• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.189-191
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• 2005

In the scalable extension of H.264/AVC, the codec is based on a layered approach to enable spatial scalability. In each layer, the basic concepts of motion compensated prediction and intra prediction are employed as in standard H.264/AVC. Additionally inter-layer prediction algorithm between successive spatial layers is applied to remove redundancy. In the inter-layer prediction, as the prediction we can use the signal that is the upsampled signal of the lower resolution layer. In this case, coding efficiency can be variable as the kinds of interpolation filter. In this paper, we investigate the approach to select the interpolation filter for residual signal in order to optimal prediction.

• Korean Journal of Remote Sensing
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• v.18 no.1
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• pp.1-12
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• 2002

Landslide prediction modeling has been regarded as one of the important environmental applications in GIS. While, landslide stability in a certain area as collateral process for prediction modeling can be characterized by DEM-based hydrological features such as flow-direction, flow-accumulation, flow-length, wetness index, and so forth. In this study, Slope-Area plot methodology followed by stability index mapping with these hydrological variables is firstly performed for stability analysis with actual landslide occurrences at Boeun area, Korea, and then Landslide prediction modeling based on likelihood ratio model for landslide potential mapping is carried out; in addition, KOMPSAT EOC imagery is used to detect the locations and scalped scale of Landslide occurrences. These two tasks are independently processed for preparation of unbiased criteria, and then results of those are qualitatively compared. As results of this case study, land stability analysis based on DEM-based hydrological variables directly reflects terrain characteristics; however, the results in the form of land stability map by landslide prediction model are not fully matched with those of hydrologic landslide analysis due to the heuristic scheme based on location of existed landslide occurrences within prediction approach, especially zones of not-investigated occurrences. Therefore, it is expected that the resets on the space-robustness of landslide prediction models in conjunction with DEM-based landslide stability analysis can be effectively utilized to search out unrevealed or hidden landslide occurrences.

• Journal of Broadcast Engineering
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• v.26 no.6
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• pp.758-765
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• 2021

Recently, various research on the application of deep learning to video encoding for enhancing coding efficiency are being actively studied. This paper proposes a deep learning based intra prediction which uses multiple models by extending Matrix-based Intra Prediction(MIP) that is a neural network-based technology adopted in VVC. It also presents an efficient learning method for the multi-model intra prediction. To evaluate the performance of the proposed method, we integrated the VVC MIP and the proposed fully connected layer based multi-model intra prediction into HEVC reference software, HM16.19 as an additional intra prediction mode. As a result of the experiments, the proposed method can obtain bit-saving coding gain up to 0.47% and 0.19% BD-rate, respectively, compared to HM16.19 and VVC MIP.

• Journal of Korea Multimedia Society
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• v.23 no.10
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• pp.1286-1295
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• 2020

It has been increasingly difficult to predict the amounts of Acer mono sap to be collected due to droughts and cold waves caused by recent climate changes with few studies conducted on the prediction of its collection volume. This study thus set out to propose a Big Data prediction system based on meteorological information for the collection of Acer mono sap. The proposed system would analyze collected data and provide managers with a statistical chart of prediction values regarding climate factors to affect the amounts of Acer mono sap to be collected, thus enabling efficient work. It was designed based on Hadoop for data collection, treatment and analysis. The study also analyzed and proposed an optimal prediction model for climate conditions to influence the volume of Acer mono sap to be collected by applying a multiple regression analysis model based on Hadoop and Mahout.

• Journal of Korea Multimedia Society
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• v.12 no.6
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• pp.876-881
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• 2009

Online game servers usually has been using the static thread pool system. But this system is not fit for huge online game server because the overhead is always up-and-down. Therefore, in this paper, we suggest the new algorithm for huge online game server. This algorithm is based on the prediction-based dynamic thread pool system. But it was developed for web servers and every 0.1 seconds the system prediction the needed numbers of threads and determine the thread pool size. Some experimental results show that the check time of 0.4 seconds is the best one for online game server and if the number of worker threads do not excess or lack to the given threshold then we do not predict and keep the current state. Otherwise we apply the prediction algorithm and change the number of threads. Some experimental results shows that this proposed algorithm reduce the overhead massively and make the performance of huge online game server improved in comparison to the static thread pool system.

• Communications for Statistical Applications and Methods
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• v.22 no.3
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• pp.241-253
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• 2015

In this paper we predict the track of typhoons using a Bayesian principal component regression model based on wind field data. Data is obtained at each time point and we applied the Bayesian principal component regression model to conduct the track prediction based on the time point. Based on regression model, we applied to variable selection prior and two kinds of prior distribution; normal and Laplace distribution. We show prediction results based on Bayesian Model Averaging (BMA) estimator and Median Probability Model (MPM) estimator. We analysis 8 typhoons in 2006 using data obtained from previous 6 years (2000-2005). We compare our prediction results with a moving-nest typhoon model (MTM) proposed by the Korea Meteorological Administration. We posit that is possible to predict the track of a typhoon accurately using only a statistical model and without a dynamical model.

• Journal of the Korea Society of Computer and Information
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• v.23 no.9
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• pp.81-86
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• 2018

Software defect severity is very important in projects with limited historical data or new projects. But general software defect prediction is very difficult to collect the label information of the training set and cross-project defect prediction must have a lot of data. In this paper, an unclassified data set with defect severity is clustered according to the distribution ratio. And defect severity-based prediction model is proposed by way of labeling. Proposed model is applied CLAMI in JM1, PC4 with the least ambiguity of defect severity-based NASA dataset. And it is evaluated the value of ACC compared to original data. In this study experiment result, proposed model is improved JM1 0.15 (15%), PC4 0.12(12%) than existing defect severity-based prediction models.

• Journal of IKEEE
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• v.24 no.2
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• pp.661-664
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• 2020

In this paper, we propose a maritime region segmentation method and a segment-based destination prediction method for vessel path prediction. In order to perform maritime segmentation, clustering on destination candidates generated from the past paths is conducted. Then the segment-based destination prediction is followed. For destination prediction, different prediction methods are applied according to whether the current region is linear or not. In the linear domain, the vessel is regarded to move constantly, and linear prediction is applied. In the nonlinear domain with an uncertainty, we assume that the vessel moves similarly to the most similar past path. Experimental results show that applying the linear prediction and the prediction method using a similar path differently depending on the linearity and the uncertainty of the path is better than applying one of them alone.

• Journal of Applied Reliability
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• v.18 no.3
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• pp.250-259
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• 2018

Purpose: Previously, missile reliability prediction is based on theoretical failure prediction model. It has shown that the predicted reliability is inadequate to real field data. Although an MTTF based reliability prediction method using real field data has recently been studied to overcome this issue. In this paper, we present a more realistic method, considering MTBF concept, to predict missile reliability. Methods: In this paper we proposed a modified survival model. This model is considering MTBF as its core concept, and failed missiles in the model are to be repaired and redeployed. We compared the modified model (MTBF) and the previous model (MTTF) in terms of fitness against the real failure data. Results: The reliability prediction result of MTBF based model is closer to fields failure data set than that of MTTF based model. Conclusion: The proposed MTBF concept is more fitted to real failure data of missile than MTTF concept. The methodology of this study can be applied to analyze field failure data of other similar missiles.

• Journal of Positioning, Navigation, and Timing
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• v.3 no.2
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• pp.83-90
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• 2014

In this study, to improve an existing ground-wave based time broadcasting system, a study that predicts the field distribution and field strength of the transmitted signal of a new ground-wave based time broadcasting system was performed. The prediction area was assumed to be the Korean peninsula; and to reflect the mountainous terrain of the Korean peninsula in the prediction of the variations of field distribution and field strength, a new prediction method based on the Monteath model was proposed and utilized. As field distribution changes depending on the position of a transmitter station, potential sites for the transmitter station were selected considering the geographical characteristics. In this regard, the ground conductivity information of North Korea cannot be obtained, and thus, the ground conductivity of the North Korean region was reflected considering the geological characteristics of South Korea and North Korea. Based on this, the variations of field distribution and field strength were predicted by setting the Korean peninsula as the prediction area, and the prediction results depending on the position of the transmitter station were discussed.