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SCOPUS & WoS (2017-2019)

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Търсене във всички колони:

Citation year	Citing paper	Cited paper

Citation year	Citing paper	Cited paper
2019	Hwang, 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/en12244615	Kostov, 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
2019	Zhang, 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/s10489-019-01531-3	Borg, 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
2019	Couceiro, 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_1	Hristoskova, 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
2019	Benabbas, 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-8-317-2019	Penkov, 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
2019	Jurman, 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_14	Shao, 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
2019	Zhang, 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.2018.8688124	Kim, 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
2019	Karaboga, 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/s10462-017-9610-2	Topalov, 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.
2019	Hiron, 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/550/1/012034	Georgiev, 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
2019	Islam, 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/10.1177/0278364919881683	Popov, 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
2019	Sardarmehni, 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.2018.5428	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

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