• Nuclear Engineering and Technology
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• v.56 no.7
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• pp.2498-2515
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• 2024

In this study, we propose a fully parallelized adaptive particle refinement (APR) algorithm for smoothed particle hydrodynamics (SPH) to construct a stable and efficient multi-resolution computing system for nuclear safety analysis. The APR technique, widely employed by SPH research groups to adjust local particle resolutions, currently operates on a serialized algorithm. However, this serialized approach diminishes the computational efficiency of the system, negating the advantages of acceleration achieved through high-performance computing devices. To address this drawback, we propose a fully parallelized APR algorithm designed to enhance both efficiency and computational accuracy, facilitated by a new adaptive smoothing length model. For model validation, we simulated both hydrostatic and hydrodynamic benchmark cases in 2D and 3D environments. The results demonstrate improved computational efficiency compared to the conventional SPH method and APR with a serialized algorithm, and the model's accuracy was confirmed, revealing favorable outcomes near the resolution interface. Through the analysis of jet breakup, we verified the performance and accuracy of the model, emphasizing its applicability in practical nuclear safety analysis.

• Korean Journal of Computational Design and Engineering
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• v.16 no.4
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• pp.285-291
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• 2011

A pose estimation process from medical images is calculating locations and orientations of objects obtained from Computed Tomography (CT) volume data utilizing X-ray images from two directions. In this process, digitally reconstructed radiograph (DRR) images of spatially transformed objects are generated and compared to X-ray images repeatedly until reasonable transformation matrices of the objects are found. The DRR generation and image comparison take majority of the total time for this pose estimation. In this paper, a fast DRR generation technique based on GPU parallel computing is introduced. A volume ray-casting algorithm is explained with brief vector operations and a parallelization technique of the algorithm using Compute Unified Device Architecture (CUDA) is discussed. This paper also presents the implementation results and time measurements comparing to those from pure-CPU implementation and open source toolkit.

• Journal of Information Processing Systems
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• v.17 no.2
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• pp.227-241
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• 2021

Object detection techniques based on deep learning such as YOLO have high detection performance and precision in a single channel video stream. In order to expand to multiple channel object detection in real-time, however, high-performance hardware is required. In this paper, we propose a novel back-end server framework, a real-time AI vision platform (RAVIP), which can extend the object detection function from single channel to simultaneous multi-channels, which can work well even in low-end server hardware. RAVIP assembles appropriate component modules from the RODEM (real-time object detection module) Base to create per-channel instances for each channel, enabling efficient parallelization of object detection instances on limited hardware resources through continuous monitoring with respect to resource utilization. Through practical experiments, RAVIP shows that it is possible to optimize CPU, GPU, and memory utilization while performing object detection service in a multi-channel situation. In addition, it has been proven that RAVIP can provide object detection services with 25 FPS for all 16 channels at the same time.

• Journal of Broadcast Engineering
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• v.18 no.6
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• pp.835-849
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• 2013

Joint Collaborative Team on Video Coding (JCT-VC), which have founded ISO/IEC MPEG and ITU-T VCEG, has standardized High Efficiency Video Coding (HEVC). Standardization of HEVC has started with purpose of twice or more coding performance compared to H.264/AVC. However, flexible and hierarchical coding block and recursive coding structure are problems to overcome of HEVC standard. Many fast encoding algorithms for reducing computational complexity of HEVC encoder have been proposed. However, it is hard to implement a real-time HEVC encoder only with those fast encoding algorithms. In this paper, for implementation of software-based real-time HEVC encoder, data-level parallelism using SIMD instructions and CPU/GPU multi-threading methods are proposed. And we also proposed appropriate operations and functional modules to apply the proposed methods on HM 10.0 software. Evaluation of the proposed methods implemented on HM 10.0 software showed 20-30fps for $832{\times}480$ sequences and 5-10fps for $1920{\times}1080$ sequences, respectively.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.381-394
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• 2016

Graphical model represents conditional dependencies between variables as a graph with nodes and edges. It is widely used in various fields including physics, economics, and biology to describe complex association. Conditional dependencies can be estimated from a inverse covariance matrix, where zero off-diagonal elements denote conditional independence of corresponding variables. This paper proposes a efficient BCDR (block coordinate descent with random permutation) algorithm using graphics processing units and random permutation for the CONCORD (convex correlation selection method) based on the BCD (block coordinate descent) algorithm, which estimates a inverse covariance matrix based on pseudo-likelihood. We conduct numerical studies for two network structures to demonstrate the efficiency of the proposed algorithm for the CONCORD in terms of computation times.

• Journal of Broadcast Engineering
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• v.27 no.3
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• pp.341-350
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• 2022

In this paper, we propose a deep learning based on-device augmented reality (AR) system in which multiple input images are used to implement the correct occlusion in a real environment. The proposed system is composed of three technical steps; camera pose estimation, depth estimation, and object augmentation. Each step employs various mobile frameworks to optimize the processing on the on-device environment. Firstly, in the camera pose estimation stage, the massive computation involved in feature extraction is parallelized using OpenCL which is the GPU parallelization framework. Next, in depth estimation, monocular and multiple image-based depth image inference is accelerated using the mobile deep learning framework, i.e. TensorFlow Lite. Finally, object augmentation and occlusion handling are performed on the OpenGL ES mobile graphics framework. The proposed augmented reality system is implemented as an application in the Android environment. We evaluate the performance of the proposed system in terms of augmentation accuracy and the processing time in the mobile as well as PC environments.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2013.06a
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• pp.119-122
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• 2013

ITU-T VCEG 과 ISO/IEC MPEG 이 공동으로 구성한 JCT-VC (Joint Collaborative Team on Video Coding)이 표준화를 진행 중인 HEVC (High Efficiency Video Coding)은 H.264/AVC 대비 약 2 배의 압축효율을 갖는다. 하지만, 계층적 구조를 갖는 가변크기 블록의 사용과 재귀적 부호화 구조에 따른 인코더의 복잡도 증가는 개선해야 할 문제점으로 지적되고 있다. 본 논문에서는 현재 표준화가 진행 중인 HEVC 인코더의 실시간 구현을 위한 SIMD 명령어를 이용한 data-level 병렬화 기법, CPU 및 GPU 를 이용한 multi-threading 기법과 같은 다양한 병렬화 기법을 소개한다. 또한, 이러한 병렬화 기법들을 HEVC 인코더에 적용하기 위해 적합한 연산 및 기능 모듈에 대하여 소개한다. 본 연구를 통하여 HM (HEVC reference model)에 적용한 결과 $832{\times}480$ 영상의 경우 20-30fps 의 부호화 속도를 나타냈으며, $1920{\times}1080$ 영상의 경우 5-10fps 의 부호화 속도를 나타내었다.

• Journal of the Korea Computer Graphics Society
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• v.30 no.3
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• pp.149-158
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• 2024

Photo booths have traditionally provided a fun and easy way to capture and print photos to cherish memories. These booths allow individuals to capture their desired poses and props, sharing memories with friends and family. To enable diverse expressions, generative AI-powered photo booths have emerged. However, existing AI photo booths face challenges such as difficulty in taking group photos, inability to accurately reflect user's poses, and the challenge of applying different concepts to individual subjects. To tackle these issues, we present CINEMAPIC, a photo booth system that allows users to freely choose poses, positions, and concepts for their photos. The system workflow includes three main steps: pre-processing, generation, and post-processing to apply individualized concepts. To produce high-quality group photos, the system generates a transparent image for each character and enhances the backdrop-composited image through a small number of denoising steps. The workflow is accelerated by applying an optimized diffusion model and GPU parallelization. The system was implemented as a prototype, and its effectiveness was validated through a user study and a large-scale pilot operation involving approximately 400 users. The results showed a significant preference for the proposed system over existing methods, confirming its potential for real-world photo booth applications. The proposed CINEMAPIC photo booth is expected to lead the way in a more creative and differentiated market, with potential for widespread application in various fields.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.153-163
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• 2017

The objective of this study is to enhance the model's speed of estimating weather variables (e.g., minimum/maximum temperature, sunshine hour, PRISM (Parameter-elevation Regression on Independent Slopes Model) based precipitation), which are applied to the Agrometeorological Early Warning System (http://www.agmet.kr). The current process of weather estimation is operated on high-performance multi-core CPUs that have 8 physical cores and 16 logical threads. Nonetheless, the server is not even dedicated to the handling of a single county, indicating that very high overhead is involved in calculating the 10 counties of the Seomjin River Basin. In order to reduce such overhead, several cache and parallelization techniques were used to measure the performance and to check the applicability. Results are as follows: (1) for simple calculations such as Growing Degree Days accumulation, the time required for Input and Output (I/O) is significantly greater than that for calculation, suggesting the need of a technique which reduces disk I/O bottlenecks; (2) when there are many I/O, it is advantageous to distribute them on several servers. However, each server must have a cache for input data so that it does not compete for the same resource; and (3) GPU-based parallel processing method is most suitable for models such as PRISM with large computation loads.