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28,188 Article Results

Temperature Control of the 25kW Parabolic Dish-Stirling Engine System

10.12928/telkomnika.v14i3.4055
Liaw Geok; Universiti Teknikal Malaysia Melaka Pheng , Mohd Ruddin; Universiti Teknikal Malaysia Melaka Ab Ghani , Chin; Universiti Teknikal Malaysia Melaka Kim Gan , Tole; Universitas Ahmad Dahlan Sutikno
This paper is under in-depth investigation due to suspicion of possible plagiarism on a high similarity indexNowadays, even though many researchers continue to investigate and study about Parabolic Dish based on Concentration Solar Power (CSP), the findings are not conclusive and do not provide accurate evidence and proof on the potential of CSP development in Malaysia. The missing link in the Parabolic Dish (PD) Stirling Engine system modelled is the control systems, which vary the amount of working gas in the Stirling engine. The temperature of the heater in PD system which has been modelled is overheated and causes damage to the heater material, low output efficiency, high thermal losses and effect to the lifespan of the PD system. Therefore, the primary aim of this paper was to design a control system to maximize output efficiency during a normal operation by maintaining the heater/absorber temperature at the highest safe operating point and preventing excessive range of threshold to prevent damage to the heater material.
Volume: 14
Issue: 3
Page: 800-806
Publish at: 2016-09-01

Distributed Clustering Based on Node Density and Distance in Wireless Sensor Networks

10.12928/telkomnika.v14i3.2712
Sasikumar; VIT University P , Shankar; VIT University T , Sibaram; VIT University Khara
Wireless Sensor Networks (WSNs) are special type of network with sensing and monitoring the physical parameters with the property of autonomous in nature. To implement this autonomy and network management the common method used is hierarchical clustering. Hierarchical clustering helps for ease access to data collection and forwarding the same to the base station. The proposed Distributed Self-organizing Load Balancing Clustering Algorithm (DSLBCA) for WSNs designed considering the parameters of neighbor distance, residual energy, and node density.  The validity of the DSLBCA has been shown by comparing the network lifetime and energy dissipation with Low Energy Adaptive Clustering Hierarchy (LEACH), and Hybrid Energy Efficient Distributed Clustering (HEED). The proposed algorithm shows improved result in enhancing the life time of the network in both stationary and mobile environment.
Volume: 14
Issue: 3
Page: 916-922
Publish at: 2016-09-01

Transformer Fault Diagnosis Method Based on Dynamic Weighted Combination Model

10.12928/telkomnika.v14i3.3545
Hongli; North China University of Water Resources and Electric Power Yun , Run; North China University of Water Resources and Electric Power Liu , Linjian; North China University of Water Resources and Electric Power Shangguan
The paper tried to integrate the DGA data with the gas production rate, which are the major indexes of transformer fault diagnosis. Duval’s triangle method, BP neural network and IEC three-ratio method were weighted. Firstly, the paper regarded the gas production rate as the independent variables, fitted the cubic curves of the gas production rate and variance of each diagnosis method, and then defined the weights of each algorithm through the data processing method of unequal precision. At last, the dynamic weighted combination diagnosis model was established. That is, the weight is different as the gas production rate changes although the method is identical. The results of diagnosis examples show that the accuracy rate of the weighted combination model is higher than any single algorithm, and it has certain stability as well.
Volume: 14
Issue: 3
Page: 815-823
Publish at: 2016-09-01

Oil Palm Yield Forecasting Based on Weather Variables Using Artificial Neural Network

10.11591/ijeecs.v3.i3.pp626-633
Nadia Dwi Kartika , I Wayan Astika , Edi Santosa
Forecasting of oil palm yield has become a main factor in the management of oil palm industries for proper planning and decision making in order to avoid monthly high cost in harvesting. Predicting future value of oil palm yield with minimum error becomes an important issue recently. A lot of factors determine the productivity of oil palm and weather variables play an important role that affect plant growth and development that may reduce yield significantly. This research used secondary data of yield and weather variables available in company administration. It proposed feed forward neural network with back propagation learning algorithm to build a monthly yield forecasting model. The optimization procedure of ANN architecture obtained the best using 60 neurons in input layer, five hidden layers and one neuron in the output layer. Training data were from January 2005 to June 2008 while testing data were from July 2008 to December 2009. ANN architecture using five hidden layers gave the best accuracy with MAE 0.5346 and MSE 0.4707 while the lowest accuracy occurred by using two hidden layers with MAE 1.5843and MSE 4.087.
Volume: 3
Issue: 3
Page: 626-633
Publish at: 2016-09-01

A Comparison of Retweet Prediction Approaches: The Superiority of Random Forest Learning Method

10.12928/telkomnika.v14i3.3150
Hendra; Maranatha Christian University Bunyamin , Tomas; Czech Technical University Tunys
We consider the following retweet prediction task: given a tweet, predict whether it will be retweeted. In the past, a wide range of learning methods and features has been proposed for this task. We provide a systematic comparison of the performance of these learning methods and features in terms of prediction accuracy and feature importance. Specifically, from each previously published approach we take the best performing features and group these into two sets: user features and tweet features. In addition, we contrast five learning methods, both linear and non-linear. On top of that, we examine the added value of a previously proposed time-sensitive modeling approach. To the authors’ knowledge this is the first attempt to collect best performing features and contrast linear and non-linear learning methods. We perform our comparisons on a single dataset and find that user features such as the number of times a user is listed, number of followers, and average number of tweets published per day most strongly contribute to prediction accuracy across selected learning methods. We also find that a random forest-based learning, which has not been employed in previous studies, achieves the highest performance among the learning methods we consider. We also find that on top of properly tuned learning methods the benefits of time-sensitive modeling are very limited.
Volume: 14
Issue: 3
Page: 1052-1058
Publish at: 2016-09-01

Flow Fair Sampling Based on Multistage Bloom Filters

10.12928/telkomnika.v14i3.3648
Liu; Yancheng Institute of Technology Yuanzhen , Huang; Yancheng Institute of Technology Shurong , Liu; Yancheng Institute of Technology Jianzhao
Network traffic distribution is heavy-tailed. Most of network flows are short and carry very few packets, and the number of large flows is small. Traditional random sampling tends to sample more large flows than short ones. However, many applications depend on per-flow traffic other than just large flows. A flow fair sampling based on multistage Bloom filters is proposed. The total measurement interval is divided into n child time intervals. In each child time interval, employ multistage Bloom filters to query the incoming packet’s flow whether exists in flow information table or not, if exists, sample the packet with static sampling rate which is inversely proportional to the estimation flow traffic up to the previous time interval. If it is a new flow’s first packet, create its flow information and insert it into the multistage Bloom filters. The results show that the proposed algorithm is accurate especially for short flows and easy to extend.
Volume: 14
Issue: 3
Page: 1142-1149
Publish at: 2016-09-01

Classification of Motorcyclists not Wear Helmet on Digital Image with Backpropagation Neural Network

10.12928/telkomnika.v14i3.3486
Sutikno; Diponegoro University Sutikno , Indra; Diponegoro University Waspada , Nurdin; Diponegoro University Bahtiar , Priyo Sidik; Diponegoro University Sasongko
One of the world’s leading causes of death is traffic accidents. Data from World Health Organization (WHO) that there are 1.25 million people in the world die each year. Meanwhile, based on data obtained from Statistics Indonesia, traffic accidents from 2006 to 2013 continue to increase. Of all these accidents, the largest accident occurred at motorcyclists, especially motorcyclists who not wearing standard helmet. In controlling the motorcyclists, police view directly at the highway, so that there are weaknesses which there are still a possibility of motorcyclist offenders who are undetectable especially for motorcyclists who are not wear helmet. This paper explains research on image classification of human head wearing a helmet and not wearing a helmet with backpropagation neural network algorithm. The test results of this analysis is the application can performs classification with 86.67% accuracy rate. This research can be developed into a larger system and integrated that can be used to detect motorcyclists who are not wearing helmet.
Volume: 14
Issue: 3
Page: 1128-1133
Publish at: 2016-09-01

A New Semi-supervised Clustering Algorithm Based on Variational Bayesian and Its Application

10.12928/telkomnika.v14i3.3805
Shoulin Yin , Jie Liu , Lin Teng
Biclustering algorithm is proposed for discovering matrix with biological significance in gene expression data matrix and it is used widely in machine learning which can cluster the row and column of matrix. In order to further improve the performance of biclustering algorithm, this paper proposes a semi-supervised clustering algorithm based on variational Bayesian. Firstly, it introduces supplementary information of row and column for biclustering process and represents corresponding joint distribution probability model. In addition, it estimates the parameter of joint distribution probability model based on variational Bayesian learning method. Finally, it estimates the performance of proposed algorithm through synthesized data and real gene expression data set. Experiments show that normalized mutual information of this paper’s new method is better than relevant biclustering algorithms for biclustering analysis.
Volume: 14
Issue: 3
Page: 1150-1156
Publish at: 2016-09-01

A Review on Modulation Strategies of Multi Level Inverter

10.11591/ijeecs.v3.i3.pp681-705
Chinnapettai Ramalingam Balamurugan , S.P. Natarajan , R. Bensraj , B. Shanthi
This review develop different switching methods for Multi Level Inverter (MLI). The switching methods proposed in this paper are to compare various methods and to predict exact switching method for different application based upon its quality of the outputs.  The performance of the inverter is analyzed with the parameters like THD (Total Harmonic Injection), VRMS (fundamental), CF (Crest Factor), FF (Form Factor) and DF (Distortion Factor). From the various non PWM (Pulse Width Modulation) and PWM methods the analysis are method to identify the exact PWM strategies for specific applications. 
Volume: 3
Issue: 3
Page: 681-705
Publish at: 2016-09-01

Compressive Sensing Algorithm for Data Compression on Weather Monitoring System

10.12928/telkomnika.v14i3.3021
Rika; Indonesian Institute of Sciences (LIPI) Sustika , Bambang; Indonesian Institute of Sciences (LIPI) Sugiarto
Compressive sensing (CS) is new data acquisition algorithm that can be used for compression. CS theory certifies that signals can be recovered from far fewer samples or measurements than Nyquist rate. On this paper, the compressive sensing technique is applied for data compression on our weather monitoring system. On this weather monitoring system, compression using compressive sensing with fewer samples or measurements means minimizing sensing and overall energy cost. Our focus on this paper lies in the selection of matrix for representation basis under which the weather data are sparsely represented. We evaluated three types of representation basis using data from real measurement. By comparing performance of data recovery, result show that DCT (Discrete Cosine Transform) is the best performance on sparsifying weather data
Volume: 14
Issue: 3
Page: 974-980
Publish at: 2016-09-01

Hybrid Hierarchical Collision Detection Based on Data Reuse

10.12928/telkomnika.v14i3.3590
Jiancai; South China University of Technology Hu , Kejing; South China University of Technology He , Xiaobin; South China University of Technology Lin , Funan; South China University of Technology Lin
To improve the efficiency of collision detection between rigid bodies in complex scenes, this paper proposes a method based on hybrid bounding volume hierarchies for collision detection. In order to improve the simulation performance, the method is based on weighted oriented bounding box and makes dense sampling on the convex hulls of the geometric models. The hierarchical bounding volume tree is composed of many layers. The uppermost layer adopts a cubic bounding box, while lower layers employ weighted oriented bounding box. In the meantime, the data of weighted oriented bounding box is reused for triangle intersection check. We test the method using two scenes. The first scene contains two Buddha models with totally 361,690 triangle facets. The second scene is composed of 200 models with totally 115, 200 triangle facets. The experiments verify the effectiveness of the proposed method.
Volume: 14
Issue: 3
Page: 1077-1082
Publish at: 2016-09-01

Study on Community’s Land Allocation in Long Pahangai District

10.11591/ijeecs.v3.i3.pp564-571
Dito Cahya Renaldi , I Nengah Surati Jaya , Omo Rusdiana
Land use allocation for community has been a crucial process for supporting the spatial allocation either at the regency or provincial level. This study was emphasized on the analysis of land allocation at the district level. The study applied a linear programming approach to optimize the land use in Long Pahangai District then linked with the spatial information. The optimization considered several factors, i.e., land productivity, the degree of erosion and the preference of the community living in the study area. To support the optimization, the availability of land use was determined by considering the land capability using the query tools in the Geographic Information System. The level of land capability applied five constraints, namely, slope, drainage, soil texture, effective depth and erosion. The study found that the optimal allocation of land use in the study area are primary forest of 6,635.11 ha (25.19%), secondary forest of 19,025.7 ha (71.9%), mixed plantation area of 289.61 ha (1.1%), settlement area of 8.3 ha (0.03%) and rice field of 487.35 ha (1.844%). This optimal allocation might increase the community income per capita by approximately 80% from 9,602,000.- to 17,275,171.-/capita/ha/year.  
Volume: 3
Issue: 3
Page: 564-571
Publish at: 2016-09-01

GPU CUDA accelerated Image Inpainting using Fourth Order PDE equation

10.12928/telkomnika.v14i3.3412
Edwin Prananta , Pranowo; Atma Jaya Yogyakarta University Pranowo , Djoko; Atma Jaya Yogyakarta University Budianto
This paper describes the technique to accelerate inpainting process using fourth order PDE equation using GPU CUDA. Inpainting is the process of filling in missing parts of damaged images based on information gleaned from surrounding areas. It uses the GPU computation advantage to process PDE equation into parallel process. Fourth order PDE will be solved using parallel computation in GPU. This method can speed up the computation time up to 36x using NVDIA GEFORCE GTX 670.
Volume: 14
Issue: 3
Page: 1009-1015
Publish at: 2016-09-01

Prediction Model of Smelting Endpoint of Fuming Furnace Based on Grey Neural Network

10.12928/telkomnika.v14i3.3713
Song; School of Mechanical Engineering, Anyang Institute of Technology,Henan,China Qiang , WU; School of Mechanical Engineering, Anyang Institute of Technology,Henan,China Yaochun
Since grey theory and neural network could improve prediction precision, the technology of combination prediction was proposed in this study. Then the algorithm was simulated by Matlab using practical data of a fuming furnace. The results reveal that the smelting endpoint of fuming furnace could be accurately predicted with this model by referring to small sample and information. Therefore, GNN model is effective with the advantages of high precision, fewer samples required and simple calculation.
Volume: 14
Issue: 3
Page: 941-947
Publish at: 2016-09-01

Recognition of Fission Signals Based on Wavelet Analysis and Neural Network

10.12928/telkomnika.v14i3.3544
Li; South West University of Science & Technology Li , Liu Keqi , Hu; Academy of Engineering Physics Gen
Because of the particularity of the uranium components, the nondestructive measuring technique is needed to detect the radioactivity of the component in certain container and identify their property to recognize all kinds of uranium components. This paper establishes a set of samples with the same shape, different weight and abundance of uranium by simulation. Secondly the cross-correlation function of time-relation signal between the source detector and the detector could be calculated. Lastly the result of cross-correlation functions is through micro-wavelet analysis to obtain feature vector which is related to the quality and abundance property of target uranium components. This vector is used to train neural network and help to identify the quality and abundance of unknown uranium components.
Volume: 14
Issue: 3
Page: 1016-1023
Publish at: 2016-09-01
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