• Journal of the Korea Society for Simulation
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• v.17 no.4
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• pp.229-239
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• 2008

A Distributed File System (DFS) provides a mechanism in which a file can be stored across several physical computer nodes ensuring replication transparency and failure transparency. Applications that process large volumes of data (such as, search engines, grid computing applications, data mining applications, etc.) require a backend infrastructure for storing data. And the distributed file system is the central component for such storing data infrastructure. There have been many projects focused on network computing that have designed and implemented distributed file systems with a variety of architectures and functionalities. In this paper, we describe a complete distributed file system which can be used in large-scale search-oriented systems.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.4
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• pp.862-870
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• 2011

In this paper, we introduce a new topology of Radial Basis Function-based Polynomial Neural Networks (RPNN) that is based on a genetically optimized multi-layer perceptron with Radial Polynomial Neurons (RPNs). This study offers a comprehensive design methodology involving mechanisms of optimization algorithms, especially Fuzzy C-Means (FCM) clustering method and Particle Swarm Optimization (PSO) algorithms. In contrast to the typical architectures encountered in Polynomial Neural Networks (PNNs), our main objective is to develop a design strategy of RPNNs as follows : (a) The architecture of the proposed network consists of Radial Polynomial Neurons (RPNs). In here, the RPN is fully reflective of the structure encountered in numeric data which are granulated with the aid of Fuzzy C-Means (FCM) clustering method. The RPN dwells on the concepts of a collection of radial basis function and the function-based nonlinear (polynomial) processing. (b) The PSO-based design procedure being applied at each layer of RPNN leads to the selection of preferred nodes of the network (RPNs) whose local characteristics (such as the number of input variables, a collection of the specific subset of input variables, the order of the polynomial, and the number of clusters as well as a fuzzification coefficient in the FCM clustering) can be easily adjusted. The performance of the RPNN is quantified through the experimentation where we use a number of modeling benchmarks - NOx emission process data of gas turbine power plant and learning machine data(Automobile Miles Per Gallon Data) already experimented with in fuzzy or neurofuzzy modeling. A comparative analysis reveals that the proposed RPNN exhibits higher accuracy and superb predictive capability in comparison to some previous models available in the literature.

• Journal of Internet Computing and Services
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• v.21 no.4
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• pp.17-23
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• 2020

Biometric information indicating measurement items related to human characteristics has attracted great attention as security technology with high reliability since there is no fear of theft or loss. Among these biometric information, fingerprints are mainly used in fields such as identity verification and identification. If there is a problem such as a wound, wrinkle, or moisture that is difficult to authenticate to the fingerprint image when identifying the identity, the fingerprint expert can identify the problem with the fingerprint directly through the preprocessing step, and apply the image processing algorithm appropriate to the problem. Solve the problem. In this case, by implementing artificial intelligence software that distinguishes fingerprint images with cuts and wrinkles on the fingerprint, it is easy to check whether there are cuts or wrinkles, and by selecting an appropriate algorithm, the fingerprint image can be easily improved. In this study, we developed a total of 17,080 fingerprint databases by acquiring all finger prints of 1,010 students from the Royal University of Cambodia, 600 Sokoto open data sets, and 98 Korean students. In order to determine if there are any injuries or wrinkles in the built database, criteria were established, and the data were validated by experts. The training and test datasets consisted of Cambodian data and Sokoto data, and the ratio was set to 8: 2. The data of 98 Korean students were set up as a validation data set. Using the constructed data set, five CNN-based architectures such as Classic CNN, AlexNet, VGG-16, Resnet50, and Yolo v3 were implemented. A study was conducted to find the model that performed best on the readings. Among the five architectures, ResNet50 showed the best performance with 81.51%.

• Journal of Cadastre & Land InformatiX
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• v.46 no.2
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• pp.211-224
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• 2016

Based on the 'Government 3.0', the government opens the public data and encourages the active use in the private sector. Recently, the spatial and statistical information that is one of the public data is being widely used in the various web business as a high value-added information. In this study, we propose an architecture of high-availability, high-reliability and high-performance open platform which can provide a variety of services such as searching, analysis, data mining, and thematic mapping. In particular, we present two different system architectures for the government and the public services, by reflecting the importance of the information security and the respective utilization in the private and public sectors. We also compared a variety of server architecture configurations such as a clustered server configuration, a cloud-based virtual server configuration, and a CDN server configuration, in order to design a cost- and performance-effective spatial-statistical information open platform.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.6
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• pp.900-911
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• 2015

In this paper, 3D face recognition model is designed by using Polynomial based RBFNN(Radial Basis Function Neural Network) and PNN(Polynomial Neural Network). Also recognition rate is performed by this model. In existing 2D face recognition model, the degradation of recognition rate may occur in external environments such as face features using a brightness of the video. So 3D face recognition is performed by using 3D scanner for improving disadvantage of 2D face recognition. In the preprocessing part, obtained 3D face images for the variation of each pose are changed as front image by using pose compensation. The depth data of face image shape is extracted by using Multiple point signature. And whole area of face depth information is obtained by using the tip of a nose as a reference point. Parameter optimization is carried out with the aid of both ABC(Artificial Bee Colony) and PSO(Particle Swarm Optimization) for effective training and recognition. Experimental data for face recognition is built up by the face images of students and researchers in IC&CI Lab of Suwon University. By using the images of 3D face extracted in IC&CI Lab. the performance of 3D face recognition is evaluated and compared according to two types of models as well as point signature method based on two kinds of depth data information.

• KIISE Transactions on Computing Practices
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• v.23 no.2
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• pp.97-103
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• 2017

Many hardware-based regular expression matching architectures have been proposed for high-performance matching. In particular, regular expression processors, which perform pattern matching by treating the regular expressions as the instruction sequence like general purpose processors, have been proposed. After instruction sequence and data are provided in the instruction memory and data memory, respectively, a regular expression processor can perform pattern matching. To use a regular expression processor as a coprocessor, we need the host interface to transfer the instruction and data into the memory of a regular expression processor. In this paper, we design and implement the host interface between a host and a regular expression processor in the DE1-SoC board and the application program interface. We verify the operations of the host interface and a regular expression processor by executing the application programs which perform pattern matching using the application program interface.

• Journal of Korea Multimedia Society
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• v.15 no.3
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• pp.408-416
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• 2012

As increasing interests for cultural content, 3D restoration about the valuable traditional architectures is in progress, nowadays. Digital restoration is generally performed with using new IT technology and equipments such as 3D scanner. From the view points of making better use of the 3D data, the methodology for 3D restoration leaves much room for improvement. When using 3D scanner, it is possible to get precise 3D data for exterior of the building but huge data size and insufficient information for the wooden intra structure might be obstacles for using them as a source of various digital contents. In traditional wooden structure, the binding rules for corresponding architectural components are important factor for realizing the architectural culture at that times. In this paper, we develop a design tool and architectural components reflecting the wooden intra structure. Moreover, we propose a new 3D restoration method from the design tool, which is good for making contents offering useful information for processes of construction and binding rule in a real time just at a glance.

• Journal of IKEEE
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• v.23 no.4
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• pp.1302-1308
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• 2019

This paper presents an approach for classifying image made by acquired position data from a 2D Lidar sensor with a convolutional neural network (CNN). Lidar sensor has been widely used for unmanned devices owing to advantages in term of data accuracy, robustness against geometry distortion and light variations. A CNN algorithm consists of one or more convolutional and pooling layers and has shown a satisfactory performance for image classification. In this paper, different types of CNN architectures based on training methods, Gradient Descent(GD) and Levenberg-arquardt(LM), are implemented. The LM method has two types based on the frequency of approximating Hessian matrix, one of the factors to update training parameters. Simulation results of the LM algorithms show better classification performance of the image data than that of the GD algorithm. In addition, the LM algorithm with more frequent Hessian matrix approximation shows a smaller error than the other type of LM algorithm.

• The Korean Journal of Petroleum Geology
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• v.7 no.1_2 s.8
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• pp.7-12
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• 1999

A new high-resolution rnagnetotelluric (MT) survey was conducted for pull-apart basin analysis (Cretaceous Eumsung Basin), combined with surface sedimentological results. Two cross-basinal MT profiles represent an asymmetric form with a subbasin in the southeastern part. These basinal architectures are well compatible with paleoflow directions and facies transitions of surface sedimentology. The results also suggest that the basin fills reflect pull-apart opening with rapid subsidence of the central blocks. Combined with the surface sedimentological data on asymmetric lithofacies distribution, facies transitions, and paleoflow directions of the alluvio-lacustrine systems, the MT data help explain basin-fill processes during the basin formation. For petroleum exploration and basin analysis, the high-frequency MT technique can be a useful substitute for the costly burden of a seismic-reflection survey on land.

• Structural Monitoring and Maintenance
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• v.1 no.3
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• pp.249-271
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• 2014

This paper presents a unique study of Structural Health Monitoring (SHM) for the maintenance decision making about a real life movable bridge. The mechanical components of movable bridges are maintained on a scheduled basis. However, it is desired to have a condition-based maintenance by taking advantage of SHM. The main objective is to track the operation of a gearbox and a rack-pinion/open gear assembly, which are critical parts of bascule type movable bridges. Maintenance needs that may lead to major damage to these components needs to be identified and diagnosed timely since an early detection of faults may help avoid unexpected bridge closures or costly repairs. The fault prediction of the gearbox and rack-pinion/open gear is carried out using two types of Artificial Neural Networks (ANNs): 1) Multi-Layer Perceptron Neural Networks (MLP-NNs) and 2) Fuzzy Neural Networks (FNNs). Monitoring data is collected during regular opening and closing of the bridge as well as during artificially induced reversible damage conditions. Several statistical parameters are extracted from the time-domain vibration signals as characteristic features to be fed to the ANNs for constructing the MLP-NNs and FNNs independently. The required training and testing sets are obtained by processing the acceleration data for both damaged and undamaged condition of the aforementioned mechanical components. The performances of the developed ANNs are first evaluated using unseen test sets. Second, the selected networks are used for long-term condition evaluation of the rack-pinion/open gear of the movable bridge. It is shown that the vibration monitoring data with selected statistical parameters and particular network architectures give successful results to predict the undamaged and damaged condition of the bridge. It is also observed that the MLP-NNs performed better than the FNNs in the presented case. The successful results indicate that ANNs are promising tools for maintenance monitoring of movable bridge components and it is also shown that the ANN results can be employed in simple approach for day-to-day operation and maintenance of movable bridges.