2018 International Conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS)最新文献_第4页

E-KUBU: Smart Home Automation System for Housing Energy Management E-KUBU:住宅能源管理智能家居自动化系统

2018 International Conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS) Pub Date : 2018-10-01 DOI: 10.1109/ICSGTEIS.2018.8709133

Gusti Ayu Mayani Kristina Dewi, N. Wirastuti, A. E. Karyawati, I. K. G. Suhartana, I. W. Santiyasa, I. K. A. Mogi, I. G. A. Wibawa, I. B. G. Dwidasmara, N. Putra

引用次数: 0

Virtual Backup Server optimization on Data Centers using Neural Network 基于神经网络的数据中心虚拟备份服务器优化

2018 International Conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS) Pub Date : 2018-10-01 DOI: 10.1109/ICSGTEIS.2018.8709101

Muhammad Riza Hilmi, M. Sudarma, Linawati

{"title":"Virtual Backup Server optimization on Data Centers using Neural Network","authors":"Muhammad Riza Hilmi, M. Sudarma, Linawati","doi":"10.1109/ICSGTEIS.2018.8709101","DOIUrl":"https://doi.org/10.1109/ICSGTEIS.2018.8709101","url":null,"abstract":"The data center is a valuable asset in the organization because it keeps all important data of the organization, good data management is needed so that data is protected. To secure a data center, duplicate servers are generally created containing data backup from the data center. Every organization must provide high operational costs in providing special hardware (dedicated servers) for backup servers and high bandwidth for backup processes because duplicated data is all data from the data center. To minimize operational costs and high bandwidth usage, this study makes a virtual server backup system using the Neural Network. Using a virtual server does not require operational costs to buy and manage special hardware, and Neural Network uses the Iterative Dychotomizer version 3 (ID3) classification method and Backpropagation can solve the problem of backup data classification so that not all data in the data center is duplicated. The use of Neural Network using a combination of ID3 and Backpropagation classification methods, can accelerate the backup process and increase the accuracy of backup data when compared without Neural Network. The backup system research built is capable of producing backup processes in incremental backups with a time acceleration of up to 56.34% compared to the backup process without a Neural Network. In testing the accuracy of backup data shows that the backup process using the Neural Network has an accuracy level of 99.84% which is able to recognize duplicated data in accordance with the formation of a classification tree using ID3 and using Backpropagation for the learning process of duplicated data.","PeriodicalId":438615,"journal":{"name":"2018 International Conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131729963","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: 0

ICSGTEIS 2018 Author Index ICSGTEIS 2018作者索引

2018 International Conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS) Pub Date : 2018-10-01 DOI: 10.1109/icsgteis.2018.8709042

引用次数: 0

Evaluation of Integrated University Management Information System Using COBIT 5 Domain DSS 基于COBIT 5域决策支持系统的综合大学管理信息系统评价

2018 International Conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS) Pub Date : 2018-10-01 DOI: 10.1109/ICSGTEIS.2018.8709144

Ayu Indah Saridewi, D. Wiharta, Nyoman Putra Sastra

引用次数: 4

Supervised Deep Learning Based for Traffic Flow Prediction 基于监督深度学习的交通流量预测

2018 International Conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS) Pub Date : 2018-10-01 DOI: 10.1109/ICSGTEIS.2018.8709102

Hendrik Tampubolon, Pao-Ann Hsiung

{"title":"Supervised Deep Learning Based for Traffic Flow Prediction","authors":"Hendrik Tampubolon, Pao-Ann Hsiung","doi":"10.1109/ICSGTEIS.2018.8709102","DOIUrl":"https://doi.org/10.1109/ICSGTEIS.2018.8709102","url":null,"abstract":"In metropolitan areas, common traffic issues include traffic congestion, traffic accidents, air pollution, and energy consumption occur. To resolve this issues, Intelligent Transportation Systems (ITS) have been evolved by many researchers. One of the important sub-systems in the development of ITS is a Traffic Management System (TMS) which attempts to reduce a traffic congestion. In fact, TMS itself relies on the estimation of traffic flow, therefore providing such an accurate traffic flow prediction is needed. For this reason, we aim to provide an accurate traffic flow prediction to facilitate this system. In this works, a Supervised Deep Learning Based Traffic Flow Prediction (SDLTFP) was proposed which is a type of fully-connected deep neural network (FC-DNN). Timely prediction is also a major issue in guaranteeing reliable traffic flow prediction. However, training a deep network could be time-consuming, and overfitting is might be happening, especially when feeding small data into the deep architecture. The network is learned perfectly during the training, but in testing with the new data, it could fail to generalize the model. We adopt the Batch Normalization (BN) and Dropout techniques to help the network training. SGD and momentum are carried out to update the weight. We then take advantage of open data as historical traffic data which are then used to predict future traffic flow with the proposed method and model above. Experiments show that the Mean Absolute Percentage Error (MAPE) for our traffic flow prediction is within 5 % using sample data and between 15% to 20% using out of the sample data. Training a deep network faster with BN and Dropout reduces the overfitting.","PeriodicalId":438615,"journal":{"name":"2018 International Conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS)","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126588642","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: 4

Feature Learning Using Convolutional Neural Network for Cardiac Arrest Detection 基于卷积神经网络特征学习的心脏骤停检测

2018 International Conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS) Pub Date : 2018-10-01 DOI: 10.1109/ICSGTEIS.2018.8709100

M. Nguyen, Kim Kiseon

引用次数: 4

Intermittent Renewable Energy Source (IRES) Penetration Level into Bangka Power System 间歇性可再生能源(IRES)在邦加电力系统中的渗透水平

2018 International Conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS) Pub Date : 2018-10-01 DOI: 10.1109/icsgteis.2018.8709119

H. B. Tambunan, K. Mangunkusumo, B. Harsono, A. Simaremare, N. W. Priambodo, B. S. Munir

引用次数: 1

Implementation of Grid-connected PV Plant in Remote Location in Sumbawa Island of Indonesia: Lesson Learned 在印度尼西亚松巴哇岛偏远地区实施并网光伏电站:经验教训

2018 International Conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS) Pub Date : 2018-10-01 DOI: 10.1109/ICSGTEIS.2018.8709105

I. Kumara, T. Urmee, Y. Divayana, I. Setiawan, Aaga Pawitra, A. Jaya

{"title":"Implementation of Grid-connected PV Plant in Remote Location in Sumbawa Island of Indonesia: Lesson Learned","authors":"I. Kumara, T. Urmee, Y. Divayana, I. Setiawan, Aaga Pawitra, A. Jaya","doi":"10.1109/ICSGTEIS.2018.8709105","DOIUrl":"https://doi.org/10.1109/ICSGTEIS.2018.8709105","url":null,"abstract":"The National Energy Policy (NEP) set 23% renewable energy target in the generation mix by 2025, where solar PV has identified as one of the feasible resources. There are many solar programs implemented in Indonesia. A large scale grid-connected solar PV (1 MW) project in Indonesia is analyzed in this study. The power plant is installed on Sumbawa island. The project is analyzed to investigate the performance of PV systems in a tropical climate. The performances were analyzed on project development, plants technical specification, energy production and performance index, and its current status. Data is collected through site visit and observations, documents review, and discussion with relevant stakeholders. PVSyst is used to simulate the potential of annual energy production and performance. Our audit found that the main components of the power plant are certified from reputable international organizations including Indonesian national standards. The simulation showed the power plant could generate 1,195 MWh annual energy with an average of 8% monthly variation which leads to an average performance index of 68.9%. Since the commissioning tests, the plant has not been operated. Consequently, no energy has been injected into the grid nor supplied to local load. Currently, the power plant is an inoperable condition due to non-technical problems. The problems are discussed, and a recommendation is presented in this paper.","PeriodicalId":438615,"journal":{"name":"2018 International Conference on Smart Green Technology in Electrical and Information Systems (ICSGTEIS)","volume":"30 8","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114060001","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