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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 22, Issue 6 - Dec 2012
Volume 22, Issue 5 - Oct 2012
Volume 22, Issue 4 - Aug 2012
Volume 22, Issue 3 - Jun 2012
Volume 22, Issue 2 - Apr 2012
Volume 22, Issue 1 - Feb 2012
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Development of Fault Diagnostic Algorithm based on Spectrum Analysis of Acceleration Signal for Wind Turbine System
Ahn, Sung-Ill ; Choi, Seong-Jin ; Kim, Sung-Ho ;
Journal of Korean Institute of Intelligent Systems, volume 22, issue 6, 2012, Pages 675~680
DOI : 10.5391/JKIIS.2012.22.6.675
Wind energy is currently the fastest growing source of renewable energy used for electrical generation around the world. Wind farms are adding a significant amount of electrical generation capacity. The increase in the number of wind farms has led to the need for more effective operation and maintenance. CMS(Condition Monitoring System) can be used to aid plant operator in achieving these goals. Its aim is to provide operators with information regarding th e health of their machine, which in turn, can help them improve operation efficiency. In this work, wind turbine fault diagnostic algorithm which can diagnose the mass unbalance and aerodynamic asymmetry of the blades is proposed. Proposed diagnostic algorithm utilizes both FFT(Fast Feurier Transform) of the signal from accelerometers installed inside of nacelle and simple diagnostic logic. Furthermore, to verify the applicability of the proposed system, 3W small sized wind turbine system is tested and physical experiments are carried out.
Robust Speech Parameters for the Emotional Speech Recognition
Lee, Guehyun ; Kim, Weon-Goo ;
Journal of Korean Institute of Intelligent Systems, volume 22, issue 6, 2012, Pages 681~686
DOI : 10.5391/JKIIS.2012.22.6.681
This paper studied the speech parameters less affected by the human emotion for the development of the robust emotional speech recognition system. For this purpose, the effect of emotion on the speech recognition system and robust speech parameters of speech recognition system were studied using speech database containing various emotions. In this study, mel-cepstral coefficient, delta-cepstral coefficient, RASTA mel-cepstral coefficient, root-cepstral coefficient, PLP coefficient and frequency warped mel-cepstral coefficient in the vocal tract length normalization method were used as feature parameters. And CMS (Cepstral Mean Subtraction) and SBR(Signal Bias Removal) method were used as a signal bias removal technique. Experimental results showed that the HMM based speaker independent word recognizer using frequency warped RASTA mel-cepstral coefficient in the vocal tract length normalized method, its derivatives and CMS as a signal bias removal showed the best performance.
Optimal Region Deployment for Cooperative Exploration of Swarm Robots
Bang, Mun Seop ; Joo, Young Hoon ; Ji, Sang Hoon ;
Journal of Korean Institute of Intelligent Systems, volume 22, issue 6, 2012, Pages 687~693
DOI : 10.5391/JKIIS.2012.22.6.687
In this paper, we propose a optimal deployment method for cooperative exploration of swarm robots. The proposed method consists of two parts such as optimal deployment and path planning. The optimal area deployment is proposed by the K-mean Algorithm and Voronoi tessellation. The path planning is proposed by the potential field method and A* Algorithm. Finally, the numerical experiments demonstrate the effectiveness and feasibility of the proposed method.
Development of 3D Inspection Equipment using White Light Interferometer with Large F.O.V.
Koo, Young Mo ; Lee, Kyu Ho ;
Journal of Korean Institute of Intelligent Systems, volume 22, issue 6, 2012, Pages 694~699
DOI : 10.5391/JKIIS.2012.22.6.694
In this paper, semiconductor package inspection results using white light interferometer with large F.O.V., in order to apply semiconductor product inspection process, are shown. Experimental 3D data repeatability test results for the same special bumps of each substrate are shown. Experimental 3D data repeatability test results for all the bumps in each substrate are also shown. Semiconductor package inspection using white light interferometer with large F.O.V. is very important for the fast 3D data inspection in semiconductor product inspection process. This paper is surely helpful for the development of in-line type fast 3D data inspection machine.
Intelligent Digital Redesign of Observer-Based Output-Feedback Fuzzy Controller Using Delta Operator
Moon, Ji Hyun ; Lee, Ho Jae ; Kim, Do Wan ;
Journal of Korean Institute of Intelligent Systems, volume 22, issue 6, 2012, Pages 700~705
DOI : 10.5391/JKIIS.2012.22.6.700
This paper addresses an intelligent digital redesign (IDR) technique for observer-based output-feedback control systems, in order to efficiently convert a pre-designed Takagi-Sugeno fuzzy model-based analog controller into a sampled-data one in the sense of state matching. A delta operator is used to get an asymptotic relation between the analog and the sampled-data control systems. The IDR problem is viewed as a minimization problem of the norm distances between linear operator to be matched. The condition is represented as linear matrix inequalities, and the separation principle on the IDR is shown.
Feature Selection to Predict Very Short-term Heavy Rainfall Based on Differential Evolution
Seo, Jae-Hyun ; Lee, Yong Hee ; Kim, Yong-Hyuk ;
Journal of Korean Institute of Intelligent Systems, volume 22, issue 6, 2012, Pages 706~714
DOI : 10.5391/JKIIS.2012.22.6.706
The Korea Meteorological Administration provided the recent four-years records of weather dataset for our very short-term heavy rainfall prediction. We divided the dataset into three parts: train, validation and test set. Through feature selection, we select only important features among 72 features to avoid significant increase of solution space that arises when growing exponentially with the dimensionality. We used a differential evolution algorithm and two classifiers as the fitness function of evolutionary computation to select more accurate feature subset. One of the classifiers is Support Vector Machine (SVM) that shows high performance, and the other is k-Nearest Neighbor (k-NN) that is fast in general. The test results of SVM were more prominent than those of k-NN in our experiments. Also we processed the weather data using undersampling and normalization techniques. The test results of our differential evolution algorithm performed about five times better than those using all features and about 1.36 times better than those using a genetic algorithm, which is the best known. Running times when using a genetic algorithm were about twenty times longer than those when using a differential evolution algorithm.
A Study of Threat Evaluation using Learning Bayesian Network on Air Defense
Choi, Bomin ; Han, Myung-Mook ;
Journal of Korean Institute of Intelligent Systems, volume 22, issue 6, 2012, Pages 715~721
DOI : 10.5391/JKIIS.2012.22.6.715
A threat evaluation is the technique which decides order of priority about tracks engaging with enemy by recognizing battlefield situation and making it efficient decision making. That is, in battle situation of multiple target it makes expeditious decision making and then aims at minimizing asset`s damage and maximizing attack to targets. Threat value computation used in threat evaluation is calculated by sensor data which generated in battle space. Because Battle situation is unpredictable and there are various possibilities generating potential events, the damage or loss of data can make confuse decision making. Therefore, in this paper we suggest that substantial threat value calculation using learning bayesian network which makes it adapt to the varying battle situation to gain reliable results under given incomplete data and then verify this system`s performance.
Model Predictive Control for Distributed Storage Facilities and Sewer Network Systems via PSO
Baek, Hyunwook ; Ryu, Jaena ; Kim, Tea-Hyoung ; Oh, Jeill ;
Journal of Korean Institute of Intelligent Systems, volume 22, issue 6, 2012, Pages 722~728
DOI : 10.5391/JKIIS.2012.22.6.722
Urban sewer systems has a limitation of capacity of rainwater storage and problem of occurrence of untreated sewage, so adopting a storage facility for sewer flooding prevention and urban non-point pollution reduction has a big attention. The Korea Ministry of Environment has recently introduced a new concept of "multi-functional storage facility", which is crucial not only in preventive stormwater management but also in dealing with combined sewer overflow and sanitary sewer discharge, and also has been promoting its adoption. However, reserving a space for a single large-scale storage facility might be difficult especially in urban areas. Thus, decentralized construction of small- and midium-sized storage facilities and its operation have been introduced as an alternative way. In this paper, we propose a model predictive control scheme for an optimized operation of distributed storage facilities and sewer networks. To this aim, we first describe the mathematical model of each component of networks system which enables us to analyze its detailed dynamic behavior. Second, overflow locations and volumes will be predicted based on the developed network model with data on the external inflow occurred at specific locations of the network. MPC scheme based on the introduced particle swarm optimization technique then produces the optimized the gate setting for sewer network flow control, which minimizes sewer flooding and maximizes the potential storage capacity. Finally, the operational efficacy of the proposed control scheme is demonstrated by simulation study with virtual rainstorm event.
Hierarchical Clustering of Symbolic Objects based on Asymmetric Proximity
Oh, Seung-Joon ; Park, Chan-Woong ;
Journal of Korean Institute of Intelligent Systems, volume 22, issue 6, 2012, Pages 729~734
DOI : 10.5391/JKIIS.2012.22.6.729
Clustering analysis has been widely used in numerous applications like pattern recognition, data analysis, intrusion detection, image processing, bioinformatics and so on. Much of previous work has been based on the numeric data only. However, symbolic data analysis has emerged to deal with variables that can have intervals, histograms, and even functions as values. In this paper, we propose a non symmetric proximity based clustering approach for symbolic objects. A method for clustering symbolic patterns based on the average similarity value(ASV) is explored. The results of the proposed clustering method differ from those of the existing methods and the results are very encouraging.
Design of Optimized Radial Basis Function Neural Networks Classifier with the Aid of Principal Component Analysis and Linear Discriminant Analysis
Kim, Wook-Dong ; Oh, Sung-Kwun ;
Journal of Korean Institute of Intelligent Systems, volume 22, issue 6, 2012, Pages 735~740
DOI : 10.5391/JKIIS.2012.22.6.735
In this paper, we introduce design methodologies of polynomial radial basis function neural network classifier with the aid of Principal Component Analysis(PCA) and Linear Discriminant Analysis(LDA). By minimizing the information loss of given data, Feature data is obtained through preprocessing of PCA and LDA and then this data is used as input data of RBFNNs. The hidden layer of RBFNNs is built up by Fuzzy C-Mean(FCM) clustering algorithm instead of receptive fields and linear polynomial function is used as connection weights between hidden and output layer. In order to design optimized classifier, the structural and parametric values such as the number of eigenvectors of PCA and LDA, and fuzzification coefficient of FCM algorithm are optimized by Artificial Bee Colony(ABC) optimization algorithm. The proposed classifier is applied to some machine learning datasets and its result is compared with some other classifiers.
Multiple Moving Objects Detection and Tracking Algorithm for Intelligent Surveillance System
Shi, Lan Yan ; Joo, Young Hoon ;
Journal of Korean Institute of Intelligent Systems, volume 22, issue 6, 2012, Pages 741~747
DOI : 10.5391/JKIIS.2012.22.6.741
In this paper, we propose a fast and robust framework for detecting and tracking multiple targets. The proposed system includes two modules: object detection module and object tracking module. In the detection module, we preprocess the input images frame by frame, such as gray and binarization. Next after extracting the foreground object from the input images, morphology technology is used to reduce noises in foreground images. We also use a block-based histogram analysis method to distinguish human and other objects. In the tracking module, color-based tracking algorithm and Kalman filter are used. After converting the RGB images into HSV images, the color-based tracking algorithm to track the multiple targets is used. Also, Kalman filter is proposed to track the object and to judge the occlusion of different objects. Finally, we show the effectiveness and the applicability of the proposed method through experiments.
Design of Face Recognition Algorithm based Optimized pRBFNNs Using Three-dimensional Scanner
Ma, Chang-Min ; Yoo, Sung-Hoon ; Oh, Sung-Kwun ;
Journal of Korean Institute of Intelligent Systems, volume 22, issue 6, 2012, Pages 748~753
DOI : 10.5391/JKIIS.2012.22.6.748
In this paper, Face recognition algorithm is designed based on optimized pRBFNNs pattern classifier using three-dimensional scanner. Generally two-dimensional image-based face recognition system enables us to extract the facial features using gray-level of images. The environmental variation parameters such as natural sunlight, artificial light and face pose lead to the deterioration of the performance of the system. In this paper, the proposed face recognition algorithm is designed by using three-dimensional scanner to overcome the drawback of two-dimensional face recognition system. First face shape is scanned using three-dimensional scanner and then the pose of scanned face is converted to front image through pose compensation process. Secondly, data with face depth is extracted using point signature method. Finally, the recognition performance is confirmed by using the optimized pRBFNNs for solving high-dimensional pattern recognition problems.
Fuzzy LP Based Power Network Peak Shaving Algorithm
Ohn, Sungmin ; Kim, Jung-Su ; Song, Hwachang ; Chang, Byunghoon ;
Journal of Korean Institute of Intelligent Systems, volume 22, issue 6, 2012, Pages 754~760
DOI : 10.5391/JKIIS.2012.22.6.754
This paper describes peak shaving algorithms as long-term cycle scheduling in the power management system (PMS) for MW-scale battery energy storage systems (BESS). The purpose of PMS is basically to manage the input and output power from battery modules placed in the systems. Assuming that an one-day ahead load curve is provided, off-line peak shaving algorithms can be employed, but applying the results of the off-line algorithm may result in the difference in the real-time performance because there is uncertainty in the provided load curve. This paper adopts fuzzy based LP (linear programming) algorithms for describing the peak shaving algorithm in PMS and discusses a solution technique and real-time operation strategies using the solution.
A Study on Development of Maritime Traffic Assessment Model
Kim, Kwang-Il ; Jeong, Jung Sik ; Park, Gyei-Kark ;
Journal of Korean Institute of Intelligent Systems, volume 22, issue 6, 2012, Pages 761~767
DOI : 10.5391/JKIIS.2012.22.6.761
Maritime traffic assessment is important to understand the characteristics of maritime traffic and to prevent maritime accidents. The maritime traffic assessment can be calculated from the ship trajectory data observed by using AIS(Automatic Identification System). This paper developes a maritime traffic assessment tool using ship`s position and speed, course, time data from ships navigating waterways. The results are represented in terms of the number of traffic quantity and traffic distribution, speed distribution, geometric collision candidates. The developed tool will contributes to advance maritime traffic safety by VTS(Vessel Traffic Services).
Electroencephalogram-based Driver Drowsiness Detection System Using AR Coefficients and SVM
Han, Hyungseob ; Chong, Uipil ;
Journal of Korean Institute of Intelligent Systems, volume 22, issue 6, 2012, Pages 768~773
DOI : 10.5391/JKIIS.2012.22.6.768
One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a drowsiness detection system using Linear Predictive Coding (LPC) coefficients and Support Vector Machine (SVM). Samples of EEG data from each predefined state were used to train the SVM program by using the proposed feature extraction algorithms. The trained SVM program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.
Optimal EEG Channel Selection using BPSO with Channel Impact Factor
Kim, Jun-Yeup ; Park, Seung-Min ; Ko, Kwang-Eun ; Sim, Kwee-Bo ;
Journal of Korean Institute of Intelligent Systems, volume 22, issue 6, 2012, Pages 774~779
DOI : 10.5391/JKIIS.2012.22.6.774
Brain-computer interface based on motor imagery is a system that transforms a subject`s intention into a control signal by classifying EEG signals obtained from the imagination of movement of a subject`s limbs. For the new paradigm, we do not know which positions are activated or not. A simple approach is to use as many channels as possible. The problem is that using many channels causes other problems. When applying a common spatial pattern (CSP), which is an EEG extraction method, many channels cause an overfit problem, in addition there is difficulty using this technique for medical analysis. To overcome these problems, we suggest a binary particle swarm optimization with channel impact factor in order to select channels close to the most important channels as channel selection method. This paper examines whether or not channel impact factor can improve accuracy by Support Vector Machine(SVM).
Development of IR Camera based Fault Detection System for Wind Turbine Generator
Kim, Se-Yoon ; Kim, Sung-Ho ;
Journal of Korean Institute of Intelligent Systems, volume 22, issue 6, 2012, Pages 780~785
DOI : 10.5391/JKIIS.2012.22.6.780
Wind energy is currently the fastest growing source of renewable energy used for electrical generation around the world. Generally, wind turbine systems are designed to be operated for twenty years long, Therefore, various faults in the wind turbine system inevitably occur during their long term period of operation. Especially, rotor shaft, gear-box and generator are installed inside of nacelle, furthermore, some cooling systems for normal operation of these devices are also required. If these cooing systems have failed in their operation, it is impossible for the entire system to be operated normally. In this work, IR(Infra Red) camera based fault detection system for the preventive detection of various cooling systems faults is proposed. To verify the applicability of the proposed system, physical implementation is embodied and various experiments are carried out.
Integrated Model Design of Microarray Data Using miRNA, PPI, Disease Information
Ha, Kyung-Sik ; Lim, Jin-Muk ; Kim, Hong-Gee ;
Journal of Korean Institute of Intelligent Systems, volume 22, issue 6, 2012, Pages 786~792
DOI : 10.5391/JKIIS.2012.22.6.786
A microarray is a collection of thousands of DNAs or RNAs arranged on a substrate, and it enables one to navigate large amounts of gene expression. However, a researcher uses his designed experimental methods to focus on particular phenotypes from the available mass of data. In this paper, we used MicroRNAs(miRNAs) and Protein-Protein Interation(PPI) databases to enhance and expand meanings in microarray data. Further, the expanded data are linked with the Online Mendelian Inheritance in Man(OMIM), and International Statistical Classification of Diseases and Related Health Problems,
Revision(ICD-10), in order to extract common genetic relationships between diseases. This approach, we expect, should provide new biological views.
Orthonormal Polynomial based Optimal EEG Feature Extraction for Motor Imagery Brain-Computer Interface
Chum, Pharino ; Park, Seung-Min ; Ko, Kwang-Eun ; Sim, Kwee-Bo ;
Journal of Korean Institute of Intelligent Systems, volume 22, issue 6, 2012, Pages 793~798
DOI : 10.5391/JKIIS.2012.22.6.793
In this paper, we explored the new method for extracting feature from the electroencephalography (EEG) signal based on linear regression technique with the orthonormal polynomial bases. At first, EEG signals from electrodes around motor cortex were selected and were filtered in both spatial and temporal filter using band pass filter for alpha and beta rhymic band which considered related to the synchronization and desynchonization of firing neurons population during motor imagery task. Signal from epoch length 1s were fitted into linear regression with Legendre polynomials bases and extract the linear regression weight as final features. We compared our feature to the state of art feature, power band feature in binary classification using support vector machine (SVM) with 5-fold cross validations for comparing the classification accuracy. The result showed that our proposed method improved the classification accuracy 5.44% in average of all subject over power band features in individual subject study and 84.5% of classification accuracy with forward feature selection improvement.
Some Topological Structures of Ordinary Smooth Topological Spaces
Lee, Jeong Gon ; Lim, Pyung Ki ; Hur, Kul ;
Journal of Korean Institute of Intelligent Systems, volume 22, issue 6, 2012, Pages 799~805
DOI : 10.5391/JKIIS.2012.22.6.799
We introduce the notions of ordinary smooth, quasi-ordinary smooth and weak ordinary smooth structure, showing that various properties of an ordinary smooth topological space can be expressed in terms of these structures. In particular, the definitions and results of [2, 4, 5] may be expressed in terms of the ordinary smooth and quasi-ordinary smooth structures. Furthermore, we present the basic concepts relating to the weak ordinary smooth structure of an ordinary smooth topological space and the fundamental properties of the objects in these structures.