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26th ACM International Conference on Hybrid Systems: Computation and control, HSCC 2023, Part of CPS-IoT Week 2023

作者： Delimpaltadakis, Giannis Lahijanian, Morteza Mazo, Manuel Laurenti, Luca Control Systems Technology Group Mechanical Engineering Eindhoven University of Technology Eindhoven Netherlands Aerospace Engineering Sciences Computer Science University of Colorado Boulder BoulderCO United States Delft Center for Systems Control Mechanical Engineering Delft University of Technology Delft Netherlands

ISBN: (纸本)9798400700330

Interval Markov Decision Processes (IMDPs) are finite-state uncertain Markov models, where the transition probabilities belong to intervals. Recently, there has been a surge of research on employing IMDPs as abstractions of stochastic systems for control synthesis. However, due to the absence of algorithms for synthesis over IMDPs with continuous action-spaces, the action-space is assumed discrete a-priori, which is a restrictive assumption for many applications. Motivated by this, we introduce continuous-action IMDPs (caIMDPs), where the bounds on transition probabilities are functions of the action variables, and study value iteration for maximizing expected cumulative rewards. Specifically, we decompose the max-min problem associated to value iteration to |Q| max problems, where |Q| is the number of states of the caIMDP. Then, exploiting the simple form of these max problems, we identify cases where value iteration over caIMDPs can be solved efficiently (e.g., with linear or convex programming). We also gain other interesting insights: e.g., in certain cases where the action set A is a polytope, synthesis over a discrete-action IMDP, where the actions are the vertices of A, is sufficient for optimality. We demonstrate our results on a numerical example. Finally, we include a short discussion on employing caIMDPs as abstractions for control synthesis. © 2023 Copyright held by the owner/author(s).

关键词： Markov processes

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The forgotten pillar of sustainability: development of the S-assessment tool to evaluate Organizational Social Sustainability

arXiv

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arXiv 2024年

作者： Annarelli, Alessandro Catarci, Tiziana Palagi, Laura DIAG Department of Computer Control and Management Engineering Antonio Ruberti

Pursuing sustainable development has become a global imperative, underscored adopting of the 2030 Agenda for Sustainable Development and its 17 Sustainable Development Goals (SDG). At the heart of this agenda lies the recognition of social sustainability as a pivotal component, emphasizing the need for inclusive societies where every individual can thrive. Despite its significance, social sustainability remains a "forgotten pillar," often overshadowed by environmental concerns. In response, this paper presents the development and validation of the S-Assessment Tool for Social Sustainability, a comprehensive questionnaire designed to evaluate organizations' performance across critical dimensions such as health and wellness, gender equality, decent work, and economic growth, reducing inequalities, and responsible production and consumption. The questionnaire was constructed on the critical dimensions identified through a systematic and narrative hybrid approach to the analysis of peer-reviewed literature. The framework has been structured around the values of the SDGs. It aims to empower organizations to better understand and address their social impact, fostering positive change and contributing to the collective effort towards a more equitable and sustainable future. Through collaborative partnerships and rigorous methodology, this research underscores the importance of integrating social sustainability into organizational practices and decision-making processes, ultimately advancing the broader agenda of sustainable development. Copyright © 2024, The Authors. All rights reserved.

关键词： Sustainable development

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Z-SASLM: Zero-Shot Style-Aligned SLI Blending Latent Manipulation

arXiv

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arXiv 2025年

作者： Borgi, Alessio Maiano, Luca Amerini, Irene Sapienza University of Rome Department of Computer Control and Management Engineering Italy

We introduce Z-SASLM, a Zero-Shot Style-Aligned SLI (Spherical Linear Interpolation) Blending Latent Manipulation pipeline that overcomes the limitations of current multi-style blending methods. Conventional approaches rely on linear blending, assuming a flat latent space leading to suboptimal results when integrating multiple reference styles. In contrast, our framework leverages the nonlinear geometry of the latent space by using SLI Blending to combine weighted style representations. By interpolating along the geodesic on the hypersphere, Z-SASLM preserves the intrinsic structure of the latent space, ensuring high-fidelity and coherent blending of diverse styles—all without the need for fine-tuning. We further propose a new metric, Weighted Multi-Style DINO VIT-B/8, designed to quantitatively evaluate the consistency of the blended styles. While our primary focus is on the theoretical and practical advantages of SLI Blending for style manipulation, we also demonstrate its effectiveness in a multi-modal content fusion setting through comprehensive experimental studies. Experimental results show that Z-SASLM achieves enhanced and robust style alignment. The implementation code can be found at: https://***/alessioborgi/ZSASLM. © 2025, CC BY.

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A Self-Supervised Contrastive Learning Method for Radar Signal Modulation Recognition

A Self-Supervised Contrastive Learning Method for Radar Sign...

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IEEE Advanced Information Management,Communicates,Electronic and Automation control Conference (IMCEC)

作者： Shiya Li Xiaolin Du School of Computer and Control Engineering Yantai University Yantai China

ISBN: (数字)9798350316537

ISBN: (纸本)9798350316544

Effective implementation of supervised learning-based radar signal modulation recognition (RSMR) techniques is heavily dependent on the quantity and quality of labeled datasets. However, the high cost and difficulty involved in analyzing and labeling radar signal samples limit its development. To address this issue, a RSMR system that utilizes self-supervised contrastive learning (SSCL) methodology is proposed. In the classical contrastive learning framework MoCo V2, a custom data augmentation method is employed to capture time-frequency features of the radar signal. Furthermore, the feature extraction network ResNet50 is enhanced by separating spatial and channel filters, resulting in increased sensitivity to time-frequency features. To improve recognition accuracy, two loss functions, alignment and uniformity, are employed in place of the info noise contrastive estimation (InfoNCE) loss, and both loss functions are optimized directly. The experiments demonstrate the effectiveness of the proposed system.

关键词： Time-frequency analysis Accuracy Sensitivity Noise Modulation Radar Feature extraction

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controlling the Movement Path of Leg Exoskeletons Through Fractional Order Sliding Mode control 21

Controlling the Movement Path of Leg Exoskeletons Through Fr...

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21st International Multi-Conference on Systems, Signals and Devices, SSD 2024

作者： Znidi, Aicha Abdelhedi, Fatma Nouri, Ahmed Said Derbel, Nabil National Engineering School of Gabes University of Gabes Lab: Numerical Control of Industrial Processes Gabes6029 Tunisia Engineering College of Engineering King Abdulaziz: University Department of Electrical and Computer Jeddah22254 Saudi Arabia National Engineering School of Sfax University of Sfax Tunisia

ISBN: (纸本)9798350374131

This research introduces a new method, Fractional Order Sliding Mode control (FOSMC), to manage leg exoskele-tons during gait rehabilitation. This innovative algorithm utilizes fractional calculus principles to precisely regulate the joints of the exoskeleton. By integrating fractional order dynamics, the controller effectively handles the complexities and uncertainties inherent in human walking patterns. This leads to more precise tracking, lower energy usage, and increased comfort for patients during rehabilitation. Additionally, the application of Lyapunov theory ensures the stability of the system. The effectiveness of this control method is assessed through simulation examples in the study. © 2024 IEEE.

关键词： Sliding mode control

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Auxiliary Diagnosis of Pneumonia Based on Convolutional Attention and Parameter Migration 18th

Auxiliary Diagnosis of Pneumonia Based on Convolutional Atte...

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18th CCF Conference on computer Supported Cooperative Work and Social Computing, ChineseCSCW 2023

作者： Zhang, Jianfei Zhang, Bo College of Computer and Control Engineering Qiqihar University Qiqihar China

ISBN: (纸本)9789819996360

Traditional deep learning models have limited ability to extract features from pneumonia images. This study combines convolutional attention modules with transfer learning to improve the model's feature extraction ability and simplify *** article has discovered an improved CBAM-Xception neural network pneumonia model. This network uses Xception as the main network, and combines with the convolutional attention CBAM module to enhance the expression of lesion information and suppress irrelevant information interference;At the same time, transfer learning is introduced to prevent over fitting when the sample data amount is small. In order to evaluate the effectiveness of the optimization model, experimental simulation tests were conducted on the Mendeley Data public pneumonia dataset. The improved model achieves 94.2% accuracy in classifying four images of COVID-19, bacterial pneumonia, viral pneumonia and normal chest on the test set, and the optimization effect is significant. In order to further verify the performance of this method, the experiment divided the small sample data set to train the model, and divided the large sample data to test the generalization performance of the model. The results show that the model in this paper has good generalization ability. This model can provide an important basis for the auxiliary diagnosis and treatment of pneumonia. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Optimal decarbonisation pathway for mining truck fleets

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Journal of Automation and Intelligence 2024年第3期3卷 129-143页

作者： Gang Yu Xianming Ye Yuxiang Ye Hongxu Huang Xiaohua Xia Department of Electrical Electronic and Computer EngineeringUniversity of PretoriaPretoria 0002South Africa School of Economics and Finance University of the WitwatersrandJohannesburg 2000South Africa School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116China

The fossil fuel powered mining truck fleets can contribute up to 80%of total emissions in open pit *** study investigates the optimal decarbonisation pathway for mining truck ***,our proposed pathway incorporates power generation,negative carbon technologies,and carbon ***,financial,and environmental models of decarbonisation technologies are established,capturing regional variations and time dynamic characteristics such as cost trends and carbon capture *** dynamic natures of characteristics pose challenges for using the cost-effective analyses approach to find the optimal decarbonisation *** address this,we introduce a mixed-integer programming optimisation framework to find the decarbonisation pathway with minimum life cycle costs during the planning period.A case study for the optimal decarbonisation pathway of truck fleets in a South African coal mine is conducted to illustrate the applicability of the proposed *** indicate that the optimal decarbonisation pathway is significantly influenced by factors such as land cost,annual budget,and carbon trading *** proposed method provides invaluable guidance for transitioning towards a cleaner and more sustainable mining industry.

关键词： Coal mine Truck fleet Carbon emission Optimal decarbonisation pathway

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State of health estimation of lithium-ion batteries based on TiDE

State of health estimation of lithium-ion batteries based on...

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2024 China Automation Congress, CAC 2024

作者： Li, Hui Cui, Fangshu Shi, Yuanhao Wang, Bohui School of Electrical and Control Engineering North University of China Taiyuan China School of Computer Science and Technology North University of China Taiyuan China School of Cyber Science and Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798350368604

Given the widespread use of lithium-ion batteries, accurately forecasting their State of Health (SOH) is crucial for ensuring the secure and reliable operation of equipment. The local capacity regeneration during battery degradation can undermine prediction accuracy. This research introduces an MDT-FOA+TiDE approach for predicting SOH in lithium-ion batteries using multidimensional time series data. Initially, capacity and various feature data from the degradation process were collected and health indicators were derived. Features with high correlation to capacity were integrated into multidimensional time series. Subsequently, the Time-series Dense Encoder (TiDE) model was employed for training and prediction, while the Fox Optimization Algorithm (FOA) was used for model optimization. Experimental results using the NASA dataset demonstrate that the proposed approach outperforms SVR and LSTM models that utilize univariate data and MDT-TiDE model without FOA optimization. © 2024 IEEE.

关键词： lithium-ion batteries local capacity regeneration multidimensional time series state of health

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Improved Prediction of Metamaterial Antenna Bandwidth Using Adaptive Optimization of LSTM

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computers, Materials & Continua 2022年第10期73卷 865-881页

作者： Doaa Sami Khafaga Amel Ali Alhussan El-Sayed M.El-kenawy Abdelhameed Ibrahim Said H.Abd Elkhalik Shady Y.El-Mashad Abdelaziz A.Abdelhamid Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Systems Engineering Faculty of Engineering at ShoubraBenha UniversityEgypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia

The design of an antenna requires a careful selection of its parameters to retain the desired ***,this task is time-consuming when the traditional approaches are employed,which represents a significant *** the other hand,machine learning presents an effective solution to this challenge through a set of regression models that can robustly assist antenna designers to find out the best set of design parameters to achieve the intended *** this paper,we propose a novel approach for accurately predicting the bandwidth of metamaterial *** proposed approach is based on employing the recently emerged guided whale optimization algorithm using adaptive particle swarm optimization to optimize the parameters of the long-short-term memory(LSTM)deep *** optimized network is used to retrieve the metamaterial bandwidth given a set of *** addition,the superiority of the proposed approach is examined in terms of a comparison with the traditional multilayer perceptron(ML),Knearest neighbors(K-NN),and the basic LSTM in terms of several evaluation criteria such as root mean square error(RMSE),mean absolute error(MAE),and mean bias error(MBE).Experimental results show that the proposed approach could achieve RMSE of(0.003018),MAE of(0.001871),and MBE of(0.000205).These values are better than those of the other competing models.

关键词： Metamaterial antenna long short term memory(LSTM) guided whale optimization algorithm(Guided WOA) adaptive dynamic particle swarm algorithm(AD-PSO)

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A Unified Planning Model for Microgrids Considering Typical Scenarios and Operational Simulations 3

A Unified Planning Model for Microgrids Considering Typical ...

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3rd Asia Power and Electrical Technology Conference, APET 2024

作者： Su, Mengmeng Peng, Jun Zhang, Yong Li, Min Li, Zhenwei Ban, Mingfei North China Branch of State GRID Corporation of China Beijing China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China College of Computer and Control Engineering Northeast Forestry University Harbin China

ISBN: (纸本)9798350367690

The structure of microgrids and the models considered have a significant impact on the planning and operational outcomes of micro grids. Additionally, micro grids encompass various models of primary equipment, including wind, solar, diesel, and storage, while the demand-side response mechanisms are continually being refined. This necessitates the construction of diverse load types, thereby increasing the complexity of micro grid planning and highlighting the need for further research into microgrid planning models. This paper presents various typical mathematical models for microgrids, employs spectral clustering algorithms to obtain representative scenarios, and constructs a unified planning model for microgrids. Furthermore, it elucidates the model adjustment methods under different planning objectives and conducts case analyses of microgrid planning issues. © 2024 IEEE.

关键词： Microgrids

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