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Abstract With large input line inductance and low dc‐link capacitance, the small dc‐link drive system shows an instability problem, which is mainly caused by LC resonance between the line side inductor and the dc‐link film capacitor. This paper proposes a grid current feedback active damping control method to improve system stability and suppress the LC resonance. The feedback variables are analyzed, and the detected grid current is selected. Thus, the damping current is consistent with the LC resonance components in grid current, which benefits the drive system power factor and grid current quality. Furthermore, the grid current feedback control loop has been established, and the feedback gain is designed based on system parameters that can be adjusted to fit different systems. The stability margin and robustness of the drive system to the changes in line inductance and dc‐link capacitance are improved. Compared with the conventional voltage‐based damping method, the power factor is increased to 0.984 and the grid current harmonics are reduced to satisfy the requirements of EN61000‐3‐2. The effectiveness of the proposed active damping method is verified by the experimental results.

Abstract Background Colposcopy is indispensable for the diagnosis of cervical lesions. However, its diagnosis accuracy for high‐grade squamous intraepithelial lesion (HSIL) is at about 50%, and the accuracy is largely dependent on the skill and experience of colposcopists. The advancement in computational power made it possible for the application of artificial intelligence (AI) to clinical problems. Here, we explored the feasibility and accuracy of the application of AI on precancerous and cancerous cervical colposcopic image recognition and classification. Methods The images were collected from 6002 colposcopy examinations of normal control, low‐grade squamous intraepithelial lesion (LSIL), and HSIL. For each patient, the original, Schiller test, and acetic‐acid images were all collected. We built a new neural network classification model based on the hybrid algorithm. EfficientNet‐b0 was used as the backbone network for the image feature extraction, and GRU(Gate Recurrent Unit)was applied for feature fusion of the three modes examinations (original, acetic acid, and Schiller test). Results The connected network classifier achieved an accuracy of 90.61% in distinguishing HSIL from normal and LSIL. Furthermore, the model was applied to “Trichotomy”, which reached an accuracy of 91.18% in distinguishing the HSIL, LSIL and normal control at the same time. Conclusion Our results revealed that as shown by the high accuracy of AI in the classification of colposcopic images, AI exhibited great potential to be an effective tool for the accurate diagnosis of cervical disease and for early therapeutic intervention in cervical precancer.

Abstract Numerous studies on short‐term load forecasting (STLF) have used feature extraction methods to increase the model's accuracy by incorporating multidimensional features containing time, weather and distance information. However, less attention has been paid to the input data size and output dimensions in STLF. To address these two issues, an STLF model is proposed based on output dimensions using only load data. First, the load data's long‐term behavior (trend and seasonality) is extracted through the long short‐term memory network (LSTM), followed by convolution to obtain the load data's non‐stationarity. Then, using the self‐attention mechanism (SAM), the crucial input load information is emphasized in the forecasting process. The calculation example shows that the proposed algorithm outperforms LSTM, LSTM‐based SAM, and CNN‐GRU‐based SAM by more than 10% in eight different buildings, demonstrating its suitability for forecasting with only load data. Additionally, compared to earlier research utilizing two well‐known public data sets, the MAPE is optimized by 2.2% and 5%, respectively. Also, the method has good prediction accuracy for a wide variety of time granularities and load aggregation levels, so it can be applied to various load forecasting scenarios and has good reference significance for load forecasting instrumentation.