2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)最新文献_第9页

Dual Frequency Continuous Wave Radar for Small Displacement Detection 用于小位移探测的双频连续波雷达

2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2018-10-01 DOI: 10.1109/EECSI.2018.8752951

Andarining Palupi, A. A. Pramudita, D. Arseno, A. D. Setiawan

引用次数: 2

Review on Adjustable Speed Drive Techniques of Matrix Converter Fed Three-Phase Induction Machine 矩阵变换器三相感应电机调速技术研究进展

2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2018-10-01 DOI: 10.1109/EECSI.2018.8752630

Arsyad Cahya Subrata, T. Sutikno, A. Z. Jidin, A. Jidin

引用次数: 4

EECSI 2018 Author Index EECSI 2018作者索引

2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2018-10-01 DOI: 10.1109/eecsi.2018.8752832

引用次数: 0

EECSI 2018 Forewords

2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2018-10-01 DOI: 10.1109/eecsi.2018.8752890

引用次数: 0

Middleware for Network Interoperability in IoT 物联网网络互操作性的中间件

2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2018-10-01 DOI: 10.1109/EECSI.2018.8752917

Eko Sakti Pramukantoro, Fariz Andri Bakhtiar, Binariyanto Aji, Rasidy Pratama

引用次数: 5

IDEnet : Inception-Based Deep Convolutional Neural Network for Crowd Counting Estimation 基于初始化的深度卷积神经网络用于人群计数估计

2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2018-10-01 DOI: 10.1109/EECSI.2018.8752637

Samuel Cahyawijaya, Bryan Wilie, W. Adiprawita

{"title":"IDEnet : Inception-Based Deep Convolutional Neural Network for Crowd Counting Estimation","authors":"Samuel Cahyawijaya, Bryan Wilie, W. Adiprawita","doi":"10.1109/EECSI.2018.8752637","DOIUrl":"https://doi.org/10.1109/EECSI.2018.8752637","url":null,"abstract":"In crowd counting task, our goals are to estimate density map and count of people from the given crowd image. From our analysis, there are two major problems that need to be solved in the crowd counting task, which are scale invariant problem and inhomogeneous density problem. Many methods have been developed to tackle these problems by designing a dense aware model, scale adaptive model, etc. Our approach is derived from scale invariant problem and inhomogeneous density problem and we propose a dense aware inception based neural network in order to tackle both problems. We introduce our novel inception based crowd counting model called Inception Dense Estimator network (IDEnet). Our IDEnet is divided into 2 modules, which are Inception Dense Block (IDB) and Dense Evaluator Unit (DEU). Some variations of IDEnet are evaluated and analysed in order to find out the best model. We evaluate our best model on UCF50 and ShanghaiTech dataset. Our IDEnet outperforms the current state-of-the-art method in ShanghaiTech part B dataset. We conclude our work with 6 key conclusions based on our experiments and error analysis.","PeriodicalId":6543,"journal":{"name":"2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"70 1","pages":"548-553"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89367043","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

Comparison Between A* And Obstacle Tracing Pathfinding In Gridless Isometric Game 无网格等距游戏中A*与障碍物追踪寻径的比较

2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2018-10-01 DOI: 10.1109/EECSI.2018.8752625

Lailatul Husniah, R. Mahendra, Ali Sofyan Kholimi, E. Cahyono

引用次数: 1

Variance and Symmetrical-based Approach for Optimal Alignment of 3D Model 基于方差和对称性的三维模型最优对齐方法

2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2018-10-01 DOI: 10.1109/EECSI.2018.8752898

Luh Putu Ayu Prapitasari, Parth Rawal, R. Grigat

引用次数: 1

Automated Diagnosis System of Diabetic Retinopathy Using GLCM Method and SVM Classifier 基于GLCM方法和SVM分类器的糖尿病视网膜病变自动诊断系统

2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2018-10-01 DOI: 10.1109/EECSI.2018.8752726

Ahmad Zoebad Foeady, D. C. R. Novitasari, Ahmad Hanif Asyhar, Muhammad Firmansjah

{"title":"Automated Diagnosis System of Diabetic Retinopathy Using GLCM Method and SVM Classifier","authors":"Ahmad Zoebad Foeady, D. C. R. Novitasari, Ahmad Hanif Asyhar, Muhammad Firmansjah","doi":"10.1109/EECSI.2018.8752726","DOIUrl":"https://doi.org/10.1109/EECSI.2018.8752726","url":null,"abstract":"Diabetic Retinopathy (DR) is the cause of blindness. Early identification needed for prevent the DR. However, High hospital cost for eye examination makes many patients allow the DR to spread and lead to blindness. This study identifies DR patients by using color fundus image with SVM classification method. The purpose of this study is to minimize the funds spent or can also be a breakthrough for people with DR who lack the funds for diagnosis in the hospital. Pre-processing process have a several steps such as green channel extraction, histogram equalization, filtering, optic disk removal with structuring elements on morphological operation, and contrast enhancement. Feature extraction of preprocessing result using GLCM and the data taken consists of contrast, correlation, energy, and homogeneity. The detected components in this study are blood vessels, microaneurysms, and hemorrhages. This study results what the accuracy of classification using SVM and feature from GLCM method is 82.35% for normal eye and DR, 100% for NPDR and PDR. So, this program can be used for diagnosing DR accurately.","PeriodicalId":6543,"journal":{"name":"2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"12 1","pages":"154-160"},"PeriodicalIF":0.0,"publicationDate":"2018-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74366186","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: 28

Indonesian ID Card Recognition using Convolutional Neural Networks 使用卷积神经网络识别印尼身份证

2018 5th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2018-10-01 DOI: 10.1109/EECSI.2018.8752769

M. O. Pratama, W. Satyawan, Bagus Fajar, Rusnandi Fikri, Haris Hamzah

引用次数: 9