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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
 9   10   11   12   13 
Hierarchical Markov Decision Based Path Planning for Palletizing Robot Jiufu Liu; Zhengqian Wang; Zhe Chen; Zhong Yang; Zhisheng Wang; Chunsheng Liu
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

On account of the complex application environment and the large number of uncertain conditions for the palletizing robot, we do path-planning for the multiple joints robot by the algorithm based on Hierarchical Markov Decision Process. First, according to the actual working environment, we set the range of the robot’s motion and select the conventional movement combination as the basic set of the robot’s behaviors. We can get the possible reward of various situations. We divide the state space in accordance with the location information of the obstacle space into a small number of state clusters, sub-level step by step to determine the precise trajectory of palletizing robots. We simulate 3D robot motion trajectory, including barrier-free and spherical obstacle conditions. Finally, experimental verification is carried out, the algorithm has been proved to control the compatible movements of each joint effectively and keep the error within the allowed range. The experiment results meet the requirement well. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.3851 
Neural Network-based Adaptive Passive Output Feedback Control for MIMO Uncertain System Yonghong Zhu; Qing Feng; Jianhong Wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 10, No 6: October 2012
Publisher : Institute of Advanced Engineering and Science

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Abstract

A neural network--based adaptive passive output feedback control problem is studied for a class of multi-input multi-output nonlinear systems with unknown nonlinearities and unknown parameters. Neural networks are used to identify unknown nonlinearities, and the update laws of weight parameters and constant parameters are proposed. The design methods of the adaptive passive controllers for this class of systems are discussed in the paper. The corresponding adaptive passive controllers and parametric adaptive laws are designed and presented respectively. It is proved that the closed-loop system composed of the original system and the designed controller is stable by the Lyapunov method, and the controller designed can render the closed system adaptive passive. Finally, a simulation example is given to prove the effectiveness and feasibility of the proposed method. DOI: http://dx.doi.org/10.11591/telkomnika.v10i6.1600 
Adverse Impact of STATCOM on the Performance of Distance Relay Elhadi Emhemad Aker; Mohammad Lutfi Othman; Ishak Aris; Noor Izzri Abdul Wahab; Hashim Hizam; Osaji Emmanuel
Indonesian Journal of Electrical Engineering and Computer Science Vol 6, No 3: June 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v6.i3.pp528-536

Abstract

FACTS devices like the Static synchronous Compensator (STATCOM), are mostly used to enhance the maximum power transfer capability of the transmission Line (TL) system. A Matlab simulation model of Distance Relay protection of TL, with connected STATCOM at the mid-point for optimum power transfer is presented. The STATCOM’s impact on the operation of the relay is assessed with the effects on the relay misoperation in the third zone of protection coverage, during fault conditions, in four different locations.The wrong measured fault impedance by relay resulted to misoperation in zone 3 (under reach phenomena). The simulation result indicates a slight increase in the measured impedance of 1.33 Ω over the actually expected impedance setting (72.02 Ω) of the relay at 220 km protection coverage of zone 3 along the TL. This variation is about 4 km distance outside the expected distance protection coverage for fault in zone 3 as proven.
Agriculture Data Analytics in Crop Yield Estimation: A Critical Review B.M. Sagar; Cauvery N K
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 3: December 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v12.i3.pp1087-1093

Abstract

Agriculture is important for human survival because it serves the basic need. A well-known fact that the majority of population (≥55%) in India is into agriculture. Due to variations in climatic conditions, there exist bottlenecks for increasing the crop production in India. It has become challenging task to achieve desired targets in Agri based crop yield. Factors like climate, geographical conditions, economic and political conditions are to be considered which have direct impact on the production, productivity of the crops. Crop yield prediction is one of the important factors in agriculture practices. Farmers need information regarding crop yield before sowing seeds in their fields to achieve enhanced crop yield. The use of technology in agriculture has increased in recent year and data analytics is one such trend that has penetrated into the agriculture field being used for management of crop yield and monitoring crop health. The recent trends in the domain of agriculture have made the people to understand the significance of          Big data. The main challenge using big data in agriculture is identification of impact and effectiveness of big data analytics.  Efforts are going on to understand how big data analytics can be used to improve the productivity in agricultural practices. The analysis of data related to agriculture helps in crop yield prediction, crop health monitoring and other such related activities. In literature, there exist several studies related to the use of data analytics in the agriculture domain. The present study gives insights on various data analytics methods applied to crop yield prediction. The work also signifies the important lacunae points’ in the proposed area of research.
Hybrid methods of brandt’s generalised likelihood ratio and short-term energy for malay word speech segmentation Noraini Seman; Ahmad Firdaus Norazam
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.pp283-291

Abstract

Speech segmentation is an important part for speech recognition, synthesizing and coding. Statistical based approach detects segmentation points via computing spectral distortion of the signal without prior knowledge of the acoustic information proved to be able to give good match, less omission but lot of insertion. In this study the segmentation is done both manually and automatically using Malay words in traditional Malay poetry. This study proposed a hybrid method of Brandt’s generalized likelihood ratio (GLR) and short-term energy algorithm. The Brandt’s algorithm tries to estimate the abrupt change in energy to determine the segmentation points. A total of five Pantun are used in read mode and spoken by one male student in a noise free room. Experiments are conducted to see the the accuracy, insertion, and omission of the segmentation points. Experimental results show on average 80% accuracy with 0.2 second time tolerance for automatic segmentation with the algorithm having no knowledge of the acoustic characteristics.
An Efficient System for Information Recommendation Zhenhua Huang; Qiang Fang
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 recommendation system is the one of the most effective tools for tackling with the problem of information overload. However, as the maturity of Web 2.0 and the emergence of massive information, the existing information recommendation systems have the serious drawbacks in the aspects of real-timing, robustness and self-adaptability. Motivated by the above facts, in this paper, we design SIRSCA, which is an efficient semantic-driven information recommendation system under the cloud architecture. Specially, the SIRSCA system mainly include four modules: semantics representation of foundation data and user preference informations; indexing mechanism of massive semantic informations under cloud architecture; recommendation approaches based on semantic computation theory; and technologies of dynamic migration under cloud architecture. We present the extensive experiments that demonstrate our improved system is both efficient and effective. DOI:  http://dx.doi.org/10.11591/telkomnika.v12i6.5445 
Similar Constructive Method for Solving a nonlinearly Spherical Percolation Model WANG Yong; BAO Xi-tao; LI Shun-chu
Indonesian Journal of Electrical Engineering and Computer Science Vol 11, No 3: March 2013
Publisher : Institute of Advanced Engineering and Science

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Abstract

In the view of nonlinear spherical percolation problem of dual porosity reservoir, a mathematical model considering three types of outer boundary conditions: closed, constant pressure, infinity was established in this paper. The mathematical model was linearized by substitution of variable and became a boundary value problem of ordinary differential equation in Laplace space by Laplace transformation. It was verified that such boundary value problem with one type of outer boundary had a similar structure of solution. And a new method: Similar Constructive Method was obtained for solving such boundary value problem. By this method, solutions with similar structure in other two outer boundary conditions were obtained. The Similar Constructive Method raises efficiency of solving such percolation model. DOI: http://dx.doi.org/10.11591/telkomnika.v11i3.2219
Multilayer neural network synchronized secured session key based encryption in wireless communication Arindam Sarkar; Joydeep Dey; Anirban Bhowmik
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.pp169-177

Abstract

Energy computation concept of multilayer neural network synchronized on derived transmission key based encryption system has been proposed for wireless transactions. Multilayer perceptron transmitting machines accepted same input array, which in turn generate a resultant bit and the networks were trained accordingly to form a protected variable length secret-key. For each session, different hidden layer of multilayer neural network is selected randomly and weights of hidden units of this selected hidden layer help to form a secret session key. A novel approach to generate a transmission key has been explained in this proposed methodology. The last thirty two bits of the session key were taken into consideration to construct the transmission key. Inverse operations were carried out by the destination perceptron to decipher the data. Floating frequency analysis of the proposed encrypted stream of bits has yielded better degree of security results. Energy computation of the processed nodes inside multi layered networks can be done using this proposed frame of work.
The Application of S-Transform to Reduce Border Distortion Effect Based on Window Length S. Habsah Asman; M. A. Talib Mat Yusoh; A. Farid Abidin
Indonesian Journal of Electrical Engineering and Computer Science Vol 9, No 1: January 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v9.i1.pp177-182

Abstract

The enhancement of powerful signal processing  tools has broadened the scope research in power quality analysis.The necessity of processing tools to compute the signals accurately without border distortion effect presence has demanded nowadays. Hence, S-Transform has been selected in this paper as a time-frequency analysis tools for power disturbance detection and localization as it capable to extract features and high resolution to deal with border distortion effect. Various window length signal has been analyzed to overcome the border distortion effect in S-Transform.To ascertain validity of the proposed scheme, it is  validated with IEEE 3 bus test system and simulation results show that the proposed technique can minimize the border effect  while detecting transient and voltage sag during fault system. As a result, the longest window length which is four cycle, outperform the least MSE value which indicate the best performance. While, the shortest window length resulting highest MSE value which indicate the worst performance.
Functional analysis of cancer gene subtype from co-clustering and classification Logenthiran Machap; Afnizanfaizal Abdullah
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 1: April 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i1.pp343-350

Abstract

Cancer is a heterogeneity genetic disease with huge phenotypic alterations among dissimilar cancers types or even between same cancer types. Recent expansions of genome-wide profiling technologies offer a chance to explore molecular changes variations throughout advancement of cancer. Therefore, various statistical and machine learning algorithms have been designed and developed for the handling and interpretation of high-throughput microarray molecular data. Discovery of molecular subtypes studies have permitted the cancer to be allocated into similar groups that are deliberated to port similar molecular and clinical characteristics. Thus, the main objective of this research is to discover cancer gene subtypes and classify genes to obtain higher accuracy. In particular improved co-clustering algorithm used to discover cancer subtypes. And then supervised infinite feature selection gene selection method was combined with multi class SVM for classification of selected genes and further biological analysis. The analysis on breast cancer and glioblastoma multiforme evidences that top genes involved in cancer and the pathways present in both cancer top genes. The functional analysis is useful in medical and pharmaceutical field for cancer diagnosis and prognosis.

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