• Advances in concrete construction
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• 제11권2호
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• pp.111-126
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• 2021

Concrete cracking due to brittle tension strength significantly prevents fully utilization of the materials for "flexural-shear failure" type shear walls. Theoretical and experimental studies applying fiber reinforced concrete (FRC) have achieved fruitful results in improving the seismic performance of "flexural-shear failure" reinforced concrete shear walls. To come to an understanding of an optimal design strategy and find common performance prediction method for design methodology in terms to FRC shear walls, seismic performance on shear walls with PVA and steel FRC at edge columns and plastic region are compared in this study. The seismic behavior including damage mode, lateral bearing capacity, deformation capacity, and energy dissipation capacity are analyzed on different fiber reinforcing strategies. The experimental comparison realized that the lateral strength and deformation capacity are significantly improved for the shear walls with PVA and steel FRC in the plastic region and PVA FRC in the edge columns; PVA FRC improves both in tensile crack prevention and shear tolerance while steel FRC shows enhancement mainly in shear resistance. Moreover, the tensile strength of the FRC are suggested to be considered, and the steel bars in the tension edge reaches the ultimate strength for the confinement of the FRC in the yield and maximum lateral bearing capacity prediction comparing with the model specified in provisions.

• 한국해양공학회지
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• 제38권1호
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• pp.18-29
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• 2024

Waves are a complex phenomenon in marine and coastal areas, and accurate wave prediction is essential for the safety and resource management of ships at sea. In this study, three types of machine learning techniques specialized in nonlinear data processing were used to predict the waves of Korea waters. An optimized algorithm for each area is presented for performance evaluation and comparison. The optimal parameters were determined by varying the window size, and the performance was evaluated by comparing the mean absolute error (MAE). All the models showed good results when the window size was 4 or 7 d, with the gated recurrent unit (GRU) performing well in all waters. The MAE results were within 0.161 m to 0.051 m for significant wave heights and 0.491 s to 0.272 s for periods. In addition, the GRU showed higher prediction accuracy for certain data with waves greater than 3 m or 8 s, which is likely due to the number of training parameters. When conducting marine and offshore research at new locations, the results presented in this study can help ensure safety and improve work efficiency. If additional wave-related data are obtained, more accurate wave predictions will be possible.

• 무역학회지
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• 제46권6호
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• pp.191-209
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• 2021

In this paper, various time series analysis models and machine learning models are presented for long-term prediction of export growth rate, and the prediction performance is compared and reviewed by RMSE and MAE. Export growth rate is one of the major economic indicators to evaluate the economic status. And It is also used to predict economic forecast. The export growth rate may have a negative (-) value as well as a positive (+) value. Therefore, Instead of using the ReLU function, which is often used for time series prediction of deep learning models, the PReLU function, which can have a negative (-) value as an output value, was used as the activation function of deep learning models. The time series prediction performance of each model for three types of data was compared and reviewed. The forecast data of long-term prediction of export growth rate was deduced by three forecast methods such as a fixed forecast method, a recursive forecast method and a rolling forecast method. As a result of the forecast, the traditional time series analysis model, ARDL, showed excellent performance, but as the time period of learning data increases, the performance of machine learning models including LSTM was relatively improved.

• 한국정보전자통신기술학회논문지
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• 제17권4호
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• pp.183-189
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• 2024

본 연구는 시계열 데이터를 효과적으로 예측하기 위해 데이터를 Seasonal-Trend Decomposition on Loess 을 통해 추세, 계절성, 잔차 성분으로 분해한 후 추세 성분에는 ARIMA, 계절성 성분에는 Fourier Series Regression, 잔차 성분에는 XGBoost를 적용하는 하이브리드 예측 모델을 제안하였다. 또한, ARIMA, XGBoost, LSTM, EMD-ARIMA, CEEMDAN-LSTM 모델을 포함한 성능 비교 실험을 수행하여 각 모델의 예측 성능을 평가하였다. 실험 결과, 제안된 하이브리드 모델은 MAPE, MAAPE, RMSE 지표에서 각각 3.8%, 3.5%, 0.35로 가장 좋은 평가 지표 값을 보이며 기존의 단일 모델보다 우수한 성능을 보였다.

• 한국도로학회논문집
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• 제18권1호
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• pp.91-97
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• 2016

PURPOSES : The objective of this paper is to develop a pavement performance model based on the Bayesian algorithm, and compare the measured and predicted performance data. METHODS : In this paper, several pavement types such as SMA (stone mastic asphalt), PSMA (polymer-modified stone mastic asphalt), PMA (polymer-modified asphalt), SBS (styrene-butadiene-styrene) modified asphalt, and DGA (dense-graded asphalt) are modeled in terms of the performance evaluation of pavement structures, using the Bayesian algorithm. RESULTS : From case studies related to the performance model development, the statistical parameters of the mean value and standard deviation can be obtained through the Bayesian algorithm, using the initial performance data of two different pavement cases. Furthermore, an accurate performance model can be developed, based on the comparison between the measured and predicted performance data. CONCLUSIONS : Based on the results of the case studies, it is concluded that the determined coefficients of the nonlinear performance models can be used to accurately predict the long-term performance behaviors of DGA and modified asphalt concrete pavements. In addition, the developed models were evaluated through comparison studies between the initial measurement and prediction data, as well as between the final measurement and prediction data. In the model development, the initial measured data were used.

• 대한기계학회논문집B
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• 제24권8호
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• pp.1077-1084
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• 2000

In this work, a modified stage-stacking method for the performance prediction of multi-stage axial flow compressors is proposed. The method is based on a simultaneous calculation of all interstage variables (temperature, pressure, flow velocity) instead of the conventional sequential stage-by-stage scheme. The method is also very useful in simulating the effect of changing angles of the inlet guide vane and stator vanes on the compressor operating characteristics. Generalized stage performance curves are used in presenting the performance characteristics of each stage. General assumptions enable determination of flow path data and stage design performance. Performance of various real compressors is predicted and comparison between prediction and field data validates the usefulness of the present method.

• Journal of Information Technology Applications and Management
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• 제31권1호
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• pp.11-25
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• 2024

The purpose of this study is to investigate the improvement of prediction performance and changes in variable importance in SEM-ANN two-stage analysis. 366 cosmetics repurchase-related survey data were analyzed and the results were presented. The results of this study are summarized as follows. First, in SEM-ANN two-stage analysis, SEM and ANN models were trained with train data and predicted with test data, respectively, and the R² was showed. As a result, the prediction performance was doubled from SEM 0.3364 to ANN 0.6836. Looking at this degree of R² improvement as the effect size f² of Cohen (1988), it corresponds to a very large effect at 110%. Second, as a result of comparing changes in normalized variable importance through SEM-ANN two-stage analysis, variables with high importance in SEM were also found to have high importance in ANN, but variables with little or no importance in SEM became important in ANN. This study is meaningful in that it increased the validity of the comparison by using the same learning and evaluation method in the SEM-ANN two-stage analysis. This study is meaningful in that it compared the degree of improvement in prediction performance and the change in variable importance through SEM-ANN two-stage analysis.

• Nuclear Engineering and Technology
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• 제54권8호
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• pp.3027-3033
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• 2022

Background: In this study, various types of deep-learning models for predicting in vitro radiosensitivity from gene-expression profiling were compared. Methods: The clonogenic surviving fractions at 2 Gy from previous publications and microarray gene-expression data from the National Cancer Institute-60 cell lines were used to measure the radiosensitivity. Seven different prediction models including three distinct multi-layered perceptrons (MLP), four different convolutional neural networks (CNN) were compared. Folded cross-validation was applied to train and evaluate model performance. The criteria for correct prediction were absolute error < 0.02 or relative error < 10%. The models were compared in terms of prediction accuracy, training time per epoch, training fluctuations, and required calculation resources. Results: The strength of MLP-based models was their fast initial convergence and short training time per epoch. They represented significantly different prediction accuracy depending on the model configuration. The CNN-based models showed relatively high prediction accuracy, low training fluctuations, and a relatively small increase in the memory requirement as the model deepens. Conclusion: Our findings suggest that a CNN-based model with moderate depth would be appropriate when the prediction accuracy is important, and a shallow MLP-based model can be recommended when either the training resources or time are limited.

• 유체기계공업학회:학술대회논문집
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• 유체기계공업학회 2000년도 유체기계 연구개발 발표회 논문집
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• pp.110-115
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• 2000

The mean streamline analysis using the empirical loss correlations has been developed for performance prediction of industrial mixed-flow fan impellers in the present study. New simple, but effective, models for the additional Euler input work characteristic and an internal recirculation loss due to internal flow reversal under the low flowrate conditions are proposed in this paper. Comparison of overall performance predictions with six sets of test data of mixed-flow fans is accomplished to demonstrate the accuracy of the proposed models. Predicted performance curves by the present set of loss models agree fairly well with experimental data for a variety of mixed-flow fan impellers over the entire operating conditions. The prediction method presented herein can be used efficiently in the conceptual design phase of mixed-flow fan impellers.

• 한국컴퓨터정보학회논문지
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• 제10권2호
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• pp.87-95
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• 2005

고성능 마이크로프로세서에서 값 예측기는 한 명령어의 결과를 미리 예측하여 명령들 간의 데이터 종속관계를 극복하고 실행함으로써 명령어 수준 병렬성(Instruction Level Parallelism, ILP)을 향상시키는 기법이다. 본 논문에서는 ILP 프로세서 명령어 수준 병렬성의 성능향상을 위하섞 값을 미리 예측하여 병렬로 이슈하고 수행하는 값 예측기를 비교 분석하여 각 테이블 갱신 시점에 따른 예측기별 평균 성능향상과 예측률 및 예측정확도를 측정하여 평가한다 이러한 타당성을 검증하기 위해 실행구동방식 시뮬레이터를 사용하여 SPECint95 벤치마크를 시뮬레이션하여 비교한다.