Automated inspection of door parts based on fuzzy recognition system

2016 ◽  
Author(s):  
Tomasz Winiarski ◽  
Wlodzimierz Kasprzak ◽  
Maciej Stefanczyk ◽  
Michal Walecki
Keyword(s):  
Recognition System ◽  
Automated Inspection ◽  
Fuzzy Recognition

Wireless image fuzzy recognition system for human activity

2017 ◽  
Vol 76 (23) ◽  
pp. 25231-25251 ◽  
Author(s):  
Hua-Ching Chen ◽  
Ching-Chang Wong ◽  
Hsuan-Ming Feng
Keyword(s):  
Human Activity ◽  
Recognition System ◽  
Fuzzy Recognition

scholarly journals The investigation on defect recognition system using gaussian smoothing and template matching approach

2020 ◽  
Vol 18 (2) ◽  
pp. 812
Author(s):  
M. H. Harun ◽  
M. F. Yaakub ◽  
A. F. Z. Abidin ◽  
A. H. Azahar ◽  
M. S. M. Aras ◽  
...  
Keyword(s):  
Defect Detection ◽  
Template Matching ◽  
Recognition Rate ◽  
Region Of Interest ◽  
Recognition System ◽  
Automated Inspection ◽  
Gaussian Smoothing ◽  
Important Region ◽  
Detection Approach ◽  
Defect Recognition

<p>This paper investigates various approaches for automated inspection of gluing process using shape-based matching application. A new supervised defect detection approach to detect a class of defects in gluing application is proposed. Creating of region of interest in important region of object is discussed. Gaussian smoothing features is proposed in determining better image processing. Template matching in differentiates between reference and tested image are proposed. This scheme provides high computational savings and results in high defect detection recognition rate. The defects are broadly classified into three classes: 1) gap defect; 2) bumper defect; 3) bubble defect. This system does lessen execution time, yet additionally produce high precision in deformity location rate. It is discovered that the proposed framework can give precision at 95.77% recognition rate in recognizing imperfection for gluing application.</p>

scholarly journals Research on adaptive gearshift decision method based on driving intention recognition

2018 ◽  
Vol 10 (10) ◽  
pp. 168781401880535 ◽  
Author(s):  
Yulong Lei ◽  
Yuanxia Zhang ◽  
Yao Fu ◽  
Ke Liu
Keyword(s):  
Clustering Analysis ◽  
Recognition System ◽  
Power Demand ◽  
Decision Method ◽  
Intention Recognition ◽  
Power Shift ◽  
Demand Factor ◽  
Driver’S Intention ◽  
Fuzzy Recognition ◽  
Real Vehicle

One of the main tasks of adaptive gearshift decision-making is to recognize the driving intention, which reflects the adaptability of the vehicle to the driver. This article proposes a method of classification and recognition to recognize this kind of intention, which based on an improved Gustafson–Kessel clustering analysis, and constructs the corresponding fuzzy recognition system based on the method of extracting the fuzzy rules of the driving intention from classification results. Driving intention recognition results as the driver power demand factor, which is the basis of adaptive gearshift decision for the vehicle to adapt to the driving intention, which reflects the driver’s demand for vehicle power. Based on the factor, by using the method of interpolation between economy and power shift line, making gearshift decision is adaptive of driver’s intention. In the end, through the real vehicle experiment, it is proved that the method can effectively recognize the driving intention and the adaptability of the decision.

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