Genetic algorithm-based analysis of heat production prediction in electronic devices

[1] Swift, D. C., Bethkenhagen, M., Correa, A. A., et al. (2020). High-temperature ion-thermal behavior from average-atom calculations. Physical Review E, 101(5), 053201. Search in Google Scholar

[2] Chen, S., Lv, Y., Peng, T., et al. (2020). Finite-Element Analysis of Pulsed High-Field Magnets With Precise Thermal and Structural Calculations. IEEE Transactions on Magnetics, PP(99), 1-1. Search in Google Scholar

[3] Castro, P., Lecuna, R., Manana, M., et al. (2020). Infrared Temperature Measurement Sensors of Overhead Power Conductors. Sensors, 20(24), 7126. Search in Google Scholar

[4] Noguera, M., Millán, B., Pérez-Paredes, J. J., et al. (2020). A New Low-Cost Device Based on Thermal Infrared Sensors for Olive Tree Canopy Temperature Measurement and Water Status Monitoring. Remote Sensing, 12(723), 1-20. Search in Google Scholar

[5] Li, D., Gong, Y., Shen, S., et al. (2020). Research and Design of Power Equipment Operation and Maintenance System Based on Big Data Technology. In 2020 Asia Energy and Electrical Engineering Symposium (AEEES). Search in Google Scholar

[6] Wan, W., Liu, Y., Han, X., et al. (2021). Evaluation Model of Power Operation and Maintenance Based on Text Emotion Analysis. Mathematical Problems in Engineering, 2021. Search in Google Scholar

[7] Zhang, B., Gao, F., Yang, N., et al. (2021). Research on Knowledge Graph Construction for Intelligent Operation and Maintenance of Electrical Transformers. Journal of Physics: Conference Series, 1971(1), 012013. Search in Google Scholar

[8] Fiok, K., Karwowski, W., & Wilamowski, M. (2020). Prediction of adverse events in Afghanistan: regression analysis of time series data grouped not by geographic dependencies. Search in Google Scholar

[9] Narayanan, M., Venugopal, T., & Thirumurugan, A. (2021). Identification of Groundwater Potential Zones Using Time Series Analysis and GIS Applications in Palar River Basin, Tamil Nadu, India. Sociological Research, 6(4), April 2021. Search in Google Scholar

[10] Gamboa, J. (2017). Deep Learning for Time-Series Analysis. Search in Google Scholar

[11] Tagliaferri, F., Viola, I. M., & Flay, R. (2015). Wind direction forecasting with artificial neural networks and support vector machines. Ocean Engineering, 97, 65-73. Search in Google Scholar

[12] Gotoh, J. Y., & Uryasev, S. (2017). Support vector machines based on convex risk functions and general norms. Annals of Operations Research. Search in Google Scholar

[13] Jie, S., Lee, W. J., Liu, Y., et al. (2015). Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines. IEEE Transactions on Industry Applications, 48(3), 1064-1069. Search in Google Scholar

[14] Lv, Z., Hu, M., Yang, Y., et al. (2021). Developing a Novel Method for Estimating the Speed of Sound in Biodiesel Known as Grey Wolf Optimizer Support Vector Machine Algorithm. BioMed Research International. Search in Google Scholar

[15] Yue, Y., Wang, Q., He, L., et al. (2022). Carbon Emission Accounting Model and Multi-dimensional Evaluation of Power System with High Proportion of New Energy. Search in Google Scholar

[16] Han, T., Gao, Z., Du, W., et al. (2022). Multi-dimensional evaluation method for new power system. Energy Reports, 8, 618-635. Search in Google Scholar

[17] Goldberg, Y., Lavie, O., Mandel, R., et al. (2016). Two-Dimensional Sonographic Evaluation of Endometrial Polyps - Parameters That Are Reassuring. Gynecologic & Obstetric Investigation, 81(4), 359-362. Search in Google Scholar

[18] Qian, Y., Jing, D., Wei, W., et al. (2015). Deep learning for steganalysis via convolutional neural networks. Proceedings of SPIE - The International Society for Optical Engineering, 9409, 94090J-94090J-10. Search in Google Scholar

[19] Nehme, E., Weiss, L. E., Michaeli, T., et al. (2018). Deep-STORM: super-resolution single-molecule microscopy by deep learning. Optica, 5(4). Search in Google Scholar

[20] Wehmeyer, C., & Noé, F. (2018). Time-lagged autoencoders: Deep learning of slow collective variables for molecular kinetics. Journal of Chemical Physics, 148(24). Search in Google Scholar

[21] Lin, K., Yang, H. F., Hsiao, J. H., et al. (2015). Deep learning of binary hash codes for fast image retrieval. In IEEE Conference on Computer Vision & Pattern Recognition Workshops. Search in Google Scholar

[22] Ding, Z., Wang, J., Cheng, Y. (2021). A Long Short-Term Memory Network-Based Intrusion Detection Method for Power Grid Middle Platform. Journal of Physics: Conference Series, 1815(1), 012007 (6pp). Search in Google Scholar

[23] Zhang, B., Zhao, W., Zou, S., et al. (2021). A Reliable Vehicle Lateral Velocity Estimation Methodology Based on SBI-LSTM During GPS-Outage. IEEE sensors journal. Search in Google Scholar

[24] Wang, Y., Yang, C., Xu, D., et al. (2021). Evaluation and Prediction Method of Rolling Bearing Performance Degradation Based on Attention-LSTM. Hindawi Limited. Search in Google Scholar

[25] Chun-feng, W., Kui, L., Pei-ping, S. (2020). A Novel Genetic Algorithm for Global Optimization. Acta Mathematicae Applicatae Sinica, 36(02), 482-491. Search in Google Scholar

[26] Yao hua, H., Chi-Wai, H. (2010). A Binary Coding Genetic Algorithm for Multi-Purpose Process Scheduling: A case study. Chemical Engineering Science, 65(16), 4816-4828. Search in Google Scholar

[27] Chun, Y., Liu, M. X., Liu, W., et al. (2019). Improved Adaptive Genetic Algorithm for the Vehicle Insurance Fraud Identification Model based on a BP Neural Network. Theoretical Computer Science, 817(3), 12-23. Search in Google Scholar

[28] Tang, K. Z., Sun, T. K., Yang, J. Y. (2010). An Improved Genetic Algorithm based on a Novel Selection Strategy for Nonlinear Programming Problems. Computers and Chemical Engineering, 35(4), 615-621. Search in Google Scholar