An SVM-Based Masquerade Detection Method with Online Update Using Co-occurrence Matrix

2006 ◽  
pp. 37-53 ◽  
Author(s):  
Liangwen Chen ◽  
Masayoshi Aritsugi
Keyword(s):  
Detection Method ◽  
Masquerade Detection ◽  
Occurrence Matrix

A co‐occurrence matrix based masquerade detection method in in‐vehicle network

2020 ◽  
Author(s):  
Bin Zhang ◽  
Xi Xiao ◽  
Weizhe Zhang ◽  
Arun Kumar Sangaiah ◽  
Ying Zhou ◽  
...  
Keyword(s):  
Detection Method ◽  
Masquerade Detection ◽  
Occurrence Matrix ◽  
Vehicle Network

Detection of the Tool Wear Condition Based on the Computer Image Processing

2008 ◽  
Vol 375-376 ◽  
pp. 553-557
Author(s):  
Ya Liang Wang ◽  
Shi Ming Ji ◽  
Li Zhang ◽  
Shou Song Jin ◽  
Yong Chen
Keyword(s):  
Image Processing ◽  
Tool Wear ◽  
Computer Image ◽  
Cutting Tool ◽  
Detection Method ◽  
Detection System ◽  
High Quality ◽  
Computer Image Processing ◽  
Occurrence Matrix ◽  
Wear Condition

The tool wear detection system based on the image processing and computer vision has better study value and foreground. The paper brings forward the detection method of the tool wear condition, which solves the two main problems. Firstly, gets the high quality images by fuzzy restoration arithmetic. Because the cutting tool is always at the movement state during the cutting, the real-time collected sequence images by CCD sensor are blurred with noise. Then, obtains the character parameter uniformity Q2 by calculating gray co-occurrence matrix, which can distinguish the cutting tool is weared or not weared. The experimental results indicate that detection of the tool wear condition by computer image processing reach our aim.

Water Boundary Line Detection for Unmanned Surface Vehicles

2020 ◽  
Vol 13 (8) ◽  
pp. 1145-1152
Author(s):  
Yuejun Liu ◽  
Liyong Ma ◽  
Wei Xie ◽  
Xiaolei Zhang ◽  
Yong Zhang
Keyword(s):  
Real Time ◽  
Autonomous Navigation ◽  
Detection Method ◽  
Line Detection ◽  
Boundary Line ◽  
Intelligent Environment ◽  
High Brightness ◽  
Unmanned Surface Vehicles ◽  
Environment Perception ◽  
Occurrence Matrix

Background: Unmanned Surface Vehicles (USV) can undertake risks or special tasks in marine independently and will be widely used in the future. In the autonomous navigation of USV equipped with vision camera, the water boundary line needs to be detected in real time and it is one of these key intelligent environment perception methods for USV. Methods: An efficient water boundary line detection method based on Gray Level Co-occurrence Matrix (GLCM) texture entropy is proposed. In image preprocessing, the high-brightness areas are eliminated to avoid the effects of water boundary line detection. Results: GLCM entropy is employed to segment water, land and air for water line regression. The proposed method is efficient for the images with high-brightness areas. Conclusion: The experimental results demonstrate that the proposed method is not only more accurate than the existing water boundary line detection method, but also has good real-time performance and is suitable for the application in USV.

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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