• Journal of the Korean Data and Information Science Society
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• v.25 no.6
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• pp.1521-1530
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• 2014

The ROC analysis is considered for multiple class diagnosis. There exist many criteria to find optimal thresholds and measure the accuracy of diagnostic tests for k dimensional ROC analysis. In this paper, we proposed a diagnostic accuracy measure called the correct classification simple rate, which is defined as the summation of true rates for each classification distribution and expressed as a function of summation of sequential true rates for two consecutive distributions. This measure does not weight accuracy across categories by the category prevalence and is comparable across populations for multiple class diagnosis. It is found that this accuracy measure does not only have a relationship with Kolmogorov - Smirnov statistics, but also can be represented as a linear function of some optimal threshold criteria. With these facts, the suggested measure could be applied to test for comparing multiple distributions.

• Journal of radiological science and technology
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• v.13 no.1
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• pp.19-24
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• 1990

In appilcation of wide latitude HR-C film to chest x-ray examination, former x-ray diagnosis area is larger and diagnostic information has great deal of promotion. HR-C film is compare to former x-ray film is larger latitude and density level is small, reading is very easily. Especially, high estimate that is in characteristic curve linearity of toe part is good, contrast of low density made good shape and not good describe to overlap is diagnostic information increase mediastinum portion etc.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.05a
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• pp.458-461
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• 2014

CRDI 시스템에서의 ECU는 센서의 정보를 분석하여 최적의 조건으로 엔진이 동작하도록 한다. 이러한 ECU의 프로그램 부분과 데이터 부분은 제작자에서만 변경할 수 있어 엔진을 진단하는 진단기의 경우 전문가가 아니면 사용하거나 내용을 이해하기가 쉽지 않다. 본 연구에서는 산업용 차량의 엔진 데이터 값을 OBD-II 표준을 사용하여 입력받아 사용자 중심의 진단기를 PC 및 모바일용으로 개발하였다. 본 연구의 진단기는 운전자 중심의 진단 서비스를 제공하며, 자동차 고장진단 신호 및 센서 출력 신호를 유선시스템과 무선 시스템인 블루투스 모듈을 이용하여 실시간 통신이 제공되도록 함으로써 엔진이상으로 인한 사고의 예방이 가능하고, 최적의 조건으로 엔진이 동작하므로 과도한 배기가스 배출이나 불완전 연소가스 배출과 같은 대기환경오염을 예방할 수 있어 최근 대두되고 있는 에코산업에도 이바지 할 수 있을 것이다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.6079-6097
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• 2018

Safety-related systems (SRSs) has widely used in shipbuilding and power generation to prevent fatal accidents and to protect life and property. Thus, SRS performance is a high priority. The safety integrity level (SIL) is the relative performance level of an SRS with regard to its ability to operate reliably in a safe manner. In this article, we proposed an optimal design procedure to achieve the targeted SIL of SRSs. In addition, a more efficient failure mode and effects diagnostic analysis (FMEDA) process and optimization model were developed to improve cost efficiency. Based on previous IEC 61508 diagnostic analyses that revealed unnecessary costs associated with excessive reliability, the new approach consists of two phases: (i) SIL evaluation by FMEDA, and (ii) solution optimization for achieving the target SIL with minimal cost using integer-programming models. The proposed procedure meets the required safety level and minimizes system costs. A case study involving a gas-detection SRS was conducted to demonstrate the effectiveness of the new procedure.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.01a
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• pp.117-118
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• 2021

본 논문에서는 창의융합적 아이디어가 세상을 바꾸는 4차 산업혁명시대를 대비하기 위해 경찰·경비분야에 요구되는 핵심역량을 도출하고 진단도구를 개발하고자 한다. 이를 위해 K시 소재 대학의 경찰·경비관련 학과에 재학중인 66명을 연구대상으로 설문조사를 실시하였다. 그 결과, 경찰·경비분야의 직무, 과업, 역할을 수행하는 데 필요한 법집행, 상황대응, 정보수집, 지휘통솔 등 직무영역에서 9개의 전공능력이 미래사회에 대비하기 위해 전공자들에게 우선적으로 요구되는 핵심역량으로 도출되었다. 이러한 결과는 역량기반 전공 교육과정 개선과 교육성과 관리를 위한 자료로 활용이 가능하다. 또한 개인의 전공능력 수준을 진단하여 역량 증진 프로그램을 실시할 수 있으며, 역량기반 교육과정에 대한 성과확산과 각종 비교과 교육프로그램 지원과 선정을 위한 교육성과 지표로도 활용이 가능할 것으로 판단된다.

• 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.

• The Journal of Korean Medicine
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• v.40 no.4
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• pp.49-60
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• 2019

Objectives: To develop a standardized diagnostic pattern identification equation for stroke patients, our group conducted a study to derive the predictive logistic equations. However, the sample size was relatively small. In the current study, we aimed to derive new predictive logistic equations for each diagnostic pattern using an expanded number of subjects. Methods: This study was a hospital-based multi-center trial recruited stroke patients within 30 days of symptom onset. Patients' general information, and the variables related to diagnostic pattern identification were measured. The diagnostic pattern of each patient was identified independently by two Korean Medicine Doctors. To derive a predictive model for pattern identification, binary logistic regression analysis was applied. Results: Among the 1,251 patients, 385 patients (30.8%) had the Fire Heat Pattern, 460 patients (36.8%) the Phlegm Dampness Pattern, 212 patients (16.9%) the Qi Deficiency Pattern, and 194 patients (15.5%) the Yin Deficiency Pattern. After the regression analysis, the predictive logistic equations for each pattern were determined. Conclusion: The predictive equations for Fire Heat, Phlegm Dampness, Qi Deficiency, and Yin Deficiency would be useful to determine individual stroke patients' pattern identification in the clinical setting. However, further studies using objective measurements are necessary to validate these data.

• Journal of radiological science and technology
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• v.22 no.2
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• pp.61-68
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• 1999

The purpose of this study is to present controversial point and reform measurements by analysing factors haying important effect on selection of upper gastrointestinal study in total health promotion. We examined 200 persons for this study, who visited for upper gastrointestinal study from January to February in 1999. We classified this group into Endoscopy, Upper gastrointestinal series, and sleeping endoscopy. We also investigated standard of satisfaction and factors having effect on selection of each study. As a results, in the motive of selection, Item of 'making accurate observation' and 'without pain' was 39.3% and 34.7%, respectively. In this study, sleeping endoscopy was 45.7%, but on the other side upper gastrointestinal series was low 22.6%(P<0.05). In the standard of preference of study, the man was 55.7% in the endoscopy, and the woman was 61.8% in the upper gastrointestinal series(P<0.05). The standard of preference of upper gastrointestinal series show that it was satisfied on the whole irrespective of sex, dwelling place, age, occupation, and level of education. In the selection of study, one's own will was showed the highest frequency, and family inducement was showed second(P<0.05). Persons over 60% were examined before the same study. Selection of upper gastrointestinal series was 47.9% of person with normal findings, and endoscopy and sleeping endoscopy was over 70% with gastritis, gastric and duodenal(P<0.01). For one's accurate selection of examination, it is important that objective and credible information should be given to a recipient for examination.

• Imaging Science in Dentistry
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• v.30 no.4
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• pp.265-274
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• 2000

Purpose: The aims of the present study are to determine the optimum compression rate in terms of file size reduction and diagnostic quality of the images after compression and evaluate the transmission speed of original or each compressed image. Materials and Methods: The material consisted of 24 extracted human premolars and molars. The occlusal surfaces and proximal surfaces of the teeth had a clinical disease spectrum that ranged from sound to varying degrees of fissure discoloration and cavitation. The images from Digora system were exported in TIFF and the images from conventional intraoral film were scanned and digitalized in TIFF by Nikon SF-200 scanner (Nikon, Japan). And six compression factors were chosen and applied on the basis of the results from a pilot study. The total number of images to be assessed were 336. Three radiologists assessed the occlusal and proximal surfaces of the teeth with 5-rank scale. Finally diagnosed as either sound or carious lesion by one expert oral pathologist. And sensitivity, specificity and k value for diagnostic agreement was calculated. Also the area (Az) values under the ROC curve were calculated and paired t-test and oneway ANOVA test was performed. Thereafter, transmission time of the image files of the each compression level was compared with that of the original image files. Results: No significant difference was found between original and the corresponding images up to 7% (1 : 14) compression ratio for both the occlusal and proximal caries (p<0.05). JPEG3 (1 : 14) image files are transmitted fast more than 10 times, maintained diagnostic information in image, compared with original image files. Conclusion: 1 : 14 compressed image file may be used instead of the original image and reduce storage needs and transmission time.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.78-83
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• 2014

Recently, a rapid growth of wind power system as a leading renewable energy source has compelled a number of companies to develop intelligent monitoring and diagnostic system. Such systems can detect early mechanical faults, which prevents from costly repairs. Generally, fault diagnostic system for wind turbines is based on vibration and process signal analysis. In this work, different type of mechanical faults such as mass unbalance and shaft misalignment which can always happen in wind turbine system is considered. The proposed intelligent fault diagnostic algorithm utilizes artificial neural network and Wavelet transform. In order to verify the feasibility of the proposed algorithm, mechanical fault generation experimental system manufactured by Gaon corporation is utilized.