• Journal of the Korean Institute of Telematics and Electronics S
• /
• v.35S no.8
• /
• pp.86-95
• /
• 1998

Intra-frame video compression, which cannot make use of temporal predictions, requires much higher bit rates compared with inter-frame schemes. In order to reduce bit rates, intra-frame predictive coding methods in DCT domain have been studied especially within the framework of the MPEG-4 video coding standard currently being developed. In this paper, we propose novel intra-frame predictive coding methods in DCT domain with the marginal complexity increase over the conventional methods . The proposed methods consist of a DC coefficient prediction method and two AC coefficient prediction methods. The proposed methods consist of a DC coefficient prediction method and two AC coefficient prediction methods. The proposed DC coefficient prediction method makes it possible to adaptively select the prediction directions without overhead bits, by comparing gradients of DC coefficients from neighboring blocks. As the AC coefficient prediction methods, first, we present an effective method which can improve the prediction directions of the MPEG-4 scheme by considering the DC coefficient of the current block to be coded. And, we present another effective method that decision on the prediction is carried out for each AC coefficient. Simulation results show that substantial bit savings can be achieved by the proposed methods.

• Journal of Korea Multimedia Society
• /
• v.18 no.4
• /
• pp.524-532
• /
• 2015

All data created in BigData times is included potentially meaning and correlation in data. A variety of data during a day in all society sectors has become created and stored. Research areas in analysis and grasp meaning between data is proceeding briskly. Especially, accuracy of meaning prediction and data imbalance problem between data for analysis is part in course of something important in data analysis field. In this paper, we proposed data prediction model based on data weights and neural network using R for meaning analysis between data. Proposed data prediction model is composed of classification model and analysis model. Classification model is working as weights application of normal distribution and optimum independent variable selection of multiple regression analysis. Analysis model role is increased prediction accuracy of output variable through neural network. Performance evaluation result, we were confirmed superiority of prediction model so that performance of result prediction through primitive data was measured 87.475% by proposed data prediction model.

• Journal of the Korean Society for Aviation and Aeronautics
• /
• v.23 no.3
• /
• pp.28-34
• /
• 2015

An analysis method of trajectory prediction has been suggested and the developed trajectory prediction module, which is an important functional component of the Arrival Manager (AMAN) of Jeju airport, has been tested by applying the suggested method. The objective of this method is to improve prediction performance of the trajectory prediction module. The trajectory prediction module predicts the trajectories based on the real-time track data and flight plans. Therefore, the suggested analysis method includes the simulation framework which is based on real-time playback, recording, and graphic display systems for testing. Besides, the definition of time error, which is a important index for the time based scheduling system, such as AMAN, is included in the suggested analysis method. An example of arrival time prediction accuracy improvement through the suggested analysis method has also been presented.

• Journal of information and communication convergence engineering
• /
• v.8 no.4
• /
• pp.370-376
• /
• 2010

Ensemble learning is a method for improving the performance of classification and prediction algorithms. However, its performance can be degraded due to multicollinearity problem where multiple classifiers of an ensemble are highly correlated with. This paper proposes genetic algorithm-based optimization techniques of SVM ensemble to solve multicollinearity problem. Empirical results with bankruptcy prediction on Korea firms indicate that the proposed optimization techniques can improve the performance of SVM ensemble.

• The Journal of the Acoustical Society of Korea
• /
• v.17 no.2E
• /
• pp.26-30
• /
• 1998

In this paper, we compose neural network predictive HMM(NNPHMM) to provide the dynamic feature of the speech pattern for the HMM. The NNPHMM is the hybrid network of neura network and the HMM. The NNPHMM trained to predict the future vector, varies each time. It is used instead of the mean vector in the HMM. In the experiment, we compared the recognition abilities of the one hundred Korean syllables according to the variation of hidden layer, state number and prediction orders of the NNPHMM. The hidden layer of NNPHMM increased from 10 dimensions to 30 dimensions, the state number increased from 4 to 6 and the prediction orders increased from 10 dimensions to 30 dimension, the state number increased from 4 to 6 and the prediction orders increased from the second oder to the fourth order. The NNPHMM in the experiment is composed of multi-layer perceptron with one hidden layer and CMHMM. As a result of the experiment, the case of prediction order is the second, the average recognition rate increased 3.5% when the state number is changed from 4 to 5. The case of prediction order is the third, the recognition rate increased 4.0%, and the case of prediction order is fourth, the recognition rate increased 3.2%. But the recognition rate decreased when the state number is changed from 5 to 6.

• Magazine of the Korean Society of Agricultural Engineers
• /
• v.39 no.4
• /
• pp.114-121
• /
• 1997

In order to provide basic information for the estimation of evapotranspiration in the ponded direct seeding paddy field, both field lysimeter experiment and model prediction were performed to estimate daily ET. Various methods were used to predict daily reference crop ET and crop coefficients. Measure4 mean daily ET during the 1995 growing season varied from 5.9 to 6.1 mm depending on the species, while it varied from 5.1 to 5.5 mm in 1996. Model predicted mean daily ET during the 1995 growing season varied from 3.9 to 4.9 mm depending on the prediction model, while it varied from 3.5 to 4.7 mm in 1996. The smaller ET values both measured and predicted in 1996 were caused by the low values of temperature, sunshine hours, and solar radiation. Crop coefficients varied from 1.20 to 1.50 in 1995 depending on the prediction model, while it varied from 1.10 to 1.47 in 1996. Comparison of the seven reference crop ET prediction methods used in this study shows that the Penman-Monteith method and the FAO-Radiation method gave the lowest ET while the corrected Penman method and the Hargreaves method gave the largest ET. Since crop coefficients vary to a large extent based on the prediction methods, reference crop ET prediction method should be carefully selected in irrigation planning.

• International journal of advanced smart convergence
• /
• v.7 no.1
• /
• pp.24-32
• /
• 2018

An outlier detection method using mixed prediction model has been described in this paper. The mixed prediction model consists of time-series model and regression model. The parameter estimation of the prediction model was performed using supervised learning and a genetic algorithm is adopted for a learning method. The experiments were performed in artificial and real data set. The prediction performance is compared with the existing prediction methods using artificial data. Outlier detection is conducted using the real sensor measurements in a dam. The validity of the proposed method was shown in the experiments.

• ETRI Journal
• /
• v.28 no.4
• /
• pp.425-434
• /
• 2006

We propose a fast macroblock mode prediction and decision algorithm based on contextual information for Pslices in the H.264/AVC video standard, in which the mode prediction part is composed of intra and inter modes. There are nine $4{\times}4$ and four $16{\times}16$ modes in the intra mode prediction, and seven block types exist for the best coding gain based on rate-distortion optimization. This scheme gives rise to exhaustive computations (search) in the coding procedure. To overcome this problem, a fast inter mode prediction scheme is applied that uses contextual mode information for P-slices. We verify the performance of the proposed scheme through a comparative analysis of experimental results. The suggested mode search procedure increased more than 57% in speed compared to a full mode search and more than 20% compared to the other methods.

• Journal of the Society of Naval Architects of Korea
• /
• v.48 no.3
• /
• pp.267-274
• /
• 2011

This paper presents the results of prediction of maneuverability of KCS about 4 degree of freedom(DOF) including roll motion. The prediction is carried out by CPMC captive model test. The CPMC(Computerized Planar Motion Carriage) with captive model test equipment including roll moment gage is installed at Ocean Engineering Tank of MOERI. KCS is the container ship open to the world by MOERI. To predict the 4 DOF maneuverability of a ship some tests with roll angle are conducted. And the prediction results of maneuverability by simulation are compared with the results of free running model test. The simulation results agree well with those of free running model tests.

• Journal of Ocean Engineering and Technology
• /
• v.9 no.2
• /
• pp.112-122
• /
• 1995

In this work, prediction of fatigue life and fatigue crack growth are studied. 4th order polynominal function is presented to describe the crack growth behaviors from artifical pit of SM45C steel. Crack growth curves obtained from 4th order polyminal growth equations are in good agreement with experimental data The crack growth behaviors at arbitrary stress levels and investigated by the concept of elastic-plastic fracture mechanics using ${\Delta}J$. Fatigue life prediction are carried out by numerical integral method. Prediction lives obtained by proposed method in this study, is in good agreement with the experimental ones. Life prediction results calculated by using of ${\Delta}J$ better than those of ${\Delta}K$.