• 대한전기학회논문지:전력기술부문A
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• 제48권5호
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• pp.658-663
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• 1999

2D sliced CT images hardly express the human disease in a space. This space expression can be reconstructed into 3D image by piling up the CT sliced image in succession. In medical image, in order to get the reconstructed 3D images, expensive system or much calculation time is needed. But by changing the method of reconstruction procedure and limit the range, the reconstruction time could be reduced. In this study, to reduce the processing time and memory, we suggested a method of interpolation and ray casting processing at the same time in a limited range. Such a limited range processing have advantages that we could reduce the unnecessary interpolation and ray casting. Through a experiment, it is founded that the reconstruction time and the memory was much reduced.

• Smart Structures and Systems
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• 제29권4호
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• pp.599-616
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• 2022

Bridge displacement contains vital information for bridge condition and performance. Due to the limits of direct displacement measurement methods, the indirect displacement reconstruction methods based on the strain or acceleration data are also developed in engineering applications. There are still some deficiencies of the displacement reconstruction methods based on strain or acceleration in practice. This paper proposed a novel method based on long short-term memory (LSTM) networks to reconstruct the bridge dynamic displacements with the strain and acceleration data source. The LSTM networks with three hidden layers are utilized to map the relationships between the measured responses and the bridge displacement. To achieve the data fusion, the input strain and acceleration data need to be preprocessed by normalization and then the corresponding dynamic displacement responses can be reconstructed by the LSTM networks. In the numerical simulation, the errors of the displacement reconstruction are below 9% for different load cases, and the proposed method is robust when the input strain and acceleration data contains additive noise. The hyper-parameter effect is analyzed and the displacement reconstruction accuracies of different machine learning methods are compared. For experimental verification, the errors are below 6% for the simply supported beam and continuous beam cases. Both the numerical and experimental results indicate that the proposed data fusion method can accurately reconstruct the displacement.

• Wind and Structures
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• 제38권1호
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• pp.75-91
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• 2024

Reliable wind signal reconstruction can be beneficial to the operational safety of long-span bridges. Non-Gaussian characteristics of wind signals make the reconstruction process challenging. In this paper, non-Gaussian wind signals are converted into a combined prediction of two kinds of features, actual wind speeds and wind angles of attack. First, two decomposition techniques, empirical mode decomposition (EMD) and variational mode decomposition (VMD), are introduced to decompose wind signals into intrinsic mode functions (IMFs) to reduce the randomness of wind signals. Their principles and applicability are also discussed. Then, four artificial intelligence (AI) algorithms are utilized for wind signal reconstruction by combining the particle swarm optimization (PSO) algorithm with back propagation neural network (BPNN), support vector regression (SVR), long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), respectively. Measured wind signals from a bridge site in a deep-cutting gorge are taken as experimental subjects. The results showed that the reconstruction error of high-frequency components of EMD is too large. On the contrary, VMD fully extracts the multiscale rules of the signal, reduces the component complexity. The combination of VMD-PSO-Bi-LSTM is demonstrated to be the most effective among all hybrid models.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권6호
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• pp.2497-2517
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• 2020

For compressed sensing (CS) applications, it is significant to construct deterministic measurement matrices with good practical features, including good sensing performance, low memory cost, low computational complexity and easy hardware implementation. In this paper, a deterministic construction method of bipolar measurement matrices is presented based on binary sequence family (BSF). This method is of interest to be applied for sparse signal restore and image block CS. Coherence is an important tool to describe and compare the performance of various sensing matrices. Lower coherence implies higher reconstruction accuracy. The coherence of proposed measurement matrices is analyzed and derived to be smaller than the corresponding Gaussian and Bernoulli random matrices. Simulation experiments show that the proposed matrices outperform the corresponding Gaussian, Bernoulli, binary and chaotic bipolar matrices in reconstruction accuracy. Meanwhile, the proposed matrices can reduce the reconstruction time compared with their Gaussian counterpart. Moreover, the proposed matrices are very efficient for sensing performance, memory, complexity and hardware realization, which is beneficial to practical CS.

• 감성과학
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• 제11권3호
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• pp.397-418
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• 2008

본 연구는 주관적 안녕감이 긍정적인 사건을 인식하는 비율과 나아가 기억을 재구성하는 인지적 과정에 있어서 어떠한 영향을 미치는지 알아보기 위해 시행되었다. 199명의 학부생들이 일상적으로 일어날법한 긍정적인 사건과 부정적인 사건 40개를 기술한 문장들을 본 후 이에 대한 회상 과제와 재인 과제를 수행하였다. 먼저 사건을 기술한 문장을 본 후 긍정적인 사건과 부정적인 사건에 대한 비율을 응답하게 한 결과, 삶에 대한 만족도가 높은 참가자들이 긍정적인 문장의 비율이 월등히 높은 조건에서 긍정적인 사건의 비율을 실제보다 더 높게 보고하였으며, 또한 긍정적인 비율이 높은 조건에서 주관적 안녕감이 높은 참가자들은 긍정적인 문장을 더 많이 회상했다. 참가자들이 처음 보고한 긍정적인 사건의 비율과 실제 회상한 기억들 중 긍정적인 사건의 비율은 정적인 상관을 가졌다. 그러나 예상과는 달리 재인 과제에서는 참가자들은 주관적 안녕감 수준에 따라 유의미하게 차이나는 오류를 보이지 않았다. 마지막으로 이 연구의 이론적 함의와 후속 연구를 위한 제언이 논의되었다.

• 한국컴퓨터그래픽스학회논문지
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• 제21권5호
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• pp.37-44
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• 2015

본 논문에서는 전통적으로 삼차원 모델 복원에 사용되는 볼륨 기반 자료 구조의 한계점을 극복하기 위해 평면 해시 구조를 볼륨 구조와 상호보완적으로 사용하는 하이브리드 모델 표현을 제안한다. 실내 환경에 대한 삼차원 모델 복원은 좁은 공간에 대한 정밀한 복원 결과를 얻기 위해 볼륨 기반의 자료 구조를 사용하였으나, 이러한 볼륨 기반의 자료 구조는 메모리의 사용량이 많아 대규모 공간에 대한 삼차원 복원으로 확장이 용이하지 못하였다. 본 논문에서는 이러한 기존 삼차원 모델 복원의 확장성을 증가시키기 위해 메모리를 효율적으로 사용하는 평면 해시 모델 구조를 제안한다. 또한 이러한 제안된 평면 해시 모델 구조를 넓고 단순한 평면 복원을 위해 사요하고, 좁고 디테일한 공간 복원에는 기존 볼륨 구조를 동시에 사용하는 하이브리드 복원 방법을 사용한다. 제안된 기법은 GPU 상에서 구현되어 공간을 실시간으로 복원 가능하다.

• 한국방송∙미디어공학회:학술대회논문집
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• 한국방송공학회 2012년도 추계학술대회
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• pp.52-53
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• 2012

Belief propagation (BP) is a commonly used global energy minimization algorithm for solving stereo matching problem in 3D reconstruction. However, it requires large memory bandwidth and data size. In this paper, we propose a novel memory-efficient algorithm of BP in stereo matching on the Graphics Processing Units (GPU). The data size and transfer bandwidth are significantly reduced by storing only a part of the whole message. In order to maintain the accuracy of the matching result, the local messages are reconstructed using shared memory available in GPU. Experimental result shows that there is almost an order of reduction in the global memory consumption, and 21 to 46% saving in memory bandwidth when compared to the conventional algorithm. The implementation result on a recent GPU shows that we can obtain 22.8 times speedup in execution time compared to the execution on CPU.

• 천문학회지
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• 제49권4호
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• pp.157-162
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• 2016

High resolution reconstruction technology is developed to help enhance the spatial resolution of observational images for ground-based solar telescopes, such as speckle masking. Near real-time reconstruction performance is achieved on a high performance cluster using the Message Passing Interface (MPI). However, much time is spent in reconstructing solar subimages in such a speckle reconstruction. We design and implement a novel parallel method for speckle masking reconstruction of solar subimage on a shared memory machine using the OpenMP. Real tests are performed to verify the correctness of our codes. We present the details of several parallel reconstruction steps. The parallel implementation between various modules shows a great speed increase as compared to single thread serial implementation, and a speedup of about 2.5 is achieved in one subimage reconstruction. The timing result for reconstructing one subimage with 256×256 pixels shows a clear advantage with greater number of threads. This novel parallel method can be valuable in real-time reconstruction of solar images, especially after porting to a high performance cluster.

• ETRI Journal
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• 제44권6호
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• pp.1034-1046
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• 2022

The learning-based multiview stereo (MVS) methods for three-dimensional (3D) reconstruction generally use 3D volumes for depth inference. The quality of the reconstructed depth maps and the corresponding point clouds is directly influenced by the spatial resolution of the 3D volume. Consequently, these methods produce point clouds with sparse local regions because of the lack of the memory required to encode a high volume of information. Here, we apply the atrous spatial pyramid pooling (ASPP) module in MVS methods to obtain dense feature maps with multiscale, long-range, contextual information using high receptive fields. For a given 3D volume with the same spatial resolution as that in the MVS methods, the dense feature maps from the ASPP module encoded with superior information can produce dense point clouds without a high memory footprint. Furthermore, we propose a 3D loss for training the MVS networks, which improves the predicted depth values by 24.44%. The ASPP module provides state-of-the-art qualitative results by constructing relatively dense point clouds, which improves the DTU MVS dataset benchmarks by 2.25% compared with those achieved in the previous MVS methods.

• Nuclear Medicine and Molecular Imaging
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• 제43권5호
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• pp.443-450
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• 2009

목적: 기존의 영상 재구성은 간소화된 투사 물리 모델을 사용하고 있다. 하지만 3D 재구성과 같은 실제적인 물리 모델은 시간이 많이 걸려서 임상에서 모든 데이터에 적용하기 힘들고, 복잡한 물리모델을 설명하기 위해 큰 메모리를 사용하면 한대의 일반적인 재구성 머신으로는 불가능하다. 개인 컴퓨터들에서도 큰 규모의 기술을 가능하게 하기위해, 병렬 연산을 이용한 빠른 재구성의 현실적인 분산메모리 모델을 제시한다. 대상 및 방법: 실제로 구현하는 가능성을 보기 위해 가상 컴퓨터들을 이용하여 선행 연구를 진행하였고, 다양한 가능성을 테스트하기 위해 상용서비스를 하고 있는 슈퍼컴퓨터(Tachyon)에서 성능 테스트를 하였다. 가장 많이 사용되는 2D 투사 영상과 실제적인 물리 모델인 3D 응답라인을 이용한 기댓값 최대화 알고리즘을 테스트하였다. 스터디 중 특정 반복횟수 이후에 속도가 최대 6배까지 느려지는 현상이 발견되어 컴파일러 최적화를 통해 병렬 효율의 극대화를 꾀하였다. 결과: Linux에서 MPICH와 NFS를 이용하여, 여러 컴퓨터에서 하나의 프로그램으로 분산 연산이 가능하였다. 병렬 연산을 했을 때 동일한 반복 연산에서 재구성된 영상간의 차이가 실수의 유효숫자(6bit) 정도임을 확인하였다. 2배의 연상장치를 사용했을 때 1.96배의 좋은 병렬화 효율을 보여주었다. 반복 연산 횟수가 증가함에 따라 느려지는 현상은 SSE를 이용한 Vectorization 방법을 사용했을 때 해결할 수 있었다. 결론: 이번 연구를 통해 일반 컴퓨터들을 이용한 현실적인 병렬 컴퓨터 시스템을 구성하여, 작은 메모리의 단일 일반 컴퓨터로는 불가능한 간단화 할 수 없는 복잡한 물리 과정도 영상 재구성 방법에 사용 가능하게 되었다.