Pub Date : 2026-05-25Epub Date: 2026-01-31DOI: 10.1016/j.ins.2026.123169
Mengmeng Liao , Jiahao Qin , Yuwei Du
This paper proposes NTLG, a novel method for small-sample facial recognition, addressing two key limitations of traditional approaches: sensitivity to data bias and ineffective use of label information. NTLG introduces three innovations: (1) decomposing complex parameter optimization into simpler subtasks, (2) enhancing inter-class discrimination via label propagation, and (3) improving robustness through feature extraction and data reconstruction. Experiments demonstrate that NTLG significantly boosts accuracy while maintaining efficiency, outperforming state-of-the-art methods in small-sample scenarios.
{"title":"Non-negative transfer space learning based on label release and graph embedding for small sample face recognition","authors":"Mengmeng Liao , Jiahao Qin , Yuwei Du","doi":"10.1016/j.ins.2026.123169","DOIUrl":"10.1016/j.ins.2026.123169","url":null,"abstract":"<div><div>This paper proposes NTLG, a novel method for small-sample facial recognition, addressing two key limitations of traditional approaches: sensitivity to data bias and ineffective use of label information. NTLG introduces three innovations: (1) decomposing complex parameter optimization into simpler subtasks, (2) enhancing inter-class discrimination via label propagation, and (3) improving robustness through feature extraction and data reconstruction. Experiments demonstrate that NTLG significantly boosts accuracy while maintaining efficiency, outperforming state-of-the-art methods in small-sample scenarios.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123169"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191055","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}
Magnetic resonance (MR) imaging plays a critical role in diagnostic workflows, yet its reliability is frequently compromised by scanner-dependent bias, contrast variability, and intensity drift. Although deep learning methods achieve high performance, they generally require extensive supervision and demonstrate limited robustness across diverse clinical settings.
To address these challenges, we propose a transparent, geometry-aware framework for annotation-free MR enhancement based on -Bézier curves. This model incorporates an adaptive deformation parameter that modulates local curvature, facilitating flexible adaptation to complex anatomical boundaries. The framework comprises three principal mechanisms: (i) adaptive for local responsiveness, (ii) monotone -Bézier tone curves for intensity standardization, and (iii) Tikhonov-regularized optimization for smooth mapping. As a result, the operator remains interpretable, operates in linear time, and provides explicit control over smoothness.
The proposed approach was validated across five public cohorts (BraTS, ACDC, PROMISE12, fastMRI, IXI), demonstrating significant improvements in image fidelity (SSIM, CNR, NIQE) and downstream segmentation accuracy (Dice, HD95) relative to variational filters and state-of-the-art foundation models. Additionally, cross-vendor experiments confirm its robustness without the need for retraining. Collectively, these findings establish -Bézier modeling as a principled, lightweight, and clinically interpretable alternative that complements deep learning by providing a geometry-aware pathway to robust MR representation.
{"title":"Interpretable structural modeling of MR images using q−Bézier curves: A geometry-aware paradigm beyond deep learning","authors":"Faruk Özger , Aytuğ Onan , Nezihe Turhan , Zeynep Ödemiş Özger","doi":"10.1016/j.ins.2026.123147","DOIUrl":"10.1016/j.ins.2026.123147","url":null,"abstract":"<div><div>Magnetic resonance (MR) imaging plays a critical role in diagnostic workflows, yet its reliability is frequently compromised by scanner-dependent bias, contrast variability, and intensity drift. Although deep learning methods achieve high performance, they generally require extensive supervision and demonstrate limited robustness across diverse clinical settings.</div><div>To address these challenges, we propose a transparent, <strong>geometry-aware framework</strong> for annotation-free MR enhancement based on <span><math><mi>q</mi></math></span><strong>-Bézier curves</strong>. This model incorporates an <strong>adaptive deformation parameter</strong> <span><math><mi>q</mi><mo>(</mo><mi>x</mi><mo>)</mo></math></span> that modulates local curvature, facilitating flexible adaptation to complex anatomical boundaries. The framework comprises three principal mechanisms: (i) adaptive <span><math><mi>q</mi><mo>(</mo><mi>x</mi><mo>)</mo></math></span> for local responsiveness, (ii) monotone <span><math><mi>q</mi></math></span>-Bézier tone curves for intensity standardization, and (iii) <strong>Tikhonov-regularized</strong> optimization for smooth mapping. As a result, the operator remains interpretable, operates in linear time, and provides explicit control over smoothness.</div><div>The proposed approach was validated across five public cohorts (BraTS, ACDC, PROMISE12, fastMRI, IXI), demonstrating significant improvements in image fidelity (SSIM, CNR, NIQE) and downstream segmentation accuracy (Dice, HD95) relative to variational filters and state-of-the-art foundation models. Additionally, cross-vendor experiments confirm its <strong>robustness without the need for retraining</strong>. Collectively, these findings establish <span><math><mi>q</mi></math></span>-Bézier modeling as a principled, lightweight, and clinically interpretable alternative that complements deep learning by providing a geometry-aware pathway to robust MR representation.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123147"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146057592","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 : 2026-05-25Epub Date: 2026-01-24DOI: 10.1016/j.ins.2026.123136
Yanfei Sun , Junyu Wang , Rui Yin
Nighttime rainy conditions severely degrade visual quality in applications such as autonomous driving and aerial surveillance, where images suffer from compounded low-light and rain degradations. Diffusion models offer strong generative priors but face limitations in image restoration, including poor controllability, structural distortion, and domain gaps with degraded images. We present MR-SDformer, a novel framework that integrates Retinex-based decomposition with diffusion priors for joint nighttime deraining and low-light enhancement. The key innovation is the Maximum-Value Retinex decomposition, which isolates high-intensity rain streaks into the illumination map and produces a rain-free reflectance map that faithfully preserves intrinsic scene content. This decomposition not only bridges the gap between rainy inputs and rain-free priors but also provides complementary guidance to the generative process. Building on this, we design an asymmetric Hybrid Conditional Transformer that leverages the decomposed illumination and reflectance maps to condition the frozen diffusion model more effectively, enabling precise multi-modal feature fusion and high-fidelity reconstruction. Extensive experiments on both synthetic and real-world datasets confirm that MR-SDformer achieves state-of-the-art performance, delivering clearer structure, enhanced illumination, and more realistic visual quality under nighttime rainy conditions.
{"title":"Maximum-Value retinex decomposition guided generative priors for joint deraining and low-light image enhancement","authors":"Yanfei Sun , Junyu Wang , Rui Yin","doi":"10.1016/j.ins.2026.123136","DOIUrl":"10.1016/j.ins.2026.123136","url":null,"abstract":"<div><div>Nighttime rainy conditions severely degrade visual quality in applications such as autonomous driving and aerial surveillance, where images suffer from compounded low-light and rain degradations. Diffusion models offer strong generative priors but face limitations in image restoration, including poor controllability, structural distortion, and domain gaps with degraded images. We present MR-SDformer, a novel framework that integrates Retinex-based decomposition with diffusion priors for joint nighttime deraining and low-light enhancement. The key innovation is the Maximum-Value Retinex decomposition, which isolates high-intensity rain streaks into the illumination map and produces a rain-free reflectance map that faithfully preserves intrinsic scene content. This decomposition not only bridges the gap between rainy inputs and rain-free priors but also provides complementary guidance to the generative process. Building on this, we design an asymmetric Hybrid Conditional Transformer that leverages the decomposed illumination and reflectance maps to condition the frozen diffusion model more effectively, enabling precise multi-modal feature fusion and high-fidelity reconstruction. Extensive experiments on both synthetic and real-world datasets confirm that MR-SDformer achieves state-of-the-art performance, delivering clearer structure, enhanced illumination, and more realistic visual quality under nighttime rainy conditions.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123136"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081928","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 : 2026-05-25Epub Date: 2026-01-30DOI: 10.1016/j.ins.2026.123178
Yong Liu , Wenhao Luo , Guangxia Xu
Delegated Proof of Stake (DPoS) is widely used in public blockchains, but static, vote-centric election rules struggle to cope with heterogeneous wide-area networks and evolving attack strategies. Producers are often chosen mainly by stake, with little regard for real-time operational quality, leading to performance bottlenecks, stake plutocracy, and unstable committees. We present ADAPT-DPoS, a dynamic, data-driven framework that casts producer selection as a multi-attribute decision-making problem. It combines entropy- and SHAP-based dynamic weighting, a two-phase TOPSIS–PROMETHEE II ranking pipeline, and an adaptive producer-count controller driven by transaction load, candidate-pool quality, and latency signals. Experiments on a heterogeneous WAN testbed with geographically distributed nodes show that, under a strict P99 latency bound of 1 s, ADAPT-DPoS drives latency-bounded throughput close to the physical limit of the deployment and achieves about 64% higher LBT than vanilla DPoS (798 vs. 486 TPS in S0). Under adversarial stress, it reduces block misses by88% (MR: 9.69%1.15%) and substantially improves decentralization and reward–contribution alignment (Nakamoto: 5.4114.89; RCA: 0.240.86), demonstrating that MADM-based, feedback-driven design can significantly enhance DPoS-style consensus.
委托权益证明（DPoS）广泛应用于公共区块链，但静态的、以投票为中心的选举规则难以应对异构广域网和不断发展的攻击策略。生产商通常主要是通过股份来选择的，很少考虑实时运营质量，从而导致绩效瓶颈、股权财阀和不稳定的委员会。我们提出ADAPT-DPoS，一个动态的，数据驱动的框架，将生产者选择作为一个多属性决策问题。它结合了基于熵和shap的动态加权、两阶段TOPSIS-PROMETHEE II排序管道，以及由事务负载、候选池质量和延迟信号驱动的自适应生产者计数控制器。在具有地理分布节点的异构WAN测试平台上进行的实验表明，在严格的P99延迟限制为1秒的情况下，ADAPT-DPoS驱动的延迟限制吞吐量接近部署的物理极限，并且比普通DPoS实现的LBT高约64% （798 TPS vs. 486 TPS）。在对抗压力下，它减少了约88%的区块失手（MR: 9.69%→1.15%），并大幅改善了去中心化和奖励贡献一致性（Nakamoto: 5.41→14.89;RCA: 0.24→0.86），表明基于madm的反馈驱动设计可以显着增强dpos风格的共识。
{"title":"ADAPT-DPoS: Data-driven producer selection in delegated proof of stake","authors":"Yong Liu , Wenhao Luo , Guangxia Xu","doi":"10.1016/j.ins.2026.123178","DOIUrl":"10.1016/j.ins.2026.123178","url":null,"abstract":"<div><div>Delegated Proof of Stake (DPoS) is widely used in public blockchains, but static, vote-centric election rules struggle to cope with heterogeneous wide-area networks and evolving attack strategies. Producers are often chosen mainly by stake, with little regard for real-time operational quality, leading to performance bottlenecks, stake plutocracy, and unstable committees. We present ADAPT-DPoS, a dynamic, data-driven framework that casts producer selection as a multi-attribute decision-making problem. It combines entropy- and SHAP-based dynamic weighting, a two-phase TOPSIS–PROMETHEE II ranking pipeline, and an adaptive producer-count controller driven by transaction load, candidate-pool quality, and latency signals. Experiments on a heterogeneous WAN testbed with geographically distributed nodes show that, under a strict P99 latency bound of 1 s, ADAPT-DPoS drives latency-bounded throughput close to the physical limit of the deployment and achieves <strong>about 64% higher LBT than vanilla DPoS</strong> (798 vs. 486 TPS in S0). Under adversarial stress, it <strong>reduces block misses by</strong> <span><math><mo>∼</mo></math></span><strong>88%</strong> (MR: 9.69%<span><math><mo>→</mo></math></span>1.15%) and substantially improves decentralization and reward–contribution alignment (Nakamoto: 5.41<span><math><mo>→</mo></math></span>14.89; RCA: 0.24<span><math><mo>→</mo></math></span>0.86), demonstrating that MADM-based, feedback-driven design can significantly enhance DPoS-style consensus.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123178"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191063","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 : 2026-05-25Epub Date: 2026-01-29DOI: 10.1016/j.ins.2026.123174
Zi-Biao Feng , Hai-Yang Jia , Sheng-Sheng Wang
Although widely used for reasoning under uncertainty, Extended Belief Rule-Based (EBRB) systems are often constrained by two fundamental deficiencies: suboptimal rule base quality due to redundancy, and a theoretically irrational conjunctive model that leads to logical fallacies. To address this research gap, this paper proposes a novel framework named SNN-EBRB. It introduces a rule generation strategy based on Shared-Nearest-Neighbor Density Peak Clustering (SNNDPC) to directly construct a compact, high-quality rule base from raw data with complex distributions. Concurrently, we establish, for the first time, a theoretical evaluation framework comprising six essential properties for the conjunctive model and design a new model that is rigorously proven to satisfy all of them, fundamentally rectifying the irrational behavior of the traditional model. Extensive experiments on 13 public UCI datasets and a real-world microseismic signal identification case study demonstrate the comprehensive superiority of SNN-EBRB: its performance is statistically superior to the majority of existing EBRB variants and it achieves the number one overall average rank. Furthermore, our method reduces the number of rules by up to 80% while exhibiting millisecond-level inference efficiency, and its introduced activation factor, , provides an effective mechanism to trade off between model performance and interpretability.
{"title":"A novel approach to conjunctive relation modeling and rule generation in extended belief rule-based expert systems for classification problems","authors":"Zi-Biao Feng , Hai-Yang Jia , Sheng-Sheng Wang","doi":"10.1016/j.ins.2026.123174","DOIUrl":"10.1016/j.ins.2026.123174","url":null,"abstract":"<div><div>Although widely used for reasoning under uncertainty, Extended Belief Rule-Based (EBRB) systems are often constrained by two fundamental deficiencies: suboptimal rule base quality due to redundancy, and a theoretically irrational conjunctive model that leads to logical fallacies. To address this research gap, this paper proposes a novel framework named SNN-EBRB. It introduces a rule generation strategy based on Shared-Nearest-Neighbor Density Peak Clustering (SNNDPC) to directly construct a compact, high-quality rule base from raw data with complex distributions. Concurrently, we establish, for the first time, a theoretical evaluation framework comprising six essential properties for the conjunctive model and design a new model that is rigorously proven to satisfy all of them, fundamentally rectifying the irrational behavior of the traditional model. Extensive experiments on 13 public UCI datasets and a real-world microseismic signal identification case study demonstrate the comprehensive superiority of SNN-EBRB: its performance is statistically superior to the majority of existing EBRB variants and it achieves the number one overall average rank. Furthermore, our method reduces the number of rules by up to 80% while exhibiting millisecond-level inference efficiency, and its introduced activation factor, <span><math><mi>λ</mi></math></span>, provides an effective mechanism to trade off between model performance and interpretability.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123174"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191059","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 : 2026-05-25Epub Date: 2026-01-30DOI: 10.1016/j.ins.2026.123176
Liang Huang, Jihao Fan
We investigate the optimal selection of high-fidelity quantum links that can preserve fragile quantum states during information transmission. However, uniformly estimating the fidelities of all links becomes prohibitively costly in large-scale networks with numerous noisy connections. To overcome this limitation, we recast link selection and fidelity inference as an optimal-action discovery task within a reinforcement learning framework. Subsequently, we propose an algorithm termed Epsilon-Greedy Quantum Link Selection (EGreedyQLiS). This algorithm effectively identifies the optimal link among numerous quantum links and provides accurate fidelity estimates with a low consumption of quantum resources. EGreedyQLiS infers link fidelities using observations obtained from a standard network benchmarking procedure and greedily optimizes link selection during the fidelity estimation procedure. This optimization strategy concentrates quantum resources on estimating high-fidelity links, thereby providing accurate fidelity estimation for these links. The results of extensive simulations demonstrate that EGreedyQLiS exceeds existing approaches in optimal link identification with reduced quantum resource overhead.
{"title":"Application and performance analysis of Epsilon-Greedy optimization strategy in quantum link selection","authors":"Liang Huang, Jihao Fan","doi":"10.1016/j.ins.2026.123176","DOIUrl":"10.1016/j.ins.2026.123176","url":null,"abstract":"<div><div>We investigate the optimal selection of <em>high-fidelity</em> quantum links that can preserve fragile quantum states during information transmission. However, uniformly estimating the fidelities of all links becomes prohibitively costly in large-scale networks with numerous noisy connections. To overcome this limitation, we recast link selection and fidelity inference as an optimal-action discovery task within a reinforcement learning framework. Subsequently, we propose an algorithm termed Epsilon-Greedy Quantum Link Selection (EGreedyQLiS). This algorithm effectively identifies the optimal link among numerous quantum links and provides accurate fidelity estimates with a low consumption of quantum resources. EGreedyQLiS infers link fidelities using observations obtained from a standard <em>network benchmarking</em> procedure and greedily optimizes link selection during the fidelity estimation procedure. This optimization strategy concentrates quantum resources on estimating high-fidelity links, thereby providing accurate fidelity estimation for these links. The results of extensive simulations demonstrate that EGreedyQLiS exceeds existing approaches in optimal link identification with reduced quantum resource overhead.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123176"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191054","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 : 2026-05-25Epub Date: 2026-01-30DOI: 10.1016/j.ins.2026.123153
Hailin Li , Jie Wang
The primary purpose of sequential recommendation is to analyze user behavior sequences, extract user preferences and identify dependencies between items to generate relevant recommendations. Although sequential recommendation models have advantages, they still face challenges in discovering complex relationships in users’ purchase histories and capturing users’ dynamic behavior patterns with shallow network structures. In this article, we propose the Visibility Graph and Convolutional Neural Networks for Sequential Recommendation (VCRec), which is the first application of visibility graphs to sequential recommendation. The VCRec model transforms users and their behavior sequences into user-embedding vectors and item-embedding matrices. The improved adaptive visibility graph algorithm is then proposed to encode the item-embedding matrices in both paired and non-paired ways, and obtains the three-dimensional tensor. High-order features of the items are extracted using residual and convolutional blocks. The resulting item features are combined with user embedding vectors to predict the subsequent item with which the user will engage. Extensive experiments on realistic datasets have demonstrated the performance of the VCRec model. These experimental results suggest that the VCRec model produces high-quality recommendations efficiently, which is of significant practical value.
{"title":"VCRec: Visibility graph and convolutional neural networks for sequential recommendation","authors":"Hailin Li , Jie Wang","doi":"10.1016/j.ins.2026.123153","DOIUrl":"10.1016/j.ins.2026.123153","url":null,"abstract":"<div><div>The primary purpose of sequential recommendation is to analyze user behavior sequences, extract user preferences and identify dependencies between items to generate relevant recommendations. Although sequential recommendation models have advantages, they still face challenges in discovering complex relationships in users’ purchase histories and capturing users’ dynamic behavior patterns with shallow network structures. In this article, we propose the Visibility Graph and Convolutional Neural Networks for Sequential Recommendation (VCRec), which is the first application of visibility graphs to sequential recommendation. The VCRec model transforms users and their behavior sequences into user-embedding vectors and item-embedding matrices. The improved adaptive visibility graph algorithm is then proposed to encode the item-embedding matrices in both paired and non-paired ways, and obtains the three-dimensional tensor. High-order features of the items are extracted using residual and convolutional blocks. The resulting item features are combined with user embedding vectors to predict the subsequent item with which the user will engage. Extensive experiments on realistic datasets have demonstrated the performance of the VCRec model. These experimental results suggest that the VCRec model produces high-quality recommendations efficiently, which is of significant practical value.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123153"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191152","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}
This paper tackles the challenge of formation tracking for multiple manipulator end effectors (MEEs) operating under aggregated perturbations. A neural estimator, which synergistically incorporates a neural network with an extended state, is proposed to achieve real-time identification and compensation of uncertainties and external disturbances, enhancing estimation convergence and precision. Concurrently, an angular artificial potential field (APF) is developed to enable smooth posture adaptation during obstacle avoidance by generating orientation-aware repulsive forces. The distributed controller guarantees the semi-global practical finite-time boundedness (SGPFTB) of formation errors, rigorously validated through Lyapunov-based theoretical proofs. Comparative simulations involving a five-manipulator system demonstrate the framework’s enhanced performance and resilience against obstacles and perturbations.
{"title":"Neural estimator-based finite-time formation control for manipulator end effectors with obstacle avoidance","authors":"Shuangsi Xue , Zihang Guo , Xiaodong Zheng , Hui Cao , Badong Chen","doi":"10.1016/j.ins.2026.123196","DOIUrl":"10.1016/j.ins.2026.123196","url":null,"abstract":"<div><div>This paper tackles the challenge of formation tracking for multiple manipulator end effectors (MEEs) operating under aggregated perturbations. A neural estimator, which synergistically incorporates a neural network with an extended state, is proposed to achieve real-time identification and compensation of uncertainties and external disturbances, enhancing estimation convergence and precision. Concurrently, an angular artificial potential field (APF) is developed to enable smooth posture adaptation during obstacle avoidance by generating orientation-aware repulsive forces. The distributed controller guarantees the semi-global practical finite-time boundedness (SGPFTB) of formation errors, rigorously validated through Lyapunov-based theoretical proofs. Comparative simulations involving a five-manipulator system demonstrate the framework’s enhanced performance and resilience against obstacles and perturbations.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123196"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191148","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 : 2026-05-25Epub Date: 2026-02-02DOI: 10.1016/j.ins.2026.123175
Vahid Safari Dehnavi, Masoud Shafiee, Mehdi Mirshahi
This paper presents a novel control framework for fractional-order singular systems (FOSS) that accounts for faults and disturbances. This framework includes four steps: homotopy-based optimal control, fault and disturbance estimation, data-driven optimal control, and data-driven estimation refinement. In the first step, we utilize the iterative homotopy algorithm, the Volterra integral, the Leibniz integral rule, and the boundary condition transformation for optimal control. This algorithm gradually transitions the simple system to the real model with guaranteed stable convergence. The second step simultaneously estimates faults and disturbances by an augmented system. The stability and convergence of the observer are analyzed via Lyapunov theory and linear matrix inequalities (LMI). In the third step, estimation errors and system uncertainties are mitigated by a data-driven approach that utilizes the Lyapunov function. This method utilizes the sampling theorem and is synchronized with the optimal control cost function. In the fourth step, an ant-grasshopper optimization algorithm with the ant’s queen model is designed to estimate uncertainties that have specific patterns. The proposed approach is validated by simulation on a multi-agent system experiencing simultaneous faults and disturbances. In the proposed method, each agent acts as an intelligent entity that interacts with others. This simulation is presented for a near-realistic situation in a biomedical application.
{"title":"Model-based fault-tolerant optimal control of fractional-order singular systems optimized via a novel data-driven approach","authors":"Vahid Safari Dehnavi, Masoud Shafiee, Mehdi Mirshahi","doi":"10.1016/j.ins.2026.123175","DOIUrl":"10.1016/j.ins.2026.123175","url":null,"abstract":"<div><div>This paper presents a novel control framework for fractional-order singular systems (FOSS) that accounts for faults and disturbances. This framework includes four steps: homotopy-based optimal control, fault and disturbance estimation, data-driven optimal control, and data-driven estimation refinement. In the first step, we utilize the iterative homotopy algorithm, the Volterra integral, the Leibniz integral rule, and the boundary condition transformation for optimal control. This algorithm gradually transitions the simple system to the real model with guaranteed stable convergence. The second step simultaneously estimates faults and disturbances by an augmented system. The stability and convergence of the observer are analyzed via Lyapunov theory and linear matrix inequalities (LMI). In the third step, estimation errors and system uncertainties are mitigated by a data-driven approach that utilizes the Lyapunov function. This method utilizes the sampling theorem and is synchronized with the optimal control cost function. In the fourth step, an ant-grasshopper optimization algorithm with the ant’s queen model is designed to estimate uncertainties that have specific patterns. The proposed approach is validated by simulation on a multi-agent system experiencing simultaneous faults and disturbances. In the proposed method, each agent acts as an intelligent entity that interacts with others. This simulation is presented for a near-realistic situation in a biomedical application.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123175"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191145","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}
Efficient and scalable path planning is a critical challenge for autonomous robotic systems, particularly in complex real-world scenarios. Traditional heuristic search algorithms like A often struggle with scalability and adaptability in such environments. To address these limitations, we improve a search framework that integrates learned, instance-specific heuristics with conventional pathfinding techniques. Leveraging autoencoder transformer networks, we predict two key heuristic functions—Correction Factor (CF) and Path Probability Map (PPM)—trained on diverse datasets—the Motion Planning (MP) and Tiled-MP datasets—to cover a wide range of path planning scenarios. When integrated with Weighted A (WA) algorithm, this approach optimally solves 88% of MP instances, with paths averaging less than 0.7% longer than optimal, and requiring nearly five times fewer node expansions. The framework demonstrates the advantages of heuristic learning in handling larger path planning problems, with inference time accounting for just 10% of the total search duration. It solves nearly half of the most complex instances optimally, showcasing strong scalability for real-time robotics applications. The framework performs well in unseen environments, solving over 25% of new problems perfectly, finding near-optimal solutions with paths less than 7% longer than optimal, and requiring fewer than two-thirds of the typical expansions. Our framework outperforms learnable planners in both scalability and generalization.
{"title":"Scalable and generalizable path planning for robotic navigation using transformer-based heuristic learning","authors":"Elie Thellier, Adolfo Perrusquía, Antonios Tsourdos","doi":"10.1016/j.ins.2026.123149","DOIUrl":"10.1016/j.ins.2026.123149","url":null,"abstract":"<div><div>Efficient and scalable path planning is a critical challenge for autonomous robotic systems, particularly in complex real-world scenarios. Traditional heuristic search algorithms like A<span><math><msup><mspace></mspace><mo>∗</mo></msup></math></span> often struggle with scalability and adaptability in such environments. To address these limitations, we improve a search framework that integrates learned, instance-specific heuristics with conventional pathfinding techniques. Leveraging autoencoder transformer networks, we predict two key heuristic functions—Correction Factor (CF) and Path Probability Map (PPM)—trained on diverse datasets—the Motion Planning (MP) and Tiled-MP datasets—to cover a wide range of path planning scenarios. When integrated with Weighted A<span><math><msup><mspace></mspace><mo>∗</mo></msup></math></span> (WA<span><math><msup><mspace></mspace><mo>∗</mo></msup></math></span>) algorithm, this approach optimally solves 88% of MP instances, with paths averaging less than 0.7% longer than optimal, and requiring nearly five times fewer node expansions. The framework demonstrates the advantages of heuristic learning in handling larger path planning problems, with inference time accounting for just 10% of the total search duration. It solves nearly half of the most complex instances optimally, showcasing strong scalability for real-time robotics applications. The framework performs well in unseen environments, solving over 25% of new problems perfectly, finding near-optimal solutions with paths less than 7% longer than optimal, and requiring fewer than two-thirds of the typical expansions. Our framework outperforms learnable planners in both scalability and generalization.</div></div>","PeriodicalId":51063,"journal":{"name":"Information Sciences","volume":"739 ","pages":"Article 123149"},"PeriodicalIF":6.8,"publicationDate":"2026-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146081916","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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