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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-normThe learned LSD can be easily integrated into a classification framework based on sparse representationResults 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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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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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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Face recognition based on subset selection via metric learning on manifold

Hong SHAO,Shuang CHEN,Jie-yi ZHAO,Wen-cheng CUI,Tian-shu YU

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 12,   Pages 1046-1058 doi: 10.1631/FITEE.1500085

Abstract: With the development of face recognition using sparse representation based classification (SRC), manyHowever, when the dictionary is large and the representation is sparse, only a small proportion of the

Keywords： Face recognition     Sparse representation     Manifold structure     Metric learning     Subset selection    

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An efficient prediction framework for multi-parametric yield analysis under parameter variations Article

Xin LI,Jin SUN,Fu XIAO

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 12,   Pages 1344-1359 doi: 10.1631/FITEE.1601225

Abstract: Due to continuous process scaling, process, voltage, and temperature (PVT) parameter variations have become one of the most problematic issues in circuit design. The resulting correlations among performance metrics lead to a significant para-metric yield loss. Previous algorithms on parametric yield prediction are limited to predicting a single-parametric yield or per-forming balanced optimization for several single-parametric yields. Consequently, these methods fail to predict the multi- parametric yield that optimizes multiple performance metrics simultaneously, which may result in significant accuracy loss. In this paper we suggest an efficient multi-parametric yield prediction framework, in which multiple performance metrics are considered as simultaneous constraint conditions for parametric yield prediction, to maintain the correlations among metrics. First, the framework models the performance metrics in terms of PVT parameter variations by using the adaptive elastic net (AEN) method. Then the parametric yield for a single performance metric can be predicted through the computation of the cumulative distribution function (CDF) based on the multiplication theorem and the Markov chain Monte Carlo (MCMC) method. Finally, a copula-based parametric yield prediction procedure has been developed to solve the multi-parametric yield prediction problem, and to generate an accurate yield estimate. Experimental results demonstrate that the proposed multi-parametric yield prediction framework is able to provide the designer with either an accurate value for parametric yield under specific performance limits, or a multi-parametric yield surface under all ranges of performance limits.

Keywords： Yield prediction     Parameter variations     Multi-parametric yield     Performance modeling     Sparse representation    

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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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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: mn> regularization method often struggles to generate sparseMJX-TeXAtom-ORD">ℓ1 sparseTo 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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Standard model of knowledge representation

Wensheng YIN

Frontiers of Mechanical Engineering 2016, Volume 11, Issue 3,   Pages 275-288 doi: 10.1007/s11465-016-0372-3

Abstract:

Knowledge representation is the core of artificial intelligence research.Knowledge representation methods include predicate logic, semantic network, computer programming languageTo establish the intrinsic link between various knowledge representation methods, a unified knowledgerepresentation model is necessary.This knowledge representation method is not a contradiction to the traditional knowledge representation

Keywords： knowledge representation     standard model     ontology     system theory     control theory     multidimensional representation    

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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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Applicability of high dimensional model representation correlations for ignition delay times of n-heptane

Wang LIU, Jiabo ZHANG, Zhen HUANG, Dong HAN

Frontiers in Energy 2019, Volume 13, Issue 2,   Pages 367-376 doi: 10.1007/s11708-018-0584-9

Abstract: In this paper, the random sampling-high dimensional model representation (RS-HDMR) methods were employed

Keywords： ignition delay     random sampling     high dimensional model representation     n-heptane     fuel kinetics    

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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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Digital representation of meso-geomaterial spatial distribution and associated numerical analysis of

YUE Zhongqi

Frontiers of Structural and Civil Engineering 2007, Volume 1, Issue 1,   Pages 80-93 doi: 10.1007/s11709-007-0008-0

Abstract: presents the author's efforts in the past decade for the establishment of a practical approach of digital representationproposed approach, digital image processing methods are used as measurement tools to construct a digital representation

Keywords： homogeneous     numerical analysis     Expanded     homogenization     meso-level    

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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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Uncertainty in Knowledge Representation

Li Deyi

Strategic Study of CAE 2000, Volume 2, Issue 10,   Pages 73-79

Abstract:

Knowledge representation in AI has been a bottleneck for years.

Keywords： knowledge representation     qualitative concept     uncertainty     cloud model     digital characteristics    

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Title Author Date Type Operation

Laplacian sparse dictionary learning for image classification based on sparse representation

Fang LI, Jia SHENG, San-yuan ZHANG

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

Asystematic review of structured sparse learning

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

Journal Article

Face recognition based on subset selection via metric learning on manifold

Hong SHAO,Shuang CHEN,Jie-yi ZHAO,Wen-cheng CUI,Tian-shu YU

Journal Article

An efficient prediction framework for multi-parametric yield analysis under parameter variations

Xin LI,Jin SUN,Fu XIAO

Journal Article

Sparse fast Clifford Fourier transform

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

Journal Article

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

Journal Article

Standard model of knowledge representation

Wensheng YIN

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

Applicability of high dimensional model representation correlations for ignition delay times of n-heptane

Wang LIU, Jiabo ZHANG, Zhen HUANG, Dong HAN

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

Digital representation of meso-geomaterial spatial distribution and associated numerical analysis of

YUE Zhongqi

Journal Article

Robust object tracking with RGBD-based sparse learning

Zi-ang MA, Zhi-yu XIANG

Journal Article

Uncertainty in Knowledge Representation

Li Deyi

Journal Article