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Optimization-oriented multi-view representation learning in implicit bi-topological spaces

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2025-02-05 DOI: 10.1016/j.ins.2025.121945

Shiyang Lan , Shide Du , Zihan Fang , Zhiling Cai , Wei Huang , Shiping Wang

Many representation learning methods have gradually emerged to better exploit the properties of multi-view data. However, these existing methods still have the following areas to be improved: 1) Most of them overlook the ex-ante interpretability of the model, which renders the model more complex and more difficult for people to understand; 2) They underutilize the potential of the bi-topological spaces, which bring additional structural information to the representation learning process. This lack is detrimental when dealing with data that exhibits topological properties or has complex geometrical relationships between different views. Therefore, to address the above challenges, we propose an optimization-oriented multi-view representation learning framework in implicit bi-topological spaces. On one hand, we construct an intrinsically interpretability end-to-end white-box model that directly conducts the representation learning procedure while improving the transparency of the model. On the other hand, the integration of bi-topological spaces information within the network via manifold learning facilitates the comprehensive utilization of information from the data, ultimately enhancing representation learning and yielding superior performance for downstream tasks. Extensive experimental results demonstrate that the proposed method exhibits promising performance and is feasible in the downstream tasks.

{"title":"Optimization-oriented multi-view representation learning in implicit bi-topological spaces","authors":"Shiyang Lan , Shide Du , Zihan Fang , Zhiling Cai , Wei Huang , Shiping Wang","doi":"10.1016/j.ins.2025.121945","DOIUrl":"10.1016/j.ins.2025.121945","url":null,"abstract":"<div><div>Many representation learning methods have gradually emerged to better exploit the properties of multi-view data. However, these existing methods still have the following areas to be improved: 1) Most of them overlook the ex-ante interpretability of the model, which renders the model more complex and more difficult for people to understand; 2) They underutilize the potential of the bi-topological spaces, which bring additional structural information to the representation learning process. This lack is detrimental when dealing with data that exhibits topological properties or has complex geometrical relationships between different views. Therefore, to address the above challenges, we propose an optimization-oriented multi-view representation learning framework in implicit bi-topological spaces. On one hand, we construct an intrinsically interpretability end-to-end white-box model that directly conducts the representation learning procedure while improving the transparency of the model. On the other hand, the integration of bi-topological spaces information within the network via manifold learning facilitates the comprehensive utilization of information from the data, ultimately enhancing representation learning and yielding superior performance for downstream tasks. Extensive experimental results demonstrate that the proposed method exhibits promising performance and is feasible in the downstream tasks.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"704 ","pages":"Article 121945"},"PeriodicalIF":8.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143377334","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

A hybrid lightweight transformer architecture based on fuzzy attention prototypes for multivariate time series classification

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2025-02-05 DOI: 10.1016/j.ins.2025.121942

Yan Gu , Feng Jin , Jun Zhao , Wei Wang

Multivariate time series classification has become a research hotspot owing to its rapid development. Existing methods mainly focus on the feature correlations of time series, ignoring data uncertainty and sample sparsity. To address these challenges, a hybrid lightweight Transformer architecture based on fuzzy attention prototypes named FapFormer is proposed, in which a convolutional spanning Vision Transformer module is built to perform feature extraction and provide inductive bias, incorporating dynamic feature sampling to select the key features adaptively for increasing the training efficiency. A progressive branching convolution (PBC) block and convolutional self-attention (CSA) block are then introduced to extract both local and global features. Furthermore, a feature complementation strategy is implemented to enable the CSA block to specialize in global dependencies, overcoming the local receptive field limitations of the PBC block. Finally, a novel fuzzy attention prototype learning method is proposed to represent class prototypes for data uncertainty, which employs the distances between prototypes and low-dimensional embeddings for classification. Experiments were conducted using both the UEA benchmark dataset and a practical industrial dataset demonstrate that FapFormer outperforms several state-of-the-art methods, achieving improved accuracy and reduced computational complexity, even under conditions of data uncertainty and sample sparsity.

{"title":"A hybrid lightweight transformer architecture based on fuzzy attention prototypes for multivariate time series classification","authors":"Yan Gu , Feng Jin , Jun Zhao , Wei Wang","doi":"10.1016/j.ins.2025.121942","DOIUrl":"10.1016/j.ins.2025.121942","url":null,"abstract":"<div><div>Multivariate time series classification has become a research hotspot owing to its rapid development. Existing methods mainly focus on the feature correlations of time series, ignoring data uncertainty and sample sparsity. To address these challenges, a hybrid lightweight Transformer architecture based on fuzzy attention prototypes named FapFormer is proposed, in which a convolutional spanning Vision Transformer module is built to perform feature extraction and provide inductive bias, incorporating dynamic feature sampling to select the key features adaptively for increasing the training efficiency. A progressive branching convolution (PBC) block and convolutional self-attention (CSA) block are then introduced to extract both local and global features. Furthermore, a feature complementation strategy is implemented to enable the CSA block to specialize in global dependencies, overcoming the local receptive field limitations of the PBC block. Finally, a novel fuzzy attention prototype learning method is proposed to represent class prototypes for data uncertainty, which employs the distances between prototypes and low-dimensional embeddings for classification. Experiments were conducted using both the UEA benchmark dataset and a practical industrial dataset demonstrate that FapFormer outperforms several state-of-the-art methods, achieving improved accuracy and reduced computational complexity, even under conditions of data uncertainty and sample sparsity.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"703 ","pages":"Article 121942"},"PeriodicalIF":8.1,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143293056","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

Short video rumor detection based on causal graph

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2025-02-04 DOI: 10.1016/j.ins.2025.121941

Donglin Cao, Xiong Tang, Yanghao Lin, Dazhen Lin

In recent years, the short video industry has experienced rapid growth, leading to the emergence of numerous knowledge-based rumors. These rumors often disguise themselves as professional knowledge, making it difficult for fact-checkers to identify their falsehoods without external expertise. Furthermore, existing Chinese short video rumor datasets lack support from external knowledge. To solve that problem and apply to a real-world scenario, this paper constructs a Chinese short video rumor dataset from Douyin, which is the largest short video platform in China, and build a related rumor evidence base. To further characterize the knowledge association between short video entities which is important for the interpretation of knowledge distortion, this paper also constructs causal relationships between entities using causal discovery algorithms. Finally, to tackle and visualize the knowledge distortion in social media short videos, this paper proposes a Causal Short Video Rumor Pretrain Model (CSVRPM). This model obtains relevant causal subgraphs from the causal knowledge repository and integrates the causal relationships within these subgraphs using an attention mechanism in the short video rumor detection model. The experiment results show that the model outperforms some state-of-the-art approaches and greatly improves the interpretability of short video rumor detection results.

{"title":"Short video rumor detection based on causal graph","authors":"Donglin Cao, Xiong Tang, Yanghao Lin, Dazhen Lin","doi":"10.1016/j.ins.2025.121941","DOIUrl":"10.1016/j.ins.2025.121941","url":null,"abstract":"<div><div>In recent years, the short video industry has experienced rapid growth, leading to the emergence of numerous knowledge-based rumors. These rumors often disguise themselves as professional knowledge, making it difficult for fact-checkers to identify their falsehoods without external expertise. Furthermore, existing Chinese short video rumor datasets lack support from external knowledge. To solve that problem and apply to a real-world scenario, this paper constructs a Chinese short video rumor dataset from Douyin, which is the largest short video platform in China, and build a related rumor evidence base. To further characterize the knowledge association between short video entities which is important for the interpretation of knowledge distortion, this paper also constructs causal relationships between entities using causal discovery algorithms. Finally, to tackle and visualize the knowledge distortion in social media short videos, this paper proposes a Causal Short Video Rumor Pretrain Model (CSVRPM). This model obtains relevant causal subgraphs from the causal knowledge repository and integrates the causal relationships within these subgraphs using an attention mechanism in the short video rumor detection model. The experiment results show that the model outperforms some state-of-the-art approaches and greatly improves the interpretability of short video rumor detection results.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"703 ","pages":"Article 121941"},"PeriodicalIF":8.1,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143293054","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

Three-way conflict analysis and resolution based on interval set information

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2025-02-04 DOI: 10.1016/j.ins.2025.121938

Sheng Gao , Hai-Long Yang , Zhi-Lian Guo

In existing three-way conflict analysis (TWCA), once the ratings are given, the relationships between agents will be determined. However, agents may compromise to achieve a common goal when conflicts arise, which leads to variable ratings. This paper will present a novel TWCA model based on interval sets, where the ratings are represented by interval sets (the lower bound of an interval set represents the preferred rating, and the upper bound indicates the range of acceptable ratings). First, we give the notion of interval set conflict systems (ISCSs) and introduce a new conflict function. Second, considering the balance of agents' opinions, we assign issue weights according to the proportion of the absolute values of the column means of all agents' ratings (ACMR) across different issues. We then discuss the trisections of agent pairs, agent set, and issue set, where the thresholds are derived by the given conflict function. We propose two methods for conflict resolution by adjusting the preference ratings of some agents to form a maximal alliance among as many agents as possible. We verify the model's stability and validity through sensitivity analysis and comparative analysis. Finally, we apply this model to a case study of enterprise bidding.

{"title":"Three-way conflict analysis and resolution based on interval set information","authors":"Sheng Gao , Hai-Long Yang , Zhi-Lian Guo","doi":"10.1016/j.ins.2025.121938","DOIUrl":"10.1016/j.ins.2025.121938","url":null,"abstract":"<div><div>In existing three-way conflict analysis (TWCA), once the ratings are given, the relationships between agents will be determined. However, agents may compromise to achieve a common goal when conflicts arise, which leads to variable ratings. This paper will present a novel TWCA model based on interval sets, where the ratings are represented by interval sets (the lower bound of an interval set represents the preferred rating, and the upper bound indicates the range of acceptable ratings). First, we give the notion of interval set conflict systems (ISCSs) and introduce a new conflict function. Second, considering the balance of agents' opinions, we assign issue weights according to the proportion of the absolute values of the column means of all agents' ratings (ACMR) across different issues. We then discuss the trisections of agent pairs, agent set, and issue set, where the thresholds are derived by the given conflict function. We propose two methods for conflict resolution by adjusting the preference ratings of some agents to form a maximal alliance among as many agents as possible. We verify the model's stability and validity through sensitivity analysis and comparative analysis. Finally, we apply this model to a case study of enterprise bidding.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"703 ","pages":"Article 121938"},"PeriodicalIF":8.1,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143293055","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

A novel robust semi-supervised stochastic configuration network for regression tasks with noise

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2025-02-04 DOI: 10.1016/j.ins.2025.121933

Shifei Ding , Zi Zhang , Li Xu , Chenglong Zhang , Lili Guo , Xuan Li

The Stochastic Configuration Network (SCN) is an incremental neural network that adjusts hidden layer inputs using a supervised mechanism and calculates the output weights using the least squares method. However, its generalization performance and robustness significantly degrade with limited labeled data and noise interference. To enhance SCN's regression performance in these scenarios, we propose a novel Robust Semi-Supervised Stochastic Configuration Network (RS³CN) and introduce its semi-supervised variant (S³CN), along with robust versions (RS³CN-Huber and RS³CN-IQR) for comparative experiments. RS³CN employs kernel density estimation (KDE) to evaluate the distribution of labeled training samples, thereby minimizing the effects of noise and outliers. Manifold regularization (MR) is also applied to learn features from unlabeled data. Combining these techniques enhances the SCN's generalization performance in such scenarios. Additionally, we introduce an L₂ regularization term to handle outliers in sparse features, reducing overfitting. Finally, we demonstrate its universal approximation property within an enhanced robust semi-supervised optimization framework. Simulation experiments on benchmark datasets show a significant improvement in both semi-supervised learning and robustness of the proposed RS³CN, compared to existing SCN-related algorithms.

{"title":"A novel robust semi-supervised stochastic configuration network for regression tasks with noise","authors":"Shifei Ding , Zi Zhang , Li Xu , Chenglong Zhang , Lili Guo , Xuan Li","doi":"10.1016/j.ins.2025.121933","DOIUrl":"10.1016/j.ins.2025.121933","url":null,"abstract":"<div><div>The Stochastic Configuration Network (SCN) is an incremental neural network that adjusts hidden layer inputs using a supervised mechanism and calculates the output weights using the least squares method. However, its generalization performance and robustness significantly degrade with limited labeled data and noise interference. To enhance SCN's regression performance in these scenarios, we propose a novel Robust Semi-Supervised Stochastic Configuration Network (RS<sup>3</sup>CN) and introduce its semi-supervised variant (S<sup>3</sup>CN), along with robust versions (RS<sup>3</sup>CN-Huber and RS<sup>3</sup>CN-IQR) for comparative experiments. RS<sup>3</sup>CN employs kernel density estimation (KDE) to evaluate the distribution of labeled training samples, thereby minimizing the effects of noise and outliers. Manifold regularization (MR) is also applied to learn features from unlabeled data. Combining these techniques enhances the SCN's generalization performance in such scenarios. Additionally, we introduce an L<sub>2</sub> regularization term to handle outliers in sparse features, reducing overfitting. Finally, we demonstrate its universal approximation property within an enhanced robust semi-supervised optimization framework. Simulation experiments on benchmark datasets show a significant improvement in both semi-supervised learning and robustness of the proposed RS<sup>3</sup>CN, compared to existing SCN-related algorithms.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"703 ","pages":"Article 121933"},"PeriodicalIF":8.1,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143293052","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

Influence contribution ratio estimation in social networks

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2025-02-04 DOI: 10.1016/j.ins.2025.121934

Yingdan Shi , Jingya Zhou , Congcong Zhang , Zhenyu Hu

Social networks are becoming an ideal choice for marketing activities and advertising campaigns due to the explosive growth of social network users. In these advertising campaigns, influential users, such as celebrities, often post or repost the ads to help disseminate product information through the ‘word-of-mouth’ effect in social networks. It is significant to allocate remuneration fairly to these influential users based on their contributions to disseminating ads. To address this, we propose a concept called the influence contribution ratio, which represents the contribution ratio of each influential user to an advertising campaign. We introduce two types of coalitional games to depict the process of influence diffusion from multiple levels, namely macro and micro coalitional games, and propose a metric called InfConR based on the Shapley value in coalitional game theory to measure the influence contribution ratio fairly. A naive method to calculate the InfConR value for each user is to use a Monte Carlo (MC) simulation to enumerate a certain number of cascades for the advertising campaign. However, this method is too time-consuming and not realistic. Therefore, we propose a scheme called ICR, which involves two components: 1) sampling algorithms for InfConR in the Independent Cascade (IC) model and Liner Threshold (LT) model, respectively, and 2) an algorithm with approximation guarantees to minimize the sampling number. Our experiments on four real-world datasets demonstrate the superiority and effectiveness of our scheme.

{"title":"Influence contribution ratio estimation in social networks","authors":"Yingdan Shi , Jingya Zhou , Congcong Zhang , Zhenyu Hu","doi":"10.1016/j.ins.2025.121934","DOIUrl":"10.1016/j.ins.2025.121934","url":null,"abstract":"<div><div>Social networks are becoming an ideal choice for marketing activities and advertising campaigns due to the explosive growth of social network users. In these advertising campaigns, influential users, such as celebrities, often post or repost the ads to help disseminate product information through the ‘word-of-mouth’ effect in social networks. It is significant to allocate remuneration fairly to these influential users based on their contributions to disseminating ads. To address this, we propose a concept called the influence contribution ratio, which represents the contribution ratio of each influential user to an advertising campaign. We introduce two types of coalitional games to depict the process of influence diffusion from multiple levels, namely macro and micro coalitional games, and propose a metric called InfConR based on the Shapley value in coalitional game theory to measure the influence contribution ratio fairly. A naive method to calculate the InfConR value for each user is to use a Monte Carlo (MC) simulation to enumerate a certain number of cascades for the advertising campaign. However, this method is too time-consuming and not realistic. Therefore, we propose a scheme called ICR, which involves two components: 1) sampling algorithms for InfConR in the Independent Cascade (IC) model and Liner Threshold (LT) model, respectively, and 2) an algorithm with approximation guarantees to minimize the sampling number. Our experiments on four real-world datasets demonstrate the superiority and effectiveness of our scheme.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"703 ","pages":"Article 121934"},"PeriodicalIF":8.1,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103849","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

RDFS-TDC: Robust discriminant feature selection based on improved trace difference criterion

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2025-02-04 DOI: 10.1016/j.ins.2025.121940

Libo Yang , Dawei Zhu , Xuemei Liu , Feiping Nie

Various discriminant feature selection models have been proposed that combine discriminant subspaces and sparse constraints. However, most scholars ignore the sensitivity of the discriminant criterion to outliers. In this study, we propose a robust discriminative feature selection method called RDFS-TDC. RDFS-TDC learns the optimal discriminative projection based on the trace-difference criterion, which provides good flexibility while avoiding singular matrices. Subsequently, the objective function was optimized using an iterative reweighting method, which reduced the impact of outliers on the discriminant subspace during the learning process. To satisfy different sparsity requirements, this study introduces the

L_{2, p}

norm constraint to impose row sparsity on the projection matrix. RDFS-TDC obtained 87.05%, 94.68%, 84.82%, and 89.60% accuracies on YaleB, COIL20, CMUPIE, and FERET, respectively, and the misclassification error rate was 0.01%-3.32% lower compared to other methods. In addition, RDFS-TDC performed better on datasets with different scenarios compared to SDFS, WDFS, Fisher Score, DLSR, ReliefF, and RFS.

{"title":"RDFS-TDC: Robust discriminant feature selection based on improved trace difference criterion","authors":"Libo Yang , Dawei Zhu , Xuemei Liu , Feiping Nie","doi":"10.1016/j.ins.2025.121940","DOIUrl":"10.1016/j.ins.2025.121940","url":null,"abstract":"<div><div>Various discriminant feature selection models have been proposed that combine discriminant subspaces and sparse constraints. However, most scholars ignore the sensitivity of the discriminant criterion to outliers. In this study, we propose a robust discriminative feature selection method called RDFS-TDC. RDFS-TDC learns the optimal discriminative projection based on the trace-difference criterion, which provides good flexibility while avoiding singular matrices. Subsequently, the objective function was optimized using an iterative reweighting method, which reduced the impact of outliers on the discriminant subspace during the learning process. To satisfy different sparsity requirements, this study introduces the <span><math><msub><mrow><mi>L</mi></mrow><mrow><mn>2</mn><mo>,</mo><mi>p</mi></mrow></msub></math></span> norm constraint to impose row sparsity on the projection matrix. RDFS-TDC obtained 87.05%, 94.68%, 84.82%, and 89.60% accuracies on YaleB, COIL20, CMUPIE, and FERET, respectively, and the misclassification error rate was 0.01%-3.32% lower compared to other methods. In addition, RDFS-TDC performed better on datasets with different scenarios compared to SDFS, WDFS, Fisher Score, DLSR, ReliefF, and RFS.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"705 ","pages":"Article 121940"},"PeriodicalIF":8.1,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143428698","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

Co-design of observer-based multiple weighting event-triggered and quadruple asynchronous control for fuzzy semi-Markov jump systems

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2025-02-03 DOI: 10.1016/j.ins.2025.121935

Yiteng Zhang , Linchuang Zhang , Wei Wang , Zhechen Zhu

This paper studies the co-design issue of observer-based dual multiple weighting event-triggered and quadruple asynchronous control for the Takagi-Sugeno fuzzy semi-Markov jump systems with actuator faults. Owing to the network-induced delay, external disturbance and other factors, the premise variables and modes information among the system, event generators, observer and controller cannot always match. In addition, there are often unmeasurable state variables and waste of limited network resources in practical systems. Therefore, an asynchronous observer-based controller and two multiple weighting asynchronous event-triggered schemes are developed simultaneously for the fuzzy semi-Markov jump systems by using hidden semi-Markov models. Based on the above quadruple asynchronous control framework, new sufficient conditions are developed to ensure that the related parameters of the designed observer, controller and event generators can be obtained. Eventually, the effectiveness of the proposed method is demonstrated by two simulation examples, with the second example ensuring the stable operation of the tunnel diode circuit system.

{"title":"Co-design of observer-based multiple weighting event-triggered and quadruple asynchronous control for fuzzy semi-Markov jump systems","authors":"Yiteng Zhang , Linchuang Zhang , Wei Wang , Zhechen Zhu","doi":"10.1016/j.ins.2025.121935","DOIUrl":"10.1016/j.ins.2025.121935","url":null,"abstract":"<div><div>This paper studies the co-design issue of observer-based dual multiple weighting event-triggered and quadruple asynchronous control for the Takagi-Sugeno fuzzy semi-Markov jump systems with actuator faults. Owing to the network-induced delay, external disturbance and other factors, the premise variables and modes information among the system, event generators, observer and controller cannot always match. In addition, there are often unmeasurable state variables and waste of limited network resources in practical systems. Therefore, an asynchronous observer-based controller and two multiple weighting asynchronous event-triggered schemes are developed simultaneously for the fuzzy semi-Markov jump systems by using hidden semi-Markov models. Based on the above quadruple asynchronous control framework, new sufficient conditions are developed to ensure that the related parameters of the designed observer, controller and event generators can be obtained. Eventually, the effectiveness of the proposed method is demonstrated by two simulation examples, with the second example ensuring the stable operation of the tunnel diode circuit system.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"703 ","pages":"Article 121935"},"PeriodicalIF":8.1,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143292637","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

Retraction notice to “Multiple attribute decision making based on MAIRCA, standard deviation-based method, and Pythagorean fuzzy sets” [Inf. Sci. 644 (2023) 119274]

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2025-02-01 DOI: 10.1016/j.ins.2024.121684

Pratibha Rani , Shyi-Ming Chen , Arunodaya Raj Mishra

引用次数: 0

Optimising RFID network planning problem using an improved automated approach inspired by artificial neural networks

IF 8.1 1区计算机科学 0 COMPUTER SCIENCE, INFORMATION SYSTEMS

Information Sciences

Pub Date : 2025-01-31 DOI: 10.1016/j.ins.2025.121927

Mustapha Maimouni , Badr Abou El Majd , Mohsine Bouya

Radio Frequency Identification (RFID) technology is recognised as an effective solution for Internet of Things (IoT) applications across various domains. However, scheduling an RFID system presents an NP-hard combinatorial optimisation problem known as the RFID network planning problem (RNP). This problem requires satisfying several criteria, including a minimum number of antennas for full coverage and a balanced load, while avoiding interference for optimal deployment. Previous studies have often relied on arbitrary initial parameters, particularly concerning the number of antennas, leading to suboptimal solutions. This study investigates a novel approach to address the RNP problem by employing a hybrid metaheuristic-based approach incorporating the advantages of artificial neural networks, whereby machine learning methods are implemented to automatically initiate the initial number of antennas. This study introduces a novel approach named ‘I-RAENNA’, which is evaluated against well-known instances and compared to established methods such as VNPSO, HPSO, CSP, RAE-NNA, and SLIWMBBO. The experimental results demonstrate that I-RAENNA significantly outperforms state-of-the-art solutions, proving its effectiveness in improving RFID system deployment.

{"title":"Optimising RFID network planning problem using an improved automated approach inspired by artificial neural networks","authors":"Mustapha Maimouni , Badr Abou El Majd , Mohsine Bouya","doi":"10.1016/j.ins.2025.121927","DOIUrl":"10.1016/j.ins.2025.121927","url":null,"abstract":"<div><div>Radio Frequency Identification (RFID) technology is recognised as an effective solution for Internet of Things (IoT) applications across various domains. However, scheduling an RFID system presents an NP-hard combinatorial optimisation problem known as the RFID network planning problem (RNP). This problem requires satisfying several criteria, including a minimum number of antennas for full coverage and a balanced load, while avoiding interference for optimal deployment. Previous studies have often relied on arbitrary initial parameters, particularly concerning the number of antennas, leading to suboptimal solutions. This study investigates a novel approach to address the RNP problem by employing a hybrid metaheuristic-based approach incorporating the advantages of artificial neural networks, whereby machine learning methods are implemented to automatically initiate the initial number of antennas. This study introduces a novel approach named ‘I-RAENNA’, which is evaluated against well-known instances and compared to established methods such as VNPSO, HPSO, CSP, RAE-NNA, and SLIWMBBO. The experimental results demonstrate that I-RAENNA significantly outperforms state-of-the-art solutions, proving its effectiveness in improving RFID system deployment.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"703 ","pages":"Article 121927"},"PeriodicalIF":8.1,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103851","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

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