检索结果-内蒙古大学图书馆

A Fast Geometric Modeling Method for Cold Blades 1

Asia-Pacific International Symposium on Aerospace Technology, APISAT 2018

作者： Liu, Kaicheng Wang, Jianjun Ni, Bowen School of Energy and Power Engineering Beihang University Beijing100191 China Cloud Computing and Big Data Center China Mobile Group Xinjiang Company Limited Xinjiang830018 China

ISBN: (数字)9789811333057

ISBN: (纸本)9789811333040

A geometric modeling method for cold blade is proposed based on hot-to-cold finite element (FE) transformation calculation. The main idea is to interpolate the deformation result calculated from hot-to-cold transformation to the control points of the hot blade profile. Subtract the interpolated displacement values from the coordinates of the hot control points then the coordinates of the cold control points are obtained. The resulting coordinates could be directly used for solid modeling in commercial CAD programs. Both the hot-to-cold FE transformation calculation part and the output part of the cold blade profile’s control point coordinates could be accomplished in a whole process automatically in the commercial FE program ANSYS15.0. The flow chart of the entire process is given attached with ANSYS APDL commands. Finally, a numerical example based on a blisk sector model is provided. The hot-to-cold FE transformation calculation is performed, the finite element model (FEM) is obtained and solid model is built. Additionally, the effect of mesh density on the modeling accuracy is investigated. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Computer aided design

来源：评论

学校读者我要写书评

暂无评论

Predicting Virtual Machine Resource Consumption Based on Optimized Grey Model

Predicting Virtual Machine Resource Consumption Based on Opt...

引用

第二届先进电子材料、计算机与材料工程国际学术会议（AEMCME 2019）

作者： Shi Aiwu Gao Di He Kai Cloud Computing and Big Data Research Center Wuhan Textile University

In order to relatively change the computing specifications of virtual machines(VMs for short) for the changing resource consumption of VMs, and reduce the impact of the changes, this paper proposes the optimized grey GM(1, 1) model to predict the resource consumption of VMs. Because VM resources may fluctuate greatly in a short time, the initial data are smoothed first, and then modeled, the model edge value is optimized according to the characteristics of cloud environment. Experiments show that the accuracy and stability of the prediction model can be improved by sorting out the initial data and optimizing the model. The prediction value is helpful to design the allocation scheme of VM resources, improve the utilization rate of physical resources in cloud environment.

关键词：

来源：评论

学校读者我要写书评

暂无评论

DCSim: Cooling Energy Aware VM Allocation Framework for a cloud data center

DCSim: Cooling Energy Aware VM Allocation Framework for a Cl...

引用

International Conference on Advances in computing and Communication Engineering (ICACCE)

作者： Priyank Bhandia Ravi S. Anupindi Pavan Yekbote Nikhil Singh H.L. Phalachandra Dinkar Sitaram Center for Cloud Computing and Big Data PES University Bangalore India

ISBN: (数字)9781728132501

ISBN: (纸本)9781728132518

Explosion of digital content has resulted in large amounts of resources being provisioned and managed for various applications in cloud data centers. Energy consumption in these large cloud data centers is a rising concern, accounting for 1.3% of the worlds electricity consumption [1]. data center cooling accounts for 40% of this energy consumption [2]. Of the various mechanisms available for studying the energy consumption in data centers, a simulation based approach is quite popular. In this paper, we propose DCSim, a configurable extension to cloudSim, a popularly used cloud infrastructure and simulation framework. cloudSim provides coarse power models to calculate total energy consumption in a data center for a given workload, but has no provision to factor in the data center topology and current cooled area into this power model. This makes building intelligent cooling energy aware allocation policies in cloudSim difficult. In DCSim, we introduce a novel data center model that addresses the shortcomings of cloudSim by encapsulating concepts of Racks, Aisles, Sectors and Zones (collectively referred to as DCObjects). We also provide the capability to model the cooling of these DCObjects. This makes the study of cooling aware resource provisioning for workloads easier. The DCObjects and the data center model presented are designed to be fully extensible to support future developments in this area. In this work we also implement a Cooling aware VM allocation policy, and demonstrate using multiple algorithms, that this VM allocation policy will effectively reduce the total data center power consumption by 18.18% over an algorithm which does not factor in the cooled DCObjects.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Research on Star/Galaxy Classification Based on Stacking Ensemble Learning

引用

Chinese Astronomy and Astrophysics 2020年第3期44卷 345-355页

作者： Chao, L.I. Wen-hui, ZHANG Ran, L.I. Jun-yi, W.A.N.G. Ji-ming, L.I.N. College of Information and Communication Engineering Guilin University of Electronic Technology Guilin 541004 Key Laboratory of Education Ministry for Cognitive Radio and Information Processing Guilin University of Electronic Technology Guilin 541004 Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin 541004 Guangxi College and University Key Laboratory for Cloud Computing and Complex Systems Guilin University of Electronic Technology Guilin 541004 Guangxi Key Laboratory for Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin 541004 Guangxi College and University Key Laboratory for Satellite Navigation and Position Sensing Guilin 541004

Machine learning has achieved great success in many areas today, but the forecast effect of machine learning often depends on the specific problem. An ensemble learning forecasts results by combining multiple base classifiers. Therefore, its ability to adapt to various scenarios is strong, and the classification accuracy is high. In response to the low classification accuracy of the darkest source magnitude set of stars/galaxies in the Sloan Digital Sky Survey (SDSS), a star/galaxy classification algorithm based on the stacking ensemble learning is proposed in this paper. The complete photometric data set is obtained from the SDSS data Release (DR) 7, and divided into the bright source magnitude set, dark source magnitude set, and darkest source magnitude set according to the stellar magnitude. Firstly, the 10-fold nested cross-validation method is used for the darkest source magnitude set, then the Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms are used to establish the base-classifier model;the Gradient Boosting Decision Tree (GBDT) is used as the meta-classifier model. Finally, based on the classification accuracy of galaxies and other indicators, the classification results are analyzed and compared with the results obtained by the Function Tree (FT), SVM, RF, GBDT, Stacked Denoising Autoencoders (SDAE), Deep Belief Nets (DBN), and Deep Perception Decision Tree (DPDT) models. The experimental results show that the stacking ensemble learning model has improved the classification accuracy of galaxies in the darkest source magnitude set by nearly 10% compared to the function tree algorithm. Compared with other traditional machine learning algorithm, stronger lifting algorithm, and deep learning algorithm, the stacking ensemble learning model also has different degrees of improvement. © 2020

关键词： Galaxies: fundamental parameters methods: data analysis Stars: fundamental parameters techniques: photometric

来源：评论

学校读者我要写书评

暂无评论

A Face Recognition Method Based on CNN

A Face Recognition Method Based on CNN

引用

2019 High Performance computing and Computational Intelligence Conference, HPCCI 2019

作者： Xie, Zhiming Li, Junjie Shi, Hui Department of Information Engineering Shanwei Polytechnic Shanwei Guangdong516600 China Research Institute of Cloud Computing and Data Center Project Design Shanwei Innovation Industrial Design and Research Institute Shanwei Guangdong516600 China Professional Committee Cloud Computing and Big Data in Higher Vocational College Higher Education Institute in Guangdong Province Shanwei Guangdong516600 China

The traditional face recognition technology is more complicated for the extraction of facial image features and the selection of classifiers, and the recognition rate is not high. With the continuous maturity of the convolutional neural network from handwritten digit recognition to face recognition, A face recognition algorithm that tests CNN using the Python+Keras framework. The method mainly involves two aspects. One is to observe the influence on the network by changing the number of neurons in the hidden layer;the other is to observe the influence on the network by changing the number of feature maps of the convolutional layer 1 and the convolutional layer 2. The best CNN model is 36-76-1024 through multiple sets of experimental tests. The model can automatically extract facial image features and classify them. Using adam optimizer and softmax classifier for face recognition can make training faster convergence and more. Effectively improve accuracy and use the Dropout method to avoid overfitting. The experimental results show that the recognition rate of the CNN model on the olivettifaces face database is 97.5%. When the optimal CNN model is used, the average recognition rate is close to 100%, which verifies the validity and accuracy of the algorithm and model. © Published under licence by IOP Publishing Ltd.

关键词： Face recognition

来源：评论

学校读者我要写书评

暂无评论

Cache Characterization of Workloads in a Microservice Environment

Cache Characterization of Workloads in a Microservice Enviro...

引用

IEEE International Conference on cloud computing in Emerging Markets (CCEM)

作者： Sanjith Athlur Nishant Sondhi Sachit Batra Subramaniam Kalambur Dinkar Sitaram Department of Computer Science Center for Cloud Computing and Big Data PES University Bengaluru

ISBN: (数字)9781728163345

ISBN: (纸本)9781728163352

The advent of Microservice based architecture has lead to the obsolescence of the tedious way of building and deploying applications in a monolithic way. The number of organizations taking advantage of cloud computation and microservice architecture is steadily increasing. From the cloud providers perspective, the purpose of cloud computation is to leverage economies of scale by co-locating several workloads dynamically on the same powerful many-core servers. In this paper, we attempt to characterize cache usage and its impact on performance when multiple workloads are collocated on multicore servers.

关键词： Servers Interference cloud computing Benchmark testing Hardware Multicore processing

来源：评论

学校读者我要写书评

暂无评论

Small Evolution Antenna Design Method Based on Dynamic Hill Climbing Algorithm and Orthogonal Experiment

Small Evolution Antenna Design Method Based on Dynamic Hill ...

引用

2019 High Performance computing and Computational Intelligence Conference, HPCCI 2019

作者： Shi, Hui Li, Junjie Xie, Zhiming Department of Information Engineering Shanwei Polytechnic Shanwei Guangdong516600 China Research Institute of Cloud Computing and Data Center Project Design Shanwei Innovation Industrial Design and Research Institute Shanwei Guangdong516600 China Professional Committee Cloud Computing and Big Data in Higher Vocational College Higher Education Institute in Guangdong Province Shanwei Guangdong516600 China

Small-scale evolution antennas have the advantages of high gain, wide bandwidth and small size, and have broad application prospects in the aerospace field. Aiming at the complex high-dimensional and dynamic problems of aerial antenna modeling, a dynamic hill-climbing algorithm based on orthogonal design is designed and implemented to simulate and optimize the single-span antenna in high-frequency operation mode. In the optimization problem, this paper defines a new individual comparison mechanism, which can reflect the number of violations of constraints and the degree of violation of each constraint, making the comparison of individuals more reasonable. The antenna simulation results demonstrate the effectiveness and feasibility of using dynamic evolutionary algorithms for antenna design. © Published under licence by IOP Publishing Ltd.

关键词： Antennas

来源：评论

学校读者我要写书评

暂无评论

Multi-Scale Region-based Fully Convolutional Networks

Multi-Scale Region-based Fully Convolutional Networks

引用

IEEE International Conference on Power, Intelligent computing and Systems (ICPICS)

作者： Chengqi Xu Xuehai Hong Yuanzhou Yao Hengheng Shen Qian Ma Hui Jiang Big data research institute Cloud Computing Center Chinese Academy of Sciences Shangrao China Institute of Computing Technology Chinese Academy of Sciences Beijing China College of Information Engineering Sichuan Agricultural University Ya’ an City Sichuan State China

ISBN: (数字)9781728198743

ISBN: (纸本)9781728198750

A multi-scale R-FCN detection algorithm is presented to solve the problem of region-based full convolution network (R-FCN) in multi-scale object detection. Firstly, in order to solve the problem that R-FCN algorithm has limited receptive field and semantic information in single feature map detection, a multi-scale proposal box (proposal) is obtained by using multi-scale feature maps in the main network. At the same time, in order to make the feature maps of different layers have rich semantic information at the same time during the two-stage detection, the feature maps of different layers are fused from top to bottom. Finally, in order to make the two-stage position-sensitive score map also have good multiscale representation ability, a multi-layer shared convolution method is used to generate the position-sensitive score map. The experimental results on PASCAL VOC dataset show that the multiscale R-FCN algorithm in this paper is better than the original R-FCN algorithm in detection accuracy. At the same time, the detection map shows that the algorithm performs better in multi-scale object detection.

关键词： Feature extraction Object detection Proposals Semantics Detection algorithms Convolution Entropy

来源：评论

学校读者我要写书评

暂无评论

Herb Pair Danggui-Baishao: Pharmacological Mechanisms Underlying Primary Dysmenorrhea by Network Pharmacology Approach 15th

Herb Pair Danggui-Baishao: Pharmacological Mechanisms Underl...

引用

15th International Conference on Intelligent computing, ICIC 2019

作者： Li, Li-Ting Qiu, Hai-Yan Liu, Mi-Mi Cai, Yong-Ming Guangdong Pharmaceutical University GuangzhouGuangdong510000 China Guangdong Provincial TCM Precision Medicine Big Data Engineering Technology Research Center GuangzhouGuangdong510000 China Guangdong University College of Precision Medicine Big Data Engineering Research Center Based on Cloud Computing GuangzhouGuangdong510000 China GuangzhouGuangdong510000 China

ISBN: (纸本)9783030269685

Epidemiological studies have shown that primary dysmenorrhea is the most common disease in gynecology, and its incidence rate is between 20% and 90%, which is a common cause of affecting women’s normal work and quality of life. Its high incidence rate, wide spread and economic losses and social harm have caused widespread concern worldwide. The present work adopted a network pharmacology-based approach to provide new insights into the active compounds and therapeutic targets of Danggui-Baishao herb pair for the treatment of primary dysmenorrhea. Fifteen active compounds of the herb pair possessing favorable pharmacokinetic profiles and biological activities were selected, interacting 17 dysmenorrhea-related targets to provide potential synergistic therapeutic actions. Systematic analysis of the constructed networks revealed that these targets such as ABCB1, ESR1, PGR, AKR1C3, PTGS2, CYP2C8, PTGS1 were mainly involved in Steroid hormone biosynthesis, Arachidonic acid metabolism, serotonergic synapse and Ovarian steroidogenesis through steroid metabolic process, steroid hormone mediated signaling pathway, cyclooxygenase pathway, response to estradiol and response to oxidative stress. © 2019, Springer Nature Switzerland AG.

关键词： Metabolism

来源：评论

学校读者我要写书评

暂无评论

Landscape of global urban environmental resistome and its association with local socioeconomic and medical status

引用

Science China(Life Sciences) 2024年第6期67卷 1292-1301页

作者： Jun Wu Yige Hu Michael H Perlin David Danko Jun Lu Manuela Oliveira Johannes Werner Maria M Zambrano Maria A Sierra Olayinka O Osuolale Paweł Łabaj Nicolás Rascovan Nur Hazlin Hazrin-Chong Soojin Jang Haruo Suzuki Marina Nieto-Caballero Bharath Prithiviraj Patrick K H Lee Agnieszka Chmielarczyk Anna Różańska Yongxiang Zhao Lan Wang Christopher E Mason Tieliu Shi Center for Bioinformatics and Computational Biology and the Institute of Biomedical SciencesSchool of Life SciencesEast China Normal UniversityShanghai 200241China Department of Biology Program on Disease EvolutionUniversity of LouisvilleLouisville 40292USA Weill Cornell Medicine New York 10065USA The Bin Talal Bin Abdulaziz Alsaud Institute for Computational Biomedicine New York 10065USA Department of Pulmonary Medicine Shanghai Chest HospitalShanghai Jiao Tong UniversityShanghai 200025China i3S-Instituto de Investiga玢o e Inova玢o em Saúde Universidade do PortoPorto 4050-290Portugal Ipatimup-Instituto de Patologia e Imunologia Molecular da Universidade do Porto Porto 4200-465Portugal Departamento de Biologia Faculdade de CiênciasUniversidade do PortoPorto 4050-290Portugal High Performance and Cloud Computing Group Zentrum für Datenverarbeitung(ZDV)Eberhard Karls University of TübingenW鋍hterstra遝7672074TübingenGermany Corporación Corpogen Research Center Bogotá110311Colombia Applied Environmental Metagenomics and Infectious Diseases Research(AEMIDR) Department of Biological SciencesElizade UniversityIlara-Mokin 340271Nigeria Maopolska Centre of Biotechnology Jagiellonian UniversityKraków 30-005Poland Aix-Marseille Université Mediterranean Institute of OceanologyUniversitéde ToulonCNRSIRDUM 110Marseille 83041France Department of Biological Sciences and Biotechnology Faculty of Science and TechnologyUniversiti Kebangsaan Malaysia43600 UKM BangiSelangor Institut Pasteur Korea Seoul 13488Republic of Korea Faculty of Environment and Information Studies Keio UniversityFujisawaKanagawa 252-0882Japan University of Colorado at Boulder CivilEnvironmental and Architectural DepartmentBoulder 80303USA Brooklyn College of the City University of New York Brooklyn 11210USA School of Energy and Environment City University of Hong KongHong Kong 999077China Department of Microbiology Faculty of MedicineJagiellonian UniversityKraków 30-005Poland Biological Targeting Diagnosis and The

Antimicrobial resistance(AMR)poses a critical threat to global health and development,with environmental factors—particularly in urban areas—contributing significantly to the spread of antibiotic resistance genes(ARGs).However,most research to date has been conducted at a local level,leaving significant gaps in our understanding of the global status of antibiotic resistance in urban *** address this issue,we thoroughly analyzed a total of 86,213 ARGs detected within 4,728 metagenome samples,which were collected by the Meta SUB International Consortium involving diverse urban environments in 60 cities of 27 countries,utilizing a deep-learning based *** findings demonstrated the strong geographical specificity of urban environmental resistome,and their correlation with various local socioeconomic and medical *** also identified distinctive evolutionary patterns of ARG-related biosynthetic gene clusters(BGCs)across different countries,and discovered that the urban environment represents a rich source of novel *** study provides a comprehensive overview of the global urban environmental resistome,and fills a significant gap in our knowledge of large-scale urban antibiotic resistome analysis.

关键词： urban environment metagenome antibiotic resistance resistance diversity biosynthetic gene clusters

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：