• 한국연소학회:학술대회논문집
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• 2012.11a
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• pp.119-122
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• 2012

Circulating fluided bed(CFB) furnace which can use a variety of low-grade fuels because of high heat capacity and good mixing characteristic in its furnace have turned out to be effective system. There is no many research to predict performance considering total boiler system with water-steam side. Most of performance prediction model have focused on hydrodynamics or chemical mechanism in furnace. so, This study is aimed to develop performance prediction model which consider water-steam side.

• MALSORI
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• no.57
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• pp.139-152
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• 2006

In this paper, we present a phrase break prediction method using CRF(Conditional Random Fields), which has good performance at classification problems. The phrase break prediction problem was mapped into a classification problem in our research. We trained the CRF using the various linguistic features which was extracted from POS(Part Of Speech) tag, lexicon, length of word, and location of word in the sentences. Combined linguistic features were used in the experiments, and we could collect some linguistic features which generate good performance in the phrase break prediction. From the results of experiments, we can see that the proposed method shows improved performance on previous methods. Additionally, because the linguistic features are independent of each other in our research, the proposed method has higher flexibility than other methods.

• Journal of information and communication convergence engineering
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• v.21 no.1
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• pp.82-89
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• 2023

Various machine-learning models may yield high predictive power for massive time series for time series prediction. However, these models are prone to instability in terms of computational cost because of the high dimensionality of the feature space and nonoptimized hyperparameter settings. Considering the potential risk that model training with a high-dimensional feature set can be time-consuming, we evaluate a feature-importance-based feature selection method to derive a tradeoff between predictive power and computational cost for time series prediction. We used two machine learning techniques for performance evaluation to generate prediction models from a retail sales dataset. First, we ranked the features using impurity- and Local Interpretable Model-agnostic Explanations (LIME) -based feature importance measures in the prediction models. Then, the recursive feature elimination method was applied to eliminate unimportant features sequentially. Consequently, we obtained a subset of features that could lead to reduced model training time while preserving acceptable model performance.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.102-114
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• 2021

Biofouling represents an important problem in the shipping industry since it causes the increase in surface roughness. The most of ships in the current world fleet do not have good coating condition which represents an important problem due to strict rules regarding ship energy efficiency. Therefore, the importance of the control and management of the hull and propeller fouling is highlighted by the International Maritime Organization and the maintenance schedule optimization became valuable energy saving measure. For adequate implementation of this measure, the accurate prediction of the effects of biofouling on the hydrodynamic characteristics is required. Although computational fluid dynamics approach, based on the modified wall function approach, has imposed itself as one of the most promising tools for this prediction, it requires significant computational time. However, during the maintenance schedule optimization, it is important to rapidly predict the effect of biofouling on the ship hydrodynamic performance. In this paper, the effect of biofilm on the ship hydrodynamic performance is studied using the proposed performance prediction method for three merchant ships. The applicability of this method in the assessment of the effect of biofilm on the ship hydrodynamic performance is demonstrated by comparison of the obtained results using the proposed performance prediction method and computational fluid dynamics approach. The comparison has shown that the highest relative deviation is lower than 4.2% for all propulsion characteristics, lower than 1.5% for propeller rotation rate and lower than 5.2% for delivered power. Thus, a practical tool for the estimation of the effect of biofouling with lower fouling severity on the ship hydrodynamic performance is developed.

• ETRI Journal
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• v.46 no.3
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• pp.461-472
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• 2024

We propose a network traffic prediction model based on linear and nonlinear model combination. Network traffic is modeled by an autoregressive moving average model, and the error between the measured and predicted network traffic values is obtained. Then, an echo state network is used to fit the prediction error with nonlinear components. In addition, an improved slime mold algorithm is proposed for reservoir parameter optimization of the echo state network, further improving the regression performance. The predictions of the linear (autoregressive moving average) and nonlinear (echo state network) models are added to obtain the final prediction. Compared with other prediction models, test results on two network traffic datasets from mobile and fixed networks show that the proposed prediction model has a smaller error and difference measures. In addition, the coefficient of determination and index of agreement is close to 1, indicating a better data fitting performance. Although the proposed prediction model has a slight increase in time complexity for training and prediction compared with some models, it shows practical applicability.

• Korean Journal of Materials Research
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• v.33 no.7
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• pp.273-278
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• 2023

In this study, based on the saturation magnetic flux density experimental values (B_s) of 622 Fe-based bulk metallic glasses (BMGs), regression models were applied to predict B_s using artificial neural networks (ANN), and prediction performance was evaluated. Model performance evaluation was investigated by using the F1 score together with the coefficient of determination (R² score), which is mainly used in regression models. The coefficient of determination can be used as a performance indicator, since it shows the predicted results of the saturation magnetic flux density of full material datasets in a balanced way. However, the BMG alloy contains iron and requires a high saturation magnetic flux density to have excellent applicability as a soft magnetic material, and in this study F1 score was used as a performance indicator to better predict B_s above the threshold value of B_s (1.4 T). After obtaining two ANN models optimized for the R² and F1 score conditions, respectively, their prediction performance was compared for the test data. As a case study to evaluate the prediction performance, new Fe-based BMG datasets that were not included in the training and test datasets were predicted using the two ANN models. The results showed that the model with an excellent F1 score achieved a more accurate prediction for a material with a high saturation magnetic flux density.

• Korean Journal of Radiology
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• v.22 no.10
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• pp.1697-1707
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• 2021

The recent introduction of various high-dimensional modeling methods, such as radiomics and deep learning, has created a much greater diversity in modeling approaches for survival prediction (or, more generally, time-to-event prediction). The newness of the recent modeling approaches and unfamiliarity with the model outputs may confuse some researchers and practitioners about the evaluation of the performance of such models. Methodological literacy to critically appraise the performance evaluation of the models and, ideally, the ability to conduct such an evaluation would be needed for those who want to develop models or apply them in practice. This article intends to provide intuitive, conceptual, and practical explanations of the statistical methods for evaluating the performance of survival prediction models with minimal usage of mathematical descriptions. It covers from conventional to deep learning methods, and emphasis has been placed on recent modeling approaches. This review article includes straightforward explanations of C indices (Harrell's C index, etc.), time-dependent receiver operating characteristic curve analysis, calibration plot, other methods for evaluating the calibration performance, and Brier score.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.22 no.1
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• pp.70-78
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• 1998

The present study has tested semi-empirical loss models for a reliable performance prediction of mixed-flow pumps with four different specific speeds. In order to improve the predictive capabilities, this paper recommends a new internal loss model and a modified parasitic loss model. The prediction method presented here is also compared with that based on two-dimensional cascade theory. Predicted performance curves by the proposed set of loss models agree fairly well with experimental data for a variety of mixed-flow pumps in the normal operating range, but further studies considering 'droop-like' head performance characteristic due to flow reversal in mixed-flow impellers at low flow range near shut-off head are needed.

• The KSFM Journal of Fluid Machinery
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• v.2 no.2 s.3
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• pp.46-56
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• 1999

This study is on the performance prediction and design of a sirocco fan. Slip coefficient is very important factor for the performance analysis of a centrifugal-type fan. Because generally used slip coefficient equations of backward curved centrifugal fan are not appropriate for forward curved sirocco fan, in this study a proper slip coefficient equation for a sirocco fan is suggested. Using this equation performance prediction program for sirocco fan is composed of and also included the total noise prediction that include the turbulent noise at the fan inlet and boundary layer noise. A comparison between the values obtained from performance prediction program and experimental values shows that the program predicts the sirocco fan performance in a practical rate.

• 유체기계공업학회:학술대회논문집
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• 1998.02a
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• pp.63-72
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• 1998

This study Is on the performance prediction and design of sirocco fan. Slip coefficient is very important factor for the performance analysis of centrifugal-type fan. Because generally used slip coefficient equations of backward curved centrifugal fan are not appropriate for forward curved sirocco fan, in this study a proper slip coefficient equation for sirocco fan is suggested. Using this equation performance prediction program for sirocco fan is composed and also included the total noise prediction that include turbulent noise at the fan Inlet and boundary layer noise. A comparison between the values obtained from performance prediction program and experimental values shows that the program predicts the sirocco fan performance in a practical rate.