2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)最新文献

Simple Spectrometer for Education Using Microcontroller 基于单片机的简易教育光谱仪

2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) Pub Date : 2020-06-01 DOI: 10.1109/ecti-con49241.2020.9158310

Kann Yingprayoon, Sansiri Tanachutiwat

{"title":"Simple Spectrometer for Education Using Microcontroller","authors":"Kann Yingprayoon, Sansiri Tanachutiwat","doi":"10.1109/ecti-con49241.2020.9158310","DOIUrl":"https://doi.org/10.1109/ecti-con49241.2020.9158310","url":null,"abstract":"A simple spectrometer is constructed using Light Emitting Diodes of different colors as sources for this spectrometer. In order to calibrate the light sources, different types of LEDs are connected to the circuit of constant voltage to give lights of different colors. The colors of these LEDs are Blue, Green, Yellow, Orange and red with different wavelengths. The emission spectra of all LEDs were obtained from commercial standard companies to give the peak wavelengths of each LED, 400nm (Violet), 470nm (Blue), 525nm (Green), 574nm (Yellow), 590nm (Orange), 610nm (Orange), 625nm (Red), 700nm (Dark Red) respectively. These peak wavelengths are used as reference light sources for this spectrometer. The results of Absorption spectrum measurement show similar spectrum from standard measurement. Raspberry Pi microcontroller was used in this study to measure, to analyze data and display the absorption spectrum. This low-cost spectrometer is good enough for education which can be used in the normal schools or educational institutions using LEDs of several standard peak wavelengths.","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"347 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115282049","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

Statistical Approach for Text and Non-text Classifier in Off-line Handwritten Document 离线手写文档中文本和非文本分类器的统计方法

2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) Pub Date : 2020-06-01 DOI: 10.1109/ecti-con49241.2020.9158334

B. Pravalpruk, S. Watcharabutsarakham

引用次数: 1

Maximized Output Power Design of Switch-Mode Power Amplifier with Thevenin Impedance 具有Thevenin阻抗的开关模式功率放大器的最大输出功率设计

2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) Pub Date : 2020-06-01 DOI: 10.1109/ecti-con49241.2020.9158226

Prateep Manasummakij, Chatrpol Pakasiri

引用次数: 1

Self Adaptive Learning – Great Deluge Based Hyper-heuristics for Solving Cross Optimization Problem Domains 自适应学习-基于大洪水的超启发式求解交叉优化问题域

2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) Pub Date : 2020-06-01 DOI: 10.1109/ECTI-CON49241.2020.9158209

Widya Saputra, A. Muklason, Baiq Z.H. Rozaliya

{"title":"Self Adaptive Learning – Great Deluge Based Hyper-heuristics for Solving Cross Optimization Problem Domains","authors":"Widya Saputra, A. Muklason, Baiq Z.H. Rozaliya","doi":"10.1109/ECTI-CON49241.2020.9158209","DOIUrl":"https://doi.org/10.1109/ECTI-CON49241.2020.9158209","url":null,"abstract":"In the literature, almost all optimization problems in NP-hard class are solved by meta-heuristics approach. However, this approach has the drawback of requiring tuning parameters for each different problem domain and different instances of the same problem. This approach is considered less effective in resolving these problems. Therefore, a new approach is needed, namely the hyper-heuristics approach that is able to solve cross-domain problems. Hyper-heuristic is one of the approximate search methods which is able to provide solutions to NP-hard problems in polynomial time, as well as giving fairly good and acceptable results. This method has two properties of search space, namely the selection of LLH and the acceptance of solutions (move acceptance). This approach works in barrier domains rather than directly working in problem domains. With these properties, hyper-heuristic is able to solve problems in different domains. In addition, hyper-heuristics has a learning mechanism through feedback from previously generated solutions. This final project tries to apply a hyperheuristic algorithm in six combinatorial optimization problem domains, namely SAT, Bin Packing, Flow Shop, Personnel Scheduling, TSP, and VRP. The method that will be used in this final project is Self Adaptive - Great Deluge (SAD-GED). The Self Adaptive mechanism is used to make LLH selection to be used, while the Great Deluge is used in determining the acceptance of solutions (move acceptance) in a hyperheuristic framework. The application of the SAD-GED algorithm is expected to be able to provide better results than the existing algorithm used previously, namely Simple Random - Simulated Annealing.","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"212 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123558085","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

Empowered PG in Forex Trading 授权PG在外汇交易

2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) Pub Date : 2020-06-01 DOI: 10.1109/ECTI-CON49241.2020.9158227

A. Suchaimanacharoen, T. Kasetkasem, S. Marukatat, I. Kumazawa, P. Chavalit

引用次数: 2

Enhancing CNN Based Knowledge Graph Embedding Algorithms Using Auxiliary Vectors: A Case Study of Wordnet Knowledge Graph 利用辅助向量增强基于CNN的知识图嵌入算法——以Wordnet知识图为例

2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) Pub Date : 2020-06-01 DOI: 10.1109/ecti-con49241.2020.9158288

Chanathip Pornprasit, Pattararat Kiattipadungkul, Peeranut Duangkaew, Suppawong Tuarob, Thanapon Noraset

{"title":"Enhancing CNN Based Knowledge Graph Embedding Algorithms Using Auxiliary Vectors: A Case Study of Wordnet Knowledge Graph","authors":"Chanathip Pornprasit, Pattararat Kiattipadungkul, Peeranut Duangkaew, Suppawong Tuarob, Thanapon Noraset","doi":"10.1109/ecti-con49241.2020.9158288","DOIUrl":"https://doi.org/10.1109/ecti-con49241.2020.9158288","url":null,"abstract":"Knowledge graphs (KGs) have been utilized by various business fields. One example is Google that stores data in knowledge graphs for searching and retrieval tasks. Even though these graphs have reached an impressive size, they are far from completeness. Missing relations in knowledge graphs is a severe problem for algorithms that operate over knowledge graphs. There are many researchers trying to develop knowledge graph embedding methods so that they can handle different types of relations. ConvKB is one of the knowledge graph embedding methods that utilize convolution neural networks (CNN). However, this method lacks the ability to handle symmetric relations. Being inspired by this limitation, we would like to enhance this method by proposing ConvKB+, which is obtained by modifying ConvKB’s CNN structure and introducing an additional relation vector. Our experiment results show that our method outperforms ConvKB by achieving higher MRR on some symmetric relations of the WN18RR dataset.","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"48 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114417246","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

ECTI-CON 2020 Cover Page ec - con 2020封面

2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) Pub Date : 2020-06-01 DOI: 10.1109/ecti-con49241.2020.9158324

引用次数: 0

Application of Association Rule Mining with Concept-Effect Relationship Model for Learning Diagnosis 概念-效果关系模型关联规则挖掘在学习诊断中的应用

2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) Pub Date : 2020-06-01 DOI: 10.1109/ecti-con49241.2020.9158099

Sudarat Saengkeaw

引用次数: 1

A Report on Human Head Exposure to a 2.6 GHz Mid-Band of 5G by Using FDTD Method 基于时域有限差分法的5G 2.6 GHz中频人体头部暴露研究

2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) Pub Date : 2020-06-01 DOI: 10.1109/ecti-con49241.2020.9158082

T. Jariyanorawiss, Wachira Chongburee

{"title":"A Report on Human Head Exposure to a 2.6 GHz Mid-Band of 5G by Using FDTD Method","authors":"T. Jariyanorawiss, Wachira Chongburee","doi":"10.1109/ecti-con49241.2020.9158082","DOIUrl":"https://doi.org/10.1109/ecti-con49241.2020.9158082","url":null,"abstract":"This paper presents a simulation result of human head exposure to 2.6 GHz, which is one of the bands used in the recently launched 5G mobile networks. The method adopted in the simulation is Finite-Difference Time-Domain (FDTD), which divides the computational domains into a physical and an artificial absorbing domains. The physical domain consists of a dipole antenna representing the mobile phone and a human head model created by a set of 53 layers from Magnetic Resonance Imaging (MRI). The artificial absorbing domain is a 3-D reflectionless boundary which can be implemented by using Perfectly Matched Layers (PML). Also, the Specific Absorption Rate (SAR) value is averaged over 1 gram of the head tissues when the dipole is placed in the range of 1-10 cm from the human head. Additionally, the total power absorption computed from the electric field is also reported. The results suggest that as the distance between the mobile phone set and the human head increases, SAR decreases monotonically and exponentially. Meanwhile, with operating frequency 2.6 GHz, the power absorption tends to decrease but possibly increases at some particular distance. The key result is that for a radiated power of 0.6 W, none of the distances under test deliver SAR value that meet the 1.6 W/kg of the FCC standard. The simulation results conclude that the radiated power of approximately 0.25 W assures the compliance with the FCC standard at the distance of 1 cm.","PeriodicalId":371552,"journal":{"name":"2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130659643","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

Neural Network for Forecasting High Price and Low Price on Foreign Exchange Market 基于神经网络的外汇市场高价和低价预测

2020 17th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON) Pub Date : 2020-06-01 DOI: 10.1109/ecti-con49241.2020.9158133

Krin Chinprasatsak, N. Niparnan, A. Sudsang

引用次数: 1