检索结果-内蒙古大学图书馆

IEEE General Meeting Power& Energy Society

作者： Sarangan Rajendran Adithya Melagoda Sangar Shanthanam Nirasha Bataduvaarachchi Arun-Kaarthick Manoharan Visvakumar Aravinthan Amirthagunaraj Yogarathinam Al Tamimi Charles R. Yokley Electrical and Computer Engineering Wichita State University Wichita KS USA Interdisciplinary Science Department Brookhaven National Laboratory Upton NY USA Transmission Planning and Policy Sunflower Electric Power Corporation Wichita KS USA

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Distributed Energy Resources (DER) aid to an electrical grid in more implicit ways beyond just supplying of power. During High Impact (HI) events caused by weather disruptions, when the grid is in a reduced state of operation, significant presence of DERs has been found to mitigate further impacts to the system. Grid operators who benefit from having DERs in their system save on system recovery costs that can be redirected back to the prosumers who contribute through DER. In this work, a methodology is introduced to value the ancillary benefits provided by the DERs during a HI event, that can be used to compensate the prosumers. The proposed method is tested on a test system with high wind energy generation, where rooftop solar PV acts as the DER, and the validity of the results are explored. The method is further expanded to include all probable variations of HI events and quantify the value of a location acting as a DER host.

关键词： Wind energy generation Measurement Costs Voltage System recovery Distributed power generation Reliability Meteorology Load modeling Load flow

来源：评论

学校读者我要写书评

暂无评论

Cross-Subcarrier Precoder Design for Massive MIMO-OFDM Downlink

Cross-Subcarrier Precoder Design for Massive MIMO-OFDM Downl...

引用

IEEE Conference on Vehicular Technology (VTC)

作者： Yuxuan Zhang An-An Lu Bingyan Liu Xiqi Gao Xiang-Gen Xia National Mobile Communications Research Laboratory Southeast University Nanjing China Purple Moutain Laborataries Nanjing China Department of Electrical and Computer Engineering University of Delaware Newark DE USA

We propose a cost efficient cross-subcarrier pre-coder design (CSPD) for massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) downlink with imperfect channel state information (CSI). To reduce the high computational complexity caused by individual precoder design for each subcarrier, we design transform domain precoding vectors (TDPVs), from which the precoders for a set of subcarriers can be obtained through a transform. The number of TDPVs is much less than that of subcarriers, and the number of total parameters to be designed can be reduced significantly. The main objective is to maximize an upper bound of the ergodic sum-rate by exploiting the a posteriori beam-based statistical channel model. We provide a concave minorizing function of the upper bound of the ergodic sum-rate and then derive the stationary points of a concave quadratic optimization problem with this minorizing function. To reduce the dimension of the matrix inversion in the stationary points, we propose an algorithm by using block coordinate descent (BCD) method with power allocation.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Dynamic Spectrum Aggregation and Access for Rescue Cognitive Networks Using Multi-Agent Actor-Critic Reinforcement Learning

引用

IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Zhang, Wensheng Ding, Wenjiao Wang, Cheng-Xiang Chen, Yunfei Abreu, Giuseppe Liu, Zhi Sun, Jian Han, Zhu Shandong University School of Information Science and Engineering Shandong Key Laboratory of Intelligent Communication and Sensing-Computing Integration Qingdao266237 China Industrial and Commercial Bank Of China Limited Shandong Branch Jinan China Southeast University National Mobile Communications Research Laboratory School of Information Science and Engineering Nanjing211189 China Purple Mountain Laboratories Pervasive Communication Research Center Nanjing211111 China Durham University Department of Engineering DurhamDH1 3LE United Kingdom Constructor University Bremen GGMBH School of Computer Science and Engineering Campus Ring 1 Bremen28757 Germany University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

Unmanned aerial vehicles (UAVs) based emergency communication is gaining more attention for disaster rescue, where the UAV can work as an aerial base station to provide spectrum access in areas with power outages or network interruptions. However, UAVs can only provide limited spectrum access for a short period of time due to limited battery energy. Aiming to providing spectrum access to more users under spectrum and energy limitations, a new dynamic spectrum aggregation and access scheme is proposed for rescue cognitive networks (RCNs), in which different kinds of users are modeled as primary users (PUs) and secondary users (SUs). In each time slot, SUs with different spectrum sensing capabilities, spectrum aggregation capabilities, and bandwidth demands need to sense and aggregate multiple spectrum slots that are not occupied by PUs for data transmission. Based on selected channel states, the reward is designed as a function of transmission rates and is fed back to SUs. A maximum entropy based multi-agent actor-critic (ME-MAAC) algorithm is proposed to optimize the action selection policy for SUs. Simulation results show that the proposed ME-MAAC algorithm can effectively achieve higher achievable rate, lower mean collision rate and lower power consumption, than state-of-the-art methods based on Deep Q-Network (DQN) algorithm. The proposed RCNs provide a novel spectrum usage paradigm for emergency communication and rescue networks. © 2015 IEEE.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Bias Correction in Urban Building Energy Modeling for Chicago Using Machine Learning 3

Bias Correction in Urban Building Energy Modeling for Chicag...

引用

3rd International Conference on Intelligent Data Science Technologies and Applications, IDSTA 2023

作者： Chowdhury, Shovan Li, Fengqi Stubbings, Avery New, Joshua Garg, Ankur Correa, Santiago Bacabac, Kevin The University of Tennessee The Bredesen Center for Interdisciplinary Research KnoxvilleTN37996 United States Oak Ridge National Laboratory Electrification and Energy Infrastructures Division Oak RidgeTN37831 United States Illinois Institute of Technology Department of Electrical and Computer Engineering ChicagoIL60616-3793 United States BlocPower BrooklynNY11210 United States

ISBN: (纸本)9798350339253

Urban-scale building energy modeling (UBEM) holds promise for optimizing energy usage across extensive geographic regions. However, there is a recognized bias between simulated energy consumption and actual measured data. This study, based on building data from Chicago, delved into bias correction techniques for enhancing the accuracy of UBEM energy consumption estimates. Initially, the AutoBEM simulation yielded a normalized mean bias error (NMBE) of 1.1% and 51% of Coefficient of the Variation of the Root Mean Square Error (CVRMSE) after outlier exclusion. To address this, three bias correction methods were deployed: Average Mean Bias Error based bias correction, Quantile mapping bias correction, and Machine learning-based bias correction using Linear Regression and Random Forest models. Post-correction results exhibited marked improvement. The NMBE values were diminished to 0 for Average MBE-based, 0.36 for Quantile Mapping, and 0 for Machine Learning-based corrections. Concurrently, the CVRMSE values registered reductions from an original 51 to 50.8 for Quantile Mapping, and 38.56 for Machine Learning-based corrections, pointing towards the effectiveness of specific bias correction methods in refining the precision of UBEM energy predictions. Such accurate estimations are paramount for informed energy planning and urban policy-making. © 2023 IEEE.

关键词： Mapping

来源：评论

学校读者我要写书评

暂无评论

Additively Manufactured Aperture-based FSS

Additively Manufactured Aperture-based FSS

引用

IEEE Instrumentation and Measurement Technology Conference

作者： Alexander Hook Doyle T. Motes Cody Morrow Kristen M. Donnell Department of Electrical and Computer Engineering Microwave Sensing (µSense) Laboratory Missouri University of Science and Technology Rolla Missouri USA Nondestructive Evaluation Division Texas Research Institute (TRI) Austin Inc. Austin Texas USA

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

Frequency selective surfaces (FSSs) are arrays of patch- or aperture-based elements with specific high frequency reflective and/or transmissive properties. The specific FSS response of a given design is dictated by the element dimensions and spacing relative to adjacent elements (collectively referred to as the unit cell), along with the substrate (and superstrate if applicable) properties. As it relates to sensing, the FSS response may be affected by environmental (e.g., temperature) or structural (e.g., strain) changes. To this end, FSS-based sensors have been considered in recent years for a myriad of sensing applications including structural health monitoring (SHM). Concurrent to this, the growth of additive manufacturing (AM) technologies has opened the door for in-situ printing of FSS-based sensors. To this end, this work studies the potential for Fused Deposition Modeling (FDM) type AM-fabricated FSS-based sensors. Of particular interest is the effect of the reduced electrical conductivity (common to currently available conductive FDM filaments) on the performance and repeatability of FDM-printed sensors. Results from this work show that aperture-based FSS designs maintain a resonant response when printed with a reduced-conductivity material, whereas patch-based designs no longer provide FSS functionality. In addition, the results of a measurement study on a set of five additive manufactured FSS (AM-FSS) sensors show good agreement between the simulations and measurements and a high level of measurement repeatability. For these reasons, aperture-based FSS sensors fabricated via the FDM-AM process using a reduced conductivity filament are a viable option for aperture-based FSS fabrication and sensing applications.

关键词： Fabrication Visualization Additives Resonant frequency Conductivity Three-dimensional printing Sensors

来源：评论

学校读者我要写书评

暂无评论

Toward Enhanced Reinforcement Learning-Based Resource Management via Digital Twin: Opportunities, Applications, and Challenges

arXiv

引用

arXiv 2024年

作者： Chen, Nan Wang, Xiucheng Li, Zan Yin, Zhisheng Luan, Tom Shen, Xuemin The State Key Laboratory of ISN School of Telecommunications Engineering Xidian University Xi’an710071 China School of Cyber Engineering Xidian University Xi’an710071 China The Department of Electrical and Computer Engineering University of Waterloo WaterlooN2L 3G1 Canada

This article presents a digital twin (DT)-enhanced reinforcement learning (RL) framework aimed at optimizing performance and reliability in network resource management, since the traditional RL methods face several unified challenges when applied to physical networks, including limited exploration efficiency, slow convergence, poor long-term performance, and safety concerns during the exploration phase. To deal with the above challenges, a comprehensive DT-based framework is proposed to enhance the convergence speed and performance for unified RL-based resource management. The proposed framework provides safe action exploration, more accurate estimates of long-term returns, faster training convergence, higher convergence performance, and real-time adaptation to varying network conditions. Then, two case studies on ultrareliable and low-latency communication (URLLC) services and multiple unmanned aerial vehicles (UAV) network are presented, demonstrating improvements of the proposed framework in performance, convergence speed, and training cost reduction both on traditional RL and neural network based Deep RL (DRL). Finally, the article identifies and explores some of the research challenges and open issues in this rapidly evolving field. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

来源：评论

学校读者我要写书评

暂无评论

Pump-induced magnon anticrossing due to three-magnon splitting and confluence

引用

Physical Review B 2025年第18期111卷 L180410-L180410页

作者： Tao Qu Yuzan Xiong Xufeng Zhang Yi Li Wei Zhang Department of Physics and Astronomy University of North Carolina at Chapel Hill Chapel Hill North Carolina 27599 USA Department of Electrical and Computer Engineering Northeastern University Boston Massachusetts 02115 USA Department of Physics Northeastern University Boston Massachusetts 02115 USA Materials Science Division Argonne National Laboratory Argonne Illinois 60439 USA

We present a plausible mechanism for achieving a magnon-magnon level repulsion spectrum originating from the oscillation between the splitting and confluence in a three-magnon scattering process. When a magnetostatic mode on a YIG sphere is pumped by a microwave signal near the magnon resonance frequency with an increasing amplitude, the generated magnon condensate at half of the pumping frequency exerts a backaction to the original magnon mode. Such a strong nonlinear coupling manifests a striking feature of a “bending effect” of the magnetostatic mode spectra, akin to the anticrossing observed in a strongly coupled magnon-photon system.

关键词： Magnetostatics

来源：评论

学校读者我要写书评

暂无评论

Multi-modal Image and Radio Frequency Fusion for Optimizing Vehicle Positioning

arXiv

引用

arXiv 2024年

作者： Huan, Ouwen Luo, Tao Chen, Mingzhe Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing100876 China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL33146 United States

In this paper, a multi-modal vehicle positioning framework that jointly localizes vehicles with channel state information (CSI) and images is designed. In particular, we consider an outdoor scenario where each vehicle can communicate with only one BS, and hence, it can upload its estimated CSI to only its associated BS. Each BS is equipped with a set of cameras, such that it can collect a small number of labeled CSI, a large number of unlabeled CSI, and the images taken by cameras. To exploit the unlabeled CSI data and position labels obtained from images, we design an meta-learning based hard expectation-maximization (EM) algorithm. Specifically, since we do not know the corresponding relationship between unlabeled CSI and the multiple vehicle locations in images, we formulate the calculation of the training objective as a minimum matching problem. To reduce the impact of label noises caused by incorrect matching between unlabeled CSI and vehicle locations obtained from images and achieve better convergence, we introduce a weighted loss function on the unlabeled datasets, and study the use of a meta-learning algorithm for computing the weighted loss. Subsequently, the model parameters are updated according to the weighted loss function of unlabeled CSI samples and their matched position labels obtained from images. Simulation results show that the proposed method can reduce the positioning error by up to 61% compared to a baseline that does not use images and uses only CSI fingerprint for vehicle positioning. Copyright © 2024, The Authors. All rights reserved.

关键词： Expectation maximization algorithm

来源：评论

学校读者我要写书评

暂无评论

PCA: Semi-Supervised Segmentation with Patch Confidence Adversarial Training

引用

IEEE Transactions on Network Science and engineering 2025年第4期12卷 2473-2486页

作者： Xu, Zhenghua Yang, Runhe Xu, Zihang Zhang, Shuo Yang, Yuchen Liu, Weipeng Xu, Weichao Chen, Junyang Lukasiewicz, Thomas Leung, Victor C. M. Hebei University of Technology State Key Laboratory of Reliability and Intelligence of Electrical Equipment School of Health Sciences and Biomedical Engineering Tianjin China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong The Johns Hopkins University Department of Applied Mathematics and Statistic Baltimore United States Hebei University of Technology School of Artificial Intelligence Tianjin China Hebei Provincial Hospital of Traditional Chinese Medicine Digestive Disease Center Shijiazhuang China Shenzhen University College of Computer Science and Software Engineering Shenzhen China University of Oxford Department of Computer Science University of Oxford United Kingdom Department of Computer Science United Kingdom Vienna University of Technology institute of Logic and Computation nstitute of Logic and Computation Vienna Austria

Deep-learning-based semi-supervised learning (SSL) methods have achieved a strong performance in medical image segmentation, which can alleviate doctors' expensive annotation by utilizing a large amount of unlabeled data. Unlike most existing semi-supervised learning methods, adversarial training methods distinguish samples from different sources by learning the data distribution of the segmentation map, leading the segmenter to generate more accurate predictions. We argue that the current performance restrictions for such approaches are the problems of feature extraction and learning preferences. In this paper, we propose a new semi-supervised adversarial method called Patch Confidence Adversarial Training (PCA) for medical image segmentation. The PCA method's discriminator penalizes patch-level structures, guiding the generator to optimize different patch areas, by leveraging pixel context, the generator is driven to focus on high-frequency features, making it harder to deceive the discriminator and easy to converge to an ideal state, which more effectively guides the segmenter to generate high-quality pseudo-labels. Furthermore, at the discriminator's input, we supplement image information constraints, making it simpler to fit the expected data distribution. Extensive experiments on the Automated Cardiac Diagnosis Challenge (ACDC) 2017 dataset and the Brain Tumor Segmentation (BraTS) 2019 challenge dataset show that our method outperforms the state-of-the-art semi-supervised methods, which demonstrates its effectiveness for medical image segmentation. © 2025 IEEE. All rights reserved.

关键词： Adversarial machine learning

来源：评论

学校读者我要写书评

暂无评论

Structural phase transitions between layered Indium Selenide for integrated photonic memory

arXiv

引用

arXiv 2025年

作者： Li, Tiantian Wang, Yong Li, Wei Mao, Dun Benmore, Chris J. Evangelista, Igor Xing, Huadan Li, Qiu Wang, Feifan Sivaraman, Ganesh Janotti, Anderson Law, Stephanie Gu, Tingyi Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States Department of Materials Science and Engineering University of Delaware NewarkDE19716 United States Los Alamos National Laboratory Computer Computational and Statistical Sciences Division Los AlamosNM87545 United States X-ray Science Division Argonne National Laboratory LemontIL60439 United States Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States

The primary mechanism of optical memoristive devices relies on the phase transitions between amorphous-crystalline states. The slow or energy hungry amorphous-crystalline transitions in optical phase-change materials are detrimental to the devices’ scalability and performance. Leveraging the integrated photonic platform, we demonstrate a single nanosecond pulse triggered nonvolatile and reversible switching between two layered structures of indium selenide (In2Se3). High resolution pair distribution function reveals the detailed atomistic transition pathways between the layered structures. With inter-layer ‘shear glide’ and isosymmetric phase transition, the switching between α- and β-structural states contain low re-configurational entropy, allowing reversible switching between layered structures. Broadband refractive index contrast, optical transparency, and volumetric effect in the crystalline-crystalline phase transition are experimentally characterized in molecular beam epitaxy-grown thin films and compared to ab-initial calculations. The nonlinear resonator transmission spectra measure incremental linear loss rate of 3.3 GHz introduced by 1.5 µm long In2Se3 covered layer, resulting from the combinations of material absorption and scattering. © 2025, CC BY.

关键词： Molecular beam epitaxy

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：