Garuda - Garba Rujukan Digital

Article Per Year (5 Year)

All

Mechatronics, Electrical Power, and Vehicular Technology

mev
Website

Published by Lembaga Ilmu Pengetahuan Indonesia

ISSN : 20873379 EISSN : 20886985 DOI : -

Core Subject : Engineering,

Electrical & Electronics Engineering

Mechatronics, Electrical Power, and Vehicular Technology (hence MEV) is a journal aims to be a leading peer-reviewed platform and an authoritative source of information. We publish original research papers, review articles and case studies focused on mechatronics, electrical power, and vehicular technology as well as related topics. All papers are peer-reviewed by at least two referees. MEV is published and imprinted by Research Center for Electrical Power and Mechatronics - Indonesian Institute of Sciences and managed to be issued twice in every volume. For every edition, the online edition is published earlier than the print edition.

Arjuna Subject : -

Articles 596 Documents

10 11 12 13 14

Preface MEV Vol 8 Iss 2 Andriani, Dian
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 8, No 2 (2017)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

The Performance of EEG-P300 Classification using Backpropagation Neural Networks Turnip, Arjon; Soetraprawata, Demi
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 4, No 2 (2013)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

Electroencephalogram (EEG) recordings signal provide an important function of brain-computer communication, but the accuracy of their classification is very limited in unforeseeable signal variations relating to artifacts. In this paper, we propose a classification method entailing time-series EEG-P300 signals using backpropagation neural networks to predict the qualitative properties of a subjectâ€™s mental tasks by extracting useful information from the highly multivariate non-invasive recordings of brain activity. To test the improvement in the EEG-P300 classification performance (i.e., classification accuracy and transfer rate) with the proposed method, comparative experiments were conducted using Bayesian Linear Discriminant Analysis (BLDA). Finally, the result of the experiment showed that the average of the classification accuracy was 97% and the maximum improvement of the average transfer rate is 42.4%, indicating the considerable potential of the using of EEG-P300 for the continuous classification of mental tasks.

Sensorless-BLDC motor speed control with ensemble Kalman filter and neural network Rif'an, Muhammad; Yusivar, Feri; Kusumoputro, Benyamin
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 10, No 1 (2019)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

The use of sensorless technology at BLDC is mainly to improve operational reliability and play a role for wider use of BLDC motors in the future. This research aims to predict load changes and to improve the accuracy of estimation results of sensorless-BLDC. In this paper, a new filtering algorithm is proposed for sensorless brushless DC motor based on Ensemble Kalman filter (EnKF) and neural network. The proposed EnKF algorithm is used to estimate speed and rotor position, while neural network is used to estimate the disturbance by simulation. The proposed algorithm requires only the terminal voltage and the current of three phases for estimated speed and disturbance. A model of non-linear systems is carried out for simulation. Variations in disturbances such as external mechanical loads are given for testing the performance of the proposed algorithm. The experimental results show that the proposed algorithm has sufficient control with error speed of 3 % in a disturbance of 50 % of the rated-torque. Simulation results show that the speed can be tracked and adjusted accordingly either by disturbances or the presence of disturbances.

Preface MEV Vol 5 Iss 2 Atmaja, Tinton D.
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 5, No 2 (2014)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

Penggunaan Extended Kalman Filter Sebagai Estimator Sikap pada Sistem Kendali Servo Visual Robot Basjaruddin, Noor Cholis
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 2, No 1 (2011)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

Extended Kalman Filter (EKF) is the non-linear version of Kalman filter and the said filter is usually used in nonlinear state estimation. In this study EKF is applied to process the image features of a single camera mounted on the end effector of a robot. Data generated by the EKF then is to be processed to obtain the motion parameters. Simulation of visual servo control system was built with the aim to examine the use of the EKF as a pose estimator. The simulation results using Matlab show that the EKF is able to well estimate the robot pose.Â

Optimized object tracking technique using Kalman filter Taylor, Liana Ellen; Mirdanies, Midriem; Saputra, Roni Permana
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 7, No 1 (2016)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

This paper focused on the design of an optimized object tracking technique which would minimize the processing time required in the object detection process while maintaining accuracy in detecting the desired moving object in a cluttered scene. A Kalman filter based cropped image is used for the image detection process as the processing time is significantly less to detect the object when a search window is used that is smaller than the entire video frame. This technique was tested with various sizes of the window in the cropping process. MATLABÂ® was used to design and test the proposed method. This paper found that using a cropped image with 2.16 multiplied by the largest dimension of the object resulted in significantly faster processing time while still providing a high success rate of detection and a detected center of the object that was reasonably close to the actual center.

Front Cover MEV Vol 2 No 2 Atmaja, Tinton Dwi
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 2, No 2 (2011)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14203/j.mev.2011.v2.%p

Preface MEV Vol 8 Iss 1 Andriani, Dian
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 8, No 1 (2017)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

IMU Application in Measurement of Vehicle Position and Orientation for Controlling a Pan-Tilt Mechanism Saputra, Hendri Maja; Abidin, Zainal; Rijanto, Estiko
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 4, No 1 (2013)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

This paper describes a modeling and designing of inertial sensor using Inertial Measurement Unit (IMU) to measure the position and orientation of a vehicle motion. Sensor modeling is used to derive the vehicle attitude models where the sensor is attached while the sensor design is used to obtain the data as the input to control the angles of a pan-tilt mechanism with 2 degrees of freedom. Inertial sensor Phidget Spatial 3/3/3, which is a combination of 3-axis gyroscope, 3-axis accelerometer and 3-axis magnetometer, is used as the research object. Software for reading the sensor was made by using Matlabâ„¢. The result shows that the software can be applied to the sensor in the real-time reading process. The sensor readings should consider several things i.e. (a) sampling time should not be less than 32 ms and (b) deviation ratio between measurement noise (r) and process noise (q) for the parameters of Kalman filter is 1:5 (i.e. r = 0.08 and q = 0.4).

Exhaust emissions analysis of gasoline motor fueled with corncob-based bioethanol and RON 90 fuel mixture Widiyanti, Widiyanti; Mizar, Muhammad Alfian; Wicaksana, Christian Asri; Nurhadi, Didik; Moses, Kriya Mateeke
Journal of Mechatronics, Electrical Power and Vehicular Technology Vol 10, No 1 (2019)
Publisher : Research Centre for Electrical Power and Mechatronics, Indonesian Istitutes of Sciences

One of the viable solutions to the fossil fuel energy crisis was to seek alternative sources of environmentally friendly energy with the same or better quality such as bioethanol. It was possible to produce bioethanol from organic waste, e.g., corncob. This research aimed to obtain the lowest exhaust emission levels of CO and CO2 generated from a gasoline motor that used a mixture of bioethanol containing 96 % corncob and RON 90 fuel. This research was experimental using Anova statistical data analysis method. The results showed that the lowest average of CO emissions was 0.177 vol% using E100 fuel, and the highest average was 2.649 vol% using 100 % RON 90 fuel, displaying a significant difference. The lowest average of CO2 emissions was 6.6 vol% using E100 fuel, and the highest was 7.51 vol% using 100 % RON 90 fuel, which was insignificantly different. The mixture variation with the lowest CO and CO2 emissions was E100.

Page 12 of 60 | Total Record : 596

10 11 12 13 14

Filter by Year

2010 2025

Filter By Issues

All Issue Vol 16, No 1 (2025) Vol 15, No 2 (2024) Vol 15, No 1 (2024) Vol 14, No 2 (2023) Vol 14, No 1 (2023) Vol 13, No 2 (2022) Vol 13, No 1 (2022) Vol 12, No 2 (2021) Vol 12, No 1 (2021) Vol 11, No 2 (2020) Vol 11, No 1 (2020) Vol 10, No 2 (2019) Vol 10, No 1 (2019) Vol 9, No 2 (2018) Vol 9, No 1 (2018) Vol 8, No 2 (2017) Vol 8, No 1 (2017) Vol 7, No 2 (2016) Vol 7, No 1 (2016) Vol 6, No 2 (2015) Vol 6, No 1 (2015) Vol 5, No 2 (2014) Vol 5, No 1 (2014) Vol 4, No 2 (2013) Vol 4, No 1 (2013) Vol 3, No 2 (2012) Vol 3, No 1 (2012) Vol 2, No 2 (2011) Vol 2, No 1 (2011) Vol 1, No 2 (2010) Vol 1, No 1 (2010) More Issue

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name
-

Contact Email
-

Phone
-

Journal Mail Official
-

Editorial Address
-

Location
Kota adm. jakarta selatan,

Dki jakarta

INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name -

Contact Email -

Phone -

Journal Mail Official -

Editorial Address -

Location Kota adm. jakarta selatan, Dki jakarta INDONESIA

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
-

Contact Email
-

Phone
-

Journal Mail Official
-

Editorial Address
-

Location
Kota adm. jakarta selatan,

Dki jakarta

INDONESIA