检索结果-内蒙古大学图书馆

114th Annual ASEE Conference and Exposition, 2007

作者： Eschenbach, Elizabeth Montgomery, Jami Johnson, James Brus, Chris Carlson, Patricia Giammar, Dan Grauer, Bette Hotaling, Liesl Oguntimein, Gbekeloluwa Safferman, Steven Wentling, Tim Humboldt State University United States Department of Environmental Resources Engineering Humboldt State University United States WATERS Network - CLEANER Project Office Network Howard University United States Department of Environmental Engineering College of Engineering Architecture and Computer Sciences Howard University United States American Academy of Environmental Engineers United States University of Iowa United States Program University of Iowa United States Rose-Hulman Institute of Technology United States Department of Rhetoric Rose-Hulman Institute of Technology United States National Research Council U. S. Air Force United States NASA's Classroom of the Future Wheeling WV United States Washington University United States Department of Energy Environmental and Chemical Engineering Washington University St. Louis United States Environmental Studies Program Center for Materials Innovation United States McPherson High School United States McPherson High School McPherson KS United States Stevens Institute of Technology United States Stevens Institute of Technology United States Morgan State University United States Michigan State University United States Biosystems and Agricultural Engineering Department Michigan State University United States University of Dayton United States Education Planning Committee National Center for Supercomputing Applications United States Department of Information Science Graduate School of Library and Information Science National Center for Supercomputing Applications University or Illinois United States Knowledge and Learning Systems Group NCSA United States

The WATERS Network (WATer and Environmental Research Systems Network) will be an integrated real-time distributed observing system which will enable academic and government scientists, engineers, educators, and practitioners to advance effective management of our nation's water resources by understanding human interactions with water and the natural and built environment. WATERS will provide easily accessible real time environmental data as well as analysis tools to engineers, scientists, educators, K- graduate students and policymakers so they can better understand how water quantity, quality and related components of the hydrologic cycle are impacted by natural and human influences. The WATERS strategic plan will be completed by July 2007 and requires constituent input. This paper describes the draft Conceptual Design and the Education Plan of WATERS Network. This paper will highlight the potential impact of WATERS on undergraduate and graduate environmental engineering education in order to elicit input from the environmental engineering education community on how WATERS Network could better meet the future needs of undergraduate and graduate students and educators. More information can be found at ***. © American Society for engineering Education, 2007.

关键词： Water resources

来源：评论

学校读者我要写书评

暂无评论

Preface

Lecture Notes in Networks and Systems

引用

Lecture Notes in Networks and Systems 2023年 782 LNNS卷 v-vi页

作者： Fong, Simon Dey, Nilanjan Joshi, Amit University of Macau China Department of Computer Science and Engineering Techno International New Town Kolkata India Global Knowledge Research Foundation Ahmedabad India

来源：评论

学校读者我要写书评

暂无评论

ShuffleUNet: Super resolution of diffusion-weighted MRIs using deep learning

arXiv

引用

arXiv 2021年

作者： Chatterjee, Soumick Sciarra, Alessandro Dünnwald, Max Mushunuri, Raghava Vinaykanth Podishetti, Ranadheer Rao, Rajatha Nagaraja Gopinath, Geetha Doddapaneni Oeltze-Jafra, Steffen Speck, Oliver Nürnberger, Andreas Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Faculty of Computer Science Otto von Guericke University Magdeburg Germany Department of Biomedical Magnetic Resonance Otto von Guericke University Magdeburg Germany MedDigit Department of Neurology Medical Faculty University Hospital Magdeburg Germany German Centre for Neurodegenerative Diseases Magdeburg Germany Center for Behavioral Brain Sciences Magdeburg Germany Leibniz Institute for Neurobiology Magdeburg Germany

Diffusion-weighted magnetic resonance imaging (DW-MRI) can be used to characterise the microstructure of the nervous tissue, e.g. to delineate brain white matter connections in a non-invasive manner via fibre tracking. Magnetic Resonance Imaging (MRI) in high spatial resolution would play an important role in visualising such fibre tracts in a superior manner. However, obtaining an image of such resolution comes at the expense of longer scan time. Longer scan time can be associated with the increase of motion artefacts, due to the patient’s psychological and physical conditions. Single Image Super-Resolution (SISR), a technique aimed to obtain high-resolution (HR) details from one single low-resolution (LR) input image, achieved with Deep Learning, is the focus of this study. Compared to interpolation techniques or sparse-coding algorithms, deep learning extracts prior knowledge from big datasets and produces superior MRI images from the low-resolution counterparts. In this research, a deep learning based super-resolution technique is proposed and has been applied for DW-MRI. Images from the IXI dataset have been used as the ground-truth and were artificially downsampled to simulate the low-resolution images. The proposed method has shown statistically significant improvement over the baselines and achieved an SSIM of 0.913 ± 0.045. Copyright © 2021, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

来源：评论

学校读者我要写书评

暂无评论

Few-shot learning based histopathological image classification of colorectal cancer

引用

Intelligent Medicine 2024年第4期4卷 256-267页

作者： Rui Li Xiaoyan Li Hongzan Sun Jinzhu Yang Md Rahaman Marcin Grzegozek Tao Jiang Xinyu Huang Chen Li Key Laboratory of Intelligent Computing in Medical Image Ministry of EducationNortheastern UniversityShenyangLiaoning 110167China Cancer Hospital China Medical UniversityShenyangLiaoning 110122China Shengjing Hospital China Medical UniversityShenyang.Liaoning 110000China Institute for Medical Informatics University of LuebeckGermany Department of Knowledge Engineering University of Economics in KatowicePoland School of Intelligent Medicine Chengdu University of Traditional Chinese MedicineChengduSichuan 610075China International Joint Institute of Robotics and Intelligent Systems Chengdu University of Information TechnologyChengduSichuan 610225China School of Computer Science and Engineering University of New South WalesSydneyNSW 2052Australia

Background Colorectal cancer is a prevalent and deadly disease worldwide,posing significant diagnostic *** histopathologic image classification is often inefficient and *** some histopathologists use computer-aided diagnosis to improve efficiency,these methods depend heavily on exten-sive data and specific annotations,limiting their *** address these challenges,this paper proposes a method based on few-shot *** This study introduced a few-shot learning approach that combines transfer learning and contrastive learning to classify colorectal cancer histopathology images into benign and malignant *** model comprises modules for feature extraction,dimensionality reduction,and classification,trained using a combi-nation of contrast loss and cross-entropy *** this paper,we detailed the setup of hyperparameters:n-way,κ-shot,β,and the creation of support,query,and test *** Our method achieved over 98% accuracy on a query dataset with 35 samples per category using only 10 training samples per *** documented the model’s loss,accuracy,and the confusion matrix of the ***,we employed the t-SNE algorithm to analyze and assess the model’s classification *** The proposed model may demonstrate significant advantages in accuracy and minimal data depen-dency,performing robustly across all tested n-way,κ-shot *** consistently achieved over 93% accuracy on comprehensive test datasets,including 1916 samples,confirming its high classification accuracy and strong generalization *** research could advance the use of few-shot learning in medical diagnostics and also lays the groundwork for extending it to deal with rare,difficult-to-diagnose cases.

关键词： Colorectal cancer Few-shot learning Transfer learning Contrastive learning Histopathological images Benign and malignant categories

来源：评论

学校读者我要写书评

暂无评论

A Study of NetCDF as an Approach for High Performance Medical Image Storage

引用

Journal of Physics: Conference Series 2012年第1期341卷

作者： Marcone Magnus Thiago Coelho Prado Aldo von Wangenhein Douglas D J de Macedo M A R Dantas Post-graduate Program in Computer Science Department of Informatics and Statistic Federal University of Santa Catarina Florianópolis SC - Brasil Post-graduate Program in Knowledge Engineering and Managemet Department of Knowledge Engineering Federal University of Santa Catarina Florianópolis SC - Brasil

The spread of telemedicine systems increases every day. The systems and PACS based on DICOM images has become common. This rise reflects the need to develop new storage systems, more efficient and with lower computational costs. With this in mind, this article discusses a study for application in NetCDF data format as the basic platform for storage of DICOM images. The study case comparison adopts an ordinary database, the HDF5 and the NetCDF to storage the medical images. Empirical results, using a real set of images, indicate that the time to retrieve images from the NetCDF for large scale images has a higher latency compared to the other two methods. In addition, the latency is proportional to the file size, which represents a drawback to a telemedicine system that is characterized by a large amount of large image files.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Development of Precision Agriculture Models for Medium and Small-Scale Agriculture in Indonesia

引用

IOP Conference Series: Materials science and engineering 2020年第1期879卷

作者： H Maulana H Kanai School of Knowledge Science Japan Advanced Institute of Science and Technology 1-1 Asahidai Nomi Ishikawa Japan. Department of Informatics Engineering and Computer Science Universitas Komputer Indonesia Indonesia.

The purpose of this research is to build a model of precision agriculture to increase the productivity of medium and small-scale agriculture in Indonesia. This research used a descriptive method by conducting a literature study on the concept of precision agriculture and analysis of data related to agriculture in Indonesia. Extreme weather changes in Indonesia have an impact on the decline in agricultural production. While an understanding of environmental conditions is the primary key to agriculture, this research explained what data is needed in precision agriculture, how to obtain and process data, also explained how to visualize information through geographic information systems. This research also conducted an assessment of the built model. The assessment process is implemented by analysing the suitability of the model based on farmers' needs. The results of this study concluded that the model can be developed in the implementation phase. This research is expected to provide a clear explanation of the system requirements and also can reduce risk at the system development and implementation stage.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Correction to: Partially Overlapped Channel Assignment for Cloud-Based Heterogeneous Cellular and Mesh Networks

引用

Wireless Personal Communications 2021年第3期122卷 2583-2583页

作者： Iqbal, Saleem Qureshi, Kashif Naseer Majeed, Saqib Ghafoor, Kayhan Zrar Jeon, Gwanggil University Institute of Information Technology PMS UAAR Rawalpindi Pakistan Department of Computer Science Bahria University Islamabad Pakistan Department of Computer Science Knowledge University Erbil Iraq Department of Embedded Systems Engineering Incheon National University Incheon South Korea

来源：评论

学校读者我要写书评

暂无评论

Drive: An Adjustable Parallel Architecture Based on Evidence Awareness for Fake News Detection

SSRN

引用

SSRN 2024年

作者： Cong, Mou Liu, Lu Han, Jiayu Zhu, Beibei Yuan, Xiaosong Peng, Tao College of Computer Science and Technology Jilin University 2699 Qianjin Street Jilin Changchun130012 China College of Software Jilin University 2699 Qianjin Street Jilin Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Ministry of Education Jilin University 2699 Qianjin Street Jilin Changchun130012 China Department of Linguistics University of Washington SeattleWA98195 United States

Fake news detection becomes a critical challenge in NLP. Batch processing with attention-derivative models is a popular solution for fake news detection. Models with attention mechanisms could mitigate the long-distance dependence problem. However, we find that extracting local dependencies using convolutional networks is as important as extracting global dependencies using attention mechanisms. Besides, fake news datasets often suffer from problems such as imbalance and are hard to understand by current models. To address these problems, we propose DRIVE, which is short for an adjustable parallel architecture based on evidence awareness for fake news detection to alleviate the above problems, in which we introduce Claim as a supporting clue to enhance the model’s comprehension ability. Specifically, we propose a novel method for unbalanced datasets and design adjustable parallel branches, using Claim Additives Network (ClaimAddNet) and the MLP with convolutional gating mechanism (CPMLP) to capture global and local information in distinct branches, respectively. Experiments conducted on 5 public datasets demonstrate that DRIVE outperforms all baselines, improving accuracy by approximately 1.7 %. Furthermore, we implement an anti-noise interference test and evaluate the weighted average of the two branches along with ablation studies. © 2024, The Authors. All rights reserved.

关键词： Parallel architectures

来源：评论

学校读者我要写书评

暂无评论

Web-based Teamwork: Distributed Software Development Course under Covid-19

Web-based Teamwork: Distributed Software Development Course ...

引用

International Conference on computer science & Education (ICCSE)

作者： Rui ZHANG Pedro Mestre Carlos Serodio Marília Prada Wenjuan GAO College of Computer Science and Technology Jilin University Key Lab of Symbolic Computation and Knowledge Engineering Chinese Ministry of Education Changchun China Centro Algoritmi University of Minho Guimaraes Portugal CITAB University of Tras-os-Montes and Alto Douro Vila Real Portugal Department of Social and Organizational Psychology Instituto Universitário de Lisboa (ISCTE-IUL) Lisboa Portugal Library of Changchun University Changchun China

ISBN: (数字)9781728172675

ISBN: (纸本)9781728172682

In a increasingly globalized world, it is very important to help students to acquire the hard and soft skills needed to cope with the challenges they will face when working in such environments. However, even though students find this subject interesting and volunteer for it, it is hard, even impossible to teach college students such a distributed fashion of teamwork. In this paper, a case study is provided to illustrate the methodology and experiences of a joint course, distributed software development, of multiple universities from Asia, Europe, North and South Americas. The results show that student can get the spirit, effective communication for such web-based teamwork environment and achieve software project development. The recent developments because of the Covid-19 pandemic are the facts that now students have to cooperate remotely with each other not only across continents but also inside their own city or country. It has increased the awareness of the need for this kind of training and effectively improved the interactions between team elements in different countries and improved the students' feeling of belonging to their team.

关键词： Software Software engineering Games Faces Teamwork Electronic mail Tools

来源：评论

学校读者我要写书评

暂无评论

A standard simulation environment: a review of preliminary requirements 94

A standard simulation environment: a review of preliminary r...

引用

Simulation Winter Conference

作者： M.A.F. Wagner S. Sevinc O. Tanir P.L. Haigh J.D. Arthur R.E. Nance H.D. Schwetman SysTech Software Solutions Annandale VA USA Knowledge Systems Group Basser University of Sydney Australia Quality Engineering and Research (QER) Group Longueuil QUE Canada High Performance Software Inc. Dayton OH USA Systems Research Center Department of Computer Science Virginia Polytechnic Institute and State University Blacksburg VA USA Mesquite Software Inc. Austin TX USA

ISBN: (纸本)9780780321090

The concept of a simulation environment combines different aspects of the simulation process into one complete powerful tool. The environment should provide the necessary state-of-the-art advances and concepts that a modeler may require during the simulation development and execution process. The "environment" requirements must address the end user needs while minimizing the necessity of learning a new language. The requirements must specify the useful features, independent of different platforms and implementation languages. Hence the opportunity exists for standardization of environments. The paper opens a discussion on challenges and rewards awaiting the simulation community with the advent of a standard environment.

关键词： Power system modeling Computational modeling computer simulation Standardization knowledge based systems Australia knowledge engineering Software performance Drives computer science

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：