• Transactions of Materials Processing
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• v.31 no.4
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• pp.173-178
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• 2022

The present study employed two different machine-learning approaches, the extreme gradient boosting (XGB) and light gradient boosting machine (LGBM), to predict a compressive deformation behavior of additively manufactured Ti-6Al-4V. Such approaches have rarely been verified in the field of metallurgy in contrast to artificial neural network and its variants. XGB and LGBM provided a good prediction for elongation to failure under an extrapolated condition of processing parameters. The predicting accuracy of these methods was better than that of response surface method. Furthermore, XGB and LGBM with optimum hyperparameters well predicted a deformation behavior of Ti-6Al-4V additively manufactured under the extrapolated condition. Although the predicting capability of two methods was comparable, LGBM was superior to XGB in light of six-fold higher rate of machine learning. It is also noted this work has verified the LGBM approach in solving the metallurgical problem for the first time.

• Korean Journal of Agricultural Science
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• v.12 no.2
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• pp.309-323
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• 1985

The Paper describes the observed behaviour in the undrained triaxial condition of marine clays remoulded at various different levels of factors, to find out the effects of restricted factors on the stress-strain characteristics. The conventional triaxial compression tests $({\sigma}1>{\sigma}2={\sigma}3)$ were carried out on the 50mm in diameter and 100mm long cylindrical specimens of Gun-san bay mud under controlled various moisture content, density, axial strain rate and passing on No. 200 sieve. Significant conclusions from this study are; 1. The compressible deviator stress at failure of pure marine clay was observed to increase with the decrease of moulding moisture content. 2. The compressible deviator stress at failure increased with the increasing of moulding dry density. 3. The interaction between moisture content and density on the stress-strain characteristics of marine clay was remarkedly significant, as the result of factorial experimental method. 4. The effect of axial strain rate on stress-strain behaviour was unsignificant in marine clay and but the secant moduli could be pronounced on a slight decreasing with increase of the strain rate. 5. With the increasing of the passing on No. 200 sieve, the deviator stress increased regularly. 6. The multiple regression equation could be modeled for the prediction of stress or strain and the comparison with experimental results relatively proved the accuracy.

• Journal of Intelligence and Information Systems
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• v.9 no.1
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• pp.227-249
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• 2003

Prediction of corporate failure using past financial data is a well-documented topic. Early studies of bankruptcy prediction used statistical techniques such as multiple discriminant analysis, logit and probit. Recently, however, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as model construction process. Irrespective of the efficiency of a teaming procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network model. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables fur neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.05a
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• pp.165-167
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• 2022

The proportion of cat cats among companion animals has been increasing at an average annual rate of 25.4% since 2012. Cats have strong wildness compared to dogs, so they have a characteristic of hiding diseases well. Therefore, when the guardian finds out that the cat has a disease, the disease may have already worsened. Symptoms such as anorexia (eating avoidance), vomiting, diarrhea, polydipsia, and polyuria in cats are some of the symptoms that appear in cat diseases such as diabetes, hyperthyroidism, renal failure, and panleukopenia. It will be of great help in treating the cat's disease if the owner can recognize the cat's polydipsia (drinking a lot of water), polyuria (a large amount of urine), and frequent urination (urinating frequently) more quickly. In this paper, 1) Efficient version of DeepLabCut for posture prediction running on an artificial intelligence server, 2) yolov4 for object detection, and 3) LSTM are used for behavior prediction. Using artificial intelligence technology, it predicts the cat's next, polyuria and frequency of urination through the analysis of the cat's behavior pattern from the home CCTV video and the weight sensor of the water bowl. And, through analysis of cat behavior patterns, we propose an application that reports disease prediction and abnormal behavior to the guardian and delivers it to the guardian's mobile and the main server system.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.7
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• pp.33-38
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• 2017

The number of damaged lifeline structures have been increasing with urban acceleration under earthquakes. To predict the damage, damage mitigation technology of lifeline structures should be analyzed using damage prediction technology. Therefore, in this paper, the degree of the fragility of structures under an earthquake was evaluated stochastically through an evaluation of the seismic fragility. The aim was to develop damage prediction technology of sewage pipes among the lifeline facilities. The site response was performed using the data from 158 boreholes in Seoul and 7 real earthquake waves to determine the responses in real urban areas. The seismic fragility was deduced through a total of 29822 time history analysis. In addition, sewer pipes were evaluated and the persisting period was passed by applying the research results of strength reduction which is due to sulphate erosion. As a result, the difference in failure probability between 300 and 800 with the smaller diameter of the representative pipes was approximately double and the size of the pipes has a significant effect on the seismic fragility function. Moreover, the failure probability of a seismic load increases by up to 10 fold as the strength reduction rate increases. The results of this study can be used as a means of predicting the damage and countermeasures of sewer pipes and might be reflected in the seismic design of underground facilities.

• Elastomers and Composites
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• v.46 no.2
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• pp.144-151
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• 2011

Intermittent CSR testing was used to investigate the degradation of a hydrogenated NBR(HNBR) O-rings, and also the prediction of its life-time. The cure system of HNBR O-ring was controlled as sulfur cure and peroxide cure system. An intermittent CSR jig was designed taking into consideration the O-ring's environment under use. The testing allowed observation of the effects of friction, heat loss, and stress relaxation by the Mullins effect. Degradation of O-rings by thermal aging was observed between 100 and $120^{\circ}C$. In the temperature range of $100-120^{\circ}C$, O-rings showed linear degradation behavior and satisfied the Arrhenius relationship. The activation energy of HNBR-S was about 70.6 kJ/mol. From Arrhenius plots, predicted life-times of HNBR-S O-ring were 31.1 years and 33.7 years for 50% and 40% failure conditions, respectively. In case of HNBR-P, the activation energy was about 72.1kJ/mol, and predicted life-times were 34.0 years and 36.5 years for 50% and 40% failure conditions, respectively. The peroxide cure system showed slower degradation rate and higher activation energy than the sulfur cure system.

• Quality Improvement in Health Care
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• v.10 no.2
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• pp.216-231
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• 2003

Objectives: To develop a model that predicts a death probability of acute myocardial infarction(AMI) patient, and to evaluate a performance of hospital services using the developed model. Methods: Medical records of 861 AMI patients in 7 general hospitals during 1996 and 1997 were reviewed by two trained nurses. Variables studied were risk factors which were measured in terms of severity measures. A risk model was developed by using the logistic regression, and its performance was evaluated using cross-validation and bootstrap techniques. The statistical prediction capability of the model was assessed by using c-statistic, $R^2$ as well as Hosmer-Lemeshow statistic. The model performance was also evaluated using severity-adjusted mortalities of hospitals. Results: Variables included in the model building are age, sex, ejection fraction, systolic BP, congestive heart failure at admission, cardiac arrest, EKG ischemia, arrhythmia, left anterior descending artery occlusion, verbal response within 48 hours after admission, acute neurological change within 48 hours after admission, and 3 interaction terms. The c statistics and $R^2$ were 0.887 and 0.2676. The Hosmer-Lemeshow statistic was 6.3355 (p-value=0.6067). Among 7 hospitals evaluated by the model, two hospitals showed significantly higher mortality rates, while other two hospitals had significantly lower mortality rates, than the average mortality rate of all hospitals. The remaining hospitals did not show any significant difference. Conclusion: The comparison of the qualities of hospital service using risk-adjusted mortality rates indicated significant difference among them. We therefore conclude that risk-adjusted mortality rate of AMI patients can be used as an indicator for evaluating hospital performance in Korea.

• Childhood Kidney Diseases
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• v.4 no.2
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• pp.144-153
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• 2000

Purpose : Effects to predict tile progression of chronic renal failure (CRF) in children, using mathematical models based on transformations of serum creatinine (Scr) concentration, have failed. Error may be introduced by age-related variations in creatinine production rate. Height (Ht) is a reliable reference for creatinine production in children. Thus, Scr, factored for Ht, could provide a more accurate predictive model. We examined this hypothesis. Methods : The progression of of was detected in 63 children who proceeded to end-stage renal disease. Derivatives of Scr, including 1/Scr, log Scr & Ht/Scr, were defined fir the period Scr was between 2 and 5 mg/dl. Regression equation were used to predict the time, in months, to Scr > 10 mg/dl. The prediction error (PE) was defined as the predicted time minus actual time for each Scr transformation. Result : The PE for Ht/Scr was lower than the PE for either 1/Scr or log Scr (median: -0.01, -2.0 & +10.6 mos respectively; P<0.0001). For children with congenital renal diseases, the PE for Ht/Scr was also lower than for the other two transformations (median: -1.2, -3.2 & +8.2 mos respectively; P<0.0001). However, the PEs for children with glomerular diseases was not as clearly different (median: +0.9, +0.5 & +9.9 respectively). In children < 13 yrs, PE for Ht/Scr was tile lowest, while in older children, 1/Scr provided the lowest PE but not significantly different from that for Ht/Scr. The logarithmic transformation tended to predict a slower progression of CRF than actually occurred. Conclusion : Scr, floored for Ht, appears to be a useful model to predict the rate of progression of CRF, particularly in the prepubertal child with congenital renal disease.

• Journal of the Korean Institute of Gas
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• v.27 no.2
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• pp.7-15
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• 2023

The electric submersible pump (ESP) has been operating in production wells around the world because of its high applicability and operational efficiency among artificial lift techniques. When operating an ESP in a reservoir, variables such as temperature, pressure, gas/oil ratio, and flow rate are factors that affect ESP performance. In particular, free gas in the production fluid is a major factor that reduces the life and operational efficiency of ESP. This study presents the flow loop system which can implement the performance and damage tests of ESP considering field operating conditions to quantitatively analyze the variables that affect ESP performance. The developed apparatus in an integrated system that can diagnose the failure and causes of ESP, and detect leak of tubing by linking ESP and tubing as one system. In this study, the flow conditions for stable operation of ESP were identified through single phase and two phase flow experiments related to evaluation for the performance of ESP. The results provide the basic data to develop the failure prediction and diagnosis program of ESP, and are expected to be used for real-time monitoring for optimal operating conditions and failure diagnosis for ESP operation.

• Journal of Korea Water Resources Association
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• v.53 no.9
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• pp.661-670
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• 2020

The water supply system has a wider installation range and various components of it than other infrastructure, making it difficult to secure stability against earthquakes. Therefore, it is necessary to develop methods for evaluating the seismic performance of water supply systems. Ground Motion Prediction Equation (GMPE) is used to evaluate the seismic performance (e.g, failure probability) for water supply facilities such as pump, water tank, and pipes. GMPE is calculated considering the independent variables such as the magnitude of the earthquake and the ground motion such as PGV (Peak Ground Velocity) and PGA (Peak Ground Acceleration). Since the large magnitude earthquake data has not accumulated much to date in Korea, this study tried to select a suitable GMPE for the domestic earthquake simulation by using the earthquake data measured in Korea. To this end, GMPE formula is calculated based on the existing domestic earthquake and presented the results. In the future, it is expected that the evaluation will be more appropriate if the determined GMPE is used when evaluating the seismic performance of domestic waterworks. Appropriate GMPE can be directly used to evaluate hydraulic seismic performance of water supply networks. In other words, it is possible to quantify the damage rate of a pipeline during an earthquake through linkage with the pipe failure probability model, and it is possible to derive more reasonable results when estimating the water outage or low-pressure area due to pipe damages. Finally, the quantifying result of the seismic performance can be used as a design criteria for preparing an optimal restoration plan and proactive seismic design of pipe networks to minimize the damage in the event of an earthquake.