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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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Optimization of classification model for electric vehicle charging station placement using dynamic graylag goose algorithm

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Frontiers in Energy Research 2024年 12卷

作者： Alhussan, Amel Ali Khafaga, Doaa Sami El-kenawy, El-Sayed M. Eid, Marwa M. Ibrahim, Abdelhameed Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University Riyadh Saudi Arabia MEU Research Unit Middle East University Amman Jordan Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Mansoura Egypt Faculty of Artificial Intelligence Delta University for Science and Technology Mansoura Egypt Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

The study of electric vehicles (EVs) aims to address the critical challenges of promoting widespread adoption. These challenges include EVs’ high upfront costs compared to conventional vehicles, the need for more sufficient charging stations, limitations in battery technology and charging speeds, and concerns about the distance EVs can travel on a single charge. This paper is dedicated to designing an innovative strategy to handle EV charging station arrangement issues in different cities. Our research will support the development of sustainable transportation by intelligently replying to the challenges related to short ranges and long recharging times through the distribution of fast and ultra-fast charge terminals by allocating demand to charging stations while considering the cost variable of traffic congestion. A hybrid combination of Dynamic Greylag Goose Optimization (DGGO) algorithm, as well as a Long Short-Term Memory (LSTM) model, is employed in this approach to determine, in a cost-sensitive way, the location of the parking lots, factoring in the congestion for traffic as a variable. This study examines in detail the experiments on the DGGO + LSTM model performance for the purpose of finding an efficient charging station place. The results show that the DGGO + LSTM model has achieved a stunning accuracy of 0.988,836, more than the other models. This approach shapes our finding’s primary purpose of proposing solutions in terms of EV charging infrastructure optimization that is fully justified to the EV’s wide diffusion and mitigating of the environmental consequences. Copyright © 2024 Alhussan, Khafaga, El-kenawy, Eid and Ibrahim.

关键词： Charging stations

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Refining quantum fidelity: approaches in quantum error correction

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Quantum Information Processing 2025年第7期24卷 1-24页

作者： Thakur, Vikram Singh Kumar, Atul Dev, Kapal Department of Electronics Engineering Indian Institute of Technology (Banaras Hindu University) IIT (BHU) Varanasi India Department of Computer Science Munster Technological University Bishopstown Cork Ireland Institute of Intelligent Systems University of Johannesburg Auckland Park 2006 South Africa

Quantum fidelity is a cornerstone metric for evaluating the accuracy and reliability of quantum information processes, particularly in noise- and error-prone environments. This study presents a comprehensive exploration of refined approaches to quantum fidelity within the framework of quantum error correction (QEC). Central to our analysis is the role of fidelity in preserving quantum states and mitigating the impact of noise across QEC codes such as the Steane code and Shor code. By leveraging density matrices, which encapsulate the statistical and quantum coherence properties of quantum systems, we conduct a mode analysis with respect to the off-diagonal elements, unveiling their critical influence on state preservation and error resilience. A key focus is the interpolation parameter $$ p $$ , which governs the transition from a maximally mixed state to the actual quantum state. Comparative analysis across fidelity computation techniques highlights the superior performance of methods such as 2x sqrtm, 3x svd, and eigvals, while methods like sqrtmh eigvalsh and sqrtm svd exhibit slower fidelity growth in these regions. The Steane QEC code demonstrates enhanced robustness over the Shor code, particularly under noisy conditions, showcasing its ability to maintain quantum state integrity. This study underscores the pivotal role of density matrices in optimizing QEC protocols, enabling precise fidelity evaluation, and ensuring the scalability and reliability of quantum technologies. This research improved the development of next-generation quantum computing and communication infrastructures.

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On-Demand Model and Client Deployment in Federated Learning with Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年

作者： Chahoud, Mario Sami, Hani Mourad, Azzam Otrok, Hadi Bentahar, Jamal Guizani, Mohsen Concordia University Concordia Institute for Information Systems Engineering Montreal Canada Mohammad Bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Lebanese American University Artificial Intelligence and Cyber Systems Research Center Department of CSM Beirut Lebanon Khalifa University KU6G Research Center Department of Computer Science United Arab Emirates Department of Computer Science Abu Dhabi United Arab Emirates

In Federated Learning (FL), the limited accessibility of data from diverse locations and user types poses a significant challenge due to restricted user participation. Expanding client access and diversifying data enhance models by incorporating diverse perspectives, thereby improving adaptability. However, in dynamic and mobile environments, the availability of FL clients fluctuates as devices may become inaccessible, leading to inefficient client selection and reduced model performance. Current solutions often fail to adapt quickly to these changes, creating a gap in achieving real-time client availability and efficient data utilization. To address this, we propose a Deep Reinforcement Learning (DRL) On-Demand solution, deploying new clients using Docker Containers on-the-fly. Our On-Demand solution, employing DRL, targets client availability and selection while considering data shifts and container deployment complexities. It employs an autonomous end-to-end approach for handling model deployment and client selection. The DRL strategy leverages a Markov Decision Process (MDP) framework, with a Master Learner and a Joiner Learner to optimize decision-making. The designed cost functions account for the complexity of dynamic client deployment and selection, ensuring effective resource management and service reliability. Simulated tests show that our architecture can easily adapt to changes in the environment and respond to On-Demand requests while reducing the number of learning rounds used by 20-50 % compared with existing approaches. This highlights its ability to improve client availability, capability, accuracy, and learning efficiency, surpassing heuristic and traditional reinforcement learning methods. © 2014 IEEE.

关键词： Federated learning

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Tackling Noisy Labels with Network Parameter Additive Decomposition

arXiv

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arXiv 2024年

作者： Wang, Jingyi Xia, Xiaobo Lan, Long Wu, Xinghao Yu, Jun Yang, Wenjing Han, Bo Liu, Tongliang The Department of Intelligent Data Science College of Computer Science and Technology National University of Defense Technology Changsha410073 China The Trustworthy Machine Learning Lab School of Computer Science Faculty of Engineering University of Sydney DarlingtonNSW2008 Australia The State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China The Department of Automation University of Science and Technology of China Hefei230026 China The Department of Computer Science Hong Kong Baptist University Hong Kong

Given data with noisy labels, over-parameterized deep networks suffer overfitting mislabeled data, resulting in poor generalization. The memorization effect of deep networks shows that although the networks have the ability to memorize all noisy data, they would first memorize clean training data, and then gradually memorize mislabeled training data. A simple and effective method that exploits the memorization effect to combat noisy labels is early stopping. However, early stopping cannot distinguish the memorization of clean data and mislabeled data, resulting in the network still inevitably overfitting mislabeled data in the early training stage. In this paper, to decouple the memorization of clean data and mislabeled data, and further reduce the side effect of mislabeled data, we perform additive decomposition on network parameters. Namely, all parameters are additively decomposed into two groups, i.e. , parameters w are decomposed as w = σ + γ. Afterward, the parameters σ are considered to memorize clean data, while the parameters γ are considered to memorize mislabeled data. Benefiting from the memorization effect, the updates of the parameters σ are encouraged to fully memorize clean data in early training, and then discouraged with the increase of training epochs to reduce interference of mislabeled data. The updates of the parameters γ are the opposite. In testing, only the parameters σ are employed to enhance generalization. Extensive experiments on both simulated and real-world benchmarks confirm the superior performance of our method. The implementation is available at: https://***/wangjingyi9924/TNLPAD. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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Machine Learning Heart Disease Prediction Using KNN and RTC Algorithm 3

Machine Learning Heart Disease Prediction Using KNN and RTC ...

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3rd International Conference on Power, Energy, Control and Transmission systems, ICPECTS 2022

作者： Senthil, G.A. Prabha, R. Razmah, M. Veeramakali, T. Sridevi, S. Yashini, R. Agni College of Technology Dept of Information Technology Chennai India Sri Sai Ram Institute of Technology Dept of Electronics and Communication Engineering Chennai India School of Computing SRM Institute of Science and Technology Dept of Data Science and Business Systems Chennai India Sri Ramachandra Engineering and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9781665462754

Heart Disease or cardiovascular disease refers to the range of heart conditions like cardiac arrest, coronary artery disease. Heart disease can be very well hindered through certain lifestyle changes. There is a significant increase in the mortality rate recently due to the distinctive heart diseases. Machine learning uses mathematical models to work efficiently with the enormous amount of data. It plays a crucial role in medical science in the prediction of distinctive diseases. Cardiologists inspects the heart functionality using electrocardiography, computed tomography. These tests are quite expensive for a common man. Recent times, the life span of a human is guaranteed only with the support of medications. As prevention is better than the cure, machine learning helps to predict the vulnerability of a heart disease with few elemental symptoms and health factors. It is been fed by the basic data of the patients like age, sex. Machine learning helps to predict the vulnerability in advance which provides the cardiologists with great acumen for the adaption of the treatment. Machine learning algorithms have proven to produce reliable and accurate output with the help of the inputs. The algorithms used in the article include KNN and decision tree classifier which is compared to yield the desired and efficient output. © 2022 IEEE.

关键词： Heart

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Reconstruction of Airports Coordinates Based on Flight Times Data 15th

Reconstruction of Airports Coordinates Based on Flight Times...

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15th International Conference on Dynamical systems: Theory and Applications, 2019

作者： Drapala, Jaroslaw Sobieraj, Michal Radliński, Lukasz Świątek, Jerzy Department of Computer Science and Systems Engineering Wroclaw University of Science and Technology Wyb. Wyspiańskiego 27 Wroclaw50-370 Poland

ISBN: (纸本)9783030773137

We propose a data-centric approach to the issue of the shape of the Earth’s surface. We base the research solely on widely available data on flight times between airports from all over the world. The airport coordinates will be treated as unknown variables to be estimated in such a way that the flight times between the estimated ones match their real counterparts as closely as possible. This leads to the non-linear optimization task that we handle using the BFGS algorithm. The real data are acquired using web scraping. The performance of the proposed method is illustrated on artificial examples: sphere, disc, circle. The results obtained for the real data are presented. Interesting properties of the task came up during the simulation study performed with the BFGS optimization routine. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Airports

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Performance Analysis of Distributed Filtering under Misspecified Noise Covariances

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IEEE Transactions on Automatic Control 2025年

作者： Lyu, Xiaoxu Wen, Guanghui Shi, Ling Duan, Peihu Duan, Zhisheng The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Southeast University School of Automation Nanjing210096 China The Hong Kong University of Science and Technology Department of Chemical and Biological Engineering Hong Kong Beijing Institute of Technology State Key Laboratory of CNS/ATM Beijing100081 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Beijing100871 China

This paper systematically investigates the performance of the consensus-based distributed filter under misspecified noise covariances. First, we introduce four quantities: the nominal filter parameter, the nominal estimation error covariance, the ideal filter parameter, and the ideal estimation error covariance. We derive the difference expressions among these quantities and establish the corresponding one-step relations. These relations reveal how performance deteriorates when noise covariances are misspecified, and demonstrate how to evaluate the estimation error covariance using the available nominal filter parameter. We particularly highlight the effect of the information fusion step number on these relations. Furthermore, recursive relations are introduced by extending the results of the one-step relations. Subsequently, we demonstrate the convergence of these quantities under the collective observability condition, and show that the convergence condition of the nominal filter parameter can guarantee the convergence of the estimation error covariance. Additionally, we provide bounds on the estimation error covariance under misspecified noise covariances by utilizing the Frobenius norms of the noise covariance deviations and the trace of the nominal filter parameter. Finally, the effectiveness of the theoretical results is verified through numerical simulations. © 1963-2012 IEEE.

关键词： consensus analysis convergence analysis Distributed filtering misspecified noise covariance performance analysis

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Leveraging Large Vision-Language Model for Better Automatic Web GUI Testing

Leveraging Large Vision-Language Model for Better Automatic ...

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International Conference on Software Maintenance (ICSM)

作者： Siyi Wang Sinan Wang Yujia Fan Xiaolei Li Yepang Liu Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The Hong Kong University of Science and Technology Hong Kong China

ISBN: (数字)9798350395686

ISBN: (纸本)9798350395693

With the rapid development of web technology, more and more software applications have become web-based in the past decades. To ensure software quality and user experience, various techniques have been proposed to automatically test web applications by interacting with their GUIs. To achieve high functional coverage, web GUI testing tools often need to generate high-quality text inputs and interact with the associated GUI elements (e.g., click submit buttons). However, developing a holistic approach that solves both subtasks is challenging because the web GUI context can be complicated and highly dynamic, which is hard to process programmatically. The recent development of large vision-language models (LVLM) provides new opportunities to handle these longstanding problems. We in this paper propose VETL, the first LVLM-driven end-to-end web testing technique. With LVLM's scene understanding capabilities, VETL can generate valid and meaningful text inputs focusing on the local context, while avoiding the need to extract precise textual attributes. The selection of associated GUI elements is formulated as a visual question answering problem, allowing LVLM to capture the logical connection between the input box and the relevant element based on visual instructions. Further, the GUI exploration is guided by a multi-armed bandit module employing a curiosity-oriented strategy. Experiments show that VETL is effective in exploring web state/action spaces and detecting bugs. Compared with WebExplor, the state-of-the-art web testing technique, VETL can discover 25% more unique web actions on benchmark websites. Moreover, it can expose functional bugs in top-ranking commercial websites, which have been confirmed by the website maintainers. Our work makes the first attempt of leveraging LVLM in end-to-end GUI testing, demonstrating promising results of this research direction.

关键词： Visualization Software maintenance computer bugs Focusing Software quality User experience Question answering (information retrieval) Space exploration Graphical user interfaces Testing

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Vision-based Inspection of Flare Stacks Operation Using a Visual Servoing Controlled Autonomous Unmanned Aerial Vehicle (UAV)* 48

Vision-based Inspection of Flare Stacks Operation Using a Vi...

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48th Annual Conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Al Radi, Muaz Karki, Hamad Werghi, Naoufel Javed, Sajid Dias, Jorge Khalifa University Department of Electrical Engineering and Computer Science Abu Dhabi United Arab Emirates Khalifa University Department of Mechanical Engineering Abu Dhabi United Arab Emirates Khalifa University Center for Autonomous Robotic Systems Abu Dhabi United Arab Emirates

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

The inspection of flare stacks' operation is a challenging task that requires technical expertise and human effort. Flare stack systems undergo various types of faults that need to be monitored in a timely manner to avoid costly and dangerous accidents. Automating this process via the application of autonomous robotic systems for collecting comprehensive data of the flare stack's operation is a promising solution for minimizing the involved hazards and costs. In this work, a novel Unmanned Aerial Vehicle (UAV)-based autonomous inspection system for flare stacks performance monitoring is proposed. The system employs a deep learning detection network that was trained for detection of flame and smoke for vision-based flaring performance analysis. A visual servoing control technique was used for guiding the UAV's movement throughout the inspection mission for collecting comprehensive visual inspection data. Simulations in a simulated petrochemical plant environment with flare stacks were performed for validating the performance of the proposed system. The proposed UAV system was able to collect the required data successfully and analysis of the obtained data returned useful information about the flare stack's operation. © 2022 IEEE.

关键词： Visual servoing

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