Fuzzy Logic Based Assignable Causes Ranking System for Control Chart Abnormity Diagnosis

2008 ◽  
Author(s):  
Hou shiwang ◽  
Tong shurong
Keyword(s):  
Fuzzy Logic ◽  
Control Chart ◽  
Ranking System

An Assessment of Imbalanced Control Chart Pattern Recognition by Artificial Neural Networks

2022 ◽  
pp. 683-702
Author(s):  
Ramazan Ünlü
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Control Chart ◽  
Minority Class ◽  
Control Chart Patterns ◽  
Abnormal Pattern ◽  
Weighting Strategy ◽  
Control Chart Pattern ◽  
Chart Patterns ◽  
Normal Class

Manual detection of abnormality in control data is an annoying work which requires a specialized person. Automatic detection might be simpler and effective. Various methodologies such as ANN, SVM, Fuzzy Logic, etc. have been implemented into the control chart patterns to detect abnormal patterns in real time. In general, control chart data is imbalanced, meaning the rate of minority class (abnormal pattern) is much lower than the rate of normal class (normal pattern). To take this fact into consideration, authors implemented a weighting strategy in conjunction with ANN and investigated the performance of weighted ANN for several abnormal patterns, then compared its performance with regular ANN. This comparison is also made under different conditions, for example, abnormal and normal patterns are separable, partially separable, inseparable and the length of data is fixed as being 10,20, and 30 for each. Based on numerical results, weighting policy can better predict in some of the cases in terms of classifying samples belonging to minority class to the correct class.

scholarly journals Intelligent Process Abnormal Patterns Recognition and Diagnosis Based on Fuzzy Logic

2016 ◽  
Vol 2016 ◽  
pp. 1-8
Author(s):  
Shi-wang Hou ◽  
Shunxiao Feng ◽  
Hui Wang
Keyword(s):  
Fuzzy Logic ◽  
Control Chart ◽  
Fuzzy Inference ◽  
Recognition Method ◽  
Inference System ◽  
Characteristic Numbers ◽  
Control Chart Patterns ◽  
Intelligent Process ◽  
Chart Patterns ◽  
Method Point

Locating the assignable causes by use of the abnormal patterns of control chart is a widely used technology for manufacturing quality control. If there are uncertainties about the occurrence degree of abnormal patterns, the diagnosis process is impossible to be carried out. Considering four common abnormal control chart patterns, this paper proposed a characteristic numbers based recognition method point by point to quantify the occurrence degree of abnormal patterns under uncertain conditions and a fuzzy inference system based on fuzzy logic to calculate the contribution degree of assignable causes with fuzzy abnormal patterns. Application case results show that the proposed approach can give a ranked causes list under fuzzy control chart abnormal patterns and support the abnormity eliminating.

An Assessment of Imbalanced Control Chart Pattern Recognition by Artificial Neural Networks

2020 ◽  
pp. 240-258
Author(s):  
Ramazan Ünlü
Keyword(s):  
Neural Networks ◽  
Fuzzy Logic ◽  
Control Chart ◽  
Minority Class ◽  
Control Chart Patterns ◽  
Abnormal Pattern ◽  
Weighting Strategy ◽  
Control Chart Pattern ◽  
Chart Patterns ◽  
Normal Class

Manual detection of abnormality in control data is an annoying work which requires a specialized person. Automatic detection might be simpler and effective. Various methodologies such as ANN, SVM, Fuzzy Logic, etc. have been implemented into the control chart patterns to detect abnormal patterns in real time. In general, control chart data is imbalanced, meaning the rate of minority class (abnormal pattern) is much lower than the rate of normal class (normal pattern). To take this fact into consideration, authors implemented a weighting strategy in conjunction with ANN and investigated the performance of weighted ANN for several abnormal patterns, then compared its performance with regular ANN. This comparison is also made under different conditions, for example, abnormal and normal patterns are separable, partially separable, inseparable and the length of data is fixed as being 10,20, and 30 for each. Based on numerical results, weighting policy can better predict in some of the cases in terms of classifying samples belonging to minority class to the correct class.

scholarly journals Fuzzy logic based assignable cause diagnosis using control chart patterns

2010 ◽  
Vol 180 (17) ◽  
pp. 3258-3272 ◽  
Author(s):  
Kudret Demirli ◽  
Sujikumar Vijayakumar
Keyword(s):  
Fuzzy Logic ◽  
Control Chart ◽  
Control Chart Patterns ◽  
Chart Patterns

Hysteresis in Categorization: Fuzzy Logic Makes Fuzzy Boundaries

2012 ◽  
Author(s):  
Thomas M. Crawford ◽  
Justin Fine ◽  
Donald Homa
Keyword(s):  
Fuzzy Logic

Continuous Estimation of CO2 Production during Exercise

1997 ◽  
Vol 36 (04/05) ◽  
pp. 368-371
Author(s):  
R. Soma ◽  
Y. Yamamoto
Keyword(s):  
Fuzzy Logic ◽  
Steady State ◽  
Infusion Rate ◽  
Feedback System ◽  
Whole Body ◽  
Co2 Production ◽  
Cycle Exercise ◽  
Continuous Estimation ◽  
Breath Measurement ◽  
13Co2 Enrichment

Abstract.A new method was developed for continuous isotopic estimation of human whole body CO2 rate of appearance (Ra) during non-steady state exercise. The technique consisted of a breath-by-breath measurement of 13CO2 enrichment (E) and a real-time fuzzy logic feedback system which controlled NaH13CO3 infusion rate to achieve an isotopic steady state. Ra was estimated from the isotope infusion rate and body 13CO2 enrichment which was equal to E at the isotopic steady state. During a non-steady state incremental cycle exercise (5 w/min or 10 w/min), NaH13CO3 infusion rate was successfully increased by the action of feedback controller so as to keep E constant.

Modified fuzzy-based greedy routing protocol for VANETs

2020 ◽  
Vol 39 (6) ◽  
pp. 8357-8364
Author(s):  
Thompson Stephan ◽  
Ananthnarayan Rajappa ◽  
K.S. Sendhil Kumar ◽  
Shivang Gupta ◽  
Achyut Shankar ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Routing Protocol ◽  
Intelligent Transportation Systems ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Research Area ◽  
Transportation Systems ◽  
Delivery Ratio ◽  
Greedy Routing ◽  
Hoc Networks

Vehicular Ad Hoc Networks (VANETs) is the most growing research area in wireless communication and has been gaining significant attention over recent years due to its role in designing intelligent transportation systems. Wireless multi-hop forwarding in VANETs is challenging since the data has to be relayed as soon as possible through the intermediate vehicles from the source to destination. This paper proposes a modified fuzzy-based greedy routing protocol (MFGR) which is an enhanced version of fuzzy logic-based greedy routing protocol (FLGR). Our proposed protocol applies fuzzy logic for the selection of the next greedy forwarder to forward the data reliably towards the destination. Five parameters, namely distance, direction, speed, position, and trust have been used to evaluate the node’s stability using fuzzy logic. The simulation results demonstrate that the proposed MFGR scheme can achieve the best performance in terms of the highest packet delivery ratio (PDR) and minimizes the average number of hops among all protocols.

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