• Korean Journal of Remote Sensing
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• v.28 no.3
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• pp.337-346
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• 2012

In the view from most application system developers and users, cloud computing becomes popular in recent years and is still evolving. But in fact it is not easy to reach at the level of actual operations. Despite, it is known that the cloud in the practical stage provides a new pattern for deploying a geo-spatial application. However, domestically geo-spatial application implementation and operation based on this concept or scheme is on the beginning stage. It is the motivation of this works. Although this study is an introductory level, a simple and practical processed result was presented. This study was carried out on Amazon web services platform, as infrastructure as a service in the geo-spatial areas. Under this environment, cloud instance, a web and mobile system being previously implemented in the multi-layered structure for geo-spatial open sources of database and application server, was generated. Judging from this example, it is highly possible that cloud services with the functions of geo-processing service and large volume data handling are the crucial point, leading a new business model for civilian remote sensing application and geo-spatial enterprise industry. The further works to extend geo-spatial applications in cloud computing paradigm are left.

• International Journal of Concrete Structures and Materials
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• v.1 no.1
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• pp.63-73
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• 2007

Optimum multi-layered feed-forward neural network (NN) models using a resilient back-propagation algorithm and early stopping technique are built to predict the shear capacity of reinforced concrete deep and slender beams. The input layer neurons represent geometrical and material properties of reinforced concrete beams and the output layer produces the beam shear capacity. Training, validation and testing of the developed neural network have been achieved using 50%, 25%, and 25%, respectively, of a comprehensive database compiled from 631 deep and 549 slender beam specimens. The predictions obtained from the developed neural network models are in much better agreement with test results than those determined from shear provisions of different codes, such as KBCS, ACI 318-05, and EC2. The mean and standard deviation of the ratio between predicted using the neural network models and measured shear capacities are 1.02 and 0.18, respectively, for deep beams, and 1.04 and 0.17, respectively, for slender beams. In addition, the influence of different parameters on the shear capacity of reinforced concrete beams predicted by the developed neural network shows consistent agreement with those experimentally observed.

• Proceedings of the Korean Geotechical Society Conference
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• 2009.03a
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• pp.456-463
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• 2009

The preconsolidation pressure has been commonly determined by oedometer test. However, it can also be determined by in-situ test, such as piezocone test with theoretical and(or) empirical correlations. Recently, Neural Network(NN) theory was applied and some models were proposed to estimate the preconsolidation pressure or OCR. However, since the optimization process of synaptic weights of NN model is dependent on the initial synaptic weights, NN models which are trained with different initial weights can't avoid the variability on prediction result for new database even though they have same structure and use same transfer function. In this study, Committee Neural Network(CNN) model is proposed to improve the initial weight dependency of multi-layered neural network model on the prediction of preconsolidation pressure of soft clay from piezocone test result. It was found that even though the NN model has the optimized structure for given training data set, it still has the initial weight dependency, while the proposed CNN model can improve the initial weight dependency of the NN model and provide a consistent and precise inference result than existing NN models.

• Journal of the Korean Institute of Intelligent Systems
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• v.24 no.1
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• pp.71-77
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• 2014

In this paper, we propose an improved numeric character recognition method which can recognize numeric characters well under low-illuminated and shade-illuminated environment. The LN(Local Normalization) preprocessing method is used in order to enhance low-illuminated and shade-illuminated image quality. The reading area is detected using line segment information extracted from the illumination-normalized meter images, and then the three-phase procedures are performed for segmentation of numeric characters in the reading area. Finally, an efficient hybrid classifier is used to classify the segmented numeric characters. The proposed numeric character classifier is a combination of multi-layered feedforward neural network and template matching module. Robust heuristic rules are applied to classify the numeric characters. Experiments using meter image database were conducted. Meter image database was made using various kinds of meters under low-illuminated and shade-illuminated environment. The experimental results indicates the superiority of the proposed numeric character recognition method.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.3C
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• pp.115-125
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• 2009

The preconsolidation pressure has been commonly determined by oedometer test. However, it can also be determined by insitu test, such as piezocone test with theoretical and(or) empirical correlations. Recently, Neural Network (NN) theory was applied and some models were proposed to estimate the preconsolidation pressure or OCR. It was already found that NN model can come over the site dependency and prediction accuracy is greatly improved when compared with present theoretical and empirical models. However, since the optimization process of synaptic weights of NN model is dependent on the initial synaptic weights, NN models which are trained with different initial weights can't avoid the variability on prediction result for new database even though they have same structure and use same transfer function. In this study, Committee Neural Network (CNN) model is proposed to improve the initial weight dependency of multi-layered neural network model on the prediction of preconsolidation pressure of soft clay from piezocone test result. Prediction results of CNN model are compared with those of conventional empirical and theoretical models and multi-layered neural network model, which has the optimized structure. It was found that even though the NN model has the optimized structure for given training data set, it still has the initial weight dependency, while the proposed CNN model can improve the initial weight dependency of the NN model and provide a consistent and precise inference result than existing NN models.

• Structural Engineering and Mechanics
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• v.57 no.4
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• pp.657-680
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• 2016

Reinforced concrete (RC) deep beams are structural members that predominantly fail in shear. Therefore, determining the shear strength of these types of beams is very important. The strut-and-tie method is commonly used to design deep beams, and this method has been adopted in many building codes (ACI318-14, Eurocode 2-2004, CSA A23.3-2004). In this study, the efficiency of artificial neural networks (ANNs) in predicting the shear strength of RC deep beams is investigated as a different approach to the strut-and-tie method. An ANN model was developed using experimental data for 214 normal and high-strength concrete deep beams from an existing literature database. Seven different input parameters affecting the shear strength of the RC deep beams were selected to create the ANN structure. Each parameter was arranged as an input vector and a corresponding output vector that includes the shear strength of the RC deep beam. The ANN model was trained and tested using a multi-layered back-propagation method. The most convenient ANN algorithm was determined as trainGDX. Additionally, the results in the existing literature and the accuracy of the strut-and-tie model in ACI318-14 in predicting the shear strength of the RC deep beams were investigated using the same test data. The study shows that the ANN model provides acceptable predictions of the ultimate shear strength of RC deep beams (maximum $R^2{\approx}0.97$). Additionally, the ANN model is shown to provide more accurate predictions of the shear capacity than all the other computed methods in this study. The ACI318-14-STM method was very conservative, as expected. Moreover, the study shows that the proposed ANN model predicts the shear strengths of RC deep beams better than does the strut-and-tie model approaches.

• The Journal of Society for e-Business Studies
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• v.15 no.3
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• pp.85-98
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• 2010

The prevailing infrastructure of ubiquitous computing paradigm on the one hand making significant development for integrating technology in the daily life but on the other hand raising concerns for privacy and confidentiality. This research presents a new privacy-preserving spatio-temporal query processing technique, in which location based services (LBS) can be serviced without revealing specific locations of private users. Existing location cloaking techniques are based on a grid-based structures such as a Quad-tree and a multi-layered grid. Grid-based approaches can suffer a deterioration of the quality in query results since they are based on pre-defined size of grids which cannot be adapted for variations of data distributions. Instead of using a grid, we propose a location-cloaking algorithm which uses the R-tree, a widely adopted spatio-temporal index structure. The proposed algorithm uses the MBRs of leaf nodes as the cloaked locations of users, since each leaf node guarantees having not less than a certain number of objects. Experimental results show the superiority of the proposed method.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.5D
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• pp.797-804
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• 2006

A development of regression model for asphalt concrete pavements using Falling Weight Deflectometer deflections is presented in this paper. A backcalculation program based on layered elastic theory was used to generate the synthetic modulus database, which was used to generate 95% confidence intervals of modulus in each layer. Using deflection basins of FWD data used in developing this procedure were collected from Pavement Management System in flexible pavements. Assumptions of back-calculation are that one is 3 layered flexible pavement structure and another is depth to bedrock is finite. It is found that difference of between 95% confidence intervals and modulus ranges of other papers does not exist. So, the data of 95% confidence intervals in each layer was used to develop multiple regression models. Multiple regression equations of each layer were established by SPSS, package of Statics analysis. These models were proved by regression diagnostics, which include case analysis, multi-collinearity analysis, influence diagnostics and analysis of variance. And these models have higher degree of coefficient of determination than 0.75. So this models were applied to predict modulus of domestic asphalt concrete pavement at FWD field test.