• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2006.05a
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• pp.352-357
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• 2006

매년 도로와 주차공간의 확장보다 차량의 수가 빠르게 증가하여 그에 따라 불법 주차 관리의 어려움이 증가하고 있다. 이러한 문제점을 해결하기 위해 지능형 주차 관리 시스템이 필요하게 되었다. 본 논문에서는 획득된 차량 영상에서 수직 에지의 특징을 이용하여 번호판 영역과 개별 코드를 추출하고, 추출된 개별 코드를 퍼지 신경망 알고리즘을 제안하여 학습 및 인식한다. 본 논문에서는 차량 번호판 영역을 검출하기 위해 프리윗 마스크를 적용하여 수직 에지를 찾고, 차량 번호판의 정보를 이용하여 잡음을 제거한 후에 차량 번호판 영역을 추출한다. 추출된 차량 번호판 영역은 반복 이진화방법을 적용하여 이진화하고, 이진화된 차량 번호판 영역에 대해서 수직 분포도와 수평 분포도를 이용하여 번호판의 개별 코드를 추출한다 추출된 개별 코드는 제안된 퍼지 신경망 알고리즘을 적용하여 인식한다. 제안된 퍼지 신경망은 입력층과 중간층간의 학습 구조로는 FCM 알고리즘을 적용하고 중간층과 출력층간의 학습 구조는 Max_Min 신경망을 적용한다. 제안된 방법의 추출 및 인식 성능을 평가하기 위하여 실제 차량 영상 150장을 대상으로 실험한 결과, 기존의 차량 번호판 인식 방법보다 효율적이고 인식 성능이 개선된 것을 확인하였다.

• The Journal of the Korea Contents Association
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• v.11 no.3
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• pp.48-55
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• 2011

In this paper, we analyze the input-output characteristics of fuzzy inference systems according to the division of entire input spaces and the fuzzy reasoning methods to identify the fuzzy model for nonlinear process. And fuzzy model is expressed by identifying the structure and parameters of the system by means of input variables, fuzzy partition of input spaces, and consequence polynomial functions. In the premise part of the rules Min-Max method using the minimum and maximum values of input data set and C-Means clustering algorithm forming input data into the hard clusters are used for identification of fuzzy model and membership function is used as a series of triangular membership function. In the consequence part of the rules fuzzy reasoning is conducted by two types of inferences. The identification of the consequence parameters, namely polynomial coefficients, of the rules are carried out by the standard least square method. And lastly, we use gas furnace process which is widely used in nonlinear process and we evaluate the performance for this nonlinear process.

• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.550-555
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• 2009

A violin plays an essential role among string instruments. It has an extremely strong expressive power that can be conveyed through beautiful timbre when played correctly. However, a beginner who attempts to play the violin will be most likely to face difficulties in placing his or her fingers on a specific point of the fingerboard or in bowing the violin on a specific string. In order to resolve this difficulty, the database of a professional violinist's bowing skills were inserted beforehand. By doing so, a beginner can emulate professional's violin by comparing his playing with the constructed database that is programmed in the computer. Accordingly, the results are displayed on the monitor after being evaluated by the performance evaluation function using a fuzzy logic.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.17-23
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• 2016

In the future network such as Internet of Things (IoT), the number of computing devices are expected to grow exponentially, and each of the things communicates with the others and acquires information by itself. Due to the growing interest in IoT applications, the broadcasting in Opportunistic ad-hoc networks such as Machine-to-Machine (M2M) is very important transmission strategy which allows fast data dissemination. In distributed networks for IoT, the energy efficiency of the nodes is a key factor in the network performance. In this paper, we propose a fuzzy logic based probabilistic multi-hop broadcast (FPMCAST) algorithm which statistically disseminates data accordingly to the remaining energy rate, the replication density rate of sending node, and the distance rate between sending and receiving nodes. In proposed FPMCAST, the inference engine is based the fuzzy rule base which is consists of 27 if-then rules. It maps input and output parameters to membership functions of input and output. The output of fuzzy system defines the fuzzy sets for rebroadcasting probability, and defuzzification is used to extract a numeric result from the fuzzy set. Here Center of Gravity (COG) method is used to defuzzify the fuzzy set. Then, the performance of FPMCAST is evaluated through a simulation study. From the simulation, we demonstrate that the proposed FPMCAST algorithm significantly outperforms flooding and gossiping algorithms. Specially, the FPMCAST algorithm has longer network lifetime because the residual energy of each node consumes evenly.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.1
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• pp.81-86
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• 2005

In this study, we introduce a new category of fuzzy inference systems based on information granulation to carry out the model identification of complex and nonlinear systems. Informally speaking, information granules are viewed as linked collections of objects (data, in particular) drawn together by the criteria of proximity, similarity, or functionality Granulation of information with the aid of Hard C-Means (HCM) clustering help determine the initial parameters of fuzzy model such as the initial apexes of the membership functions and the initial values of polynomial functions being used in the premise and consequence part of the fuzzy rules. And the initial parameters are tuned effectively with the aid of the genetic algorithms(GAs) and the least square method (LSM). An aggregate objective function with a weighting factor is also used in order to achieve a balance between performance of the fuzzy model. The proposed model is evaluated with using a numerical example and is contrasted with the performance of conventional fuzzy models in the literature.

• Journal of the Korean Institute of Intelligent Systems
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• v.7 no.2
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• pp.96-105
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• 1997

In this paper, A new design method of fuzzy modeling is presented for the model identification of nonlinear complex systems. The proposed FPNN(Fuzzy Polynomial Neural Network) modeling implements system structure and parameter identification using GMDH(Group Method of Data Handling) method and linguistic fuzzy implication rules from input and output data of processes. In order to identify premise structure and parameter of fuzzy implication rules, GMDH method and regression polynomial fuzzy reasoning method are used and the least square method is utilized for the identification of optimum consequence parameters. Time series data for gas furnace and those for wastewater treatment process are used for the purpose of evaluating the performance of the proposed FPNN modeling. The results show that the proposed method can produce the fuzzy model with higher accuracy than other works achieved previously.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.11
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• pp.7971-7977
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• 2015

This paper attempts to select the attributes of job creation and to rank them for evaluation of companies' job creation. And the results of this paper are expected to provide the information for the polices of job creation. In doing so, this paper applies fuzzy decision making analysis that reflects ambiguity and uncertainty in decision-making process. According to the results, the weight of quality of employment is similar with that of quantity of employment. In addition, annual employment growth rate, annual net employment are ranked as first and the percentage of irregular employment, the average length of employment of all workers, average monthly wages of all workers, and employment growth over sales growth rate are next ranked.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.11
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• pp.5379-5388
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• 2012

In fuzzy modeling for nonlinear process, the fuzzy rules are typically formed by selection of the input variables, the number of space division and membership functions. The Generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, complex nonlinear process can be modeled by generating the fuzzy rules by means of fuzzy division of input space. Therefore, in this paper, rules of non-fuzzy inference systems are generated by partitioning the input space in the scatter form using HCM clustering algorithm. The premise parameters of the rules are determined by membership matrix by means of HCM clustering algorithm. The consequence part of the rules is represented in the form of polynomial functions and the consequence parameters of each rule are identified by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process. Through this experiment, we showed that high-dimensional nonlinear systems can be modeled by a very small number of rules.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.4
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• pp.224-231
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• 2012

In this paper, we introduce a fuzzy inference systems based on fuzzy c-means clustering algorithm for fuzzy modeling of nonlinear process. Typically, the generation of fuzzy rules for nonlinear processes have the problem that the number of fuzzy rules exponentially increases. To solve this problem, the fuzzy rules of fuzzy model are generated by partitioning the input space in the scatter form using FCM clustering algorithm. The premise parameters of the fuzzy rules are determined by membership matrix by means of FCM clustering algorithm. The consequence part of the rules is expressed in the form of polynomial functions and the coefficient parameters of each rule are determined by the standard least-squares method. And lastly, we evaluate the performance and the nonlinear characteristics using the data widely used in nonlinear process.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.501-501
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• 2023

하천에서의 여가활동에 대한 수요가 증가함에 따라 각종 친수활동에 대한 안전도 평가가 사고예방을 위해 중요해지고 있다. 친수 활동의 안전은 수리 및 수질 인자에 크게 영향을 받지만 기존 친수지수는 수질 인자에만 집중되어 개발되어왔다. 하지만, 세일링, 패들링, 저동력보트 등 입수형 친수활동의 경우, 다양한 수리 현상에 큰 영향을 받기 때문에 유속, 흐름 방향, 수심 및 수면 폭 등의 수리인자를 친수지수에 반영할 필요가 있다. 또한, 친수활동에 위험이 되는 수리적 조건은 유량 조건과 하천의 평면적 공간에 따라 상이하게 발생하기에 이를 공간적으로 평가하는 것 역시 필요한 실정이다. 본 연구에서는 수리학적 요소를 기반으로 하천 친수 활동에 대한 안전도를 평가하기 위해 공간적으로 친수활동의 안정성을 평가할 수 있는 SRRI (Spatial River Recreation Index)를 제안하였다. SRRI의 개발을 위해 1단계에서는 다양한 유량 조건에서 EFDC 동수역학모형을 이용하여 수리 인자들의 공간적 분포를 재현한 후, 2단계에서는 퍼지합성법 (FSE)를 적용하여 수리인자의 모든 소속도와 가중치를 종합하여 하천 지점별 하천친수지수를 산정하였다. 개발한 SRRI를 낙동강-금호강 합류부에 적용한 결과, 유량 및 지형 조건에 따라 각 수리인자가 친수활동 안전성에 미치는 영향이 공간적으로 매우 상이하게 나타났다. 유향(흐름 방향)은 합류지점 부근에서 친수활동의 위험성을 크게 증가시키는 반면, 사행구간에서는 수심이 중요한 요인으로 나타났다. 고유량 조건에서는 유속이 세일링 및 패들링에서 가장 큰 영향을 미치는 요소로 작용하였다. 특히 세일링은 유량 변화에 민감하여 고유량시에는 주흐름부와 합류부 부근을 제외하고 일부 공간에서만 안전하게 이용이 가능한 것으로 나타났다. 반면 무동력 및 저동력보트는 유량 변화에 덜 민감하여 고유량 조건에서도 부분적으로 허용될 수 있었지만 사행구간의 고수심부에서는 위험 등급으로 권고되었다. 이러한 결과를 바탕으로 SRRI는 다양한 수리학적 조건을 기반으로 공간적 안전정보를 제공함으로써 많은 이용자들이 하천에서 보다 안전한 친수활동을 즐기는 데에 기여할 수 있을 것으로 판단된다.