scholarly journals Grounding grid corrosion detection based on mini-batch gradient descent and greedy method

AIP Advances ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 065034
Author(s):  
Hongpeng Xie ◽  
Fan Yang ◽  
Mingsheng Hua ◽  
Sen Liu ◽  
Jiayuan Hu ◽  
...  
Keyword(s):  
Gradient Descent ◽  
Greedy Method ◽  
Corrosion Detection ◽  
Grounding Grid

scholarly journals Advances and Challenges of Corrosion and Topology Detection of Grounding Grid

2019 ◽  
Vol 9 (11) ◽  
pp. 2290 ◽  
Author(s):  
Zhihong Fu ◽  
Xiujuan Wang ◽  
Qian Wang ◽  
Xiaobin Xu ◽  
Nengyi Fu ◽  
...  
Keyword(s):  
Power System ◽  
Field Method ◽  
Imaging Method ◽  
Safe Operation ◽  
Detection Distance ◽  
Intelligent Computing ◽  
Corrosion Detection ◽  
Electromagnetic Imaging ◽  
Grounding Grid ◽  
Detection Technology

The grounding device plays performs the role of releasing a lightning current and a fault current in the power system, and the corrosion of the conductor will cause damage to the grounding body, which threatens the safe operation of the power system. The grounding grid corrosion detection technology and equipment guarantee the safe operation of the power system. This paper discusses the research status of grounding corrosion and topological detection in detail and introduces the basic principles, research difficulties and existing problems of the methods such as the electric network method, electromagnetic field method, electrochemical method, ultrasonic detection method and electromagnetic imaging method. The methods of electromagnetic imaging and time difference positioning proposed in recent years have been also discussed in detail. The paper points out that the application of grounding grid corrosion detection distance engineering still faces great challenges and that multi-disciplinary, multi-information fusion, new sensing technology, big data platforms and intelligent computing will be the trends to follow in research on grounding grid fault, corrosion detection and life prediction.

Comparison of SVM, RF and SGD Methods for Determination of Programmer's Performance Classification Model in Social Media Activities

2020 ◽  
Vol 4 (2) ◽  
pp. 329-335
Author(s):  
Rusydi Umar ◽  
Imam Riadi ◽  
Purwono
Keyword(s):  
Social Media ◽  
Gradient Descent ◽  
Classification Model ◽  
Stochastic Gradient Descent ◽  
Support Vector ◽  
Svm Algorithm ◽  
Vector Machines ◽  
Performance Patterns ◽  
A Company

The failure of most startups in Indonesia is caused by team performance that is not solid and competent. Programmers are an integral profession in a startup team. The development of social media can be used as a strategic tool for recruiting the best programmer candidates in a company. This strategic tool is in the form of an automatic classification system of social media posting from prospective programmers. The classification results are expected to be able to predict the performance patterns of each candidate with a predicate of good or bad performance. The classification method with the best accuracy needs to be chosen in order to get an effective strategic tool so that a comparison of several methods is needed. This study compares classification methods including the Support Vector Machines (SVM) algorithm, Random Forest (RF) and Stochastic Gradient Descent (SGD). The classification results show the percentage of accuracy with k = 10 cross validation for the SVM algorithm reaches 81.3%, RF at 74.4%, and SGD at 80.1% so that the SVM method is chosen as a model of programmer performance classification on social media activities.

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