Ms. Jun Wu Profile

Jun Wu

at Liaoning Electric Power Co., Ltd.

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Publications (2)

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Proceedings Article | 12 March 2020 Paper

Design of chip character recognition system based on neural network

Yutong Hu, Peng Wang, Jun Wu, Tingting Xu, Jiahan Wang

Proceedings Volume 11439, 1143908 (2020) https://doi.org/10.1117/12.2541127

KEYWORDS: Image segmentation, Optical character recognition, Neural networks, Image processing, Convolutional neural networks, Edge detection, Detection and tracking algorithms, Feature extraction, Image filtering

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At present, the sample comparison of intelligent electric meters in power grid companies mainly relies on manual inspection. With the development of semiconductor technology and the increasing demand of intelligent electric meters, the disadvantages of this method, such as low detection efficiency, high misjudgment rate, are becoming more prominent. In this paper, a method for automatically detecting and identifying the intelligent electric meter circuit board chip is proposed, and a chip character recognition system based on convolutional neural network (CNN) is designed. The system is mainly divided into two parts: chip positioning and character recognition. The chip is positioned based on the method of layout analysis and edge detection. According to the difference between the characteristics of the chip and the characteristics of the PCB background, preprocess the images, detect the chip identification and obtain multiple candidate regions. Finally, candidate regions are screened based on chip characteristics. The gray-level projection method is used to segment characters. A single character image is obtained by row segmentation and column segmentation. At the same time, the optimization algorithm for character adhesion and fracture problem is proposed to improve the segmentation accuracy. For the character recognition module, build a convolutional neural network to extract character features, and the normalized character is input into the trained neural network for recognition. The recognition accuracy of test sets is high, and the time for recognizing a single character is about 0.35 seconds. Compared with the traditional detection methods, the proposed method has higher detection efficiency and recognition accuracy.

Proceedings Article | 24 October 2017 Paper

A whole process quality control system for energy measuring instruments inspection based on IOT technology

Bo Yin, Li Liu, Jiahan Wang, Xiran Li, Zhenbo Liu, Dewei Li, Jun Wang, Lu Liu, Jun Wu, Tingting Xu, He Cui

Proceedings Volume 10458, 104580D (2017) https://doi.org/10.1117/12.2282189

KEYWORDS: Process control, Control systems, Inspection

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Electric energy measurement as a basic work, an accurate measurements play a vital role for the economic interests of both parties of power supply, the standardized management of the measurement laboratory at all levels is a direct factor that directly affects the fairness of measurement. Currently, the management of metering laboratories generally uses one-dimensional bar code as the recognition object, advances the testing process by manual management, most of the test data requires human input to generate reports. There are many problems and potential risks in this process: Data cannot be saved completely, cannot trace the status of inspection, the inspection process isn’t completely controllable and so on. For the provincial metrology center’s actual requirements of the whole process management for the performance test of the power measuring appliances, using of large-capacity RF tags as a process management information media, we developed a set of general measurement experiment management system, formulated a standardized full performance test process, improved the raw data recording mode of experimental process, developed a storehouse automatic inventory device, established a strict test sample transfer and storage system, ensured that all the raw data of the inspection can be traced back, achieved full life-cycle control of the sample, significantly improved the quality control level and the effectiveness of inspection work.

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