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2025 IEEE Texas power and Energy Conference, TPEC 2025

作者： Mishra, Aniruddh Barnes, Arthur K. Tabarez, Jose E. Mate, Adam University of Texas at Austin Department of Electrical and Computer Engineering AustinTX United States Information Systems and Modeling Group Los Alamos National Laboratory Los AlamosNM United States

ISBN: (纸本)9798331541125

Geomagnetic disturbances are a threat to the reliability and security of our national critical energy infrastructures. These events specifically result in geomagnetically induced currents, which can cause damage to transformers due to magnetic saturation. In order to mitigate these effects, blocker devices must be placed in optimal locations. Finding this placement requires a dc representation of the ac transmission lines, which this paper discusses. Different decisions in this process, including the method of representing the blocking devices, result in significant variations to the power loss calculations. To analyze these effects, we conclude the paper by comparing the losses on a sample network with different modeling implementations. © 2025 IEEE.

关键词： Transformer windings

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Robust Error State Sage-Husa Adaptive Kalman Filter for UWB Localization

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IEEE Sensors Journal 2025年第9期25卷 16034-16049页

作者： Juston, Marius Gupta, Soumil Mathur, Shrey Norris, William R. Nottage, Dustin Soylemezoglu, Ahmet University of Illinois Urbana-Champaign The Grainger College of Engineering Industrial and Enterprise Systems Engineering Department UrbanaIL61801 United States University of Illinois Urbana-Champaign The Grainger College of Engineering Electrical and Computer Engineering Department UrbanaIL61801 United States Construction Engineering Research Laboratory U.S. Army Corps of Engineers Engineering Research and Development Center ChampaignIL61822 United States

Given the sensors’ path and interference mitigation capabilities, ultra-wideband (UWB)-based positioning systems have demonstrated high accuracy and reliability. This work aims to improve the Sage-Husa fuzzy adaptive filter (SHFAF) proposed in previous works by modifying the motion model to a 3-D ground-based differential drive robot using IMU and wheel encoder kinematic fused control inputs. In addition to the changed motion model kinematics, this article improved the positive definite constraint on P and R during dynamic estimations, thus making the filter more robust to outliers. An improvement to the computation and derivation of the fuzzy logic system for the SHFAF based on the adaptive neuro-fuzzy inference system (ANFIS) structure was developed, and training the fuzzy system using gradient descent was applied to improve the system’s accuracy. Experimental validation was conducted using real-world data from a Clearpath Jackal robot equipped with Qorvo UWB sensors and static nodes. Regarding localization accuracy, the proposed velocity-based SHFAF (VelSHFAF) system outperformed the previous SHFAF implementation by approximately 30%–25% across two test courses, demonstrating its enhanced performance and reliability. © 2001-2012 IEEE.

关键词： Adaptive filters

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Guest Editorial: Special Issue on Human-Machine Fusion Decision-Making for Emergency Handling

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IEEE Transactions on Automation Science and engineering 2025年 22卷 4427-4433页

作者： Wu, Edmond Q. Li, Jianqiang Chen, Guimin Yuce, Mehmet R. Islam, Shafiqul Ser, Javier Del Yu, Hui Liu, Peter Xiaoping Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China College of Computer and Software Engineering Shenzhen University Shenzhen518060 China State Key Laboratory for Manufacturing Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Systems Engineering Monash University MelbourneVIC3800 Australia Xavier University of Louisiana New OrleansLA70125 United States Tecnalia Institute Bizkaia48160 Spain School of Creative Technologies University of Portsmouth PortsmouthPO1 2UP United Kingdom Department of Systems and Computer Engineering Carleton University OttawaONKIS 5B6 Canada

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TopoEEG: A Timesformer-Based Topographic Image Representation Method for Single-Trial Early Detection of P300

TopoEEG: A Timesformer-Based Topographic Image Representatio...

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IEEE International Symposium on Biomedical Imaging

作者： Jiazhen Hong Laleh Najafizadeh Department of Electrical and Computer Engineering Integrated Systems and NeuroImaging Laboratory Rutgers University

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Detecting P300 event-related potentials (ERPs) from electroencephalography (EEG) finds applications in several domains including brain computer interfaces (BCIs). Due to the low signal-to-noise ratio (SNR), however, detecting P300 from single trials has been challenging. As a result, multiple repetitions are typically needed, extending the BCI decision time. To address this problem, in this paper, we present TopoEEG, a novel method that generates a sequence of topography-based images from EEG, to capture both spatial and temporal information. These sequences are subsequently processed using the state-of-the-art video classification technique, TimeSformer. Evaluated using the BCI Competition II (IIb) dataset, results demonstrate that TopoEEG achieves single-trial P300 detection with F1-scores of 0.6830 and 0.7134 within the first 500 ms and 600 ms, respectively. These results highlight TopoEEG's potential to enhance the efficiency and speed of BCIs by reducing the need for multiple repetitions and the time required for decision-making in single-trial P300 detection.

关键词： Electric potential Attention mechanisms Decision making Image representation Electroencephalography Brain-computer interfaces Signal to noise ratio Biomedical imaging

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A variable structure model of neurons’ application in computer vision

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Multimedia Tools and Applications 2025年 1-11页

作者： Sridi, Alaeddine Attia, Mouna Bouallegue, Kais Electronics and Micro-Electronics Laboratory Faculty of Sciences of Monastir University of Monastir Monastir Tunisia The Higher Institute of Computer Science and Communication Technologies of Hammam Sousse University of Sousse Sousse Tunisia Laboratory of Computer Science Parallelism and Intelligent Communicating Systems Faculty of Sciences of Tunisia University of Tunis ELManar Tunis Tunisia Department of Electrical Engineering Higher Institute of Applied Sciences and Technology of Sousse University of Sousse Sousse Tunisia

The neuron doctrine defines the neuron as the basic unit of the nervous system, which drives the dynamic behavior of our organs. This has led to neurons becoming the focus of modern neuroscience research and to the rise of neurocomputing, in which we try to digitally simulate human behavior. The goal being to better understand the human nervous system and gain more control over neuron-related diseases, helping doctors establish faster and more reliable medical diagnoses. In this context, we aim to simulate eye diseases and vision problems by convolving input images using a new class of neural networks based on a variable structure model of neurons presented in [1]. In this work, we focus on recreating the common convolution filters as a first step. This is the first time in neurocomputing literature that dendritic neurons have been used for dynamic convolution kernels generation and eye diseases simulation. We start with a presentation of our upgrades to the model’s flexibility and accuracy. Then, we propose a novel kernel generator algorithm to convolve the input images. Finally, we show its promising results in creating the outline, pixelization, and night convolution kernels, as well as control some metrics, including brightness and data retention. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Neurons

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Multi-Quadrotor Cooperative Encirclement and Capture Approach in Obstacle Environments

Multi-Quadrotor Cooperative Encirclement and Capture Approac...

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2025 IEEE International Conference on Mechatronics, ICM 2025

作者： Li, Zhenyu Wu, Ruixin Wang, Jin Hu, Chuanqi Zheng, Zhi Meng, Jun Lu, Guodong Zhejiang University State Key Laboratory of Fluid Power and Mechatronic Systems Hangzhou310058 China Zhejiang University Zhejiang Key Laboratory of Industrial Big Data and Robot Intelligent Systems Hangzhou310058 China Robotics Research Center of Yuyao City Ningbo315400 China College of Electrical Engineering Zhejiang University Hangzhou310027 China Center for Data Mining and Systems Biology College of Electrical Engineering Zhejiang University Hangzhou310027 China

ISBN: (纸本)9798331533892

This article proposes an algorithm designed for autonomous quadrotors to capture target quadrotors in complex obstacle environments, enabling multiple pursuers to perform distributed capture while simultaneously avoiding obstacles and collisions. We first introduce a 3D obstacle space representation method based on safety-priority reachable region (SPRR) and implement real-time construction for general point cloud maps. Subsequently, leveraging SPRR, we employ an area minimization approach to ensure the effective capture of the evader within a limited time. The outcomes are then integrated into a MINCO-based trajectory planning framework to ensure their practical execution by real quadrotors. Relying on SPRR, the optimization problem is simplified and the solution efficiency is improved. In the scenario of simulating the dynamics of real quadrotors, we compare the proposed algorithm with state-of-the-art evasion and pursuit methods, validating its effectiveness and advancements. This work presents a practical solution for multi-quadrotor collaborative capture. © 2025 IEEE.

关键词： Motion planning

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Reference Model Design in Multiple Timescale Adaptive Control

Reference Model Design in Multiple Timescale Adaptive Contro...

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AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Eves, Kameron Valasek, John Electrical and Computer Engineering Engineering Department Utah Tech University Saint GeorgeUT84770 United States Vehicle Systems and Control Laboratory Aerospace Engineering Department Texas A and M University College StationTX77843-3141 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

The method [K]control of Adaptive Multiple-timescale systems (KAMS) has been used as a method of adaptive control for systems with states that evolve at vastly different rates and with uncertain parameters. Prior research on KAMS has shown a relationship between the asymptotic behavior of the reference models and the behavior of the full-order closed-loop system. Poor reference model design can cause steady-state error or instability even if that reference model is acceptable in a single timescale context, due to adverse feedback between the reference models and the fast-state manifold. This paper simplifies KAMS control design and provides insight into multiple timescale reference models by identifying design considerations for the fast and slow reference models. For fast reference models, it is shown that the stability proofs used in KAMS control design necessarily require the manifold to be an asymptotically stable equilibrium of the fast reference model, despite prior work that allowed other possibilities. In the case of a slow reference model that is not asymptotically stable to its reference signal, it is common for the inner connection conditions to be dependent on the reference signal. This can cause the interconnection conditions to be ill-formatted for the application of the stability proofs used in KAMS control design. This paper addresses that case and proposes the use of Raffoul’s condition as a method to prove stability in the sense of Lyapunov. A linear quadrotor attitude stabilization problem is used as a framework for the slow reference model analysis. Results presented in the paper show that 1) the asymptotic stability of the fast reference model is a necessary condition for the KAMS stability proofs used in prior work and 2) Raffoul’s condition is a sufficient condition for Lyapunov sense stability even when the inner connection conditions are ill-formatted for the KAMS stability proofs used in prior work. © 2025, American Institute of Aeronautics

关键词： Model reference adaptive control

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A Novel Consensus Mechanism Based on Dynamic Sharding 20th

A Novel Consensus Mechanism Based on Dynamic Sharding

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20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： Zhang, Jingyu Teng, Yilong Sun, Yongtao Zhu, Shi Yang, Fangliao Luo, Lailong School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China Science and Technology on Information Systems Engineering Laboratory School of Systems Engineering National University of Defense Technology Changsha China Data Operations Center State Grid Hunan Electric Power Information and Communication Corporation Hunan Changsha China

ISBN: (纸本)9789819628292

Blockchain, as an emerging technology, has been widely studied by the researchers from academia and industry. Alliance chain, as an important form of blockchain, is often applied to smart grids, smart city and the Internet of Things to ensure the authenticity and reliability of data. Practical Byzantine Fault Tolerance (PBFT) algorithm is a key technology to ensure data consistency in the alliance chains, and it plays a critical role in improving system performance, security and scalability. However, with the increase of the number of nodes and transactions, the ability of PBFT to process transactions is challenged. In particular, PBFT’s linear transaction processing also limits its ability to process transactions in parallel. In view of the problems existing in PBFT, this paper studies the consensus mechanism optimization, and puts forward a consensus mechanism with better performance. In this paper, in order to solve the problem of weak parallel transaction processing capability of PBFT, we propose a novel Consensus Mechanism based on Dynamic Sharding (CMDS). CMDS firstly constructs a dynamic fragmentation model based on consistent hash algorithm to segment nodes, and then constructs a voting consensus mechanism based on reputation weight to achieve global state consistency. The experimental results show that CMDS can significantly improve the ability of parallel transaction processing and the performance of blockchain system. © IFIP International Federation for Information Processing 2025.

关键词： Blockchain

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Disturbance Rejection Control for PMSGs Based on Novel Sliding Mode Control Law and Unknown System Disturbance Estimator

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IEEE Transactions on Energy Conversion 2025年

作者： Wei, Chun Yin, Tianqi Qiao, Wei Zhu, Ying Zhejiang University of Technology College of Information Engineering Zhejiang Hangzhou310023 China University of Nebraska-Lincoln Power and Energy Systems Laboratory Department of Electrical and Computer Engineering LincolnNE68588 United States Hohai University School of Electrical and Power Engineering Nanjing211100 China

This paper proposes a novel terminal sliding mode control strategy for the speed control of permanent magnet synchronous generator (PMSG)-based wind energy conversion systems (WECSs) to improve wind power generation efficiency. Firstly, a novel terminal sliding mode function based on exponential convergence is constructed, and a controller is designed accordingly, effectively addressing the system's nonlinear characteristics while achieving high precision tracking and chattering suppression with rapid convergence. Secondly, an unknown system dynamic estimator (USDE) is designed to estimate disturbances caused by external environments and inaccurate model parameters, further enhancing the system's disturbance rejection capability. Finally, the proposed USDE-based novel exponential sliding mode control is applied in a 1000 W PMSG-based WECS hardware simulator. Experimental results indicate that compared to the full-order sliding mode controller, the proposed control method exhibits stronger speed tracking, chattering suppression, and disturbance rejection capabilities. © 1986-2012 IEEE.

关键词： Sliding mode control

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Uncovering Spatiotemporal Differences in Cortical Activity Corresponding to two Tasks Using Data-Driven ADASSO Algorithm

Uncovering Spatiotemporal Differences in Cortical Activity C...

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IEEE International Symposium on Biomedical Imaging

作者： Siwei Mai Ali E. Haddad Laleh Najafizadeh Department of Electrical and Computer Engineering Integrated Systems and NeuroImaging Laboratory Rutgers University Department of Computer Engineering University of Basrah

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Identifying spatiotemporal differences in brain functional dynamics corresponding to two tasks is critical for under-standing how specific neural processes contribute to distinct tasks or cognitive functions. Traditional methods rely on imposing assumptions and limits on the location and timing of activities, while machine-learning-based methods generally lack offering interpretable insights. This highlights the need for new data-driven approaches to capture spatial and temporal differences in brain activity between two tasks, while also providing interpretable explanations of the neural processes underlying these differences. In this work, we formulate the problem of finding the spatial and temporal differences in the dynamics of brain function corresponding to two motor imagery (MI) tasks (left hand movement vs right hand movement) as a discriminative discrete basis problem (DDBP). We apply the data-driven asymmetric discriminative associative algorithm (ADASSO) to EEG data collected during these tasks to uncover the key functional components of the brain's functional dynamics that differentiate between them. Results suggest that hand movements are strongly associated with high-confidence activation in the motor cortex, verifying the effectiveness of the ADASSO algorithm in identifying the location and timing of cortical activities that distinguish between the two task classes.

关键词： Hands Brain Heuristic algorithms Motors Electroencephalography Brain-computer interfaces Spatiotemporal phenomena Timing Biomedical imaging

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