• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.8
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• pp.4809-4814
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• 2014

The aim of the present study was to estimate the prediction performance of a FDS (Fire Dynamic Simulator) to simulate the fire behaviors and suppression characteristics by operating a water-mist. Rosin-Rammler/log-normal distribution function was used to determine the initial droplet distribution of water-mist and the effects of its model constant were considered. In addition, the simulation models were validated by a comparison of the predicted fire suppression characteristics with water-mist injection pressures to the previous experiments, and the thermal flow behaviors and gaseous concentration variations were analyzed. The results showed that water-mists with the same mean diameter were affected by the characteristics of the droplet size distribution, which have different size and velocity distributions at the downstream location. The fire simulations conducted in this study determine the initial droplet size distribution tuned to the base of the spray characteristics measured by previous experiments. The simulation results showed good agreement with the previous measurements for temperature variations and fire suppression characteristics. In addition, it was confirmed that the FDS simulation with a water-mist operation supplies useful details on estimations of the thermal flow fields and gaseous concentration under water mist operation conditions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.3
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• pp.1111-1116
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• 2011

Heat exchangers have been used for the automotive, HVAC systems, and other various industrial facilities, so the market is very wide. In general, high-efficiency heat exchangers with louver fins are used in the dust-free environment while heat exchangers with wavy fins are used for dusty environment such as construction site, etc. In this study, numerical analysis has been performed for typical heat exchangers, used as oil coolers or fuel coolers, with dual cell model that can handle different grids for the air-side and oil-side of heat exchangers. First wind tunnel tests were conducted to obtain one-dimensional thermal performance data of heat exchangers. Then, heat release rates with varying air flows were numerically predicted using the three-dimensional dual-cell model. The model can greatly enhance the accuracy of thermal design since it includes the effects of nonuniformity of air flows across heat exchangers.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.35 no.3
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• pp.159-166
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• 2022

In this study, slender reinforced concrete columns subjected to high temperatures and eccentric axial loads are evaluated by finite element analysis employing Abaqus (a finite element analysis program). Subsequently, the analysis results are compared and assessed. The sequentially coupled thermal stress analysis provided by Abaqus was employed to reflect the condition of an axially loaded column exposed to fire. First, heat transfer analysis was performed on the column cross-section. After verifying the results, another analysis was conducted: the cross-section was transformed into a three-dimensional element and then structural analyzed. In the analysis process, the column was modeled by accounting for the effects of tension stiffening and initial imperfection that could affect convergence and accuracy. The analysis results were compared with 74 experimental records, and an average error of 6% was observed based on the fire exposure and resistance. The foregoing indicates that the fire resistance performance of reinforced concrete columns can be predicted through finite element analysis.

• The Journal of Bigdata
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• v.5 no.2
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• pp.155-170
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• 2020

The purpose of this study was to apply machine learning techniques (Decision Tree, Random Forest, XGBoost) to data from the 4th~6th year of the Seoul Education Longitudinal Study to find the factors predicting the career maturity of middle school students in Seoul city. In order to evaluate the machine learning application result, the performance of the model according to the indicators was checked. In addition, the model was analyzed using the XGBoostExplainer package, and R and R Studio tools were used for this study. As a result, there was a slight difference in the ranking of variable importance by each model, but the rankings were high in 'Achievement goal awareness', 'Creativity', 'Self-concept', 'Relationship with parents and children', and 'Resilience'. In addition, using the XGBoostExplainer package, it was found that the factors that protect and deteriorate career maturity by panel and 'Achievement goal awareness' is the top priority factor for predicting career maturity. Based on the results of this study, it was suggested that a comparative study of machine learning and variable selection methods and a comparative study of each cohort of the Seoul Education Termination Study should be conducted.

• Journal of the Korean Society of Propulsion Engineers
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• v.17 no.1
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• pp.35-41
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• 2013

Torque characteristics of a turbopump turbine was analyzed using the turbine performance test result. Specific torque of the subject turbine could be expressed as a linear function of corrected rotor speed at a fixed pressure ratio and it has been confirmed by the test result. It also found that corrected rotor speed-specific torque characteristics does not change anymore if the turbine pressure ratio is set bigger than a certain value. Using the turbine torque characteristics and pyro-starter performance test results, rotational speed development behavior of the turbopump was predicted. Prediction revealed that the lap time reaching 50% of turbopump design speed is less than 0.7 second. Effect of the thermal loss between pyro-starter and turbopump was negligible.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2012.05a
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• pp.4-10
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• 2012

Torque characteristics of a 75-tonf turbopump turbine was analyzed using the turbine performance test result. Specific torque of the subject turbine could be expressed as a linear function of corrected rotor speed at a fixed pressure ratio and it has been confirmed by the test result. It also found that corrected rotor speed-specific torque characteristics does not change anymore if the turbine pressure ratio is set bigger than a certain value. Using the turbine torque characteristics and pyro-starter performance test results, rotational speed development behavior of the turbopump was predicted. Prediction revealed that the lap time reaching 50% of turbopump design speed is less than 0.7 second. Effect of the thermal loss between pyro-starter and turbopump was negligible.

• The Journal of Engineering Geology
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• v.33 no.4
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• pp.673-687
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• 2023

Exploratory data analysis is the process of observing and understanding data collected from various sources to identify their distributions and correlations through their structures and characterization. This process can be used to identify correlations among conditioning factors and select the most effective factors for analysis. This can help the assessment of landslide susceptibility, because landslides are usually triggered by multiple factors, and the impacts of these factors vary by region. This study compared two stages of exploratory data analysis to examine the impact of the data exploration procedure on the landslide prediction model's performance with respect to factor selection. Deep-learning-based landslide susceptibility analysis used either a combinations of selected factors or all 23 factors. During the data exploration phase, we used a Pearson correlation coefficient heat map and a histogram of random forest feature importance. We then assessed the accuracy of our deep-learning-based analysis of landslide susceptibility using a confusion matrix. Finally, a landslide susceptibility map was generated using the landslide susceptibility index derived from the proposed analysis. The analysis revealed that using all 23 factors resulted in low accuracy (55.90%), but using the 13 factors selected in one step of exploration improved the accuracy to 81.25%. This was further improved to 92.80% using only the nine conditioning factors selected during both steps of the data exploration. Therefore, exploratory data analysis selected the conditioning factors most suitable for landslide susceptibility analysis and thereby improving the performance of the analysis.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.8
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• pp.339-346
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• 2022

Recently, the resource depletion and climate change problem caused by the massive usage of fossil fuels for electric power generation has become a critical issue worldwide. According to this issue, interest in renewable energy resources that can replace fossil fuels is increasing. Especially, photovoltaic power has gaining much attention because there is no risk of resource exhaustion compared to other energy resources and there are low restrictions on installation of photovoltaic system. In order to use the power generated by the photovoltaic system efficiently, a more accurate photovoltaic power forecasting model is required. So far, even though many machine learning and deep learning-based photovoltaic power forecasting models have been proposed, they showed limited success in terms of interpretability. Deep learning-based forecasting models have the disadvantage of being difficult to explain how the forecasting results are derived. To solve this problem, many studies are being conducted on explainable artificial intelligence technique. The reliability of the model can be secured if it is possible to interpret how the model derives the results. Also, the model can be improved to increase the forecasting accuracy based on the analysis results. Therefore, in this paper, we propose an explainable photovoltaic power forecasting scheme based on BiLSTM (Bidirectional Long Short-Term Memory) and SHAP (SHapley Additive exPlanations).

• The Journal of Society for e-Business Studies
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• v.26 no.3
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• pp.97-117
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• 2021

Predicting term deposit subscriptions is one of representative financial marketing in banks, and banks can build a prediction model using various customer information. In order to improve the classification accuracy for term deposit subscriptions, many studies have been conducted based on machine learning techniques. However, even if these models can achieve satisfactory performance, utilizing them is not an easy task in the industry when their decision-making process is not adequately explained. To address this issue, this paper proposes an explainable scheme for term deposit subscription forecasting. For this, we first construct several classification models using decision tree-based ensemble learning methods, which yield excellent performance in tabular data, such as random forest, gradient boosting machine (GBM), extreme gradient boosting (XGB), and light gradient boosting machine (LightGBM). We then analyze their classification performance in depth through 10-fold cross-validation. After that, we provide the rationale for interpreting the influence of customer information and the decision-making process by applying Shapley additive explanation (SHAP), an explainable artificial intelligence technique, to the best classification model. To verify the practicality and validity of our scheme, experiments were conducted with the bank marketing dataset provided by Kaggle; we applied the SHAP to the GBM and LightGBM models, respectively, according to different dataset configurations and then performed their analysis and visualization for explainable term deposit subscriptions.

• Journal of the Korea Institute of Building Construction
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• v.22 no.6
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• pp.619-630
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• 2022

The concrete mix design and compressive strength evaluation are used as basic data for the durability of sustainable structures. However, the recent diversification of mixing factors has created difficulties in calculating the correct mixing factor or setting the reference value concrete mixing design. The purpose of this study is to design a predictive model of bidirectional analysis that calculates the mixing elements of ternary concrete using deep learning, one of the artificial intelligence techniques. For the DNN-based predictive model for calculating the concrete mixing factor, performance evaluation and comparison were performed using a total of 8 models with the number of layers and the number of hidden neurons as variables. The combination calculation result was output. As a result of the model's performance evaluation, an average error rate of about 1.423% for the concrete compressive strength factor was achieved. and an average MAPE error of 8.22% for the prediction of the ternary concrete mixing factor was satisfied. Through comparing the performance evaluation for each structure of the DNN model, the DNN5L-2048 model showed the highest performance for all compounding factors. Using the learned DNN model, the prediction of the ternary concrete formulation table with the required compressive strength of 30 and 50 MPa was carried out. The verification process through the expansion of the data set for learning and a comparison between the actual concrete mix table and the DNN model output concrete mix table is necessary.