Pub Date : 2025-01-08DOI: 10.1016/j.ins.2024.121867
Junping Xie , Jing Yang , Jinhai Li , Mingwei He , Huaxiang Song
In three-way concept analysis, how to quickly construct object-induced three-way concept lattices and acquire three-way decision association rules deserves to be studied. Based on this, the main work is done in this study as follows. Firstly, we raise a novel fast algorithm of setting up object-induced three-way concept lattices, which includes quickly generating object-induced three-way concepts and establishing the partial order among these concepts, and carry out experiments to verify the high efficiency of the algorithm. Secondly, we define three-way decision association rules, which can express richer knowledge than two-way decision association rules, explore the relationship between three-way decision association rules and two-way decision association rules, and give a new algorithm to extract three-way decision association rules grounded on object-induced three-way concept lattices. Finally, we apply the proposed algorithms to cause analysis of traffic accidents for thoroughly identifying the coupling factors of traffic accidents.
{"title":"Three-way concept lattice construction and association rule acquisition","authors":"Junping Xie , Jing Yang , Jinhai Li , Mingwei He , Huaxiang Song","doi":"10.1016/j.ins.2024.121867","DOIUrl":"10.1016/j.ins.2024.121867","url":null,"abstract":"<div><div>In three-way concept analysis, how to quickly construct object-induced three-way concept lattices and acquire three-way decision association rules deserves to be studied. Based on this, the main work is done in this study as follows. Firstly, we raise a novel fast algorithm of setting up object-induced three-way concept lattices, which includes quickly generating object-induced three-way concepts and establishing the partial order among these concepts, and carry out experiments to verify the high efficiency of the algorithm. Secondly, we define three-way decision association rules, which can express richer knowledge than two-way decision association rules, explore the relationship between three-way decision association rules and two-way decision association rules, and give a new algorithm to extract three-way decision association rules grounded on object-induced three-way concept lattices. Finally, we apply the proposed algorithms to cause analysis of traffic accidents for thoroughly identifying the coupling factors of traffic accidents.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"701 ","pages":"Article 121867"},"PeriodicalIF":8.1,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105411","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 : 2025-01-08DOI: 10.1016/j.ins.2024.121866
Tengfei Ren , Qiusheng Lian , Jiale Chen
Occluded person re-identification (ReID) aims to address the potential occlusion problem when matching occluded or holistic pedestrians from different camera views. Currently, occlusion augmentation-based methods have not fully exploited the occlusion attributes, resulting in suboptimal results. We introduce a novel ReID framework, dubbed Occlusion Attributes boosted Occluded Person Re-Identification (OA-ReID), aimed at leveraging the occlusion attributes for pedestrian-focused feature learning. Firstly, we propose an occlusion emulator (OE) that generates artificially occluded images towards emulating the occlusion scenarios. Both the original image and the corresponding artificially occluded image are jointly used for model training. Secondly, we present two crucial components, namely the inductive hard (IH) sample mining and the Occlusion-Informed Part Transformer (OIPT). The IH sample mining leverages the obstacle category to construct inductive triplets, which induces the model to extract identity-relevant features. The OIPT integrates the obstacle position information into our ReID framework to rectify the erroneous attention on occlusions, promoting reliable target pedestrian localization. Through extensive experiments, we show OA-ReID achieves state-of-the-art performance on both occluded and holistic person ReID benchmarks.
{"title":"Boosting occluded person re-identification by leveraging occlusion attributes","authors":"Tengfei Ren , Qiusheng Lian , Jiale Chen","doi":"10.1016/j.ins.2024.121866","DOIUrl":"10.1016/j.ins.2024.121866","url":null,"abstract":"<div><div>Occluded person re-identification (ReID) aims to address the potential occlusion problem when matching occluded or holistic pedestrians from different camera views. Currently, occlusion augmentation-based methods have not fully exploited the occlusion attributes, resulting in suboptimal results. We introduce a novel ReID framework, dubbed Occlusion Attributes boosted Occluded Person Re-Identification (OA-ReID), aimed at leveraging the occlusion attributes for pedestrian-focused feature learning. Firstly, we propose an occlusion emulator (OE) that generates artificially occluded images towards emulating the occlusion scenarios. Both the original image and the corresponding artificially occluded image are jointly used for model training. Secondly, we present two crucial components, namely the inductive hard (IH) sample mining and the Occlusion-Informed Part Transformer (OIPT). The IH sample mining leverages the obstacle category to construct inductive triplets, which induces the model to extract identity-relevant features. The OIPT integrates the obstacle position information into our ReID framework to rectify the erroneous attention on occlusions, promoting reliable target pedestrian localization. Through extensive experiments, we show OA-ReID achieves state-of-the-art performance on both occluded and holistic person ReID benchmarks.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"701 ","pages":"Article 121866"},"PeriodicalIF":8.1,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105410","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}
In this article, the problem of global prescribed performance tracking control for multi-input multi-output (MIMO) nonlinear systems with sensor faults and unmeasured states is investigated. By constructing a state observer that incorporates an adaptive sensor fault compensation mechanism, the impact of the loss of sensor effectiveness is alleviated through the cubic absolute-value Lyapunov function analysis method. Based on several transformation functions and a time-varying scaling function, the tracking errors are restricted within the global prescribed performance without the constrained initial conditions. Considering the abrupt change in tracking errors due to the existence of sensor faults, a monitoring function to supervise the excessive loss of sensor effectiveness is designed. Furthermore, a novel reconfigurable controller can be constructed with the detected fault time instant and the global prescribed performance. The analysis demonstrates that all signals remain bounded and the tracking errors are maintained within the designed global prescribed performance regardless of sensor faults. Finally, the efficacy of the presented control scheme is demonstrated by the simulation results.
{"title":"Adaptive fault compensation for global performance tracking control of sensor faulty MIMO nonlinear systems with unmeasured states","authors":"Liuliu Zhang, Lingchen Zhu, Cheng Qian, Changchun Hua","doi":"10.1016/j.ins.2024.121862","DOIUrl":"10.1016/j.ins.2024.121862","url":null,"abstract":"<div><div>In this article, the problem of global prescribed performance tracking control for multi-input multi-output (MIMO) nonlinear systems with sensor faults and unmeasured states is investigated. By constructing a state observer that incorporates an adaptive sensor fault compensation mechanism, the impact of the loss of sensor effectiveness is alleviated through the cubic absolute-value Lyapunov function analysis method. Based on several transformation functions and a time-varying scaling function, the tracking errors are restricted within the global prescribed performance without the constrained initial conditions. Considering the abrupt change in tracking errors due to the existence of sensor faults, a monitoring function to supervise the excessive loss of sensor effectiveness is designed. Furthermore, a novel reconfigurable controller can be constructed with the detected fault time instant and the global prescribed performance. The analysis demonstrates that all signals remain bounded and the tracking errors are maintained within the designed global prescribed performance regardless of sensor faults. Finally, the efficacy of the presented control scheme is demonstrated by the simulation results.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"700 ","pages":"Article 121862"},"PeriodicalIF":8.1,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143150774","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 : 2025-01-07DOI: 10.1016/j.ins.2024.121839
KyungSoo Kim, PooGyeon Park
This paper aims to explore control synthesis for interval type-2 Takagi–Sugeno fuzzy systems using novel relaxation techniques. To tackle the conservatism of the previous works with respect to the utilization of membership properties, relaxation techniques are proposed within two distinct aspects. Based on a comprehensive interpretation of the properties of membership functions, an extrema-based algorithm to develop a convex polytope enclosing type-2 fuzzy membership distributions is proposed for membership-dependent stability analysis. To account for real-world conditions, this work utilizes constraints on the derivatives of lower and upper membership functions rather than the embedded membership function, in contrast to recent studies. In such a way as Finsler's lemma and relaxation principle, relaxation techniques that exploit lower, upper, and embedded membership properties with the reasonable constraints are established within a membership-quadratic framework with a matrix shrink lemma to save computational resources. As a result, the less conservative stabilization condition in the shape of the membership-quadratic framework is facilitated by the proposed algorithm and relaxation techniques. Finally, the effectiveness of the proposed methods is demonstrated through numerical examples.
{"title":"H∞ control for interval type-2 Takagi–Sugeno fuzzy systems via the membership-quadratic framework","authors":"KyungSoo Kim, PooGyeon Park","doi":"10.1016/j.ins.2024.121839","DOIUrl":"10.1016/j.ins.2024.121839","url":null,"abstract":"<div><div>This paper aims to explore <span><math><msub><mrow><mi>H</mi></mrow><mrow><mo>∞</mo></mrow></msub></math></span> control synthesis for interval type-2 Takagi–Sugeno fuzzy systems using novel relaxation techniques. To tackle the conservatism of the previous works with respect to the utilization of membership properties, relaxation techniques are proposed within two distinct aspects. Based on a comprehensive interpretation of the properties of membership functions, an extrema-based algorithm to develop a convex polytope enclosing type-2 fuzzy membership distributions is proposed for membership-dependent stability analysis. To account for real-world conditions, this work utilizes constraints on the derivatives of lower and upper membership functions rather than the embedded membership function, in contrast to recent studies. In such a way as Finsler's lemma and relaxation principle, relaxation techniques that exploit lower, upper, and embedded membership properties with the reasonable constraints are established within a membership-quadratic framework with a matrix shrink lemma to save computational resources. As a result, the less conservative stabilization condition in the shape of the membership-quadratic framework is facilitated by the proposed algorithm and relaxation techniques. Finally, the effectiveness of the proposed methods is demonstrated through numerical examples.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"701 ","pages":"Article 121839"},"PeriodicalIF":8.1,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105394","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 : 2025-01-07DOI: 10.1016/j.ins.2024.121838
Md. Aquil Khan, Ranjan
The necessity lower approximation operator defined on subset approximation structures is studied in this paper. We propose a semantics of the basic modal language based on this operator, offering a formal framework for analysis. The study focuses on the axiomatization, expressiveness, and invariance properties of the proposed semantics. Our findings contribute to a comprehensive understanding of the necessity lower approximation operator, shedding light on its properties.
{"title":"A semantics of the basic modal language based on a generalized rough set model","authors":"Md. Aquil Khan, Ranjan","doi":"10.1016/j.ins.2024.121838","DOIUrl":"10.1016/j.ins.2024.121838","url":null,"abstract":"<div><div>The necessity lower approximation operator defined on subset approximation structures is studied in this paper. We propose a semantics of the basic modal language based on this operator, offering a formal framework for analysis. The study focuses on the axiomatization, expressiveness, and invariance properties of the proposed semantics. Our findings contribute to a comprehensive understanding of the necessity lower approximation operator, shedding light on its properties.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"701 ","pages":"Article 121838"},"PeriodicalIF":8.1,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143105407","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 : 2025-01-07DOI: 10.1016/j.ins.2024.121864
Ma Yinghua , Ahmad Khan , Yang Heng , Fiaz Gul Khan , Farman Ali , Yasser D. Al-Otaibi , Ali Kashif Bashir
Cancer is a highly complex and fatal disease that affects various human organs. Early and accurate cancer analysis is crucial for timely treatment, prognosis, and understanding of the disease's development. Recent research utilizes deep learning-based models to combine multi-omics data for tasks such as cancer classification, clustering, and survival prediction. However, these models often overlook interactions between different types of data, which leads to suboptimal performance. In this paper, we present a Contrastive Multi-Modal Encoder (CMME) that integrates and maps multi-omics data into a lower-dimensional latent space, enabling the model to better understand relationships between different data types. The challenging distribution and organization of the data into anchors, positive samples, and negative samples encourage the model to learn synergies among different modalities, pay attention to both strong and weak modalities, and avoid biased learning. The performance of the proposed model is evaluated on downstream tasks such as clustering, classification, and survival prediction. The CMME achieved an accuracy of 98.16% and an F1 score of 98.09% in classifying breast cancer subtypes. For clustering tasks across ten cancer types based on TCGA data, the adjusted Rand index reached 0.966. Additionally, survival analysis results highlighted significant differences in survival rates between different cancer subtypes. The comprehensive qualitative and quantitative results demonstrate that the proposed method outperforms existing methods.
{"title":"A deep contrastive multi-modal encoder for multi-omics data integration and analysis","authors":"Ma Yinghua , Ahmad Khan , Yang Heng , Fiaz Gul Khan , Farman Ali , Yasser D. Al-Otaibi , Ali Kashif Bashir","doi":"10.1016/j.ins.2024.121864","DOIUrl":"10.1016/j.ins.2024.121864","url":null,"abstract":"<div><div>Cancer is a highly complex and fatal disease that affects various human organs. Early and accurate cancer analysis is crucial for timely treatment, prognosis, and understanding of the disease's development. Recent research utilizes deep learning-based models to combine multi-omics data for tasks such as cancer classification, clustering, and survival prediction. However, these models often overlook interactions between different types of data, which leads to suboptimal performance. In this paper, we present a Contrastive Multi-Modal Encoder (CMME) that integrates and maps multi-omics data into a lower-dimensional latent space, enabling the model to better understand relationships between different data types. The challenging distribution and organization of the data into anchors, positive samples, and negative samples encourage the model to learn synergies among different modalities, pay attention to both strong and weak modalities, and avoid biased learning. The performance of the proposed model is evaluated on downstream tasks such as clustering, classification, and survival prediction. The CMME achieved an accuracy of 98.16% and an F1 score of 98.09% in classifying breast cancer subtypes. For clustering tasks across ten cancer types based on TCGA data, the adjusted Rand index reached 0.966. Additionally, survival analysis results highlighted significant differences in survival rates between different cancer subtypes. The comprehensive qualitative and quantitative results demonstrate that the proposed method outperforms existing methods.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"700 ","pages":"Article 121864"},"PeriodicalIF":8.1,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143150718","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 : 2025-01-07DOI: 10.1016/j.ins.2024.121858
Xuepeng Ren , Maocai Wang , Guangming Dai , Lei Peng , Xiaoyu Chen , Zhiming Song
In the study of decomposition-based multi-objective evolutionary algorithms, the adaptive weight vector approach effectively balances algorithm convergence and diversity. A common method for weight vector adaptation uses a population sparsity strategy, which calculates sparsity via Euclidean distance. However, this method causes individuals with low sparsity to cluster at the center of the objective space, while those with high sparsity spread to the edges, disrupting the convergence-diversity balance. To address this issue, this paper proposes using a Gaussian mixture model. This model treats data as a mix of multiple Gaussian distributions, partitioning the data space more flexibly. First, the paper analyzes various algorithms that adjust weight vectors using the sparsity strategy, highlighting their shortcomings. Then, it demonstrates how Gaussian mixture models can better divide the space and accurately identify individuals with different sparsity levels, correcting traditional sparsity calculation flaws. Since the population in the objective space changes during evolution, selecting appropriate component parameters is crucial. This paper uses the elbow rule to adaptively select these parameters. The experimental section includes three sets of experiments comparing the proposed algorithm with several popular algorithms, including a study on real mechanical bearing optimization. Results show that the proposed algorithm is highly competitive.
{"title":"Balancing convergence and diversity: Gaussian mixture models in adaptive weight vector strategies for multi-objective algorithms","authors":"Xuepeng Ren , Maocai Wang , Guangming Dai , Lei Peng , Xiaoyu Chen , Zhiming Song","doi":"10.1016/j.ins.2024.121858","DOIUrl":"10.1016/j.ins.2024.121858","url":null,"abstract":"<div><div>In the study of decomposition-based multi-objective evolutionary algorithms, the adaptive weight vector approach effectively balances algorithm convergence and diversity. A common method for weight vector adaptation uses a population sparsity strategy, which calculates sparsity via Euclidean distance. However, this method causes individuals with low sparsity to cluster at the center of the objective space, while those with high sparsity spread to the edges, disrupting the convergence-diversity balance. To address this issue, this paper proposes using a Gaussian mixture model. This model treats data as a mix of multiple Gaussian distributions, partitioning the data space more flexibly. First, the paper analyzes various algorithms that adjust weight vectors using the sparsity strategy, highlighting their shortcomings. Then, it demonstrates how Gaussian mixture models can better divide the space and accurately identify individuals with different sparsity levels, correcting traditional sparsity calculation flaws. Since the population in the objective space changes during evolution, selecting appropriate component parameters is crucial. This paper uses the elbow rule to adaptively select these parameters. The experimental section includes three sets of experiments comparing the proposed algorithm with several popular algorithms, including a study on real mechanical bearing optimization. Results show that the proposed algorithm is highly competitive.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"700 ","pages":"Article 121858"},"PeriodicalIF":8.1,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143150770","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 : 2025-01-07DOI: 10.1016/j.ins.2024.121863
Libao Deng , Guanyu Yuan , Chunlei Li , Lili Zhang
Compared to unconstrained global optimization, constrained optimization problems (COPs) introduce extra complexity to the search space due to constraints, making the issues more challenging to solve. COPs necessitate an algorithm with enhanced exploration and exploitation capabilities, as well as a constraint handling technique (CHT) that can be easily integrated and effectively balance constraints and objectives. To fully leverage evolutionary information in solving COPs, this paper proposes a constrained differential evolution algorithm (called IUCDE) based on the utilization of individual and correlation information. The constraints, objectives, and distances of individuals in the population are integrated into individual information, which is represented by individual performance scores. Based on the individual information, individuals are categorized into three subpopulations with distinct search characteristics to maximize their potential. Based on the correlation information generated in each iteration of the population, a dynamic feasibility rule is proposed, which, combined with the original feasibility rule, is adaptively selected to handle constraints based on the proportion of feasible solutions in the population. The proposed IUCDE algorithm is compared with five state-of-the-art constrained optimization algorithms across 22 test problems from the CEC 2017 benchmark, demonstrating superior performance. Furthermore, IUCDE exhibits a competitive advantage in solving 41 test problems from the CEC 2020 real-world constrained optimization test benchmark. Extensive experiments have validated the efficient execution of IUCDE and the effectiveness of its components.
{"title":"Differential evolution with individual and correlation information utilization for constrained optimization problems","authors":"Libao Deng , Guanyu Yuan , Chunlei Li , Lili Zhang","doi":"10.1016/j.ins.2024.121863","DOIUrl":"10.1016/j.ins.2024.121863","url":null,"abstract":"<div><div>Compared to unconstrained global optimization, constrained optimization problems (COPs) introduce extra complexity to the search space due to constraints, making the issues more challenging to solve. COPs necessitate an algorithm with enhanced exploration and exploitation capabilities, as well as a constraint handling technique (CHT) that can be easily integrated and effectively balance constraints and objectives. To fully leverage evolutionary information in solving COPs, this paper proposes a constrained differential evolution algorithm (called IUCDE) based on the utilization of individual and correlation information. The constraints, objectives, and distances of individuals in the population are integrated into individual information, which is represented by individual performance scores. Based on the individual information, individuals are categorized into three subpopulations with distinct search characteristics to maximize their potential. Based on the correlation information generated in each iteration of the population, a dynamic feasibility rule is proposed, which, combined with the original feasibility rule, is adaptively selected to handle constraints based on the proportion of feasible solutions in the population. The proposed IUCDE algorithm is compared with five state-of-the-art constrained optimization algorithms across 22 test problems from the CEC 2017 benchmark, demonstrating superior performance. Furthermore, IUCDE exhibits a competitive advantage in solving 41 test problems from the CEC 2020 real-world constrained optimization test benchmark. Extensive experiments have validated the efficient execution of IUCDE and the effectiveness of its components.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"700 ","pages":"Article 121863"},"PeriodicalIF":8.1,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143150772","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 : 2025-01-07DOI: 10.1016/j.ins.2024.121831
Jianxi Yang , Die Liu , Lu Zhao , Xiangli Yang , Ren Li , Shixin Jiang , Jianming Li
The Stochastic Configuration Network (SCN) is a powerful incremental learning algorithm that dynamically generates network structures during training. However, as a fully connected neural network, it is not adept at capturing the internal dynamic changes of monitoring data and suffers from node redundancy. To address the inadequacy of SCN in handling multi-sensor monitoring data, this paper proposes a feature extraction method called Mean of Positive Values (MPV) to randomly extract the intrinsic features of monitoring data, thereby reconfiguring the original SCN. This improved SCN based on random convolution is named SCN based on Improved Random Convolution (IRC-SCN). Furthermore, to enhance the efficiency of SCN, this study introduces a Random Node Removal based on Importance Ranking (RNR-IR) algorithm. The proposed methods are evaluated on two bridge monitoring datasets for damage identification and anomaly detection, demonstrating their effectiveness. The model based on MPV achieves an accuracy increase of approximately 1% compared to the comparative methods on the test set. Unlike traditional node deletion algorithms, RNR-IR can improve the performance of model by approximately 2% with the removal of around 10% of neurons.
{"title":"Improved stochastic configuration network for bridge damage and anomaly detection using long-term monitoring data","authors":"Jianxi Yang , Die Liu , Lu Zhao , Xiangli Yang , Ren Li , Shixin Jiang , Jianming Li","doi":"10.1016/j.ins.2024.121831","DOIUrl":"10.1016/j.ins.2024.121831","url":null,"abstract":"<div><div>The Stochastic Configuration Network (SCN) is a powerful incremental learning algorithm that dynamically generates network structures during training. However, as a fully connected neural network, it is not adept at capturing the internal dynamic changes of monitoring data and suffers from node redundancy. To address the inadequacy of SCN in handling multi-sensor monitoring data, this paper proposes a feature extraction method called Mean of Positive Values (MPV) to randomly extract the intrinsic features of monitoring data, thereby reconfiguring the original SCN. This improved SCN based on random convolution is named SCN based on Improved Random Convolution (IRC-SCN). Furthermore, to enhance the efficiency of SCN, this study introduces a Random Node Removal based on Importance Ranking (RNR-IR) algorithm. The proposed methods are evaluated on two bridge monitoring datasets for damage identification and anomaly detection, demonstrating their effectiveness. The model based on MPV achieves an accuracy increase of approximately 1% compared to the comparative methods on the test set. Unlike traditional node deletion algorithms, RNR-IR can improve the performance of model by approximately 2% with the removal of around 10% of neurons.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"700 ","pages":"Article 121831"},"PeriodicalIF":8.1,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143150776","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 : 2025-01-07DOI: 10.1016/j.ins.2024.121860
Weihua Xu , Zhenyuan Tian
Multi-source Decision-Making Information Systems (DMSs) demonstrate superior capabilities in integrating and analyzing a diverse array of information sources, providing enhanced functionality over single-source systems. Within these systems, feature selection is crucial for identifying key attributes, which reduces information and enhance the efficiency of the decision-making process. However, current established information fusion techniques in multi-source DMSs, which integrate various sources into a unified framework, tend to be computationally intensive and are not adept at handling interval-valued data. This paper introduces an innovative feature selection model specifically developed for multi-source DMSs, employing the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE). The model initiates by establishing the neighbourhood relationships among objects across different attributes. It then utilizes the PROMETHEE algorithm to rank these attributes based on their comparative strengths and weaknesses, facilitating the pinpointing of the most valuable features. The model further refines the selection process by quantifying the consensus level, thereby discovering the most reliable information sources. Our some experiments, performed utilizing a broad and comprehensive dataset, have validated both the model and its underlying algorithm. The results obtained provide compelling evidence of the model's effectiveness, especially highlighting its proficiency in handling interval-valued data. Furthermore, the outcomes illustrate the model's significance to the enhancement of decision-making processes within multi-source Decision-Making Information Systems (DMSs).
{"title":"Feature selection and information fusion based on preference ranking organization method in interval-valued multi-source decision-making information systems","authors":"Weihua Xu , Zhenyuan Tian","doi":"10.1016/j.ins.2024.121860","DOIUrl":"10.1016/j.ins.2024.121860","url":null,"abstract":"<div><div>Multi-source Decision-Making Information Systems (DMSs) demonstrate superior capabilities in integrating and analyzing a diverse array of information sources, providing enhanced functionality over single-source systems. Within these systems, feature selection is crucial for identifying key attributes, which reduces information and enhance the efficiency of the decision-making process. However, current established information fusion techniques in multi-source DMSs, which integrate various sources into a unified framework, tend to be computationally intensive and are not adept at handling interval-valued data. This paper introduces an innovative feature selection model specifically developed for multi-source DMSs, employing the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE). The model initiates by establishing the neighbourhood relationships among objects across different attributes. It then utilizes the PROMETHEE algorithm to rank these attributes based on their comparative strengths and weaknesses, facilitating the pinpointing of the most valuable features. The model further refines the selection process by quantifying the consensus level, thereby discovering the most reliable information sources. Our some experiments, performed utilizing a broad and comprehensive dataset, have validated both the model and its underlying algorithm. The results obtained provide compelling evidence of the model's effectiveness, especially highlighting its proficiency in handling interval-valued data. Furthermore, the outcomes illustrate the model's significance to the enhancement of decision-making processes within multi-source Decision-Making Information Systems (DMSs).</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"700 ","pages":"Article 121860"},"PeriodicalIF":8.1,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143151865","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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