• KIISE Transactions on Computing Practices
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• v.21 no.6
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• pp.393-402
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• 2015

Human resources are one of the most essential elements of an organization. In particular, the more closed a group is, the higher the value each member has. Previous studies have focused on personal attributes of individual, such as medical history, and have depended upon self-diagnosis to manage structures. However, this method has weak points, such as the timeconsuming process required, the potential for concealment, and non-disclosure of participants' mental states, as this method depends on self-diagnosis through extensive questionnaires or interviews, which is solved in an interactive way. It also suffers from another problem in that relations among people are difficult to express. In this paper, we propose a multi-faced diagnosis model based on social network analysis which overcomes former weaknesses. Our approach has the following steps : First, we reveal the states of those in a social network through 9 questions. Next, we diagnose the social network to find out specific individuals such as victims or leaders using the proposed algorithm. Experimental results demonstrated our model achieved 0.62 precision rate and identified specific people who are not revealed by the existing methods.

• Journal of Biomedical Engineering Research
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• v.42 no.4
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• pp.150-158
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• 2021

Intracranial hemorrhage (ICH) refers to acute bleeding inside the intracranial vault. Not only does this devastating disease record a very high mortality rate, but it can also cause serious chronic impairment of sensory, motor, and cognitive functions. Therefore, a prompt and professional diagnosis of the disease is highly critical. Noninvasive brain imaging data are essential for clinicians to efficiently diagnose the locus of brain lesion, volume of bleeding, and subsequent cortical damage, and to take clinical interventions. In particular, computed tomography (CT) images are used most often for the diagnosis of ICH. In order to diagnose ICH through CT images, not only medical specialists with a sufficient number of diagnosis experiences are required, but even when this condition is met, there are many cases where bleeding cannot be successfully detected due to factors such as low signal ratio and artifacts of the image itself. In addition, discrepancies between interpretations or even misinterpretations might exist causing critical clinical consequences. To resolve these clinical problems, we developed a diagnostic model predicting intracranial bleeding and its subtypes (intraparenchymal, intraventricular, subarachnoid, subdural, and epidural) by applying deep learning algorithms to CT images. We also constructed a visualization tool highlighting important regions in a CT image for predicting ICH. Specifically, 1) 27,758 CT brain images from RSNA were pre-processed to minimize the computational load. 2) Three different CNN-based models (ResNet, EfficientNet-B2, and EfficientNet-B7) were trained based on a training image data set. 3) Diagnosis performance of each of the three models was evaluated based on an independent test image data set: As a result of the model comparison, EfficientNet-B7's performance (classification accuracy = 91%) was a way greater than the other models. 4) Finally, based on the result of EfficientNet-B7, we visualized the lesions of internal bleeding using the Grad-CAM. Our research suggests that artificial intelligence-based diagnostic systems can help diagnose and treat brain diseases resolving various problems in clinical situations.

• Korean Journal of Poultry Science
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• v.21 no.1
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• pp.21-34
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• 1994

The objective of this study was to develop a user-friendly computer model for economic analysis on the commercial egg production that could help the egg farmer make managerial diagnosis and rational decision in the changing environment. To raise the adequacy of the model, the program was run for every sample and adjusted to fit the data. The model, programmed with Microsoft QuickBASIC, was a user-friendly computer program in supporting the Korean language. The basic analytical tool used in the study was an engineering-type computerized simulation model which incorporates a cost-benefit analysis of a full-time egg farmer. The computer model developed in this study may be the powerful analytical tool used to evaluate both a managerial decision whether to alter the production system and its impact on production, costs, revenue, and profits. Ultimately, the program is expected to enable the egg farmer to make managerial planning and diagnosis. The program can also calculate the values of economic variables at user-chosen incremental values of market eggs and feed prices. It provides the information on the profit and cost. This may lead the egg farmer, by allowing to establish the best managerial strategy, to increase the profit aor to lessen the cost. The results of this study could be utilized in the evaluation and improvement of the management. It also may be utilized for the researchers and guiding farmers in collecting and analyzing the data on the laying hen. In particular, such a program would be potentially useful to researchers who wish to quickly estimate profits associated with various laying hen treatments. The program could also benefit the egg farmer interested in making managerial decisions based on either current or predicted market conditions. The model would make the egg farmer respond actively to the information-oriented society by promoting to use personal computer.

• Proceedings of the Korean Institute of Communication Sciences Conference
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• 1983.10a
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• pp.112-117
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• 1983

In this paper, a functional m-redundant system, which is me-fault tolerant, is defined based on the graph-theory. This system is designed to be t fault-diagnosable by comparing its unit's outcomes without additive test functions, and so, the system down for diagnosis is not needed. the diagnostic model for this system is presented and this effectively uses system's redundancy. It is shown that this model can be converted into Preparata's model. Thus, the diagnostic characteristics of a functional m-redundant system is analyzed by the methods originated by Preparata et al..

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1114-1117
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• 1993

The paper presents an effective method for finding the propagation structure of the real origin of a system malfunction. It uses a combined system model consisting of Structural Model (SM) in the form of Fuzzy Directed Graph and Behavior Model (BM) as a set of Fuzzy Relational Equations $A\;{\circ}\;R\;=\;B$. Here a specially proposed fuzzy inference technique is checked and investigated. Finally a test example for fault diagnosis of an industrial system is given and analyzed.

• Journal of the Korea Convergence Society
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• v.11 no.2
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• pp.331-339
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• 2020

The purpose of this study was to develop and validate future teacher competencies diagnosis tools required for pre-service teachers. In this study, the hypothesis model was established by hierarchizing basic competency and job competency in three dimensions such as knowledge, practice, and personality as teachers' competencies required in future society. Based on this hypothesis model, 54 preliminary questions were developed, and competencies diagnosis test was conducted for 237 pre-service teachers in J area, Korea. The results of this study are as follows: First, as a result of this study, a total of 53 questions were extracted, including 18 questions with 6 factors in the knowledge dimension, 17 questions with 6 factors in the practice dimension, and 18 questions with 6 factors in the personality dimension. Second, the goodness-of-fit of future teacher competencies diagnosis model required was verified, and convergence and discriminant validity were verified. The results of this study were discussed. Finally, the implications and suggestions for further research were presented.

• The Journal of Korean Association of Computer Education
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• v.16 no.1
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• pp.73-80
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• 2013

Government promotes that the strategy of national R&D converts from catch-up R&D type to leading R&D type for the future growth and national competitiveness according to the recent paradigm shift in the research and development. So the many national researches about foundation, source and core technology are actively being made. As a result of these researches, the security has become an important part of success factor in R&D. And so various security diagnosis and evaluation is being conducted about national R&D program. Existing the research security evaluation models are classified domains in terms of security management and created evaluation indicators according to the domains. However the models are inappropriate in case of researchers doing self-diagnosis of research security. This paper set up the domains in aspect of research management and then proposed the evaluation indicator of research security according to the domains. The evaluation indicator model that is suggested can be utilized in self-diagnosis of research security effectively.

• Convergence Security Journal
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• v.14 no.7
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• pp.45-51
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• 2014

The most important point in the QoS management system to ensure the quality of the IoT system design goal is quality measurement system and the quality evaluation system. This research study is a matrix model for the IoT based on key quality measures by diagnosis system structure and function. Developing for the quality metrics measured Internet of Things environment will provide the foundation for the Internet of Things quality measurement/analysis. IoT matrix system for quality evaluation is a method to describe the functional requirements and the quality requirements in a single unified table for quality estimation performed. Comprehensive functional requirements and quality requirements by assessing the association can improve the reliability and usability evaluation. When applying the proposed method IoT quality can be improved while reducing the QoS signaling, the processing, the basis for more efficient quality assurances as a whole.

• Journal of Korean society of Dental Hygiene
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• v.11 no.5
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• pp.603-613
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• 2011

Objectives : This study is to investigate factors that predispose the oral health education patterns of teachers at preschool institutions such as kindergartens and day nurseries, for which a comparison was made among the patterns, whereto the PRECEDE model was applied. Methods : A survey was conducted by two visits, a preliminary survey and a main survey, and teachers at the foregoing institutions personally filled in the questionnaire. Results : 1. With relation to epidemiological and social diagnosis, the largest number of respondents (53.7%) agreed on the need for oral health education, but at the same time, the largest number of respondents (40.3%) was unsatisfactory with oral health education given by them. 2. With relation to behavioral diagnosis, there were many cases where respondents taught their students to brush their teeth after meals and snacks. Oral health education was focused on safety and injuries. There was no significant intergroup difference (p>0.05). 3. Predisposing factors (a subcategory of educational diagnosis) showed the following results: As for the frequency of oral health education, most respondents at both institutions answered preferred once every six month (p>0.05). In the case of oral health checkup, 75.4% of respondents at kindergartens preferred once a year. 72.2% of respondents at day nurseries preferred the same frequency. They showed a statistically significant difference (p<0.05). In enabling factors, it was found that most respondents at both institutions collected information and teaching materials from mass media and public health centers respectively. In enabling factors, insufficient teaching materials, media and knowledge were found to be obstacles to oral health education. Conclusions : Oral healthcare providers' cooperation is required to diversify away from tooth brushing-centered education and to enrich oral health education. In addition, continuous supplements are required to make teachers at preschool institutions acquire expert knowledge and give oral health education with confidence. Moreover, it needs to train them for various education programs as well as to support them with educational media. Lastly, family members' cooperation is required to develop oral health education programs.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.5 no.3
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• pp.144-151
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• 2012

Chronic gastritis is the disease that is occuring in one in every 10 persons in Korea. In western medicine, endoscopy is needed to diagnose chronic gastritis, but it causes patients a pain and budget of expense. According to the TEM (Traditional Eastern Medicine), on the other hand, the 'Guan' position of the right wrist is related to a stomach. Thus we can diagnosis chronic gastritis by analyzing of pulse signal. However, pulse signal diagnosis is depended on oriental doctor's knowledge and experience. In this study, a systematic approach is proposed to analyze the computerized pulse signal. The pulse signals are firstly pre-processed, Gaussian model is adopted to fit the pulse signal, and then some related parameters are extracted from the model. Consequently, disease-sensitive parameters are selected by T-test and statistical difference. Finally, the selected parameters are entered into a Fuzzy C-Means (FCM) algorithm for classification. Classification results show that healthy persons and chronic gastritis patients are 95% and 87%, respectively.