• The Journal of Korean Institute of Communications and Information Sciences
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• v.38B no.9
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• pp.736-743
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• 2013

In IRST(infrared search and track) system, the small target detection is very difficult because the IR(infrared) image have various clutter and sensor noise. The noise and clutter similar to the target intensity value produce many false alarms. In this paper. We propose the adaptive detection method which obtains optimal target detection using the image intensity information and the prior information of target. In order to enhance the target, we apply the human visual system. we determine the adaptive threshold value using image intensity and distance measure in target enhancement image. The experimental results indicate that the proposed method can efficiently extract target region in various IR images.

• The Research Journal of the Costume Culture
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• v.29 no.4
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• pp.522-537
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• 2021

The purpose of this study is to develop a knitwear design with the potential for practical use through a combination of science and design by examining the concept and formative characteristics of fractal geometry and applying them to the development of 3D virtual clothing knitwear design. This study produced five main conclusions. First, the sub-concepts of "Repeatability," "Scale variability," and "complexity," which are based on self-similarity, appear together with simple regularity in the fractal formative characteristics shown in fashion design. Second, fashion fields apply fractal geometry in three-dimensional surface textures and optical textile patterns as a method of expression. Third, it was confirmed that various expressions can be created with fractal patterns by using the SDS-ONE APEX 3-4 design system; moreover, fractal patterns are a suitable design source for the development of Jacquard knitwear patterns. Fourth, in the development of knitted jacquard fractal patterns, by arranging the patterns in perspective, the effect of emphasizing or reducing the human body by optical illusion was shown. Fifth, a knit Jacquard structure with a pattern that exhibits fractal modeling characteristics and applying it to a 3D virtual clothing sample design reduces the time required for sample production while expanding the knit design's expression area and reducing costs. Thus, the clothing sample confirmed the effectiveness of practical knitwear design development.

• International conference on construction engineering and project management
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• 2022.06a
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• pp.752-759
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• 2022

The instantiation of spaces as a discrete entity allows users to utilize BIM models in a wide range of analyses. However, in practice, their utility has been limited as spaces are erroneously entered due to human error and often omitted entirely. Recent studies attempted to automate space allocation using artificial intelligence approaches. However, there has been limited success as most studies focused solely on the use of geometric features to distinguish spaces. In this study, in addition to geometric features, semantic relations between spaces and elements were modeled and used to improve space classification in BIM models. Graph Convolutional Networks (GCN), a deep learning algorithm specifically tailored for learning in graphs, was deployed to classify spaces via a similarity graph that represents the relationships between spaces and their surrounding elements. Results confirmed that accuracy (ACC) was +0.08 higher than the baseline model in which only geometric information was used. Most notably, GCN was able to correctly distinguish spaces with no apparent difference in geometry by discriminating the specific elements that were provided by the similarity graph.

• Journal of Pharmacopuncture
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• v.27 no.3
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• pp.223-233
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• 2024

Objectives: The phytochemicals in the aerial parts of Euphorbia paralias (also known as Sea Spurge) and their anti-inflammatory and antimicrobial activities were investigated. Methods: The methanolic extract was characterized using GC-MS and HPLC techniques. The anti-inflammatory feature was estimated through a Human Red Blood Cell (HRBC) membrane stabilization technique, while the antimicrobial feature was evaluated by the disc diffusion agar technique, minimum bactericidal concentration, and minimum inhibitory concentration (MIC) via micro-broth dilution method. Results: The GC/MS results demonstrated the existence of various phytochemicals, such as n-hexadecenoic acid, cis-11-eicosenoic acid, and methyl stearate, recognized for their anti-inflammatory and antibacterial features. The similarity of the phytochemical composition with other Euphorbia species emphasizes the genus-wide similarity. The anti-inflammatory activity exhibited a noteworthy inhibitory effect comparable to the reference drug indomethacin. The extract's antimicrobial potential was tested against a range of microorganisms, demonstrating significant action against Gram-positive bacteria and Candida albicans. The quantification of total phenolics and flavonoids further supported the therapeutic potential of the extract. Conclusion: The methanolic extract from E. paralias emerges as a successful natural source of important active constituents with potential applications as anti-inflammatory and antimicrobial agents. This research provides a first step to valorize Euphorbia paralias insights as a source of worthwhile phytochemicals that have potential applications in the pharmaceutical industry.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.1-18
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• 2015

Collaborative filtering(CF) algorithm has been popularly used for recommender systems in both academic and practical applications. A general CF system compares users based on how similar they are, and creates recommendation results with the items favored by other people with similar tastes. Thus, it is very important for CF to measure the similarities between users because the recommendation quality depends on it. In most cases, users' explicit numeric ratings of items(i.e. quantitative information) have only been used to calculate the similarities between users in CF. However, several studies indicated that qualitative information such as user's reviews on the items may contribute to measure these similarities more accurately. Considering that a lot of people are likely to share their honest opinion on the items they purchased recently due to the advent of the Web 2.0, user's reviews can be regarded as the informative source for identifying user's preference with accuracy. Under this background, this study proposes a new hybrid recommender system that combines with users' review mining. Our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and his/her text reviews on the items when calculating similarities between users. In specific, our system creates not only user-item rating matrix, but also user-item review term matrix. Then, it calculates rating similarity and review similarity from each matrix, and calculates the final user-to-user similarity based on these two similarities(i.e. rating and review similarities). As the methods for calculating review similarity between users, we proposed two alternatives - one is to use the frequency of the commonly used terms, and the other one is to use the sum of the importance weights of the commonly used terms in users' review. In the case of the importance weights of terms, we proposed the use of average TF-IDF(Term Frequency - Inverse Document Frequency) weights. To validate the applicability of the proposed system, we applied it to the implementation of a recommender system for smartphone applications (hereafter, app). At present, over a million apps are offered in each app stores operated by Google and Apple. Due to this information overload, users have difficulty in selecting proper apps that they really want. Furthermore, app store operators like Google and Apple have cumulated huge amount of users' reviews on apps until now. Thus, we chose smartphone app stores as the application domain of our system. In order to collect the experimental data set, we built and operated a Web-based data collection system for about two weeks. As a result, we could obtain 1,246 valid responses(ratings and reviews) from 78 users. The experimental system was implemented using Microsoft Visual Basic for Applications(VBA) and SAS Text Miner. And, to avoid distortion due to human intervention, we did not adopt any refining works by human during the user's review mining process. To examine the effectiveness of the proposed system, we compared its performance to the performance of conventional CF system. The performances of recommender systems were evaluated by using average MAE(mean absolute error). The experimental results showed that our proposed system(MAE = 0.7867 ~ 0.7881) slightly outperformed a conventional CF system(MAE = 0.7939). Also, they showed that the calculation of review similarity between users based on the TF-IDF weights(MAE = 0.7867) leaded to better recommendation accuracy than the calculation based on the frequency of the commonly used terms in reviews(MAE = 0.7881). The results from paired samples t-test presented that our proposed system with review similarity calculation using the frequency of the commonly used terms outperformed conventional CF system with 10% statistical significance level. Our study sheds a light on the application of users' review information for facilitating electronic commerce by recommending proper items to users.

• Parasites, Hosts and Diseases
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• v.52 no.2
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• pp.197-199
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• 2014

Diphyllobothrium latum and Diphyllobothrium nihonkaiense are the 2 reported main causes of human diphyllobothriasis in the Republic of Korea. However, the differentiation of these 2 species based on morphologic features alone is difficult. The authors used nucleotide sequencing of the mitochondrial cytochrome c oxidase subunit 1 (cox1) gene to diagnose Diphyllobothrium spp. Two patients visited the emergency room at Kyungpook National University Hospital on 3 April and 12 April 2013, respectively, with fragments of parasites found while defecating. The parasites were identified as Diphyllobothrium spp. based on morphologic characteristics, and subsequent cox1 gene sequencing showed 99.9% similarity (1,478/1,480 bp) with D. nihonkaiense. Our findings support the hypothesis that D. nihonkaiense is a dominant species in Korea.

• Journal of Ecology and Environment
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• v.34 no.2
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• pp.237-249
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• 2011

Land-use changes due to the socio-economic environment influence landscape patterns and processes, which affect habitats and biodiversity. This study considers the effects of such land-use changes, particularly on the traditional rural "Maeul" forested landscape, by analyzing landscape structure and vegetation changes. Three study areas were examined that have seen their populations decrease and age over the last few decades. Five types of plant life-forms (Raunkier life-forms) were distinguished to investigate ecosystem function. Principle component analysis was used to understand vegetation dynamics and community characteristics based on a vegetation similarity index. Ordination analysis transformed species-coverage data was introduced to clarify vegetation dynamics. Landscape indices, such as area metrics, edge metrics, and shape metrics, showed that spatial heterogeneity has increased over time in all areas. Pinus densiflora was the main land-use plant type in all study areas but decreased over time, whereas Quercus spp. increased. Over a decade, P. densiflora communities shifted to deciduous oak and plantation. These findings indicate that the impact of human activities on the Maeul landscape is twofold. While forestry activities caused heavy disturbances, the abandonment of traditional human activities has led to natural succession. Furthermore, it can be concluded that the type and intensity of these human impacts on landscape heterogeneity relate differently to vegetation succession. This reflects the cause and consequence of patch dynamics. We discuss an approach for sustainable landscape planning and management of the Maeul landscape based on traditional management.

• Journal of Korea Multimedia Society
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• v.12 no.11
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• pp.1661-1670
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• 2009

In this paper, by analyzing the nonverbal communication of personified animals and human animation characters, the differences of gesture and the amount of similarity between personified animal characters and human characters were studied. Accordingly, personified characters were classified into three categories: humanoid characters, animal-like characters and mixed-type characters which possess characteristics of both human and animals. Furthermore, we proposed a 7-step process for the personification of animal characters. Diverse information of personification was provided for animation designers in this paper. A DB system for animal character designing and automation for animation utilizing personified characters are expected to be constructed.

• Korean Journal of Human Ecology
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• v.14 no.3
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• pp.453-466
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• 2005

The purpose of this study is to investigate the origin of make-up and the beginning of 'Yoenji' cosmetics. The result of this study is as follows: 1. The origin of make-up can be summarized as being originated from the sexual display to maintain tribes and incantatory religion for their existence. The reason for the preference to Yeonji in make-up is that Yeonji has a characteristic of red in color, which seems to hold effective for the sexual display of human beings. 2. The origin of Yeonji (焉支; Safflower) was from Egypt, but its inception as a cosmetic product was with Huns. 'Yeonji' was a term of Hungro race, and was also called Unji (焉支), Yeonji (燕支), Inji (姻支), Urji (閼氏), following the similarity of the sound. These terms were not only the interpretation of the Hunnish sound into Chinese, but also allegorical expression. Unji (焉支), Yeonji (燕支), and Inji (姻支) meant Safflower. Urji(閼氏) meant 'Empress' or 'Wife,' which was pronounced Yeonji (燕支) and Asi (閼氏).

• Proceedings of the IEEK Conference
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• 2008.06a
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• pp.683-688
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• 2008

The emotional information processing is to simulate and recognize human sensibility, sensuality or emotion, to realize natural and harmonious human-machine interface. This paper proposes an emotion-based image retrieval method. In this method, user can choose a linguistic query among some emotional adjectives. Then the system shows some corresponding representative images that are pre-evaluated by experts. Again the user can select a representative one among the representative images to initiate traditional content-based image retrieval (CBIR). By this proposed method any CBIR can be easily expanded as emotion-based image retrieval. In CBIR of our system, we use several color and texture visual descriptors recommended by MPEG-7. We also propose a fuzzy similarity measure based on Choquet integral in the CBIR system. For the communication between system and user, a relevance feedback mechanism is used to represent human subjectivity in image retrieval. This can improve the performance of image retrieval, and also satisfy the user's individual preference.