• Genomics & Informatics
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• v.17 no.4
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• pp.41.1-41.12
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• 2019

Survival analysis mainly deals with the time to event, including death, onset of disease, and bankruptcy. The common characteristic of survival analysis is that it contains "censored" data, in which the time to event cannot be completely observed, but instead represents the lower bound of the time to event. Only the occurrence of either time to event or censoring time is observed. Many traditional statistical methods have been effectively used for analyzing survival data with censored observations. However, with the development of high-throughput technologies for producing "omics" data, more advanced statistical methods, such as regularization, should be required to construct the predictive survival model with high-dimensional genomic data. Furthermore, machine learning approaches have been adapted for survival analysis, to fit nonlinear and complex interaction effects between predictors, and achieve more accurate prediction of individual survival probability. Presently, since most clinicians and medical researchers can easily assess statistical programs for analyzing survival data, a review article is helpful for understanding statistical methods used in survival analysis. We review traditional survival methods and regularization methods, with various penalty functions, for the analysis of high-dimensional genomics, and describe machine learning techniques that have been adapted to survival analysis.

• International Journal of Advanced Culture Technology
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• v.11 no.2
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• pp.191-195
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• 2023

An X-event is an event that is difficult to predict and unlikely to occur, but if it occurs, it has a very large ripple effect, such as loss of life, property, territory, and emotional turmoil. Extreme events are unlikely to occur, but they can happen someday, and if they do, they have a great impact on society as a whole, so they must be prepared to minimize the impact and impact. For this purpose, we collected opinions from low-level experts at the Korea Army Research Center for Future & Innovation and the Army College on extreme events that can trigger the near future (10 years) in the field of high-tech science and technology, which is currently developing rapidly after the 4th Industrial Revolution. The researchers intend to synthesize and analyze this data to derive implications and provide a response direction to alleviate the ultra-uncertainty of extreme events and provide a cornerstone for crisis management strategies for the occurrence of serial and simultaneous extreme events.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.32 no.2
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• pp.135-141
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• 2024

Despite the innovative technological advances in the aviation industry, hard landing events that occur during aircraft landing account for 13% of all accidents. Hard landing when landing an aircraft affects normal operation by generating a large load on the landing gear and the fuselage. In order to identify these risk factors, the airline monitors the high vertical acceleration event, a precursor to hard landing, through QAR (Quick Access Recorder) flight data analysis, and prepares and implements mitigation measures. In this study, it is intended to contribute to safety management based on flight data analysis that identifies the characteristics of high vertical acceleration G event data that can cause such hard landing and detailed parameters of precursor signs, and to identify the causal relationship of the occurrence of the event by applying statistical analysis methods such as variance analysis, correlation analysis, and regression analysis models to identify the characteristics of the event occurrence and eliminate the cause in advance.

• Journal of the Korean Home Economics Association
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• v.33 no.5
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• pp.89.1-114
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• 1995

This study examined whether specific coping strategies for the university entrance examination stress are related to high school seniors' depression and whether these strategies moderate the relationship between additional life event stress and depression. A sample of 358 high school seniors reported their level of additional life event stress, coping strategies they were using to handle examination stress, and their levels of depression. Problem-focused coping strategies and affective-regulation coping strategies were found to be related to reduction in depression. And, coping with examination stress was found to be related to depression independent of the level of additional life event stress.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.30 no.3
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• pp.37-43
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• 2007

We use influence diagrams to describe event trees used in safety analyses of low-probability high-risk incidents. This paper shows how the branch parameters used in the event tree models can be updated by a bayesian method based on the observed counts of certain well-defined subsets of accident sequences. We focus on the analysis of the shared branch parameters, which may frequently often in the real accident initiation and propagation to more severe accident. We also suggest the way to utilize different levels of accident data to forecast low-probability high-risk accidents.

• The Korean Journal of Emergency Medical Services
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• v.12 no.3
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• pp.5-15
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• 2008

Purpose : This study is a descriptive research to provide basic factors of posttraumatic stress in Firefighters. This study was carried out to develop the effective program for the fire fighters to cope with the posttraumatic stress following the disasters. Methods : The questionnaires were collected among fire fighters who serviced in K and I community from April 1 to June 30 in 2008. Total 304 questionnaires were analyzed by SPSS WIN program for descriptive statistics, Pearson's correlation coefficient and t-test. Results : 1. 48.0% of 300 fire fighters were at the age of 31-40, and 42.3% were under 30. 2. Work burden had a significant difference of 2.30 in low-risk group, 2.60 in high-risk group(t-value=-3.85, p=0.00). However, life event had no significant difference 0.79 event in low-risk group, 1.41 event in high-risk group(t-value=-2.27, p=0.24). 3. Concerning posttraumatic stress factors, there was positive correlation between mobilization impact level r=0.38(P<0.01), work burden r=0.38(p<0.01), and life event r=0.27(p<0.01). 4. According to the Symptom Check List-Revised(SCL-90-R), somatization had a significant differences(t-value=5.46, p=0.00), obsessive-compulsive(t-value=7.16, p=0.00), interpersonal sensitivity(t-value=6.15, p=0.00), depression(t-value=6.62, p=0.00), anxiety (t-value=7.33, p=0.00), hostility(t-value=5.94, p=0.00), phobia anxiety(t-value=6.85, p=0.00), paranoid ideation(t-value=5.55, p=0.00), psychotism(t-value=6.52, p=0.00) in low-risk and high-risk group. Conclusion : As a consequence, mobilization impact, work burden, and life event were the influential factors on posttraumatic stress. Also, high-risk group revealed significantly higher score on all 9 scales. The information obtained from surveys made recommendation to develop the intervention of stress management to control mobilization impact and posttraumatic stress.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.30 no.6_1
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• pp.549-557
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• 2012

Spatial phenomena such as environment pollution, disease and the risk of spreading information need a rapid initial response to perceive spread event. Moving data perceive spread event through real-time processing and analysis. To process and analysis the event, spatial-temporal complex event processing is used. Previous spatialtemporal complex event processing is possible basis spatial operator but insufficient apply to design spatialtemporal complex event processing to perceive spatial phenomena of high complexity. This study proposed hierarchical spatio-temporal CEP design which will efficiently manage the fast growing incoming sensor data. The implementation of the proposed design is evaluated with GPS location data of moving vehicles which are used as the incoming data stream for identifying spatial events. The spatial component of existing CEP software engine has been extended during the implementation phase to broaden the capabilities of processing spatio-temporal events.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3965-3982
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• 2015

Event detection is one of the key issues in many wireless sensor network (WSN) applications. The uncertainties that are derived from the instability of sensor node, measurement noise and incomplete sampling would influence the performance of event detection to a large degree. Many of the present researches described the sensor readings with crisp values, which cannot adequately handle the uncertainties inhered in the imprecise sensor readings. In this paper, a fault-tolerant event detection algorithm is proposed based on Dempster-Shafer (D-S) theory (also called evidence theory). Instead of crisp values, all possible states of the event are represented by the Basic Probability Assignment (BPA) functions, with which the output of each sensor node are characterized as weighted evidences. The combination rule was subsequently applied on each sensor node to fuse the evidences gathered from the neighboring nodes to make the final decision on whether the event occurs. Simulation results show that even 20% nodes are faulty, the accuracy of the proposed algorithm is around 80% for event region detection. Moreover, 97% of the error readings have been corrected, and an improved detection capability at the boundary of the event region is gained by 75%. The proposed algorithm can enhance the detection accuracy of the event region even in high error-rate environment, which reflects good reliability and robustness. The proposed algorithm is also applicable to boundary detection as it performs well at the boundary of the event.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.6
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• pp.1237-1252
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• 2010

This paper proposes an intelligent technique for data sharing and event transition among personal devices including smart phones, laptops, and desktops. We implemented the PES (Personal Event Service) system that proactively executes appropriate tasks across multiple devices without explicit user requests by sharing the data used by the user and recognizing user intention based on the observed actions of the user for specific devices. The client module of PES installed on each device monitors the user actions and recognizes the intention of the user. The server provides data sharing and maintenance for clients. The connection between client and server is established by Java RMI (Remote Method Invocation). A series of experiments were performed to evaluate user satisfaction and system accuracy, and the results showed that the PES system can proactively provide appropriate, personalized services with a high degree of satisfaction to the user in an effective and efficient manner.

• Geomechanics and Engineering
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• v.38 no.5
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• pp.467-476
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• 2024

This study investigates Tunnel Boring Machine (TBM) performance prediction by employing discrete event simulation technique, which is a potential remedy highlighting its stochastic adaptability to the complex nature of TBM tunnelling activities. The new discrete event simulation model using AnyLogic software was developed and validated by comparing its results with actual performance data for Daegok-Sosa railway project that Earth Pressure Balance (EPB) TBM machine was used in Korea. The results showed the successful implementation of predicting TBM performance. However, it necessitates high-quality database establishment including geological formations, machine specifications, and operation settings. Additionally, this paper introduces a novel methodology for daily performance updates during construction, using automated data processing techniques. This approach enables daily updates and predictions for the ongoing projects, offering valuable insights for construction management. Overall, this study underlines the potential of discrete event simulation in predicting TBM performance, its applicability to other tunneling projects, and the importance of continual database expansion for future model enhancements.