• The Transactions of the Korea Information Processing Society
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• v.4 no.10
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• pp.2485-2492
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• 1997

Nowadays the traffic conditions have been getting worse due to continuous increase in the number of vehicles. So it has become more important to manage traffic signal lights efficiently. Recently fuzzy logic is introduced to control the cycle time of traffic lights adaptively. Conventional fuzzy logic controller adjusts the extension time of current green phase by using the fuzzy input variables such as the number of entering vehicles at the green light and the number of waiting vehicle during the red light. However this scheme is inadequate for an intersection with variable traffic densities. In this paper, a new FLC with asymmetric membership functions that reflects more exactly traffic flows than other FLCs with symmetric ones regardless of few control rules is propsed. The effectiveness of the proposed method was shown through simulation of a single intersection. The experimental results yielded the superior performance of the proposed FLC in terms of the average delay time, the number of passed vehicles, and the degree of saturation.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.3
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• pp.27-36
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• 2004

Detection and recognition of human faces in images can be considered as an important aspect for applications that involve interaction between human and computer. In this paper, we propose a face recognition method using eigenfaces and fuzzy neural networks. The Principal Components Analysis (PCA) is one of the most successful technique that have been used to recognize faces in images. In this technique the eigenvectors (eigenfaces) and eigenvalues of an image is extracted from a covariance matrix which is constructed form image database. Face recognition is Performed by projecting an unknown image into the subspace spanned by the eigenfaces and by comparing its position in the face space with the positions of known indivisuals. Based on this technique, we propose a new algorithm for face recognition consisting of 5 steps including preprocessing, eigenfaces generation, design of fuzzy membership function, training of neural network, and recognition. First, each face image in the face database is preprocessed and eigenfaces are created. Fuzzy membership degrees are assigned to 135 eigenface weights, and these membership degrees are then inputted to a neural network to be trained. After training, the output value of the neural network is intupreted as the degree of face closeness to each face in the training database.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.4
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• pp.563-568
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• 2007

This paper presents a methodology for predicting nonlinear time series based on the neural network with weighted fuzzy membership functions (NEWFM). The degree of classification intensity is obtained by bounded sum of weighted fuzzy membership functions extracted by NEWFM, then weighted average defuzzification is used for predicting nonlinear time series. The experimental results demonstrate that NEWFM has the classification capability of 92.22% against the target class of GDP. The time series created by NEWFM model has a relatively close approximation to the GDP which is a typical business cycle indicator, and has been proved to be a useful indicator which has the turning point forecasting capability of average 12 months in the peak point and average 6 months in the trough point during 5th to 8th cyclical period. In addition, NEWFM measures the efficiency of the economic indexes by the feature selection and enables the users to forecast with reduced numbers of 7 among 10 leading indexes while improving the classification rate from 90% to 92.22%.

• The Journal of the Korea Contents Association
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• v.9 no.7
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• pp.1-8
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• 2009

In this paper, we propose a home medical system that integrates a self-diagnosing disease classification system and a tele-consulting system by communication technology. The proposed disease classification system supports to self-diagnose the health condition based on oriental medical science using fuzzy neural network algorithm. The prepared database includes 72 different diseases and their associated symptoms based on a famous medical science book "Dong-eui-bo-gam". The proposed system extracts three most prospective diseases from user's symptoms by analyzing disease database with fuzzy neural network technology. Technically, user's symptoms are used as an input vector and the clustering algorithm based upon a fuzzy neural network is performed. The degree of fuzzy membership is computed for each probable cluster and the system infers the three most prospective diseases with their degree of membership. Such information should be sent to medical doctors via our tele-consulting system module. Finally a user can take an appropriate consultation via video images by a medical doctor. Oriental medical doctors verified the accuracy of disease diagnosing ability and the efficacy of overall system's plausibility in the real world.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.10 no.4
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• pp.722-727
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• 2009

The technology of distinguishing human motion states is required in the areas of measuring and analyzing biosignals changing according to physical activities, diagnosing sleep disorder, screening the effect of treatment, examining chronic patients' kinetic state, prescribing exercise therapy, etc. The present study implemented a fuzzy inference system based on fuzzy rules that distinguish human motion states (tying, sitting, walking, and running) by acquiring and processing data of LAA, TAA, L-MAD, and T-MAD using ADXL202AE of Analog Devices embedded in an armband. The membership degree and fuzzy rules in each area of input (LAA, TAA, L-MAD, and T-MAD) and output (tying, sitting, walking, and running) data used here were determined using numeric data obtained from experiment. In the results of analyzing data for simulation generated in order of tying$\rightarrow$walking$\rightarrow$running$\rightarrow$tying, the sorting rate for motion states tying, sitting, walking, and running was 100% for each motion.

• The KIPS Transactions:PartB
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• v.8B no.5
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• pp.463-468
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• 2001

We propose a new similarity measure based on relative membership function (RMF). In this paper, the RMF is suggested to represent the relativity between fuzzy subsets easily. Since the shape of the RMF is determined according to the values of its parameters, we can easily represent the relativity between fuzzy subsets by adjusting only the values of its parameters. Hence, we can easily reflect the relativity among individuals or cultural differences when we represent the subjectivity by using the fuzzy subsets. In this case, these parameters may be regarded as feature points for determining the structure of fuzzy subset. In the sequel, the degree of similarity between fuzzy subsets can be quickly computed by using the parameters of the RMF. We use Euclidean distance to compute the degree of similarity between fuzzy subsets represented by the RMF. In the meantime, we present a new linguistic approximation method as an application area of the proposed similarity measure and show its numerical example.

• Journal of Korea Trade
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• v.27 no.3
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• pp.161-178
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• 2023

Purpose - purpose of this paper is to analyze the effects of the investment facilitation levels of 11 RCEP countries (excluding Myanmar, Brunei, and Laos due to lack of data) on China's outward foreign direct investments(OFDI) using balanced panel data from 2010 to 2019. Design/methodology - First, four investment facilitation measurement indicators (regulatory environment, infrastructure, financial market, ease of doing business) were selected,investment facilitation scores of the 11 countries were obtained using the principal component analysis, an investment gravity model was established with nine explanatory variables (investment facilitation level, market size, population, geographic distance, degree of opening, tax level, natural resources, whether the country is an APEC member or not, and whether a valid bilateral investment treaty with China has been concluded) were used to establish an investment gravity model, and regression analyses were conducted with OLS and system GMM. Findings - The results of the regression analyses showed that investment facilitation levels had the greatest effect on China's OFDI, all four first-level indicators had positive effects on China's OFDI, and among them, the institutional environment had the greatest effect. In addition, it was shown that explanatory variables such as market size, population, geographical distance, degree of openness, natural resources, and whether or not a valid bilateral investment treaty has been concluded would have positive effects on China's OFDI, while tax levels and APEC membership would impede China's OFDI to some extent. Originality/value - Since the Regional Comprehensive Economic Partnership (RCEPT) came into effect not long ago, there are not so many studies on the effects of investment facilitation levels of RCEP member states on China's OFDI, and the investment facilitation measurement index constructed in this paper is relatively systematic and scientific because it includes all the contents of investment facilitation related to the life cycle of company's foreign direct investments.

• Journal of the Korea Society of Computer and Information
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• v.14 no.12
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• pp.55-62
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• 2009

This paper proposes a decision method of fuzzy weights by utilizing degrees of contribution in order to classify insect footprint patterns having difficulties to classify species clearly. Insect footprints revealed delicately in the form of scattered spots since they are very small. Therefore it is not easy to define shape of footprints unlike other species, and there are lots of noises in the footprint patterns so that it is difficult to distinguish those from correct data. For these reasons, the extracted feature set has obvious feature values with some uncertain feature values, so we estimate weights according to degrees of contribution. If the one of feature values has distinct difference enough to decide a class among other classes, high weight is assigned to make classification. A calculated weight determines the membership values by fuzzy functions and objects are classified into the class having a superior value.atu present experimental resultseighrontribution. Iinsect footprints with noises by the proposed method.

• International Journal of Control, Automation, and Systems
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• v.6 no.6
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• pp.928-938
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• 2008

This study presents an approach for approximation an unknown function from a numerical data set based on the synthesis of a neuro-fuzzy model. An adaptive input data space parting method, which is used for building hyperbox-shaped clusters in the input data space, is proposed. Each data cluster is implemented here as a fuzzy set using a membership function MF with a hyperbox core that is constructed from a min vertex and a max vertex. The focus of interest in proposed approach is to increase degree of fit between characteristics of the given numerical data set and the established fuzzy sets used to approximate it. A new cutting procedure, named NCP, is proposed. The NCP is an adaptive cutting procedure using a pure function $\Psi$ and a penalty function $\tau$ for direction the input data space parting process. New algorithms named CSHL, HLM1 and HLM2 are presented. The first new algorithm, CSHL, built based on the cutting procedure NCP, is used to create hyperbox-shaped data clusters. The second and the third algorithm are used to establish adaptive neuro- fuzzy inference systems. A series of numerical experiments are performed to assess the efficiency of the proposed approach.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.6
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• pp.731-737
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• 2007

In this paper, we propose a system for analyzing nutrition status based on hierarchical fuzzy inference approach, where the hierarchical fuzzy approach used to analyze the transition process on the nutritional status from an obesity degree, the previous nutritional status, and the eating pattern with an individual. Moreover we discussed about the selection method of fuzzy membership intervals of the next layer to improve the reliability of inference results in hierarchical fuzzy system, where their intervals are modified by using statistical information of the defuzzified results obtained from the previous layer. To show the effectiveness of this system, we evaluated the nutritional status from the information of anthropometric measurement, biochemical test, and INQ on 113 people over the age of 65, and also analyzed their nutritional status.