检索结果-内蒙古大学图书馆

3rd IEEE International Conference on Device Intelligence, Computing and Communication Technologies, DICCT 2025

作者： Ranjan, Raj Upadhyay, Deepak Soni, Mukesh Sharma, Rashmi Gupta, Mridul Venu, Nookala Graphic Era Hill University Dept. of Computer Science and Engineering Dehradun India University Centre for Research & Development Chandigarh University Department of Cse Punjab Mohali India Department of Cse Nagpur Campus Pune India Graphic Era deemed to be University Dept. of Electronics and Communication Engineering Dehradun India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (纸本)9798331543358

This study explores the effectiveness of spatial diversity techniques in log-normal shadowing channels, which are crucial for enhancing the reliability and efficiency of wireless communication systems. Extensive simulation and analysis are done to investigate different spatial diversity techniques i.e. maximal ratio combining (MRC), space-time block codes (STBC) and selection combining (SC) in combination with different signal-to-noise ratio (SNR) and shadowing variance conditions. We evaluate the effectiveness of the techniques through performance metrics including Bit Error Rate (BER), spectral efficiency, and signal-to-noise ratio. The results show that MRC and STBC perform better in most scenarios, which highlight the necessity of antenna diversity for system reliability and spectral efficiency. These findings while signaling the possible future enhancements of the wireless communication systems working under shadowed propagation environments are of paramount value in optimizing the system designs. © 2025 IEEE.

关键词： Fading channels

来源：评论

学校读者我要写书评

暂无评论

Digital-Twin-Driven PI Parameter Evaluation Method for Grid-Connected Converters

Digital-Twin-Driven PI Parameter Evaluation Method for Grid-...

引用

Power engineering (ICPE), International Conference on

作者： Xu Cheng Jianfu Chen Yong Chen Xiaoyan Zhao Zhipeng Zhou Qifan Yang Ning Yi Dai Research Center Guangdong Power Grid Co. Ltd. Zhuhai Power Supply Bureau and DC Power Distribution and Consumption Technology Zhuhai China Zhuhai Power Supply Bureau Guangdong Power Grid Co. Ltd. Zhuhai China Department of Electrical and Computer Engineering SKL of Internet of Things for Smart City University of Macau Macao China Department of Electrical and Computer Engineering SKL of Internet of Things for Smart City University of Macau and Zhuhai UM Science & Technology Research Institute Macao and Zhuhai China

Digital twin technology describes and models the characteristics, behavior, formation process and real-world performance of physical objects by using digital technologies such as sensors, internet of things (IoT), and artificial intelligence (AI). It is one of the key technologies to realize real-time perception and operation optimization of electric power equipment. Proportional-integral (PI) modules are the most commonly used blocks in the control system of power converters. But choosing the parameters with the best control effect requires repeated experiments. Therefore, this paper proposes a data-driven model that can evaluate the control performance of different PI parameters accurately and efficiently. First, the concept of digital twins and a general architecture applicable to the key power electronic equipment are demonstrated. Then, taking the grid-connected converter as an example, a data-driven model based on artificial neural network (ANN) and Levenberg-Marquardt (LM) algorithm is built to realize the evaluation of PI controller parameters. Comparisons with simulation models are also provided to show the validity of the digital-twin driven control parameter evaluation method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Resource Optimization for LEO Constellation Networks: A Multi-satellite Cooperative Coverage Design

引用

IEEE Transactions on Communications 2025年

作者： Ma, Xu Zhang, Haijun Song, Wei Lu, Yang Wu, Yuan Leung, Victor C.M University of Science and Technology Beijing Beijing Engineering and Technology Research Center for Convergence Networks and Ubiquitous Services Beijing100083 China China Electric Power Research Institute Co. Ltd Beijing102209 China University of Macau State Key Laboratory of Internet of Things for Smart City Macau China University of Macau Department of Computer and Information Science Macau China Shenzhen University Department of Computer Science and Software Engineering Shenzhen518061 China

With its low latency, high throughput, good deployment flexibility, and cost-effectiveness, the low earth orbit (LEO) constellation is regarded as a promising technology for seamless coverage. Unlike geosynchronous orbiting (GSO) satellites, the highly dynamic evolution of LEO constellation topology poses a significant challenge to the traditional scheme of allocating wireless resources. This paper proposes a multisatellite cooperative coverage resource allocation for the LEO constellation. A multi-objective optimization problem focusing on throughput and coverage time is proposed for the overall service quality of the LEO constellation. To solve the complex coupling between multi-domain resources, the optimization problem is decomposed into three key subproblems, which are the beam placement problem, the joint beam association with power allocation problem, and the beam hopping time slot allocation problem. For each subproblem, an adaptive algorithm is proposed that effectively exploits the payload of LEO satellites to ensure the coverage performance of the constellation network. Simulation results demonstrate that the proposed coverage scheme enables fast convergence, enhances network throughput utility, and guarantees the stability and fairness of the communication services. © 1972-2012 IEEE.

关键词： Multiobjective optimization

来源：评论

学校读者我要写书评

暂无评论

Environment Transformer and Policy Optimization for Model-Based Offline Reinforcement Learning

Environment Transformer and Policy Optimization for Model-Ba...

引用

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

作者： Pengqin Wang Meixin Zhu Shaojie Shen Division of Emerging Interdisciplinary Areas Hong Kong University of Science and Technology Hong Kong SAR China Systems Hub Hong Kong University of Science and Technology (Guangzhou) Civil and Environmental Engineering Department Sensing and Computation for Ubiquitous Internet of Things Hong Kong University of Science and Technology and Guangdong Provincial Key Lab of Integrated Communication Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Hong Kong SAR China

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Interacting with the actual environment to acquire data is often costly and time-consuming in robotic tasks. Model-based offline reinforcement learning (RL) provides a feasible solution. On the one hand, it eliminates the requirements of interaction with the actual environment. On the other hand, it learns the transition dynamics and reward function from the offline datasets and generates simulated rollouts to accelerate training. Previous model-based offline RL methods adopt probabilistic ensemble neural networks (NN) to model aleatoric uncertainty and epistemic uncertainty. However, this results in a great increase in training time and computing resource requirements. Furthermore, these methods are easily disturbed by the accumulative errors of the environment dynamics models when simulating long-term rollouts. To solve the above problems, we propose an uncertainty-aware sequence modeling architecture called Environment Transformer. It models the probability distribution of the environment dynamics and reward function to capture aleatoric uncertainty and treats epistemic uncertainty as a learnable noise parameter. Benefiting from the accurate modeling of the transition dynamics and reward function, Environment Transformer can be combined with arbitrary planning, dynamics programming, or policy optimization algorithms for offline RL. In this case, we perform Conservative Q-Learning (CQL) to learn a conservative Q-function. Through simulation experiments, we demonstrate that our method achieves or exceeds state-of-the-art performance in widely studied offline RL benchmarks. Moreover, we show that Environment Transformer's simulated rollout quality, sample efficiency, and long-term rollout simulation capability are superior to those of previous model-based offline RL methods.

关键词： Training Uncertainty Q-learning Computational modeling Transformers Probabilistic logic Probability distribution Dynamic programming Planning Optimization

来源：评论

学校读者我要写书评

暂无评论

Reconfigurable Intelligent Surface Aided Vehicular Edge Computing: Joint Phase-shift Optimization and Multi-User Power Allocation

arXiv

引用

arXiv 2024年

作者： Qi, Kangwei Wu, Qiong Fan, Pingyi Cheng, Nan Chen, Wen Letaief, Khaled B. School of Internet of Things Engineering Jiangnan University Wuxi214122 China Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China State Key Lab. of ISN School of Telecommunications Engineering Xidian University Xi’an710071 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Hong Kong

Vehicular edge computing (VEC) is an emerging technology with significant potential in the field of internet of vehicles (IoV), enabling vehicles to perform intensive computational tasks locally or offload them to nearby edge devices. However, the quality of communication links may be severely deteriorated due to obstacles such as buildings, impeding the offloading process. To address this challenge, we introduce the use of Reconfigurable Intelligent Surfaces (RIS), which provide alternative communication pathways to assist vehicular communication. By dynamically adjusting the phase-shift of the RIS, the performance of VEC systems can be substantially improved. In this work, we consider a RIS-assisted VEC system, and design an optimal scheme for local execution power, offloading power, and RIS phase-shift, where random task arrivals and channel variations are taken into account. To address the scheme, we propose an innovative deep reinforcement learning (DRL) framework that combines the Deep Deterministic Policy Gradient (DDPG) algorithm for optimizing RIS phase-shift coefficients and the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm for optimizing the power allocation of vehicle user (VU). Simulation results show that our proposed scheme outperforms the traditional centralized DDPG, Twin Delayed Deep Deterministic Policy Gradient (TD3) and some typical stochastic schemes. 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

DRL-Based Optimization for AoI and Energy Consumption in C-V2X Enabled IoV

arXiv

引用

arXiv 2024年

作者： Zhang, Zheng Wu, Qiong Fan, Pingyi Cheng, Nan Chen, Wen Letaief, Khaled B. The School of Internet of Things Engineering Jiangnan University Wuxi214122 China The Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China The State Key Lab. of ISN School of Telecom-munications Engineering Xidian University Xi’an710071 China The Department of Electronic Engineering Shanghai JiaoTong University Shanghai200240 China Hong Kong

To address communication latency issues, the Third Generation Partnership Project (3GPP) has defined Cellular-Vehicle to Everything (C-V2X) technology, which includes Vehicle-to-Vehicle (V2V) communication for direct vehicle-to-vehicle communication. However, this method requires vehicles to autonomously select communication resources based on the Semi-Persistent Scheduling (SPS) protocol, which may lead to collisions due to different vehicles sharing the same communication resources, thereby affecting communication effectiveness. Non-Orthogonal Multiple Access (NOMA) is considered a potential solution for handling large-scale vehicle communication, as it can enhance the Signal-to-Interference-plus-Noise Ratio (SINR) by employing Successive Interference Cancellation (SIC), thereby reducing the negative impact of communication collisions. When evaluating vehicle communication performance, traditional metrics such as reliability and transmission delay present certain contradictions. Copyright © 2024, The Authors. All rights reserved.

关键词： Magnetic levitation vehicles

来源：评论

学校读者我要写书评

暂无评论

Full-Space Wireless Sensing Enabled by Multi-Sector Intelligent Surfaces

引用

IEEE Transactions on Wireless Communications 2025年

作者： Zhang, Yumeng Shao, Xiaodan Li, Hongyu Clerckx, Bruno Zhang, Rui Hong Kong University of Science and Technology Electronic and Computing Engineering Hong Kong Hong Kong University of Waterloo Department of Electrical and Computer Engineering WaterlooONN2L 3G1 Canada Internet of Things Thrust Guangdong Guangzhou511400 China Imperial College London Department of Electrical and Electronic Engineering LondonSW7 2AZ United Kingdom Kyung Hee University Seoul Korea Republic of The Chinese University of HongKong School of Science and Engineering Shenzhen Research Institute of Big Data Guangdong Shenzhen518172 China National University of Singapore Department of Electrical and Computer Engineering Singapore117583 Singapore

The multi-sector intelligent surface (IS), benefiting from a smarter wave manipulation capability, has been shown to enhance channel gain and offer full-space coverage in communications. However, the benefits of multi-sector IS in wireless sensing remain unexplored. This paper introduces the application ofmulti-sector IS for wireless sensing/localization. Specifically, we propose a new self-sensing system, where an active source controller uses the multi-sector IS geometry to reflect/scatter the emitted signals towards the entire space, thereby achieving full-space coverage for wireless sensing. Additionally, dedicated sensors are installed aligned with the IS elements at each sector, which collect echo signals fromthe target and cooperate to sense the target angle. In this context, we develop a maximum likelihood estimator of the target angle for the proposed multi-sector IS self-sensing system, along with the corresponding theoretical limits defined by the Craḿer-Rao Bound. The analysis reveals that the advantages of the multi-sector IS self-sensing system stem from two aspects: enhancing the probing power on targets (thereby improving power efficiency) and increasing the rate of target angle (thereby enhancing the transceiver's sensitivity to target angles). Finally, our analysis and simulations confirm that the multi-sector IS self-sensing system, particularly the 4-sector architecture, achieves full-space sensing capability beyond the single-sector IS configuration. Furthermore, similarly to communications, employing directive antenna patterns on each sector's IS elements and sensors significantly enhances sensing capabilities. This enhancement originates from both aspects of improved power efficiency and target angle sensitivity, with the former also being observed in communications while the latter being unique in sensing. © 2002-2012 IEEE.

关键词： Directive antennas

来源：评论

学校读者我要写书评

暂无评论

Implicit Supervision-Assisted Graph Collaborative Filtering for Third-Party Library Recommendation

引用

IEEE Transactions on Services Computing 2025年第3期18卷 1459-1471页

作者： Lianrong Chen Mingdong Tang Naidan Mei Fenfang Xie Guo Zhong Qiang He School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China Unicom (Guangdong) Industry Internet Company Guangzhou China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Computing Technologies Swinburne University of Technology Melbourne VIC Australia

Third-party libraries (TPLs) play a crucial role in software development. Utilizing TPL recommender systems can aid software developers in promptly finding useful TPLs. A number of TPL recommendation approaches have been proposed and among them graph neural network (GNN)-based recommendation is attracting the most attention. However, GNN-based approaches generate node representations through multiple convolutional aggregations, which is prone to introducing noise, resulting in the over-smoothing issue. In addition, due to the high sparsity of labelled data, node representations may be biased in real-world scenarios. To address these issues, this paper presents a TPL recommendation method named Implicit Supervision-assisted Graph Collaborative Filtering (ISGCF). Specifically, it takes the App-TPL interaction relationships as input and employs a popularity-debiased method to generate denoised App and TPL graphs. This reduces the noise introduced during graph convolution and alleviates the over-smoothing issue. It also employs a novel implicitly-supervised loss function to exploit the labelled data to learn enhanced node representations. Extensive experiments on a large-scale real-world dataset demonstrate that ISGCF achieves a significant performance advantage over other state-of-the-art TPL recommendation methods in Recall, NDCG and MAP. The experiments also validate the superiority of ISGCF in mitigating the over-smoothing problem.

关键词： Vectors Noise Training Collaborative filtering Recommender systems Libraries Predictive models Graph neural networks Electronic mail Convolution

来源：评论

学校读者我要写书评

暂无评论

Red Piranha Optimization Based Fast and Robust Fuzzy C-Means Clustering for Precise Glioma Segmentation in Therapeutic Applications

Red Piranha Optimization Based Fast and Robust Fuzzy C-Means...

引用

International Conference on Advanced Computing and Communication Systems (ICACCS)

作者： Sophia G Kottaimalai Ramaraj Karthikeyan K Prasath Alias Surendhar S Ambika B Thilagaraj M Department of Computer Science and Engineering MV J College of Engineering Bengaluru India Department of Electronics and Communication Engineering Kalasalingam Academy of Research and Education Krishnankoil Tamil Nadu India Department of Electrical and Electronics Engineering Ramco Institute of Technology Rajapalayam Tamil Nadu India Department of Biomedical Engineering Aarupadai Veedu Institute of Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Thiagarajar College of Engineering Madurai Tamil Nadu India Department of Industrial Internet of Things MVJ College of Engineering Bengaluru India

ISBN: (数字)9798350384369

ISBN: (纸本)9798350384376

Gliomas have cells that resemble glial cells., which are normal brain cells. They are the prevalent kind of tumors; however., in patients with several years to live., they may be a lesser threat., or in patients with only two years to live., they may be more deadly. Chemotherapy and radiation are able to be utilized to decrease the development of brain tumors which are unable to be practically taken away even though medical intervention is still the preferred therapy for these tumors. Magnetic resonance imaging (MRI) for tumor diagnosis because they offer comprehensive images of the structure of the brain. Accurately segmenting tumors from MRls is crucial for both preparing for surgery and diagnostic purposes. Further investigations may not be able to justify manual tumor segmentation. Furthermore., tumor delineation can significantly influence the scheduling of treatment., rate of growth forecasting., and better diagnosis. The present study uses an integrated segmentation algorithm that combines optimization and clustering techniques. One method for creating clusters or groups of identical pixels is fuzzy C-means (FCM) clustering., an unsupervised learning technique. Fast and Robust FCM (FRFCM) addresses the drawbacks of traditional method. Red Piranha Optimization (RPO) is a recently introduced optimization technique that draws inspiration from nature and emulates the community-based hunting act of red piranhas. The suggested RPO-based FCM method was evaluated on a number of BraTS challenge datasets to guarantee its effectiveness. The results of the dissection are contrasted with the corresponding ground truth images. The enhanced dice score value of 92 % is obtained using the developed method. Medical professionals can use findings to help schedule surgical procedures., diagnose patients promptly, and treat them.

关键词： Image segmentation Schedules Magnetic resonance imaging Surgery Manuals Glial cells Forecasting Optimization Medical diagnostic imaging Unsupervised learning

来源：评论

学校读者我要写书评

暂无评论

Prototype-Wise Self-Knowledge Distillation for Few-Shot Segmentation

SSRN

引用

SSRN 2023年

作者： Chen, Yadang Xu, Xinyu Wei, Chenchen Yang, Zhi-Xin School of Computer and Software Nanjing University of Information Science and Technology Nanjing210044 China Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing210044 China The State Key Laboratory of Internet of Things for Smart City Department of Electromechanical Engineering University of Macau 999078 China

Few-shot segmentation was proposed to obtain segmentation results for a image with an unseen class by referring to a few labeled samples. However, due to the limited number of samples, many few-shot segmentation models suffer from poor generalization. Prototypical network-based few-shot segmentation still has issues with spatial inconsistency and prototype bias. Since the target class has different appearance in each image, some specific features in the prototypes generated from the support image and its mask do not accurately reflect the generalized features of the target class. To address the support prototype consistency issue, we put forward two modules: Data Augmentation Self-knowledge Distillation (DASKD) and Prototype-wise Regularization (PWR). The DASKD module focuses on enhancing spatial consistency by using data augmentation and self-knowledge distillation. Self-knowledge distillation helps the model acquire generalized features of the target class and learn hidden knowledge from the support images. The PWR module focuses on obtaining a more representative support prototype by conducting prototype-level loss to obtain support prototypes closer to the category center. Broad evaluation experiments on PASCAL-5i and COCO-20i demonstrate that our model outperforms the prior works on few-shot segmentation. Our approach surpasses the state of the art by 7.5% in PASCAL-5i and 4.2% in COCO-20i. © 2023, The Authors. All rights reserved.

关键词： Distillation

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：