• 한국자동차공학회논문집
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• 제15권5호
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• pp.48-55
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• 2007

A bushing component of a vehicle suspension system is tested to capture the nonlinear behavior of rubber bushing element using the MTS 3-axes rubber test machine. The results of the tests are used to model the artificial neural network bushing model. The performances from the neural network model usually are dependent on the structure of the neural network. In this paper, maximum error, peak error, root mean square error, and error-to-signal ratio are employed to evaluate the performances of the neural network bushing model. A simple simulation is carried out to show the usefulness of the developed procedure.

• 컴퓨터교육학회논문지
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• 제6권3호
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• pp.47-56
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• 2003

We describe a new method for removing non-linear phased array antenna aberration called "squint" problem. To develop a compensation scheme. theoretical antenna and artificial neural networks were used. The purpose of using the artificial neural networks is to develop an antenna system model that represents the steering function of an actual array. The artificial neural networks are also used to implement an inverse model which when concatenated with the antenna or antenna model will correct the "squint" problem. Combining the actual steering function and the inverse model contained in the artificial neural network, alters the steering command to the antenna so that the antenna will point to the desired position instead of squinting. The use of an artificial neural network provides a method of producing a non-linear system that can correct antenna performance. This paper demonstrates the feasibility of generating an inverse steering algorithm with artificial neural networks.

• Structural Engineering and Mechanics
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• 제26권4호
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• pp.377-392
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• 2007

A discrete sliding mode control (SMC) method based on hybrid model of neural network and nominal model is proposed to reduce the vibration of flexible structures, which is a robust active controller developed by using a sliding manifold approach. Since the thick boundary layer will reduce the virtue of SMC, the multilayer feed-forward neural network is adopted to model the uncertainty part. The neural network is trained by Levenberg-Marquardt backpropagation. The design objective of the sliding mode surface is based on the quadratic optimal cost function. In course of running, the input signal of SMC come from the hybrid model of the nominal model and the neural network. The simulation shows that the proposed control scheme is very effective for large uncertainty systems.

• Asia pacific journal of information systems
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• 제9권3호
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• pp.93-110
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• 1999

The previous studies regarding the stock returns have advocated that industry effects exist over entire industry. As the industry categories are more rigid, the demand for predicting the industry sectors is rapidly increasing. The advances in Artificial Intelligence and Neural Networks suggest the feasibility of a valuable computational model for stock returns prediction. We propose a sector-factor model for predicting the return on industry stock index using neural networks. As a substitute for the traditional models, neural network model may be more accurate and effective alternative when the dynamics between the underlying industry features are not well known or when the industry specific asset pricing equation cannot be solved analytically. To assess the potential value of neural network model, we simulate the resulting network and show that the proposed model can be used successfully for banks and general construction industry. For comparison, we estimate models using traditional statistical method of multiple regression. To illustrate the practical relevance of neural network model, we apply it to the predictions of two industry stock indexes from 1980 to 1995.

• 대한화학회지
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• 제57권3호
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• pp.370-376
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• 2013

A back propagation artificial neural network model with one hidden layer is established to correlate the liquid-liquid equilibrium data of hydrocarbon-water systems. The model has four inputs and two outputs. The network is systematically trained with 48 data points in the range of 283.15 to 405.37K. Statistical analyses show that the optimised neural network model can yield excellent agreement with experimental data(the average absolute deviations equal to 0.037% and 0.0012% for the correlated mole fractions of hydrocarbon in two coexisting liquid phases respectively). The comparison in terms of average absolute deviation between the correlated mole fractions for each binary system and literature results indicates that the artificial neural network model gives far better results. This study also shows that artificial neural network model could be developed for the phase equilibria for a family of hydrocarbon-water binaries.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1997년도 추계학술대회 논문집
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• pp.454-457
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• 1997

The purpose of this study is to develop an optimal model, using existing models, that is able to estimate the amount of spatter utilizing artificial neural network in the short circuit transfer mode of gas metal arc (GMA) welding. The amount of spatter generated during welding can become a barometer which represents the process stability of metal transfer in GMA welding, and it depends on some factors which constitute a periodic waveforms of welding current and arc voltage in short circuit GMA welding. So, the 12 factors, which could express the characteristics for the waveforms, and the amount of spatter are used as input and output variables of the neural network, respectively. Two neural network models to estimate the amount of spatter are proposed: A neural network model, where arc extinction is not considered, and a combined neural network model where it is considered. In order to reduce the calculation time it take to produce an output, the input vector and hidden layers for each model are optimized using the correlation coefficients between each factor and the amount of spattcr. The est~mation performance of each optimized model to the amount of spatter IS assessed and compared to the est~mation performance of the model proposed by Kang. Also, through the evaluation for the estimation performance of each optimized model, it is shown that the combined neural network model can almost perfectly predict the amount of spatter.

• 한국정밀공학회지
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• 제12권5호
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• pp.157-165
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• 1995

In this paper the comparison between the neural networks and traditional approaches as nonlinear system identification methods are considered. Two model structures of neural networks are the state space model and the input output model neural networks. The traditional methods are the AutoRegressive eXogeneous Input model and the Nonlinear AutoRegressive eXogeneous Input model. Computer simulation for an analytic dynamic model of a single input single output nonlinear system has been done for all the chosen models. Model validation for the obtained models also has been done with testing inputs of the sinusoidal, ramp and the noise ramp.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -1
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• pp.18-21
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• 2002

We present a phase covariance model that can well represent stimulus intensity as well af feature binding (i.e., covariance). The model is represented by complex neural equations, which is a mean field model of stochastic neural model such as Boltzman machine and sigmoid belief networks.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1994년도 Proceedings of the Korea Automatic Control Conference, 9th (KACC) ; Taejeon, Korea; 17-20 Oct. 1994
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• pp.134-139
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• 1994

In this paper the comparison between the neural networks and traditional approaches as system identification method are considered. Two model structures of neural networks are the state space model and the input output model neural networks. The traditional methods are the AutoRegressive eXogeneous Input model and the Nonlinear AutoRegressive eXogeneous Input model. The examples considered do not represent any physical system, no a priori knowledge concerning their structure has been used in the identification process. Testing inputs for comparison are the sinusoidal, ramp and the noise ramp.

• 문화기술의 융합
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• 제9권1호
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• pp.649-654
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• 2023

본 논문은 그래프 합성곱 신경망을 이용한 신경망 구조 탐색 모델 설계를 제안한다. 딥 러닝은 블랙박스로 학습이 진행되는 특성으로 인해 설계한 모델이 최적화된 성능을 가지는 구조인지 검증하지 못하는 문제점이 존재한다. 신경망 구조 탐색 모델은 모델을 생성하는 순환 신경망과 생성된 네트워크인 합성곱 신경망으로 구성되어있다. 통상의 신경망 구조 탐색 모델은 순환신경망 계열을 사용하지만 우리는 본 논문에서 순환신경망 대신 그래프 합성곱 신경망을 사용하여 합성곱 신경망 모델을 생성하는 GC-NAS를 제안한다. 제안하는 GC-NAS는 Layer Extraction Block을 이용하여 Depth를 탐색하며 Hyper Parameter Prediction Block을 이용하여 Depth 정보를 기반으로 한 spatial, temporal 정보(hyper parameter)를 병렬적으로 탐색합니다. 따라서 Depth 정보를 반영하기 때문에 탐색 영역이 더 넓으며 Depth 정보와 병렬적 탐색을 진행함으로 모델의 탐색 영역의 목적성이 분명하기 때문에 GC-NAS대비 이론적 구조에 있어서 우위에 있다고 판단된다. GC-NAS는 그래프 합성곱 신경망 블록 및 그래프 생성 알고리즘을 통하여 기존 신경망 구조 탐색 모델에서 순환 신경망이 가지는 고차원 시간 축의 문제와 공간적 탐색의 범위 문제를 해결할 것으로 기대한다. 또한 우리는 본 논문이 제안하는 GC-NAS를 통하여 신경망 구조 탐색에 그래프 합성곱 신경망을 적용하는 연구가 활발히 이루어질 수 있는 계기가 될 수 있기를 기대한다.