• Journal of Life Science
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• v.19 no.3
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• pp.349-355
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• 2009

Metabolic fingerprinting of microbial communities was investigated with Biolog GN2 plates using samples of biofilm and aeration tanks from an RABC (rotating activated Bacillus contactor) system - an advanced wastewater treatment system for the food wastewater of pig slaughterhouses. Aerobic and anaerobic results revealed the following four aspects. First, simple matching and pairs t-test of daily variation showed more defined qualitative and quantitative relatedness of active microbial communities than that of mere optical densities. Second, metabolic potentials were higher in biofilm than in aeration tanks (p<0.01), meaning higher activity of biofilm. Third, two aeration tanks showed the highest similarity (78%) and similar metabolic power (p=0.287). However, actively used carbon sources were different among samples, signifying change of active communities at each wastewater treatment step. Finally, aerobic and anaerobic metabolic fingerprinting patterns were different for the same samples representing activities of microaerophilic and/or anaerobic communities. These results suggest that daily variation and anaerobic incubation would help in the comparison of metabolic fingerprintings.

• The Journal of Society for e-Business Studies
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• v.15 no.3
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• pp.131-145
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• 2010

Until now, popular paradigms to provide e-catalog documents that are adapted to users' needs are keyword search or collaborative filtering based recommendation. Since users' queries are too short to represent what users want, it is hard to provide the users with e-catalog documents that are adapted to their needs(i.e., queries and preferences). Although various techniques have beenproposed to overcome this problem, they are based on index term matching. A conventional Bayesian belief network-based approach represents the users' needs and e-catalog documents with their corresponding concepts. However, since the concepts are the index terms that are extracted from the e-catalog documents, it is hard to represent relationships between concepts. In our work, we extend the conventional Bayesian belief network based approach to represent users' needs and e-catalog documents with a concept network which is derived from the Web directory. By exploiting the concept network, it is possible to search conceptually relevant e-catalog documents although they do not contain the index terms of queries. Furthermore, by computing the conceptual similarity between users, we can exploit a semantic collaborative filtering technique for recommending e-catalog documents.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.39 no.1
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• pp.97-107
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• 2002

In this paper, we propose the realization of on-line handwritten Chinese character recognition for mobile personal digital assistants (PDA). We focus on the development of an algorithm having a high recognition performance under the restriction that PDA requires small memory storage and less computational complexity in comparison with PC. Therefore, we use index matching method having computational advantage for fast recognition and we suggest a unit reconstruction method to minimize the memory size to store the character models and to accomodate the various changes in stroke order and stroke number of each person in handwriting Chinese characters. We set up standard model consisting of 1800 characters using a set of pre-defined units. Input data are measured by similarity among candidate characters selected on the basis of stroke numbers and region features after preprocessing and feature extracting. We consider 1800 Chinese characters adopted in the middle and high school in Korea. We take character sets of five person, written in printed style, irrespective of stroke ordering and stroke numbers. As experimental results, we obtained an average recognition time of 0.16 second per character and the successful recognition rate of 94.3% with MIPS R4000 CPU in PDA.

• The Research Journal of the Costume Culture
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• v.16 no.6
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• pp.1050-1063
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• 2008

A movie is a fiction made on a basis of an author's and a writer's imagination, but all sorts of properties mixed with each other and most realistically expresses the era which becomes the background of a movie and acts as a carrier that connects designers with consumers. Thus, this study was carried out to review how the fashion products that designer's intention and commercial value added are expressed in collections by comparing and analysing the costumes in the movie "The Great Gatsby" that described the life of America's upper-class in 1920s and the 04 S/S Y&Kei collection which were proceeding after getting inspiration from this movie. For this, literature materials were inspected in order to make a theoretical review on social and cultural background and costumes history background in 1920s and the photo materials on movie costume were collected and analysed using DVD video captures, as well as the photo materials on 04 S/S Y&Kei were collected and analyzed through the institute providing domestic fashion information. The following conclusion was deduced through this study. First, in 1920s which becomes the background of this study, the slim shape of Flapper which looks like a young and boy became an ideal figure condition and the straight silhouette with low waist line and the short skirt that rose to knee was popular. Second, as a result of analysing movie costume by classifying it in silhouette, colors, and materials, straight silhouette of low waistline with a near colored - tone seen in the pastel series, including white, beige, pink, and gray was mainly constituted and the metal colors like silver and gold were used. As a material, chiffon, satin, velvet, flower patterned prints, and beads were used, which represented luxurious life of women in the upper classes. Third, as a result of comparing and analysing, it turned out that there was a similarity. However, in dress collection for a heroine, some dissimilarity differentiated from a movie costumes was found out in that the dresses in collection expressed moderate beauty and modernism and elegant beauty at the same time by matching a variety of materials and using black color.

• Journal of the Korean Wood Science and Technology
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• v.37 no.1
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• pp.37-47
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• 2009

For the inspection of wood, machine vision is the most common automated inspection method used at present. It is required to sort wood products by grade and to locate surface defects prior to cut-up. Many different sensing methods have been applied to inspection of wood including optical, ultrasonic, X-ray sensing in the wood industry. Nowadays the scanning system mainly employs CCD line-scan camera to meet the needs of accurate detection of lumber defects and real-time image processing. But this system needs exact feeding system and low deviation of lumber thickness. In this study low cost CCD area sensor was used for the development of image processing system for lumber being fed. When domestic red pine being fed on the conveyer belt, lumber images of irregular term of captured area were acquired because belt conveyor slipped between belt and roller. To overcome incorrect image merging by the unstable feeding speed of belt conveyor, it was applied template matching algorithm which was a measure of the similarity between the pattern of current image and the next one. Feeding the lumber over 13.8 m/min, general area sensor generates unreadable image pattern by the motion blur. The red channel of RGB filter showed a good performance for removing background of the green conveyor belt from merged image. Threshold value reduction method that was a image-based thresholding algorithm performed well for knot detection.

• Archives of design research
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• v.14 no.3
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• pp.37-48
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• 2001

This paper is a study on how women consumers purchase are affected by models who appear in advertisements for cosmetics, focusing especially on studies concerning the impact that models have on advertisement strategies of the cosmetic industry in korea. In surveys conducted, consumers responded that cosmetic advertisement models should examplify a expertness and trustworthiness attitude more than just display their own physical attractiveness. The consumers who bought cosmetic products based on its endorsement from particular models responded that they had a positive reaction to the models physical attractiveness and likability while experiencing a negative reaction to the model's expertness and trustworthiness attitude. Women consumers are interested in cosmetic advertisement models, but do not necessarily trust them. Hence, the use of a Particular model does not directly affect the consumers Purchasing decision. Famous stars often appear in cosmetic advertisements in korea, and targeted consumers have a very positive response to their physical attractiveness, familiarity and perceived likability. However, the consumers have a completely negative response to the models in regards to their expertness, trustworthiness, and their sense of similarity with the model. The models, then, should be used in these advertisements to try and uphold the fellowing qualities. expertness in regards to having some knowledge of, experience with, and expertness in using the cosmetic produces, trustworthiness when expressing their own opinion of the product, matching image of products and targeted consumers.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.2
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• pp.37-47
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• 2011

In the recent years, malicious codes called malware are having shown significant increase due to the code obfuscation to evade detection mechanisms. When the code obfuscation technique is applied to malwares, they can change their instruction sequence and also even their signature. These malwares which have same functionality and different appearance are able to evade signature-based AV products. Thus, AV venders paid large amount of cost to analyze and classify malware for generating the new signature. In this paper, we propose a novel approach for detecting metamorphic malwares. The proposed mechanism first converts malware's API call sequences to call graph through dynamic analysis. After that, the callgraph is converted to semantic signature using 128 abstract nodes. Finally, we extract all subgraphs and analyze how similar two malware's behaviors are through subgraph similarity. To validate proposed mechanism, we use 273 real-world malwares include obfuscated malware and analyze 10,100 comparison results. In the evaluation, all metamorphic malwares are classified correctly, and similar module behaviors among different malwares are also discovered.

• Asia Marketing Journal
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• v.9 no.3
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• pp.117-149
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• 2007

It can be explained by congruity theory as a process that consumers engage in a matching process to identify personal community that is congruent with their self-images to find the identification between the self and the personal community. Personal community cues that evoke certain images are viewed as activating similar beliefs about the self (e.g., high status). Individuals prompt a comparison process to determine whether the personal community and self-image are congruent and imagine prototypical users of the personal community and select ones that maximize similarity to their actual or desired self-concept. Identity is devided into personal identity and social identity. Consumers are likely to be influenced by both personal identity and social identity. In this article the influencing factors of the commitment to on-line personal community are explored by the sources of both personal identification and social identification. The results are as follows. The maintenance expression and enhancement expression of personal self influence the level of personal identification positively and the maintenance expression and enhancement expression of social self influence the level of social identification positively. The level of both social and personal identification positively influence the commitment to on-line personal community which gives positive responses to the source enterprise that allows the cyberspace and the other benefits to be used.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.221-241
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• 2018

As we enter the knowledge society, the importance of information as a new form of capital is being emphasized. The importance of information classification is also increasing for efficient management of digital information produced exponentially. In this study, we tried to automatically classify and provide tailored information that can help companies decide to make technology commercialization. Therefore, we propose a method to classify information based on Korea Standard Industry Classification (KSIC), which indicates the business characteristics of enterprises. The classification of information or documents has been largely based on machine learning, but there is not enough training data categorized on the basis of KSIC. Therefore, this study applied the method of calculating similarity between documents. Specifically, a method and a model for presenting the most appropriate KSIC code are proposed by collecting explanatory texts of each code of KSIC and calculating the similarity with the classification object document using the vector space model. The IPC data were collected and classified by KSIC. And then verified the methodology by comparing it with the KSIC-IPC concordance table provided by the Korean Intellectual Property Office. As a result of the verification, the highest agreement was obtained when the LT method, which is a kind of TF-IDF calculation formula, was applied. At this time, the degree of match of the first rank matching KSIC was 53% and the cumulative match of the fifth ranking was 76%. Through this, it can be confirmed that KSIC classification of technology, industry, and market information that SMEs need more quantitatively and objectively is possible. In addition, it is considered that the methods and results provided in this study can be used as a basic data to help the qualitative judgment of experts in creating a linkage table between heterogeneous classification systems.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.47-73
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• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.