• Journal of Cardiovascular Imaging
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• v.31 no.4
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• pp.169-177
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• 2023

BACKGROUND: Acute worsening of cardiac function frequently leads to kidney dysfunction. This study aimed to identify clinical and imaging parameters associated with impaired kidney function in patients with acute decompensated heart failure with reduced ejection fraction (HFrEF). METHODS: Data from 131 patients hospitalized with acute decompensated HFrEF (left ventricular ejection fraction, < 40%) were analyzed. Patients were divided into two groups according to the glomerular filtration rate (GFR) at admission (those with preserved kidney function [GFR ≥ 60 mL/min/1.73 m²] and those with reduced kidney function [GFR < 60 mL/min/1.73 m²]). Various echocardiographic parameters and perirenal fat thicknesses were assessed by computed tomography. RESULTS: There were 71 patients with preserved kidney function and 60 patients with reduced kidney function. Increased age (odds ratio [OR], 1.07; 95% confidence interval [CI], 1.04-1.12; p = 0.005), increased log N-terminal pro b-type natriuretic peptide (OR, 1.74; 95% CI, 1.14-2.66; p = 0.010), and increased perirenal fat thickness (OR, 1.19; 95% CI, 1.10-1.29; p < 0.001) were independently associated with reduced kidney function, even after adjusting for variable clinical and echocardiographic parameters. The optimal average perirenal fat thickness cut-off value of > 12 mm had a sensitivity of 55% and specificity of 83% for kidney dysfunction prediction. CONCLUSIONS: Thick perirenal fat was independently associated with impaired kidney function in patients hospitalized for acute decompensated HFrEF. Measurement of perirenal fat thickness may be a promising imaging marker for the detection of HFrEF patients who are more susceptible to kidney dysfunction.

• Journal of the Korea Furniture Society
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• v.15 no.2
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• pp.19-27
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• 2004

As glulam is a woody material, it is necessary to be more careful in a gluing process. It takes 6-7 hours at $40-60^{\circ}C$ to harden PRF resin used in making structural glulam, and about 24 hours at room temperature. In the present process which can not use a press continuously, reducing the hardening time is necessary to increase production. The experiment was done to compare the adhesive properties of PRF resin and MUF resin through bending test, block shear strength test and water soaking test. In comparing the bending strength of prediction MOE is 1.2 times higher that actual MOE. PRF and MUF do not show significant difference in MOE and MOR, and in block shear strength test, such as shear strength and wood failure rate. However, in water soaking and boiling water soaking tests PRF and MUF show the significant difference in delamination rate.

• Journal of Applied Reliability
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• v.15 no.1
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• pp.44-51
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• 2015

In this study, we propose reliability prediction of a satellite by function analysis. To do so, the intended functions of the satellite are derived from using function structure block diagram, and defined as main, sub, and detailed functions. Furthermore, in order to generate function and reliability structure table, reliability model rule, duty cycle, and types of switch are assigned to the classified functions. This study also establishes reliability block diagram and mathematical reliability models to schematize the relationship among the functions. The reliability of the classified function is estimated by calculating the failure rate of parts comprising them. Finally, we apply the proposed method to a small satellite as a case study. The result shows that the reliability for the detailed function and the sub function as well as the main function could be predicted quantitatively and accurately by the proposed approach.

• Journal of Aerospace System Engineering
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• v.18 no.2
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• pp.21-28
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• 2024

In light of the rapid development of the space industry, there has been increased attention on small satellites. These satellites rely on components that are considered to have lower reliability compared to larger-scale satellites. As a result, predicting reliability becomes even more crucial in this context. Therefore, this study aims to compare three reliability prediction techniques: MIL-HDBK-217F, RiAC-HDBK-217Plus, and FIDES. The goal is to determine a suitable reliability standard specifically for nano-satellites. Furthermore, we have refined the quality assurance factors of the manufacturing company. These factors have been adjusted to be applicable across various industrial sectors, with a particular focus on the selected FIDES prediction standard. This approach ensures that the scoring system accurately reflects the suitability for the aerospace industry. Finally, by implementing this refined system, we confirm the impact of the manufacturer's quality assurance level on the total failure rate.

• Journal of Korean Academy of Nursing
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• v.53 no.4
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• pp.468-479
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• 2023

Purpose: This study aimed to evaluate the use of the respiratory rate oxygenation (ROX) index, ROX-heart rate (ROX-HR) index, and saturation of percutaneous oxygen/fraction of inspired oxygen ratio (SF ratio) to predict weaning from high-flow nasal cannula (HFNC) in patients with respiratory distress in a pediatric intensive care unit. Methods: A total of 107 children admitted to the pediatric intensive care unit were enrolled in the study between January 1, 2017, and December 31, 2021. Data on clinical and personal information, ROX index, ROX-HR index, and SF ratio were collected from nursing records. The data were analyzed using an independent t-test, χ² test, Mann-Whitney U test, and area under the curve (AUC). Results: Seventy-five (70.1%) patients were successfully weaned from HFNC, while 32 (29.9%) failed. Considering specificity and sensitivity, the optimal cut off points for predicting treatment success and failure of HFNC oxygen therapy were 6.88 and 10.16 (ROX index), 5.23 and 8.61 (ROX-HR index), and 198.75 and 353.15 (SF ratio), respectively. The measurement of time showed that the most significant AUC was 1 hour before HFNC interruption. Conclusion: The ROX index, ROX-HR index, and SF ratio appear to be promising tools for the early prediction of treatment success or failure in patients initiated on HFNC for acute hypoxemic respiratory failure. Nurses caring for critically ill pediatric patients should closely observe and periodically check their breathing patterns. It is important to continuously monitor three indexes to ensure that ventilation assistance therapy is started at the right time.

• Journal of Korea Water Resources Association
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• v.42 no.7
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• pp.525-535
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• 2009

In this paper a heuristic method for identifying individual pipes in water pipe networks to determine specific sections of the pipes that need to be replaced due to deterioration. An appropriate minimum pipe length is determined by selecting the pipe length that has the greatest variance of the average cumulative break number slopes among the various pipe lengths used. As a result, the minimum pipe length for the case study water network is determined as 4 m and a total of 39 individual pipe IDs are obtained. The economically optimal replacement times of the individual pipe IDs are estimated by using the threshold break rate of an individual pipe ID and the pipe break trends models for which the General Pipe Break Prediction Model(Park and Loganathan, 2002) that can incorporate the linear, exponential, and in-between of the linear and exponetial failure trends and the ROCOFs based on the modified time scale(Park et al., 2007) are used. The maximum log-likelihoods of the log-linear ROCOF and Weibull ROCOF estimated for the break data of a pipe are compared and the ROCOF that has a greater likelihood is selected for the pipe of interest. The effects of the social costs of a pipe break on the optimal replacement time are also discussed.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.16 no.4
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• pp.173-179
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• 2023

This paper analyzes the time-dependent dielectric breakdown(TDDB) degradation mechanism for each stress region of Peri devices manufactured by 4th generation VNAND process, and presents a complementary lifetime prediction model that improves speed and accuracy in a wider reliability evaluation region compared to the conventional model presented. SiON dielectric nMOSFETs were measured 10 times each under 5 constant voltage stress(CVS) conditions. The analysis of stress-induced leakage current(SILC) confirmed the significance of the field-based degradation mechanism in the low electric field region and the current-based degradation mechanism in the high field region. Time-to-failure(TF) was extracted from Weibull distribution to ascertain the lifetime prediction limitations of the conventional E-model and 1/E-model, and a parallel complementary model including both electric field and current based degradation mechanisms was proposed by extracting and combining the thermal bond breakage rate constant(k) of each model. Finally, when predicting the lifetime of the measured TDDB data, the proposed complementary model predicts lifetime faster and more accurately, even in the wider electric field region, compared to the conventional E-model and 1/E-model.

• The Journal of Engineering Geology
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• v.18 no.4
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• pp.447-457
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• 2008

Laboratory tests for single plane sliding were conducted using the model rock slope to investigate the cut slope deformability and failure mechanism due to combined effect of engineering characteristics such as angle of sliding plane, water force, joint roughness and infillings. Also the possibility of prediction of slope failure through displacement monitoring was explored. The joint roughness was prepared in forms of saw-tooth type having different roughness specifications. The infillings was maintained between upper and lower roughness plane from zero to 1.2 times of the amplitude of the surface projections. Water force was expressed as the percent filling of tension crack from dry (0%) to full (100%), and constantly increased from 0% at the rate of 0.5%/min and 1%/min upto failure. Total of 50 tests were performed at sliding angles of $30^{\circ}$ and $35^{\circ}$ based on different combinations of joint roughness, infilling thickness and water force increment conditions. For smooth sliding plane, it was found that the linear type of deformability exhibited irrespective of the infilling thickness and water force conditions. For sliding planes having roughness, stepping or exponential types of deformability were predominant under condition that the infilling thickness is lower or higher than asperity height, respectively. These arise from the fact that, once the infilling thickness exceeds asperities, strength and deformability of the sliding plane is controlled by the engineering characteristics of the infilling materials. The results obtained in this study clearly show that the water force at failure was found to increase with increasing joint roughness, and to decrease with increasing filling thickness. It seems possible to estimate failure time using the inverse velocity method for sliding plane having exponential type of deformability. However, it is necessary to estimate failure time by trial and error basis to predict failure of the slope accurately.

• Nuclear Engineering and Technology
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• v.28 no.1
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• pp.17-26
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• 1996

The in-reactor fuel rod support conditions against the fretting wear-induced damage can be evaluated by residual spacer grid spring deflection or rod-to-grid gap. In order to evaluate the impact of fuel design parameters on the fretting wear-induced damage, a simulation methodology of the in-reactor fuel rod supporting conditions as a function of burnup has been developed and implemented in the GRIDFORCE program. The simulation methodology takes into account cladding creep rate, initial spring deflection, initial spring force, and spring force relaxation rate as the key fuel design parameters affecting the in-reactor fuel rod supporting conditions. Based on the parametric studies on these key parameters, it is found that the initial spring deflection, the spring force relaxation rate and cladding creepdown rate are in the order of the impact on the in-reactor fuel rod supporting conditions. Application of this simulation methodology to the fretting wear-induced failure experienced in a commercial plant indicates that this methodology can be utilized as an effective tool in evaluating the capability of newly developed cladding materials and/or new spacer grid designs against the fretting wear-induced damage.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.121-139
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• 2014

Predicting corporate failure has been an important topic in accounting and finance. The costs associated with bankruptcy are high, so the accuracy of bankruptcy prediction is greatly important for financial institutions. Lots of researchers have dealt with the topic associated with bankruptcy prediction in the past three decades. The current research attempts to use ensemble models for improving the performance of bankruptcy prediction. Ensemble classification is to combine individually trained classifiers in order to gain more accurate prediction than individual models. Ensemble techniques are shown to be very useful for improving the generalization ability of the classifier. Bagging is the most commonly used methods for constructing ensemble classifiers. In bagging, the different training data subsets are randomly drawn with replacement from the original training dataset. Base classifiers are trained on the different bootstrap samples. Instance selection is to select critical instances while deleting and removing irrelevant and harmful instances from the original set. Instance selection and bagging are quite well known in data mining. However, few studies have dealt with the integration of instance selection and bagging. This study proposes an improved bagging ensemble based on instance selection using genetic algorithms (GA) for improving the performance of SVM. GA is an efficient optimization procedure based on the theory of natural selection and evolution. GA uses the idea of survival of the fittest by progressively accepting better solutions to the problems. GA searches by maintaining a population of solutions from which better solutions are created rather than making incremental changes to a single solution to the problem. The initial solution population is generated randomly and evolves into the next generation by genetic operators such as selection, crossover and mutation. The solutions coded by strings are evaluated by the fitness function. The proposed model consists of two phases: GA based Instance Selection and Instance based Bagging. In the first phase, GA is used to select optimal instance subset that is used as input data of bagging model. In this study, the chromosome is encoded as a form of binary string for the instance subset. In this phase, the population size was set to 100 while maximum number of generations was set to 150. We set the crossover rate and mutation rate to 0.7 and 0.1 respectively. We used the prediction accuracy of model as the fitness function of GA. SVM model is trained on training data set using the selected instance subset. The prediction accuracy of SVM model over test data set is used as fitness value in order to avoid overfitting. In the second phase, we used the optimal instance subset selected in the first phase as input data of bagging model. We used SVM model as base classifier for bagging ensemble. The majority voting scheme was used as a combining method in this study. This study applies the proposed model to the bankruptcy prediction problem using a real data set from Korean companies. The research data used in this study contains 1832 externally non-audited firms which filed for bankruptcy (916 cases) and non-bankruptcy (916 cases). Financial ratios categorized as stability, profitability, growth, activity and cash flow were investigated through literature review and basic statistical methods and we selected 8 financial ratios as the final input variables. We separated the whole data into three subsets as training, test and validation data set. In this study, we compared the proposed model with several comparative models including the simple individual SVM model, the simple bagging model and the instance selection based SVM model. The McNemar tests were used to examine whether the proposed model significantly outperforms the other models. The experimental results show that the proposed model outperforms the other models.