• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.2
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• pp.329-338
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• 2021

This study predicts the amount of wind power generation for rational operation plan of wind power generation and capacity calculation of ESS. For forecasting, we present a method of predicting wind power generation by combining a physical approach and a statistical approach. The factors of wind power generation are analyzed and variables are selected. By collecting historical data of the selected variables, the amount of wind power generation is predicted using deep learning. The model used is a hybrid model that combines a bidirectional long short term memory (LSTM) and a convolution neural network (CNN) algorithm. To compare the prediction performance, this model is compared with the model and the error which consist of the MLP(:Multi Layer Perceptron) algorithm, The results is presented to evaluate the prediction performance.

• 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.

• Journal of Biosystems Engineering
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• v.43 no.4
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• pp.352-361
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• 2018

Purpose: The objective of this study was to develop a multilayer perceptron (MLP) model to estimate solar radiation using a solar module. Methods: Data for the short-circuit current of a solar module and other environmental parameters were collected for a year. For MLP learning, 14,400 combinations of input variables, learning rates, activation functions, numbers of layers, and numbers of neurons were trained. The best MLP model employed the batch backpropagation algorithm with all input variables and two hidden layers. Results: The root-mean-squared error (RMSE) of each learning cycle and its average over three repetitions were calculated. The average RMSE of the best artificial neural network model was $48.13W{\cdot}m^{-2}$. This result was better than that obtained for the regression model, for which the RMSE was $66.67W{\cdot}m^{-2}$. Conclusions: It is possible to utilize a solar module as a power source and a sensor to measure solar radiation for an agricultural sensor node.

• Smart Structures and Systems
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• v.30 no.2
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• pp.195-207
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• 2022

Computational drawbacks associated with regular predictive models have motivated engineers to use hybrid techniques in dealing with complex engineering tasks like simulating the compressive strength of concrete (CSC). This study evaluates the efficiency of tree potential metaheuristic schemes, namely shuffled complex evolution (SCE), multi-verse optimizer (MVO), and beetle antennae search (BAS) for optimizing the performance of a multi-layer perceptron (MLP) system. The models are fed by the information of 1030 concrete specimens (where the amount of cement, blast furnace slag (BFS), fly ash (FA1), water, superplasticizer (SP), coarse aggregate (CA), and fine aggregate (FA2) are taken as independent factors). The results of the ensembles are compared to unreinforced MLP to examine improvements resulted from the incorporation of the SCE, MVO, and BAS. It was shown that these algorithms can considerably enhance the training and prediction accuracy of the MLP. Overall, the proposed models are capable of presenting an early, inexpensive, and reliable prediction of the CSC. Due to the higher accuracy of the BAS-based model, a predictive formula is extracted from this algorithm.

• Proceedings of the IEEK Conference
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• 2002.07c
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• pp.1772-1775
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• 2002

In this paper, we propose a Hangul character recognition method in which new letter components as recognition units are introduced and the MLP (multilayer perceptrons) neural networks are employed for two-step recognition of Hangul. To recognize Hangul character, we divide it into two or three recognition units and extract the direction angle features of them to be fed to the corresponding neural network recognizers. The recognition results of neural network recognizers are combined by another neural network. The experiments were conducted on the Hangul characters from real letter envelopes which are collected in the mail centers in Korea and the results showed that our method performs better than the conventional one.

• Proceedings of the KIEE Conference
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• 2007.07a
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• pp.1422-1423
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• 2007

This study deals with various neural network algorithms for the on-site partial discharge pattern recognition. For the purpose, the pattern recognition has been carried out on partial discharge data for the typical artificial defect using 9 different neural network models. In order to enhance on-site applicability, artificial defects were installed in the insulation joint box of extra-high voltage xLPE cables and partial discharges were measured by use of the metal foil sensor and a HFCT as a sensor. As the result, it is found out that the accuracy of pattern recognition could be enhanced through the application of the Sigmoid function, the Momentum algorithm and the Genetic algorism on the artificial neural networks. Although Multilayer Perceptron (MLP) algorism showed the best result among 9 neural network algorisms, it is thought that more researches on others would be needed in consideration of on-site application.

• Journal of the Korea Convergence Society
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• v.10 no.10
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• pp.7-13
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• 2019

The natural gas (NG), mostly methane leaks into the air, it is a big problem for the climate. detected NG leaks under U.S. city streets and collected data. In this paper, we introduced a Deep Neural Network (DNN) classification of prediction for a level of NS leak. The proposed method is OrdinalEncoder(OE) based K-means clustering and Multilayer Perceptron(MLP) for predicting NG leak. The 15 features are the input neurons and the using backpropagation. In this paper, we propose the OE method for labeling target data using k-means clustering and compared normalization methods performance for NG leak prediction. There five normalization methods used. We have shown that our proposed OE based MLP method is accuracy 97.7%, F1-score 96.4%, which is relatively higher than the other methods. The system has implemented SPSS and Python, including its performance, is tested on real open data.

• Journal of the Institute of Convergence Signal Processing
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• v.13 no.3
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• pp.136-141
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• 2012

One of the main reasons for serious road accidents is driving while drowsy. For this reason, drowsiness detection and warning system for drivers has recently become a very important issue. Monitoring physiological signals provides the possibility of detecting features of drowsiness and fatigue of drivers. One of the effective signals is to measure electroencephalogram (EEG) signals and electrooculogram (EOG) signals. The aim of this study is to extract drowsiness-related features from a set of EEG signals and to classify the features into three states: alertness, drowsiness, sleepiness. This paper proposes a neural-network-based drowsiness detection system using Linear Predictive Coding (LPC) coefficients as feature vectors and Multi-Layer Perceptron (MLP) as a classifier. Samples of EEG data from each predefined state were used to train the MLP program by using the proposed feature extraction algorithms. The trained MLP program was tested on unclassified EEG data and subsequently reviewed according to manual classification. The classification rate of the proposed system is over 96.5% for only very small number of samples (250ms, 64 samples). Therefore, it can be applied to real driving incident situation that can occur for a split second.

• Steel and Composite Structures
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• v.44 no.2
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• pp.241-254
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• 2022

The cables in a cable-stayed bridge are critical load-carrying parts. The potential damage to cables should be identified early to prevent disasters. In this study, an efficient deep learning model is proposed for the damage identification of cables using both a multi-layer perceptron (MLP) and a graph neural network (GNN). Datasets are first generated using the practical advanced analysis program (PAAP), which is a robust program for modeling and analyzing bridge structures with low computational costs. The model based on the MLP and GNN can capture complex nonlinear correlations between the vibration characteristics in the input data and the cable system damage in the output data. Multiple hidden layers with an activation function are used in the MLP to expand the original input vector of the limited measurement data to obtain a complete output data vector that preserves sufficient information for constructing the graph in the GNN. Using the gated recurrent unit and set2set model, the GNN maps the formed graph feature to the output cable damage through several updating times and provides the damage results to both the classification and regression outputs. The model is fine-tuned with the original input data using Adam optimization for the final objective function. A case study of an actual cable-stayed bridge was considered to evaluate the model performance. The results demonstrate that the proposed model provides high accuracy (over 90%) in classification and satisfactory correlation coefficients (over 0.98) in regression and is a robust approach to obtain effective identification results with a limited quantity of input data.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.4
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• pp.35-42
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• 2024

In this study, we propose a method for real-time recognition and analysis of dog behavior using a motion sensor and deep learning techonology. The existing home CCTV (Closed-Circuit Television) that recognizes dog behavior has privacy and security issues, so there is a need for new technologies to overcome them. In this paper, we propose a system that can analyze and care for a dog's behavior based on the data measured by the motion sensor. The study compares the MLP (Multi-Layer Perceptron) and CNN (Convolutional Neural Network) models to find the optimal model for dog behavior analysis, and the final model, which has an accuracy of about 82.19%, is selected. The model is lightened to confirm its potential for use in embedded environments.