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Uzbek Commands Recognition By Processing The Spectrogram Image, M M. Musayev, I Sh Khujayorov, M I. Abdullaeva, M M. Ochilov
Uzbek Commands Recognition By Processing The Spectrogram Image, M M. Musayev, I Sh Khujayorov, M I. Abdullaeva, M M. Ochilov
Technical science and innovation
This paper describes the most common algorithms with image approach convolutional neural network and two-dimensional DCT with machine learning classification KNN, SVM and RF. These algorithms are evaluated for applicability to the Uzbek language and a comparative analysis on the accuracy and recognition rate. The command words of the Uzbek language were chosen for the experiments. According to the results, it was found that both methods give high rates of recognition accuracy and are 92% (CNN) and 90% (2DDCT+Zigzag+SVM). Also the combinations of 2D-DCT+Zigzag+ KNN and 2D-DCT+Zigzag+ RF with average recognition accuracy of 86% and 85%, respectively, were considered in …
A Comprehensive Survey For Non-Intrusive Load Monitoring, Efe İsa Tezde, Eray Yildiz
A Comprehensive Survey For Non-Intrusive Load Monitoring, Efe İsa Tezde, Eray Yildiz
Turkish Journal of Electrical Engineering and Computer Sciences
Energy-saving and efficiency are as important as benefiting from new energy sources to supply increasing energy demand globally. Energy demand and resources for energy saving should be managed effectively. Therefore, electrical loads need to be monitored and controlled. Demand-side energy management plays a vital role in achieving this objective. Energy management systems schedule an optimal operation program for these loads by obtaining more accurate and precise residential and commercial loads information. Different intellegent measurement applications and machine learning algorithms have been proposed for the measurement and control of electrical devices/loads used in buildings. Of these, nonintrusive load monitoring (NILM) is …