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REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
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Journal of Korean Institute of Intelligent Systems
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Journal DOI :
Korean Institute of Intelligent Systems
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Volume & Issues
Volume 24, Issue 6 - Dec 2014
Volume 24, Issue 5 - Oct 2014
Volume 24, Issue 4 - Aug 2014
Volume 24, Issue 3 - Jun 2014
Volume 24, Issue 2 - Apr 2014
Volume 24, Issue 1 - Feb 2014
Selecting the target year
Development of Torque simulator for the performance analysis of the 10kW wind turbine system
Kim, Se-Yoon ; Kim, Sung-Ho ; Lee, Jong-Hee ; Moon, Jin-Young ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 6, 2014, Pages 579~585
DOI : 10.5391/JKIIS.2014.24.6.579
10kW wind turbine is widely studied in the field of renewable energy for the merits of easy installation to the confined area such as hill, park and urban areas. The performance of various electrical devices such as converter and inverter in the wind turbine system should be systematically analyzed for various wind speeds. However, it is impossible to apply these devices directly to practical wind turbine system for the safety of wind turbine system. Therefore, it is required to develop torque simulator which can generate corresponding torque according to its wind speed. In this work, 10kW torque simulator which consists of three phase torque control inverter, 3 phase induction motor and PMSG(Permanent Magnet Synchronous Generator) is developed.
Design of RBFNN-Based Pattern Classifier for the Classification of Precipitation/Non-Precipitation Cases
Choi, Woo-Yong ; Oh, Sung-Kwun ; Kim, Hyun-Ki ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 6, 2014, Pages 586~591
DOI : 10.5391/JKIIS.2014.24.6.586
In this study, we introduce Radial Basis Function Neural Networks(RBFNNs) classifier using Artificial Bee Colony(ABC) algorithm in order to classify between precipitation event and non-precipitation event from given radar data. Input information data is rebuilt up through feature analysis of meteorological radar data used in Korea Meteorological Administration. In the condition phase of the proposed classifier, the values of fitness are obtained by using Fuzzy C-Mean clustering method, and the coefficients of polynomial function used in the conclusion phase are estimated by least square method. In the aggregation phase, the final output is obtained by using fuzzy inference method. The performance results of the proposed classifier are compared and analyzed by considering both QC(Quality control) data and CZ(corrected reflectivity) data being used in Korea Meteorological Administration.
The Wilcoxon Signed-Rank Fuzzy Test on Rate of Internal Division
Kang, Man Ki ; Choi, Seung Bae ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 6, 2014, Pages 592~596
DOI : 10.5391/JKIIS.2014.24.6.592
We shall consider fuzzy hypotheses test for signed-rank Wilcoxon fuzzy test by fuzzy difference on rate of internal division. Fundamental to these discussion are fuzzy number data and Wilcoxon signed-rank fuzzy test of a fuzzy hypothesis
which is based upon a fuzzy statistics whose distribution does not depend upon the specified distribution or any parameters.
Analyzing Human`s Motion Pattern Using Sensor Fusion in Complex Spatial Environments
Tark, Han-Ho ; Jin, Taeseok ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 6, 2014, Pages 597~602
DOI : 10.5391/JKIIS.2014.24.6.597
We propose hybrid-sensing system for human tracking. This system uses laser scanners and image sensors and is applicable to wide and crowded area such as hallway of university. Concretely, human tracking using laser scanners is at base and image sensors are used for human identification when laser scanners lose persons by occlusion, entering room or going up stairs. We developed the method of human identification for this system. Our method is following: 1. Best-shot images (human images which show human feature clearly) are obtained by the help of human position and direction data obtained by laser scanners. 2. Human identification is conducted by calculating the correlation between the color histograms of best-shot images. It becomes possible to conduct human identification even in crowded scenes by estimating best-shot images. In the experiment in the station, some effectiveness of this method became clear.
A Study on Near-miss Incidents from Maritime Traffic Flow by Clustering Vessel Positions
Kim, Kwang-Il ; Jeong, Jung Sik ; Park, Gyei-Kark ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 6, 2014, Pages 603~608
DOI : 10.5391/JKIIS.2014.24.6.603
In the maritime traffic environment, the near-miss between vessels is the situation approaching on collision course but collision accident is not occurred. In this study, in order to calculate the near-miss between navigating vessels, the discriminating equation using ship bumper theory and vessel position clustering methods are proposed. Applying proposed module to the vessel trajectories of the WANDO waterway, we assessment navigational risk factors of vessel type, navigational speed, meeting situation.
Motor Imagery based Brain-Computer Interface for Cerebellar Ataxia
Choi, Young-Seok ; Shin, Hyun-Chool ; Ying, Sarah H. ; Newman, Geoffrey I. ; Thakor, Nitish ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 6, 2014, Pages 609~614
DOI : 10.5391/JKIIS.2014.24.6.609
Cerebellar ataxia is a steadily progressive neurodegenerative disease associated with loss of motor control, leaving patients unable to walk, talk, or perform activities of daily living. Direct motor instruction in cerebella ataxia patients has limited effectiveness, presumably because an inappropriate closed-loop cerebellar response to the inevitable observed error confounds motor learning mechanisms. Recent studies have validated the age-old technique of employing motor imagery training (mental rehearsal of a movement) to boost motor performance in athletes, much as a champion downhill skier visualizes the course prior to embarking on a run. Could the use of EEG based BCI provide advanced biofeedback to improve motor imagery and provide a "backdoor" to improving motor performance in ataxia patients? In order to determine the feasibility of using EEG-based BCI control in this population, we compare the ability to modulate mu-band power (8-12 Hz) by performing a cued motor imagery task in an ataxia patient and healthy control.
Mathematical Modelling and Behavior Analysis of Addiction of Physical Exercise
Bae, Young-Chul ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 6, 2014, Pages 615~621
DOI : 10.5391/JKIIS.2014.24.6.615
The Addiction problems have been became a social problem; the social efforts continue to solve these problems. One of those efforts, we need to establish a mathematical modeling for an addictive model to perform analysis of behavior by using this modeling. We need to process the research that can be judged before and after addictive status with result of the behavior analysis. We have to process an observation of transition from before to after addictive status. According to those necessary, this paper proposed the physical exercise model that is composed by novel second order system, which consisted of Spring-Damper-Mass system with equivalence in order to evolve an addictive equation for physical exercise. This paper also is analyzed by the behaviors for those the addictive equation of physical exercise.
Infrared Gait Recognition using Wavelet Transform and Linear Discriminant Analysis
Kim, SaMun ; Lee, DaeJong ; Chun, MyungGeun ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 6, 2014, Pages 622~627
DOI : 10.5391/JKIIS.2014.24.6.622
This paper proposes a new method which improves recognition rate on the gait recognition system using wavelet transform, linear discriminant analysis and genetic algorithm. We use wavelet transform to obtain the four sub-bands from the gait energy image. In order to extract feature data from sub-bands, we use linear discriminant analysis. Distance values between training data and four sub-band data are calculated and four weights which are calculated by genetic algorithm is assigned at each sub-band distance. Based on a new fusion distance value, we conducted recognition experiments using k-nearest neighbors algorithm. Experimental results show that the proposed weight fusion method has higher recognition rate than conventional method.
Performance Improvement of Double Talk Detection before Convergence of the Echo Canceller by Using Linear Predictive Coding Filter Gain of the Primary Input Signal
Yoo, Jae-Ha ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 6, 2014, Pages 628~633
DOI : 10.5391/JKIIS.2014.24.6.628
This paper proposes a performance improvement method of the conventional double talk detection method which can operate before convergence of the echo canceller. The proposed method estimates the coefficients of the linear predictive coding(LPC) filter by using the primary input signal. The time-varying threshold for double talk detection is determined based on the LPC filter gain of the primary input signal level. The proposed method can reduce not only false detection rate which means wrong detection of single talk as double talk but also double talk detection delay. Computer simulation was performed using a long-term real speech signals. It is shown that the proposed method improves the conventional method in terms of lowering the false detection rate and shortening the detection delay.
Design and Analysis of Collision Alarm Using Infrared Distance Sensor
Kim, Byoung-Ho ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 6, 2014, Pages 634~639
DOI : 10.5391/JKIIS.2014.24.6.634
This paper specifies a collision alarm using an infrared distance sensor that can identify the dangerousness of collision of active mobile robotic systems to various objects, such as unknown objects or another robots. And we analyse the major operating signals and features of the collision alarm for effective industrial applications. For the purpose, we consider a typical parking situation of a mobile robotic system with the collision alarm designed. As a result, it is shown that the proposed collision alarm is applicable for effective collision avoidance and safe driving of various mobile robots or vehicles.
A Reply Graph-based Social Mining Method with Topic Modeling
Lee, Sang Yeon ; Lee, Keon Myung ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 6, 2014, Pages 640~645
DOI : 10.5391/JKIIS.2014.24.6.640
Many people use social network services as to communicate, to share an information and to build social relationships between others on the Internet. Twitter is such a representative service, where millions of tweets are posted a day and a huge amount of data collection has been being accumulated. Social mining that extracts the meaningful information from the massive data has been intensively studied. Typically, Twitter easily can deliver and retweet the contents using the following-follower relationships. Topic modeling in tweet data is a good tool for issue tracking in social media. To overcome the restrictions of short contents in tweets, we introduce a notion of reply graph which is constructed as a graph structure of which nodes correspond to users and of which edges correspond to existence of reply and retweet messages between the users. The LDA topic model, which is a typical method of topic modeling, is ineffective for short textual data. This paper introduces a topic modeling method that uses reply graph to reduce the number of short documents and to improve the quality of mining results. The proposed model uses the LDA model as the topic modeling framework for tweet issue tracking. Some experimental results of the proposed method are presented for a collection of Twitter data of 7 days.
Feature Selection of Fuzzy Pattern Classifier by using Fuzzy Mapping
Roh, Seok-Beom ; Kim, Yong Soo ; Ahn, Tae-Chon ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 6, 2014, Pages 646~650
DOI : 10.5391/JKIIS.2014.24.6.646
In this paper, in order to avoid the deterioration of the pattern classification performance which results from the curse of dimensionality, we propose a new feature selection method. The newly proposed feature selection method is based on Fuzzy C-Means clustering algorithm which analyzes the data points to divide them into several clusters and the concept of a function with fuzzy numbers. When it comes to the concept of a function where independent variables are fuzzy numbers and a dependent variable is a label of class, a fuzzy number should be related to the only one class label. Therefore, a good feature is a independent variable of a function with fuzzy numbers. Under this assumption, we calculate the goodness of each feature to pattern classification problem. Finally, in order to evaluate the classification ability of the proposed pattern classifier, the machine learning data sets are used.
A Prediction Method of the Gas Pipeline Failure Using In-line Inspection and Corrosion Defect Clustering
Kim, Seong-Jun ; Choe, Byung Hak ; Kim, Woosik ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 6, 2014, Pages 651~656
DOI : 10.5391/JKIIS.2014.24.6.651
Corrosion has a significant influence upon the reliability assessment and the maintenance planning of gas pipeline. Corrosion defects occurred on the underground pipeline can be obtained by conducting periodic in-line inspection (ILI). However, little study has been done for practical use of ILI data. This paper deals with remaining lifetime prediction of the gas pipeline in the presence of corrosion defects. Because a pipeline parameter includes uncertainty in its operation, a probabilistic approach is adopted in this paper. A pipeline fails when its operating pressure is larger than the pipe failure pressure. In order to estimate the failure probability, this paper uses First Order Reliability Method (FORM) which is popular in the field of structural engineering. A well-known Battelle code is chosen as the computational model for the pipe failure pressure. This paper develops a Matlab GUI for illustrating failure probability predictions Our result indicates that clustering of corrosion defects is helpful for improving a prediction accuracy and preventing an unnecessary maintenance.
A Study of Quality Evaluation for SDR System Operating Software
Kim, Min-Soo ; Lee, Kun-Joon ; Ha, Sung-Jae ; Cho, Sang-Young ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 6, 2014, Pages 657~664
DOI : 10.5391/JKIIS.2014.24.6.657
In this paper, we described evaluation software that can propose rating scale and evaluate automatically on SDR(Software-Defined Radio) transmitter system. To deduct suitable rating scale, we extracted requirements based on the operating environment, operating application and operating method because SDR operating software is subordinative to the hardware. And we implementation evaluation software to automatically quality evaluation using rating scale draw from the requirements. The implemented evaluation software has automatically evaluation functions using script, can update the evaluation factor and show visual evaluation result. The implemented evaluation software is used to useful the high quality SDR operating software development.
Analysis and Detection Method for Line-shaped Echoes using Support Vector Machine
Lee, Hansoo ; Kim, Eun Kyeong ; Kim, Sungshin ;
Journal of Korean Institute of Intelligent Systems, volume 24, issue 6, 2014, Pages 665~670
DOI : 10.5391/JKIIS.2014.24.6.665
A SVM is a kind of binary classifier in order to find optimal hyperplane which separates training data into two groups. Due to its remarkable performance, the SVM is applied in various fields such as inductive inference, binary classification or making predictions. Also it is a representative black box model; there are plenty of actively discussed researches about analyzing trained SVM classifier. This paper conducts a study on a method that is automatically detecting the line-shaped echoes, sun strobe echo and radial interference echo, using the SVM algorithm because the line-shaped echoes appear relatively often and disturb weather forecasting process. Using a spatial clustering method and corrected reflectivity data in the weather radar, the training data is made up with mean reflectivity, size, appearance, centroid altitude and so forth. With actual occurrence cases of the line-shaped echoes, the trained SVM classifier is verified, and analyzed its characteristics using the decision tree method.