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IEEE Conference on Decision and control

作者： Christos Mavridis Aris Kanellopoulos Henrik Sandberg Karl Henrik Johansson Division of Decision and Control School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We investigate the problem of deceiving a malicious agent employing an identification method to estimate the closed-loop dynamics of a cyber-physical system. In particular, we propose a moving target defense mechanism that utilizes stochastic switching between linear closed-loop dynamics to drive a linear system identification process of a potential adversary to sub-optimal solutions with non-vanishing error. We provide a statistical analysis of the induced identification error and show that it is not possible for any linear system identification method to reconstruct the average dynamics of a stochastic switched linear system. Finally, we utilize the theory of Markov jump linear systems to guarantee asymptotic stability of the switching system, and formulate the switching control problem as an optimization problem that guarantees stability while taking into account the trade-off between security and switching effort. Simulation results showcase the efficacy of the proposed approach in inducing identification error for the adversary using minimal switching.

关键词： Linear systems Asymptotic stability Switching systems Statistical analysis Simulation Switches Cyber-physical systems control systems Stability analysis Security

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Spatial-Temporal Graph U-Net for Skeleton-Based Human Motion Infilling

Spatial-Temporal Graph U-Net for Skeleton-Based Human Motion...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Leiyang Xu Qiang Wang Chenguang Yang Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Computer Science University of Liverpool Liverpool UK

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

Motion infilling is a fundamental and challenging research field in human motion modeling and analysis, which aims to generate natural and visually coherent transitions to fill in missing motion frames based on the start and end motion sequences. However, most current methods ignore the spatial structure formed by joints, which may lose some spatial information. This work proposes a novel spatiotemporal graph U-Net that supports flexible inputs for skeleton-based motion infilling. We apply spatiotemporal graph convolutional layers, skeleton pooling layers, and skeleton unpooling layers to extract spatial and temporal features in the motion sequence. At the same time, we use the U-Net structure to integrate the information in the start and end motion sequences. In addition, the generative adversarial mechanism is introduced to ensure the generated skeleton poses are smooth and natural. We conduct experiments on two motion datasets, including one large-scale public dataset and one self-built dataset. The model inputs are joint quaternions or joint coordinates. Experimental results show that our method can improve the performance of skeleton-based motion infilling and achieve state-of-the-art results when using joint coordinates as model input.

关键词： Analytical models Quaternions Feature extraction Skeleton Data models Spatiotemporal phenomena

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A Versatile Low-Light Enhancement Module for Improved Workpiece Detection 13

A Versatile Low-Light Enhancement Module for Improved Workpi...

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13th International Conference of Information and Communication Technology, ICTech 2024

作者： Yin, ZhenYu Lin, DaHeng Wang, XiaoHui Xin, Qi Zhang, XueMei Shenyang Institute of Computing Technology Chinese Academy of Sciences Shenyang110168 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing100049 China Liaoning Key Laboratory of Domestic Industrial Control Platform Technology on Basic Hardware & Software China School of Computer Science and Technology Shenyang University of Chemical Technology Shenyang110168 China

ISBN: (纸本)9798350376258

This study presents a versatile Low-Light Enhancement Module (LLEM) to enhance object detection models, particularly for workpieces in challenging lighting conditions. The LLEM seamlessly integrates into existing models, addressing low-light challenges through a denoising branch, a global branch, and a Structural Perception loss for image quality improvement. The denoising branch utilizes Residual Denoising Modules to effectively reduce noise and preserve low-level features. Meanwhile, the global branch focuses on color recovery and gamma correction. Experimental results show YOLOv3+LLEM significantly improves detection performance, achieving a mAP increase from 88.0% to 91.9%. This enhancement, especially with the introduction of Structural Perception loss, maintains computational efficiency, validating its practicality for industrial workpiece detection in low-light conditions. ©2024 IEEE.

关键词： Image enhancement

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Adversarial Training with Maximal Coding Rate Reduction

Adversarial Training with Maximal Coding Rate Reduction

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Asilomar Conference on Signals, Systems & computers

作者： Hsiang-Yu Chu Hongbo Zhao Markus Flierl Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Control Science and Engineering Harbin Institute of Technology Harbin China

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Deep convolutional networks can solve various complex tasks in the field of image processing. However, adversarial attacks have been shown to have the ability of fooling deep learning models. Adversarial training is one commonly used strategy to improve the robustness of deep learning models against adversarial examples, which is performed by incorporating adversarial examples into the training process. Traditionally, during this process, cross-entropy loss is used as the loss function. In order to improve the robustness of deep learning models against adversarial examples, we propose in this paper two new methods of adversarial training by applying the principle of Maximal Coding Rate Reduction (MCR 2 ). We evaluate the performance of different adversarial training methods by comparing the clean accuracy and adversarial accuracy. It is shown that adversarial training with the MCR 2 loss function yields a more robust network than the traditional adversarial training method. In our experiments, adversarial accuracies are improved by up to 10%. The two loss functions are discussed by using a model.

关键词： Training Deep learning Hands Accuracy Image coding Computational modeling Robustness Data models Encoding Convolutional neural networks

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Fractional-order RISE control for Reducing Torques dedicated for Rehabilitation Robots 4

Fractional-order RISE Control for Reducing Torques dedicated...

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4th International Conference on Applied Automation and Industrial Diagnostics, ICAAID 2022

作者： Maalej, Boutheina Khlif, Rim Jallouli Derbel, Nabil University of Sfax ENIS Laboratory of Control and Energy Management Sfax Tunisia Higher Institute of Computer Science and Multimedia of Sfax Sfax Tunisia

ISBN: (纸本)9781728189390

In rehabilitation, lower limb exoskeletons are the most used devices to help disabled persons, to relearn the walking process. In fact, referring to statistics offered by hospital centers, 40% of handicapped kids became normal if they are followed by physiotherapist doctors who force the lower limbs to do series of movements, manually. Moreover, the use of robotic systems facilitates considerably the results of such movements. The paper's interest concerns the control of exoskeletons using robust *** paper aims to implement a fractional-order RISE control for 2 degrees of freedom exoskeleton. © 2022 IEEE.

关键词： Exoskeleton (Robotics)

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Results From a Large-Scale IoT-based Intervention for Energy Saving and Sustainability Awareness via Behavior Change in 25 K-12 Schools in Europe

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IEEE Internet of Things Journal 2025年第12期12卷 19308-19325页

作者： Mylonas, Georgios Amaxilatis, Dimitrios Paganelli, Federica Chatzigiannakis, Ioannis Koulouris, Pavlos Falkouskaya, Yelizaveta Markopoulos, Panos Athena Research Center Industrial Systems Institute Patras26504 Greece Comp. Tech. Institute and Press "Diophantus" 26504 Greece Spark Works Ltd GalwayH91 AHR1 Ireland Univ. of Pisa Dpt. of Computer Science Pisa56127 Italy CNIT Florence50134 Italy Sapienza University of Rome Dpt. of Computer Control and Management Engineering 00185 Italy Ellinogermaniki Agogi Athens Greece Eindhoven University of Technology AI Systems Institute Industrial Design Noord-Brabant Eindhoven5612 AE Netherlands

The unfolding climate crisis has resulted in a rising interest for increasing sustainability awareness and achieving energy savings worldwide. Several interventions within educational environments have been aimed at mobilizing younger audiences towards such goals. However, most of the interventions carried out so far are based on a subset of possible tools (e.g., IoT monitoring, gamification) and offer "all or nothing"approaches, which do not cope well with the uniqueness of schools and the need to adapt interventions to specific contexts (e.g. school size, building, curriculum, teacher involvement). We present a large-scale intervention in 25 school buildings in Europe over the course of two school years. Our intervention was based on the installation of a relatively low-cost IoT infrastructure in schools, that produced real-time energy-related data from the school buildings involved, and the application of a strategy combining, in a flexible and customizable way, educational activities, monitoring tools, gamification, and competition to motivate behavior change and achieve energy savings. Overall, 2983 students and 204 educators were directly involved to a variable degree of participation. Our results indicate a positive overall result in terms of short-term energy savings in most schools involved, with an average reduction of 20-25% in the energy that could be affected by end-users, as well as a 32.9% increase after the intervention in students self-reporting good or very good sustainability awareness. These findings suggest that such interventions can be a valuable step towards educating young students regarding sustainability and towards sustainable schools, if designed and implemented properly. © 2014 IEEE.

关键词： Sustainable development Buildings Energy conservation Europe Meteorology Internet of Things Energy consumption Real-time systems Energy efficiency Psychology

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BigFeat: Scalable and Interpretable Automated Feature Engineering Framework

BigFeat: Scalable and Interpretable Automated Feature Engine...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Eldeeb, Hassan Amashukeli, Shota Elshawi, Radwa University of Tartu Institute of Computer Science Tartu Estonia Tanta University Faculty of Engineering Computers and Automatic Control Department Tanta Egypt

ISBN: (纸本)9781665480451

Feature engineering is a crucial step in building well-performing machine learning pipelines. However, manually constructing highly predictive features is time-consuming and requires domain knowledge. Although the research area of automated feature engineering has attracted much interest lately, both in academia and industry, the scalability and efficiency of the existing systems and tools are still practically unsatisfactory. This paper presents a scalable and interpretable automated feature engineering framework, BigFeat, that optimizes input features' quality to maximize the predictive performance according to a user-defined metric. BigFeat employs a dynamic feature generation and selection mechanism that constructs a set of expressive features that improve the prediction performance while retaining interpretability. Extensive experiments are conducted, and the results show that BigFeat provides superior performance compared to the state-of-the-art automated feature engineering framework, AutoFeat, on a wide range of datasets. We show that BigFeat significantly improves the F1-Score of 8 classifiers by 4.59%, on average. In addition, the performance improvement achieved by integrating BigFeat into different AutoML frameworks is higher than that achieved by integrating AutoFeat into the same frameworks. Besides, the scalability of BigFeat is confirmed by its linear complexity, parallel design, and execution time which is, on average, 22x faster than AutoFeat. © 2022 IEEE.

关键词： Scalability

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TSQ-2024: A Categorized Dataset of 2D LiDAR Images of Moving Dump Trucks in Various Environment Conditions 17

TSQ-2024: A Categorized Dataset of 2D LiDAR Images of Moving...

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17th International Conference on Machine Vision, ICMV 2024

作者： Kokhan, V.V. Konyushenko, I.D. Bocharov, D.A. Seleznev, I.O. Nikolaev, I.P. Nikolaev, D.P. Institute for Information Transmission Problems Russian Academy of Sciences Moscow Russia Moscow Institute of Physics and Technology Dolgoprudny Russia GLAVKARIER LLC Borovsk Russia Federal Research Center "Computer Science and Control" Russian Academy of Sciences Moscow Russia Smart Engines Service LLC Moscow117312 Russia

ISBN: (纸本)9781510688278

LiDARs are powerful and common sensors used for complex 3D environment analysis tasks. They are widely utilized as data providers in industrial measurements, robotics and unmanned technologies. Due to physical properties of such sensors they suffer from environmental heterogenity resulting in distortions of obtained measurements because of natural clutter such as cloud dust, snow and fog. Nevertheless the problem of robust clutter filtering algorithms is of significant importance, we highlight the lack of LiDAR measurements datasets containing natural clutter. The current study presents a novel publicly available and expertly annotated 2D LiDAR measurements dataset TSQ-2024 that contains 120 measurements of passing trucks under natural conditions including sand dust clutter data. The proposed dataset was utilized in the task of truck body segmentation. The experiments section provides the performance evaluation of the proposed truck body segmentation algorithm on the TSQ-2024 dataset which is also published along with the data. © 2025 SPIE.

关键词： Radar clutter

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Keeping Eyes on the Road: Understanding Driver Attention and Its Role in Safe Driving 9

Keeping Eyes on the Road: Understanding Driver Attention and...

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9th Scholar's Yearly Symposium of Technology, Engineering and Mathematics, SYSTEM 2023

作者： Fiani, Francesca Ponzi, Valerio Russo, Samuele Department of Computer Control and Management Engineering Sapienza University of Rome Roma00185 Italy Institute for Systems Analysis and Computer Science Italian National Research Council Roma00185 Italy Department of Psychology Sapienza University of Rome Roma00185 Italy

Monitoring the driver’s attention is an important task to maintain the driver’s safety. The estimation of the driver’s gaze direction can help us to evaluate if the drivers are not focusing their attention on the street. For an evaluation of this type, comparing the inside view and outside scenery of the vehicle is essential, therefore we decided to create a specific dataset for this task. In this work, we realize a machine-learning-oriented approach to driver’s attention evaluation using a coupled visual perception system. By analyzing the road and the driver’s gaze simultaneously it is possible to understand if the driver is looking at the traffic signs detected. We evaluate if a determined Region Of Interest (ROI) contains a road sign or not through YOLOv8. © 2023 Copyright for this paper by its authors.

关键词： Machine learning

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Phase chimera states on nonlocal hyperrings

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Physical Review E 2024年第2期109卷 L022201-L022201页

作者： Riccardo Muolo Thierry Njougouo Lucia Valentina Gambuzza Timoteo Carletti Mattia Frasca Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo 152-8552 Japan Department of Mathematics University of Namur B5000 Namur Belgium naXys Namur Institute for Complex Systems University of Namur B5000 Namur Belgium Faculty of Computer Science University of Namur B5000 Namur Belgium Department of Electrical and Electronic Engineering University of Buea P.O. Box 63 Buea Cameroon Department of Electrical Electronics and Computer Science Engineering University of Catania 95125 Catania Italy Istituto di Analisi dei Sistemi ed Informatica “A. Ruberti” IASI-CNR 00185 Roma Italy

Chimera states are dynamical states where regions of synchronous trajectories coexist with incoherent ones. A significant amount of research has been devoted to studying chimera states in systems of identical oscillators, nonlocally coupled through pairwise interactions. Nevertheless, there is increasing evidence, also supported by available data, that complex systems are composed of multiple units experiencing many-body interactions that can be modeled by using higher-order structures beyond the paradigm of classic pairwise networks. In this work we investigate whether phase chimera states appear in this framework, by focusing on a topology solely involving many-body, nonlocal, and nonregular interactions, hereby named nonlocal d-hyperring, (d+1) being the order of the interactions. We present the theory by using the paradigmatic Stuart-Landau oscillators as node dynamics, and we show that phase chimera states emerge in a variety of structures and with different coupling functions. For comparison, we show that, when higher-order interactions are “flattened” to pairwise ones, the chimera behavior is weaker and more elusive.

关键词： Chimera states Collective behavior in networks Dynamics of networks Pattern formation Synchronization Synchronization transition

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