• Journal of Fashion Business
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• v.26 no.1
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• pp.1-15
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• 2022

Digital transformation has induced changes in human life patterns; consumption patterns are also changing to digitalization. Entering the era of industry 4.0 with the 4th industrial revolution, it is important to pay attention to a new paradigm in the fashion industry, the shift from developer-centered to user-centered in the era of the 3rd industrial revolution. The meaning of storing users' changing life and consumption patterns and analyzing stored big data are linked to consumer sentiment. It is more valuable to read emotions, then develop and distribute products based on them, rather than developer-centered processes that previously started in the fashion market. An AI(Artificial Intelligence) deep learning algorithm that analyzes user emotion big data from user experience(UX) to emotion and uses the analyzed data as a source has become possible. By combining AI technology, the fashion industry can develop various new products and technologies that meet the functional and emotional aspects required by consumers and expect a sustainable user experience structure. This study analyzes clear and useful user experience in the fashion industry to derive the characteristics of AI algorithms that combine emotions and technologies reflecting users' needs and proposes methods that can be used in the fashion industry. The purpose of the study is to utilize information analysis using big data and AI algorithms so that structures that can interact with users and developers can lead to a sustainable ecosystem. Ultimately, it is meaningful to identify the direction of the optimized fashion industry through user experienced emotional fashion technology algorithms.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.8
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• pp.481-490
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• 2016

Today, the advance of a community tool such as a SNS enables users to easily share information each other and quickly deliver various opinions to suppliers. The suppliers are trying to create new value through co-creation strategies in order to satisfy rapidly and variously changing user's needs. User-participation-based idea platforms are emerging as a open innovation space that can provide user participation and interaction between suppliers and users. However, although each idea platform has specific advantages in each stage of a development process, this development process is not generalized as one complete, integrated development process. Thus, the development of an effectively integrated process, which includes advantages in each stage of the process, is needed. This research aims to analysis the characteristics of idea platforms based on the classification of idea platforms as a basic study for proposing a new development processes of them. This research will contribute to the guideline for developing a new user-participation-based idea platform.

• The Journal of Bigdata
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• v.7 no.1
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• pp.89-97
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• 2022

Various efforts are needed to prevent accidents because ship collisions can cause various negative situations such as economic losses and casualties. Therefore, research to prevent accidents is being actively conducted, and in this study, new leading indicators for preventing ship collision accidents is proposed. In previous studies, the risk of collision was expressed in consideration of the distance between ships in a specific sea area, but there is a disadvantage that a new model needs to be developed to apply this to other sea areas. In this study, the density-based ship domain DESD (Density-based Empirical Ship Domain) including the environment and operating characteristics of the sea area was defined using AIS (Automatic Identification System) data, which is ship operation information. Deep clustering is applied to two-dimensional DESDs created for each sea area to cluster the seas with similar operating environments. Through the analysis of the relationship between clustered sea areas and ship collision accidents, it was statistically tested that the occurrence of accidents varies by characteristic of each sea area, and it was proved that DESD can be used as a leading indicator of accidents.

• Korean Journal of Exercise Nutrition
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• v.13 no.2
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• pp.101-107
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• 2009

The objectives of this study were to investigate 1) the nutritional knowledge, 2) the use of nutritional supplements, and 3) nutrient intakes of male elite bodybuilders (n=20). Participants carried out a comprehensive survey, anthropometric assessment, and 1 day food record. Daily nutrient intakes of the subjects were analyzed using Computer Aided Nutritional Analysis Program (Can-pro 3.0). The mean age of the subjects was 23.4 years. The mean duration of exercise was 5.3 years. The average scores of nutritional knowledge were 71.0%. The subjects were gathered nutrition information from nutrition book (65%), mass communication (50%), friends (50%) and coach (30%) in order. Ninety percentage of the subjects reported that they were taking nutritional supplements. Major reasons for taking nutritional supplements were to improve performance and to build-up muscle. The most frequently taken nutritional supplements were protein powder (85%), multivitamin/mineral (75%), BCAA (60%) and glutamine (55%) in order. The average daily energy intakes of the subjects were 4,248.7 kcal. The mean intake of protein was 370.3 g/day (3.93 g/kg BW). The ratio of total energy intake from carbohydrate, protein and lipid was 51 : 34 : 15. The intakes of most vitamin and minerals through food and nutrition supplements were much higher than those of each nutrient of the RDAs. Especially, vitamin B complex and vitamin C intakes were ranged from 500 to 3,000% of KNHNES. More research needs to be conducted to determine the optimal amounts of carbohydrates, protein, lipid and micro-nutrients for the bodybuilders.

• Journal of The Korean Society of Integrative Medicine
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• v.12 no.2
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• pp.177-186
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• 2024

Purpose : The purpose of this study was to use oral instruction and taping to induce abdominal draw-in in the previous study, but there is a lack of research on the comparison of effects. Therefore, in this study, we would like to study how the effect of oral instruction, taping, and no feedback can affect muscle activity when performing abdominal draw-in pier exercises. Methods : Before the experiment, group A trained piers and applied only draw-in piers, group B applied taping feedback, and group C were divided into three groups per week for six weeks, and three sets of contractions for five seconds per group were performed five times, and a minute break was provided between each exercise. As a measurement tool, a wireless surface electromyography (EMG) was used to measure surface electromyography (Telemyo-DTS, NORAXON, USA) that derives a synthetic unit of muscle using a surface electrode. For statistical processing of data, the analysis is performed using the SPSS/PC Version 25.0 statistical program for Windows, and the statistical significance level is a=.05 Results : First, the duration was somewhat shorter in order to more accurately compare the muscle activity of the core muscles. Therefore, future research needs to be research that can be exercises with precise biofeedback and taping applied over a longer period of time. Secondly, it is believed that there is no significant difference, since the subjects were studied in healthy young adult men and women, not patients. Thirdly, they could not control the daily life of the subjects. Through future research, it is necessary to analyze the correlation between muscle activity and abdominal muscle thickness by adding ultrasound in addition to muscle activity in various age groups. Conclusion : During draw-in bridge exercise according to feedback, the muscle activity of the back muscle increased, and there was no significant difference in the muscle activity of the back muscle according to each feedback. Therefore, both the feedback applied during the draw-in pier exercise and the control group are effective in changing the thickness of the abdominal muscles.

• Journal of Agricultural Extension & Community Development
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• v.31 no.2
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• pp.41-56
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• 2024

This study aims to examine the causal relationshps between self-efficacy and job satisfaction of young farmers by verifying the mediating effect of self-efficacy in the relationship between job ability and job satisfaction of young farmers, and to improve the job ability necessary for agriculture, and to provide implications for the development and operation of curriculum that meets the needs of farmers. To this end, an online survey was conducted on 300 young farmers under the age of 40 among 4-H members in Korea from June 17 to 28, 2022. The results of the analysis to find out the mediating effect of self-efficacy in the relationship between job ability and job satisfaction of young farmers are as follows. First, it was verified that self-efficacy plays a partial mediating role in affecting job satisfaction by verifying whether the job ability of young farmers mediates self-efficacy in the relationship between job satisfaction and job satisfaction. It can be said that the higher the job ability, the more positive the job satisfaction and the more positive the self-efficacy, which ultimately has a positive effect on job satisfaction. Second, as a result of verifying whether self-efficacy is mediated in the relationship between job satisfaction and job satisfaction by sub-factors of job competency in detail, self-efficacy plays a partial mediating role between agricultural production ability, agricultural machine management ability, and job satisfaction, and agricultural management ability and agricultural information utilization ability do not have a direct significant effect on job satisfaction.

• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.81-86
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• 2024

This study aimed to assess the level of intention to tampon use among female university students and to identify factors influencing intention to tampon use. The participants were 140 female university students. Data collection was performed through an online survey using chi-squared test, spearman's correlation coefficients, and multivariable logistic regression analysis from March 13 to March 30, 2024. In this study, it was found that 63.6% of female university students had an intention to use tampon and factors influencing intention to tampon use were marital status, length of menstruation, and prior experience with tampon use. Specifically, participants who were 'unmarried', 'had menstruation lasting 5 days or shorter', and 'had previous experience with tampon use' demonstrated a higher intention to use tampons. Therefore, it is necessary to continuously monitor the changing patterns of women's menstrual product usage and intention, identify influencing factors, and establish educational and promotional strategies to provide information on the characteristics and usage of menstrual products, enabling women to choose the best products suited to their menstrual needs.

• Annals of Hepato-Biliary-Pancreatic Surgery
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• v.28 no.3
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• pp.371-380
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• 2024

Backgrounds/Aims: The published data had contradictory information on the role of adjuvant therapy on resected periampullary carcinomas (PACA). The study was performed to evaluate the survival benefit of adjuvant treatment. Methods: This was a propensity score matched case-control study from a prospectively maintained database from 2004-2019. The study included patients with nonpancreatic PACA who underwent curative resection. The patients (cases) who received adjuvant chemotherapy were compared with patients (controls) who were observed alone after surgery. Results: Of 510 patients with PACA, 230 patients (cases = 107, controls = 123) formed the unmatched study cohort. After propensity score matching, 140 patients (cases = 70, controls = 70) formed the matched study cohort. The median overall survival (OS) was similar in cases than controls in the unmatched population but doubled non-significantly in cases after matching (unmatched population, 54 months vs. 54 months, p-value = 0.624; matched population, 71 months vs. 36 months, p-value = 0.087). However, the median recurrence-free survival (RFS) was non significantly higher in the control group (unmatched population, 59 months vs. 38 months, p-value = 0.195; matched population, 53 months vs. 40 months, p-value = 0.797). In cox regression analysis, age < 60 years, advanced T stage, and presence of perineural invasion were independent factors for worse RFS, while tumor recurrence was an independent factor for poor OS. Conclusions: Patients with nonpancreatic PACA may have an OS benefit from adjuvant chemotherapy, and this needs to be validated with large prospective randomized studies.

• International conference on construction engineering and project management
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• 2024.07a
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• pp.1284-1284
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• 2024

Leveraging large language models and safety accident report data has unique potential for analyzing construction accidents, including the classification of accident types, injured parts, and work processes, using unstructured free text accident scenarios. We previously proposed a novel approach that harnesses the power of fine-tuned Generative Pre-trained Transformer to classify 6 types of construction accidents (caught-in-between, cuts, falls, struck-by, trips, and other) with an accuracy of 82.33%. Furthermore, we proposed a novel methodology, saliency visualization, to discern which words are deemed important by black box models within a sentence associated with construction accidents. It helps understand how individual words in an input sentence affect the final output and seeks to make the model's prediction accuracy more understandable and interpretable for users. This involves deliberately altering the position of words within a sentence to reveal their specific roles in shaping the overall output. However, the validation of saliency visualization results remains insufficient and needs further analysis. In this context, this study aims to qualitatively validate the effectiveness of saliency visualization methods. In the exploration of saliency visualization, the elements with the highest importance scores were qualitatively validated against the construction accident risk factors (e.g., "the 4m pipe," "ear," "to extract staircase") emerging from Construction Safety Management's Integrated Information data scenarios provided by the Ministry of Land, Infrastructure, and Transport, Republic of Korea. Additionally, construction accident precursors (e.g., "grinding," "pipe," "slippery floor") identified from existing literature, which are early indicators or warning signs of potential accidents, were compared with the words with the highest importance scores of saliency visualization. We observed that the words from the saliency visualization are included in the pre-identified accident precursors and risk factors. This study highlights how employing saliency visualization enhances the interpretability of models based on large language processing, providing valuable insights into the underlying causes driving accident predictions.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.39-70
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• 2017

Data-drive analytics techniques have been recently applied to public surveys. Instead of simply gathering survey results or expert opinions to research the preference for a recently launched product, enterprises need a way to collect and analyze various types of online data and then accurately figure out customer preferences. In the main concept of existing data-based survey methods, the sentiment lexicon for a particular domain is first constructed by domain experts who usually judge the positive, neutral, or negative meanings of the frequently used words from the collected text documents. In order to research the preference for a particular product, the existing approach collects (1) review posts, which are related to the product, from several product review web sites; (2) extracts sentences (or phrases) in the collection after the pre-processing step such as stemming and removal of stop words is performed; (3) classifies the polarity (either positive or negative sense) of each sentence (or phrase) based on the sentiment lexicon; and (4) estimates the positive and negative ratios of the product by dividing the total numbers of the positive and negative sentences (or phrases) by the total number of the sentences (or phrases) in the collection. Furthermore, the existing approach automatically finds important sentences (or phrases) including the positive and negative meaning to/against the product. As a motivated example, given a product like Sonata made by Hyundai Motors, customers often want to see the summary note including what positive points are in the 'car design' aspect as well as what negative points are in thesame aspect. They also want to gain more useful information regarding other aspects such as 'car quality', 'car performance', and 'car service.' Such an information will enable customers to make good choice when they attempt to purchase brand-new vehicles. In addition, automobile makers will be able to figure out the preference and positive/negative points for new models on market. In the near future, the weak points of the models will be improved by the sentiment analysis. For this, the existing approach computes the sentiment score of each sentence (or phrase) and then selects top-k sentences (or phrases) with the highest positive and negative scores. However, the existing approach has several shortcomings and is limited to apply to real applications. The main disadvantages of the existing approach is as follows: (1) The main aspects (e.g., car design, quality, performance, and service) to a product (e.g., Hyundai Sonata) are not considered. Through the sentiment analysis without considering aspects, as a result, the summary note including the positive and negative ratios of the product and top-k sentences (or phrases) with the highest sentiment scores in the entire corpus is just reported to customers and car makers. This approach is not enough and main aspects of the target product need to be considered in the sentiment analysis. (2) In general, since the same word has different meanings across different domains, the sentiment lexicon which is proper to each domain needs to be constructed. The efficient way to construct the sentiment lexicon per domain is required because the sentiment lexicon construction is labor intensive and time consuming. To address the above problems, in this article, we propose a novel product reputation mining algorithm that (1) extracts topics hidden in review documents written by customers; (2) mines main aspects based on the extracted topics; (3) measures the positive and negative ratios of the product using the aspects; and (4) presents the digest in which a few important sentences with the positive and negative meanings are listed in each aspect. Unlike the existing approach, using hidden topics makes experts construct the sentimental lexicon easily and quickly. Furthermore, reinforcing topic semantics, we can improve the accuracy of the product reputation mining algorithms more largely than that of the existing approach. In the experiments, we collected large review documents to the domestic vehicles such as K5, SM5, and Avante; measured the positive and negative ratios of the three cars; showed top-k positive and negative summaries per aspect; and conducted statistical analysis. Our experimental results clearly show the effectiveness of the proposed method, compared with the existing method.