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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： George, Deborah Venayagamoorthy, Ganesh K. Clemson University Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering ClemsonSC29634 United States

ISBN: (纸本)9781665430654

Amidst the challenges posed by the high penetration of distributed energy resources (DERs), particularly a number of distributed photovoltaic plants (DPVs), in modern electric power distribution systems (MEPDS), the integration of new technologies and frameworks become crucial for addressing operation, management, and planning challenges. Situational awareness (SA) and situational intelligence (SI) over multi-time scales is essential for enhanced and reliable PV power generation in MEPDS. In this paper, data-driven digital twins (DTs) are developed using AI paradigms to develop actual and/or virtual models of DPV s, These DTs are then applied for estimating and forecasting the power outputs of physical and virtual PV plants. Virtual weather stations are used to estimate solar irradiance and temperature at user-selected locations in a localized region, using inferences from physical weather stations. Three case studies are examined based on data availability: physical PV plant, hybrid PV plants, and virtual PV plants, generating real-time estimations and short-term forecasts of PV power production that can support distribution system studies and decision-making. © 2023 IEEE.

关键词： - AI digital twin distributed energy resources power distribution systems virtual systems weather stations

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Low 3D-HEVC Depth Map Intra Modes Selection Complexity Based on Clustering Algorithm and an Efficient Edge Detection 1st

Low 3D-HEVC Depth Map Intra Modes Selection Complexity Based...

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1st edition of the International Conference on Artificial Intelligence and Green Computing, ICAIGC 2023

作者： Hamout, Hamza Elyousfi, Abderrahmane Computer Systems and Vision Laboratory Faculty of Sciences Ibn-Zohr University Agadir80000 Morocco Department of Computer Science National Engineering School of Applied Sciences Ibn-Zohr University Agadir80000 Morocco

ISBN: (纸本)9783031465833

In 2016, ITU-T Video Coding Experts Group (VCEG) and ISO/IEC MPEG finalize the recent 3-dimensional video coding standard founded on Multi-view texture Videos plus Depth maps (MVD) format called, the 3D extension of High-Efficiency Video Coding (3D-HEVC). In the current design of 3D-HEVC, the conventional intra-prediction modes and Depth Modeling Modes are exploited sophistically to improve the depth map coding performance. However, the coding efficiency improvement is achieved at the cost of computational complexity load, which obstructs 3D-High Efficiency Video Coding standards from practical world applications. In the 3D-HEVC, the depth maps are described by enormous homogeneous regions, and it is observed that for most of the cases, the DMMs and all or the majority of angular intra modes could be skipped for the homogenous area. Thus, we propose an efficient intra-coding model decision for a depth map founded on the clustering algorithm and edge detection to resolve the above-mentioned complexity increase. Experimental results demonstrate that the proposed intra-coding model decision for depth map can decrease the encoding time up to 41.35%, with insignificant loss of rate-distortion performance. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Edge detection

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Modeling and Clustering of Parabolic Granular Data

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第7期5卷 3728-3742页

作者： Tang, Yiming Gao, Jianwei Pedrycz, Witold Hu, Xianghui Xi, Lei Ren, Fuji Hu, Min Hefei University of Technology Anhui Province Key Laboratory of Affective Computing and Advanced Intelligent Machine School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2V4 Canada Hefei University of Technology School of Computer and Information Hefei230601 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Polish Academy of Sciences Systems Research Institute Warsaw00-901 Poland Istinye University Research Center of Performance and Productivity Analysis Istanbul34010 Turkey Southeast University School of Computer Science and Engineering Nanjing211189 China University of Electronic Science and Technology of China School of Computer Science and Engineering Sichuan Chengdu611731 China

At present, there exist some problems in granular clustering methods, such as lack of nonlinear membership description and global optimization of granular data boundaries. To address these issues, in this study, revolving around the parabolic granular data, we propose an overall architecture for parabolic granular modeling and clustering. To begin with, novel coverage and specificity functions are established, and then a parabolic granular data structure is proposed. The fuzzy c-means (FCM) algorithm is used to obtain the numeric prototypes, and then particle swarm optimization (PSO) is introduced to construct the parabolic granular data from the global perspective under the guidance of principle of justifiable granularity (PJG). Combining the advantages of FCM and PSO, we propose the parabolic granular modeling and optimization (PGMO) method. Moreover, we put forward attribute weights and sample weights as well as a distance measure induced by the Gaussian kernel similarity, and then come up with the algorithm of weighted kernel fuzzy clustering for parabolic granularity (WKFC-PG). In addition, the assessment mechanism of parabolic granular clustering is discussed. In summary, we set up an overall architecture including parabolic granular modeling, clustering, and assessment. Finally, comparative experiments on artificial, UCI, and high-dimensional datasets validate that our overall architecture delivers a good improvement over previous strategies. The parameter analysis and time complexity are also given for WKFC-PG. In contrast with related granular clustering algorithms, it is observed that WKFC-PG performs better than other granular clustering algorithms and has superior stability in handling outliers, especially on high-dimensional datasets. © 2020 IEEE.

关键词： Fuzzy clustering

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Optimal Control of Connected Automated Vehicles with Event-Triggered Control Barrier Functions: a Test Bed for Safe Optimal Merging

Optimal Control of Connected Automated Vehicles with Event-T...

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2023 IEEE Conference on Control Technology and Applications, CCTA 2023

作者： Sabouni, Ehsan Ahmad, H.M. Sabbir Xiao, Wei Cassandras, Christos G. Li, Wenchao Boston University Division of Systems Engineering Department of Electrical & Computer Engineering BostonMA United States Massachusetts Institute of Technology Computer Science & Artificial Intelligence Laboratory CambridgeMA United States

ISBN: (纸本)9798350335446

We address the problem of safely coordinating a network of Connected and Automated Vehicles (CAVs) in conflict areas of a traffic network. Such problems can be solved through a combination of tractable optimal control problems and Control Barrier Functions (CBFs) that guarantee the satisfaction of all constraints. These solutions can be reduced to a sequence of Quadratic Programs (QPs) which are efficiently solved online over discrete time steps. However, guaranteeing the feasibility of the CBF-based QP method within each discretized time interval requires the careful selection of time steps which need to be sufficiently small. This creates computational requirements and communication rates between agents which may limit the controller's application to real CAVs. We tackle this limitation by adopting an event-triggered control approach for CAVs such that the next QP is triggered by properly defined events with a safety guarantee. We present a laboratory-scale test bed developed to emulate merging roadways using mobile robots as CAVs. We present results to demonstrate how the event-triggered scheme is computationally efficient and can handle measurement uncertainties and noise compared to time-driven control while guaranteeing safety. © 2023 IEEE.

关键词： Merging

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ETAG: An Energy-Neutral Ear Tag for Real-Time Body Temperature Monitoring of Dairy Cattle 23

ETAG: An Energy-Neutral Ear Tag for Real-Time Body Temperatu...

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29th Annual International Conference on Mobile Computing and Networking, MobiCom 2023

作者： Vu, Hien Chung, Hanwook Choi, Christopher Kim, Younghyun Department of Electrical and Computer Engineering College of Engineering Wisconsin Embedded Systems and Computing Laboratory University of Wisconsin-Madison MadisonWI60032179 United States

ISBN: (纸本)9781450399906

Heat stress, caused by a warming climate and the increasingly high milk-producing dairy cattle, is one of the major threats to the well-being of dairy cattle as well as the economic, environmental, and social sustainability of dairy farming around the world. Timely identification of cows under heat stress is crucial to improving animal welfare, preventing milk production losses, and preserving water and energy for *** paper presents a smart ear tag, named eTag, and an associated system that can read a passive microchip temperature sensor subcutaneously injected into the animal with minimal discomfort. It features a lightweight design using a single coil shared for microchip scanning and wireless charging. eTag is autonomously recharged by a wireless charger over the head during daily milking sessions, enabling perpetual operation without battery replacement after deployment. The real-world performance of the proposed system was examined intensively in a three-week deployment on seven lactating Holstein cows. We demonstrate that eTag can reliably collect accurate body temperature in real time while maintaining a positive energy flow. The deployment of eTag will enable the timely detection of heat stress and facilitate precision control of barn cooling systems. © 2023 ACM.

关键词： Energy harvesting

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Joint Optimizations for Double-IRS' Cooperative Positioning and Beamforming Over Massive-MIMOAP Based 6G Secure Mobile Wireless Networks

Joint Optimizations for Double-IRS' Cooperative Positioning ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Wang, Jiaojie Wang, Fei Zhang, Xi Yang, Yuanyuan Southwest University College of Electronic and Information Engineering Chongqing400715 China Texas A&m University Networking and Information Systems Laboratory Department of Electrical and Computer Engineering College StationTX77843 United States Stony Brook University Department of Electrical and Computer Engineering Stony BrookNY11794 United States

ISBN: (纸本)9798350351255

Intelligent reflecting surface (IRS) has been widely recognized as one of the key techniques to improve secure communications performances. However, most existing works mainly focus on the passive beamforming, i.e., phase-shifts, design of a single IRS, without taking into account the cooperations among multiple IRSs and the optimizations of their relative positions. To overcome these deficiencies, in this paper we propose the joint optimizations between the transmit beamforming of massive multiple-input multiple-output (massive-MIMO) access point (AP) and double-IRS' positions and passive beamforming over 6G secure mobile networks. In our proposed networking architectures, AP transmits data to multiple mobile users (MUs) through reflections of two cooperative IRSs under the existence of one eavesdropper (Eve). First, we formulate a secrecy rate optimization problem to maximize the expectations of all MUs' aggregate secrecy rates when Eve's exact position is unknown. Second, leveraging the deep reinforcement learning (DRL), we develop two joint deploying and beamforming schemes to tackle the uncertainty of Eve's exact position. Finally, we validate and evaluate our developed schemes by conducting the extensive simulations, showing the significant performances improvements of our developed schemes through jointly optimizing IRSs' positions/orientations and transmit-beamforming of massive-MIMOAP as the function of the predicted Eve's deploying areas. © 2024 IEEE.

关键词： Deep reinforcement learning

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MaDEVIoT: Cyberattacks on EV Charging Can Disrupt Power Grid Operation

MaDEVIoT: Cyberattacks on EV Charging Can Disrupt Power Grid...

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2024 IEEE Power and Energy Society Innovative Smart Grid Technologies Conference, ISGT 2024

作者： Acharya, Samrat Khan, Hafiz Anwar Ullah Karri, Ramesh Dvorkin, Yury Pacific Northwest National Laboratory RichlandWA99354 United States New York University Department of Electrical and Computer Engineering BrooklynNY11201 United States Johns Hopkins University Ralph o'Connor Sustainable Energy Institute Department of Electrical and Computer Engineering Department of Civil and Systems Engineering BaltimoreMD21218 United States

ISBN: (纸本)9798350313604

This paper examines the feasibility of demand-side cyberattacks on power grids launched via internet-connected high-power EV Charging Stations (EVCSs). By distorting power grid frequency and voltage, these attacks can trigger system-wide outages. Our case study focuses on Manhattan, New York, and reveals that such attacks will become feasible by 2030 with increased EV adoption. With a single EVCS company dominating Manhattan, compromising a single EVCS server raises serious power grid security concerns. These attacks can overload power lines and trip over-frequency (OF) protection relays, resulting in a power grid blackout. This study serves as a crucial resource for planning authorities and power grid operators involved in the EV charging infrastructure roll-out, highlighting potential cyberthreats to power grids stemming from high-power EVCSs. © 2024 IEEE.

关键词： Relay protection

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Real-time binaural speech reproduction with deep neural networks and dynamic head tracking 156

Real-time binaural speech reproduction with deep neural netw...

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AES Madrid 2024: 156th Audio engineering Society Convention

作者： Papavasileiou, Ioannis Thoidis, Iordanis Sevastiadis, Christos Vryzas, Nikolaos Papanikolaou, George Laboratory of Electroacoustics and TV Systems School of Electrical and Computer Engineering Faculty of Engineering Aristotle University of Thessaloniki Thessaloniki Greece Laboratory of Electronic Media Department of Journalism and Mass Media Aristotle University of Thessaloniki Thessaloniki Greece

This study examines the efficacy of a deep learning-based binaural speech reproduction system via headphones that integrates real-time head tracking to simulate a stable speech source in a virtual environment. We developed a binaural speech reproduction system that operates in real time using a custom head-tracking device based on the Arduino platform and a pre-trained neural network model for binaural speech synthesis. The system dynamically adapts audio output based on the head orientation of the listener, operating with an overall latency of 70 ms on standard computing hardware. We conducted a subjective speech localization task with 15 young, normal-hearing participants. Initial results showed a 40% accuracy in localizing a static speech source, which significantly improved with dynamic head movements. Participants reported minimal dizziness and distortion, indicating good tolerability and sound quality. Despite some limitations, such as non-personalized settings and perceivable latency, the findings demonstrate the potential of deep learning approaches to enhance realism in virtual auditory environments. Further research is needed to refine these technologies for broader applications in immersive audio settings. © AES Madrid 2024: 156th Audio engineering Society Convention. All rights reserved.

关键词： Deep neural networks

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Equalizer Zero-Determinant Strategy in Discounted Repeated Stackelberg Asymmetric Game

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Journal of systems Science & Complexity 2024年第1期37卷 184-203页

作者： CHENG Zhaoyang CHEN Guanpu HONG Yiguang Key Laboratory of Systems and Control Academy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical Sciences University of Chinese Academy of SciencesBeijing 100049China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology10044 StockholmSweden. Department of Control Science and Engineering Tongji UniversityShanghai 201804China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 210201China

This paper focuses on the performance of equalizer zero-determinant(ZD)strategies in discounted repeated Stackelberg asymmetric *** the leader-follower adversarial scenario,the strong Stackelberg equilibrium(SSE)deriving from the opponents’best response(BR),is technically the optimal strategy for the ***,computing an SSE strategy may be difficult since it needs to solve a mixed-integer program and has exponential complexity in the number of *** this end,the authors propose an equalizer ZD strategy,which can unilaterally restrict the opponent’s expected *** authors first study the existence of an equalizer ZD strategy with one-to-one situations,and analyze an upper bound of its performance with the baseline SSE *** the authors turn to multi-player models,where there exists one player adopting an equalizer ZD *** authors give bounds of the weighted sum of opponents’s utilities,and compare it with the SSE ***,the authors give simulations on unmanned aerial vehicles(UAVs)and the moving target defense(MTD)to verify the effectiveness of the proposed approach.

关键词： Discounted repeated Stackelberg asymmetric game equalizer zero-determinant strategy strong Stackelberg equilibrium strategy

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Uncovering Spatiotemporal Differences in Cortical Activity Corresponding to two Tasks Using Data-Driven ADASSO Algorithm

Uncovering Spatiotemporal Differences in Cortical Activity C...

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IEEE International Symposium on Biomedical Imaging

作者： Siwei Mai Ali E. Haddad Laleh Najafizadeh Department of Electrical and Computer Engineering Integrated Systems and NeuroImaging Laboratory Rutgers University Department of Computer Engineering University of Basrah

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Identifying spatiotemporal differences in brain functional dynamics corresponding to two tasks is critical for under-standing how specific neural processes contribute to distinct tasks or cognitive functions. Traditional methods rely on imposing assumptions and limits on the location and timing of activities, while machine-learning-based methods generally lack offering interpretable insights. This highlights the need for new data-driven approaches to capture spatial and temporal differences in brain activity between two tasks, while also providing interpretable explanations of the neural processes underlying these differences. In this work, we formulate the problem of finding the spatial and temporal differences in the dynamics of brain function corresponding to two motor imagery (MI) tasks (left hand movement vs right hand movement) as a discriminative discrete basis problem (DDBP). We apply the data-driven asymmetric discriminative associative algorithm (ADASSO) to EEG data collected during these tasks to uncover the key functional components of the brain's functional dynamics that differentiate between them. Results suggest that hand movements are strongly associated with high-confidence activation in the motor cortex, verifying the effectiveness of the ADASSO algorithm in identifying the location and timing of cortical activities that distinguish between the two task classes.

关键词： Hands Brain Heuristic algorithms Motors Electroencephalography Brain-computer interfaces Spatiotemporal phenomena Timing Biomedical imaging

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