• Journal of the Korean Geographical Society
• /
• v.30 no.3
• /
• pp.296-310
• /
• 1995

Classification of spatial units into meaningful sets is an important procedure in spatial analysis. It is crucial in characterizing and identifying spatial structures. But traditional classification methods such as cluster analysis require an exact database and impose a clear-cut boundary between classes. Scrutiny of realistic classification problems, however, reveals that available infermation may be vague and that the boundary may be ambiguous. The weakness of conventional methods is that they fail to capture the fuzzy data and the transition between classes. Fuzzy subsets theory is useful for solving these problems. This paper aims to come to the understanding of theoretical foundations of fuzzy spatial analysis, and to find the characteristics of fuzzy classification methods. It attempts to do so through the literature review and the case study of urban classification of the Cities and Eups of Kyung-Nam Province. The main findings are summarized as follows: 1. Following Dubois and Prade, fuzzy information has an imprecise and/or uncertain evaluation. In geography, fuzzy informations about spatial organization, geographical space perception and human behavior are frequent. But the researcher limits his work to numerical data processing and he does not consider spatial fringe. Fuzzy spatial analysis makes it possible to include the interface of groups in classification. 2. Fuzzy numerical taxonomic method is settled by Deloche, Tranquis, Ponsard and Leung. Depending on the data and the method employed, groups derived may be mutually exclusive or they may overlap to a certain degree. Classification pattern can be derived for each degree of similarity/distance $\alpha$. By takina the values of $\alpha$ in ascending or descending order, the hierarchical classification is obtained. 3. Kyung-Nam Cities and Eups were classified by fuzzy discrete classification, fuzzy conjoint classification and cluster analysis according to the ratio of number of persons employed in industries. As a result, they were divided into several groups which had homogeneous characteristies. Fuzzy discrete classification and cluste-analysis give clear-cut boundary, but fuzzy conjoint classification delimit the edges and cores of urban classification. 4. The results of different methods are varied. But each method contributes to the revealing the transparence of spatial structure. Through the result of three kinds of classification, Chung-mu city which has special characteristics and the group of Industrial cities composed by Changwon, Ulsan, Masan, Chinhai, Kimhai, Yangsan, Ungsang, Changsungpo and Shinhyun are evident in common. Even though the appraisal of the fuzzy classification methods, this framework appears to be more realistic and flexible in preserving information pertinent to urban classification.

• Journal of Institute of Control, Robotics and Systems
• /
• v.7 no.12
• /
• pp.973-979
• /
• 2001

Sliding mode control guarantees robustness in the presence of modeling uncertainties and external disturbances. However, this can be obtained at the cost of high control activity that may lead to chattering As one way to alleviate this problem a boundary layer around sliding surface is typically used. In this case the selection of controller gain, control ban width and boundary layer thickness is a crucial problem for the trade-off between tracking error and chattering. The parameter tuning is usually done by trail-and-error in practice causing significant effort and time. An auto tuning method based on fuzzy rules is proposed in the paper in this method tracking error and chattering are monitored by performance indices and the controller tunes the design parameters intelligently in order to compromise both indices. To demonstrate the efficiency of the propose method a mass-spring translation system and a roboic control system are simulated and tested It is shown that the proposed algorithm is effective to facilitae the parameter tuning for sliding mode controllers.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.14 no.6
• /
• pp.713-719
• /
• 2004

This paper presents an interval type-2 fuzzy C-spherical shells (FCSS) algorithm that is an extension of the type-1 FCSS algorithm proposed in (1). In our proposed method, the membership values for each pattern vector are extended as interval type-2 fuzzy memberships by assigning uncertainty to the type-1 memberships. By doing so, the cluster boundary obtained by the interval type-2 FCSS can be found to be more desirable than that of type-1 FCSS in the presence of noise. Experimental results are given to show the effectiveness of our method.

• Journal of Advanced Marine Engineering and Technology
• /
• v.16 no.5
• /
• pp.39-49
• /
• 1992

The dynamic design object of rotor system is to optimize the system in stability at the operating speed, unbalance response in the vicinity of the rotor critical speed, bearing weighting and system weighting. In conventional optimization method, designers have to set mathematical modeling, such as objective function, constraints and design parameters, strictly and quantitavely. But in actual design process, they do not treat all of these values strictly and some of them are somehow "fuzziness". So, considering boundary conditions of seal diameter, clearance, and length in a typical double suction centrifugal pump is fuzzy, this paper is considered fuzzy in constraints. Fuzzy method is used .alpha.-level cut method. Then, the optimum dimensions of seal according to values are obtained and vibration characteristics are investigated.estigated.

• The Transactions of the Korean Institute of Electrical Engineers C
• /
• v.50 no.11
• /
• pp.569-574
• /
• 2001

In this paper, we described the new IEC method with fuzzy theory for detecting abnormal causes within transformer. The proposed technique presented the solution of limitation in case of lying nearly boundary conditions and not having codes for measured gas values in IEC code. Also, we proved the confidence of diagnosed results in the use of the gases values in real fault transformers.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.31B no.12
• /
• pp.132-136
• /
• 1994

Presented is the study on design and implementation of a fuzzy system to approximately reason using measured actual storage rate in irrigation reservoirs. To design Fuzzy reasoning systems. Minimum Operation Rule by Mamdani was applied. Fuzzy variable and membership functions are determined after identifying storage-rate affecting factor and followed simulation. Hydrological model to express actual situation within drought areal boundary is generally too complex. Hereby, considering irregularity of time-rate storage change during irrigation, this system uses irrigation water and meteorological data as a IN-data. It was abvious the results were closely corresponding to the actual data observed.

• Journal of the Korean Society for Precision Engineering
• /
• v.16 no.5 s.98
• /
• pp.48-57
• /
• 1999

Applications of thresholding technique are based on the assumption that object and background pixels in a digital image can be distinguished by their gray level values. For the segmentation of more complex images, it is necessary to resort to multiple threshold selection techniques. This paper describes a new method for multiple threshold selection of gray level images which are not clearly distinguishable from the background. The proposed method consists of three main stages. In the first stage, a probability distribution function for a gray level histogram of an image is derived. Cluster points are defined according to the probability distribution function. In the second stage, fuzzy partition matrix of the probability distribution function is generated through the fuzzy clustering process. Finally, elements of the fuzzy partition matrix are classified as clusters according to gray level values by using max-membership method. Boundary values of classified clusters are selected as multiple threshold. In order to verify the performance of the developed algorithm, automatic inspection process of ball grid array is presented.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.2
• /
• pp.164-170
• /
• 2002

Sliding mode control, as a typical method of variable structure control, has the robust characteristics for the uncertainty and the disturbance of the nonlinear system. Because, however, sliding mode control input includes a sign function that Is discontinuous on the predefined switching surface, its applications are primarily limited by the need of alleviation or reduction of chattering. In this paper, we propose a chattering alleviation strategy based on a special nonlinear function and a fuzzy system. By using the proposed control scheme, we can reduce the steady state error. Its tracking performance is as fast as that of conventional method using the fixed boundary layer. Especially, in the proposed method, we can adjust the trade-off between the steady state error and the degree of chattering by regulating the proper range of the output variable of the fuzzy system. To verify the validity of the proposed algorithm, the analysis of the control method using the fixed boundary layer and the computer simulations are shown to compare with them.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.24 no.2
• /
• pp.161-165
• /
• 2014

In this paper, we proposed the fuzzy prototype pattern classifier. In the proposed classifier, each prototype is defined to describe the related sub-space and the weight value is assigned to the prototype. The weight value assigned to the prototype leads to the change of the boundary surface. In order to define the prototypes, we use Fuzzy C-Means Clustering which is the one of fuzzy clustering methods. In order to optimize the weight values assigned to the prototypes, we use the Differential Evolutionary Algorithm. We use Linear Discriminant Analysis to estimate the coefficients of the polynomial which is the structure of the consequent part of a fuzzy rule. Finally, in order to evaluate the classification ability of the proposed pattern classifier, the machine learning data sets are used.

• Journal of KIISE:Software and Applications
• /
• v.31 no.9
• /
• pp.1171-1179
• /
• 2004

We describe word boundary detection that extracts the boundary between speech and non-speech. The proposed method uses two features. One is the normalized root mean square of speech signal, which is insensitive to white noises and represents temporal information. The other is the normalized met-frequency band energy of voice signal, which is frequency information of the signal. Our method detects word boundaries using a recurrent fuzzy associative memory(RFAM) that extends FAM by adding recurrent nodes. Hebbian learning method is employed to establish the degree of association between an input and output. An error back-propagation algorithm is used for teaming the weights between the consequent layer and the recurrent layer. To confirm the effectiveness, we applied the suggested system to voice data obtained from KAIST.