• The Korean Journal of Archival Studies
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• no.18
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• pp.221-254
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• 2008

Community Archpe is . Community Archpe is as close as a kind of a complex of culture space or community center which puts individuals and small community together with culture soil in a central position. For example Community Archpe can include community library, community archive, community historical center, community recovery center, community commencement of an enterprise center, etc. We need small library, archive and historian rather than big scale institution and professional system to take care of culture soil which belongs to an individual and community. Community Archpe is located in coordinates of two intention points. First intention is, a 'Heterogenous Smithy'. Heterogeneity deals with Community Archpe's life. Second intention is, a 'Feminine Smithy'. Community Archpe can be a recovery community when we are in the recovery context, which understand and support a person through archives and history. Then, what can Community Archpe do? First, it can be a new movement of the community. Second, it can also be a centripetal point of classic life. Community Archpe surly locates in the central of Community. Therefore, it will be a cultural literary soil and be a smithy of community history and culture. Thus Community Archpe will change a lot of things on people's life. Community Archpe will be a small happiness to ordinary people, even though it is not a state organ realizing large values.

• Journal of Popular Narrative
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• v.26 no.4
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• pp.9-41
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• 2020

Park Jong-won's debut film "Guro Arirang," based on a short story of the same title by Lee Moon-yeol, is the first commercial film to deal with labor struggles from a worker's point of view in the wake of the 1987 democratic movement, and a pioneering work in terms of representing female workers the Korean cinema has traditionally turned away from. In this film Park Jong-won tried to win the sympathy of the middle class for labor movement in spite of the red scare which still stood firm in the Korean society at that time. To convey its progressive message in a form acceptable to the middle class public, the film portrays labor issues in the light of universal humanity and ethics, not in terms of class hostility or struggle. Park Jong-won calls this point of view "common sense of normal people" and emphasizes its universality and objectivity. This study critically examines the cinematic strategies to deal with labor issues in a form acceptable to the public in a conventional and commercial film and the ideological implications of the "common sense of normal people" reflected in such strategies. The first chapter of the study reveals that the film destroys the irony of the original story and reduces the complex constellation of the characters to the conflict between pure good and evil, creating a melodramatic composition in which the good falls victim to evil. The tragedies suffered by the workers in the film are of course intended to arouse the audience's strong sympathy and solidarity with them. The second chapter shows that the film's various scenes and episodes converge on the them of compassion and grief, and are mostly based on cultural and real experiences and events that caused great public sensations at that time. Especially in the last decisive scene of the movie, the memory of the June 1987 uprising is strongly recalled. So "Guro Arirang" can be seen as a patchwork of proven cases of compassion and grief. The third chapter examines the implications of the scene where the workers turn back demands for wages and put the issues of human treatment and trust to the forefront at the crucial moment of their struggle. It appeals to universal moral values and sentiments that everyone has to acknowledge and removes the political dimension from the workers' campaign. While the film tends to become a pure story of humanity marginalizing irreconcilable conflicts of class interest, the workers fall to the position of passive victims who can be deeply sympathetic on the one hand, and on the other, are idealized as leaders with noble attitude keeping themselves aloof from the hard reality. As a result, the movie loses its realistic ground and weakens its narrative probability. The scenes reminiscent of the 1987 uprising which evoke the solidarity between working and middle class fail to integrate harmoniously into the whole story of the film and remain only as fragmentary parts of the patchwork of compassion and grief.

• Journal of the Korean Society of Radiology
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• v.17 no.4
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• pp.607-614
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• 2023

Clear overlapping of the bilateral epicondyle and proper separation of the elbow joint are crucial for obtaining accurate lateral general radiographs of the elbow. However, due to the complex anatomical structure of the elbow, achieving optimal positioning is challenging, leading to the need for repeated x-ray examinations. Therefore, the purpose of this study was to investigate the angle of the forearm in patients where accurate lateral images of the elbow joint can't be obtained after vertical incidence using a styrofoam device during elbow joint lateral x-ray imaging. Twenty patients were enrolled in our study following the established protocol. First, a vertical x-ray at an angle of 0° between the forearm and the table was taken (control group). Here, if the lateral image of the elbow joint was deemed inadequate, the forearm angle was adjusted using custom-made styrofoam supports with 5° and 10° inclinations (experimental groups). For the evaluation method, two assessors utilized a 5-point Likert scale to assess the images. The reliability of the assessments was analyzed using Cronbach's alpha coefficient. As a result, patients with inadequate overlap of the bilateral epicondyle and separation of the elbow joint in the initial examination (control group) were able to obtain the best images when setting a 10° angle between the forearm and the table. The subjective evaluation was 1.6 ± 0.8 points at 0°, 2.7 ± 0.8 points at 5°, and 4.4 ± 1.3 points at 10°, respectively. The reliability analysis for the angles of 0°, 5°, and 10° yielded Cronbach's alpha values of 0.867, 0.697, and 0.922, respectively. In conclusion, when it is not possible to obtain accurate images using the conventional position and X-ray beam direction, it is considered that by initially acquiring images with an angle of 10° between the forearm and the table, and gradually decreasing the angle while obtaining images, it would be possible to achieve the optimal image while reducing the number of repeat examinations.

• Journal of the Korean Society of Radiology
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• v.17 no.3
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• pp.305-314
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• 2023

With the development of medical technology and radiation treatment equipment, the frequency of high-precision radiation therapy such as intensity modulation radiation therapy has increased. Image-guided radiation therapy has become essential for radiation therapy in precise and complex treatment plans. In particular, with the introduction of imaging equipment for diagnosis in a linear accelerator, CBCT scanning became possible, which made it possible to calibrate and correct the patient's posture through 3D images. Although more precise reproduction of the patient's posture has become possible, the exposure dose delivered to the patient during the image acquisition process cannot be ignored. Radiation optimization is necessary in the field of radiation therapy, and efforts to reduce exposure are necessary. However, when acquiring 3D CBCT images by changing the imaging conditions to reduce exposure, there should be no image quality or artefacts that would make it impossible to align the patient's position. In this study, Rando phantom was used to scan and evaluate images for each shooting condition. The highest SNR was obtained at 100 kV 80 mA 25 ms F1 filter 180°. As the tube voltage and tube current increased, the noise decreased, and the bowtie filter showed the optimal effect at high tube current. Based on the actual scanned images, it was confirmed that patient alignment was possible under all imaging conditions, and that image-guided radiation therapy for patient alignment was possible under the condition of 70 kV 10 mA 20 ms F0 filter 180°, which showed the lowest SNR. In this study, image evaluation was conducted according to the imaging conditions, and low tube voltage, tube current, and small rotation angle scan are expected to be effective in reducing radiation exposure. Based on this, the patient's exposure dose should be kept as low as possible during CBCT imaging.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.