• Korean Journal of Radiology
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• v.21 no.8
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• pp.1007-1017
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• 2020

Objective: The purpose of our study was to investigate the predictive abilities of clinical and computed tomography (CT) features for outcome prediction in patients with coronavirus disease (COVID-19). Materials and Methods: The clinical and CT data of 238 patients with laboratory-confirmed COVID-19 in our two hospitals were retrospectively analyzed. One hundred sixty-six patients (103 males; age 43.8 ± 12.3 years) were allocated in the training cohort and 72 patients (38 males; age 45.1 ± 15.8 years) from another independent hospital were assigned in the validation cohort. The primary composite endpoint was admission to an intensive care unit, use of mechanical ventilation, or death. Univariate and multivariate Cox proportional hazard analyses were performed to identify independent predictors. A nomogram was constructed based on the combination of clinical and CT features, and its prognostic performance was externally tested in the validation group. The predictive value of the combined model was compared with models built on the clinical and radiological attributes alone. Results: Overall, 35 infected patients (21.1%) in the training cohort and 10 patients (13.9%) in the validation cohort experienced adverse outcomes. Underlying comorbidity (hazard ratio [HR], 3.35; 95% confidence interval [CI], 1.67-6.71; p < 0.001), lymphocyte count (HR, 0.12; 95% CI, 0.04-0.38; p < 0.001) and crazy-paving sign (HR, 2.15; 95% CI, 1.03-4.48; p = 0.042) were the independent factors. The nomogram displayed a concordance index (C-index) of 0.82 (95% CI, 0.76-0.88), and its prognostic value was confirmed in the validation cohort with a C-index of 0.89 (95% CI, 0.82-0.96). The combined model provided the best performance over the clinical or radiological model (p < 0.050). Conclusion: Underlying comorbidity, lymphocyte count and crazy-paving sign were independent predictors of adverse outcomes. The prognostic nomogram based on the combination of clinical and CT features could be a useful tool for predicting adverse outcomes of patients with COVID-19.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.34 no.4
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• pp.437-442
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• 2010

Flow-accelerated corrosion (FAC) leads to thinning of steel pipe walls that are exposed to flowing water or wet steam. From experience, it is seen that FAC damage to piping at fossil and nuclear plants can result in outages that require expensive repairs and can affect plant reliability and safety. CHECWORKS have been utilized in domestic nuclear plants as a predictive tool to assist FAC engineers in planning inspections and evaluating the inspection data so that piping failures caused by FAC can be prevented. However, CHECWORKS may be occasionally ignore local susceptible portions when predicting FAC damage in a group of pipelines after constructing a database for all the secondary side piping in nuclear plants. This paper describes the methodologies that can complement CHECWORKS and the verifications of CHECWORKS prediction results using numerical analysis. FAC susceptible locations determined using CHECWORKS for two pipeline groups of a nuclear plant was compared with determined using the numerical-analysis-based FLUENT.

• Journal of Korean Society of Environmental Engineers
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• v.39 no.3
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• pp.118-123
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• 2017

This study investigated the feasibility of a statistical approach for the prediction of BaP and total PAHs as pyrogenic sources. As results of regression, excellent linear and multiple correlations ($r^2$ > 0.94) were observed between BaP (or ${\Sigma}PAH$) and Pyr concentrations. When a developed prediction equation was applied to other investigations as validation and application studies, outstanding prediction results were obtained. The predictive model showed very good correlation between the measured and calculated ${\Sigma}PAH$. From this equation, Pyr was an apparently important hydrocarbon for the prediction of PAH. This model might provide a potentially useful tool for the calculation of average BaP and ${\Sigma}PAH$ in a certain region without additional tests.

• Journal of Convergence for Information Technology
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• v.7 no.1
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• pp.55-60
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• 2017

In order to provide intelligent services without human intervention in the Internet of Things environment, it is necessary to analyze the big data generated by the IoT device and learn the normal pattern, and to predict the abnormal symptoms such as faulty or malfunction based on the learned normal pattern. The purpose of this study is to implement a machine learning model that can predict product failure by analyzing big data generated in various devices of product process. The machine learning model uses the big data analysis tool R because it needs to analyze based on existing data with a large volume. The data collected in the product process include the information about product faulty, so supervised learning model is used. As a result of the study, I classify the variables and variable conditions affecting the product failure, and proposed a prediction model for the product failure based on the decision tree. In addition, the predictive power of the model was significantly higher in the conformity and performance evaluation analysis of the model using the ROC curve.

• Food Science of Animal Resources
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• v.38 no.5
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• pp.1043-1054
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• 2018

Salmonellosis caused by chicken consumption has been a critical issue in food safety worldwide, including in Korea. The probability of illness from consumption of chicken was simulated in study, based on the recipe of Dakgalbi, a commonly eaten chicken dish in Korea. Additionally, the processing stage at slaughterhouses to decrease Salmonella concentration in broilers was modeled to explore its effect on the likelihood of illness. A Monte Carlo simulation model was created using @RISK. Prevalence of Salmonella in chickens at the retail stage was found to be predominantly important in determining the probability of illness. Other than the prevalence, cooking temperature was found to have the largest impact on the probability of illness. The results also demonstrated that, although chlorination is a powerful tool for decreasing the Salmonella concentration in chicken, this effect did not last long and was negated by the following stages. This study analyzes the effects of variables of the retail-to-table pathway on the likelihood of salmonellosis in broiler consumption, and also evaluates the processing step used to decrease the contamination level of Salmonella in broilers at slaughterhouses. According to the results, we suggest that methods to decrease the contamination level of Salmonella such as chlorination had little effect on decreasing the probability of illness. Overall, these results suggest that preventing contamination of broiler with Salmonella must be a top priority and that methods to reduce the concentration of Salmonella in broilers at slaughterhouses hardly contribute to safe consumption of Salmonella-contaminated chicken.

• Pediatric Gastroenterology, Hepatology & Nutrition
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• v.21 no.2
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• pp.118-126
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• 2018

Purpose: Classical criteria for diagnosis of autoimmune hepatitis (AIH) are intended as research tool and are difficult to apply at patient's bedside. We aimed to study the accuracy of simplified criteria and the concordance with the expert diagnosis based on the original criteria. Methods: A cohort of children under study for liver disorder was selected through consecutive sampling to obtain the prevalence of AIH within the group of differential diagnoses. AIH was defined, based on classical criteria, through committee review of medical reports. Validity indicators of the simplified criteria were obtained in an intention to diagnose approach. Optimal cut-off and the area under the receiver operating characteristic (ROC) curve were calculated. Results: Out of 212 cases reviewed, 47.2% were AIH. For the optimal cut-off (6 points), the simplified criteria showed a sensitivity of 72.0% and a specificity of 96.4%, with a 94.7% positive and a 79.4% negative predictive value. The area under the ROC curve was 94.3%. There was a good agreement in the pre-treatment concordance between the classical and the simplified criteria (kappa index, 0.775). Conclusion: Simplified criteria provide a moderate sensitivity for the diagnosis of AIH, but may help in indicating treatment in cases under suspicion with 6 or more points.

• Journal of Environmental Impact Assessment
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• v.8 no.4
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• pp.47-57
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• 1999

This study is aimed at the development of comprehensive environmental management system, which can be operated on the basis of world wide web, as a topic of G7 project. Even though there should be lots of works remaining to achieve this goal, preliminary products can be summarized as follows : 1) integrated environmental information management system, 2) web based control engine, 3) surface water environment management system, 4) subsurface water environment management system, 5) sewer and waterworks management system. The core methodology of the engine is the generalized multidimensional finite element matrices to depict the terms in the analysis of various partial differential equations. Spatial information management system (ArcView) and Visual Basic were extensively employed to construct GUI oriented web based engine. The developed systems were composed of very intense computer codes due to the necessity of combinatory management of environmental problems. The web based engine could be served as a decision tool for the integrated management of environmental projects in Korea.

• The Journal of the Korea Contents Association
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• v.13 no.6
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• pp.20-29
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• 2013

The compression of video data is intended for real-time transmission of band-limited channels. Compressed video bit-streams are very sensitive to transmission error. If we lose packets or receive them with errors during transmission, not only the current frame will be corrupted, but also the error will propagate to succeeding frames due to the spatio-temporal predictive coding structure of sequences. Error detection and concealment is a good approach to reduce the bad influence on the reconstructed visual quality. To increase concealment efficiency, we need to get some more accurate error detection algorithm. In this paper, We hide specific data into the motion vector difference of each macro-block, which is obtained from the procedure of inter prediction mode in H.264/AVC. Then, the location of errors can be detected easily by checking transmitted specific data in decoder. We verified that the proposed algorithm generates good performances in PSNR and subjective visual quality through the computer simulation by H.324M mobile simulation tool.

• Journal of Korean Neurosurgical Society
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• v.58 no.5
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• pp.448-453
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• 2015

Objective : Recently, the survival of patients with hepatocellular carcinoma (HCC) has been prolonged with improvements in various diagnostic tools and medical treatment modalities. Consequently, spine metastases from HCC are being diagnosed more frequently. The accurate prediction of prognosis plays a critical role in determining a patient's treatment plan, including surgery for patients with spinal metastases of HCC. We investigated the clinical features, surgical outcomes, and prognostic factors of HCC presenting with spine metastases, in patients who underwent surgery. Methods : A retrospective review was conducted on 33 HCC patients who underwent 36 operations (three patients underwent surgical treatment twice) from February 2006 to December 2013. The median age of the patients was 56 years old (range, 28 to 71; male : female=30 : 3). Results : Overall survival was not correlated with age, sex, level of metastases, preoperative Child-Pugh classification, preoperative ambulatory function, preoperative radiotherapy, type of operation, administration of Sorafenib, or the Tokuhashi scoring system. Only the Tomita scoring system was shown to be an independent prognostic factor for overall survival. Comparing the Child-Pugh classification and ambulatory ability, there were no statistically differences between patients pre- and post-operatively. Conclusion : The Tomita scoring system represents a practicable and highly predictive prognostic tool. Even though surgical intervention may not restore ambulatory function, it should be considered to prevent deterioration of the patient's overall condition. Additionally, aggressive management may be needed if there is any ambulatory ability remaining.

• Journal of Korean Tunnelling and Underground Space Association
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• v.16 no.1
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• pp.13-24
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• 2014

This paper presents development of artificial neural network(ANN) based prediction method for section forces of TBM tunnel segment lining in an effort to develop an automatized design technique. A series of design cases were first developed and subsequently analyzed using the two-ring beam finite element model. The results were then used to form a database for use as training and validation data sets for ANN development. Using the database, optimized ANNs were developed that can readily be used to predict maximum sectional forces and their distributions. It is shown that the compute maximum section forces and their distributions by the developed ANNs are almost identical to the computed by the two-ring beam finite element model, implying that the developed ANNs can be used as design tools which expedite routine design calculation process. The results of this study indicate that the neural network model can be effectively used as a reliable and simple predictive tool for the prediction of segment sectional forces for design.