Study on Detection Method of Electrical Shock Current Based on Sliding Window and Wavelet Transform

2013 ◽  
Vol 860-863 ◽  
pp. 2035-2039
Author(s):  
Chun Lan Li ◽  
Song Huai Du ◽  
Zhai Shi ◽  
Juan Su

With the popularization of the rural electricity utilization, the rural electric safety was still an immediate problem to be solved. It was difficult to exactly detect and judge electric shock signals in the summation leakage current on the low-voltage electric power grid. A detection method of electrical contact signals based on sliding window and wavelet multi-resolution method was proposed. Under the different signal-to-noise ratio level, the summation leakage current was reconstructed by wavelet decomposition reconstruction algorithm. According to the characteristic of the slow change of the normal leakage current and the rapid change of the electric shock current within a short time, the electric shock current were extracted from the restructured summation leakage current signal by sliding window method. The mean square error and correlation analysis between the extracted signal and the actual testing results were studied. The analysis results show that the proposed method could identify electric shock current in the summation leakage current among noise, and its detection precision was superior to single sliding window method.

Author(s):  
Jyoti Malik ◽  
G. Sainarayanan ◽  
Ratna Dahiya

Authentication time is the main and important part of the authentication system. Normally the response time should be fast but as the number of persons in the database increases, there is probability of more response time taken for authentication. The need of fast authentication system arises so that authentication time (matching time) is very less. This paper proposes a sliding window approach to make fast authentication system. The highlight of sliding window method is constant matching time, fast and can match translated images also. Several palmprint matching methods like match by correlation etc. are dependent upon the number of corners detected and so is the matching time. In sliding window method, matching time is constant as the numbers of matching operations are limited and the matching time is independent of the number of corners detected. The palmprint corner features extracted using two approaches Phase Congruency Corner Detector and Harris Corner Detector are binarized so that only useful information (features) is matched. The two approaches of Phase Congruency Corner Detector and Harris Corner Detector, when matched with hamming distance using sliding window can achieve recognition rate of 97.7% and 97.5% respectively.


2017 ◽  
Vol 4 (1) ◽  
pp. 1304499 ◽  
Author(s):  
Adamu Muhammad Noma ◽  
Abdullah Muhammed ◽  
Zuriati Ahmad Zukarnain ◽  
Muhammad Afendee Mohamed ◽  
Duc Pham

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