• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.2
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• pp.312-320
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• 2012

In this study, we introduce a new design methodology of a granular-oriented self-organizing polynomial neural networks (GoSOPNNs) that is based on multi-layer perceptron with Context-based Polynomial Neurons (CPNs) or Polynomial Neurons (PNs). In contrast to the typical architectures encountered in polynomial neural networks (PNN), our main objective is to develop a methodological design strategy of GoSOPNNs as follows : (a) The 1st layer of the proposed network consists of Context-based Polynomial Neuron (CPN). In here, CPN is fully reflective of the structure encountered in numeric data which are granulated with the aid of Context-based Fuzzy C-Means (C-FCM) clustering method. The context-based clustering supporting the design of information granules is completed in the space of the input data while the build of the clusters is guided by a collection of some predefined fuzzy sets (so-called contexts) defined in the output space. (b) The proposed design procedure being applied at each layer of GoSOPNN leads to the selection of preferred nodes of the network (CPNs or PNs) whose local characteristics (such as the number of contexts, the number of clusters, a collection of the specific subset of input variables, and the order of the polynomial) can be easily adjusted. These options contribute to the flexibility as well as simplicity and compactness of the resulting architecture of the network. For the evaluation of performance of the proposed GoSOPNN network, we describe a detailed characteristic of the proposed model using a well-known learning machine data(Automobile Miles Per Gallon Data, Boston Housing Data, Medical Image System Data).

• The Journal of the Acoustical Society of Korea
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• v.39 no.6
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• pp.592-599
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• 2020

This paper proposes a Convolutional Recurrent Neural Net (CRNN) structure that can simultaneously reflect both static and dynamic characteristics of seismic waveforms for various earthquake events classification. Addressing various earthquake events, including not only micro-earthquakes and artificial-earthquakes but also macro-earthquakes, requires both effective feature extraction and a classifier that can discriminate seismic waveform under noisy environment. First, we extract the static characteristics of seismic waveform through an attention-based convolution layer. Then, the extracted feature-map is sequentially injected as input to a multi-input single-output Long Short-Term Memory (LSTM) network structure to extract the dynamic characteristic for various seismic event classifications. Subsequently, we perform earthquake events classification through two fully connected layers and softmax function. Representative experimental results using domestic and foreign earthquake database show that the proposed model provides an effective structure for various earthquake events classification.

• Journal of Korea Game Society
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• v.15 no.1
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• pp.135-144
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• 2015

Smart TV has been the most popular communication device since 2010. It is the multi-functional device conjugated a variety of features such as Apps, Web surfing, viewing VOD and so on. However, the contents and development tools for smart TV are still relatively small and research for game contents development tools is also insufficient. In this study, we are to design and develop a framework for developers to make Smart TV game contents easily. The STGF (Smart TV Game Framework) we developed is made up of 3 managers such as input manager, screen data process manager, and game status process manager. We verified the usefulness of STGF through developing "Pentomiro" App launched commercially in 2013. With STGF development time would be expected to be reduced, because we spend little time in basic development steps such as input, output, and data processing and error correction processing.

• Journal of KIISE:Information Networking
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• v.36 no.1
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• pp.57-67
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• 2009

Packet forwarding in the Internet routers is to find out the longest prefix that matches the destination address of an input packet and to forward the input packet to the output port designated by the longest matched prefix. The IP address lookup is the key of the packet forwarding, and it is required to have efficient data structures and search algorithms to provide the high-speed lookup performance. In this paper, an efficient IP address lookup algorithm using binary search is investigated. Most of the existing binary search algorithms are not efficient in search performance since they do not provide a balanced search. The proposed binary search algorithm performs perfectly balanced binary search using switch pointers. The performance of the proposed algorithm is evaluated using actual backbone routing data and it is shown that the proposed algorithm provides very good search performance without increasing the memory amount storing the forwarding table. The proposed algorithm also provides very good scalability since it can be easily extended for multi-way search and for large forwarding tables

• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.1
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• pp.73-80
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• 2016

The Car License Plate Recognition(: CLPR) is required in searching the hit-and-run car, measuring the traffic density, investigating the traffic accidents as well as in pursuing vehicle crimes according to the increasing in number of vehicles. The captured images on the real environment of the CLPR is contaminated not only by snow and rain, illumination changes, but also by the geometrical distortion due to the pose changes between camera and car at the moment of image capturing. We propose homographic transformation and intensity histogram of vertical image projection so as to transform the distorted input to the original image and cluster the character and number, respectively. Especially, in this paper, the Multilayer Perceptron Algorithm(: MLP) in the CLPR is used to not only recognize the charcters and car license plate, but also determine the optimized ratio among the number of input, hidden and output layers by the real experimental result.

• 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.

• Geomechanics and Engineering
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• v.12 no.3
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• pp.441-464
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• 2017

The determination of the mixture parameters of stabilization has become a great concern in geotechnical applications. This paper presents an effort about the application of artificial intelligence (AI) techniques including radial basis neural network (RBNN), multi-layer perceptrons (MLP), generalized regression neural network (GRNN) and adaptive neuro-fuzzy inference system (ANFIS) in order to predict the unconfined compressive strength (UCS) of silty soil stabilized with bottom ash (BA), jute fiber (JF) and steel fiber (SF) under different freeze-thaw cycles (FTC). The dosages of the stabilizers and number of freeze-thaw cycles were employed as input (predictor) variables and the UCS values as output variable. For understanding the dominant parameter of the predictor variables on the UCS of stabilized soil, a sensitivity analysis has also been performed. The performance measures of root mean square error (RMSE), mean absolute error (MAE) and determination coefficient ($R^2$) were used for the evaluations of the prediction accuracy and applicability of the employed models. The results indicate that the predictions due to all AI techniques employed are significantly correlated with the measured UCS ($p{\leq}0.05$). They also perform better predictions than nonlinear regression (NLR) in terms of the performance measures. It is found from the model performances that RBNN approach within AI techniques yields the highest satisfactory results (RMSE = 55.4 kPa, MAE = 45.1 kPa, and $R^2=0.988$). The sensitivity analysis demonstrates that the JF inclusion within the input predictors is the most effective parameter on the UCS responses, followed by FTC.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.8 no.1
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• pp.18-27
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• 2004

In this paper, we presents an analytical simulation procedure for evaluation in baseband digital modulated signals distortions in the present of RF power amplifier(SSPA) nonlinear behavior and backoff operations of OFDM wireless transmission system. we obtained the optimum nonlinear transfer function of designed SSPA with the SiGe HBT bias currents of OFDM multi-channel wireless transmission system and compared this transfer function to SSPA nonlinear modeling functions mathematically, we finds optimum bias conditions of designed SSPA. With the derived nonlinear modeling function of SSPA, We analysed the PSD characteristics of in-band and out-band output powers of SSPA EVM measurement results of distorted constellation signals with the input power levels of SSPA. The results of paper can be applied to find the SSPA linearly with optimum bias currents and determine the SSPA input backoff bias for AGC control circuits of SSPA.

• Journal of KIISE:Computer Systems and Theory
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• v.36 no.6
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• pp.511-520
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• 2009

In this paper, we present a new iso-density surface reconstruction scheme based on a hierarchy on the input volume data and the output mesh data. From the input volume data, we construct a hierarchy of volumes, called a volume pyramid, based on a 3D dilation filter. After constructing the volume pyramid, we extract a coarse base mesh from the coarsest resolution of the pyramid with the Cell-boundary representation scheme. We iteratively fit this mesh to the iso-points extracted from the volume data under O(3)-adjacency constraint. For the surface fitting, the shrinking process and the smoothing process are adopted as in the SWIS (Shrink-wrapped isosurface) algorithm[6], and we subdivide the mesh to be able to reconstruct fine detail of the isosurface. The advantage of our method is that it generates a mesh which can be utilized by several multiresolution algorithms such as compression and progressive transmission.

• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.137-146
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• 2011

In order to support increased consumer awareness regarding energy consumption, we present new ways of monitoring and predicting with energy in electric appliances. The proposed system is a design of a common electrical power outlet called smart plug that measures the amount of current passing through current sensor at 0.5 second. To acquire data for training and testing the proposed neural network, weather parameters used include average temperature of day, min and max temperature, humidity, and sunshine hour as input data, and power consumption as target data from smart plug. Using the experimental data for training, the neural network model based on Back-Propagation algorithm was developed. Multi layer perception network was used for nonlinear mapping between the input and the output data. It was observed that the proposed neural network model can predict the power consumption quite well with correlation coefficient was 0.9965, and prediction mean square error was 0.02033.