• 한국HCI학회:학술대회논문집
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• 2007.02a
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• pp.982-987
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• 2007

The necessity of data compression scheme for volume data has been increased because of the increase of data capacity and the amount of network uses. Now we have various kinds of compression schemes, and we can choose one of them depending on the data types, application fields, the preferences, etc. However, the capacity of data which is produced by application scientists has been excessively increased, and the format of most scientific data is 3D volume. For 2D image or 3D moving pictures, many kinds of standards are established and widely used, but for 3D volume data, specially time-varying volume data, it is very difficult to find any applicable compression schemes. In this paper, we present a compression scheme for encoding time-varying volume data. This scheme is aimed to encoding time-varying volume data for visualization. This scheme uses MPEG's I- and P-frame concept for raising compression ratio. Also, it transforms volume data using Daubechies D4 filter before encoding, so that the image quality is better than other wavelet-based compression schemes. This encoding scheme encodes time-varying volume data composed of single precision floating-point data. In addition, this scheme provides the random reconstruction accessibility for an unit, and can be used for compressing large time-varying volume data using correlation between frames while preserving image qualities.

• Journal of the Korea Society of Computer and Information
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• v.12 no.3
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• pp.85-93
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• 2007

This paper describes a compression scheme for volumetric video data(3D space X 1D time) there each frame of the volume is decompressed and rendered in real-time. Since even one frame size of volume is very large, runtime decompression can be a bottleneck for real-time playback of time-varying volume data. To increase the run-time decompression speed and compression ratio, we decompose the volume into small blocks and only update significantly changing blocks. The results show that our compression scheme compromises decompression speed and image quality well enough for interactive time-varying visualization.

• Journal of information and communication convergence engineering
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• v.7 no.2
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• pp.199-202
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• 2009

Adaptive memory management is a serious issue in data stream management. Data stream differ from the traditional stored relational model in several aspect such as the stream arrives online, high volume in size, skewed data distributions. Data skew is a common property of massive data streams. We propose the predicted allocation strategy, which uses predictive processing to cope with time varying data skew. This processing includes memory usage estimation and indexing with timestamp. Our experimental study shows that the predictive strategy reduces both required memory space and latency time for skewed data over varying time.

• Journal of the Korea Computer Graphics Society
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• v.29 no.3
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• pp.105-115
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• 2023

In this paper, we describe the process and method of converting tens of TB of time-varying AMR (adaptive mesh refinement) volume data generated as a result of numerical model simulation into optimized data that can be used for various XR devices. AMR volume data is a useful data format for complex modeling and simulation, and it can efficiently express materials such as star clusters and gases that exist in the very wide outer space used in this study. we analyzes the metadata of AMR data, samples it at low resolution, optimizes information in important areas, and converts it into a data set that can be used even on relatively low performance XR devices. Finally, we introduces how the optimized data was utilized and visualized through the development of immersive XR content using the data set.

• Journal of the Korea Computer Graphics Society
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• v.7 no.2
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• pp.21-28
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• 2001

As a result of the recent explosive growth of scientific data, extremely large volume datasets have become increasingly commonplace. While several texture-based volume rendering algorithms have been proposed, most of them focused on volumes smaller than the hardware's available texture memory. This paper presents a new parallel volume rendering scheme for very large static and time-varying data on a multipipe system architecture. Our scheme subdivides large volumes dynamically into smaller bricks, and assigns them adaptively to graphics pipes to minimize the costs of texture swapping. With the new method, Phong shaded images can be easily created by computing the gradients on the fly and using the color matrix feature of OpenGL. We report experimental results on an SGI Onyx2 for the various large datasets.

• Environmental and Resource Economics Review
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• v.33 no.2
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• pp.135-157
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• 2024

In this study, we analyzed the changes in copper consumption according to copper price fluctuations and identified the domestic copper price elasticity. A total of 408 time series data from January 1989 to December 2022 were analyzed using the vector autoregressive (VAR) model with net import volume, price, and production index as variables. In addition, to identify changes in the correlation between variables over time, the dynamic relationship between variables was identified using the time-varying vector autoregressive (TV-VAR) model. As a result of the analysis, it was confirmed that the negative price elasticity for copper is -0.1835. In addition, the interquartile range was -0.3130 ~ 0.0886, with no consistent trend over time, but mainly negative elasticity. This study can be used to quantify the expected impact of various policy proposals and changes related to minerals.

• Journal of Korean Society of Transportation
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• v.33 no.1
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• pp.61-69
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• 2015

This study compares the forecasting accuracy of five econometric models on domestic total freight volume in South Korea. Applied five models are as follows: Ordinary Least Square model, Partial Adjustment model, Reduced Autoregressive Distributed Lag model, Vector Autoregressive model, Time Varying Parameter model. Estimating models and forecasting are carried out based on annual data of domestic freight volume and an index of industrial production during 1970~2011. 1-year, 3-year, and 5-year ahead forecasting performance of five models was compared using the recursive forecasting method. Additionally, two forecasting periods were set to compare forecasting accuracy according to the size of future volatility. As a result, the Time Varying Parameter model showed the best accuracy for forecasting periods having fluctuations, whereas the Vector Autoregressive model showed better performance for forecasting periods with gradual changes.

• Journal of Korean Society of Transportation
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• v.14 no.3
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• pp.7-26
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• 1996

The estimation of real-time Origin-Destination(O-D) parameters, which gives travel demand between combinations of origin and destination points on a urban freeway network, from on-line surveillance traffic data is essential in developing an efficient ATMS strategy. On this need a real-time O-D parameter estimation model is formulated as a parameter adaptive filtering model based on the extended Kalman Filter. A Monte Carlo test have shown that the estimation of time-varying O-D parameter is possible using only traffic counts. Tests with field data produced the interesting finding that off-ramp volume predictions generated using a constant freeway O-D matrix was replaced by real-time estimates generated using the parameter adaptive filter.

• The Korean Journal of Applied Statistics
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• v.34 no.5
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• pp.773-794
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• 2021

Prediction of vehicle traffic volume is very important in planning municipal administration. It may help promote social and economic interests and also prevent traffic congestion costs. Traffic volume as a time-varying trajectory is considered as functional data. In this paper we study three functional regression models that can be used to predict an unseen trajectory of traffic volume based on already observed trajectories. We apply the methods to highway tollgate traffic volume data collected at some tollgates in Seoul, Chuncheon and Gangneung. We compare the prediction errors of the three models to find the best one for each of the three tollgate traffic volumes.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04a
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• pp.889-891
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• 2004

본 논문은 약 134 GB에 이르는 시간 가변 해양 볼륨 데이터론 효과적으로 가시화 하기 위한 두 가지 접근 방법을 제시한다. 첫 번째 방법은 고화질의 동영상을 생성하기 위한 오프라인 병렬 볼륨 렌더링 기법으로, 볼륨광선추적법과 등가면 기법을 통합한 렌더링 알고리즘을 적용하여 고해상도의 영상을 생성할 수 있다. 두 번째 방법은, 그래픽스 하드웨어 가속기능을 통해 대화식 가시화가 가능한 멀티 파이프 렌더링을 구현하는 것으로, 복수개의 그래픽스 파이프라인과 3차원 텍스춰 맵핑 가속기능을 이용해 시간의 변화에 따른 해양의 변화를 효과적으로 가시화하고 분석할 수 있다.