• The Bulletin of The Korean Astronomical Society
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• v.37 no.2
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• pp.129.1-129.1
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• 2012

서포트 벡터 머신(Support Vector Machine, SVM)과 인공신경망 모형(Neural Network, NN)을 사용하여 태양 양성자 현상(Solar proton event, SPE)의 플럭스 세기를 예측해 보았다. 이번 연구에서는 1976년부터 2011년까지 10MeV이상의 에너지를 가진 입자가 10개 cm-1 sec-1 ster -1 이상 입사할 경우를 태양 양성자 현상으로 정의한 NOAA의 태양 고에너지 입자 리스트와 GOE위성의 X-ray 플레어 데이터를 사용하였다. 여기에서 C, M, X 등급의 플레어와 관련있는 178개 이벤트를 모델의 훈련을 위한 데이터(training data) 89개와 예측을 위한 데이터(prediction data) 89개로 구분하였다. 플러스 세기의 예측을 위하여, 우리는 로그 플레어 세기, 플레어 발생위치, Rise time(플레어 시작시간부터 최대값까지의 시간)을 모델 입력인자로 사용하였다. 그 결과 예측된 로그 플럭스 세기와 관측된 로그 플럭스 세기 사이의 상관계수는 SVM과 NN에서 각각 0.32와 0.39의 값을 얻었다. 또한 두 값 사이의 평균 제곱근 오차(Root mean square error)는 SVM에서 1.17, NN에서는 0.82로 나왔다. 예측된 플럭스 세기와 관측된 플럭스 세기의 차이를 계산해 본 결과, 오차 범위가 1이하인 경우가 SVM에서는 약 68%이고 NN에서는 약 80%의 분포를 보였다. 이러한 결과로부터 우리는 NN모델이 SVM모델보다 플럭스 세기를 잘 예측하는 것을 알 수 있었다.

• Smart Structures and Systems
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• v.10 no.6
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• pp.557-572
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• 2012

This paper focuses on the vibration control of long-span reticulated steel structures under multi-dimensional earthquake excitation. The control system and strategy are constructed based on Magneto-Rheological (MR) dampers. The LQR and Hrovat controlling algorithm is adopted to determine optimal MR damping force, while the modified Bingham model (MBM) and inverse neural network (INN) is proposed to solve the real-time controlling current. Three typical long-span reticulated structural systems are detailedly analyzed, including the double-layer cylindrical reticulated shell, single-layer spherical reticulated shell, and cable suspended arch-truss structure. Results show that the proposed control strategy can reduce the displacement and acceleration effectively for three typical structural systems. The displacement control effect under the earthquake excitation with different PGA is similar, while for the cable suspended arch-truss, the acceleration control effect increase distinctly with the earthquake excitation intensity. Moreover, for the cable suspended arch-truss, the strand stress variation can also be effectively reduced by the MR dampers, which is very important for this kind of structure to ensure that the cable would not be destroyed or relaxed.

• International Journal of Naval Architecture and Ocean Engineering
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• v.10 no.6
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• pp.661-669
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• 2018

Engineering system problems consist of multi-objective optimisation and the performance analysis is generally time consuming. To optimise the system concerning its performance, many researchers perform the optimisation using an approximation model. The Response Surface Method (RSM) is usually used to predict the system performance in many research fields, but it shows prediction errors for highly nonlinear problems. To create an appropriate metamodel for marine systems, Lee (2015) compares the prediction accuracy of the approximation model, and multi-objective optimal design framework is proposed based on a confirmed approximation model. The proposed framework is composed of three parts: definition of geometry, generation of approximation model, and optimisation. The major objective of this paper is to confirm the applicability/usability of the proposed optimal design framework and evaluate the prediction accuracy based on sensitivity analysis. We have evaluated the proposed framework applicability in derrick structure optimisation considering its structural performance.

• Journal of the Semiconductor & Display Technology
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• v.19 no.2
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• pp.92-95
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• 2020

In this paper, the new architecture of seizure prediction using CNN and LSTM and DWT was presented. In the proposed architecture, EEG data was labeled into a preictal and interictal section, and DWT was adopted to the preprocessing process to apply the characteristics of the time and frequency domain of the processed EEG signal. Also, CNN was applied to extract the spatial characteristics of each electrode used for EEG measurement, and LSTM neural network was applied to verify the logical order of the preictal section. The learning of the proposed architecture utilizes the CHB-MIT Scalp EEG dataset, and the sliding window technique is applied to balance the dataset between the number of interictal sections and the number of preictal sections. As a result of the simulation of the proposed architecture, a sensitivity of 81.22% and an FPR of 0.174 were obtained.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.779-783
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• 2010

최근 들어 그 심각성을 더하고 있는 이상기후 현상으로 가용 수자원의 변동이 커지고 있으며, 이에 따라 수자원의 효율적인 운영이 요구되고 있다. 그러나 효율적인 운영을 위해서는 미래 유입량의 불확실성의 고려하고, 홍수 조절용량의 확보하면서도, 용수공급을 위한 저수량을 확보하고, 수력 발전을 해야 하는 복잡한 상황을 모두 고려하여야한다. 이러한 복잡한 시스템에서 하나의 최적화 기법으로는 모든 고려사항들을 만족시키는 최적해를 찾는 것은 사실상 불가능에 가깝다. 그러므로 저수지 운영의 최적화를 위한 연구에서 한 가지 이상의 기법을 조합하는 기법을 사용하게 되었다. 이러한 기법은 각 기법의 장점을 취하고 각각의 한계를 극복하기 위해 주로 사용되었다. 본 연구에서는 저수지 운영 최적화를 모의하기 위하여 대청댐에서의 저수위, 유입량, 용수이용량 등을 고려하여 방류량의 예측을 동적 계획법(Dynamic Programming Model)으로부터 동적 신경망(Dynamic Neural Network Model)과 적응 퍼지 제어기법(Adaptive Neuro-Fuzzy Inference System)을 개발하여 실제 방류량과 세 가지 최적화 방법에 의한 결과를 비교 검정하였다. 본 연구의 수행으로 인해 얻어진 결과를 요약하면 다음과 같다. 첫째, 동적 신경망과 적응 퍼지 제어기법에 의한 최적화 모의가 동적 계획법에 비해 시스템의 구축이 쉽고 유연하다. 둘째, 퍼지추론의 Membership 함수의 구축에 따라 단시간에 많은 양의 강우가 발생하는 국지성 강우에 대해서도 최적 방류량을 예측할 수 있다. 셋째, 저수지 운영 과거자료의 부족과 불확실성을 해결하면, 보다 용이하고 양호한 예측결과를 얻을 수 있을 것이다.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.2
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• pp.163-172
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• 2001

This study present the quadratic programming as the classification method of remote sensed data applying to the extraction of landcover and examine it's applicable capability by comparing the classification accuracy of quadratic programming with that of neural network and maximum likelihood method which are used in the extraction of thematic layer. As the results, as drawing the more improved classification results by 6% than maximum likelihood method, we could discern that the method of quadratic programming is appliable to classifying the remote sensed data. Also, in the classification of quadratic programming method, we could definitely indicate the results which was ignored in the previous extreme(binary) classification method by affecting the class decision with the class composition proportion.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.18 no.2
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• pp.157-168
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• 2000

MDD(Mountainous District Development) has more available area than flatland come into solution for many urban problem arisen from the insufficiency of urban-purpose land and space. When develope the mountainous district, the development must be a development considered the viewscape. To efficiently accomplish the MDD viewscape was considered, firstly we have to know which viewscape element effect on many observers when they rate the viewscpe preference. As a basical study for MDD viewscape was considered, we used GSIS and ANN(Artificial Neural Network) in cause analysis of viewscape preference. Many observers firstly influenced by“height difference”that represent the difference between elevation of a mountainous and buildings, and secondly floor-area-ratio has influence on viewscape preference, thirdly many observer affected by building coverage among many viewscape elecments considered in this study.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.532-538
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• 1998

In this paper, a new adaptive fuzzy inference method using neural network based fuzzy reasoning is proposed to make a fuzzy logic control system more adaptive and more effective. In most cases, the design of a fuzzy inference system rely on the method in which an expert or a skilled human operator would operate in that special domain. However, if he has not expert knowledge for any nonlinear environment, it is difficult to control in order to optimize. Thus, using the proposed adaptive structure for the fuzzy reasoning system can controled more adaptive and more effective in nonlinear environment for changing input membership functions and output membership functions. The proposed fuzzy inference algorithm is called adaptive neuro-fuzzy control(ANFC). ANFC can adapt a proper membership function for nonlinear plant, based upon a minimum number of rules and an initial approximate membership function. Nonlinear function approximation and rotary inverted pendulum control system ar employed to demonstrate the viability of the proposed ANFC.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2002.12a
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• pp.313-316
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• 2002

이동 물체의 이동 거리 추적이나 대상 물체의 인식과 판별 물체의 특징 추출과 같은 응용분야에서 컴퓨터(Computer)와 비젼시스템(vision system)을 이용한 영상 데이터 처리 분야에 대한 이용률이 증가하면서, 그에 따른 연구가 활발히 진행되고 있다. 따라서 CCD 카메라(Charge-Couple Device Camera)로부터 입력된 그레이 레벨(Gray Level)의 영상을 입력받아 처리과정을 거쳐 위치정보를 전송하는 과정에서 정확한 정보를 얻기 위한 전처리 과정 방법을 제안하고, 실제 시스템에 적용한 결과를 제시한다. 여기서 영상의 전처리 과정 중 입력 영상에서 불필요한 부분을 제거하거나, 배경과 대상물의 분리, 내포된 잡음을 없애기 위하여 흔히 이진화 방법을 많이 사용한다 특히 이진화 과정에서 그레이 레벨의 입력영상에서 히스토그램(histogram) 정보를 이용하여 영상의 이진화시의 임계값을 찾는 것은 아주 중요한 요인이다 따라서 본 논문에서는 신경회로망을 이용하여 실시간으로 CCD 카메라를 통하여 입력되는 그레이 레벨의 입력 영상에 대하여 동적으로 적당한 임계값을 .찾는 방법을 제안하고자한다. 또한 제안한 신경회로망을 이용한 임계값 추출 알고리즘(algorithms)을 구현한 시스템(system)에 적용하여 일반적인 방법과 비교 검토하고 응용 가능성을 확인한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.15 no.7
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• pp.618-627
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• 1990

In this paper a new algorithm for separation of contacting subpattern and connective feature extraction of strokes is proposed. This algorithm is able to classification of the type of contacting parts, connective feature extreaction of strokes, separate the phoneme of contacting parts between strokes, classify the character types by feature classification of connecting parts and analysis of connecting attribute. Also, shape normalize into formal patterns and decide on the input pattern from position value of bending feature of this normalized shape and make an recognition experiment by neural network using BEP learining algorithm. This algorithm represents the good achievement ratio by separation of phoneme, classification of character type, connective feature extraction of stroke and recognition experiment.