• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.1144-1148
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• 1996

This paper presents WMR modelling and path tracking algorithm using Inertial Navigation System. The error models of gyroscope and accelerometers in INS are derived by Gauss-Newton method which is nonlinear regression model. Then, to test availability of error model, we pursue the fitness diagnosis about probability characteristic for real data and estimated data. Performance of inertial sensor with error model and Kalman filter is pursued by comparing with one without them. The computer simulation shows that position error remarkably decrease when error compensation is applied.

• 대한예방의학회:학술대회논문집
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• 1994.02b
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• pp.150-153
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• 1994

Receiver operating characteristic (ROC) curves have been frequently used to compare probability models applied to medical problems. Though the curves are a measure of the discriminatory power of a model. they do not reflect the model's accuracy. A supplementary accuracy curve is derived which will be coincident with the ROC curve if the model is reliable. will be above the ROC curve if the model's probabilities are too high or below if they are too low. A clinical example of this new graphical presentation is given.

• The Journal of the Korea Contents Association
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• v.15 no.10
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• pp.561-569
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• 2015

Many start-ups have many realistic hardships in getting management diagnosis about whether their business model are properly going. For this reason, there is the need for an easy and simple method that makes it possible to conduct a strategic management through the analysis and management diagnosis of the business model. The Business Model Canvas(BMC) has been popularized as a tool to help entrepreneurs describe, design, challenge, invent and pivot their business model. This model gives a framework to describe the most important building blocks(9 blocks) of existing business. Entrepreneurs can make their own business analyses and craft their own solutions through using this model. In this study, we conducted consulting by using the BMC on the NARUATO which is a small start-up in the healthy food industry. This case study can use as a learning material for entrepreneurship education.

• Korean Journal of Biological Psychiatry
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• v.27 no.1
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• pp.18-26
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• 2020

Objectives The aim was to find effective vectorization and classification models to predict a psychiatric diagnosis from text-based medical records. Methods Electronic medical records (n = 494) of present illness were collected retrospectively in inpatient admission notes with three diagnoses of major depressive disorder, type 1 bipolar disorder, and schizophrenia. Data were split into 400 training data and 94 independent validation data. Data were vectorized by two different models such as term frequency-inverse document frequency (TF-IDF) and Doc2vec. Machine learning models for classification including stochastic gradient descent, logistic regression, support vector classification, and deep learning (DL) were applied to predict three psychiatric diagnoses. Five-fold cross-validation was used to find an effective model. Metrics such as accuracy, precision, recall, and F1-score were measured for comparison between the models. Results Five-fold cross-validation in training data showed DL model with Doc2vec was the most effective model to predict the diagnosis (accuracy = 0.87, F1-score = 0.87). However, these metrics have been reduced in independent test data set with final working DL models (accuracy = 0.79, F1-score = 0.79), while the model of logistic regression and support vector machine with Doc2vec showed slightly better performance (accuracy = 0.80, F1-score = 0.80) than the DL models with Doc2vec and others with TF-IDF. Conclusions The current results suggest that the vectorization may have more impact on the performance of classification than the machine learning model. However, data set had a number of limitations including small sample size, imbalance among the category, and its generalizability. With this regard, the need for research with multi-sites and large samples is suggested to improve the machine learning models.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.12 no.2
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• pp.858-864
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• 2011

There is no standard model for a Stated Period Check and a Precise Safety Diagnosis to remove electric fire and shock in the university Lab and institute. Especially, the research for the Stated Period Check and the Precise Safety Diagnosis of the Lab related to electrical field is very weak currently, and it is very necessary to build a detail safety plan. This paper informs the specific standard guideline of the safety check list, method and equipment and it shows the way to evaluate safety grade too. This paper also provides the information of R&D process through the analysis of electrical safety check list of ordinary R&D Lab. It shows a new detail guideline to R&D Lab, and the new guideline removes existing problem and deliver the effective standard model to each R&D Lab. The standard model developed in this research adopts the clear guideline of each check list for the electrical environment of current R&D Lab. This standard model can be applied for every R&D Lab to detect routine safety check and detail safety check immediately. This Research will generally improve not only the effective safety check, but also the safety level for R&D Lab to prevent the electrical accidents.

• Korean Journal of Oriental Medicine
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• v.14 no.3
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• pp.103-111
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• 2008

Recently, skin diagnosis has been suggested as a promising tool for discrimination of Sasang Constitution, reported by examining the skin characteristics such as thickness, stiffness, slip, and skin textures like wrinkles and furrows. However, the works had a limitation in that clinical decision on the skin characteristics was made by relying upon oriental medicine doctors' subjective sense of touch. In order to objectify the skin diagnosis and claim its efficacy on the discrimination of the Sasang Constitutions, it is necessary to demonstrate its discrimination capability by providing numerical values in terms of physical quantities obtained from measurements using today's sensors and equipment technologies, which motivated this work as a priliminary step towards objectification of skin diagnosis. The skin characteristics focused in this work is the slip property of the back-hand skin that has been exploited using the dynamic friction measurement system. First, curved geometric effects of the back-hand skin on the measured lateral/vertical force signals were estimated using the artificially designed silicon coated structures, which led to a suggestion on a quality controlled experimental design based upon a empirical analysis model. Second, the experimental design thus suggested has been applied to the measurement of dynamic friction coefficients for two healthy male subjects of Taeumin (TE) and Soyangin (SY), respectively. The result shows that the dynamic friction coefficient is less for the SY subject than for the TE subject around the area of the skin used for diagnosis by the oriental medicine doctor, implying the TE subject's skin is more slippery than the SE subject's that is consistent with the oriental medicine doctor's diagnosis. Hopefully, this work can provide guidelines for obtaining quality data in friction measurement to be collected for discussion on the efficacy of the skin diagnosis and its objectification through statistical analysis.

• Tuberculosis and Respiratory Diseases
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• v.73 no.3
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• pp.162-168
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• 2012

Background: Idiopathic pulmonary fibrosis (IPF) is a progressive disease. Effective treatment is not currently available and the prognosis is poor. The aim of our study was to identify clinical predictors of survival in patients with IPF. Methods: By using medical record database of a university hospital, we reviewed the records of patients who had been diagnosed as having IPF from January 1996 through December 2007. Results: Among 89 patients considered as having interstitial lung disease (ILD) on computed tomography (CT) of the chest, 22 were excluded because of the diagnosis of other ILDs or connective tissue disease, and finally, 67 met the criteria of IPF. The mean age at the diagnosis of IPF was 70 years (range, 41~87 years) and 43 (64%) were male. The mean survival time following the diagnosis of IPF was 40 months (range, 0~179 months). Among them, 28 cases were diagnosed as the progressive state of IPF on the follow-up CT examination, and the mean duration between diagnosis of IPF and progression was 31 months. Multivariate analysis using Cox regression model revealed that body mass index (BMI) less than 18.5 $kg/m^2$ (p=0.030; hazard ratio [HR], 12.085; 95% confidence interval [CI], 1.277~114.331) and CT progression before 36 months from the diagnosis of IPF (p=0.042; HR, 13.564; 95% CI, 1.101~167.166) were independently associated with mortality. Conclusion: Since low BMI at the diagnosis of IPF and progression on follow-up CT were associated with poor prognosis, IPF patients with low BMI and/or progression before 36 months following the diagnosis should be closely monitored.

• The Journal of the Korea Contents Association
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• v.17 no.5
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• pp.221-233
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• 2017

To survive in the ever-changing environment, organizations need to improve or innovate their business processes. As a result, to attain this objective, BPM (Business Process Management) concept is widely adopted in modern enterprises. BPM life cycle consists of diagnosis, design, implementation and enactment. Conventionally, diagnosis of business process within the BPM life cycle is usually conducted by manual methods such as interviews, questionnaires and direct observations of process. And (re)designing business processes is also usually done manually under supervision of business experts from scratch. It is time-consuming and error-prone tasks. The objective of this research is to integrate the diagnosis and (re)design phase of BPM life cycle by sharing automatically generated process model and basic statistics in the diagnosis phase based on the process mining method. Eventually, this approach will lead to automate the tasks of diagnosis and design of business process. To implement and to show the usefulness of the proposed framework, two case studies were conducted in this research.

• Journal of Korea Multimedia Society
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• v.22 no.10
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• pp.1168-1177
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• 2019

In order to diagnose and prevent Alzheimer's Disease (AD), it is becoming increasingly important to develop a CAD(Computer-aided Diagnosis) system for AD diagnosis, which provides effective treatment for patients by analyzing 3D MRI images. It is essential to apply powerful deep learning algorithms in order to automatically classify stages of Alzheimer's Disease and to develop a Alzheimer's Disease support diagnosis system that has the function of detecting hippocampus and CSF(Cerebrospinal fluid) which are important biomarkers in diagnosis of Alzheimer's Disease. In this paper, for AD diagnosis, we classify a given MRI data into three categories of AD, mild cognitive impairment, and normal control according by applying 3D brain MRI image to the Faster R-CNN model and detect hippocampus and CSF in MRI image. To do this, we use the 2D MRI slice images extracted from the 3D MRI data of the Faster R-CNN, and perform the widely used majority voting algorithm on the resulting bounding box labels for classification. To verify the proposed method, we used the public ADNI data set, which is the standard brain MRI database. Experimental results show that the proposed method achieves impressive classification performance compared with other state-of-the-art methods.

• Journal of Agricultural Extension & Community Development
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• v.21 no.4
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• pp.1149-1189
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• 2014

Nowadays, many farmers use online management diagnosis tool developed by Rural development agency(RDA) for the purpose of self-diagnosis of their farm management. Database(DB) was created using the diagnosis results and has been used for agri-firm management consulting. However, the amount of diagnosis data in the DB has been decreasing year by year. This means that the diagnosis tool of RDA did not reach farmers' expectation. Therefore it is necessary to develop a practical consulting tool which is applicable for various types of agri-firm management. This study introduces a management diagnosis tool and consulting method based on multi-contingency organization theory and value chain model for the purpose of improving existing tools and methods. The consulting method based on multi-contingency organization theory shows the core strategy of agri-firms by two different ways such as "efficiency-oriented" direction and "effectiveness-orientated" direction. Also, this method emphasizes that the performance of firm can be achieved when subelements of firm activities follow the same direction with the orientation of core strategy. The important thing is the right firm management activity fitted to its strategic direction. Through this action, limited firm resources can be optimized. In order to make itself understand, this study shows a practical example applied by this method from actual agri-firms.