• Journal of the Korean Society of Radiology
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• v.16 no.4
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• pp.477-490
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• 2022

Serious Accident Punishment Act(SAPA) went into effect as of Jan. 27, 2022. The subject of study was the worker of the nuclear medicine department and the investigation was aimed at identifying the present situation of their understanding on the issue in the here and now, which can be utilized as basic research for further study. The survey was conducted on 51 people of the worker in the nuclear medicine department. The general factors were classified by their gender, the scale of the hospitals, the period of career, and the detailed occupational categories. The conclusion was drawn, including 1 missing data in gender and 2 in the type of occupation. The targeted hospitals were tertiary hospital, university hospital, and general hospital which have nuclear medicine department in. The period of subjects' career was categorized by less than 3 years, 3 to 5 years, 5 to 10 years, and more than 10 years. The specific occupation was classified by in-vivo radiological technologist, radiation safety manager and others. The amount of pressure that the job entails was highest in the category of general hospital, the period of 3 to 5 years of job experience, and radiation safety manager each. The system of the code was well constructed in the category of general hospital, the period of less than 3-year career, and radiation safety manager, as they responded. The blood transmissible disease had the largest number of outbreak of accidents related to the serious industrial accident. In addition, the radiopharmaceutical dosing error had the highest number of outbreak of accidents related to the serious civil accident. Therefore, we need to improve SAPA, facility inspection, security of budget, security of professional manpower. It will help the stable use of radiation and ensure patient safety.

• The Korean Society of Law and Medicine
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• v.24 no.2
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• pp.35-77
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• 2023

As the amendment to the Personal Information Protection Act, which newly established the basis for the right to request transmission of personal information, was promulgated through the plenary session of the National Assembly, MyData, which was previously applied only to the financial sector, could spread to all fields. The right to request transmission of personal information is the right of the information subject to be guaranteed for the realization of MyData. However, since the right to request transmission of personal information stipulated in the Personal Information Protection Act is designed to be applied to all fields, not a special field such as the medical field, it has many shortcomings to act as a core basis for implementing MyData in Medicine. Based on this awareness of the problem, this paper compares and analyzes major legal trends related to the right to portability of personal health information at home and abroad, and examines the limitations of Korea's Personal Information Protection Act and Medical Act in realizing Medical MyData. Under the Personal Information Protection Act, the right to request transmission of personal information is insufficient to apply to the medical field, such as the scope of information to be transmitted, the transmission method, and the scope of the person obligated to perform the transmission, etc.. Regulations on the right to access medical information and transmission of medical records under the Medical Act also have limitations in implementing the full function of Medical My Data in that the target information and the leading institution are very limited. In order to overcome these limitations, this paper prepared a separate and independent special law to regulate matters related to the use and protection of personal health information as a measure to improve the legal system that can effectively guarantee the right to portability of personal health information, taking into account the specificity of the medical field. It was proposed to specifically regulate the contents of the movement and transmission system of personal health information.

• Korean Journal of Culture and Social Issue
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• v.9 no.spc
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• pp.107-122
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• 2003

The object of this study is to evaluate the prevailing physical and psychosocial conditions regarding occupational low back injury. This study consists of two parts. In the first part of the study, analytic biomechanical model and NIOSH guidelines are applied to evaluate risk levels of low back injury for automobile assembly jobs. Total of 246 workers are analysed. There are 20 jobs having greater back compressive forces than 300kg at L5/S1. Also, there are 44 jobs over Action Limit with respect to 1981 NIOSH guidelines. The relationship between psychosocial factors and low back injury was examined in the second part of the study. A battery of questionnaires concerning the psychosocial stress based on PWI (Psychosocial Well-being Index) and musculoskeletal pain symptoms at low back was completed by 246 workers at the same plant. Results showed that 207 out 246 workers experienced the symptoms and 27 workers were diagnosed as patients. Two groups(low stressed, high stressed) based on PWI score had no significant relationships with both symptoms and results of diagnosis. The relationships between physical work load and psychosocial stress were also analysed. Specifically, some postural factors(vertical deviation angle of forearm, horizontal deviation angle of upperarm, vertical deviation angle of thigh, etc) were highly correlated with psychosocial stress. The results illustrated that PWI scores were associated with some physical workloads. However, psychosocial stress levels couldn't be well related with the pain symptom as well as the actual incidence of low back injury since pain or discomfort regarding low back injury were more complex than that of other musculoskeletal disorders.

• The Korean Journal of Coaching Psychology
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• v.7 no.3
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• pp.153-196
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• 2023

The purpose of this study is to development and validate kkondae tendency scale. Kkondae tendencies are defined as "a response pattern to others in a way that values authority in social relationships, is self-centered, and does not accept other people's opinions," and the subjects of the study are workers aged 19 or older who act as seniors, seniors, and bosses in the workplace. In Study 1, 65 preliminary questions were produced with 7 factors for the compositional concept of kkondae tendency through literature review, expert interviews, and open questionnaire survey. In Study 2, a preliminary survey was conducted with 65 questions derived from Study 1. Exploratory factor analysis was conducted based on the responses of a total of 395 people, and 22 items for 4 factors were derived. In Study 3, this survey was conducted with 22 questions derived from Study 2. A total of 880 responses were analyzed, and cross-validation verification was conducted by dividing the data into two groups (Group 1 and Group 2). Exploratory factor analysis was conducted on Group 1 (N=429) to derive 19 items with 4 factors. The four factors are authoritarianism(3 items), egocentrism (5 items), inertial thinking (5 itemss), and one-sided communication (6 items). A confirmatory factor analysis was conducted on 19 questions obtained from Group 1 for Group 2 (N = 451), and 19 questions of four factors were accepted due to the good fit of the model. To verify the convergent validity of the Kkondae tendency scale, the correlation with the Kkondae scale was examined, and to verify the criterion-related validity, the relationship between self-reflection, relationship conflict, social connectedness was examined. All were statistically significant, and convergence validity and criterion-related validity were verified. Finally, discussions on the process and results of this study, differences from related measures, academic significance, practical implications, limitations of the study, and future research directions were presented.

• Journal of Korean Society of Forest Science
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• v.110 no.3
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• pp.393-405
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• 2021

With increasing anthropogenic emission sources, air pollutants are emerging as a severe environmental problem worldwide. Accordingly, the importance of landscape trees is emerging as a potential solution to reduce air pollutants, especially in urban areas. This study quantified and compared NO₂ and SO₂ reduction abilities of ten major landscape tree species and analyzed the relationship between reduction ability and physiological and morphological characteristics. The results showed NO₂ reduction per leaf area was greatest in Cornus officinalis (19.81 ± 3.84 ng cm^-2 hr^-1) and lowest in Pinus strobus (1.51 ± 0.81 ng cm^-2 hr^-1). In addition, NO₂ reduction by broadleaf species (14.72 ± 1.32 ng cm^-2 hr^-1) was 3.1-times greater than needleleaf species (4.68 ± 1.26 ng cm^-2hr^-1; P < 0.001). Further, SO₂ reduction per leaf area was greatest in Zelkova serrata (70.04 ± 7.74 ng cm^-2 hr^-1) and lowest in Pinus strobus (4.79 ± 1.02 ng cm^-2 hr^-1). Similarly, SO₂ reduction by broadleaf species (44.21 ± 5.01 ng cm^-2 hr^-1) was 3.9-times greater than needleleaf species (11.47 ± 3.03 ng cm^-2 hr^-1; P < 0.001). Correlation analysis revealed differences in NO₂ reduction was best explained by chlorophyll b content (R² = 0.671, P = 0.003) and SO₂ reduction was best described by SLA and length of margin per leaf area (R² = 0.456, P = 0.032 and R² = 0.437, P = 0.001, R² = 0.872, P < 0.001, respectively). In summary, the ability of trees to reduce air pollutants was related to photosynthesis, evapotranspiration, stomatal conductance, and leaf thickness. These findings highlight effective reduction of air pollutants by landscaping trees requires comprehensively analyzing physiological and morphological species characteristics.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.

• Journal of Intelligence and Information Systems
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• v.27 no.2
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• pp.33-54
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• 2021

With the fourth industrial revolution and the arrival of the New Normal era due to Corona, the importance of Non-contact technologies such as artificial intelligence and big data research has been increasing. Convergent research is being conducted in earnest to keep up with these research trends, but not many studies have been conducted in the area of nuclear research using artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. This study was conducted to confirm the applicability of data science analysis techniques to the field of nuclear research. Furthermore, the study of identifying trends in nuclear spent fuel recognition is critical in terms of being able to determine directions to nuclear industry policies and respond in advance to changes in industrial policies. For those reasons, this study conducted a media trend analysis of pyroprocessing, a spent nuclear fuel treatment technology. We objectively analyze changes in media perception of spent nuclear fuel dry treatment techniques by applying text mining analysis techniques. Text data specializing in Naver's web news articles, including the keywords "Pyroprocessing" and "Sodium Cooled Reactor," were collected through Python code to identify changes in perception over time. The analysis period was set from 2007 to 2020, when the first article was published, and detailed and multi-layered analysis of text data was carried out through analysis methods such as word cloud writing based on frequency analysis, TF-IDF and degree centrality calculation. Analysis of the frequency of the keyword showed that there was a change in media perception of spent nuclear fuel dry treatment technology in the mid-2010s, which was influenced by the Gyeongju earthquake in 2016 and the implementation of the new government's energy conversion policy in 2017. Therefore, trend analysis was conducted based on the corresponding time period, and word frequency analysis, TF-IDF, degree centrality values, and semantic network graphs were derived. Studies show that before the 2010s, media perception of spent nuclear fuel dry treatment technology was diplomatic and positive. However, over time, the frequency of keywords such as "safety", "reexamination", "disposal", and "disassembly" has increased, indicating that the sustainability of spent nuclear fuel dry treatment technology is being seriously considered. It was confirmed that social awareness also changed as spent nuclear fuel dry treatment technology, which was recognized as a political and diplomatic technology, became ambiguous due to changes in domestic policy. This means that domestic policy changes such as nuclear power policy have a greater impact on media perceptions than issues of "spent nuclear fuel processing technology" itself. This seems to be because nuclear policy is a socially more discussed and public-friendly topic than spent nuclear fuel. Therefore, in order to improve social awareness of spent nuclear fuel processing technology, it would be necessary to provide sufficient information about this, and linking it to nuclear policy issues would also be a good idea. In addition, the study highlighted the importance of social science research in nuclear power. It is necessary to apply the social sciences sector widely to the nuclear engineering sector, and considering national policy changes, we could confirm that the nuclear industry would be sustainable. However, this study has limitations that it has applied big data analysis methods only to detailed research areas such as "Pyroprocessing," a spent nuclear fuel dry processing technology. Furthermore, there was no clear basis for the cause of the change in social perception, and only news articles were analyzed to determine social perception. Considering future comments, it is expected that more reliable results will be produced and efficiently used in the field of nuclear policy research if a media trend analysis study on nuclear power is conducted. Recently, the development of uncontact-related technologies such as artificial intelligence and big data research is accelerating in the wake of the recent arrival of the New Normal era caused by corona. Convergence research is being conducted in earnest in various research fields to follow these research trends, but not many studies have been conducted in the nuclear field with artificial intelligence and big data-related technologies such as natural language processing and text mining analysis. The academic significance of this study is that it was possible to confirm the applicability of data science analysis technology in the field of nuclear research. Furthermore, due to the impact of current government energy policies such as nuclear power plant reductions, re-evaluation of spent fuel treatment technology research is undertaken, and key keyword analysis in the field can contribute to future research orientation. It is important to consider the views of others outside, not just the safety technology and engineering integrity of nuclear power, and further reconsider whether it is appropriate to discuss nuclear engineering technology internally. In addition, if multidisciplinary research on nuclear power is carried out, reasonable alternatives can be prepared to maintain the nuclear industry.

• Journal of Korean Society of Occupational and Environmental Hygiene
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• v.25 no.3
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• pp.380-391
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• 2015

Objectives: The objective of this study is to assess airborne particulate matter pollution and its effect on health of residents living near Ansim Briquette Fuel Complex and its vicinities. Also, this study measured and analyzed the concentration of TSP, $PM_{10}$, $PM_{2.5}$, and heavy metals which influences on the environmental and respiratory disease in Ansim Briquette Fuel Complex, Daegu, Korea. Methods: In this study, we analyzed various environmental pollutants such as particulate matter and heavy metals from Ansim Briquette Fuel Complex that adversely affected local residents's health. In particular, we verified the concentration distribution and characteristics of exposure for TSP, $PM_{10}$, and $PM_{2.5}$ among particulate matters, and heavy metals(Cd, Cr, Cu, Mn, Ni, Pb, Fe, Zn, and Mg). In that regard, the official test method on air pollution in Korea for analysis of particulate matter and heavy metal in atmosphere were conducted. The large capacity air sampling method by the official test method on air pollution in Korea were applied for sampling of heavy metals in atmosphere. In addition, we evaluated the concentration of seasonal environmental pollutants for each point of residence in Ansim Briquette Fuel Complex and surrounding area. The sampling measured periods for air pollutants were from August 11, 2013 to February 21, 2014. Furthermore, we measured and analyzed the seasonal concentrations(summer, autumn and winter). Results: The average concentration for TSP, $PM_{10}$, and $PM_{2.5}$ by direct influence area at Ansim Briquette Fuel Complex were 1.7, 1.4 and 1.9 times higher than reference region. In analysis results of seasonal concentrations for particulate matter in four direct influence and reference area, concentration levels for winter were generally somewhat higher than concentrations for summer and autumn. The average concentrations for Cd, Cr, Mn, Ni, Pb, Fe, and Zn in direct influence area at Ansim Briquette Fuel Complex were $0.0008{\pm}0.0004{\mu}g/Sm^3$, $0.0141{\pm}0.0163{\mu}g/Sm^3$, $0.0248{\pm}0.0059{\mu}g/Sm^3$, $0.0026{\pm}0.0011{\mu}g/Sm^3$, $0.0272{\pm}0.0084{\mu}g/Sm^3$, $0.4855{\pm}0.1862{\mu}g/Sm^3$, and $0.3068{\pm}0.0631{\mu}g/Sm^3$, respectively. In particularly, the average concentrations for Cd, Cr, Mn, Ni, Pb, Fe, and Zn in direct influence area at Ansim Briquette Fuel Complex were 1.9, 3.6, 2.1, 1.9, 1.4, 2.6, and 1.2 times higher than reference area, respectively. The continuous monitoring and management were required for some heavy metals such as Cr and Ni. Moreover, the average concentration in winter for particulate matter in direct influence area at Ansim Briquette Fuel Complex were generally higher than concentrations in summer and autumn. Also, average concentrations for TSP, $PM_{10}$, and $PM_{2.5}$ were from 1.5 to 2.0 times, 1.2 to 1.8 times, and 1.1 to 2.3 times higher than reference area, respectively. In results for seasonal atmospheric environment, TSP, $PM_{10}$, $PM_{2.5}$, and heavy metal concentrations in direct influence area were higher than reference area. Especially, the concentrations in C station were a high level in comparison with other area. Conclusions: In the results, some particulate matters and heavy metals were relatively high concentration, in order to understand the environmental pollution level and health effect in surrounding area at Ansim Briquette Fuel Complex. The concentration of some heavy metals emitted from direct influence area at Ansim Briquette Fuel Complex were relatively higher than reference area. In particular, average concentration for heavy metals in this study were higher than average concentrations in air quality monitoring station for heavy metal for 7 years in Deagu metropolitan region. Especially, the residents near Ansim Briquette Fuel Complex may be exposed to the pollutants(TSP, $PM_{10}$, $PM_{2.5}$, and heavy metals, etc) emitted from the factories in Ansim Briquette Fuel Complex.

• Korean Journal of Environmental Agriculture
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• v.18 no.3
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• pp.250-258
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• 1999

The purpose of this study was to compare heavy metal concentrations in uncontaminated soil with those in soil influenced by industrial activities, and to investigate the relationship between change of heavy metal content and the kind of industry at the Iksan 2nd Industrial Complex that has started since 1995. Soils sampled in 0-3 cm and 3-6 cm soil depth, respectively were analized for content of Cd, Cu, Ni, Pb and Zn. The content of Cd in soil layer of 0 to 3 cm was 0.07-4.37ppm range, average concentration was 0.516ppm and 3-6 cm was 0.07-8.52ppm range, average concentration was 0.380ppm. Area of the chemicals, dyes and metal products manufacturing were higher than the other manufacturing area in Industrial Complex. The content of Cu in soil layer of 0 to 3 cm was 0.61-42.62ppm range, average concentration was 11.087ppm and 3-6 cm was 0.16-35.45ppm range, average concentration was 7.578ppm. Area of the metal products manufacturing were higher than the other manufacturing area in Industrial Complex. The content of Ni in soil layer of 0 to 3 cm was 0.19-15.93ppm range, average concentration was 5.525ppm and 3-6 cm was 0.39-15.59ppm range, average concentration was 5.310ppm. Area of the metal and chemical products manufacturing were higher than the other manufacturing area in Industrial Complex. The content of Pb in soil layer of 0 to 3 cm was 3.10-55.75ppm range, average concentration was 23.543ppm and 3-6 cm was 3.35-46.55ppm range, average concentration was 19.198ppm. Area of the chemicals and metal products manufacturing were higher than the other manufacturing area in Industrial Complex. The content of Zn in soil layer of 0 to 3 cm was 26.50-943.00ppm range, average concentration was 158.329ppm and 3-6 cm was 35.45-882.45ppm range, average concentration was 127.914ppm. Area of the chemicals and metal products manufacturing were higher than the other manufacturing area in Industrial Complex. As the result, this study was to compare Cd, Cu, Ni, Pb, Zn average concentration in uncontaminated soil of world with those in soil, that Cu, Ni were uncontaminated concentration level, Cd was somewhat higher compare with the concentration level of world, Pb and Zn were very higher. Soil contaminated degree of Iksan 2nd Industrial Complex was known a difference by type of industrial activities(chemical, dyes and metal of products)

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.