• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.102-108
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• 2023

Recently in Korea, YouTube stock channels increased rapidly due to the high social interest in the stock market during the COVID-19 period. Accordingly, the role of new media channels such as YouTube is attracting attention in the process of generating and disseminating market information. Nevertheless, prior studies on the market forecasting power of YouTube stock channels remain insignificant. In this study, the market forecasting power of the information from the YouTube stock channel was examined and compared with traditional news media. To measure information from each YouTube stock channel and news media, positive and negative opinions were extracted. As a result of the analysis, opinion in channels operated by media outlets were found to be leading indicators of KOSPI market returns among YouTube stock channels. The prediction accuracy by using logistic regression model show 74%. On the other hand, Sampro TV, a popular YouTube stock channel, and the traditional news media simply reported the market situation of the day or instead showed a tendency to lag behind the market. This study is differentiated from previous studies in that it verified the market predictive power of the information provided by the YouTube stock channel, which has recently shown a growing trend in Korea. In the future, the results of advanced analysis can be confirmed by expanding the research results for individual stocks.

• Asia Marketing Journal
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• v.13 no.3
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• pp.163-182
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• 2011

The focus of this paper is to investigate cognitive development of brand heuristics in the mind of a young consumer as the consumer matures. This issue was examined by comparing the nature of the set of associations (that form the brand heuristic) given by consumers across four different age groups, with each age group representing a distinct stage of cognitive maturity. It is found that there are fundamental differences in the way the different age groups perceive the brand. The research method uses the novel approach of classifying the elicited associations into the three types of brand associations: attributes, benefits and attitudes. This classification enables comparisons of the nature of brand associations and the changes that occur as a consumer matures. To conclude, implications for theory and practice are discussed.

• Informatization Policy
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• v.30 no.4
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• pp.89-112
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• 2023

Traffic safety policies have been established and carried out every five years according to the Traffic Safety Act. In addition to policies that are planned and carried out in the long run, there are also policies established to prevent the recurrence of various social issues and accidents. Citizens' participation in administrative affairs has recently seized the spotlight, and has become an efficient means of realizing administrative democracy. Based on big data analysis, this study aims to present how the "Kim Min-sik Case," which recently brought to the fore a social issue of strengthening laws on child school zones, has realized administrative democracy and contributed to legislation due to the emergence of the online platform called "national petition." Policy changes according to the cycle of issues are divided according to time series classification and what contents are devised in each section through text mining analysis. In this regard, the results of this study are expected to provide useful theoretical and practical implications for researchers and policymakers by presenting policy implications that it is important to prepare practical and realistic alternatives in solving policy problems.

• Proceedings of the Plant Resources Society of Korea Conference
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• 2020.08a
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• pp.27-27
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• 2020

Daphne L. (Thymelaceae) comprises about 95 species distributing worldwide from N Africa, N India, SE Asia to E Asia and the coast of the Mediterranean of Europe. In Korea, five species of this genus have been described. In this study, we included four species (D. genkwa, D. pseudomezereum, D. kiusiana, D. jejudoensis) from Korea, excluding cultivated D. odora. The morphological characters through local surveys and the re-classification of the specimens collected in the Korea National Herbarium (KH) were carried out and distribution maps for each taxon were also prepared. The major characters include habit, trichomes in winter bud, leaf, and twig, phyllotaxis, inflorescence, size of calyx lobe and trichomes in the calyx tube, etc. The distribution map showed that D. genkwa is mainly distributed in the coastal area of Hwanghaenam-do, Pyeongannam-do, Jeollabuk-do and Jeollanam-do, whereas D. pseudomezereum is distributed in the limestone zone of Gangwon-do, Jeollabuk-do, and Gyeongsangbuk-do. Similarly, D. kiusiana is mostly found in Jeollanam-do, Gyeongsangnam-do, and Jeju-do. In addition, D. jejudoensis is known to be distributed in forests of Murueng, Andeok, and Seonheul-ri in Jeju-do, but recently, new habitat is discovered in the island forest areas of Jeollanam-do. However, some of these individuals showed the characteristics of D. kiusiana, thus before come to any conclusion detailed taxonomic review of D. jejudoensis and D. kiusiana is required.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.21-26
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• 2023

Knowledge distillation (KD) is a model lightening technology that transfers the knowledge of deep models to light models. Most KD methods have been developed for classification models, and there have been few KD studies in the field of super-resolution (SR). In this paper, various KD methods are applied to an SR model and their performance is compared. Specifically, we modified the loss function to apply each KD method to the SR model and conducted an experiment to learn a student model that was about 27 times lighter than the teacher model and to double the image resolution. Through the experiment, it was confirmed that some KD methods were not valid when applied to SR models, and that the performance was the highest when the relational KD and the traditional KD methods were combined.

• Journal of the Korea Society of Computer and Information
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• v.29 no.2
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• pp.51-59
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• 2024

This study examines the relationship between the release of the news and the individual stock returns. Investors utilize a variety of information sources to maximize stock returns when establishing investment strategies. News companies publish their articles based on stock recommendation reports of analysts, enhancing the reliability of the information. Defining release of a stock-recommendation news article as an event, we examine its economic impacts and propose a binary classification model that predicts the stock return 10 days after the event. XGBoost and LightGBM models are applied for the study with accuracy of 75%, 71% respectively. In addition, after categorizing the recommended stocks based on the listed market(KOSPI/KOSDAQ) and market capitalization(Big/Small), this study verifies difference in the accuracy of models across four sub-datasets. Finally, by conducting SHAP(Shapley Additive exPlanations) analysis, we identify the key variables in each model, reinforcing the interpretability of models.

• Archives of Plastic Surgery
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• v.50 no.6
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• pp.550-556
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• 2023

Blunt breast trauma occurs in 2% of blunt chest injuries. This study aimed to evaluate the evidence on breast reconstruction after blunt trauma associated with the use of a seat belt. Also, we describe the first case of breast reconstruction using the Ribeiro technique. In November 2022, a systematic search of MEDLINE, EMBASE, and Google Scholar databases was conducted. The literature was screened independently by two reviewers, and the data was extracted. Our search terms included breast, mammoplasty, blunt injury, and seat belts. In addition, we present the case of a woman with a left breast deformity and her reconstruction using the inferior Ribeiro flap technique. Six articles were included. All included studies were published between 2010 and 2021. The studies recruited seven patients. According to the Teo and Song classification, seven class 2b cases were reported. In five cases a breast reduction was performed in the deformed breast with different types of pedicles (three superomedial flaps, one lower flap, one superior flap). Only one case presented complications. The case here presented was a type 2b breast deformity in which the lower Ribeiro pedicle was used successfully without complications during follow-up. Until now there has been no consensus on reconstructive treatment due to the rarity of this entity. However, we must consider surgical treatment individually for each patient. We believe that the Ribeiro technique is a feasible and safe alternative in the treatment of posttraumatic breast deformities, offering very good long-term results.

• Journal of Intelligence and Information Systems
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• v.25 no.4
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• pp.67-88
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• 2019

Webtoon is a Korean-style digital comics platform that distributes comics content produced using the characteristic elements of the Internet in a form that can be consumed online. With the recent rapid growth of the webtoon industry and the exponential increase in the supply of webtoon content, the need for effective webtoon content recommendation measures is growing. Webtoons are digital content products that combine pictorial, literary and digital elements. Therefore, webtoons stimulate consumer sentiment by making readers have fun and engaging and empathizing with the situations in which webtoons are produced. In this context, it can be expected that the sentiment that webtoons evoke to consumers will serve as an important criterion for consumers' choice of webtoons. However, there is a lack of research to improve webtoons' recommendation performance by utilizing consumer sentiment. This study is aimed at developing consumer sentiment pattern maps that can support effective recommendations of webtoon content, focusing on consumer sentiments that have not been fully discussed previously. Metadata and consumer sentiments data were collected for 200 works serviced on the Korean webtoon platform 'Naver Webtoon' to conduct this study. 488 sentiment terms were collected for 127 works, excluding those that did not meet the purpose of the analysis. Next, similar or duplicate terms were combined or abstracted in accordance with the bottom-up approach. As a result, we have built webtoons specialized sentiment-index, which are reduced to a total of 63 emotive adjectives. By performing exploratory factor analysis on the constructed sentiment-index, we have derived three important dimensions for classifying webtoon types. The exploratory factor analysis was performed through the Principal Component Analysis (PCA) using varimax factor rotation. The three dimensions were named 'Immersion', 'Touch' and 'Irritant' respectively. Based on this, K-Means clustering was performed and the entire webtoons were classified into four types. Each type was named 'Snack', 'Drama', 'Irritant', and 'Romance'. For each type of webtoon, we wrote webtoon-sentiment 2-Mode network graphs and looked at the characteristics of the sentiment pattern appearing for each type. In addition, through profiling analysis, we were able to derive meaningful strategic implications for each type of webtoon. First, The 'Snack' cluster is a collection of webtoons that are fast-paced and highly entertaining. Many consumers are interested in these webtoons, but they don't rate them well. Also, consumers mostly use simple expressions of sentiment when talking about these webtoons. Webtoons belonging to 'Snack' are expected to appeal to modern people who want to consume content easily and quickly during short travel time, such as commuting time. Secondly, webtoons belonging to 'Drama' are expected to evoke realistic and everyday sentiments rather than exaggerated and light comic ones. When consumers talk about webtoons belonging to a 'Drama' cluster in online, they are found to express a variety of sentiments. It is appropriate to establish an OSMU(One source multi-use) strategy to extend these webtoons to other content such as movies and TV series. Third, the sentiment pattern map of 'Irritant' shows the sentiments that discourage customer interest by stimulating discomfort. Webtoons that evoke these sentiments are hard to get public attention. Artists should pay attention to these sentiments that cause inconvenience to consumers in creating webtoons. Finally, Webtoons belonging to 'Romance' do not evoke a variety of consumer sentiments, but they are interpreted as touching consumers. They are expected to be consumed as 'healing content' targeted at consumers with high levels of stress or mental fatigue in their lives. The results of this study are meaningful in that it identifies the applicability of consumer sentiment in the areas of recommendation and classification of webtoons, and provides guidelines to help members of webtoons' ecosystem better understand consumers and formulate strategies.

• Journal of Broadcast Engineering
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• v.19 no.5
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• pp.578-589
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• 2014

Window image is displayed through a monitor screen when we execute the application programs on the computer. This includes webpage, video player and a number of applications. The webpage delivers a variety of information by various types in comparison with other application. Unlike a natural image captured from a camera, the window image like a webpage includes diverse components such as text, logo, icon, subimage and so on. Each component delivers various types of information to users. However, the components with different characteristic need to be divided locally, because text and image are served by various type. In this paper, we divide window images into many sub blocks, and classify each divided region into background, text and subimage. The detected subimages can be applied into 2D-to-3D conversion, image retrieval, image browsing and so forth. There are many subimage classification methods. In this paper, we utilize AdaBoost for verifying that the machine learning-based algorithm can be efficient for subimage detection. In the experiment, we showed that the subimage detection ratio is 93.4 % and false alarm is 13 %.

• Journal of KIISE:Software and Applications
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• v.31 no.3
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• pp.379-386
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• 2004

Video segmentation is a fundamental task in video indexing and it includes two kinds of shot change detections such as the abrupt transition and the gradual transition. The abrupt shot boundaries are detected by computing the image-based distance between adjacent frames and comparing this distance with a pre-determined threshold value. However, the gradual shot boundaries are difficult to detect with this approach. To overcome this difficulty, we propose the method that detects gradual transition in the MPEG compressed video using the HMM (Hidden Markov Model). We take two different HMMs such as a discrete HMM and a continuous HMM with a Gaussian mixture model. As image features for HMM's observations, we use two distinct features such as the difference of histogram of DC images between two adjacent frames and the difference of each individual macroblock's deviations at the corresponding macroblock's between two adjacent frames, where deviation means an arithmetic difference of each macroblock's DC value from the mean of DC values in the given frame. Furthermore, we obtain the DC sequences of P and B frame by the first order approximation for a fast and effective computation. Experiment results show that we obtain the best detection and classification performance of gradual transitions when a continuous HMM with one Gaussian model is taken and two image features are used together.