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2024 IEEE International Conference on robotics and Biomimetics, ROBIO 2024

作者： Kaya, Mertcan Kuhnlenz, Kolja Coburg University of Applied Sciences and Arts Robotics Research Lab Department of Electrical Engineering and Computer Science Coburg Germany

ISBN: (纸本)9781665481090

This paper investigates the influence of a static robot head on deviations of human hand movements from task direction (motor interference) during simultaneous human and robot arm movements using a collaborative robot arm. Synchronous vertical and horizontal human and robot arm movements are conducted in all combinations (within-subjects factors direction and congruency). The presence of the head (head/ no head) is varied as a between-subjects factor. In this first step, only its motionless presence is deliberately focused at, in order to exclude contributions by eye- or head-movement behavior or facial expressions being difficult variables to generalize from. Participants' hands are tracked optically marker-based and deviations from task-related vertical, respectively, horizontal movements are evaluated. In contrast to expectations, results do not show a significant impact of the robot head on motor interference. Thus, there is evidence, that appearance might not be an activating factor for motor interference in HRI. © 2024 IEEE.

关键词： Robotic arms

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Autonomous Drifting Based on Maximal Safety Probability Learning 27

Autonomous Drifting Based on Maximal Safety Probability Lear...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Hoshino, Hikaru Li, Jiaxing Menon, Arnav Dolan, John M. Nakahira, Yorie University of Hyoto Department of Electrical Materials and Engineering Japan Carnegie Mellon University Department of Electrical and Computer Engineering United States Robotics Institute Carnegie Mellon University United States

ISBN: (纸本)9798331505929

This paper proposes a novel learning-based framework for autonomous driving based on the concept of maximal safety probability. Efficient learning requires rewards that are informative of desirable/undesirable states, but such rewards are challenging to design manually due to the difficulty of differentiating better states among many safe states. On the other hand, learning policies that maximize safety probability does not require laborious reward shaping but is numerically challenging because the algorithms must optimize policies based on binary rewards sparse in time. Here, we show that physics-informed reinforcement learning can efficiently learn this form of maximally safe policy. Unlike existing drift control methods, our approach does not require a specific reference trajectory or complex reward shaping, and can learn safe behaviors only from sparse binary rewards. This is enabled by the use of the physics loss that plays an analogous role to reward shaping. The effectiveness of the proposed approach is demonstrated through lane keeping in a normal cornering scenario and safe drifting in a high-speed racing scenario. © 2024 IEEE.

关键词： Contrastive Learning

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Network Embedding Methods: Study and Comparison

EEA - Electrotehnica, Electronica, Automatica

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EEA - Electrotehnica, Electronica, Automatica 2024年第4期72卷 72-78页

作者： Benbatata, Sabrina Saoud, Bilal LIMPAF laboratory Department of Computer Sciences University of Bouira Bouira10000 Algeria Electrical Engineering Department Sciences and Applied Sciences Faculty LISEA laboratory University of Bouira Bouira10000 Algeria

Graph is a powerful language that can model many systems in different fields such as information sciences, social sciences, Biology, mathematics, physics, etc. Graphs can capture very well the relationships between nodes and their structure. Representing data through graphs has some limitations and is challenging to use them like input in machine learning and deep learning models. This challenge can be overcome by using network embedding. Embedding represents a network into low-dimensional vector space. Several methods have been proposed to embed networks. Methods like DeepWalk, Node2Vec and GraphSAGE have gained traction. DeepWalk employs random walks to capture local neighborhood information, generating embeddings using skip-gram models. Node2Vec extends this by incorporating biased random walks, balancing exploration and exploitation of the network. GraphSAGE adopts a neighborhood aggregation strategy, aggregating features from a node's local graph structure through different convolutional layers. These methods facilitate downstream tasks like node classification, link prediction and clustering. Challenges include scalability to large networks, handling heterogeneous information and preserving structural characteristics. Recent advancements integrate techniques like attention mechanisms and reinforcement learning for improved embeddings. Network embedding methods continue to evolve, catering to diverse applications in social networks, biology and recommendation systems, offering insights into complex network structures. In this paper we provide networks embedding concepts and some methods. A comparison between embedding methods is presented. Embedding network methods on node classification tasks on different benchmark datasets have been conducted in order to evaluate them. Through rigorous evaluation, we assess their effectiveness in capturing latent features and improving classification accuracy across various domains. © Editura ELECTRA 2024. All rights reserved

关键词： Graph embeddings

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Dynamics-incorporated Modeling Framework for Stability Constrained Scheduling under High-penetration of Renewable Energy

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IEEE Transactions on Sustainable Energy 2025年第3期16卷 1673-1685页

作者： Wang, Jinning Li, Fangxing Fang, Xin Cui, Hantao She, Buxin Shuai, Hang Zhang, Qiwei Tomsovic, Kevin University of Tennessee Department of Electrical Engineering and Computer Science KnoxvilleTN37996 United States Mississippi State University Department of Electrical and Computer Engineering StarkvilleMS39762 United States NC State University Department of Electrical and Computer Engineering RaleighNC27606 United States Pacific Northwest National Laboratory RichlandWA99352 United States Johns Hopkins University Department of Environmental Health and Eng. BaltimoreMD21205 United States

In this paper, a modularized modeling framework is designed to enable a dynamics-incorporated power system scheduling under high-penetration of renewable energy. This unique framework incorporates an adapted hybrid symbolic-numeric approach to scheduling models, effectively bridging the gap between device- and system-level optimization models and streamlining the scheduling modeling effort. The adaptability of the proposed framework stems from four key aspects: extensible scheduling formulations through modeling blocks, scalable performance via effective vectorization and sparsity-aware techniques, compatible data structure aligned with dynamic simulators by common power flow data, and interoperable dynamic interface for bi-direction data exchange between steady-state generation scheduling and time-domain dynamic simulation. Through extensive benchmarks with various usage scenarios, the framework's accuracy and scalability are validated. The case studies also demonstrate the efficient interoperation of generation scheduling and dynamics, significantly reducing the modeling conversion work in stability-constrained grid operation towards high-penetration of renewable energy. © 2025 IEEE.

关键词： Adaptation models Dynamic scheduling Numerical models Power system stability Power system dynamics Stability analysis Aerodynamics Transient analysis Computational modeling Renewable energy sources

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Attack-detection and multi-clock source cooperation-based accurate time synchronization for PLC-AIoT in smart parks

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Digital Communications and Networks 2024年第6期10卷 1732-1740页

作者： Zhigang Du Sunxuan Zhang Zijia Yao Zhenyu Zhou Muhammad Tariq State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China Electrical Engineering Department National University of Computer and Emerging SciencesIslamabad 44000Pakistan

Power Line Communications-Artificial Intelligence of Things(PLC-AIo T)combines the low cost and high coverage of PLC with the learning ability of Artificial Intelligence(AI)to provide data collection and transmission capabilities for PLC-AIo T devices in smart *** the development of smart parks,their emerging services require secure and accurate time synchronization of PLC-AIo T ***,the impact of attackers on the accuracy of time synchronization cannot be *** solve the aforementioned problems,we propose a tampering attack-aware Deep Q-Network(DQN)-based time synchronization ***,we construct an abnormal clock source detection ***,the abnormal clock source is detected and excluded by comparing the time synchronization information between the device and the ***,the proposed algorithm realizes the joint guarantee of high accuracy and low delay for PLC-AIo T in smart parks by intelligently selecting the multi-clock source cooperation strategy and timing *** results show that the proposed algorithm has better time synchronization delay and accuracy performance.

关键词： Smart park Power line communications Artificial intelligence of things Tampering attack awareness Abnormal clock source detection Multi-clock source cooperation

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Rotor Angle Stability Prediction Using Temporal and Topological Embedding Deep Neural Network Based on Grid-informed Adjacency Matrix

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 695-706页

作者： Peiyuan Sun Long Huo Xin Chen Siyuan Liang the School of Electrical Engineering Xi’an Jiaotong UniversityXi’an 710049China the School of Electrical Engineering and the Center of Nanomaterials for Renewable EnergyState Key Laboratory of Electrical Insulation and Power EquipmentXi’an Jiaotong UniversityXi’an 710049China the Department of Computer Science and Engineering The Chinese University of Hong KongShatin999077Hong KongChina

Rotor angle stability(RAS)prediction is critically essential for maintaining normal operation of the interconnected synchronous machines in power *** wide deployment of phasor measurement units(PMUs)promotes the development of data-driven methods for RAS *** paper proposes a temporal and topological embedding deep neural network(TTEDNN)model to accurately and efficiently predict RAS by extracting the temporal and topological features from the PMU *** grid-informed adjacency matrix incorporates the structural and electrical parameter information of the power *** the small-signal RAS with disturbance under initial operating conditions and the transient RAS with short circuits on transmission lines are *** studies of the IEEE 39-bus and IEEE 300-bus power systems are used to test the performance,scalability,and robustness against measurement uncertainties of the TTEDNN *** show that the TTEDNN model performs best among existing deep learning ***,the superior transfer learning ability from small-signal RAS conditions to transient RAS conditions has been proved.

关键词： Rotor angle stability prediction topological embedding deep learning graph convolution network

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Optimization of Residential Net Load using Demand Response and Battery Storage 3

Optimization of Residential Net Load using Demand Response a...

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3rd International Conference on Energy Transition in the Mediterranean Area, SyNERGY MED 2024

作者： Bracho, Jorge Ilia, Neofytos Makrides, George Tseng, Alex R. Herodotou, Panagiotis Georgiou, George E. PV Technology Laboratory Department of Electrical and Computer Engineering Cyprus for Intelligent Nicosia Cyprus Princeton University Electrical and Computer Engineering NJ United States

ISBN: (纸本)9798350375923

Demand side management (DSM) and solar-plus-storage systems are enabling technologies that facilitate consumers to increase self-consumption (SC), while reducing electricity costs through efficient and intelligent electricity usage. Even though many demand response (DR) strategies have been presented for the residential sector, a major challenge remains the joint optimal utilization and synergies of DR and the battery systems flexibility scheduling. This paper explores how the optimization of DR and battery energy storage systems (BESS) scheduling can notably improve the net load profile of residential users. The obtained results indicate that the optimized DR model and battery storage effectively support load shifting and peak energy utilization. Specifically, the analysis suggests that the combination of DR and an 8kWh battery storage system increased the SC from 55.04% to 95.84% and the SS from 37.50% to 66.88%, compared to a baseline scenario (without battery storage and DR actions). Ultimately, the proposed optimization achieved a higher degree of energy autonomy for the yearly profile of a median household by minimizing the sum of absolute values of the net load with load shifting and BESS charging/discharging. Finally, the optimization model can be used by aggregators and system operators for scheduling and optimizing flexibility actions. © 2024 IEEE.

关键词： Demand response

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A Deep Joint Source-Channel Coding Scheme for Hybrid Mobile Multi-hop Networks

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IEEE Journal on Selected Areas in Communications 2025年第7期43卷 2543-2559页

作者： Bian, Chenghong Shao, Yulin Gündüz, Deniz Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom University of Macau State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering SAR China

Efficient data transmission across mobile multi-hop networks that connect edge devices to core servers presents significant challenges, particularly due to the variability in link qualities between wireless and wired segments. This variability necessitates a robust transmission scheme that transcends the limitations of existing deep joint source-channel coding (Deep-JSCC) strategies, which often struggle at the intersection of analog and digital methods. Addressing this need, this paper introduces a novel hybrid DeepJSCC framework, h-DJSCC, tailored for effective image transmission from edge devices through a network architecture that includes initial wireless transmission followed by multiple wired hops. Our approach harnesses the strengths of DeepJSCC for the initial, variable-quality wireless link to avoid the cliff effect inherent in purely digital schemes. For the subsequent wired hops, which feature more stable and high-capacity connections, we implement digital compression and forwarding techniques to prevent noise accumulation. This dual-mode strategy is adaptable even in scenarios with limited knowledge of the image distribution, enhancing the framework's robustness and utility. Extensive numerical simulations demonstrate that our hybrid solution outperforms traditional fully digital approaches by effectively managing transitions between different network segments and optimizing for variable signal-to-noise ratios (SNRs). We also introduce a fully adaptive h-DJSCC architecture with both SNR-adaptive (SA) and rate-adaptive (RA) modules capable of adjusting to different network conditions and achieving diverse rate-distortion objectives, thereby reducing the memory requirements on network nodes. © 1983-2012 IEEE.

关键词： Channel coding

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MIMO performance enhancement of MIMO arrays using PCS-based near-field optimization technique

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Science China(Information Sciences) 2023年第6期66卷 170-185页

作者： Yipeng WANG Xiaoming CHEN Huiling PEI Wei E.I.SHA Yi HUANG Ahmed A.KISHK School of Information and Communications Engineering Xi'an Jiaotong University Key Laboratory of Micro-Nano Electronic Devices and Smart Systems of Zhejiang Province College of Information Science and Electronic EngineeringZhejiang University Department of Electrical Engineering and Electronics University of Liverpool Department of Electrical and Computer Engineering Concordia University

Extensive efforts have been made in designing large multiple-input multiple-output(MIMO)arrays. Nevertheless, improvements in conventional antenna characteristics cannot ensure significant MIMO performance improvement in realistic multipath environments. Array decorrelation techniques have been proposed, achieving correlation reductions by either tilting the antenna beams or shifting the phase centers away from each other. Hence, these methods are mainly limited to MIMO terminals with small arrays. To avoid such problems, this work proposes a decorrelation optimization technique based on phase correcting surface(PCS)that can be applied to large MIMO arrays, enhancing their MIMO performances in a realistic(non-isotropic)multipath environment. First, by using a near-field channel model and an optimization algorithm, a near-field phase distribution improving the MIMO capacity is obtained. Then the PCS(consisting of square elements)is used to cover the array's aperture, achieving the desired near-field phase *** examples demonstrate the effectiveness of this PCS-based near-field optimization technique. One is a1 × 4 dual-polarized patch array(working at 2.4 GHz)covered by a 2 × 4 PCS with 0.6λ center-to-center distance. The other is a 2 × 8 dual-polarized dipole array, for which a 4 × 8 PCS with 0.4λ center-to-center distance is designed. Their MIMO capacities can be effectively enhanced by 8% and 10% in single-cell and multi-cell scenarios, respectively. The PCS has insignificant effects on mutual coupling, matching, and the average radiation efficiency of the patch array, and increases the antenna gain by about 2.5 dB while keeping broadside radiations to ensure good cellular coverage, which benefits the MIMO performance of the *** proposed technique offers a new perspective for improving large MIMO arrays in realistic multipath in a statistical sense.

关键词： antenna correlation array antenna multiple-input multiple-output (MIMO) capacity

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A Multi-Scale Control Framework for Electric and Automated Buses in Intercity Lines 63

A Multi-Scale Control Framework for Electric and Automated B...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Graffione, S. Bracco, S. Pasquale, C. Sacone, S. Siri, S. Ferrara, A. University of Genova Department of Informatics Bioengineering Robotics and Systems Engineering Italy University of Genova Department of Electrical Electronics and Telecommunication Engineering and Naval Architecture Italy University of Pavia Department of Electrical Computer and Biomedical Engineering Italy

ISBN: (纸本)9798350316339

This paper proposes a multi-scale control scheme aimed at controlling speed, dwell time, and charging time of electric and automated buses performing the transport service on lines where there are no reserved lanes. The proposed control scheme has two layers characterized by different control objectives, level of detail and time scales. The high-level controller periodically solves a multi-objective optimal control problem formalized to ensure an adequate transport service, while the low-level controller, being based on a more accurate description of the bus dynamics and energy consumption, seeks to follow the high-level control actions while taking into account the effective traffic conditions encountered on the line and the effective dynamic behaviour of the bus. The paper concludes with the application to a case study in order to demonstrate the effectiveness of the proposed control scheme. © 2024 IEEE.

关键词： Bus transportation

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