• Journal of The Korean Association For Science Education
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• v.43 no.3
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• pp.209-223
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• 2023

This study was conducted to evaluate the effectiveness of a polar literacy education program for elementary and middle school students, and to derive implications for new education to respond to climate change. We developed modular education programs based on the seven principles of polar literacy established by the Polar-ICE team. We divided them into two courses, one emphasizing science concepts and another emphasizing humanities and sociological issues. We then selected and structured detailed programs suitable for the two courses. These two courses were applied to 26 elementary and middle school students for approximately 69 hours in a Saturday science class hosted by the Department of Science Education at a university in Seoul. The 26 students were divided into three groups. Two groups completed the science education program for polar literacy and a humanities and social studies education program for polar literacy, respectively. The third group, the control group, received general science education unrelated to polar literacy. Before and after running the programs, all three groups responded to a polar literacy test and questionnaires that used vocabulary and presented scenes associated with polar regions. The test results were expressed using Wilcoxon signed ranks, which is a non-parametric test method, and improvements made upon completion of the program were analyzed. From a cognitive aspect, all three groups showed improvement after completing the program in the knowledge area; however, the experimental groups showed a greater degree of improvement than the control group, and there was a clear difference in the contents or materials explicitly covered. From an affective aspect, the difference between before and after the program was minor, but the group that focused on humanities and social issues showed a statistically significant improvement. Regarding changes in polar imagery, the two experimental groups tended to diverge from monotonous images to more diverse images compared to the control group. Based on the above results, we suggested methods to increase the effectiveness of polar literacy education programs, the importance of polar literacy as appropriate material for scientific thinking and earth system education, measures to improve attitudes related to the polar region, and the need to link to school curriculums.

• Journal of Service Research and Studies
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• v.11 no.4
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• pp.1-20
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• 2021

This study was conducted to derive a model of the legal system that is the basis for realizing the service economy, political administration, and social education system. Based on the experience of mankind's legal system operation in the historical era for the past 5,000 years, a legal system model that will make the future human society sustainable has been established. The problems of the current legal system were analyzed at the fundamental level. The root cause of injustice and unfairness was analyzed and a new legal system was designed. Through the legal systems of various national societies that have been attempted in the history of mankind, the structure of the legal system that is desirable for the modern society was designed. Human society, which has experienced how much good legal system has been and is being abused by human irrationality and nonsense, needs to make an effort to change the legal system paradigm itself by learning lessons from failure. This study derives the basis for a legal system that can realize justice and a fair society in the long term. It proposed a model for improving the legal system that allows human society to be happy for a long time. To this end, the fundamental role of the legal system was analyzed at the ideological level and the problems of the current legal system were presented. In addition, the problem of fundamental assumptions about human nature was analyzed and improved assumptions were presented. The structural system of the current legal system was analyzed and a new structure was proposed. In addition, a plan for the operation of a new legal system based on a new structure was suggested. The new legal system was named servicism system. This is because it is a model centered on thorough checks and balances between all opponents, not a simple linear one-dimensional legal system, but a multidimensional legal system, and because it is a viewpoint that clearly recognizes both human reason and desire. The new system is a model that reflects the confrontation between the rule of law and the non-law rule and the confrontation between the power people and the general public. A follow-up study is needed on a concrete plan for transitioning from the current legal system to a new legal system.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.

• Journal of the Korean Society of Radiology
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• v.7 no.2
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• pp.113-120
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• 2013

Nowadays, the medical system towards patients changes into the medical services. As the human rights are improved and the capitalism is enlarged, the rights and needs of patients are gradually increasing. Also, based on this change, several systems in hospitals are revised according to the convenience and needs of patients. Thus, the cases of mobile portable among examinations are getting augmented. Because the number of mobile portable examinations in patient's room, intensive care unit, operating room and recovery room increases, neighboring patients are unnecessarily exposed to radiation so that the examination is legally regulated. Hospitals have to specify that "In case that the examination is taken out of the operating room, emergency room or intensive care units, the portable medical X-ray protective blocks should be set" in accordance with the standards of radiation protective facility in diagnostic radiological system. Some keep this regulation well, but mostly they do not keep. In this study, we shielded around the Collimator where the radiation is detected and then checked the change of dose regarding that of angles in portable tube and collimator before and after shielding. Moreover, we tried to figure out the effects of shielding on dose according to the distance change between patients' beds. As a result, the neighboring areas around the collimator are affected by the shielding. After shielding, the radiation is blocked 20% more than doing nothing. When doing the portable examination, the exposure doses are increased $0^{\circ}C$, $90^{\circ}C$ and $45^{\circ}C$ in order. At the time when the angle is set, the change of doses around the collimator decline after shielding. In addition, the exposure doses related to the distance of beds are less at 1m than 0.5m. In consideration of the shielding effects, putting the beds as far as possible is the best way to block the radiation, which is close to 100%. Next thing is shielding the collimator and its effect is about 20%, and it is more or less 10% by controlling the angles. When taking the portable examination, it is better to keep the patients and guardians far enough away to reduce the exposure doses. However, in case that the bed is fixed and the patient cannot move, it is suggested to shield around the collimator. Furthermore, $90^{\circ}C$ of collimator and tube is recommended. If it is not possible, the examination should be taken at $0^{\circ}C$ and $45^{\circ}C$ is better to be disallowed. The radiation-related workers should be aware of above results, and apply them to themselves in practice. Also, it is recommended to carry out researches and try hard to figure out the ways of reducing the exposure doses and shielding the radiation effectively.

• 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.

• Advances in pediatric surgery
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• v.8 no.2
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• pp.143-155
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• 2002

A survey on biliary atresia was made among 26 members of the Korean Association of Pediatric Surgeons. The members were required to complete a questionnaire and a case registration form for each patient during the twentyone-year period of 1980-2000. Three hundred and eighty patients were registered from 18 institutions. The average number of patients per surgeon was one to two every year. The male to female ratio was 1:1.3. The age of patients on diagnosis with biliary atresia was on average $65.4{\pm} 36.2$ days old. The national distribution was 32.8% in Seoul, 25.3% in Gyoungki-Do, 21.6% in Gyoungsang-Do, 9.27% in Choongchung-Do, etc. in order. The most common clinical presentation was jaundice (98.4%) and change of stool color (86.2%) was second. Two hundred eighty (74.7%) of 375 patients were operated by 80 days of age. Three hundred thirty six (9 1.9%) of 366 patients were operated on by the original Kasai procedure, and 305 (84.3%) of 362 patients were observed by bile-drainage postoperatively. The overall postoperative complication rate was 18.5% and the overall postoperative mortality rate was 6.8%. The associated anomalies were observed in 72 cases (22.5%). One hundred ninty five (64.7%) of 302 patients have been alive in follow-up and 49 (25.1%) have survived over 5 years without problem after operation. Ascending cholangitis, varices and ascites affected survival significantly, and the important long-term prognostic factor was the occurrence of complications.