• Journal of Mechanical Science and Technology
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• v.19 no.3
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• pp.846-859
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• 2005

Support vector machines (SVMs) have become one of the most popular approaches to learning from examples and have many potential applications in science and engineering. However, their applications in fault diagnosis of rotating machinery are rather limited. Most of the published papers focus on some special fault diagnoses. This study covers the overall diagnosis procedures on most of the faults experienced in rotating machinery and examines the performance of different SVMs strategies. The excellent characteristics of SVMs are demonstrated by comparing the results obtained by artificial neural networks (ANNs) using vibration signals of a fault simulator.

• Earthquakes and Structures
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• v.8 no.5
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• pp.1171-1190
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• 2015

Enhancement of strength and ductility is the main reason for the extensive use of FRP jackets to provide external confinement to reinforced concrete columns especially in seismic areas. Therefore, numerous researches have been carried out in order to provide a better description of the behavior of FRP-confined concrete for practical design purposes. This study presents a new approach to obtain strength enhancement of CFRP (carbon fiber reinforced polymer) confined concrete cylinders by applying artificial neural networks (ANNs). The proposed ANN model is based on experimental results collected from literature. It represents the ultimate strength of concrete cylinders after CFRP confinement which is also given in explicit form in terms of geometrical and mechanical parameters. The accuracy of the proposed ANN model is quite satisfactory when compared to experimental results. Moreover, the results of the proposed ANN model are compared with five important theoretical models proposed by researchers so far and considered to be in good agreement.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.34-41
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• 2008

This paper concerns the development of a IT-based tunnel design system within the framework of artificial neural networks(ANNs). The system is aimed at expediting a routine cavern design works such as determination of support patterns and stability analysis of the selected support patterns. The detailed system development process and functions of sub modules are provided in this paper.

• Proceedings of the KOSOMBE Conference
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• v.1998 no.11
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• pp.205-206
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• 1998

In this paper, we present the detection algorithm of interictal epileptic waveform using LVQ network and wavelet transform. First wavelet coefficients is used to represent the characteristics of a single channel EEG wave, and make a number of neural network input node smaller. Then, three-layer neural network employing LVQ network is trained and tested using parameters obtained from the first stage. This study showed that preprocessed EEG data can be successfully used to train ANNs to detect epileptogenic discharges with a high success.

• Proceedings of the Korean Powder Metallurgy Institute Conference
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• 1993.11a
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• pp.4-4
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• 1993

Ferrous powder metallurgy in Japan has developed in the last four decades, where every decade is featured by certain breakthroughs in materials. The progress in PM materials is closely related to newly developed powders. Low alloy steel powders for high strength PM components are grouped into three types: Ni and/or Mo containing completely alloyed powders, Ni containing partially alloyed powders, and Cr containing completely alloyed powders. Every type has its special characteristics. The tensile strength of PM materials is improved up to 2 GPa. The hardness is also increased to exceed 500 HV with normal hardening methods, and 700 HV with novel surface treatment techniques. The present maximum of fatigue strength is 550 MPa, and that of impact energy is 100 J. Novel PM materials with improved properties are applied to a variety of automobile and other components: power steering pumps, rocker anns, valve guides and inserts, bearings, torque sensors, etc. The future outlook for the ferrous PM is Quite positive, and the industry is expected to show renewed growth by applying many types of alloy steel powders and new ferrous PM materials.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.05a
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• pp.443-448
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• 1998

With using artificial neural networks (ANNs), an analytical study related to the heated length effect on critical heat flux(CHF) has been carried out to make an improvement of the CHF prediction accuracy based on local condition correlations or table. It has been carried out to suggest a feasible criterion of the threshold length-to-diameter (L/D) value in which heated length could affect CHF. And within the criterion, a L/D correction factor has been developed through conventional regression. In order to validate the developed L/D correction factor, CHF experiment for various heated lengths have been carried out under low and intermediate pressure conditions. The developed threshold L/D correlation provides a new feasible criterion of L/D threshold value. The developed correction factor gives a reasonable accuracy fur the original database, showing the error of -2.18% for average and 27.75% for RMS, and promising results for new experimental data.

• Computers and Concrete
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• v.3 no.5
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• pp.357-373
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• 2006

The concepts of four parameters of nominal water-cement ratio, equivalent water-cement ratio, average paste thickness, fly ash-binder ratio were introduced. It was verified that the four parameters and the mix proportion of mortar can be transformed each other. The behaviors (strength, workability, et al.) of mortar primarily determined by the mix proportion of mortar now depend on the four parameters. The prediction models of strength and workability of mortar were built based on artificial neural networks (ANNs). The calculation models of average paste thickness and equivalent water-cement ratio of mortar can be obtained by the reversal deduction of the two prediction models, respectively. A mortar mix proportion design algorithm was proposed. The proposed mortar mix proportion design algorithm is expected to reduce the number of trial and error, save cost, laborers and time.

• Journal of Industrial Technology
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• v.18
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• pp.379-384
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• 1998

Conventional short-term load forecasting techniques have limitation in their use on holidays due to dissimilar load behaviors of holidays and insufficiency of pattern data. Thus, a new short-term load forecasting method for special days in anomalous load conditions is proposed in this paper. The proposed method uses two Artificial Neural Networks(ANN); one is for the estimation of load curve, and the other is for the estimation of minimum and maximum value of load. The forecasting procedure is as follows. First, the normalized load curve is estimated by ANN. At next step, minimum and maximum values of load in a special day are estimated by another ANN. Finally, the estimate of load in a whole special day is obtained by combining these two outputs of ANNs. The proposed method shows a good performance, and it may be effectively applied to the practical situations.

• Computers and Concrete
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• v.11 no.3
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• pp.237-252
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• 2013

This paper presents the application of artificial neural network (ANN) to predict deep beam deflection using experimental data from eight high-strength-self-compacting-concrete (HSSCC) deep beams. The optimized network architecture was ten input parameters, two hidden layers, and one output. The feed forward back propagation neural network of ten and four neurons in first and second hidden layers using TRAINLM training function predicted highly accurate and more precise load-deflection diagrams compared to classical linear regression (LR). The ANN's MSE values are 40 times smaller than the LR's. The test data R value from ANN is 0.9931; thus indicating a high confidence level.

• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.2977-2979
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• 1999

본 논문에서는 MEBP(Modified Error Back-Propagation) 학습 규칙을 간단한 비선형 회로를 이용하여 구현하였다. 인공 신경 회로망(ANNs : Artificial Neural Networks)은 많은 수의 뉴런을 필요하기 때문에 표준 CMOS 기술을 이용하는 간단한 비선형 시냅스(synapse) 회로는 인공 신경 회로망 구현에 적합하다. 학습회로는 비선형 시냅스 회로. 시그모이드(sigmoid) 회로. 그리고 선형 곱셈기로 구성되어 있다. 학습 회로의 출력은 각 입력 패턴에 따라 유일한 값으로 결정되어진다. 제안한 학술회로를 $2{\times}2{\times}1$과 $2{\times}3{\times}1$ 다층 feedforward 신경 회로망 모델에 적용하였다. MEBP 하드웨어 구현은 HSPICE 회로 시뮬레이터를 이용하여 검증하였다. 제안한 학술 회로는 on-chip 학습회로를 포함한 대규모 신경회로망 구현에 매우 적합하리라 예상된다.