• Sleep Medicine and Psychophysiology
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• v.18 no.2
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• pp.63-66
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• 2011

It has been controversial whether upper airway resistance syndrome (UARS) is a distinct syndrome or not since it was reported in 1993. The International Classification of Sleep Disorders classified UARS under obstructive sleep apnea syndrome (OSAS) in 2005. UARS can be diagnosed when the apnea-hypopnea index (AHI) is fewer than 5 events per hour, the simultaneously calculated respiratory disturbance index (RDI) is more than 5 events per hour due to abnormal non-apneic non-hypopneic respiratory events accompanying respiratory effort related arousals (RERAs), and oxygen saturation is greater than 92% at termination of an abnormal breathing event. Although esophageal pressure measurement remains the gold standard for detecting subtle breathing abnormality other than hypopnea and apnea, nasal pressure transducer has been most commonly used. RERAs include phase A2 of cyclical alternating patterns (CAPs) associated with EEG changes. Symptoms of OSAS can overlap with UARS, but chronic insomnia tends to be more common in UARS than in OSAS and clinical symptoms similar with functional somatic syndrome are also more common in UARS. In this journal, diagnostic and clinical differences between UARS and OSAS are reviewed.

• Proceedings of the Korean Nuclear Society Conference
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• 2001.05a
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• pp.120.2-120
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• 2001

• KIISE Transactions on Computing Practices
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• v.21 no.12
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• pp.768-773
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• 2015

Emergence events are the cause of much economic damage. In order to minimize the damage that these events cause, it must be possible to predict what will happen in the future. Accordingly, many researchers have focused on real-time monitoring, detecting events, and investigating events. In addition, there have also been many studies on predictive analysis for forecasting of future trends. However, most studies provide future tendency per event without contextual compositive analysis. In this paper, we present a predictive visual analytics system using topic composition to provide future trends per event. We first extract abnormal topics from social media data to find interesting and unexpected events. We then search for similar emergence patterns in the past. Relevant topics in the past are provided by news media data. Finally, the user combines the relevant topics and a new context is created for contextual prediction. In a case study, we demonstrate our visual analytics system with two different cases and validate our system with possible predictive story lines.

• International Commerce and Information Review
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• v.9 no.3
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• pp.305-320
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• 2007

With expanded use of B2B(between enterprises), B2G(between enterprises and government) and EDI(Electronic Data Interchange), and increased amount of available network information and information protection threat, as it was judged that security can not be perfectly assured only with security technology such as electronic signature/authorization and access control, Bayesian networks have been developed for protection of information. Therefore, this study speculates Bayesian networks system, centering on ERP(Enterprise Resource Planning). The Bayesian networks system is one of the methods to resolve uncertainty in electronic data interchange and is applied to overcome uncertainty of abnormal invasion detection in ERP. Bayesian networks are applied to construct profiling for system call and network data, and simulate against abnormal invasion detection. The host-based abnormal invasion detection system in electronic trade analyses system call, applies Bayesian probability values, and constructs normal behavior profile to detect abnormal behaviors. This study assumes before and after of delivery behavior of the electronic document through Bayesian probability value and expresses before and after of the delivery behavior or events based on Bayesian networks. Therefore, profiling process using Bayesian networks can be applied for abnormal invasion detection based on host and network. In respect to transmission and reception of electronic documents, we need further studies on standards that classify abnormal invasion of various patterns in ERP and evaluate them by Bayesian probability values, and on classification of B2B invasion pattern genealogy to effectively detect deformed abnormal invasion patterns.

• Journal of muscle and joint health
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• v.27 no.2
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• pp.122-131
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• 2020

Purpose: This study aims to investigate major traumatic events experienced by nurses in regional trauma centers and explore the relationship among their traumatic events experience, perceived stress, and stress coping. Methods: Data were collected from 208 nurses in the trauma emergency room (trauma-bay) and trauma intensive care unit at four regional trauma centers. Results: The mean score of the traumatic events experience was 44.3 out of 76 points. The scores for physical injuries caused by traffic accidents or falls as well as patient care with abnormal behaviors were high. Significantly positive correlations among traumatic events experience, perceived stress, and stress coping were identified. Conclusion: Nurses working in the regional trauma centers experienced many various traumatic events, leading to high levels of stress. This study suggests that it is necessary to establish a regular surveillance system for nurses' traumatic events experience and perceived stress.

• Nuclear Engineering and Technology
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• v.53 no.4
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• pp.1181-1188
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• 2021

When abnormal events occur in a nuclear power plant, operators must conduct appropriate abnormal operating procedures. It is burdensome though for operators to choose the appropriate procedure considering the numerous main plant parameters and hundreds of alarms that should be judged in a short time. Recently, various research has applied deep-learning algorithms to support this problem by classifying each abnormal condition with high accuracy. Most of these models are trained with simulator data because of a lack of plant data for abnormal states, and as such, developed models may not have tolerance for plant data in actual situations. In this study, two approaches are investigated for a deep-learning model trained with simulator data to overcome the performance degradation caused by noise in actual plant data. First, a preprocessing method using several filters was employed to smooth the test data noise, and second, a data augmentation method was applied to increase the acceptability of the untrained data. Results of this study confirm that the combination of these two approaches can enable high model performance even in the presence of noisy data as in real plants.

• Journal of Integrative Natural Science
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• v.17 no.1
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• pp.1-12
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• 2024

Many abnormal climate events are occurring around the world. The cause of abnormal climate is related to temperature. Factors that affect temperature include excessive emissions of carbon and greenhouse gases from a global perspective, and air circulation from a local perspective. Due to the air circulation, many abnormal climate phenomena such as abnormally high temperature and abnormally low temperature are occurring in certain areas, which can cause very serious human damage. Therefore, the problem of abnormal temperature should not be approached only as a case of climate change, but should be studied as a new category of climate crisis. In this study, we proposed a model for the classification of abnormal temperature using random forests based on various meteorological data such as longitudinal observations, yellow dust, ultraviolet radiation from 2018 to 2022 for each region in Korea. Here, the meteorological data had an imbalance problem, so the imbalance problem was solved by oversampling. As a result, we found that the variables affecting abnormal temperature are different in different regions. In particular, the central and southern regions are influenced by high pressure (Mainland China, Siberian high pressure, and North Pacific high pressure) due to their regional characteristics, so pressure-related variables had a significant impact on the classification of abnormal temperature. This suggests that a regional approach can be taken to predict abnormal temperatures from the surrounding meteorological environment. In addition, in the event of an abnormal temperature, it seems that it is possible to take preventive measures in advance according to regional characteristics.

• Journal of Korean Critical Care Nursing
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• v.14 no.2
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• pp.70-81
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• 2021

Purpose : The purpose of this study was to identify the moderating effects of emotional intelligence on the relationship between traumatic events and turnover intention among nurses working in intensive care units (ICUs). Method : In this predictive correlation study, the convenience sample included 133 ICU nurses. Data were collected using an online, structured self-report survey. The collected data were analyzed by descriptive statistics, an independent t-test, an analysis of variance, Pearson's correlation coefficient, and a hierarchical multiple regression analysis using SPSS/WIN 25.0. Results : The most frequently experienced traumatic events in ICUs were "nursing patients with abnormal behavior, including shouting and delirium," "end-of-life care," and "nursing patients with a risk of disease transmission, including AIDS and tuberculosis." The moderating effect of emotional intelligence was found to be statistically significant on the relationship between traumatic events and turnover intentions (𝛽=-0.15, p =.029). Conclusion : Intervention to improve the emotional intelligence of ICU nurses can be a salient strategy to reduce turnover intention resulting from traumatic events.

• Korean Journal of Microbiology
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• v.17 no.3
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• pp.137-151
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• 1979

Germinating conidia of Aspergillus nidulans diploid heterozygous for color and other genetic markers were used to direct and distinguish genetic events such as mutation, mitotic crossingover and nondisjunction in a single test after treatment with N-methyl-N'-nitro-N-nitrosoguanidine (NG), mitomycin C(MC), and chloral hydrate(CH). The following results were obtained : 1. NG reduced the survival of conidia and increased the frequencies of miototic segregants about sevenfoli over the control ; among the mitotic segregants the predominant genetic event was mitotic crossingover. NG also produced many abnormal colonies, which appeared to be of the types caused by induced semidominant lethals or chromosomal aberrations, and the aneuploid types found spontaneously. 2. After treatment with MC the survival of conidia was reduced but few abnormal colonies were produced. The frequencies of miotic segregants were increased about threefold over the control ; in the mitotic segeregants the induced genetic event was mitotic crossingover. 3. CH gave no apparent effect on the survival of conidia and the frequencies of mitotic segregants. However, CH generated abnormal colonies, very greatly, which turned out to be of the aneuploid types. This result suggests that CH interferes with the normal distribution of chromosomes in mitosis.

• 한국수산학회:학술대회논문집
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• 2005.05a
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• pp.161-161
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• 2005