• Journal of the Korean Society of Clothing and Textiles
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• v.31 no.6 s.165
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• pp.848-858
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• 2007

Sontag and Lee (2004) recently developed an objectively measurable instrument, the Proximity of Clothing to Self(PCS) Scale, which measured the psychological closeness of clothing to self. They validated a 4-factor, 24-item PCS Scale for use with adolescents and identified the need for confirmation of the factor structure with other age groups. This paper extends the work of Sontag and Lee by employing the PCS Scale with older persons, age 65 and over, and reports the validation of a 3-factor, 19-item PCS Scale for older persons. A mail survey was sent to a national random sample of 1,700 older Persons by means of a list purchased from a U.S. survey sampling company in late November 2004. Total usuable number of respondents was 250 with an adjusted response rate of 15.6 percent. Three analytical rounds of confirmatory factor analysis(CFA) to test the construct validity of the PCS Scale were conducted by using AMOS 5.0(Analysis of Moment Structures), one of several structural equation modeling(SEM) programs. Completion of three rounds of the CFA resulted in a 3-factor, 19-item PCS Scale with demonstrated construct validity and reliability for older persons. The three PCS dimensions are clothing in relation to 1) self as structure-process(PCS Dimension 1-2-3 combined), 2) self-esteem-evaluative and affective processes(PCS Dimension 4-5 combined), and 3) body image and body cathexis(PCS Dimension 6). The initially hypothesized 6-factor scale(Sontag & Lee, 2004) was not confirmed for adolescents in their study nor with older persons in this study. In addition, the 4-factor solution for the adolescent group did not hold for older persons. It appears that the self-system of older persons is more integrated than may be true for younger individuals. Recommendations for future testing of construct validity of the PCS Scale are made.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.1
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• pp.25-39
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• 2018

NIST(National Institute of Standards and Technology) has recently published SP 800-90B second draft which is the document for evaluating security of entropy source, a key element of a cryptographic random number generator(RNG), and provided a tool implemented on Python code. In SP 800-90B, the security evaluation of the entropy sources is a process of estimating min-entropy by several estimators. The process of estimating min-entropy is divided into IID track and non-IID track. In IID track, the entropy sources are estimated only from MCV estimator. In non-IID Track, the entropy sources are estimated from 10 estimators including MCV estimator. The running time of the NIST's tool in non-IID track is approximately 20 minutes and the memory usage is over 5.5 GB. For evaluation agencies that have to perform repeatedly evaluations on various samples, and developers or researchers who have to perform experiments in various environments, it may be inconvenient to estimate entropy using the tool and depending on the environment, it may be impossible to execute. In this paper, we propose high-speed implementations and an efficient memory usage technique for min-entropy estimation algorithm of SP 800-90B. Our major achievements are the three improved speed and efficient memory usage reduction methods which are the method applying advantages of C++ code for improving speed of MultiMCW estimator, the method effectively reducing the memory and improving speed of MultiMMC by rebuilding the data storage structure, and the method improving the speed of LZ78Y by rebuilding the data structure. The tool applied our proposed methods is 14 times faster and saves 13 times more memory usage than NIST's tool.

• The Korean Society of Law and Medicine
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• v.19 no.2
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• pp.41-72
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• 2018

In the division of labor (or teamwork) in medicine, the responsibility of medical and nursing staff should be separated or distributed to justify negligent criminal offenses. The present work refers to the standards by which the due diligence and responsibility of the individual persons are to be determined and delimited. In this context, it has been proven that objective theory as a measure of due diligence is appropriate. From a moral point of view, when assessing due diligence, it makes sense to impose greater individual or higher performance demands on the perpetrator, but law and order require that due diligence should result from socially relevant human behavior. To give objective measure of negligence and to provide the highest level of personal responsibility, so that man can not be burdened too much responsibility and it is accordingly with an equality theorem. Afterwards some points are presented, which should be considered in a concrete fact in the determination of the medical negligence. Medical action has specific characteristics such as professionalism, discretionary and exclusive, unbalance of information. These characteristics distinguish medical actions from general negligence. The general level of knowledge, the urgency, working condition and working environment of the medical facility, duration of the professional practice, assessment of the medical activity are crucial in this context. As a standard of delineation of due diligence, I have used the permitted risk and the principle of trust. In the horizontal division of labor, the principle of trust applies. The principle of trust applies in principle in cases of division of labor interaction, when doctors in the same hospital exercise their own specific occupational field or everyone works in another hospital. However, this is not true for every case. In the vertical division of labor, the principle of trust does not apply and the senior physician can not trust the assistant doctors. In this case, the principle of trust is converted into a duty of supervision for assistant doctors by the senior physician. This supervision requirement could be used as a random check.

• Journal of Life Science
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• v.31 no.9
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• pp.840-848
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• 2021

This study was conducted to estimate annual trends and the environmental effects in the racing records of Jeju horses. The Korean Racing Authority (KRA) collected 48,645 observations for 2,167 Jeju horses from 2002 to 2019. Racing records were preprocessed to eliminate errors that occur during the data collection. Racing times were adjusted for comparison between race distances. A stepwise Akaike information criterion (AIC) variable selection method was applied to select appropriate environment variables affecting racing records. The annual improvement of the race time was -0.242 seconds. The model with the lowest AIC value was established when variables were selected in the following order: year, budam classification, jockey ranking, trainer ranking, track condition, weather, age, and gender. The most suitable model was constructed when the jockey ranking and age variables were considered as random effects. Our findings have potential for application as basic data when building models for evaluating genetic abilities of Jeju horses.

• Sleep Medicine and Psychophysiology
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• v.28 no.1
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• pp.27-33
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• 2021

Objectives: The purpose of this study is to investigate the difference in the effects of eye-mask and light-off on sleep status according to a commercial fitness tracker and a sleep diary of psychiatric in-patients in correctional facilities where nocturnal light is compulsory. Methods: This study was conducted over 3 consecutive nights. In-patients of the National Forensic Psychiatric Hospital (n = 29) were assigned random subject numbers and slept as usual in the light-on condition on the first night. The subjects slept with eye-masks in the light-on condition on another night and without an eye-mask in the light-off condition on the other night. Subjects were asked to sleep wearing a commercial fitness tracker and to keep a sleep diary. The order of these changes in bedroom lighting condition on the second and third nights was assigned randomly to participants. Results: In comparison of the sleep variables between the light-on condition and the eye-mask condition, the Wakefullness After Sleep Onset (WASO) was shorter and sleep satisfaction was higher in the latter.(respectively, Z = 3.66, p < 0.017 ; Z = 2.69, p < 0.017) In comparison of the sleep variables between the light-on and light-off conditions, the WASO was shorter and sleep efficiency and sleep satisfaction were higher in the latter (respectively, Z = 2.40, p < 0.017 ; Z = 3.02, p < 0.017 ; Z = 3.88, p < 0.017). However, there were no differences in the sleep variables between the eye-mask condition and the light-off condition. Conclusion: Subjective improvements in sleep variables were noted in sleep diaries of institutionalized psychiatric patients under either the 'eye-mask' or 'light-off' condition. However, there were no significant differences between the 'eye-mask' and 'light-off' conditions. Therefore, we suggest that psychiatric patients in correctional facilities use eye-masks when sleeping.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.1031-1042
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• 2023

In this study, a deep learning model was developed to predict the yield of cabbage and radish, one of the five major supply and demand management vegetables, using satellite images of Landsat 8. To predict the yield of cabbage and radish in Gangwon-do from 2015 to 2020, satellite images from June to September, the growing period of cabbage and radish, were used. Normalized difference vegetation index, enhanced vegetation index, lead area index, and land surface temperature were employed in this study as input data for the yield model. Crop yields can be effectively predicted using satellite images because satellites collect continuous spatiotemporal data on the global environment. Based on the model developed previous study, a model designed for input data was proposed in this study. Using time series satellite images, convolutional neural network, a deep learning model, was used to predict crop yield. Landsat 8 provides images every 16 days, but it is difficult to acquire images especially in summer due to the influence of weather such as clouds. As a result, yield prediction was conducted by splitting June to July into one part and August to September into two. Yield prediction was performed using a machine learning approach and reference models , and modeling performance was compared. The model's performance and early predictability were assessed using year-by-year cross-validation and early prediction. The findings of this study could be applied as basic studies to predict the yield of field crops in Korea.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.