• Publications of The Korean Astronomical Society
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• v.26 no.1
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• pp.25-35
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• 2011

A diagnostic tool has been proposed to convert the observed surface distribution of hydrogen recombination line intensities into the radial distributions of the electron temperature and the density in HII regions. The observed line intensity is given by an integral of the volume emission coefficient along the line of sight, which comprises the Abel type integral equation for the volume emission coefficient. As the emission coefficient at a position is determined by the temperature and density of electrons at the position, the local emission coefficient resulted from the solution of the Abel equation gives the radial distribution of the temperature and the density. A test has been done on the feasibility of our diagnostic approach to probing of HII regions. From model calculations of an HII region of pure hydrogen, we have theoretically generated the observed surface brightness of hydrogen recombination line intensities and analyzed them by our diagnostic tool. The resulting temperatures and densities are then compared with the model values. For this case of uniform density, errors in the derived density are not large at all the positions. For the electron temperature, however, the largest errors appear at the central part of the HII region. The errors in the derived temperature decrease with the radial distance, and become negligible in the outer part of the model HII region.

• Proceedings of the Korea Contents Association Conference
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• 2014.06a
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• pp.191-192
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• 2014

In this study, diagnostic model was proposed to evaluate and rate the innovation capability of SMEs and suggest alternatives to insufficient capabilities and optimum supporting programs for SMEs from literature survey, GIC model was composed based on KIS value and ASTI(Associate of Science and Technology information) SMEs database, thus, sample deviation can be caused and securing accurate data is insufficient. To compose model by analyzing characteristics of companies accurately, various companies' data for long period will be required.

• Phonetics and Speech Sciences
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• v.8 no.2
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• pp.17-22
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• 2016

This paper devises a model to diagnose physical fatigue using speech features. This paper presents a machine learning method through an SVM algorithm using the various feature parameters. The parameters used include the significant speech parameters, questionnaire responses, and bio-signal parameters obtained before and after the experiment imposing the fatigue. The results showed that performance rates of 95%, 100%, and 90%, respectively, were observed from the proposed model using three types of the parameters relevant to the fatigue. These results suggest that the method proposed in this study can be used as the physical fatigue diagnostic model, and that fatigue can be easily diagnosed by speech technology.

• Proceedings of the Korea Water Resources Association Conference
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• 1996.05a
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• pp.683-688
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• 1996

The purpose of this study is to find seasonal variation of the water circulation in the Chenbuk coastal sea region. Chenbuk coastal sea is investigated with use of a robust diagnostic numerical model. Water circulations in four seasons are calculated diagnostically from the observed water temperature and salinity data and wind data from Kunsan mereorologcal station.

• Clinical Psychopharmacology and Neuroscience
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• v.16 no.4
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• pp.489-493
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• 2018

Objective: This study was to identify whether working memory (WM) can be clearly subdivided according to auditory and visual modality. To do this, we administered the most recent and universal clinical WM measures in a mixed psychiatric sample. Methods: A total of 115 patients were diagnosed on the basis of DSM-IV diagnostic criteria and with MINI-Plus 5.0, a structured diagnostic interview. WM subtests of Korean version of Wechsler Adult Intelligence Scale-IV and Korean version of Wechsler Memory Scale-IV were administered to assess WM. Confirmatory factor analysis (CFA) was used to observe whether WM measures fit better to a one-factor or two-factor model. Results: CFA results demonstrated that a two factor model fits the data better than one-factor model as expected. Conclusion: Our study supports a modality model of WM, or the existence of modality-specific WM systems, and thus poses a clinical significance of assessing both auditory and visual WM tests.

• Biomolecules & Therapeutics
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• v.30 no.2
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• pp.179-183
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• 2022

Traditionally, pathologists microscopically examine tissue sections to detect pathological lesions; the many slides that must be evaluated impose severe work burdens. Also, diagnostic accuracy varies by pathologist training and experience; better diagnostic tools are required. Given the rapid development of computer vision, automated deep learning is now used to classify microscopic images, including medical images. Here, we used a Inception-v3 deep learning model to detect mouse lung metastatic tumors via whole slide imaging (WSI); we cropped the images to 151 by 151 pixels. The images were divided into training (53.8%) and test (46.2%) sets (21,017 and 18,016 images, respectively). When images from lung tissue containing tumor tissues were evaluated, the model accuracy was 98.76%. When images from normal lung tissue were evaluated, the model accuracy ("no tumor") was 99.87%. Thus, the deep learning model distinguished metastatic lesions from normal lung tissue. Our approach will allow the rapid and accurate analysis of various tissues.

• Journal of the Korean Statistical Society
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• v.22 no.2
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• pp.283-306
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• 1993

An outlier detection diagnostic for the detection of k-consecutive atypical observations is considered. The proposed diagnostic is based on the innovational variance estimate utilizing both the interpolated and the predicted residuals. We adopt the interpolation method to construct the proposed diagnostic by replacing atypical observations. The perfomance of the proposed diagnositc is investigated by simulation. A real example is presented.

• Journal of Korean Society for Quality Management
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• v.28 no.1
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• pp.119-131
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• 2000

This paper suggests a new diagnostic measure for detecting influential observations in linear discriminant analysis (LDA). It is developed from a Bayesian point of view using a default Bayes factor obtained from the imaginary training sample methodology. The Bayes factor is taken as a criterion for testing homogeneity of covariance matrices in LDA model. It is noted that the effect of an observation over the criterion is fully explained by the diagnostic measure. We suggest a graphical method that can be taken as a tool for interpreting the diagnostic measure and detecting influential observations. Performance of the measure is examined through an illustrative example.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.28 no.3
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• pp.201-207
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• 2015

In this paper, in order to avoid equipment malfunction due to electromagnetic waves, which can occur when high-voltage live line diagnostic device fabrication, the enclosure structure of the diagnostic device with power lines that can minimize the EMI (electromagnetic interference) was modeled using the FEM (finite element method). Simulation examined the strength of the electric field in the required thickness, material and regions where there is a control board while changing the curvature radius of the corner making the enclosure, and By applying a mechanical design and simulation results that occur during the actual production has been designed for the final design. Most of the simulation results for the electric field is concentrated in the final model, the inner edge of the enclosure could be confirmed that the stable structure.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.3
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• pp.25-38
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• 2019

This study is to develop a diagnostic model for the effective introduction of smart factories in the manufacturing industry, to diagnose SMEs that have difficulties in building their own smart factory compared to large enterprise, to identify the current level and to present directions for implementation. IT, AT, and OT experts diagnosed 18 SMEs using the "Smart Factory Capacity Diagnosis Tool" developed for smart factory level assessment of companies. They analyzed the results and assessed the level by smart factory diagnosis categories. Companies' smart factory diagnostic mean score is 322 out of 1000 points, between 1 level (check) and 2 level (monitoring). According to diagnosis category, Factory Field Basic, R&D, Production/Logistics/Quality Control, Supply Chain Management and Reference Information Standardization are high but Strategy, Facility Automation, Equipment Control, Data/Information System and Effect Analysis are low. There was little difference in smart factory level depending on whether IT system was built or not. Also, Companies with large sales amount were not necessarily advantageous to smart factories. This study will help SMEs who are interested in smart factory. In order to build smart factory, it is necessary to analyze the market trends, SW/ICT and establish a smart factory strategy suitable for the company considering the characteristics of industry and business environment.