• The Korean Journal of Applied Statistics
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• v.28 no.6
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• pp.1103-1111
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• 2015

This paper presents a new prediction method based on relative error incorporated with a penalized regression. The proposed method consists of fully data-driven procedures that is fast, simple, and easy to implement. An example of real data analysis and some simulation results were given to prove that the proposed approach works in practice.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.23 no.4
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• pp.42-48
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• 2015

This paper has its purpose on constructing a prediction model of the arrival sequencing strategy which reflects the actual sequencing patterns of air traffic controllers. As the first step, we analyzed a pair-wise sequencing of two aircraft entering TMA from different entering points. Based on the historical trajectory data, several traffic factors such as time, speed and traffic density were examined for the model. With statistically significant factors, we constructed a prediction model of arrival sequencing through a binary logistic regression analysis. With the estimated coefficients, the performance of the model was conducted through a cross validation.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.17-21
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• 2023

The primary objective of this research is to utilize machine learning models to analyze factors tailored to each dataset for predicting mental health conditions. The study aims to develop appropriate models based on specific datasets, with the goal of accurately predicting mental health states through the analysis of distinct factors present in each dataset. This approach seeks to design more effective strategies for the prevention and intervention of depression, enhancing the quality of mental health services by providing personalized services tailored to individual circumstances. Overall, the research endeavors to advance the development of personalized mental health prediction models through data-driven factor analysis, contributing to the improvement of mental health services on an individualized basis.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.45 no.3
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• pp.18-30
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• 2022

Predicting remaining useful life (RUL) becomes significant to implement prognostics and health management of industrial systems. The relevant studies have contributed to creating RUL prediction models and validating their acceptable performance; however, they are confined to drive reasonable preventive maintenance strategies derived from and connected with such predictive models. This paper proposes a data-driven preventive maintenance method that predicts RUL of industrial systems and determines the optimal replacement time intervals to lead to cost minimization in preventive maintenance. The proposed method comprises: (1) generating RUL prediction models through learning historical process data by using machine learning techniques including random forest and extreme gradient boosting, and (2) applying the system failure time derived from the RUL prediction models to the Weibull distribution-based minimum-repair block replacement model for finding the cost-optimal block replacement time. The paper includes a case study to demonstrate the feasibility of the proposed method using an open dataset, wherein sensor data are generated and recorded from turbofan engine systems.

• Geomechanics and Engineering
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• v.38 no.1
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• pp.69-77
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• 2024

The development of shield-driven cross-river tunnels in China is witnessing a notable shift towards larger diameters, longer distances, and higher water pressures due to the more complex excavation environment. Complex geological formations, such as fault and karst cavities, pose significant construction risks. Real-time adjustment of shield tunneling parameters based on parameter prediction is the key to ensuring the safety and efficiency of shield tunneling. In this study, prediction models for the torque and thrust of the cutter plate of ultra-large diameter slurry shield TBMs is established based on integrated learning algorithms, by analyzing the real data of Heyan Road cross-river tunnel. The influence of geological complexities at the excavation face, substantial burial depth, and high water level on the slurry shield tunneling parameters are considered in the models. The results reveal that the predictive models established by applying Random Forest and AdaBoost algorithms exhibit strong agreement with actual data, which indicates that the good adaptability and predictive accuracy of these two models. The models proposed in this study can be applied in the real-time prediction and adaptive adjustment of the tunneling parameters for shield tunneling under complex geological conditions.

• International Journal of Air-Conditioning and Refrigeration
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• v.9 no.1
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• pp.11-19
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• 2001

To establish optimum cycle of the inverter-driven heat pump with a variation of frequency, the bypass orifice, which was a short tube haying a bypass hole in the middle, was designed and tested. Flow characteristics of the bypass orifice were measured as a function of orifice geometry and operating conditions. Flow trends with respect to frequency were compared with those of short tube orifices and capillary tubes. Generally, the bypass orifice showed the best flow trends among them. and it would enhance the seasonal energy efficiency ratio of an inverter heat pump system, Based on experimental data, a semi-empirical flow model was developed to predict mass flow rate through bypass orifices. The maximum difference between measured data and model`s prediction was within $\pm$5%.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.11 no.1
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• pp.109-116
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• 1999

To establish optimum cycle of the inverter-driven heat pump with a variation of frequency, the bypass orifice, which is a short tube having a bypass hole in the middle, was designed and tested. Flow characteristics of the bypass orifice were measured as a function of orifice geometry and operating conditions. Flow trends with respect to frequency were compared with those of short tube orifices and capillary tubes. Generally, the bypass orifice showed the best flow trends among them, that will enhance the seasonal energy efficiency ratio of an inverter heat pump system. Based on experimental data, the semi-empirical flow model was developed to predict mass flow rate through bypass orifices. The maximum difference between measured data and model's prediction was within ${\pm}5%$.

• The Journal of the Korea Contents Association
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• v.20 no.4
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• pp.14-24
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• 2020

This paper aims to establish an efficient national R&D Application Programming Interface (API) management system for national R&D data-driven management and explore the possibility of using operational data according to the recent global data openness and sharing policy. In accordance with the trend of opening and sharing of national R&D data, we plan to improve management efficiency by analyzing operational data of the national R&D API service. For this purpose, we standardized the parameters for the national R&D APIs that were distributed separately by integrating the individual APIs to build a national R&D API management system. The results of this study revealed that the service call traffic of the national R&D API has shown 554.5% growth in the year as compared to the year 2015 when the measurement started. In addition, this paper also evaluations the possibility of using operational data through data preparation, analysis, and prediction based on service operations management data in the actual operation of national R&D integrated API management system.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.10
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• pp.387-396
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• 2018

Stock price prediction has been a difficult problem to solve. There have been many studies to predict stock price scientifically, but it is still impossible to predict the exact price. Recently, a variety of types of cryptocurrency has been developed, beginning with Bitcoin, which is technically implemented as the concept of distributed ledger. Various approaches have been attempted to predict the price of cryptocurrency. Especially, it is various from attempts to stock prediction techniques in traditional stock market, to attempts to apply deep learning and reinforcement learning. Since the market for cryptocurrency has many new features that are not present in the existing traditional stock market, there is a growing demand for new analytical techniques suitable for the cryptocurrency market. In this study, we first collect and process seven cryptocurrency price data through Bithumb's API. Then, we use the gradient boosting model, which is a data-driven learning based machine learning model, and let the model learn the price data change of cryptocurrency. We also find the most optimal model parameters in the verification step, and finally evaluate the prediction performance of the cryptocurrency price trends.

• International Journal of Advanced Culture Technology
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• v.7 no.1
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• pp.8-13
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• 2019

Despite technical advances in healthcare, the rates of hospital-acquired pressure injury (HAPI) are still high although many are potentially preventable. The purpose of this study was to determine whether tree-based prediction modeling is suitable for assessing the risk of HAPI in ICU patients. Retrospective cohort study has been carried out. A decision tree model was constructed with Age, Weight, eTube, diabetes, Braden score, Isolation, and Number of comorbid conditions as decision nodes. We used RStudio for model training and testing. Correct prediction rate of the final prediction model was 92.4 and the Area Under the ROC curve (AUC) was 0.699, which means there is about 70% chance that the model is able to distinguish between HAPI and non-HAPI. The results of this study has limited generalizability as the data were from a single academic institution. Our research finding shows that the data-driven tree-based prediction modeling may potentially support ICU sensitive risk assessment for HAPI prevention.