Pub Date : 2026-03-18DOI: 10.1109/TCYB.2026.3668829
Huafeng Qin, Yuming Fu, Huiyan Zhang, Mounim A El-Yacoubi, Xinbo Gao, Qun Song, Jun Wang
Deep neural networks have recently achieved promising performance in the vein recognition task and have shown an increasing application trend. However, they are prone to adversarial attacks by adding imperceptible perturbations to the input, resulting in incorrect recognition. To address this issue, we propose a novel defense model named MsMemoryGAN, which aims to filter the perturbations from adversarial samples before recognition. First, we design a multiscale memory autoencoder (MsMemoryAE) to achieve high-quality reconstruction, where the memory module (MM) within it is capable of learning the detailed patterns of normal samples at different scales. Second, to overcome the limitations of handcrafted similarity metrics, we propose an MM with learnable similarity (LSMM), which retrieves the most relevant memory items to purify the input feature. Finally, the perceptual loss and adversarial loss are integrated with the pixel loss to further enhance the quality of the reconstructed image. During the training phase, the MsMemoryGAN learns to reconstruct the input by merely using fewer prototypical elements of the normal patterns recorded in the memory. At the testing stage, given an adversarial sample, the MsMemoryGAN retrieves its most relevant normal patterns in MMs for reconstruction. Perturbations in the adversarial sample are usually not reconstructed well, resulting in adversarial purification. We conduct extensive experiments on two public vein datasets under different adversarial attack methods to evaluate the performance of the proposed approach. The experimental results show that our approach removes a wide variety of adversarial perturbations, allowing vein classifiers to achieve the highest recognition accuracy.
{"title":"MsMemoryGAN: A Multiscale Memory GAN for Palm-Vein Adversarial Purification.","authors":"Huafeng Qin, Yuming Fu, Huiyan Zhang, Mounim A El-Yacoubi, Xinbo Gao, Qun Song, Jun Wang","doi":"10.1109/TCYB.2026.3668829","DOIUrl":"https://doi.org/10.1109/TCYB.2026.3668829","url":null,"abstract":"<p><p>Deep neural networks have recently achieved promising performance in the vein recognition task and have shown an increasing application trend. However, they are prone to adversarial attacks by adding imperceptible perturbations to the input, resulting in incorrect recognition. To address this issue, we propose a novel defense model named MsMemoryGAN, which aims to filter the perturbations from adversarial samples before recognition. First, we design a multiscale memory autoencoder (MsMemoryAE) to achieve high-quality reconstruction, where the memory module (MM) within it is capable of learning the detailed patterns of normal samples at different scales. Second, to overcome the limitations of handcrafted similarity metrics, we propose an MM with learnable similarity (LSMM), which retrieves the most relevant memory items to purify the input feature. Finally, the perceptual loss and adversarial loss are integrated with the pixel loss to further enhance the quality of the reconstructed image. During the training phase, the MsMemoryGAN learns to reconstruct the input by merely using fewer prototypical elements of the normal patterns recorded in the memory. At the testing stage, given an adversarial sample, the MsMemoryGAN retrieves its most relevant normal patterns in MMs for reconstruction. Perturbations in the adversarial sample are usually not reconstructed well, resulting in adversarial purification. We conduct extensive experiments on two public vein datasets under different adversarial attack methods to evaluate the performance of the proposed approach. The experimental results show that our approach removes a wide variety of adversarial perturbations, allowing vein classifiers to achieve the highest recognition accuracy.</p>","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"PP ","pages":""},"PeriodicalIF":10.5,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147480509","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}
Pub Date : 2026-03-18DOI: 10.1109/tcyb.2026.3671337
{"title":"IEEE Women in Engineering Membership Benefits","authors":"","doi":"10.1109/tcyb.2026.3671337","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3671337","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"13 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147489406","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}
Pub Date : 2026-03-18DOI: 10.1109/tcyb.2026.3670025
Kai Rao,Huaicheng Yan,Yunkai Lv,Zeming Wu,Xiaojun Wu,Youmin Zhang
This article addresses the pursuit-evasion problem among differential drive robots in an obstacle environment with perception uncertainty. To calculate probabilistic collision-free trajectories during the pursuit process, this article introduces the chance-constraint pursuit Voronoi cell (CCPVC), which consists of separation hyperplanes between robots and separation hyperplanes between robots and obstacles. The optimization problems are formulated to compute the separation hyperplanes, and the solution methods are provided. By incorporating two buffer terms, CCPVC exhibits favorable probabilistic collision avoidance properties. Furthermore, a nearest point finding algorithm specifically designed for pursuit scenarios, along with a distributed pursuit control policy tailored for differential drive robots are proposed based on CCPVC. Rigorous proofs for the probabilistic collision avoidance guarantees of CCPVC and the control law during the pursuit process are provided, respectively. Finally, the effectiveness of the proposed methods is validated through simulations and experiments.
{"title":"Decentralized Pursuit of an Evader With Probabilistic Collision-Free for Differential Drive Robots.","authors":"Kai Rao,Huaicheng Yan,Yunkai Lv,Zeming Wu,Xiaojun Wu,Youmin Zhang","doi":"10.1109/tcyb.2026.3670025","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3670025","url":null,"abstract":"This article addresses the pursuit-evasion problem among differential drive robots in an obstacle environment with perception uncertainty. To calculate probabilistic collision-free trajectories during the pursuit process, this article introduces the chance-constraint pursuit Voronoi cell (CCPVC), which consists of separation hyperplanes between robots and separation hyperplanes between robots and obstacles. The optimization problems are formulated to compute the separation hyperplanes, and the solution methods are provided. By incorporating two buffer terms, CCPVC exhibits favorable probabilistic collision avoidance properties. Furthermore, a nearest point finding algorithm specifically designed for pursuit scenarios, along with a distributed pursuit control policy tailored for differential drive robots are proposed based on CCPVC. Rigorous proofs for the probabilistic collision avoidance guarantees of CCPVC and the control law during the pursuit process are provided, respectively. Finally, the effectiveness of the proposed methods is validated through simulations and experiments.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"7 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147478611","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}
Pub Date : 2026-03-18DOI: 10.1109/tcyb.2026.3669690
{"title":"IEEE Transactions on Cybernetics","authors":"","doi":"10.1109/tcyb.2026.3669690","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3669690","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"419 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147489405","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}
Pub Date : 2026-03-17DOI: 10.1109/tcyb.2026.3673274
Dan Liu, Shikun Zhang, Binrui Wang, Xiaohang Li
{"title":"Prescribed-Time Containment Control for Multiple Euler–Lagrange Systems Against DoS Attacks","authors":"Dan Liu, Shikun Zhang, Binrui Wang, Xiaohang Li","doi":"10.1109/tcyb.2026.3673274","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3673274","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"27 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147470913","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}
Pub Date : 2026-03-17DOI: 10.1109/tcyb.2026.3668117
Zilong Tan, Gaochang Wu, Yang Liu
{"title":"Prescribed-Time Fuzzy Adaptive Consensus Control for Photovoltaic Systems With Dead-Zone Input and Actuator Faults","authors":"Zilong Tan, Gaochang Wu, Yang Liu","doi":"10.1109/tcyb.2026.3668117","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3668117","url":null,"abstract":"","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"25 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147470912","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}
Pub Date : 2026-03-16DOI: 10.1109/tcyb.2026.3671185
Pedro Henrique Silva Coutinho,Paulo S P Pessim,Iury Bessa,Marcia Luciana da Costa Peixoto,Reinaldo Martinez Palhares
This article deals with periodic event-triggered control (PETC) of nonlinear systems, considering an equivalent quasi-linear parameter-varying (quasi-LPV) polytopic representation of the nonlinear plant and a gain-scheduled controller for stabilization. Although gain-scheduling approaches allow one to improve the results and extend the set of feasible solutions to the co-design problem, the event-based sampling induces the so-called asynchronous scheduling functions, which void the gain-scheduling advantages, leading to conservative results, especially in the PETC framework. The dominant approaches for dealing with this issue consider a bounding assumption on the mismatched scheduling functions, but do not guarantee that those bounds cannot be violated during the closed-loop operation. To properly manage the asynchronous phenomenon, we propose a novel PETC scheme. Based on the looped-functional approach and a nonquadratic Lyapunov function, we derive linear matrix inequality (LMI)-based conditions to co-design the event-triggering mechanism and the gain-scheduled controller. These conditions are incorporated into a multiobjective optimization problem to maximize the estimate of the region of attraction of the origin and minimize the number of transmissions of the PETC scheme. We prove that the closed-loop trajectories initiated in the estimated region of attraction converge toward the origin without violating the boundedness of the mismatched scheduling functions during operation. Two numerical examples are provided to illustrate the methodology.
{"title":"Handling Asynchronous Scheduling Functions in Periodic Event-Triggered Gain-Scheduled Control With Guaranteed Polytopic Inclusion.","authors":"Pedro Henrique Silva Coutinho,Paulo S P Pessim,Iury Bessa,Marcia Luciana da Costa Peixoto,Reinaldo Martinez Palhares","doi":"10.1109/tcyb.2026.3671185","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3671185","url":null,"abstract":"This article deals with periodic event-triggered control (PETC) of nonlinear systems, considering an equivalent quasi-linear parameter-varying (quasi-LPV) polytopic representation of the nonlinear plant and a gain-scheduled controller for stabilization. Although gain-scheduling approaches allow one to improve the results and extend the set of feasible solutions to the co-design problem, the event-based sampling induces the so-called asynchronous scheduling functions, which void the gain-scheduling advantages, leading to conservative results, especially in the PETC framework. The dominant approaches for dealing with this issue consider a bounding assumption on the mismatched scheduling functions, but do not guarantee that those bounds cannot be violated during the closed-loop operation. To properly manage the asynchronous phenomenon, we propose a novel PETC scheme. Based on the looped-functional approach and a nonquadratic Lyapunov function, we derive linear matrix inequality (LMI)-based conditions to co-design the event-triggering mechanism and the gain-scheduled controller. These conditions are incorporated into a multiobjective optimization problem to maximize the estimate of the region of attraction of the origin and minimize the number of transmissions of the PETC scheme. We prove that the closed-loop trajectories initiated in the estimated region of attraction converge toward the origin without violating the boundedness of the mismatched scheduling functions during operation. Two numerical examples are provided to illustrate the methodology.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"9 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147464946","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}
Pub Date : 2026-03-16DOI: 10.1109/tcyb.2026.3667145
Chao Zeng,Jun Zhang,Sam Kwong
In recent years, the task of detecting salient objects in optical remote-sensing images has posed a significant and formidable challenge. The existing approaches heavily rely on a limited amount of label saliency masks and usually utilize convolutional neural networks (CNNs) for feature decoding. In this article, we introduce the conditional diffusion transformer network (CDTNet), a novel architecture meticulously designed to learn contextualized and diffusion-guided features for optical remote sensing image salient object detection (ORSI SOD). Our work presents a Transformer-based progressive cross-stage fusion (PCSF) module. This module serves as the decoding unit for saliency prediction, enabling the seamless integration of multiscale features from different stages of the network. Through this fusion, the model can better understand the inner structure of the image and enhance the accuracy of saliency prediction. Moreover, we develop a patch strategy (PS). This strategy is dedicated to fine-grained feature aggregation, allowing the network to focus on detailed information within individual feature patches and thus making better use of transformer layers. In addition, the encoder feature enhancement (EFE) module is applied to enhance the extracted features from the backbone network by utilizing spatial and channel attention. We conduct comprehensive experiments on various benchmark datasets and evaluation metrics. The experimental results unequivocally demonstrate the superiority of the proposed CDTNet over the comparison SOTA methods.
{"title":"Learning Conditional Diffusion Transformer for Salient Object Detection in Optical Remote Sensing Images.","authors":"Chao Zeng,Jun Zhang,Sam Kwong","doi":"10.1109/tcyb.2026.3667145","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3667145","url":null,"abstract":"In recent years, the task of detecting salient objects in optical remote-sensing images has posed a significant and formidable challenge. The existing approaches heavily rely on a limited amount of label saliency masks and usually utilize convolutional neural networks (CNNs) for feature decoding. In this article, we introduce the conditional diffusion transformer network (CDTNet), a novel architecture meticulously designed to learn contextualized and diffusion-guided features for optical remote sensing image salient object detection (ORSI SOD). Our work presents a Transformer-based progressive cross-stage fusion (PCSF) module. This module serves as the decoding unit for saliency prediction, enabling the seamless integration of multiscale features from different stages of the network. Through this fusion, the model can better understand the inner structure of the image and enhance the accuracy of saliency prediction. Moreover, we develop a patch strategy (PS). This strategy is dedicated to fine-grained feature aggregation, allowing the network to focus on detailed information within individual feature patches and thus making better use of transformer layers. In addition, the encoder feature enhancement (EFE) module is applied to enhance the extracted features from the backbone network by utilizing spatial and channel attention. We conduct comprehensive experiments on various benchmark datasets and evaluation metrics. The experimental results unequivocally demonstrate the superiority of the proposed CDTNet over the comparison SOTA methods.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"12 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147464945","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}
Pub Date : 2026-03-13DOI: 10.1109/tcyb.2026.3670840
Zhihua Guo,Qinglai Wei,Xudong Zhao,Bohui Wang,Ben Niu,Hao Liu
This article concentrates on the attack detection and active attack defense strategies for discrete-time linear cyber-physical systems (CPSs) with unknown but bounded (UBB) disturbance and noise in the presence of both actuator and sensor attacks. First, a novel zonotopic observer is constructed to estimate the set-valued state and actuator attack by introducing augmentation techniques. To mitigate the effects of uncertainty and enhance estimation accuracy, the $H_{infty }$ technique is introduced to construct the observer. Unlike most existing works, the constructed observer simultaneously estimates the system state and actuator attacks. Then, by combining the designed observer with reachability analysis, a set-valued abnormal detector and a residual-based abnormal detector are designed to detect actuator and sensor attacks, respectively. In addition, by incorporating the obtained state reachable sets and the $H_{infty }$ technique, an active attack defense mechanism is designed to mitigate the impact of attacks on system performance. The proposed defense strategy does not introduce any performance loss in the absence of attacks. Finally, the superiority of the developed method is demonstrated by its application to a numerical simulation and an autonomous aircraft system.
{"title":"Attack Detection and Active Attack Defense for Cyber-Physical Systems via Zonotopic Observer and Reachability Analysis.","authors":"Zhihua Guo,Qinglai Wei,Xudong Zhao,Bohui Wang,Ben Niu,Hao Liu","doi":"10.1109/tcyb.2026.3670840","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3670840","url":null,"abstract":"This article concentrates on the attack detection and active attack defense strategies for discrete-time linear cyber-physical systems (CPSs) with unknown but bounded (UBB) disturbance and noise in the presence of both actuator and sensor attacks. First, a novel zonotopic observer is constructed to estimate the set-valued state and actuator attack by introducing augmentation techniques. To mitigate the effects of uncertainty and enhance estimation accuracy, the $H_{infty }$ technique is introduced to construct the observer. Unlike most existing works, the constructed observer simultaneously estimates the system state and actuator attacks. Then, by combining the designed observer with reachability analysis, a set-valued abnormal detector and a residual-based abnormal detector are designed to detect actuator and sensor attacks, respectively. In addition, by incorporating the obtained state reachable sets and the $H_{infty }$ technique, an active attack defense mechanism is designed to mitigate the impact of attacks on system performance. The proposed defense strategy does not introduce any performance loss in the absence of attacks. Finally, the superiority of the developed method is demonstrated by its application to a numerical simulation and an autonomous aircraft system.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"16 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147447046","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}
Pub Date : 2026-03-13DOI: 10.1109/tcyb.2026.3670956
Kui Wang,Yun Feng,Bing-Chuan Wang,Yu Zhou,Peng Wei,Liqun Chen,Han-Xiong Li
Many industrial processes, such as heat transfer and chemical diffusion reactions, are typical distributed parameter systems (DPSs) characterized by strong spatiotemporal (S-T) coupling. Any component within these systems may malfunction and result in significant safety risks. This article proposes a model-based framework for the collaborative diagnosis of S-T faults and sensor anomalies in DPSs. First, based on the reduced-order model obtained through the spectral method, two sets of observers are established for process faults and sensor anomalies, respectively. Fault detection and isolation (FDI) algorithms are developed by leveraging the characteristics of these two fault types. Next, using an unknown input observer (UIO), a cooperative fault estimation algorithm capable of handling the coexistence of both fault types is designed. The stability and convergence of the proposed method are ensured through the Lyapunov direct method. Finally, numerical simulations are conducted on a heat-transfer rod. The results demonstrate that the FDI algorithm can detect and isolate S-T faults and sensor anomalies effectively. Moreover, the root-mean-square error (RMSE) of the intensity estimation remains below 0.31, further verifying the effectiveness of the proposed collaborative diagnosis algorithm.
{"title":"Collaborative Diagnosis of Spatiotemporal Faults and Sensor Anomalies in Parabolic Distributed Parameter Systems.","authors":"Kui Wang,Yun Feng,Bing-Chuan Wang,Yu Zhou,Peng Wei,Liqun Chen,Han-Xiong Li","doi":"10.1109/tcyb.2026.3670956","DOIUrl":"https://doi.org/10.1109/tcyb.2026.3670956","url":null,"abstract":"Many industrial processes, such as heat transfer and chemical diffusion reactions, are typical distributed parameter systems (DPSs) characterized by strong spatiotemporal (S-T) coupling. Any component within these systems may malfunction and result in significant safety risks. This article proposes a model-based framework for the collaborative diagnosis of S-T faults and sensor anomalies in DPSs. First, based on the reduced-order model obtained through the spectral method, two sets of observers are established for process faults and sensor anomalies, respectively. Fault detection and isolation (FDI) algorithms are developed by leveraging the characteristics of these two fault types. Next, using an unknown input observer (UIO), a cooperative fault estimation algorithm capable of handling the coexistence of both fault types is designed. The stability and convergence of the proposed method are ensured through the Lyapunov direct method. Finally, numerical simulations are conducted on a heat-transfer rod. The results demonstrate that the FDI algorithm can detect and isolate S-T faults and sensor anomalies effectively. Moreover, the root-mean-square error (RMSE) of the intensity estimation remains below 0.31, further verifying the effectiveness of the proposed collaborative diagnosis algorithm.","PeriodicalId":13112,"journal":{"name":"IEEE Transactions on Cybernetics","volume":"171 1","pages":""},"PeriodicalIF":11.8,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147447044","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}
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