• Journal of IKEEE
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• v.24 no.4
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• pp.1176-1179
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• 2020

Vessel path prediction methods generally predict the latitude and longitude of a future location directly. However, in the case of direct prediction, errors could be large since the possible output range is too broad. In addition, error accumulation could occur since recurrent neural networks-based methods employ previous predicted data to forecast future data. In this paper, we propose a vessel path prediction method that does not directly predict the longitude and latitude. Instead, the proposed method predicts the acceleration of the vessel. Then the acceleration is employed to generate the velocity and direction, and the values decide the longitude and latitude of the future location. In the experiment, we show that the proposed method makes smaller errors than the direct prediction method, while both methods employ the same model.

• Transactions of the Korean Society of Pressure Vessels and Piping
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• v.10 no.1
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• pp.64-69
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• 2014

Gr. 91 steel is used for the major structural components of Generation-IV reactor systems, such as a very high temperature reactor(VHTR) and sodium-cooled fast reactor(SFR). Since these structures are designed for up to 60 years at elevated temperatures, the prediction of long-term creep life is important for a design application of Gr. 91 steel. In this study, a number of creep rupture data were collected through world-wide literature surveys, and using these data, the long-term creep life was predicted in terms of three methods: the single-C method in Larson-Miller(L-M) parameter, multi-C constant method in the L-M parameter, and a modified method("sinh" equation) in the L-M parameter. The results of the creep-life prediction were compared using the standard deviation of error value, respectively. Modified method proposed by the "sinh" equation revealed better agreement in creep life prediction than the single-C L-M method.

• Korean Journal of Microbiology
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• v.39 no.2
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• pp.67-75
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• 2003

One large topic in comparative genomics is to predict functional annotation by classifying protein sequences. Computational approaches for function prediction include protein structure prediction, sequence alignment and domain prediction or binding site prediction. This paper is on another computational approach searching for sets of homologous sequences from sequence similarity graph. Methods based on similarity graph do not need previous knowledges about sequences, but largely depend on the researcher's subjective threshold settings. In this paper, we propose a genome sequence clustering method of iterative testing and graph decomposition, and a simple method to calculate a strict threshold having biochemical meaning. Proposed method was applied to known bacterial genome sequences and the result was shown with the BAG algorithm's. Result clusters are lacking some completeness, but the confidence level is very high and the method does not need user-defined thresholds.

• Journal of Ocean Engineering and Technology
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• v.22 no.6
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• pp.95-99
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• 2008

The block assembly of ship consists of a certain type of heat processes such as cutting, bending welding residual stress relaxation and fairing. The residual deformation due to welding is inevitable at each assembly stage. The geometric inaccuracy caused by the welding deformation tends to preclude the introduction of automation and mechanization and needs the additional man-hours for the adjusting work at the following assembly stage. To overcome this problem, a distortion control method should be applied. For this purpose, it is necessary to develop an accurate prediction method which can explicitly account for the influence of various factors on the welding deformation. The validity of the prediction method must be also clarified through experiments. This paper proposes a simplified analysis method to predict the welding deformation of panel block structure. For this purpose, a simple prediction model for fillet welding deformations has been derived based on numerical and experimental results through the regression analysis. On the basis of these results, the simplified analysis method has been applied to some examples to show its validity.

• Proceedings of the Korea Institute of Convergence Signal Processing
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• 2001.06a
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• pp.213-216
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• 2001

The H.263 Annex I method for the intraframe coding is based on the prediction in DCT domain, unlike JPEG, MPEG-1, and MPEG-2 where the intraframe coding uses block DCT, independent of the neighboring blocks. In this paper, we show the ineffectiveness of H.263 Annex I prediction method by mathematically deriving the spatial domain meaning of H.263 Annex I prediction method. Based on the derivation, we propose a prediction method which is based on the spatial correlation property of image signals. From the experiment and derivation, we verified the proposed method.

• International journal of advanced smart convergence
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• v.7 no.2
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• pp.67-72
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• 2018

On the internal based search, the big data inference, which is failed in the president's election in the United States of America in 2016, is failed, because the prediction method is used on the base of the searching numerical value of a candidate for the presidency. Also the Flu Trend service is opened by the Google in 2008. But the Google was embarrassed for the fame's failure for the killing flu prediction system in 2011 and the prediction of presidential election in 2016. In this paper, using the virtual vote algorithm for virtual election and data mining method, the election prediction algorithm is proposed and unpacked. And also the WEKA DB is unpacked. Especially in this paper, using the K means algorithm and XEDOS tools, the prediction of election results is unpacked efficiently. Also using the analysis of the WEKA DB, the smart election prediction system is proposed in this paper.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.66 no.12
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• pp.1799-1803
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• 2017

This paper introduces the prediction methods of windstorm advisory using GP nonlinear compensation and SVM. The existing special report prediction is not specialized for strong wind, such as windstorm, because it is based on the wide range of predicted values for wind speed from low to high. In order to improve the performance of strong wind reporting prediction, a method that can efficiently classify boundaries of strong wind is necessary. First, evolutionary nonlinear regression based compensation technique is applied to obtain more accurate values of prediction for wind speed using UM data. Based on the prediction wind speed, the windstorm advisory is determined. Second, SVM method is applied to classify directly using the data of UM predictors and windstorm advisory. Above two methods are compared to evaluate of the performances for the windstorm data in Jeju Island in South Korea. The data of 2007-2009, 2011 year is used for training, and 2012 year is used for test.

• ETRI Journal
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• v.46 no.1
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• pp.118-126
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• 2024

In human conversations, listeners often utilize brief backchannels such as "uh-huh" or "yeah." Timely backchannels are crucial to understanding and increasing trust among conversational partners. In human-machine conversation systems, users can engage in natural conversations when a conversational agent generates backchannels like a human listener. We propose a method that simultaneously predicts backchannels and recognizes speech in real time. We use a streaming transformer and adopt multitask learning for concurrent backchannel prediction and speech recognition. The experimental results demonstrate the superior performance of our method compared with previous works while maintaining a similar single-task speech recognition performance. Owing to the extremely imbalanced training data distribution, the single-task backchannel prediction model fails to predict any of the backchannel categories, and the proposed multitask approach substantially enhances the backchannel prediction performance. Notably, in the streaming prediction scenario, the performance of backchannel prediction improves by up to 18.7% compared with existing methods.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1406-1420
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• 2017

Artificial neural networks (ANNs) play an important role in the fields of function approximation, prediction, and classification. ANN performance is critically dependent on the input parameters, including the number of neurons in each layer, and the optimal values of weights and biases assigned to each neuron. In this study, we apply the particle swarm optimization method, a popular optimization algorithm for determining the optimal values of weights and biases for every neuron in different layers of the ANN. Several regression models, including general linear regression, Fourier regression, smoothing spline, and polynomial regression, are conducted to evaluate the proposed method's prediction power compared to multiple linear regression (MLR) methods. In addition, residual analysis is conducted to evaluate the optimized ANN accuracy for both training and test datasets. The experimental results demonstrate that the proposed method can effectively determine optimal values for neuron weights and biases, and high accuracy results are obtained for prediction applications. Evaluations of the proposed method reveal that it can be used for prediction and estimation purposes, with a high accuracy ratio, and the designed model provides a reliable technique for optimization. The simulation results show that the optimized ANN exhibits superior performance to MLR for prediction purposes.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.47 no.5
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• pp.206-211
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• 2010

This paper describes a skipping method for handling various illuminating effects in video sequences. The weighted prediction in H.264/AVC improves coding efficiency and image quality. However, it requires massive computation overheads for entire system, and thus, reducing the computation complexity becomes more important. Compared to the weighted prediction method in the H.264/AVC, the proposed method can decrease the bitrate down to 15%. Moreover, the proposed algorithm can reduce computation complexity down to 30%, compared to the localized weighted prediction which does not skip unnecessary calculation.