Research on Abnormal Data Detection Method of Power Measurement Automation System

2021 ◽  
pp. 217-228
Author(s):  
Ming-fei Qu ◽  
Nan Chen
Keyword(s):  
Detection Method ◽  
Power Measurement ◽  
Automation System ◽  
Data Detection ◽  
Measurement Automation

scholarly journals An Improved Data Detection Method for Mesoscale Vortex Flow Field Based on Optimum Launching Environment of Major Launch Weapons

2019 ◽  
Vol 310 ◽  
pp. 052086
Author(s):  
Yang Jifeng ◽  
Chen Jie ◽  
Liu Bingjie ◽  
Qin Feng
Keyword(s):  
Flow Field ◽  
Vortex Flow ◽  
Detection Method ◽  
Data Detection ◽  
Mesoscale Vortex

Intelligent Intrusion Detection of Measurement Automation System Based on Deep Learning

2021 ◽  
pp. 70-83
Author(s):  
Tao Liu ◽  
Shaocheng Wu ◽  
Ziyv Guo ◽  
Jie Zhao ◽  
Wenlong Sun ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Automation System ◽  
Measurement Automation

An Image Hided-Data Detection Method Combining Markov Chain and Support Vector Machines

2011 ◽  
Vol 128-129 ◽  
pp. 520-524
Author(s):  
Hui Min Zhao ◽  
Li Zhu
Keyword(s):  
Markov Chain ◽  
Support Vector Machines ◽  
Spread Spectrum ◽  
Detection Method ◽  
Markov Chain Model ◽  
Support Vector ◽  
Data Detection ◽  
Chain Model ◽  
Detection Rates ◽  
Vector Machines

An image hided-data detection method is proposed combining 2-D Markov chain model and Support Vector Machines (SVM) by the paper, in which image pixels are predicted with their neighboring pixels, and the prediction-error image is generated by subtracting the prediction value from the pixel value. Support vector machines are utilized as classifier. As embedding data rate being 0.1 bpp, experimental investigation utilizing spread spectrum (SS) and a Quantization Index Modulation (QIM) method data hiding method respectively , correction detection rates are all above 90% . For optimum LSB method ,the method achieves a detection rate from 50% to 90% above with 0.01bpp-0.3bpp various embedding data rates.

Anomaly detection of bridge health monitoring data based on KNN algorithm

2020 ◽  
Vol 39 (4) ◽  
pp. 5243-5252
Author(s):  
Zhen Lei ◽  
Liang Zhu ◽  
Youliang Fang ◽  
Xiaolei Li ◽  
Beizhan Liu
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Health Monitoring ◽  
Detection Method ◽  
Detection Algorithm ◽  
Monitoring Data ◽  
Data Detection ◽  
Multiple Time ◽  
Time Zone ◽  
Bridge Health Monitoring

Pattern recognition technology is applied to bridge health monitoring to solve abnormalities in bridge health monitoring data. Testing is of great significance. For abnormal data detection, this paper proposes a single variable pattern anomaly detection method based on KNN distance and a multivariate time series anomaly detection method based on the covariance matrix and singular value decomposition. This method first performs compression and segmentation on the original data sequence based on important points to obtain multiple time subsequences, then calculates the pattern distance between each time subsequence according to the similarity measure of the time series, and finally selects the abnormal mode according to the KNN method. In this paper, the reliability of the method is verified through experiments. The experimental results in this paper show that the 5/7/9 / 11-nearest neighbors point to a specific number of nodes. Combined with the original time series diagram corresponding to the time zone view, in this paragraph in the time, the value of the temperature sensor No. 6 stays at 32.5 degrees Celsius for up to one month. The detection algorithm controls the number of MTS subsequences through sliding windows and sliding intervals. The execution time is not large, and the value of K is different. Although the calculated results are different, most of the most obvious abnormal sequences can be detected. The results of this paper provide a certain reference value for the study of abnormal detection of bridge health monitoring data.

An Advanced Data Detection Method for 34.6-Gbit/in2 Multilevel Recording using a Blue Laser and NA0.85 Optics

2006 ◽  
Author(s):  
M. Yamamoto ◽  
J. Sumioka ◽  
K. Okamoto ◽  
Y. Miyaoka ◽  
E. Fujii
Keyword(s):  
Detection Method ◽  
Blue Laser ◽  
Data Detection

A novel trust-based false data detection method for power systems under false data injection attacks

2021 ◽  
Vol 358 (1) ◽  
pp. 56-73 ◽  
Author(s):  
Bin Xie ◽  
Chen Peng ◽  
Minjing Yang ◽  
Xiaobing Kong ◽  
Tengfei Zhang
Keyword(s):  
Power Systems ◽  
Detection Method ◽  
Data Detection ◽  
False Data Injection ◽  
Injection Attacks ◽  
False Data Detection

Novel Abnormal Data Detection Method in Environmental Wireless Sensor Networks

2012 ◽  
Vol 6 (18) ◽  
pp. 234-241
Author(s):  
Li Guanghui ◽  
Pan Yuanyang ◽  
Xu Yongjun
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Detection Method ◽  
Wireless Sensor ◽  
Data Detection

Robust Bad Data Detection Method for Microgrid Using Improved ELM and DBSCAN Algorithm

2018 ◽  
Vol 144 (3) ◽  
pp. 04018026 ◽  
Author(s):  
Heming Huang ◽  
Fei Liu ◽  
Xiaoming Zha ◽  
Xiaoqi Xiong ◽  
Tinghui Ouyang ◽  
...  
Keyword(s):  
Detection Method ◽  
Data Detection ◽  
Bad Data Detection ◽  
Dbscan Algorithm ◽  
Bad Data

Security Analysis of Measurement Automation System Based on Threat Intelligence

2021 ◽  
pp. 601-610
Author(s):  
Shaocheng Wu ◽  
Tao Liu ◽  
Jin Li ◽  
Hefang Jiang ◽  
Xiaowei Chen ◽  
...  
Keyword(s):  
Security Analysis ◽  
Automation System ◽  
Measurement Automation ◽  
Threat Intelligence
Sign in / Sign up

Export Citation Format

Share Document