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INDONESIA
Indonesian Journal of Electrical Engineering and Computer Science
Published by Institute of Advanced Engineering and Science
ISSN : 25024752     EISSN : 25024760     DOI : -
Core Subject :
Arjuna Subject : -
Articles 9,199 Documents
 161   162   163   164   165 
A Curve-fitting Calibration Method applied for Ultrasonic Flow-meter Yong Luo; Rangding Wang; Ling Yao
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 10: October 2013
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

 As the influence of fluid distribution in the internal pipe, the measurement characteristics of theory and practice exist significant differences in Ultrasonic Flow-meter(USF). Through analysis of fluid state, the method of curve-fitting is applied for the calibration of USF. Experimental results show that the USF can achieve level-1 accuracy with just a correction of 5 flow points, and this method performs a low computational complexity and strong practicality. DOI: http://dx.doi.org/10.11591/telkomnika.v11i10.3392 
Competitive analysis of single and multi-path routing protocols in mobile Ad-Hoc network Mohammed Ahmed Jubair; Mustafa Hamid Hassan; Salama A. Mostafa; Hairulnizam Mahdin; Aida Mustapha; Luqman Hanif Audah; Farooq Sijal Shaqwi; Ali Hashim Abbas
Indonesian Journal of Electrical Engineering and Computer Science Vol 19, No 1: July 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v19.i1.pp293-300

Abstract

A Mobile Ad-hoc Network (MANET) refers to a dynamic and wireless network, which can be designed without an existing infrastructure as every node serves as a router. A MANET is a self-configuring system of mobile nodes that are connected wirelessly. Every node serves as a sink, as well as a router to send packets. The movement of the nodes is not restricted as they can move in any direction, and they have the ability to get organized into a network. Due to their free and independent movement, they do not have a fixed position; they often change positions. In this study, the Dynamic Source Routing (DSR) and Ad-hoc On Multipath Demand Distance Vector (AOMDV) protocols are compared using Network Simulator NS2.35. DSR is a reactive gateway discovery algorithm whereby the connection of a MANET mobile device is established only on demand. Basically, AOMDV was specially tailored for ad-hoc networks that are highly dynamic to respond to link failures and breakages in the network. It ensures that the paths for destinations are sustained, and it defines the new routing information using destination serial numbers to ensure loop freedom always while avoiding problems. More so, it is a protocol that is based on a timer that can discover ways through which the mobile nodes respond to link breakages and change in topology. A comparison of protocols has been carried out individually and jointly with the aim of evaluating their performance. The performance is measured in terms of End-to-End Delay, Packet Delivery Ratio, Packet Loss Ratio, and Routing Overhead Ratio. The performance of the routing protocols was done using two scenarios; when there is a change in the simulation time and when there is a change in the number of nodes.
Electric Vehicle Lithium Ion Batteries Thermal Management Gaoussou Hadia Fofana; You Tong Zhang
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 3: March 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

The lithium ion batteries, thanks to their high densities and high power, became promotes element for hybrid-electric and plug-in electric vehicles. Thermal management of lithium ion battery is important for many reasons, including thermal runaway, performance and maintains a constant temperature during the operating, security, lifecycle. However, in a battery pack, the batteries are stacked against each other without cooling surfaces except the outer surface of the package and the cell in the center of pack are exposed to overheating and thermal runaway. After several recent researches, it has been proved that lithium ion batteries are currently confronts a problem of temperature rise during their operation discharge, which affects the batteries performance, efficiency and reduces the life of lithium ion batteries.  However, this work is set to access the three dimensional analytical modeling based on Green’s Function technique to study the thermal behavior of lithium ion battery during discharge with different discharge rates (0.3C, C/2, 1C, 2C) and strategies natural convection cooling on the surface of the battery is performed. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4026
Different Algorithms for Improving Detection Power of Atomic Fluorescence Spectrometry Ning Cui; Jian Cui
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 7: November 2012
Publisher : Institute of Advanced Engineering and Science

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Abstract

The purpose of detecting trace concentrations of analytes often is hindered by occurring noise in the signal curves of analytical methods. This is also a problem when different arsenic species (organic arsenic species such as arsanilic acid, nitarsone and roxarsone) are to be determined in animal meat by HPLC-UV-HG-AFS, which is the basis of this work. In order to improve the detection power, methods of signal treatment may be applied. We show a comparison of convolution with Gaussian distribution curves, Fourier transform, and wavelet transform. It is illustrated how to estimate decisive parameters for these techniques. All methods result in improved limits of detection. Furthermore, applying baselines and evaluating peaks thoroughly is facilitated. However, there are differences. Fourier transform may be applied, but convolution with Gaussian distribution curves shows better results of improvement. The best of the three is wavelet transform, whereby the detection power is improved by factors of about 2.4. DOI: http://dx.doi.org/10.11591/telkomnika.v10i7.1581
A Hybrid Feature Selection Based on Mutual Information and Genetic Algorithm Yuan-Dong Lan
Indonesian Journal of Electrical Engineering and Computer Science Vol 7, No 1: July 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v7.i1.pp214-225

Abstract

Feature selection aims to choose an optimal subset of features that are necessary and sufficient to improve the generalization performance and the running efficiency of the learning algorithm. To get the optimal subset in the feature selection process, a hybrid feature selection based on mutual information and genetic algorithm is proposed in this paper. In order to make full use of the advantages of filter and wrapper model, the algorithm is divided into two phases: the filter phase and the wrapper phase. In the filter phase, this algorithm first uses the mutual information to sort the feature, and provides the heuristic information for the subsequent genetic algorithm, to accelerate the search process of the genetic algorithm. In the wrapper phase, using the genetic algorithm as the search strategy, considering the performance of the classifier and dimension of subset as an evaluation criterion, search the best subset of features. Experimental results on benchmark datasets show that the proposed algorithm has higher classification accuracy and smaller feature dimension, and its running time is less than the time of using genetic algorithm.
Integration of synthetic minority oversampling technique for imbalanced class Noviyanti Santoso; Wahyu Wibowo; Hilda Hikmawati
Indonesian Journal of Electrical Engineering and Computer Science Vol 13, No 1: January 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v13.i1.pp102-108

Abstract

In the data mining, a class imbalance is a problematic issue to look for the solutions. It probably because machine learning is constructed by using algorithms with assuming the number of instances in each balanced class, so when using a class imbalance, it is possible that the prediction results are not appropriate. They are solutions offered to solve class imbalance issues, including oversampling, undersampling, and synthetic minority oversampling technique (SMOTE). Both oversampling and undersampling have its disadvantages, so SMOTE is an alternative to overcome it. By integrating SMOTE in the data mining classification method such as Naive Bayes, Support Vector Machine (SVM), and Random Forest (RF) is expected to improve the performance of accuracy. In this research, it was found that the data of SMOTE gave better accuracy than the original data. In addition to the three classification methods used, RF gives the highest average AUC, F-measure, and G-means score.
Research and Design of Embedded uC/OS-II Network Storage System Xiaowei Zhang; Fensu Shi
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 4: April 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

This article describes a network storage system, which is developed on the embedded system platform. It uses the open-source embedded real-time operating system kernel uCOS-II. Because the kernel is far from complete, such as the lack of a file system, device management, network protocol stack, graphical user interface, but it is small and open source.We extended its functions, such as to add LwIP network protocol stack, IDE hard drive, DM9000 NIC driver, NOR FALSH drive and a file system that supports large-capacity memory and etc, making it an embedded network storage system based on uCOS-II. DOI: http://dx.doi.org/10.11591/telkomnika.v11i4.2373
Artificial neural network and partial least square in predicting blood hemoglobin using near-infrared spectrum Mohd Nazrul Effendy Mohd Idrus; Kim Seng Chia
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 2: November 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i2.pp701-708

Abstract

Predictive models is crucial in near-infrared (NIR) spectroscopic analysis. Partial least square - artificial neural network (PLS-ANN) is a hybrid method that may improve the performance of prediction in NIR spectroscopic analysis. This study investigates the advantage of PLS-ANN over the well-known modelling in spectroscopy analysis that is partial least square (PLS) and artificial neural network (ANN). The results show that ANN that coupled with first order SG derivatives achieved the best prediction with root mean square error of prediction (RMSEP) of 0.3517 gd/L and coefficient of determination ( ) of 0.9849 followed by PLS-ANN with RMSEP of 0.4368 gd/L and  of 0.9787, and PLS with RMSEP of 0.4669 gd/L and  of 0.9727. This suggests that the spectrum information may unable to be totally represented by the first few latent variables of PLS and a nonlinear model is crucial to model these nonlinear information in NIR spectroscopic analysis.
Adaptive Hybrid Synchronization of Lorenz-84 System with Uncertain Parameters Edwin Albert Umoh
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 7: July 2014
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i7.pp5251-5260

Abstract

This paper presents the adaptive control and hybrid synchronization of Lorenz-84 chaotic system using a master-slave topology. The Lorenz-84 is an 11-term dissipative system that possessed  four quadratic nonlinearities in its coupled algebraic structure which results to the evolution of  a dense chaotic attractors in both 2-D and 3-D spaces. Firstly, an adaptive nonlinear feedback controller was designed to suppress the chaotic dynamics of the system. By using Lyapunov stability criterion, the asymptotic stability of the error states was guaranteed and the state dynamics were stabilized. Secondly, adaptive nonlinear feedback controllers were designed to guarantee the co-existence of synchronization and anti-synchronization of the system. By suitable selection of feedback coefficients and Lyapunov function candidate, the uncertain parameters of the slave system were estimated. Numerical simulations via MATLAB show the convergence of the uncertain parameters to their true values after a transient time while the two systems synchronized completely.
Comparison of feature selection techniques in classifying stroke documents Nur Syaza Izzati Mohd Rafei; Rohayanti Hassan; RD Rohmat Saedudin; Anis Farihan Mat Raffei; Zalmiyah Zakaria; Shahreen Kasim
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 3: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i3.pp1244-1250

Abstract

The amount of digital biomedical literature grows that make most of the researchers facing the difficulties to manage and retrieve the required information from the Internet because this task is very challenging. The application of text classification on biomedical literature is one of the solutions in order to solve problem that have been faced by researchers but managing the high dimensionality of data being a common issue on text classification. Therefore, the aim of this research is to compare the techniques that could be used to select the relevant features for classifying biomedical text abstracts. This research focus on Pearson’s Correlation and Information Gain as feature selection techniques for reducing the high dimensionality of data. Towards this effort, we conduct and evaluate several experiments using 100 abstract of stroke documents that retrieved from PubMed database as datasets. This dataset underwent the text pre-processing that is crucial before proceed to feature selection phase. Features selection phase is involving Information Gain and Pearson Correlation technique. Support Vector Machine classifier is used in order to evaluate and compare the effectiveness of two feature selection techniques. For this dataset, Information Gain has outperformed Pearson’s Correlation by 3.3%. This research tends to extract the meaningful features from a subset of stroke documents that can be used for various application especially in diagnose the stroke disease.

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