• Journal of the Korea Computer Industry Society
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• v.8 no.3
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• pp.147-154
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• 2007

Many studies have reported that each evaluation had a stronger effect on the development of a student's teaming ability. Nevertheless, in reality schools rely on the results of summative evaluation after the lesson only for the purpose of learning evaluation. Such a method of evaluation is a result-oriented learning evaluation, with no consideration of developing process of loaming ability of each student. Existing learning evaluation has been considered difficult to process learning performance ability in a clearer manner, as it examines teaming performance ability by diagnostic evaluation and learning ability improvement by formative evaluation, separately. Therefore, this paper proposes a learning evaluation method incorporating diagnostic and formative evaluation, using a Fuzzy inference, for a more objective assessment of performance ability. The proposed method assessed teaming ability based on different weight values, in order to reflect the level of diagnostic and formative evaluation.

• Asian Pacific Journal of Cancer Prevention
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• v.15 no.13
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• pp.5123-5126
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• 2014

Background: To evaluate the diagnostic performance of IOTA simple rules in differentiating between benign and malignant ovarian tumors. Materials and Methods: A study of diagnostic performance was conducted on women scheduled for elective surgery due to ovarian masses between March 2007 and March 2012. All patients underwent ultrasound examination for IOTA simple rules within 24 hours of surgery. All examinations were performed by the authors, who had no any clinical information of the patients, to differentiate between benign and malignant adnexal masses using IOTA simple rules. Gold standard diagnosis was based on pathological or operative findings. Results: A total of 398 adnexal masses, in 376 women, were available for analysis. Of them, the IOTA simple rules could be applied in 319 (80.1%) including 212 (66.5%) benign tumors and 107 (33.6%) malignant tumors. The simple rules yielded inconclusive results in 79 (19.9%) masses. In the 319 masses for which the IOTA simple rules could be applied, sensitivity was 82.9% and specificity 95.3%. Conclusions: The IOTA simple rules have high diagnostic performance in differentiating between benign and malignant adnexal masses. Nevertheless, inconclusive results are relatively common.

• The KIPS Transactions:PartA
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• v.16A no.6
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• pp.481-488
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• 2009

GIS is used to isolate large power electrical equipment using SF6 gas. While GIS has simple structure, it has few break down, relatively high reliability. But it is hard to check up faults for reason of pressure. Faults of GIS should have a ripple effect on community and be hard to recovery. Consequently, GIS imports a preventive diagnostic system to find internal faults in advance. It is most important that reliability on the GIS preventive diagnostic system, because it estimates abnormality of system by analysis result of collected data. But, exist system which used central data management is low efficiency, and hard to guarantee timeliness and accuracy of data. To guarantee timeliness and accuracy, the GIS preventive diagnostic system needs accordingly to use a real-time middleware. So, in this paper, to improve reliability of the GIS preventive diagnostic system, we use a middleware based on TMO for guaranteeing timeliness of real-time distributed computing. And we propose an improved GIS preventive diagnostic system applying data acquisition, monitoring and control methods based on the TMO model. The presented system uses the Communication Control Unit(CCU) for distributed data handling which is supported by TMO. CCU can improve performance of the GIS preventive diagnostic system by guaranteeing timeliness of data handling process and increasing reliability of data through the TMO middleware. And, it has designed to take full charge of overload on a data acquisition task had been processed in an exist server. So, it could reduce overload of the server and apply distribution environment from now. Therefore, the proposed system can improve performance and reliability of the GIS preventive diagnostic system and contribute to stable operation of GIS.

• Korean Journal of Radiology
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• v.22 no.7
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• pp.1213-1224
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• 2021

Objective: To develop a machine learning (ML) pipeline based on radiomics to predict Coronavirus Disease 2019 (COVID-19) severity and the future deterioration to critical illness using CT and clinical variables. Materials and Methods: Clinical data were collected from 981 patients from a multi-institutional international cohort with real-time polymerase chain reaction-confirmed COVID-19. Radiomics features were extracted from chest CT of the patients. The data of the cohort were randomly divided into training, validation, and test sets using a 7:1:2 ratio. A ML pipeline consisting of a model to predict severity and time-to-event model to predict progression to critical illness were trained on radiomics features and clinical variables. The receiver operating characteristic area under the curve (ROC-AUC), concordance index (C-index), and time-dependent ROC-AUC were calculated to determine model performance, which was compared with consensus CT severity scores obtained by visual interpretation by radiologists. Results: Among 981 patients with confirmed COVID-19, 274 patients developed critical illness. Radiomics features and clinical variables resulted in the best performance for the prediction of disease severity with a highest test ROC-AUC of 0.76 compared with 0.70 (0.76 vs. 0.70, p = 0.023) for visual CT severity score and clinical variables. The progression prediction model achieved a test C-index of 0.868 when it was based on the combination of CT radiomics and clinical variables compared with 0.767 when based on CT radiomics features alone (p < 0.001), 0.847 when based on clinical variables alone (p = 0.110), and 0.860 when based on the combination of visual CT severity scores and clinical variables (p = 0.549). Furthermore, the model based on the combination of CT radiomics and clinical variables achieved time-dependent ROC-AUCs of 0.897, 0.933, and 0.927 for the prediction of progression risks at 3, 5 and 7 days, respectively. Conclusion: CT radiomics features combined with clinical variables were predictive of COVID-19 severity and progression to critical illness with fairly high accuracy.

• Korean Journal of Digital Imaging in Medicine
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• v.11 no.2
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• pp.69-77
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• 2009

This study gives an account of the collateral standards in IEC 60601-1-3: 2008 specifying the general requirements for basic safety and essential performance of diagnostic X-ray equipment regarding radiation protection as it pertains to the production of X-rays. The collateral standards establish general requirements for safety regarding ionization radiation in diagnostic radiation systems and describe a verifiable evaluation method of suitable requirements regarding control over the lowest possible dose equivalent for patients, radiologic technologists, and others. The particular standards for each equipment can be determined by the general requirements in the collateral standard and the particular standard is followed in the risk management file. The guidelines for radiation safety of diagnostic radiation systems is written up in ISO 13485, ISO 14971, IEC 60601-1-3(2002)1st edition, medical electric equipment part 1-3, and the general requirements for safety-collateral standards: programmable electrical medical systems. Therefore the diagnostic radiation system protects citizens' health rights with the establishment and revisions of laws and standards for diagnostic radiation systems as a background for the general requirements of radiation safe guards applies, as an international trend, standards regarding the medical radiation safety management. The diagnostic radiation system will also assure competitive power through a conforming evaluation unifying the differing standards, technical specifications, and recognized processes.

• Imaging Science in Dentistry
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• v.41 no.4
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• pp.143-150
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• 2011

Purpose : The aim of this study was to compare the diagnostic accuracy of cone-beam computed tomography (CBCT) images and bitewing images in detection of secondary caries. Materials and Methods : One hundred and twenty proximal slots of Class II cavities were randomly prepared on human premolar and molar teeth, and restored with amalgam (n=60) and composite resin (n=60). Then, artificial secondary caries lesions were randomly created using round steel No. 4 bur. The teeth were radiographed with a conventional bitewing technique and two CBCT systems; Pax-500ECT and Promax 3D. All images were evaluated by five observers. The area under the receiver operating characteristic (ROC) curve ($A_z$) was used to evaluate the diagnostic accuracy. Significant difference was tested using the Friedman test (p value<0.05). Results : The mean $A_z$ values for bitewing, Pax-500ECT, and Promax 3D imaging systems were 0.882, 0.995, and 0.978, respectively. Significant differences were found between the two CBCT systems and film (p=0.007). For CBCT systems, the axial plane showed the greatest $A_z$ value. Conclusion : Based on the design of this study, CBCT images were better than bitewing radiographs in detection of secondary caries.

• Journal of Advanced Marine Engineering and Technology
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• v.29 no.5
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• pp.502-508
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• 2005

Fouling causes serious maintenance problems on heat exchanger tubes and process facilities. To avoid such fouling problems, numerous efforts have been tried. e.g., diagnosis of fouling, reducing and eliminating the fouling. etc.. The objective of the present study is to develop an innovative diagnostic system of fouling, which can detect the scaling attached to the wall non-homogeneously. The performance of the diagnostic system has been evaluated with a scaling simulator that generates scaling on tested tube wall. The measured values with the diagnostic system were compared with the amounts of the scaling generated by the simulator. In addition to, we showed the data that have been executed in field test for reliability verification.

• 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 Society for Precision Engineering
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• v.21 no.12
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• pp.75-83
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• 2004

With the rotating machinery getting more accurate and diversified, the necessity fur an appropriate diagnosis technique and maintenance system has been greatly recognized. However, until now, the operator has executed a monitoring of the machine by the senses or simple the change of RMS (root mean Square) value. So, the diagnostic expert system using the fuzzy inference which the operator can judge easily and expertly a condition of the machine is developed through this study. In this paper, the hardware and software of the diagnostic expert system was composed and the identification of the diagnostic performance of the developed system for 5 fault phenomena was carried out.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.499-507
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• 2016

Medical imaging techniques such as Magnetic Resonance Imaging (MRI), Computed Tomography (CT) and Ultrasound (US) produce a large amount of digital medical images. Hence, compression of digital images becomes essential and is very much desired in medical applications to solve both storage and transmission problems. But at the same time, an efficient image compression scheme that reduces the size of medical images without sacrificing diagnostic information is required. This paper proposes a novel threshold-based medical image compression algorithm to reduce the size of the medical image without degradation in the diagnostic information. This algorithm discusses a novel type of thresholding to maximize Compression Ratio (CR) without sacrificing diagnostic information. The compression algorithm is designed to get image with high optimum compression efficiency and also with high fidelity, especially for Peak Signal to Noise Ratio (PSNR) greater than or equal to 36 dB. This value of PSNR is chosen because it has been suggested by previous researchers that medical images, if have PSNR from 30 dB to 50 dB, will retain diagnostic information. The compression algorithm utilizes one-level wavelet decomposition with threshold-based coefficient selection.