Corona Discharge Detection System Based on Ultraviolet Sensor and Optical Lens

2019 ◽  
Vol 14 (12) ◽  
pp. 1686-1692
Author(s):  
Li Luo ◽  
Qing Yang ◽  
Jin-Long Gong ◽  
Yi-Fan Wang
Keyword(s):  
Corona Discharge ◽  
Detection Method ◽  
Detection System ◽  
Ultraviolet Rays ◽  
Detection Distance ◽  
External Interference ◽  
Optical Lens ◽  
Ultraviolet Detection ◽  
Research Results ◽  
Ultraviolet Sensor

Ultraviolet radiation produced by corona discharge can be used for discharge diagnosis, but the ultraviolet imager is expensive and the ultraviolet signal is susceptible to external interference during the day, so it can not accurately identify the occurrence of corona discharge. In this paper, an optical lens is designed to collect ultraviolet signal. In this paper, a corona detection method based on ultraviolet sensor and optical lens was proposed. The design of optical lens to concentrate the ultraviolet signal so as to suppress external interference was presented. By carrying out insulator corona discharge experiments, the feasibility of the proposed method was studied. Finally, a comparison and verification was made with the combination of corona discharge images collected by UV imager. Research results by this paper indicate that, when the detection distance is smaller than 5 m, and the power supply is about 700 V, the proposed corona detection method can effectively collect the ultraviolet rays generated by corona discharge, thus realizing the feasible recognition of corona discharge. Moreover, this method can feasibly reflect the changing of UV intensity with applied voltage and detection distance. The research results can provide important reference for the development and calibration of ultraviolet detection equipment.

Image detection and parameterization for different components in cross-sections of radial tires

2021 ◽  
pp. 095440702110236
Author(s):  
Chen Liu ◽  
Yude Dong ◽  
Yanli Wei ◽  
Jiangtao Wang ◽  
Hongling Li
Keyword(s):  
Image Processing ◽  
Digital Image Processing ◽  
Cross Sections ◽  
Digital Image ◽  
Detection Method ◽  
Detection System ◽  
Practical Significance ◽  
Vehicle Safety ◽  
Wire Steel ◽  
Good Detection

The internal structure analysis of radial tires is of great significance to improve vehicle safety and during tire research. In order to perform the digital analysis and detection of the internal composition in radial tire cross-sections, a detection method based on digital image processing was proposed. The research was carried out as follows: (a) the distribution detection and parametric analysis of the bead wire, steel belt, and carcass in the tire section were performed by means of digital image processing, connected domain extraction, and Hough transform; (b) using the angle of location distribution and area relationship, the detection data were optimized through coordinate and quantity relationship constraints; (c) a detection system for tire cross-section components was designed using the MATLAB platform. Our experimental results showed that this method displayed a good detection performance, and important practical significance for the research and manufacture of tires.

scholarly journals Examination of Abnormal Behavior Detection Based on Improved YOLOv3

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 197
Author(s):  
Meng-ting Fang ◽  
Zhong-ju Chen ◽  
Krzysztof Przystupa ◽  
Tao Li ◽  
Michal Majka ◽  
...  
Keyword(s):  
Detection Method ◽  
Abnormal Behavior ◽  
Detection Accuracy ◽  
Average Precision ◽  
Research Results ◽  
Behavior Detection ◽  
Abnormal Behavior Detection ◽  
The Mean ◽  
Examination Room ◽  
Detection Speed

Examination is a way to select talents, and a perfect invigilation strategy can improve the fairness of the examination. To realize the automatic detection of abnormal behavior in the examination room, the method based on the improved YOLOv3 (The third version of the You Only Look Once algorithm) algorithm is proposed. The YOLOv3 algorithm is improved by using the K-Means algorithm, GIoUloss, focal loss, and Darknet32. In addition, the frame-alternate dual-thread method is used to optimize the detection process. The research results show that the improved YOLOv3 algorithm can improve both the detection accuracy and detection speed. The frame-alternate dual-thread method can greatly increase the detection speed. The mean Average Precision (mAP) of the improved YOLOv3 algorithm on the test set reached 88.53%, and the detection speed reached 42 Frames Per Second (FPS) in the frame-alternate dual-thread detection method. The research results provide a certain reference for automated invigilation.

Research on Several Key Techniques of Power Tool Rest Noise Detection System

2012 ◽  
Vol 630 ◽  
pp. 271-275
Author(s):  
Xiao Hong Lu ◽  
Yong Yan Shang ◽  
Peng Zhuo Han ◽  
Guang Jun Li ◽  
Wen Yi Wu
Keyword(s):  
Spectrum Analysis ◽  
Detection Method ◽  
Detection System ◽  
Sampled Data ◽  
Noise Detection ◽  
Machining Center ◽  
Cnc Lathe ◽  
Spectrum Transformation ◽  
Key Techniques

The scarcity and imperfection of power tool rest noise detection method have seriously limited the development of the industry of CNC lathe, lathe and milling composite machining center. A noise detection system based on LabVIEW is developed. The developed system adopts a noise sensor as noise detection component to test the noise information of the power tool rest. To enhance the anti-interference ability of this system, the sampled signals are amplified and adjusted by the signal disposal instrument. Through the spectrum transformation and spectrum analysis of the sampled noise signals, the noise causes of the power tool rest can be inquired and the concerned measurements can be taken to reduce the noise effectively. Finally, the sampled data is stored by the data saving function.

scholarly journals An Automata Based Intrusion Detection Method for Internet of Things

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Yulong Fu ◽  
Zheng Yan ◽  
Jin Cao ◽  
Ousmane Koné ◽  
Xuefei Cao
Keyword(s):  
Open Access ◽  
Internet Of Things ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection Method ◽  
Detection System ◽  
Network Communication ◽  
5G Networks ◽  
Machine To Machine ◽  
Security Of Iot

Internet of Things (IoT) transforms network communication to Machine-to-Machine (M2M) basis and provides open access and new services to citizens and companies. It extends the border of Internet and will be developed as one part of the future 5G networks. However, as the resources of IoT’s front devices are constrained, many security mechanisms are hard to be implemented to protect the IoT networks. Intrusion detection system (IDS) is an efficient technique that can be used to detect the attackers when cryptography is broken, and it can be used to enforce the security of IoT networks. In this article, we analyzed the intrusion detection requirements of IoT networks and then proposed a uniform intrusion detection method for the vast heterogeneous IoT networks based on an automata model. The proposed method can detect and report the possible IoT attacks with three types: jam-attack, false-attack, and reply-attack automatically. We also design an experiment to verify the proposed IDS method and examine the attack of RADIUS application.

scholarly journals The Ultraviolet Detection of Corona Discharge in Power Transmission Lines

2013 ◽  
Vol 05 (04) ◽  
pp. 1298-1302 ◽  
Author(s):  
Lan Chen ◽  
Lin Lin ◽  
Mimi Tian ◽  
Xingming Bian ◽  
Liming Wang ◽  
...  
Keyword(s):  
Corona Discharge ◽  
Transmission Lines ◽  
Power Transmission ◽  
Power Transmission Lines ◽  
Ultraviolet Detection

Semi supervised inspection algorithm of automatic packaging curve based on deep learning

2021 ◽  
pp. 1-10
Author(s):  
Yong He
Keyword(s):  
Deep Learning ◽  
Supervised Learning ◽  
Optimization Algorithm ◽  
Posterior Probability ◽  
Detection Method ◽  
Detection System ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Data Set ◽  
Packaging Process

The current automatic packaging process is complex, requires high professional knowledge, poor universality, and difficult to apply in multi-objective and complex background. In view of this problem, automatic packaging optimization algorithm has been widely paid attention to. However, the traditional automatic packaging detection accuracy is low, the practicability is poor. Therefore, a semi-supervised detection method of automatic packaging curve based on deep learning and semi-supervised learning is proposed. Deep learning is used to extract features and posterior probability to classify unlabeled data. KDD CUP99 data set was used to verify the accuracy of the algorithm. Experimental results show that this method can effectively improve the performance of automatic packaging curve semi-supervised detection system.

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