检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Liu, Bangzhen Zheng, Chenxi Xu, Xuemiao Xu, Cheng Zhang, Huaidong He, Shengfeng South China University of Technology Guangzhou China Guangdong Engineering Center for Large Model and GenAI Technology The State Key Laboratory of Subtropical Building and Urban Science The Ministry of Education Key Laboratory of Big Data and Intelligent Robot The Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information China Singapore Management University Singapore

The vulnerability of 3D point cloud analysis to unpredictable rotations poses an open yet challenging problem: orientation-aware 3D domain generalization. Cross-domain robustness and adaptability of 3D representations are crucial but not easily achieved through rotation augmentation. Motivated by the inherent advantages of intricate orientations in enhancing generalizability, we propose an innovative rotation-adaptive domain generalization framework for 3D point cloud analysis. Our approach aims to alleviate orientational shifts by leveraging intricate samples in an iterative learning process. Specifically, we identify the most challenging rotation for each point cloud and construct an intricate orientation set by optimizing intricate orientations. Subsequently, we employ an orientation-aware contrastive learning framework that incorporates an orientation consistency loss and a margin separation loss, enabling effective learning of categorically discriminative and generalizable features with rotation consistency. Extensive experiments and ablations conducted on 3D cross-domain benchmarks firmly establish the state-of-the-art performance of our proposed approach in the context of orientation-aware 3D domain generalization. © 2025, CC BY-NC-SA.

关键词： Contrastive Learning

来源：评论

学校读者我要写书评

暂无评论

Robot and its living space:A roadmap for robot development based on the view of living space

引用

Digital Communications and Networks 2021年第4期7卷 505-517页

作者： Xiaofeng Cai Huansheng Ning Sahraoui Dhelim Rongjuan Zhou Tao Zhang Yang Xu Yueliang Wan School of Computer and Communication Engineering University of Science and Technology Beijing Beijing 100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing 100083China School of Foreign Studies University of Science and Technology Beijing Beijing 100083China Key Lab of Information Network Security Ministry of Public SecurityChina Run Technologies Co. LidBeijing China

Robot’s living space Human-robot relationship Robot humanization Hybrid intelligence Human-robot integrated society Robots,as the creation of humans,became an irreplaceable component in human *** the advancement of technologies,robots have become more and more intelligent and have been widely used in many fields,such as disease diagnosis,customer services,healthcare for the older people,and so *** robots made our lives much more convenient than ever before,they also brought many potential risks and challenges in technology,security,and *** better understand the development of robots,we proposed a concept of a robot’s living space and analyzed the role of robots in our *** this paper,we focus on setting a theoretical framework of the robot’s living space to further understand the human-robot *** research in this paper contains three central ***,we interpret the concept of the robot’s living space and the functions of each ***,we analyze and summarize the relative technologies which support robots living well in each ***,we provide advice and improvement measures based on a discussion of potential problems caused by the developments of *** the trend of robots humanization and human-robot society integration,we should seriously consider how to collaborate with intelligent robots to achieve hybrid *** build a harmonious human-robot integrated society,studying the robot’s living space and its relationship with humans is the prerequisite and roadmap.

关键词： Robot's living space Human-robot relationship Robot humanization Hybrid intelligence Human-robot integrated society

来源：评论

学校读者我要写书评

暂无评论

An Improved YOLOv3 Network for PCB Defect Detection

An Improved YOLOv3 Network for PCB Defect Detection

引用

2021 China Automation Congress, CAC 2021

作者： Shao, Guifang Zheng, Yuyun Luo, Jincai Gao, Fengqiang Li, Tiejun Ma, Jianqi Wu, Jianyi Liu, Tundong Department of Automation Xiamen Key Lab. of Big Data Intelligent Analysis and Decision Xiamen University Xiamen China School of Aerospace Engineering Xiamen University Xiamen China School of Information Engineering Jimei University Xiamen China

ISBN: (纸本)9781665426473

Different defects with various features and shapes may appeared in one printed circuit board (PCB), making a great challenge for defect detection. To deal with the contradiction between the speed and accuracy of current deep learning networks, we put forward a MobileNet-YOLOv3 network for PCB defect detection. The MobileNet is introduced to replace the backbone network Darknet-53 of YOLOv3, so as to obtain a lightweight network. Meanwhile, the feature pyramid network (FPN) of YOLOv3 is improved by channel reorganization and down-sampling to obtain higher accuracy. Experiments on public data set images with 6 types of defects are conducted, comparative results reveal that the proposed MobileNet-YOLOv3 can achieve higher accuracy than YOLOv3 and YOLOv3-Tiny. According to the frames per second factor compared with YOLOv3-Tiny and YOLOv3, the proposed network is faster than YOLOv3. © 2021 IEEE

关键词： Printed circuit boards

来源：评论

学校读者我要写书评

暂无评论

Snapshot boosting: a fast ensemble framework for deep neural networks

引用

Science China(Information Sciences) 2020年第1期63卷 77-88页

作者： Wentao ZHANG Jiawei JIANG Yingxia SHAO Bin CUI Center for Data Science Peking University National Engineering Laboratory for Big Data Analysis and Applications Department of Computer Science Beijing Key Lab of Intelligent Telecommunications Software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications Key Lab of High Confidence Software Technologies Department of Computer SciencePeking University

Boosting has been proven to be effective in improving the generalization of machine learning models in many fields. It is capable of getting high-diversity base learners and getting an accurate ensemble model by combining a sufficient number of weak learners. However, it is rarely used in deep learning due to the high training budget of the neural network. Another method named snapshot ensemble can significantly reduce the training budget, but it is hard to balance the tradeoff between training costs and diversity. Inspired by the ideas of snapshot ensemble and boosting, we propose a method named snapshot boosting. A series of operations are performed to get many base models with high diversity and accuracy, such as the use of the validation set, the boosting-based training framework, and the effective ensemble strategy. Last, we evaluate our method on the computer vision(CV) and the natural language processing(NLP) tasks, and the results show that snapshot boosting can get a more balanced trade-off between training expenses and ensemble accuracy than other well-known ensemble methods.

关键词： ensemble learning deep learning boosting neural network snapshot ensemble

来源：评论

学校读者我要写书评

暂无评论

CGAN-Based data Augmentation for Enhanced Channel Prediction in Massive MIMO under Subway Tunnels

引用

IEEE Communications Letters 2025年

作者： Wu, Yating Li, Yifeng Liang, Rubin Zheng, Guoxin Wang, Xiaoyong Shanghai University Key Lab of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science Shanghai200072 China CASCO Signal Ltd Shanghai Engineering Research Center of Driverless Train Control of Urban Guided Transport Shanghai200072 China

Massive multiple-input multiple-output (MIMO) channel modeling poses a crucial yet challenging task for wireless communication systems under subway tunnel scenarios. Due to the high cost associated with on-site measurement campaigns, the amount of channel data is often limited and insufficient. This paper proposes a conditional generative adversarial network (cGAN)-based channel model that can learn from the limited measurement data and generate realistic samples of channel impulse response (CIR) matrices specific to given conditions. The proposed channel model provides a data augmentation approach to enhance the training of neural networks for channel prediction, overcoming the prediction performance loss caused by insufficient number of training samples. Simulation results validate that the generated data exhibits high consistency with the actual channel and accurately captures the statistical properties in both delay and spatial domains. By expanding the available training dataset effectively, it is shown that the accuracy of neural network-based channel prediction has improved significantly with the proposed channel data augmentation. © 1997-2012 IEEE.

关键词： Impulse response

来源：评论

学校读者我要写书评

暂无评论

A Review of Atmospheric Electricity Research in China from 2019 to 2022

引用

Advances in Atmospheric Sciences 2023年第8期40卷 1457-1484页

作者： Weitao LYU Dong ZHENG Yang ZHANG Wen YAO Rubin JIANG Shanfeng YUAN Dongxia LIU Fanchao LYU Baoyou ZHU Gaopeng LU Qilin ZHANG Yongbo TAN Xuejuan WANG Yakun LIU Shaodong CHEN Lyuwen CHEN Qingyong LI Yijun ZHANG State Key Laboratory of Severe Weather Chinese Academy of Meteorological SciencesBeijing 100081China Laboratory of Lightning Physics and Protection Engineering Chinese Academy of Meteorological SciencesBeijing 100081China Key Laboratory of Middle Atmosphere and Global Environment Observation Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Key Laboratory of Transportation Meteorology of China Meteorological Administration Nanjing Joint Institute for Atmospheric SciencesNanjing 210041China Chinese Academy of Sciences Key Laboratory of Geospace Environment School of Earth and Space SciencesUniversity of Science and Technology of ChinaHefei 230026China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD) Nanjing University of Information Science&TechnologyNanjing 210044China Department of Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200030China Institute of Tropical and Marine Meteorology China Meteorological AdministrationGuangzhou 510641China Beijing Key Lab of Transportation Data Analysis and Mining Beijing Jiaotong UniversityBeijing 100044China Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences Fudan UniversityShanghai 200438China

Atmospheric electricity is composed of a series of electric phenomena in the *** advances in atmospheric electricity research conducted in China have been achieved in recent *** this paper,the research progress on atmospheric electricity achieved in China during 2019-22 is reviewed focusing on the following aspects:(1)lightning detection and location techniques,(2)thunderstorm electricity,(3)lightning forecasting methods and techniques,(4)physical processes of lightning discharge,(5)high energy emissions and effects of thunderstorms on the upper atmosphere,and(6)the effect of aerosol on lightning.

关键词： atmospheric electricity thunderstorm lightning lightning detection lightning physics

来源：评论

学校读者我要写书评

暂无评论

FM-FCN:A Neural Network with Filtering Modules for Accurate Vital Signs Extraction

引用

Research 2025年第1期2024卷 92-106页

作者： Fangfang Zhu Qichao Niu Xiang Li Qi Zhao Honghong Su Jianwei Shuai Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine and State Key Laboratory of Cellular Stress BiologyInnovation Center for Cell Signaling NetworkXiamen UniversityXiamen 361005China Vitalsilicon Technology Co.Ltd. JiaxingZhejiang 314006China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan 114051China Yangtze Delta Region Institute of Tsinghua University ZhejiangJiaxing 314006China Wenzhou Institute University of Chinese Academy of SciencesWenzhou 325001China Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health)Wenzhou 325001China

Neural networks excel at capturing local spatial patterns through convolutional modules,but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological *** this work,we propose a novel network named filtering module fully convolutional network(FM-FCN),which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress ***,instead of using a fully connected layer,we use an FCN to preserve the time-dimensional correlation information of physiological signals,enabling multiple cycles of signals in the network and providing a basis for signal ***,we introduce the FM as a network module that adapts to eliminate unwanted interference,leveraging the structure of the *** approach builds a bridge between deep learning and signal processing ***,we evaluate the performance of FM-FCN using remote *** results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse(BVP)signal and heart rate(HR)*** substantially improves the quality of BVP waveform reconstruction,with a decrease of 20.23%in mean absolute error(MAE)and an increase of 79.95%in signal-to-noise ratio(SNR).Regarding HR estimation accuracy,FM-FCN achieves a decrease of 35.85%in MAE,29.65%in error standard deviation,and 32.88%decrease in 95%limits of agreement width,meeting clinical standards for HR accuracy *** results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal *** codes and datasets are available online at https://***/zhaoqi106/FM-FCN.

关键词： physiological signalsin filtering module fully convolutional network fm fcn which vital signs extraction amplify physiological signals convolutional modulesbut neural networks filtering module capturing local spatial patterns

来源：评论

学校读者我要写书评

暂无评论

Analysis of Moving Target Defense in Unbalanced and Multiphase Distribution Systems Considering Voltage Stability

Analysis of Moving Target Defense in Unbalanced and Multipha...

引用

2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids, SmartGridComm 2021

作者： Liu, Mengxiang Zhao, Chengcheng Zhang, Zhenyong Deng, Ruilong Cheng, Peng College of Control Science and Engineering Zhejiang University State Key Lab. of Industrial Control Technology Hangzhou China College of Computer Science and Technology Guizhou University State Key Lab. of Public Big Data Guiyang China

ISBN: (纸本)9781665415026

Moving Target Defense (MTD) is a new technology to defend against the false data injection attack (FDIA) on distribution system state estimation (DSSE). It works by proactively perturbing the branch reactance. However, due to the challenges induced by the nonlinear dynamics and the coupling phases in the three-phase AC DSSE model, the analysis on the effectiveness and hiddenness of MTD, which are two essential performance metrics, has not yet been conducted. In this paper, we attempt to optimize the effectiveness and hiddenness of MTD while considering voltage stability. Firstly, we quantify the two metrics with approximated measurement residuals. Based on the quantified metrics, we formulate an optimization problem to maximize the effectiveness with guaranteed hiddenness and ensure voltage stability by minimizing the voltage variation induced by MTD. The original problem is transformed to a polynomial optimization problem based on the observation that the alteration of the projection matrix caused by reactance perturbation is neglectable, such that the near-optimal result can be obtained. Finally, extensive simulations are conducted on the IEEE 13-bus test feeder to evaluate the performance of the proposed MTD. © 2021 IEEE.

关键词： Network security

来源：评论

学校读者我要写书评

暂无评论

Adaptive Perception for Unified Visual Multi-modal Object Tracking

arXiv

引用

arXiv 2025年

作者： Hu, Xiantao Zhong, Bineng Liang, Qihua Mo, Zhiyi Shi, Liangtao Tai, Ying Yang, Jian Guangxi Key Lab of Multi-Source Information Mining & Security Guangxi Normal University Guilin541004 China School of Data Science and Software Engineering Wuzhou University Wuzhou543002 China School of intelligence Science and Technonolgy Nanjing University Nanjing210008 China PCA-Lab School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210094 China

Recently, many multi-modal trackers prioritize RGB as the dominant modality, treating other modalities as auxiliary, and fine-tuning separately various multi-modal tasks. This imbalance in modality dependence limits the ability of methods to dynamically utilize complementary information from each modality in complex scenarios, making it challenging to fully perceive the advantages of multi-modal. As a result, a unified parameter model often underperforms in various multi-modal tracking tasks. To address this issue, we propose APTrack, a novel unified tracker designed for multi-modal adaptive perception. Unlike previous methods, APTrack explores a unified representation through an equal modeling strategy. This strategy allows the model to dynamically adapt to various modalities and tasks without requiring additional fine-tuning between different tasks. Moreover, our tracker integrates an adaptive modality interaction (AMI) module that efficiently bridges cross-modality interactions by generating learnable tokens. Experiments conducted on five diverse multi-modal datasets (RGBT234, LasHeR, VisEvent, DepthTrack, and VOT-RGBD2022) demonstrate that APTrack not only surpasses existing state-of-the-art unified multi-modal trackers but also outperforms trackers designed for specific multi-modal tasks. © 2025, CC BY.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

Overview of Speaker Modeling and Its Applications: From the Lens of Deep Speaker Representation Learning

arXiv

引用

arXiv 2024年

作者： Wang, Shuai Chen, Zhengyang Lee, Kong Aik Qian, Yanmin Li, Haizhou Shenzhen Research Institute of Big Data School of Data Science The Chinese University of Hong Kong Guangdong Shenzhen518172 China Auditory Cognition and Computational Acoustics Lab The Department of Computer Science and Engineering The MoE Key Laboratory of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai200240 China Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong

Speaker individuality information is among the most critical elements within speech signals. By thoroughly and accurately modeling this information, it can be utilized in various intelligent speech applications, such as speaker recognition, speaker diarization, speech synthesis, and target speaker extraction. In this overview, we present a comprehensive review of neural approaches to speaker representation learning from both theoretical and practical perspectives. Theoretically, we discuss speaker encoders ranging from supervised to self-supervised learning algorithms, standalone models to large pretrained models, pure speaker embedding learning to joint optimization with downstream tasks, and efforts toward interpretability. Practically, we systematically examine approaches for robustness and effectiveness, introduce and compare various open-source toolkits in the field. Through the systematic and comprehensive review of the relevant literature, research activities, and resources, we provide a clear reference for researchers in the speaker characterization and modeling field, as well as for those who wish to apply speaker modeling techniques to specific downstream tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

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：