2021 International Conference on Fuzzy Theory and Its Applications (iFUZZY)最新文献

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Fault Detection Method Using Inverse Distance Weight-based Local Outlier Factor 基于逆距离权重的局部离群因子故障检测方法
2021 International Conference on Fuzzy Theory and Its Applications (iFUZZY) Pub Date : 2021-10-05 DOI: 10.1109/iFUZZY53132.2021.9605086
Minseok Kim, Seunghwan Jung, Sungshin Kim
{"title":"Fault Detection Method Using Inverse Distance Weight-based Local Outlier Factor","authors":"Minseok Kim, Seunghwan Jung, Sungshin Kim","doi":"10.1109/iFUZZY53132.2021.9605086","DOIUrl":"https://doi.org/10.1109/iFUZZY53132.2021.9605086","url":null,"abstract":"In modern complex industrial processes, unexpected shutdown not only shorten the lifespan of the main equipment, but also causes huge maintenance costs. To prevent such a problem, a method for detection equipment failure is required. Therefore, in this paper, we propose a fault detection method using local outlier factor (LOF). Unlike statistical methods such as principal component analysis (PCA) and independent component analysis (ICA), which assume that the data follows a specific distribution (Gaussian, binomial, exponential, etc.), LOF using the density of neighbors does not require distribution assumptions on the data. Thus, it is attracting attention in non-linear system, multimode and non-stationary processes. However, LOF is affected by the distance of neighbors due to characteristic of using density, this paper proposes a method to improve the fault detection performance of an existing LOF in the form of subtracting a weigh proportional to the distance to each neighbor. To verify the performance of the proposed method, it was applied to the Tennessee Eastman process, which is used for the evaluation of fault detect and diagnosis. The experimental results confirmed that the proposed method can properly detect a fault and reduce the occurrence of inappropriate false alarm compared to the conventional PCA and LOF.","PeriodicalId":442344,"journal":{"name":"2021 International Conference on Fuzzy Theory and Its Applications (iFUZZY)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126133063","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
The Detection of Change-Point of Partial Discharges on Power Cable Joints 电力电缆接头局部放电变化点的检测
2021 International Conference on Fuzzy Theory and Its Applications (iFUZZY) Pub Date : 2021-10-05 DOI: 10.1109/iFUZZY53132.2021.9605082
Chien-Kuo Chang, B. Boyanapalli
{"title":"The Detection of Change-Point of Partial Discharges on Power Cable Joints","authors":"Chien-Kuo Chang, B. Boyanapalli","doi":"10.1109/iFUZZY53132.2021.9605082","DOIUrl":"https://doi.org/10.1109/iFUZZY53132.2021.9605082","url":null,"abstract":"A total of 6 sets of partial discharge test data of underground cable joints, including 3 sets of defect type A, gaps, and 3 sets of defect type B, holes, were analyzed in this paper. Each test was measured from the accelerating aging experiment which applied voltage from partial discharge inception voltage to insulation breakdown voltage. A statistical method, change-point, was proposed to find the transition of partial discharge status. This method calculated 2 optimal transition points from 2 partial discharge features data. For each test sample, three stages were provided, named as the initial stage, middle stage, and final stage.","PeriodicalId":442344,"journal":{"name":"2021 International Conference on Fuzzy Theory and Its Applications (iFUZZY)","volume":"98 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134097592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Autonomous Vehicle Trajectory Combined Prediction model based on C-LSTM 基于C-LSTM的自动驾驶车辆轨迹组合预测模型
2021 International Conference on Fuzzy Theory and Its Applications (iFUZZY) Pub Date : 2021-10-05 DOI: 10.1007/s40815-022-01288-x
Runmei Li, Zherui Zhong, Jin Chai, Jian Wang
{"title":"Autonomous Vehicle Trajectory Combined Prediction model based on C-LSTM","authors":"Runmei Li, Zherui Zhong, Jin Chai, Jian Wang","doi":"10.1007/s40815-022-01288-x","DOIUrl":"https://doi.org/10.1007/s40815-022-01288-x","url":null,"abstract":"","PeriodicalId":442344,"journal":{"name":"2021 International Conference on Fuzzy Theory and Its Applications (iFUZZY)","volume":"57 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131289584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Malicious Flows Generator Based on Data Balanced Algorithm 基于数据均衡算法的恶意流量生成器
2021 International Conference on Fuzzy Theory and Its Applications (iFUZZY) Pub Date : 2021-10-05 DOI: 10.1109/iFUZZY53132.2021.9605084
I. Liu, Cheng-En Hsieh, Weixing Lin, Chu-Fen Li, Jung-Shian Li
{"title":"Malicious Flows Generator Based on Data Balanced Algorithm","authors":"I. Liu, Cheng-En Hsieh, Weixing Lin, Chu-Fen Li, Jung-Shian Li","doi":"10.1109/iFUZZY53132.2021.9605084","DOIUrl":"https://doi.org/10.1109/iFUZZY53132.2021.9605084","url":null,"abstract":"As Internet technology gradually matures, the network structure becomes more complex. Therefore, the attack methods of malicious attackers are more diverse and change faster. Fortunately, due to the substantial increase in computer computing power, machine learning is valued and widely used in various fields. It has also been applied to intrusion detection systems. This study found that due to the imperfect data ratio of the unbalanced flow dataset, the model will be overfitting and the misjudgment rate will increase. In response to this problem, this research proposes to use the Cuckoo system to induce malicious samples to generate malicious traffic, to solve the data proportion defect of the unbalanced traffic dataset.","PeriodicalId":442344,"journal":{"name":"2021 International Conference on Fuzzy Theory and Its Applications (iFUZZY)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114660415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Generalized Fuzzy Logic with twofold fuzzy set: Learning through Neural Net and Application to Business Intelligence 双模糊集广义模糊逻辑:神经网络学习及其在商业智能中的应用
2021 International Conference on Fuzzy Theory and Its Applications (iFUZZY) Pub Date : 2021-10-05 DOI: 10.1109/iFUZZY53132.2021.9605090
P. V. Reddy
{"title":"Generalized Fuzzy Logic with twofold fuzzy set: Learning through Neural Net and Application to Business Intelligence","authors":"P. V. Reddy","doi":"10.1109/iFUZZY53132.2021.9605090","DOIUrl":"https://doi.org/10.1109/iFUZZY53132.2021.9605090","url":null,"abstract":"The fuzzy logic deals with incomplete, inconsistent, uncertain, vagueand undecided information with belief. Fuzzy logic with twofold fuzzy set will give more accuracy then single membership function. In this paper, fuzzy logic with twofold fuzzy sets are studied. Fuzzy neural net used learn fuzzy inference. Fuzz certainity factor (FCF) is studied to eliminate conflict between two membership functions. Sometimes decision has to be taken under risk. Fuzzy Decision set is defined with fuzzy certainty factor (FCF). Different fuzzy resoning methods are discussed. Fuzzy inference anr reasoning mrthods are given for business intelligence.","PeriodicalId":442344,"journal":{"name":"2021 International Conference on Fuzzy Theory and Its Applications (iFUZZY)","volume":"169 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122786170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
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