2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)最新文献_第2页

Leakage Current Monitoring for Electrical Loads Based on Internet of Things 基于物联网的电力负荷漏电流监测

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2021-10-20 DOI: 10.23919/eecsi53397.2021.9624294

Riky Tri Yunardi, E. Sutanto, Aji Akbar Firdaus, Elsyea Adia Tunggadewi, Hammam Ali, Silvi Nurwahyuni

引用次数: 3

Design of Autonomous Vehicle Navigation Using GNSS Based on Pixhawk 2.1 基于Pixhawk 2.1的GNSS自动车辆导航设计

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2021-10-20 DOI: 10.23919/eecsi53397.2021.9624244

Muhammad Khosyi'in, E. N. Budisusila, Sri Arttini Dwi Prasetyowati, B. Suprapto, Z. Nawawi

{"title":"Design of Autonomous Vehicle Navigation Using GNSS Based on Pixhawk 2.1","authors":"Muhammad Khosyi'in, E. N. Budisusila, Sri Arttini Dwi Prasetyowati, B. Suprapto, Z. Nawawi","doi":"10.23919/eecsi53397.2021.9624244","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624244","url":null,"abstract":"The autonomous vehicle navigation system is heavily dependent on the use of GPS sensors. The poor accuracy of GPS sensors can be circumvented by combining other sensors using a Kalman filter-based method. Combining GPS and IMU sensors or other sensors is an option because of the relatively low cost. The use of controllers to build an autonomous vehicle navigation system becomes an important thing, so the controller devices used must have excellent performance, and it is found in UA V technology with autopilot-based controllers. The sensor combination method is carried out by combining the IMU sensor on the pixhawk 2.1 controller module with the Here2 GPS/GNSS Module, which is expected to make autonomous vehicles' position and heading orientation more accurate. The initial test results show that the navigation system accuracy is very good with the orientation angle towards the vehicle as expected. However, there was a problem in sending data from the vehicle to the ground station via telemetry radio due to distance and buildings constraints.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125467724","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

Mobile Application for Unmanned Ship Monitoring Based on LoRA Communication 基于LoRA通信的无人船监控移动应用

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2021-10-20 DOI: 10.23919/eecsi53397.2021.9624270

L. Subiyanto, A. Arfianto, S. Alfuat, D. P. Riananda, E. Supriyanto, F. Nofandi, T. Santoso, N. Gunantara, V. Ardhana

{"title":"Mobile Application for Unmanned Ship Monitoring Based on LoRA Communication","authors":"L. Subiyanto, A. Arfianto, S. Alfuat, D. P. Riananda, E. Supriyanto, F. Nofandi, T. Santoso, N. Gunantara, V. Ardhana","doi":"10.23919/eecsi53397.2021.9624270","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624270","url":null,"abstract":"The potential of fish in the sea is a hope for fishers. During fishing, fishers must go around and map the fishing location because the distance and direction of the ship to the fishing location is not yet known accurately. Fishers only rely on the experience and information from other fishers to find out the location of fish distribution, and this will cause boat fuel losses because of the uncertain goals. In this research will shown the making process of the design of mobile navigation applications to find the location of the fish distribution. The device works based on information data in the form of latitude and longitude data from the Center for Marine Research and Observation. By entering the fish distribution data, the distance and direction of the location can be known. Android mobile application called Mobile Virtual Assistant (MVA) works by using smartphone GPS data, maps, and gyroscope compass sensors to locate fish distribution and data analysis will be compared with the Android application “Polaris Navigation” to find out the coordinates. From 10 tests, the Android Virtual Mobile Assistant application successfully headed to the destination location with a different accuracy of 24 meters from the Mobile Navigation Apps coordinate point detector. So it can be concluded that the android mobile application that is made can read the coordinates accurately.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116112379","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

Load Effect on Switched Reluctance Motor Using Hysteresis Current and Voltage Control 磁滞电流电压控制开关磁阻电机的负载效应

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2021-10-20 DOI: 10.23919/eecsi53397.2021.9624267

A. A. Nugroho, Muhammad Khosyi'in, Bustanul Arifin, B. Suprapto, Muhamad Haddin, Z. Nawawi

引用次数: 1

Ultrasonic Multi-Sensor Detection Patterns On Autonomous Vehicles Using Data Stream Method 基于数据流方法的自动驾驶汽车超声多传感器检测模式

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2021-10-20 DOI: 10.23919/eecsi53397.2021.9624313

E. N. Budisusila, Muhammad Khosyi'in, S. Prasetyowati, B. Suprapto, Z. Nawawi

{"title":"Ultrasonic Multi-Sensor Detection Patterns On Autonomous Vehicles Using Data Stream Method","authors":"E. N. Budisusila, Muhammad Khosyi'in, S. Prasetyowati, B. Suprapto, Z. Nawawi","doi":"10.23919/eecsi53397.2021.9624313","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624313","url":null,"abstract":"Autonomous vehicles need sensors to detect the surroundings of the vehicle, especially if there are obstructions that could harm the vehicle or the object itself. The goal is to avoid accidents by detecting as early as possible if there are obstacles. In this study, a series of ultrasonic sensors are used and placed in strategic positions around the vehicle. They are placed in front, in side and in rear of vehicle. When the sensor detects an object, each sensor provides information on the existence of the object in the form of a detection point. These points are still formed as detection points as the results of individual detection from each sensor. In order to integrate all the resulting points, it is necessary to establish a comprehensive detection pattern, to provide information about the safe distance of the vehicle from surrounding objects. The sensors are connected to a programmable microcontroller unit to monitor and control the transmitted signal and sensor detection results. The signal obtained by the microcontroller is fed to the computer unit through the serial port, which is then read using the Data Stream to be displayed on the monitor screen. With this method, the data will be reprocessed to display an integrated detection pattern from all sensors graphically. There are two kinds of graphical pattern formed, spider web pattern and bar pattern.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"31 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120983751","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

A Convolutional Neural Network for Arrhythmia Classification: A Review 卷积神经网络在心律失常分类中的应用综述

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2021-10-20 DOI: 10.23919/eecsi53397.2021.9624304

Sarah Kamil, L. Muhammed

{"title":"A Convolutional Neural Network for Arrhythmia Classification: A Review","authors":"Sarah Kamil, L. Muhammed","doi":"10.23919/eecsi53397.2021.9624304","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624304","url":null,"abstract":"Arrhythmia is a heart condition that occurs due to abnormalities in the heartbeat, which means that the heart's electrical signals do not work properly, resulting in an irregular heartbeat or rhythm and thus defeating the pumping of blood. Some cases of arrhythmia are not considered serious, while others are very dangerous, life-threatening, and cause death in a short period of time. In the clinical routine, cardiac arrhythmia detection is performed by electrocardiogram (ECG) signals. The ECG is a significant diagnosis tool that is used to record the electrical activity of the heart, and its signals can reveal abnormal heart activity. However, because of their small amplitude and duration, visual interpretation of ECG signals is difficult. Many deep and machine learning approaches have been proposed for automatically arrhythmia classification. Convolutional neural networks (CNNs) have been achieved promising performances in this field that proved the efficiency of deep convolutional neural networks in automated detection and, therefore, cardiovascular disease protection as well as help cardiologists in medical practice by saving.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115152231","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

Review of Method for System Identification on Motors 电机系统辨识方法综述

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2021-10-20 DOI: 10.23919/eecsi53397.2021.9624259

Bustanul Arifin, A. A. Nugroho, B. Suprapto, S. Prasetyowati, Z. Nawawi

{"title":"Review of Method for System Identification on Motors","authors":"Bustanul Arifin, A. A. Nugroho, B. Suprapto, S. Prasetyowati, Z. Nawawi","doi":"10.23919/eecsi53397.2021.9624259","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624259","url":null,"abstract":"The industry is closely related to motors. Motor is used as the prime mover to run the production machines. Control of the motor is needed so that it can work according to its designation. Motor parameters must be known to control it. The required parameters include electrical and mechanical parameters. These parameters are often not easy to obtain then one way to find out is by identifying the system. This paper aimed to convey the various methods that have been used in motor identification systems. Brushed DC motor, brushless DC motor, servo motor, stepper motor, induction motor, and switch reluctance motor were motors analyzed. These methods included the least square, recursive least square in the form of autoregressive with exogenous input, autoregressive moving average with exogenous. Another system identification method utilizes artificial intelligence. This method used fuzzy logic, neural network, genetic algorithm, particle swarm optimization, and various combinations of these methods. The review results showed that the artificial intelligence method was very interesting and promising because it has advantages compared to conventional methods. Modification or combination of two or more artificial intelligence methods would get better and closer results to the actual situation.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133019593","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

Design and Implementation of Interactive Virtual Museum based on Hand Tracking OpenCV in Indonesia 基于OpenCV手部追踪的印尼交互式虚拟博物馆的设计与实现

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2021-10-20 DOI: 10.23919/eecsi53397.2021.9624265

W. A. A. Praditasari, Ria Aprilliyani, I. Kholis

引用次数: 3

Colorimetric System Based on Android Smartphone: Study Case of Total Chlorine Level Prediction 基于Android智能手机的比色系统:总氯水平预测研究案例

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2021-10-20 DOI: 10.23919/eecsi53397.2021.9624234

Agnes Diza Fahira, A. H. Saputro

{"title":"Colorimetric System Based on Android Smartphone: Study Case of Total Chlorine Level Prediction","authors":"Agnes Diza Fahira, A. H. Saputro","doi":"10.23919/eecsi53397.2021.9624234","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624234","url":null,"abstract":"Colorimetric is a system used to measure and describe color. Several previous studies have successfully implemented this system using a smartphone camera for image acquisition of test strips. But unfortunately, most of these studies still transfer image data manually to a computer for processing. In this study, the colorimetric system applied to predict the value of total chlorine levels was made as an Android application. The application can take a picture and directly get results on the smartphone screen. This makes the system work more portable than previous studies. The application is made in a client-server architectural style with RESTful API communication and has two servers, one server is used to transfer images and the other is used to process images into total chlorine values. The application's success rate to reach the two servers is 100%, with the average time required is 2.58 seconds to reach the upload server and 2.68 seconds to reach the computational server. The evaluation results of the regression model used in the application are 0.31 to 0.13 RMSE. These results indicate that the regression model, Artificial Neural Network with Levenberg-Marquardt function, can be used for total chlorine levels prediction system on test strip based on colorimetric.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116607476","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

Automated feature extraction in deep learning models: A boon or a bane? 深度学习模型中的自动特征提取:是福还是祸?

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2021-10-20 DOI: 10.23919/eecsi53397.2021.9624287

D. Hemanth

引用次数: 0