Go to the main menu
Skip to content
Go to bottom
REFERENCE LINKING PLATFORM OF KOREA S&T JOURNALS
> Journal Vol & Issue
Journal of Korean Institute of Intelligent Systems
Journal Basic Information
Journal DOI :
Korean Institute of Intelligent Systems
Editor in Chief :
Volume & Issues
Volume 17, Issue 7 - Dec 2007
Volume 17, Issue 6 - Dec 2007
Volume 17, Issue 5 - Oct 2007
Volume 17, Issue 4 - Aug 2007
Volume 17, Issue 3 - Jun 2007
Volume 17, Issue 2 - Apr 2007
Volume 17, Issue 1 - Feb 2007
Selecting the target year
A note on entropy defined by Choquet integral on interval-valued fuzzy sets
Jang, Lee-Chae ;
Journal of Korean Institute of Intelligent Systems, volume 17, issue 2, 2007, Pages 149~153
DOI : 10.5391/JKIIS.2007.17.2.149
In this paper, we consider interval-valued fuzzy sets which were suggested by Wang and Li(1998) and Turksen(1986) and investigate entropy defined by Choquet integral on interval-valued fuzzy sets. Furthermore, we discuss some properties of them and give some examples related this entropy. This tool has drawn much attention due to numerous applications areas, such as decision making and information theory on interval-valued fuzzy sets.
A Study on Prediction of Wake Distribution by Neuro-Fuzzy System
Shin, Sung-Chul ;
Journal of Korean Institute of Intelligent Systems, volume 17, issue 2, 2007, Pages 154~159
DOI : 10.5391/JKIIS.2007.17.2.154
Wake distribution data of stem flow fields have been accumulated systematically by model tests. If the correlation between geometrical hull information and wake distribution is grasped through the accumulated data, this correlation can be helpful to designing similar ships. In this paper, Neuro-Fuzzy system that is emerging as a new knowledge over a wide range of fields nowadays is tried to estimate the wake distribution on the propeller plan. Neuro-Fuzzy system is well known as one of prospective and representative analysis method for prediction, classification, diagnosis of real complicated world problem, and it is widely applied even in the engineering fields. For this study three-dimensional stern hull forms and nominal wake values from a model test ate structured as processing elements of input and output layer, respectively. The proposed method is proved as an useful technique in ship design by comparing measured wake distribution with predicted wake distribution.
Constructing User Preferred Anti-Spam Ontology using Data Mining Technique
Kim, Jong-Wan ; Kim, Hee-Jae ; Kang, Sin-Jae ;
Journal of Korean Institute of Intelligent Systems, volume 17, issue 2, 2007, Pages 160~166
DOI : 10.5391/JKIIS.2007.17.2.160
When a mail was given to users, each user`s response could be different according to his or her preference. This paper presents a solution for this situation by constructing a user preferred ontology for anti-spam systems. To define an ontology for describing user behaviors, we applied associative classification mining to study preference information of users and their responses to emails. Generated classification rules can be represented in a formal ontology language. A user preferred ontology can explain why mail is decided to be spam or ron-spam in a meaningful way. We also suggest a new rule optimization procedure inspired from logic synthesis to improve comprehensibility and exclude redundant rules.
An Introduction of Two-Step K-means Clustering Applied to Microarray Data
Park, Dae-Hoon ; Kim, Youn-Tae ; Kim, Sung-Shin ; Lee, Choon-Hwan ;
Journal of Korean Institute of Intelligent Systems, volume 17, issue 2, 2007, Pages 167~172
DOI : 10.5391/JKIIS.2007.17.2.167
Long gene sequences and their products have been studied by many methods. The use of DNA(Deoxyribonucleic acid) microarray technology has resulted in an enormous amount of data, which has been difficult to analyze using typical research methods. This paper proposes that mass data be analyzed using division clustering with the K-means clustering algorithm. To demonstrate the superiority of the proposed method, it was used to analyze the microarray data from rice DNA. The results were compared to those of the existing K-meansmethod establishing that the proposed method is more useful in spite of the effective reduction of performance time.
A Design of Fire Monitoring System Based On Unmaned Helicopter and Sensor Network
Yun, Dong-Yol ; Kim, Sung-Ho ;
Journal of Korean Institute of Intelligent Systems, volume 17, issue 2, 2007, Pages 173~178
DOI : 10.5391/JKIIS.2007.17.2.173
Recently, fires happen to occur owing to various factors. However, the demage caused by the fire is eyer increasing because timely actions could not be taken. To reduce the demage, a development of fire detection system which makes it possible to take adequate actions is requited. In this work, a sensor network-based fire detection system which utilizes both sensor nodes equipped with smoke sensor and unmaned helicopter is proposed. The proposed system is composed of unmaned helicopter which can gather the measurement data from the deployed sensor nodes and the embedded system which can get visual information on the firing spot and transmit these images to a remote server computer. The proposed system is applied to actual test bed to verify its feasibility.
Context-aware application for smart home based on Bayesian network
Chung, Woo-Yong ; Kim, Eun-Tai ;
Journal of Korean Institute of Intelligent Systems, volume 17, issue 2, 2007, Pages 179~184
DOI : 10.5391/JKIIS.2007.17.2.179
This paper deals with a context-aware application based on Bayesian network in the smart home. Bayesian network is a powerful graphical tool for learning casual dependencies between various context events and obtaining probability distributions. So we can recognize the resident`s activities and home environment based on it. However as the sensors become various, learning the structure become difficult. We construct Bayesian network simple and efficient way with mutual information and evaluated the method in the virtual smart home.
A Fault Detection system Design for Uncertain Nonlinear Systems
Yoo, Seog-Hwan ; Choi, Byung-Jae ;
Journal of Korean Institute of Intelligent Systems, volume 17, issue 2, 2007, Pages 185~189
DOI : 10.5391/JKIIS.2007.17.2.185
This paper deals with a fault detection system design for nonlinear systems with uncertain time varying parameters modelled as a T-S fuzzy system. A coprime factorization for T-S fuzzy systems is defined and a residual generator is designed using a left coprime factor. A fault detection criteria derived from the residual generator is also suggested. In order to demonstrate the efficacy of the suggested method, the fault defection method is applied to an inverted pendulum system and computer simulations are performed.
Control of Ubiquitous Environment using Sensors Module
Jung, Tae-Min ; Choi, Woo-Kyung ; Kim, Seong-Joo ; Jeon, Hong-Tae ;
Journal of Korean Institute of Intelligent Systems, volume 17, issue 2, 2007, Pages 190~195
DOI : 10.5391/JKIIS.2007.17.2.190
As Ubiquitous era comes, it became necessary to construct environment which can provide more useful information to human in the spaces where people live like homes or offices. On this account, network of the peripheral devices of Ubiquitous should constitute efficiently. For it, this paper researched human pattern by classified motion recognition using sensors module data. (This data processing by Neural network and fuzzy algorithm.) This pattern classification can help control home network system communication. I suggest the system which can control home network system more easily through patterned movement, and control Ubiquitous environment by grasp human`s movement and condition.
A Density Estimation based Fuzzy C-means Algorithm for Image Segmentation
Ko, Jeong-Won ; Choi, Byung-In ; Rhee, Frank Chung-Hoon ;
Journal of Korean Institute of Intelligent Systems, volume 17, issue 2, 2007, Pages 196~201
DOI : 10.5391/JKIIS.2007.17.2.196
The Fuzzy E-means (FCM) algorithm is a widely used clustering method that incorporates probabilitic memberships. Due to these memberships, it can be sensitive to noise data. In this paper, we propose a new fuzzy C-means clustering algorithm by incorporating the Parzen Window method to include density information of the data. Several experimental results show that our proposed density-based FCM algorithm outperforms conventional FCM especially for data with noise and it is not sensitive to initial cluster centers.
The Definition of Context-Ontology for Context-Awareness Framework in Ubiquitous Environment
Lee, Ki-Chul ; Kim, Jung-Hoon ; Kim, Dong-Moon ; Lee, Jee-Hyong ;
Journal of Korean Institute of Intelligent Systems, volume 17, issue 2, 2007, Pages 202~207
DOI : 10.5391/JKIIS.2007.17.2.202
Ontology has many merits such as high expression power and extensibility etc. and there are some suggestion on development of ubiquitous environment using ontology. For collecting and analysing the context information in ubiquitous environment, we need a context-awareness system. For these reason, some context-awareness systems have been developed using ontology, but they have insufficient representation power of context information and are independent to domain. In this paper, we define the context type to improve the extensibility of context framework, and develop the context ontology adopting the defined contort type to represent or inference various context information. We define the ontology to be easily adopted the and domain. Also, we use SWRL to represent numerical formulas and Horn-Logic expression.
Object Position Tracking Algorithm of Intelligent Robot using Sound Source and Absolute Orientation
Park, Kyoung-Jin ; Lee, Hae-Gang ; Jang, In-Hun ; Sim, Kwee-Bo ;
Journal of Korean Institute of Intelligent Systems, volume 17, issue 2, 2007, Pages 208~213
DOI : 10.5391/JKIIS.2007.17.2.208
As recent research on home service robot has been performed actively in these days. It becomes very important for the robot to react upon voice and sound source, and then tracks an object position in dynamic environment like a home. When people choose a path for finding a destination of objects, in case of sound, they track a direction of the sound source. Or in case as a position of the object be girded with a point on map, people track the position according to absolute orientation of the present position and the sound source position. In this paper, In this manner we had views on what people decide own direction when they react one`s voice or go some directions. We suggest a algorithm that intelligent mobile robots on which we installed a sound source tracking board and a digital magnetic compass board go some object`s positions by the direction of sound source and absolute orientation.
Magnetic Position Sensing System for Autonomous Vehicle and Robot Guidance
Jung, Young-Yoon ; Kim, Geun-Mo ; Ryoo, Young-Jae ;
Journal of Korean Institute of Intelligent Systems, volume 17, issue 2, 2007, Pages 214~219
DOI : 10.5391/JKIIS.2007.17.2.214
In this paper, a new magnetic position sensing mettled for autonomous vehicle and robot guidance is presented. In autonomous vehicle and robot control, position sensing is an important task for the identification of their locations, such as the current position within a trajectory. The magnet based autonomous vehicle and robot was identified position via magnetic materials. In the magnetic sensing system, the Earth field is one of the largest disturbance. To removal of the Earth field, this paper proposes 1-dimensional magnetic field sensors array and develops precise petition sensing system using linear operating region of the magnetic field sensor. This proposal is verified a feasible magnetic position sensing system for autonomous vehicle and robot guidance by the experimental results.
Input Variables Selection by Principal Component Analysis and Mutual Information Estimation
Cho, Yong-Hyun ; Hong, Seong-Jun ;
Journal of Korean Institute of Intelligent Systems, volume 17, issue 2, 2007, Pages 220~225
DOI : 10.5391/JKIIS.2007.17.2.220
This paper presents an efficient input variable selection method using both principal component analysis(PCA) and adaptive partition mutual information(AP-MI) estimation. PCA which is based on 2nd order statistics, is applied to prevent a overestimation by quickly removing the dependence between input variables. AP-MI estimation is also applied to estimate an accurate dependence information by equally partitioning the samples of input variable for calculating the probability density function. The proposed method has been applied to 2 problems for selecting the input variables, which are the 7 artificial signals of 500 samples and the 24 environmental pollution signals of 55 samples, respectively. The experimental results show that the proposed methods has a fast and accurate selection performance. The proposed method has also respectively better performance than AP-MI estimation without the PCA and regular partition MI estimation.
Colored Object Extraction using Fuzzy Neural Network
Kim, Yong-Soo ; Chung, Seung-Won ;
Journal of Korean Institute of Intelligent Systems, volume 17, issue 2, 2007, Pages 226~231
DOI : 10.5391/JKIIS.2007.17.2.226
This paper presents a method of colored object extraction from an image using the fuzzy neural network. Fuzzy neural network divides an image into two clusters. It extracts the prototypes of Cb and Cr of object and background by controlling the vigilance parameter. The proposed method extracted object regardless of the position, the size, and the intensity of object. We compared the performance of the proposed method with that of the method of using subjective threshold value. And, we compared the performance of the proposed method with that of the method of using subjective threshold value by using several images with added noises.
Job Level Determination of Organizational Member Using Fuzzy Theory
Heo, Sik ; Hwang, Seung-Gook ;
Journal of Korean Institute of Intelligent Systems, volume 17, issue 2, 2007, Pages 232~237
DOI : 10.5391/JKIIS.2007.17.2.232
In this paper, we suggest the model how to evaluate the job level of the member of Nong-Hyup branch, using fuzzy subordination relation by estimating the relationship of criteria and eigenvector method. The criteria for the evaluation of job levels are divided into two groups, that is, the job group to do in Nong-Hyup and the Job demanding details group that is needed to do this job. The study method used adding weight on the job group and the present level, the itemized weight about job demanding details and the present level, the relationship the job group and the job demanding details. This paper shows that there is room for improvement in the present evaluation method, which regards the job level of each branch as equal, evaluates each branch and ranks. Therefore we will expect to utilize it a lot when the Nong-Hyup and the branchs and places of like this company are estimated.
Pattern Analysis of Organizational Leader Using Fuzzy TAM Network
Park, Soo-Jeom ; Hwang, Seung-Gook ;
Journal of Korean Institute of Intelligent Systems, volume 17, issue 2, 2007, Pages 238~243
DOI : 10.5391/JKIIS.2007.17.2.238
The TAM(Topographic Attentive Mapping) network neural network model is an especially effective one for pattern analysis. It is composed of of Input layer, category layer, and output layer. Fuzzy rule, lot input and output data are acquired from it. The TAM network with three pruning rules for reducing links and nodes at the layer is called fuzzy TAM network. In this paper, we apply fuzzy TAM network to pattern analysis of leadership type for organizational leader and show its usefulness. Here, criteria of input layer and target value of output layer are the value and leadership related personality type variables of the Egogram and Enneagram, respectively.
Personalized Media Control Method using Probabilistic Fuzzy Rule-based Learning
Lee, Hyong-Euk ; Kim, Yong-Hwi ; Lee, Tae-Youb ; Park, Kwang-Hyun ; Kim, Yong-Soo ; Cho, Joon-Myun ; Bien, Z. Zenn ;
Journal of Korean Institute of Intelligent Systems, volume 17, issue 2, 2007, Pages 244~251
DOI : 10.5391/JKIIS.2007.17.2.244
Intention reading technique is essential to provide personalized services toward more convenient and human-friendly services in complex ubiquitous environment such as a smart home. If a system has knowledge about an user`s intention of his/her behavioral pattern, the system can provide mote qualified and satisfactory services automatically in advance to the user`s explicit command. In this sense, learning capability is considered as a key function for the intention reading technique in view of knowledge discovery. In this paper, ore introduce a personalized media control method for a possible application iii a smart home. Note that data pattern such as human behavior contains lots of inconsistent data due to limitation of feature extraction and insufficiently available features, where separable data groups are intermingled with inseparable data groups. To deal with such a data pattern, we introduce an effective engineering approach with the combination of fuzzy logic and probabilistic reasoning. The proposed learning system, which is based on IFCS (Iterative Fuzzy Clustering with Supervision) algorithm, extract probabilistic fuzzy rules effectively from the given numerical training data pattern. Furthermore, an extended architectural design methodology of the learning system incorporating with the IFCS algorithm are introduced. Finally, experimental results of the media contents recommendation system are given to show the effectiveness of the proposed system.
A Study on the Face Recognition Using PCA Algorithm
Lee, John-Tark ; Kueh, Lee-Hui ;
Journal of Korean Institute of Intelligent Systems, volume 17, issue 2, 2007, Pages 252~258
DOI : 10.5391/JKIIS.2007.17.2.252
In this paper, a face recognition algorithm system using Principal Component Analysis (PCA) is proposed. The algorithm recognized a person by comparing characteristics (features) of the face to those of known individuals of Intelligent Control Laboratory (ICONL) face database. Simulations are carried out to investigate the algorithm recognition performance, which classified the face as a face or non-face and then classified it as known or unknown one. Particularly, a Principal Components of Linear Discriminant Analysis (PCA + LDA) face recognition algorithm is also proposed in order to confirm the recognition performances and the adaptability of a proposed PCA for a certain specific system.
The Target Searching Method in the Chaotic Mobile Robot Embedding BVP Model
Bae, Young-Chul ; Kim, Yi-Gon ; Koo, Young-Duk ;
Journal of Korean Institute of Intelligent Systems, volume 17, issue 2, 2007, Pages 259~264
DOI : 10.5391/JKIIS.2007.17.2.259
In this paper, we composed chaos mobile robot by embedding many type of chaos circuit including Arnold Equation and Chua`s Equation and proposed method of evaluation of obstacles when it meets or approaches an obstacle while the mobile robot searches an any plane with chaos trajectory and method of concentrating search when it faces target and verified these results. For obstacles avoidance, we developed algorithm that evades an obstacles with chaos trajectory by assuming fixed obstacle, obstacles using VDP model, hidden obstacles using BVP model as obstacles and for searching an object, we developed algorithm of searching with a chaos trajectory by assuming BVP model as an object, verified the results and confirmed reasonability of them.
Gastric Cancer Extraction of Electronic Endoscopic Images using IHb and HSI Color Information
Kim, Kwang-Baek ; Lim, Eun-Kyung ; Kim, Gwang-Ha ;
Journal of Korean Institute of Intelligent Systems, volume 17, issue 2, 2007, Pages 265~269
DOI : 10.5391/JKIIS.2007.17.2.265
In this paper, we propose an automatic extraction method of gastric cancer region from electronic endoscopic images. We use the brightness and saturation of HSI in removing noises by illumination and shadows by the crookedness occurring in the endoscopic process. We partition the image into several areas with similar pigments of hemoglobin using IHb. The candidate areas for gastric cancer are defined as the areas that have high hemoglobin pigments and high value in every channel of RGB. Then the morphological characteristics of gastric cancer are used to decide the target region. In experiment, our method is sufficiently accurate in that it correctly identifies most cases (18 out of 20 cases) from real electronic endoscopic images, obtained by expert endoscopists.
Storing and Retrieving Motion Capture Data based on Motion Capture Markup Language and Fuzzy Search
Lee, Sung-Joo ; Chung, Hyun-Sook ;
Journal of Korean Institute of Intelligent Systems, volume 17, issue 2, 2007, Pages 270~275
DOI : 10.5391/JKIIS.2007.17.2.270
Motion capture technology is widely used for manufacturing animation since it produces high quality character motion similar to the actual motion of the human body. However, motion capture has a significant weakness due to the lack of an industry wide standard for archiving and retrieving motion capture data. In this paper, we propose a framework to integrate, store and retrieve heterogeneous motion capture data files effectively. We define a standard format for integrating different motion capture file formats. Our standard format is called MCML (Motion Capture Markup Language). It is a markup language based on XML (eXtensible Markup Language). The purpose of MCML is not only to facilitate the conversion or integration of different formats, but also to allow for greater reusability of motion capture data, through the construction of a motion database storing the MCML documents. We propose a fuzzy string searching method to retrieve certain MCML documents including strings approximately matched with keywords. The method can be used to retrieve desired series of frames included in MCML documents not entire MCML documents.
Structure Identification of a Neuro-Fuzzy Model Can Reduce Inconsistency of Its Rulebase
Wang, Bo-Hyeun ; Cho, Hyun-Joon ;
Journal of Korean Institute of Intelligent Systems, volume 17, issue 2, 2007, Pages 276~283
DOI : 10.5391/JKIIS.2007.17.2.276
It has been shown that the structure identification of a neuro-fuzzy model improves their accuracy performances in a various modeling problems. In this paper, we claim that the structure identification of a neuro-fuzzy model can also reduce the degree of inconsistency of its fuzzy rulebase. Thus, the resulting neuro-fuzzy model serves as more like a structured knowledge representation scheme. For this, we briefly review a structure identification method of a neuro-fuzzy model and propose a systematic method to measure inconsistency of a fuzzy rulebase. The proposed method is applied to problems or fuzzy system reproduction and nonlinear system modeling in order to validate our claim.