• Korean Journal of Radiology
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• v.19 no.6
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• pp.1021-1030
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• 2018

Objective: We evaluated the effect of various patient characteristics and time-density curve (TDC)-factors on the test bolus-affected vessel enhancement on coronary computed tomography angiography (CCTA). We also assessed the value of generalized linear regression models (GLMs) for predicting enhancement on CCTA. Materials and Methods: We performed univariate and multivariate regression analysis to evaluate the effect of patient characteristics and to compare contrast enhancement per gram of iodine on test bolus (${\Delta}HUTEST$) and CCTA (${\Delta}HUCCTA$). We developed GLMs to predict ${\Delta}HUCCTA$. GLMs including independent variables were validated with 6-fold cross-validation using the correlation coefficient and Bland-Altman analysis. Results: In multivariate analysis, only total body weight (TBW) and ${\Delta}HUTEST$ maintained their independent predictive value (p < 0.001). In validation analysis, the highest correlation coefficient between ${\Delta}HUCCTA$ and the prediction values was seen in the GLM (r = 0.75), followed by TDC (r = 0.69) and TBW (r = 0.62). The lowest Bland-Altman limit of agreement was observed with GLM-3 (mean difference, $-0.0{\pm}5.1$ Hounsfield units/grams of iodine [HU/gI]; 95% confidence interval [CI], -10.1, 10.1), followed by ${\Delta}HUCCTA$ ($-0.0{\pm}5.9HU/gI$; 95% CI, -11.9, 11.9) and TBW ($1.1{\pm}6.2HU/gI$; 95% CI, -11.2, 13.4). Conclusion: We demonstrated that the patient's TBW and ${\Delta}HUTEST$ significantly affected contrast enhancement on CCTA images and that the combined use of clinical information and test bolus results is useful for predicting aortic enhancement.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.90-93
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• 2014

In this paper, we have developed a new fault detection method based on vibration signal for rotor machinery. Generally, many methods related to detection of rotor fault exist and more advanced methods are continuously developing past several years. However, there are some problems with existing methods. Oftentimes, the accuracy of fault detection is affected by vibration signal change due to change of operating environment since the diagnostic model for rotor machinery is built by the data obtained from the system. To settle a this problems, we build a rotor diagnostic model by using feature residual based on vibration signal. To prove the algorithm's performance, a comparison between proposed method and the most used method on the rotor machinery was conducted. The experimental results demonstrate that the new approach can enhance and keeps the accuracy of fault detection exactly although the algorithm was applied to various systems.

• Journal of radiological science and technology
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• v.42 no.1
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• pp.9-17
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• 2019

Although digital mammography is a representative method for breast cancer detection. It has a limitation in detecting and classifying breast tumor due to superimposed structures. Machine learning, which is a part of artificial intelligence fields, is a method for analysing a large amount of data using complex algorithms, recognizing patterns and making prediction. In this study, we proposed a technique to improve the diagnostic accuracy of energy-selective mammography by training data using the machine learning algorithm and using dual-energy measurements. A dual-energy images obtained from a photon-counting detector were used for the input data of machine learning algorithms, and we analyzed the accuracy of predicted tumor thickness for verifying the machine learning algorithms. The results showed that the classification accuracy of tumor thickness was above 95% and was improved with an increase of imput data. Therefore, we expect that the diagnostic accuracy of energy-selective mammography can be improved by using machine learning.

• IE interfaces
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• v.25 no.4
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• pp.376-381
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• 2012

As the number, complexity and interaction of electrical, electronic and programmable electronic (E/E/PE) systems increase, a growing emphasis has been placed on the concept of functional safety during product development. IEC 61508 provides guidelines and standardized procedures in the development of reliable and dependable E/E/PE systems to assure functional safety. Determining risk classes (i.e., safety integrity levels, SILs) associated to a specific E/E/PE item may be recognized as one of the most crucial activities in the product development per IEC 61508 since SILs are used to specify necessary safety requirements for achieving an acceptable residual risk. This article presents a case study on the assessment of SILs applying failure modes, effects and diagnostic analysis (FMEDA) from which failure rates may be derived for each important failure category by combining a standard FMEA with online diagnostic techniques.

• Journal of Biomedical Engineering Research
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• v.38 no.5
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• pp.271-276
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• 2017

Cancer biomarkers are using in the diagnosis, staging, prognosis and prediction of disease progression. But, there are not sufficiently profiled and validated in early detection and risk classification of prostate cancer. In this study, we have devoted to finding a panel of serum biomarkers that are able to detect the diagnosis of prostate cancer. The serum samples were consisted of 111 prostate cancer and 343 control samples and examined. Eleven biomarkers were constructed in this study, and then nine biomarkers were relevant to candidate biomarkers by using t test. Finally, four biomarkers, PSA, ApoA2, CYFRA21.1 and TTR, were selected as the prostate cancer biomarker panel, logistic regression was used to identify algorithms for diagnostic biomarker combinations(AUC = 0.9697). A panel of combination biomarkers is less invasive and could supplement clinical diagnostic accuracy.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.27 no.5
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• pp.553-560
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• 2003

On-site diagnosis of chiller performance is an essential step fur energy saving business. The main purpose of the on-site diagnosis is to predict the COP of a target chiller. Many models based on thermodynamics background have been proposed for this purpose. However, they have to be modified from chiller to chiller and require deep insight into thermodynamics that most of field engineers are often lacking in. This study focuses on developing an easy-to-use diagnostic technique that is based on adaptive neuro-fuzzy inference system (ANFIS). Quality of the training data for ANFIS, sampled over June through September, is assessed by checking COP prediction errors. The architecture of the ANFIS, its error bounds, and collection of training data are described in detail.

• Nuclear Medicine and Molecular Imaging
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• v.42 no.sup1
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• pp.52-59
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• 2008

In the management of colo-retal and anal cancer, accurate staging, treatment evaluation, early detection of recurrence are main clinical problems. F-18 FDG PET (PET/CT) has been reported as useful in the management of colo-rectal and anal cancer because that PET has high diagnostic performance comparing to conventional studies. In case of liver metastases, for confirmation of no extrahepatic metastases, in case of high risk of metastasis, for avoiding unnecessary operation, PET (PET/CT) is expected more useful. In anal cancer, PET is expected useful in lymph node staging. For the early prediction of chemotherapy or radiation therapy effect PET has been reported as useful, also. In early detection of recurrence by PET, cost-benefit advantages has been suggested, also. PET/CT is expected to have higher diagnostic performance than PET alone.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2006.04a
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• pp.82-88
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• 2006

In People usually think that smart materials and smart structures have not been developed until recent years. But those kinds of sensors have already been used for sensing damage in a variety of materials and structures. Two typical examples are piezoelectric materials (e.g., PZT) and electric strain gauges. Load cell is an example that utilizes the piezoelectric property to measure the change in physical quantities occurred by applied loads, while strain gauges are used to measure the deformation of compressive and tension members. The feasibility of using smart materials is realized for a monitoring technology when those sensors are used to monitor damages at inside or outsider of the structures. In this study, a fundamental study on the development of self diagnostic smart concrete using PZT, and unsaturated polyester electric resistance sensor.

• Proceedings of the KIEE Conference
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• 2000.07b
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• pp.1330-1332
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• 2000

In general, the expected life of power semiconductor elements is known for semi-permanent, but actual characteristics are changed according to the such environment conditions obviously because of using time or operating condition. Specially, in case of using at the power plant it is very important to sustain reliability for power semiconductor which it affect to stop operating condition as deterioration or break. Therefore, we need to apply maintenance technics to got the reliability which is a prediction method of life cycle. This paper shows the result of the analyzed data for element characteristic and effects used practically and we had developed the effective equipment which for diagnostic the semiconductor performance.

• Journal of the Semiconductor & Display Technology
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• v.20 no.3
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• pp.161-166
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• 2021

To prevent accidents caused by changes in the surrounding environment or other factors, various protection facilities are installed at the photovoltaic connection module. The main causes of fire are sparks due to foreign substances inside the photovoltaic connection module through high temperature rise and dew condensation in the photovoltaic connection module, and fire due to heat from the power diode. The proposed method can predict the fire by measuring flame, carbon dioxide, carbon monoxide, temperature, humidity, input voltage, and current on the photovoltaic connection module, and when the fire conditions are reached, fire alarm and power off can be sent to managers and users in real time to prevent fire in advance.