• Journal of Korea Multimedia Society
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• v.14 no.11
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• pp.1467-1477
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• 2011

This paper proposes a HPPS(Hybrid Preference Prediction System) design using the analysis of user profile and of the similarity among users precisely to predict the preference for custom-tailored service. Contrary to the existing NBCFA(Neighborhood Based Collaborative Filtering Algorithm), this paper is designed using these following rules. First, if there is no neighbor's commodity rating value in a preference prediction formula, this formula uses the rating average value for a commodity. Second, this formula reflects the weighting value through the analysis of a user's characteristics. Finally, when the nearest neighbor is selected, we consider the similarity, the commodity rating, and the rating frequency. Therefore, the first and second preference prediction formula made HPPS improve the precision by 97.24%, and the nearest neighbor selection method made HPPS improve the precision by 75%, compared with the existing NBCFA.

• International Journal of Naval Architecture and Ocean Engineering
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• v.13 no.1
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• pp.260-277
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• 2021

The appropriate radiation conditions of ship motion problem with advancing speed in frequency domain are investigated from a theoretical and practical point of view. From extensive numerical experiments that have been conducted for evaluation of the relevant radiation conditions, a hybrid radiation technique is proposed in which the Sommerfeld radiation condition and the free surface damping are mixed. Based on the comparison with the results of the translating and pulsating Green function method, the optimal damping factor of the hybrid radiation technique is selected, and the observed limitations of the proposed hybrid radiation technique are discussed, along with its accuracy obtained from the numerical solutions. Comparative studies of the forward-speed seakeeping prediction methods available confirm that the results of applying the hybrid radiation technique are relatively similar to those obtained from the translating and pulsating Green function method. This confirmation is made in comparisons with the results of solely applying either the free surface damping, or the Sommerfeld radiation condition. By applying the proposed hybrid radiation technique, the wave patterns, hydrodynamic coefficients, and motion responses of the Wigley III hull are finally calculated, and compared with those of model tests. It is found that, in comparison with the model test results, the three-dimensional Rankine source method adopting the proposed hybrid radiation technique is more robust in terms of accuracy and numerical stability, as well as in obtaining the forward speed seakeeping solution.

• International Journal of Naval Architecture and Ocean Engineering
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• v.10 no.3
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• pp.270-281
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• 2018

Application of the LeMoS hybrid (LH) URANS/LES method for the wake parameters prediction is considered. The wake fraction coefficient is calculated for inland ship model M1926 under shallow water conditions and compared to results of PIV measurements. It was shown that due to lack of the resolved turbulence at the interface between LES and RANS zones the artificial grid induced separations can occur. In order to overcome this drawback, a shielding function is introduced into LH model. The new version of the model is compared to the original one, RANS $k-{\omega}$ SST and SST-IDDES models. It is demonstrated that the proposed modification is robust and capable of wake prediction with satisfactory accuracy.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.1281-1284
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• 1998

This paper proposes an efficient time series prediction of the nonlinear dynamical discrete-time systems using multilayer neural networks of a hybrid learning algorithm. The proposed learning algorithm is a hybrid backpropagation algorithm based on the steepest descent for high-speed optimization and the dynamic tunneling for global optimization. The proposed algorithm has been applied to the y00 samples of 700 sequences to predict the next 100 samples. The simulation results shows that the proposed algorithm has better performances of the convergence and the prediction, in comparision with that using backpropagation algorithm based on the gradient descent for multilayer neural network.

• The Transactions of the Korea Information Processing Society
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• v.7 no.7
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• pp.2196-2203
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• 2000

As processor's operational frequency increases and processors execute multiple instructions per cycle, the processor performance becomes more dependent on the load operand referencing latency and the data dependency. To reduce the operand fetch latency and to increase ILP by breaking the data dependency, we propose a value-address hybrid predictor using a reasonable size prediction buffer and analyse the performance improvement by the proposed predictor. Through the extensive simulation of 5 benchmark programs, the proposed hybrid prediction scheme accurately predicts 62.72% of all loads which are 12.64% higher than the value prediction scheme and show its cost-effectiveness compared to the address predition scheme. In addition, we analyse the performance improvement achieved by the stride management and the history of previous predictions.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.63 no.3
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• pp.189-194
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• 2014

Due to the increasing of power consumption, it is difficult to construct accurate prediction model for daily peak power demand. It is very important work to know power demand in next day for manager and control power system. In this research, we develop a daily peak power demand prediction method based on hybrid type composed of AR and Neuro-Fuzzy model. Using data sets between 2006 and 2010 in Korea, the proposed method has been intensively tested. As the prediction results, we confirm that the proposed method makes it possible to effective estimate daily peak power demand than conventional methods.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.24 no.4
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• pp.26-34
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• 2016

In this study, reliability prediction of the ignition system of hybrid rocket is performed. The FMECA is preceded to the reliability prediction. To this end, the ignition system is divided into 5 components and 19 potential failure modes. The failure cause and effects are identified and criticality analysis is carried out for each failure mode, in which the criticality number is estimated using the failure rate databases. Among the numbers, the failure modes and components with higher criticality and severity are chosen and allocated with higher weighting factor. The reliability predictions are performed using the failure rate databases, from which the current ignition system is found to satisfy the target reliability.

• Proceedings of the KIEE Conference
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• 1987.07b
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• pp.1431-1433
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• 1987

To transmit the image data at low rate, motion compensated(MC) hybrid coding scheme is used generally. In this scheme since coder performance could be decreased when scene change happens, another prediction method must be employed. In this paper we present two prediction methods. One is using the mean of neighboring block which was already transmitted. The others is estimating the current block with the neighboring blocks. When the proposed methods are applied to the conventional Me hybrid coding scheme, it is found that SNR gain of 7 dB is achieved and bit rate can be also reduced above 30%.

• Journal of the Korea Society for Simulation
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• v.8 no.3
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• pp.17-28
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• 1999

Recently studies have shown that conditional branches can be accurately predicted by recording the path leading up to the branch. But path predictors are more complex and uncompatible with existing pattern branch predictors. In order to solve these problems, we propose a simple path branch predictor(SPBP) that hashes together two most recent branch instruction addresses. In addition, we propose a pattern/path hybrid branch predictor composed of the SPBP and existing pattern branch predictors. Through the trace-driven simulation of six benchmark programs, the performance improvement by the proposed pattern/path hybrid branch prediction is analysed and validated. The proposed predictor can improve the prediction accuracy from 94.21% to 95.03%.

• The Transactions of the Korean Institute of Power Electronics
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• v.27 no.1
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• pp.48-55
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• 2022

This paper proposes a battery remaining useful life (RUL) prediction method using a deep learning-based EMD-CNN-LSTM hybrid method. The proposed method pre-processes capacity data by applying empirical mode decomposition (EMD) and predicts the remaining useful life using CNN-LSTM. CNN-LSTM is a hybrid method that combines convolution neural network (CNN), which analyzes spatial features, and long short term memory (LSTM), which is a deep learning technique that processes time series data analysis. The performance of the proposed remaining useful life prediction method is verified using the battery aging experiment data provided by the NASA Ames Prognostics Center of Excellence and shows higher accuracy than does the conventional method.