IEEE Transactions on Fuzzy Systems最新文献_第9页

Active Domain Adaptation Based on Probabilistic Fuzzy C-Means Clustering for Pancreatic Tumor Segmentation 基于概率模糊c均值聚类的主动域自适应胰腺肿瘤分割

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-04-08 DOI: 10.1109/TFUZZ.2025.3555281

Chendong Qin;Yongxiong Wang;Fubin Zeng;Jiapeng Zhang;Yangsen Cao;Xiaolan Yin;Shuai Huang;Di Chen;Huojun Zhang;Zhiyong Ju

{"title":"Active Domain Adaptation Based on Probabilistic Fuzzy C-Means Clustering for Pancreatic Tumor Segmentation","authors":"Chendong Qin;Yongxiong Wang;Fubin Zeng;Jiapeng Zhang;Yangsen Cao;Xiaolan Yin;Shuai Huang;Di Chen;Huojun Zhang;Zhiyong Ju","doi":"10.1109/TFUZZ.2025.3555281","DOIUrl":"10.1109/TFUZZ.2025.3555281","url":null,"abstract":"Pancreatic cancer is a highly lethal disease, for which mortality closely parallels incidence. Precise delineation of the Gross Tumor Volume (GTV) plays a pivotal role in ensuring effective radiotherapy for pancreatic cancer. Although recent methods have achieved promising results in GTV segmentation, it remains challenging to generalize from one domain to another, especially when the target domain is a different modality with severe domain shifts. To address this challenge, we propose a novel active domain adaptation framework to enhance domain adaptation for GTV segmentation. Specifically, we refer to the latent feature space of the synthesized target domain to select domain-specific representative samples from a specific target domain for annotation and model fine-tuning. To suppress noise and enhance the edge information, we decouple the network into an additional edge regression task that is used to further mine the contextual information of the edge pixels. Experiments on our self-collected pancreas tumor dataset and a public dataset show that our method outperforms state-of-the-art methods by a significant margin, achieving an average Dice score improvement of 2.16% and 1.86% in the two target domains on the pancreas tumor dataset, respectively.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 6","pages":"2016-2026"},"PeriodicalIF":10.7,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143805573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Interval Observer-Based Secure Estimation for T–S Fuzzy Systems Under Stealthy Attacks 隐身攻击下基于区间观测器的T-S模糊系统安全估计

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-04-07 DOI: 10.1109/TFUZZ.2025.3557896

Zhi-Hui Li;Guang-Hong Yang

引用次数: 0

A Transformation Method of Noncooperative to Cooperative Behavior by Trust Propagation in Social Network Group Decision Making 基于信任传播的社会网络群体决策非合作行为向合作行为的转化方法

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-04-04 DOI: 10.1109/TFUZZ.2025.3557904

Tiantian Gai;Francisco Chiclana;Weidong Jin;Mi Zhou;Jian Wu

{"title":"A Transformation Method of Noncooperative to Cooperative Behavior by Trust Propagation in Social Network Group Decision Making","authors":"Tiantian Gai;Francisco Chiclana;Weidong Jin;Mi Zhou;Jian Wu","doi":"10.1109/TFUZZ.2025.3557904","DOIUrl":"10.1109/TFUZZ.2025.3557904","url":null,"abstract":"In the consensus reaching process (CRP) of social network group decision making, the noncooperative behavior exhibited by experts will hinder the achievement of group consensus. This article develops a noncooperative behavior management framework based on trust propagation and dynamic cooperation index under bidirectional feedback context. On the one hand, a trust propagation operator with trust decay is established to enhance the trust relationship between noncooperative experts. On the other hand, the fuzzy preference relations are utilized as preference expression structure, and the mutual reinforcing effect between consensus and trust is explored to achieve the dynamic enhancement of cooperation index, thereby facilitating the transformation of nonscooperative behavior. Specifically, a cooperation index is formulated to identify the noncooperation behavior. Subsequently, a noncooperative behavior transformation method by dynamic cooperation index is investigated. Finally, a bidirectional feedback mechanism is provided for group consensus reaching. This paper provides an innovative strategy for detecting and managing noncooperative behavior, an illustrative example and some analyses are presented to verify the validity of proposed method.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 7","pages":"2238-2250"},"PeriodicalIF":10.7,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multiview Unsupervised Representation Learning via Integration of Fuzzy Rules and Graph-Based Adaptive Regularization 基于模糊规则和基于图的自适应正则化的多视图无监督表示学习

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-04-03 DOI: 10.1109/TFUZZ.2025.3554030

Yi Zhu;Dong Li;Chao Xi;Witold Pedrycz

{"title":"Multiview Unsupervised Representation Learning via Integration of Fuzzy Rules and Graph-Based Adaptive Regularization","authors":"Yi Zhu;Dong Li;Chao Xi;Witold Pedrycz","doi":"10.1109/TFUZZ.2025.3554030","DOIUrl":"10.1109/TFUZZ.2025.3554030","url":null,"abstract":"With the rapid advancement of data acquisition technologies, multiview data have been widely applied in fields such as social networks, computer vision, and natural language processing. Multiview data typically contain information arising from different views or sensors, offering more perspectives for observation. The multiview nature also brings challenges, such as high dimensionality, noise, heterogeneity, and redundancy. Particularly, in scenarios with limited labeled data, traditional single-view learning methods often struggle to handle these complex issues. To address this, this article proposes an unsupervised multiview learning framework that integrates Takagi–Sugeno–Kang fuzzy systems and graph-based adaptive regularization (MvTSK-GAR) to handle the heterogeneity and redundancy in multiview data effectively. Specifically, this article first captures the uncertainty in multiview data through fuzzy rules and models the structural relationships between the data using graph-based adaptive regularization. The framework does not rely on a large amount of labeled data. Instead, it integrates complementary information coming from different views to automatically mine latent patterns, thus generating more accurate and stable data representations. Experimental results demonstrate that the proposed framework performs well in various real-world applications, particularly excelling in high-dimensional data processing and noise reduction. Good performance in multiple publicly available datasets validates the effectiveness of our approach.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 7","pages":"2226-2237"},"PeriodicalIF":10.7,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143775531","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Output-Feedback Proportional-Integral Fuzzy Control of Unknown Nonlinear Systems With Prescribed Performance 给定性能的未知非线性系统的输出反馈比例积分模糊控制

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-04-02 DOI: 10.1109/TFUZZ.2025.3557029

Jin-Zi Yang;Jin-Xi Zhang;Tianyou Chai

引用次数: 0

IEEE Computational Intelligence Society Information 电气和电子工程师学会计算智能学会信息

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-04-01 DOI: 10.1109/TFUZZ.2025.3553017

引用次数: 0

IEEE Transactions on Fuzzy Systems Publication Information IEEE模糊系统学报

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-04-01 DOI: 10.1109/TFUZZ.2025.3553019

引用次数: 0

Fuzzy Optimization Feature Fusion for Enhanced Fine-Grained Visual Classification in Sustainable Manufacturing using Vision Transformer 基于视觉变压器的模糊优化特征融合增强可持续制造细粒度视觉分类

IF 11.9 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-03-29 DOI: 10.1109/tfuzz.2025.3555523

Chin-Feng Lai, Yi-Wei Lai, Shih-Yeh Chen, Chi-Hsuan Lee, Mu-Yen Chen

引用次数: 0

Fractional Exponent-Based Looped-Lyapunov Functional for Mode-Dependent Sampled-Data Control of T–S Fuzzy Markovian Jump Systems With $mathscr {H}_{infty }$ Performance 基于分数指数的T-S模糊马尔可夫跳跃系统模相关采样数据控制的Lyapunov泛函 $mathscr {H}_{infty }$ 表演

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-03-26 DOI: 10.1109/TFUZZ.2025.3554270

Karpagavalli Sundararajan;Padmaja Narasimman;Tae H. Lee;Lakshmanan Shanmugam

{"title":"Fractional Exponent-Based Looped-Lyapunov Functional for Mode-Dependent Sampled-Data Control of T–S Fuzzy Markovian Jump Systems With $mathscr {H}_{infty }$ Performance","authors":"Karpagavalli Sundararajan;Padmaja Narasimman;Tae H. Lee;Lakshmanan Shanmugam","doi":"10.1109/TFUZZ.2025.3554270","DOIUrl":"10.1109/TFUZZ.2025.3554270","url":null,"abstract":"In this article, a novel fractional exponent (FE)-based looped-Lyapunov functional (FEBLLF) is proposed to analyze the stochastic stability (SS) criteria of a nonlinear Markovian jump systems (MJSs) under a mode-dependent sampled-data control through the Takagi–Sugeno (T–S) fuzzy approach. An FE, represented by an exponential function with a fractional parameter, is introduced to define looped FEs (LFEs) <inline-formula><tex-math>$ ({mathsf {1-e}}^{-hat{upalpha} (upepsilon_{1}(mathsf{t}))})$</tex-math></inline-formula> and <inline-formula><tex-math>$ ({mathsf {1-e}}^{-hat{upalpha}(upepsilon_{2}(mathsf{t}))})$</tex-math></inline-formula>, where <inline-formula><tex-math>$ upepsilon_{1}(mathsf {t})= {{mathsf{t}}-{{mathsf{t}}_{mathsf{k}}}},$</tex-math></inline-formula> <inline-formula><tex-math>$boldsymbol {upepsilon_{2}} {(mathsf {t})= {{{mathsf{t}}_{mathsf{k+1}}}-{mathsf{t}}},} {hat{boldsymbol{upalpha}} in (0,1)}$</tex-math></inline-formula>. These LFEs are utilized to construct a novel FEBLLF and to partition the sampling interval into four distinct sampling subintervals, providing detailed information about the state within each subinterval. Subsequently, the sampling-dependent sufficient conditions are obtained as linear matrix inequalities to ensure the SS of the T–S fuzzy MJSs with <inline-formula><tex-math>$mathscr {H}_{infty }$</tex-math></inline-formula> performance level <inline-formula><tex-math>$upgamma$</tex-math></inline-formula> and to verify the effectiveness of the proposed results, a nonlinear mass–spring system is assessed. In addition, comparative examples are discussed to illustrate the better conservative results of the proposed approaches.","PeriodicalId":13212,"journal":{"name":"IEEE Transactions on Fuzzy Systems","volume":"33 7","pages":"2174-2188"},"PeriodicalIF":10.7,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10938236","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143723221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

New Result on Mismatched Double Fuzzy Summation Inequality 关于不匹配双模糊和不等式的新结果

IF 10.7 1区计算机科学

IEEE Transactions on Fuzzy Systems Pub Date : 2025-03-26 DOI: 10.1109/TFUZZ.2025.3554757

Feng Li;Zhenghao Ni;Sangmoon Lee;Hao Shen

引用次数: 0