检索结果-内蒙古大学图书馆

IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Zhipeng Li Benfei Wang Ping Wang Yu Han Cristian Garcia Jose Rodriguez School of Intelligent Systems Engineering Shenzhen Campus of Sun Yat-sen University Shenzhen China Guangdong Provincial Key Laboratory of Fire Science and Intelligent Emergency Technology Guangzhou China Department of Electrical Engineering Universidad de Talca Curico Chile Faculty of Engineering Universidad San Sebastian Santiago Santiago Chile

ISBN: (纸本)9781665409858

Recently, recursive least square (RLS) based model free predictive control (MF-PC) has been widely applied to model estimation for superior performance to model-based method. In most previous researches the forgetting factor which is a tradeoff between the convergence speed and the steady-state tracking error is usually neglected, thus selected as a constant to ensure robustness. In this paper, a dynamic forgetting factor based bias compensated RLS (DFF-BCRLS) is proposed for model free predictive current control of a voltage source inverter. The dynamic forgetting factor is calculated by local optimal forgetting factor (LOFF) algorithm to improve data convergency and steady-state robustness. A bias compensation (BC) scheme is presented to further increase the accuracy of parameter estimation and immune input noise. The simulation results show that the proposed strategy has smaller tracking error in steady-state and better performance against model or parameter mismatches compared to the conventional RLS.

关键词： Current control Parameter estimation Voltage source inverters Heuristic algorithms Simulation Estimation Predictive models

来源：评论

学校读者我要写书评

暂无评论

Enhancing Recommendation Representations Through Alignment and Uniformity with Integrated Contrastive Learning and Collaborative Filtering

Enhancing Recommendation Representations Through Alignment a...

引用

IEEE International Conference on Smart Internet of Things (SmartIoT)

作者： Linxian Li Yuzhu Hu Yulong Zhao Xiaoqian Xi Jiaxin Chen Wei Wang School of Computer Science Beijing Institute of Technology Beijing China Guangdong-Hong Kong-Macao Joint Laboratory for Emotion Intelligence and Pervasive Computing Artificial Intelligence Research Institute Shenzhen MSU - BIT University Shenzhen China School of Intelligent Systems Engineering Sun Vat-sen University Shenzhen China Faculty of Engineering Shenzhen MSU-BIT University Shenzhen China

ISBN: (数字)9798350366440

ISBN: (纸本)9798350366457

Collaborative Filtering(CF) is a widely studied recommendation method, and learning high-quality representations is crucial for it. Recent research has focused on improving the quality of learned representations by designing better optimization objectives, such as optimizing alignment and uniformity, which enhance recommendation performance. However, supervised learning alone cannot address inherent issues like sparse supervision signals in recommendation systems. Therefore, many studies use contrastive learning (CL) from self-supervised learning to provide additional self-supervised signals, and often jointly optimize InfoNCE loss from CL and BPR loss from CF. Considering that both InfoNCE and BPR losses can be analyzed through the key properties of alignment and uniformity, we propose a loss function that integrates CL and CF into alignment and uniformity. This integration ensures that the learned representations are both representative and suitable for recommendation tasks. Specifically, we use LightGCN to extract high-order collaborative signals from the user-item interaction graph, and to enable self-supervised learning, we add uniform noise perturbations at each layer. We then optimize the learned representations using our proposed loss function. We conduct experiments on a benchmark dataset to demonstrate the superiority of our framework.

关键词： Collaborative filtering Perturbation methods Supervised learning Noise Collaboration Contrastive learning Information filters Internet of Things Recommender systems Optimization

来源：评论

学校读者我要写书评

暂无评论

Consistency Approximation: Incremental Feature Selection Based on Fuzzy Rough Set Theory

SSRN

引用

SSRN 2023年

作者： Zhao, Jie Wu, Daiyang Wu, JiaXin Ye, Wenhao Huang, Faliang Wang, Jiahai See-To, Wing-Kuen Eric Department of Data Science and Business Intelligence Guangdong University of Technology Guangzhou China Guangxi Key Lab of Human-machine Interaction and Intelligent Decision Nanning Normal University Nanning China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Computing and Decision Sciences Faculty of Business Lingnan University Hong Kong

Fuzzy Rough Set Theory (FRST)-based feature selection has been widely used as a preprocessing step to handle dynamic and large datasets. However, large-scale or high-dimensional datasets remain intractable for FRST-based feature selection approaches due to high space complexity and unsatisfactory classification performance. To overcome these challenges, we propose a Consistency Approximation (CA)-based framework for incremental feature selection. By exploring CA, we introduce a novel significance measure and a tri-accelerator. The CA-based significance measure provides a mechanism for each sample in the universe to keep members with different class labels within its fuzzy neighbourhood as far as possible, while keeping members with the same label as close as possible. Furthermore, our tri-accelerator reduces the search space and decreases the computational space with a theoretical lower bound. The experimental results demonstrate the superiority of our proposed algorithm compared to state-of-the-art methods on efficiency and classification accuracy, especially for large-scale and high-dimensional datasets. © 2023, The Authors. All rights reserved.

关键词： Feature Selection

来源：评论

学校读者我要写书评

暂无评论

Technology Roadmap for Flexible Sensors

引用

ACS NANO 2023年第6期17卷 5211-5295页

作者： Luo, Yifei Abidian, Mohammad Reza Ahn, Jong-Hyun Akinwande, Deji Andrews, Anne M. Antonietti, Markus Bao, Zhenan Berggren, Magnus Berkey, Christopher A. Bettinger, Christopher John Chen, Jun Chen, Peng Cheng, Wenlong Cheng, Xu Choi, Seon-Jin Chortos, Alex Dagdeviren, Canan Dauskardt, Reinhold H. Di, Chong-an Dickey, Michael D. Duan, Xiangfeng Facchetti, Antonio Fan, Zhiyong Fang, Yin Feng, Jianyou Feng, Xue Gao, Huajian Gao, Wei Gong, Xiwen Guo, Chuan Fei Guo, Xiaojun Hartel, Martin C. He, Zihan Ho, John S. Hu, Youfan Huang, Qiyao Huang, Yu Huo, Fengwei Hussain, Muhammad M. Javey, Ali Jeong, Unyong Jiang, Chen Jiang, Xingyu Kang, Jiheong Karnaushenko, Daniil Khademhosseini, Ali Kim, Dae-Hyeong Kim, Il-Doo Kireev, Dmitry Kong, Lingxuan Lee, Chengkuo Lee, Nae-Eung Lee, Pooi See Lee, Tae-Woo Li, Fengyu Li, Jinxing Liang, Cuiyuan Lim, Chwee Teck Lin, Yuanjing Lipomi, Darren J. Liu, Jia Liu, Kai Liu, Nan Liu, Ren Liu, Yuxin Liu, Yuxuan Liu, Zhiyuan Liu, Zhuangjian Loh, Xian Jun Lu, Nanshu Lv, Zhisheng Magdassi, Shlomo Malliaras, George G. Matsuhisa, Naoji Nathan, Arokia Niu, Simiao Pan, Jieming Pang, Changhyun Pei, Qibing Peng, Huisheng Qi, Dianpeng Ren, Huaying Rogers, John A. Rowe, Aaron Schmidt, Oliver G. Sekitani, Tsuyoshi Seo, Dae-Gyo Shen, Guozhen Sheng, Xing Shi, Qiongfeng Someya, Takao Song, Yanlin Stavrinidou, Eleni Su, Meng Sun, Xuemei Takei, Kuniharu Tao, Xiao-Ming Tee, Benjamin C. K. Thean, Aaron Voon-Yew Trung, Tran Quang Wan, Changjin Wang, Huiliang Wang, Joseph Wang, Ming Wang, Sihong Wang, Ting Wang, Zhong Lin Weiss, Paul S. Wen, Hanqi Xu, Sheng Xu, Tailin Yan, Hongping Yan, Xuzhou Yang, Hui Yang, Le Yang, Shuaijian Yin, Lan Yu, Cunjiang Yu, Guihua Yu, Jing Yu, Shu-Hong Yu, Xinge Zamburg, Evgeny Zhang, Haixia Zhang, Xiangyu Zhang, Xiaosheng Zhang, Xueji Zhang, Yihui Zhang, Yu Zhao, Siyuan Zhao, Xuanhe Zheng, Yuanjin Zheng, Yu-Qing Zheng, Zijian Zhou, Tao Zhu, Bowen Zhu, Ming Zhu, Rong Zhu, Yangzhi Zhu, Yong Zou, Guijin Chen, Xiaodong 08-03 Innovis Singapore 138634 Republic of Singapore Innovative Centre for Flexible Devices (iFLEX) School of Materials Science and Engineering Nanyang Technological University Singapore 639798 Singapore Department of Biomedical Engineering University of Houston Houston Texas 77024 United States School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea Department of Electrical and Computer Engineering The University of Texas at Austin Austin Texas 78712 United States Microelectronics Research Center The University of Texas at Austin Austin Texas 78758 United States Department of Chemistry and Biochemistry California NanoSystems Institute and Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior and Hatos Center for Neuropharmacology University of California Los Angeles Los Angeles California 90095 United States Colloid Chemistry Department Max Planck Institute of Colloids and Interfaces 14476 Potsdam Germany Department of Chemical Engineering Stanford University Stanford California 94305 United States Laboratory of Organic Electronics Department of Science and Technology Campus Norrköping Linköping University 83 Linköping Sweden Wallenberg Initiative Materials Science for Sustainability (WISE) and Wallenberg Wood Science Center (WWSC) SE-100 44 Stockholm Sweden Department of Materials Science and Engineering Stanford University Stanford California 94301 United States Department of Biomedical Engineering and Department of Materials Science and Engineering Carnegie Mellon University Pittsburgh Pennsylvania 15213 United States Department of Bioengineering University of California Los Angeles Los Angeles California 90095 United States School of Chemistry Chemical Engineering and Biotechnology Nanyang Technological University Singapore 637457 Singapore Nanobionics Group Department of Chemical and Biological Engineering Monash University Clayton Australia 3800 Monash

Humans rely increasingly on sensors to address grand challenges and to improve quality of life in the era of digitalization and big data. For ubiquitous sensing, flexible sensors are developed to overcome the limitations of conventional rigid counterparts. Despite rapid advancement in bench-side research over the last decade, the market adoption of flexible sensors remains limited. To ease and to expedite their deployment, here, we identify bottlenecks hindering the maturation of flexible sensors and propose promising solutions. We first analyze continued...challenges in achieving satisfactory sensing performance for real-world applications and then summarize issues in compatible sensor-biology interfaces, followed by brief discussions on powering and connecting sensor networks. Issues en route to commercialization and for sustainable growth of the sector are also analyzed, highlighting environmental concerns and emphasizing nontechnical issues such as business, regulatory, and ethical considerations. Additionally, we look at future intelligent flexible sensors. In proposing a comprehensive roadmap, we hope to steer research efforts towards common goals and to guide coordinated development strategies from disparate communities. Through such collaborative

关键词： soft materials mechanics engineering flexible electronics conformable sensors bioelectronics human-machine interfaces body area sensor networks technology translation sustainable electronics

来源：评论

学校读者我要写书评

暂无评论

Clustering analysis of learners' watching sequences on MOOC videos 16

Clustering analysis of learners' watching sequences on MOOC ...

引用

16th IEEE International Conference on Computer science and Education, ICCSE 2021

作者： Liao, Peijie Xu, Jing Gong, Shimin Liu, Wei Yi, Yingying Huazhong University of Science and Technology Smart Internet Technology Lab School of Electronic Information and Communications Wuhan China Sun Yat-sen University School of Intelligent Systems Engineering Shenzhen China The Chinese University of Hong Kong Faculty of Education Hong Kong

ISBN: (纸本)9781665414685

In MOOC community, it is a challenge to analyze online learners' video watching behaviors based on the MOOC videos' log data. In this paper, we propose a research model on video watching behaviors of MOOC learners, by encoding the behavior sequences retrieved from the videos' log data, evaluating the similarity between different online learners, clustering and visualizing the similarity results. To verify our findings, we present a case study on four courses from a popular Chinese MOOC platform. In particular, we divide the learners into 5 different clusters based on the learners' behavior patterns. The clustering results show that the video watching coverage and completion rate are key attributes that affect the clustering of learners. Besides, the ordering and distribution of the learners' different activities are also important factors that decide the learning patterns in each cluster. The distribution of learners is also affected by different types of courses in each cluster. Based on the exercise log data in the same MOOC platform, we further calculate average assessment scores in each cluster, which reveal obvious differences across different clusters. © 2021 IEEE.

关键词： Clustering algorithms

来源：评论

学校读者我要写书评

暂无评论

Efficient and Trustworthy Block Propagation for Blockchain-enabled Mobile Embodied AI Networks: A Graph Resfusion Approach

arXiv

引用

arXiv 2025年

作者： Kang, Jiawen Liao, Jiana Gao, Runquan Wen, Jinbo Huang, Huawei Zhang, Maomao Yi, Changyan Zhang, Tao Niyato, Dusit Zheng, Zibin Anhui Engineering Research Center for Agricultural Product Quality Safety Digital Intelligence Fuyang Normal University Fuyang236037 China School of Automation Guangdong University of Technology Guangzhou510006 China School of Software Engineering Sun Yat-Sen University Zhuhai519082 China School of Intelligent Systems Engineering Sun Yat-Sen University Shenzhen518107 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China School of Cyberspace Science and Technology Beijing Jiaotong University Beijing100044 China College of Computing and Data Science Nanyang Technological University Singapore

By synergistically integrating mobile networks and embodied artificial intelligence (AI), Mobile Embodied AI Networks (MEANETs) represent an advanced paradigm that facilitates autonomous, context-aware, and interactive behaviors within dynamic environments. Nevertheless, the rapid development of MEANETs is accompanied by challenges in trustworthiness and operational efficiency. Fortunately, blockchain technology, with its decentralized and immutable characteristics, offers promising solutions for MEANETs. However, existing block propagation mechanisms suffer from challenges such as low propagation efficiency and weak security for block propagation, which results in delayed transmission of vehicular messages or vulnerability to malicious tampering, potentially causing severe traffic accidents in blockchain-enabled MEANETs. Moreover, current block propagation strategies cannot effectively adapt to real-time changes of dynamic topology in MEANETs. Therefore, in this paper, we propose a graph Resfusion model-based trustworthy block propagation optimization framework for consortium blockchain-enabled MEANETs. Specifically, we propose an innovative trust calculation mechanism based on the trust cloud model, which comprehensively accounts for randomness and fuzziness in the miner trust evaluation. Furthermore, by leveraging the strengths of graph neural networks and diffusion models, we develop a graph Resfusion model to effectively and adaptively generate the optimal block propagation trajectory. Simulation results demonstrate that the proposed model outperforms other routing mechanisms in terms of block propagation efficiency and trustworthiness. Additionally, the results highlight its strong adaptability to dynamic environments, making it particularly suitable for rapidly changing MEANETs. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

来源：评论

学校读者我要写书评

暂无评论

Deformation And Penetration Hybrid Detection-Net For Parcels Inspection In Industrial Supply Chain

Deformation And Penetration Hybrid Detection-Net For Parcels...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Zhi Chen Cuifeng Du Xiujie Huang Zelong Lin Yuyu Zhou Quanlong Guan Zhefu Li Shuanghuan Lv Xiaofeng Wu Xiaotian Zhuang College of Information Science and Technology Jinan University Guangzhou China Guangdong Institute of Smart Education Jinan University Guangzhou China Cetc Potevio Science&Technology Co. Ltd Guangzhou China Guangdong Key Laboratory of Data Security and Privacy Preserving Guangzhou China School of Intelligent Systems Science and Engineering Jinan University Zhuhai China Guangzhou Polytechnic of Sports Guangzhou China Beijing JD Zhenshi Information Technology Co. Ltd Beijing China

The express delivery industry has become integral to modern social life, but supply chain parcels, especially those made of corrugated cardboard, are at risk of damage during transportation. Although corrugated cardboard boxes offer some impact resistance, they can still experience deformation and penetration damage. To address this issue, we propose a hybrid model called Parcels-DNet. Parcels-DNet adopts a lightweight feature extraction backbone network, enabling deployment in resource-constrained scenarios like mobile or embedded devices. Additionally, our experimental results demonstrate that Parcels-DNet effectively captures the features of parcel deformation and penetration damage. This improves the safety and efficiency of express supply chain parcel transportation, offering greater convenience and economic benefits for the logistics industry.

关键词：

来源：评论

学校读者我要写书评

暂无评论

BSH-Det3D: Improving 3D Object Detection with BEV Shape Heatmap

BSH-Det3D: Improving 3D Object Detection with BEV Shape Heat...

引用

IEEE/RSJ International Conference on intelligent Robots and systems (IROS)

作者： You Shen Yunzhou Zhang Yanmin Wu Zhenyu Wang Linghao Yang Sonya Coleman Dermot Kerr College of Information Science and Engineering Northeastern University Shenyang China School of Electronic and Computer Engineering Peking University Shenzhen China Department of Electronic and Computer Engineering Faculty of Robotics and Engineering North-eastern University Technical University in Munich Germany Shenyang China Department of Electronic and Computer Engineering Technical University Munich Germany School of Computing Engineering and Intelligent Systems Ulster University N. Ireland UK

The progress of LiDAR-based 3D object detection has significantly enhanced developments in autonomous driving and robotics. However, due to the limitations of LiDAR sensors, object shapes suffer from deterioration in occluded and distant areas, which creates a fundamental challenge to 3D perception. Existing methods estimate specific 3D shapes and achieve remarkable performance. However, these methods rely on extensive computation and memory, causing imbalances between accuracy and real-time performance. To tackle this challenge, we propose a novel LiDAR-based 3D object detection model named BSH-Det3D, which applies an effective way to enhance spatial features by estimating complete shapes from a bird's eye view (BEV). Specifically, we design the Pillar-based Shape Completion (PSC) module to predict the probability of occupancy whether a pillar contains object shapes. The PSC module generates a BEV shape heatmap for each scene. After integrating with heatmaps, BSH-Det3D can provide additional information in shape deterioration areas and generate high-quality 3D proposals. We also design an attention-based densification fusion module (ADF) to adaptively associate the sparse features with heatmaps and raw points. The ADF module integrates the advantages of points and shapes knowledge with negligible overheads. Extensive experiments on the KITTI benchmark achieve state-of-the-art (SOTA) performance in terms of accuracy and speed, demonstrating the efficiency and flexibility of BSH-Det3D. The source code is available on https://***/mystorm16/BSH-Det3D.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A hybrid data harmonization workflow using word embeddings for the interlinking of heterogeneous cross-domain clinical data structures

A hybrid data harmonization workflow using word embeddings f...

引用

2021 IEEE EMBS International Conference on Biomedical and Health Informatics, BHI 2021

作者： Pezoulas, Vasileios C. Sakellarios, Antonis Kleber, Marcus Bosch, Jos A. van der Laan, Sander W. Lamers, Femke Lehtimäki, Terho März, Winfried Fotiadis, Dimitrios I. Unit of Medical Technology and Intelligent Information Systems Department of Materials Science and Engineering University of Ioannina Ioannina Greece Unit of Medical Technology and Intelligent Information Systems Department of Materials Science and Engineering FORTH-IMBB University of Ioannina Ioannina Greece Mannheim Medical Faculty Department of Internal Medicine V University of Heidelberg Germany Faculty of Social and Behavioural Sciences Dept. of Psychology University of Amsterdam Amsterdam Netherlands Experimental Cardiology and Laboratory of Clinical Chemistry UMCU Utrecht Netherlands Dept. of Psychiatry Amsterdam Public Health Research Institute Amsterdam UMC Vrije Universiteit Netherlands Faculty of Medicine and Health Technology Dept. of Clinical Chemistry Tampere University Tampere Finland Mannheim Medical Faculty Department of Internal Medicine V University of Heidelberg Mannheim Germany Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering FORTH-IMBB University of Ioannina Ioannina Greece

ISBN: (纸本)9781665403580

Retrospective data harmonization is an open issue in healthcare due to the emerging need to interlink data from multiple clinical centers with the absence of standardized data collection protocols. In this work, we present an automated data harmonization workflow which utilizes lexical and semantic analysis based on word embeddings and relational modeling to detect terminologies with common lexical and conceptual basis. The method is built on top of a knowledge base to enable the interlinking of heterogeneous cross-domain data. A case study is applied in two clinical domains, namely the cardiovascular disease (CVD) and the mental disorders, where the proposed method yielded matched terminologies with 85% precision in less execution time than the application of lexical analysis and manual mapping which yielded 10% less precision. © 2021 IEEE

关键词： Terminology

来源：评论

学校读者我要写书评

暂无评论

Permutation polynomials x~(2k+1+3)+ax2k+2+bx over F_(22k) and their differential uniformity

引用

science China(Information sciences) 2020年第10期63卷 255-257页

作者： Jie PENG Lijing ZHENG Chunsheng WU Haibin KAN Mathematics and Science College Shanghai Normal University School of Mathematics and Physics University of South China Department of Mathematics Lianyungang Normal University Shanghai Key Laboratory of Intelligent Information Processing School of Computer ScienceFudan University Fudan-Zhongan Joint Laboratory of Blockchain and Information Security Shanghai Blockchain Engineering Research Center Shanghai Institute for Advanced Communication and Data Science Shanghai Institute of Intelligent Electronics & Systems

Dear editor,A polynomial f（x）∈Fq[x] is called a permutation polynomial （PP） over the finite field Fq if the associated mapping f:c （?） f（c） from Fq to itself is bijective.A PP f is called a complete permutation polynomial if f（x）+x is a *** over finite fields of even characteristic have wide applications,including cryptography,coding theory,and communication *** many block ciphers with substitution-permutation network structure,the substitution box is usually a PP over F_(22k)for some positive integer *** resist the differential attacks,the differential uniformity of this

关键词： Finite fields Permutation polynomial Binomial Trinomial Differential uniformity

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：