• 제어로봇시스템학회논문지
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• 제14권11호
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• pp.1180-1188
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• 2008

As many systems depend on electronics, concern for fault tolerance is growing rapidly in the safety critical system such as intelligent vehicle. In order to make system fault tolerant, there has been a body of research mainly from aerospace field including predictive hybrid redundancy by Lee. Although the predictive hybrid redundancy has the fault tolerant mechanism to satisfy the fault tolerant requirement of safety crucial system such as x-by-wire system, it suffers form the variability of prediction performance according to the input feature of system. As an alternative to the prediction method of predictive hybrid redundancy for robust fault tolerant, Kalman prediction has attracted some attention because of its well-known and often-used with its structure called Kalman hybrid redundancy. In addition, several numerical simulation results are given where the Kalman hybrid redundancy outperforms with predictive smoothing voter.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.145-145
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• 2000

In this paper, we propose a hybrid d-step predictor which is composed of an adaptive predictor and a Kalman predictor. We prove the performance limit of the proposed predictor. Simulation is conducted to examine the performance of the proposed predictor. Simulation results show that the proposed combined predictor is superior to the adaptive predictor and the Kalman predictor. Proposed predictor is used for prediction of gun tip vibration of k1 tank. The result is compared with that of conventional adaptive predictor.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 1996년도 추계학술대회발표논문집; 고려대학교, 서울; 26 Oct. 1996
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• pp.203-206
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• 1996

Prediction of firm bankruptcy have been extensively studied in accounting, as all stockholders in a firm have a vested interest in monitoring its financial performance. The objective of this paper is to develop the hybrid models for bankruptcy prediction. The proposed hybrid models are two phase. Phase one are (a) DA-assisted neural network, (b) Logit-assisted neural network, and (c) Genetic-assisted neural network. And, phase two are (a) DA-assisted Case based reasoning, and (b) Genetic-assisted Case based reasoning. In the variables selection, We are focusing on three alternative methods - linear discriminant analysis, logit analysis and genetic algorithms - that can be used empirically select predictors for hybrid model in bankruptcy prediction. Empirical results using Korean medium-sized firms data show that hybrid models are very promising neural network models and case based reasoning for bankruptcy prediction in terms of predictive accuracy and adaptability.

• Smart Structures and Systems
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• 제14권6호
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• pp.1269-1289
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• 2014

With the sub-stepping technique, the numerical analysis in real-time dynamic hybrid testing is split into the response analysis and signal generation tasks. Two target computers that operate in real-time may be assigned to implement these two tasks, respectively, for fully extending the simulation scale of the numerical substructure. In this case, the integration time-step of solving the dynamic response of the numerical substructure can be dozens of times bigger than the sampling time-step of the controller. The time delay between the real and desired feedback forces becomes more striking, which challenges the well-developed delay compensation methods in real-time dynamic hybrid testing. This paper focuses on displacement prediction and force correction for delay compensation in the real-time dynamic hybrid testing with a large integration time-step. A new displacement prediction scheme is proposed based on recently-developed explicit integration algorithms and compared with several commonly-used prediction procedures. The evaluation of its prediction accuracy is carried out theoretically, numerically and experimentally. Results indicate that the accuracy and effectiveness of the proposed prediction method are of significance.

• 한국분무공학회지
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• 제7권3호
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• pp.1-6
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• 2002

In this study numerical and experimental study on the spray atomization characteristics of a GDI injector is performed. To carry out numerical analysis, four hybrid models that are composed of conical sheet disintegration model, LISA model, DDB model, and RT model are used. The experimental results to evaluate the prediction accuracy of hybrid models are obtained by using phase Doppler particle analyzer and spray visualization system. It is shown that the prediction accuracy of hybrid model concerning spray developing process and spray tip penetration is good for all hybrid models, but the hybrid breakup models show different prediction of accuracy in the case of local radial SMD distribution.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1987년도 전기.전자공학 학술대회 논문집(II)
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• pp.1379-1382
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• 1987

IN ADAPTIVE LINEAR PREDICTION, AN ADAPTIVE CAPABILITY IS BUILT INTO THE PROCESSOR SUCH THAT AS THE IMAGE STATISTICS CHANGE, THE PREDICTION FILTER COEFFICIENTS THEMSELVES CHANGE, PRODUCING A NEW FILTER MORE CLOSELY OPTIMIZED TO THE NEW SET OF IMAGES STATISTICS. THE LMS ALGORITHM MAY BE USED TO ADAPT THE COEFFICIENT OF AN ADAPTIVE PREDICTION FILTER FOR IMAGE SOURCE ENCODING. IN THIS PAPER, TWO CODING SYSTEMS USING DPCM AND LMS ALGORITHMS RESPECTIVELY FOR OBTAINING THE FIRST TRANSFORMED COEFFICIENT IN HYBRID CODING ARE COMPARED.

• 한국컴퓨터정보학회논문지
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• 제28권2호
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• pp.201-207
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• 2023

본 논문에서는 Multi-Step Time Series의 세 가지 전략을 비교 분석하기 위해 LGBM, MLP, LSTM, GRU를 사용하여 농산물 중장기 가격 예측에 대한 최적의 모형을 제안한다. 제안 모형은 다각도로 전략을 선택하여 모델과 전략간 최적의 조합을 찾도록 설계되었다. 기존 농산물 가격 예측 연구에서는 전통 계량경제 모델인 ARIMA를 비롯하여 LSTM 계열 모델이 주로 사용된 반면 Multi-Step Time Series 관련 농산물 가격 예측 연구는 매우 제한적이다. 본 연구에서는 농산물 가격의 변동성 정도에 따라 두 개의 기간으로 나누어 실험을 진행하였으며, Direct, Hybrid, Multiple Outputs 등 세 전략의 중장기 가격 예측 결과 Hybrid 접근법이 상대적으로 우수한 성능을 보였다.본 연구 결과는 중장기 일별 가격 예측을 고도화할 수 있는 효과적인 대안을 제시한다는 측면에서 학술적, 실무적 의의를 갖는다.

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2006년도 PARALLEL CFD 2006
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• pp.275-278
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• 2006

Japan Aerospace Exploration Agency has introduced a new terascale clusterd SMP system as a main compute engine of Numerical Simulator III for aerospace science and engineering research purposes. The system is using Fujitsu PRIMEPOWER HPC2500; it has computing capability of 9.3Tflop/s peak performance and 3.6TB of user memory, with about 1,800 scalar processors for computation. In this paper, we first present the performance evaluation results for aerospace CFD applications with hybrid programming paradigm used at JAXA. Next we propose a performance prediction formula for hybrid codes based on a simple extension of AMhhal's law, and discuss about the predicted and measured performances for some typical hybrid CFD codes.

• 한국정보처리학회논문지
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• 제5권10호
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• pp.2676-2685
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• 1998

본 논문에서는 파이프라인 프로세서의 분기 명령어 처리 성능 향상을 목적으로, BTB의 미스율을 줄이고 분기 예측의 정확도를 개선하기 위해 victim cache를 활용한 2-단계 BTB 구조를 제안한다. 2-단계 BTB는 기존의 BTB에 작은 크기의 victim BTB를 추가한 구조로, 적은 비용으로 BTB 미스율을 개선하고, 동적 예측(dynamic prediction)과 정적 예측 (static prediction)이 함께 사용되는 기존의 통합 분기 예측(Hybrid Branch Prediction) 구조의 예측 정확도를 높이도록 운영된다. 본 논문에서 제안된 2-단계 BTB에 의한 성능 개선을 4개 벤치마크 프로그램에 대한 trace-driven 시뮬레이션을 통해 검증한 결과, 기존의 BTB에 비해 2.5∼8.5%의 비용 증가로 BTB 미스율이 26.5% 개선되고, 기존의 gshare에 비해 64%의 비용 증가로 예측 정확도는 26.75% 개선되었다.

• 한국경영과학회지
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• 제23권3호
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• pp.185-207
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• 1998

This research investigates computer generated hybrid second-order model of two numerically based approaches to risk classification : discriminant analysis and neural networks. The hybrid second-order models are derived by rule induction using the ID3 and tested in the several different kinds of data. This new hybrid approach is designed to combine the high prediction accuracy and robustness of DA or NN with perspicuity of ID3. The hybrid model also eliminates the problem of contradictory inputs of ID3. After doing empirical test for the validity of hybrid model using small and medium companies' bankrupt data, hybrid model shows high perspicuity, high prediction accuracy for bankrupt, and simplicity for rules. The hybrid model also shows high performance regardless the type of data such as numeric data, non-numeric data, and combined data.