cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
-
Contact Email
-
Phone
-
Journal Mail Official
-
Editorial Address
-
Location
Kota yogyakarta,
Daerah istimewa yogyakarta
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 102 Documents
 7   8   9   10   11 
Search results for , issue "Vol 12, No 4: April 2014" : 102 Documents clear
Robust Adaptive Fuzzy Sliding Mode Control Based on Fuzzy Compensation for Ammunition Auto-loading Robot Yufeng Li; Kuiwu Li; Yutian Pan; Kelei Li
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 4: April 2014
Publisher : Institute of Advanced Engineering and Science

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

Abstract

Aimed at the problems of low control accuracy and weak robustness influenced by external disturbance, friction, load changes, modeling errors and other issues in ammunition auto-loading robot control system, a new robust adaptive fuzzy sliding mode controller based on fuzzy compensation is proposed. The control architecture employs fuzzy systems to compensate adaptively for plant uncertainties to distinguish different disturbance compensation terms and approximate each of them respectively. The stability of the robust adaptive fuzzy sliding mode control (SMC) and the convergence of the tracking errors are ensured by using the Lyapunov theory. By analyzing and comparing the simulation results, it is obviously shown that the control system can lighten the effect on the control system caused by different disturbance factors and eliminate the system chattering instead of traditional SMC. As a result, the control system has great dynamic features and robust stability and meets the requirement that the actual motion of ammunition auto-loading robot quickly tracks the scheduled trajectory. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4342
An Adaptive Detection Method of Multiple Faces Wei Li
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 4: April 2014
Publisher : Institute of Advanced Engineering and Science

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

Abstract

The appearance of multiple faces is influenced by abnormal exposure, interfering backgrounds or fake objects greatly in the color face image. A multiple-face detection method based on the adaptive dual skin model and improved fuzzy C-mean clustering was presented in this study. First an adaptive skin-color model and an adaptive skin-probability model were built to acquire the skin likelihood for clustering, the adaptive initial clustering centers, and the adaptive clustering weights. Then the skin-likelihood image was segmented dynamically by improved fuzzy C-mean clustering. Finally the multiple-face targets were distinguished and extracted by jointly using the effective areas, circumferences and circularities of connected targets. Experiment showed that the algorithm had good results and high speed, accuracy, and adaptability of face detection. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4368 
Process Model and Digitalization of the Coal Gas Outburst Prevention Shao-jie Hou; Yu-wei Zhang; Yuan-ping Cheng
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 4: April 2014
Publisher : Institute of Advanced Engineering and Science

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

Abstract

Regional coal gas outburst prevention has become the prerequisite of coal mining in the most China underground collieries. It touched on miners’ lives so closely, but especially lacked of digitalization due to the hostile working environment, sightless strata reserves, complicated and long-time workflow. By synthesizing the vital rules issued by China governments and various techniques of coal gas outburst prevention, we proposed a novel process model for them embodied as a logical workflow. The model consisted of two operation links and two judging nodes, and dealt with three types of data. Then an easy-use and practical process data management software system was developed. By testing in Qinan colliery, the system was proved to be fully considering user experience and helpful to promote digitalization of coal gas outburst prevention. Compared with the traditional management, the digitalization might help engineers identify anomalies more quickly and avoid gas accidents in time. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4240
An Intelligent Course Scheduling Model Based on Genetic Algorithm Guofeng Qin; Haibin Ma
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 4: April 2014
Publisher : Institute of Advanced Engineering and Science

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

Abstract

With the university expansion, how to maintain teaching order using limited resources make the intelligent course scheduling become a multiple-constraint and multi-objective optimization problem. Traditional intelligent course scheduling algorithm is inefficient, cannot solve curriculum conflict question and meet the requirements of the modern university education management. Given this situation, this paper analyzes the university timetabling problem, and establishes a general course scheduling model; then proposes an improved genetic algorithm to sovle the intelligent course scheduling problem. It can meet all of the education resources’ constraints and the teachers’ personal demands as much as possible. Test the performance of between the improved genetic algorithm and simple genetic algorithm under different scenarios, the experimental results show that the improved genetic algorithm has better performance, can schedule courses reasonable. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4798 
Study on Measuring and Forecasting of Fully Mechanized Working Face Roof Pressure System Yong Zhang; XueQiang Yang; ZengXin Wang
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 4: April 2014
Publisher : Institute of Advanced Engineering and Science

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

Abstract

China is one of the largest coal producer and consumer countries in the world. However, due to the complexity of coal resources, storage conditions, geological disaster-prone coal mines, it is also a coal mine accidents multiple country, coal mine accidents and deaths of China accounted to about 80% total of the world. In the coal mine accident occurred, roof accident has accounted for around 40%,such as the roof collapsed, slipped, deformation, obstruction and so on. So the monitor and early warning of roof is particularly important. State of motion is closely related to mine roof pressure. Roof support pressure or resistance can be measured by the pressure sensor. The data send to the ring Ethernet underground and transmit to the monitoring center of ground. Through information analysis processing, it could provide real-time data and early warning, alarm information. Applied time series theory analysis and forecasting future pressure changes, can master the roof movement trends and regularity, guide safe production. So the decisions have some practical significance. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4779 
The Combined Forecasting Model of Discrete Verhulst-BP Neural Network Based on Linear Time-Varying Shang Hongchao; Long Xia; He Tingjie
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 4: April 2014
Publisher : Institute of Advanced Engineering and Science

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

Abstract

Firstly, this paper, aiming at the problem of errors produced by the transformation of differential equation directly into difference equation from traditional gray Verhulst model,through generating reciprocal for the original data sequence, constructs the discrete Vrhulst model based on linear time-varying（LTDVM model);And then we, taking the LTDVM predicted value as an input value and the original data as a mentor training value, put forward the combined forecasting model of discrete Verhulst-BP neural network based on linear time-varying. Meanwhile, in order to improve the training speed and agility and effectively avoid the saturation region of S-type function, this article normalized in advance the input data and mentor training values to better ensure the usefulness, self-learning ability and fault tolerance of the model. At last, we will study the cases to demonstrate that the model has high modeling and forecasting accuracy. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4938
Switching Surface Design for Nonlinear Systems: the Ship Dynamic Positioning Diallo Thierno Mamadou Pathe; Hongsheng Li; Guangrong Bian
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 4: April 2014
Publisher : Institute of Advanced Engineering and Science

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

Abstract

In this paper a design of the switching-surface for the nonlinear system is studied. The aim was to prove that with the linear matrix inequality the coefficients of the sliding surface can be determined optimally for the control law structure. The advantages of the use of the linear matrix inequality reside in the accurate determination of the coefficients of the sliding surface. The sliding mode control for dynamic positioning of the ship with our proposed switching-surface is done. The objective of this control was to make sure that the ship follows a predetermined track. The good trackings are observed from the simulation results which confirm the robustness of the control law obtained by our proposed switching-surface. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4739
Recognition Based on Metric-optimized Neighborhood Preserving Embedding Bo Chen; Ye Zhang
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 4: April 2014
Publisher : Institute of Advanced Engineering and Science

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

Abstract

Face recognition is a biometric technology with great developable potential. It has a great deal of potential applications in public security and information security. To overcome the problem in the high-dimensional face data processing, the k-nearest neighbors is chose by Linear Discriminate Analysis (LDA). A Metric-optimized is proposed for Neighborhood Preserving Embedding (MONPE).MONPE algorithm, with the dimensions of data reduced by LDA, will be reasonable in NPE algorithm. On the other hand, LDA maximizes the between-class scatter and minimizes the within-class scatter, so the neighbors of a sample will have higher possibility to be picked from the same class .With the ORL face database and the Yale database, the recognition rate and run time is compared among NPE, MONPE and CLMONPE. The simulation results show that CLMONPE has obvious advantage in application DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4825 
The Optimal Design of Communication Module for Campus Smart Card Zhu Lin
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 4: April 2014
Publisher : Institute of Advanced Engineering and Science

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

Abstract

Campus Card is an important part in the digital campus life. However, due to the problems of current network design, in the course of card using, the signal channels are seized by a large number of communication tags, which leads to the phenomena of blocked communication process and unsmooth communication. In order to solve this problem, the communication module of card system has been optimized in this paper. The module consists of two parts, which are writing system and reading system. A multi-tasking multi-point mapping decomposition technique was introduced to the designation of communication module. Using the method based on the combination of Map and Reduce function to make the task decomposition which is from a sudden increase of network traffic characteristics and database, obtain a large number of sub-tasks, and accomplish the management of mutation network traffic. Experimental results show that in the task scheduling of optimized module, the solution time is short and response is fast. It can solve the communication blocked problems in the process of card using. Furthermore, it provides theoretical references for the improvement of communication system design of smart card, and promotes the construction of digital campus evolving. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4251
A P2P Traffic Identification Approach Based on SVM and BFA Chunzhi Wang; Zeqi Wang; Zhiwei Ye; Hongwei Chen
Indonesian Journal of Electrical Engineering and Computer Science Vol 12, No 4: April 2014
Publisher : Institute of Advanced Engineering and Science

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

Abstract

Nowadays new peer to peer (P2P) traffic with dynamic port and encrypted technology makes the identification of P2P traffic become more and more difficult. As one of the optimal classifiers, support vector machine (SVM) has special advantages with avoiding local optimum, overcoming dimension disaster, resolving small samples and high dimension for P2P classification problems. However, to employ SVM, the parameters selection of SVM should be considered and thus some optimization methods have been put forward to deal with it, still, it is not fully solved. Hence, in the paper, a peer to peer traffic identification approach based on support vector machine and bacterial foraging algorithm is proposed for better identification of P2P network traffic. First, the best parameters for SVM are tuned with bacterial foraging algorithm. Subsequently, SVM set with the best parameters is used to identify P2P traffic. Finally, experimental results show the proposed approach can effectively improve the accuracy of P2P network traffic identification. DOI : http://dx.doi.org/10.11591/telkomnika.v12i4.4736

Page 9 of 11 | Total Record : 102

 7   8   9   10   11 

Filter by Year

2014 2014


Reset
Filter By Issues
All Issue Vol 40, No 2: November 2025 Vol 40, No 1: October 2025 Vol 39, No 3: September 2025 Vol 39, No 2: August 2025 Vol 39, No 1: July 2025 Vol 38, No 3: June 2025 Vol 38, No 2: May 2025 Vol 38, No 1: April 2025 Vol 37, No 3: March 2025 Vol 37, No 2: February 2025 Vol 37, No 1: January 2025 Vol 36, No 3: December 2024 Vol 36, No 2: November 2024 Vol 36, No 1: October 2024 Vol 35, No 3: September 2024 Vol 35, No 2: August 2024 Vol 35, No 1: July 2024 Vol 34, No 3: June 2024 Vol 34, No 2: May 2024 Vol 34, No 1: April 2024 Vol 33, No 3: March 2024 Vol 33, No 2: February 2024 Vol 33, No 1: January 2024 Vol 32, No 3: December 2023 Vol 32, No 1: October 2023 Vol 31, No 3: September 2023 Vol 31, No 2: August 2023 Vol 31, No 1: July 2023 Vol 30, No 3: June 2023 Vol 30, No 2: May 2023 Vol 30, No 1: April 2023 Vol 29, No 3: March 2023 Vol 29, No 2: February 2023 Vol 29, No 1: January 2023 Vol 28, No 3: December 2022 Vol 28, No 2: November 2022 Vol 28, No 1: October 2022 Vol 27, No 3: September 2022 Vol 27, No 2: August 2022 Vol 27, No 1: July 2022 Vol 26, No 3: June 2022 Vol 26, No 2: May 2022 Vol 26, No 1: April 2022 Vol 25, No 3: March 2022 Vol 25, No 2: February 2022 Vol 25, No 1: January 2022 Vol 24, No 3: December 2021 Vol 24, No 2: November 2021 Vol 24, No 1: October 2021 Vol 23, No 3: September 2021 Vol 23, No 2: August 2021 Vol 23, No 1: July 2021 Vol 22, No 3: June 2021 Vol 22, No 2: May 2021 Vol 22, No 1: April 2021 Vol 21, No 3: March 2021 Vol 21, No 2: February 2021 Vol 21, No 1: January 2021 Vol 20, No 3: December 2020 Vol 20, No 2: November 2020 Vol 20, No 1: October 2020 Vol 19, No 3: September 2020 Vol 19, No 2: August 2020 Vol 19, No 1: July 2020 Vol 18, No 3: June 2020 Vol 18, No 2: May 2020 Vol 18, No 1: April 2020 Vol 17, No 3: March 2020 Vol 17, No 2: February 2020 Vol 17, No 1: January 2020 Vol 16, No 3: December 2019 Vol 16, No 2: November 2019 Vol 16, No 1: October 2019 Vol 15, No 3: September 2019 Vol 15, No 2: August 2019 Vol 15, No 1: July 2019 Vol 14, No 3: June 2019 Vol 14, No 2: May 2019 Vol 14, No 1: April 2019 Vol 13, No 3: March 2019 Vol 13, No 2: February 2019 Vol 13, No 1: January 2019 Vol 12, No 3: December 2018 Vol 12, No 2: November 2018 Vol 12, No 1: October 2018 Vol 11, No 3: September 2018 Vol 11, No 2: August 2018 Vol 11, No 1: July 2018 Vol 10, No 3: June 2018 Vol 10, No 2: May 2018 Vol 10, No 1: April 2018 Vol 9, No 3: March 2018 Vol 9, No 2: February 2018 Vol 9, No 1: January 2018 Vol 8, No 3: December 2017 Vol 8, No 2: November 2017 Vol 8, No 1: October 2017 Vol 7, No 3: September 2017 Vol 7, No 2: August 2017 Vol 7, No 1: July 2017 Vol 6, No 3: June 2017 Vol 6, No 2: May 2017 Vol 6, No 1: April 2017 Vol 5, No 3: March 2017 Vol 5, No 2: February 2017 Vol 5, No 1: January 2017 Vol 4, No 3: December 2016 Vol 4, No 2: November 2016 Vol 4, No 1: October 2016 Vol 3, No 3: September 2016 Vol 3, No 2: August 2016 Vol 3, No 1: July 2016 Vol 2, No 3: June 2016 Vol 2, No 2: May 2016 Vol 2, No 1: April 2016 Vol 1, No 3: March 2016 Vol 1, No 2: February 2016 Vol 1, No 1: January 2016 Vol 16, No 3: December 2015 Vol 16, No 2: November 2015 Vol 16, No 1: October 2015 Vol 15, No 3: September 2015 Vol 15, No 2: August 2015 Vol 15, No 1: July 2015 Vol 14, No 3: June 2015 Vol 14, No 2: May 2015 Vol 14, No 1: April 2015 Vol 13, No 3: March 2015 Vol 13, No 2: February 2015 Vol 13, No 1: January 2015 Vol 12, No 12: December 2014 Vol 12, No 11: November 2014 Vol 12, No 10: October 2014 Vol 12, No 9: September 2014 Vol 12, No 8: August 2014 Vol 12, No 7: July 2014 Vol 12, No 6: June 2014 Vol 12, No 5: May 2014 Vol 12, No 4: April 2014 Vol 12, No 3: March 2014 Vol 12, No 2: February 2014 Vol 12, No 1: January 2014 Vol 11, No 12: December 2013 Vol 11, No 11: November 2013 Vol 11, No 10: October 2013 Vol 11, No 9: September 2013 Vol 11, No 8: August 2013 Vol 11, No 7: July 2013 Vol 11, No 6: June 2013 Vol 11, No 5: May 2013 Vol 11, No 4: April 2013 Vol 11, No 3: March 2013 Vol 11, No 2: February 2013 Vol 11, No 1: January 2013 Vol 10, No 8: December 2012 Vol 10, No 7: November 2012 Vol 10, No 6: October 2012 Vol 10, No 5: September 2012 Vol 10, No 4: August 2012 Vol 10, No 3: July 2012 More Issue