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Asystematic review of structured sparse learning Review

Lin-bo QIAO, Bo-feng ZHANG, Jin-shu SU, Xi-cheng LU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 4,   Pages 445-463 doi: 10.1631/FITEE.1601489

Abstract: from diverse scientific research fields and industrial development have led to increased interest in sparseStructured sparse learning encodes the structural information of the variables and has been quite successfulThese regularizations have greatly improved the efficacy of sparse learning algorithms through the useIn this article, we present a systematic review of structured sparse learning including ideas, formulationsimplementations, and compare the computational complexity of typical optimization methods to solve structured sparse

Keywords： Sparse learning     Structured sparse learning     Structured regularization    

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Non-convex sparse optimization-based impact force identification with limited vibration measurements

Frontiers of Mechanical Engineering 2023, Volume 18, Issue 3, doi: 10.1007/s11465-023-0762-2

Abstract: method often struggles to generate sparse solutions.MJX-TeXAtom-ORD">ℓ1 sparseregularization, while promoting sparsity, underestimates the amplitude of impact forces, resulting inTo alleviate such limitations, a novel non-convex sparse regularization method that uses the non-convexrealize simultaneous impact localization and time history reconstruction with an under-determined, sparse

Keywords： impact force identification     inverse problem     sparse regularization     under-determined condition     alternating    

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A regularization scheme for explicit level-set XFEM topology optimization

Markus J. GEISS, Jorge L. BARRERA, Narasimha BODDETI, Kurt MAUTE

Frontiers of Mechanical Engineering 2019, Volume 14, Issue 2,   Pages 153-170 doi: 10.1007/s11465-019-0533-2

Abstract: Regularization of the level-set (LS) field is a critical part of LS-based topology optimization (TO)This paper introduces a novel LS regularization approach based on a signed distance field (SDF) which

Keywords： level-set regularization     explicit level-sets     XFEM     CutFEM     topology optimization     heat method     signed distance    

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Sparse fast Clifford Fourier transform Article

Rui WANG, Yi-xuan ZHOU, Yan-liang JIN, Wen-ming CAO

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 8,   Pages 1131-1141 doi: 10.1631/FITEE.1500452

Abstract: The sparse fast Fourier transform (sFFT) theory deals with the big data problem by using input data selectivelyThis has inspired us to create a new algorithm called sparse fast CFT (SFCFT), which can greatly improve

Keywords： Sparse fast Fourier transform (sFFT)     Clifford Fourier transform (CFT)     Sparse fast Clifford Fourier    

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Uncertainty propagation analysis by an extended sparse grid technique

X. Y. JIA, C. JIANG, C. M. FU, B. Y. NI, C. S. WANG, M. H. PING

Frontiers of Mechanical Engineering 2019, Volume 14, Issue 1,   Pages 33-46 doi: 10.1007/s11465-018-0514-x

Abstract: In this paper, an uncertainty propagation analysis method is developed based on an extended sparse gridSubsequently, within the sparse grid numerical integration framework, the statistical moments of the

Keywords： uncertainty propagation analysis     extended sparse grid     maximum entropy principle     extended Gauss integration    

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Laplacian sparse dictionary learning for image classification based on sparse representation Article

Fang LI, Jia SHENG, San-yuan ZHANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1795-1805 doi: 10.1631/FITEE.1600039

Abstract: Sparse representation is a mathematical model for data representation that has proved to be a powerfulAs one of the building blocks of the sparse representation method, dictionary learning plays an importantWe incorporate the Laplacian weighted graph in the sparse representation model and impose the l1-normAn LSD is a sparse overcomplete dictionary that can preserve the intrinsic structure of the data andResults show the advantages of the proposed LSD algorithm over state-of-the-art sparse representation

Keywords： Sparse representation     Laplacian regularizer     Dictionary learning     Double sparsity     Manifold    

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Home location inference from sparse and noisy data: models and applications

Tian-ran HU,Jie-bo LUO,Henry KAUTZ,Adam SADILEK

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 5,   Pages 389-402 doi: 10.1631/FITEE.1500385

Abstract: In particular, the sparse and noisy nature of social media data poses serious challenges in pinpointingknowledge, this is the first time home location has been detected at such a fine granularity using sparse

Keywords： Home location     Mobility patterns     Healthcare    

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Robust object tracking with RGBD-based sparse learning Article

Zi-ang MA, Zhi-yu XIANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 7,   Pages 989-1001 doi: 10.1631/FITEE.1601338

Abstract: In this paper, a novel RGBD and sparse learning based tracker is proposed.The range data is integrated into the sparse learning framework in three respects.demonstrate that the proposed tracker outperforms the state-of-the-art tracking algorithms, including both sparse

Keywords： Object tracking     Sparse learning     Depth view     Occlusion templates     Occlusion detection    

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Adaptive simulation of wave propagation problems including dislocation sources and random media

Hassan YOUSEFI, Jamshid FARJOODI, Iradj MAHMOUDZADEH KANI

Frontiers of Structural and Civil Engineering 2019, Volume 13, Issue 5,   Pages 1054-1081 doi: 10.1007/s11709-019-0536-4

Abstract: An adaptive Tikhonov regularization is integrated with an h-adaptive grid-based scheme for simulationdeveloping of non-dissipative spurious oscillations, numerical stability is guaranteed by the Tikhonov regularizationTo preserve waves of small magnitudes, an adaptive regularization is utilized: using of smaller amount

Keywords： wavelet     adaptive smoothing     discontinuous solutions     stochastic media     spurious oscillations     Tikhonov regularization    

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Unsupervised feature selection via joint local learning and group sparse regression Regular Papers

Yue WU, Can WANG, Yue-qing ZHANG, Jia-jun BU

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 4,   Pages 538-553 doi: 10.1631/FITEE.1700804

Abstract: issue, we propose a novel unsupervised feature selection algorithm via joint local learning and group sparseregression in a single formulation, and seeks features that respect both the manifold structure and group sparse

Keywords： Unsupervised     Local learning     Group sparse regression     Feature selection    

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Efficient scheme of low-dose CT reconstruction using TV minimization with an adaptive stopping strategy and sparse Article

Yong DING, Tuo HU

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 12,   Pages 2001-2008 doi: 10.1631/FITEE.1700287

Abstract: imaging, we propose a promising reconstruction scheme which combines total-variation minimization and sparse

Keywords： Low-dose computed tomography (CT)     CT imaging     Total variation     Sparse dictionary learning    

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Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation None

Ji-jun TONG, Peng ZHANG, Yu-xiang WENG, Dan-hua ZHU

Frontiers of Information Technology & Electronic Engineering 2018, Volume 19, Issue 4,   Pages 471-480 doi: 10.1631/FITEE.1620342

Abstract: We propose a fully automatic brain tumor segmentation method based on kernel sparse coding.Sparse coding is performed on the feature vectors extracted from the original MRI images, which are a

Keywords： Brain tumor segmentation     Kernel method     Sparse coding     Dictionary learning    

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Variational Bayesian multi-sparse component extraction for damage reconstruction of space debris hypervelocity Research Article

Xuegang HUANG, Anhua SHI, Qing LUO, Jinyang LUO,emei-126@126.com

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 4,   Pages 530-541 doi: 10.1631/FITEE.2000575

Abstract: To improve the survivability of orbiting spacecraft against space debris impacts, we propose an impact method. First, a multi-area damage mining model, which can describe damages in different spatial layers, is built based on an infrared thermal image sequence. Subsequently, to identify different impact damage types from infrared image data effectively, the inference is used to solve for the parameters in the model. Then, an image-processing framework is proposed to eliminate errors and compare locations of different damage types. It includes an image segmentation algorithm with an energy function and an image fusion method with . In the experiment, the proposed method is used to evaluate the complex damages caused by the impact of the secondary debris cloud on the rear wall of the typical Whipple shield configuration. Experimental results show that it can effectively identify and evaluate the complex damage caused by , including surface and internal defects.

Keywords： Hypervelocity impact     Variational Bayesian     Sparse representation     Damage assessment    

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Short-term Load Forecasting Using Neural Network

Luo Mei

Strategic Study of CAE 2007, Volume 9, Issue 5,   Pages 77-80

Abstract:  Bayesian regularization can overcome the over fitting and improve the generalization of ANN.

Keywords： short-term load forecasting(STLF)     ANN     Levenberg-Marquardt     Bayesian regularization     optimized algorithms    

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Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder Article

Zong-feng QI, Qiao-qiao LIU, Jun WANG, Jian-xun LI

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 12,   Pages 1991-2000 doi: 10.1631/FITEE.1601395

Abstract: To solve this problem, an improved Kullback-Leibler divergence sparse autoencoder (KL-SAE) is proposed

Keywords： Battle damage assessment     Improved Kullback-Leibler divergence sparse autoencoder     Structural optimization    

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Title Author Date Type Operation

Asystematic review of structured sparse learning

Lin-bo QIAO, Bo-feng ZHANG, Jin-shu SU, Xi-cheng LU

Journal Article

Non-convex sparse optimization-based impact force identification with limited vibration measurements

Journal Article

A regularization scheme for explicit level-set XFEM topology optimization

Markus J. GEISS, Jorge L. BARRERA, Narasimha BODDETI, Kurt MAUTE

Journal Article

Sparse fast Clifford Fourier transform

Rui WANG, Yi-xuan ZHOU, Yan-liang JIN, Wen-ming CAO

Journal Article

Uncertainty propagation analysis by an extended sparse grid technique

X. Y. JIA, C. JIANG, C. M. FU, B. Y. NI, C. S. WANG, M. H. PING

Journal Article

Laplacian sparse dictionary learning for image classification based on sparse representation

Fang LI, Jia SHENG, San-yuan ZHANG

Journal Article

Home location inference from sparse and noisy data: models and applications

Tian-ran HU,Jie-bo LUO,Henry KAUTZ,Adam SADILEK

Journal Article

Robust object tracking with RGBD-based sparse learning

Zi-ang MA, Zhi-yu XIANG

Journal Article

Adaptive simulation of wave propagation problems including dislocation sources and random media

Hassan YOUSEFI, Jamshid FARJOODI, Iradj MAHMOUDZADEH KANI

Journal Article

Unsupervised feature selection via joint local learning and group sparse regression

Yue WU, Can WANG, Yue-qing ZHANG, Jia-jun BU

Journal Article

Efficient scheme of low-dose CT reconstruction using TV minimization with an adaptive stopping strategy and sparse

Yong DING, Tuo HU

Journal Article

Kernel sparse representation for MRI image analysis in automatic brain tumor segmentation

Ji-jun TONG, Peng ZHANG, Yu-xiang WENG, Dan-hua ZHU

Journal Article

Variational Bayesian multi-sparse component extraction for damage reconstruction of space debris hypervelocity

Xuegang HUANG, Anhua SHI, Qing LUO, Jinyang LUO,emei-126@126.com

Journal Article

Short-term Load Forecasting Using Neural Network

Luo Mei

Journal Article

Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder

Zong-feng QI, Qiao-qiao LIU, Jun WANG, Jian-xun LI

Journal Article