• Title/Summary/Keyword: Threat score

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Comparison of Risk and Safety Perceptions of Industrial Hygienist (산업위생 분야 종사자들의 사회 안전의식변화에 관한 조사)

  • Lim, Dae Sung;Lee, Seung kil
    • Journal of Korean Society of Occupational and Environmental Hygiene
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    • v.30 no.4
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    • pp.331-341
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    • 2020
  • Objectives: This study was conducted to evaluate perceptions of safety and risk among Korean industrial hygienists and the change between before and after the Sewol Ferry Disaster in 2014. Two surveys with questionnaires composed of 51 questions were completed by attendees of the Korea Industrial Hygiene Association(KIHA) conference. Methods: One was conducted at the 2013 KIHA Fall Conference(N=181) and the other was from the 2014 KIHA Summer Conference(N=123). Between these two surveys was the Sewol Ferry Disaster on April 14, 2014, which was believed to seriously affect safety and risk perceptions in Korea. Results: It was revealed that industrial hygienists' awareness of safety rules strengthened after the Sewol Ferry Disaster(p<0.05). It was apparent that people over the age of 30 were more sensitive to social safety. There was no significant difference in the evaluation and attitude regarding governmental safety policy between the years of 2013 and 2014. The credibility of public organizations responsible for the disaster management system decreased. The self-evaluation of respondents' safety level also decreased. This trend shows mainly in the younger generation. It was evaluated that the overall social safety level decreased and the anxiety level increased. The score on social safety on a ±5 Likert scale was 0.68 in the 2013 survey and -0.33 in the 2014 survey(p<0.05). It was reported that the most serious threat factors for accident or disaster were 'building collapse > illegalities and corruption > side effects of radiation therapy >accidents in normal activity > occupational disease,' in order. They picked 'safety ignorance > hurry-up habits and culture > focusing on short-term benefit > easy-going attitude > insufficient safety education' for the causes of low social safety levels in 2013. In 2014, they were 'safety ignorance > easy-going attitude > focusing on short-term benefit > insufficient safety education > hurry-up habits and culture'. Conclusions: This study has some limitations because it was originally not designed to survey attitudes prior to the Sewol Ferry disaster in 2013. In addition, the survey targets are industrial hygienists who are familiar with occupational disease and injury.

Detection of Marine Oil Spills from PlanetScope Images Using DeepLabV3+ Model (DeepLabV3+ 모델을 이용한 PlanetScope 영상의 해상 유출유 탐지)

  • Kang, Jonggu;Youn, Youjeong;Kim, Geunah;Park, Ganghyun;Choi, Soyeon;Yang, Chan-Su;Yi, Jonghyuk;Lee, Yangwon
    • Korean Journal of Remote Sensing
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    • v.38 no.6_2
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    • pp.1623-1631
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    • 2022
  • Since oil spills can be a significant threat to the marine ecosystem, it is necessary to obtain information on the current contamination status quickly to minimize the damage. Satellite-based detection of marine oil spills has the advantage of spatiotemporal coverage because it can monitor a wide area compared to aircraft. Due to the recent development of computer vision and deep learning, marine oil spill detection can also be facilitated by deep learning. Unlike the existing studies based on Synthetic Aperture Radar (SAR) images, we conducted a deep learning modeling using PlanetScope optical satellite images. The blind test of the DeepLabV3+ model for oil spill detection showed the performance statistics with an accuracy of 0.885, a precision of 0.888, a recall of 0.886, an F1-score of 0.883, and a Mean Intersection over Union (mIOU) of 0.793.

Malicious Traffic Classification Using Mitre ATT&CK and Machine Learning Based on UNSW-NB15 Dataset (마이터 어택과 머신러닝을 이용한 UNSW-NB15 데이터셋 기반 유해 트래픽 분류)

  • Yoon, Dong Hyun;Koo, Ja Hwan;Won, Dong Ho
    • KIPS Transactions on Software and Data Engineering
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    • v.12 no.2
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    • pp.99-110
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    • 2023
  • This study proposed a classification of malicious network traffic using the cyber threat framework(Mitre ATT&CK) and machine learning to solve the real-time traffic detection problems faced by current security monitoring systems. We applied a network traffic dataset called UNSW-NB15 to the Mitre ATT&CK framework to transform the label and generate the final dataset through rare class processing. After learning several boosting-based ensemble models using the generated final dataset, we demonstrated how these ensemble models classify network traffic using various performance metrics. Based on the F-1 score, we showed that XGBoost with no rare class processing is the best in the multi-class traffic environment. We recognized that machine learning ensemble models through Mitre ATT&CK label conversion and oversampling processing have differences over existing studies, but have limitations due to (1) the inability to match perfectly when converting between existing datasets and Mitre ATT&CK labels and (2) the presence of excessive sparse classes. Nevertheless, Catboost with B-SMOTE achieved the classification accuracy of 0.9526, which is expected to be able to automatically detect normal/abnormal network traffic.

COVID-19-related Korean Fake News Detection Using Occurrence Frequencies of Parts of Speech (품사별 출현 빈도를 활용한 코로나19 관련 한국어 가짜뉴스 탐지)

  • Jihyeok Kim;Hyunchul Ahn
    • Journal of Intelligence and Information Systems
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    • v.29 no.2
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    • pp.267-283
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    • 2023
  • The COVID-19 pandemic, which began in December 2019 and continues to this day, has left the public needing information to help them cope with the pandemic. However, COVID-19-related fake news on social media seriously threatens the public's health. In particular, if fake news related to COVID-19 is massively spread with similar content, the time required for verification to determine whether it is genuine or fake will be prolonged, posing a severe threat to our society. In response, academics have been actively researching intelligent models that can quickly detect COVID-19-related fake news. Still, the data used in most of the existing studies are in English, and studies on Korean fake news detection are scarce. In this study, we collect data on COVID-19-related fake news written in Korean that is spread on social media and propose an intelligent fake news detection model using it. The proposed model utilizes the frequency information of parts of speech, one of the linguistic characteristics, to improve the prediction performance of the fake news detection model based on Doc2Vec, a document embedding technique mainly used in prior studies. The empirical analysis shows that the proposed model can more accurately identify Korean COVID-19-related fake news by increasing the recall and F1 score compared to the comparison model.

Identification and Measurement of Hospital-Related Fears in Hospitalized School-Aged Children (학령기 입원아동의 병원관련 공포에 관한 탐색연구)

  • 문영임
    • Journal of Korean Academy of Nursing
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    • v.25 no.1
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    • pp.61-79
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    • 1995
  • When children are admitted to hospital, they have to adapt to new and unfamiliar stimuli. Children may respond with fear to stimuli such as pain or unfamiliar experiences. One goal of nursing is to help hospitalized children to adapt effectively to their hospital experience. Accordingly, nurses need to assess childrens' fears of their hospital experience to contribute to the planning of care to alleviate these fears. The problem addressed by this study was to identify and measure hospital-related fears(hereafter called HRF) in hospitalized school-aged children. The study was conceptualized with Roy's model. A descriptive qualitative approach was used first, followed by a quantitative approach. This study was conducted from November 30, 1989 to January 12, 1991. The sample consisted of 395 hospitalized school-aged children selected through an allocated sampling technique in nine general hospitals. The HRF questionnaire (three point likert scale ) was developed by a delphi technique. The data were analyzed by an SAS program. Factor analysis was used for the examination of component factors. Differences in the HRF related to demographic variables were examined by t-test, analysis of variance and the Scheffe test. The crude scores of the HRF scale were transformed into T- scores to calculate the standard scores. The results included the following : 1. Forty-four items were derived from 188 statements identifying the childrens' hospital-re-lated fears. These items clustered into 14 factors, fear of injections, operations, bodily harm others' pain, medical rounds, physical examinations, medical staff, disease process, blood and X-rays, drugs and cockroaches, tests, harsh discipline from parents or staff, being absent from school, and separation from family. The 14 factors was classified into four categories,'pain','the unfamiliar','the un-known' and 'separation'. 2. The reliability of the HRF instruments was .92(Cronbach's alpha). In the factor analysis, Cronbach's alpha coefficients for the 14 factors ranged from .84 to .86 and Cronbach's alpha coefficients for the four categories ranged from .70 to .84. Pearson correlation coefficient scores for relationships among the 14 factors ranged from ,11 to .50, and among the four categories, from ,44 to ,63, indicating their relative independence. 3. The total group HRF score ranged from 45 to 130 in a possible range of H to 132, with a mean of 74.51. The fears identified by the children were, in order, injections, harsh discipline by parents or staff, bodily harm, operations, medical staff, disease process, and medical rounds ; the least feared was others' pain. The fear item with the highest mean score was surgery and the lowest was examination by a doctor. HRF scores were higher for girls than for boys, and for grade 1 students than for grade 6 students. HRF scores were lower for children whose fathers were over 40 than for those whose fathers were in the 30 to 39 age group, and whose mothers were over 35 than for those whose mothers were in the 20 to 34 age group. HRF scores were lower when the mother rather than any other person stayed with the child. The expressed fear of pain, the unfamiliar, the un-known and of separation directs nurses' concern to the threat felt by hospitalized children to their concept of self. This study contributes to the assessment of fears of hospitalized children and of stimuli impinging on those fears. Accordingly, nursing practice will be directed to the alleviation of pain, pre-admission orientation to the hospital setting and routines, initiation of information about procedures and experiences and arrangments for mothers to stay with their children. Recommendations were made for further research in different settings and for development and testing of the instrument.

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The NCAM Land-Atmosphere Modeling Package (LAMP) Version 1: Implementation and Evaluation (국가농림기상센터 지면대기모델링패키지(NCAM-LAMP) 버전 1: 구축 및 평가)

  • Lee, Seung-Jae;Song, Jiae;Kim, Yu-Jung
    • Korean Journal of Agricultural and Forest Meteorology
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    • v.18 no.4
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    • pp.307-319
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    • 2016
  • A Land-Atmosphere Modeling Package (LAMP) for supporting agricultural and forest management was developed at the National Center for AgroMeteorology (NCAM). The package is comprised of two components; one is the Weather Research and Forecasting modeling system (WRF) coupled with Noah-Multiparameterization options (Noah-MP) Land Surface Model (LSM) and the other is an offline one-dimensional LSM. The objective of this paper is to briefly describe the two components of the NCAM-LAMP and to evaluate their initial performance. The coupled WRF/Noah-MP system is configured with a parent domain over East Asia and three nested domains with a finest horizontal grid size of 810 m. The innermost domain covers two Gwangneung deciduous and coniferous KoFlux sites (GDK and GCK). The model is integrated for about 8 days with the initial and boundary conditions taken from the National Centers for Environmental Prediction (NCEP) Final Analysis (FNL) data. The verification variables are 2-m air temperature, 10-m wind, 2-m humidity, and surface precipitation for the WRF/Noah-MP coupled system. Skill scores are calculated for each domain and two dynamic vegetation options using the difference between the observed data from the Korea Meteorological Administration (KMA) and the simulated data from the WRF/Noah-MP coupled system. The accuracy of precipitation simulation is examined using a contingency table that is made up of the Probability of Detection (POD) and the Equitable Threat Score (ETS). The standalone LSM simulation is conducted for one year with the original settings and is compared with the KoFlux site observation for net radiation, sensible heat flux, latent heat flux, and soil moisture variables. According to results, the innermost domain (810 m resolution) among all domains showed the minimum root mean square error for 2-m air temperature, 10-m wind, and 2-m humidity. Turning on the dynamic vegetation had a tendency of reducing 10-m wind simulation errors in all domains. The first nested domain (7,290 m resolution) showed the highest precipitation score, but showed little advantage compared with using the dynamic vegetation. On the other hand, the offline one-dimensional Noah-MP LSM simulation captured the site observed pattern and magnitude of radiative fluxes and soil moisture, and it left room for further improvement through supplementing the model input of leaf area index and finding a proper combination of model physics.