With the development of a smart grid, the automatic location of power equipment is becoming a trend. In this study, a method for automatic location identification and diagnosis of external power insulation equipment based on YOLOv3 is proposed. This deep learning algorithm is used to extract the characteristics of image data under the visible light channel of the insulator. It learns and trains the collected data to realise the rapid location identification and frame selection of the external insulation equipment and extract discharge characteristics of the target box under the ultraviolet channel. According to the number of photons and the spot area information, the operating status of the equipment is determined. The results show that the YOLOv3 algorithm with a training rate of 0.005 achieved a fast convergence of the location recognition model. The average recognition accuracy was 88.7% and the average detection time was 0.0182 s. The combination of visible light path insulator target recognition and ultraviolet light path diagnosis can realise a lean and intelligent diagnosis of power equipment. This method had good real-time performance, accuracy, and robustness to the background. It provides a new concept for intelligent diagnosis and location analysis of power equipment.
Lung cancer is one of the leading causes of cancer-associated deaths worldwide. 1 Nonsmall cell lung cancer (NSCLC) is known as the main subtypes of lung cancer, among which, lung adenocarcinoma (LUAD) is a inducer of the high incidence rate and mortality. Great effort has been made in the therapeutic methods, such as chemotherapy, whereas late diagnosis and tumor metastasis lead to the low treatment efficiency. 2 Cisplatin is a common chemotherapeutic agent using for solid tumors. 3 Nevertheless, the efficiency of cisplatin to tumors is limit due to the chemoresistance in the late stage. 4 Therefore,
The generalized oscillator strengths (GOSs) of the valence-shell excitations of CH3Cl have been determined at an incident electron energy of 1500 eV and an energy resolution of about 70 meV. The momentum transfer dependence behaviors of the GOSs of the valence-shell excitations have been carefully analyzed, and the A band shows a strong dipole-forbidden characteristic. By extrapolating the GOSs to the limit of a zero squared momentum transfer, the optical oscillator strengths have been obtained, which provide an independent cross-check for the previous experimental and theoretical results. The BE-scaled integral cross sections (where B is the binding energy and E is the excitation energy) of the valence-shell excitations of CH3Cl have been derived systematically from the threshold to 5000 eV with the aid of a BE-scaling method. The results provide the fundamental spectroscopic data of CH3Cl and have important applications in photochemical modeling for atmospheric physics.
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