• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권4호
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• pp.1110-1127
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• 2022

To improve the training efficiency and generalization performance of a support vector machine (SVM) in a large-scale set, an optimal SVM learning method based on adaptive sparse sampling and the granularity shift factor is presented. The proposed method combines sampling optimization with learner optimization. First, an adaptive sparse sampling method based on the potential function density clustering is designed to adaptively obtain sparse sampling samples, which can achieve a reduction in the training sample set and effectively approximate the spatial structure distribution of the original sample set. A granularity shift factor method is then constructed to optimize the SVM decision hyperplane, which fully considers the neighborhood information of each granularity region in the sparse sampling set. Experiments on an artificial dataset and three benchmark datasets show that the proposed method can achieve a relatively higher training efficiency, as well as ensure a good generalization performance of the learner. Finally, the effectiveness of the proposed method is verified.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제12권10호
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• pp.5015-5038
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• 2018

In this paper, a novel spectral-spatial joint sparse representation algorithm for hyperspectral image classification is proposed based on multi-layer superpixels in various scales. Superpixels of various scales can provide complete yet redundant correlated information of the class attribute for test pixels. Therefore, we design a joint sparse model for a test pixel by sampling similar pixels from its corresponding superpixels combinations. Firstly, multi-layer superpixels are extracted on the false color image of the HSI data by principal components analysis model. Secondly, a group of discriminative sampling pixels are exploited as reconstruction matrix of test pixel which can be jointly represented by the structured dictionary and recovered sparse coefficients. Thirdly, the orthogonal matching pursuit strategy is employed for estimating sparse vector for the test pixel. In each iteration, the approximation can be computed from the dictionary and corresponding sparse vector. Finally, the class label of test pixel can be directly determined with minimum reconstruction error between the reconstruction matrix and its approximation. The advantages of this algorithm lie in the development of complete neighborhood and homogeneous pixels to share a common sparsity pattern, and it is able to achieve more flexible joint sparse coding of spectral-spatial information. Experimental results on three real hyperspectral datasets show that the proposed joint sparse model can achieve better performance than a series of excellent sparse classification methods and superpixels-based classification methods.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권5호
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• pp.2468-2483
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• 2017

The construction of completely random sensing matrices of Compressive Sensing requires a large number of random numbers while that of deterministic sensing operators often needs complex mathematical operations. Thus both of them have difficulty in acquiring large signals efficiently. This paper focuses on the enhancement of the practicability of the structurally random matrices and proposes a semi-deterministic sensing matrix called Partial Kronecker product of Identity and Hadamard (PKIH) matrix. The proposed matrix can be viewed as a sub matrix of a well-structured, sparse, and orthogonal matrix. Only the row index is selected at random and the positions of the entries of each row are determined by a deterministic sequence. Therefore, the PKIH significantly decreases the requirement of random numbers, which has a complex generating algorithm, in matrix construction and further reduces the complexity of sampling. Besides, in order to process large signals, the corresponding fast sampling algorithm is developed, which can be easily parallelized and realized in hardware. Simulation results illustrate that the proposed sensing matrix maintains almost the same performance but with at least 50% less random numbers comparing with the popular sampling matrices. Meanwhile, it saved roughly 15%-35% processing time in comparison to that of the SRM matrices.

• 한국방사선학회논문지
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• 제11권7호
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• pp.655-661
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• 2017

방사선 치료 전 환자 위치 확인을 위해 수행하는 콘빔 CT 촬영에서 환자 선량 감소를 위해 Sparse view CT가 사용되고 있다. 본 연구는 시뮬레이션과 실험을 통해 선형보간법과 inpainting 방법을 이용하여 사이노그램의 sparse 데이터 복원하고 평가하는 것이다. 사이노그램 복원은 여러 간격의 각도로 획득된 영상에 적용되었다. 복원된 사이노그램은 역투영재구성법으로 재구성되었고, 그 결과를 평균제곱근오차와 영상의 프로파일로 나타내었다. 결과에 따르면, 평균제곱근오차와 영상 프로파일은 투영 각도와 복원법에 의존하였다. 시뮬레이션과 실험 결과에서 inpainting 복원법은 선형보간법에 비해 사이노그램의 복원 측면에서 개선된 결과를 보여주었다. 따라서, inpainting 방법은 환자 선량을 감소시키면서 영상화질을 유지시키는데 기여할 수 있을 것이다.

• 한국의학물리학회지:의학물리
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• 제27권3호
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• pp.105-110
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• 2016

Sparse angular sampling has been studied recently owing to its potential to decrease the radiation exposure from computed tomography (CT). In this study, we investigated the analytic reconstruction algorithm in sparse angular sampling using the sinogram interpolation method for improving image quality and computation speed. A prototype of the spectral CT system, which has a 64-pixel Cadmium Zinc Telluride (CZT)-based photon-counting detector, was used. The source-to-detector distance and the source-to-center of rotation distance were 1,200 and 1,015 mm, respectively. Two energy bins (23~33 keV and 34~44 keV) were set to obtain two reconstruction images. We used a PMMA phantom with height and radius of 50.0 mm and 17.5 mm, respectively. The phantom contained iodine, gadolinium, calcification, and lipid. The Feld-kamp-Davis-Kress (FDK) with the sinogram interpolation method and Maximum Likelihood Expectation Maximization (MLEM) algorithm were used to reconstruct the images. We evaluated the signal-to-noise ratio (SNR) of the materials. The SNRs of iodine, calcification, and liquid lipid were increased by 167.03%, 157.93%, and 41.77%, respectively, with the 23~33 keV energy bin using the sinogram interpolation method. The SNRs of iodine, calcification, and liquid state lipid were also increased by 107.01%, 13.58%, and 27.39%, respectively, with the 34~44 keV energy bin using the sinogram interpolation method. Although the FDK algorithm with the sinogram interpolation did not produce better results than the MLEM algorithm, it did result in comparable image quality to that of the MLEM algorithm. We believe that the sinogram interpolation method can be applied in various reconstruction studies using the analytic reconstruction algorithm. Therefore, the sinogram interpolation method can improve the image quality in sparse-angular sampling and be applied to CT applications.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제14권5호
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• pp.2141-2155
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• 2020

In order to meet the requirements of background change, illumination variation, moving shadow interference and high accuracy in object detection of moving camera, and strive for real-time and high efficiency, this paper presents an object detection algorithm based on sparse approximation recursion and sparse coding migration in subspace. First, low-rank sparse decomposition is used to reduce the dimension of the data. Combining with dictionary sparse representation, the computational model is established by the recursive formula of sparse approximation with the video sequences taken as subspace sets. And the moving object is calculated by the background difference method, which effectively reduces the computational complexity and running time. According to the idea of sparse coding migration, the above operations are carried out in the down-sampling space to further reduce the requirements of computational complexity and memory storage, and this will be adapt to multi-scale target objects and overcome the impact of large anomaly areas. Finally, experiments are carried out on VDAO datasets containing 59 sets of videos. The experimental results show that the algorithm can detect moving object effectively in the moving camera with uniform speed, not only in terms of low computational complexity but also in terms of low storage requirements, so that our proposed algorithm is suitable for detection systems with high real-time requirements.

• 디지털콘텐츠학회 논문지
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• 제16권4호
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• pp.605-613
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• 2015

본 논문은 패턴인식에서 자주 사용되는 투영행렬을 희소화하는 문제를 다룬다. 최근 임베디드 시스템이 널리 사용됨에 따라 탑재되는 프로그램의 용량이 제한받는 경우가 빈번히 발생한다. 개발된 프로그램은 상수 데이터를 포함하는 경우가 많다. 예를 들어, 얼굴인식과 같은 패턴인식 프로그램의 경우 고차원 벡터를 저차원 벡터로 차원을 축소하는 투영행렬을 사용하는 경우가 많다. 인식성능 향상을 위해 영상으로부터 매우 높은 차원의 고차원 특징벡터를 추출하는 경우 투영행렬의 사이즈는 매우 크다. 최근 라소 회귀분석 방법을 이용한 RSR(rotated sparse regression) 방법론[1]이 제안되었다. 이 방법론은 여러 실험을 통해 희소행렬을 구하는 가장 우수한 알고리즘 중 하나로 평가받고 있다. 우리는 본 논문에서 RSR을 개선할 수 있는 세 가지 방법론을 제안한다. 즉, 학습데이터에서 이상치를 제거하여 일반화 성능을 높이는 방법, 학습데이터를 랜덤 샘플링하여 희소율을 높이는 방법, RSR의 목적함수에 엘라스틱 넷 회귀분석의 패널티 항을 사용한 E-RSR(elastic net-RSR) 방법을 제안한다. 우리는 실험을 통해 제안한 방법론이 인식률을 희생하지 않으며 희소율을 크게 증가시킴으로써 기존 RSR 방법론을 개선할 수 있음을 보였다.

• Current Optics and Photonics
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• 제6권3호
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• pp.288-296
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• 2022

We propose and experimentally demonstrate a novel photonic compressive sensing (CS) scheme for acquiring sparse radio frequency signals with adjustable compression ratio in this paper. The sparse signal to be measured and a pseudo-random binary sequence are modulated on consecutively connected chirped pulses. The modulated pulses are compressed into short pulses after propagating through a dispersive element. A programmable optical filter based on spatial light modulator is used to realize spectral segmentation and demultiplexing. After spectral segmentation, the compressed pulses are transformed into several sub-pulses and each of them corresponds to a measurement in CS. The major advantage of the proposed scheme lies in its adjustable compression ratio, which enables the system adaptive to the sparse signals with variable sparsity levels and bandwidths. Experimental demonstration and further simulation results are presented to verify the feasibility and potential of the approach.

• 대한전자공학회논문지SP
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• 제46권5호
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• pp.25-31
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• 2009

Compressed sensing은 기존의 Nyquist sampling 이론에 기반을 두었던 dynamic MRI에서의 시 공간 해상도의 제한을 획기적으로 향상시킴으로써, 최근 몇 년 사이, MR reconstruction 분야에서 가장 큰 이슈가 되고 있는 연구주제이다. Dynamic MRI 는 대부분 시간방향의 redundancy 가 매우 크므로, 쉽게 sparse 변환이 가능하다. 따라서 sparsity를 기본 조건으로 하는 compressed sensing은 거의 모든 dynamic MRI 에 대해 효과적으로 적용될 수 있다. 본 review 페이퍼에서는 최근 compressed sensing 에 기반을 두거나 영상의 sparsity를 이용하여 개발된 dynamic MR imaging algorithm 들을 간략히 소개하고, 비교 분석함으로써, compressed sensing과 같은 새로운 접근 방식의 dynamic MRI가 실제 임상에서 가져다 줄 발전 가능성을 제시한다.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.86-89
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• 2003

To model a numerical problem space under the limitation of available data, we need to extract sparse but key points from the space and to efficiently approximate the space with them. This study proposes a sampling method based on the search process of genetic algorithm and a space modeling method based on least-squares approximation using the summation of Gaussian functions. We conducted simulations to evaluate them for several kinds of problem spaces: DeJong's, Schaffer's, and our original one. We then compared the performance between our sampling method and sampling at regular intervals and that between our modeling method and modeling using a polynomial. The results showed that the error between a problem space and its model was the smallest for the combination of our sampling and modeling methods for many problem spaces when the number of samples was considerably small.