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IEEE Conference on Global Communications (GLOBECOM)

作者： Shupei Zhang Boya Di Hongliang Zhang H. Vincent Poor State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Beijing China Department of Electrical and Computer Engineering Princeton University USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Reconfigurable holographic surfaces (RHSs) have been proposed as a cost-effective and power-efficient solution for extremely large-scale arrays, where the amplitude of electromagnetic waves radiated at each element is controlled to achieve high directive gain. However, the complexity of acquiring real-time channel state information (CSI) required for beamforming is prohibitively high, especially when the near-field expansion brought by the large-scale RHS is considered. In this paper, we propose a codebook-based beam training scheme for a large-scale RHS-enabled communication system to bypass CSI estimation. Unlike traditional phase-controlled arrays, the amplitude-controlled property of the RHS implies that each RHS element can be selectively activated. This motivates an array reconfiguration method where a scale-changeable RHS array is constructed to generate gain-flat beams with different coverage in the angle-range domain. A hierarchical RHS codebook is then proposed where the coverage of the codewords in each layer is progressively refined. To address the substantial beam search overhead in the near-far field, a two-stage beam training scheme is performed in the proposed codebook, thereby reducing the overhead to a logarithmic level of the element number. The simulation results show that the proposed scheme performs better than phased arrays given the same input power in terms of sum rate, and it also approaches the upper bound achieved by the exhaustive search at a significantly reduced overhead.

关键词： Training Phased arrays Upper bound Surface waves Simulation Electromagnetic scattering Estimation Real-time systems Complexity theory Global communication

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Orthogonal projection for anomaly detection in networking datasets

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Journal of Ambient Intelligence and Humanized computing 2023年第6期14卷 7957-7966页

作者： Cortes-Polo, David Jimenez, Luis I. Paoletti, Mercedes E. Calle-Cancho, Jesus Rico-Gallego, Juan A. Department of Signal Theory and Communication University Rey Juan Carlos Cam. del Molino 5 Madrid Fuenlabrada28942 Spain Intelligent Systems Group Department of Computer Science University of Valladolid P.O de Belén 15 Valladolid47011 Spain Hyperspectral Computing Laboratory Department of Technology of Computers and Communication University of Extremadura Av. de la Universidad s/n Caceres10003 Spain Calle Sola 1 Extremadura Trujillo10200 Spain Computer and Telematics Systems Engineering University of Extremadura Av. de la Universidad s/n Caceres10003 Spain

In recent years, the impressive growth of new wireless technologies, together with the appearance of new requirements in applications and services, is progressively changing the use of networks. Due to the high mobility required, the network must adapt to the infrastructure to meet the demands of the users. As a result, service providers currently have to over-provision network capacity, which is costly. In addition, considering efficient resource planning in advance involves a lot of labor-intensive efforts. Consequently, network usage analysis is a very useful tool that allows network administrators to find patterns and anomalies. Whilst pattern detection provides administrators the ability to define the infrastructure, anomaly detection provides rich and valuable information for certain applications, for example, to avoid network saturation in urban areas during peak hours. This article proposes a new methodology based on orthogonal projections over Call Detail Records (CDR) for anomaly detection to help in the dynamic management of the network in an urban area. The method is evaluated in a real scenario provided by an Italian telecommunications operator, considering different locations in the Milan metropolitan area, differentiated by the geographic resolution of the data, reaching F1 scores above 0.8. In addition, a new ground truth is presented, hoping it will become a reference data set for the community, in the form of a set of locations that have been corroborated for use in evaluating anomaly detection techniques. © 2023, The Author(s).

关键词： Anomaly detection

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Adaptive Automatic Generation Control for Improved Stability of Power systems with Utility-Scale Photovoltaic Plants

Adaptive Automatic Generation Control for Improved Stability...

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Renewable Energy and Hydrogen Technologies (GlobConHT), IEEE IAS Global Conference on

作者： Rajan Ratnakumar Ganesh K. Venayagamoorthy Holcombe Department of Electrical and Computer Engineering Real-Time Power and Intelligent Systems Laboratory Clemson University Clemson USA

Stability problems arise when large utility-scale solar photovoltaic (PV) plants are integrated into bulk power systems. The intermittent nature of solar radiation results in PV power generation variations, which must be compensated by the conventional power plants (synchronous generators) to regulate the frequency of the power system by balancing generation-load. The compensation involves generation dispatch changes of the synchronous generators. The PV plant variations and synchronous generators' dispatch changes could give rise to electromechanical oscillations (EMOs) in the power system. Severe EMOs can lead to power system instability, and this can be avoided by intelligently modulating the rate of change of dispatch of the synchronous generators during PV plant output variations. The intelligent modulation of the rate of change of dispatch can be implemented via an adaptive automatic generation control (A-AGC). This paper presents the development of an A-AGC based on an EMO index derived from phasor measurement units to ensure the stability of the power system. Typical results are presented to illustrate the operation and performance of A-AGC during PV power variations.

关键词： Photovoltaic systems Renewable energy sources Stability criteria Automatic generation control Power system stability Synchronous generators Table lookup

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Digital Twins for Creating Virtual Models of Solar Photovoltaic Plants

Digital Twins for Creating Virtual Models of Solar Photovolt...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Deborah George Ganesh K. Venayagamoorthy Department of Electrical and Computer Engineering Real-Time Power and Intelligent Systems Laboratory Clemson University Clemson SC USA

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.

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Robotic fish:Opening a new era of underwater detection

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The Innovation 2025年第3期6卷 13-14页

作者： Dongfang Li Suet To Zhicheng Xu Guanghong Liu Rob Law Edmond Q.Wu Limin Zhu State Key Laboratory of Ultraprecision Machining Technology Department of Industrial and Systems EngineeringThe Hong Kong Polytechnic UniversityHong Kong 999077China School of Electrical Engineering and Automation Fuzhou UniversityFujian 350108China China Electronics Technology Group Corporation Information Science Research InstituteBeijing 100086China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China Asia-Pacific Academy of Economics and Management Faculty of Business AdministrationUniversity of MacaoMacao 999078China Department of Computer Science and Engineering Shanghai JiaoTong UniversityShanghai 200240China

Escalating competition within aquatic environments,particularly in oceans,underscores the imperative for innovative solutions in underwater *** solutions necessitate the development of intelligent underwater equipment,with robotic fish emerging as a promising contender.1 Leveraging advancements in robotics and artificial intelligence,robotic fish offer a suite of advantages that position them as transformative assets in underwater exploration and *** fish are autonomous robots designed based on biomimetics principles that mimic the appearance of fish and can autonomously swim and perform specific tasks in water.

关键词： robotic fish intelligent underwater equipmentwith autonomous robots underwater detection oceans artificial intelligencerobotic fish aquatic environments innovative solutions

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Personalized Federated Learning with Collaborative Aggregation Networks for Multi-Site Brain Disorder Diagnosis 4

Personalized Federated Learning with Collaborative Aggregati...

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4th International Conference on Industrial Automation, Robotics and Control engineering, IARCE 2024

作者： Si, Qian Li, Yang School of Cyber Science and Technology Beihang University Beijing China Department of Automation Science and Electrical Engineering The Beijing Advanced Innovation Center for Big Data and Brain Computing State Key Laboratory of Virtual Reality Technology and Systems Beijing China Advanced Institute of Information Technology Peking University Beijing China Beihang University Beijing China

ISBN: (纸本)9798350380323

In multi-site brain disease diagnosis studies, traditional centralized training methods necessitate sharing medical data, posing significant privacy risks. Federated learning (FL) offers a privacy-preserving solution by enabling global model training through aggregating locally trained models from multiple data centers without sharing raw data. However, current FL approaches rely on a server-based network topology, where central server failure disrupts training. Additionally, data heterogeneity across sites often slows convergence and reduces accuracy. To overcome these issues, we introduce a decentralized personalized federated learning collaborative aggregation network (pFedCAN). This framework has two core components: (1) separating local models into shared and personalized layers, and (2) forming a collaborative aggregation network via similarity detection in the shared layers. Specifically, each center trains its local model, then separates it into shared and personalized layers. The shared layer is exchanged with other centers, while the personalized layer remains local. Data centers analyze similarities in received shared layers to build a collaborative network, where shared layers from similar centers are aggregated to refine the model. This approach flexibly adapts to varying levels of data heterogeneity, enhancing model training efficiency. Validation on public datasets, ABIDE I and ADHD, shows that the proposed method outperforms current leading techniques. © 2024 IEEE.

关键词： Federated learning

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State of Charge Estimation Using Data-Driven Models for Inverter-Based systems

State of Charge Estimation Using Data-Driven Models for Inve...

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Design Methodologies Conference (DMC), IEEE

作者： Hossam Hussein Abhishek Donekal Ahmed Aghmadi S M Sajjad Hossain Rafin Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami Fl 33174 USA

Lithium-ion batteries are playing a critical role in many applications nowadays, from small-scale electronic devices to grid-scale storage systems. To maintain its continuous operation and increase its life span, the state of charge of the battery should be determined to ensure safe operating conditions. Among the existing charge estimation methods, data-driven models are flourishing these days. This work presents a state of charge estimation for the eFlex 52.8V/5.4 kWh lithium iron phosphate battery pack at the Energy systems Research laboratory (ESRL) at FIU. Three different machine learning models were implemented and trained through Python code to achieve the most accurate SoC estimation. Although the three proposed models can efficiently estimate the battery’s SoC with an acceptable error percentage, the random forest regression model has proven its outperformance among the selected models with a percentage mean square error less than 0.01.

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Data-Driven Digital Twins for Power Estimations of a Solar Photovoltaic Plant

Data-Driven Digital Twins for Power Estimations of a Solar P...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Michael Walters John Yonce Ganesh K. Venayagamoorthy Holcombe Department of Electrical and Computer Engineering Realtime Power and Intelligent Systems Laboratory Clemson University Clemson United States

Renewable energy generation sources (RESs) are gaining increased popularity due to global efforts to reduce carbon emissions and mitigate effects of climate change. Planning and managing increasing levels of RESs, specifically solar photovoltaic (PV) generation sources is becoming increasingly challenging. Estimations of solar PV power generations provide situational awareness in distribution system operations. A digital twin (DT) can replicate PV plant behaviors and characteristics in a virtual platform, providing realistic solar PV estimations. Furthermore, neural networks, a popular paradigm of artificial intelligence may be used to adequately learn and replicate the relationship between input and output variables for data-driven DTs (DD-DTs). In this paper, DD-DTs are developed for Clemson University's 1 MW solar PV plant located in South Carolina, USA to perform realistic solar PV power estimations. The DD-DTs are implemented utilizing multilayer perceptron (MLP) and Elman neural networks. Typical practical results for two DD-DT architectures are presented and validated.

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Regression coefficient estimation from remote sensing maps

arXiv

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arXiv 2024年

作者： Lu, Kerri Kluger, Dan M. Bates, Stephen Wang, Sherrie Laboratory for Information and Decision Systems MIT United States Department of Electrical Engineering and Computer Science MIT United States Department of Mechanical Engineering MIT United States Institute for Data Systems and Society MIT United States

Remote sensing map products are used to estimate regression coefficients relating environmental variables, such as the effect of conservation zones on deforestation. However, the quality of map products varies, and — because maps are outputs of complex machine learning algorithms that take in a variety of remotely sensed variables as inputs — errors are difficult to characterize. Thus, population-level estimates from such maps may be biased. In this paper, we apply prediction-powered inference (PPI) to regression coefficient estimation. PPI generates unbiased estimates by using a small amount of randomly sampled ground truth data to correct for bias in large-scale remote sensing map products. Applying PPI across multiple remote sensing use cases in regression coefficient estimation, we find that it results in estimates that are (1) more reliable than using the map product as if it were 100% accurate and (2) have lower uncertainty than using only the ground truth and ignoring the map product. Empirically, we observe effective sample size increases of up to 17-fold using PPI compared to only using ground truth data. This is the first work to estimate remote sensing regression coefficients without assumptions on the structure of map product errors. Data and code are available at https://***/Earth-Intelligence-Lab/ uncertainty-quantification. © 2024, CC BY.

关键词： Regression analysis

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Dynamic Pulsed Load Mitigation in PV-Battery-Supercapacitor systems: A Hybrid PI-NN Controller Approach

Dynamic Pulsed Load Mitigation in PV-Battery-Supercapacitor ...

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Design Methodologies Conference (DMC), IEEE

作者： Ahmed Aghmadi Ola Ali Hossam Hussein Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami FL 33174 USA

DC microgrids are becoming more and more popular, however there are still difficulties involving ongoing instability that are mostly caused by imbalances in energy supply and demand, particularly in situations with pulse load or variable pulse load patterns. Innovative solutions are required since these disruptive load dynamics significantly decrease the effectiveness of conventional PV-Battery control systems. conventional Proportional-Integral (PI) controllers, although widely employed, may encounter limitations when confronted with the unique challenges posed by specific load patterns, notably pulse load scenarios. These dynamic load profiles, characterized by rapid and unpredictable changes. The inherent non-linearity and variability in such load conditions can result in suboptimal control performance, potentially leading to voltage instability and transients within the microgrid. To overcome these issues and improve control performance, the introduction of Neural Network (NN) controllers promises to be a potential approach. NN controllers have a remarkable capacity to learn and adapt from data, making them particularly well-suited for situations characterized by non-linear and dynamic load behavior. In this study, a hybrid PI-NN (Proportional-Integral Neural Network) controller is introduced to reduce load effects, especially pulsed loads, in PV systems with batteries and supercapacitors. This method efficiently optimizes energy storage. For voltage control, it utilizes a PI system, which divides the reference current into low and high-frequency components for battery and supercapacitor control. For enhanced sensitivity and dynamic responsiveness, neural network control replaces conventional PIs. The performance of the hybrid PI-NN controller is evaluated in this work, with an emphasis on its potential to improve PV system efficiency in severe load circumstances.

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