• Journal of Physiology & Pathology in Korean Medicine
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• v.25 no.6
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• pp.1113-1118
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• 2011

In this paper, when a physician make a diagnosis of the pattern identification (PI) in Korean stroke patients, the development methods of the PI classification function is considered by diagnostic questionnaire of the PI for stroke patients. Clinical data collected from 1,502 stroke patients who was identically diagnosed for the PI subtypes diagnosed by two physicians with more than 3 years experiences in 13 oriental medical hospitals. In order to develop the classification function into PI using Korean Stroke Syndrome Differentiation Standard was consist of the 44 items (Fire heat(19), Qi deficiency(11), Yin deficiency(7), Dampness-phlegm(7)). Using the 44 items, we took diagnostic and prediction accuracy rate through of discriminant model. The overall diagnostic and prediction accuracy rate of the PI subtypes for discriminant model was 74.37%, 70.88% respectively.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.493-505
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• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

• Nuclear Engineering and Technology
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• v.56 no.2
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• pp.558-567
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• 2024

Diagnostic support systems are being researched to assist operators in identifying and responding to abnormal events in a nuclear power plant. Most studies to date have considered single abnormal events only, for which it is relatively straightforward to obtain data to train the deep learning model of the diagnostic support system. However, cases in which multiple abnormal events occur must also be considered, for which obtaining training data becomes difficult due to the large number of combinations of possible abnormal events. This study proposes an approach to maintain diagnostic performance for multiple abnormal events by training a deep learning model with data on single abnormal events only. The proposed approach is applied to an existing algorithm that can perform feature selection and multi-label classification. We choose an extremely randomized trees classifier to select dedicated monitoring parameters for target abnormal events. In diagnosing each event occurrence independently, two-channel convolutional neural networks are employed as sub-models. The algorithm was tested in a case study with various scenarios, including single and multiple abnormal events. Results demonstrated that the proposed approach maintained diagnostic performance for 15 single abnormal events and significantly improved performance for 105 multiple abnormal events compared to the base model.

• Health Policy and Management
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• v.28 no.2
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• pp.138-144
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• 2018

Background: Diagnostic imaging fee had been reduced in May 2011, but it was recovered after 6 months because of strong opposition of medical providers. This study aimed to analyze the behavior of medical providers according to fee changes. Methods: The National Health Insurance claims data between November 2010 and December 2012 were used. The number of exams per computed tomography was analyzed to verify that the fee changes increased or decreased the number of exams. Multivariate regression model were applied. Results: The monthly number of exams increased by 92.5% after fee reduction, so the diagnostic imaging spending were remained before it. But medical provider decreased the number of exams after fee return. After adjusting characteristic of hospitals, fee reduction increased the monthly number of exams by 48.0% in a regression model. Regardless type of hospitals and severity of disease, the monthly number of exams increased during period of fee reduction. The number of exams in large-scaled hospitals (tertiary and general hospital) were increased more than those of small-scaled hospitals. Conclusion: Fee-reduction increased unnecessary diagnostic exams under the fee-for-service system. It is needed to define appropriate exam and change reimbursement system on the basis of guideline.

• Korean Journal of Agricultural Science
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• v.37 no.3
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• pp.515-522
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• 2010

The purpose of this research was to construct an efficient management system in developing and supplying a 'management standard diagnostic table' for the improvement of chestnut cultivation farmhouse. 'Chestnut management standard diagnostic table' were based from the actual condition of chestnut management in Chungcheongnam-do, selected 'appraisal factor item and by consulting 'agricultural plant standard diagnostic table' and various kinds of data which had already been developed. This research also consulted the classification systems and degree of importance. The developed 'Chestnut management diagnostic table' consisted of 3 first classified items, 19 second classified items and 2 product indicator items.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.130.6-130
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• 2001

On-Board diagnostic systems are installed in passenger cars and light trucks on today. During the 1970's and early 1980's manufacturers started using electronic means to control engine functions and diagnose engine problems. This wa primarily to meet EPA emission standards. The CARB requires that, by model year 1996, all vehicle sold in California contain a certain minimum "On-Board Diagnostic" capability to diagnose emissions-related failures of the engine control system. These diagnostic requirements have been designated as OBD with a goal of monitoring all of the emissions-related components on-board the vehicle for proper operation. Part of the intent of CARB´s OBD program is that a single diagnostic tester can be used to read the diagnostic information from any OBD-compliant vehicle. A tester which ...

• Journal of Physiology & Pathology in Korean Medicine
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• v.23 no.5
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• pp.938-941
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• 2009

In spite of abundant clinical resources of stroke patients, the objective and logical data analyses or diagnostic systems were not established in oriental medicine. In the present study we tried to develop the statistical diagnostic tool discriminating the subtypes of oriental medicine diagnostic system, syndrome differentiation (SD). Discriminant analysis was carried out using clinical data collected from 1,478 stroke patients with the same subtypes diagnosed identically by two clinical experts with more than 3 year experiences. Numerical discriminant models were constructed using important 61 symptom and syndrome indices. Diagnostic accuracy and prediction rate of 5 SD subtypes: The overall diagnostic accuracy of 5 SD subtypes using 61 indices was 74.22%. According to subtypes, the diagnostic accuracy of "phlegm-dampness" was highest (82.84%), and followed by "qi-deficiency", "fire/heat", "static blood", and "yin-deficiency". On the other hand, the overall prediction rate was 67.12% and that of qi-deficiency was highest (73.75%). Diagnostic accuracy and prediction rate of 4 SD subtypes: The overall diagnostic accuracy and prediction rate of 4 SD subtypes except "static blood" were 75.06% and 71.63%, respectively. According to subtypes, the diagnostic accuracy and prediction rate was highest in the "phlegm-dampness" (82.84%) and qi-deficiency (81.69%), respectively. The statistical discriminant model of constructed using 4 SD subtypes, and 61 indices can be used in the field of oriental medicine contributing to the objectification of SD.

• Korean Journal of Comparative Education
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• v.28 no.5
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• pp.1-19
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• 2018

The purpose of educational assessments such as the Trends in International Mathematics and Science Study (TIMSS) is to compare groups such as countries. When the unit of measurement is above the student level, group-level diagnostic assessment based on multilevel item response theory (ML-IRT) can be considered just as cognitive diagnosis models are developed from item response theory. This study suggests an ML-IRT-based group-level diagnostic assessment model by modifying an item feature model by Park and bolt (2008). The model is illustrated on the recently released TIMSS 2015 Grade 8 mathematics assessment. The results provide skill profiles for the studied countries and the nine cognitive attributes; that is, the attribute effects can be compared across the countries and also across the attributes. By controlling unexplained variance, the suggested model may provide more reliable and more informative group-level comparisons. The results are interpreted using an example. Limitations and directions for future research are also discussed.

• Journal of The Korean Society of Agricultural Engineers
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• v.66 no.5
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• pp.1-13
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• 2024

In this study, we applied an on-site diagnostic method for estimating the structural safety of a plastic greenhouse. A three-dimensional light detection and ranging (3D LiDAR) sensor was used to scan the greenhouse to extract point cloud data (PCD). Differential thresholds of the color index were applied to the partitions of raw PCD to separate steel frames from plastic films. Additionally, the K-means algorithm was used to convert the steel frame PCD into the nodes of unit members. These nodes were subsequently transformed into structural shape data. To verify greenhouse shape reproducibility, the member lengths of the scan and blueprint models were compared with the measurements along the X-, Y-, and Z-axes. The error of the scan model was accurate at 2%-3%, whereas the error of the blueprint model was 5.4%. At a maximum snow depth of 0.5 m, the scan model revealed asymmetric horizontal deflection and extreme bending stress, which indicated that even minor shape irregularities could result in critical failures in extreme weather. The safety factor for bending stress in the scan model was 18.7% lower than that in the blueprint model. This phenomenon indicated that precise shape estimation is crucial for safety diagnostic. Future studies should focus on the development of an automated process based on supervised learning to ensure the widespread adoption of greenhouse safety diagnostics.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.306-319
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• 2008

Reaching fault tolerance in technological systems requires to detect malfunctions. This paper presents a diagnostic method that is robust with respect to unknown-but-bounded uncertainties of the dynamical model and the measurements. By using models of the faultless and the faulty behaviours, a state-set observer computes polyhedral sets from which the consistency of the models with the interval measurements is determined. The diagnostic result is proven to be complete, i.e., the set of faults obtained by the diagnostic algorithm includes the actual fault. The algorithm is illustrated by an application example.