• Journal of Chest Surgery
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• v.25 no.11
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• pp.1218-1220
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• 1992

Pneumonectomy on a patient with documented poor pulmonary function indicating a contraindication to surgery can be associated with a high risk of serious postoperative morbidity or mortality. However the usual criterias, on the performance of a pneumonectomy on a high risk patient based on the preoperative assessment of the pulmonary function may not sometimes predict with accuracy the operative outcome in the postoperative period. We recently performed pneumonectomy with good results on a patient with poor pulmonary function that would otherwise have been an absolute contraindication to surgery by usual criteria.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.133-134
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• 2022

Recently, the criteria for assessing industrial accidents have been replaced by the mortality rate. It was found that the number of deaths from excavation work was the highest among construction machinery. The risk assessment is being conducted, however the industrial accident mortality rate has not decreased. Accordingly, this study aims to provide the basic for the create of a risk assessment model specialized in construction work at excavator. It provides absolute value from the risk model which is capable of delivery the probability of a disaster. In addition, we provide a relative risk model that compares the risk through scores between detailed works. The relative risk model is combined by likelihood and severity; the likelihood indicates the frequency of accidents and the severity indicates seriousness of fatal accidents. A variable that reflects the conditions of the construction site was added to the risk assessment model based on past disaster cases. And using the concepts of probability and average, the risk assessment process was quantified and used as an objective indicator. Therefore, the model is expected to reduce disasters by raising the awareness of disasters.

• The Korean Journal of Nuclear Medicine Technology
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• v.13 no.3
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• pp.137-142
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• 2009

Purpose: World Health Organization (WHO) have suggested that an individual's 10-year absolute fracture risk is more reliable than Bone Mineral Density (BMD) measurement as the predictor of osteoporotic fracture. In 2008, Fracture Risk Assessment Tool ($FRAX^{TM}$) was developed by WHO to evaluate fracture risk of patients based on individual's clinical risk factors. The purpose of this study is to offer the comparative analysis of the existing GE prodigy and $FRAX^{TM}$ Tool in Absolute Fracture Risk Assessment Tool. Materials and Methods: 201 women ($55{\pm}3.5$ years) underwent femoral neck BMD measurement using GE Prodigy. The 10-year probability (%) of hip fracture (or a major osteoporosis-related fracture) was estimated using T-scores of GE prodigy and $FRAX^{TM}$. We made a comparative analysis of these data using SPSS (Ver.12). Results: There was a significant difference statistically between T-score ($-0.52{\pm}0.97$) of GE prodigy and T-score ($-1.45{\pm}0.81$) of $FRAX^{TM}$ (r=0.977, p=0.000). Also, there was a significant difference statistically between a major osteoporosis- related fracture ($9.15{\pm}3.71$) of GE prodigy and a major osteoporosis-related fracture ($4.87{\pm}1.51$) of $FRAX^{TM}$ (r=0.909, p=0.000). Moreover, a statistically significant difference was found in the 10-year probability of hip fracture of GE prodigy ($1.56{\pm}1.48$) and of hip fracture ($0.53{\pm}0.61$) of $FRAX^{TM}$ (r=0.905, p=0.000). Conclusions: There was a significant difference statistically between GE prodigy and $FRAX^{TM}$ Tool in Absolute Fracture Risk Assessment Tool. Especially, T-score, a major osteoporosis-related fracture and the 10-year probability of hip fracture that were estimated using GE prodigy tended to show the higher results than one evaluated by $FRAX^{TM}$ Tool. In conclusion, $FRAX^{TM}$ Tool may provide a better tool. The application of $FRAX^{TM}$ Tool as a fracture predictor remains to be clarified.

• Journal of the Chungcheong Mathematical Society
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• v.27 no.2
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• pp.219-225
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• 2014

I use the dynamic programming approach to study the optimal consumption and investment problem with regime-switching and constant labor income. I derive the optimal solutions in closed-form with constant absolute risk aversion (CARA) utility and constant disutility.

• Journal of the Chungcheong Mathematical Society
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• v.26 no.3
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• pp.571-576
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• 2013

In this paper, we solve the optimal investment problem when the investor gets labor income and may have a wage increase. We assume the investor has constant absolute risk aversion(CARA) utility and obtain closed form solutions.

• Journal of the Chungcheong Mathematical Society
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• v.26 no.1
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• pp.85-90
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• 2013

We use the dynamic programming method to investigate the optimal consumption and investment problem with regime-switching. We derive the optimal solutions in closed-form with constant absolute risk aversion (CARA) utility.

• Communications for Statistical Applications and Methods
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• v.30 no.1
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• pp.1-19
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• 2023

Observational data with missing or incomplete data are common in biomedical research. Multiple imputation is an effective approach to handle missing data with the ability to decrease bias while increasing statistical power and efficiency. In recent years propensity score (PS) matching has been increasingly used in observational studies to estimate treatment effect as it can reduce confounding due to measured baseline covariates. In this paper, we describe in detail approaches to competing risk analysis in the setting of incomplete observational data when using PS matching. First, we used multiple imputation to impute several missing variables simultaneously, then conducted propensity-score matching to match statin-exposed patients with those unexposed. Afterwards, we assessed the effect of statin exposure on the risk of heart failure-related hospitalizations or emergency visits by estimating both relative and absolute effects. Collectively, we provided a general methodological framework to assess treatment effect in incomplete observational data. In addition, we presented a practical approach to produce overall cumulative incidence function (CIF) based on estimates from multiple imputed and PS-matched samples.

• International Journal of Computer Science & Network Security
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• v.23 no.5
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• pp.179-192
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• 2023

The widespread use of Cloud Computing, Internet of Things (IoT), and social media in the Information Communication Technology (ICT) field has resulted in continuous and unavoidable cyber-attacks on users and critical infrastructures worldwide. Traditional security measures such as firewalls and encryption systems are not effective in countering these sophisticated cyber-attacks. Therefore, Intrusion Detection and Prevention Systems (IDPS) are necessary to reduce the risk to an absolute minimum. Although IDPSs can detect various types of cyber-attacks with high accuracy, their performance is limited by a high false alarm rate. This study proposes a new technique called Fuzzy Logic - Objective Risk Analysis (FLORA) that can significantly reduce false positive alarm rates and maintain a high level of security against serious cyber-attacks. The FLORA model has a high fuzzy accuracy rate of 90.11% and can predict vulnerabilities with a high level of certainty. It also has a mechanism for monitoring and recording digital forensic evidence which can be used in legal prosecution proceedings in different jurisdictions.

• Earthquakes and Structures
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• v.17 no.5
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• pp.511-519
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• 2019

The concrete gravity dam is one of the most important parts of the nation's infrastructure. Besides the benefits, the dam also has some potentially catastrophic disasters related to the life of citizens directly. During the lifetime of service, some degradations in a dam may occur as consequences of operating conditions, environmental aspects and deterioration in materials from natural causes, especially from dynamic loads. Cumulative Absolute Velocity (CAV) plays a key role to assess the operational condition of a structure under seismic hazard. In previous researches, CAV is normally used in Nuclear Power Plant (NPP) fields, but there are no particular criteria or studies that have been made on dam structure. This paper presents a method to calculate the limitation of CAV for the Bohyeonsan Dam in Korea, where the critical Peak Ground Acceleration (PGA) is estimated from twelve sets of selected earthquakes based on High Confidence of Low Probability of Failure (HCLPF). HCLPF point denotes 5% damage probability with 95% confidence level in the fragility curve, and the corresponding PGA expresses the crucial acceleration of this dam. For determining the status of the dam, a 2D finite element model is simulated by ABAQUS. At first, the dam's parameters are optimized by the Minitab tool using the method of Central Composite Design (CCD) for increasing model reliability. Then the Response Surface Methodology (RSM) is used for updating the model and the optimization is implemented from the selected model parameters. Finally, the recorded response of the concrete gravity dam is compared against the results obtained from solving the numerical model for identifying the physical condition of the structure.

• Food Science and Industry
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• v.54 no.3
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• pp.160-170
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• 2021

A recent advance in communication technologies accelerates the spread of food safety issues once presented by the news media. To respond to those safety issues and take steps in a timely manner, automatically detecting related information from the news data matters. This work presents an AI-based system that detects risk information within a food-related news article. Experts in food safety areas participated in labeling risk information from the food-related news articles; we acquired 43,527 articles in which food names and risk information are marked as labels. Based on the news document, our system automatically detects food names and risk information by analyzing similarities between words within a text by leveraging learned word embedding vectors. Our AI-based system shows higher detection accuracy scores over a non-AI rule-based system: achieving an absolute gain of +32.94% in F1 for the food name category and +41.53% for the risk information category.