Fuzzy Identification by Means of an Auto-Tuning Algorithm and a Weighted Performance Index

오성권,Oh, Sung-Kwun Korean Institute of Intelligent Systems 1998 한국지능시스템학회논문지 Vol.8 No.6

The study concerns a design procedure of rule-based systems. The proposed rule-based fuzzy modeling implements system structure and parameter identification in the efficient from of "IF..., THEN..." statements, and exploits the theory of system optimization and fuzzy implication rules. The method for rule-based fuzzy modeling concerns the from of the conclusion part of the the rules that can be constant. Both triangular and Gaussian-like membership function are studied. The optimization hinges on an autotuning algorithm that covers as a modified constrained optimization method known as a complex method. The study introduces a weighted performance index (objective function) that helps achieve a sound balance between the quality of results produced for the training and testing set. This methodology sheds light on the role and impact of different parameters of the model on its performance. The study is illustrated with the aid of two representative numerical examples.

PCA알고리즘을 이용한 최적 pRBFNNs 기반 나이트비전 얼굴인식 시스템 설계

오성권,장병희,Oh, Sung-Kwun,Jang, Byoung-Hee 대한전자공학회 2013 전자공학회논문지 Vol.50 No.9

본 연구에서는 PCA알고리즘을 이용한 최적 pRBFNNs 기반 나이트비전 얼굴인식 시스템을 설계 하고자 한다. 조명이 없는 주위 상태 하에서 조도가 낮기 때문에 CCD 카메라를 이용하여 영상을 획득하는 것이 어렵다. 본 논문에서는 낮은 조도에 의해 왜곡된 이미지의 품질을 나이트 비전 카메라와 히스토그램 평활화를 사용하여 향상시킨다. 그리고 얼굴과 비얼굴 이미지 영역 사이에서 얼굴 이미지를 검출하기 위하여 Ada-Boost 알고리즘을 사용한다. 추출된 고차원 특징 데이터를 저차원의 특징 데이터로 변환하기 위하여 데이터 차원축소 기법인 주성분 분석법(Principal Components Analysis; PCA)을 사용한다. 또한 인식 모듈로서 pRBFNNs(Polynomial- based Radial Basis Function Neural Networks) 패턴분류기를 소개한다. 제안된 다항식 기반 RBFNNs은 조건부, 결론부, 추론부 세 가지의 기능적 모듈로 구성되어 있다. 조건부는 FCM (Fuzzy C-means) 클러스터링을 사용하여 입력공간을 분할하고, 결론부는 분할된 로컬 영역을 다항식 함수로 표현한다. 그리고 차분진화 (Differential Evolution; DE) 알고리즘을 사용하여 모델의 파라미터를 최적화 한다. In this study, we propose the design of optimized pRBFNNs-based night vision face recognition system using PCA algorithm. It is difficalt to obtain images using CCD camera due to low brightness under surround condition without lighting. The quality of the images distorted by low illuminance is improved by using night vision camera and histogram equalization. Ada-Boost algorithm also is used for the detection of face image between face and non-face image area. The dimension of the obtained image data is reduced to low dimension using PCA method. Also we introduce the pRBFNNs as recognition module. The proposed pRBFNNs consists of three functional modules such as the condition part, the conclusion part, and the inference part. In the condition part of fuzzy rules, input space is partitioned by using Fuzzy C-Means clustering. In the conclusion part of rules, the connection weights of pRBFNNs is represented as three kinds of polynomials such as linear, quadratic, and modified quadratic. The essential design parameters of the networks are optimized by means of Differential Evolution.

클러스터링 방법을 이용한 TSK 퍼지추론 시스템의 설계 및 해석

오성권,Oh, Sung-Kwun 한국정보전자통신기술학회 2014 한국정보전자통신기술학회논문지 Vol.7 No.3

본 논문에서는 주어진 데이터 전처리를 통한 새로운 형태의 TSK기반 퍼지 추론 시스템을 제안한다. 제안된 모델은 주어진 데이터의 효율적인 처리를 위해 클러스터링 기법인 Fuzzy C-Means 클러스터링 방법을 이용하였다. 제안된 새로운 형태의 퍼지추론 시스템의 전반부는 FCM 을 통하여 정규화된 멤버쉽 함수와 클러스터 수를 결정하기 때문에, 멤버쉽함수의 형태 및 개수를 정의할 필요가 없어, 모델의 구조 또한 간단한 형태를 이룬다. 본 논문에서 사용된 후반부는 4가지 형태로-간략추론, 1차선형추론, 2차선형추론, 변형된 2차선형추론-가 있으며, 이는 효율적인 후반부구조를 찾는데 주도적인 역할을 한다. 또한 제안된 모델의 후반부 파라미터 계수는 Weighted Least Squares Estimation(WLSE)을 사용하여 동정하며, Least Squares Estimation(LSE)를 적용한 모델의 성능과 비교한다. 마지막으로, Boston housing 데이터를 사용하여 제안된 모델의 성능을 평가하였다. We introduce a new architecture of TSK-based fuzzy inference system. The proposed model used fuzzy c-means clustering method(FCM) for efficient disposal of data. The premise part of fuzzy rules don't assume any membership function such as triangular, gaussian, ellipsoidal because we construct the premise part of fuzzy rules using FCM. As a result, we can reduce to architecture of model. In this paper, we are able to use four types of polynomials as consequence part of fuzzy rules such as simplified, linear, quadratic, modified quadratic. Weighed Least Square Estimator are used to estimates the coefficients of polynomial. The proposed model is evaluated with the use of Boston housing data called Machine Learning dataset.

개선된 공간 탐색 알고리즘을 이용한 정보입자 기반 퍼지모델 설계

오성권(Sung-Kwun Oh),김현기(Hyun-Ki Kim) 한국지능시스템학회 2011 한국지능시스템학회논문지 Vol.21 No.6

This study is concerned with the identification of fuzzy models. To address the optimization of fuzzy model, we proposed an improved space search evolutionary algorithm (ISSA) which is realized with the combination of space search algorithm and Gaussian mutation. The proposed ISSAis exploited here as the optimization vehicle for the design of fuzzy models. Considering the design of fuzzy models, we developed a hybrid identification method using information granulation and the ISSA. Information granules are treated as collections of objects (e.g. data) brought together by the criteria of proximity, similarity, or functionality. The overall hybrid identification comes in the form of two optimization mechanisms: structure identification and parameter identification. The structure identification is supported by the ISSA and C-Means while the parameter estimation is realized via the ISSA and weighted least square error method. A suite of comparative studies show that the proposed model leads to better performance in comparison with some existing models.

최적화된 pRBF 뉴럴 네트워크에 이용한 삼상 부분방전 패턴분류에 관한 연구

오성권(Sung-Kwun Oh),김현기(Hyun-Ki Kim),김정태(Jung-Tae Kim) 대한전기학회 2013 전기학회논문지 Vol.62 No.4

In this paper, we propose the pattern classifier of Radial Basis Function Neural Networks(RBFNNs) for diagnosis of 3-phase partial discharge. Conventional methods map the partial discharge/noise data on 3-PARD map, and decide whether the partial discharge occurs or not from 3-phase or neutral point. However, it is decided based on his own subjective knowledge of skilled experter. In order to solve these problems, the mapping of data as well as the classification of phases are considered by using the general 3-PARD map and PA method, and the identification of phases occurring partial discharge/noise discharge is done. In the sequel, the type of partial discharge occurring on arbitrary random phase is classified and identified by fuzzy clustering-based polynomial Radial Basis Function Neural Networks(RBFNN) classifier. And by identifying the learning rate, momentum coefficient, and fuzzification coefficient of FCM fuzzy clustering with the aid of PSO algorithm, the RBFNN classifier is optimized. The virtual simulated data and the experimental data acquired from practical field are used for performance estimation of 3-phase partial discharge pattern classifier.

악천후 상황에서 Laser Range-Gate 방식을 이용한 원거리 영상 감시 및 추적 시스템에 대한 연구

오성권(Sung-Kwun Oh),유성훈(Sung-Hoon Yoo),구경완(Kyong-Wan Ku),김수찬(Su-Chan Kim) 대한전기학회 2013 전기학회논문지 Vol.62 No.2

In case of image observation equipments, CCTV for short distance visual field is usually installed and operated mostly as the means of crime-prevention. However, the extensive demand for monitoring problems in case of the increase in intelligent crimes and disasters has led to the necessity of the development of long-distance observation equipments embedded with Night View functions. In case of the Night View equipments, the relevant market is set up to be focused mostly on Thermal Observation Device(hereinafter, TOD), but some shortcomings such as the limitation of image visibility and excessive maintenance cost, etc. have actually caused the necessity of new long distance Night View equipment. Moreover there might follow lots of difficulties in long-distance visualization in the event that irregular reflection is generated by minute particles in the atmosphere such as fog, smog, and dust, etc. These factors are motivate the work presented in this study. Our study is aimed at the realization of Pulsed Laser Illuminator and newly proposed Range-Gated image acquisition technology. And also the implementation of Tracker for continuous trace of the objects of interest from the obtained sequence images.

얼굴인식을 위한 ELM 기반 퍼지 패턴분류기

오성권(Sung-Kwun Oh),노석범(Seok-Beom Roh) 대한전기학회 2015 대한전기학회 학술대회 논문집 Vol.2015 No.7

기상레이더를 이용한 뉴로-퍼지 알고리즘 기반 에코 분류기 설계

오성권(Sung-Kwun Oh),고준현(Jun-Hyun Ko) 대한전기학회 2014 전기학회논문지 Vol.63 No.5

In this paper, precipitation echo(PRE) and non-precipitaion echo(N-PRE)(including ground echo and clear echo) through weather radar data are identified with the aid of neuro-fuzzy algorithm. The accuracy of the radar information is lowered because meteorological radar data is mixed with the PRE and N-PRE. So this problem is resolved by using RBFNN and judgement module. Structure expression of weather radar data are analyzed in order to classify PRE and N-PRE. Input variables such as Standard deviation of reflectivity(SDZ), Vertical gradient of reflectivity(VGZ), Spin change(SPN), Frequency(FR), cumulation reflectivity during 1 hour(1hDZ), and cumulation reflectivity during 2 hour(2hDZ) are made by using weather radar data and then each characteristic of input variable is analyzed. Input data is built up from the selected input variables among these input variables, which have a critical effect on the classification between PRE and N-PRE. Echo judgment module is developed to do echo classification between PRE and N-PRE by using testing dataset. Polynomial-based radial basis function neural networks(RBFNNs) are used as neuro-fuzzy algorithm, and the proposed neuro-fuzzy echo pattern classifier is designed by combining RBFNN with echo judgement module. Finally, the results of the proposed classifier are compared with both CZ and DZ, as well as QC data, and analyzed from the view point of output performance.

지능형 알고리즘 기반 RGBW Dimming control LED 감성조명 시스템 개발

오성권(Sung-Kwun Oh),임승준(Sung-Joon Lim),마창민(Chang-Min Ma),김진율(Jin-Yul Kim) 한국지능시스템학회 2011 한국지능시스템학회논문지 Vol.21 No.3

본 연구는 감성 공학과 인공 지능 알고리즘의 하나인 퍼지 추론을 이용하여 LED 색온도 제어시스템의 체계적인 제어를 위한 퍼지 추론 기반 LED 감성 조명 시스템을 개발하고자 한다. 감성공학 영역에서 하나의 형용사 언어로 표현되는 감성과 색상과의 관계를 고려하여 감성언어를 결정하고, 인간의 뇌에서 나오는 뇌파의 파장과 색온도와의 관계를 고려하여 수업과목의 종류를 결정한다. 결정된 감성언어와 수업과목의 종류를 이용하여 RGB LED의 색온도를 조정한다. 더불어 GPS(Global Positioning System)로 위도와 경도의 정보를 이용하여 실시간으로 태양의 고도를 산출하고, 온도 및 습도센서의 정보를 이용하여 불쾌지수를 산출한다. 결과로 나온 태양의 고도와 불쾌지수의 변화에 따라 LED 조명시스템의 White LED의 조도와 RGB LED의 색온도를 조정한다. 개발된 LED 감성조명 시스템은 상황에 알맞은 분위기를 연출함으로써 학습능력과 업무능력의 효율 향상 등을 이끌어 낼 수 있을 것이다. The study uses department of the sensitivity and fuzzy reasoning, one of artificial intelligence algorithms, so that develop LED lighting system based on fuzzy reasoning for systematical control of the LED color temperature. In the area of sensitivity engineering, by considering the relation between color and emotion expressed as an adjective word, the corresponding sensitivity word can be determined, By taking into consideration the relation between the brain wave measured from the human brain and the color temperature, the preferred lesson subject can be determined. From the decision of the sensitivity word and the lesson subject, we adjust the color temperature of RGB (Red, Green, Blue) LED. In addition, by using the information of the latitude and the longitude from GPS(Global Positioning System), we can calculate the on-line moving altitude of sun. By using the sensor information of both temperature and humidity, we can calculate the discomfort index. By considering the altitude of sun as well as the value of the discomfort index, the illumination of W(white) LED and the color temperature of RGB LED can be determined. The (LED) sensitivity lighting control system is bulit up by considering the sensitivity word, the lesson subject, the altitude of sun, and the discomfort index The developed sensitivity lighting control system leads to more suitable atmosphere and also the enhancement of the efficiency of lesson subjects as well as business affairs.

실시간 이미지 획득을 통한 pRBFNNs 기반 얼굴인식 시스템 설계

오성권(Sung-Kwun Oh),석진욱(Jin-Wook Seok),김기상(Ki-Sang Kim),김현기(Hyun-Ki Kim) 제어로봇시스템학회 2010 제어·로봇·시스템학회 논문지 Vol.16 No.12

In this study, the Polynomial-based Radial Basis Function Neural Networks is proposed as one of the recognition part of overall face recognition system that consists of two parts such as the preprocessing part and recognition part. The design methodology and procedure of the proposed pRBFNNs are presented to obtain the solution to high-dimensional pattern recognition problem. First, in preprocessing part, we use a CCD camera to obtain a picture frame in real-time. By using histogram equalization method, we can partially enhance the distorted image influenced by natural as well as artificial illumination. We use an AdaBoost algorithm proposed by Viola and Jones, which is exploited for the detection of facial image area between face and non-facial image area. As the feature extraction algorithm, PCA method is used. In this study, the PCA method, which is a feature extraction algorithm, is used to carry out the dimension reduction of facial image area formed by high-dimensional information. Secondly, we use pRBFNNs to identify the ID by recognizing unique pattern of each person. The proposed pRBFNNs architecture consists of three functional modules such as the condition part, the conclusion part, and the inference part as fuzzy rules formed in "If-then" format. In the condition part of fuzzy rules, input space is partitioned with Fuzzy C-Means clustering. In the conclusion part of rules, the connection weight of pRBFNNs is represented as three kinds of polynomials such as constant, linear, and quadratic. Coefficients of connection weight identified with back-propagation using gradient descent method. The output of pRBFNNs model is obtained by fuzzy inference method in the inference part of fuzzy rules. The essential design parameters (including learning rate, momentum coefficient and fuzzification coefficient) of the networks are optimized by means of the Particle Swarm Optimization. The proposed pRBFNNs are applied to real-time face recognition system and then demonstrated from the viewpoint of output performance and recognition rate.