• 한국항행학회논문지
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• 제12권1호
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• pp.22-27
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• 2008

본 논문에서는 기존의 MMOSPRED(MultiMedia One Step Prediction)에 의한 멀티미디어 호의 자원 요구량(채널 수)의 예측방법을 개선한 적응 MMOSPRED 기법을 제안하고, 이 기법을 사용한 멀티미디어 무선망의 호 수락제어의 성능을 분석한다. 제안된 적응기법은 자원 요구량의 예측시간 동안 고정된 표준 정규분포의 확률변수 값을 갖는 기존의 MMOSPRED 방법과는 다르게 LMS 알고리즘을 사용하여 자원의 예측 오차량을 최소화시킨다. 시뮬레이션을 통하여 제안된 방법에 의한 자원의 예측 오차량이 기존의 방법보다 감소함을 보이고, 제안된 적응예측기법을 사용한 호 수락제어는 기존의 방법보다 미래의 핸드오프 호 가 요구하는 자원의 양을 상대적으로 정확히 예측함으로써, 원하는 핸드오프 호 손실확률에서 신규 호의 수락율을 증가시킴으로써 호 수락제어의 성능이 향상됨을 보인다.

• 제어로봇시스템학회논문지
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• 제20권7호
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• pp.730-735
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• 2014

This paper introduces a GP (Genetic Programming) based robust technique for the prediction of a heavy rain newsflash. The nature of prediction for precipitation is very complex, irregular and highly fluctuating. Especially, the prediction of heavy precipitation is very difficult. Because not only it depends on various elements, such as location, season, time and geographical features, but also the case data is rare. In order to provide a robust model for precipitation prediction, a nonlinear and symbolic regression method using GP is suggested. The remaining part of the study is to evaluate the performance of prediction for a heavy rain newsflash using a GP based nonlinear regression technique in Korean regions. Analysis of the feature selection is executed and various fitness functions are proposed to improve performances. The KLAPS data of 2006-2010 is used for training and the data of 2011 is adopted for verification.

• 한국통신학회논문지
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• 제34권1A호
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• pp.27-33
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• 2009

본 논문에서는 고속 이동성을 갖는 통신 시스템에서 회귀 신경망을 기반으로 한 페이딩 신호 예측 기법을 제안하고, 이를 이용한 송신 전력 제어를 제안하였다. 회귀 신경망의 연산 결과를 해석적으로 도출하여, 신경망 특유의 회로 복잡도 문제를 해결하고, 연산된 채널 예측치를 이용하여 최대비 결합(maximum ratio combining)방식으로 여러 개의 송신 안테나에 대하여 채널 이득을 산출하고, 이 산출된 값으로 송신 안테나 각각에 대한 송신 전력을 제어하였다. 모의 실험 결과 채널 예측 기반 전력 제어를 하지 않은 것에 비해 쥐어난 성능을 나타냄을 보여준다. 기존의 대부분의 연구들이 페이딩 신호에 강인한 수신기술에 대하여 연구를 하였거나 페이딩 신호에 대한 채널 예측도 저속의 이동성에 국한되어진 것에 비하여, 제안된 채널예측 방법은 개회로 전력제어에 적용하는 경우 송신단에서 페이딩의 영향을 제거하여 신호를 송신하기 때문에 수신 단에서 여타의 요소기술들을 매우 단순하게 설계하거나 시스템의 복잡도를 획기적으로 개선시킬 수 있는 가능성을 제시하였다.

• Geomechanics and Engineering
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• 제13권5호
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• pp.861-879
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• 2017

Coal and gas outburst is a serious dynamic disaster that occurs during coal mining and threatens the lives of coal miners. Currently, coal and gas outburst is commonly predicted using single indicator and its critical value. However, single indicator is unable to fully reflect all of the factors impacting outburst risk and has poor prediction accuracy. Therefore, a more accurate prediction method is necessary. In this work, we first analyzed on-site impacting factors and precursors of coal and gas outburst; then, we constructed a Fisher discriminant analysis (FDA) index system using the gas adsorption index of drilling cutting ${\Delta}h_2$, the drilling cutting weight S, the initial velocity of gas emission from borehole q, the thickness of soft coal h, and the maximum ratio of post-blasting gas emission peak to pre-blasting gas emission $B_{max}$; finally, we studied an FDA-based multiple indicators discriminant model of coal and gas outburst, and applied the discriminant model to predict coal and gas outburst. The results showed that the discriminant model has 100% prediction accuracy, even when some conventional indexes are lower than the warning criteria. The FDA method has a broad application prospects in coal and gas outburst prediction.

• 제어로봇시스템학회논문지
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• 제15권9호
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• pp.929-938
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• 2009

This paper presents a novel control methodology for communication network based nonlinear systems with time delay nature. We construct a nominal nonlinear control law for representing a linear model and a reset control system which is aimed for corrective control strategy to compensate system error due to uncertain time delay through wireless communication network. Next, online neural control approach is proposed for overcoming nonstationary statistical nature in the network topology. Additionally, DBN (Dynamic Bayesian Network) technique is accomplished for modeling of its dynamics in terms of casuality, which is then utilized for estimating prediction of system output. We evaluate superiority and reliability of the proposed control approach through numerical simulation example in which a nonlinear inverted pendulum model is employed as a networked control system.

• 전력전자학회논문지
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• 제11권5호
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• pp.426-430
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• 2006

본 논문에서는 제어 시지연을 갖는 고성능 PI 전류제어기에 대한 새로운 예측전류 적용방법을 모색한다. 먼저 선형 영구자석 동기전동기를 사용한 선형 서보 제어시스템에 존재하는 불가피한 전류예측 오차원인을 분석하고, 전류예측 오차와 제어 시지연을 고려한 전류제어 성능 개선 방법으로 수정된 동기좌표계 비간섭 PI 전류제어기를 제안한다. 그리고 시뮬레이션 및 실험 결과를 통하여 제안된 전류제어기가 서보 제어시스템에 존재하는 전류예측 오차와 제어 시지연이 있는 경우에도 개선된 전류제어응답을 보임을 검증하였다.

• Nuclear Engineering and Technology
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• 제52권7호
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• pp.1429-1435
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• 2020

Applying an accurate parametric prediction model to identify abnormal or false pressurizer water levels (PWLs) is critical to the safe operation of marine pressurized water reactors (PWRs). Recently, deep-learning-based models have proved to be a powerful feature extractor to perform high-accuracy prediction. However, the effectiveness of models still suffers from two issues in PWL prediction: the correlations shifting over time between PWL and other feature parameters, and the example imbalance between fluctuation examples (minority) and stable examples (majority). To address these problems, we propose a cost-sensitive mechanism to facilitate the model to learn the feature representation of later examples and fluctuation examples. By weighting the standard mean square error loss with a cost-sensitive factor, we develop a Cost-Sensitive Long Short-Term Memory (CSLSTM) model to predict the PWL of PWRs. The overall performance of the CSLSTM is assessed by a variety of evaluation metrics with the experimental data collected from a marine PWR simulator. The comparisons with the Long Short-Term Memory (LSTM) model and the Support Vector Regression (SVR) model demonstrate the effectiveness of the CSLSTM.

• 전기학회논문지
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• 제59권6호
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• pp.1159-1166
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• 2010

To improve prediction quality of a nonlinear prediction system, the system's capability for uncertainty of nonlinear data should be satisfactory. This paper presents a TSK fuzzy prediction system that can consider and deal with the uncertainty of nonlinear data sufficiently. In the design procedures of the proposed system, HCBKA(Hierarchical Correlationship-Based K-means clustering Algorithm) was used to generate the accurate fuzzy rule base that can control output according to input efficiently, and the first-order difference method was applied to reflect various characteristics of the nonlinear data. Also, multiple prediction systems were designed to analyze the prediction tendencies of each difference data generated by the difference method. In addition, to enhance the prediction quality of the proposed system, an error compensation method was proposed and it compensated the prediction error of the systems suitably. Finally, the prediction performance of the proposed system was verified by simulating two typical time series examples.

• 에너지공학
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• 제4권3호
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• pp.338-343
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• 1995

The objective of this study is to predict the reliability of the electronic control module at ROD control system in nuclear power plant. Maintaining of the reliability is important issue in the complext system like nuclear plower plant, military equipment, satelite system, etc., because the failure of reliability brings etravagant economic loss and deteriorates public acceptance. In addition to the prediction of reliability, the fators affect the reliability including operating condition, environment, temperature and quality factors were analyzed and simulated. The result shows that the quality factors are more critical for the higher reliability than other two factors.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1993년도 Fifth International Fuzzy Systems Association World Congress 93
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• pp.1013-1016
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• 1993

This paper describes a new type of neuron model, the inputs of which are interfered with one another. It has a high mapping ability with only single unit. The learning speed is considerably improved compared with the conventional linear type neural networks. The proposed neuron model was successfully applied to the prediction problem of chaotic time series signal.