检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Shek, Chak Lam Bedi, Amrit Singh Basak, Anjon Novoseller, Ellen Waytowich, Nick Narayanan, Priya Manocha, Dinesh Tokekar, Pratap Department of Electrical and Computer Engineering University of Maryland College ParkMD United States Department of Computer Science University of Central Florida FL United States DEVCOM US Army Research Laboratory AdelphiMD United States Department of Computer Science University of Maryland College ParkMD United States

In this work, we present a novel cooperative multiagent reinforcement learning method called Locality based Factorized Multi-Agent Actor-Critic (Loc-FACMAC). Existing state-of-the-art algorithms, such as FACMAC, rely on global reward information, which may not accurately reflect the quality of individual robots’ actions in decentralized systems. We integrate the concept of locality into critic learning, where strongly related robots form partitions during training. Robots within the same partition have a greater impact on each other, leading to more precise policy evaluation. Additionally, we construct a dependency graph to capture the relationships between robots, facilitating the partitioning process. This approach mitigates the curse of dimensionality and prevents robots from using irrelevant information. Our method improves existing algorithms by focusing on local rewards and leveraging partition-based learning to enhance training efficiency and performance. We evaluate the performance of Loc-FACMAC in three environments: Hallway, Multi-cartpole, and Bounded-Cooperative-Navigation. We explore the impact of partition sizes on the performance and compare the result with baseline MARL algorithms such as LOMAQ, FACMAC, and QMIX. The experiments reveal that, if the locality structure is defined properly, Loc-FACMAC outperforms these baseline algorithms up to 108%, indicating that exploiting the locality structure in the actor-critic framework improves the MARL performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Multipurpose robots

来源：评论

学校读者我要写书评

暂无评论

Microwave to Millimeter-Wave (mmWave) Tightly Coupled Dipole Array (TCDA) With 20:1 Bandwidth Using a Hybrid PCB-3D Printing Fabrication Approach

IEEE Open Journal of Antennas and Propagation

引用

IEEE Open Journal of Antennas and Propagation 2025年

作者： Anastasiadis, Michail O. Islam, Md Rakibul Troccola, Jorge A. Caripidis Venkatakrishnan, Satheesh Bojja Mitchell, Gregory Volakis, John L. Florida International University Department of electrical and computer engineering MiamiFL33174 United States Amazon Project Kuiper RedmondWA United States DEVCOM Army Research Laboratory AdelphiMD20783 United States

The vast majority of mmWave arrays utilize planar topologies due to fabrication and assembly constraints. However, planar architectures bring the antenna element too close to the ground plane, limiting the achievable bandwidth (BW). In this paper, we introduce a vertically integrated UWB array that operates from microwave to mmWave frequencies. The novel antenna array operates from 2 to 40 GHz, i.e 20:1 bandwidth. The enabling novelty is the combination of PCB fabrication technologies with 3D-printing technology. Specifically, unlike previous designs, all components of the array are printed on the same plane and the vertical dipole boards are assembled using a 3D-printed structure. The array employs the concept of tightly coupled dipoles to achieve Ultra-Wideband (UWB) operation. Infinite array simulations showed a VSWR © 2020 IEEE.

关键词： Microwave antennas

来源：评论

学校读者我要写书评

暂无评论

Past, present, and future of microconcentrating photovoltaics

引用

Progress in Quantum Electronics 2025年 100-101卷

作者： Grede, Alex J. Sun, Maxwell Giebink, Noel C. Electronics Science and Technology Division U.S. Naval Research Laboratory Washington DC200375 United States Department of Electrical Engineering and Computer Science University of Michigan Ann ArborMI48109 United States

Concentrating photovoltaics (CPV) use inexpensive optics to concentrate sunlight onto high efficiency solar cells. Over the past decade, the field of CPV has evolved from large systems aimed at grid-scale power generation toward microconcentrating photovoltaics (µCPV) that employ miniaturized cells and compact optics to address new, performance-driven applications such as agrivoltaics and space power. This review summarizes the development, present status, and future prospects of this emerging subfield. We discuss the main components that make up a typical µCPV system and highlight some of the key results achieved to date before concluding with a look forward at the milestones that will be needed to transition µCPV out of the lab and into the real world. © 2025 Elsevier Ltd

关键词： Microscale Multijunction Optical concentrator Solar cell µCPV

来源：评论

学校读者我要写书评

暂无评论

A comparative study of the performance of ten metaheuristic algorithms for parameter estimation of solar photovoltaic models

引用

PeerJ computer Science 2025年 11卷 e2646-e2646页

作者： Zga, Adel Zitouni, Farouq Harous, Saad Sallam, Karam Almazyad, Abdulaziz S. Xiong, Guojiang Mohamed, Ali Wagdy Department of Computer Science and Information Technology Laboratory of Artificial Intelligence and Information Technology Kasdi Merbah University Ouargla Algeria Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh Saudi Arabia Guizhou Key Laboratory of Intelligent Technology in Power System College of Electrical Engineering Guizhou University Guiyang China Operations Research Department Faculty of Graduate Studies for Statistical Research Cairo University Giza Egypt Applied Science Research Center Applied Science Private University Amman Jordan

This study conducts a comparative analysis of the performance of ten novel and wellperforming metaheuristic algorithms for parameter estimation of solar photovoltaic models. This optimization problem involves accurately identifying parameters that reflect the complex and nonlinear behaviours of photovoltaic cells affected by changing environmental conditions and material inconsistencies. This estimation is challenging due to computational complexity and the risk of optimization errors, which can hinder reliable performance predictions. The algorithms evaluated include the Crayfish Optimization Algorithm, the Golf Optimization Algorithm, the Coati Optimization Algorithm, the Crested Porcupine Optimizer, the Growth Optimizer, the Artificial Protozoa Optimizer, the Secretary Bird Optimization Algorithm, the Mother Optimization Algorithm, the Election Optimizer Algorithm, and the Technical and Vocational Education and Training-Based Optimizer. These algorithms are applied to solve four well-established photovoltaic models: the singlediode model, the double-diode model, the triple-diode model, and different photovoltaic module models. The study focuses on key performance metrics such as execution time, number of function evaluations, and solution optimality. The results reveal significant differences in the efficiency and accuracy of the algorithms, with some algorithms demonstrating superior performance in specific models. The Friedman test was utilized to rank the performance of the various algorithms, revealing the Growth Optimizer as the top performer across all the considered models. This optimizer achieved a root mean square error of 9.8602187789E−04 for the single-diode model, 9.8248487610E−04 for both the double-diode and triplediode models and 1.2307306856E−02 for the photovoltaic module model. This consistent success indicates that the Growth Optimizer is a strong contender for future enhancements aimed at further boosting its efficiency and effectiveness. Its

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

Human-Guided Artificial Intelligence (HGAI), a Framework for Overcoming AI's Blind Spots

Human-Guided Artificial Intelligence (HGAI), a Framework for...

引用

IEEE Southeastcon

作者： Ziyu Chen Selam B. Kelil Mohammad Y. M. Naser Jason S. Metcalfe Sylvia Bhattacharya Electrical & Computer Engineering Department Kennesaw State University Marietta USA Computer Science Department Kennesaw State University Marietta USA US DEVCOM Army Research Laboratory Aberdeen Proving Ground Aberdeen USA Engineering Technology Department Kennesaw State University Marietta USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

Artificial Intelligence (AI) systems frequently exhibit systematic blind spots, often referred to as hallucinations in Large Language Models (LLMs), posing risks in high-stakes applications such as autonomous systems, security, and military operations. This paper explores how human intuitive responses, along with conscious reasoning processes, can be integrated to mitigate AI blind spots and enhance decision-making effectiveness within Human-AI teams. We introduce Human-Guided Artificial Intelligence (HGAI) as a framework for achieving this goal. Specifically, we examine the role of System-1 intuitive processing, as captured through physiological signals such as electroencephalography (EEG) and electrocardiography (ECG), System-2 reasoning-based decision-making, and Multi-Modal Fusion (MMF) mechanisms while assessing the knowledge required to develop more reliable, context-aware, and ethically aligned intelligent decision-making systems for highly complex environments.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Methodology, progress and challenges of geoscience knowledge graph in International Big Science Program of Deep-Time Digital Earth

引用

Journal of Geographical Sciences 2025年第5期35卷 1132-1156页

作者： ZHU Yunqiang WANG Qiang WANG Shu SUN Kai WANG Xinbing LV Hairong HU Xiumian ZHANG Jie WANG Bin QIU Qinjun YANG Jie ZHOU Chenghu State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources ResearchCAS Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application University of Chinese Academy of Sciences School of Electronic Information and Electrical EngineeringShanghai Jiao Tong University Department of Automation Tsinghua University State Key Laboratory for Mineral Deposits Research School of Earth Sciences and EngineeringNanjing University State Key Laboratory of Information Photonics and Optical Communications Beijing University of Posts and Telecommunications Command Center of Natural Resources Comprehensive Survey School of Computer Science China University of Geosciences

Deep-time Earth research plays a pivotal role in deciphering the rates, patterns, and mechanisms of Earth's evolutionary processes throughout geological history, providing essential scientific foundations for climate prediction, natural resource exploration, and sustainable planetary stewardship. To advance Deep-time Earth research in the era of big data and artificial intelligence, the International Union of Geological Sciences initiated the “Deeptime Digital Earth International Big Science Program”(DDE) in 2019. At the core of this ambitious program lies the development of geoscience knowledge graphs, serving as a transformative knowledge infrastructure that enables the integration, sharing, mining, and analysis of heterogeneous geoscience big data. The DDE knowledge graph initiative has made significant strides in three critical dimensions:(1) establishing a unified knowledge structure across geoscience disciplines that ensures consistent representation of geological entities and their interrelationships through standardized ontologies and semantic frameworks;(2) developing a robust and scalable software infrastructure capable of supporting both expert-driven and machine-assisted knowledge engineering for large-scale graph construction and management;(3) implementing a comprehensive three-tiered architecture encompassing basic, discipline-specific, and application-oriented knowledge graphs, spanning approximately 20 geoscience disciplines. Through its open knowledge framework and international collaborative network, this initiative has fostered multinational research collaborations, establishing a robust foundation for next-generation geoscience research while propelling the discipline toward FAIR(Findable, Accessible, Interoperable, Reusable) data practices in deep-time Earth systems research.

关键词： Deep-time Digital Earth International Big Science Program deep-time Earth geoscience knowledge graph

来源：评论

学校读者我要写书评

暂无评论

QoE-oriented Communication Service Provision for Annotation Rendering in Mobile Augmented Reality

arXiv

引用

arXiv 2025年

作者： Sun, Lulu Zhou, Conghao Hu, Shisheng Zhu, Yupeng Cheng, Nan Xia, Xu School of Telecommunications Engineering Xidian University China Department of Electrical and Computer Engineering University of Waterloo Canada The State Key Laboratory of ISN Xidian University China China Telecom Research Institute China

As mobile augmented reality (MAR) continues to evolve, future 6G networks will play a pivotal role in supporting immersive and personalized user experiences. In this paper, we address the communication service provision problem for annotation rendering in edge-assisted MAR, with the objective of optimizing spectrum resource utilization while ensuring the required quality of experience (QoE) for MAR users. To overcome the challenges of user-specific uplink data traffic patterns and the complex operational mechanisms of annotation rendering, we propose a digital twin (DT)-based approach. We first design a DT specifically tailored for MAR applications to learn key annotation rendering mechanisms, enabling the network controller to access MAR application-specific information. Then, we develop a DT-based QoE modeling approach to capture the unique relationship between individual user QoE and spectrum resource demands. Finally, we propose a QoE-oriented resource allocation algorithm that decreases resource utilization compared to conventional network slicing-based approaches. Simulation results demonstrate that our DT-based approach outperforms benchmark approaches in the accuracy and granularity of QoE modeling. Copyright © 2025, The Authors. All rights reserved.

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

Nonparametric Sparse Online Learning of the Koopman Operator

arXiv

引用

arXiv 2025年

作者： Hou, Boya Sanjari, Sina Dahlin, Nathan Koppel, Alec Bose, Subhonmesh Carl R. Woese Institute for Genomic Biology University of Illinois Urbana-Champaign United States Department of Mathematics and Computer Science Royal Military College of Canada Canada Department of Electrical and Computer Engineering University at Albany SUNY United States Artificial Intelligence Research JP Morgan Chase & Co Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois Urbana-Champaign United States

The Koopman operator provides a powerful framework for representing the dynamics of general nonlinear dynamical systems. Data-driven techniques to learn the Koopman operator typically assume that the chosen function space is closed under system dynamics. In this paper, we study the Koopman operator via its action on the reproducing kernel Hilbert space (RKHS), and explore the mis-specified scenario where the dynamics may escape the chosen function space. We relate the Koopman operator to the conditional mean embeddings (CME) operator and then present an operator stochastic approximation algorithm to learn the Koopman operator iteratively with control over the complexity of the representation. We provide both asymptotic and finite-time last-iterate guarantees of the online sparse learning algorithm with trajectory-based sampling with an analysis that is substantially more involved than that for finite-dimensional stochastic approximation. Numerical examples confirm the effectiveness of the proposed algorithm. © 2025, CC BY-NC-ND.

关键词： Dynamical systems

来源：评论

学校读者我要写书评

暂无评论

NARCE: A Mamba-Based Neural Algorithmic Reasoner Framework for Online Complex Event Detection

arXiv

引用

arXiv 2025年

作者： Han, Liying Dong, Gaofeng Ouyang, Xiaomin Kaplan, Lance Cerutti, Federico Srivastava, Mani Department of Electrical and Computer Engineering University of California Los Angeles United States Department of Computer Science and Engineering Hong Kong University of Science and Technology China US Army DEVCOM Army Research Laboratory United States Department of Information Engineering University of Brescia Italy Amazon United States

Current machine learning models excel in short-span perception tasks but struggle to derive high-level insights from long-term observation, a capability central to understanding complex events (CEs). CEs, defined as sequences of short-term atomic events (AEs) governed by spatiotemporal rules, are challenging to detect online due to the need to extract meaningful patterns from long and noisy sensor data while ignoring irrelevant events. We hypothesize that state-based methods are well-suited for CE detection, as they capture event progression through state transitions without requiring long-term memory. Baseline experiments validate this, demonstrating that the state-space model Mamba outperforms existing architectures. However, Mamba’s reliance on extensive labeled data, which are difficult to obtain, motivates our second hypothesis: decoupling CE rule learning from noisy sensor data can reduce data requirements. To address this, we propose NARCE, a framework that combines Neural Algorithmic Reasoning (NAR) to split the task into two components: (i) learning CE rules independently of sensor data using synthetic concept traces generated by LLMs and (ii) mapping sensor inputs to these rules via an adapter. Our results show that NARCE outperforms baselines in accuracy, generalization to unseen and longer sensor data, and data efficiency, significantly reducing annotation costs while advancing robust CE detection. © 2025, CC BY.

关键词： Labeled data

来源：评论

学校读者我要写书评

暂无评论

MN-Net: Speech Enhancement Network via Modeling the Noise

IEEE Transactions on Audio, Speech and Language Processing

引用

IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 1208-1219页

作者： Ying Hu Qin Yang Wenbing Wei Li Lin Liang He Zhijian Ou Wenzhong Yang School of Computer Science and Technology Key Laboratory of Signal Detection and Processing Xinjiang University Urumqi China Xinjiang Institute of Electronics Research Shares Company Ltd. Urumqi China Department of Electrical Engineering Tsinghua University Beijing China Beijing National Research Center for information Science and Technology Department of Electronics Tsinghua University Beijing China

Currently, deep learning-based speech enhancement methods generally focus on target speech extraction while neglecting modeling the other sound sources in the mixture. These methods still can't distinguish the target speech from the interference well. In this paper, we present a monaural speech enhancement network via Modeling the Noise (MN-Net), which includes a shared Encoder and three separate Decoders for parallel modeling the magnitude and phase spectrogram of target speech, and the complex spectrogram of noise. Specifically, we propose a Multi-Branch Feature Extractor (MBFE) module to capture the richer contextual information in mixture, and a Spatial Reconstruction Unit (SRU) to remove the redundancy from extracted features. We compared our proposed MN-Net with 18 classical speech enhancement methods on the VoiceBank+DEMAND dataset, and with 9 ones on DNS-Challenge dataset for denoising task, and with 7 ones on the WHAMR! dataset for simultaneous denoising & de-reverberation task. Our proposed MBFE module was applied to two classical speech enhancement methods, DB-AIAT and CMGAN, replacing their DenseBlocks module. The results demonstrate that applying the MBFE module can boost their performances while keeping smaller model size. A series of visualization analysis intuitively verify that modeling the noise can enable the network to distinguish the target speech from noise and other interference more accurately.

关键词： Noise Feature extraction Spectrogram Speech enhancement Decoding Kernel Transformers Noise reduction Data mining Training

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：