• 대한기계학회논문집
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• 제18권11호
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• pp.3039-3045
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• 1994

The k-$\varepsilon$ turbulence models by Launder et al.(1977, LPS) and Leschziner and Rodi(1981, LR) are modified to account for the secondary straining effect with having a generality in the present paper. The modified models are obtained by replacing the gradient Richardson number used to account for the secondary straining effect in the original models by a new parameter with a tensor-invariant correction form. These two modified models are used to predict the turbulent flow over a backward-facing step. In contrast to both standard and modified LR models, the modified LPS model is found to predict the reattachment point fairy well, as well as mean velocity, wall static pressure, turbulent kinetic energy and Reynolds shear stress in the recirculating region.

• Computers and Concrete
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• 제30권6호
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• pp.377-391
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• 2022

The use of carbon fiber-reinforced polymers (CFRP) has widely increased due to its enhancement in the ultimate strength and ductility of the reinforced concrete (RC) structures. This study presents a prediction model for the axial compressive strength and strain of normal-strength concrete cylinders confined with CFRP. Besides, soft computing approaches have been extensively used to model in many areas of civil engineering applications. Therefore, the genetic expression programming (GEP) models to predict axial compressive strength and strain of CFRP-confined concrete specimens were used in this study. For this purpose, the parameters of 283 CFRP-confined concrete specimens collected from 38 experimental studies in the literature were taken into account as input variables to predict GEP based models. Then, the results of GEP models were statistically compared with those of models proposed by various researchers. The values of R² for strength and strain of CFRP-confined concrete were obtained as 0.897 and 0.713, respectively. The results of the comparison reveal that the proposed GEP-based models for CFRP-confined concrete have the best efficiency among the existing models and provide the best performance.

• 대한산업공학회지
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• 제25권3호
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• pp.393-403
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• 1999

In this research, a reach posture prediction based on a two-segment trunk model was made. Recently, reach posture prediction models have used inverse kinematics to provide a single posture that a person naturally takes, with a single segment trunk model that had some shortcomings. A two-segment trunk model was first developed with two links; pelvis link and lumbar-thoracic link. The former refers to the link from the hip joint to L5/S1 joint while the latter does the link from L5/S1 to the shoulder joint. Second, a reach prediction model was developed using the two-segment trunk model. As a result, more reliable equations for two-segment trunk motion were obtained, and the lean direction which refers to the movement direction of the trunk was not found to have a significant effect on the two-segment trunk motion. The results also showed that the hip joint is more preferred over L5/S1 to serve as a reference point for trunk models and the reach prediction model being developed predicted the real posture accurately.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2007년도 춘계학술대회논문집
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• pp.882-886
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• 2007

People living in the large cities are exposed to high level noise due to road-traffic, railway-traffic and aircraft. Nowadays, some researches are ongoing to reduce the noise by using noise map. However it has to be decided which prediction model is the most suitable in Korea. In this study, it has been focused on railway noise prediction models which are employed in a commercial software(Sound Plan) and developed by Korea Railroad Research Institute, and comparative study of the prediction models has been made.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2000년도 가을 학술발표회 논문집
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• pp.377-384
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• 2000

The characteristics and prediction model of the shear strength for unsaturated residual soils was studied. In order to investigate the influence of the initial water content on the shear strength, unsaturated triaxial tests were carried out varying the initial water content, and the applicability of existing prediction models for the unsaturated shear strength was testified. It was shown that the soil - water characteristic curve and the shear strength of the unsaturated soil varied with the change of the initial water content. A sample compacted in the lower initial water content needs a higher suction to get the same degree of saturation while the shear strength of a sample with the lower initial water content displays a lower value. In order to apply the existing prediction models of the unsaturated shear strength to granite residual soils, a correction coefficient, α, on the internal friction angle, ø'was added.

• 한국공작기계학회논문집
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• 제17권1호
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• pp.137-143
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• 2008

Thermal fatigue life prediction for solder joints becomes the most critical issue in present microelectronic packaging industry. And lead-free solder is quickly becoming a reality in electronic manufacturing fields. This trend requires life prediction models for new solder alloy systems. This paper describes the life prediction models for SnAgCu and SnPb solder joints, based upon non-linear finite element analysis (FEA). In case of analyses of the SnAgCu solder joints, two kinds of shapes are used. As a result, it is found that the SnAgCu solder has longer fatigue life than the SnPb solder in temperature cycling analyses.

• 한국IT서비스학회지
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• 제14권4호
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• pp.121-135
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• 2015

Ensemble classification is to utilize multiple classifiers instead of using a single classifier. Recently ensemble classifiers have attracted much attention in data mining community. Ensemble learning techniques has been proved to be very useful for improving the prediction accuracy. Bagging, boosting and random subspace are the most popular ensemble methods. In random subspace, each base classifier is trained on a randomly chosen feature subspace of the original feature space. The outputs of different base classifiers are aggregated together usually by a simple majority vote. In this study, we applied the random subspace method to the bankruptcy problem. Moreover, we proposed a method for optimizing the random subspace ensemble. The genetic algorithm was used to optimize classifier subset of random subspace ensemble for bankruptcy prediction. This paper applied the proposed genetic algorithm based random subspace ensemble model to the bankruptcy prediction problem using a real data set and compared it with other models. Experimental results showed the proposed model outperformed the other models.

• Journal of information and communication convergence engineering
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• 제19권4호
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• pp.241-247
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• 2021

Particulate matter has emerged as a serious global problem, necessitating highly reliable information on the matter. Therefore, various algorithms have been used in studies to predict particulate matter. In this study, we compared the prediction performance of neural network models that have been actively studied for particulate matter prediction. Among the neural network algorithms, a deep neural network (DNN), a recurrent neural network, and long short-term memory were used to design the optimal prediction model using a hyper-parameter search. In the comparative analysis of the prediction performance of each model, the DNN model showed a lower root mean square error (RMSE) than the other algorithms in the performance comparison using the RMSE and the level of accuracy as metrics for evaluation. The stability of the recurrent neural network was slightly lower than that of the other algorithms, although the accuracy was higher.

• 한국터널지하공간학회 논문집
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• 제16권6호
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• pp.533-542
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• 2014

본 연구에서는 Gehring, CSM, NTNU 모델을 이용한 디스크 커터 수명 예측 방법과 각 모델이 가지는 특징을 살펴보았다. 디스크 커터 수명에 크게 영향을 주는 요소인 관입깊이, 암석의 일축압축강도, 마모지수의 변화가 각각의 예측 모델들에 미치는 영향을 분석하였다. 디스크 커터 수명은 1회전당 관입깊이에 선형적으로 증가하였고, 일축압축강도의 증가에 따라 감소하는 경향을 보였다. 마모지수인 CAI 값이 증가함에 따라 Gehring과 CSM 모델에서의 디스크 커터 수명은 감소하였으나, CLI 값이 증가할수록 NTNU 모델의 디스크 커터 수명은 증가하는 경향을 보였다. 그리고 실제 현장 자료를 이용하여 디스크 커터 수명을 상호 비교하였다.

• 전자공학회논문지SC
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• 제38권2호
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• pp.20-30
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• 2001

본 논문에서는 신경회로망 기반의 다중 모델을 이용한 예측제어 방법에 간하여 기술한다. 플랜트의 특정한 피라미터 값들에 대해 다중의 모델을 구성하고, 이들 중 현재 시간에서 최적의 예측 값을 제공하는 모델을 스위칭 기법으로 선택한다. 선택된 모델의 예측 값을 기반으로 비선형 프로그래밍 방법으로 현재 시간에서의 제어 입력 값을 구하여 예측제어를 수행한다. 제안한 방법을 시간지연 값이 변하거나 매개변수 값이 가변하는 시스템에 적용하여 그 유용성을 보이고, 부하가 변동하는 자기부상열차 시스템의 부상제어에 이용한 모의 실험 결과를 보인다.