• 한국해안해양공학회지
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• 제2권4호
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• pp.183-189
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• 1990

방파제의 뒤에서 파랑이 회절될 때와 같이 여러 방향으로 전파되는 경우 파랑의 변형을 계산하기 위하여 각스펙트럼 모델이 개발되어왔다. 본 논문에서는 우선 각스펙트럼의 개념을 설명하고, 최근에 이를 토대로 하여 개발된 각스펙트럼 모델들을 소개한다.

• 한국통신학회논문지
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• 제28권4A호
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• pp.238-246
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• 2003

본 논문에서는 현재까지 셀룰라 서비스와 PCS서비스를 위해 얻어진 전파전파 측정에 대한 전체적인 요약을 하였다. 이렇게 얻어진 협대역신호 기반의 측정치와 광대역 신호에 의한 이동통신 채널 측정에 대해 고찰을 한 후 미국 캘리포니아의 오클랜드시에서 얻어진 측정치를 이용하여 불규칙한 높이의 건물로 이뤄진 도시 환경에서 쓰일 수 있는 마이크로셀용 신호 감쇄 예측 공식을 만들고, 이를 균일한 높이의 건물로 이뤄진 환경에서 얻은 신호 감쇄 예측 공식과 비교하였다.

• 대한기계학회논문집
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• 제18권10호
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• pp.2787-2796
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• 1994

The modeling of combustion process is an important part in an engine simulation program. In this study, calculated results using a conventional B-K model and the other model which is called GESIM were compared with experimentally measured data of a three-cylinder spark-ignition engine under wide range of operating conditions. The burn rates calculated from the combustion models were compared with the burn rate calculated from the one-zone heat release analysis that uses measured pressure data as an input data. As a result of the two models' comparison, the GESIM combustion model conformed to be closer to the data acquired from the experiment in wide operating ranges. The GESIM model has been improved by introducing a variable that considers the flame size, the area of flame conacting the piston surface into the model, based on the comparison between the experimental result and the calculated results. The improved combustion model predicts experimental results more precisely than that of GESIM combustion model.

• 한국경영과학회지
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• 제20권3호
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• pp.17-29
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• 1995

Recently, neural network models have been employed as an alternative to regression analysis for point estimation or function fitting in various field. Thus far, however, no theoretical or empirical guides seem to exist for selecting the tool which the most suitable one for a specific function-fitting problem. In this paper, we evaluate performance of three major function-fitting techniques, regression analysis and two neural network models, back-propagation and linear-Hebbian-learning neural networks. The functions to be fitted are simple linear ones of a single independent variable. The factors considered are size of noise both in dependent and independent variables, portion of outliers, and size of the data. Based on comutational results performed in this study, some guidelines are suggested to choose the best technique that can be used for a specific problem concerned.

• 한국전자파학회논문지
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• 제9권3호
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• pp.410-418
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• 1998

밀리미터파대역의 강우감쇄와 교차편파를 여러 나라의 측정치와 비교하였다. 그리고 국내환경에 적용할 수 있도록 $\tau$분 강우율을 1분 가우율롤 변환할 수 있는 변환모델을 제시하였다. 변환한 국내의 가웅율 데이타를 이요하여, 강우감쇄와 교차편파의 확률분포를 다양한 모델을 이용하여 계산하고, 그 결과를 서로 비교하였다.

• 한국철도학회:학술대회논문집
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• 한국철도학회 2011년도 정기총회 및 추계학술대회 논문집
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• pp.2650-2652
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• 2011

The fiber reinforced composite materials have many advantages due to their high strength-to-density ratios. Thus they have been widely used in many industrial applications. As the wave propagation are closely related to dynamic analysis of structures, it is very important to predict them. This paper presents a wave propagation in the axially loaded axial-bending-shear coupled composite laminated beams which are represented by the Timoshenko beam models based on the first-order shear deformation theory.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2012년도 추계학술발표대회
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• pp.814-817
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• 2012

It has to secure the reliability for the propagation performance in the physical layer of the products for comprising system in order to satisfy the service quality. The radiowave environment under the actual service circumstance is measured. By using the channel model which models and which it obtains, the performance about the access device tries to be tested under the laboratory environment and there is the object of channel modeling.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.20-20
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• 2000

Back-propagation neural network (BPNN) is the most prevalently used paradigm in modeling semiconductor manufacturing processes, which as a neuron activation function typically employs a bipolar or unipolar sigmoid function in either hidden and output layers. In this study, applicability of another linear function as a neuron activation function is investigated. The linear function was operated in combination with other sigmoid functions. Comparison revealed that a particular combination, the bipolar sigmoid function in hidden layer and the linear function in output layer, is found to be the best combination that yields the highest prediction accuracy. For BPNN with this combination, predictive performance once again optimized by incrementally adjusting the gradients respective to each function. A total of 121 combinations of gradients were examined and out of them one optimal set was determined. Predictive performance of the corresponding model were compared to non-optimized, revealing that optimized models are more accurate over non-optimized counterparts by an improvement of more than 30%. This demonstrates that the proposed gradient-optimized teaming for BPNN with a linear function in output layer is an effective means to construct plasma models. The plasma modeled is a hemispherical inductively coupled plasma, which was characterized by a 24 full factorial design. To validate models, another eight experiments were conducted. process variables that were varied in the design include source polver, pressure, position of chuck holder and chroline flow rate. Plasma attributes measured using Langmuir probe are electron density, electron temperature, and plasma potential.

• Computational Structural Engineering : An International Journal
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• 제2권1호
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• pp.33-42
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• 2002

A computationally efficient stiffness design method for building structures is proposed in which dynamic soil-structure interaction based on the wave-propagation theory is taken into account. A sway-rocking shear building model with appropriate ground impedances derived from the cone models due to Meek and Wolf (1994) is used as a simplified design model. Two representative models, i.e. a structure on a homogeneous half-space ground and a structure on a soil layer on rigid rock, are considered. Super-structure stiffness satisfying a desired stiffness performance condition are determined via an inverse problem formulation for a prescribed ground-surface response spectrum. It is shown through a simple yet reasonably accurate model that the ground conditions, e.g. homogeneous half-space or soil layer on rigid rock (frequency-dependence of impedance functions), ground properties (shear wave velocity), depth of surface ground, have extensive influence on the super-structure design.

• Computers and Concrete
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• 제29권 6호
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• pp.433-444
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• 2022

Concrete's compressive strength is widely studied in order to understand many qualities and the grade of the concrete mixture. Conventional civil engineering tests involve time and resources consuming laboratory operations which results in the deterioration of concrete samples. Proposing efficient non-destructive models for the prediction of concrete compressive strength will certainly yield advancements in concrete studies. In this study, the efficiency of using radial basis function neural network (RBFNN) which is not common in this field, is studied for the concrete compressive strength prediction. Complementary studies with back propagation neural network (BPNN), which is commonly used in this field, have also been carried out in order to verify the efficiency of RBFNN for compressive strength prediction. A total of 13 input parameters, including novel ones such as cement's and fly ash's compositional information, have been employed in the prediction models with RBFNN and BPNN since all these parameters are known to influence concrete strength. Three different train: test ratios were tested with both models, while different hidden neurons, epochs, and spread values were introduced to determine the optimum parameters for yielding the best prediction results. Prediction results obtained by RBFNN are observed to yield satisfactory high correlation coefficients and satisfactory low mean square error values when compared to the results in the previous studies, indicating the efficiency of the proposed model.