• East Asian mathematical journal
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• v.38 no.1
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• pp.95-105
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• 2022

In this paper, we consider a quadratic matrix equation Q(X) = AX² + BX + C = 0 where A, B, C ∈ ℝ^n×n. A new approach is proposed to find solutions of Q(X), using the novel structure of the information processing system. We also present some numerical experimetns with Artificial Neural Network.

• Structural Engineering and Mechanics
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• v.85 no.5
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• pp.635-644
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• 2023

The design of honeycomb sandwich structures is often challenging because these structures can be tailored from a variety of possible cores and face sheets configurations, therefore, the design of sandwich structures is characterized as a time-consuming and complex task. A data-driven computational approach that integrates the analytical method and Artificial Neural Network (ANN) is developed by the authors to rapidly predict the design of sandwich structures for a targeted maximum structural deflection. The elaborated ANN reverse design approach is applied to obtain the thickness of the sandwich core, the thickness of the laminated face sheets, and safety factors for composite sandwich structure. The required data for building ANN model were obtained using the governing equations of sandwich components in conjunction with the Monte Carlo Method. Then, the functional relationship between the input and output features was created using the neural network Backpropagation (BP) algorithm. The input variables were the dimensions of the sandwich structure, the applied load, the core density, and the maximum deflection, which was the reverse input given by the designer. The outstanding performance of reverse ANN model revealed through a low value of mean square error (MSE) together with the coefficient of determination (R2) close to the unity. Furthermore, the output of the model was in good agreement with the analytical solution with a maximum error 4.7%. The combination of reverse concept and ANN may provide a potentially novel approach in designing of sandwich structures. The main added value of this study is the elaboration of a reverse ANN model, which provides a low computational technique as well as savestime in the design or redesign of sandwich structures compared to analytical and finite element approaches.

• Journal of the Korean Geotechnical Society
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• v.21 no.9
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• pp.25-34
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• 2005

This paper proposes a selection methodology composed of neural network (NN) and genetic algorithm (GA) to design optimal NN structure. We combine the characteristics of GA and NN to reduce the computational complexity of artificial intelligence applications and increase the precision of NN' prediction in the design of NN structure. Genetic selection approach of design parameters of NN is introduced to obtain optimal NN structure. Analyzed results for geotechnical problems are given to evaluate the performance of the proposed hybrid methodology.

• Journal of the Korean Geotechnical Society
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• v.36 no.8
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• pp.7-15
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• 2020

Constructing the maximum displacement and shear force database for the seismic performance of building with soil-structure interaction under varied earthquake scenarios and geotechnical conditions is critical in developing the neural network-based prediction models. However, using the available 3D FEM-based computer simulation techniques causes high computation costs in developing the database. This study introduces the framework of developing the artificial neural network (ANN) model to predict the seismic performance of building at given Poisson's ratio and shear wave velocity of soil. The simple Single-Degree-Of-Freedom system was used to develop the database and the performance of the developed neural network model is discussed through the evaluated coefficient of determination (R²). In addition, ANN models were developed for 90~100% percentile of the database to assess the accuracy of the developed ANN models in each percentile.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 1999.10a
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• pp.365-373
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• 1999

Recently, numerous studies have demonstrated that artificial intelligence such as neural networks can be an alternative methodology for classification problems to which traditional statistical methods have long been applied. In building neural network model, the selection of independent and dependent variables should be approached with great care and should be treated as a model construction process. Irrespective of the efficiency of a learning procedure in terms of convergence, generalization and stability, the ultimate performance of the estimator will depend on the relevance of the selected input variables and the quality of the data used. Approaches developed in statistical methods such as correlation analysis and stepwise selection method are often very useful. These methods, however, may not be the optimal ones for the development of neural network models. In this paper, we propose a genetic algorithms approach to find an optimal or near optimal input variables for neural network modeling. The proposed approach is demonstrated by applications to bankruptcy prediction modeling. Our experimental results show that this approach increases overall classification accuracy rate significantly.

• The KIPS Transactions:PartD
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• v.16D no.1
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• pp.139-144
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• 2009

Neural network is a very exciting and generic framework to develop almost all ranges of machine learning technologies and its potential is far beyond its current capabilities. Among other characteristics, neural network acts as associative memory obtained from the values structurally stored in synaptic inherent structure. Due to innate complexity of neural networks system, in its practical implementation and maintenance, multifaceted problems are known to be unavoidable. In this paper, we present design and implementation details of GUI software which can be valuable tool to maintain and develop neural networks. It has capability of displaying every state of synaptic weights with network nodal relation in each learning step.

• Journal of the Korean Society of Safety
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• v.37 no.1
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• pp.41-48
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• 2022

Bridge engineering documents have essential contents that must be referenced continuously throughout a structure's entire life cycle, but research related to the quality of the contents is still lacking. XML schema matching is an excellent technique to improve the quality of stored data; however, it takes excessive computing time when applied to documents with many contents and a deep hierarchical structure, such as bridge engineering documents. Moreover, it requires a manual parametric study for matching elements' weight factors, maintaining a high matching accuracy. This study proposes an efficient weight-factor determination method based on an artificial neural network (ANN) model using the simplified XML schema-matching method proposed in a previous research to reduce the computing time. The ANN model was generated and verified using 580 data of document properties, weight factors, and matching accuracy. The proposed ANN-based schema-matching method showed superiority in terms of accuracy and efficiency compared with the previous study on XML schema matching for bridge engineering documents.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.101-104
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• 2008

In this study, rainfall adjust and forecasting using artificial neural network(ANN) which includes a correlation coefficient is application in Seoul region. It analyzed one-hour rainfall data which has been reported in 25 region in seoul during from 2000 to 2006 at rainfall observatory by AWS. The ANN learning algorithm apply for input data that each region using cross-correlation will use the highest correlation coefficient region. In addition, rainfall adjust analyzed the minimum error based on correlation coefficient and determination coefficient related to the input region. ANN model used back-propagation algorithm for learning algorithm. In case of the back-propagation algorithm, many attempts and efforts are required to find the optimum neural network structure as applied model. This is calculated similar to the observed rainfall that the correlation coefficient was 0.98 in missing rainfall adjust at 10 region. As a result, ANN model has been for suitable for rainfall adjust. It is considered that the result will be more accurate when it includes climate data affecting rainfall.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1998.04a
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• pp.216-225
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• 1998

Although artificial neural network based on backpropagation algorithm is an excellent system simulator, it has still unsolved problems of its structure-decision and learning method. That is, we cannot find a general approach to decide the structure of the neural network and cannot train it satisfactorily because of the local optimum point which it frequently falls into. In addition, although there are many successful applications using backpropagation learning algorithm, there are few efforts to improve the learning algorithm itself. In this study, we suggest a general way to construct the hidden layer of the neural network using binary genetic algorithm and also propose the various learning methods by which the global minimum value of the teaming error can be obtained. A XOR problem and line heating problems are investigated as examples.

• Journal of the Korea Society of Computer and Information
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• v.24 no.1
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• pp.33-39
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• 2019

In this paper, we propose deep convolutional neural network(CNN) with bottleneck structure which improves the performance of earthquake classification. In order to address all possible forms of earthquakes including micro-earthquakes and artificial-earthquakes as well as large earthquakes, we need a representation and classifier that can effectively discriminate seismic waveforms in adverse conditions. In particular, to robustly classify seismic waveforms even in low snr, a deep CNN with 1x1 convolution bottleneck structure is proposed in raw seismic waveforms. The representative experimental results show that the proposed method is effective for noisy seismic waveforms and outperforms the previous state-of-the art methods on domestic earthquake database.