• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.5
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• pp.70-77
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• 1999

본 논문에서는 Wavelet을 이용한 위장 영상의 질환 부위 특징을 추출하여 질환 부위 패턴을 인식할 수 있는 알고리즘을 제안하였다. 전처리 과정으로서 위장 영상이 형태정보는 입력 영상을 DWT(Discrete wavelet transform)에 의해 4레벨 DWT 계수 행렬을 구하고 계수 행렬의 특징에 따라 저주파 계수 행렬로부터 저주파 특징 파라미터 32개, 수평 고주파 계수 행렬로부터 수평 고주파 특징 파라미터 16개, 수직 고주파 계수 행렬로부터 수직 고주파 특징 파라미터 16개, 그리고, 대각 고주파 계수 행렬로부터 대각 고주파 특징 파라미터 32개 등 모두 96개의 특징 파라미터를 추출한 후 각각의 특징 파라미터를 최대 값+0.5로 최소 값을 -0.5로 정규화 하여 신경회로망의 입력 벡터로 사용하였다. 위장 영상 패턴 인식을 위한 신경회로망은 교사 학습을 요구하는 다층 구조의 오차 역전파(Error back propagation)알고리즘으로 하였고 구조적 특성을 이용하여 입력층, 중간층, 출력층의 계층 구조로 설계하였다. 설계된 신경회로망의 학습은 학습계수를 0.2로 모우멘텀을 0.6으로 설정하여 출력층 최대오차가 0.01보다 작을 때까지 수행하였으며 약 8000회 정도 학습한 결과 설정값 보다 작은 결과를 얻었고 질환의 종류나 위치, 크기에 관계없이 100%의 인식률을 얻었다.

• Proceedings of the Korea Water Resources Association Conference
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• 2009.05a
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• pp.1141-1144
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• 2009

인공신경망(artificial neural network) 기법은 인간의 두뇌 신경세포의 활동을 모형화한 것으로 오랜 시간동안 발전해 왔으며 여러 분야에서 활용되고 있고 수문분야에서도 인공신경망을 이용한 연구가 활발히 진행되어 왔다. RDAPS와 같은 단기수치예보 자료는 강우의 유무 판단과 같은 정성적인 분석에서 비교적 정확도가 높지만 정확한 강우량의 추정과 같은 정량적인 부분에서는 정확도가 매우 낮으므로 인공신경망 기법과 같은 후처리 기법을 통해서 정확도를 높이게 된다. 인공신경망 기법을 수행할 때, 가장 중요한 것은 입력변수선택(input variable selection)으로 입력 변수의 적절한 선택이 결과값에 큰 영향을 주게 된다. 본 연구에서는 mutual information을 입력 변수 선택 기법으로 채택하여, 인공신경망의 입력변수 선정의 정확도를 알아보고자 한다. Mutual information은 주어진 자료의 엔트로피값을 이용하여 변수들 간의 독립과 종속의 관계를 나타내는 기법으로서, MI값은 '0'에서 '1'의 값을 가지며 '0'에 가까울수록 변수들 간의 관계가 독립적이고 '1'에 가까울수록 종속적인 관계를 나타낸다. 인공신경망의 입력변수선정에 대한 mutual information의 정확도를 알아보기 위해, 기존 입력변수선택 기법과 mutual information을 이용했을 경우의 인공신경망의 처리능력, 정확도를 비교 검토하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.47-50
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• 2000

In this paper, an optimal identification method using Multi-FNN(Fuzzy-Neural Network) is proposed for model ins of nonlinear complex system. In order to control of nonlinear process with complexity and uncertainty of data, proposed model use a HCM clustering algorithm which carry out the input-output data preprocessing function and Genetic Algorithm which carry out optimization of model. The proposed Multi-FNN is based on Yamakawa's FNN and it uses simplified inference as fuzzy inference method and Error Back Propagation Algorithm as learning rules. HCM clustering method which carry out the data preprocessing function for system modeling, is utilized to determine the structure of Multi-FNN by means of the divisions of input-output space. Also, the parameters of Multi-FNN model such as apexes of membership function, learning rates and momentum coefficients are adjusted using genetic algorithms. Also, a performance index with a weighting factor is presented to achieve a sound balance between approximation and generalization abilities of the model, To evaluate the performance of the proposed model, we use the time series data for gas furnace and the numerical data of nonlinear function.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.292-297
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• 2000

This paper proposes a new concept of coordinating green time which controls 10 traffic intersection systems. For instance, if we have a baseball game at 8 pm today, traffic volume toward the baseball game at 8 pm today, traffic volume toward the baseball game will be increased 1 hour or 1 hour and 30 minutes before the baseball game. At that time we can not predict optimal green time Even though there have smart elctro-sensitive traffic light system. Therefore, in this paper to improve average vehicle speed and reduce average vehicle waiting time, we created optimal green time using fuzzy rules and neural network. Computer simulation results proved reducing average vehicle waiting time which proposed coordinating green time better than electro-sensitive traffic light system dosen't consider coordinating green time.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.1
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• pp.61-66
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• 2002

A position control algorithm for a flexible manipulator is studied. The proposed algorithm is based on a fuzzy theory with a Steady State Genetic Algorithm(SSGA) and an Adaptive Genetic Algorithms(AGA). The proposed controller for a flexible manipulator have decreased 90.8%, 31.8%, 31.3% in error when compared with a conventional fuzzy controller, fuzzy controller using neural network, fuzzy controller using evolution strategies, respectively when the weight and the velocity of end-point are 0.8k9 and 1m/s, respectively.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.24 no.5
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• pp.16-28
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• 2010

This paper proposes maximum torque control of SynRM drive using artificial intelligent(AI)PI and artificial neural network(ANN). The control method is applicable over the entire speed range and considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal axis current for maximum torque operation is derived. The proposed control algorithm is applied to SynRM drive system controlled AIPI and ANN controller and the operating characteristics controlled by maximum torque control are examined in detail.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2008.10a
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• pp.315-318
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• 2008

In this paper proposed the solar tracking system to use a fuzzy control order to increase an output of the PV(Photovoltaic) array. The solar tracking system operated two DC motors driving by signal of photo sensor. The control of dual axes is not an easy task due to nonlinear dynamics and unavailability of the parameters. Recently, artificial intelligent control of the fuzzy control, neural-network and genetic algorithm etc. have been studied. The fuzzy control made a nonlinear dynamics to well perform and had a robust and highly efficient characteristic about a parameter variable as well as a nonlinear characteristic. Hence the fuzzy control was used to perform the tracking system after comparing with error values of setting-up, nonlinear altitude and azimuth. In this paper designed a fuzzy controller for improving output of PV array and evaluated comparison with efficient of conventional PI controller. The data which were obtained by experiment were able to show a validity of the proposed controller.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.596-599
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• 2001

Extracting of car licens plate is important for identifying the car. Since there are some problems such as poor ambient lighting problem, bad weather problem and so on, the car images we distorted and the tar license plate is difficult to be extracted. This paper proposes a method of extracting car license plate using motor vehicle regulation. In this method, some features of car license plate according to motor vehicle regulation such as color information, shape are applied to determine the candidate of car license plates. For the result of recognition by neural network, the candidate which has characters and numbers patterns according to motor vehicle regulation is certified as license-plate region. The results of the experiments with 70 samples of real car images shoe the performance of car license-plate extraction by 84.29%, and the recognition rate is 80.81%.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2001.10a
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• pp.479-482
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• 2001

IDS has been studied mainly in the field of the detection decision and collecting of audit data. The detection decision should decide whether successive behaviors are intrusions or not, the collecting of audit data needs ability that collects precisely data for intrusion decision. Artificial methods such as rule based system and neural network are recently introduced in order to solve this problem. However, these methods have simple host structures and defects that can't detect transformed intrusion patterns. So, we propose the method using data mining that can retrieve and estimate the patterns and retrieval of user's behavior in the distributed different hosts.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2002.05a
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• pp.619-622
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• 2002

IDS has been studied mainly in the field of the detection derision and collecting of audit data. The detection decision should decide whether successive behaviors are intrusions or not , the collecting of audit data needs ability that collects precisely data for intrusion decision. Artificial methods such as rule based system and neural network are recently introduced in order to solve this problem. However, these methods have simple host structures and defects that can't detect transformed intrusion patterns. So, we propose the method using data mining agent that can retrieve and estimate the patterns and retrieval of user's behavior in the distributed different hosts.