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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： M. Nasir R. Fareh J. Ghommam Om Gad S. Khadraoui M. Bettayeb Department of Electrical Engineering University of Sharjah Sharjah UAE Department of Electrical and Computer Engineering Sultan Qaboos University Oman Control and Energy Management Laboratory (CEM Lab) Ecole National d'Ingenieur de Sfax Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

This study presents an adaptive fractional sliding mode control (AFSMC) strategy for finite-time trajectory tracking of differential drive mobile robots (DDMR) in the presence of system uncertainties and external disturbances. The proposed control strategy employs a fractional-order sliding surface and an optimized reaching law to ensure finite-time convergence of tracking errors while retaining robustness against external disturbances. Particle Swarm Optimization (PSO) is employed for offline parameter tuning, where its capability to efficiently handle a large parameter space makes it an ideal choice for optimizing the control system, ensuring both enhanced performance and computational feasibility. Simulation tests comparing the AFSMC with a conventional SMC show that the AFSMC exhibits higher tracking accuracy, robustness, and precise linear and angular velocity tracking. The results demonstrate the AFSMC's resilience to disturbances and minimal steady-state error, even under challenging conditions.

关键词： Adaptive systems Uncertainty Accuracy Torque Trajectory tracking Robustness Mobile robots Sliding mode control Optimization Tuning

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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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One-dimensional Continuous Dynamic Modeling and Optimal Sensor Placement Strategy of Heat Storage Tank

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Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical engineering 2025年第8期45卷 3120-3131页

作者： Zhang, Yi Liu, Jianbang Yang, Tingting Liu, Jinfeng Fang, Fang School of Control and Computer Engineering North China Electric Power University Changping District Beijing102206 China School of Electrical and Information Engineering Jiangsu University Jiangsu Province Zhenjiang212013 China Department of Chemical & Materials Engineering University of Alberta EdmontonABT6G 1H9 Canada

Thermocline heat storage tanks, known for their high net energy and exergy efficiency, are becoming a mainstream thermal energy storage solution. However, flow direction switching caused by real-time variations in charging/discharging mass flow creates discontinuities in conventional temperature stratification models. To address this, this study employs continuous smooth functions to approximate the 0-1 variables characterizing working fluid flow direction, resulting in a one-dimensional continuous dynamic stratified temperature model along the tank height. Building on this model, we propose an optimal sensor placement strategy based on sensitivity matrices. The system observability index is redefined using information derived from sensitivity matrices, with the minimum sensor count and optimal positions determined by each location's contribution to system observability. Simulation results demonstrate that the proposed model accurately captures vertical temperature stratification across various operating scenarios when an appropriate smoothing parameter μ is selected. Among all observable sensor combinations with equal sensor numbers, this strategy achieves the smallest mean and variance of RMSE in moving horizon estimation (MHE) observation errors, confirming its superior system observability performance. © 2025 Chinese Society for Electrical engineering. All rights reserved.

关键词： Tanks (containers)

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Designing a stakeholder engagement framework with critical success factors for Hubs for Circularity

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Journal of Environmental Management 2025年 384卷 125324页

作者： Tleuken, Aidana Rogetzer, Patricia Fraccascia, Luca Yazan, Devrim Murat Department Industrial Engineering and Business Information Systems University of Twente Drienerlolaan 5 Enschede PO Box 217 7500 AE Netherlands Department of Computer Control and Management Engineering "Antonio Ruberti" Sapienza University of Rome Via Ariosto 25 Roma00185 RM Italy

This paper introduces a framework aimed at evaluating stakeholder involvement within Hubs for Circularity (H4C), which play a critical role in advancing the circular economy. It demonstrates the significance of collaboration among stakeholders and the need for a structured approach to assess the effectiveness of H4C initiatives. As a result of the literature review synthesis, the paper proposes a novel and actionable framework for stakeholder engagement. It is composed of several key elements: it begins with the identification and analysis of the different stakeholder groups participating in H4C projects. Next, it advocates for a comprehensive review of the factors influencing H4C implementation through an analysis of drivers, barriers, and enablers. It also emphasizes the development of success criteria aligned with the Sustainable Development Goals to measure the outcomes of H4C initiatives. The paper concludes by proposing future research directions, such as integrating the Societal Readiness Level and utilizing social network analysis and regional input–output modeling to assess the socio-economic impacts of H4C projects. Overall, the paper highlights the critical role of stakeholder engagement and effective evaluation frameworks in the success of H4C initiatives and the advancement of sustainability goals. © 2025 The Authors

关键词： Economic and social effects

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Smooth finite-dimensional time-varying feedback for chained nonholonomic systems with distributed input delays

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Automatica 2025年 177卷

作者： Zhang, Kang-Kang Yang, Xuefei Zhang, Kai Department of Mechanical Engineering University of Hong Kong Hong Kong Department of Computer Science KU Leuven HeverleeB-3001 Belgium Center for Control Theory and Guidance Technology Harbin Institute of Technology Harbin150001 China Department of Biomedical Engineering City University of Hong Kong Hong Kong

This paper investigates the smooth finite-dimensional time-varying control problem for chained nonholonomic systems with distributed input delays. For the scalar distributed delay system, a smooth finite-dimensional nonhomogeneous controller is proposed. By using a smooth time-varying state transformation, a chained nonholonomic system with distributed input delay is transformed into a linear time-varying system with a distributed input delay. With the aid of the inherent structural properties of chained nonholonomic system, smooth finite-dimensional linear time-varying controllers are constructed. Unlike traditional predictor feedback, which involves infinite-dimensional terms, the proposed controllers are finite-dimensional and easier to implement. By utilizing Barbalat's lemma, it is shown that the proposed control laws can drive the states to zero. Both state feedback and observer-based output feedback are considered. The effectiveness of the proposed methods is verified through a numerical example. © 2025 Elsevier Ltd

关键词： Delay control systems

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Personalized Semantic Trajectory Privacy Protection in Location-Based Services: A TD3-Based Approach

Personalized Semantic Trajectory Privacy Protection in Locat...

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IEEE Conference on Wireless Communications and Networking

作者： Jun Ye Minghui Dai Minghui Min Jinling Song Hongliang Zhang Zhu Han School of Information and Control Engineering China University of Mining and Technology Xuzhou China School of Electronics Peking University Beijing China Department of Electrical and Computer Engineering University of Houston Houston TX USA

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

The swift advancement of Location-Based Services (LBSs) raises the danger of trajectory privacy being breached, since the location semantic tags can easily disclose users' sensitive information. Additionally, attackers can exploit temporal correlations to infer sensitive personal information. This paper formulates a personalized semantic trajectory privacy protection framework designed to protect locations with varying sensitivities on the trajectory from the attacker with temporal correlation information. We model the trajectory privacy protection problem as a Markov Decision Process (MDP) and introduce the Reinforcement Learning (RL) technique to dynamically adjust the privacy parameters. Specifically, we leverage the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm to enhance the stability and accuracy of policy evaluation, enabling efficient learning of optimal policies in continuous action spaces. Simulation results indicate that the TD3-based personalized semantic trajectory privacy protection mechanism effectively balances the Quality of Service and semantic trajectory privacy while realizing personalized trajectory privacy protection.

关键词： Privacy Correlation Sensitivity Simulation Semantics Quality of service Reinforcement learning Stability analysis Trajectory Protection

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Text-to-Decision Agent: Learning Generalist Policies from Natural Language Supervision

arXiv

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arXiv 2025年

作者： Zhang, Shilin Hu, Zican Wu, Wenhao Xie, Xinyi Tang, Jianxiang Chen, Chunlin Dong, Daoyi Cheng, Yu Sun, Zhenhong Wang, Zhi Department of Control Science and Intelligent Engineering Nanjing University China School of Engineering Australian National University Australia Australian Artificial Intelligence Institute University of Technology Sydney Australia Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong

RL systems usually tackle generalization by inferring task beliefs from high-quality samples or warmup explorations. The restricted form limits their generality and usability since these supervision signals are expensive and even infeasible to acquire in advance for unseen tasks. Learning directly from the raw text about decision tasks is a promising alternative to leverage a much broader source of supervision. In the paper, we propose Text-to-Decision Agent (T2DA), a simple and scalable framework that supervises generalist policy learning with natural language. We first introduce a generalized world model to encode multi-task decision data into a dynamics-aware embedding space. Then, inspired by CLIP, we predict which textual description goes with which decision embedding, effectively bridging their semantic gap via contrastive language-decision pre-training and aligning the text embeddings to comprehend the environment dynamics. After training the text-conditioned generalist policy, the agent can directly realize zero-shot text-to-decision generation in response to language instructions. Comprehensive experiments on MuJoCo and Meta-World benchmarks show that T2DA facilitates high-capacity zero-shot generalization and outperforms various types of baselines. Copyright © 2025, The Authors. All rights reserved.

关键词： Supervised learning

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Concept-Level Semantic Transfer and Context-Level Distribution Modeling for Few-Shot Segmentation

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Luo, Yuxuan Chen, Jinpeng Cong, Runmin Ip, Horace Ho Shing Kwong, Sam City University of Hong Kong Department of Computer Science Hong Kong Shandong University School of Control Science and Engineering Jinan250061 China Ministry of Education Key Laboratory of Machine Intelligence and System Control Jinan250061 China Lingnan University Hong Kong

Few-shot segmentation (FSS) methods aim to segment objects using only a few pixel-level annotated samples. Current approaches either derive a generalized class representation from support samples to guide the segmentation of query samples, which often discards crucial spatial contextual information, or rely heavily on spatial affinity between support and query samples, without adequately summarizing and utilizing the core information of the target class. Consequently, the former struggles with fine detail accuracy, while the latter tends to produce errors in overall localization. To address these issues, we propose a novel FSS framework, CCFormer, which balances the transmission of core semantic concepts with the modeling of spatial context, improving both macro and micro-level segmentation accuracy. Our approach introduces three key modules: (1) the Concept Perception Generation (CPG) module, which leverages pre-trained category perception capabilities to capture high-quality core representations of the target class;(2) the Concept-Feature Integration (CFI) module, which injects the core class information into both support and query features during feature extraction;and (3) the Contextual Distribution Mining (CDM) module, which utilizes a Brownian Distance Covariance matrix to model the spatial-channel distribution between support and query samples, preserving the fine-grained integrity of the target. Experimental results on the PASCAL-5i and COCO-20i datasets demonstrate that CCFormer achieves state-of-the-art performance, with visualizations further validating its effectiveness. Our code is available at ***/lourise/ccformer. © 1991-2012 IEEE.

关键词： Semantic Segmentation

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Post-Processing of Thresholding or Deep Learning Methods for Enhanced Tissue Segmentation of Whole-Slide Histopathological Images

Post-Processing of Thresholding or Deep Learning Methods for...

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Marczyk, Michal Wrobel, Agata Merta, Julia Polanska, Joanna Department of Data Science and Engineering Silesian University of Technology Gliwice44-100 Poland Yale Cancer Center Yale School of Medicine New HavenCT06511 United States Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice44-100 Poland

Digital pathology allows for the efficient storage and advanced computational analysis of stained histopathological slides of various tissues. Tissue segmentation is a crucial first step of digital pathology aimed at eliminating background, pen markings, and other artifacts, reducing image size, and increasing the efficiency of further analysis. In most cases, color thresholding or deep learning models are used, but their effectiveness is reduced due to complex artifacts and huge color variations between slides. We propose a post processing method to increase the tissue segmentation performance of any initial segmentation algorithm. Using a set of 197 manually annotated histopathological images of breast cancer patients and 63 images of endometrial cancer patients, we tested our method with 3 thresholding techniques and 3 deep learning-based algorithms by calculating the Dice index, Jaccard index, precision, and recall. In both datasets, applying post processing increased precision and recall for thresholding methods and mostly precision for deep learning models. Overall, applying post-processing gave better tissue segmentation performance than initial segmentation methods, significantly increasing Dice and Jaccard indices. Our results proved that thanks to post-processing, the tissue segmentation pipeline is more robust to noises and artifacts commonly present in histopathological images. © 2025 by SCITEPRESS– Science and Technology Publications, Lda.

关键词： Image segmentation

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Service Function Chain Dynamic Scheduling in Space-Air-Ground Integrated Networks

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IEEE Transactions on Vehicular Technology 2025年

作者： Jia, Ziye Cao, Yilu He, Lijun Wu, Qihui Zhu, Qiuming Niyato, Dusit Han, Zhu Nanjing University of Aeronautics and Astronautics College of Electronic and Information Engineering Nanjing211106 China Xidian University State Key Laboratory of ISN Xian710071 China China University of Mining and Technology School of Information and Control Engineering Xuzhou221116 China Nanyang Technological University School of Computer Science and Engineering Singapore639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

As an important component of the sixth generation communication technologies, the space-air-ground integrated network (SAGIN) attracts increasing attentions in recent years. However, due to the mobility and heterogeneity of the components such as satellites and unmanned aerial vehiclesin multi-layer SAGIN, the challenges of inefficient resource allocation and management complexity are aggregated. To this end, the network function virtualization technology is introduced and can be implemented via service function chains (SFCs) deployment. However, urgent unexpected tasks may bring conflicts and resource competition during SFC deployment, and how to schedule the SFCs of multiple tasks in SAGIN is a key issue. In this paper, we address the dynamic and complexity of SAGIN by presenting a reconfigurable time extension graph and further propose the dynamic SFC scheduling model. Then, we formulate the SFC scheduling problem to maximize the number of successful deployed SFCs within limited resources and time horizons. Since the problem is in the form of integer linear programming and intractable to solve, we propose the algorithm by incorporating deep reinforcement learning. Finally, simulation results show that the proposed algorithm has better convergence and performance compared to other benchmark algorithms. © 2025 IEEE.

关键词： Network function virtualization

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