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arXiv 2025年

作者： Belabbas, Mohamed-Ali Chen, Xudong Electrical and Computer Engineering Department and Coordinated Science Laboratory University of Illinois Urbana-Champaign United States Electrical and Systems Engineering Washington University in St. Louis United States

We address the infinite-horizon minimum energy control problem for linear time-invariant finite-dimensional systems (A, B). We show that the problem admits a solution if and only if (A, B) is stabilizable and A does n... 详细信息

关键词： Linear time-invariant system

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Taming "data-hungry" reinforcement learning? Stability in continuous state-action spaces 38

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

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38th Conference on Neural Information Processing systems, NeurIPS 2024

作者： Duan, Yaqi Wainwright, Martin J. Department of Technology Operations and Statistics Stern School of Business New York University New YorkNY10012 United States Laboratory for Information and Decision Systems Statistics and Data Science Center Department of Electrical Engineering and Computer Science Department of Mathematics Massachusetts Institute of Technology CambridgeMA02139 United States

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. © 2024 Neural information processing systems foundation. All rights reserved.

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IRS/UAV-Based Edge-Computing and Traffic-Offioading Over 6G THz Mobile Wireless Networks

IRS/UAV-Based Edge-Computing and Traffic-Offioading Over 6G ...

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2023 IEEE International Conference on Communications, ICC 2023

作者： Wang, Fei Zhang, Xi Chongqing Key Laboratory of Non-linear Circuit and Intelligent Information Processing College of Electronic and Information Engineering Southwest University Chongqing400715 China Networking and Information Systems Laboratory Department of Electrical and Computer Engineering Texas A&m University College StationTX77843 United States

ISBN: (纸本)9781538674628

Intelligent reflecting surface (IRS), unmanned aerial vehicle (UAV), and Terahertz (THz) communications, which are recognized as 6G promising techniques, have attracted significant attentions. We propose UAV energy minimization schemes for IRS/UAV-based mobile-edge-computing (MEC) and traffic-offloading over broadband THz mobile networks. In the networks, multiple UAVs serving as MEC-servers collect data from multiple ground users (GUs) with the assistance of a set of passive IRSs. First, we formulate a UAV energy minimization problem, which jointly optimizes the IRSs' phase shifts, UAVs' trajectories, and system computation and communication resources. For THz communications, since the composite channel power gains of GUs are complicated functions of UAVs' trajectories and IRSs' phase shifts, the formulated optimization problem is non-convex. Then, using the alternating optimization (AO) technique, we decompose this non-convex optimization problem into three sub-problems which then can be iteratively solved. Finally, we validate and evaluate the proposed schemes by numerical analyses, which show that the energy consumption of UAVs can be reduced by around 40% by using IRSs. © 2023 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Enhancing Energy Management System for a Hybrid Wind Solar Battery Based Standalone Microgrid

Enhancing Energy Management System for a Hybrid Wind Solar B...

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International Conference on Environment and electrical engineering (EEEIC)

作者： Ahmed Aghmadi Hossam Hussein Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami USA

This paper introduces a highly efficient energy management system for a microgrid that combines PV system, wind turbine, and battery. The study presents an effective energy management system specifically designed for a small-scale hybrid microgrid, focusing on the development of solar and wind energy conversion systems and battery storage systems. Power electronic converters and control algorithms are implemented to evaluate the microgrid's operation. The energy management system plays a crucial role in maintaining power balance amidst varying renewable energy generation and load demand conditions. The microgrid is a testing platform to evaluate control algorithms, energy management systems, and various test scenarios. It provides a controlled environment for assessing the performance and efficiency of these systems. The efficient energy management system comprises photovoltaic (PV) modules, wind turbines, and batteries. The proposed strategy effectively manages the switching between the PV panel and the battery storage system while prioritizing power generation from the wind turbine. The efficacy and success of this strategy in microgrid control under various operational circumstances are demonstrated through MATLAB simulation results.

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Wavelet Convolutional Neural Network for Low-Resolution Brain MRI Images 22

Wavelet Convolutional Neural Network for Low-Resolution Brai...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Oh, Soo Min Li, Yifan Chong, Jo Woon Massachusetts Institute of Technology Wireless Information and Network Sciences Laboratory CambridgeMA02139 United States Massachusetts Institute of Technology Laboratory for Information and Decision Systems CambridgeMA02139 United States Texas A&m University Department of Statistics College StationTX77843 United States Texas Tech University Department of Electrical and Computer Engineering LubbockTX79409 United States Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of

ISBN: (纸本)9798331520526

Low-resolution images inherently contain less information, making effective feature extraction more challenging and posing difficulties for training neural networks. However, if neural networks can be trained successfully on low-resolution images, this could significantly reduce memory storage requirements and computational costs. In this study, we address the limitations of low-resolution brain MRI images by enhancing the available information through wavelet transform techniques. Specifically, we leverage the high-frequency coefficients obtained from wavelet transforms and the Hurst exponent to improve feature representation and optimize model training for convolutional neural networks (CNN), which is referred to as the wavelet CNN (WCNN). We demonstrate that WCNN outperforms standard CNNs in multi-class classification tasks, distinguishing among four categories of brain MRI images such as normal, glioma, meningioma, and pituitary tumor images. This approach highlights the potential to achieve high classification accuracy even with low-resolution data, ultimately reducing the memory and computational resources required for data processing and model training. © 2025 IEEE.

关键词： Convolutional neural networks

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Enhancing Solar Radiation Forecasting with Hybrid Ensemble Empirical Mode Decomposition and Machine Learning Techniques

Enhancing Solar Radiation Forecasting with Hybrid Ensemble E...

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International Conference on Environment and electrical engineering (EEEIC)

作者： Ahmed. Aghmadi Osama Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami USA

The importance of short-term solar radiation forecasting for power system use and management cannot be overstated. However, Non-stationarity and unpredictability make accurate forecasting difficult. Time series approaches are suitable for forecasting stationary time series derived from a non-stationary sequence. Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) are time-domain decomposition methods used to separate the components of the original solar radiation time series that have distinct time 1 -scale characteristics. As a result, the components are forecasted using the Back Propagation Neural Network (BPNN) model, which is a simple and effective machine learning method. This paper presents a hybrid EMD/EEMD-BPNN model for predicting short-term solar irradiance. To identify unique data information at different time scales, EMD/EEMD first breaks up the solar radiation time series into several stationary or fundamental sub-series. The next step is to create BPNN models with specified parameters for each subsequence to forecast new ones. Finally, each subsequence's projected value is combined to generate the final forecast result. The accuracy of solar forecasts has greatly increased, especially when utilizing hybrid methods. Furthermore, using the proposed hybrid technique for multistep forecasting resulted in even more improvement. The basic BPNN model for 15 min time step achieves predicting with a root mean square error (RMSE) of roughly 416.04 W/m2 for 15min; however, this error decreases to 65.25 W/m2 with the EMD-Hybrid Model, and 32.86 W/m2 with the EEMD-Hybrid Model. In addition, the results of the skill score have proven clearly that EMD/EEMD-BPNN Model performs better compared to typical BPNN using several meteorological inputs

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Deep-subwavelength engineering of stealthy hyperuniformity

arXiv

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arXiv 2024年

作者： Park, Jusung Park, Seungkyun Kim, Kyuho Kwak, Jeonghun Yu, Sunkyu Park, Namkyoo Photonic Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Intelligent Wave Systems Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of Department of Electrical and Computer Engineering Inter-university Semiconductor Research Center SOFT Foundry Institute Seoul National University 1 Gwanak-ro Gwanak-gu Seoul08826 Korea Republic of

Light behaviours in disordered materials have been of research interest primarily at length scales beyond or comparable to the wavelength of light, because order and disorder are often believed to be almost indistinguishable in the subwavelength regime according to effective medium theory (EMT). However, it was recently demonstrated that the breakdown of EMT occurs even at deep-subwavelength scales when interface phenomena, such as the Goos-Hänchen effect, dominate light flows. Here we develop the engineering of disordered multilayers at deep-subwavelength scales to achieve angle-selective manipulation of wave localization. To examine the disorder-dependent EMT breakdown, we classify the intermediate regime of microstructural phases between deep-subwavelength crystals and uncorrelated disorder through the concept of stealthy hyperuniformity (SHU). In this classification, we devise nontrivial order-to-disorder transitions by selectively tailoring the short-range and long-range order in SHU multilayers, achieving angle-selective control of wave localization. The result paves the way to the realization of deep-subwavelength disordered metamaterials, bridging the gap between the fields of disordered photonics and metamaterials. © 2024, CC BY.

关键词： Multilayers

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Identifying Mild Traumatic Brain Injury via Vision Transformer and Bag of Visual Features

Identifying Mild Traumatic Brain Injury via Vision Transform...

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International IEEE/EMBS Conference on Neural engineering, CNE

作者： Fatemeh Koochaki Laleh Najafizadeh Department of Electrical and Computer Engineering Integrated Systems and NeuroImaging Laboratory Rutgers University Piscataway NJ USA

Due to lack of established criteria and reliable biomarkers, timely diagnosis of mild traumatic brain injury (mTBI) has remained a challenging problem. Widefield optical imaging of cortical activity in animals provides a unique opportunity to study injury-induced alterations of brain function. Motivated by the results of medical-imaging studies that employ patch-level-based approaches, this paper proposes to use two patch-based deep learning techniques for classifying brain images of mTBI and healthy Thyl-GCaMP6s transgenic mice. The first approach uses a Bag of Visual Word (BoVW) technique to represent each image as a histogram of local features derived from patches from all training data. The local features are extracted using an unsupervised convolutional autoencoder (CAE). The second approach employs a pre-trained vision transformer (ViT) model. The average accuracy for classifying mTBI and healthy brains for the CAE-BoVW and the ViT are 96.8% and 97.78%, respectively, outperforming results of a convolutional neural network (CNN) model. This work suggests that attention-based models can be utilized for the problem of classifying mTBI and healthy brain images.

关键词： Training Visualization Transfer learning Training data Brain modeling Transformers Optical imaging

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Robust Control of Feeding Speed for Coal Mine Tunnel Drilling Machines 25

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

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25th IEEE International Conference on Industrial Technology, ICIT 2024

作者： Liu, Xiao Chen, Luefeng Wu, Min Cao, Weihua Lu, Chengda Pedrycz, Witold 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 The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Department of Computer Engineering Sariyer Istanbul34396 Turkey

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. © 2024 IEEE.

关键词： Robust control

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Toward Scene Understanding with Depth and Object-Aware Clustering in Contested Environment 22

Toward Scene Understanding with Depth and Object-Aware Clust...

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22nd IEEE International Conference on Machine Learning and Applications, ICMLA 2023

作者： Bhurtel, Manish Siwakoti, Yuba R. Rawat, Danda B. Sadler, Brian M. Fossaceca, John M. Rice, Daniel O. Howard University Department of Electrical Engineering and Computer Science WashingtonDC United States U.S. Army Research Laboratory AdelphiMD United States Bae Systems Fast Labs BurlingtonMA United States

ISBN: (纸本)9798350345346

Scene understanding in a contested battlefield is one of the very difficult tasks for detecting and identifying threats. In a complex battlefield, multiple autonomous robots for multi-domain operations are likely to track the activities of the same threat/objects leading to inefficient and redundant tasks. To address this problem, we propose a novel and effective object clustering framework that takes into account the position and depth of objects scattered in the scene. This framework enables the robot to focus solely on the objects of interest. Our system model incorporates YOLOv5 object detection model (ODM) and a pre-trained depth estimation model (DEM), whose outputs are fed into the KMeans Clustering (KM C) model. To train YOLOv5, we create our own battlefield dataset entitled 'Common Objects in Battlefield (COBA)' which is split into a training dataset and a validation dataset using our customized object-aware stratified 7-fold cross-validation technique. We present the creation of minimum optimal clusters assuming the minimum number of available battlefield robots. These clusters exhibit accurate relative distances among objects, further validating the effectiveness of our approach for depth and object-aware clustering for scene understanding. Overall, our proposed approach facilitates efficient work division among robots, enhances decision-making capabilities, and helps to improve situational awareness in battlefield scenarios. © 2023 IEEE.

关键词： clustering depth estimation object detection scene understanding situational awareness task allocation

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