• Anxiety and mood
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• v.5 no.2
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• pp.80-88
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• 2009

Disorder of Extreme Stress, Not Otherwise Specified (DESNOS) is the proposed diagnosis that meets the severe, complex, and prolonged psychological sequela of victims with chronic traumatization (e.g., family violence, incest, and childhood sexual or physical abuse). The hallmarks of DESNOS are a multiplicity of symptoms (e.g., somatization, dissociation, and depression), pathological changes in relationships, identity disturbances, and a propensity to experience repeated harm and injury at the hands of oneself and others. DESNOS can be directly assessed by Structured Interview of Disorder of Extreme Stress (SIDES) and Self- Report Inventory of Disorder of Extreme Stress (SIDES-SR). The treatment of DESNOS should be phaseoriented and involve movement back and forth among three basic stages : 1) stabilization ; 2) trauma processing ; 3) reintegration.

• Journal of the Korean Society of Industry Convergence
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• v.8 no.2
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• pp.85-95
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• 2005

The margin level in the futures market platys an important role in balancing the default probability with the investor's opportunity cost. In this paper, we investigate whether the movement of KOSPI200 futures daily prices can be modeled with the extreme value theory. Based on this investigation, we examine the validity of the margin level set by the extreme value theory. Moreover, we propose an expected profit-maximization model for securities companies. In this model, the extreme value theory is used for cost estimation, and a regression analysis is used for revenue calculation. Computational results are presented to compare the extreme value distribution with the empirical distribution of margin violation in KOSPI200 and to examine the suitability of the expected profit-maximization model.

• Wind and Structures
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• v.2 no.2
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• pp.85-94
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• 1999

The EARPG(1)/UPS was first developed by Seong (1993) and has been tested for wind pressure time series simulations (Seong and Peterka 1993, 1997, 1998) to prove its excellent performance for generating non-Gaussian time series, in particular, with large amplitude sharp peaks. This paper presents a parametric study focused on simulation of extreme value statistics based on the synthetic realizations of the EARPG(1)/UPS. The method is shown to have a great capability to simulate a wide range of non-Gaussian statistic values and extreme value statistics with exact target sample power spectrum. The variation of skewed long tail in PDF and extreme value distribution are illustrated as function of relevant parameters.

• Korean Management Science Review
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• v.24 no.2
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• pp.115-126
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• 2007

Weekly minima of daily log returns of Korean composite stock price index 200 and its five industry-based business divisions over the period from January 1990 to December 2005 are fitted using two block-based extreme distributions: Generalized Extreme Value(GEV) and Generalized Logistic(GLO). Parameters are estimated using the probability weighted moments. Applicability of two distributions is investigated using the Monte Carlo simulation based empirical p-values of Anderson Darling test. Our empirical results indicate that both the GLO and GEV models seem to be comparably applicable to the weekly minima. These findings are against the evidences in Gettinby et al.[7], who claimed that the GEV model was not valid in many cases, and supported the significant superiority of the GLO model.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.581-585
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• 2010

This study introduced a Bayesian based frequency analysis in which the statistical trend seasonal analysis for hydrologic extreme series is incorporated. The proposed model employed Gumbel and GEV extreme distribution to characterize extreme events and a fully coupled bayesian frequency model was finally utilized to estimate design rainfalls in Seoul. Posterior distributions of the model parameters in both trend and seasonal analysis were updated through Markov Chain Monte Carlo Simulation mainly utilizing Gibbs sampler. This study proposed a way to make use of nonstationary frequency model for dynamic risk analysis, and showed an increase of hydrologic risk with time varying probability density functions. In addition, full annual cycle of the design rainfall through seasonal model could be applied to annual control such as dam operation, flood control, irrigation water management, and so on. The proposed study showed advantage in assessing statistical significance of parameters associated with trend analysis through statistical inference utilizing derived posterior distributions.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.50-50
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• 2018

As the frequency of drought due to climate change is increasing and the severity of drought becomes severe, it is urgent to prepare measures against extreme drought. Despite the significant impacts of drought on the coupled human-environment system, we have not fully understood the consequences of extreme droughts affecting all parts of the environment and our communities, and there is no system to assess environmental droughts quantitatively. Even if a drought disaster occurs on the same scale, the severity of the drought depends on the vulnerability of the region. Therefore, this study proposes environmental drought assessment based on water quality vulnerability to extreme drought for the resilient proactive response.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.15-15
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• 2016

In South Korea, annual maximum precipitation often occurs in association with mature typhoons in the western Pacific and from summer monsoon rains. In addition, certain years have no significant typhoon activity. Therefore, the characteristics of frequency distributions differ between extreme typhoons and monsoon events. Those extremes are also influenced from climate conditions in a different way. Application of nonstationary frequency analysis to the AMP data combined with typhoon and monsoon events might not always be reasonable. Therefore, we propose a novel approach of nonstationary frequency analysis to integrate extreme events of AMP induced from two main sources such as typhoons and monsoon in the current study. In this way, we were able to model the nonstationarity of extreme events from tropical storms and monsoon separately.

• ALGAE
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• v.36 no.1
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• pp.61-72
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• 2021

Increased proliferation of bacterial resistance to antibiotics is a critical issue that has increased the demand for novel antibacterial compounds. Antibacterial activities have been evaluated in extracts from photosynthetic green microalgae, with varying levels of subsequent potential for development based on the strain of algae, strain of bacterial pathogen, and solvent used to extract the metabolites. Green microalgae from extreme environmental conditions have had to adapt to conditions that exclude many other organisms. The production of antibacterial compounds aids directly or indirectly in the survival of green microalgae in these extreme environments, as well as potentially serve other roles. This review investigates antibacterial activities of green microalgae from both extreme in-situ environmental conditions and induced extreme laboratory conditions and highlights.

• International Journal of Advanced Culture Technology
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• v.10 no.3
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• pp.57-62
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• 2022

This paper is intended to study the tragedy of Kim Sung-han's short stories 'Extreme' and 'Return'. The author describes the postwar chaotic reality as a tragic reality in the novel as the pain of the times people experience. In the novels "Extreme" and "Return," war is violence, and all human beings who participate in it are victims of the violence of war. However, in "Extreme," Tatsuko expresses her will to fight against the tragic reality in "Return." Kim Sung-han never wants them to stay in the tragic world, although the tragic reality of the main characters in his novel ends in a tragic ending. He wants them to fight against the tragic reality.

• Communications for Statistical Applications and Methods
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• v.29 no.3
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• pp.319-331
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• 2022

In this paper, we develop a new statistical model to forecast the PM_2.5 level in Seoul, South Korea. The proposed model is based on the extreme quantile regression model with lasso penalty. Various meteorological variables and air pollution variables are considered as predictors in the regression model, and the lasso quantile regression performs variable selection and solves the multicollinearity problem. The final prediction model is obtained by combining various extreme lasso quantile regression estimators and we construct a binary classifier based on the model. Prediction performance is evaluated through the statistical measures of the performance of a binary classification test. We observe that the proposed method works better compared to the other classification methods, and predicts 'very bad' cases of the PM_2.5 level well.