• International conference on construction engineering and project management
• /
• 2013.01a
• /
• pp.60-65
• /
• 2013

Introducing the concept of construction safety in the design/engineering phase can improve the efficiency and effectiveness of safety management on construction sites. In this sense, further improvements for safety can be made in the design/engineering phase through the development of (1) an automated hazard identification process that is little dependent on user knowledge, (2) an automated construction schedule generation to accommodate varying hazard information over time, and (3) a visual representation of the results that is easy to understand. In this paper, we formulate an automated hazard identification framework for construction safety by extracting hazard information from related regulations to eliminate human interventions, and by utilizing a visualization technique in order to enhance users' understanding on hazard information. First, the hazard information is automatically extracted from textual safety and health regulations (i.e., Occupational Safety Health Administration (OSHA) Standards) by using natural language processing (NLP) techniques without users' interpretations. Next, scheduling and sequencing of the construction activities are automatically generated with regard to the 3D building model. Then, the extracted hazard information is integrated into the geometry data of construction elements in the industry foundation class (IFC) building model using a conformity-checking algorithm within the open source 3D computer graphics software. Preliminary results demonstrate that this approach is advantageous in that it can be used in the design/engineering phases of construction without the manual interpretation of safety experts, facilitating the designers' and engineers' proactive consideration for improving safety management.

• The KIPS Transactions:PartB
• /
• v.15B no.6
• /
• pp.553-560
• /
• 2008

The amount of image data used in medical institution is increasing rapidly with great development of medical technology. Therefore, an automation method that use image processing description, rather than manual macrography of doctors, is required for the analysis large medical data. Specially, medical image registration, which is the process of finding the spatial transform that maps points from one image to the corresponding points in another image, and 3D analysis and visualization skills for a series of 2D images are essential technologies. However, a high establishment cost raise a budget problem, and hence small scaled hospitals hesitate importing these medical visualizing system. In this paper, we propose a visualization system which allows user to manage datasets and manipulates medical images registration using an open source graphics tool - VTK(Visualization Tool Kit). The propose of our research is to get more accurate 3D diagnosis system in less expensive price, compared to existing systems.

• Journal of Biomedical Engineering Research
• /
• v.34 no.3
• /
• pp.135-140
• /
• 2013

We have developed an ultra wide-field of view Optical Coherence Tomography(OCT) which has capability to 2D and 3D views of cross-sectional structure of in vivo human retina. Conventional OCT has a limitation in visualizing the entire retina due to a reduced field of view. We designed an optical setup to significantly improve the lateral scanning range to be more than 20 mm. The entire human retinal structure in 2D and 3D was reported in this paper with the developed OCT system. Also, we empirically searched an optimized image size for real time visualization by analyzing variation of the frame rate with different lateral scan points. The size was concluded to be $1024{\times}2000{\times}300$ pixels which took 9 seconds for visualization.

• Proceedings of the Korean Society of Computer Information Conference
• /
• 2021.07a
• /
• pp.655-658
• /
• 2021

본 논문에서는 GPU 아키텍처를 이용하여 적응형 부호 거리장을 최적화하여 빠르게 구축하고 시각화 할 수 있는 방법에 대해 제안한다. 쿼드트리를 효율적으로 GPU 메모리로 전달하고, 이를 활용하여 삼각형에 대해 유클리디안 거리를 각 스레드 별로 병렬처리하여 최단 거리를 찾는다. 이 과정에서 GPU를 사용하여 삼각형으로 구성된 3D 메쉬로부터 빠르게 적응형 부호 거리장을 계산할 수 있는 최적화 기법과 절단면 보기, 특정 위치의 값 조회, 실시간 레이트레이싱 및 충돌처리 작업을 빠르고 효율적으로 수행할 수 있는지를 보여준다. 또한, 제안하는 프레임워크를 활용하면 하이 폴리곤 메쉬도 1초 내외로 부호 거리장을 계산할 수 있기 때문에 강체뿐만 아니라 변형체에도 충분히 활용될 수 있다.

• Journal of Broadcast Engineering
• /
• v.24 no.6
• /
• pp.1064-1071
• /
• 2019

Multiview stereo matching algorithm is used to reconstruct 3D shape from a set of 2D images. Conventional multiview stereo algorithms have been implemented on high-performance hardware due to the heavy complexity that contains a large number of calculations in each step. However, as the performance of mobile graphics processors has recently increased rapidly, complex computer vision algorithms can now be implemented on mobile devices like a smartphone and an embedded board. In this paper we parallelize an multiview stereo algorithm using OpenCL on mobile GPU and provide various optimization techniques on the embedded hardware with limited resource.

• Journal of Biomedical Engineering Research
• /
• v.20 no.2
• /
• pp.155-164
• /
• 1999

In this paper, the prototype of SIS(Simulated Implantation System) for human femoral head is introduced. SIS is a software which carries on a virtual femoral head replacement surgery including 3d visualization as well as various numeric analyses between a patient's femur and artificial femur through certain stages of the image processing and of the computer graphics. Also, processes required after acquiring consecutive CT-images and projected image of an artificial femur are discussed, and the corresponding results including prototype of SIS are given.

• Journal of Broadcast Engineering
• /
• v.19 no.6
• /
• pp.866-876
• /
• 2014

Generating of digital hologram of video contents with computer graphics(CG) requires natural fusion of 3D information between real and virtual. In this paper, we propose the system which can fuse real-virtual 3D information naturally and fast generate the digital hologram of fused results using multiple-GPUs based computer-generated-hologram(CGH) computing part. The system calculates camera projection matrix of RGB-Depth camera, and estimates the 3D information of virtual object. The 3D information of virtual object from projection matrix and real space are transmitted to Z buffer, which can fuse the 3D information, naturally. The fused result in Z buffer is transmitted to multiple-GPUs based CGH computing part. In this part, the digital hologram of fused result can be calculated fast. In experiment, the 3D information of virtual object from proposed system has the mean relative error(MRE) about 0.5138% in relation to real 3D information. In other words, it has the about 99% high-accuracy. In addition, we verify that proposed system can fast generate the digital hologram of fused result by using multiple GPUs based CGH calculation.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2010.11a
• /
• pp.1784-1787
• /
• 2010

General Purpose Graphic Processing Unit (GPGPU) computing is a technique that utilizes the high-performance many-core processors of high-end graphic cards for general-purpose computations such as 3D graphics, video/image processing, computer vision, scientific computing, HPC and many more. GPGPUs offer a vast amount of raw computing power, but programming is extremely challenging because of hardware idiosyncrasies. The open computing language (OpenCL) has been proposed as a vendor-independent GPGPU programming interface. OpenCL is very close to the hardware and thus does little to increase GPGPU programmability. In this paper we present how a set of digital camera image signal processing (ISP) filters can be realized efficiently on GPGPUs using OpenCL. Although we found ISP filters to be memory-bound computations, our GPGPU implementations achieve speedups of up to a factor of 64.8 over their sequential counterparts. On GPGPUs, our proposed optimizations achieved speedups between 145% and 275% over their baseline GPGPU implementations. Our experiments have been conducted on a Geforce GTX 275; because of OpenCL we expect our optimizations to be applicable to other architectures as well.

• Journal of Radiation Protection and Research
• /
• v.47 no.3
• /
• pp.111-133
• /
• 2022

The exciting advancement related to the "modeling of digital human" in terms of a computational phantom for radiation dose calculations has to do with the latest hype related to deep learning. The advent of deep learning or artificial intelligence (AI) technology involving convolutional neural networks has brought an unprecedented level of innovation to the field of organ segmentation. In addition, graphics processing units (GPUs) are utilized as boosters for both real-time Monte Carlo simulations and AI-based image segmentation applications. These advancements provide the feasibility of creating three-dimensional (3D) geometric details of the human anatomy from tomographic imaging and performing Monte Carlo radiation transport simulations using increasingly fast and inexpensive computers. This review first introduces the history of three types of computational human phantoms: stylized medical internal radiation dosimetry (MIRD) phantoms, voxelized tomographic phantoms, and boundary representation (BREP) deformable phantoms. Then, the development of a person-specific phantom is demonstrated by introducing AI-based organ autosegmentation technology. Next, a new development in GPU-based Monte Carlo radiation dose calculations is introduced. Examples of applying computational phantoms and a new Monte Carlo code named ARCHER (Accelerated Radiation-transport Computations in Heterogeneous EnviRonments) to problems in radiation protection, imaging, and radiotherapy are presented from research projects performed by students at the Rensselaer Polytechnic Institute (RPI) and University of Science and Technology of China (USTC). Finally, this review discusses challenges and future research opportunities. We found that, owing to the latest computer hardware and AI technology, computational human body models are moving closer to real human anatomy structures for accurate radiation dose calculations.

• Journal of the Institute of Electronics Engineers of Korea CI
• /
• v.48 no.1
• /
• pp.90-100
• /
• 2011

We present a parallel bi-conjugate gradient (Bi-CG) matrix solver for large scale Bio-FET simulations based on recent graphics processing units (GPUs) which can realize a large-scale parallel processing with very low cost. The proposed method is focused on solving the Poisson equation in a parallel way, which requires massive computational resources in not only semiconductor simulation, but also other various fields including computational fluid dynamics and heat transfer simulations. As a result, our solver is around 30 times faster than those with traditional methods based on single core CPU systems in solving the Possion equation in a 3D FDM (Finite Difference Method) scheme. The proposed method is implemented and tested based on NVIDIA's CUDA (Compute Unified Device Architecture) environment which enables general purpose parallel processing in GPUs. Unlike other similar GPU-based approaches which apply usually 32-bit single-precision floating point arithmetics, we use 64-bit double-precision operations for better convergence. Applications on the CUDA platform are rather easy to implement but very hard to get optimized performances. In this regard, we also discuss the optimization strategy of the proposed method.