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International Multidisciplinary Conference on computer and Energy Science (SpliTech)

作者： David Bač nar Lolita Leytner Rene Prenc Veljko Jardas Jonatan Lerga Department of Computer Engineering Faculty of Engineering University of Rijeka Rijeka Croatia Automation and Computer Engineering Department University of Angers Polytech Angers Angers France Department of Electric Power Systems Faculty of Engineering University of Rijeka Rijeka Croatia Jatro d.o.o. Rijeka Croatia

ISBN: (纸本)9781665488280

Smart meters are modernizing the electrical energy systems, solving numerous problems of the classical systems, and introducing new functionalities to the grid. This paper aims to give an insight into these systems in terms of their security and privacy aspects. Special attention is paid to the energy theft problem (meter tampering, physical anti-tampering measures, and theft detection methods). We, next, address other security issues present in remote metering (attack types, risks, and threats to the system). Also, necessary security requirements and signature-based, anomaly-based, and specification-based Intrusion Detection Systems (IDS) are defined. We conclude by listing privacy concerns arising due to the remote meters and advanced metering infrastructures (AMI) being installed more and more.

关键词： Meters Privacy Energy measurement Intrusion detection Aging Smart meters Security

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Robust Vehicle Detection in High-Resolution Aerial Images With Imbalanced Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2021年第3期2卷 238-250页

作者： Li, Xianghui Li, Xinde Li, Zhijun Xiong, Xinran Khyam, Mohammad Omar Sun, Changyin The Laboratory of Measurement Control of CSE Ministry of Education School of Automation Southeast University Nanjing210096 China School of Cyber Science and Engineering Southeast University Nanjing210096 China The Department of Automation University of Science Technology of China Hefei230027 China The Department of Faculty of Science McGill University MontréalQCH3A 0G4 Canada The School of Engineering and Technology Central Queensland University MelbourneVIC3000 Australia

Vehicle detection in images from unmanned aerial vehicles (UAVs) plays an important role in traffic surveillance and urban planning due to the popularity of UAVs. However, the class imbalance problem is an important factor that restricts the performance of vehicle detectors. There are two types of class imbalance in UAV images, i.e., foreground-background imbalance and foreground-foreground imbalance. For anchor-based single stage detector, as many ground truths cannot be assigned to corresponding anchors because of low intersection over union, it makes the foreground-background imbalance problem more severe. Therefore, we propose a novel bag-based single-stage detector, which treats each position on the feature map as a bag. A simple and adaptive definition of bags is proposed along with the positive sample definition method, which is utilized to ensure more ground truths can be assigned to proper bags. In addition, we utilize online hard example mining method to control the proportion of positive and negative samples during the training process. To address the foreground-foreground imbalance, we propose a novel data augmentation algorithm, which allows us to create appropriate visual context for under-represented class. Extensive experiments demonstrate the superiority of the proposed algorithm, compared with other state-of-the-art solutions. Impact Statement-Recently, unmanned aerial vehicles (UAVs) are widely used in intelligent transportation due to their low price and high flexibility, which makes vehicle detection in UAV images important for automatically gathering of traffic information. However, the class imbalance problem, which is common in object detection where some classes have far fewer frequencies in the dataset, has an adverse effect on the performance of vehicle detectors. The data augmentation method and deep learning based vehicle detector proposed in this article are able to reduce the negative impact and improve detection performance by at leas

关键词： Object detection

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A New Solution to the PID18 Challenge:Reinforcement-Learning-based PI control

A New Solution to the PID18 Challenge:Reinforcement-Learning...

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第34届中国控制与决策会议

作者： Yuting Wu Lantao Xing Xiao-Kang Liu Fanghong Guo the School of Electrical and Electronic Engineering Nanyang Technological University School of Control Science and Engineering Shandong Unvieristy the School of Artificial Intelligence and Automation Huazhong University of Science and Technology the Department of Automation Zhejiang University of Technology

The PID18 benchmark challenge,first presented at the 3rd IFAC Conference on Advances in 2018,is widely used to test the effectiveness of proportional-integral-derivative(PID) *** this challenge,a 2-Input and 2-Output vapor-compression refrigeration system with strong nonlinearities and couplings is used as the test control *** this paper,we address this challenge by proposing a new reinforcement-learning-based PI *** current existing results,the PI parameters of each RL-based PI controller are determined by two adaptive signals,which are generated by a reinforcement learning(RL) agent,aiming at achieving better control *** comparisons with existing methods have been conducted,and it is shown that our proposed strategy yields the best control performance thus far.

关键词： PI control Reinforcement learning PID18 benchmark refrigeration system

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Behavioural Cloning for Serious Games in Support of Pediatric Neurorehabilitation

Behavioural Cloning for Serious Games in Support of Pediatri...

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Mediterranean Conference on control and automation (MED)

作者： Federico Baldisseri Edoardo Montecchiani Arturo Maiani Danilo Menegatti Alessandro Giuseppi Antonio Pietrabissa Vincenzo Fogliati Francesco Delli Priscoli Dept. of Computer Control and Management Engineering (DIAG) Sapienza University of Rome Italy 4You – Association for Social Advancement Rome Italy Consortium for the Research in Automation and Telecommunication (CRAT) Rome Italy

Behavioural Cloning is a Machine Learning method concerning how a machine attempts to autonomously mimic the actions of a human, or in general a complex controller, performing a given task. This work innovatively exploits Behavioural Cloning in support of Pediatric Neurorehabilitation. In particular, an Artificial Neural Network Classifier has been implemented to autonomously adapt the difficulty, through a set of tunable parameters, of a Serious Game that was specifically developed to stimulate some relevant cognitive capabilities of the patient. Data augmentation via Behavioural Cloning allows such autonomous difficulty adaptation system to improve its classification performances and, thus, to enforce a control logic that, in turn, improves the effectiveness of the cognitive training. The system is validated through an experimental assessment on a Serious Game that trains motor coordination: experimental results of children gameplay are analyzed and discussed.

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Transfer and Alignment Network for Generalized Category Discovery

arXiv

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arXiv 2023年

作者： An, Wenbin Tian, Feng Shi, Wenkai Chen, Yan Wu, Yaqiang Wang, Qianying Chen, Ping School of Automation Science and Engineering Xi’an Jiaotong University China School of Computer Science and Technology Xi’an Jiaotong University China National Engineering Laboratory for Big Data Analytics China Lenovo Research China Department of Engineering University of Massachusetts Boston United States

Generalized Category Discovery (GCD) is a crucial real-world task that aims to recognize both known and novel categories from an unlabeled dataset by leveraging another labeled dataset with only known categories. Despite the improved performance on known categories, current methods perform poorly on novel categories. We attribute the poor performance to two reasons: biased knowledge transfer between labeled and unlabeled data and noisy representation learning on the unlabeled data. The former leads to unreliable estimation of learning targets for novel categories and the latter hinders models from learning discriminative features. To mitigate these two issues, we propose a Transfer and Alignment Network (TAN), which incorporates two knowledge transfer mechanisms to calibrate the biased knowledge and two feature alignment mechanisms to learn discriminative features. Specifically, we model different categories with prototypes and transfer the prototypes in labeled data to correct model bias towards known categories. On the one hand, we pull instances with known categories in unlabeled data closer to these prototypes to form more compact clusters and avoid boundary overlap between known and novel categories. On the other hand, we use these prototypes to calibrate noisy prototypes estimated from unlabeled data based on category similarities, which allows for more accurate estimation of prototypes for novel categories that can be used as reliable learning targets later. After knowledge transfer, we further propose two feature alignment mechanisms to acquire both instance- and category-level knowledge from unlabeled data by aligning instance features with both augmented features and the calibrated prototypes, which can boost model performance on both known and novel categories with less noise. Experiments on three benchmark datasets show that our model outperforms SOTA methods, especially on novel categories. Theoretical analysis is provided for an in-depth understanding

关键词： Alignment

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Corrections to "computer Vision Aided mmWave Beam Alignment in V2X Communications"

arXiv

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arXiv 2024年

作者： Xu, Weihua Gao, Feifei Tao, Xiaoming Zhang, Jianhua Alkhateeb, Ahmed Department of Automation Tsinghua University Beijing100084 China Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering University of Texas at Austin AustinTX78712-1687 United States

In this document, we revise the results of [1] based on more reasonable assumptions regarding data shuffling and parameter setup of deep neural networks (DNNs). Thus, the simulation results can now more reasonably demonstrate the performance of both the proposed and compared beam alignment methods. We revise the simulation steps and make moderate modifications to the design of the vehicle distribution feature (VDF) for the proposed vision based beam alignment when the MS location is available (VBALA). Specifically, we replace the 2D grids of the VDF with 3D grids and utilize the vehicle locations to expand the dimensions of the VDF. Then, we revise the simulation results of Fig. 11, Fig. 12, Fig. 13, Fig. 14, and Fig. 15 in [1] to reaffirm the validity of the conclusions. Copyright © 2024, The Authors. All rights reserved.

关键词： Vehicle-to-grid

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Reciprocal Safety Velocity Cones for Decentralized Collision Avoidance in Multi-Agent Systems

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IFAC-PapersOnLine 2023年第2期56卷 8024-8029页

作者： Soulaimane Berkane Dimos V. Dimarogonas Department of Computer Science and Engineering University of Quebec in Outaouais 101 St-Jean Bosco Gatineau QC J8X 3X7 Canada Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology SE-100 44 Stockholm Sweden

In this paper, we solve the inter-agent collision avoidance problem in an arbitrary n-dimensional Euclidean space using reciprocal safety velocity cones (RSVCs). We propose a decentralized feedback control strategy that guarantees simultaneously asymptotic stabilization to a reference and collision avoidance. Our algorithm is purely decentralized in the sense that each agent uses only local information about its neighbouring agents. Moreover, the proposed solution can be implemented using only inter-agent bearing measurements. Therefore, the algorithm is a sensor-based control strategy which is practically implementable using a wide range of sensors such as vision systems and range scanners. Simulation results in a two dimensional environment cluttered with agents shows that the number of possible deadlocks is marginal and decrease with the decrease in the clutteredness of the workspace.

关键词： Collision avoidance multi-agent systems sensor-based methods motion planning feedback control

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" Investigating the Effectiveness of Sand Fill Additives for Enhancing Heat Exchanging Capacity of Sustainable Spiral Ground Looping "

SSRN

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SSRN 2024年

作者： Elsherief, Abdelrahman A. Reda, Ahmed M. Hafez, Ali G. Eltohamy, Ahmed M. Madkour, Hany Abdelrahman, Kamal Fnais, Mohammed S. Khodary, Safia M. Department of Civil Engineering Aswan University Aswan Egypt Faculty of Energy Engineering Aswan university Aswan Egypt Department of Seismology National Research Institute of Astronomy and Geophysics Cairo Egypt Department of Civil Engineering Beni Suef University Beni Suef Egypt Department of Civil Engineering Nahda university Beni Suef Egypt Department of Control and Computer Engineering AlMaaqal University Basra Iraq Department of Geology and Geophysics College of Science King Saud University P.O. Box 2455 Riyadh11451 Saudi Arabia

This study investigates the performance of Horizontal Spiral Ground Heat Exchangers (HSGHE) by analyzing the effects of various parameters such as flow rate, inlet water temperature, surface temperature, and backfill material composition. In a laboratory system that simulated summer climate conditions in Egypt, experiments were performed using various backfill materials, such as native sand soil and sand/limestone powder blend. The heat transfer dynamics within the HSGHE system have therefore been investigated through experimental results. It was observed that limestone powder blend gives an average increase in heat exchange rates of 24.17% approximately when compared with native sand soil. The results show that the blend always performs better than natural sand soil with various operational situations since it has higher heat exchange rates and efficiencies. Moreover, higher initial water temperatures initially boost heat exchange rates. This effect diminishes over time. Additionally, the influence of surface temperatures becomes less significant at a depth of 0.9 meters. Overall this study provides information on optimizing the design and selection of backfill materials to improve efficiency in HSGHEs contributing to advancements, in sustainable energy systems and environmental engineering. © 2024, The Authors. All rights reserved.

关键词： Surface temperature

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Pinching Antennas: Principles, Applications and Challenges

arXiv

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arXiv 2025年

作者： Yang, Zheng Wang, Ning Sun, Yanshi Ding, Zhiguo Schober, Robert Karagiannidis, George K. Wong, Vincent W.S. Dobre, Octavia A. Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application Fujian Normal University Fuzhou350117 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Khalifa University Abu Dhabi United Arab Emirates University of Manchester ManchesterM1 9BB United Kingdom Germany Group Electrical & Computer Engineering Dept. Aristotle University of Thessaloniki Thessaloniki54 124 Greece Department of Electrical and Computer Engineering The University of British Columbia Vancouver Canada Department of Electrical and Computer Engineering Memorial University St. John’sNL Canada

Flexible-antenna systems, such as fluid antennas and movable antennas, have been recognized as key enabling technologies for sixth-generation (6G) wireless networks, as they can intelligently reconfigure the effective channel gains of the users and hence significantly improve their data transmission capabilities. However, existing flexible-antenna systems have been designed to combat small-scale fading in non-line-of-sight (NLoS) conditions. As a result, they lack the ability to establish line-of-sight links, which are typically 100 times stronger than NLoS links. In addition, existing flexible-antenna systems have limited flexibility, where adding/removing an antenna is not straightforward. This article introduces an innovative flexible-antenna system called pinching antennas, which are realized by applying small dielectric particles to waveguides. We first describe the basics of pinching-antenna systems and their ability to provide strong LoS links by deploying pinching antennas close to the users as well as their capability to scale up/down the antenna system. We then focus on communication scenarios with different numbers of waveguides and pinching antennas, where innovative approaches to implement multiple-input multiple-output and non-orthogonal multiple access are discussed. In addition, promising 6G-related applications of pinching antennas, including integrated sensing and communication and next-generation multiple access, are presented. Finally, important directions for future research, such as waveguide deployment and channel estimation, are highlighted. Copyright © 2025, The Authors. All rights reserved.

关键词： Channel estimation

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Faulty diagnostics model for wind power plant application using AI

引用

Measurement: Sensors 2023年 25卷

作者： Sudhakar, Puladasu Kamble, Nitin K. K, Geetha Turukmane, Anil V. Perli, Suresh Babu P, Jayaraman Department of Electrical and Electronics Engineering Sree Visvesvaraya Institute of Technology and Science Telangana Mahabubnagar509204 India Department of Robotics and Automation D. Y. Patil College of Engineering Pradhikaran Akurdi Pune411044 India Department of Computing Technologies School of Computing SRM Institute of Science and Technology Chengalpattu District Kattankulathur603203 India Department of Computer Science & Engineering VIT- AP University AmaravatiVijaywada India Department of Electrical Engineering NIT Warangal Telangana WARANGAL506004 India Department of Mechanical Engineering Prathyusha Engineering College Aranvoyalkuppam Tiruvallur Tamil Nadu Chennai602025 India

Researchers desperately ought to build an efficient turbine problem detection & simulation environment given the quick increase in wind energy capacity and the steadily rising operational cost duration of wind generators. The two aspects of fault identification & fault prediction are employed to describe the primary problem features of wind generators. We examine and synthesize the studies on fault detection techniques that rely on vibrating, electrical impulse evaluation, and pattern matching to address the challenging difficulties of defect detection. During the same period, we highlight the computationally efficient, constraints, and potential directions of distinct approaches. We review recent study developments & suggest a defect forecasting model that utilizes a physical breakdown prototype with data analysis theory fused depending on the mechanics and electronic subsystem degradation features of wind energy. In this study, we employ the Internet of Things (IoT) on Deep Learning (DL) architecture to forecast and identify wind energy generation issues. The testing findings demonstrate the method's ability to diagnose faults logically and forecast their types. © 2022 The Authors

关键词： Pattern matching

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