• Journal of KIBIM
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• v.4 no.1
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• pp.1-7
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• 2014

The construction method using PC(Precast Concrete) has been widely used because the method can shorten the construction period and improve construction quality. In this paper, through the analysis of present design process for PC railway platform, design parameters on the geometry and properties were extracted and 3D information models for PC railway platform were constructed by the parametric modeling technique. Furthermore, the interface module was developed to link 3D models to the structural analysis/design sheet and database program using VBA(Visual Basic Application). This information model could be used in various areas including structural analysis and design, 2D drawing, quantity estimation and 4D simulation including clash detection.

• Journal of KIBIM
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• v.3 no.4
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• pp.11-18
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• 2013

Modular technology has become a major issue of the construction industries to enhance their productivity. Modular bridge construction generally requires the collaboration between the contractor, designer, fabricator and constructor. Therefore, a readily accessible information model based on BIM technology should be provided for their communication during a construction project life-cycle. In this study, BIM based 3D information modeling was carried out for the modular bridge pier. First, the product breakdown structure (PBS) and level of detail (LOD) of the pier were defined. Based on them, 3D models were created by using parametric modeling method. In addition, database was constructed for the exchange of geometry and property data of 3D models. Finally, application areas of 3D information model were suggested, including the quantity estimation and the 4D simulation.

• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.3
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• pp.9-15
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• 2013

Recent works on ad hoc network study have shown that achievable throughput can be made to scale linearly with the number of receive antennas even if the transmitter has only a single antenna. In this paper, we propose a non-parametric linear minimum mean square error (MMSE) receiver for achieving further gain in performance when the channel state information at receiver (CSIR) of interferers is imperfect. The key feature to make our approach effective is to exploit the autocorrelation of the received signal. In fact, by incorporating the desired channel information on top of the observations including interference and noise only, the proposed method achieves large fraction of the optimal MMSE transmission capacity without transmission rate loss. From the SINR analysis as well as transmission capacity simulations in realistic ad hoc network system, we show that the proposed non-parametric linear MMSE receiver brings substantial performance gain over existing multiple receive antenna algorithms.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.1-10
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• 2022

Deep neural networks are widely used to solve various problems. In a fully connected neural network, the nonlinear activation function is a function that nonlinearly transforms the input value and outputs it. The nonlinear activation function plays an important role in solving the nonlinear problem, and various nonlinear activation functions have been studied. In this study, we propose a combined parametric activation function that can improve the performance of a fully connected neural network. Combined parametric activation functions can be created by simply adding parametric activation functions. The parametric activation function is a function that can be optimized in the direction of minimizing the loss function by applying a parameter that converts the scale and location of the activation function according to the input data. By combining the parametric activation functions, more diverse nonlinear intervals can be created, and the parameters of the parametric activation functions can be optimized in the direction of minimizing the loss function. The performance of the combined parametric activation function was tested through the MNIST classification problem and the Fashion MNIST classification problem, and as a result, it was confirmed that it has better performance than the existing nonlinear activation function and parametric activation function.

• 제어로봇시스템학회:학술대회논문집
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• 1999.10a
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• pp.125-128
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• 1999

A linear time-invariant model can be described either by a parametric model or by a nonparametric model. Nonparametric models, for which a priori information is not necessary, are basically the response of the dynamic system such as impulse response model and frequency models. Parametric models, such as transfer function models, can be easily described by a small number of parameters. In this paper aiming to take benefit from both types of models, we will use linear-combination of basis fuctions in an impulse response using a few parameters. We will expand and generalize the Kautz functions as basis functions for dynamical system representations and we will consider estimation problem of transfer functions using Kautz function. And so we will present the influences of poles settings of Kautz function on the identification accuracy.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.557-557
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• 2015

To overcome main drawbacks of parametric models, k-nearest neighbor resampling (KNNR) is suggested for water quality analysis involving geographic information. However, with KNNR nonparametric model, Geographic information is not properly handled. In the current study, to manipulate geographic information properly, we introduce a depth function which is a novel statistical concept in the classical KNNR model for stormwater quality simulation. An application is presented for a case study of the total suspended solids throughout the entire United States. Total suspended solids concentration data of stormwater demonstrated that the proposed model significantly improves the simulation performance rather than the existing KNNR model.

• Environmental and Resource Economics Review
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• v.14 no.2
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• pp.447-474
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• 2005

Respondents, in the willingness to pay (WTP) survey, may have preference intensity about their stated WTP values. This study elicited a post-decisional intensity measure for each observed WTP answer for gathering information on the degree of preference intensity. In order to deal with the WTP data with preference intensity, this paper considers using the Type 3 Tobit model. This is usually estimated by the parametric two-stage estimation method assuming homoskedastic and bivariate normal error structure. However, if the assumptions are not satisfied, the estimates are inconsistent. The author has tested the hypotheses of homoskedasticity and normality, and could not accept them at the 1% level. The assumptions required to estimate the parametric Type 3 model are, therefore, too strong to be satisfied. As an alternative the parametric model, this study applies a semiparametric Type 3 Tobit model. The results show that the semiparametric model significantly outperforms the parametric model, and that more importantly, the mean WTP from the parametric model is significantly different from that from the semiparametric model.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.207-211
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• 2008

The importance of cost management in early stage has been increasing due to market change and competition severence in construction industry. Because the adjustable budget is only 20% after finishing design stage, the critical decision is made in the early stage. However, in the early stage, the design information is not enough to make crucial decision. Therefore, this research suggests the predicting method on the purpose of accurate cost estimation. The parametric estimation is appropriate for the early stage, especially it has the strength of rapidity in cost estimation. This research analyzes 84 actual data of public apartment on the scale of $11{\sim}15$ stories, and then performs the correlation analysis between cost and influence factors. After eliminating the parameters which causes the problem of multicollinearity, this research derived the formula through the multi-regression analysis.

• KIPS Transactions on Software and Data Engineering
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• v.10 no.10
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• pp.407-420
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• 2021

Deep neural networks are widely used to solve various problems. However, the deep neural network with a deep hidden layer frequently has a vanishing gradient or exploding gradient problem, which is a major obstacle to learning the deep neural network. In this paper, we propose a parametric activation function to alleviate the vanishing gradient problem that can be caused by nonlinear activation function. The proposed parametric activation function can be obtained by applying a parameter that can convert the scale and location of the activation function according to the characteristics of the input data, and the loss function can be minimized without limiting the derivative of the activation function through the backpropagation process. Through the XOR problem with 10 hidden layers and the MNIST classification problem with 8 hidden layers, the performance of the original nonlinear and parametric activation functions was compared, and it was confirmed that the proposed parametric activation function has superior performance in alleviating the vanishing gradient.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.9
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• pp.371-380
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• 2022

Convolutional neural networks are widely used to manipulate data arranged in a grid, such as images. A general convolutional neural network consists of a convolutional layers and a fully connected layers, and each layer contains a nonlinear activation functions. This paper proposes a combined parametric activation function to improve the performance of convolutional neural networks. The combined parametric activation function is created by adding the parametric activation functions to which parameters that convert the scale and location of the activation function are applied. Various nonlinear intervals can be created according to parameters that convert multiple scales and locations, and parameters can be learned in the direction of minimizing the loss function calculated by the given input data. As a result of testing the performance of the convolutional neural network using the combined parametric activation function on the MNIST, Fashion MNIST, CIFAR10 and CIFAR100 classification problems, it was confirmed that it had better performance than other activation functions.