• Journal of the Institute of Electronics and Information Engineers
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• v.50 no.8
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• pp.245-249
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• 2013

In this paper, we propose two rate control algorithms for multi-view extension of HEVC with two rate control algorithms adopted in HEVC and analyze the multi-view rate control performance. The proposed multi-view rate controls are designed on HEVC-based multi-view video coding (MV-HEVC) platform with consideration of high-level syntax, inter-view prediction, etc. not only for the base view but also for the extended views using the rate control algorithms based on URQ (Unified Rate-Quantization) and R-lambda model adopted in HEVC. The proposed multi-view rate controls also contain view-wise target bit allocation for providing the compatibility to the base view. By allocating the target bitrates for each view, the proposed multi-view rate control based on URQ model achieved about 1.83% of average bitrate accuracy and 1.73dB of average PSNR degradation. In addition, about 2.97% of average bitrate accuracy and 0.31dB of average PSNR degradation are achieved with the proposed multi-view rate control based on R-lambda model.

• Journal of the Korean Data and Information Science Society
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• v.14 no.3
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• pp.641-651
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• 2003

This study suggests the hybrid models for interest rate forecasting using structural changes (or change points). The basic concept of this proposed model is to obtain significant intervals caused by change points, to identify them as the change-point groups, and to reflect them in interest rate forecasting. The model is composed of three phases. The first phase is to detect successive structural changes in the U. S. Treasury bill rate dataset. The second phase is to forecast the change-point groups with data mining classifiers. The final phase is to forecast interest rates with backpropagation neural networks (BPN). Based on this structure, we propose three hybrid models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported model, (2) case-based reasoning (CBR)-supported model, and (3) BPN-supported model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the prediction ability of hybrid models to reflect the structural change.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.106-111
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• 1994

We proposed a new variable sampling rate model which expresses the phenomena with both rapid and slow components. A method for determining the variable sampling rate and the older of the time series model was explained. The proposed variable sampling rate model was evaluated based oil an information criterion(AIC). Tile variable sampling rate model brought smaller an information criterion than one of a constant sampling rate model of conventional type, and was proved to be effective as a prediction model of the system with both rapid and slow components.

• Computers and Concrete
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• v.8 no.2
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• pp.177-192
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• 2011

This research paper aims at computer based modeling of carbonation induced corrosion under extreme conditions and its experimental verification by incorporating enhanced electrochemical and mass balance equations based on thermo-hygro physics with strong coupling of mass transport and equilibrium in micro-pore structure of carbonated concrete for which the previous research data is limited. In this paper the carbonation induced electrochemical corrosion model is developed and coupled with carbon dioxide transport computational model by the use of a concrete durability computer based model DuCOM developed by our research group at concrete laboratory in the University of Tokyo and its reliability is checked in the light of experiment results of carbonation induced corrosion mass loss obtained in this research. The comparison of model analysis and experiment results shows a fair agreement. The carbonation induced corrosion model computation reasonably predicts the quantitative behavior of corrosion rate for normal air dry relative humidity conditions. The computational model developed also shows fair qualitative corrosion rate simulation and analysis for various pH levels and coupled environmental actions of chloride and carbonation. Detailed verification of the model for the quantitative carbonation induced corrosion rate computation under varying relative conditions, different pH levels and combined effects of carbonation and chloride attack remain as scope for future research.

• 한국데이터정보과학회:학술대회논문집
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• 2003.10a
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• pp.57-72
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• 2003

This article provides integrated neural network models for the interest rate forecasting using change-point detection. The model is composed of three phases. The first phase is to detect successive structural changes in interest rate dataset. The second phase is to forecast change-point group with data mining classifiers. The final phase is to forecast the interest rate with BPN. Based on this structure, we propose three integrated neural network models in terms of data mining classifier: (1) multivariate discriminant analysis (MDA)-supported neural network model, (2) case based reasoning (CBR)-supported neural network model and (3) backpropagation neural networks (BPN)-supported neural network model. Subsequently, we compare these models with a neural network model alone and, in addition, determine which of three classifiers (MDA, CBR and BPN) can perform better. For interest rate forecasting, this study then examines the predictability of integrated neural network models to represent the structural change.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.652-657
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• 2004

As the real-time multimedia applications through Internet increase, the bandwidth available to TCP connections is oppressed by the UDP traffic, result in the performance of overall system is extremely deteriorated. Therefore, developing a new transmission protocol is necessary. The TCP-friendly algorithm is an example meeting this necessity. The TCP-friendly (TFRC) is an UDP-based protocol that controls the transmission rate based on the available round transmission time (RTT) and the packet loss rate (PLR). In the data transmission processing, transmission rate is determined based on the conditions of the previous transmission period. If the one-step ahead predicted values of the control factors are available, the performance will be improved significantly. This paper proposes a prediction model of transmission rate control factors that will be used for the transmission rate control, which improves the performance of the networks. The model developed through this research is predicting one-step ahead variables of RTT and PLR. A multiplayer perceptron neural network is used as the prediction model and Levenberg-Marquardt algorithm is used for the training. The values of RTT and PLR were collected using TFRC protocol in the real system. The obtained prediction model is validated using new data set and the results show that the obtained model predicts the factors accurately.

• Nuclear Engineering and Technology
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• v.48 no.5
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• pp.1264-1272
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• 2016

The accurate estimation of leak rate through cracks is crucial in applying the leak before break (LBB) concept to pipeline design in nuclear power plants. Because of its importance, several programs were developed based on the several proposed flow models, and used in nuclear power industries. As the flow models were developed for a homogeneous pipe material, however, some difficulties were encountered in estimating leak rates for bimaterial pipes. In this paper, a flow model is proposed to estimate leak rate in bimaterial pipes based on the modified Henry-Fauske flow model. In the new flow model, different crack morphology parameters can be considered in two parts of a flow path. In addition, based on the proposed flow model, a program was developed to estimate leak rate for a crack with linearly varying cross-sectional area. Using the program, leak rates were calculated for through-thickness cracks with constant or linearly varying cross-sectional areas in a bimaterial pipe. The leak rate results were then compared and discussed in comparison with the results for a homogeneous pipe. The effects of the crack morphology parameters and the variation in cross-sectional area on the leak rate were examined and discussed.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.17 no.3
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• pp.8-13
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• 2008

The paper proposes a model-based measurement of unsteady flow rate by using distributed parameter pipeline model and the measured pressure values at two distant points along the pipeline. The distributed parameter model of hydraulic pipeline is applied with consideration of frequency dependent viscosity friction and unsteady velocity distribution at a cross section of a pipeline. By using the self-diagnostics functions of the measurement method, the validity is investigated by comparison with the measured and estimated pressure and flow rate wave forms at the halfway section on the pipeline. The results show good agreement between the estimated flow rate wave forms and theoretical those under unsteady laminar flow conditions. The method proposed here is useful in estimating unsteady flow rate through an arbitrary cross section in hydraulic pipeline and components without installing an instantaneous flowmeter.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.4
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• pp.385-391
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• 2005

As the Internet real-time multimedia applications increases, the bandwidth available to TCP connections is oppressed by the UDP traffic, result in the performance of overall system is extremely deteriorated. Therefore, developing a new transmission protocol is necessary. The TCP-friendly algorithm is an example satisfying this necessity. The TCP-Friendly Rate Control (TFRC) is an UDP-based protocol that controls the transmission rate that is based on the available round trip time (RTT) and the packet loss rate (PLR). In the data transmission processing, transmission rate is determined based on the conditions of the previous transmission period. If the one-step ahead predicted values of the control factors are available, the performance will be improved significantly. This paper proposes a prediction model of transmission rate control factors that will be used in the transmission rate control, which improves the performance of the networks. The model developed through this research is predicting one-step ahead variables of RTT and PLR. A multiplayer perceptron neural network is used as the prediction model and Levenberg-Marquardt algorithm is used for the training. The values of RTT and PLR were collected using TFRC protocol in the real system. The obtained prediction model is validated using new data set and the results show that the obtained model predicts the factors accurately.

• Journal of KIISE:Software and Applications
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• v.29 no.7
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• pp.425-435
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• 2002

This paper introduces a macroblock-based rate control algorithm using the neural classifier based in Self Organization feature Maps (SOFM). In contrast to the conventional rate control methods based on the mathematical rate distortion (RD) model and the feedback regression, proposed method can actively adapt to the rapid-varying image characteristics by establishing the global model for bitrate control and by using the SOFM based neural classifier to manage that model. Proposed rate control algorithm has 0.2 dB ~ 0.6 dB better performances than MPEG-4 macroblock-based rate control algorithm by evaluating with the encoded Peak Signal to Noise Ratios while maintaining similar overall computational complexity.