scholarly journals A Simultaneous Sparse Learning Algorithm of Structured Approximation with Transformation Analysis Embedded in Bayesian Framework

2021 ◽  
Author(s):  
Guisheng Wang
Keyword(s):  
Bayesian Learning ◽  
High Efficiency ◽  
Learning Algorithm ◽  
Bayesian Framework ◽  
Sparse Approximation ◽  
Sparse Learning ◽  
Sparse Signals ◽  
Sparse Models ◽  
Wide Range ◽  
Feasible Solutions

<div>Sparse approximation is critical to the applications of signal or image processing, and it is conducive to estimate the sparse signals with the joint efforts of transformation analysis. In this study, a simultaneous Bayesian framework was extended for sparse approximation by structured shared support, and a simultaneous sparse learning algorithm of structured approximation (SSL-SA) is proposed with transformation analysis which leads to the feasible solutions more sensibly. Then the improvements of sparse Bayesian learning and iterative reweighting were embedded in the framework to achieve speedy convergence as well as high efficiency with robustness. Furthermore, the iterative optimization and transformation analysis were embedded in the overall learning process to obtain the relative optima for sparse approximation. Finally, compared to conventional reweighting algorithms for simultaneous sparse models with l1 and l2, simulation results present the preponderance of the proposed approach to solve the sparse structure and iterative redundancy in processing sparse signals. The fact indicates that proposed method will be effective to sparsely approximate the various signals and images, which does accurately analyse the target in optimal transformation. It is envisaged that the proposed model could be suitable for a wide range of data in sparse separation and signal denosing.</div>

scholarly journals A Simultaneous Sparse Learning Algorithm of Structured Approximation with Transformation Analysis Embedded in Bayesian Framework

2021 ◽  
Author(s):  
Guisheng Wang
Keyword(s):  
Bayesian Learning ◽  
High Efficiency ◽  
Learning Algorithm ◽  
Bayesian Framework ◽  
Sparse Approximation ◽  
Sparse Learning ◽  
Sparse Signals ◽  
Sparse Models ◽  
Wide Range ◽  
Feasible Solutions

<div>Sparse approximation is critical to the applications of signal or image processing, and it is conducive to estimate the sparse signals with the joint efforts of transformation analysis. In this study, a simultaneous Bayesian framework was extended for sparse approximation by structured shared support, and a simultaneous sparse learning algorithm of structured approximation (SSL-SA) is proposed with transformation analysis which leads to the feasible solutions more sensibly. Then the improvements of sparse Bayesian learning and iterative reweighting were embedded in the framework to achieve speedy convergence as well as high efficiency with robustness. Furthermore, the iterative optimization and transformation analysis were embedded in the overall learning process to obtain the relative optima for sparse approximation. Finally, compared to conventional reweighting algorithms for simultaneous sparse models with l1 and l2, simulation results present the preponderance of the proposed approach to solve the sparse structure and iterative redundancy in processing sparse signals. The fact indicates that proposed method will be effective to sparsely approximate the various signals and images, which does accurately analyse the target in optimal transformation. It is envisaged that the proposed model could be suitable for a wide range of data in sparse separation and signal denosing.</div>

Simultaneous sparse learning algorithm of structured approximation with transformation analysis embedded in Bayesian framework

2021 ◽  
Vol 30 (05) ◽  
Author(s):  
Guisheng Wang ◽  
Yequn Wang ◽  
Guoce Huang ◽  
Qinghua Ren ◽  
Tingting Ren
Keyword(s):  
Learning Algorithm ◽  
Bayesian Framework ◽  
Sparse Learning

scholarly journals Block Sparse Bayesian Recovery with Correlated LSM Prior

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Juan Zhao ◽  
Xia Bai ◽  
Tao Shan ◽  
Ran Tao
Keyword(s):  
Bayesian Learning ◽  
Learning Algorithm ◽  
Simulated Data ◽  
Sampling Theorem ◽  
Sparse Signals ◽  
Sparse Bayesian Learning ◽  
Recovery Algorithm ◽  
Low Snr ◽  
Variational Bayesian Inference ◽  
Recovery Performance

Compressed sensing can recover sparse signals using a much smaller number of samples than the traditional Nyquist sampling theorem. Block sparse signals (BSS) with nonzero coefficients occurring in clusters arise naturally in many practical scenarios. Utilizing the sparse structure can improve the recovery performance. In this paper, we consider recovering arbitrary BSS with a sparse Bayesian learning framework by inducing correlated Laplacian scale mixture (LSM) prior, which can model the dependence of adjacent elements of the block sparse signal, and then a block sparse Bayesian learning algorithm is proposed via variational Bayesian inference. Moreover, we present a fast version of the proposed recovery algorithm, which does not involve the computation of matrix inversion and has robust recovery performance in the low SNR case. The experimental results with simulated data and ISAR imaging show that the proposed algorithms can efficiently reconstruct BSS and have good antinoise ability in noisy environments.

THE IDENTIFICATION OF COMPLEX SPATIOTEMPORAL PATTERNS USING COUPLED MAP LATTICE MODELS

2008 ◽  
Vol 18 (04) ◽  
pp. 997-1013 ◽  
Author(s):  
Y. PAN ◽  
S. A. BILLINGS
Keyword(s):  
Spatial Data ◽  
Bayesian Learning ◽  
Learning Algorithm ◽  
Lattice Models ◽  
Sampling Procedure ◽  
Coupled Map Lattice ◽  
Spatiotemporal Patterns ◽  
Data Sampling ◽  
Wide Range ◽  
Correlation Analysis Method

Many complex and interesting spatiotemporal patterns have been observed in a wide range of scientific areas. In this paper, two kinds of spatiotemporal patterns including spot replication and Turing systems are investigated and new identification methods are proposed to obtain Coupled Map Lattice (CML) models for this class of systems. Initially, a new correlation analysis method is introduced to determine an appropriate temporal and spatial data sampling procedure for the identification of spatiotemporal systems. A new combined Orthogonal Forward Regression and Bayesian Learning algorithm with Laplace priors is introduced to identify sparse and robust CML models for complex spatiotemporal patterns. The final identified CML models are validated using correlation-based model validation tests for spatiotemporal systems. Numerical results illustrate the identification procedure and demonstrate the validity of the identified models.

L1-Norm Sparse Learning and its Application

2011 ◽  
Vol 88-89 ◽  
pp. 379-385 ◽  
Author(s):  
Xin Feng Zhu ◽  
Bin Li ◽  
Jian Dong Wang
Keyword(s):  
Bayesian Learning ◽  
Sparse Representations ◽  
Multivariate Curve Resolution ◽  
Sparse Learning ◽  
Sparse Bayesian Learning ◽  
L1 Norm ◽  
Curve Resolution ◽  
Feasible Solutions ◽  
Sparse Solutions

The need on finding sparse representations has attracted more and more people to research it. Researchers have developed many approaches (such as nonnegative constraint, l1-norm sparsity regularization and sparse Bayesian learning with independent Gaussian prior) for encouraging sparse solutions and established some conditions under which the feasible solutions could be found by those approaches. This paper commbined the L1-norm regularization and bayesian learning, called L1-norm sparse bayesian learning, which was inspired by RVM (relative vector machine). L1-norm sparse bayesian learning has found its applications in many fields such as MCR (multivariate curve resolution) and so on. We proposed a new method called BSMCR (bayesian sparse MCR) to enhance the quality of resolve result.

Synthesis of Elaborate Benzofuran-2-Carboxamide Derivatives Through a Combination of 8-Aminoquinoline Directed C–H Arylations and Transamidations

2019 ◽  
Author(s):  
Michael Oschmann ◽  
Linus Johansson Holm ◽  
Oscar Verho
Keyword(s):  
Small Molecule ◽  
High Efficiency ◽  
Synthetic Strategy ◽  
Small Molecule Screening ◽  
Physiological Properties ◽  
Wide Range ◽  
Directing Group ◽  
Synthetic Sequence

Benzofurans are everywhere in nature and they have been extensively studied by medicinal chemists over the years because of their chemotherapeutic and physiological properties. Herein, we describe a strategy that can be used to access elaborate benzo-2-carboxamide derivatives, which involves a synthetic sequence of 8-aminoquinoline directed C–H arylations followed by transamidations. For the directed C–H arylations, Pd catalysis was used to install a wide range of aryl and heteroaryl substituents at the C3 position of the benzofuran scaffold in high efficiency. Directing group cleavage and further diversification of the C3-arylated benzofuran products were then achieved in a single synthetic operation through the utilization of a two-step transamidation protocol. By bocylating the 8-aminoquinoline amide moiety of these products, it proved possible to activate them towards aminolysis with different amine nucleophiles. Interestingly, this aminolysis reaction was found to proceed efficiently without the need of any additional catalyst or additive. Given the high efficiency and modularity of this synthetic strategy, it constitute a very attractive approach for generating structurally-diverse collections of benzofuran derivatives for small molecule screening.

To the 70th anniversary of World Meteorological Organization. Scientific and technical cooperation of Hydrometeorological Center of the USSR/Russia in the Programs of World Meteorological Organization

2020 ◽  
pp. 117-138
Author(s):  
S.V. Borshch ◽  
◽  
R.M. Vil’fand ◽  
D.B. Kiktev ◽  
V.M. Khan ◽  
...  
Keyword(s):  
Environmental Monitoring ◽  
High Efficiency ◽  
World Meteorological Organization ◽  
70Th Anniversary ◽  
Technical Level ◽  
Technical Cooperation ◽  
Wide Range

The paper presents the summary and results of long-term and multi-faceted experience of international scientific and technical cooperation of Hydrometeorological Center of Russia in the field of hydrometeorology and environmental monitoring within the framework of WMO programs, which indicates its high efficiency in performing a wide range of works at a high scientific and technical level. Keywords: World Meteorological Organization, major WMO programs, representatives of Hydrometeorological Center of Russia in WMO

Integrated Design and Multi-Objective Optimization of a Single Stage Heat-Pump Turbocompressor

2013 ◽  
Author(s):  
J. Schiffmann
Keyword(s):  
Design Optimization ◽  
High Efficiency ◽  
Integrated Design ◽  
Heat Pumps ◽  
Small Scale ◽  
Multi Objective Optimization ◽  
Design Constraints ◽  
Multi Objective ◽  
Wide Range ◽  
Standard Design

Small scale turbomachines in domestic heat pumps reach high efficiency and provide oil-free solutions which improve heat-exchanger performance and offer major advantages in the design of advanced thermodynamic cycles. An appropriate turbocompressor for domestic air based heat pumps requires the ability to operate on a wide range of inlet pressure, pressure ratios and mass flows, confronting the designer with the necessity to compromise between range and efficiency. Further the design of small-scale direct driven turbomachines is a complex and interdisciplinary task. Textbook design procedures propose to split such systems into subcomponents and to design and optimize each element individually. This common procedure, however, tends to neglect the interactions between the different components leading to suboptimal solutions. The authors propose an approach based on the integrated philosophy for designing and optimizing gas bearing supported, direct driven turbocompressors for applications with challenging requirements with regards to operation range and efficiency. Using previously validated reduced order models for the different components an integrated model of the compressor is implemented and the optimum system found via multi-objective optimization. It is shown that compared to standard design procedure the integrated approach yields an increase of the seasonal compressor efficiency of more than 12 points. Further a design optimization based sensitivity analysis allows to investigate the influence of design constraints determined prior to optimization such as impeller surface roughness, rotor material and impeller force. A relaxation of these constrains yields additional room for improvement. Reduced impeller force improves efficiency due to a smaller thrust bearing mainly, whereas a lighter rotor material improves rotordynamic performance. A hydraulically smoother impeller surface improves the overall efficiency considerably by reducing aerodynamic losses. A combination of the relaxation of the 3 design constraints yields an additional improvement of 6 points compared to the original optimization process. The integrated design and optimization procedure implemented in the case of a complex design problem thus clearly shows its advantages compared to traditional design methods by allowing a truly exhaustive search for optimum solutions throughout the complete design space. It can be used for both design optimization and for design analysis.

scholarly journals Low Power Cusped Field Thruster Developed for the Space-Borne Gravitational Wave Detection Mission in China

2021 ◽  
Vol 11 (14) ◽  
pp. 6549
Author(s):  
Hui Liu ◽  
Ming Zeng ◽  
Xiang Niu ◽  
Hongyan Huang ◽  
Daren Yu
Keyword(s):  
Low Power ◽  
Gravitational Wave ◽  
Attitude Control ◽  
High Efficiency ◽  
Experimental Investigations ◽  
Attitude Control System ◽  
Gravitational Wave Detection ◽  
Wave Detection ◽  
Wide Range ◽  
Institute Of Technology

The microthruster is the crucial device of the drag-free attitude control system, essential for the space-borne gravitational wave detection mission. The cusped field thruster (also called the High Efficiency Multistage Plasma Thruster) becomes one of the candidate thrusters for the mission due to its low complexity and potential long life over a wide range of thrust. However, the prescribed minimum of thrust and thrust noise are considerable obstacles to downscaling works on cusped field thrusters. This article reviews the development of the low power cusped field thruster at the Harbin Institute of Technology since 2012, including the design of prototypes, experimental investigations and simulation studies. Progress has been made on the downscaling of cusped field thrusters, and a new concept of microwave discharge cusped field thruster has been introduced.

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