• Journal of Korea Multimedia Society
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• v.6 no.1
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• pp.48-57
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• 2003

This paper describes an approach for classifying myoelectric patterns using a multilayer perceptrons (MLP's) with genetic algorithm and hidden Markov models (HMM's) hybrid classifier. Genetic Algorithms play a role of selecting Multilayer Perceptron's optimized initial connection weights by its typical global search. The dynamic aspects of EMG are important for tasks such as continuous prosthetic control or various time length EMG signal recognition, which have not been successfully mastered by the most neural approaches. It is known that the hidden Markov model (HMM) is suitable for modeling temporal patterns. In contrast, the multilayer feedforward networks are suitable for static patterns. And, a lot of investigators have shown that the HMM's to be an excellent tool for handling the dynamical problems. Considering these facts, we suggest the combination of ANN and HMM algorithms that might lead to further improved EMG recognition systems.

• Steel and Composite Structures
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• v.45 no.4
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• pp.501-511
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• 2022

In this study, a two-dimensional model of the contact problem has been examined using the finite element method (FEM) based software ANSYS and based on the multilayer perceptron (MLP), an artificial neural network (ANN). For this purpose, a functionally graded (FG) half-infinite layer (HIL) with a crack pressed by means of two rigid blocks has been solved using FEM. Mass forces and friction are neglected in the solution. Since the problem is analyzed for the plane state, the thickness along the z-axis direction is taken as a unit. To check the accuracy of the contact problem model the results are compared with a study in the literature. In addition, ANSYS and MLP results are compared using Root Mean Square Error (RMSE) and coefficient of determination (R2), and good agreement is found. Numerical solutions are made by considering different values of external load, the width of blocks, crack depth, and material properties. The stresses on the contact surfaces between the blocks and the FG HIL are examined for these values, and the results are presented. Consequently, it is concluded that the considered non-dimensional quantities have a noteworthy influence on the contact stress distributions, and also, FEM and ANN can be efficient alternative methods to time-consuming analytical solutions if used correctly.

• KIEE International Transactions on Power Engineering
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• v.5A no.4
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• pp.350-356
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• 2005

Earth equipments are essential to protect humans and other types of equipment from abnormal conditions. Earth resistance and potential must be restricted within a low value. An estimation algorithm of earth parameters and equivalent resistivity is introduced to calculate reliable earth resistance in this research. The proposed algorithm is based on the relationship between apparent resistances and earth parameters. The proposed algorithm, which approximates the non-linear characteristics of earth by using the Artificial Neural Network (ANN), estimates the earth parameters and equivalent resistivity. The effectiveness of the proposed method is verified with case studies.

• Journal of Ocean Engineering and Technology
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• v.35 no.4
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• pp.287-295
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• 2021

This paper describes a recurrent neural network (RNN) for the fault classification of a blade pitch system of a spar-type floating wind turbine. An artificial neural network (ANN) can effectively recognize multiple faults of a system and build a training model with training data for decision-making. The ANN comprises an encoder and a decoder. The encoder uses a gated recurrent unit, which is a recurrent neural network, for dimensionality reduction of the input data. The decoder uses a multilayer perceptron (MLP) for diagnosis decision-making. To create data, we use a wind turbine simulator that enables fully coupled nonlinear time-domain numerical simulations of offshore wind turbines considering six fault types including biases and fixed outputs in pitch sensors and excessive friction, slit lock, incorrect voltage, and short circuits in actuators. The input data are time-series data collected by two sensors and two control inputs under the condition that of one fault of the six types occurs. A gated recurrent unit (GRU) that is one of the RNNs classifies the suggested faults of the blade pitch system. The performance of fault classification based on the gate recurrent unit is evaluated by a test procedure, and the results indicate that the proposed scheme works effectively. The proposed ANN shows a 1.4% improvement in its performance compared to an MLP-based approach.

• Computers and Concrete
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• v.25 no.6
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• pp.551-563
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• 2020

This paper investigates the artificial neural network (ANN) to predict the dimensionless parameters for the maximum contact pressures and contact areas of a contact problem. Firstly, the problem is formulated and solved theoretically by using Theory of Elasticity and Integral Transform Technique. Secondly, the contact problem has been extended based on the ANN. The multilayer perceptron (MLP) with three-layer was used to calculate the contact distances. External load, distance between the two quarter planes, layer heights and material properties were created by giving examples of different values were used at the training and test stages of ANN. Program code was rewritten in C++. Different types of network structures were used in the training process. The accuracy of the trained neural networks for the case was tested using 173 new data which were generated via theoretical solutions so as to determine the best network model. As a result, minimum deviation value (difference between theoretical and C++ ANN results) of was obtained for the network model. Theoretical results were compared with artificial neural network results and well agreements between them were achieved.

• Steel and Composite Structures
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• v.50 no.1
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• pp.77-87
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• 2024

In this study, the two-dimensional crack problem was investigated by using the finite element method (FEM)-based ANSYS package program and the artificial neural network (ANN)-based multilayer perceptron (MLP) method. For this purpose, a half-infinite functionally graded (FG) layer with a crack pressed through two rigid blocks was analyzed using FEM and ANN. Mass forces and friction were neglected in the solution. To control the validity of the crack problem model exercised, the acquired results were compared with a study in the literature. In addition, FEM and ANN results were checked using Root Mean Square Error (RMSE) and coefficient of determination (R²), and a well agreement was found. Numerical solutions were made considering different geometric parameters and material properties. The stress intensity factor (SIF) was examined for these values, and the results were presented. Consequently, it is concluded that the considered non-dimensional quantities have a noteworthy influence on the SIF. Also FEM and ANN can be logical alternative methods to time-consuming analytical solutions if used correctly.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.86-86
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• 2016

본 연구는 가뭄 예측에 대한 오차를 알고리즘과 결합하여 다층 퍼셉트론 (Multi-layer Perceptron, MLP) 네트워크 구조를 인공신경망 모형에 적용하고, 표준강수지수(Standard Precipitation Index, SPI)를 입 력 및 출력 변수로 구성하여 가뭄예측을 시도하였다. 예측모델을 평가하기 위해 기상청 산하의 59개 관측소에 대한 1980년부터 2015년까지의 기상자료를 적용하였으며, 수립된 자료를 활용하여 한반도 전역의 가뭄에 대한 시공간적인 분석을 수행하였다. 단기가뭄 예측성능을 평가하기 위해 2000년에서 2015년까지 16년간의 모의결과를 ROC 분석을 통하여 시공간적 단기가뭄 예측성능을 평가하고 혼동행렬(Conversion Matrix) 구성에 대한 조건적 확률의 다각적 검토를 통해 모델 예측에 대한 정확성(Accuracy), 신뢰성(Precision) 등 다양한 예측성능에 대한 평가를 수행하고 2016년 가뭄전망을 제시하고자 한다.

• Journal of Korean Society for Quality Management
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• v.50 no.4
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• pp.701-716
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• 2022

Purpose: This study aims to predict the dry cargo transportation market economy. The subject of this study is the BDI (Baltic Dry Index) time-series, an index representing the dry cargo transport market. Methods: In order to increase the accuracy of the BDI time-series, we have pre-processed the original time-series via time-series decomposition and data augmentation techniques and have used them for ANN learning. The ANN algorithms used are Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) to compare and analyze the case of learning and predicting by applying time-series decomposition and data augmentation techniques. The forecast period aims to make short-term predictions at the time of t+1. The period to be studied is from '22. 01. 07 to '22. 08. 26. Results: Only for the case of the MAPE (Mean Absolute Percentage Error) indicator, all ANN models used in the research has resulted in higher accuracy (1.422% on average) in multivariate prediction. Although it is not a remarkable improvement in prediction accuracy compared to uni-variate prediction results, it can be said that the improvement in ANN prediction performance has been achieved by utilizing time-series decomposition and data augmentation techniques that were significant and targeted throughout this study. Conclusion: Nevertheless, due to the nature of ANN, additional performance improvements can be expected according to the adjustment of the hyper-parameter. Therefore, it is necessary to try various applications of multiple learning algorithms and ANN optimization techniques. Such an approach would help solve problems with a small number of available data, such as the rapidly changing business environment or the current shipping market.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.62 no.4
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• pp.186-191
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• 2013

Earth equipments are essential to protect ITS facilities from abnormal situation. In this research, an estimation algorithm of earth parameters and equivalent resistivity is introduced. Traditional estimation methods can be divided into graphic method and numerical method. The result of graphic method is varied by the ability of expert or repeated calculation and it is hard to estimate the parameters precisely. The numerical method requires special techniques such as optimizing theory, and numerous calculations, whose results can be varied with initial values. The proposed algorithm is based on the relationship between apparent resistances and earth parameters and approximates the nonlinear characteristics of earth using ANN(artificial neural networks). The effectiveness of proposed method is verified in case studies.

• Structural Monitoring and Maintenance
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• v.9 no.3
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• pp.221-235
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• 2022

Among nondestructive damage detection methods, impedance-based methods have been recognized as an effective technique for damage identification in many kinds of structures. This paper proposes a method to detect cracks in metal structures by combining electro-mechanical impedance (EMI) responses and artificial neural networks (ANN). Firstly, the theories of EMI responses and impedance-based damage detection methods are described. Secondly, the reliability of numerical simulations for impedance responses is demonstrated by comparing to pre-published results for an aluminum beam. Thirdly, the proposed method is used to detect cracks in the beam. The RMSD (root mean square deviation) index is used to alarm the occurrence of the cracks, and the multi-layer perceptron (MLP) ANN is employed to identify the location and size of the cracks. The selection of the effective frequency range is also investigated. The analysis results reveal that the proposed method accurately detects the cracks' occurrence, location, and size in metal structures.