2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)最新文献

Optimization Methodology Of Small Scale Horizontal Axis Shrouded Tidal Current Turbine 小型水平轴冠式潮流水轮机优化方法

2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2019-12-01 DOI: 10.1109/CSDE48274.2019.9162407

I. Kim, Ali Alkhabbaz, Hyun-il Jeong, Young Ho Lee

{"title":"Optimization Methodology Of Small Scale Horizontal Axis Shrouded Tidal Current Turbine","authors":"I. Kim, Ali Alkhabbaz, Hyun-il Jeong, Young Ho Lee","doi":"10.1109/CSDE48274.2019.9162407","DOIUrl":"https://doi.org/10.1109/CSDE48274.2019.9162407","url":null,"abstract":"An investigation of the performance of a small-scale horizontal axis shrouded tidal current turbine was carried out numerically and experimentally. Blade Element Momentum (BEM) theory is used to find out the optimum parameters of the rotor blades such as chord length, and twist angle distribution. Since the power output is proportional to the third order of incoming velocity, so, even a slight increase of the fluid velocity will produce a notable increment of the power output. Thus, a shrouded duct equipped with a ring-type flange has used to enhance the turbine performance. The paper aims to find out the optimum diffuser configuration to minimize the duct size and reduce the structural loads. A surface response method of the ANSYS-Workbench has used to optimize four design parameters including the curvature of the diffuser, flange height, inlet, and outlet length. All calculations were carried out using a commercial software ANSYS-CFX17.2. Results showed that the optimum parameters of the shrouded turbine could produce a high power output with a rate of increase up to 25 % as compared with a conventional turbine. An experiment of a small-scale tidal current turbine was performed. A comparison between CFD results and experimental data has done. The experimental results showed a good agreement with those from CFD analysis.","PeriodicalId":238744,"journal":{"name":"2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127265211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Using Graph Convolution Network for Predicting Performance of Automatically Generated Convolution Neural Networks 用图卷积网络预测自动生成卷积神经网络的性能

2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2019-12-01 DOI: 10.1109/CSDE48274.2019.9162354

Enzhi Zhang, Tomohiro Harada, R. Thawonmas

{"title":"Using Graph Convolution Network for Predicting Performance of Automatically Generated Convolution Neural Networks","authors":"Enzhi Zhang, Tomohiro Harada, R. Thawonmas","doi":"10.1109/CSDE48274.2019.9162354","DOIUrl":"https://doi.org/10.1109/CSDE48274.2019.9162354","url":null,"abstract":"In this paper, we propose a model using a graph convolution network for predicting the accuracy of the automatically generated convolution neural network (CNN). In recent years, there have been many excellent results in the automatic designing of deep learning network structures and their hyperparameters. These works mainly use evolutionary algorithms and reinforcement learning methods. But to verify the accuracy of newly generated neural network samples, it requires a lot of computational time and large scale of hardware. Therefore, how to reduce training time and improve the utilization of samples has become an important problem of automatic design for deep learning networks. On the other hand, the effectiveness of the graph convolution network (GCN) has been shown in processing graph data on the tasks of graph node classification, link prediction, and clustering. We are considering using GCN to predict the accuracy of the automatically designed neural networks. We compare GCN, a multi-layer neural network(NN), and CNN on the accuracy prediction task of automatically generated CNN for image classification tasks on the CIFAR-10 dataset. Our experiment demonstrates the advantages of GCN on small datasets compared to the multi-layer neural network and CNN, while GCN achieves the equivalent accuracy to the other methods on large datasets.","PeriodicalId":238744,"journal":{"name":"2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127378794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Proposal Specifications of Building Data Centre for Virtual GlobalNets 虚拟全球网络数据中心建设建议规范

2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2019-12-01 DOI: 10.1109/CSDE48274.2019.9162370

Aiman Al-Sabaawi, Hassan M. Ibrahim, Mohammed Abdal Baqee Mahmood Almalullah, Jasmeet Kaur, Khamael Al-Dulaimi, Amani Zwayen

引用次数: 1

Influence of spring constant and electromagnetic damping coefficient on measurement performance in a checkweigher with force balance system using Voice Coil Actuator 弹簧常数和电磁阻尼系数对音圈式力平衡校验秤测量性能的影响

2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2019-12-01 DOI: 10.1109/CSDE48274.2019.9162415

JiYun An, Jaeseong Choi, Gil-Seung Lee, Junghwan Ahn, Hwa-Young Kim

引用次数: 0

Impact of E-Commence Technology Adoption in Dubai SMEs 迪拜中小企业采用电子商务技术的影响

2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2019-12-01 DOI: 10.1109/CSDE48274.2019.9162358

Z. H. A. Almtiri, S. Miah

{"title":"Impact of E-Commence Technology Adoption in Dubai SMEs","authors":"Z. H. A. Almtiri, S. Miah","doi":"10.1109/CSDE48274.2019.9162358","DOIUrl":"https://doi.org/10.1109/CSDE48274.2019.9162358","url":null,"abstract":"E-commerce is becoming more and more relevant in different business markets, bringing benefits and convenience to all stakeholders involved. Although e-commerce is developing at a higher speed in the UAE country context comparing to other countries, there is still skepticism and other barriers to its full adoption. Therefore, this research aims to reveal the positive effect of technology on e-commerce and encourage small and medium enterprises (SMEs) to incorporate technology into their business plans. For this, the research uses quantitative methods of analysis, namely, a questionnaire, with a positivist interpretation of the collected data. To analyse the data accurately, the study will use Descriptive Statistics in SPSS statistics. The main limiting factors for the adoption of e-commerce technologies in the UAE have to do with the perception of e-commerce amongst the SMEs managers. Organizations are eager to use new technologies, realizing their benefits. E-commerce, on the other hand, is blurry in the minds of the employees of the UAE SMEs, which makes them distrustful, and hence resistant to the adoption of new technologies that impact in developing e-commerce strategies.","PeriodicalId":238744,"journal":{"name":"2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122864696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Cloud Enterprise Resource Planning Implementation: A Systematic Literature Review of Critical Success Factors 云企业资源规划实施:关键成功因素的系统文献综述

2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2019-12-01 DOI: 10.1109/CSDE48274.2019.9162373

Sunchai Tongsuksai, Sanjay Mathrani, Nazim Taskin

{"title":"Cloud Enterprise Resource Planning Implementation: A Systematic Literature Review of Critical Success Factors","authors":"Sunchai Tongsuksai, Sanjay Mathrani, Nazim Taskin","doi":"10.1109/CSDE48274.2019.9162373","DOIUrl":"https://doi.org/10.1109/CSDE48274.2019.9162373","url":null,"abstract":"Enterprise resource planning (ERP) is a well-known business management system that enhances functional efficiencies through integration of processes in a company. Cloud ERP systems have recently evolved which are taking precedence over on-premise ERP due to the convenience of remote information access in real-time. The purpose of this study is to investigate and identify the critical success factors (CSFs) and the organisational, environmental, technological and individual characteristics that impact implementation of cloud ERP systems. 81 articles on cloud ERP implementation were evaluated from various conferences and journals. This study identifies 32 CSFs which influence implementation of these systems and also develops an integrative model based on the organisational, environmental, technological and individual characteristics that could impact deployment of cloud ERP systems. This study provides new insights into cloud ERP adoption, its CSFs and offers more clarity to information technology (IT) practitioners into the characteristics that may impact cloud ERP implementation.","PeriodicalId":238744,"journal":{"name":"2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114411567","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Google App Analysis 谷歌应用分析

2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2019-12-01 DOI: 10.1109/CSDE48274.2019.9162379

Rui Cai, Xiawen Zhang

引用次数: 0

Feasibility Study of Carbon capture and storage (CCS) technology in Energy-Environmental Technology 能源环境技术中碳捕集与封存技术的可行性研究

2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2019-12-01 DOI: 10.1109/CSDE48274.2019.9162364

A. Kim, S. Kwon, S. Pyo

引用次数: 3

Using Ensemble Decision Tree Model to Predict Student Dropout in Computing Science 基于集成决策树模型的计算机科学专业学生退学预测

2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2019-12-01 DOI: 10.1109/CSDE48274.2019.9162389

M. Naseem, K. Chaudhary, B. Sharma, Aman Goel Lal

{"title":"Using Ensemble Decision Tree Model to Predict Student Dropout in Computing Science","authors":"M. Naseem, K. Chaudhary, B. Sharma, Aman Goel Lal","doi":"10.1109/CSDE48274.2019.9162389","DOIUrl":"https://doi.org/10.1109/CSDE48274.2019.9162389","url":null,"abstract":"Science, Technology, Engineering and Mathematics (STEM) professionals play a key role in the development of an economy. STEM workers are critical thinkers as they contribute immensely by driving innovations. There is a high demand for professionals in the STEM fields but there is also a shortage of human resource in these areas. One way to reduce this problem is by identifying students who are at-risk of dropping out and then intervening with focused strategies that will ensure that these students remain in same the programme till graduation. Therefore, this research aims to use a data mining classification technique to identify students who are at-risk of dropping out from their Computing Science (CS) degree programmes. The Random Forest (RF) decision tree algorithm is used to learn patterns from historical data about first-year undergraduate CS students who are enrolled in a tertiary institute in the South Pacific. A number of factors are used which comprise of students demographic information, previous education background, financial information as well as data about students’ academic interaction. Feature selection is performed to determine which factors have greater influence in students’ decision in dropping out. Cross-validation techniques are used to ensure that the models are not over-fitted. Two models were built using a 5fold and 10-fold cross-validation and the results were compared using several measures of model performance. The results show that the factors corresponding to students’ academic performance in a first-year programming course had the greatest impact student attrition in CS.","PeriodicalId":238744,"journal":{"name":"2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"11 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123690463","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Iteration split with Firefly Algorithm and Genetic Algorithm to Solve Multidimensional Knapsack Problems 基于萤火虫算法和遗传算法的迭代分割求解多维背包问题

2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) Pub Date : 2019-12-01 DOI: 10.1109/CSDE48274.2019.9162422

Ravneil Nand, Priynka Sharma

{"title":"Iteration split with Firefly Algorithm and Genetic Algorithm to Solve Multidimensional Knapsack Problems","authors":"Ravneil Nand, Priynka Sharma","doi":"10.1109/CSDE48274.2019.9162422","DOIUrl":"https://doi.org/10.1109/CSDE48274.2019.9162422","url":null,"abstract":"When we talk about optimization, we mean to get the best or the optimal solutions from some set of available substitutes for the problems. If constraints are introduced in the problem, the feasible range would change. As we venture further in optimization, different types of problems need different approaches. One very common problem is combinatorial optimization problems. Combinatorial optimization is a topic that consists of finding an optimal object from a finite set of objects. In simple terms, finding optimal solutions from some set of available datasets of a problem. Multi Knapsack Problem (MKP) is NP-hard combinational optimization problem better known as the multi-constraint knapsack problem. It is one of the extensively studied problems in the field as it has a variety of real world problems associated with it. In this paper, the Firefly algorithm is used with the Genetic algorithm to solve the Multidimensional Knapsack Problem (MKP). By using the properties of flashing behavior of fireflies together with genetic evolution, some benchmark problems are solved. The results are compared with some work from the literature.","PeriodicalId":238744,"journal":{"name":"2019 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126339529","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3