• Archives of design research
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• v.16 no.1
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• pp.5-14
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• 2003

As the digital technology has improved, the numbers of Hangul font users have increased and their individual needs and taste are diversified. Therefore new and various Hangul fonts out of traditional form are developed and used. But under the present font classification system, it is hard to compare and analyze these various fonts. And the present classification system is hard to be the font user's guide for proper use of various Hangul fonts. For the better use of Hangul font, to diversify the font classification system is needed. So we propose the development of these thru classification standards. First, structural classification based on the structural character of Hangul. Second, image classification based on the visual images of each font. And third, usage classification based on the fonts proper usage in various media. For the development of various typographically balanced fonts and for the suitable and effective use of the various font, we must try to build the font classification system based on the diversified classification standards and build Hangul font database based on this classification system. Through these studies, we can expect the development of good quality fonts and the better use of these fonts.

• Journal of the Korean Society for Library and Information Science
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• v.58 no.3
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• pp.65-89
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• 2024

This study developed a model to classify news articles into categories of topic, genre, and region using a Korean Pre-trained Language model. To achieve this, a new news article classification system was designed by referring to the classification systems of domestic media outlets. The topic and genre classification models were implemented as hierarchical classification models that link the main categories and subcategories, and their performance was compared with that of an integrated category model. The evaluation results showed that the hierarchical structure classification model had the advantage of providing more precise categorization in ambiguous or overlapping categories compared to the integrated category model. For regional classification of news articles, a model was built to classify into 18 categories, and for regional news articles, the regional characteristics were clearly reflected in the text, resulting in high performance. This study demonstrated the effectiveness of classifying news articles from multiple perspectives-topic, genre, and region-and emphasized the significance of suggesting the potential for a multi-dimensional news article classification service that meets user needs.

• Journal of the Society of Disaster Information
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• v.14 no.1
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• pp.28-35
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• 2018

The following research has focused and implemented on designing a system that classifies the severity of mass casualty situations across both normal and disaster levels. The system's algorithm has implemented requirements such as accuracy as well as user convenience. The developed e-Triage System has applied various severity classification algorithms implemented from IoT concepts. In order to overcome flaws of currently used severity classification systems, the e-Triage System used electronic elements including the NFC module. By using the mobile application's severity classification algorithm the system demonstrated quick and accurate assessment of patient. Four different LED lamps visualized the severity classification results and RTS scores were portrayed through FND(Flexible Numeric Display) after a two wave classification.

• KCID journal
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• v.17 no.2
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• pp.17-27
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• 2010

In order to manage the water quality in reservoir, it is necessary to understand the temporal and spatial variation of reservoirs and to classify the reservoirs. In this research, agricultural reservoirs are classified according to physical characteristics (depth, residence time, shape of the reservoir etc) and water quality using multivatriate analysis (PCA and CA). CA (Cluster Analysis) method classify reservoirs into several groups as a similarity of the reservoirs, but it is difficult to indicate a full list to the one table. In case of PCA (Principle Component Analysis) method, it has the advantage for the classification on the reservoirs depending on the water quality similarity and also it is useful to analyze the relationship between related factors through correlation analysis. However PCA is limited to classify into several groups based on the characteristics of the reservoirs and each user should be classified as randomly subjective according to the relative position of the reservoir in the figure. In conclusions, compared to conventional reservoirs classification methods, both CA and PCA methods are considered to be a classification method that describes the nature of the reservoir well, but classification results has a restriction on use, so further research will be needed to complement.

• International Journal of Advanced Culture Technology
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• v.7 no.4
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• pp.321-326
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• 2019

As AI technology is recently introduced into various fields, it is being applied to the fashion field. This paper proposes a system for recommending cody clothes suitable for a user's selected clothes. The proposed system consists of user app, cody recommendation module, and server interworking of each module and managing database data. Cody recommendation system classifies clothing images into 80 categories composed of feature combinations, selects multiple representative reference images for each category, and selects 3 full body cordy images for each representative reference image. Cody images of the representative reference image were determined by analyzing the user's preference using Google survey app. The proposed algorithm classifies categories the clothing image selected by the user into a category, recognizes the most similar image among the classification category reference images, and transmits the linked cody images to the user's app. The proposed system uses the ResNet-50 model to categorize the input image and measures similarity using ORB and HOG features to select a reference image in the category. We test the proposed algorithm in the Android app, and the result shows that the recommended system runs well.

• Journal of The Institute of Information and Telecommunication Facilities Engineering
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• v.9 no.1
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• pp.24-28
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• 2010

The Quality of Experience (QoE) of next Generation wired/wireless network services based upon IP networking is becoming a popular issue in recent years. The user experience of Internet services such as IPTV, online game, web surfing and etc, are becoming the most desirable factors to service providers to improve service performance and customer's satisfaction. However, collecting user experience from customers and obtaining the QoE parameters from the Quality of Service (QoS) parameters such as bandwidth, delay, jitter or admission control algorithm, are difficult subjects because of the various service types and user characteristics. In this paper, we propose a framework which contains service classification, QoE analysis and service enhancement steps for a suitable QoE measurement and management protocol. We define the user satisfaction indicators of the Internet services, classify the categories of each type of services, and analyse the Key Performance Indicator (KPI) in each type of services to perform the QoS parameters and improving the service qualities.

• Journal of Korean Academy of Nursing
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• v.17 no.1
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• pp.9-23
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• 1987

Nursing service, as the largest user of labor resources, has become concerned about appropriate allocation of staffing resources. Therefore, this project was designed to measure quantitatively the direct nursing care provided to patients and to develop a new patient classification system based on the direct nursing care activities. The initial step in the development of the classification instrument was to identify the content of direct nursing activities. The frequency with which these activities were carried out, the total time spent in carrying them out and the average time for one performance of each of the nursing activities was calculated. The next step was to select the items for the classification instrument taking into account these direct nursing activities. A list of 40 items was prepared. These items were then classified into 8 major categories: personal hygiene, moving ＆ exercise, nutrition & elimination, observation, medication, treatment, collecting specimens and other care activities for severity ill patients. Each item was assigned a value unit based on the average time required by the nursing staff to complete the specific item. The third step was to determine the practicality of the items and value units, so an attempt was made to establish content validity for these items and units by obtaing a consensus from 8 head nurses, representing eight different departments. The 4th step was to conducted a pilot study to establish the score range for the classification boundaries. For this purpose an instrument was designed using the list of items and value units and a prepared classification criteria as a guideline to validate the patient classification. A judgment group consisting of 52 supervisory nurses and head nurses were asked to select the proper patient to fit each classification criteria and to fill out the instrument for each patient. The total value unit and the frequency for each classification group was calculated. According to the frequency distribution, the score range for the classification group was determined as follows : 0~15 for groupI, 16~30 for group II, 31~50 for group III, and above 51 for group IV. Finally a patient classification form was developed.

• Journal of the Korea Convergence Society
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• v.8 no.1
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• pp.25-34
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• 2017

With the recent spread of smartphones and the introduction of web services, online users can access large-scale content regardless of time or place. However, users have had trouble finding the content they wanted among large-scale content. To solve this problem, user modeling and content recommendation system have been actively studied in various fields. However, in spite of active changes in senior groups according to the changes in information environment, research on user modeling and content recommendation system focused on senior groups are insufficient. In this paper, we propose a method of modeling smart senior based on their preference, and further develop a smart senior classification model using machine learning methods. As a result, we can not only grasp the preferences of smart seniors, but also develop a smart senior classification model, which is the foundation for the research of a recommendation system which will provide the activities and contents most suitable for senior groups.

• Journal of Biomedical Engineering Research
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• v.37 no.5
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• pp.161-167
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• 2016

Recent technological advances in sensor fabrication and bio-signal processing enabled non-constraint and non-intrusive measurement of human bio-signals. Especially, non-constraint measurement of ECG makes it available to estimate various human health parameters such as heart rate. Additionally, non-constraint ECG measurement of wheelchair user provides real-time health parameter information for emergency response. For accurate emergency response with low false alarm rate, it is necessary to discriminate quality levels of ECG measured using non-constraint approach. Health parameters acquired from low quality ECG results in inaccurate information. Thus, in this study, a machine learning based approach for three-class classification of ECG quality level is suggested. Three sensors are embedded in the back seat, chest belt, and handle of automatic wheelchair. For the two sensors embedded in back seat and chest belt, capacitively coupled electrodes were used. The accuracy of quality level classification was estimated using Monte Carlo cross validation. The proposed approach demonstrated accuracy of 94.01%, 95.57%, and 96.94% for each channel of three sensors. Furthermore, the implemented algorithm enables classification of user posture by detection of contacted electrodes. The accuracy for posture estimation was 94.57%. The proposed algorithm will contribute to non-constraint and robust estimation of health parameter of wheelchair users.

• Journal of KIISE
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• v.42 no.12
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• pp.1611-1622
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• 2015

Although Android systems provide a permission-based access control mechanism and demand a user to decide whether to install an app based on its permission list, many users tend to ignore this phase. Thus, an improved method is necessary for users to intuitively make informed decisions when installing a new app. In this paper, with regard to the permission-based access control system, we present a novel approach based on a machine-learning technique in order to support a user decision-making on the fly. We apply the K-NN (K-Nearest Neighbors) classification algorithm with necessary weighted modifications for malicious app classification, and use 152 Android permissions as features. Our experiment shows a superior classification result (93.5% accuracy) compared to other previous work. We expect that our method can help users make informed decisions at the installation step.