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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Jaewon Kim Marija Ilic Le Xie Laboratory for Information & Decision Systems Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Electrical & Computer Engineering Texas A&M University College Station TX USA

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

In this paper, we introduce a systematic design of an end-to-end cyber-secure monitoring and safe operation framework for solar photovoltaic (PV)-rich microgrids, by focusing on the use of on-line attack detector for defending against potential cyber-physical attacks and sensor measurement data processing for operational safety. The proposed approach is based on: 1) identifying component models using their input and output measurements; 2) dynamic watermarking (DW) methodology for on-line cyber-attack detection and its timing; and 3) an adaptive event-driven safe shutdown procedure after attack detection. It is shown how these three components interactive steps jointly to ensure safe operation under both normal operational scenarios and compromised operational scenarios with some malicious sensors caused by cyber-attacks on solar PV microgrids. Through this on-line integration, the proposed framework dynamically adapts to varying solar irradiance, load demands, operating conditions, and potential cyber-physical attacks. This forms a cyber-physical system which can pro-actively detect and mitigate potential cyber-physical attacks on solar PV microgrids. We describe its proof-of-concept implementation using real-world industrial solar farm data provided by the CenterPoint Energy. This study contributes to enhancing the cyber-security and safe operation of solar PVrich power systems, offering a scalable solution to other types of emerging renewable-rich energy distribution systems.

关键词： Photovoltaic systems Adaptation models Systematics Solar farms Power system dynamics Microgrids Watermarking Timing Monitoring Cyberattack

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Multi-behavior Learning for Socially Compatible Autonomous Driving

Multi-behavior Learning for Socially Compatible Autonomous D...

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International Conference on Intelligent Transportation

作者： Sanjula Jayawardana Vindula Jayawardana Kaneeka Vidanage Cathy Wu University of Westminster UK Informatics Institute of Technology Colombo Sri Lanka Department of Electrical Engineering and Computer Science MIT Laboratory for Information & Decision Systems Cambridge MA USA Department of Civil and Environmental Engineering MIT Laboratory for Information Decision Systems Institute of Data Systems and Society Cambridge MA USA

Autonomous vehicles (AVs) have the potential to revolutionize transportation, but their effective integration into the real world requires addressing the challenge of interacting with human drivers. real-world driving involves negotiating and cooperating with fellow drivers through social cues, necessitating AVs to also demonstrate such social compatibilities. However, despite the popularity, current learning-based control methods for AV policy synthesis often overlook this crucial aspect. In this work, we look at the problem of enabling socially compatible driving when AV control policies are learned. We leverage human driving data to learn a social preference model of human driving and then integrate it with reinforcement learning-based AV policy synthesis using Social Value Orientation theory. In particular, we propose to use multi-task reinforcement learning to learn diverse social compatibility levels in driving (ex: altruistic, prosocial, individualistic, and competitive), focusing on the requirement of having diverse behaviors in real-world driving. Using highway driving scenarios, we demonstrate through experiments that socially compatible AV driving not only enables naturalistic driving behaviors but also reduces collision rates from the baseline. Our findings reveal that without social compatibility, AV policies tend to adopt dangerously competitive driving behaviors, while the incorporation of social compatibility fosters smoother vehicle maneuvers.

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eVision: Respiratory Syncytial Virus (RSV) Seasonal Trend Forecasting

eVision: Respiratory Syncytial Virus (RSV) Seasonal Trend Fo...

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Biomedical engineering International Conference (BMEiCON)

作者： Divya Syal Juan Zuluaga Navid Shaghaghi Department of Computer Science & Engineering (CSEN) and Information Systems & Analytics (ISA) Ethical Pragmatic and Intelligent Computing (EPIC) Research Laboratory Santa Clara University (SCU) Santa Clara California USA

ISBN: (数字)9798331505431

ISBN: (纸本)9798331505448

Respiratory Syncytial Virus (RSV) is a flu-like illness annually responsible for 100K-160K hospitalizations and 6K-10K deaths in adults above the age of 65 as well as 58K-80K hospitalizations and 100–300 deaths in children under 5 years of age in the United States. A live prediction of the rate of growth of RSV cases during the RSV season can empower healthcare organizations to best prepare for RSV peaks and ultimately reduce the RSV mortality rate without over-budgeting, be it pharmaceutical companies producing too many perishable testing kits, vaccines and medications or hospitals keeping too many beds and staffing in reserve and thus away from other needed patients. This study adds a new RSV forecasting model to eVision, the collection of disease-forecasting Long Short-Term Memory (LSTM) neural networks, developed by Santa Clara University's EPIC (Ethical, Pragmatic, and Intelligent Computing) Research lab. This model leverages data from the Centers of Disease Control and Prevention (CDC) and Google Trends between 2021–2024 to predict the trend of RSV cases through the typical RSV season and beyond in the United States. Given the lack of extensive RSV data - which has only been collected by the CDC on a national level since 2021 - and how easily RSV is mistaken for other diseases, this model has a limitation on the number of weeks ahead in which its prediction can maintain a high accuracy for. This model successfully predicts percent positive RSV Polymerase Chain Reaction (PCR) tests with 94.45 % forecasting accuracy three weeks in advance at the national level, for the 2023–2024 RSV season.

关键词： Accuracy computer viruses Computational modeling Biological system modeling Buildings Predictive models Market research Data models Forecasting Long short term memory

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2021 Monthly Rice Production in Chinese Coastal Provinces 3

2021 Monthly Rice Production in Chinese Coastal Provinces

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3rd International Conference on Big Data Analytics and Practices, IBDAP 2022

作者： Parmar, Ajeet Munasinghe, Thilanka Tubbs, Heidi Anyamba, Assaf Rensselaer Polytechnic Institute Department of Electrical Computer and Systems Engineering Troy United States Rensselaer Polytechnic Institute Department of Information Technology and Web Science Troy United States Oak Ridge National Laboratory Geospatial Science and Human Security Division Oak Ridge United States

ISBN: (数字)9781665469388

ISBN: (纸本)9781665469388

This paper explores the dynamics of rice production in the Chinese provinces of Liaoning, Jilin, Heilongjiang, Shanghai, Jiangsu, and Zhejiang and seeks to predict monthly rice production in the months of April through October using precipitation and Normalized Difference Vegetation Index as the predictor variables available. We utilize ridge and lasso regression models to predict the rice yield. Results indicate that a lasso regression model with an R2 value of 0.9991501 with an adjusted R2 value of 0.9991502 and a ridge regression model with an R2 value of 0.9865443 with an adjusted R2 value of 0.9870637 are possible. The lasso regression model does not account for all predictor variables while the ridge regression model does. Both models could be expanded upon to include more observations. © 2022 IEEE.

关键词： Regression analysis

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SLAM algorithm based on bounding box and deep continuity in dynamic scene

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International Journal of Wireless and Mobile Computing 2021年第4期21卷 349-364页

作者： Fang, Baofu Han, Xiumeng Wang, Zaijun Yuan, Xiaohui School of Computer Science and Information Engineering Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Anhui Hefei230009 China Key Laboratory of Flight Techniques and Flight Safety Civil Aviation Flight University of China Sichuan Guanghan618307 China Department of Computer Science and Engineering University of North Texas DentonTX311277 United States

To solve the problems of low positioning accuracy and poor robustness caused by the inability of Simultaneous Localisation and Mapping (SLAM) algorithm to deal with dynamic targets in dynamic scenes, a SLAM algorithm based on bounding box and depth continuity in dynamic scenes is proposed. Firstly, the pixel random search filling process is carried out in combination with the depth and the bounding box to obtain the pixel-level segmentation result of the prior dynamic target. Then, the dynamic features are screened to eliminate the influence of dynamic targets. According to the screening results, relative static features which can be used for posture optimisation are selected. Posture optimisation is carried out by combining the static features to obtain the optimised camera posture. All experimental results show that the proposed algorithm significantly improves the positioning accuracy and real-time performance of SLAM algorithm in complex dynamic scenes. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Mapping

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A K -Band Four-Beam Transmitter with Decoupled Phase/Gain Control and Enhanced Power Back off Efficiency for SATCOM

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IEEE Transactions on Microwave Theory and Techniques 2024年第11期72卷 6443-6459页

作者： Gao, Huiyan Li, Nayu Xie, Xinhong Wang, Shaogang Lu, Hang Kuan, Yen-Cheng Yu, Xiaopeng Song, Chunyi Jane Gu, Qun Xu, Zhiwei Institute of Marine Electronics and Intelligent Systems Ocean College Zhejiang University Zhoushan316021 China Engineering Research Center of Oceanic Sensing Technology and Equipment Ministry of Education Zhoushan316021 China Donghai Laboratory Zhoushan316021 China National Yang Ming Chiao Tung University International College of Semiconductor Technology Hsinchu30010 Taiwan Institute of VLSI Design and Research College of Information Science and Electronic Engineering Zhejiang University Hangzhou310058 China University of California at Davis Department of Electrical and Computer Engineering DavisCA95616 United States

This article presents a 17.7-20.2 GHz eight-element four-beam RF-beamforming transmitter in 65-nm CMOS for satellite communication (SATCOM). The transmitter utilizes an analog scheme in the variable-gain amplifier (VGA) to achieve dB-in-linear gain control with a high dynamic range (DR), together with a current combining crossbar for a compact footprint. The vector-modulation-based (VM-based) phase shifter (PS) adopts the cancellation-based Gm units for low switching variation. Furthermore, eight PSs of each beam are split into two groups, with each group sharing the same tunable I/Q generator for low power consumption. Considering the increased peak-to-average power ratio (PAPR) caused by high-order modulation schemes and multibeam operation, the transmitter incorporates compact transformer-based Doherty power amplifiers (DPAs) with adaptive bias control to improve the power efficiency at back-off. The chip achieves a 31-dB gain-tuning range with a 0.283-dB step and a 360° phase-shifting range with a calibrated 7-bit resolution. The measured root-mean-square (RMS) phase/gain errors induced by the VGA/PS are below 2.05°/0.147 dB, respectively, which decouples phase/gain control. The transmitter achieves a maximum output 1-dB gain compression point (OP1dB) of 17.9 dBm and a maximum saturated power (Psat) of 18.9 dBm. The output element exhibits 29.3% drain efficiency (DE) at 6-dB power back off (PBO) from Psat , which is 1.56/3.12 times over the normalized class-B/class-A implementations, demonstrating Doherty PBO efficiency enhancement. The chip occupies an area of 8.59 × 4.17 mm2 and consumes 398 mW per element at OP1dB. Additionally, a 16-element linear array has been developed to showcase the capabilities of the multibeam operation and gain tapering. © 1963-2012 IEEE.

关键词： Efficiency

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Optimizing Bidding Curves for Renewable Energy in Two-Settlement Electricity Markets

arXiv

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arXiv 2025年

作者： Zhao, Dongwei Delikaraoglou, Stefanos Dvorkin, Vladimir Lamadrid, Alberto J.L. Botterud, Audun Argonne National Laboratory LemontIL United States Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI United States Department of Economics Department of Industrial and Systems Engineering Lehigh University BethlehemPA United States

Coordination of day-ahead and real-time electricity markets is imperative for cost-effective electricity supply and also to provide efficient incentives for the energy transition. Although stochastic market designs feature the least-cost coordination, they are incompatible with current deterministic markets. This paper proposes a new approach for compatible coordination in two-settlement markets based on benchmark bidding curves for variable renewable energy. These curves are optimized based on a bilevel optimization problem, anticipating per-scenario responses of deterministic market-clearing problems and ultimately minimizing the expected cost across day-ahead and real-time markets. Although the general bilevel model is challenging to solve, we theoretically prove that a single-segment bidding curve with a zero bidding price is sufficient to achieve system optimality if the marginal cost of variable renewable energy is zero, thus addressing the computational challenge. In practice, variable renewable energy producers can be allowed to bid multi-segment curves with non-zero prices. We test the bilevel framework for both single- and multiple-segment bidding curves under the assumption of fixed bidding prices. We leverage duality theory and McCormick envelopes to derive the linear programming approximation of the bilevel problem, which scales to practical systems such as a 1576-bus NYISO system. We benchmark the proposed coordination and find absolute dominance over the baseline solution, which assumes that renewables agnostically bid their expected forecasts. We also demonstrate that our proposed scheme provides a good approximation of the least-cost, yet unattainable in practice, stochastic market outcome. © 2025, CC BY.

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Empowering Leather Quality Assurance: Leveraging Convolutional Neural Networks for Precise Defect Detection and Classification

Empowering Leather Quality Assurance: Leveraging Convolution...

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International Symposium on Advanced Computing and Communication (ISACC)

作者： Logeswaran K Suresh P Savitha S Swati Sah Prasanna Kumar Kr Rajadevi R Vasugi M Sujit B Akilesh T Department of Artificial Intelligence Center of Excellence in Real-Time Analytic Kongu Engineering College Erode Tamilnaud India Department of Database Systems Vellore Institute of Technology Vellore Tamil Nadu India Department of Computer Science and Engineering K.S.R College of Engineering Tiruchengode Tamil Nadu India School of Engineering and Technology Sharda University Greater Noida Uttar Pradesh India Department of Information Technology Kongu Engineering College Erode Tamilnaud India Department of Artificial Intelligence Kongu Engineering College Erode Tamilnaud India

ISBN: (数字)9798331523893

ISBN: (纸本)9798331523909

Ensuring the development of high-quality leather products in the industrial sector requires a strong focus on leather quality assurance. In this study, a unique method for enhancing leather quality assurance using Convolutional Neural Networks (CNNs) is presented. Our suggested solution is able to detect and classify leather defects with amazing accuracy and efficiency by utilizing deep learning. CNN integration for leather fault categorization and detection offers a more reliable and affordable solution, ensuring better product quality and satisfied customers. Existing methods for inspecting leather products often suffer from inconsistent quality control, subjective manual inspection processes, and the complexity of accurately identifying and classifying various types of leather defects. By addressing these challenges, the proposed system aims to empower leather manufacturers with a reliable and efficient solution for ensuring consistent product quality and reducing production costs. Convolutional Neural Networks (CNNs) are integrated to provide a highly automated and effective solution that overcomes these scaling challenges. The proposed CNN-based method uses deep learning algorithms to decode images, which allows the system to swiftly and precisely analyse enormous volumes of visual data. To make sure the CNN can differentiate between various defect classes and keep a low rate of false positives and false negatives, its accuracy, precision, recall, and F1 score should be carefully assessed.

关键词： Deep learning Visualization Quality assurance Accuracy Quality control Product design Quality assessment Convolutional neural networks Reliability Defect detection

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Taming "data-hungry" reinforcement learning? stability in continuous state-action spaces 24

Taming "data-hungry" reinforcement learning? stability in co...

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Proceedings of the 38th International Conference on Neural information Processing systems

作者： Yaqi Duan Martin J. Wainwright Department of Technology Operations and Statistics Stern School of Business New York University New York NY Laboratory for Information and Decision Systems Statistics and Data Science Center and Department of Electrical Engineering and Computer Science and Department of Mathematics Massachusetts Institute of Technology Cambridge MA

ISBN: (纸本)9798331314385

We introduce a novel framework for analyzing reinforcement learning (RL) in continuous state-action spaces, and use it to prove fast rates of convergence in both off-line and on-line settings. Our analysis highlights two key stability properties, relating to how changes in value functions and/or policies affect the Bellman operator and occupation measures. We argue that these properties are satisfied in many continuous state-action Markov decision processes. Our analysis also offers fresh perspectives on the roles of pessimism and optimism in off-line and on-line RL.

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Continual Reinforcement Learning for Digital Twin Synchronization Optimization

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IEEE Transactions on Mobile Computing 2025年

作者： Tong, Haonan Chen, Mingzhe Zhao, Jun Hu, Ye Yang, Zhaohui Liu, Yuchen Yin, Changchuan Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence Beijing Advanced Information Network Laboratory Beijing China College of Computing and Data Science Singapore University of Miami Department of Electrical and Computer Engineering Frost Institute for Data Science and Computing Coral Gables33146 United States University of Miami Department of Industrial and Systems Engineering Coral Gables33146 United States Zhejiang University College of Information Science and Electronic Engineering Hangzhou310027 China North Carolina State University Department of Computer Science RaleighNC27695 United States Beijing Key Laboratory of Network System Architecture and Convergence China Beijing University of Posts and Telecommunications Beijing Advanced Information Network Laboratory Beijing100876 China

This article investigates the adaptive resource allocation scheme for digital twin (DT) synchronization optimization over dynamic wireless networks. In our considered model, a base station (BS) continuously collects factory physical object state data from wireless devices to build a real-time virtual DT system for factory event analysis. Due to continuous data transmission, maintaining DT synchronization must use extensive wireless resources. To address this issue, a subset of devices is selected to transmit their sensing data, and resource block (RB) allocation is optimized. This problem is formulated as a constrained Markov process (CMDP) problem that minimizes the long-term mismatch between the physical and virtual systems. To solve this CMDP, we first transform the problem into a dual problem that refines RB constraint impacts on device scheduling strategies. We then propose a continual reinforcement learning (CRL) algorithm to solve the dual problem. The CRL algorithm learns a stable policy across historical experiences for quick adaptation to dynamics in physical states and network capacity. Simulation results show that the CRL can adapt quickly to network capacity changes and reduce normalized root mean square error (NRMSE) between physical and virtual states by up to 55.2%, using the same RB number as traditional methods. © 2025 IEEE. All rights reserved.

关键词： Markov processes

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