• 대한토목학회논문집
• /
• 제26권6C호
• /
• pp.421-429
• /
• 2006

본 연구에서는 국내 연약지반의 신뢰성 있는 강성지수 추정을 위하여 인공신경망기법을 적용하였다. 실내시험을 통한 물성치결과들을 통하여 인공신경망을 위한 입력자료를 확보한 뒤 이를 이용하여 모델을 학습시킨 후 모델검증을 실시하였다. 개발된 모델의 검증결과 측정값과 예측값의 상관관계가 매우 높게 나타났으며 이를 통하여 수학적 모델 수립이 곤란한 국내 연약지반의 신뢰성 있는 강성지수 추정의 전반적인 고찰의 기초를 확립하였다.

• 한국물환경학회지
• /
• 제29권1호
• /
• pp.19-28
• /
• 2013

Artificial neural networks were used for time series modelling of algal dynamics of whole year and by season at the Paldang dam station (confluence area). The modelling was based on comprehensive weekly water quality data from 1997 to 2004 at the Paldang dam station. The results of validation of seasonal models showed that the timing and magnitude of the observed chlorophyll a concentration was predicted better, compared with the ANN model for whole year. Internal weightings of the inputs in trained neural networks were obtained by sensitivity analysis for identification of the primary driving mechanisms in the system dynamics. pH, COD, TP determined most the dynamics of chlorophyll a, although these inputs were not the real driving variable for algal growth. Short-term prediction models that perform one or two weeks ahead predictions of chlorophyll a concentration were designed for the application of Harmful Algal Alert System in Lake Paldang. Short-term-ahead ANN models showed the possibilities of application of Harmful Algal Alert System after increasing ANN model's performance.

• 대한인간공학회:학술대회논문집
• /
• 대한인간공학회 1993년도 추계학술대회논문집
• /
• pp.124-132
• /
• 1993

This study compared two prediction methods-regression and artificial neural network (ANN) on the visual search performance when monitoring a multi-parameter screen with different occurrence frequencies. Under the highlighting condition for the highest occurrence frequency parameter as a search cue, it was found from the requression analysis that variations of mean search time (MST) could be expained almost by three factors such as the number of parameters, the target occurrence frequency of a highlighted parameter, and the highlighted parameter size. In this study, prediction performance of ANN was evaluated as an alternative to regression method. Backpropagation method which was commonly used as a pattern associator was employed to learn a search behavior of subjects. For the case of increased number of parameters and incresed target occurrence frequency of a highlighted parameter, ANN predicted MST's moreaccurately than the regression method (p<0.000). Only the MST's predicted by ANN did not statistically differ from the true MST's. For the case of increased highlighted parameter size. both methods failed to predict MST's accurately, but the differences from the true MST were smaller when predicted by ANN than by regression model (p=0.0005). This study shows that ANN is a good predictor of a visual search performance and can substitute the regression method under certain circumstances.

• Computers and Concrete
• /
• 제17권5호
• /
• pp.613-628
• /
• 2016

In this study, the influence of coarse aggregate size and type on chloride penetration of concrete was investigated, and the grey correlation analysis was applied to find the key influencing factor. Furthermore, the proposed 6-10-1 artificial neural network (ANN) model was constructed, and performed under the MATLAB program. Training, testing and validation of the model stages were performed using 81 experiment data sets. The results show that the aggregate type has less effect on the concrete permeability, compared with the size effect. For concrete with a lower w/b, the coarse aggregate with a larger particle size should be chose, however, for concrete with a higher w/c, the aggregate with a grading of 5-20 mm is preferred, too large or too small aggregates are adverse to concrete chloride diffusivity. A new idea for the optimum selection of aggregate to prepare concrete with a low penetration is provided. Moreover, the ANN model predicted values are compared with actual test results, and the average relative error of prediction is found to be 5.62%. ANN procedure provides guidelines to select appropriate coarse aggregate for required chloride penetration of concrete and will reduce number of trial and error, save cost and time.

• Structural Engineering and Mechanics
• /
• 제37권6호
• /
• pp.575-592
• /
• 2011

For seismic resistant design of critical structures, a dynamic analysis, either response spectrum or time history is frequently required. Owing to the lack of recorded data and the randomness of earthquake ground motion that may be experienced by structure in the future, usually it is difficult to obtain recorded data which fit the requirements (site type, epicenteral distance, etc.) well. Therefore, the artificial seismic records are widely used in seismic designs, verification of seismic capacity and seismic assessment of structures. The purpose of this paper is to develop a numerical method using Artificial Neural Network (ANN) and wavelet packet transform in best basis method which is presented for the decomposition of artificial earthquake records consistent with any arbitrarily specified target response spectra requirements. The ground motion has been modeled as a non-stationary process using wavelet packet. This study shows that the procedure using ANN-based models and wavelet packets in best-basis method are applicable to generate artificial earthquakes compatible with any response spectra. Several numerical examples are given to verify the developed model.

• 조명전기설비학회논문지
• /
• 제20권10호
• /
• pp.47-57
• /
• 2006

본 논문은 ALM-FNN 제어기와 ANN 제어기를 사용하여 SynRM 드라이브의 최대토크 제어를 제시한다. 이 제어기는 인버터의 정격 전류와 전압 제한을 고려하고 전 속도 영역에 적용된다. 각 제어모드를 위하여 최대토크를 위한 최적의 d-축 전류 $^i{_d}$를 결정한다. 제시된 제어 알고리즘은 ALM-FNN 제어기와 ANN 제어기로 SynRM 드라이브 시스템을 제어하는데 적용된다. 최대토크 제어에 의하여 제어된 동작 특성은 실험을 통하여 상세히 설명한다. 또한 본 눈문은 ALM-FNN 제어기와 ANN 제어기 결과분석을 통하여 타당성을 입증한다.

• Advances in concrete construction
• /
• 제15권4호
• /
• pp.269-286
• /
• 2023

In the present study, we aim to utilize the numerical solution frequency results of functionally graded beam under thermal and dynamic loadings to train and test an artificial neural network. In this regard, shear deformable functionally-graded beam structure is considered for obtaining the natural frequency in different conditions of boundary and material grading indices. In this regard, both analytical and numerical solutions based on Navier's approach and differential quadrature method are presented to obtain effects of different parameters on the natural frequency of the structure. Further, the numerical results are utilized to train an artificial neural network (ANN) using AdaGrad optimization algorithm. Finally, the results of the ANN and other solution procedure are presented and comprehensive parametric study is presented to observe effects of geometrical, material and boundary conditions of the free oscillation frequency of the functionally graded beam structure.

• 대한전기학회논문지:시스템및제어부문D
• /
• 제53권10호
• /
• pp.690-696
• /
• 2004

This paper is proposed a hybrid intelligent controller based on the vector controlled surface permanent magnet synchronous motor(SPMSM) drive system. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper is proposed speed control of SPMSM using neural network-fuzzy(NNF) control and speed estimation using artificial neural network(ANN) Controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The error between the desired state variable and the actual one is back-propagated to adjust the rotor speed, so that the actual state variable will coincide with the desired one. The back propagation mechanism is easy to derive and the estimated speed tracks precisely the actual motor speed. This paper is proposed the theoretical analysis as well as the simulation results to verify the effectiveness of the new method.

• 대한토목학회논문집
• /
• 제30권2B호
• /
• pp.159-167
• /
• 2010

본 논문의 목적은 지상 우량계의 강우자료와 레이더 강우자료를 신경망(Artificial Neural Network, ANN)의 학습을 통하여 지상 우량계가 가지는 지점 강우의 정확성과 강우레이더가 가지는 강우의 공간정보의 장점을 최대화하여 강우자료를 보정하는 것이다. 따라서 본 논문에서는 2가지 신경망 모형(Model I, Model II)을 사용하였다. Model II의 입력층은 지체시간을 고려한 4개의 레이더 강우로 구성하였고 Model I의 입력층은 4개의 레이더 강우와 지상우량계 강우로 구성하여 안성천 유역의 레이더 강우를 보정하였다. 3가지 강우사상 중에서 총 2개의 사상을 신경망 모형 I, II에 입력하여 최적 매개변수인 연결강도를 추정한 후에 나머지 사상을 사용하여 검증을 실행하였다. 신경망에 의해 보정된 레이더 강우 자료(ANN 강우)와 보정을 하지 않은 레이더 강우를 준분포형 모형인 Modclark 모형에 입력하여 유출을 모의하고, 실제 유출 수문곡선과 비교 분석 하였다. 분석 결과, 보정을 하지 않은 레이더 강우에 의한 유출량은 전체적으로 실제 수문곡선보다 과소 추정되었다. 반면에 ANN 강우의 유출량은 관측유출량과 비교하였을 때 첨두시간과 첨두유량을 가장 근접하게 모의하는 결과를 나타내었다.

• Steel and Composite Structures
• /
• 제47권2호
• /
• pp.167-184
• /
• 2023

In this study, a new recentering friction device (RFD) to retrofit steel moment frame structures is introduced. The device provides both self-centering and energy dissipation capabilities for the retrofitted structure. A hybrid performance-based seismic design procedure considering multiple limit states is proposed for designing the device and the retrofitted structure. The design of the RFD is achieved by modifying the conventional performance-based seismic design (PBSD) procedure using computational intelligence techniques, namely, genetic algorithm (GA) and artificial neural network (ANN). Numerous nonlinear time-history response analyses (NLTHAs) are conducted on multi-degree of freedom (MDOF) and single-degree of freedom (SDOF) systems to train and validate the ANN to achieve high prediction accuracy. The proposed procedure and the new RFD are assessed using 2D and 3D models globally and locally. Globally, the effectiveness of the proposed device is assessed by conducting NLTHAs to check the maximum inter-story drift ratio (MIDR). Seismic fragilities of the retrofitted models are investigated by constructing fragility curves of the models for different limit states. After that, seismic life cycle cost (LCC) is estimated for the models with and without the retrofit. Locally, the stress concentration at the contact point of the RFD and the existing steel frame is checked being within acceptable limits using finite element modeling (FEM). The RFD showed its effectiveness in minimizing MIDR and eliminating residual drift for low to mid-rise steel frames models tested. GA and ANN proved to be crucial integrated parts in the modified PBSD to achieve the required seismic performance at different limit states with reasonable computational cost. ANN showed a very high prediction accuracy for transformation between MDOF and SDOF systems. Also, the proposed retrofit showed its efficiency in enhancing the seismic fragility and reducing the LCC significantly compared to the un-retrofitted models.