Keywords
How to Cite
Abstract
Ushbu maqolada kompyuter tizimlarida energiya iste'molini real vaqt “termokartina” (issiqlik xaritasi) ma’lumotlari asosida prognozlovchi sun’iy intellekt modulini yaratish tamoyillari yoritiladi. Taklif etilayotgan model infratovush yoki termal kameralar yordamida yig‘ilgan issiqlik ma’lumotlarini chuqur o‘rganish algoritmlari orqali tahlil qiladi va energiya iste’moli bo‘yicha aniq bashoratlarni shakllantiradi. Ushbu yondashuv sanoat korxonalari, server markazlari, IoT qurilmalarida xarajatlarni optimallashtirishga yordam beradi.
References
1.Uzbekiston Respublikasi Axborot Texnologiyalari Vazirligi. Kompyuter tizimlarida energiya samaradorligini oshirish bo‘yicha metodik qo‘llanma, 2022.
2.Bishop, C. M. Pattern Recognition and Machine Learning. Springer, 2006.
3.O’Reilly, T. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. O’Reilly Media, 2020.
4.Gonzalez, R. C., & Woods, R. E. Digital Image Processing. Pearson, 2018.
5.Szegedy, C., et al. "Going Deeper with Convolutions." IEEE Conference on CVPR, 2015.
6.Krizhevsky, A., Sutskever, I., & Hinton, G. “ImageNet Classification with Deep Convolutional Neural Networks.” NIPS, 2012.
7.Yamashita, R., Nishio, M., Do, R. K., & Togashi, K. “Convolutional Neural Networks: An Overview and Applications.” Insights into Imaging, 2018.
8.FLIR Systems Inc. Thermal Imaging Guidebook for Industrial Applications. FLIR Publications, 2017.
9.Mahdavian, M., & Esmaeili, M. “Thermal Image Analysis in Computer Systems.” Journal of Electronics Cooling and Thermal Control, 2020.
This work is licensed under a Creative Commons Attribution 4.0 International License.
