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- W4312291036 abstract "In a transferred arc plasma system, the position of the cathode is difficult to detect during the smelting process as it remains inside the cylindrical anode. Real-time and accurate cathode position detection leads to efficient smelting operation with optimal use of electrical energy. In this article, a machine learning technique is proposed to accurately detect the position of the cathode in a direct current (DC) transferred arc plasma system. The measured voltage signal sampled at 20 kHz is processed using a tunable Q-factor wavelet transform (TQWT) followed by statistical features extraction and a machine learning algorithm to provide accurate cathode position information. Two different machine learning algorithms are used in this work, namely, single hidden layer neural network (SHLNN) and single-layer extreme learning machine (SELM). The output of these machine learning algorithms provides accurate position information and is also compared to the traditional voltage-related position information. The experimental signal of a 30-kW DC plasma system and cathode position detection results is shown." @default.
- W4312291036 created "2023-01-04" @default.
- W4312291036 creator A5056936832 @default.
- W4312291036 creator A5067372010 @default.
- W4312291036 creator A5086166223 @default.
- W4312291036 date "2023-03-01" @default.
- W4312291036 modified "2023-09-25" @default.
- W4312291036 title "Cathode Position Detection in a Transferred Arc Plasma Using Artificial Neural Network" @default.
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- W4312291036 doi "https://doi.org/10.1109/tps.2022.3229199" @default.
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