• 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.

• Information Systems Review
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• v.18 no.2
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• pp.173-198
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• 2016

Financial distress can damage stakeholders and even lead to significant social costs. Thus, financial distress prediction is an important issue in macroeconomics. However, most existing studies on building a financial distress prediction model have only considered idiosyncratic risk factors without considering systematic risk factors. In this study, we propose a prediction model that considers both the idiosyncratic risk based on a financial ratio and the systematic risk based on a business cycle. Ultimately, we build several IT artifacts associated with financial ratio and add them to the idiosyncratic risk factors as well as address the imbalanced data problem by using an oversampling technique and synthetic minority oversampling technique (SMOTE) to ensure good performance. When considering systematic risk, our study ensures that each data set consists of both financially distressed companies and financially sound companies in each business cycle phase. We conducted several experiments that change the initial imbalanced sample ratio between the two company groups into a 1:1 sample ratio using SMOTE and compared the prediction results from the individual data set. We also predicted data sets from the subsequent business cycle phase as a test set through a built prediction model that used business contraction phase data sets, and then we compared previous prediction performance and subsequent prediction performance. Thus, our findings can provide insights into making rational decisions for stakeholders that are experiencing an economic crisis.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.2
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• pp.51-62
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• 2022

This study suggests a design of predictive modeling for a hospital fall risk based on inpatients' posture. Inpatient's profile, medical history, and body measurement data along with basic information about a bed they use, were used to predict a fall risk and suggest an algorithm to determine the level of risk. Fall risk prediction is largely divided into two parts: a real-time fall risk evaluation and a qualitative fall risk exposure assessment, which is mostly based on the inpatient's profile. The former is carried out by recognizing an inpatient's posture in bed and extracting rule-based information to measure fall risk while the latter is conducted by medical staff who examines an inpatient's health status related to hospital fall risk and assesses the level of risk exposure. The inpatient fall risk is determined using a sigmoid function with recognized inpatient posture information, body measurement data and qualitative risk assessment results combined. The procedure and prediction model suggested in this study is expected to significantly contribute to tailored services for inpatients and help ensure hospital fall prevention and inpatient safety.

• Journal of Navigation and Port Research
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• v.33 no.4
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• pp.255-261
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• 2009

Prediction of the stability for ships is very complex in reality. In this paper, risk based approach is applied to predict the probability of capsize for a certified ship, which is effected by the forces of sea especially the wave loading Safety assessment and risk analysis process are also applied for the probabilistic prediction of stability for ships. The probability of shipsencountering different waves at sea is calculated by the existed statistics data and risk based models. Finally, ship capsizing probability is calculated according to single degree of freedom(SDF) rolling differential equation and basin erosion theory of nonlinear dynamics. Calculation results show that the survival probabilities of ship excited by the forces of the seas, especially in the beam seas status, can be predicted by the risk based method.

• 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.

• Fire Science and Engineering
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• v.30 no.6
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• pp.130-136
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• 2016

A university needs to analyze and improve its curricula with the perspective of the consumer to develop a syllabus for the training of industry-demand customized human resources. Accordingly, this paper surveyed the demand of fire-related industry workers to evaluate the major fitness of the curriculum of fire risk prediction and assessment and carried out descriptive statistical analysis, factor analysis, cluster analysis, and one-way ANOVA based on the results. According to the analysis, fire-related industry workers reported that the curriculum of fire risk prediction and assessment is suitable for majors. In addition, they were greatly aware of the necessity of basic major and common major subjects among subjects of fire risk prediction and assessment. The results of this analysis will provide the basic data to improve the curriculum continuously in the future.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.16
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• pp.6811-6817
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• 2014

Background: Breast cancer risk prediction models are widely used in clinical practice. They should be useful in identifying high risk women for screening in limited-resource countries. However, previous models showed poor performance in derived and validated settings. Therefore, we aimed to develop and validate a breast cancer risk prediction model for Thai women. Materials and Methods: This cross-sectional study consisted of derived and validation phases. Data collected at Ramathibodi and other two hospitals were used for deriving and externally validating models, respectively. Multiple logistic regression was applied to construct the model. Calibration and discrimination performances were assessed using the observed/expected ratio and concordance statistic (C-statistic), respectively. A bootstrap with 200 repetitions was applied for internal validation. Results: Age, menopausal status, body mass index, and use of oral contraceptives were significantly associated with breast cancer and were included in the model. Observed/expected ratio and C-statistic were 1.00 (95% CI: 0.82, 1.21) and 0.651 (95% CI: 0.595, 0.707), respectively. Internal validation showed good performance with a bias of 0.010 (95% CI: 0.002, 0.018) and C-statistic of 0.646(95% CI: 0.642, 0.650). The observed/expected ratio and C-statistic from external validation were 0.97 (95% CI: 0.68, 1.35) and 0.609 (95% CI: 0.511, 0.706), respectively. Risk scores were created and was stratified as low (0-0.86), low-intermediate (0.87-1.14), intermediate-high (1.15-1.52), and high-risk (1.53-3.40) groups. Conclusions: A Thai breast cancer risk prediction model was created with good calibration and fair discrimination performance. Risk stratification should aid to prioritize high risk women to receive an organized breast cancer screening program in Thailand and other limited-resource countries.

• Communications for Statistical Applications and Methods
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• v.25 no.6
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• pp.605-618
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• 2018

Risk management has been a crucial part of the daily operations of the financial industry over the past two decades. Value at Risk (VaR), a quantitative measure introduced by JP Morgan in 1995, is the most popular and simplest quantitative measure of risk. VaR has been widely applied to the risk evaluation over all types of financial activities, including portfolio management and asset allocation. This paper uses the implementations of multivariate GARCH models and copula methods to illustrate the performance of a one-day-ahead VaR prediction modeling process for high-dimensional portfolios. Many factors, such as the interaction among included assets, are included in the modeling process. Additionally, empirical data analyses and backtesting results are demonstrated through a rolling analysis, which help capture the instability of parameter estimates. We find that our way of modeling is relatively robust and flexible.

• 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 Korean Institute of Building Construction Conference
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• 2020.11a
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• pp.69-70
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• 2020

Accidents in the construction industry are very high compared to other industries, and the number is also increasing steeply every year. Relevant studies were limited for solving the problems. The purpose of this study is to develop a comprehensive risk prediction process for personnel deployed at construction sites on safety management. First of all, the variables were divided into fixed, real-time and working types variables, and the relevant comprehensive data were collected. Second, the probability of a disaster was derived based on the collected data, and weights for each variable were calculated using the dummy regression analysis method using statistical methodology. Lastly, the resulting weighting and disaster probability equation was constructed, and The Final Risk Calculation Formula was developed. The Final Risk Calculation Formula presented in this study is expected to have a significant impact on the establishment of effective safety management measures to prevent possible safety accidents at construction sites