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63rd IEEE Conference on Decision and control, CDC 2024

作者： Judy, Rachael L. Fuchs, Zachariah E. Casbeer, David W. University of Cincinnati Department of Electrical Engineering and Computer Science CincinnatiOH United States Control Science Center of Excellence Air Force Research Laboratory 2210 8th St. WPAFBOH45433 United States

ISBN: (纸本)9798350316339

An Escort and Defense Scenario is presented in which a high-value Target maneuvers through a high-risk region while being escorted by a mobile, defensive agent. Along this trajectory, an Attacker may be launched from one of several possible launch locations. If a launch occurs, the three agents play out a Target-Attacker-Defender (TAD) differential game in which the Defender attempts to intercept the Attacker at maximal distance from the Target while the Attacker strives to maneuver as close as possible to the Target. Prior to launch, the Target and Defender strive to preposition themselves in advantageous positions to effectively respond to a potential threat while simultaneously moving towards a safety region. An augmented collocation-based direct optimal control method is developed to solve the escort problem by solving and utilizing the value of the TAD differential subgame at each collocation point while simultaneously optimizing the primary optimal control problem. © 2024 IEEE.

关键词： Optimal control systems

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Binary Bleed: Fast Distributed and Parallel Method for Automatic Model Selection

Binary Bleed: Fast Distributed and Parallel Method for Autom...

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2024 IEEE High Performance Extreme Computing Conference, HPEC 2024

作者： Barron, Ryan Eren, Maksim E. Bhattarai, Manish Boureima, Ismael Matuszek, Cynthia Alexandrov, Boian S. Theoretical Division Los Alamos National Laboratory Los Alamos United States University of Maryland Baltimore County Department of Computer Science and Electrical Engineering MD United States Advanced Research in Cyber Systems Los Alamos National Laboratory Los Alamos United States

ISBN: (纸本)9798350387131

In several Machine Learning (ML) clustering and dimensionality reduction approaches, such as nonnegative matrix factorization (NMF), RESCAL, and K-Means clustering, users must select a hyper-parameter k to define the number of clusters or components that yield an ideal separation of samples or clean clusters. This selection, while difficult, is crucial to avoid overfitting or underfitting the data. Several ML applications use scoring methods (e.g., Silhouette and Davies Boulding scores) to evaluate the cluster pattern stability for a specific k. The score is calculated for different trials over a range of k, and the ideal k is heuristically selected as the value before the model starts overfitting, indicated by a drop or increase in the score resembling an elbow curve plot. While the grid-search method can be used to accurately find a good k value, visiting a range of k can become time-consuming and computationally resource-intensive. In this paper, we introduce the Binary Bleed method based on binary search, which significantly reduces the k search space for these grid-search ML algorithms by truncating the target k values from the search space using a heuristic with thresholding over the scores. Binary Bleed is designed to work with single-node serial, single-node multi-processing, and distributed computing resources. In our experiments, we demonstrate the reduced search space gain over a naive sequential search of the ideal k and the accuracy of the Binary Bleed in identifying the correct k for NMFk, K-Means pyDNMFk, and pyDRESCALk with Silhouette and Davies Boulding scores. We make our implementation of Binary Bleed for the NMF algorithm available on GitHub. © 2024 IEEE.

关键词： Dimensionality reduction

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Automated explainable and interpretable framework for anomaly detection and human activity recognition in smart homes

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Neural Computing and Applications 2025年第16期37卷 9295-9308页

作者： Kim, Tae Hoon Ojo, Stephen Krichen, Moez Alamro, Meznah A. Mihoub, Alaeddine Sampedro, Gabriel Avelino School of Information and Electronic Engineering Zhejiang University of Science and Technology No. 318 Zhejiang Hangzhou China Department of Electrical and Computer Engineering College of Engineering Anderson University AndersonSC29621 United States Al-Baha University Al-Baha65528 Saudi Arabia ReDCAD Laboratory University of Sfax Sfax3038 Tunisia Department of Information Technology College of Computer and Information Science Princess Nourah Bint Abdul Rahman University Riyadh11564 Saudi Arabia Department of Management Information Systems College of Business and Economics Qassim University P.O. Box: 6640 Buraidah51452 Saudi Arabia Department of Computer Science University of the Philippines Diliman Quezon City1101 Philippines

This paper presents a comprehensive framework for activity recognition and anomaly detection in smart home environments, targeting applications in convenience, efficiency, responsiveness, and healthcare. The proposed framework incorporates explainable artificial intelligence (XAI) to interpret feature impacts on learning models and optimize feature selection for activity recognition and anomaly detection. The study examines the recognition of two activity sets: Activities Set I, comprising 8 activities, and Activities Set II, comprising 11 activities. Various machine learning models (random forest (RF), decision tree (DT), and XGBoost (XGB)) are employed, along with an ensemble voting classifier (EVC) with these machine learning. EVC achieved 91% accuracy for Activities Set I and 90–91% accuracy for Activities Set II. The model demonstrated strong precision, recall, and F1-score across most classes. For anomaly detection, the Isolation Forest and H2O Isolation Forest Estimator algorithms are utilized to uncover irregularities in daily routines. The Isolation Forest identified anomalies in activities such as chores and desk-related tasks, while the H2O Isolation Forest Estimator detected no anomalies in either activity set. This study underscores the potential of the proposed framework to enhance smart home functionality and highlights avenues for future research and system improvements. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Adversarial machine learning

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Cyber-Security Knowledge Graph Generation by Hierarchical Nonnegative Matrix Factorization 12

Cyber-Security Knowledge Graph Generation by Hierarchical No...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Barron, Ryan Eren, Maksim E. Bhattarai, Manish Wanna, Selma Solovyev, Nicholas Rasmussen, Kim Alexandrov, Boian S. Nicholas, Charles Matuszek, Cynthia Los Alamos National Laboratory Theoretical Division Los Alamos United States Los Alamos National Laboratory Advanced Research in Cyber Systems Los Alamos United States University of Maryland Department of Computer Science and Electrical Engineering Baltimore CountyMD United States

ISBN: (纸本)9798350330366

Much of human knowledge in cybersecurity is encapsulated within the ever-growing volume of scientific papers. As this textual data continues to expand, the importance of document organization methods becomes increasingly crucial for extracting actionable insights hidden within large text datasets. Knowledge Graphs (KGs) serve as a means to store factual information in a structured manner, providing explicit, interpretable knowledge that includes domain-specific information from the cybersecurity scientific literature. One of the challenges in constructing a KG from scientific literature is the extraction of ontology from unstructured text. In this paper, we address this topic and introduce a method for building a multi-modal KG by extracting structured ontology from scientific papers. We demonstrate this concept in the cybersecurity domain. One modality of the KG represents observable information from the papers, such as the categories in which they were published or the authors. The second modality uncovers latent (hidden) patterns of text extracted through hierarchical and semantic non-negative matrix factorization (NMF), such as named entities, topics or clusters, and keywords. We illustrate this concept by consolidating more than two million scientific papers uploaded to arXiv into the cyber-domain, using hierarchical and semantic NMF, and by building a cyber-domain-specific KG. © 2024 IEEE.

关键词： Cybersecurity

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Adaptive Time Period Aggregation for Day-ahead Unit Commitment

Adaptive Time Period Aggregation for Day-ahead Unit Commitme...

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2022 Tsinghua-IET electrical engineering Academic Forum, TDF 2022

作者： Yu, Zekuan Zhong, Haiwang Yan, Xinfei Department of Electrical Engineering Tsinghua University Beijing China State Key Laboratory of Control and Simulation of Power Systems and Generation Equipments Beijing China

ISBN: (数字)9781839537998

ISBN: (纸本)9781839537998

The growing penetration of renewables calls for finer temporal resolutions of the day-ahead unit commitment (DA-UC) problem in order to accommodate the fluctuations and improve the performance of UC schedules, leading to the expansion of the DA-UC problem scale. However, due to its high computational burden, the viability of adopting larger UC models in practice becomes a challenge. In this paper, an adaptive time period aggregation strategy and a corresponding DA-UC model are proposed to reduce the model scale while preserving feasibility and accuracy. The aggregation strategy integrates the statistical characteristics of net demand with the operational characteristics of thermal units to effectively capture the consistency in on/off statuses. Case study based on the IEEE 118-bus system validates that, combined with a post-processing procedure, the proposed strategy and model can provide near-optimal UC schedules with approximately 5 times acceleration for various demand profiles, enhancing computational efficiency. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Computational efficiency

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Integrated Routing and Traffic Signal control for CAVs via Reinforcement Learning Approach 27

Integrated Routing and Traffic Signal Control for CAVs via R...

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27th IEEE International Conference on Intelligent Transportation systems, ITSC 2024

作者： Park, Jiho Zhang, Guohui Wang, Chieh [Ross] Wang, Hong Jiang, Zhong-Ping Tandon School of Engineering New York University Control and Networks Lab Department of Electrical and Computer Engineering BrooklynNY11201 United States University of Hawaii at Manoa Department of Civil and Environmental Engineering HonoluluHI96822 United States Oak Ridge National Laboratory Oak RidgeTN37831 United States

ISBN: (纸本)9798331505929

Incorporating Connected and Automated Vehicles (CAVs) into urban traffic networks presents opportunities and challenges for traffic management systems. This paper aims to develop an integrated routing and traffic signal control system designed explicitly for CAVs, utilizing a Reinforcement Learning (RL) approach. The objective is to enhance traffic flow and improve overall transportation efficiency in the controlled areas. We propose an innovative framework that employs the Deep Reinforcement Learning (DRL) algorithm, especially the Deep Q-network (DQN), to dynamically adjust the number of vehicles in the routes and the duration of traffic signals. Our simulation results demonstrate that a DQN agent successfully optimizes the number of vehicles in the routes and traffic signal timings of traffic signal controllers, eventually reducing total travel time. The study illustrates the potential usage of RL-based systems in managing routing and traffic signals for CAVs, offering a promising opportunity for future urban traffic management strategies. © 2024 IEEE.

关键词： Travel time

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On Improved Commutation for Moving-Magnet Planar Actuators

arXiv

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arXiv 2023年

作者： Broens, Yorick Butler, Hans Tóth, Roland The Department of Electrical Engineering Eindhoven University of Technology Eindhoven Netherlands ASML Veldhoven Netherlands The Systems and Control Laboratory Institute for Computer Science and Control Hungary

The demand for high-precision and high-throughput motion control systems has increased significantly in recent years. The use of moving-magnet planar actuators (MMPAs) is gaining popularity due to their advantageous characteristics, such as complete environmental decoupling and reduction of stage mass. Nonetheless, model-based commutation techniques for MMPAs are compromised by misalignment between the mover and coil array and mismatch between the ideal electromagnetic model and the physical system, often leading to decreased system performance. To address this issue, a novel improved commutation approach is proposed in this paper, which is applicable for general planar motor applications, by means of dynamic regulation of the position dependence of the ideal model-based commutation algorithm, which allows for attenuation of magnetic misalignment, manufacturing inaccuracies and other unmodelled phenomena. The effectiveness of the proposed approach is validated through experiments using a state-of-the-art moving-magnet planar actuator *** Codes 37M15 © 2023, CC BY.

关键词： Alignment

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Demand Response Strategy for HVAC Based on Reinforcement Learning and Imitation Learning

Demand Response Strategy for HVAC Based on Reinforcement Lea...

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2022 Tsinghua-IET electrical engineering Academic Forum, TDF 2022

作者： He, Yiliu Zhong, Haiwang Zhang, Guanglun Ruan, Guangchun Department of Electrical Engineering Tsinghua University Beijing China State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Beijing China

ISBN: (数字)9781839537998

ISBN: (纸本)9781839537998

Heating, ventilation and air conditioning (HVAC) loads have great potential for demand response due to the buildings' thermal inertia. Modelling the complex thermal dynamics of realistic buildings is essential for the building energy optimization. In this paper, Deep Q-Network (DQN), a reinforcement learning (RL) algorithm, is used to decide the indoor temperature setpoint in response to the real-time price. The agent can adjust the HVAC load to reduce the electricity cost while considering the occupants' comfort requirement. To significantly accelerate the training process, imitation learning is adopted to pre-train the RL agent using historical data. The results of the case study show that the agent's performance improves steadily and exceeds the benchmark after a short period of training. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Reinforcement learning

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AUTONOMOUS control MANAGEMENT OF SYNCHRONOUS GENERATORS IN MICROGRID SYSTEM

AUTONOMOUS CONTROL MANAGEMENT OF SYNCHRONOUS GENERATORS IN M...

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2025 International Conference on Energy, Power, Environment, control and Computing, ICEPECC 2025

作者： Qamar, Hafiz Ghulam Murtza Ashraf, Adil Ahmad, Fareed Riaz, Ayesha Imran, Muhammad Yousaf, Muhammad Zain Khan, Imran School of Electrical Engineering Yanshan University Qinhuangdao066004 China Department of Electrical Electronics and Computer Systems University of Sargodha Pakistan Center for Renewable Energy and Microgrids Huanjiang Laboratory Zhejiang University Zhejiang Zhuji311816 China

ISBN: (纸本)9781837243235

Decentralized excitation control based on local asynchronous clock feedback measurement leads to the problem of decentralized incompatibility of excitation controllers, which reduces the ability of excitation control design to improve the stability of power systems. Therefore, the problem of decentralized compatible excitation control is proposed, and the effect of excitation control on the dynamic characteristics of the system under compatible and incompatible modes is compared and analyzed, and the shortcomings of decentralized incompatible excitation control and the advantages of decentralized compatible excitation control are given. © The Institution of engineering & Technology 2025.

关键词： Decentralized control

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Energy-balancing dual-port grid-forming control for VSC-HVDC systems

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IFAC-PapersOnLine 2023年第2期56卷 6447-6452页

作者： Irina Subotić Dominic Groß Automatic Control Laboratory ETH Zürich Switzerland Department of Electrical and Computer Engineering University of Wisconsin-Madison USA

This work examines energy-balancing dual port grid-forming (GFM) control for high-voltage direct current (HVDC) transmission. In contrast to the state-of-the-art, HVDC converters controlled in this way do not require assigning GFM and grid-following roles to different converters. Moreover, this control enables primary frequency control and inertia support through HVDC links. A detailed stability and steady-state analysis results in conditions on the control gains such that i) the overall hybrid dc/ac system is stable, ii) asynchronous ac areas are quasi-synchronous, and iii) circulating power in cyclic topologies is avoided. Finally, a high-fidelity case study is used to illustrate and verify the analytical results.

关键词： Power systems Power systems stability Grid-forming control HVDC transmission

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