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Robust Sliding Mode control Based on Fractional Order Reaching Law for Rehabilitation Robots

IEEE Access 2025年 13卷 75744-75753页

作者： Rim Jallouli-Khlif Fatma Abdelhedi Chadia Zayane Ahmed Said Nouri Nabil Derbel Higher Institute of Computer Science and Multimedia of Sfax University of Sfax Sfax Tunisia Department of Electrical and Computer Engineering College of Engineering King Abdulaziz University Jeddah Saudi Arabia Center of Excellence in Intelligent Engineering Systems (CEIES) King Abdulaziz University Jeddah Saudi Arabia Research Laboratory of Numerical Control of Industrial Processes National Engineering School of Gabes University of Gabes Gabes Tunisia Control and Energy Management Laboratory (CEM-Lab) National Engineering School of Sfax University of Sfax Sfax Tunisia

Rehabilitation robots, particularly lower-limb exoskeletons, are transforming healthcare by assisting individuals with mobility impairments. This study introduces a novel Sliding Mode control (SMC) system based on a fractional-order reaching law, designed to enhance control performance and robustness. The proposed approach effectively manages the exoskeleton’s dynamic behavior, particularly during the transient regime, by reducing initial torque energy demand during start-up, ensuring precise trajectory tracking, and prioritizing patient safety and comfort. The method’s effectiveness is validated through MATLAB simulations and supported by a rigorous dual stability analysis, demonstrating asymptotic and finite-time convergence of the system in the reaching and sliding phases. A Comparison study with traditional SMC techniques proves that the FO-RL-SMC significantly improves energy efficiency during the transient phase and the overall dynamical behavior of the system. These results highlight the potential of the proposed FO-RL-SMC system to advance the performance of rehabilitation robots, emphasizing its value in addressing complex control challenges and improving patient outcomes.

关键词： Exoskeletons Mathematical models Sliding mode control Robustness Robots Assistive robots Stability analysis Biological system modeling Asymptotic stability Transient analysis

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Applying System Thinking to Learn from Accident of Modern Automatic control Systems

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Chinese Journal of Electronics 2025年第2期23卷 409-414页

作者： Ru Niu Yuan Cao Xiaocheng Ge Tao Tang State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China School of Electronics and Information Engineering National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China Department of Computer Science University of York York UK

Modern societies increasingly rely on automatic control systems. These systems are hardly pure technical systems; instead they are complex socio-technical systems, which consist of technical elements and social components. It is necessary to have a systematic approach to analyze these systems because it is growing evidence that accidents from these systems usually have complex causal factors which form an interconnected network of events, rather than a simple cause-effect chain. We take railway Train control systems (TCS) as an example to demonstrate the importance of the socio-technical approach to analyze the system. The paper presents an investigation of recent high-speed railway accident by applying STAMP - one of the most notable socio-technical system analysis techniques, outlines improvements to the system which could avoid similar accidents in the future. We also provide our valuable feedback for the use of STAMP.

关键词： Sociotechnical systems Analytical models Systematics Railway accidents Transportation Medical services Rail transportation Systems thinking Research and development Accidents

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VFL+: Low-Coupling Vertical Federated Learning With Privileged Information Paradigm

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IEEE Transactions on Emerging Topics in Computational Intelligence 2025年

作者： Dai, Wei Cui, Teng Zhang, Tong Chen, Badong China University of Mining Technology School of Information and Control Engineering Xuzhou221116 China South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Xi'an Jiaotong University Institute of Artificial Intelligence and Robotics Xi'an710049 China

Vertical Federated Learning (VFL) enables the construction of models by combining clients with different features without compromising privacy. Existing VFL methods exhibit tightly coupled participant parameters, resulting in substantial interdependencies among clients during the prediction phase, which significantly hampers the model's usability. To tackle these challenges, this paper studies a VFL approach with low coupling of parameters between clients. Drawing inspiration from federated cooperation and teacher-supervised learning, we propose a low-coupling vertical federated learning with privileged information paradigm (VFL+), allowing participants to make autonomous predictions. Specifically, VFL+ treats information from other clients as privileged data during the training phase rather than the testing phase, thereby achieving independence in individual model predictions. Subsequently, this paper further investigates three typical scenarios of vertical cooperation and designs corresponding cooperative frameworks. Systematic experiments on real data sets demonstrate the effectiveness of the proposed method. © 2017 IEEE.

关键词： Federated learning

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Exploring Token-Level Augmentation in Vision Transformer for Semi-Supervised Semantic Segmentation

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IEEE Signal Processing Letters 2025年 32卷 1885-1889页

作者： Zhang, Dengke Tang, Quan Liu, Fagui Mei, Haiqing Chen, C. L. Philip South China University of Technology School of Computer Science and Engineering Guangzhou510006 China Pengcheng Laboratory Department of New Network Shenzhen518000 China China Tobacco Guangdong Industrial Corporation Guangzhou510385 China Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems Institute of Automation Beijing100080 China

Semi-supervised semantic segmentation has witnessed remarkable advancements in recent years. However, existing algorithms are based on convolutional neural networks, and directly applying them to Vision Transformers poses certain limitations due to conceptual disparities. To this end, we propose TokenSwap, a data augmentation technique designed explicitly for semi-supervised semantic segmentation with Vision Transformers. TokenSwap aligns well with the global attention mechanism by mixing images at the token level, enhancing the learning capability for contextual information among image patches and the utilization of unlabeled data. We further incorporate image augmentation and feature augmentation to promote the diversity of augmentation. Moreover, to enhance consistency regularization, we propose a dual-branch framework where each branch applies image and feature augmentation to the input image. We conduct extensive experiments across multiple benchmark datasets, including Pascal VOC 2012, Cityscapes, and COCO. Results suggest that the proposed method outperforms state-of-the-art algorithms with notably observed accuracy improvement, especially under limited fine annotations. © 1994-2012 IEEE.

关键词： Semantic Segmentation

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Oral Cancer Classification Using a Hybrid Attention-Aided Deep Learning Model 5th

Oral Cancer Classification Using a Hybrid Attention-Aided D...

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5th International Conference on Medical Imaging and computer Aided Diagnosis, MICAD 2024

作者： Dutta, Srinjan Roy, Jibesh Mandal, Diptarka Kaplun, Dmitrii Shpakovskaya, Irina Sarkar, Ram Department of Computer Science and Engineering Jalpaiguri Government Engineering College Jalpaiguri India School of Computer Engineering and Mathematical Sciences Defence Institute of Advanced Technology Pune India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China Department of Automation and Control Process Saint Petersburg Electrotechnical University "LETI" Saint Petersburg Russia Department of Computer Science and Engineering Jadavpur University West Bengal Kolkata India I.P. Pavlov Saint Petersburg First State Medical University St. Petersburg Russia

ISBN: (纸本)9789819638628

Oral cancer remains a critical global health challenge, characterized by high morbidity and mortality due to late-stage diagnosis. This paper addresses the need for improved diagnostic accuracy by introducing a novel hybrid-attention aided deep learning model for oral cancer classification. Our approach integrates the EfficientNetB0 model for feature extraction with a customized hybrid attention module, combining self, spatial, and soft attention mechanisms to emphasize crucial features. The model has been trained and evaluated on both microscopic and macroscopic oral cancer datasets, demonstrating its generalizability. Our proposed model achieves an accuracy of 96.97% on a macroscopic dataset and 92% on a microscopic dataset, with respective F1-scores of 0.97 and 0.89, outperforming baseline models. This hybrid attention-aided deep learning model shows significant potential in improving early detection and treatment planning of oral cancer. Source code and additional results of this work can be found at the GitHub repository. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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A Survey of fMRI to Image Reconstruction

arXiv

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arXiv 2025年

作者： Guo, Weiyu Sun, Guoying He, Jianxiang Shao, Tong Wang, Shaoguang Chen, Ziyang Hong, Meisheng Sun, Ying Xiong, Hui Thrust of Artificial Intelligence The Hong Kong University of Science and Technology Guangzhou China College of Computer Science and Technology Harbin Institute of Technology Shenzhen China School of Control Science and Engineering Shandong University China

Functional magnetic resonance imaging (fMRI)based image reconstruction plays a pivotal role in decoding human perception, with applications in neuroscience and brain-computer interfaces. While recent advancements in deep learning and large-scale datasets have driven progress, challenges such as data scarcity, cross-subject variability, and low semantic consistency persist. To address these issues, we introduce the concept of fMRI-to-Image Learning (fMRI2Image) and present the first systematic review in this field. This review highlights key challenges, categorizes methodologies such as fMRI signal encoding, feature mapping, and image generator. Finally, promising research directions are proposed to advance this emerging frontier, providing a reference for future studies. © 2025, CC BY.

关键词： Image reconstruction

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Enhancing Speech Emotion Recognition With Conditional Emotion Feature Diffusion and Progressive Interleaved Learning Strategy

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1787-1800页

作者： Yang Liu Xin Chen Zhichao Peng Yongwei Li Xingfeng Li Peng Song Masashi Unoki Zhen Zhao School of Information Science and Technology Qingdao University of Science and Technology Qingdao China School of Information Hunan University of Humanities Science and Technology Loudi China CAS Key Laboratory of Behavioral Science Institute of Psychology Chinese Academy of Sciences Beijing China Faculty of Data Science City University of Macau Macau China School of Computer and Control Engineering Yantai University Yantai China School of Information Science Japan Advanced Institute of Science and Technology Nomi Japan

Speech emotion recognition (SER) aims to identify the speaker's emotional states in specific utterances accurately. However, existing methods still face feature confusion when attempting to recognize certain emotions because traditional acoustic feature extraction methods fail to capture dynamic emotional changes, blurring emotional boundaries. Additionally, existing classification networks (CNs) are constrained by fixed learning strategies, hindering their ability to capture subtle emotional nuances and resulting in label confusion. To address these two issues, we introduce 3D multiresolution modulation filtered cochleogram (MMCG) features by computing the deltas and delta-deltas of MMCG features to enhance the dynamic emotional changes and produce distinct emotional boundaries. We then customize a conditional emotion feature diffusion (CEFD) module, which progressively diffuses features based on emotional context to retain emotional nuances effectively and reduce reliance on conditioned information. In addition, a confidence filtering module is used to filter diffused features based on confidence-based posterior probabilities to ensure enhanced feature discrimination. We design a flexible training strategy named the progressive interleaved learning strategy (PILS) to learn further complex emotional nuances, which consists of two alternating stages: fine-tuning the CN parameters and supervising the CEFD output. Testing on the IEMOCAP, CASIA, and EMODB corpora demonstrates significant performance improvements in SER.

关键词： Feature extraction Emotion recognition Training Three-dimensional displays Diffusion models Speech enhancement Electronic mail Speech recognition Semantics Face recognition

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A third-party platoon coordination service: Pricing under government subsidies

A third-party platoon coordination service: Pricing under go...

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作者： Bai, Ting Johansson, Alexander Li, Shaoyuan Johansson, Karl Henrik Mårtensson, Jonas School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden KTH Royal Institute of Technology Stockholm Sweden Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China Digital Futures Stockholm Sweden

This paper models a platooning system consisting of trucks and a third-party service provider (TPSP), which performs platoon coordination, distributes the platooning profit in platoons, and charges trucks in exchange for the services. Government subsidies used to incentivize platooning are also considered. We propose a pricing rule for the TPSP, which keeps part of the platooning profit including the subsidy each time a platoon is formed. In addition, a platoon coordination solution based on the distributed model predictive control (MPC) is proposed, in which the pricing rule under government subsidies is integrated. We perform a realistic simulation over the Swedish road network to evaluate the impact of the pricing rule and subsidies on the achieved profits and fuel savings. Our results show that subsidies are an effective mean to boost fuel savings from platooning. Moreover, the simulation study indicates that high pricing corresponds to a low platooning rate of the system, as trucks' incentives for platooning decrease. © 2023 The Authors. Asian Journal of control published by John Wiley & Sons Australia, Ltd on behalf of Chinese Automatic control Society.

关键词： Trucks

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Efficient Coordination and Synchronization of Multi-Robot Systems Under Recurring Linear Temporal Logic

arXiv

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arXiv 2025年

作者： Peron, Davide Fernandez-Ayala, Victor Nan Vlahakis, Eleftherios E. Dimarogonas, Dimos V. Department of Information Engineering University of Padova Padova35122 Italy Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm10044 Sweden

We consider multi-robot systems under recurring tasks formalized as linear temporal logic (LTL) specifications. To solve the planning problem efficiently, we propose a bottom-up approach combining offline plan synthesis with online coordination, dynamically adjusting plans via real-time communication. To address action delays, we introduce a synchronization mechanism ensuring coordinated task execution, leading to a multi-agent coordination and synchronization framework that is adaptable to a wide range of multi-robot applications. The software package is developed in Python and ROS2 for broad deployment. We validate our findings through lab experiments involving nine robots showing enhanced adaptability compared to previous methods. Additionally, we conduct simulations with up to ninety agents to demonstrate the reduced computational complexity and the scalability features of our work. © 2025, CC BY.

关键词： Multipurpose robots

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Toward Using Fuzzy Grey Cognitive Maps in Manned and Autonomous Collision Avoidance at Sea

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IEEE Journal of Oceanic engineering 2025年第2期50卷 1210-1230页

作者： Gil, Mateusz Poczęta, Katarzyna Wróbel, Krzysztof Yang, Zaili Chen, Pengfei Gdynia Maritime University Research Group on Maritime Transportation Risk and Safety Faculty of Navigation Gdynia81-345 Poland Faculty of Electrical Engineering Automatics Control and Computer Science Kielce University of Technology Kielce25-314 Poland Research Institute LiverpoolL3 3AF United Kingdom Wuhan University of Technology School of Navigation Wuhan430063 China Hubei Key Laboratory of Inland Shipping Technology Wuhan430063 China

With Maritime Autonomous Surface Ships (MASS) slowly but steadily nearing full-scale implementation, the question of their safety persists. Regardless of being a disruptive technology, they will likely be subject to the same factors shaping their safety performance as manned ships nowadays are. Yet, the impact of these factors may be different in each case. The current study presents an application of Fuzzy Grey Cognitive Maps (FGCMs) to the comparative evaluation of factors affecting collision avoidance at sea. To this end, subject matter experts have been elicited, and the data obtained from them have been analyzed, concerning how changes in the intensity of given factors would affect safety performance. The obtained results showed that with the use of FGCM, it was possible to model the relative impact of selected factors both on a specific phase of the maritime collision avoidance process as well as on its entirety. The conducted analysis shows noticeable variability of the influence of some factors, depending on the timing of their activation during the process (time dependence), and using FGCM, it was possible to assess its quantification. Furthermore, the results indicate that greater differences can be found between the factors’ impact on phases of an encounter than between manned and autonomous ships. The outcome of this study may be found interesting for all parties involved in maritime safety modeling as well as working on the forthcoming introduction of autonomous ships. © 1976-2012 IEEE.

关键词： Fuzzy Cognitive Maps

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