• 한국물환경학회지
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• 제22권5호
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• pp.952-957
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• 2006

The present study applied wavelet transform and artificial neural networks (ANNs) for the prediction of daily TOC data. TOC data were transformed into denoised data by the wavelet transform and the noise-reduced data were used for the prediction model by artificial neural networks. For the application of wavelet transform, Daubechies wavelet of order 10 ('db10') was used as a basis function and decomposed the TOC data up to fifth level with five detail components and one approximation component. ANNs were calibrated with the input data of the segregated TOC data corresponding to the details from second to fifth level and the approximation. Consequently, the ANNs model for the prediction of daily TOC data showed the best result when it had seventeen hidden nodes in its layer.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1998년도 가을 학술발표회 논문집
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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.

• 한국지반공학회:학술대회논문집
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• 한국지반공학회 2000년도 가을 학술발표회 논문집
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• pp.673-680
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• 2000

Artificial neural networks are efficient computing techniques that are widely used to solve complex problems in many fields. In this study, a back-propagation neural network model for estimating a consolidation behavior of clay from soil parameter, site investigation data and the first settlement curve is proposed. The training and testing of the network were based on a database of 63 settlement curve from two different sites. Five different network models were used to study the ability of the neural network to predict the desired output to increasing degree of accuracy. The study showed that the neural network model predicted a consolidation behavior of clay reasonably well.

• 디지털콘텐츠학회 논문지
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• 제17권6호
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• pp.449-459
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• 2016

본 논문에서는 기존의 인공신경망을 이용한 자동작곡 방법에서 발생한 세 가지 문제점을 개선하는 새로운 방법을 제안한다. 첫 번째 문제는 인공신경망이 출력한 곡의 박자를 음악이론에 맞도록 후처리 하는 것에서 모든 경우를 처리하지 못하여 발생한 문제이다. 두 번째 문제는 음표를 학습하는 인공신경망에 음표와 구분되는 큰 값으로 쉼표를 같이 학습하다보니 음표공간이 왜곡되어 발생하는 문제이다. 마지막 문제는 새로운 곡 작곡 시 사용자가 작곡해서 넣어준 초기 멜로디와 박자가 인공신경망이 출력하는 나머지 멜로디와 박자와 어울리지 못하여 발생하는 문제이다. 본 논문에서는 이러한 문제를 해결하기 위하여 개선된 박자 후처리 알고리즘과 초기 멜로디 처리 방법을 제안하였으며 쉼표용 인공신경망을 새로이 도입하였다. 실험결과 새로 제안한 방법이 기존의 방법에서 발생한 세 가지 문제점을 모두 해결하는 것으로 판명되었다.

• 반도체디스플레이기술학회지
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• 제21권3호
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• pp.135-139
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• 2022

The current-voltage modeling plays an important role in characterizing photovoltaic systems. A solar cell has a nonlinear characteristic with various parameters influenced by the external environments such as the irradiance and the temperature. In order to accurately predict current-voltage characteristics at low irradiance, the artificial neural networks are applied to effectively quantify nonlinear behaviors. In this paper, a multi-layer perceptron scheme that can make accurate predictions is employed to learn complex formulas for large amounts of continuous data. The simulated results of artificial neural networks model show the accuracy improvement by using MATLAB/Simulink.

• Computers and Concrete
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• 제24권5호
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• pp.399-412
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• 2019

In this investigation, Elman neural networks were utilized for predicting the mechanical properties of Self-Compacting Concretes (SCCs). Elman models were designed by using experimental data of many different concrete mixdesigns of various types of SCC that were collected from the literature. In order to investigate the effectiveness of the selected input variables on the network performance in predicting intended properties, utilized data in artificial neural networks were considered in two sets of 8 and 140 input variables. The obtained outcomes showed that not only can the developed Elman ANNs predict the mechanical properties of SCCs with high accuracy, but also for all of the desired outputs, networks with 140 inputs, compared to ones with 8, have a remarkable percent improvement in the obtained prediction results. The prediction accuracy can significantly be improved by using a more complete and accurate set of key factors affecting the desired outputs, as input variables, in the networks, which is leading to more similarity of the predicted results gained from networks to experimental results.

• 한국콘크리트학회:학술대회논문집
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• 한국콘크리트학회 2002년도 가을 학술발표회 논문집
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• pp.647-652
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• 2002

In the previous study, authors presented the I-ProConS (Intelligent PREdiction system of CONcrete Strength) using artificial neural networks (ANN) that provides in-place strength information of the concrete to facilitate concrete form removal and scheduling for construction. The serious problem of the system has occured, which it cannot appropriately predict the concrete strength when the curing temperature of a curing day is changed. This is because it uses the single neural networks, which all nodes are fully connected, and thus it cannot smoothly respond for external impact. However this paper presents that the problem can be solved by multiple neural networks, which is composed of five ANNs.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2000년도 하계종합학술대회 논문집(4)
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• pp.242-245
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• 2000

In this paper, we present a color interpolation technique based on artificial neural networks for a single-chip CCD (charge-coupled device) camera with a Bayer color filter array (CFA). Single-chip digital cameras use a color filter array and an interpolation method in order to regenerate high quality color images from sparsely sampled images. We applied 3-layer feedforward neural networks in order to interpolate missing pixel from surrounding pixels. And we compared the proposed method with conventional interpolation methods such as the proposed interpolation algorithm based on neural networks provides a better performance than the conventional interpolation algorithms.

• 전자통신동향분석
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• 제35권4호
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• pp.34-41
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• 2020

The existing Von Neumann architecture places limits to data processing in AI, a booming technology. To address this issue, research is being conducted on computing architectures and artificial neural networks that simulate neurons and synapses, which are the hardware of the human brain. With high-speed, high-throughput data communication infrastructures, photonic solutions today are a mature industrial reality. In particular, due to the recent outstanding achievements of artificial neural networks, there is considerable interest in improving their speed and energy efficiency by exploiting photonic-based neuromorphic hardware instead of electronic-based hardware. This paper covers recent photonic neuromorphic studies and a classification of existing solutions (categorized into multilayer perceptrons, convolutional neural networks, spiking neural networks, and reservoir computing).

• Structural Engineering and Mechanics
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• 제52권4호
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• pp.739-753
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• 2014

There have been many packages that can be employed to analyze plane frames. However, because most structural analysis packages suffer from closeness of system, it is very difficult to integrate it with an optimization package. To overcome the difficulty, we proposed a possible alternative, DAMDO, which integrate Design, Analysis, Modeling, Definition, and Optimization phases into an integrative environment. The DAMDO methodology employs neural networks to integrate structural analysis package and optimization package so as not to need directly to integrate these two packages. The key problem of the DAMDO approach is how to generate a set of reasonable random designs in the first phase. According to the characteristics of optimized plane frames, we proposed the ratio variable approach to generate them. The empirical results show that the ratio variable approach can greatly improve the accuracy of the neural networks, and the plane frame optimization problems can be solved by the DAMDO methodology.