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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 9,174 Documents
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Implementation of Stereo Vision Semi-Global Block Matching Methods for Distance Measurement Raden Arief Setyawan; Rudy Sunoko; Mochammad Agus Choiron; Panca Mudji Rahardjo
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 2: November 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i2.pp585-591

Abstract

Stereo vision has become an attractive topic research in the last decades. Many implementations such as the autonomous car, 3D movie, 3D object generation, are produced using this technique. The advantages of using two cameras in stereo vision are the disparity map between images. Disparity map will produce distance estimation of the object. Distance measurement is a crucial parameter for an autonomous car. The distance between corresponding points between the left and right images must be precisely measured to get an accurate distance. One of the most challenging in stereo vision is to find corresponding points between left and right images (stereo matching). This paper proposed distance measurement using stereo vision using Semi-Global Block Matching algorithm for stereo matching purpose. The object is captured using a calibrated stereo camera. The images pair then optimized using WLS Filter to reduce noises. The implementation results of this algorithm are furthermore converted to a metric unit for distance measurement. The result shows that the stereo vision distance measurement using Semi-Global Block Matching gives a good result. The obtained best result of this work contains error of less than 1% for 1m distance
Compressed Sensing Speech Signal Enhancement Research Kuangfeng Ning; Guojun Qin
Indonesian Journal of Electrical Engineering and Computer Science Vol 6, No 1: April 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v6.i1.pp26-35

Abstract

The proposed Compressive sensing method is a new alternative method, it is used to eliminate noise from the input signal, and the quality of the speech signal is enhanced with fewer samples, thus it is required for the reconstruction than needed in some of the methods like Nyquist sampling theorem. The basic idea is that the speech signals are sparse in nature, and most of the noise signals are non-sparse in nature, and Compressive Sensing(CS) eliminates the non-sparse components and it reconstructs only the sparse components of the input signal. Experimental results prove that the average segmental SNR (signal to noise ratio) and PESQ (perceptual evaluation of speech quality) scores are better in the compressed domain．
Evolving spiking neural networks methods for classification problem: a case study in flood events risk assessment Mohd Hafizul Afifi Abdullah; Muhaini Othman; Shahreen Kasim; Siti Aisyah Mohamed
Indonesian Journal of Electrical Engineering and Computer Science Vol 16, No 1: October 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v16.i1.pp222-229

Abstract

Analysing environmental events such as predicting the risk of flood is considered as a challenging task due to the dynamic behaviour of the data. One way to correctly predict the risk of such events is by gathering as much of related historical data and analyse the correlation between the features which contribute to the event occurrences. Inspired by the brain working mechanism, the spiking neural networks have proven the capability of revealing a significant association between different variables spike behaviour during an event. Personalised modelling, on the other hand, allows a personal model to be created for a specific data model and experiment. Therefore, a personalised modelling method incorporating spiking neural network is used to create a personalised model for assessing a real-world flood case study in Kuala Krai, Kelantan based on historical data of 2012-2016 provided by Malaysian Meteorological Department. The result shows that the method produces the highest accuracy among the selected compared algorithms.
Optimal Placement and Sizing of TCSC using Gravitational Search Algorithm Purwoharjono Purwoharjono; Muhammad Abdillah; Ontoseno Penangsang; Adi Soeprijanto
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 5: September 2012
Publisher : Institute of Advanced Engineering and Science

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

Abstract

This paper represents the GSA that can be used to determine the optimal location and rating of FACTS devices. They are devices used to regulate and improve the power flow in the power system. The method used in this study was GSA. FACTS types used were TCSC implemented on 500kV Java-Bali Power System. Load flow results before optimization showed that the active power loss was 297.607MW. While the load flow results after optimization using GSA with 5-TCSC obtained were 287.926MW of active power loss, with 10-TCSC, it was obtained 281.143MW of active power loss. In addition, using 15-TCSC, the active power loss obtained was 279.405MW. GSA methods can be used to minimize power losses and transmission lines as well as to improve the value of the voltage in the range of 0.95+ 1.05pu compared with load flow results before optimization. DOI: http://dx.doi.org/10.11591/telkomnika.v10i5.1183
PSO Algorithm Based on Accumulation Effect and Mutation Ji Wei Dong; Zhang Jun
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 12: December 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

Particle SwarmOptimization (PSO) algorithm is a new swarm intelligence optimization technique, because of its simplicity, fewerparameters and good effects, PSO has been widely used to solve various complexoptimization problems. particle swarm optimization(PSO) exist the problems ofpremature and local convergence, we proposed an improved particle swarm optimization based on aggregation effect and with mutation operator, whichdetermines whether the aggregation occurs in searching, if there is then theGaussian  mutation is detected to theglobal extremum, to overcome particle swarm optimization falling into localoptimal solution defects.  Testing thenew algorithm by a typical test function, the results show that, compared withthe conventional genetic algorithm (SGA), it improves the ability of globaloptimization, but also effectively avoid the premature convergence. DOI: http://dx.doi.org/10.11591/telkomnika.v11i12.3703
DOA Estimation by Fourth-Order Cumulants without Source Enumeration and Eigendecomposition Elhafiz Abdalla Bakhit Yagoup; Zhiwen Liu; Yougen Xu
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 6: June 2014
Publisher : Institute of Advanced Engineering and Science

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Abstract

A new algorithm for direction of arrival (DOA) estimation is proposed.Using fourth-order cumulants and modified MUSIC (MUliplle SIgnal Classification) algorithm. However, it does not require any eigendecomposition of the cumulant matrix of the received data and source enumeration. It also eliminates the need for knowledge of the spatial characteristics of the noise and interference. This method only uses the conjugate spatial signal of different sensor positions. Computer simulation results are provided to demonstrate the performance of the proposed approach and compare them to DCI (Diagonally-loaded Conjugate correlation matrix Inverse power) method. DOI : http://dx.doi.org/10.11591/telkomnika.v12i6.4679
Mechanical Behavior of MTMoCr under High Temperature and High Strain-rate Jingkui Ruan; Zhengwei Dong
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 2: February 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

MTMoCr is a kind of Mo-Cr alloy cast iron often used to make automobile panel dies. To study high-speed machining process of automobile panel dies, the material’s elastic modulus and rupture critical values of MTMoCr at 20℃-800℃ were studied based on the high temperature elongation test. The material’s stress-strain diagram at various temperatures set-points (20℃-500℃) and various strain-rates (500/s-5000/s) were studied and the dynamic tensile yield strength values were obtained by dynamic SHPB (Split Hopkinson Pressure Bar) high-speed compression test. The experimental results indicate that MTMoCr has heat resistance and its behavior is between toughness and brittleness materials. Its toughness is enhanced with temperature increasing. The strain-rate strengthening effect prevails over temperature softening effect. DOI: http://dx.doi.org/10.11591/telkomnika.v11i2.2064
Prospect Convenient Steadfast Procedure in Wireless Sensor Network Jewan Singh; Vibhakar Mansotra
Indonesian Journal of Electrical Engineering and Computer Science Vol 9, No 3: March 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v9.i3.pp613-615

Abstract

This article objective is to improve the steadfast routing in Wireless Sensor Networks with little interfering and avoid packet collision. In the scheme, the entire node has the option of electing next Data Communication Node (DCN). The next data communication node is chosen depend on the intensity of link, remaining energy, and the node with distance towards the Base Station. Thus, the sender node transmits the information to the best DCN. Instantly, the DCN sends the acknowledgement (ACK) along with the number of packets received back to the node from which it obtains the data. The sender node assures the delivery of the transmitted packets by comparing the value of number of packets sent with the value obtained with the acknowledgement. If they are equivalent, it will send the verification identity to the DCN. If it is not equivalent, it will decide another node with highest link intensity. After that, the data chooses the DCN and repeat the process until the data reaches the Base Station.
Comparing bags of features, conventional convolutional neural network and AlexNet for fruit recognition Nik Noor Akmal Abdul Hamid; Rabiatul Adawiya Razali; Zaidah Ibrahim
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 1: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i1.pp333-339

Abstract

This paper presents a comparative study between Bag of Features (BoF), Conventional Convolutional Neural Network (CNN) and Alexnet for fruit recognition.  Automatic fruit recognition can minimize human intervention in their fruit harvesting operations, operation time and harvesting cost.  On the other hand, this task is very challenging because of the similarities in shapes, colours and textures among various types of fruits. Thus, a robust technique that can produce good result is necessary. Due to the outstanding performance of deep learning like CNN and its pre-trained models like AlexNet in image recognition, this paper investigates the accuracy of conventional CNN, and Alexnet in recognizing thirty different types of fruits from a publicly available dataset.  Besides that, the recognition performance of BoF is also examined since it is one of the machine learning techniques that achieves good result in object recognition.   The experimental results indicate that all of these three techniques produce excellent recognition accuracy. Furthermore, conventional CNN achieves the fastest recognition result compared to BoF, and Alexnet.
Improving Relevance Feedback in Image Retrieval by Incorporating Unlabelled Images Guizhi Li
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 7: July 2013
Publisher : Institute of Advanced Engineering and Science

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

Abstract

In content-base image retrieval, relevance feedback (RF) schemes based on support vector machine (SVM) have been widely used to narrow the semantic gap between low-level visual features and high-level human perception. However, the performance of image retrieval with SVM active learning is known to be poor when the training data is insufficient. In this paper, the problem is solved by incorporating the unlabelled images into the learning process. We proposed a semi-supervised active learning algorithm which uses not only labeled training samples but also unlabeled ones to build better models. In relevance feedback, active learning algorithm is often used to reduce the cost of labeling by selecting only the most informative data. In addition, we introduced a semi-supervised approach which employed Nearest-Neighbor technique to label the unlabeled sample with a certain degree of uncertainty in its class information. Using these samples, Fuzzy support vector machine (FSVM) which takes into account the fuzzy nature of some training samples during its training is trained. We compared our method with standard active SVM on a database of 10,000 images, the experiment results show that the efficiency of SVM active learning can be improved by incorporating unlabelled images, and thus improve the overall retrieval performance. DOI: http://dx.doi.org/10.11591/telkomnika.v11i7.2807

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