2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE)最新文献_第4页

The control speed of DC motor with adaptive compensator add integrate 采用自适应补偿器对直流电动机进行速度控制

2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE) Pub Date : 2017-10-01 DOI: 10.1109/ICITEED.2017.8250485

Napassadol Singhata

引用次数: 0

Cell and RBs selection scheme for power consumption reduction in femtocell networks using Discrete Bacterial Foraging Optimization 基于离散细菌觅食优化的飞蜂窝网络低功耗小区和RBs选择方案

2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE) Pub Date : 2017-10-01 DOI: 10.1109/ICITEED.2017.8250480

I. Mustika, Sahirul Alam, S. Sulistyo

引用次数: 1

Design of virtual process control laboratory (VPCL) using first principle method and interactive PID control toolkit using Labview 利用第一性原理法和Labview交互式PID控制工具箱设计虚拟过程控制实验室(VPCL)

2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE) Pub Date : 2017-10-01 DOI: 10.1109/ICITEED.2017.8250460

S. Sundaram

{"title":"Design of virtual process control laboratory (VPCL) using first principle method and interactive PID control toolkit using Labview","authors":"S. Sundaram","doi":"10.1109/ICITEED.2017.8250460","DOIUrl":"https://doi.org/10.1109/ICITEED.2017.8250460","url":null,"abstract":"First principle method of modelling is relatively easy to implement and analyze a process because of its simple equations. If the process is properly modelled, the model can be used effectively to understand the process behavior. In this paper, it is aimed to design a virtual level process control system to understand the working of the process with PID control. Interactive Graphical User Interface (GUI) is developed using Labview. This GUI can be used as a training toolkit to understand the effect of PID action in a level process system. The toolkit made in the form of an .exe file so as to make it platform independent. The GUI has added features than a real-time single loop PID level process control system. User can perform experiments to understand the features such as Auto / Manual operations, Direct / Reverse settings, Constant / Non-Constant outflow & PV, dead time, anti-reset windup, bump less transfer and load disturbances. The simulated results are compared with a real-time level control system with Yokogawa PID controller and the results are found to be closely matching.","PeriodicalId":267403,"journal":{"name":"2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125261278","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: 8

Comparison of vowel feature extraction on time and frequency domain 时域和频域元音特征提取的比较

2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE) Pub Date : 2017-10-01 DOI: 10.1109/ICITEED.2017.8250477

Risanuri Hidayat, Deni Yulian, Agus Bejo, Sujoko Sumaryono

引用次数: 2

Vehicle classification in congested traffic based on 3D point cloud using SVM and KNN 基于SVM和KNN的三维点云交通拥堵车辆分类

2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE) Pub Date : 2017-10-01 DOI: 10.1109/ICITEED.2017.8250451

Porn-anan Raktrakulthum, C. Netramai

{"title":"Vehicle classification in congested traffic based on 3D point cloud using SVM and KNN","authors":"Porn-anan Raktrakulthum, C. Netramai","doi":"10.1109/ICITEED.2017.8250451","DOIUrl":"https://doi.org/10.1109/ICITEED.2017.8250451","url":null,"abstract":"The vehicle classification in congested traffic is a big challenge due to the difficulty to segment packs of different vehicles that stand still next to each other or travel at a very low speed. In this work, a low-cost vision system was designed and built to acquire the image and to generate 3D point cloud to be used as input for the classification process. The vehicle classification uses machine learning K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) with radial basis function kernel to classify two types of vehicle which are car and motorcycle based on 3D point cloud. The processing of the training data and test data can be divided into filtering, segmentation, tracking, and feature extraction, respectively. The extracted feature vectors are then used for both KNN and SVM classifiers. The results show that the proposed performs well even in high congested traffic with a mix of both vehicle's type. This can be seen from the TPR for car classification from both KNN and SVM which is relatively high (KNN=95.8% and SVM=95.8%) compared to other existing systems. In case of motorcycle classification, the SVM classifier performs better compared to KNN in all three different traffic conditions.","PeriodicalId":267403,"journal":{"name":"2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"31 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113956514","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: 10

Three-stages hard exudates segmentation in retinal images 视网膜图像硬渗出物的三阶段分割

2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE) Pub Date : 2017-10-01 DOI: 10.1109/ICITEED.2017.8250438

Worapan Kusakunniran, Qiang Wul, Panrasee Ritthipravad, Jian Zhang

{"title":"Three-stages hard exudates segmentation in retinal images","authors":"Worapan Kusakunniran, Qiang Wul, Panrasee Ritthipravad, Jian Zhang","doi":"10.1109/ICITEED.2017.8250438","DOIUrl":"https://doi.org/10.1109/ICITEED.2017.8250438","url":null,"abstract":"This paper proposes a three-stages method of hard exudate segmentation in retinal images. The first stage is the pre-processing. The color transfer is applied to make all retinal images to have the same color characteristics, based on statistical analysis. Then, only a yellow channel of each image is used in the further analysis. The second stage is the blob initialization. The blob detection based on color, size, and shape including circularity and convexity is used to identify initial pixels of hard exudates. The detected blobs must not be inside the optic disk. The third stage is the segmentation. The graph cut is iteratively applied on partitions of the image. The fine-tune segmentation in sub-images is necessary because the portion of hard exudates is significantly less than the portion of non-hard exudates. The proposed method is evaluated using the two well-known datasets, namely e_ophtha and DIARETDB1, in both aspects of pixel-level and image-level. Based on the comprehensive comparisons with the existing works, the proposed method is shown to be very promising. In the image-level, it achieves 96% sensitivity and 94% specificity for the e_ophtha dataset, and 96% sensitivity and 98% specificity for the DIARETDB1 dataset.","PeriodicalId":267403,"journal":{"name":"2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"18 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114107327","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

Segmentation of coring images using fully convolutional neural networks 使用全卷积神经网络分割核心图像

2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE) Pub Date : 2017-10-01 DOI: 10.1109/ICITEED.2017.8250490

S. Fazekas, S. Obrochta, Tatsuhiko Sato, A. Yamamura

引用次数: 2

Iterative decremented step-size scanning-based MPPT algorithms for photovoltaic systems 基于迭代递减步长扫描的光伏系统MPPT算法

2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE) Pub Date : 2017-10-01 DOI: 10.1109/ICITEED.2017.8250463

Faizal Arya Samman, Universitas Hasanuddin, A. Rahmansyah, Syafaruddin, Politeknik Bosowa

引用次数: 3

Fall detection using Gaussian mixture model and principle component analysis 基于高斯混合模型和主成分分析的跌落检测

2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE) Pub Date : 2017-10-01 DOI: 10.1109/ICITEED.2017.8250441

Arisa Poonsri, W. Chiracharit

引用次数: 20

Extraction and evaluation of popular online trends: A case of Pantip.com 网络流行趋势的提取与评价——以Pantip.com为例

2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE) Pub Date : 2017-10-01 DOI: 10.1109/ICITEED.2017.8250454

Bundit Thanasopon, Nattawut Sumret, Jirawin Buranapanitkij, P. Netisopakul

{"title":"Extraction and evaluation of popular online trends: A case of Pantip.com","authors":"Bundit Thanasopon, Nattawut Sumret, Jirawin Buranapanitkij, P. Netisopakul","doi":"10.1109/ICITEED.2017.8250454","DOIUrl":"https://doi.org/10.1109/ICITEED.2017.8250454","url":null,"abstract":"Popular online trends detection from crowd becomes more and more essential for both trend followers and online sellers. However, huge amount of online posts, both text and images, has prevented trends detection to be manually processed. This article, focusing on text mining, aims to automatically extract popular online trends. A case study is performed on one of the most popular discussion forum websites in Thailand — i.e., Pantip.com. The approach involves employing several unsupervised text mining techniques, namely, TF-IDF and HTML scores, and supervised learning sentiment classification, to extract key topics and assess sentiment results, respectively. Also, we conducted an experiment on the performance of sentiment classification and found that support vector machine (SVM) outperformed other learning techniques. In addition, the authors developed a web- application incorporating the proposed approach. The application collects data from Pantip.com, identifies key concepts of posts and calculates the popularity of each key concept based on statistics and sentiment results.","PeriodicalId":267403,"journal":{"name":"2017 9th International Conference on Information Technology and Electrical Engineering (ICITEE)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127390194","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: 2