• 한국정밀공학회지
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• 제24권4호
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• pp.93-101
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• 2007

Aluminum alloy which is one of the light materials has been tried to apply to light weight vehicle body. In order to do that, welding technology is very important. In case of the aluminum laser welding, the strength of welded part is reduced due to porosity, underfill, and magnesium loss. To overcome these problems, laser welding of aluminum with filler wire was suggested. In this study, experiment about laser welding of AA5182 aluminum alloy with AA5356 filler wire was performed according to process parameters such as laser power, welding speed and wire feed rate. The tensile strength was measured to find the weldability of laser welding with filler wire. The models to estimate tensile strength were suggested using three regression models and one neural network model. For regression models, one was the multiple linear regression model, another was the second order polynomial regression model, and the other was the multiple nonlinear regression model. Neural network model with 2 hidden layers which had 5 and 3 nodes respectively was investigated to find the most suitable model for the system. Estimation performance was evaluated for each model using the average error rate. Among the three regression models, the second order polynomial regression model had the best estimation performance. For all models, neural network model has the best estimation performance.

• 설비공학논문집
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• 제15권8호
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• pp.619-629
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• 2003

A grease filter is used to remove grease generated from a cooking appliance in a kitchen. Since the inertial impaction is a dominant particle removal mechanism of the grease filter, the performance of the filter is greatly affected by the geometry. This numerical study has been conducted to investigate the effect of geometry on the performance of grease filters for four models having nominal flowrate of 100 m$^3$/h. Four models were designed by changing the shape of impaction surface, the length of eyelid, and the number of eyelids of the grease filter. The flow field and particle trajectories in the grease filter with a flow chamber were simulated using the commercial code of STAR-CD. The difference of air velocity and pressure distributions among four models was discussed in detail. The collection efficiency curves and the pressure drops of four models were also compared. It was found that the grease filter model with flat top surfaces shows highest performance among four models, having high particle collection efficiency and relatively low pressure drop. The cutoff diameter of this model representing 50-% collection efficiency is about 7.1 ${\mu}{\textrm}{m}$ for water droplets at 100 m$^3$/h.

• Advances in concrete construction
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• 제7권4호
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• pp.211-218
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• 2019

Predictions about shrinkage and creep of concrete are very important for evaluating time-dependent effects on structural performance. Some prediction models and formulas of concrete shrinkage and creep have been proposed with diversity. However, the influence of reinforcement ratio on shrinkage and creep of concrete has been ignored in most prediction models and formulas. In this paper, the concrete shrinkage and creep with different ratios of reinforcement were studied. Firstly, the shrinkage performance was tested by the 10 reinforced concrete beams specimens with different reinforcement ratios for 200 days. Meanwhile, the creep performance was tested by the 5 reinforced concrete beams specimens with different ratios of reinforcement under sustained load for 200 days. Then, the test results were compared with the prediction models and formulas of CEB-FIP 90, ACI 209, GL 2000 and JTG D 62-2004. At last, based on ACI 209, an improved prediction models and formulas of concrete shrinkage and creep considering reinforcement ratio was derived. The results from improved prediction models and formulas of concrete shrinkage and creep are in good agreement with the experimental results.

• Nuclear Engineering and Technology
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• 제55권8호
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• pp.2747-2756
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• 2023

Reinforced Concrete (RC) shear walls are one of the civil structures in nuclear power plants to resist lateral loads such as earthquakes and wind loads effectively. Risk-informed and performance-based regulation in the nuclear industry requires considering possible accidents and determining desirable performance on structures. As a result, rather than predicting only the ultimate capacity of structures, the prediction of performances on structures depending on different damage states or various accident scenarios have increasingly needed. This study aims to develop machine-learning models predicting drifts of the RC shear walls according to the damage limit states. The damage limit states are divided into four categories: the onset of cracking, yielding of rebars, crushing of concrete, and structural failure. The data on the drift of shear walls at each damage state are collected from the existing studies, and four regression machine-learning models are used to train the datasets. In addition, the bagging ensemble method is applied to improve the accuracy of the individual machine-learning models. The developed models are to predict the drifts of shear walls consisting of various cross-sections based on designated damage limit states in advance and help to determine the repairing methods according to damage levels to shear walls.

• Nuclear Engineering and Technology
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• 제39권2호
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• pp.133-142
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• 2007

The Electric Power Research Institute (EPRI) demonstrated a method for monitoring the performance of instrument channels in Topical Report (TR) 104965, 'On-Line Monitoring of Instrument Channel Performance.' This paper presents the results of several models originally developed by EPRI to monitor three nuclear plant sensor sets: Pressurizer Level, Reactor Protection System (RPS) Loop A, and Reactor Coolant System (RCS) Loop A Steam Generator (SG) Level. The sensor sets investigated include one redundant sensor model and two non-redundant sensor models. Each model employs an Auto-Associative Kernel Regression (AAKR) model architecture to predict correct sensor behavior. Performance of each of the developed models is evaluated using four metrics: accuracy, auto-sensitivity, cross-sensitivity, and newly developed Error Uncertainty Limit Monitoring (EULM) detectability. The uncertainty estimate for each model is also calculated through two methods: analytic formulas and Monte Carlo estimation. The uncertainty estimates are verified by calculating confidence interval coverages to assure that 95% of the measured data fall within the confidence intervals. The model performance evaluation identified the Pressurizer Level model as acceptable for on-line monitoring (OLM) implementation. The other two models, RPS Loop A and RCS Loop A SG Level, highlight two common problems that occur in model development and evaluation, namely faulty data and poor signal selection

• 한국인공지능학회지
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• 제12권2호
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• pp.1-7
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• 2024

In this paper, we compare three models (logistic regression, Random Forest, and XGBoost) for predicting stroke occurrence using data from the Korea National Health and Nutrition Examination Survey (KNHANES). We evaluated these models using various metrics, focusing mainly on recall and F1 score to assess their performance. Initially, the logistic regression model showed a satisfactory recall score among the three models; however, it was excluded from further consideration because it did not meet the F1 score threshold, which was set at a minimum of 0.5. The F1 score is crucial as it considers both precision and recall, providing a balanced measure of a model's accuracy. Among the models that met the criteria, XGBoost showed the highest recall rate and showed excellent performance in stroke prediction. In particular, XGBoost shows strong performance not only in recall, but also in F1 score and AUC, so it should be considered the optimal algorithm for predicting stroke occurrence. This study determines that the performance of XGBoost is optimal in the field of stroke prediction.

• 한국통신학회논문지
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• 제33권5B호
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• pp.364-378
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• 2008

Data Envelopment Analysis(DEA) 모형은 다수출력/다수입력을 갖는 IT중소기업 정부자금지원정책 성과평가에 활용가능하다. DEA효율성지수의 정확성 제고를 위해 기업역량을 반영한 외생고정변수를 DEA모형에서 고려할 수 있다. 또한, 다수 DEA기본모형과 확장모형을 활용한 성과평가를 시도함으로써, 단일 DEA모형에 의존하는 성과평가의 한계를 완화할 수 있다. 본 연구는, IT중소기업 정부자금지원시점에서의 기업자산, 매출액, 종업원수를 외생고정변수로 갖는; 1)DEA자료구조 정립; 2)DEA기본모형과 확장모형 수립; 3)실증자료를 이용한 사례분석을 예시한다. DEA기본모형으로 CCR, BCC, Super-efficiency모형, DEA확장모형으로 비제어변수(noncontrollable variables), 비자유변수(nondiscretionary variables)를 갖는 모형을 수립한다. DEA모형 비교 및 Analytic Hierarchy Process(AHP) 가중치를 이용한 통합 활용절차가 설명된다. 모수 비모수분산분석에 의한 기술분야별 DEA효율성지수로써의 성과유의차를 판정한다.

• 유체기계공업학회:학술대회논문집
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• 유체기계공업학회 2005년도 연구개발 발표회 논문집
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• pp.378-383
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• 2005

The performances of side channel type regenerative blowers were evaluated by the blower performance test, 1-D performance prediction and CFD. The performance prediction method was modified using the results of the performance test and CFD and applied to the design of the new regenerative blowers. The major geometric parameters such as channel height, channel area and expansion angle were decided from the performance prediction method for the improved models and the predicted results were compared with CFD and experimental data. Both of the modified models showed improved efficiency at the operating condition. Especially, model3 could be possible to reduce operating rotating speed, that is benefit to noise performance, because of the high head performance at the design point. The CFD results showed that the performance of the regenerative blower was influenced by the secondary circulatory flow in the channel.

• 한국물환경학회지
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• 제33권3호
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• pp.326-333
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• 2017

The lakes and reservoirs have been reported as important sources of carbon emissions to the atmosphere in many countries. Although field experiments and theoretical investigations based on the fundamental gas exchange theory have proposed the quantitative amounts of Net Atmospheric Flux (NAF) in various climate regions, there are still large uncertainties at the global scale estimation. Mechanistic models can be used for understanding and estimating the temporal and spatial variations of the NAFs considering complicated hydrodynamic and biogeochemical processes in a reservoir, but these models require extensive and expensive datasets and model parameters. On the other hand, data driven machine learning (ML) algorithms are likely to be alternative tools to estimate the NAFs in responding to independent environmental variables. The objective of this study was to develop random forest (RF) and multi-layer artificial neural network (ANN) models for the estimation of the daily $CO_2$ NAFs in Daecheong Reservoir located in Geum River of Korea, and compare the models performance against the multiple linear regression (MLR) model that proposed in the previous study (Chung et al., 2016). As a result, the RF and ANN models showed much enhanced performance in the estimation of the high NAF values, while MLR model significantly under estimated them. Across validation with 10-fold random samplings was applied to evaluate the performance of three models, and indicated that the ANN model is best, and followed by RF and MLR models.

• 대한전자공학회논문지SD
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• 제45권4호
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• pp.78-85
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• 2008

네트워크 SoC 칩을 대상으로 SystemC를 이용한 High-level 설계 방법을 연구하였다. 실제 Verilog RTL 모델과 비교하여 깊이있는 Architecture 구조탐색과 정확한 SystemC 모델 cycle 검증을 토대로 하여 High-level 설계를 강조할 것이다. 대다수 High-level 설계와 접근방법과 다르게, SystemC 모델과 Verilog RTL 모델의 성능을 비교해 보고, SystemC-based platform을 검증하기 위해 On-chip test board 측정 데이터를 이용하였다. 이 논문에서는 High-level 설계기법이 RTL 모델과 같은 정확성을 얻을 수 있을 뿐만 아니라, RTL 모델보다 100배 이상 빠른 시뮬레이션 속도를 달성할 수 있음을 보여 주었다. 그리고, 아키텍처 구조탐색을 통해서 시스템 성능하락의 원인을 파악하고, 대안을 찾아보았다.