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IEEE General Meeting Power& Energy Society

作者： Jonathan Lee Rodrigo Henriquez-Auba Bala Poolla Duncan Callaway Energy and Resources Group University of California Berkeley Electrical Engineering and Computer Sciences University of California Berkeley Power Systems Engineering Center National Renewable Energy Laboratory

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408240

Efforts to utilize 100% renewable energy in community microgrids require new approaches to energy markets and transactions to efficiently address periods of scarce energy supply. In this paper we contribute to the promising approach of peer-to-peer (P2P) energy trading in two main ways: analysis of a centralized, welfare-maximizing economic dispatch that characterizes optimal price and allocations, and a novel P2P system for negotiating energy trades that yields physically feasible and at least weakly Pareto-optimal outcomes. Our main results are 1) that optimal pricing is insufficient to induce agents with batteries to take optimal actions, 2) a novel P2P algorithm to address this while keeping private information, 3) a formal proof that this algorithm converges to the centralized solution in the case of two agents negotiating for a single period, and 4) numerical simulations of the P2P algorithm performance with up to 10 agents and 24 periods that show it converges on average to total welfare within 0.1% of the social optimum in on the order of 10s to 100s of iterations, increasing with the number of agents, time periods, and total storage capacity.

关键词： Economics Renewable energy sources Pricing Microgrids Numerical simulation Peer-to-peer computing Batteries

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Sliding Mode Control for a DC-DC Buck Converter Feeding a Constant Power Load: A Practical Implementation

Sliding Mode Control for a DC-DC Buck Converter Feeding a Co...

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Advanced Technology in Electronic and electrical engineering (ICATEEE), International Conference of

作者： Khalil Louassaa Aissa Chouder Mahdi Boukerdja Abdelhafid Cherifi Ali Aillane Electrical Engineering Department Electrical Engineering Laboratory University of M’sila M’sila Algeria Materials and Electronic Systems Laboratory University of Bordj Bou Arreridj Bordj Bou Arreridj Algeria Laboratory of Sciences and Techniques of Automatic Control and Computer Engineering (LabSTA) National School of Engineering of Sfax (ENIS) University of Sfax Sfax Tunisia

DC microgrids have gained in popularity in recent years due to features such as greater reliability, high efficiency, and control simplicity. Constant power loads (CPLs), on the other hand, are a problem and an effective cause of instability in DC MGs. To address the aforementioned issue, this paper discusses the stabilization of a buck power converter that supplies CPL via a regulated boost power converter. A robust sliding mode control is proposed, which stabilizes the output voltage and ensures the CPL's required power. The system is investigated in the context of external perturbations such as fluctuating input voltage and sudden change in the CPL. The robustness and efficacy of the proposed method have been demonstrated to confirm the system's overall stability. Simulation and experimentation are employed to verify the provided controller. The results verify the suggested controller's superiority.

关键词： Damping Voltage measurement Perturbation methods Microgrids Stability analysis Robustness Steady-state

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Disco Intelligent Omni-Surfaces: 360◦ Fully-Passive Jamming Attacks

arXiv

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arXiv 2024年

作者： Huang, Huan Zhang, Hongliang Yuan, Jide Sun, Luyao Wang, Yitian Mei, Weidong Di, Boya Cai, Yi Han, Zhu School of Electronic and Information Engineering Soochow University Jiangsu Suzhou215006 China School of Electronics Peking University Beijing100871 China National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China Chengdu611731 China Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of

Intelligent omni-surfaces (IOSs) with 360o electromagnetic radiation significantly improves the performance of wireless systems, while an adversarial IOS also poses a significant potential risk for physical layer security. In this paper, we propose a "DISCO" IOS (DIOS) based fully-passive jammer (FPJ) that can launch omnidirectional fully-passive jamming attacks. In the proposed DIOS-based FPJ, the interrelated refractive and reflective (R&R) coefficients of the adversarial IOS are randomly generated, acting like a "DISCO ball" that distributes wireless energy radiated by the base station. By introducing active channel aging (ACA) during channel coherence time, the DIOS-based FPJ can perform omnidirectional fully-passive jamming without neither jamming power nor channel knowledge of legitimate users (LUs). To characterize the impact of the DIOS-based PFJ, we derive the statistical characteristics of DIOS-jammed channels based on two widely-used IOS models, i.e., the constant-amplitude model and the variable-amplitude model. Consequently, the asymptotic analysis of the ergodic achievable sum rates under the DIOS-based omnidirectional fully-passive jamming is given based on the derived stochastic characteristics for both the two IOS models. Based on the derived analysis, the omnidirectional jamming impact of the proposed DIOS-based FPJ implemented by a constant-amplitude IOS does not depend on either the quantization number or the stochastic distribution of the DIOS coefficients, while the conclusion does not hold on when a variable-amplitude IOS is used. Numerical results based on one-bit quantization of the IOS phase shifts are provided to verify the effectiveness of the derived theoretical analysis. The proposed DIOS-based FPJ can not only launch omnidirectional fully-passive jamming, but also improve the jamming impact by about 55% at 10 dBm transmit power per LU. Copyright © 2024, The Authors. All rights reserved.

关键词： Stochastic models

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Design and Development of a Microcontroller Based Fingerprint-Attendance System

Design and Development of a Microcontroller Based Fingerprin...

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Ubiquitous Computing and Intelligent Information systems (ICUIS), International Conference on

作者： Kaloso Mbengwa Ravi Samikannu R. Rohini K.Maruliya Begam G. Kanimozhi Abid Yahya Department of Electrical and Communications Systems Engineering Botswana International University of Science and Technology Palapye Botswana Department of ECE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of Computer Science and Engineering Vivekanandha College of Engineering for Women Tiruchengode India Department of Electrical and Electronics Engineering Periyar Maniammai Institute of Science and Technology Thanjavur India Center for Smart Grid Technologies School of Electrical Engineering Vellore Institute of Technology Chennai Tamil Nadu India

ISBN: (数字)9798331529635

ISBN: (纸本)9798331529642

In the modern era of rapidly advancing technology, organizations across various sectors are continuously seeking innovative solutions to enhance operational efficiency, streamline processes, and improve overall productivity. One area that significantly impacts these goals is the management of employee/student attendance. Traditional manual attendance tracking methods often lead to inaccuracies, inefficiencies, and increased administrative overhead. Recognizing this challenge, this proposal is to bring forth developing a microcontroller-based implementation of a Fingerprint-Based Attendance System. The system uses the power of biometric authentication to improve how attendance is recorded and managed. Firstly, this system provides real-time attendance tracking, reducing the delay in data processing and enabling immediate access to attendance records. Secondly, the proposed system ensures a high level of accuracy in attendance recording. Thirdly, this system promotes enhanced security.

关键词： Accuracy Simulation Fingerprint recognition Writing Ubiquitous computing Real-time systems Recording Security Reliability Testing

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Federated Learning Cost Disparity for IoT Devices

arXiv

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arXiv 2022年

作者： Alvi, Sheeraz A. Hong, Yi Durrani, Salman School of Engineering The Australian National University CanberraACT2601 Australia Department of Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia

Federated learning (FL) promotes predictive model training at the Internet of things (IoT) devices by evading data collection cost in terms of energy, time, and privacy. We model the learning gain achieved by an IoT device against its participation cost as its utility. Due to the device-heterogeneity, the local model learning cost and its quality, which can be time-varying, differs from device to device. We show that this variation results in utility unfairness because the same global model is shared among the devices. By default, the master is unaware of the local model computation and transmission costs of the devices, thus it is unable to address the utility unfairness problem. Also, a device may exploit this lack of knowledge at the master to intentionally reduce its expenditure and thereby enhance its utility. We propose to control the quality of the global model shared with the devices, in each round, based on their contribution and expenditure. This is achieved by employing differential privacy to curtail global model divulgence based on the learning contribution. In addition, we devise adaptive computation and transmission policies for each device to control its expenditure in order to mitigate utility unfairness. Our results show that the proposed scheme reduces the standard deviation of the energy cost of devices by 99% in comparison to the benchmark scheme, while the standard deviation of the training loss of devices varies around 0.103. © 2022, CC0.

关键词： Internet of things

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A NEW APPROACH ON CONDUCTING FLEXIBILITY STUDIES BASED ON NEURAL NETWORKS - APPLICATION ON THE HELLENIC TRANSMISSION SYSTEM 14

A NEW APPROACH ON CONDUCTING FLEXIBILITY STUDIES BASED ON NE...

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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Kaskouras, Christos D. Krommydas, Konstantinos F. Baltas, Ioannis Papaioannou, George P. Papayiannis, Georgios I. Yannacopoulos, Athanasios N. Department of Statistics Athens University of Economics and Business Athens Greece Stochastic Modelling and Applications Laboratory Athens University of Economics and Business Athens Greece Research Technology and Development Department Independent Power Transmission Operator S.A. Athens Greece Department of Electrical and Computer Engineering University of Patras Rion Greece Department of Financial and Management Engineering University of Aegean Chios Greece Center for Research and Applications of Nonlinear Systems CRANS University of Patras Rion Greece Department of Statistics and Insurance Science University of Piraeus Piraeus Greece

ISBN: (纸本)9781837242689

In order to meet the environmental targets, a large scale integration of renewable energy sources (RESs) has been taking place in power systems. Although those sources contribute to more clean energy and help to reduce the green house gasses emissions, they pose significant challenges to the system's stability due to the intermittency and the high level of dependence to the weather conditions. One of the main challenges is to sustain the system's balance and to guarantee the flexibility adequacy. For this purpose, the need for more comprehensive flexibility studies will increase in the future, since different scenarios should be examined according to various level of RESs, demand and weather conditions. In this work, we propose a novel approach for the unit commitment (UC) algorithm that is implemented in the conventional flexibility studies that aims to reduce the computational time and simultaneously sustain the results' accuracy in a satisfactory level. For this scope, we designed a nested-neural network system of two recurrent neural networks with long short-term memory, which are integrated and aim to reproduce the results of the complex conventional UC algorithm. Results show that the predicted insufficient ramping resource expectation of the two solutions are similar, while the computational time is being reduced by almost 99%. © The Institution of engineering & Technology 2024.

关键词： FLEXIBILITY RECURRENT NEURAL NETWORKS TRANSMISSION SYSTEM

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Flexible Humidity Sensors with Electrodes Composed of Laser-Induced Graphene and Sputtered Sensitive Films All Derived from Poly(Ether-Ether-Ketone)

SSRN

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SSRN 2022年

作者： Wang, Junjie Wang, Nan Xu, Da Tang, Lei Sheng, Bin a School of Optical Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai 200093 China. b Shanghai Key Laboratory of Modern Optical Systems Engineering Research Center of Optical Instruments and Systems Shanghai 200093 China.

Currently, there are still challenges in the practical applications of flexible, efficient, and large-scale production humidity sensors. In the work, we put forward a type of convenient, efficient, durable, and flexible wearable method to prepare humidity sensors. Flexible substrates based on laser direct writing technology in poly(ether-ether-ketone) (PEEK) film was used for the preparation of laser-induced graphene interdigital electrode (LIG-IDE) , which did not require high investment costs and could be implemented for the large-scale preparation of a flexible base. A flexible impedance hygroscopic element based on poly(ether-ether-ketone) sputtered and polymerized (PEEK-SP) as a hygroscopic layer with high sensitivity was obtained, with a fast response/recovery time (4.2/6.8 s,) and a wide operating range of relative humidity (11% RH~97% RH). These advantages extended the sensor's versatility, from contactless humidity sensing to breath detection for human applications. Therefore, a high-performance flexible humidity sensor based entirely on the PEEK polymer material is reported here, which provides a new approach for the development of flexible gas and humidity sensors. © 2022, The Authors. All rights reserved.

关键词： Graphene

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UAV-Assisted Data Synchronization for Digital-Twin-Enabled Vehicular Networks

UAV-Assisted Data Synchronization for Digital-Twin-Enabled V...

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IEEE International Conference on Communications in China (ICCC)

作者： Guoquan Wu Jiangtian Nie Jianhang Tang Yang Zhang Jiang Xiao Zehui Xiong School of Information Science and Engineering Yanshan University China School of Computer Science and Engineering Nanyang Technological University Singapore State Key Laboratory of Public Big Data Guizhou University China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics China School of Computer Science and Technology Huazhong University of Science and Technology China Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore

Digital twin (DT) is becoming a promising solution for vehicular networks to improve the interoperability of distributed autonomous driving systems. Mobile edge computing (MEC) has been introduced to provide low-latency services for DT-enabled vehicular networks at the edge of the network. However, it is hard to obtain the dynamic network topology for moving vehicles by the ground-based MEC system, which may deteriorate the service quality for DT synchronization. In this paper, we propose a novel unmanned aerial vehicle (UAV)-assisted synchronization framework for DT-enabled vehicular networks. With the proposed framework, an intelligent resource allocation algorithm is developed to improve UAV resource utilization and maximize the synchronization completion ratio. By leveraging an advantage actor-critic (A2C) algorithm, the synchronization decisions are obtained with low time complexity. Experiment results demonstrate that the proposed algorithm can reduce the synchronization latency and improve the synchronization completion ratio effectively.

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Robust computation of subcortical functional connectivity guided by quantitative susceptibility mapping: An application in Parkinson's disease diagnosis

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NeuroImage 2025年 314卷 121256-121256页

作者： Qin, Jianmei Wu, Haoting Wu, Chenqing Guo, Tao Zhou, Cheng Duanmu, Xiaojie Tan, Sijia Wen, Jiaqi Zheng, Qianshi Yuan, Weijin Zhu, Zihao Chen, Jingwen Wu, Jingjing He, Chenyu Ma, Yiran Liu, Chunlei Xu, Xiaojun Guan, Xiaojun Zhang, Minming Department of Radiology the Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China Joint Laboratory of Clinical Radiology the Second Affiliated Hospital Zhejiang University School of Medicine Hangzhou China State Key Laboratory of Computer-aided Design & Computer Graphics Zhejiang University College of Computer Science and technology Hangzhou China State Key Laboratory of Industrial Control Technology Zhejiang University College of Control Science and Engineering Hangzhou China Department of Electrical Engineering and Computer Sciences University of California Berkeley CA United States Helen Wills Neuroscience Institute University of California Berkeley CA United States

Previous resting state functional MRI (rs-fMRI) analyses of the basal ganglia in Parkinson's disease heavily relied on T1-weighted imaging (T1WI) atlases. However, subcortical structures are characterized by subtle contrast differences, making their accurate delineation challenging on T1WI. In this study, we aimed to introduce and validate a method that incorporates quantitative susceptibility mapping (QSM) into the rs-fMRI analytical pipeline to achieve precise subcortical nuclei segmentation and improve the stability of RSFC measurements in Parkinson's disease. A total of 321 participants (148 patients with Parkinson's Disease and 173 normal controls) were enrolled. We performed cross-modal registration at the individual level for rs-fMRI to QSM (FUNC2QSM) and T1WI (FUNC2T1), *** consistency and accuracy of resting state functional connectivity (RSFC) measurements in two registration approaches were assessed by intraclass correlation coefficient and mutual information. Bootstrap analysis was performed to validate the stability of the RSFC differences between Parkinson's disease and normal controls. RSFC-based machine learning models were constructed for Parkinson's disease classification, using optimized hyperparameters (RandomizedSearchCV with 5-fold cross-validation). The consistency of RSFC measurements between the two registration methods was poor, whereas the QSM-guided approach showed better mutual information values, suggesting higher registration accuracy. The disruptions of RSFC identified with the QSM-guided approach were more stable and reliable, as confirmed by bootstrap analysis. In classification models, the QSM-guided method consistently outperformed the T1WI-guided method, achieving higher test-set ROC-AUC values (FUNC2QSM: 0.87–0.90, FUNC2T1: 0.67–0.70). The QSM-guided approach effectively enhanced the accuracy of subcortical segmentation and the stability of RSFC measurement, thus facilitating future biomarker development in Parkinso

关键词： Parkinson's disease Quantitative susceptibility mapping Resting-state functional connectivity Subcortical nuclei

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Digital Labeling of the Vasculature: Toward Label-Free and Labor-Free Vessel Segmentation

Digital Labeling of the Vasculature: Toward Label-Free and L...

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Bio-Optics: Design and Application, BODA 2023 - Part of Optica Biophotonics Congress: Optics in the Life Sciences 2023

作者： Lapierre-Landry, Maryse Liu, Yehe Bayat, Mahdi Wilson, David L. Jenkins, Michael W. Department of Biomedical Engineering School of Medicine Case Western Reserve University 10900 Euclid Ave ClevelandOH44106 United States Department of Electrical Computer and Systems Engineering School of Medicine Case Western Reserve University 10900 Euclid Ave ClevelandOH44106 United States Department of Radiology School of Medicine Case Western Reserve University 10900 Euclid Ave ClevelandOH44106 United States Department of Pediatrics School of Medicine Case Western Reserve University 10900 Euclid Ave ClevelandOH44106 United States

We propose digital labeling, a method for automated, three-dimensional segmentation of blood vessels without vascular contrast agents. Our deep learning approach greatly simplifies the sample preparation required for ... 详细信息

ISBN: (纸本)9781957171210

关键词： Deep learning

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