• Journal of the Korea Institute of Military Science and Technology
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• v.12 no.2
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• pp.199-205
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• 2009

A simple and effective analysis method is presented for predicting the best infiltration area. Based on geospatial information, numerical estimation barometer for degree of infiltration risk has been derived. The dominant geospatial features influencing infiltration risk have been found to be area altitude, degree of surface gradient, relative direction of surface gradient to the surveillance line, degree of surface gradient repetition, regional forest information. Each feature has been numerically expressed corresponding to the degree of infiltration risk of that area. Four different detection probability maps of infiltration risk for the surveillance area are drawn on the actual map with respect to the numerically expressed five dominant factors of infiltration risks. By combining the four detection probability maps, the complete picture of thr best infiltration area has been drawn. By using the map and the analytic method the effectiveness of surveillance operation can be improved.

• Asian Pacific Journal of Cancer Prevention
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• v.14 no.7
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• pp.4167-4175
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• 2013

A najor current challenge and constraint in cervical cancer research is the development of vaccines against human papilloma virus (HPV) epitopes. Although many studies are done on epitope identification on HPVs, no computational work has been carried out for high risk forms which are considered to cause cervical cancer. Of all the high risk HPVs, HPV 16, HPV 18 and HPV 45 are responsible for 94% of cervical cancers in women worldwide. In this work, we computationally predicted the promiscuous epitopes among the E6 proteins of high risk HPVs. We identified the conserved residues, HLA class I, HLA class II and B-cell epitopes along with their corresponding secondary structure conformations. We used extremely precise bioinformatics tools like ClustalW2, MAPPP, NetMHC, Epi,Jen, EpiTop 1.0, ABCpred, BCpred and PSIPred for achieving this task. Our study identified specific regions 'FAFR(K)DL' followed by 'KLPD(Q)LCTEL' fragments which proved to be promiscuous epitopes present in both human leukocyte antigen (HLA) class I, class II molecules and B cells as well. These fragments also follow every suitable character to be considered as promiscuous epitopes with supporting evidences of previously reported experimental results. Thus, we conclude that these regions should be considered as the important for design of specific therapeutic vaccines for cervical cancer.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.15
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• pp.6353-6356
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• 2014

Background: The purpose of this study was to investigate and evaluate risk factors for lymph node metastases (LNM) in cases of endometrial cancer (EC). Materials and Methods: A retrospective single institution analysis of patients surgically staged for EC at Ankara Oncology Education and Research Hospital from 1996 to 2010 was performed. Roles of prognostic factors, such as age, histological type, grade, depth of myometrial invasion, cervical involvement, peritoneal cytology, and tumor size, in the prediction of LNM were evaluated. Fisher's exact test and logistic regression analysis were used to assess the effects of various factors on LNM. Results: LNM was observed in 22 out of 247 patients (8.9%) and was significantly more common in the presence of tumors of higher grade, deep myometrial invasion (DMI), cervical involvement, size >2cm, and with positive peritoneal cytology. Logistic regression analysis revealed that DMI remained the only independent risk factor for LNM. NPV, PPV, sensitivity, and specificity for satisfying LNM risk were 98.0, 19.5, 86.3, and 65.3%, respectively for DMI. Conclusions: The incidence of LNM is influenced independently by DMI. If data support a conclusion of DMI, LND should be seriously considered.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.41-44
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• 2021

In this paper, we propose a monitoring system that can monitor gas leakage concentrations in real time and forecast the amount of gas leaked after one minute. When gas leaks happen, they typically lead to accidents such as poisoning, explosion, and fire, so a monitoring system is needed to reduce such occurrences. Previous research has mainly been focused on analyzing explosion characteristics based on gas types, or on warning systems that sound an alarm when a gas leak occurs in industrial areas. However, there are no studies on creating systems that utilize specific gas explosion characteristic analysis or empirical urban gas data. This research establishes a deep learning model that predicts the gas explosion risk level over time, based on the gas data collected in real time. In order to determine the relative risk level of a gas leak, the gas risk level was divided into five levels based on the lower explosion limit. The monitoring platform displays the current risk level, the predicted risk level, and the amount of gas leaked. It is expected that the development of this system will become a starting point for a monitoring system that can be deployed in urban areas.

• Journal of muscle and joint health
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• v.31 no.2
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• pp.65-74
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• 2024

Purpose: This study was conducted to verify fall predictive power and reasonable fall risk assessment tool by a comparative analysis of the sensitivity, specificity, positive forecast and negative forecast of each tool by applying Morse Fall Scale (MFS), Johns Hopkins Fall Risk Assessment Tool (JHFRAT), and Fall Assessment Scale-Korean version (FAS-K) through electronic medical records to adult patients hospitalized in a general hospital in Korea. Methods: We performed a retrospective evaluation study from January to December 2018, 123 fall groups experiencing falls during hospitalization and 123 non-falls groups were selected. Data presented a reasonable assessment tool that predicts and distinguishes fall high-risk patients through area comparison based on the ROC curve for each tool. Results: In the ROC curve analysis by fall risk assessment group, the AUC of MFS is shown to be .706 (good), JHFRAT is shown to be .649 (sufficient) and FAS-K is shown to be .804 (very good). FAS-K at a cut-off score of 4, sensitivity, specificity, and positive and negative prediction values were 83.7%, 60.2%, 67.8%, and 78.7%, respectively. Conclusion: Based on the above findings, it is believed that the FAS-K was presented as a suitable and reasonable tool for predicting falls for adult patients in general hospitals.

• Journal of the Korean Institute of Intelligent Systems
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• v.25 no.2
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• pp.191-196
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• 2015

There have been many studies on statistical forecasting on firm's performance and stock price by applying various financial indicators such as debt ratio and sales growth rate. Selecting predictors for constructing a prediction model among the various financial indicators is very important for precise prediction. Most of the previous studies applied variable selection algorithms for selecting predictors. However, the variable selection algorithm is considered to be at risk of eliminating certain amount of information from the indicators that were excluded from model construction. Therefore, we propose a firm's performance prediction model which principal component analysis is applied instead of the variable selection algorithm, in order to reduce dimensionality of input variables of the prediction model. In this study, we constructed the proposed prediction model by using financial data of American IT companies to empirically analyze prediction performance of the model.

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.559-575
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• 2021

Due to the increase in ground excavation work, the possibility of ground subsidence accidents is increasing. And it is very difficult to prevent these risk fundamentally through institutional reinforcement such as the special law for underground safety management. As for the various cases of urban ground excavation practice, the ground subsidence behavior characteristics which is predicted using various information before excavation showed a considerable difference that could not be ignored compared to the results real construction data. Changes in site conditions such as seasonal differences in design and construction period, changes in construction methods depending on the site conditions and long-term construction suspension due to various reasons could be considered as the main causes. As the countermeasures, the safety management system through various construction information is introduced, but there is still no suitable system which can predict the effect of excavation and risk assessment. In this study, a web-based system was developed in order to predict the degree of impact on the ground subsidence and surrounding structures in advance before ground excavation and evaluate the risk in the design and construction of urban ground excavation projects. A system was built using time series analysis technique that can predict the current and future behavior characteristics such as ground water level and settlement based on past field construction records with field monitoring data. It was presented as a geotechnical data visualization (GDV) technology for risk reduction and disaster management based on web-based system, Using this newly developed web-based assessment system, it is possible to predict ground excavation impact prediction and risk assessment.

• Journal of the Korean Society of Safety
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• v.38 no.2
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• pp.96-103
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• 2023

Generally, human stampedes and crowd collapses occur when people press against each other, causing falls that may result in death or injury. Particularly, crowd accidents have become increasingly common since the 1990s, with an average of 380 deaths annually. For instance, in Korea, a stampede occurred during the Itaewon Halloween festival on October 29, 2022, when several people crowded onto a narrow, downhill road, which was 45 meters long and between 3.2 and 4 meters wide. Precisely, this stampede was primarily due to the excessive number of people relative to the road size. Essentially, stampedes can occur anywhere and at any time, not just at events, but also in other places where large crowds gather. More specifically, the likelihood of accidents increases when the crowd density exceeds a turbulence threshold of 5-6 /m². Meanwhile, festivals and events, which have become more frequent and are promoted through social media, garner people from near and far to a specific location. Besides, as cities grow, the number of people gathering in one place increases. While stampedes are rare, their impact is significant, and the uncertainty associated with them is high. Currently, there is no scientific system to analyze the risk of stampedes due to crowd concentration. Consequently, to prevent such accidents, it is essential to prepare for crowd disasters that reflect social changes and regional characteristics. Hence, this study proposes using digital topographic maps and crowd-density risk simulations to develop a 3D model of the region. Specifically, the crowd density simulation allows for an analysis of the density of people walking along specific paths, which enables the prediction of danger areas and the risk of crowding. By using the simulation method in this study, it is anticipated that safety measures can be rationally established for specific situations, such as local festivals, and preparations may be made for crowd accidents in downtown areas.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2015.11a
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• pp.60-61
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• 2015

In recent years, the accidents in construction projects are continuously increasing due to their complexity and variety. However, few studies have been conducted regarding the risk prediction model and the database of risk assessment in construction projects. To address of these issues, the objective of this study is to analyze the accident causes by using insured claim payouts of insurance companies. First, the descriptive analysis of accidents causes is conducted according to scheduling rate, season, and total construction costs. Second, the correlation analysis is conducted between accidents causes and total construction costs. In the future, the risk assessment model can be developed to quantify the accident causes in construction projects to estimate claim payouts of insurance companies.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.7
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• pp.273-280
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• 2006

This paper suggests the method that forecasts Dynamic Line Rating (DLR). Thermal Overload Risk Probability (TORP) of the next time is forecasted based on the present weather conditions and DLR value by Monte Carlo Simulation (MCS). To model weather elements of transmission line for MCS process, this paper will propose the use of statistical weather models that time series is applied. Also, through the case study, it is confirmed that the forecasted TORP can be utilized as a criterion that decides DLR of next time. In short, proposed method may be used usefully to keep security and reliability of transmission line by forecasting transmission capacity of the next time.