• 한국통신학회논문지
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• 제39A권12호
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• pp.739-745
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• 2014

압축 센싱에서 측정 행렬 A의 3s-Restricted Isometry Constant가 1/2 혹은 $1/\sqrt{3}$보다 작다면 모든 s-Sparse 벡터 $x{\in}R^N$는 측정 벡터 y=Ax 또는 잡음이 섞인 벡터 y=Ax+e로부터 Iterative Hard Thresholding (IHT) 알고리즘에 의해 복원될 수 있다. 하지만, 이러한 복원은 신호 획득 기법의 특정한 가정 하에서 실질적인 알고리즘들에 의해 보장된다. 복원을 위한 핵심적인 가정 중에 하나는 측정 행렬이 Restricted Isometry Property (RIP)를 만족해야만 하는 것인데, 이 조건은 압축 센싱의 실제 응용 환경에서 종종 만족되지 않는다. 본 논문에서는 이방성 (Anisotropic) 경우에서 Restricted Biorthogonality Property (RBOP)로 불리는 RIP의 일반화와 Oblique Pursuit으로 불리는 새로운 복구 알고리즘들을 분석한다. 또한, IHT 알고리즘들을 위해 Restricted Biorthogonality Constant의 관점에서 성공적인 Sparse 신호 복원에 대한 분석을 제시한다.

• Smart Structures and Systems
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• 제25권3호
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• pp.369-384
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• 2020

A wavefield sparse reconstruction technique based on compressed sensing is developed in this work to dramatically reduce the number of measurements. Firstly, a severely underdetermined representation of guided wavefield at a snapshot is established in the spatial domain. Secondly, an optimal compressed sensing model of guided wavefield sparse reconstruction is established based on l₁-norm penalty, where a suite of discrete cosine functions is selected as the dictionary to promote the sparsity. The regular, random and jittered undersampling schemes are compared and selected as the undersampling matrix of compressed sensing. Thirdly, a gradient projection method is employed to solve the compressed sensing model of wavefield sparse reconstruction from highly incomplete measurements. Finally, experiments with different excitation frequencies are conducted on an aluminum plate to verify the effectiveness of the proposed sparse reconstruction method, where a scanning laser Doppler vibrometer as the true benchmark is used to measure the original wavefield in a given inspection region. Experiments demonstrate that the missing wavefield data can be accurately reconstructed from less than 12% of the original measurements; The reconstruction accuracy of the jittered undersampling scheme is slightly higher than that of the random undersampling scheme in high probability, but the regular undersampling scheme fails to reconstruct the wavefield image; A quantified mapping relationship between the sparsity ratio and the recovery error over a special interval is established with respect to statistical modeling and analysis.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권5호
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• pp.2539-2554
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• 2017

Regression-based image super resolution (SR) methods have shown great advantage in time consumption while maintaining similar or improved quality performance compared to other learning-based methods. In this paper, we propose a novel single image SR method based on hierarchical regression to further improve the quality performance. As an improvement to other regression-based methods, we introduce a hierarchical scheme into the process of learning multiple regressors. First, training samples are grouped into different clusters according to their geometry similarity, which generates the structure layer. Then in each cluster, a compact dictionary can be learned by Sparse Coding (SC) method and the training samples can be further grouped by dictionary atoms to form the detail layer. Last, a series of projection matrixes, which anchored to dictionary atoms, can be learned by linear regression. Experiment results show that hierarchical scheme can lead to regression that is more precise. Our method achieves superior high quality results compared with several state-of-the-art methods.

• Journal of Information Processing Systems
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• 제17권2호
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• pp.411-425
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• 2021

In this paper, a vehicle recognition algorithm based on deep convolutional neural network and compression dictionary is proposed. Firstly, the network structure of fine vehicle recognition based on convolutional neural network is introduced. Then, a vehicle recognition system based on multi-scale pyramid convolutional neural network is constructed. The contribution of different networks to the recognition results is adjusted by the adaptive fusion method that adjusts the network according to the recognition accuracy of a single network. The proportion of output in the network output of the entire multiscale network. Then, the compressed dictionary learning and the data dimension reduction are carried out using the effective block structure method combined with very sparse random projection matrix, which solves the computational complexity caused by high-dimensional features and shortens the dictionary learning time. Finally, the sparse representation classification method is used to realize vehicle type recognition. The experimental results show that the detection effect of the proposed algorithm is stable in sunny, cloudy and rainy weather, and it has strong adaptability to typical application scenarios such as occlusion and blurring, with an average recognition rate of more than 95%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권7호
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• pp.3194-3216
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• 2018

Slow Feature Discriminant Analysis (SFDA) is a supervised feature extraction method inspired by biological mechanism. In this paper, a novel method called Two Dimensional Slow Feature Discriminant Analysis via $L_{2,1}$ norm minimization ($2DSFDA-L_{2,1}$) is proposed. $2DSFDA-L_{2,1}$ integrates $L_{2,1}$ norm regularization and 2D statically uncorrelated constraint to extract discriminant feature. First, $L_{2,1}$ norm regularization can promote the projection matrix row-sparsity, which makes the feature selection and subspace learning simultaneously. Second, uncorrelated features of minimum redundancy are effective for classification. We define 2D statistically uncorrelated model that each row (or column) are independent. Third, we provide a feasible solution by transforming the proposed $L_{2,1}$ nonlinear model into a linear regression type. Additionally, $2DSFDA-L_{2,1}$ is extended to a bilateral projection version called $BSFDA-L_{2,1}$. The advantage of $BSFDA-L_{2,1}$ is that an image can be represented with much less coefficients. Experimental results on three face databases demonstrate that the proposed $2DSFDA-L_{2,1}/BSFDA-L_{2,1}$ can obtain competitive performance.

• Animal cells and systems
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• 제1권2호
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• pp.381-388
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• 1997

Glutamate is a putative excitatory neurotransmitter in the central nervous system. The present study utilizing monoclonal antibodies against fixative-modified glutamate analyzed the distribution of glutamate-immunoreactive neuronal elements in the dog basilar pons. The glutamatergic neurons were present throughout the rostrocaudal extent of the basilar pons, predominantly to the medial and ventral subdivisions. Labelled cells were relatively sparse in the midline region of the medial nucleus and most lateral area of the lateral nucleus. The majority of glutamate-immunoreactive neuronal somata in the basilar pons was multipolar-shaped, and the size was in the range of 15-25 ${\mu}$m in diameter. Glutamate-immunoreactive axons and terminals were also observed at specific regions of the basilar pons. These observations provide evidence that this excitatory neural element functions in a multisynaptic pathway involving glutamatergic afferents to the basilar pons, pontocerebellar projection neurons, and the granule cells of the cerebellar cortex.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2000년도 추계학술대회 학술발표 논문집
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• pp.480-483
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• 2000

공학의 많은 응용분야에서 큰 회소 행렬(Large Sparse Matrices)에 대한 가장 작거나 또는 가장 큰 고유치(Eigenvalues)들을 요구하게 되는데, 이때 많이 이용되는 것은 Krylov Subspace로의 Projection방법이다. 대칭 행렬에 대해서는 Lanczos방법을, 비대칭 행렬에 대해서는 Biorhtogonal Lanczos방법을 이용할 수 있다. 이러한 기존의 알고리즘들은 새롭게 제안되는 병렬처리 시스템에서 효과적이지 못하다. 많은 프로세서를 가지는 병렬처리 컴퓨터 중에서도 분산 기억장치 시스템(Distributed Memory System)에서는 프로세서들 사이의 Data Communication에 필요한 시간을 줄이도록 해야한다. 본 논문에서는 기존의 Lanczos 알고리즘을 수정함으로써, 알고리즘의 동기점(Synchronization Point)을 줄이고 병렬화를 위한 입상(Granularity)을 증가시켜서 MPP인 Cray T3E에서 Data Communication에 필요한 시간을 줄인다. 많은 프로세서를 사용하는 경우 수정된 알고리즘이 기존의 알고리즘에 비해 더 나은 speedup을 보여준다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권3호
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• pp.1287-1300
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• 2018

Target-tracking algorithm based on ad hoc camera sensor networks (ACSNs) utilizes the distributed observation capability of nodes to achieve accurate target tracking. A compressed sensing-based multiple-target tracking algorithm (CSMTTA) for ACSNs is proposed in this work based on the study of camera node observation projection model and compressed sensing model. The proposed algorithm includes reconfiguration of observed signals and evaluation of target locations. It reconfigures observed signals by solving the convex optimization of L1-norm least and forecasts node group to evaluate a target location by the motion features of the target. Simulation results show that CSMTTA can recover the subtracted observation information accurately under the condition of sparse sampling to a high target-tracking accuracy and accomplish the distributed tracking task of multiple mobile targets.

• 한국원자력학회:학술대회논문집
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• 한국원자력학회 1997년도 추계학술발표회논문집(1)
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• pp.329-334
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• 1997

A tomography algorithm to maximize the entropy of image using Lagrangian multiplier technique and conjugate gradient method has been designed for the measurement of 2D spatial distribution of intense neutral beams of KSTAR NBI(Korea Superconducting Tokamak Advanced Research Neutral Beam Injector) which is now being designed. A possible detection system was assumed and a numerical simulation has been implemented to test the reconstruction quality of given beam profiles. This algorithm has the good applicability for sparse projection data and thus, can be used for the neutral beam tomography.

• Current Optics and Photonics
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• 제5권5호
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• pp.562-575
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• 2021

As a new type of spectrometer, that based on filters with different transmittance features attracts a lot of attention for its advantages such as small-size, low cost, and simple optical structure. It uses post-processing algorithms to achieve target spectrum reconstruction; therefore, the performance of the spectrometer is severely affected by noise. The influence of noise on the spectral reconstruction results is studied in this paper, and suggestions for solving the spectral reconstruction problem under noisy conditions are given. We first list different spectral reconstruction methods, and through simulations demonstrate that these methods show unsatisfactory performance under noisy conditions. Then we propose to apply the gradient projection for sparse reconstruction (GRSR) algorithm to the spectral reconstruction method. Simulation results show that the proposed method can significantly reduce the influence of noise on the spectral reconstruction process. Meanwhile, the accuracy of the spectral reconstruction results is dramatically improved. Therefore, the practicality of the filter-based spectrometer will be enhanced.