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arXiv 2025年

作者： Bongini, Pietro Mandelli, Sara Montibeller, Andrea Casu, Mirko Pontorno, Orazio Ragaglia, Claudio Vittorio Zanchetta, Luca Aquilina, Mattia Wani, Taiba Majid Guarnera, Luca Tondi, Benedetta Boato, Giulia Bestagini, Paolo Amerini, Irene De Natale, Francesco Battiato, Sebastiano Barni, Mauro University of Siena Department of Information Engineering an Mathematics Italy Politecnico di Milano Department of Electronics Informatics and Bioengineering Italy University of Trento Department of Information Engineering and Computer Science Italy University of Catania Department of Mathematics and Computer Science Italy Sapienza University of Rome Departement of Computer Control and Management Engineering Italy

Synthetic image source attribution is an open challenge, with an increasing number of image generators being released yearly. The complexity and the sheer number of available generative techniques, as well as the scarcity of high-quality open source datasets of diverse nature for this task, make training and benchmarking synthetic image source attribution models very challenging. WILD1 is a new in-the-Wild Image Linkage Dataset designed to provide a powerful training and benchmarking tool for synthetic image attribution models. The dataset is built out of a closed set of 10 popular commercial generators, which constitutes the training base of attribution models, and an open set of 10 additional generators, simulating a real-world in-the-wild scenario. Each generator is represented by 1,000 images, for a total of 10,000 images in the closed set and 10,000 images in the open set. Half of the images are post-processed with a wide range of operators. WILD allows benchmarking attribution models in a wide range of tasks, including closed and open set identification and verification, and robust attribution with respect to post-processing and adversarial attacks. Models trained on WILD are expected to benefit from the challenging scenario represented by the dataset itself. Moreover, an assessment of seven baseline methodologies on closed and open set attribution is presented, including robustness tests with respect to post-processing. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep learning

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Rough inference system for automated failure prediction in cooling devices 1

Rough inference system for automated failure prediction in c...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Kokosinski, Zbigniew Szydlowski, Piotr Cracow University of Technology Department of Automatic Control and Computer Engineering Kraków31-155 Poland Efento Sp. Z O.o. Kraków31-123 Poland

ISBN: (纸本)9798350328042

This article presents a versatile model of failure prediction in control systems of objects with large time constants. The basic model of a control unit consists of deterministic finite automaton (DFA) which states correspond to states of the system where system variables satisfy a given set of constraints. System failures can be predicted on the basis of time series of the selected signals. Further diagnosis is performed on the basis of rough set theory and original algorithms for attributes' reduction and decision rule generation. A versatile failure prediction algorithm is constructed that integrates all involved techniques. Cooling systems are application examples of the proposed methodology. © 2023 IEEE.

关键词： Approximation algorithms

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Scalable and Low-Cost Remote Lab Platforms: Teaching Industrial Robotics Using Open-Source Tools and Understanding Its Social Implications 16th

Scalable and Low-Cost Remote Lab Platforms: Teaching Indust...

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16th International Conference on Social Robotics, ICSR + AI 2024

作者： Kumar, Amit Jose, Jaison Jain, Archit Kulkarni, Siddharth Arya, Kavi Centre for Systems and Control Indian Institute of Technology Bombay Mumbai India Embedded Real-Time Systems / e-Yantra Lab Indian Institute of Technology Bombay Mumbai India Computer Science and Engineering Department Indian Institute of Technology Bombay Mumbai India

ISBN: (纸本)9789819635214

With recent advancements in industrial robots, educating students in new technologies and preparing them for the future is imperative. However, access to industrial robots for teaching poses challenges, such as the high cost of acquiring these robots, the safety of the operator and the robot, and complicated training material. This paper proposes two low-cost platforms built using open-source tools like Robot Operating System (ROS) and its latest version ROS 2 to help students learn and test algorithms on remotely connected industrial robots. Universal Robotics (UR5) arm and a custom mobile rover were deployed in different life-size testbeds, a greenhouse, and a warehouse to create an Autonomous Agricultural Harvester System (AAHS) and an Autonomous Warehouse Management System (AWMS). These platforms were deployed for a period of 7 months and were tested for their efficacy with 1,433 and 1,312 students, respectively. The hardware used in AAHS and AWMS was controlled remotely for 160 and 355 hours, respectively, by students over a period of 3 months. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Costs

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FusionGCNN: An IoT-Based Novel Spatiotemporal Graph Convolutional Network for ECG Arrhythmia Detection

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IEEE Internet of Things Journal 2025年

作者： Iqbal, Saeed Zhong, Xiaopin Alhussein, Musaed Wu, Zongze Aurangzeb, Khursheed Liu, Weixiang Zhang, Yudong Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China King Saud University College of Computer and Information Sciences Department of Computer Engineering Riyadh11543 Saudi Arabia Henan Polytechnic University School of Computer Science and Technology Henan Jiaozuo China

Electrocardiogram (ECG) arrhythmia identification is critical for early cardiovascular disease diagnosis and monitoring in Internet of Things (IoT) industry. Still, it is difficult due to complicated waveforms, individual variability, and the requirement for real-time analysis on resource-limited equipment. Traditional approaches sometimes fail to detect complicated spatial-temporal correlations in ECG data, limiting their efficiency in identifying arrhythmias. Furthermore, deploying these models in tinyML contexts, such as edge and IoT devices limited by large computational and memory needs, emphasizes the importance of lightweight, accurate models for real-time applications. Our suggested solution consists of three main components: SigNet, DualGCNN, and FusionGCNN. SigNet uses Separable Convolution layers to effectively extract local spatial features, making it ideal for IoT-based healthcare deployment. DualGCNN combines dual Graph Convolutional layers with spatial attention, allowing the model to capture local and global dependencies for better classification of arrhythmia. FusionGCNN combines the capabilities of GCN and SigNet with an effective feature fusion technique to improve feature representation while remaining computationally economical. Ablation tests show that FusionGCNN improves performance considerably, with greater accuracy (0.9641), lower training error (0.0004), and a higher F1 Score (0.9645) across a variety of ECG patterns. FusionGCNN, with its low training error, high stability, and computational economy, is well-suited to tinyML requirements, allowing implementation on edge and IoT devices for scalable, real-time ECG monitoring in healthcare. © 2014 IEEE.

关键词： Electrocardiograms

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Renewable integration and energy reduction in multiple stage evaporator

Renewable integration and energy reduction in multiple stage...

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作者： Pati, Smitarani Kumar Sharma, Tarun Kumar Goyal, Kapil Prakash Verma, Om Department of Instrumentation and Control Engineering Dr B R Ambedkar National Institute of Technology Jalandhar Punjab Jalandhar144011 India Department of Computer Science and Engineering Shobhit University UP Saharanpur-247341 India Department of Industrial and Production Engineering Dr B R Ambedkar National Institute of Technology Jalandhar Punjab Jalandhar144011 India

Multiple Effect Evaporator (MEE) is an imperative part of the paper industry. It is used to process the waste byproduct named Black Liquor to enhance its solid concentration that may be further utilized to produce biomass-based energy. This process accounts very high energy consumption which leads to the energy intensiveness of this unit. A substantial amount of energy-saving may be accumulated by integrating various Energy Reduction Schemes (ERSs) with the MEE. Steam splitting, feed splitting, feed liquor preheating, thermo-vapor compressor, etc. are the well-known ERSs that may be incorporated with the MEE to reduce energy utilization. Hence, this study investigates the hybrid ERSs incorporated with MEE to observe their energy efficiency in an optimal manner. The prime focus of this investigation is to achieve enhanced energy efficiency in teams of maximum steam efficiency which is computed by formulating the nonlinear mathematical models of the MEE. To achieve the maximized energy efficiency, an optimization function has been developed under a constraint environment and solved by using metaheuristic-based approach named Flow Direction Algorithm. Another ERSs named flash tanks can also be integrated with this system to encourage waste heat re-utilization and ensures an increase in energy efficiency with constant steam consumption. This work also deals with the incorporation of the Linear Fresnel Reflector with ERSs integrated MEE which is capable of reducing the conventional energy load. The overall integrated MEE model achieves an energy-efficient and renewably operated MEE unit that may enhance the self-sustainability of the industry. © 2022

关键词： Energy efficiency

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A Novel Quantum Binary Evolutionary Algorithm for EM Applications: bGA-QCO

A Novel Quantum Binary Evolutionary Algorithm for EM Applica...

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： F. Martinez E. Gabriel Alessandro Niccolai Eleonora L. Zich Riccardo E. Zich Department of Energy Politecnico di Milano Milano Italy Department of Control and Computer Engineering Politecnico di Torino Torino Italy

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

It is possible to effectively study most of engineering optimization problems suitably rephrased in binary via Evolutionary Algorithms. Even so, the fitness function computation, typically dealing with antenna design, may be very time consuming. Therefore, it is very important to speed up the convergency and to improve the performances of these algorithms, and the introduction of quantum computing seems to open new perspectives, where new approaches have to be considered to exploit the specificity of these instruments. In this paper, a novel quantum version of the binary Genetic Algorithm, bGA, has been introduced, bGA-QCO, and tested on a benchmark function and on a thinned array design.

关键词： Quantum computing Instruments Conferences Evolutionary computation Benchmark testing Optimization Antennas

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An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

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computers, Materials & Continua 2022年第4期71卷 199-213页

作者： Abdelhameed Ibrahim Hattan F.Abutarboush Ali Wagdy Mohamed Mohamad Fouad El-Sayed M.El-kenawy Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electrical Engineering Department College of EngineeringTaibah UniversityMedina42353Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyEgypt

Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their *** size affects the quality factor and the radiation loss of the *** antennas can overcome the limitation of bandwidth for small *** learning(ML)model is recently applied to predict antenna *** can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated *** accuracy of the prediction depends mainly on the selected *** models combine two or more base models to produce a better-enhanced *** this paper,a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial *** base models are used namely:Multilayer Perceptron(MLP)and Support Vector Machines(SVM).To calculate the weights for each model,an optimization algorithm is used to find the optimal weights of the *** Group-Based Cooperative Optimizer(DGCO)is employed to search for optimal weight for the base *** proposed model is compared with three based models and the average ensemble *** results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

关键词： Metamaterial antenna machine learning ensemble model multilayer perceptron support vector machines

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Strong bounds for large-scale Minimum Sum-of-Squares Clustering

arXiv

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arXiv 2025年

作者： Croella, Anna Livia Piccialli, Veronica Sudoso, Antonio M. Department of Computer Control and Management Engineering Antonio Ruberti Sapienza University of Rome Via Ariosto 25 00185 Italy

Clustering is a fundamental technique in data analysis and machine learning, used to group similar data points together. Among various clustering methods, the Minimum Sum-of-Squares Clustering (MSSC) is one of the most widely used. MSSC aims to minimize the total squared Euclidean distance between data points and their corresponding cluster centroids. Due to the unsupervised nature of clustering, achieving global optimality is crucial, yet computationally challenging. The complexity of finding the global solution increases exponentially with the number of data points, making exact methods impractical for large-scale datasets. Even obtaining strong lower bounds on the optimal MSSC objective value is computationally prohibitive, making it difficult to assess the quality of heuristic solutions. We address this challenge by introducing a novel method to validate heuristic MSSC solutions through optimality gaps. Our approach employs a divide-and-conquer strategy, decomposing the problem into smaller instances that can be handled by an exact solver. The decomposition is guided by an auxiliary optimization problem, the "anticlustering problem", for which we design an efficient heuristic. Computational experiments demonstrate the effectiveness of the method for large-scale instances, achieving optimality gaps below 3% in most cases while maintaining reasonable computational times. These results highlight the practicality of our approach in assessing feasible clustering solutions for large datasets, bridging a critical gap in MSSC evaluation. Copyright © 2025, The Authors. All rights reserved.

关键词： Large datasets

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements 63

A Multi-Player Potential Game Approach for Sensor Network Lo...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Xu, Gehui Chen, Guanpu Fidan, Baris Hong, Yiguang Qi, Hongsheng Parisini, Thomas Johansson, Karl H. Kth Royal Institute of Technology Division of Decision and Control Systems School of Electrical Engineering and Computer Science Stockholm100 44 Sweden University of Waterloo Department of Mechanical and Mechatronics Engineering WaterlooONN2L 3G1 Canada Tongji University Department of Control Science and Engineering shanghai201804 China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai201210 China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China University of Chinese Academy of Sciences School of Mathematical Sciences Beijing China Aalborg University Department of Electronic Systems Denmark Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom University of Trieste Department of Engineering and Architecture Italy

ISBN: (纸本)9798350316339

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies. © 2024 IEEE.

关键词： Nash equilibrium

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DOMAIN RANDOMIZATION VIA ENTROPY MAXIMIZATION 12

DOMAIN RANDOMIZATION VIA ENTROPY MAXIMIZATION

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12th International Conference on Learning Representations, ICLR 2024

作者： Tiboni, Gabriele Klink, Pascal Peters, Jan Tommasi, Tatiana D'Eramo, Carlo Chalvatzaki, Georgia Department of Control and Computer Engineering Politecnico di Torino Italy Department of Computer Science Technische Universität Darmstadt Germany Center for Artificial Intelligence and Data Science University of Würzburg Germany Darmstadt Germany Centre for Cognitive Science TU Darmstadt Germany Germany Center for Mind Brain and Behavior Uni. Marburg and JLU Giessen Germany

Varying dynamics parameters in simulation is a popular Domain Randomization (DR) approach for overcoming the reality gap in Reinforcement Learning (RL). Nevertheless, DR heavily hinges on the choice of the sampling distribution of the dynamics parameters, since high variability is crucial to regularize the agent's behavior but notoriously leads to overly conservative policies when randomizing excessively. In this paper, we propose a novel approach to address sim-to-real transfer, which automatically shapes dynamics distributions during training in simulation without requiring real-world data. We introduce DOmain RAndomization via Entropy MaximizatiON (DORAEMON), a constrained optimization problem that directly maximizes the entropy of the training distribution while retaining generalization capabilities. In achieving this, DORAEMON gradually increases the diversity of sampled dynamics parameters as long as the probability of success of the current policy is sufficiently high. We empirically validate the consistent benefits of DORAEMON in obtaining highly adaptive and generalizable policies, i.e. solving the task at hand across the widest range of dynamics parameters, as opposed to representative baselines from the DR literature. Notably, we also demonstrate the Sim2Real applicability of DORAEMON through its successful zero-shot transfer in a robotic manipulation setup under unknown real-world parameters. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Reinforcement learning

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