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24,883 Article Results

Application of Chaotic Particle Swarm Optimization in Wavelet Neural Network

10.12928/telkomnika.v12i4.533
Cuijie; Tianjin University of Finance and Economics Zhao , Guozhen; Bohai Professional and Technical College Wang
Currently, the method of optimizing the wavelet neural network with particle swarm plays a certain role in improving the convergence speed and accuracy; however, it is not a good solution for problems of turning into local extrema and poor global search ability. To solve these problems, this paper, based on the particle swarm optimization, puts forward an improved method, which is introducing the chaos mechanism into the algorithm of chaotic particle swarm optimization. Through a series of comparative simulation experiments, it proves that applying this algorithm to optimize the wavelet neural network can successfully solve the problems of turning into local extrema, and improve the convergence speed of the network, in the meantime, reduce the output error and improve the search ability of the algorithm. In general, it helps a lot to improve the overall performance of the wavelet neural network.
Volume: 12
Issue: 4
Page: 997-1004
Publish at: 2014-12-01

A New Algorithm for Detecting Local Community Based on Random Walk

10.12928/telkomnika.v12i4.438
Yueping; Shenzhen Polytechnic Li , Weikun; Shenzhen Institute of Information Technology Zheng
This paper presents one new algorithm for local community discovery. It employs a new vertex selection strategy which considers not only the boundary structure of candidate local community but also the probability which the investigated vertex will return to the candidate local community. A local random walk is adopted to compute this return probability which does not require the global information. We choose four algorithms for comparison which are the best ones existed by far. For better evaluation, the datasets include not only the computer generated graphs in standard benchmark but also the real-world networks which are classical ones in global community discovery. The experimental results show our algorithm outperforms the other ones on the computer generated graphs. The performance of our algorithm is approximately the same with the algorithm proposed by Luo, Wang and Promislow on real-world networks.
Volume: 12
Issue: 4
Page: 1005-1016
Publish at: 2014-12-01

Dynamic DEMATEL Group Decision Approach Based on Intuitionistic Fuzzy Number

10.12928/telkomnika.v12i4.787
Hui; University of Science and Technology Xie , Wanchun; University of Science and Technology Duan , Yonghe; University of Science and Technology Sun , Yuanwei; University of Science and Technology Du
With respect to the problems of aggregation about group experts’ information and dynamic decision in DEMATEL(decision making trial and evaluation laboratory), a dynamic DEMATEL group expert decision-making method on intuitionistic fuzzy number(IFN) is presented. Firstly using IFN instead of original point estimates to reflect the experts’ preference, the group experts’ information are integrated horizontally at each period. Then the aggregation information at different periods are aggregated vertically again by dynamic intuitionistic fuzzy weighted averaging (DIFWA) operator so as to obtain the dynamic intuitionistic fuzzy DEMATEL total relation matrix. Thirdly, through the analysis of center and reason degree, the positions of the various factors in the system are clear and definite, and the inner structure of system has been revealed. Finally, the feasibility and practicability of the proposed method is shown through an illustrative example of a process of course selection in a school.
Volume: 12
Issue: 4
Page: 1064-1072
Publish at: 2014-12-01

Review of Local Descriptor in RGB-D Object Recognition

10.12928/telkomnika.v12i4.388
Ema; Bandung Institute of Technology Rachmawati , Iping Supriana; Bandung Institute of Technology Suwardi , Masayu Leylia; Bandung Institute of Technology Khodra
The emergence of an RGB-D (Red-Green-Blue-Depth) sensor which is capable of providing depth and RGB images gives hope to the computer vision community. Moreover, the use of local features began to increase over the last few years and has shown impressive results, especially in the field of object recognition. This article attempts to provide a survey of the recent technical achievements in this area of research. We review the use of local descriptors as the feature representation which is extracted from RGB-D images, in instances and category-level object recognition. We also highlight the involvement of depth images and how they can be combined with RGB images in constructing a local descriptor. Three different approaches are used in involving depth images into compact feature representation, that is classical approach using distribution based, kernel-trick, and feature learning. In this article, we show that the involvement of depth data successfully improves the accuracy of object recognition.
Volume: 12
Issue: 4
Page: 1132-1141
Publish at: 2014-12-01

A Review of Parabolic Dish-Stirling Engine System Based on Concentrating Solar Power

10.12928/telkomnika.v12i4.1132
Liaw Geok; Universiti Teknikal Malaysia Melaka Pheng , Rosnani; Universiti Teknikal Malaysia Melaka Affandi , Mohd Ruddin; Universiti Teknikal Malaysia Melaka Ab Ghani , Chin Kim; Universiti Teknikal Malaysia Melaka Gan , Zanariah; Universiti Teknikal Malaysia Melaka Jano , Tole; Universitas Ahmad Dahlan Sutikno
A solar thermal technology which is also known as concentrating solar power (CSP) uses thermal energy from the sun to generate electricity. The electricity generation from solar thermal can be produced with four technologies of concentrating solar systems which are parabolic trough, linear Fresnel reflector, solar tower, and parabolic dish-Stirling engine system. This paper reviews the parabolic dish-stirling based on CSP technology by taking into account the performance, the global performance, site for parabolic dish and levelized cost of energy (LCOE). Generally, the parabolic dish applications have barriers in terms of the technology and the high capital cost compared to the others CSP technologies. 
Volume: 12
Issue: 4
Page: 1142-1152
Publish at: 2014-12-01

Sparsity Properties of Compressive Video Sampling Generated by Coefficient Thresholding

10.12928/telkomnika.v12i4.296
Ida Wahidah; Institut Teknologi Bandung Hamzah , Tati Latifah; Institut Teknologi Bandung R. Mengko , Andriyan; Institut Teknologi Bandung B. Suksmono , Hendrawan; Institut Teknologi Bandung Hendrawan
We study the compressive sampling (CS) and its application in video encoding framework. The video input is firstly transformed into suitable domain in order to achieve sparser configuration of coefficients. Then, we apply coefficient thresholding to classify which frames to be sampled compressively or conventionally. For frames chosen to undergo compressive sampling, the coefficient vectors will be projected into smaller vectors using random measurement matrix. As CS requires two main conditions, i.e. sparsity and matrix incoherence, this research is emphasized on the enhancement of sparsity property of the input signal. It was empirically proven that the sparsity enhancement could be reached by applying motion compensation and thresholding to the non-significant coefficient count. At the decoder side, the reconstruction algorithm can employ basis pursuit or L1 minimization algorithm.
Volume: 12
Issue: 4
Page: 897-904
Publish at: 2014-12-01

Pre-Timed and Coordinated Traffic Controller Systems Based on AVR Microcontroller

10.12928/telkomnika.v12i4.497
Freddy; Department of Electrical Engineering, Sekolah Tinggi Teknologi Adisutjipto Kurniawan , Denny; Department of Electrical Engineering, Sekolah Tinggi Teknologi Adisutjipto Dermawan , Okto; Department of Mechanical Engineering, Sekolah Tinggi Teknologi Adisutjipto Dinaryanto , Mardiana; Department of Informatics Engineering, Sekolah Tinggi Teknologi Adisutjipto Irawati
The major weaknesses of traffic controllers in Indonesia are unable to accommodate the variety of traffic volume and unable to be coordinated. To solve the problem, a pre-timed and coordinated traffic controller system is build. The system consists of a master and a local controller. Each controller has a database containing signal-timing plans that would be allocated to manage vehicle flows. To synchronize the signal-timing, the master controller sends the synchronization data to the local controller wirelessly and the local controller shifts the end of a cycle by adding or subtracting the green interval of any phases. The transition time for synchronization only takes one to several cycles. The algorithm for controlling the traffic including coordination can be done by an AVR microcontroller. Memory usage of the microcontroller is lower than 10% meanwhile the CPU utilization is no more than 1%, thus the systems could be widely developed.
Volume: 12
Issue: 4
Page: 787-794
Publish at: 2014-12-01

A Design and Analysis of Voltage Source Inverters for Renewable Energy Applications

https://ijeecs.iaescore.com/index.php/IJEECS/article/view/3926
M. Murali , Arulmozhiyal Arulmozhiyal , P Sundaramoorthy
The paper proposes design of voltage source inverters for renewable energy applications such as HEV. The wind and solar are growing energy sources to world this sources to be converted alternating one for grid interfacing.  Conventional inverters are electronic thyristor which has some drawbacks.  To improve its efficiency and performance MOSFET based inverters using controllers has been designedusing PIC controllers. In this project the hardware details of three phases, 50Hz, 60W, 180 degree conduction mode of VSI output waveforms under various load conditions were presented and discussed. This paper will be a significant contributes for   forthcoming development of Hybrid Electric Vehicle (HEV).In which Voltage source inverters is operation is performed in Single PIC microcontroller.  http://dx.doi.org/10.11591/telkomnika.v12i12.6893 
Volume: 12
Issue: 12
Page: 8114-8119
Publish at: 2014-12-01

Clustering Algorithm Combined with Hill Climbing for Classification of Remote Sensing Image

https://ijece.iaescore.com/index.php/IJECE/article/view/5510
B.Sai Chandana , K. Srinivas , R. Kiran Kumar
Clustering is an unsupervised classification method widely used for classification of remote sensing images.  As the spatial resolution of remote sensing images getting higher and higher, the complex structure is the simple objects becomes obvious, which makes the classification algorithm based on pixels being losing their advantages. In this paper, four different clustering algorithms such as K-means, Moving K-means, Fuzzy K-means and Fuzzy Moving K-means are used for classification of remote sensing images. In all the traditional clustering algorithms, number of clusters and initial centroids are randomly selected and often specified by the user. In this paper, a hill climbing algorithm for the histogram of the input image will generate the number of clusters and initial centroids required for clustering.   It overcomes the shortage of random initialization in traditional clustering and achieves high computational speed by reducing the number of iterations. The experimental results show that Fuzzy Moving K-means has classified the remote sensing image more accurately than other three algorithms.DOI:http://dx.doi.org/10.11591/ijece.v4i6.6608
Volume: 4
Issue: 6
Page: 923-930
Publish at: 2014-12-01

Wireless Sensor Based Hybrid Architecture for Vehicular Ad hoc Networks

10.12928/telkomnika.v12i4.537
Kashif Naseer; Universiti Teknologi Malaysia Qureshi , Abdul Hanan; Universiti Teknologi Malaysia Abdullah , Raja Waseem; Universiti Teknologi Malaysia Anwar
A vehicular Ad hoc network is an emerging and widely adopted technology because of their potentiality for innovative applications in transportation sector. Recently, the technology has been faced various challenges and rely on expensive architecture. The implementation of Wireless Sensor Network (WSN) in vehicular networks reduces the required investment and improves intelligent applications performance for driving safety and traffic efficiency. In this paper, we propose a wireless sensor based hybrid architecture for navigation systems for vehicular Ad hoc networks. The architecture is suitable for mountain range roads, where vehicles cannot communication properly. The propose architecture is used to exchange and perceive roadside information and helpful in navigation decision process and for alert messages.
Volume: 12
Issue: 4
Page: 942-949
Publish at: 2014-12-01

Diagnostic Study Based on Wavelet Packet Entropy and Wear Loss of Support Vector Machine

10.12928/telkomnika.v12i4.305
Yunjie; Zhejiang Agricultural & Forestry University Xu , Shudong; Zhejiang Agricultural & Forestry University Xiu
Against the problems, the ratio of signal to noise of bearing wear is low, the feature extraction is difficult, there are few fault samples and it is difficult to establish the reliable fault recognition model, the diagnostic method is put forward based on wavelet packet features and bearing wear loss of SVM. Firstly, choose comentropy with strong fault tolerance as characteristic parameter, then through wavelet packet decomposition, extract feature entropy of wavelet packet in fault sensitivity band as input vector and finally, apply the Wrapper method of least square SVM to choose optimal character subset. The application in actual bearing fault diagnosis indicates the effectiveness of the proposed method in the article.
Volume: 12
Issue: 4
Page: 847-854
Publish at: 2014-12-01

A Novel Intrusion Detection Approach using Multi-Kernel Functions

10.12928/telkomnika.v12i4.889
Lijiao; Yiwu Industrial & Commercial College Pan , Weijian; Yiwu Industrial & Commercial College Jin , Jun; Yiwu Industrial & Commercial College Wu
Network intrusion detection finds variant applications in computer and network industry. How to achieve high intrusion detection accuracy and speed is still received considerable attentions in this field. To address this issue, this work presents a novel method that takes advantages of multi-kernel computation technique to realize speedy and precise network intrusion detection and isolation. In this new development the multi-kernel function based kernel direct discriminant analysis (MKDDA) and quantum particle swarm optimization (QPSO) optimized kernel extreme learning machine (KELM) were appropriately integrated and thus form a novel method with strong intrusion detection ability. The MKDDA herein was firstly employed to extract distinct features by projecting the original high dimensionality of the intrusion features into a low dimensionality space. A few distinct and efficient features were then selected out from the low dimensionality space. Secondly, the KELM was proposed to provide quick and accurate intrusion recognition on the extracted features. The only parameter need be determined in KELM is the neuron number of hidden layer. Literature review indicates that very limited work has addressed the optimization of this parameter. Hence, the QPSO was used for the first time to optimize the KELM parameter in this paper. Lastly, experiments have been implemented to verify the performance of the proposed method. The test results indicate that the proposed LLE-PSO-KELM method outperforms its rivals in terms of both recognition accuracy and speed. Thus, the proposed intrusion detection method has great practical importance.
Volume: 12
Issue: 4
Page: 1088-1095
Publish at: 2014-12-01

Guidelines to Establish Secure Structure in Communication Networks used in the Smart Grid

https://ijeecs.iaescore.com/index.php/IJEECS/article/view/3914
Siamak Rezaei
Currently, an extensive worldwide movement to implement electrical Smart Grid and replacing traditional grids has been started. In the past, since each electrical grid was usually using its specific systems and protocols, the possibility of system penetration and consequently security concerns was in low levels. But in recent years, with implementation of electrical Smart Grid, using open standards and popular network protocols, such as the technology which is used in the Internet (Internet Protocol), in electrical Smart Grids has been considered. This is due to advantages like efficiency, accessibility and low cost of these technologies. But, on the other hand, this increases the security concerns. Thus, selecting the appropriate communication mechanism for Smart Grid, is one of the most important challenges. Therefore, in this paper we introduce and study traditional communication networks in Smart Grids and discuss security capabilities of them. http://dx.doi.org/10.11591/telkomnika.v12i12.6725 
Volume: 12
Issue: 12
Page: 8022-8027
Publish at: 2014-12-01

Prediction and Realization of DO in Sewage Treatment Based on Machine Vision and BP Neural Network

10.12928/telkomnika.v12i4.437
Liu; Hebei United University Liping , Sunjin; Hebei United University Sheng , Yin; Hebei Energy College of Vocation and Technology Jing-tao , Liang; Hebei United University Na
Dissolved Oxygen (DO) is one of the most important parameters describing biochemical process in wastewater treatment. It is usually measured with dissolved oxygen meters, and currently galvanic and polarographic electrodes are the predominant methods. Expensive, membrane surface inactivation, and especially need of cleaning and calibrating very frequently are common disadvantages of electrode-type measuring sensors. In our work, a novel method for Prediction and Realization dissolved oxygen based-on Machine Vision and BP Neural Network was researched. Pictures of the water-body surface in aeration basins are captured and transformed into HSI space data. These data plus the correspondent measured DO values are processed with a neural network. Using the well-trained neural network, a satisfied result for classifying dissolved oxygen according to the water-body pictures has been realized.
Volume: 12
Issue: 4
Page: 890-896
Publish at: 2014-12-01

Image Deblurring via an Adaptive Dictionary Learning Strategy

10.12928/telkomnika.v12i4.532
Lei; Beijing Forestry University Li , Ruiting; Beijing Technology and Business University Zhang , Jiangmin; Beijing Forestry University Kan , Wenbin; Beijing Forestry University Li
Recently, sparse representation has been applied to image deblurring. The dictionary is the fundamental part of it and the proper selection of dictionary is very important to achieve super performance. The global learned dictionary might achieve inferior performances since it could not mine the specific information such as the texture and edge which is contained in the blurred image. However, it is a computational burden to train a new dictionary for image deblurring which requires the whole image (or most parts) as input; training the dictionary on only a few patches would result in over-fitting. To address the problem, we instead propose an online adaption strategy to transfer the global learned dictionary to a specific image. In our deblurring algorithm, the sparse coefficients, latent image, blur kernel and the dictionary are updated alternatively. And in every step, the global learned dictionary is updated in an online form via sampling only a few training patches from the target noisy image. Since our adaptive dictionary exploits the specific information, our deblurring algorithm shows superior performance over other state-of-the-art algorithms. 
Volume: 12
Issue: 4
Page: 855-864
Publish at: 2014-12-01
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