• Journal of Animal Environmental Science
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• v.7 no.1
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• pp.45-56
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• 2001

The following statistics are the results of a survey that analyzed the classification of livestock area and spatial mobility based upon the number of livestock and an area of 151 towns and cities from 1975 to 1995. 1. As a results of analysis about the degree of location concentration using C.V., Korean native cattles (HanWoo) and swines are becoming more centralized while dairies and chickens are becoming decentralized. 2. 49 regions, that is 32.5%, were classified as growing regions, 30 regions (19.9%) were stagnant regions and 72 regions (47.7%) were withering regions. The classification was based upon the calculation according to the numbers of converted grown animals and growth index. Kyonggi-do and Chungchongnam-do, specifically, took up 26.6% and 24.5% of the developing regions which shows that these two regions are the dominant regions for livestock. 3. Kyongsangbuk-do and Chungchongnam-do play significant roles for overall livestock, and Chollanam-do is considering a transition from swines to Korean native cattles and Kyongsangbuk-do is shifting from Korean native cattles to swines.

• International Journal of Highway Engineering
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• v.16 no.4
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• pp.127-134
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• 2014

PURPOSES : To operate more efficient traffic management system, it is utmost important to detect the change in congestion level on a freeway segment rapidly and reliably. This study aims to develop classification method of congestion change type. METHODS: This research proposes two classification methods to capture the change of the congestion level on freeway segments using the dedicated short range communication (DSRC) data and the vehicle detection system (VDS) data. For developing the classification methods, the decision tree models were employed in which the independent variable is the change in congestion level and the covariates are the DSRC and VDS data collected from the freeway segments in Korea. RESULTS : The comparison results show that the decision tree model with DSRC data are better than the decision tree model with VDS data. Specifically, the decision tree model using DSRC data with better fits show approximately 95% accuracies. CONCLUSIONS : It is expected that the congestion change type classified using the decision tree models could play an important role in future freeway traffic management strategy.

• IMMUNE NETWORK
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• v.21 no.3
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• pp.21.1-21.22
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• 2021

Myeloid-derived suppressor cells (MDSCs) have strong immunosuppressive activity and are morphologically similar to conventional monocytes and granulocytes. The development and classification of these cells have, however, been controversial. The activation network of MDSCs is relatively complex, and their mechanism of action is poorly understood, creating an avenue for further research. In recent years, MDSCs have been found to play an important role in immune regulation and in effectively inhibiting the activity of effector lymphocytes. Under certain conditions, particularly in the case of tissue damage or inflammation, MDSCs play a leading role in the immune response of the central nervous system. In cancer, however, this can lead to tumor immune evasion and the development of related diseases. Under cancerous conditions, tumors often alter bone marrow formation, thus affecting progenitor cell differentiation, and ultimately, MDSC accumulation. MDSCs are important contributors to tumor progression and play a key role in promoting tumor growth and metastasis, and even reduce the efficacy of immunotherapy. Currently, a number of studies have demonstrated that MDSCs play a key regulatory role in many clinical diseases. In light of these studies, this review discusses the origin of MDSCs, the mechanisms underlying their activation, their role in a variety of clinical diseases, and their function in immune response regulation.

• Korean Institute of Interior Design Journal
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• v.16 no.5
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• pp.71-80
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• 2007

Although the history of the contemporary museum architecture is relatively short, the concept of its existence has changed owing to its openness to the spectators at large. Within the short period of time, it has developed into a multi functional architecture with eduinfortainment function for the general publics in concert of the changes of its social activities in addition to its innate function as a museum to meet the intellectual desires of the spectators. Therefore, this study looks Into how to suffice the ever changing Intellectual desires of the spectators and the various spatial correspondences in accordance with the social and cultural roles of the museum with purpose to present the materials of the typological characteristics of the third generation museum architecture, which shows diversifying propensity, by means of an analytical study on the characteristics of the third generation museum architecture with confidence in mind that such materials are needed in the early planning stage. The chapter 2 divides the museum architecture into three generations for a comparative analytical study and presents the three classification standards thru the preceding studies related to the museum typological classifications. In accordance with the standards, 60 selective art museums have been classified by their typological patterns. The chapter 3 shows the result of the typological space classification of the 60 art museums through an analyzation on the typological characteristics and the interrelations of them. Such study is considered to furnish important measures for the realization of the substance of the museum architecture. At the same time, it Is also judged to play an instrumental role for the theoretical system of the communication function and classification required in the early designing stage as well as to play an educational role important as the designing guide line.

• Journal of Korean Society for Geospatial Information Science
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• v.4 no.2 s.8
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• pp.181-193
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• 1996

A new technique for land cover classification which applies digital image pre-classified by unsupervised classification technique, clustering, to Canonical Correlation Analysis(CCA) was proposed in this paper. Compared with maximum likelihood classification, the proposed technique had a good flexibility in selecting training areas. This implies that any selected position of training areas has few effects on classification results. Land cover of each cluster designated by CCA after clustering is able to be used as prior information for maximum likelihood classification. In case that the same training areas are used, accuracy of classification using Canonical Correlation Analysis after cluster analysis is better than that of maximum likelihood classification. Therefore, a new technique proposed in this study will be able to be put to practical use. Moreover this will play an important role in the construction of GIS database

• The Journal of Society for e-Business Studies
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• v.24 no.2
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• pp.1-14
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• 2019

Because of the characteristics of game software, it is important to quickly identify and reflect users' needs into game software after its launch. However, most sites such as the Google Play Store, where users can download games and post reviews, provide only very limited and ambiguous classification categories for game reviews. Therefore, in this paper, we develop an automatic classification system for game reviews that categorizes reviews into categories that are clearer and more useful for game providers. The developed system converts words in reviews into vectors using word2vec, which is a representative word embedding model, and classifies reviews into the most relevant categories by measuring the similarity between those vectors and each category. Especially, in order to choose the best similarity measure that directly affects the classification performance of the system, we have compared the performance of three representative similarity measures, the Euclidean similarity, cosine similarity, and the extended Jaccard similarity, in a real environment. Furthermore, to allow a review to be classified into multiple categories, we use a threshold-based multi-category classification method. Through experiments on real reviews collected from Google Play Store, we have confirmed that the system achieved up to 95% accuracy.

• International Journal of Contents
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• v.10 no.3
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• pp.1-8
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• 2014

In this paper, we collect and analyze the status of mobile phone applications (hereafter apps) in the healthcare and fitness category of the Apple iTunes App Store and Google Play Store. We determine the number of apps and analyze statistical aspects such as classifications, age rating, fees, and user evaluation of the popular items. As of September 30, 2013, there were 236 popular apps available from iTunes. Google Play offered 720 apps. We discover that apps for healthcare and fitness are diverse. Apps for physical exercise have the greatest popularity. The proportions of apps that are suitable for all ages among the Google and iTunes popular apps are 55.8% and 89.4%, respectively. The user evaluation of apps in iTunes is relatively less positive. We determine that the proportion of paid apps to free apps in Google is higher than that of the apps in iTunes. We perform hypothesis tests and find statistically significant differences in age rating and perceived satisfaction between the apps of the Apple iTunes App Store and Google Play Store. However, we find no meaningful differences in the classification and price of the apps between the two app stores. We perform hypothesis tests to verify the differences in age rating and perceived satisfaction between the paid and free apps within and across the Google Play Store and iTunes App Store. There are statistically significant differences in the age rating between the paid and free apps in the Google play store, between the Google free and iTunes free apps, between the Google paid and iTunes paid apps, between the Google free and iTunes paid apps, and between the Google paid and iTunes free apps. There are statistically significant differences in the perceived satisfaction between the Google free and iTunes free apps, between the Google paid and iTunes paid apps, between the Google free and iTunes paid apps, and between the Google paid and iTunes free apps.

• The Journal of Society for e-Business Studies
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• v.8 no.3
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• pp.219-239
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• 2003

The world of business is being profoundly transformed by the Internet and electronic commerce. E-commerce is driven by Internet and e-commerce technology. That is, the new e-commerce is commonly associated with highly developed technical elements, ranging from web , graphic design. payment systems and network infrastructure. Thus, it is necessary to decide which technologies are important and how they are related to each other. To anticipate the future of each information communication technology and electronic commerce accurately , we have attempted to develop a classification model of electronic commerce technology. A classification model for EC technologies consists of three categories: basic technology, base technology, and application technology. This model can play a role as a guideline in classifying EC technologies into three hierarchical category and in comparing the relative relationships of each electronic commerce technology. It will also provide an impetus for the study of electronic commerce technologies and for the shaping process of electronic commerce generally.

• Journal of the Korean Institute of Intelligent Systems
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• v.20 no.3
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• pp.337-342
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• 2010

The rise of music resources has led to a parallel rise in the need to manage thousands of songs on user devices. So users are tend to build play-list for manage songs. However the manual selection of songs for creating play-list is bothersome task. This paper proposes an auto play-list recommendation system considering user's context of use and preference. This system has two separate systems: mood and emotion classification system and music recommendation system. Users need to choose just one seed song for reflection their context of use and preference. The system recommends songs before the current song ends in order to fill up user play-list. User also can remove unsatisfied songs from recommended song list to adapt user preferences of the system for the next recommendation precess. The generated play-lists show well defined mood and emotion of music and provide songs that user preferences are reflected.

• Smart Structures and Systems
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• v.29 no.1
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• pp.105-116
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• 2022

Stay cables play an essential role in cable-stayed bridges. Severe vibrations and/or harsh environment may result in cable failures. Therefore, an efficient structural health monitoring (SHM) solution for cable damage detection is necessary. This study proposes a data-driven method for immediately detecting cable damage from measured cable forces by recognizing pattern transition from the intact condition when damage occurs. In the proposed method, pattern recognition for cable damage detection is realized by time series classification (TSC) using a deep learning (DL) model, namely, the long short term memory fully convolutional network (LSTM-FCN). First, a TSC classifier is trained and validated using the cable forces (or cable force ratios) collected from intact stay cables, setting the segmented data series as input and the cable (or cable pair) ID as class labels. Subsequently, the classifier is tested using the data collected under possible damaged conditions. Finally, the cable or cable pair corresponding to the least classification accuracy is recommended as the most probable damaged cable or cable pair. A case study using measured cable forces from an in-service cable-stayed bridge shows that the cable with damage can be correctly identified using the proposed DL-TSC method. Compared with existing cable damage detection methods in the literature, the DL-TSC method requires minor data preprocessing and feature engineering and thus enables fast and convenient early detection in real applications.