• Korean journal of aerospace and environmental medicine
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• v.30 no.3
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• pp.83-85
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• 2020

In commemoration of Vol. 30, Issue 3, our journal prepared five review articles and one original paper. The global outbreak of COVID-19 in 2020 has impacted our society, and especially the aviation and travel industries have been severely damaged. Kwon presented the aviation medical examination regulations related to COVID-19 announced by the Ministry of Land, Infrastructure, and Transport of the Republic of Korea. Lim summarized various efforts of airlines to overcome the crisis in the aviation industry. He also discussed the management of these aircraft as the number of airplanes landing for long periods increased. Finally, he suggested various quarantine guidelines at airports and onboard aircraft. COVID-19 has had a profound impact on mental health as well as physical effects. Kim investigated the impact of COVID-19 on mental health and suggested ways to manage the stress caused by it. The Internet of Things (IoT) refers to a technology in which devices communicate with each other through wired or wireless communication. Hyun explained the current state of the technology of the IoT and how it could be used, especially in the aviation field. In the area of airline service, various situations arise between passengers and crew. Therefore, role-playing is useful in performing education to prepare and respond to passengers' different needs appropriately. Ra introduced the conceptual background and general concepts of role-playing and presented the actual role-play's preparation process, implementation, evaluation, and feedback process. For a fighter to fly for a long time and perform a rapid air attack, air refueling is essential, which serves refueling from the air rather than from the aircraft base. Koo developed a questionnaire based on the HFACS (Human Factors Analysis and Classification System) model and used it to conduct a fighter pilot survey and analyze the results.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.6
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• pp.1139-1150
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• 2022

With the rapid development of SW Industry, softwares are everywhere in our daily life. The number of vulnerabilities are also increasing with a large amount of newly developed code. Vulnerabilities can be exploited by hackers, resulting the disclosure of privacy and threats to the safety of property and life. In particular, since the large numbers of increasing code, manually analyzed by expert is not enough anymore. Machine learning has shown high performance in object identification or classification task. Vulnerability detection is also suitable for machine learning, as a reuslt, many studies tried to use RNN-based model to detect vulnerability. However, the RNN model is also has limitation that as the code is longer, the earlier can not be learned well. In this paper, we proposed a novel method which applied BERT to detect vulnerability. The accuracy was 97.5%, which increased by 1.5%, and the efficiency also increased by 69% than Vuldeepecker.

• Asia-Pacific Journal of Business
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• v.13 no.1
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• pp.153-166
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• 2022

Purpose - ESG(Environmental, Social and Governance) rating is an indicator to predict the sustainable development and long-term value creation of enterprises, which is becoming more and more important. This study divided the ESG rating into each sector(E, S and G) to identify which ESG elements are effective in enhancing enterprise value according to the characteristics of the enterprise, which is different from previous studies. Design/methodology/approach - In this study, Bloomberg ESG Disclosure Score was used to empirically analyze the relationship between ESG ratings and corporate value by taking the listed companies of China's Shanghai Composite Index from 2017 to 2020 as the object. Findings - First, the relationship between ESG ratings and enterprise value shows a statistically significant positive correlation, which supports the results of previous studies. Second, the analysis results from the classification of ownership structure of enterprises (state-owned enterprises and non-state-owned enterprises) show that compared with state-owned enterprises, the ESG ratings of non-state-owned enterprises is more closely related to enterprise value. Third, the analysis of various industries (manufacturing and non-manufacturing) shows that compared with manufacturing, ESG scores of non-manufacturing has a more positive effect on enterprise value. Lastly, the analysis by industry type (heavy-contaminated companies, non-contaminated companies) confirmed that ESG scores of non-contaminated companies has a positive effect on corporate value than heavy-contaminated companies. Research implications or Originality - This study classified ESG evaluation grades(E, S and G) for listed companies in China and analyzed in detail how they affect corporate value according to corporate characteristics, drawing implications for what ESG indicators should be focused on to increase corporate value.

• Journal of the Korea Society of Computer and Information
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• v.27 no.10
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• pp.1-9
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• 2022

In this paper, we propose the Deep Learning-Based Companion Animal Abnormal Behavior Detection Service, which using video and sensor data. Due to the recent increase in households with companion animals, the pet tech industry with artificial intelligence is growing in the existing food and medical-oriented companion animal market. In this study, companion animal behavior was classified and abnormal behavior was detected based on a deep learning model using various data for health management of companion animals through artificial intelligence. Video data and sensor data of companion animals are collected using CCTV and the manufactured pet wearable device, and used as input data for the model. Image data was processed by combining the YOLO(You Only Look Once) model and DeepLabCut for extracting joint coordinates to detect companion animal objects for behavior classification. Also, in order to process sensor data, GAT(Graph Attention Network), which can identify the correlation and characteristics of each sensor, was used.

• Fashion & Textile Research Journal
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• v.25 no.4
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• pp.460-471
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• 2023

This study aimed to analyze the consumption value and clothing pursuit benefits of genderless fashion based on gender identity. The study questionnaire was distributed to and collected from men and women in their 20s and 30s living in Seoul City and the Gyeonggi province. The collected data were analyzed by using Cronbachs α, factor analysis, K-means group classification analysis, and ANOVA. The study results were as follows. First, gender identity was categorized into three groups: the genderless group, the traditional gender rejection group, and the traditional gender acceptance group. Therefore, it is necessary to subdivide gender identity rather than acceptance and rejection of traditional gender roles. Second, an analysis of consumption value based on gender identity showed significant differences in terms of fashion value and expressive value. Therefore, it is important to establish a differentiated strategy based on the relevant gender identity group when establishing genderless fashion design or marketing strategy. Finally, the study results showed that clothing pursuit benefits based on gender identity, there was a significant difference in terms of individuality pursuit, deviation from the norm, and fashion pursuit. In particular, since the genderless phenomenon agrees with the characteristics of the MZ generation, it will be necessary to share brand information or product information through digital media or to utilize a sharing culture-that is, 'meaning out' tendency and 'flex culture' (i.e., conspicuous consumption).

• International conference on construction engineering and project management
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• 2022.06a
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• pp.353-360
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• 2022

Due to the dense and complicated working environment, the construction industry is susceptible to many accidents. Worker's fall is a severe problem at the construction site, including falling into holes or openings because of the inadequate coverings as per the safety rules. During the construction or demolition of a building, openings and holes are formed in the floors and roofs. Many workers neglect to cover openings for ease of work while being aware of the risks of holes, openings, and gaps at heights. However, there are safety rules for worker safety; the holes and openings must be covered to prevent falls. The safety inspector typically examines it by visiting the construction site, which is time-consuming and requires safety manager efforts. Therefore, this study presented a worker-driven approach (the worker is involved in the reporting process) to facilitate safety managers by developing integrated computer vision and inertia sensors-based mobile applications to identify openings. The TensorFlow framework is used to design Convolutional Neural Network (CNN); the designed CNN is trained on a custom dataset for binary class openings and covered and deployed on an android smartphone. When an application captures an image, the device also extracts the accelerometer values to determine the inclination in parallel with the classification task of the device to predict the final output as floor (openings/ covered), wall (openings/covered), and roof (openings / covered). The proposed worker-driven approach will be extended with other case scenarios at the construction site.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.1
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• pp.57-65
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• 2023

A vibration response-based detection system was used to investigate the adhesive areas of single-lap joints using a nonlinear transformation approach for deep learning. In industry or engineering fields, it is difficult to know the condition of an invisible part within a structure that cannot easily be disassembled and the conditions of adhesive areas of adhesively bonded structures. To address these issues, a detection method was devised that uses nonlinear transformation to determine the adhesive areas of various single-lap-jointed specimens from the vibration response of the reference specimen. In this study, a frequency response function with nonlinear transformation was employed to identify the vibration characteristics, and a virtual spectrogram was used for classification in convolutional neural network based deep learning. Moreover, a vibration experiment, an analytical solution, and a finite-element analysis were performed to verify the developed method with aluminum, carbon fiber composite, and ultra-high-molecular-weight polyethylene specimens.

• Food Science of Animal Resources
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• v.42 no.2
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• pp.313-320
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• 2022

Brazil is considered as a great broiler feet exporter, especially for the Chinese trade. Contact lesions at the tibiotarsal region are responsible for economic losses and there is no model for its classification, thereby this study presents a fast and practical grade system to be used in the poultry industry and proposes these lesion characterizations into three different grades. For this, correlation was made between macroscopic, histological findings and microbiological quantification (Escherichia coli, Staphylococcus spp., Streptococcus spp. and sulphite-reducing clostridia) from contact lesions in the tibiotarsal region of 112 broiler carcasses, divided in four groups (n=28), accordingly to the lesion's intensity. There were no significant differences in microbiological quantification among the groups (p>0.05) except for the grade 3 group, as grade 1 and 2 lesions were in the early stages and histopathological changes such as ulceration were not observed. In grade 3 lesion group, it was observed bacterial cocci grume and ulceration at the articular region and significantly higher microbiological count (p<0.05) for E. coli and Staphylococcus spp. In conclusion, the visual standard proposed in this work, correlated and confirmed by the histopathologic, and microbiologic characterization, allows to precise and fast ascertainment of the contact lesion grade in the tibiotarsal regions of broiler carcasses. Moreover, it should be highlighted that grades 1 and 2 alterations are not caused by an inflammatory process caused by pathogenic agents and should not be considered a public health risk.

• Journal of the Korean Society of Food Culture
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• v.25 no.6
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• pp.680-707
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• 2010

Research was conducted on vinegar types and characteristics with reference to representative literature such as SanGaYoRok, SikRyoChanYo, SooEunJabBang, GoSaChalYo, DongEuiBoGam, SaSiChanYoCho, JuBangMoon, ShinGanGuWhang, ChalYoSeo, EumSikDiMiBang, YoRok, ChiSengYoRam, SanRimKyoungJae,EumSikBo,OnJuBeop, SulManDeuNunBeop, KyuHapChongSeo, ImWonSipYukJi, JungIlDangJabJi, SulBitNeunBeop, SiUiJeonSeo, and BuInPilJi from the 15th to the 19th centuries of the Joseon Dynasty. Based on this research, a classification of materials used in vinegar, knowledge on treatment, preparation of ingredients, capturing flavor, storage of vinegar, as well as the favorable days that vinegar can be manufactured were studied and analyzed based on the different aspect of vinegar. Vinegar is a wellknown condiment throughout the world and has the potential of becoming a luxurious food. Replication and further analysis to expand the properties of vinegar is necessary using old literature, together with the literature identified above. Based on ongoing research, it is foreseeable that the development of a vinegar with unique characteristics and improved standards will be the foundation for the globalization of Korean cuisine, which is our current focus.

• Journal of Fashion Business
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• v.27 no.3
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• pp.50-62
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• 2023

The e-commerce fashion market has experienced a remarkable growth, leading to an overwhelming availability of shared information and numerous choices for users. In light of this, chatbots have emerged as a promising technological solution to enhance personalized services in this context. This study aimed to develop user-product attributes for a chatbot-based personalized fashion recommendation service using big data text mining techniques. To accomplish this, over one million consumer reviews from Coupang, an e-commerce platform, were collected and analyzed using frequency analyses to identify the upper-level attributes of users and products. Attribute terms were then assigned to each user-product attribute, including user body shape (body proportion, BMI), user needs (functional, expressive, aesthetic), user TPO (time, place, occasion), product design elements (fit, color, material, detail), product size (label, measurement), and product care (laundry, maintenance). The classification of user-product attributes was found to be applicable to the knowledge graph of the Conversational Path Reasoning model. A testing environment was established to evaluate the usefulness of attributes based on real e-commerce users and purchased product information. This study is significant in proposing a new research methodology in the field of Fashion Informatics for constructing the knowledge base of a chatbot based on text mining analysis. The proposed research methodology is expected to enhance fashion technology and improve personalized fashion recommendation service and user experience with a chatbot in the e-commerce market.