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Annual North-American Power Symposium

作者： Mushfiqul Abedin Khan Mona Ghassemi Department of Electrical and Computer Engineering Zero Emission Realization of Optimized Energy Systems (ZEROES) Laboratory The University of Texas at Dallas Richardson TX USA

Corona effects are one of the most important factors to take into consideration when designing an overhead transmission line. Corona discharges cause power loss which should be considered during transmission line design. Unconventional high surge impedance loading (HSIL) lines have subconductors placed anywhere in space and have no bundle symmetry. They have the potential to produce greater natural power than conventional lines and conventional HSIL lines. This paper calculates corona loss in both fair and foul weather conditions for an envisaged unconventional HSIL line. It is seen that the unconventional lines under discussion undergo much greater corona loss than the conventional lines and conventional HSIL lines.

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An Actor-Critic Reinforcement Learning Scheme for Reactive 3D Optimal Motion Planning Based on Fluid Dynamics

An Actor-Critic Reinforcement Learning Scheme for Reactive 3...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Marios Malliaropoulos Panagiotis Rousseas Charalampos P. Bechlioulis Kostas J. Kyriakopoulos School of Mechanical Engineering Control Systems Laboratory National Technical University of Athens Department of Electrical and Computer Engineering University of Patras Center of AI & Robotics (CAIR) New York University Abu Dhabi

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

This work proposes a novel and provably correct method for three-dimensional optimal motion planning in complex environments. Our approach models the 3D motion planning problem by solving streamlines of the potential fluid flow, filling a gap in traditional motion planning techniques by guaranteeing a closed-loop, smooth and natural-looking navigation solution. Special emphasis is given to an inherent challenge of artificial potential field (APF) methods, namely establishing proofs of safety and stability over the entire optimization process. A model-based actor-critic reinforcement learning algorithm is introduced to approximate the optimal solution to the Hamilton-Jacobi-Bellman equation and update the controller parameters in a deterministic manner. Through a series of ROS-Gazebo software-in-the-loop simulations the proposed methodology demonstrates robustness and outperforms widely used methods such as the RRT ∗ , highlighting its contribution to the field of 3D optimal motion planning.

关键词： Solid modeling Three-dimensional displays Navigation Reinforcement learning Mathematical models Stability analysis Robustness Planning Safety Optimization

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Cyber-Physical Reconfiguration for Disaster Resilience of Power Distribution systems

Cyber-Physical Reconfiguration for Disaster Resilience of Po...

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IEEE International Conference on Smart Grid Communications (SmartGridComm)

作者： Md. Zahidul Islam Yuzhang Lin Vinod M. Vokkarane Yiyun Yao Fei Ding Department of Electrical and Computer Engineering University of Massachusetts Lowell Lowell MA USA Power Systems Engineering Center National Renewable Energy Laboratory Golden CO

Cyber-physical distribution systems (CPDS) have emerged from the integration of information technology into distribution systems. While offering substantial benefits, this integration also introduces vulnerabilities. The interaction between cyber networks and distribution systems renders CPDS susceptible to disasters. To ensure critical load supply and system resilience, rapid post-disaster load restoration is required. The paper proposes a critical load restoration (CLR) framework in CPDS using a network reconfiguration approach that exploits the existing post-disaster resources to restore critical loads within the shortest possible time. Using graph theory, the cyber network and distribution system are integrated into a single digraph, minimizing the CLR complexity in CPDS. A cost metric is also defined to satisfy network-specific objectives and constraints. A heuristic is proposed to guide the load restoration process using the cost metric within the integrated digraph. Simulation results confirm the framework’s superiority over existing literature, which either overlooks cyber components or prolongs restoration with additional resource deployment.

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Matrix Factorization for Inferring Associations and Missing Links

arXiv

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arXiv 2025年

作者： Barron, Ryan Eren, Maksim E. Truong, Duc P. Matuszek, Cynthia Wendelberger, James Dorn, Mary F. Alexandrov, Boian Theoretical Division Los Alamos National Laboratory United States Information Systems and Modeling Los Alamos National Laboratory United States Department of Computer Science and Electrical Engineering University of Maryland Baltimore County United States Computer Computational and Statistical Sciences Los Alamos National Laboratory United States

Missing link prediction is a method for network analysis, with applications in recommender systems, biology, social sciences, cybersecurity, information retrieval, and Artificial Intelligence (AI) reasoning in Knowledge Graphs. Missing link prediction identifies unseen but potentially existing connections in a network by analyzing the observed patterns and relationships. In proliferation detection, this supports efforts to identify and characterize attempts by state and non-state actors to acquire nuclear weapons or associated technology - a notoriously challenging but vital mission for global security. Dimensionality reduction techniques like Non-Negative Matrix Factorization (NMF) and Logistic Matrix Factorization (LMF) are effective but require selection of the matrix rank parameter, that is, of the number of hidden features, k, to avoid over/under-fitting. We introduce novel Weighted (WNMFk), Boolean (BNMFk), and Recommender (RNMFk) matrix factorization methods, along with ensemble variants incorporating logistic factorization, for link prediction. Our methods integrate automatic model determination for rank estimation by evaluating stability and accuracy using a modified bootstrap methodology and uncertainty quantification (UQ), assessing prediction reliability under random perturbations. Additionally, we incorporate Otsu threshold selection and k-means clustering for Boolean matrix factorization, comparing them to coordinate descent-based Boolean thresholding. Our experiments highlight the impact of rank k selection, evaluate model performance under varying test-set sizes, and demonstrate the benefits of UQ for reliable predictions using abstention. We validate our methods on three synthetic datasets (Boolean and Gaussian distributed) and benchmark them against LMF and symmetric LMF (symLMF) on five real-world protein-protein interaction networks, showcasing an improved prediction performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Non-negative matrix factorization

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Average Coverage Probability for Base-Station-to-UAV Communications Over 6G Multiple Access Wireless Networks

Average Coverage Probability for Base-Station-to-UAV Communi...

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IEEE International Symposium on Information Theory

作者： Xi Zhang Qixuan Zhu H. Vincent Poor Department of Electrical and Computer Engineering Networking and Information Systems Laboratory Texas A&#x0026 M University TX USA Department of Electrical and Computer Engineering Princeton University Princeton USA

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

While the fifth generation (5G) of wireless networks is currently being rolled out, wireless networks still need further development to meet the requirements of dramatically increasing numbers of users and new applications and the resulting traffic expected in the coming decade and beyond. The sixth generation (6G) of wireless networks is envisioned to respond to this by providing services with massive access, ultra-reliability, low latency, intelligence, and security while maximizing the spectral/energy/cost efficiency. Unmanned aerial vehicles (UAVs) have attracted considerable research attention due to their mobility and ability to line-of-sight (LoS) coverage in areas that suffer from low channel quality. However, how to characterize a UAV’s coverage area is a challenging problem and has not been thoroughly studied. To address this issue, in this paper we investigate the coverage performance of base station (BS) to UAV communications with a number of interfering mobile users. We first establish a Nakagami-m fading channel model for BS-toUAV wireless communications. Then, we derive a closed-form expression for the UAV’s average coverage probability under the scenario of interfering mobile users. Finally, numerical results confirm our derived analytical results and evaluate the UAV’s performance under different scenarios that anticipate 6G wireless networking models.

关键词： 6G mobile communication Fading channels Base stations Closed-form solutions Wireless networks Probability Autonomous aerial vehicles

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A Survey of Integrating Generative Artificial Intelligence and 6G Mobile Services: Architectures, Solutions, Technologies and Outlooks

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IEEE Transactions on Cognitive Communications and Networking 2025年第3期11卷 1334-1356页

作者： Liu, Yi-Jing Du, Hongyang Xu, Xinyi Zhang, Ruichen Feng, Gang Cao, Bin Niyato, Dusit Kim, Dong In Jamalipour, Abbas Letaief, Khaled B. Tafazolli, Rahim University of Electronic Science and Technology of China National Key Lab on Wireless Communications Chengdu China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of University of Sydney School of Electrical and Computer Engineering Sydney Australia Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong University of Surrey Institute for Communication Systems School of Computer Science and Electronic Engineering United Kingdom

Generative artificial intelligence (GenAI) is rapidly driving a new phase of artificial intelligence revolution, marked by various applications such as ChatGPT, Sora and DeepSeek. With powerful capabilities in content generation, personalization and user interaction, GenAI can drive significant enhancements in various mobile services to satisfy individual user preferences. Meanwhile, mobile services offer substantial support for accessibility, functionality and intelligence of GenAI applications. However, current research on integrating GenAI and mobile services remains nascent, lacking necessary theoretical and technical support. In this survey, we break down a new vision to guide research on integrating GenAI and mobile services, aiming to improve 6G intelligence levels and enable 6G mobile networks to support the integration of GenAI and mobile services effectively. We begin by examining the necessity of integrating GenAI with 6G mobile services and exploring potential learning architectures of GenAI models within mobile networks. Next, we analyze potential solutions from two perspectives: how 6G mobile networks empower GenAI and how GenAI enables mobile services. We subsequently explore a promising use case, along with essential features and techniques, to facilitate the integration of GenAI and mobile services. Finally, we discuss future research directions, aiming at realizing intelligent 6G networks. © 2015 IEEE.

关键词： Generative adversarial networks

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Sequential fusion estimation for multisensor systems with non-Gaussian noises

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Science China(Information Sciences) 2020年第12期63卷 153-165页

作者： Liping YAN Chenying DI Q.M.Jonathan WU Yuanqing XIA Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology Department of Electrical and Computer Engineering University of Windsor

The sequential fusion estimation for multisensor systems disturbed by non-Gaussian but heavytailed noises is studied in this paper. Based on multivariate t-distribution and the approximate t-filter,the sequential fusion algorithm is presented. The performance of the proposed algorithm is analyzed and compared with the t-filter-based centralized batch fusion and the Gaussian Kalman filter-based optimal centralized fusion. Theoretical analysis and exhaustive experimental analysis show that the proposed algorithm is effective. As the generalization of the classical Gaussian Kalman filter-based optimal sequential fusion algorithm, the presented algorithm is shown to be superior to the Gaussian Kalman filter-based optimal centralized batch fusion and the optimal sequential fusion in estimation of dynamic systems with non-Gaussian noises.

关键词： state estimation sequential fusion non-Gaussian disturbance heavy-tailed noise multivariate t-distribution

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Robust Control of Feeding Speed for Coal Mine Tunnel Drilling Machines

Robust Control of Feeding Speed for Coal Mine Tunnel Drillin...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Xiao Liu Luefeng Chen Min Wu Weihua Cao Chengda Lu Witold Pedrycz School of Automation China University of Geosciences Wuhan China School of Automation the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems and the Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Department of Electrical and Computer Engineering University of Alberta Edmonton AB Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Department of Computer Engineering Istinye University Sariyer/Istanbul Turkey

ISBN: (数字)9798350340266

ISBN: (纸本)9798350340273

Changes in coal seam hardness cause fluctuations in the feed resistance at the drill bit during the drilling process, leading to unstable feeding speed. This paper proposes a robust dynamic output feedback controller to suppress disturbances caused by the variations in coal seam hardness in the feed system. Firstly, an unknown parameter measuring coal seam hardness is introduced, and an uncertain model of the feeding system is established based on the finite element model of the drill string. By designing weighted functions based on industrial field requirements and constructing a generalized plant, the controller achieves loop shaping, reducing the low-frequency impact of coal seam hardness variations on the feed system and suppressing the systems resonance peak. Simulation results demonstrate that the controller effectively suppresses parameter variations and external disturbances caused by changes in coal seam hardness, achieving stable control of the drilling speed.

关键词： Drilling Robust control Uncertainty Robust stability Simulation Coal Resonance

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IMPS: Informative Map Point Selection for Visual-Inertial SLAM

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2024年第9期9卷 5595-5605页

作者： Changxiang Liu Hongshan Yu Qiang Fu Xieyuanli Chen Naveed Akhtar Zhi-Hong Mao College of Electrical and Information Engineering School of Robotics Quanzhou Institute of Industrial Design and Machine Intelligence Innovation Hunan University Changsha China National University of Defense Technology Changsha China School of Computing and Information Systems University of Melbourne Melbourne VIC Australia Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh PA USA

Visual-inertial SLAM (VINS) stands at the forefront of advancements in computer vision, robotics, and autonomous driving, revolutionizing the way we perceive and navigate the world. The typical approach in optimization-based methods for VINS is to estimate camera poses by minimizing the reprojection errors of all corresponding map points. However, not all map points are suitable for accurate pose estimation. Some map points introduce noise and can compromise the accuracy of the estimation. Current methods primarily focus on removing 2D noise points, which proves to be ineffective since triangulation can increase the uncertainty of map points. To address this problem, we propose a new method for Informative Map Point Selection (IMPS) in VINS systems. IMPS identifies the most informative map points by utilizing mutual and geometric information as the optimization target. We integrate IMPS into a VINS system and evaluate its performance using publicly available EuRoC, TUM and M2DGR datasets, as well as our own data. Experimental results demonstrate that our method surpasses existing VINS methods and achieves state-of-the-art pose estimation performance. Importantly, IMPS functions as an independent module with strong generalization capabilities, allowing for easy integration into other VINS systems and enhancing pose estimation performance. This integration not only elevates pose estimation accuracy but also holds great promise for advancing applications in intelligent driving and unmanned systems.

关键词： Simultaneous localization and mapping Optimization Cameras Pose estimation Robots Mutual information Intelligent vehicles

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Inspection of a helicopter blade using drone based active thermography 45

Inspection of a helicopter blade using drone based active th...

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Thermosense: Thermal Infrared Applications XLV 2023

作者： Pant, Shashank Genest, Marc Ibarra-Castanedo, Clemente Avdelidis, Nicolas P. Valdes, Julio Deane, Shakeb Zolotas, Argyrios Maldague, Xavier P. National Research Council Canada OttawaONK1A 0R6 Canada Computer Vision and Systems Laboratory Department of Electrical and Computer Engineering Laval University Quebec CityQCG1V 0A6 Canada School of Aerospace Transport and Manufacturing Cranfield University CranfieldMK43 0AL United Kingdom

ISBN: (纸本)9781510661868

A drone-based inspection system that can move "freely" around an aircraft to perform the inspection of all the areas of interest in a fast and effective manner can have significant impact in reducing inspection time and cost. However, active thermography inspection using drone is challenging because the drone carrying the optical and thermal cameras is subjected to vibration and undesired motion. Since active thermography relies on the pixel temperature evolution over time, an unstable thermal video from a flying drone can cause error in the output results as any movement between the acquired images will affect the pixel position in the successive frames and thus disrupt the monitoring of the temperature evolution. This paper presents the outcome of experimental runs, where a commercially available drone equipped with both thermal and optical cameras was used to inspect a helicopter Main Rotor Blade (MRB) in a laboratory environment. © 2023 SPIE.

关键词： Inspection

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