• 시스템엔지니어링학술지
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• 제16권2호
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• pp.38-46
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• 2020

The accurate measurement of critical heat flux (CHF) in flow boiling is important for the safety requirement of the nuclear power plant to prevent sharp degradation of the convective heat transfer between the surface of the fuel rod cladding and the reactor coolant. In this paper, a System Engineering approach is used to develop a model that predicts the CHF using machine learning. The model is built using artificial neural network (ANN). The model is then trained, tested and validated using pre-existing database for different flow conditions. The Talos library is used to tune the model by optimizing the hyper parameters and selecting the best network architecture. Once developed, the ANN model can predict the CHF based solely on a set of input parameters (pressure, mass flux, quality and hydraulic diameter) without resorting to any physics-based model. It is intended to use the developed model to predict the DNBR under a large break loss of coolant accident (LBLOCA) in APR1400. The System Engineering approach proved very helpful in facilitating the planning and management of the current work both efficiently and effectively.

• 한국산업융합학회 논문집
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• 제16권3호
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• pp.61-68
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• 2013

Long-term runoff model can be used to establish the effective plan of water reources allocation and the determination of the storage capacity of reservoir. So this study aims at the development of monthly runoff model using artificial neural network technique. For this, it was selected multi-layer neural network(MLN) and radial basis function neural network(RFN) model. In this study, it was applied model to analysis monthly runoff process at the Wi stream basin in Nakdong river which is representative experimental river basin of IHP. For this, multi-layer neural network model tried to construct input 3, hidden 7, and output 1 for each number of layer. As the result of analysis of monthly runoff process using models connected with artificial neural network technique, it showed that these models were effective in the simulation of monthly runoff.

• 한국공간구조학회논문집
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• 제20권2호
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• pp.51-58
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• 2020

Recently, deep learning that is the most popular and effective class of machine learning algorithms is widely applied to various industrial areas. A number of research on various topics about structural engineering was performed by using artificial neural networks, such as structural design optimization, vibration control and system identification etc. When nonlinear semi-active structural control devices are applied to building structure, a lot of computational effort is required to predict dynamic structural responses of finite element method (FEM) model for development of control algorithm. To solve this problem, an artificial neural network model was developed in this study. Among various deep learning algorithms, a recurrent neural network (RNN) was used to make the time history response prediction model. An RNN can retain state from one iteration to the next by using its own output as input for the next step. An eleven-story building structure with semi-active tuned mass damper (TMD) was used as an example structure. The semi-active TMD was composed of magnetorheological damper. Five historical earthquakes and five artificial ground motions were used as ground excitations for training of an RNN model. Another artificial ground motion that was not used for training was used for verification of the developed RNN model. Parametric studies on various hyper-parameters including number of hidden layers, sequence length, number of LSTM cells, etc. After appropriate training iteration of the RNN model with proper hyper-parameters, the RNN model for prediction of seismic responses of the building structure with semi-active TMD was developed. The developed RNN model can effectively provide very accurate seismic responses compared to the FEM model.

• Journal of Electrical Engineering and Technology
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• 제10권4호
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• pp.1822-1829
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• 2015

In this study, artificial neural networks were used to determine the intensity of brightness in interior spaces. The illumination elements to illuminate indoor spaces were considered, not individually, but as a system. So, during the planned maintenance periods of an illumination system, after its design and installation, simple brightness level measurements must be taken. For a three-dimensional evaluation of the brightness level in indoor spaces in a speedy and accurate manner, the obtained brightness level measurement results and artificial neural network model were used. Upon estimation of the most suitable brightness level for indoor spaces by using the artificial neutral network model, the energy demands required by the illumination elements decreased. Consequently, in this study, with estimations of brightness levels, the extent to which the artificial neutral networks become successful was observed and more correct results have been obtained in terms of both economy and usage.

• 대한의용생체공학회:의공학회지
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• 제13권2호
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• pp.87-96
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• 1992

In thIns paper, a numerical simulation of steady laminar and turbulent flow in a two dimensional model for the total artificial heart is'presented. A trlleaflet polyurethane valve was simulated at the outflow orifice while the Inflow orifice had a trileaflet or a flap valve. The finite analytic numerical method was employed to obtain solutions to the governing equations in the Cartesian coordinates. The closure for turbulence model was achieved by employing the k-$\varepsilon$-E model. The SIMPLER algo rithm was used to solve the problem in primitive variables. The numerical solutions of the slulated model show that regions of relative stasis and trapped vortices were smaller within the ventricular chamber with the flap valve at the Inflow orifice than that with the trileaflet valve. The predicted Reynolds stresses distal to the inflow valve within the ventricular chamber were also found to be smaller wlth the flap valve than with the trlleaflet valve. These resu1ts also suggest a correlation be- tween high turbulent stresses and the presence of thrombus In the vicinity of the valves in the total artificial hearts. The computed velocity vectors and trubulent stresses were comparable with previ ously reported in vitro measurements in artificial heart chambers. Analysis of the numerical solo talons suggests that geometries similar to the flap valve(or a tilting disc valve) results in a better flow dynamics within the total artificial heart chamber compared to a trileaflet valve.

• 한국해안해양공학회지
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• 제15권1호
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• pp.59-65
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• 2003

자연 연안 사주와 고정된 인공 연안 사주가 있는 해안에서의 해양 물리 환경 변화에 따른 해빈 단면변화를 수치모델을 통하여 연구하였다. 수치 모델은 준3차원 파랑-흐름-퇴적물 이동모델에 인공 구조물 위에서의 퇴적물 이동에 대한 경계조건을 제안하여 적용하였다. 수치실험 결과 자연 사주의 경우 해수면이나 파고의 변화에 따라 위치를 이동하며 적을 하였으나 고정된 인공 사주의 경우 사주 후면에 새로운 자연 사주를 형성시키거나 혹은 사주 후면에서 세굴 현상이 발생하기도 하였다. 본 연구는 잠제 설치에 따른 해안 지형 변화를 연구하는데 응용될 수 있다.

• 한국인공지능학회지
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• 제11권3호
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• pp.29-34
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• 2023

Recently, the rapid development of artificial intelligence technology, many studies are being conducted to predict the risk of heart disease in order to lower the mortality rate of cardiovascular diseases worldwide. This study presents exercise or dietary improvement contents in the form of a software app or web to patients with cardiovascular disease, and cardiovascular disease through digital devices such as mobile phones and PCs. LR, LDA, SVM, XGBoost for the purpose of developing "Life style Improvement Contents (Digital Therapy)" for cardiovascular disease care to help with management or treatment We compared and analyzed cardiovascular disease prediction models using machine learning algorithms. Research Results XGBoost. The algorithm model showed the best predictive model performance with overall accuracy of 80% before and after. Overall, accuracy was 80.0%, F1 Score was 0.77~0.79, and ROC-AUC was 80%~84%, resulting in predictive model performance. Therefore, it was found that the algorithm used in this study can be used as a reference model necessary to verify the validity and accuracy of cardiovascular disease prediction. A cardiovascular disease prediction analysis algorithm that can enter accurate biometric data collected in future clinical trials, add lifestyle management (exercise, eating habits, etc.) elements, and verify the effect and efficacy on cardiovascular-related bio-signals and disease risk. development, ultimately suggesting that it is possible to develop lifestyle improvement contents (Digital Therapy).

• 소성∙가공
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• 제27권4호
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• pp.227-235
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• 2018

The flow stresses have been identified prior to a numerical simulation for predicting a deformation of materials using the experimental or analytical analysis. Recently, the flow stress models considering the temperature effect have been developed to reduce the number of experiments. Artificial neural network can provide a simple procedure for solving a problem from the analytical models. The objective of this paper is the prediction of flow stress on the fiber metal laminate using the artificial neural network. First, the training data were obtained by conducting the uniaxial tensile tests at the various temperature conditions. After, the artificial neural network has been trained by Levenberg-Marquardt method. The numerical results of the trained model were compared with the analytical models predicted at the previous study. It is noted that the artificial neural network can predict flow stress effectively as compared with the previously-proposed analytical models.

• 대한치과보철학회지
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• 제29권2호
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• pp.49-57
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• 1991

The magnets were widely used to increase the retention of overdentures. The purpose of this study was to compare the break load between overdentures and edentulous models. For this study, Model former(U-402) was used for model fabrication and four different magnets were used for evaluation of break load. The artificial saliva was used between overdenture and model. Breakaway loads were tested with an Instron 1122 at a speed of 2mm/min. The results were as follows. 1. The retentivee forces complete dentures with artificial saliva were than the retentive forces of complete detures without artificial saliva. 2. Magnetic overdenture with artificial saliva showed best retentive force, magnetic overdenture without artificial saliva showed the next retentive force, and the complete denture without artificial saliva showed the worst retention. 3. As the magnetic sizes increased, the retentive forces of magnetics were increased. 4. The retentive force of nipple shape magnet is greater than the retentive force of flate shape magnet in the same size.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1998년도 가을 학술발표회 논문집
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• pp.497-504
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• 1998

In the nonlinear dynamic structural analysis, the given ground excitation as an input should be well defined. Because of the lack of recorded accelerograms in Korea, it is required to generate an artificial earthquake by a stochastic model of ground excitation with various dynamic properties rather than recorded accelerograms. It is well own that earthquake motions are generally non-stationary with time-varying intensity and frequency content. Many researchers have proposed non-stationary random process models. Yeh and Wen (1990) proposed a non-stationary stochastic process model which can be modeled as components with an intensity function, a frequency modulation function and a power spectral density function to describe such non-stationary characteristics. This model is based on the simulation for the strong-motion earthquakes with magnitude greater than approximately 5.0~6.0, because it will be not only expected to cause structural damage but also involved the characteristics of earthquake motions. Also, the recorded earthquake motion within this range are still very scarce in Korea. Thus, it is necessary to verify the model by the application of it to the mid-magnitude (approximately 4.0~6.0) earthquakes actually recorded in domestic or foreign area. The purpose of the paper is to generate an artificial earthquake using the model of Yeh and Wen in the area with low seismicity.