• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2002년도 가을 학술발표회 논문집
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• pp.513-520
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• 2002

The stability analysis of rock slope can be predicted using a suitable field data but it cannot be predicted unless suitable field data was taken. In this study, artificial neural networks theory is applied to predict plane failure that has a few data. It is well known that human brain has the advantage of handling disperse and parallel distributed data efficiently. On the basis of this fact, artificial neural networks theory was developed and has been applied to various fields of science successfully In this study, error back-propagation algorithm that is one of the teaching techniques of artificial neural networks is applied to predict plane failure. In order to verify the applicability of this model, a total of 30 field data results are used. These data are used for training the artificial neural network model and compared between the predicted and the measured. The simulation results show the potentiality of utilizing the neural networks for effective safety factor prediction of plane failure. In conclusion, the well-trained artificial neural network model could be applied to predict the plane failure stability of rock slope.

• 설비공학논문집
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• 제12권11호
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• pp.1038-1047
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• 2000

The objective of this study is to develop an optimized Artificial Neural Network(ANN) model to predict the descending time of room air temperature. For this, program for predicting room air temperature and ANN program using generalized delta rule were collected through simulation for predicting room air temperature. ANN was trained and the ANN model having the optimized values-learning rate, moment, bias, number of hidden layer, and number of neuron of hidden layer was presented.

• 대한흉부심장혈관외과학회:학술대회논문집
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• 대한흉부외과학회 1993년도 제25차년차학술대회 및 총회
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• pp.128-128
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• 1993

• 한국전산유체공학회:학술대회논문집
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• 한국전산유체공학회 2001년도 추계 학술대회논문집
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• pp.11-19
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• 2001

An adaptive nonlinear artificial dissipation model is presented for performing aeroacoustic computations by the high-order and high-resolution numerical schemes based on the central finite differences. An effective formalism of it is devised by combining a selective background smoothing term and a well-established nonlinear shock-capturing term which is for the temporal accuracy as well as the numerical stability. A conservative form of the selective background smoothing term is presented to keep accurate phase speeds of the propagating nonlinear waves. The nonlinear shock-capturing term that has been modeled by the second-order derivative term is combined with it to improve the resolution of discontinuities and stabilize the strong nonlinear waves. It is shown that the improved artificial dissipation model with an adaptive control constant which is independent of problem types reproduces the correct profiles and speeds of nonlinear waves, suppresses numerical oscillations near discontinuity and avoids unnecessary damping on the smooth linear acoustic waves. The feasibility and performance of the adaptive nonlinear artificial dissipation model are investigated by the applications to actual computational aeroacoustics problems.

• 한국정보시스템학회지:정보시스템연구
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• 제18권3호
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• pp.375-399
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• 2009

In this study, we propose a integrated model of logistic regression, artificial neural networks, support vector machines(SVM), with case-based reasoning(CBR). To predict respondents in the direct marketing is the binary classification problem as like bankruptcy prediction, IDS, churn management and so on. To solve the binary problems, we employed logistic regression, artificial neural networks, SVM. and CBR. CBR is a problem-solving technique and shows significant promise for improving the effectiveness of complex and unstructured decision making, and we can obtain excellent results through CBR in this study. Experimental results show that the classification accuracy of integration model using CBR is superior to logistic regression, artificial neural networks and SVM. When we apply the customer response model to predict respondents in the direct marketing, we have to consider from the view point of profit/cost about the misclassification.

• 한국정밀공학회지
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• 제15권3호
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• pp.107-118
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• 1998

Two-phase neuro-system identification method is presented. The 1$^{st}$-phase identification uses conventional neural network mapping for modeling an input-output system. The 2$^{nd}$ -phase modeling is also performed sequentially using the 1$^{st}$-phase modeling errors. In the 2$^{nd}$ a phase modeling, newly generated input signals, which are obtained by summing the 1st-phase modeling error and artificially generated uniform series, are utilized as system's I-O mapping elements. The 1$^{st}$-phase identification is interpreted as a “Real Model” system identification because it uses system's real data(i.e., observations and control inputs) while the 2$^{nd}$ -phase identification as a “Artificial Model” identification because of using artificial data. Experimental results are given to verify that the two-phase neuro-system identification could reduce the overall modeling errors.rrors.

• 한국항해항만학회지
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• 제44권3호
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• pp.187-194
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• 2020

항만의 주요 정책 및 향후 운영계획 수립 시 정확한 물동량 예측에 관한 연구는 매우 중요하며 이러한 중요성으로 인해 관련 연구가 활발히 수행되고 있다. 본 논문에서는 국내 최대 석탄 및 철광석 처리 항만인 광양항을 대상으로 단계적 회귀분석과 인공신경망모형을 활용하여 모형간 예측력을 비교하였다. 2009년 1월부터 2019년 1월까지 총 121개월의 월별자료를 활용하였으며 석탄 및 철광석 물동량에 영향을 주는 요인을 선정하여 공급관련요인과 시장·경제관련요인으로 분류하였다. 단계적 회귀분석 결과, 광양항 석탄 물동량 예측모형의 경우, 입항선박 톤수, 석탄가격 및 대미환율이 최종변수로 선정되었고 철광석 물동량 예측모형의 경우, 입항선박 톤수, 철광석가격이 최종변수로 선정되었다. 인공신경망모형의 경우, 모델 성능에 영향을 미치는 다양한 Hyper-parameters를 조정하며 최적 모델을 선정하는 시행착오법을 사용하였다. 분석결과 인공신경망모형이 단계적 회귀분석에 비해 우수한 예측성능을 나타내었으며 예측 모형별 예측값과 실측값을 그래프 상 비교 시에도 인공신경망모형이 단계적 회귀분석에 비해 고·저점을 유사하게 나타냈다.

• 대한치과기공학회지
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• 제43권2호
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• pp.48-55
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• 2021

Purpose: This study aimed to analyze deformation according to post-curing of complete arch artificial teeth for temporary dentures printed with a digital light processing (DLP) printer. Methods: An edentulous model was prepared and an occlusal rim was produced. The edentulous model and occlusal rim were scanned using a model scanner. A complete denture was designed using a dental computer-aided design, and the denture base and artificial tooth were separated. Ten complete arch artificial teeth were printed using a 3D printer (DLP). Complete arch artificial teeth was classified into the following three groups: a group no post-curing (NC), a group with 10 minutes post-curing (10M), and a group with 20 minutes post-curing (20M). Specimens were scanned using a model scanner. The scanned data were overlapped with the reference data. Statistical analysis was performed using one-way ANOVA analysis of variance, Kruskal-Wallis test, and Mann-Whitney U test (α=0.05). Results: Regarding the overall deviation of complete arch artificial teeth, the NC group showed the lowest mean deviation of 111.13 ㎛ and the 20M group showed the highest mean deviation of 131.03 ㎛. There were statistically significant differences among the three groups (p<0.05). Conclusion: The complete arch artificial tooth showed deformation due to post-curing. In addition, the largest shrinkage deformation was observed at 10 minutes of post-curing, whereas the least deformation was observed at 20 minutes.

• 한국정밀공학회지
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• 제24권8호통권197호
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• pp.122-129
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• 2007

To evaluate the effect of artificial disc implantation and fusion on the biomechanics of adjacent motion segment, a nonlinear three-dimensional finite element model of whole lumbar spine (L1-S1) was developed. Biomechanical analysis was performed for two different types of artificial disc, ProDisc and SB $Charit{\acute{e}}$ III model, inserted at L4-L5 level and these results were also compared with fusion case. Angular motion of vertebral body, forces on the spinal ligaments and facet joint under sagittal plane loading with a compressive preload of 150 N at a nonlinear three-dimensional finite element model of Ll-S1 were compared. The implant did not significantly alter the kinematics of the motion segment adjacent to the instrumented level. However, $Charit{\acute{e}}$ III model tend to decrease its motion on the adjacent levels, especially in extension motion. Contrast to motion and ligament force changes, facet contact forces were increased in the adjacent levels as well as implanted level for constrained instantaneous center of rotation model, i.e. ProDisc model.

• 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.