• Journal of the Society of Cosmetic Scientists of Korea
• /
• v.40 no.1
• /
• pp.55-88
• /
• 2014

In this study, we examined the use of cosmetics among adolescents in our country and their purchasing behavior, as the use of cosmetics has increasingly been prevailing in the younger generation. The aims of this study were to investigate the state of the youth cosmetics market, to grasp the needs of youth and ultimately to suggest the right directions for the youth cosmetics market. The subjects in this study were 1,092 boys and girls from 10 selected secondary schools in Seoul. After a survey was conducted, the answer sheets from 1,074 respondents were gathered, and the collected data were analyzed by the statistical package (SPSS WIN 18.0). The vast majority of the teens participated in the survey were answered to use basic cosmetics daily, and there was a gradual increase in the frequency that they used color cosmetics. They started to use cosmetics earlier in ages than the older generations. Most of them were in trouble due to acne, and the most dominant way to get rid of their skin troubles was by using cosmetics. The most common place at which they purchased cosmetics were brand shops, and they gave priority to the function of the products when they bought cosmetics. The adolescents were still told by their schools to abstain from using cosmetics, and that was the case for social climate as well. Yet they definitely wanted to be allowed to use cosmetics.

• The Journal of Korean Academic Society of Nursing Education
• /
• v.3
• /
• pp.34-45
• /
• 1997

Aspects of nursing student's clinical experiences are anxiety provoking. High anxiety may contribute to decreased learning. The purpose of this study was to identify the level of anxiety and potentially anxiety-producing clinical experience, the relation between the level of anxiety and their Trait-Anxiety and State-Anxiety. Finally, it is aimed at getting preparing data for guidance of students which can enhance learning effect of students for clinical experience. The samples of this study were 36 junior students(1 semester experience) and 44 senior students (3 semester experience) from Junior College of Nursing in Seoul on September 1996. The tools of this study were two kinds ; questionare of Spielberg' STAI measuring State and Trait-Anxiety, and author's for measuring the level of Anxiety producing situations and 10cm visual analogue scale was also used for measuring self stated level of anxiety on clinical setting. The collected data were analyzed by SPSS using percentage, t-test, ANOVA and Pearson correlation coefficient. The results of this study were as follows : 1. The self perception of anxiety level was 4.3/10cm and the level of anxiety in clinical setting situations was 3.5/5. 2. Among 20 questions for perception of anxiety-producing situations in the clinical setting. 'deficit of nursing knowledge' was the highest item(4.18), 'vagueness of role'(4.11), 'lack of nursing skill'(4.00), 'evaluation by faculty'(4.00) 'fear of making mistakes'(3.81) 'initial clinical experience on a unit'(3.76) 'initial application of nursing knowledge'(3.74) in turn. 3. The level of State-anxiety of senior students was higher than junior's (p=0.005)and the level of Trait-Anxiety of insufficient interpersonal relationship and unhealthy students were higher than others (p=0.015) There was no differences according to the student's grade in level of anxiety. 4. Both of self-stated anxiety and situationa anxiety of unhealthy students were high (p=0.007, p=0.000) and the level of self-stated anxiety of unsatisfied students for selection major and clinical experience were high (p=0.050, p=0.009). 5. Self-stated anxiety and situation anxiety (p=0.0000), self-stated- anxiety and Trait-anxiety(p=0.003), situation anxiety and Trait-anxiety(p=0.004), and Trait-anxiety and state-anxiety(p=0.000) of the students were interrelated. By the above conclusion, the nursing students still feel anxiety on clinical experience and on making a mistake due to the lack of their nursing knowledge and skill. And the students are afraid of the faculties' evaluation. In addition, the students who are not healthy and have not sufficiently interpersonal relationship feel more anxiety. But, since there was no difference significantly between each grade, we think it is needed that further study on the same topic in large samples. And, we have to equip the students with much nursing knowledge and philosophy apparently before the students have clinical experience. Finally, the faculty have to reduce the students' anxiety by making a climate of acceptance in clinical setting with good personality.

• Korean Journal of Remote Sensing
• /
• v.36 no.5_3
• /
• pp.1077-1093
• /
• 2020

Sea Surface Temperature (SST) is an important environmental indicator that affects climate coupling systems around the world. In particular, coastal regions suffer from abnormal SST resulting in huge socio-economic damage. This study used Long Short Term Memory (LSTM) and Convolutional Long Short Term Memory (ConvLSTM) to predict SST up to 7 days in the south sea region in South Korea. The results showed that the ConvLSTM model outperformed the LSTM model, resulting in a root mean square error (RMSE) of 0.33℃ and a mean difference of -0.0098℃. Seasonal comparison also showed the superiority of ConvLSTM to LSTM for all seasons. However, in summer, the prediction accuracy for both models with all lead times dramatically decreased, resulting in RMSEs of 0.48℃ and 0.27℃ for LSTM and ConvLSTM, respectively. This study also examined the prediction of abnormally high SST based on three ocean heatwave categories (i.e., warning, caution, and attention) with the lead time from one to seven days for an ocean heatwave case in summer 2017. ConvLSTM was able to successfully predict ocean heatwave five days in advance.

• Land and Housing Review
• /
• v.12 no.3
• /
• pp.109-119
• /
• 2021

Global warming caused by greenhouse gas emissions has rapidly increased abnormal climate events and geotechnical engineering hazards in terms of their size and frequency accordingly. Biopolymer-based soil treatment (BPST) in geotechnical engineering has been implemented in recent years as an alternative to reducing carbon footprint. Furthermore, thermo-gelating biopolymers, including agar gum, gellan gum, and xanthan gum, are known to strengthen soils noticeably. However, an explicitly detailed evaluation of the correlation between the factors, that have a significant influence on the strengthening behavior of BPST, has not been explored yet. In this study, machine learning regression analysis was performed using the UCS (unconfined compressive strength) data for BPST tested in the laboratory to evaluate the factors influencing the strengthening behavior of gellan gum-treated soil mixtures. General linear regression, Ridge, and Lasso were used as linear regression methods; the key factors influencing the behavior of BPST were determined by RMSE (root mean squared error) and regression coefficient values. The results of the analysis showed that the concentration of biopolymer and the content of clay have the most significant influence on the strength of BPST.

• Korean Journal of Remote Sensing
• /
• v.37 no.2
• /
• pp.291-304
• /
• 2021

Updating a forest type map is essential for sustainable forest resource management and monitoring to cope with climate change and various environmental problems. According to the necessity of efficient and wide-area forestry remote sensing, CAS500-4 (Compact Advanced Satellite 500-4; The agriculture and forestry satellite) project has been confirmed and scheduled for launch in 2023. Before launching and utilizing CAS500-4, this study aimed to pre-evaluation the possibility of satellite-based tree species classification using RapidEye, which has similar specifications to the CAS500-4. In this study, the study area was the Chuncheon forest management complex, Gangwon-do. The spectral information was extracted from the growing season image. And the GLCM texture information was derived from the growing and non-growing seasons NIR bands. Both information were used to classification with random forest machine learning method. In this study, tree species were classified into nine classes to the coniferous tree (Korean red pine, Korean pine, Japanese larch), broad-leaved trees (Mongolian oak, Oriental cork oak, East Asian white birch, Korean Castanea, and other broad-leaved trees), and mixed forest. Finally, the classification accuracy was calculated by comparing the forest type map and classification results. As a result, the accuracy was 39.41% when only spectral information was used and 69.29% when both spectral information and texture information was used. For future study, the applicability of the CAS500-4 will be improved by substituting additional variables that more effectively reflect vegetation's ecological characteristics.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.19 no.12
• /
• pp.218-226
• /
• 2018

The purpose of this study was to examine the validity and reliability of the Korean version of the nursing students' perception of instructor caring (NSPIC) scale developed by Wade and Kasper for nursing students. We collected data from 219 nursing students in the 3rd to 4th year at three nursing colleges from June 4 to June 20, 2018. Data were analyzed using SPSS 21.0 and AMOS 21.0. An exploratory factor analysis with varimax rotation was performed, resulting in five factors (respectful sharing, confidence through caring, control versus flexibility, supportive learning climate, appreciation of life's meanings) with a total of 27 items. Confirmatory factor analysis supported good convergent and discriminant validities. In addition, the concurrent validity test confirmed that the K-NSPIC scale was a validity tool as the correlation of the clinical learning environment (CLE) scale appeared as r=.64 (p<.001). The Cronbach's alpha coefficient of the K-NSPIC was .88, and Cronbach's alpha coefficient for each of the five factors was .91, .86, .80, .76, and .85; internal consistency was confirmed. It is significant that the K-NSPIC proved applicable as a useful tool for assessing instructor caring. It is also expected that it will assist in the design of programs to improve the caring ability of instructors.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1047-1056
• /
• 2022

Sea ice, frozen sea water, in the Artic is a primary indicator of global warming. Due to its importance to the climate system, shipping-route navigation, and fisheries, Arctic sea ice prediction has gained increased attention in various disciplines. Recent advances in artificial intelligence (AI), motivated by a desire to develop more autonomous and efficient future predictions, have led to the development of new sea ice prediction models as alternatives to conventional numerical and statistical prediction models. This study aims to evaluate the performance of the two-stream convolutional long-and short-term memory (TS-ConvLSTM) AI model, which is designed for learning both global and local characteristics of the Arctic sea ice changes, for the minimum September Arctic sea ice from 2001 to 2021, and to show the possibility for an operational prediction system. Although the TS-ConvLSTM model generally increased the prediction performance as training data increased, predictability for the marginal ice zone, 5-50% concentration, showed a negative trend due to increasing first-year sea ice and warming. Additionally, a comparison of sea ice extent predicted by the TS-ConvLSTM with the median Sea Ice Outlooks (SIOs) submitted to the Sea Ice Prediction Network has been carried out. Unlike the TS-ConvLSTM, the median SIOs did not show notable improvements as time passed (i.e., the amount of training data increased). Although the TS-ConvLSTM model has shown the potential for the operational sea ice prediction system, learning more spatio-temporal patterns in the difficult-to-predict natural environment for the robust prediction system should be considered in future work.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1273-1283
• /
• 2022

Recently, forest fires have frequently occurred due to climate change, leading to human and property damage every year. The forest fire monitoring technique using remote sensing can obtain quick and large-scale information of fire-damaged areas. In this study, the Gangneung and Donghae forest fires that occurred in March 2022 were analyzed using the spectral band of Sentinel-2, the normalized difference vegetation index (NDVI), and the normalized difference water index (NDWI) to classify the affected areas of forest fires. The U-net based convolutional neural networks (CNNs) model was simulated for the fire-damaged areas. The accuracy of forest fire classification in Donghae and Gangneung classification was high at 97.3% (f₁=0.486, IoU=0.946). The same model used in Donghae and Gangneung was applied to Uljin and Samcheok areas to get rid of the possibility of overfitting often happen in machine learning. As a result, the portion of overlap with the forest fire damage area reported by the National Institute of Forest Science (NIFoS) was 74.4%, confirming a high level of accuracy even considering the uncertainty of the model. This study suggests that it is possible to quantitatively evaluate the classification of forest fire-damaged area using a spectral band and indices similar to that of the Compact Advanced Satellite 500 (CAS500-4) in the Sentinel-2.

• Journal of Korea Water Resources Association
• /
• v.54 no.spc1
• /
• pp.1195-1204
• /
• 2021

Recently, local torrential rain have become more frequent and severe due to abnormal climate conditions, causing a surge in human and properties damage including infrastructures along the river. In this study, water surface elevation prediction algorithm was developed using the LSTM (Long Short-term Memory) technique specialized for time series data among Machine Learning to estimate and prevent flooding of the facilities. The study area is Jamsu Bridge, the study period is 6 years (2015~2020) of June, July and August and the water surface elevation of the Jamsu Bridge after 3 hours was predicted. Input data set is composed of the water surface elevation of Jamsu Bridge (EL.m), the amount of discharge from Paldang Dam (m³/s), the tide level of Ganghwa Bridge (cm) and the number of tweets in Seoul. Complementary data were constructed by using not only structured data mainly used in precedent research but also unstructured data constructed through wordcloud, and the role of unstructured data was presented through comparison and analysis of whether or not unstructured data was used. When predicting the water surface elevation of the Jamsu Bridge, the accuracy of prediction was improved and realized that complementary data could be conservative alerts to reduce casualties. In this study, it was concluded that the use of complementary data was relatively effective in providing the user's safety and convenience of riverside infrastructure. In the future, more accurate water surface elevation prediction would be expected through the addition of types of unstructured data or detailed pre-processing of input data.

• Korean Journal of Environmental Biology
• /
• v.40 no.2
• /
• pp.214-223
• /
• 2022

Among migratory insect pests, Mythimna seperata and Cnaphalocrocis medinalis are invasive pests introduced into South Korea through westerlies from southern China. M. seperata and C. medinalis are insect pests that use rice as a host. They injure rice leaves and inhibit rice growth. To understand the distribution of M. seperata and C. medinalis, it is important to understand environmental factors such as temperature and humidity of their habitat. This study predicted current and future habitat suitability models for understanding the distribution of M. seperata and C. medinalis. Occurrence data, SSPs (Shared Socio-economic Pathways) scenario, and RCP (Representative Concentration Pathway) were applied to MaxEnt (Maximum Entropy), a machine learning model among SDM (Species Distribution Model). As a result, M. seperata and C. medinalis are aggregated on the west and south coasts where they have a host after migration from China. As a result of MaxEnt analysis, the contribution was high in the order of Land-cover data and DEM (Digital Elevation Model). In bioclimatic variables, BIO_4 (Temperature seasonality) was high in M. seperata and BIO_2 (Mean Diurnal Range) was found in C. medinalis. The habitat suitability model predicted that M. seperata and C. medinalis could inhabit most rice paddies.