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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 11, Issue 9 - Dec 2001
Volume 11, Issue 8 - Dec 2001
Volume 11, Issue 7 - Dec 2001
Volume 11, Issue 6 - Dec 2001
Volume 11, Issue 5 - Oct 2001
Volume 11, Issue 4 - Aug 2001
Volume 11, Issue 3 - Jun 2001
Volume 11, Issue 2 - Apr 2001
Volume 11, Issue 1 - Feb 2001
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An Image Contrast Enhancement Technique Using the Improved Integrated Adaptive Fuzzy Clustering Model
Journal of Korean Institute of Intelligent Systems, volume 11, issue 9, 2001, Pages 777~781
This paper presents an image contrast enhancement technique for improving the low contrast images using the improved IAFC(Integrated Adaptive Fuzzy Clustering) model. The low pictorial information of a low contrast image is due to the vagueness or fuzziness of the multivalued levels of brightness rather than randomness. Fuzzy image processing has three main stages, namely, image fuzzification, modification of membership values, and image defuzzification. Using a new model of automatic crossover point selection, optimal crossover point is selected automatically. The problem of crossover point selection can be considered as the two-category classification problem. The improved IAFC model is used to classify the image into two classes. The proposed method is applied to several experimental images with 256 gray levels and the results are compared with those of the histogram equalization technique. We utilized the index of fuzziness as a measure of image quality. The results show that the proposed method is better than the histogram equalization technique.
Adaptive Structure of Modular Wavelet Neural Network
Journal of Korean Institute of Intelligent Systems, volume 11, issue 9, 2001, Pages 782~787
In this paper, we propose an growing and pruning algorithm to design the adaptive structure of modular wavelet neural network(MWNN) with F-projection and geometric growing criterion. Geometric growing criterion consists of estimated error criterion considering local error and angel criterion which attempts to assign wavelet function that is nearly orthogonal to all other existing wavelet functions. There criteria provide a methodology that a network designer can constructs wavelet neural network according to one s intention. The proposed growing algorithm grows the module and the size of modules. Also, the pruning algorithm eliminates unnecessary node of module or module from constructed MWNN to overcome the problem due to localized characteristics of wavelet neural network which is used to modules of MWNN. We apply the proposed constructing algorithm of the adaptive structure of MWNN to approximation problems of 1-D function and 2-D function, and evaluate the effectiveness of the proposed algorithm.
A Study on the Adaptive Fuzzy Nonlinear VSS
Journal of Korean Institute of Intelligent Systems, volume 11, issue 9, 2001, Pages 788~792
Although the general sliding model control has the robust property, bounds on the disturbances and parameter variations should be known a prior to the designer of the control system. However, these bounds may not be easily obtained. Fuzzy logic provides an effective way to design a controller of the system with disturbances and parameter variations. Therefore, combination of the best feature of the fuzzy logic control and the sliding mode control is considered. In this paper, the adaptive fuzzy variable structure controller developed for variables of fuzzy logic. A variable length pendulum system is used to demonstrate the availability of the proposed algorithm.
Stabilization Analysis for Switching-Type Fuzzy-Model-Based Controller
Journal of Korean Institute of Intelligent Systems, volume 11, issue 9, 2001, Pages 793~800
This paper deals with a new design methodology for a switching-type fuzzy-model-based controller in continuous and discrete-time system. Takagi-Sugeno (TS) fuzzy model is employed to design the switching-type fuzzy-model-based controller. A switching-type fuzzy-model-based controller is constructed based on the spirit of “divide and conquer”. The global system which has several rules in divided into several subsystems and then, a solution is found at each subsystem. The global solution is determined by a conjunction of the solutions of each subsystem. The design conditions are formulated in terns of linear matrix inequalities (LMIs), which guarantee the stabilization of a given TS fuzzy system. Simulation examples are included for ensuring the proposed control method.
Self-Recognition Algorithm of Artificial Immune System
Journal of Korean Institute of Intelligent Systems, volume 11, issue 9, 2001, Pages 801~806
According as many people use a computer newly, damage of computer virus and hacking is rapidly increasing by the crucial users A computer virus is one of program in computer and has abilities of self reproduction ad destruction like a virus of biology. And hacking is to rob a person's data in a intruded computer and to delete data in a person s computer from the outside. To block hacking that is intrusion of a person s computer and the computer virus that destroys data, a study for intrusion-detection of system and virus detection using a biological immune system is in progress. In this paper, we make a model of positive selection and negative selection of self-recognition process that is ability of T-cytotoxic cell that plays an important part in biological immune system. So we embody a self-nonself distinction algorithm in computer, which is an important part when we detect an infected data by computer virus and a modified data by intrusion from the outside. The composed self-recognition process distinguishes self-file from the changed files. To prove the efficacy of self-recognition algorithm, we use simulation by a cell change and a string change of self file.
Intelligent Diagnosis System for an Electronic Weighting Machine
Journal of Korean Institute of Intelligent Systems, volume 11, issue 9, 2001, Pages 807~810
Election Weighting Machine is used an electronic scale which has many trouble because of broken load cells. In this paper, we propose an Intelligent Diagnosis System will for an electronic weighting machine using fuzzy logic. It s purpose be detect of the load cell s trouble. The electronic circuit of system, which call junction box , will be connected resistance in a series at circuit of Wheatstone Bridge for monitoring the condition of load cells.
A Study on Improving the Effectiveness Using Term Reweighting for Information Retreival
Journal of Korean Institute of Intelligent Systems, volume 11, issue 9, 2001, Pages 811~816
Spare Representation Learning of Kernel Space Using the Kernel Relaxation Procedure
Journal of Korean Institute of Intelligent Systems, volume 11, issue 9, 2001, Pages 817~821
In this paper, a new learning methodology for kernel methods that results in a sparse representation of kernel space from the training patterns for classification problems is suggested. Among the traditional algorithms of linear discriminant function, this paper shows that the relaxation procedure can obtain the maximum margin separating hyperplane of linearly separable pattern classification problem as SVM(Support Vector Machine) classifier does. The original relaxation method gives only the necessary condition of SV patterns. We suggest the sufficient condition to identify the SV patterns in the learning epoches. For sequential learning of kernel methods, extended SVM and kernel discriminant function are defined. Systematic derivation of learning algorithm is introduced. Experiment results show the new methods have the higher or equivalent performance compared to the conventional approach.
Realtime Face Recognition by Analysis of Feature Information
Journal of Korean Institute of Intelligent Systems, volume 11, issue 9, 2001, Pages 822~826
The statistical analysis of the feature extraction and the neural networks are proposed to recognize a human face. In the preprocessing step, the normalized skin color map with Gaussian functions is employed to extract the region of face candidate. The feature information in the region of the face candidate is used to detect the face region. In the recognition step, as a tested, the 120 images of 10 persons are trained by the backpropagation algorithm. The images of each person are obtained from the various direction, pose, and facial expression. Input variables of the neural networks are the geometrical feature information and the feature information that comes from the eigenface spaces. The simulation results of 10 persons show that the proposed method yields high recognition rates.
GA based Sequential Fuzzy Modeling Using Fuzzy Equalization and Linguistic Hedge
Journal of Korean Institute of Intelligent Systems, volume 11, issue 9, 2001, Pages 827~832
In this paper, we propose a sequentially optimization method for fuzzy inference system using fuzzy equalization and linguistic hedge. The fuzzy equalization does not require the usual learning step for generating fuzy rules. However, it is too sensitive for the given input-output data set. So, we adopt a sequential scheme which sequentially optimizes the fuzzy inference system. Here, the parameters of fuzzy membership function obtained from the fuzzy equalization are optimized by the genetic algorithm, and then they are also modified to increase the performance index using the linguistic hedge. Finally, we applied it to rice taste data and got better results than previous ones.
Diagnosis of the Drill Wear Based on Fuzzy Logic
Journal of Korean Institute of Intelligent Systems, volume 11, issue 9, 2001, Pages 833~836
One of the most important technology in Factory Automation and Unmanned Automation is to construct the diagnostic system for manufacturing process. To improve the productivity in cutting process, the state of tools such as bite, drill, endmill should be monitored continuously. In this study, fuzzy logic was used to check the wear of drill in drilling process. The input variables to construct the fuzzy rules are cutting force and the rate of cutting force's change. The experiment was done with the fixed spindle speed and feed rate in cutting condition. The proposed algorithm is verified by comparing Fuzzy wear with real wear measured.
Representative Keyword Extraction from Few Documents through Fuzzy Inference
Journal of Korean Institute of Intelligent Systems, volume 11, issue 9, 2001, Pages 837~843
In this work, we propose a new method of extracting and weighting representative keywords(RKs) from a few documents that might interest a user. In order to extract RKs, we first extract candidate terms and them choose a number of terms called initial representative keywords (IRKs) from them through fuzzy inference. Then, by expanding and reweighting IRKs using term co-occurrence similarity, the final RKs are obtained. Performance of our approach is heavily influenced by effectiveness of selection method of IRKs so that we choose fuzzy inference because it is more effective in handling the uncertainty inherent in selecting representative keywords of documents. The problem addressed in this paper can be viewed as the one of calculating center of document vectors. So, to show the usefulness of our approach, we compare with two famous methods - Rocchio and Widrow-Hoff - on a number of documents collections. The result show that our approach outperforms the other approaches.
Knowledge-Based Unmanned Automation and Control Systems for the Wastewater Treatment Processes
Journal of Korean Institute of Intelligent Systems, volume 11, issue 9, 2001, Pages 844~848
This paper introduces unmaned fully automation systems, which are applied for the CSTR(Continuously Stirred Tank Reactor) and SBR (Sequencing Batch Reactor) wastewater treatment system. The pilot plant is constructed in the country side which is little far from a main city. So networks and wireless modules are employed for the data transmission. The SBR plant has a local control and the remote monitoring system which is contained communication parts which consist of ADSL (Asymmetric Digital Subscriber Line) network and CDMA (Code Division Multiple Access) Wireless module. Remote control and monitoring systems are constructed at laboratory in a metropolis.
Bayesian Model for Probabilistic Unsupervised Learning
Journal of Korean Institute of Intelligent Systems, volume 11, issue 9, 2001, Pages 849~854
GTM(Generative Topographic Mapping) model is a probabilistic version of the SOM(Self Organizing Maps) which was proposed by T. Kohonen. The GTM is modelled by latent or hidden variables of probability distribution of data. It is a unique characteristic not implemented in SOM model, and, therefore, it is possible with GTM to analyze data accurately, thereby overcoming the limits of SOM. In the present investigation we proposed a BGTM(Bayesian GTM) combined with Bayesian learning and GTM model that has a small mis-classification ratio. By combining fast calculation ability and probabilistic distribution of data of GTM with correct reasoning based on Bayesian model, the BGTM model provided improved results, compared with existing models.
QUASI FUZZY CONNECTEDNESS BETWEEN FUZZY SETS
Park, Jin-Han ; Park, Jin-Keun ; Son, Mi-Jung ;
Journal of Korean Institute of Intelligent Systems, volume 11, issue 9, 2001, Pages 855~858
In this paper the concept of fuzzy connectedness between fuzzy sets  is generalized to fuzzy bitopological spaces and some of its properties are studied.