检索结果-内蒙古大学图书馆

8th International Symposium on Project Management, ISPM 2020

作者： Guo, Zhong-Hua Jiang, Hui You, Wan School of Engineering Science University of Chinese Academy of Sciences Beijing100049 China School of Economics and Management Engineering Beijing Univ. of Civil Engineering and Architecture Beijing100044 China

ISBN: (纸本)9781921712722

An effective legal system of construction project can not only guide the construction project activities, but also improve the trial efficiency of related cases.. Most of the current research on the construction engineering laws and regulations explore the relationship and conflict of different laws and regulations with qualitative analysis. However, we can not have a comprehensive understanding of the legal system of China's construction industry with qualitative analysis alone. At the same time, based on the analysis of experience or a few cases, it is impossible to find out the real situation of the application of law in judicial practice. Using the social network analysis method, this paper analyzes the 1581 effective judgments of construction contract disputes in beijing since 2001. First of all, legal logic of the judge is obtained through analyzing the judgment of Construction project contract legal disputes. Furthermore, through the analysis of the relationship between the law and the legal provisions, the root of the problems existing in the legal system of the construction engineering in our country can be identified. Finally, some suggestions are put forward to improve the legislation. © 8th International Symposium on Project Management, ISPM 2020. All rights reserved.

关键词： Construction industry

来源：评论

学校读者我要写书评

暂无评论

Deployment and Application of Deep Learning Models under Computational Constraints

Deployment and Application of Deep Learning Models under Com...

引用

IEEE International Conference on Big Data

作者： Jinwen Li Wendong Wang Beijing Univ. of Posts and Telecom Beijing China International School Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications

Deep learning is a computationally demanding field. So the GPU used has a very important impact on the effectiveness of deep learning. In addition, deep learning requires huge amounts of memory. High throughput means better performance, faster iterations, and more efficient completion of experiments. Therefore, the choice of GPU basically determines the success and experience of deep learning. This paper analyzes four commonly used computer deep learning model structures, VGG, ResNet50, MobileNet, InceptionV3, and their memory requirement. At the same time, this paper also analyzes the relationship between computing power and memory of several mainstream graphics cards produced by Nvidia which are often used for deep learning, including GeForce, Tesla, and etc. Therefore, in the case of limited medical conditions, such as the inability to purchase expensive graphics cards, or do not have enough memory graphics cards, an existing suitable computer model can be found to help medical personnel to perform gland image segmentation, so as to achieve a more efficient, fast and economical scheme.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Fast Event-based Double Integral for Real-time Robotics

arXiv

引用

arXiv 2023年

作者： Lin, Shijie Zhang, Yingqiang Huang, Dongyue Zhou, Bin Luo, Xiaowei Pan, Jia The Department of Computer Science The University of Hong Kong Hong Kong The Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong The School of Computer Science and Engineering Beihang University Beijing China The Department of Architecture and Civil Engineering City University of Hong Kong Hong Kong Peng Cheng Laboratory Guangdong Shenzhen China

Motion deblurring is a critical ill-posed problem that is important in many vision-based robotics applications. The recently proposed event-based double integral (EDI) provides a theoretical framework for solving the deblurring problem with the event camera and generating clear images at high frame-rate. However, the original EDI is mainly designed for offline computation and does not support real-time requirement in many robotics applications. In this paper, we propose the fast EDI, an efficient implementation of EDI that can achieve real-time online computation on single-core CPU devices, which is common for physical robotic platforms used in practice. In experiments, our method can handle event rates at as high as 13 million event per second in a wide variety of challenging lighting conditions. We demonstrate the benefit on multiple downstream real-time applications, including localization, visual tag detection, and feature matching. © 2023, CC0.

关键词： Robotics

来源：评论

学校读者我要写书评

暂无评论

A 3D Framework for Brain Tumor Segmentation from Multi-modal MR Images

A 3D Framework for Brain Tumor Segmentation from Multi-modal...

引用

IEEE International Conference on Big Data

作者： Shuo Yan Zheng Zhang Bo Zhang Wendong Wang Beijing Univ. of Posts and Telecom Beijing China State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications School of Modern Post Beijing University of Posts and Telecommunications

Segmentation of brain tumors from multi-modal magnetic resonance (MR) images is a crucial task in medical imaging, with significant clinical implications for the diagnosis and treatment planning of relevant brain disorders. In this paper, we present a 3D framework, that utilizes multi-modal MR images, including T1-weighted, T1ce-weighted, T2-weighted, and FLAIR images, to capture comprehensive information about the tumor for precise and automated brain tumor segmentation. The proposed framework leverages a multi-modal MRI fusion task to facilitate the optimization and training of brain tumor segmentation. Comparative experiments have indicated the effectiveness and superiority of the proposed framework for brain tumor segmentation tasks.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Breast Regions Segmentation Based on U-net++ from DCE-MRI Image Sequences 5

Breast Regions Segmentation Based on U-net++ from DCE-MRI Im...

引用

2020 5th International Seminar on computer Technology, Mechanical and Electrical engineering, ISCME 2020

作者： Sui, Dong Huang, Zixuan Song, Xinwei Zhang, Yue Wang, Yantao Zhang, Lei School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing102612 China College of Computer Science and Technology Harbin Engineering University Harbin150001 China Zhou-kou-dian Middle School Beijing Academy of Education Sciences Beijing102405 China Diagnostic Radiology and Nuclear Medicine University of Maryland BaltimoreMD21201-1531 United States

Background analysis of breast cancer can depict the progress and states of the tumour, which is based on the whole breast segmentation from MRI images. The focus of this paper is to construct a pipeline for breast region segmentation for the possibility of breast cancer automatic diagnosis by using MRI image serials. Studies of breast region segmentation based on traditional and deep learning methods have undergone several years, but most of them have not achieved a satisfactory consequence for the following background analysis. In this paper, we proposed a novel pipeline for whole breast region segmentation method based on U-net++, that can achieve a better result compared with the traditional U-net model which is the most common used medical image analysis model and achieve a better IoU than CNN models. We have evaluated the U-net++ model with tradition U-net, our experiments demonstrate that the U-net++ with deep supervision achieves a higher IoU over U-net model. © Published under licence by IOP Publishing Ltd.

关键词： Pipelines

来源：评论

学校读者我要写书评

暂无评论

Characterizing Data Sharing in civil Infrastructure engineering: Current Practice, Future Vision, Barriers, and Promotion Strategies

引用

Journal of Computing in civil engineering 2023年第2期37卷

作者： Wang, Yanyu Tang, Pingbo Liu, Kaijian Cai, Jiannan Ren, Ran Lin, Jacob J. Cai, Hubo Zhang, Jiansong El-Gohary, Nora Berges, Mario Golparvar Fard, Mani Dept. of Civil and Environmental Engineering Carnegie Mellon Univ. 500 Forbes Ave. PittsburghPA15213 United States Dept. of Civil Environmental and Ocean Engineering Stevens Institute of Technology 1 Castle Point Terrace HobokenNJ07030 United States School of Civil and Environmental Engineering and Construction Management Univ. of Texas at San Antonio San AntonioTX78207 United States Lab School of Construction Management Technology Purdue Univ. 401 N. Grant St. West LafayetteIN47907 United States Dept. of Civil Engineering National Taiwan Univ. No. 1 Section 4 Roosevelt Rd. Da'an District Taipei10617 Taiwan Lyles School of Civil Engineering Purdue Univ. 550 Stadium Mall Dr. West LafayetteIN47907 United States Dept. of Civil and Environmental Engineering Univ. of Illinois at Urbana-Champaign Mathews Ave. UrbanaIL16801 United States Dept. of Civil and Environmental Engineering Computer Science and Tech Entrepreneurship Univ. of Illinois at Urbana-Champaign 205 N Mathews Ave. UrbanaIL16801 United States

Data sharing between different organizations is critical in supporting decision making in civil infrastructure engineering projects (e.g., transportation projects). Understanding the characteristics of data-sharing-related factors in civil infrastructure engineering is crucial for the civil engineering community to identify the priorities in promoting data sharing. The ASCE Data Sensing and Analysis (DSA) Committee initiated an investigation on data-sharing barriers in civil infrastructure engineering. This work produced (1) a classification of data-collection and data-use contexts in the domain of civil infrastructure engineering;(2) a comprehensive classification of data-sharing barriers and data-sharing promotion strategies in this domain;and (3) a synthesis of the opinions about the current practices, future vision, barriers, and data-sharing promotion strategies from civil infrastructure professionals and researchers using an online survey. The survey results revealed that (1) more data-sharing activities are occurring within the organizations, such as academia, government agencies, and for-profit corporations, compared with data exchanges among these communities;(2) "if there is a cost sharing in the data sharing,"is the condition that makes data providers more willing to share data with others;and (3) compared with technology improvements, the protocol of data sharing in the business domain has more potential in promoting data sharing. Meanwhile, this research identified the top five priorities among 20 data-sharing promotion strategies, which are protocol in industry and organization, data-standard regulation, data validity, data security protection, and large-scale data-sharing technology. These five priorities play critical roles in a roadmap showing how policy, business models, and technical solutions should cooperate to promote data sharing in civil infrastructure engineering. © 2023 American Society of civil Engineers.

关键词： Decision making

来源：评论

学校读者我要写书评

暂无评论

Development of a Student-Centric Cyber-Physical System (SCPS): An Android App for Interactive Learning of Structural Analysis and Dynamics

Development of a Student-Centric Cyber-Physical System (SCPS...

引用

2021 International Conference on Computing in civil engineering, I3CE 2021

作者： Sastha Kumar, Arvind Han, Kevin Ham, Youngjib Yoon, Hyungchul Dept. of Computer Science North Carolina State Univ. RaleighNC United States Dept. of Civil Construction and Environmental Engineering North Carolina State Univ. RaleighNC United States Dept. of Construction Science Texas A&m Univ. College StationTX United States School of Civil Engineering Chungbuk National Univ. Cheongju Korea Republic of

ISBN: (纸本)9780784483893

Although there have been many attempts to increase student engagement and interaction in class through virtual reality and augmented reality (VR/AR), structural engineering has remained as one of the disciplines that lacks interactive learning. There was an earlier attempt by the authors to improve student learning in structural analysis and dynamics through a student-centric cyber-physical system (SCPS) that reacts to student movement and outcomes structural behavior. This system used mobile devices as sensors and sent data to be processed by a server. As a result, it suffered from the inherent limitation of Transmission Control Protocol/Internet Protocol (TCP/IP)-lags and instability. Therefore, this paper presents the development of a new SCPS tool with a new system architecture that is much more stable and provide better user experience. This SCPS captures and uses student movement (i.e., walking, turning, and jumping) as point loads on a virtual bridge. As students move around, structural behavior (i.e., deflection) is simulated and visualized in real time. © 2021 Computing in civil engineering 2021 - Selected Papers from the ASCE International Conference on Computing in civil engineering 2021. All rights reserved.

关键词： Students

来源：评论

学校读者我要写书评

暂无评论

Sub-Loc:predicting protein sub-mitochondrial localization based on sequence embedding

Sub-Loc:predicting protein sub-mitochondrial localization ba...

引用

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xiaoting Wang Juan Wang Haodong Bian Maozu Guo School of Computer Science Inner Mongolia University and Ecological Big Data Engineering Research Center of the Ministry of Education Hohhot China School of Electrical and Information Engineering Beijing University of Civil Engineering and Architecture Beijing P.R. China Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing P.R. China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Mitochondria are subcellular organelles existing in most eukaryotic organisms. They have a pivotal role in lots of bio-chemical processes for cells. Proteins in different compartments of mitochondria have their transport routes. Locating proteins in mitochondria can provide a solid foundation for mitochondrial pathologies. So far, there have been several computational methods for solving the issue. However, their accuracy is so low that they do not identify the localization precisely. We develop an unsupervised learning model to represent mitochondrial proteins as n-dimensional vectors, called sequence embedding, which can learn the global and context information from mitochondrial proteins. We design a new model, called Sub-Loc, to predict the sub-mitochondrial localization of proteins using the SVM classifier and the sequence embedding method. The sequence embedding method and the Sub-Loc are tested by experiments. Experimental results show the sequence embedding method remarkably enhances the performance of prediction for sub-mitochondrial localization of proteins compared with other feature representations. The Sub-Loc outperforms other approaches for predicting sub-mitochondrial localization of proteins.

关键词： Proteins Location awareness Support vector machines Deep learning Pathology Predictive models Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Assessing the Institutional- and Project-Level Determinants of Corruption in Infrastructure Projects: Evidence from World Bank–Funded Projects

ASCE Open: Multidisciplinary Journal of Civil Engineering

引用

ASCE Open: Multidisciplinary Journal of civil engineering 2024年第1期2卷

作者： Cui, Zhipeng Wang, Dan Casady, Carter B. Yin, Hang Dong, Anglong School of Urban Economics and Management Beijing Univ. of Civil Engineering and Architecture No. 1 Zhanlanguan St. Xicheng District Beijing100044 China School of Public Policy and Administration Chongqing Univ. No. 174 Shazheng St. Shapingba District Chongqing400044 China Center for Sustainable Development and Global Competitiveness Stanford Univ. 473 Via Ortega StanfordCA94305 United States Co. No. 76 Lixing St. Yubei District Chongqing400044 China School of Engineering and Applied Science Univ. of Pennsylvania 220 S 33rd St. PhiladelphiaPA19104 United States

To successfully deliver infrastructure projects, it is essential to prevent corruption—i.e., the act of offering, giving, receiving, or soliciting anything of value, directly or indirectly, with the intention of improperly influencing the actions of another party in the procurement of financed projects. However, there is little to no research exploring the institutional- and project-level determinants of corruption in infrastructure projects. To fill this gap, this research develops a conceptual model that shows the effects of contract size, repeat winners, and public sector corruption on project corruption. Using data collected from the World Bank and Varieties of Democracy (V-Dem) database, 330 projects located in 61 countries between 2000 and 2018 were analyzed using multilevel logistic regression models. The results show the average contract size and the number of repeat winners in a project are important determinants of corruption risk in infrastructure projects. Higher levels of corruption in the public sector also significantly magnify the likelihood that increases in contract value will result in project corruption. This research deepens our understanding of corruption in infrastructure projects across diverse institutional environments and shows project managers and policymakers how different project characteristics influence the likelihood of corruption occurring in these localized settings. © 2024 American Society of civil Engineers (ASCE). All rights reserved.

关键词： Project management

来源：评论

学校读者我要写书评

暂无评论

Diagnosis of COVID-19 Pneumonia via a Novel Deep Learning architecture

引用

Journal of computer science & Technology 2022年第2期37卷 330-343页

作者： Xin Zhang Siyuan Lu Shui-Hua Wang Xiang Yu Su-Jing Wang Lun Yao Yi Pan Yu-Dong Zhang Department of Medical Imaging The Fourth People's Hospital of Huai'anHuai'an 223002China School of Informatics University of LeicesterLeicesterLE17RHU.K. School of Architecture Building and Civil Engineering Loughborough UniversityLoughboroughLE113TUU.K. School of Mathematics and Actuarial Science University of LeicesterLeicesterLE17RHU.K. Key Laboratory of Behavior Sciences Institute of PsychologyChinese Academy of SciencesBeijing 100101China Department of Psychology University of the Chinese Academy of SciencesBeijing 100101China Department of Infection Diseases The Fourth People's Hospital of Huai'anHuaian 223002China Department of Computer Science Georgia State UniversityAtlanta 30302-5060U.S.A. Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz University Jeddah 21589Saudi Arabia

COVID-19 is a contagious infection that has severe effects on the global economy and our daily *** diagnosis of COVID-19 is of importance for consultants,patients,and *** this study,we use the deep learning network AlexNet as the backbone,and enhance it with the following two aspects:1)adding batch normalization to help accelerate the training,reducing the internal covariance shift;2)replacing the fully connected layer in AlexNet with three classifiers:SNN,ELM,and ***,we have three novel models from the deep COVID network(DC-Net)framework,which are named DC-Net-S,DC-Net-E,and DC-Net-R,*** comparison,we find the proposed DC-Net-R achieves an average accuracy of 90.91%on a private dataset(available upon email request)comprising of 296 images while the specificity reaches 96.13%,and has the best performance among all three proposed *** addition,we show that our DC-Net-R also performs much better than other existing algorithms in the literature.

关键词： pneumonia COVID-19 convolutional neural network AlexNet deep 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：