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The Design of Management Software for Network Device Faults
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作者 KOU Ya-nan, LI Guo-li, LI Zeng-zi Institute of Computer Architecture & Network, Xi’an Jiaotong University, Xi’an 710038, P. R. China 《International Journal of Plant Engineering and Management》 2001年第1期52-56,共5页
Effective network management software ensures networks to run credibly. In this paper we discuss the design and implementation of network device fault management based on Pure Java. It includes designs of general func... Effective network management software ensures networks to run credibly. In this paper we discuss the design and implementation of network device fault management based on Pure Java. It includes designs of general functions, server functions, client functions and a database table. The software can make it convenient to monitoring a network device, and improve network efficiency. 展开更多
关键词 network management device fault SERVER CLIENT DATABASE
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Power Grid Fault Diagnosis Based on Deep Pyramid Convolutional Neural Network
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作者 Xu Zhang Huiting Zhang +4 位作者 Dongying Zhang Yixian Wang Ruiting Ding Yuchuan Zheng Yongxu Zhang 《CSEE Journal of Power and Energy Systems》 SCIE EI CSCD 2023年第6期2188-2203,共16页
Existing power grid fault diagnosis methods relyon manual experience to design diagnosis models, lack theability to extract fault knowledge, and are difficult to adaptto complex and changeable engineering sites. Consi... Existing power grid fault diagnosis methods relyon manual experience to design diagnosis models, lack theability to extract fault knowledge, and are difficult to adaptto complex and changeable engineering sites. Considering thissituation, this paper proposes a power grid fault diagnosismethod based on a deep pyramid convolutional neural networkfor the alarm information set. This approach uses the deepfeature extraction ability of the network to extract fault featureknowledge from alarm information texts and achieve end-to-endfault classification and fault device identification. First, a deeppyramid convolutional neural network model for extracting theoverall characteristics of fault events is constructed to identifyfault types. Second, a deep pyramidal convolutional neuralnetwork model for alarm information text is constructed, thetext description characteristics associated with alarm informationtexts are extracted, the key information corresponding to faultsin the alarm information set is identified, and suspicious faultydevices are selected. Then, a fault device identification strategythat integrates fault-type and time sequence priorities is proposedto identify faulty devices. Finally, the actual fault cases and thefault cases generated by the simulation are studied, and theresults verify the effectiveness and practicability of the methodpresented in this paper. 展开更多
关键词 Alarm information deep pyramid convolutional neural network fault classification fault device identification feature extraction key information
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