scholarly journals A fast cycle detection method for power grids based on graph computing

2021 ◽  
Keyword(s):  
Detection Method ◽  
Power Grids ◽  
Graph Computing ◽  
Cycle Detection

scholarly journals Intrusion Detection Method of Electric Power Information Network in Cloud Computing Environment

2021 ◽  
Vol 2113 (1) ◽  
pp. 012050
Author(s):  
Jiaqi Zhang ◽  
Guoping Feng ◽  
Dexi Zhou ◽  
Mingjiu Li
Keyword(s):  
Cloud Computing ◽  
Intrusion Detection ◽  
Electric Power ◽  
Detection Method ◽  
Power Grids ◽  
Normal Operation ◽  
Computing Environment ◽  
Bp Network ◽  
Widespread Application ◽  
Cloud Computing Environment

Abstract With the widespread application of power grid systems, the information security problems faced by power grids have become more obvious. Various internal and external intrusion attacks that occur frequently have become an important issue affecting the normal operation of power generation and operations. The purpose of this paper is to study the intrusion detection method of electric power information(PI) network in the cloud computing environment. With the help of the cloud platform’s ability to process big data, and based on the analysis of the PI network structure, a DBN optimized BP network algorithm is proposed, and the optimized BP neural network is used as a runtime classification program. Experimental results show that MR-DBN-BP has a detection rate of 96.7% for intrusion detection of PI networks, which can effectively detect intrusions and effectively protect the power dispatch system network.

Rapid Double-Layer Identification Method of Multi-Relevant Bad Data in Large-Scale Power Grids

2014 ◽  
Vol 672-674 ◽  
pp. 1294-1300
Author(s):  
Zhao Kun ◽  
Xing Ying ◽  
Jie Xu ◽  
Yin Zhang ◽  
Yan Lei ◽  
...  
Keyword(s):  
Double Layer ◽  
Large Scale ◽  
Detection Method ◽  
Power Grid ◽  
Power Grids ◽  
Data Detection ◽  
Bad Data Detection ◽  
Identification Method ◽  
Detection And Identification ◽  
Bad Data

Bad data detection and identification is an important part of state estimation. When the relevant bad data appears, however, there is residual pollution and residual submerged condition in currently available methods of bad data detection and identification. In view of the above problem, this article presents a double-layer bad data detection and identification technique. At first, it is based on regularization residual detection method (Rn detection method) to identify the suspect measurement sets. And then, it presents a fast search technique of interrelated suspect measurements to search interrelated measurements in all the suspect measurements of the entire power grid and produce interrelated suspect measurement sets. Furthermore, use double-layer identification method to fast identify the bad data in interrelated suspect measurement sets, in other words, identify all the bad data in entire power grid. At last, taking IEEE39 node power grid for example, this detection method of bad data is analyzed, the accuracy and effectiveness of this method is to be verified.

scholarly journals Cell Cycle Kinetic Analysis of Colorectal Neoplasms Using a New Automated Immunohistochemistry-Based Cell Cycle Detection Method

Medicine ◽  
2015 ◽  
Vol 94 (4) ◽  
pp. e501 ◽  
Author(s):  
Ayako Tomono ◽  
Tomoo Itoh ◽  
Emmy Yanagita ◽  
Naoko Imagawa ◽  
Yoshihiro Kakeji
Keyword(s):  
Cell Cycle ◽  
Kinetic Analysis ◽  
Colorectal Neoplasms ◽  
Detection Method ◽  
Cell Cycle Kinetic ◽  
Cycle Detection

Solid-phase immunoelectron microscopy for rapid diagnosis of enteroviruses

1984 ◽  
Vol 42 ◽  
pp. 226-227 ◽  
Author(s):  
K. Pegg-Feige ◽  
F. W. Doane
Keyword(s):  
Electron Microscopy ◽  
Cell Culture ◽  
Immunoelectron Microscopy ◽  
Detection Method ◽  
Solid Phase ◽  
Protein A ◽  
Plant Viruses ◽  
Rapid Diagnosis ◽  
Virus Diagnosis ◽  
Antiviral Antibody

Immunoelectron microscopy (IEM) applied to rapid virus diagnosis offers a more sensitive detection method than direct electron microscopy (DEM), and can also be used to serotype viruses. One of several IEM techniques is that introduced by Derrick in 1972, in which antiviral antibody is attached to the support film of an EM specimen grid. Originally developed for plant viruses, it has recently been applied to several animal viruses, especially rotaviruses. We have investigated the use of this solid phase IEM technique (SPIEM) in detecting and identifying enteroviruses (in the form of crude cell culture isolates), and have compared it with a modified “SPIEM-SPA” method in which grids are coated with protein A from Staphylococcus aureus prior to exposure to antiserum.

A Fault Detection Approach Based on Sound Signal Analysis for Equipment Monitoring

2020 ◽  
pp. 129-139
Author(s):  
Weihai Sun ◽  
Lemei Han
Keyword(s):  
Fault Detection ◽  
Signal Analysis ◽  
Energy Analysis ◽  
Detection Method ◽  
Sound Signal ◽  
Practical Significance ◽  
Time Frequency ◽  
Detection Approach ◽  
Machine Fault ◽  
Learning Data

Machine fault detection has great practical significance. Compared with the detection method that requires external sensors, the detection of machine fault by sound signal does not need to destroy its structure. The current popular audio-based fault detection often needs a lot of learning data and complex learning process, and needs the support of known fault database. The fault detection method based on audio proposed in this paper only needs to ensure that the machine works normally in the first second. Through the correlation coefficient calculation, energy analysis, EMD and other methods to carry out time-frequency analysis of the subsequent collected sound signals, we can detect whether the machine has fault.

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