Цитирания

SCOPUS & WoS (2017-2019)

Citation yearCiting paperCited paper
2019Hwang, M.-H., Lee, H.-S., Yang, S.-H., Cha, H.-R., Park, S.-J., 2019. Electromagnetic field analysis and design of an efficient outer rotor inductor in the low-speed section for driving electric vehicles. Energies 12. https://doi.org/10.3390/en12244615Kostov, I., Spasov, V., Rangelova, V., "Application of genetic algorithms for determining the parameters of induction motors," (2009) Tehnicki Vjesnik, 16 (2), pp. 49-53, ISSN: 13303651, https://www.scopus.com/inward/record.uri?eid=2-s2.0-70749138441&partnerID=40&md5=e302755b6e6ecb76ffccfe892d71689d
2019Zhang, Z., Huang, J., Hao, J., Gong, J., Chen, H., 2019. Extracting relations of crime rates through fuzzy association rules mining. Applied Intelligence. https://doi.org/10.1007/s10489-019-01531-3Borg, A., Boldt, M., Lavesson, N., Melander, U., Boeva, V., 2014. Detecting serial residential burglaries using clustering. Expert Systems with Applications 41, 5252–5266. https://doi.org/10.1016/j.eswa.2014.02.035
2019Couceiro, M., Napoli, A., 2019. Elements about exploratory, knowledge-based, hybrid, and explainable knowledge discovery. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11511 LNAI, 3–16. https://doi.org/10.1007/978-3-030-21462-3_1Hristoskova, A., Boeva, V., Tsiporkova, E., 2012. An integrative clustering approach combining particle swarm optimization and formal concept analysis, in: Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). pp. 84–98. doi:10.1007/978-3-642-32395-9_7
2019Benabbas, A., Geißelbrecht, M., Martin Nikol, G., Mahr, L., Nähr, D., Steuer, S., Wiesemann, G., Müller, T., Nicklas, D., Wieland, T., 2019. Measure particulate matter by yourself: Data-quality monitoring in a citizen science project. Journal of Sensors and Sensor Systems 8, 317–328. https://doi.org/10.5194/jsss-8-317-2019Penkov, S., Taneva, A., Kalkov, V., Ahmed, S., 2017. Industrial network design using Low-Power Wide-Area Network, in: 2017 4th International Conference on Systems and Informatics, ICSAI 2017. pp. 40–44. https://doi.org/10.1109/ICSAI.2017.8248260
2019Jurman, G., Filosi, M., Visintainer, R., Riccadonna, S., Furlanello, C., 2019. Stability in GRN Inference. Methods in Molecular Biology 1883, 323–346. https://doi.org/10.1007/978-1-4939-8882-2_14Shao, B., Lavesson, N., Boeva, V., Shahzad, R.K., 2016. A mixture-of-experts approach for gene regulatory network inference. International Journal of Data Mining and Bioinformatics 14, 258–275. https://doi.org/10.1504/IJDMB.2016.074876
2019Zhang, Q., Sun, X., Tong, F., Chen, H., 2019. A Review of Intelligent Control Algorithms Applied to Robot Motion Control, in: 8th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent Systems, CYBER 2018. pp. 105–109. https://doi.org/10.1109/CYBER.2018.8688124Kim, C.-J., Park, M.-S., Topalov, A.V., Chwa, D., Hong, S.-K., "Unifying strategies of obstacle avoidance and shooting for soccer robot systems," (2007) ICCAS 2007 - International Conference on Control, Automation and Systems, art. no. 4406909, pp. 207-211, DOI: 10.1109/ICCAS.2007.4406909, ISBN: 8995003871; 9788995003879
2019Karaboga, D., Kaya, E., 2019. Adaptive network based fuzzy inference system (ANFIS) training approaches: a comprehensive survey. Artificial Intelligence Review 52, 2263–2293. https://doi.org/10.1007/s10462-017-9610-2Topalov, A.V., Kayacan, E., Oniz, Y., Kaynak, O., "Adaptive neuro-fuzzy control with sliding mode learning algorithm: Application to antilock braking system," (2009) Proceedings of 2009 7th Asian Control Conference, ASCC 2009, art. no. 5276234, pp. 784-789, ISBN: 9788995605691.
2019Hiron, N., Andang, A., Busaeri, N., 2019. Investigation of NdFeB N52 magnet field as advanced material at air gap of axial electrical generator, in: Aripin Joni I.M., P.C.S.C.A.M.T.H.R.K.A.M.C.A.L.K.B.A.L.G.I.K.N.K.H.A.C.S.K.L.E.C.-L. (Ed.), IOP Conference Series: Materials Science and Engineering. https://doi.org/10.1088/1757-899X/550/1/012034Georgiev, N., "A model of a three-phase two-rotor axial generator," (2017) EEA - Electrotehnica, Electronica, Automatica, 65 (3), pp. 90-96, ISSN: 15825175, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029767240&partnerID=40&md5=7cb9770b72eaa1bea24edd7b94992585
2019Islam, M.J., Hong, J., Sattar, J., 2019. Person-following by autonomous robots: A categorical overview. International Journal of Robotics Research 38, 1581–1618. https://doi.org/10.1177/0278364919881683Popov, V.L., Ahmed, S.A., Shakev, N.G., Topalov, A. V, 2018. Detection and Following of Moving Targets by an Indoor Mobile Robot using Microsoft Kinect and 2D Lidar Data, in: 2018 15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018. pp. 280–285. https://doi.org/10.1109/ICARCV.2018.8581231
2019Sardarmehni, T., Heydari, A., 2019. Sub-optimal switching in anti-lock brake systems using approximate dynamic programming. IET Control Theory and Applications 13, 1413–1424. https://doi.org/10.1049/iet-cta.2018.5428Topalov, A. V, Oniz, Y., Kayacan, E., Kaynak, O., 2011. Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm. Neurocomputing 74, 1883–1893. https://doi.org/10.1016/j.neucom.2010.07.035
2019Jerald, F., Anand, M., Deepika, N., 2019. Design of an industrial IOT architecture based on MQTT protocol for end device to cloud communication. International Journal of Recent Technology and Engineering 7, 552–554.Ahmed, S., Topalov, A., Shakev, N., 2017. A robotized wireless sensor network based on MQTT cloud computing, in: Proceedings of the 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and Their Application to Mechatronics, ECMSM 2017. https://doi.org/10.1109/ECMSM.2017.7945897
2019Maharaja, R., Iyer, P., Ye, Z., 2019. A hybrid fog-cloud approach for securing the Internet of Things. Cluster Computing. https://doi.org/10.1007/s10586-019-02935-zKakanakov, N., Shopov, M., 2017. Adaptive models for security and data protection in IoT with Cloud technologies, in: 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2017 - Proceedings. https://doi.org/10.23919/MIPRO.2017.7973570
2019Munih, T., Miljavec, D., Čorović, S., 2019. A novel design concept of electromagnetic valve actuator with high starting force. Energies 12. https://doi.org/10.3390/en12173300Malamov, D., Hadzhiev, I., Yatchev, I., 2017. Influence of the pole shapes on the force characteristics of a DC solenoid actuator, in: 2017 15th International Conference on Electrical Machines, Drives and Power Systems, ELMA 2017 - Proceedings. pp. 435–438. https://doi.org/10.1109/ELMA.2017.7955480
2019Balabozov, I., 2019. Development of Energy Efficient Control System for Polarized Electromagnets, in: 2018 10th Electrical Engineering Faculty Conference, BulEF 2018. https://doi.org/10.1109/BULEF.2018.8646919Malamov, D., Hadzhiev, I., Yatchev, I., 2017. Influence of the pole shapes on the force characteristics of a DC solenoid actuator, in: 2017 15th International Conference on Electrical Machines, Drives and Power Systems, ELMA 2017 - Proceedings. pp. 435–438. https://doi.org/10.1109/ELMA.2017.7955480
2019Balabozov, I., 2019. Development of Energy Efficient Control System for Polarized Electromagnets, in: 2018 10th Electrical Engineering Faculty Conference, BulEF 2018. https://doi.org/10.1109/BULEF.2018.8646919Hadzhicv, I., Malamov, D., Yatchev, I., 2018. Influence of the heating on the force of a solenoid actuator with T-shaped armature, in: 2018 20th International Symposium on Electrical Apparatus and Technologies, SIELA 2018 - Proceedings. https://doi.org/10.1109/SIELA.2018.8447087
2019Aruna Deepthi, S., Sreenivasa Rao, E., Giriprasad, M.N., 2019. Design of various image compression methods in wireless sensor networks. International Journal of Engineering and Advanced Technology 9, 2608–2615. https://doi.org/10.35940/ijeat.A9851.109119Ahmed, S., Topalov, A., Shakev, N., 2017. A robotized wireless sensor network based on MQTT cloud computing, in: Proceedings of the 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and Their Application to Mechatronics, ECMSM 2017. https://doi.org/10.1109/ECMSM.2017.7945897
2019Qazi, N., Wong, B.L.W., 2019. An interactive human centered data science approach towards crime pattern analysis. Information Processing and Management 56. https://doi.org/10.1016/j.ipm.2019.102066Borg, A., Boldt, M., Lavesson, N., Melander, U., Boeva, V., 2014. Detecting serial residential burglaries using clustering. Expert Systems with Applications 41, 5252–5266. https://doi.org/10.1016/j.eswa.2014.02.035
2019Jiménez, M., Triguero, I., John, R., 2019. Handling uncertainty in citizen science data: Towards an improved amateur-based large-scale classification. Information Sciences 479, 301–320. https://doi.org/10.1016/j.ins.2018.12.011Tsiporkova, E., Boeva, V., "Multi-step ranking of alternatives in a multi-criteria and multi-expert decision making environment," (2006) Information Sciences, 176 (18), pp. 2673-2697, DOI: 10.1016/j.ins.2005.11.010, ISSN: 00200255
2019Madzharov, N., Ilarionov, R., Petkov, L., 2019. Experimental studies of a method of simultaneous contactless transmission of energy and information signals through a common inductive link, in: 2019 28th International Scientific Conference Electronics, ET 2019 - Proceedings. https://doi.org/10.1109/ET.2019.8878648Vuchev, A., Grigorova, T., Madankov, Y., 2018. Static Characteristics of a Phase-Shift Controlled Series Resonant DC-DC Converter, in: 2018 IEEE 27th International Scientific Conference Electronics, ET 2018 - Proceedings. https://doi.org/10.1109/ET.2018.8549660
2019Bogdanov, L., Ivanov, R., 2019. Flash programming low power microcontrollers over the internet, in: 2019 28th International Scientific Conference Electronics, ET 2019 - Proceedings. https://doi.org/10.1109/ET.2019.8878542Spasov, G., Tsvetkov, V., Petrova, G., 2018. Implementation of Internet of Things based solutionof wireless infrared camera with MLX90621 sensor, in: 2018 IEEE 27th International Scientific Conference Electronics, ET 2018 - Proceedings. https://doi.org/10.1109/ET.2018.8549641
2019Vuchev, S.A., Arnaudov, D.D., 2019. Characteristics of a Series-Resonant DC-DC Converter with Voltage Clamping Applied to Part of the Tank Capacitor, in: 10th National Conference with International Participation, ELECTRONICA 2019 - Proceedings. https://doi.org/10.1109/ELECTRONICA.2019.8825627Grigorova, T.G., Vuchev, A.S., 2018. DC Characteristics of a Series Resonant DC/DC Converter at Variable-Frequency Control Methods, in: 2018 IEEE 27th International Scientific Conference Electronics, ET 2018 - Proceedings. https://doi.org/10.1109/ET.2018.8549614
2019Oziel, M., Korenstein, R., Rubinsky, B., 2019. Non-Contact Monitoring of Temporal Volume Changes of a Hematoma in the Head by a Single Inductive Coil: A Numerical Study. IEEE Transactions on Biomedical Engineering 66, 1328–1336. https://doi.org/10.1109/TBME.2018.2872851Scharfetter, H., Ninaus, W., Puswald, B., Petrova, G.I., Kovachev, D., Hutten, H., "Inductively coupled wideband transceiver for bioimpedance spectroscopy (IBIS)," (1999) Annals of the New York Academy of Sciences, 873, pp. 322-334, DOI: 10.1111/j.1749-6632.1999.tb09480.x, ISSN: 00778923.
2019Rakotomanga, P., Soussen, C., Khairallah, G., Amouroux, M., Zaytsev, S., Genina, E., Chen, H., Delconte, A., Daul, C., Tuchin, V., Blondel, W., 2019. Source separation approach for the analysis of spatially resolved multiply excited autofluorescence spectra during optical clearing of ex vivo skin. Biomedical Optics Express 10, 3410–3424. https://doi.org/10.1364/BOE.10.003410Borisova, E., Pavlova, P., Pavlova, E., Troyanova, P., Avramov, L., 2012. Optical biopsy of human skin - A tool for cutaneous tumours’ diagnosis. International Journal Bioautomation 16, 53–72, ISSN: 13141902, https://www.scopus.com/inward/record.uri?eid=2-s2.0-84865073895&partnerID=40&md5=f45f5ef20cd4a8deeeb3f34e09e4926e
2019Rakotomanga, P., Soussen, C., Khairallah, G., Amouroux, M., Zaytsev, S., Genina, E., Chen, H., Delconte, A., Daul, C., Tuchin, V., Blondel, W., 2019. Source separation approach for the analysis of spatially resolved multiply excited autofluorescence spectra during optical clearing of ex vivo skin. Biomedical Optics Express 10, 3410–3424. https://doi.org/10.1364/BOE.10.003410Borisova, E., Troyanova, P., Pavlova, P., Avramov, L., "Diagnostics of pigmented skin tumors based on laser-induced autofluorescence and diffuse reflectance spectroscopy," (2008) Quantum Electronics, 38 (6), pp. 597-605, DOI: 10.1070/QE2008v038n06ABEH013891, ISSN: 10637818.
2019Petranović, D., Marušić, A., Havelka, J., 2019. Optimum wire busbar design by genetic algorithm. Tehnicki Vjesnik 26, 156–162. https://doi.org/10.17559/TV-20180618080043Kostov, I., Spasov, V., Rangelova, V., "Application of genetic algorithms for determining the parameters of induction motors," (2009) Tehnicki Vjesnik, 16 (2), pp. 49-53, ISSN: 13303651, https://www.scopus.com/inward/record.uri?eid=2-s2.0-70749138441&partnerID=40&md5=e302755b6e6ecb76ffccfe892d71689d
2019Bakhirev, I. V, Kavalerov, B. V, 2019. Adaptive Control of the Rotational Frequency of a Gas-Turbine Unit with Allowance for Electrical Disturbances, in: 2019 International Multi-Conference on Industrial Engineering and Modern Technologies, FarEastCon 2019. https://doi.org/10.1109/FarEastCon.2019.8933989Topalov, A.V., Kaynak, O., "Online learning in adaptive neurocontrol schemes with a sliding mode algorithm," (2001) IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 31 (3), pp. 445-450, DOI: 10.1109/3477.931542, ISSN: 10834419.
2019Ibrahim, F., Abouelsoud, A.A., Fath Elbab, A.M.R., Ogata, T., 2019. Path following algorithm for skid-steering mobile robot based on adaptive discontinuous posture control. Advanced Robotics 33, 439–453. https://doi.org/10.1080/01691864.2019.1597764Ahmed, S.A., Petrov, M.G., 2015. Trajectory Control of Mobile Robots using Type-2 Fuzzy-Neural PID Controller. IFAC-PapersOnLine 48, 138–143. https://doi.org/10.1016/j.ifacol.2015.12.071
2019Jurado, F., Vázquez, L.A., Castañeda, C.E., Garcia-Hernandez, R., Llama, M.A., 2019. Neural Block Control via Integrator Backstepping for a Robotic Arm in Real-Time. Neural Processing Letters 49, 1139–1155. https://doi.org/10.1007/s11063-018-9860-2Shiev, K., Shakev, N., Topalov, A. V, Ahmed, S., 2012. Trajectory control of manipulators using type-2 fuzzy neural friction and disturbance compensator, in: IS’2012 - 2012 6th IEEE International Conference Intelligent Systems, Proceedings. pp. 324–329. https://doi.org/10.1109/IS.2012.6335155
2019Shi, K., Yuan, X., He, Q., 2019. Double-layer Dynamic Decoupling Control System for the Yaw Stability of Four Wheel Steering Vehicle. International Journal of Control, Automation and Systems. https://doi.org/10.1007/s12555-018-0694-5Ahmed, S., Ganchev, I., Taneva, A., Petrov, M., 2016. Decoupling neuro-fuzzy model predictive controllers applied to quadruple tanks, in: 2016 IEEE 8th International Conference on Intelligent Systems, IS 2016 - Proceedings. pp. 610–615. https://doi.org/10.1109/IS.2016.7737490
2019Sadio, O., Ngom, I., Lishou, C., 2019. Lightweight Security Scheme for MQTT/MQTT-SN Protocol, in: Alsmirat M., J.Y. (Ed.), 2019 6th International Conference on Internet of Things: Systems, Management and Security, IOTSMS 2019. pp. 119–123. https://doi.org/10.1109/IOTSMS48152.2019.8939177Ahmed, S., Topalov, A., Shakev, N., 2017. A robotized wireless sensor network based on MQTT cloud computing, in: Proceedings of the 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and Their Application to Mechatronics, ECMSM 2017. https://doi.org/10.1109/ECMSM.2017.7945897
2019Liu, X., Song, G., Wang, X., 2019. HATDC: A holistic approach for time series data repairing. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11440 LNAI, 553–564. https://doi.org/10.1007/978-3-030-16145-3_43Kostadinova, E., Boeva, V., Boneva, L., Tsiporkova, E., 2012. An integrative DTW-based imputation method for gene expression time series data, in: IS’2012 - 2012 6th IEEE International Conference Intelligent Systems, Proceedings. pp. 258–263. https://doi.org/10.1109/IS.2012.6335145
2019Khristoforova, Y.A., Bratchenko, I.A., Myakinin, O.O., Artemyev, D.N., Moryatov, A.A., Orlov, A.E., Kozlov, S. V, Zakharov, V.P., 2019. Portable spectroscopic system for in vivo skin neoplasms diagnostics by Raman and autofluorescence analysis. Journal of Biophotonics 12. https://doi.org/10.1002/jbio.201800400Borisova, E., Troyanova, P., Pavlova, P., Avramov, L., "Diagnostics of pigmented skin tumors based on laser-induced autofluorescence and diffuse reflectance spectroscopy," (2008) Quantum Electronics, 38 (6), pp. 597-605, DOI: 10.1070/QE2008v038n06ABEH013891, ISSN: 10637818.
2019Bacha, S., Ayad, M.Y., Saadi, R., Kraa, O., Aboubou, A., Hammoudi, M.Y., 2019. Autonomous Vehicle Path Tracking Using Nonlinear Steering Control and Input-Output State Feedback Linearization., in: Menaa M., B.M. (Ed.), Proceedings of 2018 3rd International Conference on Electrical Sciences and Technologies in Maghreb, CISTEM 2018. https://doi.org/10.1109/CISTEM.2018.8613365Ahmed, S.A., Petrov, M.G., 2015. Trajectory Control of Mobile Robots using Type-2 Fuzzy-Neural PID Controller. IFAC-PapersOnLine 48, 138–143. https://doi.org/10.1016/j.ifacol.2015.12.071
2019Ferdaus, M.M., Pratama, M., Anavatti, S.G., Garratt, M., 2019. A Generic Self-Evolving Neuro-Fuzzy Controller Based High-Performance Hexacopter Altitude Control System, in: Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018. pp. 2784–2791. https://doi.org/10.1109/SMC.2018.00475Topalov, A.V., Kaynak, O., "Online learning in adaptive neurocontrol schemes with a sliding mode algorithm," (2001) IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 31 (3), pp. 445-450, DOI: 10.1109/3477.931542, ISSN: 10834419.
2019López, M.G., Ponce, P., Soriano, L.A., Molina, A., Rodríguez, J.J., 2019. Improvement of lifetime in the power electronic drive of a BLDCM through the optimization of Fuzzy Logic Control [Mejora de la Vida Útil en los Módulos de Electrónica de Potencia de un BLDCM Mediante la Optimización de un Control Difuso]. RIAI - Revista Iberoamericana de Automatica e Informatica Industrial 16, 66–78. https://doi.org/10.4995/riai.2017.9078Ahmed, S., Topalov, A., Dimitrov, N., Bonev, E., 2016. Industrial implementation of a fuzzy logic controller for brushless DC motor drives using the PicoMotion control framework, in: 2016 IEEE 8th International Conference on Intelligent Systems, IS 2016 - Proceedings. pp. 629–634. https://doi.org/10.1109/IS.2016.7737493
2019Li, Y.-S., Qi, M.-L., 2019. An approach for understanding offender modus operandi to detect serial robbery crimes. Journal of Computational Science 36. https://doi.org/10.1016/j.jocs.2019.101024Borg, A., Boldt, M., Lavesson, N., Melander, U., Boeva, V., 2014. Detecting serial residential burglaries using clustering. Expert Systems with Applications 41, 5252–5266. https://doi.org/10.1016/j.eswa.2014.02.035
2019Raggiunto, S., Belli, A., Lorenzo, P., Piergiovanni, C., Gattari, M., Pierleoni, P., 2019. An efficient method for LED light sources characterization. Electronics (Switzerland) 8. https://doi.org/10.3390/electronics8101089Rachev, I., Djamiykov, T., Marinov, M., Hinov, N., 2019. Improvement of the approximation accuracy of LED radiation patterns. Electronics (Switzerland) 8. https://doi.org/10.3390/electronics8030337
2019Mani, S., Parthiban, L., 2019. Bicluster method to predict gene patterns to classify differential gene expressions in non-small cell lung cancer. International Journal of Innovative Technology and Exploring Engineering 9, 5057–5065. https://doi.org/10.35940/ijitee.A5349.119119Hristoskova, A., Boeva, V., Tsiporkova, E., 2014. A formal concept analysis approach to consensus clustering of multi-experiment expression data. BMC Bioinformatics 15. https://doi.org/10.1186/1471-2105-15-151
2019Brahimi, S., Azouaoui, O., Loudini, M., 2019. Intelligent mobile robot navigation using a neuro-fuzzy approach. International Journal of Computer Aided Engineering and Technology 11, 710–726. https://doi.org/10.1504/IJCAET.2019.102500Kim, C.-J., Park, M.-S., Topalov, A.V., Chwa, D., Hong, S.-K., "Unifying strategies of obstacle avoidance and shooting for soccer robot systems," (2007) ICCAS 2007 - International Conference on Control, Automation and Systems, art. no. 4406909, pp. 207-211, DOI: 10.1109/ICCAS.2007.4406909, ISBN: 8995003871; 9788995003879
2019Ertürk, M.A., Aydin, M.A., Büyükakkaşlar, M.T., Evirgen, H., 2019. A survey on LoRaWAN architecture, protocol and technologies. Future Internet 11. https://doi.org/10.3390/fi1110216Penkov, S., Taneva, A., Kalkov, V., Ahmed, S., 2017. Industrial network design using Low-Power Wide-Area Network, in: 2017 4th International Conference on Systems and Informatics, ICSAI 2017. pp. 40–44. https://doi.org/10.1109/ICSAI.2017.8248260
2019Le, T.-L., 2019. Self-organizing recurrent interval type-2 Petri fuzzy design for time-varying delay systems. IEEE Access 7, 10505–10514. https://doi.org/10.1109/ACCESS.2018.2889226Penkov, S., Taneva, A., Kalkov, V., Ahmed, S., 2017. Industrial network design using Low-Power Wide-Area Network, in: 2017 4th International Conference on Systems and Informatics, ICSAI 2017. pp. 40–44. https://doi.org/10.1109/ICSAI.2017.8248260
2019Mendes, M.P., Cherubini, P., Plieninger, T., Ribeiro, L., Costa, A., 2019. Climate effects on stem radial growth of Quercus suber L.: Does tree size matter? Forestry 92, 73–84. https://doi.org/10.1093/forestry/cpy034Boeva, V., 2014. Clustering approaches for dealing with multiple DNA microarray datasets. Journal of Computational Science 5, 368–376. https://doi.org/10.1016/j.jocs.2013.05.003
2019Jittawiriyanukoon, C., 2019. Proposed algorithm for Regression-based prediction with bulk noise. Indonesian Journal of Electrical Engineering and Computer Science 17, 543–550. https://doi.org/10.11591/ijeecs.v17.i1.pp543-550Boeva, V., Lundberg, L., Angelova, M., Kohstall, J., 2018. Cluster Validation Measures for Label Noise Filtering, in: JardimGoncalves, R and Mendonca, JP and Jotsov, V and Marques, M and Martins, J and Bierwolf, R (Ed.), 2018 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS (IS). IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA, pp. 109–116, ISBN: 978-1-5386-7097-2.
2019Moosapour, S.S., Fazeli Asl, S.B., Azizi, M., 2019. Adaptive fractional order fast terminal dynamic sliding mode controller design for antilock braking system (ABS). International Journal of Dynamics and Control 7, 368–378. https://doi.org/10.1007/s40435-018-0450-yTopalov, A. V, Oniz, Y., Kayacan, E., Kaynak, O., 2011. Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm. Neurocomputing 74, 1883–1893. https://doi.org/10.1016/j.neucom.2010.07.035
2019Al Kindhi, B., Sardjono, T.A., Purnomo, M.H., Verkerke, G.J., 2019. Hybrid K-means, fuzzy C-means, and hierarchical clustering for DNA hepatitis C virus trend mutation analysis. Expert Systems with Applications 121, 373–381. https://doi.org/10.1016/j.eswa.2018.12.019Boeva, V., 2014. Clustering approaches for dealing with multiple DNA microarray datasets. Journal of Computational Science 5, 368–376. https://doi.org/10.1016/j.jocs.2013.05.003
2019Dong, J., He, B., 2019. Novel fuzzy PID-type iterative learning control for quadrotor UAV. Sensors (Switzerland) 19. https://doi.org/10.3390/s19010024Shakev, N.G., Topalov, A. V, Kaynak, O., Shiev, K.B., 2012. Comparative results on stabilization of the quad-rotor rotorcraft using bounded feedback controllers. Journal of Intelligent and Robotic Systems: Theory and Applications 65, 389–408. https://doi.org/10.1007/s10846-011-9583-3
2019Abdul-Adheem, W.R., 2019. Design and simulation of a normalized fuzzy logic controller for the quadruple-tank process. Indonesian Journal of Electrical Engineering and Computer Science 18, 227–234. https://doi.org/10.11591/ijeecs.v18.i1.pp227-234Ahmed, S., Ganchev, I., Taneva, A., Petrov, M., 2016. Decoupling neuro-fuzzy model predictive controllers applied to quadruple tanks, in: 2016 IEEE 8th International Conference on Intelligent Systems, IS 2016 - Proceedings. pp. 610–615. https://doi.org/10.1109/IS.2016.7737490
2019Pérez-Alcocer, R., Moreno-Valenzuela, J., 2019. A novel Lyapunov-based trajectory tracking controller for a quadrotor: Experimental analysis by using two motion tasks. Mechatronics 61, 58–68. https://doi.org/10.1016/j.mechatronics.2019.05.006Shakev, N.G., Topalov, A. V, Kaynak, O., Shiev, K.B., 2012. Comparative results on stabilization of the quad-rotor rotorcraft using bounded feedback controllers. Journal of Intelligent and Robotic Systems: Theory and Applications 65, 389–408. https://doi.org/10.1007/s10846-011-9583-3
2019Zhu, Q., Zhang, F., Liu, S., Li, Y., 2019. An anticrime information support system design: Application of K-means-VMD-BiGRU in the city of Chicago. Information and Management. https://doi.org/10.1016/j.im.2019.103247Borg, A., Boldt, M., Lavesson, N., Melander, U., Boeva, V., 2014. Detecting serial residential burglaries using clustering. Expert Systems with Applications 41, 5252–5266. https://doi.org/10.1016/j.eswa.2014.02.035
2019Venu, G., Kalyani, S.T., 2019. Design of fractional order based super twisting algorithm for BLDC motor, in: Proceedings of the International Conference on Trends in Electronics and Informatics, ICOEI 2019. pp. 271–277. https://doi.org/10.1109/ICOEI.2019.8862677Ahmed, S., Topalov, A., Dimitrov, N., Bonev, E., 2016. Industrial implementation of a fuzzy logic controller for brushless DC motor drives using the PicoMotion control framework, in: 2016 IEEE 8th International Conference on Intelligent Systems, IS 2016 - Proceedings. pp. 629–634. https://doi.org/10.1109/IS.2016.7737493
2019Ferdaus, M.M., Anavatti, S.G., Pratama, M., Garratt, M.A., 2019. A Novel Self-Organizing Neuro-Fuzzy based Intelligent Control System for a AR.Drone Quadcopter, in: S., S. (Ed.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. pp. 2026–2032. https://doi.org/10.1109/SSCI.2018.8628815Topalov, A.V., Kaynak, O., "Online learning in adaptive neurocontrol schemes with a sliding mode algorithm," (2001) IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, 31 (3), pp. 445-450, DOI: 10.1109/3477.931542, ISSN: 10834419.
2019Ferdaus, M.M., Anavatti, S.G., Pratama, M., Garratt, M.A., 2019. A Novel Self-Organizing Neuro-Fuzzy based Intelligent Control System for a AR.Drone Quadcopter, in: S., S. (Ed.), Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence, SSCI 2018. pp. 2026–2032. https://doi.org/10.1109/SSCI.2018.8628815Topalov, Andon V., Kim, Kwang-Choon, Kim, Jong-Hwan, Lee, Bong-Kuk, "Fast genetic on-line learning algorithm for neural network and its application to temperature control," (1996) Proceedings of the IEEE Conference on Evolutionary Computation, pp. 649-654, https://www.scopus.com/inward/record.uri?eid=2-s2.0-0029700467&partnerID=40&md5=066c3cdf6789bb42e46d2f228c615f9b
2019Le, T.-L., Lin, C.-M., Huynh, T.-T., 2019. Interval Type-2 Petri CMAC Design for 4D Chaotic System, in: Proceedings of 2019 International Conference on System Science and Engineering, ICSSE 2019. pp. 420–424. https://doi.org/10.1109/ICSSE.2019.8823251Shiev, K., Ahmed, S., Shakev, N., Topalov, A. V, 2016. Trajectory control of manipulators using an adaptive parametric type-2 fuzzy CMAC friction and disturbance compensator. Studies in Computational Intelligence 586, 63–82. https://doi.org/10.1007/978-3-319-14194-7_4
2019Ma, Z., Xu, K., Zhou, B., Zhang, J., Shao, X., 2019. Motion track extraction based on empirical mode decomposition of endpoint effect suppression for double-rotor drone. IEICE Transactions on Communications E102B, 1967–1974. https://doi.org/10.1587/transcom.2018DRP0029Popov, V.L., Shiev, K.B., Topalov, A. V, Shakev, N.G., Ahmed, S.A., 2016. Control of the flight of a small quadrotor using gestural interface, in: 2016 IEEE 8th International Conference on Intelligent Systems, IS 2016 - Proceedings. pp. 622–628. https://doi.org/10.1109/IS.2016.7737492
2019Vuchev, S.A., Arnaudov, D.D., 2019. Design Considerations for Stages of Modular Topology for Fast Charging of Electric Vehicles, in: 10th National Conference with International Participation, ELECTRONICA 2019 - Proceedings. https://doi.org/10.1109/ELECTRONICA.2019.8825632Grigorova, T.G., Vuchev, A.S., 2018. DC Characteristics of a Series Resonant DC/DC Converter at Variable-Frequency Control Methods, in: 2018 IEEE 27th International Scientific Conference Electronics, ET 2018 - Proceedings. https://doi.org/10.1109/ET.2018.8549614
2019Washizaki, H., Yoshioka, N., Hazeyama, A., Kato, T., Kaiya, H., Ogata, S., Okubo, T., Fernandez, E.B., 2019. Landscape of iot patterns, in: Proceedings - 2019 IEEE/ACM 1st International Workshop on Software Engineering Research and Practices for the Internet of Things, SERP4IoT 2019. pp. 57–60. https://doi.org/10.1109/SERP4IoT.2019.00017Shopov, M.P., 2017. IoT gateway for smart metering in electrical power systems - Software architecture, in: 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2017 - Proceedings. https://doi.org/10.23919/MIPRO.2017.7973565
2019Wang, M., Ding, L., Xu, M., Xie, J., Wu, S., Xu, S., Yao, Y., Liu, Q., 2019. A novel method detecting the key clinic factors of portal vein system thrombosis of splenectomy & cardia devascularization patients for cirrhosis & portal hypertension. BMC Bioinformatics 20. https://doi.org/10.1186/s12859-019-3233-3Borg, A., Lavesson, N., Boeva, V., 2013. Comparison of clustering approaches for gene expression data. Frontiers in Artificial Intelligence and Applications 257, 55–64. https://doi.org/10.3233/978-1-61499-330-8-55
2019Zaidi, I., Chtourou, M., Djemel, M., 2019. Robust Neural Control of Discrete Time Uncertain Nonlinear Systems Using Sliding Mode Backpropagation Training Algorithm. International Journal of Automation and Computing 16, 213–225. https://doi.org/10.1007/s11633-017-1062-2Topalov, A.V., Kaynak, O., "Robust neural identification of robotic manipulators using discrete time adaptive sliding mode learning," (2005) IFAC Proceedings Volumes (IFAC-PapersOnline), 16, pp. 336-341, ISSN: 14746670, ISBN: 008045108X; 9780080451084.
2019Zaidi, I., Chtourou, M., Djemel, M., 2019. Robust Neural Control of Discrete Time Uncertain Nonlinear Systems Using Sliding Mode Backpropagation Training Algorithm. International Journal of Automation and Computing 16, 213–225. https://doi.org/10.1007/s11633-017-1062-2Topalov, A.V., Kaynak, O., "A sliding mode strategy for adaptive learning in multilayer feedforward neural networks with a scalar output," (2003) Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2, pp. 1636-1641, ISSN: 08843627.
2019Zaidi, I., Chtourou, M., Djemel, M., 2019. Robust Neural Control of Discrete Time Uncertain Nonlinear Systems Using Sliding Mode Backpropagation Training Algorithm. International Journal of Automation and Computing 16, 213–225. https://doi.org/10.1007/s11633-017-1062-2Topalov, A.V., Kaynak, O., Shakev, N.G., "Variable Structure Systems Approach for Online Learning in Multilayer Artificial Neural Networks," (2003) IECON Proceedings (Industrial Electronics Conference), 3, pp. 2989-2994, DOI: 10.1109/IECON.2003.1280724.
2019Zaidi, I., Chtourou, M., Djemel, M., 2019. Robust Neural Control of Discrete Time Uncertain Nonlinear Systems Using Sliding Mode Backpropagation Training Algorithm. International Journal of Automation and Computing 16, 213–225. https://doi.org/10.1007/s11633-017-1062-2Topalov, A.V., Kaynak, O., "Neural network modeling and control of cement mills using a variable structure systems theory based on-line learning mechanism," (2004) Journal of Process Control, 14 (5), pp. 581-589, DOI: 10.1016/j.jprocont.2003.10.005, ISSN: 09591524.
2019Yuan, S., Zhang, Y., Tang, J., Hall, W., Cabotà, J.B., 2019. Expert finding in community question answering: a review. Artificial Intelligence Review. https://doi.org/10.1007/s10462-018-09680-6Boeva, V., Angelova, M., Tsiporkova, E., 2017. Data-driven techniques for expert finding, in: ICAART 2017 - Proceedings of the 9th International Conference on Agents and Artificial Intelligence. pp. 535–542.
2019Hosseini, B., Kiani, K., 2019. A big data driven distributed density based hesitant fuzzy clustering using Apache spark with application to gene expression microarray. Engineering Applications of Artificial Intelligence 79, 100–113. https://doi.org/10.1016/j.engappai.2019.01.006Kostadinova, E., Boeva, V., Lavesson, N., 2011. Clustering of multiple microarray experiments using information integration. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6865 LNCS, 123–137. https://doi.org/10.1007/978-3-642-23208-4_12
2019Gonçalves, R., Dorneles, C.F., 2019. Automated expertise retrieval: A taxonomy-based survey and open issues. ACM Computing Surveys 52. https://doi.org/10.1145/3331000Boeva, V., Boneva, L., Tsiporkova, E., 2014. Semantic-aware expert partitioning. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8722, 13–24.
2019Hadini, Y., Galadi, A., Echchelh, A., 2019. An accurate NPT-IGBT SPICE model with simple parameter extraction method. Lecture Notes in Electrical Engineering 519, 273–282. https://doi.org/10.1007/978-981-13-1405-6_33Asparuhova, K., Grigorova, T., "IGBT behavioral PSPICE model," (2006) 2006 25th International Conference on Microelectronics, MIEL 2006 - Proceedings, art. no. 1650931, pp. 215-218, DOI: 10.1109/ICMEL.2006.1650931, ISBN: 1424401178; 9781424401178.
2019Chiem, N.X., Anh, N.D., Lukianov, A.D., Tung, P.D., Long, H.D., Linh, N.D., 2019. Design real-time embedded optimal PD fuzzy controller by PSO algorithm for autonomous vehicle mounted camera, in: Mladenovic V. Tung P.D., S.I.B.N.T.A.P.M. (Ed.), AIP Conference Proceedings. https://doi.org/10.1063/1.5138401Ahmed, S.A., Petrov, M.G., 2015. Trajectory Control of Mobile Robots using Type-2 Fuzzy-Neural PID Controller. IFAC-PapersOnLine 48, 138–143. https://doi.org/10.1016/j.ifacol.2015.12.071
2019Chiem, N.X., Anh, N.D., Lukianov, A.D., Tung, P.D., Long, H.D., Linh, N.D., 2019. Design real-time embedded optimal PD fuzzy controller by PSO algorithm for autonomous vehicle mounted camera, in: Mladenovic V. Tung P.D., S.I.B.N.T.A.P.M. (Ed.), AIP Conference Proceedings. https://doi.org/10.1063/1.5138401Petrov, M., Ganchev, I., Taneva, A., "Fuzzy PID control of nonlinear plants," (2002) 2002 1st International IEEE Symposium, 1, art. no. 1044224, pp. 30-35, DOI: 10.1109/IS.2002.1044224, ISBN: 0780371348; 9780780371347.
2019Kavitha, E., Tamilarasan, R., 2019. AGGLO-Hi clustering algorithm for gene expression micro array data using proximity measures. Multimedia Tools and Applications. https://doi.org/10.1007/s11042-018-7112-0Boeva, V., Tsiporkova, E., 2010. A multi-purpose time series data standardization method. Studies in Computational Intelligence 299, 445–460. https://doi.org/10.1007/978-3-642-13428-9_22
2019Balabozov, I., Hinov, K., 2019. Experimental Research with Energy Efficient Control System for Polarized Electromagnets, in: 2018 10th Electrical Engineering Faculty Conference, BulEF 2018. https://doi.org/10.1109/BULEF.2018.8646948Malamov, D., Hadzhiev, I., Yatchev, I., 2017. Influence of the pole shapes on the force characteristics of a DC solenoid actuator, in: 2017 15th International Conference on Electrical Machines, Drives and Power Systems, ELMA 2017 - Proceedings. pp. 435–438. https://doi.org/10.1109/ELMA.2017.7955480
2019Balabozov, I., Hinov, K., 2019. Experimental Research with Energy Efficient Control System for Polarized Electromagnets, in: 2018 10th Electrical Engineering Faculty Conference, BulEF 2018. https://doi.org/10.1109/BULEF.2018.8646948Hadzhicv, I., Malamov, D., Yatchev, I., 2018. Influence of the heating on the force of a solenoid actuator with T-shaped armature, in: 2018 20th International Symposium on Electrical Apparatus and Technologies, SIELA 2018 - Proceedings. https://doi.org/10.1109/SIELA.2018.8447087
2019Kayacan, E., 2019. Sliding mode learning control of uncertain nonlinear systems with Lyapunov stability analysis. Transactions of the Institute of Measurement and Control 41, 1750–1760. https://doi.org/10.1177/0142331218788125Topalov, A.V., Cascella, G.L., Giordano, V., Cupertino, F., Kaynak, O., "Sliding mode neuro-adaptive control of electric drives," (2007) IEEE Transactions on Industrial Electronics, 54 (1), pp. 671-679, DOI: 10.1109/TIE.2006.888930, ISSN: 02780046.
2019Kayacan, E., 2019. Sliding mode learning control of uncertain nonlinear systems with Lyapunov stability analysis. Transactions of the Institute of Measurement and Control 41, 1750–1760. https://doi.org/10.1177/0142331218788125Topalov, A.V., Kaynak, O., Aydin, G., "Neuro-adaptive sliding-mode tracking control of robot manipulators," (2007) International Journal of Adaptive Control and Signal Processing, 21 (8-9), pp. 674-691, DOI: 10.1002/acs.982, ISSN: 08906327.
2019Parthasarathy, P., Vivekanandan, S., 2019. Erratum: A numerical modelling of an amperometric-enzymatic based uric acid biosensor for GOUT arthritis diseases (Informatics in Medicine Unlocked (2019) 16, (S235291481930276X), (10.1016/j.imu.2019.100246)). Informatics in Medicine Unlocked 16. https://doi.org/10.1016/j.imu.2019.100233Neykov, A., Rangelova, V.m "Mathematical modeling of the biosensor systems," (1998) Biotechnology and Biotechnological Equipment, 12 (2), pp. 100-109, DOI: 10.1080/13102818.1998.10819000, ISSN: 13102818.
2019Le, T.-L., 2019. Intelligent fuzzy controller design for antilock braking systems. Journal of Intelligent and Fuzzy Systems 36, 3303–3315. https://doi.org/10.3233/JIFS-181014Ahmed, S., Shakev, N., Topalov, A., Shiev, K., Kaynak, O., 2012. Sliding mode incremental learning algorithm for interval type-2 Takagi-Sugeno-Kang fuzzy neural networks. Evolving Systems 3, 179–188. https://doi.org/10.1007/s12530-012-9053-6
2019Chervenkov A. , Yanev A. Chervenkova, “Performance analysis and modelling of grid-connected small photovoltaic system”, Proceedings of XVI-th International Conference on Electrical Machines, Drives and Power Systems ELMA 2019, 6-8 June 2019, Varna, Bulgaria, pp. 500-503, DOI: 10.1109/ELMA.2019.8771501S. Ivanov, Y. Ivanova, “Research of the impacy of an active driver
circuit with di / dt feedback on dc motor speed”, Proc. IX National
Conference with International Participation "Electronica 2018", May
17 - 18, 2018, Sofia, Bulgaria, pp. 51-54,DOI: 10.1109/ELECTRONICA.2018.8439363
2019De Caropreso, R.T., Fernandes, R.A.S., Osorio, D.P.M., Silva, I.N., 2019. An Open-Source Framework for Smart Meters: Data Communication and Security Traffic Analysis. IEEE Transactions on Industrial Electronics 66, 1638–1647. https://doi.org/10.1109/TIE.2018.2808927Shopov, M.P., 2017. IoT gateway for smart metering in electrical power systems - Software architecture, in: 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2017 - Proceedings. https://doi.org/10.23919/MIPRO.2017.7973565
2019Pachidis, T., Vrochidou, E., Papadopoulou, C.I., Kaburlasos, V.G., Kostova, S., Bonković, M., Papić, V., "Integrating Robotics In Education And Vice Versa; Shifting From Blackboard To Keyboard," (2019) International Journal of Mechanics and Control, 20 (1), pp. 53-69, ISSN: 15908844, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85068621784&partnerID=40&md5=2cf1728e02c873c11eb4e1c3539d7ec5Chavdarov, I., Trifonov, R., Pavlova, G., Budakova, D., 2018. Manipulability and kinematic dependences of a leg of the six-legged robot, in: ACM International Conference Proceeding Series. pp. 116–119. https://doi.org/10.1145/3274005.3274010
2019chev Y., V. Mateev, R. Tzeneva, "3D coupled electric-thermal-fluid analysis of bolted busbar connection", Proceedings of the 16-th International conference on electrical machines, drives and power systems – ELMA 2019, 6 - 8 June, 2019, Varna, Bulgaria, ISBN 978-1-7281-1413-2, DOI: 10.1109/ELMA.2019.8771547, https://www.scopus.com/record/display.uri?eid=2-s2.0-85070467622&origin=resultslist&sort=plf-f&src=s&sid=dba543f24a501e9831998c9fc6709e34&sot=autdocs&sdt=autdocs&sl=17&s=AU-ID%289733187200%29&relpos=2&citeCnt=0&searchTerm=Hadzhiev, I., Malamov, D., Yatchev, I.
Study of the influence of the force on the contact resistance between copper busbars
VII-th Conference of Faculty of Electrical Engineering-EF 2015, Proceedings of Technical University of Sofia, 66 (1), pp. 549-556.
19-21 September 2015, Sozopol, Bulgaria, ISSN 1311-0829. (in Bulgarian)
2019S. Zh. Ibadullaeva, N. O. Appazov, Yu. S. Tarahovsky, E. A. Zamyatina, M. G. Fomkina & Yu. A. Kim, Amperometric Multi-Enzyme Biosensors: Development and Application, a Short Review,
Biophysics volume 64, pp. 696–707 (2019),.https://doi.org/10.1134/S0006350919050063, ISSN:0006-3509, https://link.springer.com/article/10.1134/S0006350919050063
Rangelova V., A. Pandelova, N. Stoyanov, "Inhibitor multienzyme biosensor system in dynamic mode – phosphate measurement", Journal of Engineering Annals of the Faculty of engineering Huhedoara, ROMANIA - ISSN 1584-2665,Tome IX, fasc(2), p.p.83 – 86, 2011, https://pdfs.semanticscholar.org/56bf/6dfcdadfec9e0f957cb5505a8a07bf58cc4f.pdf
2019Stoykova, S., Spasov, V., "Determining the parameters of induction motors by Genetic Algorithms," (2019) IOP Conference Series: Materials Science and Engineering, 618 (1), art. no. 012023, DOI: 10.1088/1757-899X/618/1/012023, ISSN: 17578981. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076099023&doi=10.1088%2f1757-899X%2f618%2f1%2f012023&partnerID=40&md5=460a2d337219a5bfbd326f328ffe3f1f Kostov I , V.Rangelova, Power factor determination of induction motor frequency controlled drives, Journal of Engineering Annals of the Faculty of engineering Huhedoara, Romania,Tome VIII, fasc(2), p.p.67-72, 2010, ISSN 1584-2665
2019T. Hristova and P. Hristov, “ Assessment of Conditions for the Applications of DLT for Smart Metering in Bulgaria According to the European Requirements,” Proceedings of XVI-th International Conference on Electrical Machines, Drives and Power Systems, ELMA 2019 -6-8 June 2019, Varna, Bulgaria, pp. 551-556, DOI: 10.1109/ELMA.2019.8771514Ivanov S., Ivanova Y., “Electronic Converter for Fiber Optic Thermometer,” Journal of the Technical University – Sofia, vol. 22, pp.76–79, 2016.ISSN 1310-8271
2018Ayawli, B.B.K., Chellali, R., Appiah, A.Y., Kyeremeh, F., 2018. An Overview of Nature-Inspired, Conventional, and Hybrid Methods of Autonomous Vehicle Path Planning. Journal of Advanced Transportation 2018. https://doi.org/10.1155/2018/8269698Kim, C.-J., Park, M.-S., Topalov, A.V., Chwa, D., Hong, S.-K., "Unifying strategies of obstacle avoidance and shooting for soccer robot systems," (2007) ICCAS 2007 - International Conference on Control, Automation and Systems, art. no. 4406909, pp. 207-211, DOI: 10.1109/ICCAS.2007.4406909, ISBN: 8995003871; 9788995003879
2018Gaye, I., Mendy, G., Ouya, S., Diop, I., Seck, D., 2018. Multi-diffusion Degree Centrality Measure to Maximize the Influence Spread in the Multilayer Social Networks. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST 208, 53–65. https://doi.org/10.1007/978-3-319-66742-3_6Tsiporkova, E., Boeva, V., "Multi-step ranking of alternatives in a multi-criteria and multi-expert decision making environment," (2006) Information Sciences, 176 (18), pp. 2673-2697, DOI: 10.1016/j.ins.2005.11.010, ISSN: 00200255
2018Hamza, M.F., Yap, H.J., Choudhury, I.A., Chiroma, H., Kumbasar, T., 2018. A survey on advancement of hybrid type 2 fuzzy sliding mode control. Neural Computing and Applications 30, 331–353. https://doi.org/10.1007/s00521-017-3144-zTopalov, A. V, Oniz, Y., Kayacan, E., Kaynak, O., 2011. Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm. Neurocomputing 74, 1883–1893. https://doi.org/10.1016/j.neucom.2010.07.035
2018Hamza, M.F., Yap, H.J., Choudhury, I.A., Chiroma, H., Kumbasar, T., 2018. A survey on advancement of hybrid type 2 fuzzy sliding mode control. Neural Computing and Applications 30, 331–353. https://doi.org/10.1007/s00521-017-3144-zAhmed, S., Shakev, N., Topalov, A., Shiev, K., Kaynak, O., 2012. Sliding mode incremental learning algorithm for interval type-2 Takagi-Sugeno-Kang fuzzy neural networks. Evolving Systems 3, 179–188. https://doi.org/10.1007/s12530-012-9053-6
2018Hamza, M.F., Yap, H.J., Choudhury, I.A., Chiroma, H., Kumbasar, T., 2018. A survey on advancement of hybrid type 2 fuzzy sliding mode control. Neural Computing and Applications 30, 331–353. https://doi.org/10.1007/s00521-017-3144-zShiev, K., Shakev, N., Topalov, A. V, Ahmed, S., Kaynak, O., 2011. An extended sliding mode learning algorithm for type-2 fuzzy neural networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 6943 LNAI, 52–63. https://doi.org/10.1007/978-3-642-23857-4_9
2018Pan, Y., Han, B., Tsang, I.W., 2018. Stagewise learning for noisy k-ary preferences. Machine Learning 107, 1333–1361. https://doi.org/10.1007/s10994-018-5716-2Tsiporkova, E., Boeva, V., "Multi-step ranking of alternatives in a multi-criteria and multi-expert decision making environment," (2006) Information Sciences, 176 (18), pp. 2673-2697, DOI: 10.1016/j.ins.2005.11.010, ISSN: 00200255
2018Batayneh, W., Jaradat, M., Bataineh, A., 2018. Intelligent adaptive control for anti-lock braking system, in: ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE). https://doi.org/10.1115/IMECE2018-87659Topalov, A. V, Oniz, Y., Kayacan, E., Kaynak, O., 2011. Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm. Neurocomputing 74, 1883–1893. https://doi.org/10.1016/j.neucom.2010.07.035
2018Alharbi, S., Rodriguez, P., Maharaja, R., Iyer, P., Bose, N., Ye, Z., 2018. FOCUS: A fog computing-based security system for the Internet of Things, in: CCNC 2018 - 2018 15th IEEE Annual Consumer Communications and Networking Conference. pp. 1–5. https://doi.org/10.1109/CCNC.2018.8319238Kakanakov, N., Shopov, M., 2017. Adaptive models for security and data protection in IoT with Cloud technologies, in: 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2017 - Proceedings. https://doi.org/10.23919/MIPRO.2017.7973570
2018Wang, H., Wang, F., 2018. Application of sensor in electrical automation based on fuzzy control algorithm. Journal of Advanced Oxidation Technologies 21. https://doi.org/10.26802/jaots.2018.06873Topalov, A. V, Oniz, Y., Kayacan, E., Kaynak, O., 2011. Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm. Neurocomputing 74, 1883–1893. https://doi.org/10.1016/j.neucom.2010.07.035
2018Hampel, H., Toschi, N., Babiloni, C., Baldacci, F., Black, K.L., Bokde, A.L.W., Bun, R.S., Cacciola, F., Cavedo, E., Chiesa, P.A., Colliot, O., Coman, C.-M., Dubois, B., Duggento, A., Durrleman, S., Ferretti, M.-T., George, N., Genthon, R., Habert, M.-O., Herholz, K., Koronyo, Y., Koronyo-Hamaoui, M., Lamari, F., Langevin, T., Lehéricy, S., Lorenceau, J., Neri, C., Nisticò, R., Nyasse-Messene, F., Ritchie, C., Rossi, S., Santarnecchi, E., Sporns, O., Verdooner, S.R., Vergallo, A., Villain, N., Younesi, E., Garaci, F., Lista, S., 2018. Revolution of Alzheimer Precision Neurology. Passageway of Systems Biology and Neurophysiology. Journal of Alzheimer’s Disease 64, S47–S105. https://doi.org/10.3233/JAD-179932Hristoskova, A., Boeva, V., Tsiporkova, E., 2014. A formal concept analysis approach to consensus clustering of multi-experiment expression data. BMC Bioinformatics 15. https://doi.org/10.1186/1471-2105-15-151
2018Jindal, S., Singh, W., Deb, S., 2018. A low-cost smart vein viewer system, in: Proceedings - 2018 IEEE 4th International Symposium on Smart Electronic Systems, ISES 2018. pp. 114–117. https://doi.org/10.1109/iSES.2018.00033Borisova, E., Pavlova, P., Pavlova, E., Troyanova, P., Avramov, L., 2012. Optical biopsy of human skin - A tool for cutaneous tumours’ diagnosis. International Journal Bioautomation 16, 53–72.
2018Arnaudov, D.D., Vuchev, S.A., 2018. Investigation of Resonant Converters Parallel Operation to a Common Load, in: 2018 IEEE 27th International Scientific Conference Electronics, ET 2018 - Proceedings. https://doi.org/10.1109/ET.2018.8549661Vuchev, A.S., Grigorova, T.G., 2018. Investigation of Snubber Capacitors Influence on the Operation of a Phase-Shift Controlled Series Resonant DC/DC Converter with Zero-Voltage Switching, in: 9th National Conference with International Participation, ELECTRONICA 2018 - Proceedings. https://doi.org/10.1109/ELECTRONICA.2018.8439221
2018Mansoor, S., Saedan, M., 2018. Software-in-the-loop simulation of a quadcopter portion for hybrid aircraft control, in: IOP Conference Series: Materials Science and Engineering. https://doi.org/10.1088/1757-899X/297/1/012044Shakev, N.G., Topalov, A. V, Kaynak, O., Shiev, K.B., 2012. Comparative results on stabilization of the quad-rotor rotorcraft using bounded feedback controllers. Journal of Intelligent and Robotic Systems: Theory and Applications 65, 389–408. https://doi.org/10.1007/s10846-011-9583-3
2018Mousavi, A., Davaie-Markazi, A.H., Masoudi, S., 2018. Comparison of Adaptive Fuzzy Sliding-Mode Pulse Width Modulation Control with Common Model-Based Nonlinear Controllers for Slip Control in Antilock Braking Systems. Journal of Dynamic Systems, Measurement and Control, Transactions of the ASME 140. https://doi.org/10.1115/1.4037296Topalov, A. V, Oniz, Y., Kayacan, E., Kaynak, O., 2011. Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm. Neurocomputing 74, 1883–1893. https://doi.org/10.1016/j.neucom.2010.07.035
2018Zhang, W., Wulan, G., Zhai, J., Xu, L., Zhao, D., Liu, X., Yang, S., Zhou, J., 2018. An intelligent power distribution service architecture using cloud computing and deep learning techniques. Journal of Network and Computer Applications 103, 239–248. https://doi.org/10.1016/j.jnca.2017.09.001Grozev, Di., Spasov, G., Shopov, M., Kakanakov, N., Petrova, G., 2016. Experimental study of Cloud Computing based SCADA in Electrical Power Systems, in: 2016 25th International Scientific Conference Electronics, ET 2016. https://doi.org/10.1109/ET.2016.7753482
2018Celen, B., Oniz, Y., 2018. Trajectory tracking of a quadcopter using fuzzy logic and neural network controllers, in: Engin S.N. Arisoy D.O., O.M.A. (Ed.), 2018 6th International Conference on Control Engineering and Information Technology, CEIT 2018. https://doi.org/10.1109/CEIT.2018.8751810Topalov, A.V., Cascella, G.L., Giordano, V., Cupertino, F., Kaynak, O., "Sliding mode neuro-adaptive control of electric drives," (2007) IEEE Transactions on Industrial Electronics, 54 (1), pp. 671-679, DOI: 10.1109/TIE.2006.888930, ISSN: 02780046.
2018Celen, B., Oniz, Y., 2018. Trajectory tracking of a quadcopter using fuzzy logic and neural network controllers, in: Engin S.N. Arisoy D.O., O.M.A. (Ed.), 2018 6th International Conference on Control Engineering and Information Technology, CEIT 2018. https://doi.org/10.1109/CEIT.2018.8751810Shakev, N.G., Topalov, A.V., Kaynak, O., "Sliding mode algorithm for online learning in analog multilayer feedforward neural networks," (2003) Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2714, pp. 1064-1072, ISSN: 03029743, https://www.scopus.com/inward/record.uri?eid=2-s2.0-35248831203&partnerID=40&md5=fab3857ae4a94b0c1b6c76937403d06a
2018Morrell, T.J., Venkataramanan, V., Srivastava, A., Bose, A., Liu, C.-C., 2018. Modeling of Electric Distribution Feeder Using Smart Meter Data, in: Proceedings of the IEEE Power Engineering Society Transmission and Distribution Conference. https://doi.org/10.1109/TDC.2018.8440540Stoyanov, S., Kakanakov, N., 2017. Big data analytics in electricity distribution systems, in: 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., pp. 205–208. https://doi.org/10.23919/MIPRO.2017.7973419
2018Lu, X., Zhao, Y., Liu, M., 2018. Self-learning interval type-2 fuzzy neural network controllers for trajectory control of a Delta parallel robot. Neurocomputing 283, 107–119. https://doi.org/10.1016/j.neucom.2017.12.043Topalov, A.V., Kayacan, E., Oniz, Y., Kaynak, O., "Adaptive neuro-fuzzy control with sliding mode learning algorithm: Application to antilock braking system," (2009) Proceedings of 2009 7th Asian Control Conference, ASCC 2009, art. no. 5276234, pp. 784-789, ISBN: 9788995605691.
2018Pei, P., Pei, Z., Tang, Z., Gu, H., Llopis-Albert, C., 2018. Position Tracking Control of PMSM Based on Fuzzy PID-Variable Structure Adaptive Control. Mathematical Problems in Engineering 2018. https://doi.org/10.1155/2018/5794067Topalov, A.V., Cascella, G.L., Giordano, V., Cupertino, F., Kaynak, O., "Sliding mode neuro-adaptive control of electric drives," (2007) IEEE Transactions on Industrial Electronics, 54 (1), pp. 671-679, DOI: 10.1109/TIE.2006.888930, ISSN: 02780046.
2018Driss, A., Maalej, S., Zaghdoudi, M.C., 2018. Electro-thermal modeling of power IGBT modules by heat pipe systems, in: Proceedings - 2017 International Conference on Engineering and MIS, ICEMIS 2017. pp. 1–7. https://doi.org/10.1109/ICEMIS.2017.8273068Asparuhova, K., Grigorova, T., "IGBT high accuracy behavioral macromodel," (2008) "2008 26th International Conference on Microelectronics, Proceedings, MIEL 2008", art. no. 4559254, pp. 185-188. DOI: 10.1109/ICMEL.2008.4559254, ISBN: 9781424418824.
2018Kiddee, K., Khan-Ngern, W., 2018. Performance evaluation of regenerative braking system based on a HESS in extended range BEV. Journal of Electrical Engineering and Technology 13, 1965–1977. https://doi.org/10.5370/JEET.2018.13.5.1965Topalov, A. V, Oniz, Y., Kayacan, E., Kaynak, O., 2011. Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm. Neurocomputing 74, 1883–1893. https://doi.org/10.1016/j.neucom.2010.07.035
2018Alharbi, S., Rodriguez, P., Maharaja, R., Iyer, P., Subaschandrabose, N., Ye, Z., 2018. Secure the internet of things with challenge response authentication in fog computing, in: 2017 IEEE 36th International Performance Computing and Communications Conference, IPCCC 2017. pp. 1–2. https://doi.org/10.1109/PCCC.2017.8280489Kakanakov, N., Shopov, M., 2017. Adaptive models for security and data protection in IoT with Cloud technologies, in: 2017 40th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2017 - Proceedings. https://doi.org/10.23919/MIPRO.2017.7973570
2018Froiz-Míguez, I., Fernández-Caramés, T.M., Fraga-Lamas, P., Castedo, L., 2018. Design, implementation and practical evaluation of an iot home automation system for fog computing applications based on MQTT and ZigBee-WiFi sensor nodes. Sensors (Switzerland) 18. https://doi.org/10.3390/s18082660Ahmed, S., Topalov, A., Shakev, N., 2017. A robotized wireless sensor network based on MQTT cloud computing, in: Proceedings of the 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and Their Application to Mechatronics, ECMSM 2017. https://doi.org/10.1109/ECMSM.2017.7945897
2018Liao, L., Li, K., Li, K., Yang, C., Tian, Q., 2018. A multiple kernel density clustering algorithm for incomplete datasets in bioinformatics. BMC Systems Biology 12. https://doi.org/10.1186/s12918-018-0630-6Borg, A., Lavesson, N., Boeva, V., 2013. Comparison of clustering approaches for gene expression data. Frontiers in Artificial Intelligence and Applications 257, 55–64. https://doi.org/10.3233/978-1-61499-330-8-55
2018Ganji, S.R.S., Rassafi, A.A., Kordani, A.A., 2018. Vehicle Safety Analysis based on a Hybrid Approach Integrating DEMATEL, ANP and ER. KSCE Journal of Civil Engineering 22, 4580–4592. https://doi.org/10.1007/s12205-018-1720-0Tsiporkova, E., Boeva, V., "Multi-step ranking of alternatives in a multi-criteria and multi-expert decision making environment," (2006) Information Sciences, 176 (18), pp. 2673-2697, DOI: 10.1016/j.ins.2005.11.010, ISSN: 00200255
2018Arnaudov, D., Vuchev, S., Hinov, N., 2018. Modelling and research of high-efficiency converter for energy storage systems, in: 2018 20th International Symposium on Electrical Apparatus and Technologies, SIELA 2018 - Proceedings. https://doi.org/10.1109/SIELA.2018.8447163Bankov, N., Grigorova, T., "Load characteristics and control system behavioural modelling under optimal trajectory control of series resonant DC/DC converters," (2005) Journal of Electrical Engineering, 56 (9-10), pp. 258-264, ISSN: 13353632, https://www.scopus.com/inward/record.uri?eid=2-s2.0-55449092989&partnerID=40&md5=275ad6747f348f7bacc380f7156eef79
2018Lin, C.-Y., Liao, K.-H., Chang, C.-H., 2018. An experimental system for MQTT/CoAP-based IoT applications in IPv6 over bluetooth low energy. Journal of Universal Computer Science 24, 1170–1191.Ahmed, S., Topalov, A., Shakev, N., 2017. A robotized wireless sensor network based on MQTT cloud computing, in: Proceedings of the 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and Their Application to Mechatronics, ECMSM 2017. https://doi.org/10.1109/ECMSM.2017.7945897
2018Mardani, A., Nilashi, M., Zavadskas, E.K., Awang, S.R., Zare, H., Jamal, N.M., 2018. Decision Making Methods Based on Fuzzy Aggregation Operators: Three Decades Review from 1986 to 2017. International Journal of Information Technology and Decision Making 17, 391–466. https://doi.org/10.1142/S021962201830001XTsiporkova, E., Boeva, V., "Multi-step ranking of alternatives in a multi-criteria and multi-expert decision making environment," (2006) Information Sciences, 176 (18), pp. 2673-2697, DOI: 10.1016/j.ins.2005.11.010, ISSN: 00200255
2018Wang, Q., Wang, X., Sun, B., 2018. PID control based on GCAQBP algorithm. IPPTA: Quarterly Journal of Indian Pulp and Paper Technical Association 30, 161–165, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85057191963&partnerID=40&md5=a661ab305fc7751b6a8ed6a620b169bcAhmed, S.A., Petrov, M.G., 2015. Trajectory Control of Mobile Robots using Type-2 Fuzzy-Neural PID Controller. IFAC-PapersOnLine 48, 138–143. https://doi.org/10.1016/j.ifacol.2015.12.071
2018Rigatos, G., Siano, P., Wira, P., Busawon, K., Jovanovic, M., 2018. Nonlinear H-Infinity Control for Optimizing Cement Production, in: 2018 UKACC 12th International Conference on Control, CONTROL 2018. pp. 248–253. https://doi.org/10.1109/CONTROL.2018.8516804Topalov, A.V., Kaynak, O., "Neural network modeling and control of cement mills using a variable structure systems theory based on-line learning mechanism," (2004) Journal of Process Control, 14 (5), pp. 581-589, DOI: 10.1016/j.jprocont.2003.10.005, ISSN: 09591524.
2018Trancǎ, D.-C., Buzilǎ, E., Rosner, D., Pǎtru, G.C., Rughiniş, R. V, 2018. Intact industrial internet of things communication solution. UPB Scientific Bulletin, Series C: Electrical Engineering and Computer Science 80, 17–30, https://www.scopus.com/inward/record.uri?eid=2-s2.0-85051419202&partnerID=40&md5=6ad08b36e5f94340badd234392d9692bPenkov, S., Taneva, A., Kalkov, V., Ahmed, S., 2017. Industrial network design using Low-Power Wide-Area Network, in: 2017 4th International Conference on Systems and Informatics, ICSAI 2017. pp. 40–44. https://doi.org/10.1109/ICSAI.2017.8248260
2018Huang, Y.-C., Pan, Y.-R., 2018. A study on improving the ranking quality under multi-work and multi-reviewer: An example of project works ranking. Journal of Quality 25, 69–88. https://doi.org/10.6220/joq.201804_25(2).0001Tsiporkova, E., Boeva, V., "Multi-step ranking of alternatives in a multi-criteria and multi-expert decision making environment," (2006) Information Sciences, 176 (18), pp. 2673-2697, DOI: 10.1016/j.ins.2005.11.010, ISSN: 00200255
2018Parthasarathy, P., Vivekanandan, S., 2018. A numerical modelling of an amperometric-enzymatic based uric acid biosensor for GOUT arthritis diseases. Informatics in Medicine Unlocked 12, 143–147. https://doi.org/10.1016/j.imu.2018.03.001Neykov, A., Rangelova, V.m "Mathematical modeling of the biosensor systems," (1998) Biotechnology and Biotechnological Equipment, 12 (2), pp. 100-109, DOI: 10.1080/13102818.1998.10819000, ISSN: 13102818.
2018Lazim, M.T., Beniyounis, M., Alkhashashna, H., 2018. Analysis and design of adaptive current controller for DC drive using Z-transform, in: 2018 15th International Multi-Conference on Systems, Signals and Devices, SSD 2018. pp. 1305–1310. https://doi.org/10.1109/SSD.2018.8570422Topalov, A.V., Cascella, G.L., Giordano, V., Cupertino, F., Kaynak, O., "Sliding mode neuro-adaptive control of electric drives," (2007) IEEE Transactions on Industrial Electronics, 54 (1), pp. 671-679, DOI: 10.1109/TIE.2006.888930, ISSN: 02780046.
2018Yao, W., Wang, J., Chi, R., 2018. Vector control of semi-submerged ship dynamic positioning based on model-free adaptive sliding mode, in: Proceedings of 2018 IEEE 7th Data Driven Control and Learning Systems Conference, DDCLS 2018. pp. 1122–1127. https://doi.org/10.1109/DDCLS.2018.8516116Topalov, A.V., Cascella, G.L., Giordano, V., Cupertino, F., Kaynak, O., "Sliding mode neuro-adaptive control of electric drives," (2007) IEEE Transactions on Industrial Electronics, 54 (1), pp. 671-679, DOI: 10.1109/TIE.2006.888930, ISSN: 02780046.
2018Lin, C.-M., Le, T.-L., Huynh, T.-T., 2018. Self-evolving function-link interval type-2 fuzzy neural network for nonlinear system identification and control. Neurocomputing 275, 2239–2250. https://doi.org/10.1016/j.neucom.2017.11.009Ahmed, S., Shakev, N., Topalov, A., Shiev, K., Kaynak, O., 2012. Sliding mode incremental learning algorithm for interval type-2 Takagi-Sugeno-Kang fuzzy neural networks. Evolving Systems 3, 179–188. https://doi.org/10.1007/s12530-012-9053-6
2018Khan, I., Hussain, I., Shah, M.Z.A., Kazi, K., Patoli, A.A., 2018. Design and simulation of anti-lock braking system based on electromagnetic damping phenomena, in: 2017 1st International Conference on Latest Trends in Electrical Engineering and Computing Technologies, INTELLECT 2017. pp. 1–8. https://doi.org/10.1109/INTELLECT.2017.8277615Topalov, A. V, Oniz, Y., Kayacan, E., Kaynak, O., 2011. Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm. Neurocomputing 74, 1883–1893. https://doi.org/10.1016/j.neucom.2010.07.035
2018Crespi, G.P., Kuroiwa, D., Rocca, M., 2018. Robust optimization: Sensitivity to uncertainty in scalar and vector cases, with applications. Operations Research Perspectives 5, 113–119. https://doi.org/10.1016/j.orp.2018.03.001Tsiporkova, E., Boeva, V., "Multi-step ranking of alternatives in a multi-criteria and multi-expert decision making environment," (2006) Information Sciences, 176 (18), pp. 2673-2697, DOI: 10.1016/j.ins.2005.11.010, ISSN: 00200255
2018Hosseini, B., Kiani, K., 2018. FWCMR: A scalable and robust fuzzy weighted clustering based on MapReduce with application to microarray gene expression. Expert Systems with Applications 91, 198–210. https://doi.org/10.1016/j.eswa.2017.08.051Boeva, V., Tsiporkova, E., Kostadinova, E., 2014. Analysis of multiple DNA microarray datasets, Springer Handbook of Bio-/Neuroinformatics. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-30574-0_14
2018Li, Y.Q., 2018. An integrated platform for the Internet of Things based on an open source ecosystem. Future Internet 10. https://doi.org/10.3390/fi10110105Shopov, M.P., 2016. An M2M solution for smart metering in electrical power systems, in: 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2016 - Proceedings. https://doi.org/10.1109/MIPRO.2016.7522311
2018Yukalov, V.I., Yukalova, E.P., Sornette, D., 2018. Information processing by networks of quantum decision makers. Physica A: Statistical Mechanics and its Applications 492, 747–766. https://doi.org/10.1016/j.physa.2017.11.004Tsiporkova, E., Boeva, V., "Multi-step ranking of alternatives in a multi-criteria and multi-expert decision making environment," (2006) Information Sciences, 176 (18), pp. 2673-2697, DOI: 10.1016/j.ins.2005.11.010, ISSN: 00200255
2018Joy, J., Ushakumari, S., 2018. Performance comparison of a bridge-less canonical switching cell and H-bridge inverter with SVPWM fed PMBLDC motor drive under fuzzy logic controller. Modelling, Measurement and Control A 91, 193–201. https://doi.org/10.18280/mmc_a.910405Ahmed, S., Topalov, A., Dimitrov, N., Bonev, E., 2016. Industrial implementation of a fuzzy logic controller for brushless DC motor drives using the PicoMotion control framework, in: 2016 IEEE 8th International Conference on Intelligent Systems, IS 2016 - Proceedings. pp. 629–634. https://doi.org/10.1109/IS.2016.7737493
2018Benhellal, B., Hamerlain, M., Rahmani, Y., 2018. Decoupled Adaptive Neuro-Interval Type-2 Fuzzy Sliding Mode Control Applied in a 3DCrane System. Arabian Journal for Science and Engineering 43, 2725–2733. https://doi.org/10.1007/s13369-017-2747-0Shakev, N.G., Topalov, A.V., Kaynak, O., "A neuro-fuzzy adaptive sliding mode controller: Application to second-order chaotic system," (2008) 2008 4th International IEEE Conference Intelligent Systems, IS 2008, 1, art. no. 4670454, pp. 914-919, DOI: 10.1109/IS.2008.4670454, ISBN: 9781424417391.
2018Zheng, S., Lin, Z., Zeng, Q., Zheng, R., Liu, C., Xiong, H., 2018. IAPcloud: A cloud control platform for heterogeneous robots. IEEE Access 6, 30577–30591. https://doi.org/10.1109/ACCESS.2018.2837904Ahmed, S., Topalov, A., Shakev, N., 2017. A robotized wireless sensor network based on MQTT cloud computing, in: Proceedings of the 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and Their Application to Mechatronics, ECMSM 2017. https://doi.org/10.1109/ECMSM.2017.7945897
2018Xu, Y., Ho, C.N.M., Ghosh, A., Muthumuni, D., 2018. A behavioral transient model of IGBT for switching cell power loss estimation in electromagnetic transient simulation, in: Conference Proceedings - IEEE Applied Power Electronics Conference and Exposition - APEC. pp. 270–275. https://doi.org/10.1109/APEC.2018.8341021Asparuhova, K., Grigorova, T., "IGBT high accuracy behavioral macromodel," (2008) "2008 26th International Conference on Microelectronics, Proceedings, MIEL 2008", art. no. 4559254, pp. 185-188. DOI: 10.1109/ICMEL.2008.4559254, ISBN: 9781424418824.
2018Phan, T.-T.-H., Bigand, A., Caillault, E.P., 2018. A New Fuzzy Logic-Based Similarity Measure Applied to Large Gap Imputation for Uncorrelated Multivariate Time Series. Applied Computational Intelligence and Soft Computing 2018. https://doi.org/10.1155/2018/9095683Kostadinova, E., Boeva, V., Boneva, L., Tsiporkova, E., 2012. An integrative DTW-based imputation method for gene expression time series data, in: IS’2012 - 2012 6th IEEE International Conference Intelligent Systems, Proceedings. pp. 258–263. https://doi.org/10.1109/IS.2012.6335145
2018Gwizdałła, T.M., 2018. The role of mapping curve in swarm-like opinion formation. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 11055 LNAI, 157–166. https://doi.org/10.1007/978-3-319-98443-8_15Hristoskova, A., Boeva, V., Tsiporkova, E., 2014. A formal concept analysis approach to consensus clustering of multi-experiment expression data. BMC Bioinformatics 15. https://doi.org/10.1186/1471-2105-15-151
2018Saha, A.K., 2018. Challenges in Power Systems Operations, Analyses, Computations and Application of Cloud Computing in Electrical Power Systems, in: 2018 IEEE PES/IAS PowerAfrica, PowerAfrica 2018. pp. 208–213. https://doi.org/10.1109/PowerAfrica.2018.8521050Grozev, Di., Spasov, G., Shopov, M., Kakanakov, N., Petrova, G., 2016. Experimental study of Cloud Computing based SCADA in Electrical Power Systems, in: 2016 25th International Scientific Conference Electronics, ET 2016. https://doi.org/10.1109/ET.2016.7753482
2018Vasyukov, I. V, Puzin, V.S., Batyukov, A. V, Pavlenko, A. V, Zhivodernikov, A. V, Shcherbakov, A. V, 2018. Calculation of the external characteristic of a switched-mode power supply, in: 2018 10th International Conference on Electrical Power Drive Systems, ICEPDS 2018 - Conference Proceedings. https://doi.org/10.1109/ICEPDS.2018.8571828Bankov, N., Grigorova, T., "Load characteristics and control system behavioural modelling under optimal trajectory control of series resonant DC/DC converters," (2005) Journal of Electrical Engineering, 56 (9-10), pp. 258-264, ISSN: 13353632, https://www.scopus.com/inward/record.uri?eid=2-s2.0-55449092989&partnerID=40&md5=275ad6747f348f7bacc380f7156eef79
2018Hadroug, N., Hafaifa, A., Batel, N., Kouzou, A., Chaibet, A., 2018. Active fault tolerant control based on a neuro fuzzy inference system applied to a two shafts gas turbine. Applied Artificial Intelligence 32, 515–540. https://doi.org/10.1080/08839514.2018.1483114Topalov, A. V, Oniz, Y., Kayacan, E., Kaynak, O., 2011. Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm. Neurocomputing 74, 1883–1893. https://doi.org/10.1016/j.neucom.2010.07.035
2018Yang, P., Zhang, Y.-J., 2018. Study on the pass measurement for wire drawing dies with laser diffraction based on internet plus. Guangdianzi Jiguang/Journal of Optoelectronics Laser 29, 64–69. https://doi.org/10.16136/j.joel.2018.01.0132Ahmed, S., Topalov, A., Shakev, N., 2017. A robotized wireless sensor network based on MQTT cloud computing, in: Proceedings of the 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and Their Application to Mechatronics, ECMSM 2017. https://doi.org/10.1109/ECMSM.2017.7945897
2018Hernández-Rojas, D.L., Fernández-Caramés, T.M., Fraga-Lamas, P., Escudero, C.J., 2018. A plug-and-play human-centered virtual TEDS architecture for the web of things. Sensors (Switzerland) 18. https://doi.org/10.3390/s18072052Ahmed, S., Topalov, A., Shakev, N., 2017. A robotized wireless sensor network based on MQTT cloud computing, in: Proceedings of the 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and Their Application to Mechatronics, ECMSM 2017. https://doi.org/10.1109/ECMSM.2017.7945897
2018G. Todorov, "Loss distribution in IPMSM at different control strategy," 2018 10th Electrical Engineering Faculty Conference (BulEF), Sozopol, Bulgaria, 2018, pp. 1-4. ISBN: 978-153867565-6, doi: 10.1109/BULEF.2018.8646958, Available: https://www.scopus.com/record/display.uri?eid=2-s2.0-85063193545&origin=resultslist&sort=plf-f&src=s&sid=7969f4976236eb61c0799cb34f8a2749&sot=autdocs&sdt=autdocs&sl=18&s=AU-ID%2835197008700%29&relpos=2&citeCnt=0&searchTerm= P. Rizov, T. Stoyanov, R. Spasov, V. Spasov, Analysis of permanent magnet synchronous machines used for hybrid vehicles, 15th International Conference on Electrical Machines, Drives and Power Systems, ELMA 2017 – Proceedings, Sofia, 1-3 June 2017, pp. 374-378, ISBN (Online) 978-1-5090-6691-9, DOI: 10.1109/ELMA.2017.7955467, Available: https://www.scopus.com/record/display.uri?eid=2-s2.0-85025432282&origin=resultslist&sort=plf-f&src=s&sid=d3d3b98794a7b7e0927df3bc3753c901&sot=autdocs&sdt=autdocs&sl=18&s=AU-ID%2835197008700%29&relpos=2&citeCnt=2&searchTerm=
2017Azar, A.T., Zhu, Q., Khamis, A., Zhao, D., 2017. Control design approaches for parallel robot manipulators: A review. International Journal of Modelling, Identification and Control 28, 199–211. https://doi.org/10.1504/IJMIC.2017.086563Topalov, A.V., Kaynak, O., "Robust neural identification of robotic manipulators using discrete time adaptive sliding mode learning," (2005) IFAC Proceedings Volumes (IFAC-PapersOnline), 16, pp. 336-341, ISSN: 14746670, ISBN: 008045108X; 9780080451084.
2017González-Díaz, C.A., Uscanga-Carmona, M.C., Lozano-Trenado, L.M., Ortíz, J.L., González, J.A., Guerrero-Robles, C.I., 2017. Clinical evaluation of inductive spectrometer to detect breast cancer, in: Bustamante J. Sierra D.A., T.I. (Ed.), IFMBE Proceedings. pp. 678–681. https://doi.org/10.1007/978-981-10-4086-3_170Scharfetter, H., Ninaus, W., Puswald, B., Petrova, G.I., Kovachev, D., Hutten, H., "Inductively coupled wideband transceiver for bioimpedance spectroscopy (IBIS)," (1999) Annals of the New York Academy of Sciences, 873, pp. 322-334, DOI: 10.1111/j.1749-6632.1999.tb09480.x, ISSN: 00778923, https://www.scopus.com/inward/record.uri?eid=2-s2.0-0032985958&doi=10.1111%2fj.1749-6632.1999.tb09480.x&partnerID=40&md5=d1c56176a052e96ef53f71a9d896779a
2017Liao, L., Li, K., Li, K., Tian, Q., Yang, C., 2017. Automatic density clustering with multiple kernels for high-dimension bioinformatics data, in: Yoo I. Zheng J.H., G.Y.H.X.T.S.C.-R.B.Y.G.J.K.D. (Ed.), Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. pp. 2105–2112. https://doi.org/10.1109/BIBM.2017.8217984Borg, A., Lavesson, N., Boeva, V., 2013. Comparison of clustering approaches for gene expression data. Frontiers in Artificial Intelligence and Applications 257, 55–64. https://doi.org/10.3233/978-1-61499-330-8-55
2017Hadizadeh, M., Farzanegan, A., Noaparast, M., 2017. Supervisory Fuzzy Expert Controller for Sag Mill Grinding Circuits: Sungun Copper Concentrator. Mineral Processing and Extractive Metallurgy Review 38, 168–179. https://doi.org/10.1080/08827508.2017.1281133Topalov, A.V., Kaynak, O., "Neural network modeling and control of cement mills using a variable structure systems theory based on-line learning mechanism," (2004) Journal of Process Control, 14 (5), pp. 581-589, DOI: 10.1016/j.jprocont.2003.10.005, ISSN: 09591524.
2017Zhang, S., Li, Z., Wei, Z., Wang, S., 2017. An automatic human fall detection approach using RGBD cameras, in: Proceedings of 2016 5th International Conference on Computer Science and Network Technology, ICCSNT 2016. pp. 781–784. https://doi.org/10.1109/ICCSNT.2016.8070265Spasova, V., Iliev, I., Petrova, G., 2016. Privacy preserving fall detection based on simple human silhouette extraction and a linear support vector machine. International Journal Bioautomation 20, 237–252.
2017Jian, G., Xiaojing, H., Zhaoguang, W., Tianyu, A., Jun, Z., 2017. Multilevel cooperative on-line computing architecture under dispatching cloud, in: Proceedings IECON 2017 - 43rd Annual Conference of the IEEE Industrial Electronics Society. pp. 339–344. https://doi.org/10.1109/IECON.2017.8216061Grozev, Di., Spasov, G., Shopov, M., Kakanakov, N., Petrova, G., 2016. Experimental study of Cloud Computing based SCADA in Electrical Power Systems, in: 2016 25th International Scientific Conference Electronics, ET 2016. https://doi.org/10.1109/ET.2016.7753482
2017Zhang, C., Rossi, C., Kayacan, E., 2017. Interval type-2 fuzzy-neuro control of nonlinear systems with proved overall system stability, in: IEEE International Conference on Fuzzy Systems. https://doi.org/10.1109/FUZZ-IEEE.2017.8015495Cascella, G.L., Cupertino, F., Topalov, A.V., Kaynak, O., Giordano, V., "Adaptive control of electric drives using sliding-mode learning neural networks,"(2005) IEEE International Symposium on Industrial Electronics, I, art. no. 1528899, pp. 125-130, DOI: 10.1109/ISIE.2005.1528899, ISBN: 0780387384; 9780780387386, https://www.scopus.com/inward/record.uri?eid=2-s2.0-33748367237&doi=10.1109%2fISIE.2005.1528899&partnerID=40&md5=85e3e916ca150354cb78ca3e2268c3cb
2017Thurow, K., Junginger, S., Roddelkopf, T., 2017. Automated tube storage system for bioanalytics and diagnostics [Automatisiertes tube storage-System für Bioanalytik und Diagnostik]. BioSpektrum 23, 531–534. https://doi.org/10.1007/s12268-017-0837-xPavlova, P.Em., Cyrrilov, K.P., Moumdjiev, I.N., "Application of HSV colour system in identification by colour of biological objects on the basis of microscopic images," (1996) Computerized Medical Imaging and Graphics, 20 (5), pp. 357-364, DOI: 10.1016/S0895-6111(96)00058-4, ISSN: 08956111, https://www.scopus.com/inward/record.uri?eid=2-s2.0-0030236570&doi=10.1016%2fS0895-6111%2896%2900058-4&partnerID=40&md5=fa83479bf8da78a636e0e8132d9b60a0
2017Li, C., Zhang, Y., Li, P., 2017. Full control of a quadrotor using parameter-scheduled backstepping method: implementation and experimental tests. Nonlinear Dynamics 89, 1259–1278. https://doi.org/10.1007/s11071-017-3514-1Shakev, N.G., Topalov, A. V, Kaynak, O., Shiev, K.B., 2012. Comparative results on stabilization of the quad-rotor rotorcraft using bounded feedback controllers. Journal of Intelligent and Robotic Systems: Theory and Applications 65, 389–408. https://doi.org/10.1007/s10846-011-9583-3
2017Ku, C.-H., Iriberri, A., Jena, G., 2017. Visual analytics for crime analysis and decision support, Decision Management: Concepts, Methodologies, Tools, and Applications. https://doi.org/10.4018/978-1-5225-1837-2.ch065Borg, A., Boldt, M., Lavesson, N., Melander, U., Boeva, V., 2014. Detecting serial residential burglaries using clustering. Expert Systems with Applications 41, 5252–5266. https://doi.org/10.1016/j.eswa.2014.02.035
2017Ji, Y., Qu, S., Yu, Z., 2017. A New Method for Solving Multiobjective Bilevel Programs. Discrete Dynamics in Nature and Society 2017. https://doi.org/10.1155/2017/2870420Tsiporkova, E., Boeva, V., "Multi-step ranking of alternatives in a multi-criteria and multi-expert decision making environment," (2006) Information Sciences, 176 (18), pp. 2673-2697, DOI: 10.1016/j.ins.2005.11.010, ISSN: 00200255
2017Aziz, M.W., Rashid, M., 2017. Extended meta-model for Service-Oriented Development of Embedded Real-time Systems, in: 2017 FIRST INTERNATIONAL CONFERENCE ON LATEST TRENDS IN ELECTRICAL ENGINEERING AND COMPUTING TECHNOLOGIES (INTELLECT). IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA.Shopov, M.P., Matev, H., Spasov, G.V., Evaluation of web services implementation for ARM-based embedded system, Proceedings of the International Scientific and Applied Science Conference ELECTRONICS (ET'2007), pp. 79-84, 19-21 Sept., 2007, Sozopol, Bulgaria, ISBN: 1313-1842.
2017Rücker, G., Schwarzer, G., 2017. Resolve conflicting rankings of outcomes in network meta-analysis: Partial ordering of treatments. Research Synthesis Methods 8, 526–536. https://doi.org/10.1002/jrsm.1270Hristoskova, A., Boeva, V., Tsiporkova, E., 2014. A formal concept analysis approach to consensus clustering of multi-experiment expression data. BMC Bioinformatics 15. https://doi.org/10.1186/1471-2105-15-151
2017Strauss, T., Von Maltitz, M.J., 2017. Generalising ward’s method for use with manhattan distances. PLoS ONE 12. https://doi.org/10.1371/journal.pone.0168288Boeva, V., Tsiporkova, E., Kostadinova, E., 2014. Analysis of multiple DNA microarray datasets, Springer Handbook of Bio-/Neuroinformatics. Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-30574-0_14
2017Dao, V.-L., Hoang, V.-P., 2017. A smart delivery system using Internet of Things, in: Bui D.-H., T.X.-T. (Ed.), Proceedings of 2017 7th International Conference on Integrated Circuits, Design, and Verification, ICDV 2017. pp. 58–63. https://doi.org/10.1109/ICDV.2017.8188639Ahmed, S., Topalov, A., Shakev, N., 2017. A robotized wireless sensor network based on MQTT cloud computing, in: Proceedings of the 2017 IEEE International Workshop of Electronics, Control, Measurement, Signals and Their Application to Mechatronics, ECMSM 2017. https://doi.org/10.1109/ECMSM.2017.7945897
2017Paulusova, J., Paulus, M., 2017. Internal model control of thermo-optical plant, in: Kvasnica M., F.M. (Ed.), Proceedings of the 2017 21st International Conference on Process Control, PC 2017. pp. 179–184. https://doi.org/10.1109/PC.2017.7976210Todorov, Y., Terzyiska, M., Ahmed, S., Petrov, M., 2013. Fuzzy-neural predictive control using Levenberg-Marquardt optimization approach, in: 2013 IEEE International Symposium on Innovations in Intelligent Systems and Applications, IEEE INISTA 2013. https://doi.org/10.1109/INISTA.2013.6577624
2017Toji, J.-I., Ichihara, H., 2017. Formation control of quadrotors based on interconnected positive systems, in: 2016 European Control Conference, ECC 2016. pp. 837–842. https://doi.org/10.1109/ECC.2016.7810393Shakev, N.G., Topalov, A. V, Kaynak, O., Shiev, K.B., 2012. Comparative results on stabilization of the quad-rotor rotorcraft using bounded feedback controllers. Journal of Intelligent and Robotic Systems: Theory and Applications 65, 389–408. https://doi.org/10.1007/s10846-011-9583-3
2017Bogónez-Franco, P., Pham, P., Gehin, C., Massot, B., Delhomme, G., McAdams, E., Guillemaud, R., 2017. Problems encountered during inappropriate use of commercial bioimpedance devices in novel applications, Progress Reports on Impedance Spectroscopy: Measurements, Modeling, and Application. https://doi.org/10.1515/9783110449822-014Petrova, G.I., "Influence of electrode impedance changes on the common-mode rejection ratio in bioimpedance measurements," (1999) Physiological Measurement, 20 (4), pp. N11-N19, DOI: 10.1088/0967-3334/20/4/401, ISSN: 09673334, https://www.scopus.com/inward/record.uri?eid=2-s2.0-0032692751&doi=10.1088%2f0967-3334%2f20%2f4%2f401&partnerID=40&md5=b55693887d144036b3f76f67d9bd12a2
2017Suwansrikham, P., Singkhamfu, P., 2017. Indoor vision based guidance system for autonomous drone and control application, in: 2nd Joint International Conference on Digital Arts, Media and Technology 2017: Digital Economy for Sustainable Growth, ICDAMT 2017. pp. 110–114. https://doi.org/10.1109/ICDAMT.2017.7904945Popov, V.L., Shiev, K.B., Topalov, A. V, Shakev, N.G., Ahmed, S.A., 2016. Control of the flight of a small quadrotor using gestural interface, in: 2016 IEEE 8th International Conference on Intelligent Systems, IS 2016 - Proceedings. pp. 622–628. https://doi.org/10.1109/IS.2016.7737492
2017Chi, H., Lin, Z., Jin, H., Xu, B., Qi, M., 2017. A decision support system for detecting serial crimes. Knowledge-Based Systems 123, 88–101. https://doi.org/10.1016/j.knosys.2017.02.017Borg, A., Boldt, M., Lavesson, N., Melander, U., Boeva, V., 2014. Detecting serial residential burglaries using clustering. Expert Systems with Applications 41, 5252–5266. https://doi.org/10.1016/j.eswa.2014.02.035
2017Fan, T.-F., Liau, C.-J., 2017. A logic for reasoning about evidence and belief, in: Proceedings - 2017 IEEE/WIC/ACM International Conference on Web Intelligence, WI 2017. pp. 509–516. https://doi.org/10.1145/3106426.3106519Tsiporkova, E., Boeva, V., De Baets, B., "Dempster-Shafer theory framed in modal logic," (1999) International Journal of Approximate Reasoning, 21 (2), pp. 157-175, DOI: 10.1016/S0888-613X(99)00011-0, ISSN: 0888613X, https://www.scopus.com/inward/record.uri?eid=2-s2.0-0040958817&doi=10.1016%2fS0888-613X%2899%2900011-0&partnerID=40&md5=71bd2a1dbf1cceba01827a9260f8b235
2017Kurtaj, L., Shatri, V., Limani, I., 2017. New model of information processing at granule cell layer makes cerebellum as biological equivalent for ANFIS and CANFIS: Sharing of processing resources and generalization, in: IEEE International Conference on Fuzzy Systems. https://doi.org/10.1109/FUZZ-IEEE.2017.8015746Shiev, K., Shakev, N., Topalov, A. V, Ahmed, S., 2012. Trajectory control of manipulators using type-2 fuzzy neural friction and disturbance compensator, in: IS’2012 - 2012 6th IEEE International Conference Intelligent Systems, Proceedings. pp. 324–329. https://doi.org/10.1109/IS.2012.6335155
2017Li, H., Sang, X., 2017. SNR and transmission error rate for remote laser communication system in real atmosphere channel. Sensors and Actuators, A: Physical 258, 156–162. https://doi.org/10.1016/j.sna.2017.03.007Ferdinandov, E., Pachedjieva, B., Bonev, B., Saparev, Sl., "Joint influence of heterogeneous stochastic factors on bit-error rate of ground-to-ground free-space laser communication systems," (2007) Optics Communications, 270 (2), pp. 121-127, DOI: 10.1016/j.optcom.2006.09.006, ISSN: 00304018, https://www.scopus.com/inward/record.uri?eid=2-s2.0-33845870455&doi=10.1016%2fj.optcom.2006.09.006&partnerID=40&md5=3eb75086576617a7e7820243e8c9d7c8
2017Phan, T.-T.-H., Caillault, E.P., Bigand, A., Lefebvre, A., 2017. DTW-Approach for uncorrelated multivariate time series imputation, in: Ueda N. Chien J.-T., M.T.L.J.W.S. (Ed.), IEEE International Workshop on Machine Learning for Signal Processing, MLSP. pp. 1–5. https://doi.org/10.1109/MLSP.2017.8168165Kostadinova, E., Boeva, V., Boneva, L., Tsiporkova, E., 2012. An integrative DTW-based imputation method for gene expression time series data, in: IS’2012 - 2012 6th IEEE International Conference Intelligent Systems, Proceedings. pp. 258–263. https://doi.org/10.1109/IS.2012.6335145
2017De Souza, R.R., Gules, R., 2017. Modeling of a high power IGBT for a 1000A DC-DC converter used to drive diesel-electric locomotive traction motors, in: 2016 12th IEEE International Conference on Industry Applications, INDUSCON 2016. https://doi.org/10.1109/INDUSCON.2016.7874500Asparuhova, K., Grigorova, T., "IGBT behavioral PSPICE model," (2006) 2006 25th International Conference on Microelectronics, MIEL 2006 - Proceedings, art. no. 1650931, pp. 215-218, DOI: 10.1109/ICMEL.2006.1650931, ISBN: 1424401178; 9781424401178.
2017Bacha, S., Saadi, R., Ayad, M.Y., Aboubou, A., Bahri, M., 2017. A review on vehicle modeling and control technics used for autonomous vehicle path following, in: International Conference on Green Energy and Conversion Systems, GECS 2017. https://doi.org/10.1109/GECS.2017.8066221Ahmed, S.A., Petrov, M.G., 2015. Trajectory Control of Mobile Robots using Type-2 Fuzzy-Neural PID Controller. IFAC-PapersOnLine 48, 138–143. https://doi.org/10.1016/j.ifacol.2015.12.071
2017Phan, T.-T.-H., Caillault, E.P., Lefebvre, A., Bigand, A., 2017. Which DTW method applied to marine univariate time series imputation, in: OCEANS 2017 - Aberdeen. pp. 1–7. https://doi.org/10.1109/OCEANSE.2017.8084598Kostadinova, E., Boeva, V., Boneva, L., Tsiporkova, E., 2012. An integrative DTW-based imputation method for gene expression time series data, in: IS’2012 - 2012 6th IEEE International Conference Intelligent Systems, Proceedings. pp. 258–263. https://doi.org/10.1109/IS.2012.6335145
2017Ahmadi, M.M., Mahdavirad, H., Bakhtiari, B., 2017. Multi-criteria analysis of site selection for groundwater recharge with treated municipal wastewater. Water Science and Technology 76, 909–919. https://doi.org/10.2166/wst.2017.273Tsiporkova, E., Boeva, V., "Multi-step ranking of alternatives in a multi-criteria and multi-expert decision making environment," (2006) Information Sciences, 176 (18), pp. 2673-2697, DOI: 10.1016/j.ins.2005.11.010, ISSN: 00200255
2017Hashlamon, I., Erbatur, K., 2017. Reduced filtered dynamic model for joint friction estimation of walking bipeds. Jordan Journal of Mechanical and Industrial Engineering 11, 147–154.Shiev, K., Shakev, N., Topalov, A. V, Ahmed, S., 2012. Trajectory control of manipulators using type-2 fuzzy neural friction and disturbance compensator, in: IS’2012 - 2012 6th IEEE International Conference Intelligent Systems, Proceedings. pp. 324–329. https://doi.org/10.1109/IS.2012.6335155
2017Sun, M.-P., Liu, J.-J., Nian, X.-H., Wang, H.-B., 2017. Robust tracking control of a quad-rotor unmanned aerial vehicle via interval matrix. Kongzhi Lilun Yu Yingyong/Control Theory and Applications 34, 168–178. https://doi.org/10.7641/CTA.2017.60559Shakev, N.G., Topalov, A. V, Kaynak, O., Shiev, K.B., 2012. Comparative results on stabilization of the quad-rotor rotorcraft using bounded feedback controllers. Journal of Intelligent and Robotic Systems: Theory and Applications 65, 389–408. https://doi.org/10.1007/s10846-011-9583-3
2017Chang, C.-W., Xiao, W.-R., Hsiao, C.-C., Chen, S.-S., Tao, C.-W., 2017. A simplified interval type-2 fuzzy CMAC, in: IFSA-SCIS 2017 - Joint 17th World Congress of International Fuzzy Systems Association and 9th International Conference on Soft Computing and Intelligent Systems. https://doi.org/10.1109/IFSA-SCIS.2017.8023236Shiev, K., Ahmed, S., Shakev, N., Topalov, A. V, 2016. Trajectory control of manipulators using an adaptive parametric type-2 fuzzy CMAC friction and disturbance compensator. Studies in Computational Intelligence 586, 63–82. https://doi.org/10.1007/978-3-319-14194-7_4
2017Lin, C.-M., Le, T.-L., 2017. PSO-Self-Organizing Interval Type-2 Fuzzy Neural Network for Antilock Braking Systems. International Journal of Fuzzy Systems 19, 1362–1374. https://doi.org/10.1007/s40815-017-0301-6Topalov, A. V, Oniz, Y., Kayacan, E., Kaynak, O., 2011. Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm. Neurocomputing 74, 1883–1893. https://doi.org/10.1016/j.neucom.2010.07.035
2017Salgado, I., Yañez, C., Camacho, O., Chairez, I., 2017. Adaptive control of discrete-time nonlinear systems by recurrent neural networks in quasi-sliding mode like regime. International Journal of Adaptive Control and Signal Processing 31, 83–96. https://doi.org/10.1002/acs.2685Topalov, A.V., Kaynak, O., Aydin, G., "Neuro-adaptive sliding-mode tracking control of robot manipulators," (2007) International Journal of Adaptive Control and Signal Processing, 21 (8-9), pp. 674-691, DOI: 10.1002/acs.982, ISSN: 08906327, https://www.scopus.com/inward/record.uri?eid=2-s2.0-35648997923&doi=10.1002%2facs.982&partnerID=40&md5=df92e7ed0dff1dc4701976dc83f7c23d
2017Terán-Jiménez, O., Rodríguez-Roldán, G., Hernández-Rivera, D., Suaste-Gómez, E., 2017. Sensors based on conducting polymers for measurement of physiological parameters. IEEE Sensors Journal 17, 2492–2497. https://doi.org/10.1109/JSEN.2017.2671448Petrova, G.I., "Influence of electrode impedance changes on the common-mode rejection ratio in bioimpedance measurements," (1999) Physiological Measurement, 20 (4), pp. N11-N19, DOI: 10.1088/0967-3334/20/4/401, ISSN: 09673334, https://www.scopus.com/inward/record.uri?eid=2-s2.0-0032692751&doi=10.1088%2f0967-3334%2f20%2f4%2f401&partnerID=40&md5=b55693887d144036b3f76f67d9bd12a2
2017Lima, S.C., Wengerkievicz, C.A.C., Batistela, N.J., Sadowski, N., Da Silva, P.A., Beltrame, A.Y., 2017. Induction motor parameter estimation from manufacturer data using genetic algorithms and heuristic relationships, in: 14th Brazilian Power Electronics Conference, COBEP 2017. pp. 1–6. https://doi.org/10.1109/COBEP.2017.8257400Kostov, I., Spasov, V., Rangelova, V., "Application of genetic algorithms for determining the parameters of induction motors," (2009) Tehnicki Vjesnik, 16 (2), pp. 49-53, ISSN: 13303651, https://www.scopus.com/inward/record.uri?eid=2-s2.0-70749138441&partnerID=40&md5=e302755b6e6ecb76ffccfe892d71689d
2017Bratchenko, I.A., Artemyev, D.N., Myakinin, O.O., Khristoforova, Y.A., Moryatov, A.A., Kozlov, S. V, Zakharov, V.P., 2017. Combined Raman and autofluorescence ex vivo diagnostics of skin cancer in near-infrared and visible regions. Journal of Biomedical Optics 22. https://doi.org/10.1117/1.JBO.22.2.027005Borisova, E., Troyanova, P., Pavlova, P., Avramov, L., "Diagnostics of pigmented skin tumors based on laser-induced autofluorescence and diffuse reflectance spectroscopy," (2008) Quantum Electronics, 38 (6), pp. 597-605, DOI: 10.1070/QE2008v038n06ABEH013891, ISSN: 10637818.
2017Wang, J.-Y., Healey, T., Barker, A., Brown, B., Monk, C., Anumba, D., 2017. Magnetic induction spectroscopy (MIS) - Probe design for cervical tissue measurements. Physiological Measurement 38, 729–744. https://doi.org/10.1088/1361-6579/aa6b4eScharfetter, H., Ninaus, W., Puswald, B., Petrova, G.I., Kovachev, D., Hutten, H., "Inductively coupled wideband transceiver for bioimpedance spectroscopy (IBIS)," (1999) Annals of the New York Academy of Sciences, 873, pp. 322-334, DOI: 10.1111/j.1749-6632.1999.tb09480.x, ISSN: 00778923, https://www.scopus.com/inward/record.uri?eid=2-s2.0-0032985958&doi=10.1111%2fj.1749-6632.1999.tb09480.x&partnerID=40&md5=d1c56176a052e96ef53f71a9d896779a
2017Reddy, K.H., Ramasamy, S., Ramanathan, P., 2017. Hybrid adaptive neuro fuzzy based speed controller for brushless DC motor. Gazi University Journal of Science 30, 93–110.Topalov, A. V, Oniz, Y., Kayacan, E., Kaynak, O., 2011. Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm. Neurocomputing 74, 1883–1893. https://doi.org/10.1016/j.neucom.2010.07.035
2017Ferreira, L.M., Pinto, C.L.N., Dias, S.M., Nobre, C.N., Zárate, L.E., 2017. Extraction of conservative rules for Translation Initiation Site prediction using formal concept analysis, in: Filipe J. Hammoudi S., S.M.C.O.F.J. (Ed.), ICEIS 2017 - Proceedings of the 19th International Conference on Enterprise Information Systems. pp. 265–271.Hristoskova, A., Boeva, V., Tsiporkova, E., 2014. A formal concept analysis approach to consensus clustering of multi-experiment expression data. BMC Bioinformatics 15. https://doi.org/10.1186/1471-2105-15-151
2017Viaud, J.-F., Bertet, K., Missaoui, R., Demko, C., 2017. Distributed and parallel computation of the canonical direct basis. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 10308 LNAI, 228–241. https://doi.org/10.1007/978-3-319-59271-8_15Tsiporkova, E., Boeva, V., Kostadinova, E., 2011. MapReduce and FCA approach for clustering of multiple-experiment data compendium, in: Belgian/Netherlands Artificial Intelligence Conference, ISSN:15687805, https://www.scopus.com/inward/record.uri?eid=2-s2.0-84874007923&partnerID=40&md5=11fbd1b648b0e2f9c302d682b74983e0
2017Blondel, W., Abdat, F., Guermeur, Y., Amouroux, M., 2017. Multimodality fibered in vivo spectroscopy applied to skin hyperplastic and dysplastic class discrimination: Spatially resolved data fusion-based classification, in: Asia Communications and Photonics Conference, ACP. https://doi.org/10.1364/ACPC.2017.S4I.1Borisova, E., Troyanova, P., Pavlova, P., Avramov, L., "Diagnostics of pigmented skin tumors based on laser-induced autofluorescence and diffuse reflectance spectroscopy," (2008) Quantum Electronics, 38 (6), pp. 597-605, DOI: 10.1070/QE2008v038n06ABEH013891, ISSN: 10637818.
2017Lyaskov, M., Spasov, G., Petrova, G., 2017. A practical implementation of smart home energy data storage and control application based on cloud services, in: 2017 26th International Scientific Conference Electronics, ET 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., pp. 1–4. https://doi.org/10.1109/ET.2017.8124387Shopov, M.P., 2016. An M2M solution for smart metering in electrical power systems, in: 2016 39th International Convention on Information and Communication Technology, Electronics and Microelectronics, MIPRO 2016 - Proceedings. https://doi.org/10.1109/MIPRO.2016.7522311
2017Radovic, M., Ghalwash, M., Filipovic, N., Obradovic, Z., 2017. Minimum redundancy maximum relevance feature selection approach for temporal gene expression data. BMC Bioinformatics 18. https://doi.org/10.1186/s12859-016-1423-9Tsiporkova, E., Boeva, V., "Nonparametric recursive aggregation process," (2004) Kybernetika, 40 (1), pp. 51-70, ISSN: 00235954, https://www.scopus.com/inward/record.uri?eid=2-s2.0-2942644872&partnerID=40&md5=8e07a386ea77af77b98e52ef9ad92c6b
2017Ferreira, B. V, Serejo, G., Ferreira, M.R., Ferreira, D.F., Cardoso, L., Yoshidome, E., Arruda, H., Lira, W., Ferreira, J., Carvalho, E., Pessin, G., Souza, C.R.B., 2017. Wearable computing for railway environments: proposal and evaluation of a safety solution. IET INTELLIGENT TRANSPORT SYSTEMS 11, 319–325. https://doi.org/10.1049/iet-its.2016.0187Shopov, M., Petrova, G., Spasov, G., "Evaluation of Zigbee-based body sensor networks", Annual Journal of Electronics, vol.5, no.2, pp. 60-63, 2011, ISSN 1314-0078.
2017Arif Wani, M., Riyaz, R., 2017. A novel point density based validity index for clustering gene expression datasets. International Journal of Data Mining and Bioinformatics 17, 66–84. https://doi.org/10.1504/IJDMB.2017.084027Boeva, V., 2014. Clustering approaches for dealing with multiple DNA microarray datasets. Journal of Computational Science 5, 368–376. https://doi.org/10.1016/j.jocs.2013.05.003
2017Sun, M.-P., Liu, J.-J., 2017. Tracking control of a quad-rotor UAV based on T - S fuzzy model, in: Liu T., Z.Q. (Ed.), Chinese Control Conference, CCC. pp. 4216–4221. https://doi.org/10.23919/ChiCC.2017.8028019Shakev, N.G., Topalov, A. V, Kaynak, O., Shiev, K.B., 2012. Comparative results on stabilization of the quad-rotor rotorcraft using bounded feedback controllers. Journal of Intelligent and Robotic Systems: Theory and Applications 65, 389–408. https://doi.org/10.1007/s10846-011-9583-3
2017Blondel, W., Abdat, F., Guermeur, Y., Amouroux, M., 2017. Multimodality fibered in vivo spectroscopy applied to skin hyperplastic and dysplastic class discrimination: Spatially resolved data fusion-based classification, in: Optics InfoBase Conference Papers. https://doi.org/10.1364/ACPC.2017.S4I.1Borisova, E., Troyanova, P., Pavlova, P., Avramov, L., "Diagnostics of pigmented skin tumors based on laser-induced autofluorescence and diffuse reflectance spectroscopy," (2008) Quantum Electronics, 38 (6), pp. 597-605, DOI: 10.1070/QE2008v038n06ABEH013891, ISSN: 10637818.
2017Teran-Jiménez, O., Hernández-Rivera, D., Suaste-Gómez, E., 2017. Electrodes based on PPy polymer for electrocardiography and impedance plethysmography, in: Bustamante J. Sierra D.A., T.I. (Ed.), IFMBE Proceedings. pp. 229–232. https://doi.org/10.1007/978-981-10-4086-3_58Petrova, G.I., "Influence of electrode impedance changes on the common-mode rejection ratio in bioimpedance measurements," (1999) Physiological Measurement, 20 (4), pp. N11-N19, DOI: 10.1088/0967-3334/20/4/401, ISSN: 09673334, https://www.scopus.com/inward/record.uri?eid=2-s2.0-0032692751&doi=10.1088%2f0967-3334%2f20%2f4%2f401&partnerID=40&md5=b55693887d144036b3f76f67d9bd12a2
2017Joy, J., Ushakumari, S., 2017. Performance comparison of a Canonical Switching Cell with SPWM and SVPWM fed sensorless PMBLDC motor drive under conventional and fuzzy logic controllers. Journal of the Franklin Institute 354, 5996–6032. https://doi.org/10.1016/j.jfranklin.2017.07.043Ahmed, S., Topalov, A., Dimitrov, N., Bonev, E., 2016. Industrial implementation of a fuzzy logic controller for brushless DC motor drives using the PicoMotion control framework, in: 2016 IEEE 8th International Conference on Intelligent Systems, IS 2016 - Proceedings. pp. 629–634. https://doi.org/10.1109/IS.2016.7737493
2017Acosta Lúa, C., Di Gennaro, S., Sánchez Morales, M.E., 2017. Nonlinear adaptive controller applied to an Antilock Braking System with parameters variations. International Journal of Control, Automation and Systems 15, 2043–2052. https://doi.org/10.1007/s12555-016-0136-1Topalov, A.V., Kayacan, E., Oniz, Y., Kaynak, O., "Adaptive neuro-fuzzy control with sliding mode learning algorithm: Application to antilock braking system," (2009) Proceedings of 2009 7th Asian Control Conference, ASCC 2009, art. no. 5276234, pp. 784-789, ISBN: 9788995605691.
2017Acosta Lúa, C., Di Gennaro, S., Sánchez Morales, M.E., 2017. Nonlinear adaptive controller applied to an Antilock Braking System with parameters variations. International Journal of Control, Automation and Systems 15, 2043–2052. https://doi.org/10.1007/s12555-016-0136-1Topalov, A. V, Oniz, Y., Kayacan, E., Kaynak, O., 2011. Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm. Neurocomputing 74, 1883–1893. https://doi.org/10.1016/j.neucom.2010.07.035
2017Acosta Lúa, C., Di Gennaro, S., Sánchez Morales, M.E., 2017. Nonlinear adaptive controller applied to an Antilock Braking System with parameters variations. International Journal of Control, Automation and Systems 15, 2043–2052. https://doi.org/10.1007/s12555-016-0136-1Topalov, A.V., Kayacan, E., Oniz, Y., Kaynak, O., "Neuro-fuzzy control of antilock braking system using variable-structure- systems-based learning algorithm," (2009) Proceedings of the 2009 International Conference on Adaptive and Intelligent Systems, ICAIS 2009, art. no. 5329523, pp. 166-171, DOI: 10.1109/ICAIS.2009.35, ISBN: 9780769538273, https://www.scopus.com/inward/record.uri?eid=2-s2.0-74549128543&doi=10.1109%2fICAIS.2009.35&partnerID=40&md5=229d07a08b00a78a44243a823f13ce11
2017Raza, K., 2017. Formal concept analysis for knowledge discovery from biological data. International Journal of Data Mining and Bioinformatics 18, 281–300. https://doi.org/10.1504/IJDMB.2017.088138Hristoskova, A., Boeva, V., Tsiporkova, E., 2014. A formal concept analysis approach to consensus clustering of multi-experiment expression data. BMC Bioinformatics 15. https://doi.org/10.1186/1471-2105-15-151
2017Li, H., Jing, H., 2017. Laser scattering characteristics of dust particle and photoelectric detection signal to noise ratio model in dust concentration testing system. Optoelectronics and Advanced Materials, Rapid Communications 11, 317–323.Ferdinandov, E., Pachedjieva, B., Bonev, B., Saparev, Sl., "Joint influence of heterogeneous stochastic factors on bit-error rate of ground-to-ground free-space laser communication systems," (2007) Optics Communications, 270 (2), pp. 121-127, DOI: 10.1016/j.optcom.2006.09.006, ISSN: 00304018, https://www.scopus.com/inward/record.uri?eid=2-s2.0-33845870455&doi=10.1016%2fj.optcom.2006.09.006&partnerID=40&md5=3eb75086576617a7e7820243e8c9d7c8
2017Pape, T., 2017. Value of agreement in decision analysis: Concept, measures and application. Computers and Operations Research 80, 82–93. https://doi.org/10.1016/j.cor.2016.11.018Tsiporkova, E., Boeva, V., "Multi-step ranking of alternatives in a multi-criteria and multi-expert decision making environment," (2006) Information Sciences, 176 (18), pp. 2673-2697, DOI: 10.1016/j.ins.2005.11.010, ISSN: 00200255
2017Farid, U., Khan, B., Ullah, Z., Ali, S.M., Mehmood, C.A., Farid, S., Sajjad, R., Sami, I., Shah, A., 2017. Control and identification of dynamic plants using adaptive neuro-fuzzy type-2 strategy, in: ICECE 2017 - 2017 International Conference on Energy Conservation and Efficiency, Proceedings. pp. 68–73. https://doi.org/10.1109/ECE.2017.8248831Topalov, A.V., Cascella, G.L., Giordano, V., Cupertino, F., Kaynak, O., "Sliding mode neuro-adaptive control of electric drives," (2007) IEEE Transactions on Industrial Electronics, 54 (1), pp. 671-679, DOI: 10.1109/TIE.2006.888930, ISSN: 02780046.
2017García, C.A., Buele, J., Espinoza, J., Castellanos, E.X., Beltrán, C., Pilatasig, M., Galarza, E., García, M. V, 2017. Fuzzy control implementation in low cost CPPS devices, in: IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems. pp. 162–167. https://doi.org/10.1109/MFI.2017.8170423Ahmed, S., Topalov, A., Dimitrov, N., Bonev, E., 2016. Industrial implementation of a fuzzy logic controller for brushless DC motor drives using the PicoMotion control framework, in: 2016 IEEE 8th International Conference on Intelligent Systems, IS 2016 - Proceedings. pp. 629–634. https://doi.org/10.1109/IS.2016.7737493
2017Amer, N.H., Zamzuri, H., Hudha, K., Kadir, Z.A., 2017. Modelling and Control Strategies in Path Tracking Control for Autonomous Ground Vehicles: A Review of State of the Art and Challenges. Journal of Intelligent and Robotic Systems: Theory and Applications 86, 225–254. https://doi.org/10.1007/s10846-016-0442-0Ahmed, S.A., Petrov, M.G., 2015. Trajectory Control of Mobile Robots using Type-2 Fuzzy-Neural PID Controller. IFAC-PapersOnLine 48, 138–143. https://doi.org/10.1016/j.ifacol.2015.12.071
2017Aguilar, J.J.C., Fernández, J.P., García, J.M. V, Carrillo, J.A.C., 2017. Regenerative intelligent brake control for electric motorcycles. Energies 10. https://doi.org/10.3390/en10101648Topalov, A. V, Oniz, Y., Kayacan, E., Kaynak, O., 2011. Neuro-fuzzy control of antilock braking system using sliding mode incremental learning algorithm. Neurocomputing 74, 1883–1893. https://doi.org/10.1016/j.neucom.2010.07.035
2017Macnab, C.J.B., 2017. Creating a CMAC with overlapping basis functions in order to prevent weight drift. Soft Computing 21, 4593–4600. https://doi.org/10.1007/s00500-016-2204-0Shiev, K., Ahmed, S., Shakev, N., Topalov, A. V, 2016. Trajectory control of manipulators using an adaptive parametric type-2 fuzzy CMAC friction and disturbance compensator. Studies in Computational Intelligence 586, 63–82. https://doi.org/10.1007/978-3-319-14194-7_4
2017Petronijević, M., Peruničić-Draženović, B., Milosavljavić, Č., Veselić, B., 2017. Discrete-time speed servo system design -A comparative study: Proportional-integral versus integral sliding mode control. IET Control Theory and Applications 11, 2671–2679. https://doi.org/10.1049/iet-cta.2016.1480Topalov, A.V., Cascella, G.L., Giordano, V., Cupertino, F., Kaynak, O., "Sliding mode neuro-adaptive control of electric drives," (2007) IEEE Transactions on Industrial Electronics, 54 (1), pp. 671-679, DOI: 10.1109/TIE.2006.888930, ISSN: 02780046.
2017Li, D., Wang, Y., Wu, S., Qi, J., Wang, T., 2017. An visual analytics approach to explore criminal patterns based on multidimensional data, in: International Geoscience and Remote Sensing Symposium (IGARSS). pp. 5563–5566. https://doi.org/10.1109/IGARSS.2017.8128264Borg, A., Boldt, M., Lavesson, N., Melander, U., Boeva, V., 2014. Detecting serial residential burglaries using clustering. Expert Systems with Applications 41, 5252–5266. https://doi.org/10.1016/j.eswa.2014.02.035
2017Isafiade, O.E., Bagula, A.B., 2017. Fostering smart city development in developing nations: A crime series data analytics approach, in: Proceedings of the 2017 ITU Kaleidoscope Academic Conference: Challenges for a Data-Driven Society, ITU K 2017. pp. 1–8. https://doi.org/10.23919/ITU-WT.2017.8246992Borg, A., Boldt, M., Lavesson, N., Melander, U., Boeva, V., 2014. Detecting serial residential burglaries using clustering. Expert Systems with Applications 41, 5252–5266. https://doi.org/10.1016/j.eswa.2014.02.035
2017Ali, R., Lee, S., Chung, T.C., 2017. Accurate multi-criteria decision making methodology for recommending machine learning algorithm. Expert Systems with Applications 71, 257–278. https://doi.org/10.1016/j.eswa.2016.11.034Lavesson, N., Boeva, V., Tsiporkova, E., Davidsson, P., 2014. A method for evaluation of learning components. Automated Software Engineering 21, 41–63. https://doi.org/10.1007/s10515-013-0123-1
2017Ali, R., Lee, S., Chung, T.C., 2017. Accurate multi-criteria decision making methodology for recommending machine learning algorithm. Expert Systems with Applications 71, 257–278. https://doi.org/10.1016/j.eswa.2016.11.034Lavesson, N., Boeva, V., Tsiporkova, E., Davidsson, P., 2014. A method for evaluation of learning components. Automated Software Engineering 21, 41–63. https://doi.org/10.1007/s10515-013-0123-1
2017Bacha, S., Ayad, M.Y., Saadi, R., Aboubou, A., Bahri, M., Becherif, M., 2017. Modeling and control technics for autonomous electric and hybrid vehicles path following, in: 2017 5th International Conference on Electrical Engineering - Boumerdes, ICEE-B 2017. pp. 1–12. https://doi.org/10.1109/ICEE-B.2017.8191998Ahmed, S.A., Petrov, M.G., 2015. Trajectory Control of Mobile Robots using Type-2 Fuzzy-Neural PID Controller. IFAC-PapersOnLine 48, 138–143. https://doi.org/10.1016/j.ifacol.2015.12.071
2017Kianimajd, A., Ruano, M.G., Carvalho, P., Henriques, J., Rocha, T., Paredes, S., 2017. Validation of a similarity measurement method for clustering cardiac signals, in: Afonso P.M. Amador M., M.M.B.R. (Ed.), ENBENG 2017 - 5th Portuguese Meeting on Bioengineering, Proceedings. https://doi.org/10.1109/ENBENG.2017.7889435Kostadinova, E., Boeva, V., Boneva, L., Tsiporkova, E., 2012. An integrative DTW-based imputation method for gene expression time series data, in: IS’2012 - 2012 6th IEEE International Conference Intelligent Systems, Proceedings. pp. 258–263. https://doi.org/10.1109/IS.2012.6335145
2017Yetgin, A.G., Turan, M., 2017. Efficiency improvement in induction motor by slitted tooth core design [Porast učinkovitosti asinkronih motora pomoću dizajna jezgre s procijepljenim zubom]. Tehnicki Vjesnik 24, 1291–1296. https://doi.org/10.17559/TV-20140325133232Kostov, I., Spasov, V., Rangelova, V., "Application of genetic algorithms for determining the parameters of induction motors," (2009) Tehnicki Vjesnik, 16 (2), pp. 49-53, ISSN: 13303651, https://www.scopus.com/inward/record.uri?eid=2-s2.0-70749138441&partnerID=40&md5=e302755b6e6ecb76ffccfe892d71689d
2017Kianimajd, A., Ruano, M.G., Carvalho, P., Henriques, J., Rocha, T., Paredes, S., Ruano, A.E., 2017. Comparison of different methods of measuring similarity in physiologic time series. IFAC-PapersOnLine 50, 11005–11010. https://doi.org/10.1016/j.ifacol.2017.08.2479Kostadinova, E., Boeva, V., Boneva, L., Tsiporkova, E., 2012. An integrative DTW-based imputation method for gene expression time series data, in: IS’2012 - 2012 6th IEEE International Conference Intelligent Systems, Proceedings. pp. 258–263. https://doi.org/10.1109/IS.2012.6335145
2017Coteli, R., Acikgoz, H., Ucar, F., Dandil, B., 2017. Design and implementation of Type-2 fuzzy neural system controller for PWM rectifiers. International Journal of Hydrogen Energy 42, 20759–20771. https://doi.org/10.1016/j.ijhydene.2017.07.032Ahmed, S.A., Petrov, M.G., 2015. Trajectory Control of Mobile Robots using Type-2 Fuzzy-Neural PID Controller. IFAC-PapersOnLine 48, 138–143. https://doi.org/10.1016/j.ifacol.2015.12.071
2017Grewal, N., Singh, S., Chand, T., 2017. Effect of Aggregation Operators on Network-Based Disease Gene Prioritization: A Case Study on Blood Disorders. IEEE/ACM Transactions on Computational Biology and Bioinformatics 14, 1276–1287. https://doi.org/10.1109/TCBB.2016.2599155Tsiporkova, E., Boeva, V., "Fusing time series expression data through hybrid aggregation and hierarchical merge," (2008) Bioinformatics, 24 (16), pp. i63-i69, DOI: 10.1093/bioinformatics/btn264, ISSN: 13674803, https://www.scopus.com/inward/record.uri?eid=2-s2.0-49549089568&doi=10.1093%2fbioinformatics%2fbtn264&partnerID=40&md5=5a5c01c5653cc0f5b9c45a38ea434815
2017Papa, U., Ponte, S., Del Core, G., 2017. Conceptual Design of a Small Hybrid Unmanned Aircraft System. Journal of Advanced Transportation 2017. https://doi.org/10.1155/2017/9834247Shakev, N.G., Topalov, A. V, Kaynak, O., Shiev, K.B., 2012. Comparative results on stabilization of the quad-rotor rotorcraft using bounded feedback controllers. Journal of Intelligent and Robotic Systems: Theory and Applications 65, 389–408. https://doi.org/10.1007/s10846-011-9583-3
2017Voloshina, O. V, Shirshin, E.A., Lademann, J., Fadeev, V. V, Darvin, M.E., 2017. Fluorescence detection of protein content in house dust: the possible role of keratin. Indoor Air 27, 377–385. https://doi.org/10.1111/ina.12326Borisova, E., Troyanova, P., Pavlova, P., Avramov, L., "Diagnostics of pigmented skin tumors based on laser-induced autofluorescence and diffuse reflectance spectroscopy," (2008) Quantum Electronics, 38 (6), pp. 597-605, DOI: 10.1070/QE2008v038n06ABEH013891, ISSN: 10637818.
2017Milošević, P., Nešić, I., Poledica, A., Radojević, D., Petrović, B., 2017. Logic-based aggregation methods for ranking student applicants. Yugoslav Journal of Operations Research 27, 463–479. https://doi.org/10.2298/YJOR161110007MTsiporkova, E., Boeva, V., "Multi-step ranking of alternatives in a multi-criteria and multi-expert decision making environment," (2006) Information Sciences, 176 (18), pp. 2673-2697, DOI: 10.1016/j.ins.2005.11.010, ISSN: 00200255