• IE interfaces
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• v.16 no.4
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• pp.485-495
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• 2003

This paper reports on the application of the integration of mathematical programming model and database in a decision support system (DSS) for the planning of the multi-reservoir operating system. The DSS is based on a multi-objective, mixed-integer goal programming (MIGP) model, which can generate efficient solutions via the weighted-sums method (WSM). The major concern of this study is seamless, efficient integration between the mathematical model and the database, because there are significant differences in structure and content between the data for a mathematical model and the data for a conventional database application. In order to load the external optimization results on the database, we developed a systematic way of naming variable/constraint so that a rapid identification of variables/constraints is possible. An efficient database structure for planning of the multi-reservoir operating system is presented by taking advantage of the naming convention of the variable/constraint.

• International Journal of Contents
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• v.13 no.1
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• pp.53-61
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• 2017

Recently, the Social Internet of Things (IoT), the follow-up of the IoT, has been studied to expand the existing IoT services, by integrating devices into the social network of people. In the Social IoT environment, humans, devices and digital contents are connected with social relationships, to guarantee the network navigability and establish levels of trustworthiness. However, this environment handles massive data, including social data of humans (e.g., profile, interest and relationship), profiles of IoT devices, and digital contents. Hence, users and service providers in the Social IoT are exposed to arbitrary data when searching for specific information. A study about the recommender system for the Social IoT environment is therefore needed, to provide the required information only. In this paper, we propose the Social correlation group based Recommender System (SRS). The SRS generates a target group, depending on the social correlation of the service requirement. To generate the target group, we have designed an architecture, and proposed a procedure of the SRS based on features of social interest similarity and principles of the Collaborative Filtering and the Content-based Recommender System. With simulation results of the target scenario, we present the possibility of the SRS to be adapted to various Social IoT services.

• Journal of Biomedical Engineering Research
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• v.31 no.1
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• pp.40-49
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• 2010

A rule weight -based fuzzy classification model is proposed to analyze the patterns of admission-discharge of patients as a previous research for differential diagnosis of dyspnea. The proposed model is automatically generated from a labeled data set, supervised learning strategy, using three procedure methodology: i) select fuzzy partition regions from spatial distribution of data; ii) generate fuzzy membership functions from the selected partition regions; and iii) extract a set of candidate rules and resolve a conflict problem among the candidate rules. The effectiveness of the proposed fuzzy classification model was demonstrated by comparing the experimental results for the dyspnea patients' data set with 11 features selected from 55 features by clinicians with those obtained using the conventional classification methods, such as standard fuzzy classifier without rule weights, C4.5, QDA, kNN, and SVMs.

• Journal of Korea Multimedia Society
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• v.21 no.8
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• pp.864-875
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• 2018

In this paper, we propose a measurement method for height of white light scanning interferometer using deep learning. In order to measure the fine surface shape, a three-dimensional surface shape measurement technique is required. A typical example is a white light scanning interferometer. In order to calculate the surface shape from the measurement image of the white light scanning interferometer, the height of each pixel must be calculated. In this paper, we propose a neural network for height calculation and use virtual data generation method to train this neural network. The accuracy was measured by inputting 57 actual data to the neural network which had completed the learning. We propose two new functions for accuracy measurement. We have analyzed the cases where there are many errors among the accuracy calculation values, and it is confirmed that there are many errors when there is no interference fringe or outside the learned range. We confirmed that the proposed neural network works correctly in most cases. We expect better results if we improve the way we generate learning data.

• Journal of the Ergonomics Society of Korea
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• v.15 no.2
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• pp.15-24
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• 1996

To generate workspace analytically using the robot kinematics, data on range of human joints motion, especially range of two degrees of freedom motion, are needed. However, these data have not been investigated up to now. Therefore, in this research, we are to investigate an interaction effect of motions with two degrees of freedom occurred simultaneously at the shoulder, virtual hip(L5/S1) and hip joints, respectively, for 47 young male students. When motion with two degrees of freedom occurred at a joint such as shoulder, virtual hip and hip joints, it was found from the results of ANOVA that the action of a degree of freedom motion may either decrease or increase the effective functioning of the other degree of freedom motion. In other words, the shoulder flexion was decreased as the shoulder was adducted or abducted to $60^{\circ}C$TEX>or abducted from $60^{\circ}C$TEX>to maximum degree of abduction, while the shoulder flexion increased as the joint was abducted from $60^{\circ}C$TEX> to $60^{\circ}C$TEX> The flexion was decreased as the virtual hip was bent laterally at the virtual hip joint, and also did as the hip was adducted or abducted from the neutral position. It is expected that workspace can be generated more precisely based the data on the range of two degrees of joint motion measured in this study.

• Journal of Korean Academy of Nursing Administration
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• v.19 no.1
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• pp.128-134
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• 2013

Purpose: The purpose of this study was to generate a grounded substantive theory for the practice adaptation process of hemodialysis unit nurses. Methods: Participants in this study were 10 nurses working in one of two hemodialysis units. Data were collected through tape recorded in-depth interviews done between December, 2011 and February, 2012. Data were analyzed using grounded theory methodology. Results: From the participants' statements, 43 concepts, 18 subcategories and 10 categories were extracted through the open cording process. The 10 categories were: "Burden", "Role conflict", "Fearful", "Conflict of emotion", "Lack systematic job training", "Lack support system", "Compassion", "Rapport created", "Sense of duty", and "Growth". The core category was discovered to be 'recognized growth'. Phenomenon was identified as 'burnout' and this series of processes was categorized as having three stages: 'conflict', 'acceptance', 'growth'. Conclusion: The results of this study provide useful information about the needs of Hemodialysis Unit Nurses during the practice adaptation process based on their stages and types of practice adaptation. Finally, this study contributes data for the development of intervention programs that support the Hemodialysis Unit Nurses' practice adaptation.

• Journal of the Korean Data and Information Science Society
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• v.22 no.5
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• pp.931-940
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• 2011

Classification is to generate a rule of classifying objects into several categories based on the learning sample. Good classification model should classify new objects with low misclassification error. Many types of classification methods have been developed including logistic regression, discriminant analysis and tree. This paper presents a new classification method using penalized partial least squares. Penalized partial least squares can make the model more robust and remedy multicollinearity problem. This paper compares the proposed method with logistic regression and PCA based discriminant analysis by some real and artificial data. It is concluded that the new method has better power as compared with other methods.

• Journal of Preventive Medicine and Public Health
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• v.41 no.4
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• pp.219-224
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• 2008

Due to heightened concerns regarding possible bioterrorist attacks, the Korea Center for Disease Control and Prevention introduced syndromic surveillance systems, which have been run by emergency departments in hospitals throughout Korea since 2002. These systems are designed to identify illness clusters before diagnoses are confirmed and reported to public health agencies, to mobilize a rapid response, and thereby to reduce morbidity and mortality. The Korea Center for Disease Control and Prevention performed drop-in syndromic surveillance successfully during the World Cup Football Games in 2002, the Universiad games in 2004, and the Asian Pacific Economic Cooperation meeting in 2005. In addition, sustainable syndromic surveillance system involving the collaborative efforts of 125 sentinel hospitals has been in operation nationwide since 2002. Because active data collection can bias decisions a physician makes, there is a need to generate an automatic and passive data collection system. Therefore, the Korea Center for Disease Control and Prevention plans to establish computerized automatic data collection systems in the near future. These systems will be used not only fur the early detection of bioterrorism but also for more effective public health responses to disease.

• Journal of Physiology & Pathology in Korean Medicine
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• v.30 no.6
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• pp.402-411
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• 2016

The concept of the meridian system is originated from an empirical, systematic references in the clinical setting, which does not always require anatomical features. As the principles of systems biology are mainly associated with regulating the body's internal environment to maintain a stable condition, they are closely similar to the theory of the meridian system. In this review, I describe the origin of the concept of the meridian system, current status of research on the meridian system and acupuncture points, and the future directions of the research. To unravel the entity of the meridian system, we have to start from understanding its origin and clinical significance. The meridian system, as a theoretical model of the indications of acupuncture points, can help to understand the interconnections that underlie the pathologies of particular diseases or symptoms. Based on the establishment of clinical data platform for acupuncture research, we can extract novel medical information from the clinical data and generate analytical models that are useful for medical knowledge discovery on acupuncture points in the future.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.14-17
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• 2008

KOMPSAT-2 is the seventh high-resolution satellite in the world that provides both 1m panchromatic images and 4m multispectral images of the GSD. It is expected to be used across many different fields including digital mapping, territorial and environmental monitoring. However, due to the complexity and security concern involved with the use of the MSC, the use of KOMPSAT-2 images are limited in terms of geometric data, such as satellite orbits and detailed mapping information. This study aims to generate the DEM and orthoimage by using the stereo images of KOMPSAT-2 and to explore the applicability of geo-spatial information with KOMPSAT-2. In order to ensure generation of DEMs of optimal accuracy, the RPCs data and a suitable number of GCPs were used. The accuracy of DEM generated in this research compared with DEM generated from 1:5,000 digital map. The mean differences between horizontal position of the orthoimage and the digital map data are found to be ${\pm}$3.1m, which is in the range of ${\pm}$3.5m, within the permitted limit of a 1:5,000 digital map. The results suggest that DEM can be adequately used to produce digital maps under 1:5,000 scale.