2018 International Conference on Information Technology (InCIT)最新文献_第2页

A Multiagent-based Video Tracking Algorithm 基于多agent的视频跟踪算法

2018 International Conference on Information Technology (InCIT) Pub Date : 2018-10-01 DOI: 10.23919/INCIT.2018.8584871

Chattrakul Sombattheera

引用次数: 0

Applying Data Analytics to Findings of User Behaviour Usage in Network Systems 将数据分析应用于网络系统中用户行为使用的发现

2018 International Conference on Information Technology (InCIT) Pub Date : 2018-10-01 DOI: 10.23919/INCIT.2018.8584865

Pongsarun Boonyopakorn

引用次数: 1

Home Automation with Voice and Mirror Control 具有语音和镜子控制的家庭自动化

2018 International Conference on Information Technology (InCIT) Pub Date : 2018-10-01 DOI: 10.23919/INCIT.2018.8584870

Kulanit Vorawitsurawathana, Shao Chuang, Natasha Ratanapan, Tanasanee Phienthrakul

引用次数: 1

Comparison of Classroom Participation Technology Uses in Higher Education 高等教育课堂参与技术应用比较

2018 International Conference on Information Technology (InCIT) Pub Date : 2018-10-01 DOI: 10.23919/INCIT.2018.8584880

Chaya Hiruncharoenvate

引用次数: 0

Load Balancer Mechanism using Optimal Parameter based on Calculus 基于微积分的最优参数负载均衡机制

2018 International Conference on Information Technology (InCIT) Pub Date : 2018-10-01 DOI: 10.23919/INCIT.2018.8584884

T. Chomsiri, D. Pansa

{"title":"Load Balancer Mechanism using Optimal Parameter based on Calculus","authors":"T. Chomsiri, D. Pansa","doi":"10.23919/INCIT.2018.8584884","DOIUrl":"https://doi.org/10.23919/INCIT.2018.8584884","url":null,"abstract":"This research proposes improvement of load balancer mechanism using technique that agent software preinstalled on servers has to read server resource information and send the information to load balancer. This technique provides reliable and up-to-date information to the load balancer because if the information, e.g. active connections, is estimated or counted by load balancer, it may have some errors or be not match with the real information. Also, putting weights of each server to the load balancer by an administrator may have unintentionally bias. With this technique, ‘plan making’ for dispatching incoming requests to servers will operate efficiently. Implementing the agent for reading and sending resource information to the server can be done easily. Similarly, an algorithm to create the plan making function is not hard because it is similar to other existing load balancing algorithms. Therefore, the main problem for this research is that how much of time interval between each plan making should be appropriate. This is because too wide of time interval can make slow reaction of system. Consequently, the overall efficiency will be dropped. Likewise, too narrow of time interval can bring the drop of overall efficiency as well. This is because the load balancer has to use almost of all its CPU time to make a dispatching plan. Thus, investigating to find the best value of time interval is the main focus of this research. Highlight of the proposed method is discovering the optimal time interval ‘w’ which can bring the highest overall efficiency ‘P’ to the network. We firstly present the function of overall efficiency in the term $P =$ ${f(w)}$ and then differentiate the function to obtain its derivative function. Consequently, we can discover the optimal ‘w’ at the point that ‘P’ can reach to its maximum.","PeriodicalId":144271,"journal":{"name":"2018 International Conference on Information Technology (InCIT)","volume":"13 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":"125536455","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

Position Quantization Approach with Multi-class Classification for Wi-Fi Indoor Positioning System 基于多类别分类的Wi-Fi室内定位系统位置量化方法

2018 International Conference on Information Technology (InCIT) Pub Date : 2018-10-01 DOI: 10.23919/INCIT.2018.8584863

Werayuth Charoenruengkit, Sunisa Saejun, Ramunya Jongfungfeuang, Kewali Multhonggad

引用次数: 2

Using User Generated Content in Mobile Application to Support Children with Special Needs 在移动应用程序中使用用户生成的内容来支持有特殊需要的儿童

2018 International Conference on Information Technology (InCIT) Pub Date : 2018-10-01 DOI: 10.23919/INCIT.2018.8584883

M. Kaenampornpan, Kaveepoj Banluewong, Nicholas John Gorman, Wutcharaporn Upapong, Patumwan Kon-roo

{"title":"Using User Generated Content in Mobile Application to Support Children with Special Needs","authors":"M. Kaenampornpan, Kaveepoj Banluewong, Nicholas John Gorman, Wutcharaporn Upapong, Patumwan Kon-roo","doi":"10.23919/INCIT.2018.8584883","DOIUrl":"https://doi.org/10.23919/INCIT.2018.8584883","url":null,"abstract":"Children with special needs are able to learn and live their life with normal people. They have certain symptoms that might affect their performance in daily life. Parents and teachers are an important part of the child’s life. They have to find the way to get child’s attention by creating content and media that suitable for them. This could be time consuming and costly. As mobile devices become more accessible to everyone. We propose an application that contains 2D animation of instructional media and interactive games. Moreover, based on the user generated content concept, we allow the parents and teachers to generate content with mobile devices’ functionalities. The content is then fresh and more relevant to each particular student. We tested the application with 15 parents and teachers. After they tested the application with their children, the result from the questionnaire shows that 33 percents of users are very satisfied with the application while the remaining 67 percents satisfied with the application. The parents and teachers gave a high satisfaction towards its beneficial with an overall average at 4.6. They would like to use the application again.","PeriodicalId":144271,"journal":{"name":"2018 International Conference on Information Technology (InCIT)","volume":"27 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":"133650846","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

Employee assessment using data mining techniques: modeling individual capability to improve the competency of companies 利用数据挖掘技术进行员工评估:对个人能力进行建模，以提高公司的竞争力

2018 International Conference on Information Technology (InCIT) Pub Date : 2018-10-01 DOI: 10.23919/INCIT.2018.8584861

Prajak Chertchom

引用次数: 0

An Integration of Requirement Forecasting and Customer Segmentation Models towards Prescriptive Analytics For Electrical Devices Production 面向电气设备生产规范分析的需求预测和客户细分模型集成

2018 International Conference on Information Technology (InCIT) Pub Date : 2018-10-01 DOI: 10.23919/INCIT.2018.8584864

S. Thammaboosadee, Preuksa Wongpitak

引用次数: 2

Daily rainfall forecast model from satellite image using Convolution neural network 基于卷积神经网络的卫星影像日降水预报模型

2018 International Conference on Information Technology (InCIT) Pub Date : 2018-10-01 DOI: 10.23919/INCIT.2018.8584886

Kitinan Boonyuen, Phisan Kaewprapha, P. Srivihok

{"title":"Daily rainfall forecast model from satellite image using Convolution neural network","authors":"Kitinan Boonyuen, Phisan Kaewprapha, P. Srivihok","doi":"10.23919/INCIT.2018.8584886","DOIUrl":"https://doi.org/10.23919/INCIT.2018.8584886","url":null,"abstract":"The purpose of this paper is to investigate the capability of artificial intelligence by using convolutional neural networks (CNN), to forecast daily rainfall. The input of this model were the satellite images of the areas in Asia. The output of the model was daily rainfall prediction. Klong Yai rain station in Rayong province of Thailand was selected as our case study. We chose Inception-v3 model, which is an advance technique in convolutional neural networks. The model got very high accuracy on the ImageNet database which is the largest database of images. We helped the model to focus by reducing the size of the images and divided them into three different datasets. We used our 3 datasets to train the inception-v3 model by using 2 methods, the first method used transfer learning technique where we used a pre-trained model to train our dataset at the last fully connected layer. The second one was done from scratch where we trained all the layers of inception-v3. The training dataset consisted of satellite images of July, August and September 2017. The testing dataset had satellite images of October 2017. The result of forecasting revealed that the models were able to predict today rainfall, 1 day ahead rainfall, 2 days ahead rainfall and 3 days ahead rainfall successfully.","PeriodicalId":144271,"journal":{"name":"2018 International Conference on Information Technology (InCIT)","volume":"137 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":"128880349","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: 11