UMĚLÁ INTELIGENCE V AUTOMOTIVE / David et al.

ference on Digital System Design . 2018: pp. 618–626. https://doi.org/10.1109/ DSD.2018.00107. [3.24] BIRCH-JENSEN, A., GREMYR, I., HALLDÓRSSON, Á. Digitally con nected services: Improvements through customer-initiated feedback. European Management Journal . 38 (5), 2020. pp. 814–825. https://doi.org/10.1016/j. emj.2020.03.008. [3.25] SINGH, H., KATHURIA, A. Analyzing driver behavior under naturalis tic driving conditions: A review, Accident Analysis & Prevention. 150, 2021. 105908. https://doi.org/10.1016/j.aap.2020.105908. [3.26] AMMAR, M., HALEEM, A., JAVAID, M., BAHL, S., VERMA, A. S. Imple menting Industry 4.0 technologies in self-healing materials and digitally ma naging the quality of manufacturing, Materials Today Proceedings . 52, 2021. pp. 2285–2294. https://doi.org/10.1016/j.matpr.2021.09.248. [3.27] JEONG, Y., SON, S., JEONG, E., LEE, B. An Integrated Self-Diagnosis Sys tem for an Autonomous Vehicle Based on an IoT Gateway and Deep Learning. Applied Sciences . 8 (7), 2018. 1164. https://doi.org/10.3390/app8071164. [3.28] KIM, K.D., SON, S.R., JEONG, Y.N., LEE, B.K. A Deep Learning Part- -diagnosis Platform (DLPP) based on an in-vehicle on-board gateway for an autonomous vehicle. KSII Transactions on Internet and Information Systems . 13, 2019. pp. 4123–4141. https://doi.org/10.3837/tiis.2019.08.017. [3.29] REVIN, A., DYGALO, V., BOYKO, G., LYASCHENKO, M., DYGALO, L. Methods of monitoring the technical condition of the braking system of an autonomous vehicle during operation. IOP Conference Series: Materials Science and Engineering . 315, 2018. 12020. https://doi.org/10.1088/1757- -899x/315/1/012020. [3.30] ZHANG, S., XU, J., GOU, H., TAN, J. A Research Review on the Key Tech nologies of Intelligent Design for Customized Products. Engineering . 3 (5), 2017. pp. 631–640. https://doi.org/10.1016/J.ENG.2017.04.005 [3.31] CHIEN, C.-F., DAUZÈRE-PÉRÈS, S., HUH, W.T., JANG, Y.J., MORRI SON, R. Artificial intelligence in manufacturing and logistics systems: algo rithms, applications, and case studies. International Journal of Production Re search . 58 (9), 2020. pp. 2730–2731. https://doi.org/10.1080/00207543.2020.1 752488 [3.32] YILDIZ, B., YILDIZ, A., ALBAK, E., ABDERAZEK, H., SAIT, S., BUREE RAT, S. Butterfly optimization algorithm for optimum shape design of auto mobile suspension components. Materials Testing . 62(4), 2020. pp. 365–370. https://doi.org/10.3139/120.111492 [3.33] ZHANG, Y., CHENG, Y., WANG, X.V., ZHONG, R.Y., ZHANG, Y., TAO, F. Data-driven smart production line and its common factors. The International Journal of Advanced Manufacturing Technology. 103, 2019. pp. 1211–1223. https://doi.org/10.1007/s00170-019-03469-9

138

Made with FlippingBook - Share PDF online