• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.10a
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• pp.447-454
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• 1998

The paper describes the second half of the research for the development of Neural-Networks-based model for the generation of an Artificial earthquake and a Response Spectrum(NNARS). Based on the redefined traditional processes related to the generation of an earthquake acceleration response spectrum and design spectrum, four neural-networks-based models are proposed to substitute the traditional processes. RS_NN tries to directly generate acceleration response spectrum with basic data that are magnitude, epicentral distance, site conditions and focal depth. The test results of RS_NN are not good because of the characteristics of white noise, which is randomly generated. ARS_NN solve this problem by the introduction of the average concept. IARS_NN has a role to inverse the ARS_NN, so that is applied to generate a ground motion accelerogram compatible with the shape of a response spectrum. Additionally, DS_NN directly produces design spectrum with basic data. As these four neural networks are simulated as a step by step, the paper describes the methods to generate a response spectrum and a design spectrum using the neural networks.

• Proceedings of the KIEE Conference
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• 2003.11b
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• pp.263-266
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• 2003

In this paper, we have proposed a realization method of tactile sense of virtual objects using multi-layer Neural Networks(NN). Inputs of the NN are position data of non-rigid objects and outputs of the NN are forces at that time and point. First, the position and forte data are measured from non-rigid objects (a sponge and a balloon) using two PHANToMS, one as a master and the other as a slave manipulator, then the data are used to train a multi-layer Neural Networks whose inputs and outputs are designed to represent tactile information. The trained Neural Networks is used to regenerate the tactile sense on the virtual objects graphically made by a computer, and one can feel a quite similar sense of touch by using the system while touching the virtual objects.

• International Journal of Highway Engineering
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• v.10 no.3
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• pp.129-138
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• 2008

The finite element method(FEM) was one of tools used to solve problem of previous Concrete Pavement and was applied to Korea Pavement Research Program Study. This study used the ABAQUS and the fortran analysis program to calculate the critical stress on jointed concrete pavement and compared and analyzed the results by using neural networks and linear regression model. In that case, which are not enough analysises by using FEM programs though many input variables, when the results of FEM with NN and linear regression models are compared, there are some differences. The other cases, which are reduced input variables and a lot of analysises each of them, results of Neural Networks(NN) and linear regression models are simulated to them of FEM. But, the result of NN is more exact than them of linear regression at the (0,0), (1,1). On the results of this study, it is suggested that the calculation of stress using NN is more compatible to Korea Pavement Research Program Study.

• Journal of Intelligence and Information Systems
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• v.1 no.2
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• pp.103-121
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• 1995

This study proposes a artificial neural network method to predict the time to buy and sell the stocks listed on the Korea Composite Stock Price Index(KOSPI). Four types (NN1, NN2, NN3, NN4) of independent networks were developed to predict KOSPIs up/down direction after four weeks. These networks have a difference only in the length of learning period. NN5 - arithmetic average of four networks outputs - shows an higher accuracy than other network types and Multiple Linear Regression (MLR), and buying and selling simulation using systems outputs produces higher reture than buy-and-hold strategy.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.10a
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• pp.439-446
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• 1998

One of the most important roles in the nonlinear dynamic structural analysis is to select a proper ground excitation, which dominates the response of a structure. Because of the lack of recorded accelerograms in Korea, a stochastic model of ground excitation with various dynamic properties rather than recorded accelerograms is necessarily required. If all information is not available at site, the information from other sites with similar features can be used by the procedure of seismic hazard analysis. Eliopoulos and Wen identified the parameters of the ground motion model by the empirical relations or expressions developed by Trifunac and Lee. Because the relations used in the parameter identification are largely empirical, it is required to apply the artificial neural networks instead of the empirical model. Additionally, neural networks have the advantage of the empirical model that it can continuously re-train the new recorded data, so that it can adapt to the change of the enormous data. Based on the redefined traditional processes, three neural-networks-based models (FAS_NN, PSD_NN and INT_NN) are proposed to individually substitute the Fourier amplitude spectrum, the parameter identification of power spectral density function and intensity function. The paper describes the first half of the research for the development of Neural-Networks-based model for the generation of an Artificial earthquake and a Response Spectrum(NNARS).

• Computers and Concrete
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• v.12 no.2
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• pp.229-241
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• 2013

The objective of this study is to reveal the sufficiency of neural networks (NN) as a securer, quicker, more robust and reliable method to be used in seismic assessment of existing reinforced concrete buildings. The NN based approach is applied as an alternative method to determine the seismic performance of each existing RC buildings, in terms of damage level. In the application of the NN, a multilayer perceptron (MLP) with a back-propagation (BP) algorithm is employed using a scaled conjugate gradient. NN based model wasd eveloped, trained and tested through a based MATLAB program. The database of this model was developed by using a statistical procedure called P25 method. The NN based model was also proved by verification set constituting of real existing RC buildings exposed to 2003 Bingol earthquake. It is demonstrated that the NN based approach is highly successful and can be used as an alternative method to determine the seismic performance of each existing RC buildings.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.2
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• pp.169-174
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• 2005

A neural networks (NN) saturation compensation scheme for DC motor systems is presented. The scheme that leads to stability, command following and disturbance rejection is rigorously proved. On-line weights tuning law, the overall closed loop performance and the boundness of the NN weights are derived and guaranteed based on Lyapunov approach. The simulation and experimental results show that the proposed scheme effectively compensate for saturation nonlinearity in the presence of system uncertainty.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.168.1-168
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• 2001

This article describes a novel model-based compensating neural network (NN) model developed to be used in our active binocular head controller, which addresses both the kinematics and dynamics aspects in trying to precisely track a moving object of interest to keep it in view. The compensating NN model is constructed using two classes of self-tuning neural models: namely Neural Gas (NG) algorithm and SoftMax function networks. The resultant servo controller is shown to be able to handle the tracking problem with a minimum knowledge of the dynamic aspects of the system.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.187-190
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• 1995

A method is proposed which searches for optimal structures of Neural Networks (NN) using Genetic Algorithm (GA). The purpose of the method lies in not only finding an optimal NN structure but also leading us to the goal of self-organized control system that acquires its structure and its functionality by itself depending on its environment.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.33B no.9
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• pp.97-106
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• 1996

In this paper, we propose a face recognition algorithm using the hidden markov model and neural networks (HMM-NN). In the preprocessing stage, we find edges of a face using the locally adaptive threshold (LAT) scheme and extract features based on generic knowledge of a face, then construct a database with extracted features. In the training stage, we generate HMM parameters for each person by using the forward-backward algorithm. In the recognition stage, we apply probability vlaues calculated by the HMM to subsequent neural networks (NN) as input data. Computer simulation shows that the proposed HMM-NN algorithm gives higher recognition rate compared with conventional face recognition algorithms.