• The Journal of Bigdata
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• v.3 no.2
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• pp.51-57
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• 2018

The purpose of this study is to find a valid link between color and tourism experience. We analyzed color that extracted by Aerial photo by IRI Image Scale to find color image. As an indicator of the experience of tourism, a review of the Tripadvisor was selected and analyzed through text mining. Results using text mining results and IRI image scales were generally inconsistent. To identify problems with aerial photo, the results of the analysis using the representative photographs provided by the Tripadvisor in the same way were the same as before. This indicate that details are key of tourism than the image of the overall background. This study presents new research directions by combining color analysis studies with text mining.

• Fashion & Textile Research Journal
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• v.23 no.2
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• pp.212-225
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• 2021

With the recognition of YouTube as an information search tool, YouTube creators have subsequently become sources of information to consumers. Accordingly, this study aims to analyze the consumers' response of famous fashion YouTubers in Korea, and to identify differences in consumer response based on the gender and generation of YouTubers. During the period from the opening of fashion creators' YouTube channels, we collected postings on blogs and Internet cafes using textom. As a result of preliminary investigation, six fashion YouTubers were selected. First, all the selected fashion YouTubers were well recognized by consumers as fashion informants. However, Milanonna has been shown to act as a life advisor and as an informant for luxury brands at the same time. Second, female fashion YouTubers were perceived with themes related to daily life, beauty, emotions, and mood rather than fashion itself; whereas, male fashion YouTubers appeared to be more interested in fashion accessories, especially with respect to the basic style. Third, Generation Z fashion YouTubers used the most non-fashion keywords, and their Millennial counterparts used keywords related to fashion items and product purchase properties. However, consumer response to OPAL fashion YouTubers have emerged with items such as life experiences, wisdom, and advice. Moreover, OPAL fashion YouTubers showed a variety of consumer assessments and the YouTuber's personal background. This study's analysis of the differences in the consumer response to fashion YouTubers based on gender and age enables the establishment of an appropriate strategy to attract target consumers and identify their appeal points.

• The Journal of the Convergence on Culture Technology
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• v.7 no.1
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• pp.184-193
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• 2021

This study aims to understand the effectiveness of the FRIENDS program in improving the resilience of adolescents. For this purpose, the following research questions were chosen: 1) What effect does the FRIENDS program have on the resilience of adolescents? 2) What difference does the FRIENDS program make for the subfactors of resilience in adolescents? 3) After completing the FRIENDS program, what sustained effects are there on adolescent resiliency? They were divided into experimental and control groups and the results from before and after group coaching as well as follow-up results were analyzed using SPSS 25.0. For measuring resilience, KRQ-53 created by Juhwan Kim was utilized in this study. Finally, based on prior research and under the guidance of his advisor, the author adapted the FRIENDS program to fit the study subjects' circumstances. This program was conducted across 10 sessions which were 50 minutes each. The study found that the FRIENDS group coaching program had a positive effect on enhancing the resilience of adolescents.Based on the discussion and research findings.

• Journal of Animal Reproduction and Biotechnology
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• v.36 no.4
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• pp.261-269
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• 2021

Because sows are industrially vital for swine production, monitoring for their health or disorder status is important to ensure high reproductive performance. Especially, ambient temperature changes in different season, especially during summer, are directly influenced to the reproductive performance of sows. Although the serum biochemical parameters are widely applied in the veterinary medicine with wide ranges for the physiological process, the values are also influenced by several factors such as age, breed, gender, and stress. In addition, domestic sows in Korea-specific reference interval (RI) for serum biochemistry has not been established yet. Therefore, the present study was aimed to evaluate seasonal variation of RIs in the serum biochemistry in domestic sows in Korea at different seasons and to establish normal RIs using a RI finding program (Reference Value Advisor). Significant difference (p < 0.05) on the different seasons were identified in several serum biochemical parameters including BUN, CRE, GGT, GLU, ALB, TP, LDH and Na in sows. Therefore, we further established RIs, specific in domestic sows in Korea regardless of season. The established RIs based on the serum biochemical values provide a baseline for interpreting biochemical results in the domestic sows in Korea, regardless of seasonal effect. It may contribute to develop a strategy for better reproductive performance by improving breeding management practice and evaluating health of pig herds, which facilitate to avert the economic loss in summer infertility in sows.

• Food Science of Animal Resources
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• v.42 no.5
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• pp.833-848
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• 2022

The objective of this study was to characterize the meat quality traits that affect the texture and savory taste of Ufipa indigenous chickens by comparing the proximate composition, physical characteristics, collagen, and nucleic acid contents with those of commercial broilers. It was found that Ufipa chicken breast and thigh meat had a higher protein content (p<0.05) than broiler chicken meat, whereas the fat content was lower (p<0.01). The moisture content of thigh meat was lower in Ufipa chicken meat than in broiler chicken meat (p<0.05). Regarding meat color, broiler chickens had considerably higher L^* and b^* than Ufipa chickens in both the breast and the thigh meat, except for a^* (p<0.01). Regarding water holding capacity, Ufipa chicken breast exhibited higher drip loss but lower thawing and cooking losses than broiler chicken (p<0.01). In contrast, its thigh meat had a much lower drip and thawing losses but higher cooking losses (p<0.01). The shear force of Ufipa chickens' breasts and thighs was higher than that of broiler chickens (p<0.05), while the amount of total collagen in the thigh meat was higher than that of broiler chickens (p<0.05). Additionally, the inosine-5'-monophosphate (IMP) of Ufipa chicken breast and thigh meat was higher than that of broiler meat (p<0.05). The principal component analysis of meat quality traits provides a correlation between the proximate and physical-chemical prosperties of both breeds with some contrast. In conclusion, the present study provides information on healthy food with good-tasting Ufipa indigenous chickens, which offer a promising market due to consumers' preferences.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.2-5
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• 2022

With the advent of deep learning and the rapid development of ICT (Information and Communication Technology), research using artificial intelligence is being actively conducted in various fields of society such as politics, economy, and culture and so on. Deep learning-based artificial intelligence technology is subdivided into various domains such as natural language processing, image processing, speech processing, and recommendation system. In particular, as the industry is advanced, the need for a recommendation system that analyzes market trends and individual characteristics and recommends them to consumers is increasingly required. In line with these technological developments, this paper extracts and classifies attribute information from structured or unstructured text and image big data through deep learning-based technology development of 'language processing intelligence' and 'image processing intelligence', and We propose an artificial intelligence-based 'customized fashion advisor' service integration system that analyzes trends and new materials, discovers 'market-consumer' insights through consumer taste analysis, and can recommend style, virtual fitting, and design support.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.125-148
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• 2018

Recently, as the demand for big data analysis increases, cases of analyzing unstructured data and using the results are also increasing. Among the various types of unstructured data, text is used as a means of communicating information in almost all fields. In addition, many analysts are interested in the amount of data is very large and relatively easy to collect compared to other unstructured and structured data. Among the various text analysis applications, document classification which classifies documents into predetermined categories, topic modeling which extracts major topics from a large number of documents, sentimental analysis or opinion mining that identifies emotions or opinions contained in texts, and Text Summarization which summarize the main contents from one document or several documents have been actively studied. Especially, the text summarization technique is actively applied in the business through the news summary service, the privacy policy summary service, ect. In addition, much research has been done in academia in accordance with the extraction approach which provides the main elements of the document selectively and the abstraction approach which extracts the elements of the document and composes new sentences by combining them. However, the technique of evaluating the quality of automatically summarized documents has not made much progress compared to the technique of automatic text summarization. Most of existing studies dealing with the quality evaluation of summarization were carried out manual summarization of document, using them as reference documents, and measuring the similarity between the automatic summary and reference document. Specifically, automatic summarization is performed through various techniques from full text, and comparison with reference document, which is an ideal summary document, is performed for measuring the quality of automatic summarization. Reference documents are provided in two major ways, the most common way is manual summarization, in which a person creates an ideal summary by hand. Since this method requires human intervention in the process of preparing the summary, it takes a lot of time and cost to write the summary, and there is a limitation that the evaluation result may be different depending on the subject of the summarizer. Therefore, in order to overcome these limitations, attempts have been made to measure the quality of summary documents without human intervention. On the other hand, as a representative attempt to overcome these limitations, a method has been recently devised to reduce the size of the full text and to measure the similarity of the reduced full text and the automatic summary. In this method, the more frequent term in the full text appears in the summary, the better the quality of the summary. However, since summarization essentially means minimizing a lot of content while minimizing content omissions, it is unreasonable to say that a "good summary" based on only frequency always means a "good summary" in its essential meaning. In order to overcome the limitations of this previous study of summarization evaluation, this study proposes an automatic quality evaluation for text summarization method based on the essential meaning of summarization. Specifically, the concept of succinctness is defined as an element indicating how few duplicated contents among the sentences of the summary, and completeness is defined as an element that indicating how few of the contents are not included in the summary. In this paper, we propose a method for automatic quality evaluation of text summarization based on the concepts of succinctness and completeness. In order to evaluate the practical applicability of the proposed methodology, 29,671 sentences were extracted from TripAdvisor 's hotel reviews, summarized the reviews by each hotel and presented the results of the experiments conducted on evaluation of the quality of summaries in accordance to the proposed methodology. It also provides a way to integrate the completeness and succinctness in the trade-off relationship into the F-Score, and propose a method to perform the optimal summarization by changing the threshold of the sentence similarity.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.141-166
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• 2019

Recently, channels like social media and SNS create enormous amount of data. In all kinds of data, portions of unstructured data which represented as text data has increased geometrically. But there are some difficulties to check all text data, so it is important to access those data rapidly and grasp key points of text. Due to needs of efficient understanding, many studies about text summarization for handling and using tremendous amounts of text data have been proposed. Especially, a lot of summarization methods using machine learning and artificial intelligence algorithms have been proposed lately to generate summary objectively and effectively which called "automatic summarization". However almost text summarization methods proposed up to date construct summary focused on frequency of contents in original documents. Those summaries have a limitation for contain small-weight subjects that mentioned less in original text. If summaries include contents with only major subject, bias occurs and it causes loss of information so that it is hard to ascertain every subject documents have. To avoid those bias, it is possible to summarize in point of balance between topics document have so all subject in document can be ascertained, but still unbalance of distribution between those subjects remains. To retain balance of subjects in summary, it is necessary to consider proportion of every subject documents originally have and also allocate the portion of subjects equally so that even sentences of minor subjects can be included in summary sufficiently. In this study, we propose "subject-balanced" text summarization method that procure balance between all subjects and minimize omission of low-frequency subjects. For subject-balanced summary, we use two concept of summary evaluation metrics "completeness" and "succinctness". Completeness is the feature that summary should include contents of original documents fully and succinctness means summary has minimum duplication with contents in itself. Proposed method has 3-phases for summarization. First phase is constructing subject term dictionaries. Topic modeling is used for calculating topic-term weight which indicates degrees that each terms are related to each topic. From derived weight, it is possible to figure out highly related terms for every topic and subjects of documents can be found from various topic composed similar meaning terms. And then, few terms are selected which represent subject well. In this method, it is called "seed terms". However, those terms are too small to explain each subject enough, so sufficient similar terms with seed terms are needed for well-constructed subject dictionary. Word2Vec is used for word expansion, finds similar terms with seed terms. Word vectors are created after Word2Vec modeling, and from those vectors, similarity between all terms can be derived by using cosine-similarity. Higher cosine similarity between two terms calculated, higher relationship between two terms defined. So terms that have high similarity values with seed terms for each subjects are selected and filtering those expanded terms subject dictionary is finally constructed. Next phase is allocating subjects to every sentences which original documents have. To grasp contents of all sentences first, frequency analysis is conducted with specific terms that subject dictionaries compose. TF-IDF weight of each subjects are calculated after frequency analysis, and it is possible to figure out how much sentences are explaining about each subjects. However, TF-IDF weight has limitation that the weight can be increased infinitely, so by normalizing TF-IDF weights for every subject sentences have, all values are changed to 0 to 1 values. Then allocating subject for every sentences with maximum TF-IDF weight between all subjects, sentence group are constructed for each subjects finally. Last phase is summary generation parts. Sen2Vec is used to figure out similarity between subject-sentences, and similarity matrix can be formed. By repetitive sentences selecting, it is possible to generate summary that include contents of original documents fully and minimize duplication in summary itself. For evaluation of proposed method, 50,000 reviews of TripAdvisor are used for constructing subject dictionaries and 23,087 reviews are used for generating summary. Also comparison between proposed method summary and frequency-based summary is performed and as a result, it is verified that summary from proposed method can retain balance of all subject more which documents originally have.

• Journal of the Korean Society of Safety
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• v.35 no.1
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• pp.87-96
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• 2020

The purpose of this paper is to develop a fire HRA (Human Reliability Analysis) procedure for full power operation of domestic NPPs (Nuclear Power Plants). For the development of fire HRA procedure, the recent research results of NUREG-1921 in an effort to meet the requirements of the ASME/ANS PRA Standard were reviewed. The K-HRA method, a standard method for HRA of a domestic level 1 PSA (Probabilistic Safety Assessment) and fire related procedures in domestic NPPs were reviewed. Based on the review, a procedure for the fire HRA required for a domestic fire PSA based on the K-HRA method was developed. To this end, HRA issues such as new operator actions required in the event of a fire and complexity of fire situations were considered. Based on the four kinds of HFE (Human Failure Event) developed for a fire HRA in this research, a qualitative analysis such as feasibility evaluation was suggested. And also a quantitative analysis process which consists of screening analysis and detailed analysis was proposed. For the qualitative analysis, a screening analysis by NUREG-1921 was used. In this research, the screening criteria for the screening analysis was modified to reduce vague description and to reflect recent experimental results. For a detailed analysis, the K-HRA method and scoping analysis by NUREG-1921 were adopted. To apply K-HRA to fire HRA for quantification, efforts to modify PSFs (Performance Shaping Factors) of K-HRA to reflect fire situation and effects were made. For example, an absence of STA (Shift Technical Advisor) to command a fire brigade at a fire area is considered and the absence time should be reflected for a HEP (Human Error Probability) quantification. Based on the fire HRA procedure developed in this paper, a case study for HEP quantification such as a screening analysis and detailed analysis with the modified K-HRA was performed. It is expected that the HRA procedure suggested in this paper will be utilized for fire PSA for domestic NPPs as it is the first attempt to establish an HRA process considering fire effects.