• Journal of the Korean Statistical Society
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• v.20 no.2
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• pp.139-146
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• 1991

A generalization of an earlier diffusion model for system subject to continuous wear is presented. It is assumed that the state of the system is modelled by Brownian motion with negative drift and an absorbing barrier at the origin. A repairman arrives according to a stationary renewal process and increases the state of the system by a random amount if the state does not exceed a threshold. Various properties of this model are investigated including the distribution of the state of the system at time t, the first passage time to state 0 and the probability that the state of the system exceeds a certain level throughout a specified interval.

• The Transactions of the Korea Information Processing Society
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• v.5 no.11
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• pp.2915-2920
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• 1998

This paper propose a new model for evaluationg the whole course of study by using the neural network. Through the experiment, we get the result that our model could evaluate effectively teh state of whole knowledge, because the neural network had the characterisitics such as a generalization and the ability which overcame the weakness of itself.

• Journal of information and communication convergence engineering
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• v.16 no.3
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• pp.173-178
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• 2018

Accurate classification of cloud images is a challenging task. Almost all the existing methods rely on hand-crafted feature extraction. Their limitation is low discriminative power. In the recent years, deep learning with convolution neural networks (CNNs), which can auto extract features, has achieved promising results in many computer vision and image understanding fields. However, deep learning approaches usually need large datasets. This paper proposes a deep learning approach for classification of cloud image patches on small datasets. First, we design a suitable deep learning model for small datasets using a CNN, and then we apply data augmentation and dropout regularization techniques to increase the generalization of the model. The experiments for the proposed approach were performed on SWIMCAT small dataset with k-fold cross-validation. The experimental results demonstrated perfect classification accuracy for most classes on every fold, and confirmed both the high accuracy and the robustness of the proposed model.

• Journal of the Korean Data and Information Science Society
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• v.8 no.1
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• pp.85-89
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• 1997

In this paper, we consider a random replacement model with minimal repair, which is a generalization of the random replacement model introduced Lee and Lee(1994). It is assumed that a system is minimally repaired when it fails and replaced only when the accumulated operating time of the system exceeds a threshold time by a supervisor who arrives at the system for inspection according to Poisson process. Assigning the corresponding cost to the system, we obtain the expected long-run average cost per unit time and find the optimum values of the threshold time and the supervisor's inspection rate which minimize the average cost.

• Asian-Australasian Journal of Animal Sciences
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• v.13 no.8
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• pp.1035-1039
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• 2000

This study developed a Poisson generalized linear mixed model and a procedure to estimate genetic parameters for count traits. The method derived from a frequentist perspective was based on hierarchical likelihood, and the maximum adjusted profile hierarchical likelihood was employed to estimate dispersion parameters of genetic random effects. Current approach is a generalization of Henderson's method to non-normal data, and was applied to simulated data. Underestimation was observed in the genetic variance component estimates for the data simulated with large heritability by using the Poisson generalized linear mixed model and the corresponding maximum adjusted profile hierarchical likelihood. However, the current method fitted the data generated with small heritability better than those generated with large heritability.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.611-613
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• 1998

A structure of musculotendon model with a fatigue profile is investigated. The Hill-type musculotendon model can predicts the decline in muscle force for a given fatigue profile. It consists of nonlinear activation and contraction dynamics based on the physiological concepts. It is normalized for generalization to deal with the various muscles. Muscle force generated by continuous tetanic electrical monophasic pulsewidth modulation stimulation is decreased in time. A fatigue profile is expressed by a function of intramuscular acidification and applied to the relationship between muscle force and shortening velocity in contraction dynamics. The results of computer simulation are well matched with data in a literature which are isometrically performed for knee extension muscles. Also change in optimal fiber length has an effect only on muscle time, constant not on the steady-state tetanic force.

• Wind and Structures
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• v.8 no.5
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• pp.373-388
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• 2005

Quasi-steady approaches have been often adopted to model wind forces on moving cylinders in cross-flow and to study instability conditions of rigid cylinders supported by visco-elastic devices. Recently, much attention has been devoted to the experimental study of inclined and/or yawed circular cylinders detecting dynamical phenomena such as galloping-like instability, but, at the present state-of-the-art, no mathematical model is able to recognize or predict satisfactorily this behaviour. The present paper presents a generalization of the quasi-steady approach for the definition of the flow-induced forces on yawed and inclined circular cylinders. The proposed model is able to replicate experimental behaviour and to predict the galloping instability observed during a series of recent wind-tunnel tests.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.130-133
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• 2000

In this paper, a new design methodology named FNNN(Fuzzy Polynomial Neural Network) algorithm is proposed to identify the structure and parameters of fuzzy model using PNN(Polynomial Neural Network) structure and a fuzzy inference method. The PNN is the extended structure of the GMDH(Group Method of Data Handling), and uses several types of polynomials such as linear, quadratic and modified quadratic besides the biquadratic polynomial used in the GMDH. The premise of fuzzy inference rules defines by triangular and gaussian type membership function. The fuzzy inference method uses simplified and regression polynomial inference method which is based on the consequence of fuzzy rule expressed with a polynomial such as linear, quadratic and modified quadratic equation are used. Each node of the FPNN is defined as fuzzy rules and its structure is a kind of neuro-fuzzy architecture Several numerical example are used to evaluate the performance of out proposed model. Also we used the training data and testing data set to obtain a balance between the approximation and generalization of proposed model.

• Journal of Korean Institute of Industrial Engineers
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• v.22 no.3
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• pp.517-532
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• 1996

The area and time-dependent vehicle scheduling problem(ATVSP) is a generalization of the vehicle scheduling problem in which the travel speed between two locations depends on the passing areas and time of day. We propose a simple model for estimating area and time-dependent travel speeds in the ATVSP that relieves much burden for the data collection and storage problems. A mixed integer nonlinear programming formulation of the ATVSP is presented. We also propose three heuristics for the ATVSP, developed by extending and modifying existing heuristics for conventional vehicle scheduling problems. The results of computational experiments demonstrate that the proposed estimation model performs well and the saving method is the best among the three heuristics.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.157-157
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• 2018

A reliable and accurate downscaling model which can provide climate change information, obtained from global climate models (GCMs), at finer resolution has been always of great interest to researchers. In order to achieve this model, linear methods widely have been studied in the past decades. However, nonlinear methods also can be potentially beneficial to solve downscaling problem. Therefore, this study explored the applicability of some nonlinear machine learning techniques such as neural network (NN), extreme learning machine (ELM), and ELM autoencoder (ELM-AE) as well as a linear method, least absolute shrinkage and selection operator (LASSO), to build a reliable temperature downscaling model. ELM is an efficient learning algorithm for generalized single layer feed-forward neural networks (SLFNs). Its excellent training speed and good generalization capability make ELM an efficient solution for SLFNs compared to traditional time-consuming learning methods like back propagation (BP). However, due to its shallow architecture, ELM may not capture all of nonlinear relationships between input features. To address this issue, ELM-AE was tested in the current study for temperature downscaling.