Application of Intelligent Remote Control Combined with Machine Vision in Coal Mine Electromechanical Equipment

[1] Wang, Y., & Wang, X. (2020). THE ON-LINE MONITORING ANALYSIS OF ELECTROMECHANICAL EQUIPMENT UNDER EMBEDDED SENSOR. International Journal of Mechatronics and Applied Mechanics, (8), 64-71. Search in Google Scholar

[2] Baduge, S. K., Thilakarathna, S., Perera, J. S., Arashpour, M., Sharafi, P., Teodosio, B., ... & Mendis, P. (2022). Artificial intelligence and smart vision for building and construction 4.0: Machine and deep learning methods and applications. Automation in Construction, 141, 104440. Search in Google Scholar

[3] Tian, H., Wang, T., Liu, Y., Qiao, X., & Li, Y. (2020). Computer vision technology in agricultural automation—A review. Information Processing in Agriculture, 7(1), 1-19. Search in Google Scholar

[4] Niu, Y., Li, Z., Wang, E., Shen, R., Cheng, Z., Gao, X., ... & Ali, M. (2020). Study on characteristics of EP responsing to coal mining. Engineering Fracture Mechanics, 224, 106780. Search in Google Scholar

[5] Kakani, V., Nguyen, V. H., Kumar, B. P., Kim, H., & Pasupuleti, V. R. (2020). A critical review on computer vision and artificial intelligence in food industry. Journal of Agriculture and Food Research, 2, 100033. Search in Google Scholar

[6] Yan, Y., & Cao, W. (2021). The mining method of anti-electromagnetic interference for electronic equipment in coal mine by considering network communication technology. Microelectronics Journal, 109, 104987. Search in Google Scholar

[7] Steger, C., Ulrich, M., & Wiedemann, C. (2018). Machine vision algorithms and applications. John Wiley & Sons. Search in Google Scholar

[8] Mavridou, E., Vrochidou, E., Papakostas, G. A., Pachidis, T., & Kaburlasos, V. G. (2019). Machine vision systems in precision agriculture for crop farming. Journal of Imaging, 5(12), 89. Search in Google Scholar

[9] Chauhan, V., & Surgenor, B. (2017). Fault detection and classification in automated assembly machines using machine vision. The International Journal of Advanced Manufacturing Technology, 90, 2491-2512. Search in Google Scholar

[10] Wang, A., Zhang, W., & Wei, X. (2019). A review on weed detection using ground-based machine vision and image processing techniques. Computers and electronics in agriculture, 158, 226-240. Search in Google Scholar

[11] Fadlullah, Z. M., Tang, F., Mao, B., Kato, N., Akashi, O., Inoue, T., & Mizutani, K. (2017). State-of-theart deep learning: Evolving machine intelligence toward tomorrow’s intelligent network traffic control systems. IEEE Communications Surveys & Tutorials, 19(4), 2432-2455. Search in Google Scholar

[12] Shang, C., & You, F. (2019). Data analytics and machine learning for smart process manufacturing: Recent advances and perspectives in the big data era. Engineering, 5(6), 1010-1016. Search in Google Scholar

[13] Du, H., Zhang, Y., Zhou, J., Chen, J., Ye, W., Zhang, X., ... & Zou, X. (2024). GaN Nanowire nin Diode Enabled High-performance UV Machine Vision System. IEEE Transactions on Nanotechnology. Search in Google Scholar

[14] Murshed, M. S., Murphy, C., Hou, D., Khan, N., Ananthanarayanan, G., & Hussain, F. (2021). Machine learning at the network edge: A survey. ACM Computing Surveys (CSUR), 54(8), 1-37. Search in Google Scholar

[15] Cui, L., Yang, S., Chen, F., Ming, Z., Lu, N., & Qin, J. (2018). A survey on application of machine learning for Internet of Things. International Journal of Machine Learning and Cybernetics, 9, 1399-1417. Search in Google Scholar

[16] Nawaz, S. J., Sharma, S. K., Wyne, S., Patwary, M. N., & Asaduzzaman, M. (2019). Quantum machine learning for 6G communication networks: State-of-the-art and vision for the future. IEEE access, 7, 46317-46350. Search in Google Scholar

[17] Wiley, V., & Lucas, T. (2018). Computer vision and image processing: a paper review. International Journal of Artificial Intelligence Research, 2(1), 29-36. Search in Google Scholar

[18] Jiang, Y., Mozumder, S. A., Ma, C., & Rob, M. A. (2024, May). Derailment Detection of Mining Shaft’s Rail Vehicle Using Machine Vision on Edge Device. In 2024 3rd International Conference on Innovations and Development of Information Technologies and Robotics (IDITR) (pp. 27-31). IEEE. Search in Google Scholar

[19] Cioffi, R., Travaglioni, M., Piscitelli, G., Petrillo, A., & De Felice, F. (2020). Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions. Sustainability, 12(2), 492. Search in Google Scholar

[20] Tang, F., Kawamoto, Y., Kato, N., & Liu, J. (2019). Future intelligent and secure vehicular network toward 6G: Machine-learning approaches. Proceedings of the IEEE, 108(2), 292-307. Search in Google Scholar

[21] Kibria, M. G., Nguyen, K., Villardi, G. P., Zhao, O., Ishizu, K., & Kojima, F. (2018). Big data analytics, machine learning, and artificial intelligence in next-generation wireless networks. IEEE access, 6, 32328-32338. Search in Google Scholar

[22] Bagheri, M., Akbari, A., & Mirbagheri, S. A. (2019). Advanced control of membrane fouling in filtration systems using artificial intelligence and machine learning techniques: A critical review. Process Safety and Environmental Protection, 123, 229-252. Search in Google Scholar

[23] Anandakumar, H., & Umamaheswari, K. (2017). Supervised machine learning techniques in cognitive radio networks during cooperative spectrum handovers. Cluster Computing, 20(2), 1505-1515. Search in Google Scholar

[24] Wang, J., Fu, P., & Gao, R. X. (2019). Machine vision intelligence for product defect inspection based on deep learning and Hough transform. Journal of Manufacturing Systems, 51, 52-60. Search in Google Scholar

[25] Ren, Z., Fang, F., Yan, N., & Wu, Y. (2022). State of the art in defect detection based on machine vision. International Journal of Precision Engineering and Manufacturing-Green Technology, 9(2), 661-691. Search in Google Scholar

[26] Xu, Y., & Brownjohn, J. M. (2018). Review of machine-vision based methodologies for displacement measurement in civil structures. Journal of Civil Structural Health Monitoring, 8, 91-110. Search in Google Scholar

[27] Zhou, L. (2022, May). Research status and trend analysis of coal mine electro-mechanical equipment maintenance under the background of smart mine construction. In Advanced Intelligent Technologies for Industry: Proceedings of 2nd International Conference on Advanced Intelligent Technologies (ICAIT 2021) (pp. 263-272). Singapore: Springer Nature Singapore. Search in Google Scholar

[28] Gerike, P. B., & Klishin, V. I. (2019, April). Vibration analysis of electromechanical equipment of mining shovels. In IOP Conference Series: Earth and Environmental Science (Vol. 262, No. 1, p. 012020). IOP Publishing. Search in Google Scholar

[29] Chen, Y., & Xu, J. (2022, March). Research and design of remote online supervision system of coal mine electromechanical equipment. In Journal of Physics: Conference Series (Vol. 2218, No. 1, p. 012026). IOP Publishing. Search in Google Scholar

[30] David Dechow.(2024).Systems Integration for Machine Vision Solutions - Driving Application Success with Current and Future Technologies.Quality(5),36-36. Search in Google Scholar

[31] David L Dechow.(2021).Machine Vision Systems Integration: Deep Learning.Quality(9),33-33. Search in Google Scholar

[32] Xia Kaishu,Saidy Clint,Kirkpatrick Max,Anumbe Noble,Sheth Amit & Harik Ramy.(2021).Towards Semantic Integration of Machine Vision Systems to Aid Manufacturing Event Understanding..Sensors (Basel, Switzerland)(13),4276-4276. Search in Google Scholar

[33] Kazunobu ISHII,Hideo TERAO & Noboru NOGUCHI.(2010).Machine Vision Integration for Vehicle Guidance.JOURNAL of the JAPANESE SOCIETY of AGRICULTURAL MACHINERY(Supplement), 535-536. Search in Google Scholar