检索结果-内蒙古大学图书馆

IEEE International Conference on Electronics and Communication engineering (ICECE)

作者： Junjie Xue Junhua Zhou Guoqiang Shi Chaoqun Feng Penghua Liu Hongyan Quan Beijing Complex Product Advanced Manufacturing Engineering Research Center Beijing Simulation Center Beijing China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing China Beijing Simulation Center Science and Technology on Space System Simulation Laboratory Beijing China School of Computer Science and Technology East China Normal University Shanghai China

ISBN: (纸本)9781728188577

In order to make full use of the concept of model-based systems engineering (MBSE) and the collaborative design capability of the open model-based engineering environment (OpenMBEE) in establishing complex product development platform, we propose an effective method to solve the problem of unified identity authentication in OpenMBEE integration. The proposed method is based on the Model View Controller(MVC) architecture in OpenMBEE, which has the characteristics strong plasticity and can be benefit for expanding the function in the controller layer. In our research, we make full use of the software Development Kit (SDK) of OpenMBEE to expand the existing functions in the controller layer, and increase the function of communicating with the application development platform, then realize the function of sharing user authentication information between the development platform and OpenMBEE. After expansion, the system includes three modules, including the Front-end Service Module(FSM), MQ based Information Receiving Service Module(IFRSM), and the OpenMBEE Backend Server Module(BSM). The effectiveness of the proposed strategies is verified by some practical instances, which verifies that our study can provide an effective design idea for the identity authentication in OpenMBEE integration.

关键词： Analytical models Databases Authentication computer architecture Control systems Data models software

来源：评论

学校读者我要写书评

暂无评论

EDOA: an efficient delay optimization approach for mixed-polarity Reed-Muller logic circuits under the unit delay model

引用

Frontiers of computer Science 2019年第5期13卷 1102-1115页

作者： Zhenxue HE Limin XIAO Fei GU Li RUAN Zhisheng HUO Mingzhe LI Mingfa ZHU Longbing ZHANG Rui LIU Xiang WANG College of Information Science and Technology Hebei Agricultural University Baoding 071001 China State Key Laboratory of Software Development Environment Beihang University Beijing 100083 China School of Computer Science and Engineering Beihang University Beijing 100083 China School of Electronic and Information Engineering Beihang University Beijing 100083 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China National Engineering Research Center for Science & Technology Resources Sharing Service Beijing 100083 China

Delay optimization has recently attracted signif-icant attention. However, few studies have focused on the delay optimization of mixed-polarity Reed-Muller (MPRM) logic circuits. In this paper, we propose an efficient delay op-timization approach (EDOA) for MPRM logic circuits under the unit delay model, which can derive an optimal MPRM logic circuit with minimum delay. First, the simplest MPRM expression with the fewest number of product terms is ob-tained using a novel Reed-Muller expression simplification approach (RMESA) considering don't-care terms. Second, a minimum delay decomposition approach based on a Huffman tree construction algorithm is utilized on the simplest MPRM expression. Experimental results on MCNC benchmark cir-cuits demonstrate that compared to the Berkeley SIS 1.2 and ABC, the EDOA can significantly reduce delay for most cir-cuits. Furthermore, for a few circuits, while reducing delay, the EDOA incurs an area penalty.

关键词： delay optimization mixed-polarity Reed-Muller logic circuits unit delay model don't-care terms,

来源：评论

学校读者我要写书评

暂无评论

Spatio-Temporal meets Wavelet: Disentangled Traffic Flow Forecasting via Efficient Spectral Graph Attention network

arXiv

引用

arXiv 2021年

作者： Fang, Yuchen Qin, Yanjun Luo, Haiyong Zhao, Fang Xu, Bingbing Wang, Chenxing Zeng, Liang School of Computer Science National Pilot Software Engineering School Beijing University of Posts and Telecommunications China Research Center for Ubiquitous Computing Systems Institute of Computing Technology Chinese Academy of Sciences China CAS Key Laboratory of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences China Tsinghua University China

Traffic forecasting is crucial for public safety and resource optimization, yet is very challenging due to three aspects: i) current existing works mostly exploit intricate temporal patterns (e.g., the short-term thunderstorm and long-term daily trends) within a single method, which fail to accurately capture spatio-temporal dependencies under different schemas;ii) the under-exploration of the graph positional encoding limit the extraction of spatial information in the commonly used full graph attention network;iii) the quadratic complexity of the full graph attention introduces heavy computational needs. To achieve the effective traffic flow forecasting, we propose an efficient spectral graph attention network with disentangled traffic sequences. Specifically, the discrete wavelet transform is leveraged to obtain the low- and high-frequency components of traffic sequences, and a dual-channel encoder is elaborately designed to accurately capture the spatio-temporal dependencies under long- and short-term schemas of the low- and high-frequency components. Moreover, a novel wavelet-based graph positional encoding and a query sampling strategy are introduced in our spectral graph attention to effectively guide message passing and efficiently calculate the attention. Extensive experiments on four real-world datasets show the superiority of our model, i.e., the higher traffic forecasting precision with lower computational cost. Copyright © 2021, The Authors. All rights reserved.

关键词： Message passing

来源：评论

学校读者我要写书评

暂无评论

Generating High-quality Movie Tags from Social Reviews: A Learning-driven Approach

Generating High-quality Movie Tags from Social Reviews: A Le...

引用

IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and Communications (GreenCom)

作者： Zhenxiao Luo Guopin Tang Chen Wang Yipeng Zhou Xi Zheng Jessie Hui Wang Gang Liu Di Wu School of Computer Science and Engineering Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Faculty of Science and Engineering Macquarie University Sydney Australia Institute for Network Sciences and Cyberspace Beijing National Research Center for Information Science and Technology Tsinghua University Beijing China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (纸本)9781665417631

As a kind of keywords to describe video contents, tags are extremely beneficial for viewers to locate videos when they search the site. Additionally, tags also help video platform operators to better organize and recommend videos to platform users. Previous approaches are mainly based on manually tagging or tag propagation via video content analysis, which is time-consuming and resource-consuming. Especially, given the high volume of fresh movies generated per year, it is difficult to accurately tag all the movies manually. Moreover, video content analysis is also not easy considering typical features (e.g., long duration, complicated scenarios) of commercial movies. In this paper, we propose an automatic tagging algorithm called TagRec that exploits crowdsourced user reviews to generate accurate movie tags. We observe that user reviews contain rich information about movies (e.g., quality, actors) which can be learned to generate high-quality movie tags. Inspired by the above observation, we choose to transform the movie video tagging problem into a tag recommendation problem, in which tags are recommended to different movies by extracting knowledge from crowdsourced movie reviews. We take latent topics, tag co-occurrence probability and tag semantics into account, and formulate the problem as a recommendation optimization problem. We evaluate the performance of our pro-posed TagRec algorithm with a large-scale real-world dataset. Extensive experiments demonstrate that TagRec achieves 7.1 % and 9.6% improvement compared with other state-of-the-art methods in terms of Hit Ratio and Normalized Discounted Cumulative Gain respectively.

关键词： Social computing Filtering Semantics Transforms Tagging Motion pictures Optimization

来源：评论

学校读者我要写书评

暂无评论

EMIS: A Uniform Management and Resolution System of Multiple Identifiers on Ethereum

EMIS: A Uniform Management and Resolution System of Multiple...

引用

IEEE International Conference on Blockchain (Blockchain)

作者： Feng Zhao Hui Li Shaoliang Peng Xiyu Wang Ping Lu Qianbin Chen Weixiang Huang Jianping Wu Xiang Zhu Selwyn Deng Hanxu Hou Jieren Cheng Han Wang School of Electronic and Computer Engineering Peking University Shenzhen China College of Computer Science and Electronic Engineering Hunan University Changsha China The State Key Laboratory of Chemo/Biosensing and Chemometrics Hunan University Changsha China ZTE Corporation Shenzhen China Chongqing University of Posts and Telecommunications Chongqing China China Mobile Internet Co. Ltd. Guangzhou China China Communication Service Construction Co. Ltd. Beijing China China United Network Communications Group Co. Ltd. Beijing China China Unicom Global Limited Hongkong China Dongguan University of Technology Dongguan China Hainan Provincial Blockchain Technology Engineering Research Center Hainan University Hainan China

The decentralized nature of blockchain technology has received increasing attention in recent years. Several researchers have explored improving legacy Domain Name System (DNS) via blockchain to address security vulnerabilities and trust risks due to its centralized management. However, these DNS alternatives mainly focus on managing and resolving specific types of resources with predefined identifiers, such as domain names in the TCP/IP architecture. As a result, they are not suitable for the future Internet with diverse identifiers. To address this issue, we propose an Ethereum-based Multi-Identifier System named EMIS to uniformly manage and resolve multiple types of identifiers such as identity, content, and geographical coordinate. It consists of four modules, namely, identifier contracts, an EMIS contract, an index system, and off-chain storage, each of which can be evolved and upgraded independently. In particular, identifier contracts are designed based on our classification of existing identifier types associated with off-chain resource data. Each identifier contract enables different functionality and is aggregated by a uniform EMIS contract. Finally, we deploy the proposed EMIS on Ganache, and the experimental results demonstrate our advantage in terms of write performance with low Gas fees.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Parallel Planning:A New Motion Planning Framework for Autonomous Driving

引用

IEEE/CAA Journal of Automatica Sinica 2019年第1期6卷 236-246页

作者： Long Chen Xuemin Hu Wei Tian Hong Wang Dongpu Cao Fei-Yue Wang IEEE School of Data and Computer Science Sun Yat-sen University the School of Computer Science and Information Engineering Hubei University Institute of Measurement and Control Systems Karlsruhe Institute of Technology Department of Mechanical and Mechatronics Engineering University of Waterloo the State Key Laboratory of Management and Control for Complex Systems.Institute of Automation Chinese Academy of Sciences the Research Center for Military Computational Experiments and Parallel Systems Technology National University of Defense Technology

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as"parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality.A deep planning model which combines a convolutional neural network(CNN) with the Long Short-Term Memory module(LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human ***, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder(VAE) and a generative adversarial network(GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.

关键词： Autonomous driving artificial traffic scene deep learning emergencies motion planning parallel planning

来源：评论

学校读者我要写书评

暂无评论

A more compact object detector head network with feature enhancement and relational reasoning

arXiv

引用

arXiv 2021年

作者： Zhang, Wenchao Fu, Chong Chang, Xiangshi Zhao, Tengfei Li, Xiang Sham, Chiu-Wing School of Computer Science and Engineering Northeastern University Shenyang110819 China Engineering Research Center of Security Technology of Complex Network System Ministry of Education China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China Baidu Smart Life Group NO. 10 Xibeiwang East Street Ke Ji Yuan Haidian District Beijing100193 China School of Computer Science University of Auckland New Zealand

Modeling implicit feature interaction patterns is of significant importance to object detection tasks. However, in the two-stage detectors, due to the excessive use of hand-crafted components, it is very difficult to reason about the implicit relationship of the instance features. To tackle this problem, we analyze three different levels of feature interaction relationships, namely, the dependency relationship between the cropped local features and global features, the feature autocorrelation within the instance, and the cross-correlation relationship between the instances. To this end, we propose a more compact object detector head network (CODH), which can not only preserve global context information and condense the information density, but also allows instance-wise feature enhancement and relational reasoning in a larger matrix space. Without bells and whistles, our method can effectively improve the detection performance while significantly reducing the parameters of the model, e.g., with our method, the parameters of the head network is 0.6× smaller than the state-of-the-art Cascade R-CNN, yet the performance boost is 1.3% on COCO test-dev. Without losing generality, we can also build a more lighter head network for other multi-stage detectors by assembling our method. Copyright © 2021, The Authors. All rights reserved.

关键词： Object detection

来源：评论

学校读者我要写书评

暂无评论

SAMamba: Adaptive state space modeling with hierarchical vision for infrared small target detection

引用

Information Fusion 2025年 124卷

作者： Wenhao Xu Shuchen Zheng Changwei Wang Zherui Zhang Chuan Ren Rongtao Xu Shibiao Xu School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing 100876 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250014 China The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing 100190 China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan 250014 China School of Software Microelectronics Peking University Beijing 100084 China

Infrared small target detection (ISTD) is vital for long-range surveillance systems, particularly in military defense, maritime monitoring, and early warning applications. Despite its strategic importance, ISTD remains challenging due to two fundamental limitations: targets typically occupy less than 0.15% of the image area and exhibit low distinguishability from complex backgrounds. While recent advances in deep learning have shown promise, existing methods struggle with information loss during downsampling and inefficient modeling of global context. This paper presents SAMamba, a novel framework that synergistically integrates SAM2’s hierarchical feature learning with Mamba’s selective sequence modeling to address these challenges. Our key innovations include: (1) Feature Selection Adapter (FS-Adapter) that enables efficient domain adaptation from natural to infrared imagery by employing a dual-stage selection mechanism, which includes token-level selection via a learnable task embedding and channel-wise refinement through adaptive transformations; (2) Cross-Channel state-Space Interaction (CSI) module that achieves efficient global context modeling through selective state space modeling with linear complexity; and (3) Detail-Preserving Contextual Fusion (DPCF) module that adaptively combines multi-scale features through learnable fusion strategies, utilizing a gating mechanism to balance contributions from high-resolution and low-resolution features. SAMamba effectively addresses the core challenges of ISTD by bridging the domain gap, maintaining fine-grained target details, and efficiently modeling long-range dependencies. Extensive experiments on NUAA-SIRST, IRSTD-1k and NUDT-SIRST datasets demonstrate that SAMamba significantly outperforms state-of-the-art methods, particularly in challenging scenarios with heterogeneous backgrounds and varying target scales. Code is available at https://***/zhengshuchen/SAMamba .

关键词：

来源：评论

学校读者我要写书评

暂无评论

Measurement-device-independent quantum secure direct communication

引用

Science China(Physics,Mechanics & Astronomy) 2020年第3期63卷 2-7页

作者： ZengRong Zhou Yu Bo Sheng PengHao Niu LiuGuo Yin GuiLu Long Lajos Hanzo State Key Laboratory of Low-dimensional Quantum Physics Beijing 100084China Department of Physics Tsinghua UniversityBeijing 100084China Collaborative Innovation Center of Quantum Matter Beijing 100084China Beijing Academy of Quantum Information Beijing 100084China Institute of Quantum Information and Technology Nanjing University of Posts and TelecommunicationsNanjing 210003China College of Telecommunications&Information Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China Key Lab of Broadband Wireless Communication and Sensor Network Technology Ministry of EducationNanjing University of Posts and TelecommunicationsNanjing 210003China Beijing National Research Center for Information Science and Technology Beijing 100084China School of Information and Technology Tsinghua UniversityBeijing 100084China School of Electronics and Computer Science University of SouthamptonSouthampton SO171BJUK

Quantum secure direct communication(QSDC)is a unique technique,which supports the secure transmission of confidential information directly through a quantum channel without the need for a secret key and for *** this secure communication protocol fundamentally differs from its conventional *** this article,we report the first measurement-deviceindependent(MDI)QSDC protocol relying on sequences of entangled photon pairs and single ***,it eliminates the security loopholes associated with the measurement ***,this MDI technique is capable of doubling the communication distance of its conventional counterpart operating without using our MDI *** also conceive a protocol associated with linear optical Bell-basis measurements,where only two of the four Bell-basis states could be *** the number of qubits in a sequence reduces to 1,the MDI-QSDC protocol degenerates to a deterministic MDI quantum key distribution protocol.

关键词： quantum information secure communication quantum communication

来源：评论

学校读者我要写书评

暂无评论

DPA-2: a large atomic model as a multi-task learner

arXiv

引用

arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task 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：