2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)最新文献_第3页

Smart Loading Management System for Hybrid Photovoltaic/Wind Power Supply 光伏/风电混合供电智能负荷管理系统

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2021-10-20 DOI: 10.23919/eecsi53397.2021.9624242

Syafii, Muhardika, Darwison, Witri Onanda

{"title":"Smart Loading Management System for Hybrid Photovoltaic/Wind Power Supply","authors":"Syafii, Muhardika, Darwison, Witri Onanda","doi":"10.23919/eecsi53397.2021.9624242","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624242","url":null,"abstract":"Photovoltaic and wind turbine generation using environmentally friendly technology in the process of harvesting energy from the nature can be a solution to future electrical energy crises so that they become the most developed and reliable alternative. However, the conversion of solar/wind energy is highly dependent on the availability of sunlight and wind speed. Therefore, it is necessary to study the PV /wind loading which aims to increase and maintain the continuity of the electricity supply to the load. Load power management follows the availability of solar and wind energy in sunny, cloudy, rainy, or evening weather by considering the remaining usable battery voltage. Comparison of data is done to determine the system constraints with the ANFIS method. In testing the data with ANFIS performed with 3 MF (High, Medium, Low). From a total of 4003 data and an error of 26% was found, the training data was then compared with the test data. After testing the data comparison between the actual data and the training data that has been processed with ANFIS, it is obtained that there are more options for the maximum load that can be supplied by PV /wind generation. This has an impact on the performance of the hybrid PV /wind standalone which is more leverage on the loading side.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"59 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":"128697706","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

Workspace and Collaboration System Design of Two Robot Manipulators 双机械手工作空间与协同系统设计

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2021-10-20 DOI: 10.23919/eecsi53397.2021.9624297

T. Dewi, Rusdianasari, R. Kusumanto, Siproni

引用次数: 0

Enhancing Low-level Wind Shear Alert System (LLWAS) to Predict Low-level Wind Shear (LLWS) Phenomenon Using Temporal Convolutional Network 利用时间卷积网络增强低空风切变预警系统(LLWAS)预测低空风切变现象

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2021-10-20 DOI: 10.23919/eecsi53397.2021.9624225

Muhammad Ryan, A. H. Saputro, A. Sopaheluwakan

{"title":"Enhancing Low-level Wind Shear Alert System (LLWAS) to Predict Low-level Wind Shear (LLWS) Phenomenon Using Temporal Convolutional Network","authors":"Muhammad Ryan, A. H. Saputro, A. Sopaheluwakan","doi":"10.23919/eecsi53397.2021.9624225","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624225","url":null,"abstract":"Low-level Wind Shear (LLWS) is a significant phenomenon in aviation that has the potential to cause aircraft accident. To avoid the potential accident, information about the potential of LLWS occurrence ahead was needed so the pilot can avoid the area where LLWS can happen. Previous several studies used statistical model to predict LLWS. The dataset comes from the equipment system for detecting LLWS. Most of the statistical models used are Multi-Layer Perceptron (MLP) and the dataset is taken from Lidar Doppler. The approach that is often used is to transform wind data from Lidar Doppler into time series data and feed it to the MLP. For this study, the statistical model used is Temporal Convolutional Network (TCN). TCN is a dedicated time-series model. The dataset for the TCN is come from Low-level Wind Shear Alert System (LLWAS). We use the model to predict LLWS occurrence 5 minutes ahead. The feature input of TCN are wind direction and speed from LLWAS that already is being transformed and arranged to timeseries data west - east component (U) and south - north component (V). The label dataset is LLWAS's warning of LLWS occurrence data. As a comparison of the proposed model, a logistic regression model and Multi-Layer Perceptron (MLP) were also used. We also use varying lengths of input data to see how they perform against the model. The results show that TCN can outperform other comparison models with perfect recall and precision values (1) when using predictor time-series data longer than 5 minutes. This result means that the proposed model works well in predicting LLWS events using LLWAS data.","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":"126995642","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

YOLO Algorithm-Based Surrounding Object Identification on Autonomous Electric Vehicle 基于YOLO算法的自动驾驶电动汽车周围目标识别

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2021-10-20 DOI: 10.23919/eecsi53397.2021.9624275

Irvine Valiant Fanthony, Zaenal Husin, Hera Hikmarika, Suci Dwijayanti, B. Suprapto

{"title":"YOLO Algorithm-Based Surrounding Object Identification on Autonomous Electric Vehicle","authors":"Irvine Valiant Fanthony, Zaenal Husin, Hera Hikmarika, Suci Dwijayanti, B. Suprapto","doi":"10.23919/eecsi53397.2021.9624275","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624275","url":null,"abstract":"An autonomous vehicle must be equipped with a camera, which works by providing visual input that is used to detect objects around the autonomous electric vehicle. Currently, no method has been implemented in real-time. Thus, this study utilized the You Only Look Once (YOLO) algorithm to detect objects in real-time around the autonomous electric vehicle. The objects were limited to humans, motorcycles, and cars. The results showed that the most compatible YOLO model for the system was the Tiny YOLOv4 model which was built with the darknet framework. The simulation experiment showed that detection accuracy was 80% and was able to transmit information in a form of data location of the object to the microcontroller. A success rate of 100% was obtained from 10 tests. Hence, it showed that the YOLO was able to detect objects and provided input to the steering control system. Meanwhile, the depth information method was used to measure the distance of the object to the vehicle in real-time with an accuracy of 60%. Real-time testing was conducted to test whether the autonomous electric vehicle can avoid objects in front of it by providing input from the detection results of the Tiny- YOLOv4 model object. The success rate of the system in real-time experiments was 100%.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"7 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":"116604963","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

The AC-DC-AC Converter Design for Parallel Asynchronous Generator Based Microhydro Power Plants 基于并联异步发电机的微型水电厂交直流交流变换器设计

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2021-10-20 DOI: 10.23919/eecsi53397.2021.9624306

Arief Marwanto, Rudi Irmawanto, Muhamad Haddin, M. Rosyadi

{"title":"The AC-DC-AC Converter Design for Parallel Asynchronous Generator Based Microhydro Power Plants","authors":"Arief Marwanto, Rudi Irmawanto, Muhamad Haddin, M. Rosyadi","doi":"10.23919/eecsi53397.2021.9624306","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624306","url":null,"abstract":"The use of micro hydro energy for electricity generation in Indonesia is currently increasing along with the launch of the energy independence village program which continues to grow in several regions. In this study, the design and analysis of AC-DC-AC converter is applied to asynchronous/induction generators connected in parallel with 50-kW Micro Hydro Power Plant. The AC-DC-AC converter consists of a Generator Side Converter (GSC) and a Load Side Converter (LSC) which are installed back to back via the DC-Link network. The effectiveness and dynamic characteristics of the controller design were evaluated and analyzed through simulation using the PSCAD program. Simulation analysis is divided into 2 fault scenarios, namely: (1) Analysis of small disturbances in the form of changes in load and generator rotation differences, and (2) Analysis of large disturbances due to short circuits in the transmission network. The results show that the control system is able to stabilize and regulate the flow of power against changes in load and permanent disturbances.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"46 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":"129694175","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

Liquid Level Monitoring With Single Layered Rubber Diaphragm Fibre Bragg Grating Sensor 用单层橡胶膜片光纤光栅传感器监测液位

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2021-10-20 DOI: 10.23919/eecsi53397.2021.9624284

S. Sopian, S. Ambran, L. Ong, P. N. Ja'afar, H. Mohamed, N. M. Yusoff

引用次数: 0

Spiral-Coupled-Line Resonators for Chipless RFID Sensors 用于无芯片RFID传感器的螺旋耦合线谐振器

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2021-10-20 DOI: 10.23919/eecsi53397.2021.9624245

W. M. Abdulkawi, A. Sheta, I. Elshafiey, M. Alkanhal

引用次数: 1

Recommender System of Final Project Topic Using Rule-based and Machine Learning Techniques 基于规则和机器学习技术的期末专题推荐系统

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2021-10-20 DOI: 10.23919/eecsi53397.2021.9624310

Cut Fiarni, Herastia Maharani, Billy Lukito

{"title":"Recommender System of Final Project Topic Using Rule-based and Machine Learning Techniques","authors":"Cut Fiarni, Herastia Maharani, Billy Lukito","doi":"10.23919/eecsi53397.2021.9624310","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624310","url":null,"abstract":"The final project is a mandatory graduation requirement for bachelor's degree students. However, students often having problems in determining topic that is suitable with their interests and competencies. As a result, some students might have to change their topics halfway, which can affect their study period. Ironically, the abundant volume of previous final project documents available in the university library only add more confusion and difficulty for the students in finding relevant references for their research topic. Therefore, the focus of this research is to implement a machine learning approach to analyze and model an algorithm to recommend final project topics, based on student's interest, competencies, and their respective supervisor. This research also aims to establish a framework to map academic attributes, as part of feature selection. As the result, we develop a recommender system based on cosine similarity algorithm to recommend topics based on similarity between student's profile and topics represented by lists of keywords. Performance is measured by comparing the recommendations given by the proposed system against the actual topic chosen by students, with a very satisfying result of 71.43% precision.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"10 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":"133820241","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

Face Shape-Based Physiognomy in LinkedIn Profiles with Cascade Classifier and K-Means Clustering 基于级联分类器和K-Means聚类的LinkedIn profile中基于脸型的面相

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2021-10-20 DOI: 10.23919/eecsi53397.2021.9624262

Purwono, A. Ma’arif, Amanah Wulandari

{"title":"Face Shape-Based Physiognomy in LinkedIn Profiles with Cascade Classifier and K-Means Clustering","authors":"Purwono, A. Ma’arif, Amanah Wulandari","doi":"10.23919/eecsi53397.2021.9624262","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624262","url":null,"abstract":"The progress of a company is influenced by the excellent performance of its employee. The recruitment process should be done in a correct procedure so that it would not have the potential to harm the company. The improved use of social media can be an aspect to be applied in a recruitment process. LinkedIn is a social media platform that has many users which focuses on the career development aspect. Profile photos are commonly used in social media. In physiognomy, a personality analysis can be carried out based on his/her outward appearance. The profile photo can be an aspect of personality analysis with this knowledge. This research aimed to predict the face shape based on LinkedIn profile photos. A Cascade classifier algorithm with a haar-like feature was used to detect the face area. Dlib library was used to detect face landmarks. K-Means algorithm was used to differentiate the border of hair and facial skin. Indicators of the face shape calculation are the value of face angle, which is the arctangent of the face landmarks matrix; similarity value from the standard deviation calculation between horizontal line 1, 2, and 3; and diameter value which resulted from the standard deviation calculation between horizontal line 2 and vertical line 4. We provide output as face shape from the LinkedIn profile photos. Based on ten profile photo samples, only two predictions were incorrect.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"4 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":"121839412","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: 6

Embedded Alcohol Sensing Design And Analysis For Air Samples 空气样本的嵌入式酒精传感设计与分析

2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) Pub Date : 2021-10-20 DOI: 10.23919/eecsi53397.2021.9624305

Munaf Ismail, Arief Marwanto, J. P. Hapsari, Muhamad Haddin

{"title":"Embedded Alcohol Sensing Design And Analysis For Air Samples","authors":"Munaf Ismail, Arief Marwanto, J. P. Hapsari, Muhamad Haddin","doi":"10.23919/eecsi53397.2021.9624305","DOIUrl":"https://doi.org/10.23919/eecsi53397.2021.9624305","url":null,"abstract":"WHO through the Global Action Plan on Sustainable Development Goals (SGDs) 2013–2030 provides direction to implement 17 SDGs goals, In this context, WHO targets to reduce alcohol consumption to at least 10% in each country and reduce the number of deaths caused by traffic accidents by as much as 50%. The high death rate from traffic accidents due to the influence of alcohol is a concern for all of us. Excessive alcohol consumption is dangerous when driving because consuming alcohol will affect a person's temperament and worsen driving behavior because it reduces awareness, resulting in accidents. This study designs and analyzes the detection of alcohol levels from a person's breath simulation. A system is needed that is able to detect the alcohol content of vehicle drivers that can be monitored and analyzed, as information to warn motorists of vehicles under the influence of alcohol in order to prevent traffic accidents. Internet of Things (IoT) based alcohol detection system design consists of a series of hardware and software applications in embedded alcohol sensing. The main components consist of the MQ3 sensor to detect the alcohol content of a person's breath and the WeMos D1 mini is the ESP8266 Wi-Fi development board module as an Internet of things (IoT) connection. The tests carried out from the research showed the results of the Blood Alcohol Content (BAC) alcohol content in the test sample by testing several times in a row with the average BAC test results: >0.09, 0.06, 0.03 and 0.00.","PeriodicalId":259450,"journal":{"name":"2021 8th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI)","volume":"15 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":"127250705","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