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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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Korean Institute of Intelligent Systems
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Volume & Issues
Volume 14, Issue 7 - Dec 2004
Volume 14, Issue 6 - Oct 2004
Volume 14, Issue 5 - Aug 2004
Volume 14, Issue 4 - Jul 2004
Volume 14, Issue 2 - Apr 2004
Volume 14, Issue 1 - Feb 2004
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Neural Network-Based Sensor Fault Diagnosis in the Gas Monitoring System
Lee, In-Soo ; Cho, Jung-Hwan ; Shim, Chang-Hyun ; Lee, Duk-Dong ; Jeon, Gi-Joon ;
Journal of Korean Institute of Intelligent Systems, volume 14, issue 1, 2004, Pages 1~8
DOI : 10.5391/JKIIS.2004.14.1.001
In this paper, we propose neural network-based fault diagnosis method to diagnose of sensor in the gas monitoring system. In the proposed method, using thermal modulation of operating temperature of sensor, the signal patterns are extracted from the voltage of load resistance. Also, ART2 neural network is used for fault isolation. The performance and effectiveness of the proposed ART2 neural network based fault diagnosis method are shown by simulation results using real data obtained from the gas monitoring system.
Learning for Environment and Behavior Pattern Using Recurrent Modular Neural Network Based on Estimated Emotion
Kim, Seong-Joo ; Choi, Woo-Kyung ; Kim, Yong-Min ; Jeon, Hong-Tae ;
Journal of Korean Institute of Intelligent Systems, volume 14, issue 1, 2004, Pages 9~14
DOI : 10.5391/JKIIS.2004.14.1.009
Rational sense is affected by emotion. If we add the factor of estimated emotion by environment information into robots, we may get more intelligent and human-friendly robots. However, various sensory information and pattern classification are prescribed for robots to learn emotion so that the networks are suitable for the necessity of robots. Neural network has superior ability to extract character of system but neural network has defect of temporal cross talk and local minimum convergence. To solve the defects, many kinds of modular neural networks have been proposed because they divide a complex problem into simple several subproblems. The modular neural network, introduced by Jacobs and Jordan, shows an excellent ability of recomposition and recombination of complex work. On the other hand, the recurrent network acquires state representations and representations of state make the recurrent neural network suitable for diverse applications such as nonlinear prediction and modeling. In this paper, we applied recurrent network for the expert network in the modular neural network structure to learn data pattern based on emotional assessment. To show the performance of the proposed network, simulation of learning the environment and behavior pattern is proceeded with the real time implementation. The given problem is very complex and has too many cases to learn. The result will show the performance and good ability of the proposed network and will be compared with the result of other method, general modular neural network.
Optimal solution search method by using modified local updating rule in Ant Colony System
Hong, Seok-Mi ; Chung, Tae-Choong ;
Journal of Korean Institute of Intelligent Systems, volume 14, issue 1, 2004, Pages 15~19
DOI : 10.5391/JKIIS.2004.14.1.015
Ant Colony System(ACS) is a meta heuristic approach based on biology in order to solve combinatorial optimization problem. It is based on the tracing action of real ants which accumulate pheromone on the passed path and uses as communication medium. In order to search the optimal path, ACS requires to explore various edges. In existing ACS, the local updating rule assigns the same pheromone to visited edge. In this paper, our local updating rule gives the pheromone according to the number of visiting times and the distance between visited cities. Our approach can have less local optima than existing ACS and find better solution by taking advantage of more informations during searching.
A Gerber-Character Recognition System with Multiple Recognizers and a Verifier
Oh, Hye-Won ; Park, Tae-Hyoung ;
Journal of Korean Institute of Intelligent Systems, volume 14, issue 1, 2004, Pages 20~27
DOI : 10.5391/JKIIS.2004.14.1.020
We propose the character recognition system for Gerber files. The Gerber file is the vector-formatted drawing file for PCB manufacturing, which includes various symbols, figures and characters. Also, the characters are written in horizontal, vertical, and reverse-vortical directions. In this paper, we newly propose the Gerber-character recognition system to recognize all of component names located in PCB. To improve the performance, we develop the multiple recognizers by neural networks and the verifier considering the structural features. The developed system has been installed to the auto-programming software for PCB assembly and inspection machines.
The error character Revision System of the Korean using Semantic relationship of sentence component
Park, Hyun-Jae ; Park, Hae-Sun ; Kang, One-Il ; Sohn, Young-Sun ;
Journal of Korean Institute of Intelligent Systems, volume 14, issue 1, 2004, Pages 28~32
DOI : 10.5391/JKIIS.2004.14.1.028
Till now, Korean spelling proofreading system has corrected words of a sentence from the relationship of a collocation or the grammatical information of the sentence. In this paper, we propose a system that corrects a word using the relationship among the sememes in a single sentence and substitutes an apt word for a word of the sentence that has the meaningful mistake by a mistyping. The proposed system makes several sentences that are able to communicate with each sememe. The substantives forms meaning tree according to the meaning of the word and the predicate of a sentence defines the meaningful relationship between a substantives of the subject and the object. After this system compares and analyzes the relationship of meaning, it corrects the mistyping of a word in a single sentence that includes an error. If the system finds out the semantic error by the mistyping, it applies the spelling proofreading method that proposed in this paper.
On comonotonically additive interval-valued functionals and interval-valued Choquet integrals(II)
Jang, Lee-Chae ; Kim, Tae-Kyun ; Jeon, Jong-Duek ;
Journal of Korean Institute of Intelligent Systems, volume 14, issue 1, 2004, Pages 33~38
DOI : 10.5391/JKIIS.2004.14.1.033
In this paper, we will define comonotonically additive interval-valued functionals which are generalized comonotonically additive real-valued functionals in Schmeidler and Narukawa, and prove some properties of them. And we also investigate some relations between comonotonically additive interval-valued functionals and interval-valued Choquet integrals on a suitable function space, cf.[9,10,11,13].
Blind Measurement of Blocking Artifacts in Block-based DCT Image Coder
Chung, Tae-Yun ; Park, Sung-Wook ;
Journal of Korean Institute of Intelligent Systems, volume 14, issue 1, 2004, Pages 39~45
DOI : 10.5391/JKIIS.2004.14.1.039
This paper proposes a new blind measurement model of blocking artifacts. This model plays an important role in the assessment and enhancement of image quality caused by block-based DCT coding system. The proposed model can measure blocking artifacts without reference to original images and consider the HVS based visual model such as frequency sensitivity and channel masking effect to detect and measure overall blocking artifacts quantitatively. The experimental results show that the proposed model is highly effective in measuring blocking artifacts.
TS Fuzzy Classifier Using A Linear Matrix Inequality
Kim, Moon-Hwan ; Joo, Young-Hoon ; Park, Jin-Bae ;
Journal of Korean Institute of Intelligent Systems, volume 14, issue 1, 2004, Pages 46~51
DOI : 10.5391/JKIIS.2004.14.1.046
his paper presents a novel design technique for the TS fuzzy classifier via linear matrix inequalities(LMI). To design the TS fuzzy classifier built by the TS fuzzy model, the consequent parameters are determined to maximize the classifier's performance. Differ from the conventional fuzzy classifier design techniques, convex optimization technique is used to resolve the determination problem. Consequent parameter identification problems are first reformulated to the convex optimization problem. The convex optimization problem is then efficiently solved by converting linear matrix inequality problems. The TS fuzzy classifier has the optimal consequent parameter via the proposed design procedure in sense of the minimum classification error. Simulations are given to evaluate the proposed fuzzy classifier; Iris data classification and Wisconsin Breast Cancer Database data classification. Finally, simulation results show the utility of the integrated linear matrix inequalities approach to design of the TS fuzzy classifier
Fuzzy Support Vector Machine for Pattern Classification of Time Series Data of KOSPI200 Index
Lee, S.Y. ; Sohn, S.Y. ; Kim, C.E. ; Lee, Y.B. ;
Journal of Korean Institute of Intelligent Systems, volume 14, issue 1, 2004, Pages 52~56
DOI : 10.5391/JKIIS.2004.14.1.052
The Information of classification and estimate about KOSPI200 index`s up and down in the stock market becomes an important standard of decision-making in designing portofolio in futures and option market. Because the coming trend of time series patterns, an economic indicator, is very subordinate to the most recent economic pattern, it is necessary to study the recent patterns most preferentially. This paper compares classification and estimated performance of SVM(Support Vector Machine) and Fuzzy SVM model that are getting into the spotlight in time series analyses, neural net models and various fields. Specially, it proves that Fuzzy SVM is superior by presenting the most suitable dimension to fuzzy membership function that has time series attribute in accordance with learning Data Base.
Word Separation in Handwritten Legal Amounts on Bank Check by Measuring Gap Distance Between Connected Components
Kim, In-Cheol ;
Journal of Korean Institute of Intelligent Systems, volume 14, issue 1, 2004, Pages 57~62
DOI : 10.5391/JKIIS.2004.14.1.057
We have proposed an efficient method of word separation in a handwritten legal amount on bank check based on the spatial gaps between the connected components. The previous gap measures all suffer from the inherent problem of underestimation or overestimation that causes a deterioration in separation performance. In order to alleviate such burden, we have developed a modified version of each distance measure. Also, 4 class clustering based method of integrating three different types of distance measures has been proposed to compensate effectively the errors in each measure, whereby further improvement in performance of word separation is expected. Through a series of word separation experiments, we found that the modified distance measures show a better performance with over 2 - 3% of the word separation rate than their corresponding original distance measures. In addition, the proposed combining method based on 4-class clustering achieved further improvement by effectively reducing the errors common to two of three distance measures as well as the individual errors.
A Study on Vehicle Tracking System for Intelligent Transport System
Seo, Chang-Jin ; Yang, Hwang-Kyu ;
Journal of Korean Institute of Intelligent Systems, volume 14, issue 1, 2004, Pages 63~68
DOI : 10.5391/JKIIS.2004.14.1.063
In this paper, we propose a method about the extraction of vehicle and tracking trajectory for moving vehicle tracking system in road. This system applied to the monitoring system of the traffic flow for ATMS(advanced traffic management system) of ITS(intelligent transport system). Also, this system can solve the problem of maintenance of loop sensor. And we detected vehicle using differential image analysis. Because of the road environment changes by real time. Therefore, the method to use background image is not suitable. And we used Kalman filter and innovation value and variable search area for vehicle tracking system. Previous method using fixed search area is sensitive to the moving trajectory and the speed of vehicle. Simulation results show that proposed method increases the possibility of traffic measurement more than fixed area traffic measurement system.
A Sequencing Problem with Fuzzy Preference Relation and its Genetic Algorithm-based Solution
Lee, Keon-Myung ; Sohn, Bong-Ki ;
Journal of Korean Institute of Intelligent Systems, volume 14, issue 1, 2004, Pages 69~74
DOI : 10.5391/JKIIS.2004.14.1.069
A sequencing problem is to find an ordered sequence of some entities which maximizes (or minimize) the domain specific objective function. As some typical examples of sequencing problems, there are traveling salesman problem, job shop scheduling, flow shop scheduling, and so on. This paper introduces a new type of sequencing problems, named a sequencing problem with fuzzy preference relation, where a fuzzy preference relation is provided for the evaluation of the quality of sequences. It presents how such a problem can be formulated in terms of objective function. It also proposes a genetic algorithm applicable to such a sequencing problem.
Adaptive Intrusion Detection Algorithm based on Learning Algorithm
Sim, Kwee-Bo ; Yang, Jae-Won ; Lee, Dong-Wook ; Seo, Dong-Il ; Choi, Yang-Seo ;
Journal of Korean Institute of Intelligent Systems, volume 14, issue 1, 2004, Pages 75~81
DOI : 10.5391/JKIIS.2004.14.1.075
Signature based intrusion detection system (IDS), having stored rules for detecting intrusions at the library, judges whether new inputs are intrusion or not by matching them with the new inputs. However their policy has two restrictions generally. First, when they couldn`t make rules against new intrusions, false negative (FN) errors may are taken place. Second, when they made a lot of rules for maintaining diversification, the amount of resources grows larger proportional to their amount. In this paper, we propose the learning algorithm which can evolve the competent of anomaly detectors having the ability to detect anomalous attacks by genetic algorithm. The anomaly detectors are the population be composed of by following the negative selection procedure of the biological immune system. To show the effectiveness of proposed system, we apply the learning algorithm to the artificial network environment, which is a computer security system.
Pattern Classification for Biomedical Signal using BP Algorithm and SVM
Kim, Man-Sun ; Lee, Sang-Yong ;
Journal of Korean Institute of Intelligent Systems, volume 14, issue 1, 2004, Pages 82~87
DOI : 10.5391/JKIIS.2004.14.1.082
ECG consists of various waveforms of electric signals of heat. Datamining can be used for analyzing and classifying the waveforms. Conventional studies classifying electrocardiogram have problems like extraction of distorted characteristics, overfitting, etc. This study classifies electrocardiograms by using BP algorithm and SVM to solve the problems. As results, this study finds that SVM provides an effective prohibition of overfitting in neural networks and guarantees a sole global solution, showing excellence in generalization performance.
Quotient semiring of a k-semiring by semiprimary k-fuzzy ideals
Kim, Chang-Bum ;
Journal of Korean Institute of Intelligent Systems, volume 14, issue 1, 2004, Pages 88~92
DOI : 10.5391/JKIIS.2004.14.1.088
In this paper, we define and study the semiprimary k-fuzzy ideals in a commutative k-semiring and characterize the quotient semiring R/A of a k-semiring R by a semiprimary k-fuzzy ideal A. In particular, we show that every zero divisor of R/A is nilpotent.
Recognition of the Korean Alphabet using Phase Synchronization of Neural Oscillator
Lee, Joon-Tark ; Bum, Kwon-Yong ;
Journal of Korean Institute of Intelligent Systems, volume 14, issue 1, 2004, Pages 93~99
DOI : 10.5391/JKIIS.2004.14.1.093
Neural oscillator can be applied to oscillatory systems such as analyses of image information, voice recognition and etc. Conventional EBPA (Error back Propagation Algorithm) is not proper for oscillatory systems with the complicate input`s patterns because of its tedious training procedures and sluggish convergence problems. However, these problems can be easily solved by using a synchrony characteristic of neural oscillator with PLL(Phase Locked Loop) function and by using a simple Hebbian learning rule. Therefore, in this paper, a technique for Recognition of the Korean Alphabet using Phase Synchronized Neural Oscillator was introduced.
Some properties of fuzzy net-convergences
Kim, Yong-Chan ; Ko, Jung-Mi ;
Journal of Korean Institute of Intelligent Systems, volume 14, issue 1, 2004, Pages 100~104
DOI : 10.5391/JKIIS.2004.14.1.100
In this paper, we introduce the fuzzy limit degrees in L-fuzzy topologies using complete MV-algebras. We investigate the properties of net-convergences (the degrees of fuzzy convergent, fuzzy cluster, fuzzy adherent points and fuzzy limits).
DNA Computing Adopting DNA coding Method to solve Traveling Salesman Problem
Kim, Eun-Gyeong ; Yun, Hyo-Gun ; Lee, Sang-Yong ;
Journal of Korean Institute of Intelligent Systems, volume 14, issue 1, 2004, Pages 105~111
DOI : 10.5391/JKIIS.2004.14.1.105
DNA computing has been using to solve TSP (Traveling Salesman Problems). However, when the typical DNA computing is applied to TSP, it can`t efficiently express vertices and weights of between vertices. In this paper, we proposed ACO (Algorithm for Code Optimization) that applies DNA coding method to DNA computing to efficiently express vertices and weights of between vertices for TSP. We applied ACO to TSP and as a result ACO could express DNA codes which have variable lengths and weights of between vertices more efficiently than Adleman`s DNA computing algorithm could. In addition, compared to Adleman`s DNA computing algorithm, ACO could reduce search time and biological error rate by 50% and could search for a shortest path in a short time.
Fuzzy Sliding Mode Control of Nonlinear System Based on T-S Fuzzy Dynamic Model
Yoo, Byung-Kook ; Yang, Keun-Ho ;
Journal of Korean Institute of Intelligent Systems, volume 14, issue 1, 2004, Pages 112~117
DOI : 10.5391/JKIIS.2004.14.1.112
This paper suggests the design and analysis of the fuzzy sliding mode control for a nonlinear system using Takagi-Sugeno(T-S) fuzzy model. In this control scheme, identifying procedure that the input gain matrices in a T-S fuzzy model are manipulated into the same one is needed. The input disturbances generated in the identifying procedure are resolved by incorporating the disturbance treatment method of the conventional sliding mode control. The proposed control strategy can also treat the input disturbances that can not be linearized in the linearization procedure of T-S fuzzy modeling. Design example for the nonlinear system, an inverted pendulum on a cart, demonstrates the utility and validity of the proposed control scheme.
Wavelet Neural Network Based Indirect Adaptive Control of Chaotic Nonlinear Systems
Choi, Yoon-Ho ; Choi, Jong-Tae ; Park, Jin-Bae ;
Journal of Korean Institute of Intelligent Systems, volume 14, issue 1, 2004, Pages 118~124
DOI : 10.5391/JKIIS.2004.14.1.118
In this paper, we present a indirect adaptive control method using a wavelet neural network (WNN) for the control of chaotic nonlinear systems without precise mathematical models. The proposed indirect adaptive control method includes the off-line identification and on-line control procedure for chaotic nonlinear systems. In the off-line identification procedure, the WNN based identification model identifies the chaotic nonlinear system by using the serial-parallel identification structure and is trained by the gradient-descent method. And, in the on-line control procedure, a WNN controller is designed by using the off-line identification model and is trained by the error back-propagation algorithm. Finally, the effectiveness and feasibility of the proposed control method is demonstrated with applications to the chaotic nonlinear systems.