检索结果-内蒙古大学图书馆

Automatic metal drum opening-sealing device prototype for nuclear power plant

Revista Tecnica de la Facultad de Ingenieria Universidad del Zulia 2016年第6期39卷 264-275页

作者： Liu, Sibang Ge, Shuzhi Sam Zhao, Qian Huang, Gang Su, Zhe School of Automation Engineering University of Electronic Science and Technology of China ChengduSichuan611731 China Center for Robotics University of Electronic Science and Technology of China ChengduSichuan611731 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore Centre for Robotics University of Electronic Science and Technology of China China

Automatic metal drum opening-sealing device is the key device of the low and intermediate level radioactive wastes treatment line for the nuclear power plant, which is used to open and seal the metal drum filled with radioactive wastes automatically. This paper firstly analyzes the key technologies of the metal drum opening-sealing device which include the technology of metal drum positioning, drum trip bolt and bolt hole positioning, drum cover depositing and bolt tightening shaft torque controlling. Then, based on the modular design method, a device prototype was designed and made and tests were carried out based on the prototype. The test results verified the design rationality and high operation efficiency of the prototype. © 2016, Revista Tecnica de la Facultad de Ingeniera. All rights reserved.

关键词： Bolt tightening

来源：评论

学校读者我要写书评

暂无评论

A Hyper-Heuristic in MOEA/D-DRA Using the Upper Confidence Bound Technique

A Hyper-Heuristic in MOEA/D-DRA Using the Upper Confidence B...

引用

Brazilian Conference on Intelligent Systems (BRACIS)

作者： Lucas Prestes Myriam Regattieri de Biase da Silva Delgado Richard Aderbal Goncalves Carolina Paula de Almeida Aurora Trinidad Pozo Universidade Tecnologica Federal do Parana Curitiba PR BR Graduate Program in Electrical and Computer Engineering UTFPR Universidade Estadual do Centro-Oeste Guarapuava PR BR Computer Science Department UNICENTRO Computer Science Department UFPR

The Multi-Objective Evolutionary Algorithm based on Decomposition with Dynamical Resource Allocation (MOEA/D-DRA) has obtained very good results on various multi-objective optimization problems in the past few years. This paper focuses on an attempt to improve even more its performance by introducing a hyper-heuristic mechanism to select the best set of its operators and parameters. In this paper we use Upper Confidence Bound (UCB) as the basis of the hyper-heuristic, and test three versions of the proposed approach. Four well known benchmarks (CEC 2009, WFG, DTLZ and ZDT) and a quality indicator (hypervolume) are used to analyze the performance of the three variants. The proposed approach is compared with the original MOEA/D-DRA and the results show that tuning the parameters via UCB is an interesting alternative for a hyper-heuristic based version of MOEA/D-DRA on the addressed problems.

关键词： Optimization Sociology Statistics Evolutionary computation Benchmark testing Resource management Approximation algorithms

来源：评论

学校读者我要写书评

暂无评论

Hierarchical learning of multi-task sparse metrics for large-scale image classification

引用

Pattern Recognition 2017年 67卷 97-109页

作者： Yu Zheng Jianping Fan Ji Zhang Xinbo Gao Department of Electronic Engineering Xidian University Xi’an 710071 PR China Department of Computer Science University of North Carolina Charlotte NC 28223 USA Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Xi’an 710049 PR China

In this paper, a novel approach is developed to learn a tree of multi-task sparse metrics hierarchically over a visual tree to achieve a fast solution to large-scale image classification, where an enhanced visual tree is first learned to organize large numbers of image categories hierarchically in a coarse-to-fine fashion. Over the visual tree, a tree of multi-task sparse metrics is learned hierarchically by: (a) performing multi-task sparse metric learning over the sibling child nodes under the same parent node to explicitly separate their commonly-shared metric from their node-specific metrics; and (b) propagating the node-specific metric for the parent node to its sibling child nodes (at the next level of the visual tree), so that more discriminative metrics can be learned for controlling inter-level error propagation effectively. We have evaluated our hierarchical multi-task sparse metric learning algorithm over three different image sets and the experimental results demonstrated that our hierarchical multi-task sparse metric learning algorithm can obtain better performance than the state-of-the-art algorithms on large-scale image classification.

关键词： Hierarchical multi-task sparse metric learning Visual tree Large-scale image classification

来源：评论

学校读者我要写书评

暂无评论

Correction: Phenotate: crowdsourcing phenotype annotations as exercises in undergraduate classes

引用

Genetics in medicine : official journal of the American College of Medical Genetics 2020年第8期22卷 1427页

作者： Willie H Chang Pouria Mashouri Alexander X Lozano Brittney Johnstone Mia Husić Annie Olry Sylvie Maiella Tugce B Balci Sarah L Sawyer Peter N Robinson Ana Rath Michael Brudno Centre for Computational Medicine The Hospital For Sick Children Toronto ON Canada. Department of Computer Science Princeton University Princeton NJ USA. Faculty of Medicine University of Toronto Toronto ON Canada. Department of Materials Science & Engineering Stanford University Stanford CA USA. Sunnybrook Health Sciences Centre Toronto ON Canada. Orphanet Institut national de la santé et de la recherche médicale Paris France. Medical Genetics Program of Southwestern Ontario London Health Sciences Centre London ON Canada. Department of Genetics Children's Hospital of Eastern Ontario and Children's Hospital of Eastern Ontario Research Institute University of Ottawa Ottawa ON Canada. The Jackson Laboratory for Genomic Medicine Farmington CT USA. Institute for Systems Genomics University of Connecticut Farmington CT USA. Centre for Computational Medicine The Hospital For Sick Children Toronto ON Canada. brudno@cs.toronto.edu. Department of Computer Science University of Toronto Toronto ON Canada. brudno@cs.toronto.edu. Genetics and Genome Biology Program The Hospital for Sick Children Toronto ON Canada. brudno@cs.toronto.edu. University Health Network Toronto ON Canada. brudno@cs.toronto.edu.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Highly accurate model for prediction of lung nodule malignancy with ct scans

arXiv

引用

arXiv 2018年

作者： Causey, Jason Zhang, Junyu Ma, Shiqian Jiang, Bo Qualls, Jake Politte, David G. Prior, Fred Zhang, Shuzhong Huang, Xiuzhen Department of Computer Science Arkansas State University JonesboroAR72467 United States Department of Industrial and Systems Engineering University of Minnesota MinneapolisMN55455 United States Department of Systems Engineering and Engineering Management Chinese University of Hong Kong Shatin N.T. Hong Kong Research Center for Management Science and Data Analytics School of Information Management and Engineering Shanghai University of Finance and Economics Shanghai200433 China Mallinckrodt Institute of Radiology Washington University St. LouisMO63110 United States Department of Biomedical Informatics University of Arkansas for Medical Sciences Little RockAR72205 United States UALR/UAMS Joint Graduate Program in Bioinformatics Little RockAR72204 United States

Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a systematic approach to predict lung nodule malignancy from CT data, based on deep learning convolutional neural networks (CNN). For training and validation, we analyze >1000 lung nodules in images from the LIDC/IDRI cohort. All nodules were identified and classified by four experienced thoracic radiologists who participated in the LIDC project. NoduleX achieves high accuracy for nodule malignancy classification, with an AUC of ~0.99. This is commensurate with the analysis of the dataset by experienced radiologists. Our approach, NoduleX, provides an effective framework for highly accurate nodule malignancy prediction with the model trained on a large patient population. Our results are replicable with software available at http://***/NoduleX. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

来源：评论

学校读者我要写书评

暂无评论

The hybrid algorithm for product design in multimedia

引用

International Journal of Applied Systemic Studies 2017年第1-3期7卷 78-91页

作者： Huang, Ching-Lien Chen, Yung-Hui Tseng, Chun-Hsiung Wan, Tian-Long Shen, Mars Department of Industrial Management Lunghwa University of Science and Technology No. 300 Sec. 1 Wanshou Rd. Guishan Taoyuan City33306 Taiwan Department of Computer Information and Network Engineering LungHwa University of Science and Technology No. 300 Sec. 1 Wanshou Rd. Guishan District Taoyuan City33306 Taiwan Department of Communications Engineering Yuan Ze University No. 135 Yuandong Rd. Zhongli Dist. Taoyuan City320 Taiwan Department of Information Management Lunghwa University of Science and Technology No. 300 Sec. 1 Wanshou Rd. Guishan Taoyuan City33306 Taiwan Department of Master’s Program of Business Administration Lunghwa University of Science and Technology No. 300 Sec. 1 Wanshou Rd. Guishan Taoyuan City33306 Taiwan

It is the key successful factors for the manufacture industry that create and select the key parameters in product design to match customer requirements in multimedia. Previous literature studies are focused on the development of customer requirements or parameter selection in product design but not the development of customer demand modelling and product parameter selections in multimedia. In this way we can achieve the purpose of meeting customer demand for product design in multimedia. Kano is a successful method to solve the customer requirements selections issues. And, the theory of inventive problem solving (TRIZ) method is a method to solve the contradiction of product design. Therefore;the integration of the Kano and theory of inventive problem solving (TRIZ) methods is proposed to solve the cable product design in multimedia. This algorithm is also verified in an electronic manufacture. In summary, the proposed algorithm can be helpful by creating a product system in multimedia in this study. Copyright © 2017 Inderscience Enterprises Ltd.

关键词： Product design

来源：评论

学校读者我要写书评

暂无评论

LifeTime and improving European healthcare through cell-based interceptive medicine (vol 587, pg 377, 2020)

引用

NATURE 2021年第7852期592卷 E8-E8页

作者： Rajewsky, Nikolaus Almouzni, Genevieve Gorski, Stanislaw A. Aerts, Stein Amit, Ido Bertero, Michela G. Bock, Christoph Bredenoord, Annelien L. Cavalli, Giacomo Chiocca, Susanna Clevers, Hans De Strooper, Bart Eggert, Angelika Ellenberg, Jan Fernandez, Xose M. Figlerowicz, Marek Gasser, Susan M. Hubner, Norbert Kjems, Jorgen Knoblich, Jurgen A. Krabbe, Grietje Lichter, Peter Linnarsson, Sten Marine, Jean-Christophe Marioni, John C. Marti-Renom, Marc A. Netea, Mihai G. Nickel, Dorthe Nollmann, Marcelo Novak, Halina R. Parkinson, Helen Piccolo, Stefano Pinheiro, Ines Pombo, Ana Popp, Christian Reik, Wolf Roman-Roman, Sergio Rosenstiel, Philip Schultze, Joachim L. Stegle, Oliver Tanay, Amos Testa, Giuseppe Thanos, Dimitris Theis, Fabian J. Torres-Padilla, Maria-Elena Valencia, Alfonso Vallot, Celine van Oudenaarden, Alexander Vidal, Marie Voet, Thierry Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine in the Helmholtz Association Berlin Germany Charité-Universitätsmedizin Berlin Germany Berlin Institute of Health (BIH) Berlin Germany German Center for Cardiovascular Research (DZHK) Partner Site Berlin Berlin Germany Institute for Biology Humboldt University of Berlin Berlin Germany Cardiovascular and Metabolic Sciences Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin Germany Department of Pediatric Oncology/Hematology Charité-Universitätsmedizin Berlin Berlin Germany Institut Curie CNRS PSL Research University Sorbonne Université Nuclear Dynamics Unit Equipe Labellisée Ligue contre le cancer Paris France VIB Center for Brain and Disease Research Leuven Belgium Department of Human Genetics KU Leuven Leuven Belgium Department of Neurosciences KU Leuven Leuven Belgium UK Dementia Research Institute at UCL University College London London UK Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK Department of Immunology Weizmann Institute of Science Rehovot Israel Centre for Genomic Regulation (CRG) Barcelona Institute of Science and Technology Barcelona Spain CNAG-CRG Centre for Genomic Regulation Barcelona Institute of Science and Technology Barcelona Spain Universitat Pompeu Fabra Barcelona Spain ICREA Barcelona Spain CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences Vienna Austria Department of Laboratory Medicine Medical University of Vienna Vienna Austria Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases Vienna Austria Department of Medical Humanities Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht The Netherlands Institute of Human Genetics UMR 9002 CNRS and University of Montpellier Montpellier France Department of Experimental Oncology IEO European Institute of Oncology IRCCS Milan Italy Department of Oncology and Hemato

来源：评论

学校读者我要写书评

暂无评论

Editorial

引用

International Journal of Social robotics 2016年第1期8卷 1-3页

作者： Ge, Shuzhi Sam Khatib, Oussama and the Robotics Research Lab Department of Electrical & Computer Engineering The National University of Singapore Singapore Singapore University of Electronic Science and Technology of China Chengdu China Department of Computer Science Artificial Intelligence Laboratory Stanford University StanfordCA United States

来源：评论

学校读者我要写书评

暂无评论

Driving in the Matrix: Can virtual worlds replace human-generated annotations for real world tasks?

Driving in the Matrix: Can virtual worlds replace human-gene...

引用

IEEE International Conference on robotics and Automation (ICRA)

作者： Matthew Johnson-Roberson Charles Barto Rounak Mehta Sharath Nittur Sridhar Karl Rosaen Ram Vasudevan Department of Naval Architecture and Marine Engineering University of Michigan Ann Arbor MI USA Electrical Engineering and Computer Science University of Michigan Ann Arbor MI USA Robotics Program University of Michigan Ann Arbor MI USA Mechanical Engineering University of Michigan Ann Arbor MI USA

Deep learning has rapidly transformed the state of the art algorithms used to address a variety of problems in computer vision and robotics. These breakthroughs have relied upon massive amounts of human annotated training data. This time consuming process has begun impeding the progress of these deep learning efforts. This paper describes a method to incorporate photo-realistic computer images from a simulation engine to rapidly generate annotated data that can be used for the training of machine learning algorithms. We demonstrate that a state of the art architecture, which is trained only using these synthetic annotations, performs better than the identical architecture trained on human annotated real-world data, when tested on the KITTI data set for vehicle detection. By training machine learning algorithms on a rich virtual world, real objects in real scenes can be learned and classified using synthetic data. This approach offers the possibility of accelerating deep learning's application to sensor-based classification problems like those that appear in self-driving cars. The source code and data to train and validate the networks described in this paper are made available for researchers.

关键词： Engines Training Machine learning Data models Robots Training data Automobiles

来源：评论

学校读者我要写书评

暂无评论

The gap values in the profile matching method by fuzzy logic

引用

Journal of Physics: Conference Series 2018年第1期978卷 012061-012061页

作者： Sitepu, S A Efendi, S Situmorang, Z Master Program (S2) of Informatics Engineering Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia. Master Program of Informatics Engineering Faculty of Computer Science and Information Technology Universitas of Sumatera Utara Medan Indonesia. Department of Computer Science Faculty of Computer Science and Information Technology Universitas St. Thomas Medan Indonesia.

In this research, the determination of the appropriate values of Gap for the assessment of promotion criteria of position in an institution / company. In this study the authors use Fuzzy Sugeno logic on the determination of Gap values used in Profile Matching method. Test results of 5 employees obtained the eligibility of promotion with the position of Z* values between in 3.20 to 4.11.

关键词：

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：