• Journal of Conservation Science
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• v.37 no.2
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• pp.120-134
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• 2021

A Woolen carpet from the late Joseon Dynasty was unearthed in the process of repairing Seongjeonggak in Changdeokgung. Since relics are rarer than documentary records, the woolen carpet is highly valued as a relics. It is presumed to have been woven in the late 19th or early 20th century because there is a record of repairing Seongjeonggak in 1907. In the carpet, a pattern is made by inserting colored yarn dyed yellow and red onto a reddish-purple ground weave. The selvage of the woolen carpet used cotton thread, and jute is used for the warp and weft of the ground weave. The colored patterns is made of wool in the form of loop pile. Cut piles may appear occasionally when the colored yarn changes, but are almost invisible from the surface because they are pressed tightly with a shuttered weft. Making carpets with jute and wool is thought to be influenced by the Brussels carpets of the mid-18th century. Furthermore, the woolen carpet is torn and the pattern is completely unclear; however, it is understandable that the pattern is partially repeated. Microscopic and Fourier transform-Infrared spectrometer(FT-IR) analyses were performed for the above investigation. To identify the dyes used in relics, we compared them with natural dyed fabric samples based on chromaticity measurements and Ultraviolet/Visible spectrophotometer(UV-Vis) analysis. These analyses revealed that the woolen carpet's dyed green yarn did not use indigo, and reddish-purple ground weave is estimated to have used Caesalpinia sappan.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.101-125
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• 2022

Recently, with the development of computing technology and the improvement of the cloud environment, deep learning technology has developed, and attempts to apply deep learning to various fields are increasing. A typical example is anomaly detection, which is a technique for identifying values or patterns that deviate from normal data. Among the representative types of anomaly detection, it is very difficult to detect a contextual anomaly that requires understanding of the overall situation. In general, detection of anomalies in image data is performed using a pre-trained model trained on large data. However, since this pre-trained model was created by focusing on object classification of images, there is a limit to be applied to anomaly detection that needs to understand complex situations created by various objects. Therefore, in this study, we newly propose a two-step pre-trained model for detecting abnormal situation. Our methodology performs additional learning from image captioning to understand not only mere objects but also the complicated situation created by them. Specifically, the proposed methodology transfers knowledge of the pre-trained model that has learned object classification with ImageNet data to the image captioning model, and uses the caption that describes the situation represented by the image. Afterwards, the weight obtained by learning the situational characteristics through images and captions is extracted and fine-tuning is performed to generate an anomaly detection model. To evaluate the performance of the proposed methodology, an anomaly detection experiment was performed on 400 situational images and the experimental results showed that the proposed methodology was superior in terms of anomaly detection accuracy and F1-score compared to the existing traditional pre-trained model.

• The Journal of Industrial Distribution & Business
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• v.13 no.6
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• pp.39-49
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• 2022

Purpose: As part of these efforts, in-depth research is needed on efficient and practical utilization of underground spaces and underground shopping malls. In addition, efforts are being made to find effective alternatives to various problems currently occurring in underground shopping malls, but it is not easy. In addition, the development entity and the maintenance entity are different from each other, and the management is not unified, making it difficult to maintain underground shopping malls. From this point of view, it can be said that it is time to actively and specifically discuss ways to revitalize underground shopping malls. Data and methodology: In the domestic distribution environment, traditional markets and shops are stagnating due to rapid changes in consumption patterns, such as the spread of large companies with advanced distribution techniques such as hypermarkets, shopping malls, and SCM, the rapid increase in Internet and home shopping, and the importance of convenience for young consumers. In order to revitalize underground shopping malls, it is necessary to strengthen the organization and self-rescue efforts of merchants' associations, change consciousness through merchant education, change to specialized markets, find nuclear stores and representative restaurants, and support the hardware sector. Results: The connection of underground shopping malls in each region of the country, where commercial districts are separated from each other, will also play an important role in reviving the function of the city in the future. To do this, it is first necessary to connect underground shopping malls that have been cut off. In other words, connection between connectable underground shopping malls should be promoted. Of course, long-term projects should be promoted step by step, and many consultations should be made on how to connect with the ground for each local government. Conclusion: This is because in the future, the underground space cannot just be a walking place, but another space of the Korean Wave where you can experience satisfying the five senses. K-shopping Hallyu content can be created by creating a characteristic story for each underground shopping mall in the city, permanently this story-oriented event, and creating a safe and elegant environment. If there is a story, so-called "Senomi Shopping" will be possible. A new Korean Wave will be created that can satisfy "the fun of writing, the fun of seeing, and the fun of feeling" at the same time.

• The Journal of Korean Medicine
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• v.43 no.2
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• pp.1-7
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• 2022

Objectives: Bee venom (BV) is a widely used therapy in Traditional East Asian Medicine (TEAM). We previously reported that BV was clinically effective for treating Parkinson's disease, that phospholipase A₂ (PLA₂) was the main component of BV, and that it induced regulatory T cells (Tregs) by binding CD206 on dendritic cells (DCs). Therefore, we aimed to reconfirm our findings in human blood samples and investigate the relationship between CD206⁺ DCs and clinical syndrome differentiation in TEAM. Methods: We surveyed 100 subjects with questionnaires on cold-heat patternization and obtained their blood samples. The obtained human peripheral blood monocytes (hPBMCs) were washed with phosphate-buffered saline (PBS). After resuspension with ex vivo media, numbers of cells were counted. Tregs were counted after culturing the samples in a 37℃ CO₂ incubator for 72 h. Results: We divided the subjects into a relatively high CD206⁺ group or a relatively low CD206⁺ group. The heat factor scores of high CD206⁺ group were significantly higher than that of low CD206⁺ group (high vs low: 239.2 ± 54.1 vs 208.4 ± 55.1, p=0.023). After culturing with PLA₂, Tregs increased in the high CD206⁺ group but decreased in the low CD206⁺ group. Conclusion: In this study, we reconfirm that CD206⁺ DCs induced Treg differentiation by incubating human blood samples with PLA₂ and that they showed an association with syndrome differentiation, especially with heat patterns, in TEAM. A heat pattern in TEAM might be one indication for PLA₂ therapy because its score was elevated in the high CD206⁺ group.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.1
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• pp.23-34
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• 2022

With the development of network technology, the application area has also been diversified, and protocols for various purposes have been developed and the amount of traffic has exploded. Therefore, it is difficult for the network administrator to meet the stability and security standards of the network with the existing traditional switching and routing methods. Software Defined Networking (SDN) is a new networking paradigm proposed to solve this problem. SDN enables efficient network management by programming network operations. This has the advantage that network administrators can flexibly respond to various types of attacks. In this paper, we design a threat level management module, an attack detection module, a packet statistics module, and a flow rule generator that collects attack information through the controller and switch, which are components of SDN, and detects attacks based on these attributes of SDN. It proposes a method to block denial of service attacks (DoS) of advanced attackers by programming and applying honeypot. In the proposed system, the attack packet can be quickly delivered to the honeypot according to the modifiable flow rule, and the honeypot that received the attack packets analyzed the intelligent attack pattern based on this. According to the analysis results, the attack detection module and the threat level management module are adjusted to respond to intelligent attacks. The performance and feasibility of the proposed system was shown by actually implementing the proposed system, performing intelligent attacks with various attack patterns and attack levels, and checking the attack detection rate compared to the existing system.

• Knowledge Management Research
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• v.23 no.2
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• pp.277-299
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• 2022

The massive card transaction data generated in the tourism industry has become an important resource that implies tourist consumption behaviors and patterns. Based on the transaction data, developing a smart service system becomes one of major goals in both tourism businesses and knowledge management system developer communities. However, the lack of rating scores, which is the basis of traditional recommendation techniques, makes it hard for system designers to evaluate a learning process. In addition, other auxiliary factors such as temporal, spatial, and demographic information are needed to increase the performance of a recommendation system; but, gathering those are not easy in the card transaction context. In this paper, we introduce CTDDTR, a novel approach using card transaction data to recommend tourism services. It consists of two main components: i) Temporal preference Embedding (TE) represents tourist groups and services into vectors through Doc2Vec. And ii) Deep tourism Recommendation (DR) integrates the vectors and the auxiliary factors from a tourism RDF (resource description framework) through MLP (multi-layer perceptron) to provide services to tourist groups. In addition, we adopt RFM analysis from the field of knowledge management to generate explicit feedback (i.e., rating scores) used in the DR part. To evaluate CTDDTR, the card transactions data that happened over eight years on Jeju island is used. Experimental results demonstrate that the proposed method is more positive in effectiveness and efficacies.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.125-144
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• 2023

Recently, the use of Large Language Models (LLMs) such as ChatGPT has been increasing in various fields such as interactive commerce and mobile financial services. However, LMMs, which are mainly created by learning existing documents, can also learn various human biases inherent in documents. Nevertheless, there have been few comparative studies on the aspects of bias and discrimination in LLMs. The purpose of this study is to examine the existence and extent of nine types of discrimination (Age, Disability status, Gender identity, Nationality, Physical appearance, Race ethnicity, Religion, Socio-economic status, Sexual orientation) in LLMs and suggest ways to improve them. For this purpose, we utilized BBQ (Bias Benchmark for QA), a tool for identifying discrimination, to compare three large-scale language models including ChatGPT, GPT-3, and Bing Chat. As a result of the evaluation, a large number of discriminatory responses were observed in the mega-language models, and the patterns differed depending on the mega-language model. In particular, problems were exposed in elder discrimination and disability discrimination, which are not traditional AI ethics issues such as sexism, racism, and economic inequality, and a new perspective on AI ethics was found. Based on the results of the comparison, this paper describes how to improve and develop large-scale language models in the future.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.600-610
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• 2022

While Korean piano pedagogy has seen a remarkable growth, the relatively weak attention to intermediate level has emerged as a pending problem. The limited literature review, specifically the lack of playing Korean original works, has been considered as a serious issue. To emphasize the usefulness of Kim Kukjin's "Pieces of Korean Melody for Piano" as an intermediate work, this study presents practical teaching guidelines by classifying of difficulty in his 25 pieces and providing step by step learning goals and teaching plan. The difficulty stage was based on Jane Magret's 10-step classification table for comparison with other intermediate piano literature, and this study more specifically classified Kim's pieces according to Korean melody, rhythm, and texture. As a result of the difficulty classification, it was found that the pieces from stages 4 to 10 was organized to systematically and comprehensively learn step by step from the most basic progression to Korean 'Jangdan' rhythm patterns, various articulations and decorations that express 'Sigimsae' of Korean Traditional Music, and heterophony texture. In addition, this study proposed the order of pieces for the effective teaching according to the characteristics and difficulty of the pieces. This study suggests that the findings lead to the expansion of Korean intermediate literature study and the revitalization of Korean original works teaching method.

• Journal of the Korean Society of Food Culture
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• v.22 no.4
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• pp.462-467
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• 2007

Volatile components of six commercial $sauces(A{\sim}F)$ and $\hat{O}yuk-jang$(G, H), a Korean traditional fermented sauce, were analyzed by electronic nose based on GC with surface acoustic wave(SAW) sensor. The obtained data were used for pattern recognition and a visual pattern called a $VaporPrint^{TM}$, derived from the frequency and chromatogram of the GC-SAW sensor. Volatile components of sauces and $\hat{O}yuk-jang$ were well discriminated with the direct use of $VaporPrint^{TM}$. Commercial sauces and $\hat{O}yuk-jang$ showed different volatile patterns, respectively, due to different major material, which meju, beef extract, pickled anchovies, and Katsuobushi were used. Volatile components of Oyuk-jang were decreased drastically during the fermentation time. After boiling $\hat{O}yuk-jang$, new several peaks were found. The responses by electronic nose were used for principal component analysis. The PCA plot showed that volatile components pattern were well discriminated by first principal component score(proportion: 96.8%), and first principal component score of $\hat{O}yuk-jang$ was between soy sauce of the liquid extracted from beef and sauce of pickled anchovies.

• (The) Research of the performance art and culture
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• no.22
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• pp.413-439
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• 2011

This study aims to look for the proper directions of following and developing Gyeonggi sinawi dance in the fashion of Kim Sukja by closely examining its kinds and patterns. First, its characteristics and education reality were investigated. Second, the seven kinds of Gyeonggi sinawi dance Kim Sukja allegedly handed down (according to the 121st Report of the Intangible Cultural Assets) were concretely examined. Third, the composition of each dance pattern was studied. Fourth, various beats used in Gyeonggi sinawi dance were revealed. The late Kim Sukja had outstanding artistic talent and ability in Gyeonggi sinawi dance movements, musical composition, gayageum accompanied singing, and pansori episodes. Behind her were master singer Kim Seokchang (grandfather), father Kim Deoksun (belonging to Hwaseong Artist Board), shaman-mother Jeong Gwiseong, and great dancer Jo Jinyeong. Kim sukja's seven Gyeonggi sinawi dance types were bujeong nori, teo beollim, jinsoe, jeseok, kkaekkeum, ollimchae, and dosal puri (designated as Important Intangible Cultural Asset in 1990). Such beats as seopchae (dosal puri), mori, bal ppeodeurae, bujeong nori, ollimchae, jinsoe, and teo beollim (ban seoreum) were mainly used in Gyeonggi sinawi dance. In sum, Kim Sukja's dance was more than an individual's dance to represent the cultural types and life at that time in Gyeonggi-do and be a very important academic historic material. Therefore, it is the responsibility of the present generation to hand down and develop such invaluable traditional cultural materials.