• Proceedings of the Korean Institute of Building Construction Conference
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• 2020.11a
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• pp.186-187
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• 2020

Damage to temporary facilities and structural members caused by excessive loads in the field continue to occur. If the load can be monitored in advance, the risk can be prevented. In this study, a load cell sensor is installed under the system support, and load data is wirelessly transmitted through a Bluetooth AP(wireless). Risk prediction system is proposed through an construction alarm when an abnormal load occurs through real-time multi-point monitoring by sensor location.

• Geomechanics and Engineering
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• v.30 no.5
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• pp.413-422
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• 2022

TBM is widely used in the construction of various underground projects in the current world, and has the unique advantages that cannot be compared with traditional excavation methods. However, due to the high cost of TBM, the damage is even greater when geological disasters such as collapse occur during excavation. At present, there is still a shortage of research on various types of risk prediction of TBM tunnel, and accurate and reliable risk prediction model is an important theoretical basis for timely risk avoidance during construction. In this paper, a prediction model is proposed to evaluate the risk level of tunnel collapse by establishing a reasonable risk index system, using analytic hierarchy process to determine the index weight, and using the normal cloud model theory. At the same time, the traditional analytic hierarchy process is improved and optimized to ensure the objectivity of the weight values of the indicators in the prediction process, and the qualitative indicators are quantified so that they can directly participate in the process of risk prediction calculation. Through the practical engineering application, the feasibility and accuracy of the method are verified, and further optimization can be analyzed and discussed.

• Journal of the Korea Society of Computer and Information
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• v.22 no.10
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• pp.9-17
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• 2017

The student dropout prediction is an indispensable for many intelligent systems to measure the educational system and success rate of all university. Therefore, in this paper, we propose an intelligent dropout prediction system that minimizes the situation by adopting the proactive process through an effective model that predicts the students who are at risk of dropout. In this paper, the main data sets for students dropout predictions was used as questionnaires and university information. The questionnaire was constructed based on theoretical and empirical grounds about factor affecting student's performance and causes of dropout. University Information included student grade, interviews, attendance in university life. Through these data sets, the proposed dropout prediction model techniques was classified into the risk group and the normal group using statistical methods and Naive Bays algorithm. And the intelligence dropout prediction system was constructed by applying the proposed dropout prediction model. We expect the proposed study would be used effectively to reduce the students dropout in university.

• Transactions of the Korean Society of Automotive Engineers
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• v.21 no.5
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• pp.136-144
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• 2013

An extensive real world in-depth crash accident data is needed to make a precise occupant injury risk prediction at crash accidents which might be a critical information from the scene of the accident in ACNS(Automatic Crash Notification System). However it is rather unfortunate that there is no such a domestic database unlike other leading countries. Therefore we propose a numerical method, i.e., crash simulation using a sled model to make a virtual database that can substitute car crash database in real world. The proposing crash injury risk prediction is validated against a limited domestic crash accident data.

• International conference on construction engineering and project management
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• 2009.05a
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• pp.1260-1265
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• 2009

Topographical features in South Korea is characterized that 70% of territory is composed of the mountains that can experience intense rainfall during storms in the summer and autumn. Efficient planning and management of landscape becomes utmost important since the cutting slopes in the mountain areas have been increased due to the limited construction areas for the roadway and residential development. This paper proposed an efficient way of slope management for the landslide risk by developing Web-GIS landslide risk management system. By deploying the Logistic Regression Analysis, the system could increase the prediction accuracy that the landslide disaster might be occurred. High resolution survey technology using GPS and Total-Station could extract the exact position and visual shape of the slopes that accurately describe the slope information. Through the proposed system, the prediction of damage areas from the landslide could also make it easy to efficiently identify the level of landslide risks via web-based user interface. It is expected that the proposed landslide risk management system can support the decision making framework during the identification, prediction, and management of the landslide risks.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2003.05a
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• pp.329-337
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• 2003

Stock market prediction is regarded as a challenging task of financial time-series prediction. There have been many studies using artificial neural networks in this area. Recently, support vector machines (SVMs) are regarded as promising methods for the prediction of financial time-series because they me a risk function consisting the empirical ewer and a regularized term which is derived from the structural risk minimization principle. In this study, I apply SVM to predicting the Korea Composite Stock Price Index (KOSPI). In addition, this study examines the feasibility of applying SVM in financial forecasting by comparing it with back-propagation neural networks and case-based reasoning. The experimental results show that SVM provides a promising alternative to stock market prediction.

• Earthquakes and Structures
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• v.18 no.1
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• pp.73-82
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• 2020

With the coal mining depth increasing, both stress and gas pressure rapidly enhance, causing coal and gas outburst risk to become more complex and severe. The conventional method for prediction of coal and gas outburst adopts one prediction index and corresponding critical value to forecast and cannot reflect all the factors impacting coal and gas outburst, thus it is characteristic of false and missing forecasts and poor accuracy. For the reason, based on analyses of both the prediction indicators and the factors impacting coal and gas outburst at the test site, this work carefully selected 6 prediction indicators such as the index of gas desorption from drill cuttings Δh₂, the amount of drill cuttings S, gas content W, the gas initial diffusion velocity index ΔP, the intensity of electromagnetic radiation E and its number of pulse N, constructed the Bayes discriminant analysis (BDA) index system, studied the BDA-based multi-index comprehensive model for forecast of coal and gas outburst risk, and used the established discriminant model to conduct coal and gas outburst prediction. Results showed that the BDA - based multi-index comprehensive model for prediction of coal and gas outburst has an 100% of prediction accuracy, without wrong and omitted predictions, can also accurately forecast the outburst risk even for the low indicators outburst. The prediction method set up by this study has a broad application prospect in the prediction of coal and gas outburst risk.

• Journal of Chest Surgery
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• v.54 no.2
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• pp.88-98
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• 2021

Background: This study aimed to develop a new risk prediction model for operative mortality in a Korean cohort undergoing heart valve surgery using the Korea Heart Valve Surgery Registry (KHVSR) database. Methods: We analyzed data from 4,742 patients registered in the KHVSR who underwent heart valve surgery at 9 institutions between 2017 and 2018. A risk prediction model was developed for operative mortality, defined as death within 30 days after surgery or during the same hospitalization. A statistical model was generated with a scoring system by multiple logistic regression analyses. The performance of the model was evaluated by its discrimination and calibration abilities. Results: Operative mortality occurred in 142 patients. The final regression models identified 13 risk variables. The risk prediction model showed good discrimination, with a c-statistic of 0.805 and calibration with Hosmer-Lemeshow goodness-of-fit p-value of 0.630. The risk scores ranged from -1 to 15, and were associated with an increase in predicted mortality. The predicted mortality across the risk scores ranged from 0.3% to 80.6%. Conclusion: This risk prediction model using a scoring system specific to heart valve surgery was developed from the KHVSR database. The risk prediction model showed that operative mortality could be predicted well in a Korean cohort.

• Journal of Navigation and Port Research
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• v.28 no.1
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• pp.17-22
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• 2004

The paper describes implementation of three-dimensional visualization system that is to provide comprehensive meaning of the statistical prediction results on the marine casualties. Graphical User Interface (GUI) and Web based Virtual Reality (VR) technology are mainly introduced in the system development. To provide daily forecasting, time based casualty prediction model and risk level index are developed in this work. As operating test results of the system, complicated statistical meaning can be shown in the three-dimensional virtual space using simple color. In addition, daily risk levels can be shown on the bar-graph.

• International Journal of Quality Innovation
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• v.7 no.2
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• pp.97-124
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• 2006

The purpose of this research is to create an expert risk-based piping system inspection model. The proposed system includes a risk-based piping inspection system and a piping inspection guideline system. The research procedure consists of three parts: the risk-based inspection model, the risk-based piping inspection model, and the piping inspection guideline system model. In this research procedure, a field plant visit is conducted to collect the related domestic information (Taiwan) and foreign standards and regulations for creating a strategic risk-based piping inspection and analysis system in accordance with the piping damage characteristics in the petrochemical industry. In accordance with various piping damage models and damage positions, petrochemical plants provide the optimal piping inspection planning tool for efficient piping risk prediction for enhancing plant operation safety.