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Your search keyword '"Ren, Xiaozhen"' showing total 206 results
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206 results on '"Ren, Xiaozhen"'

20. Rapid detection method for pork freshness using fusion spectroscopy and improved BAS-LSSVM.

Author

WANG Yao and REN Xiaozhen

Subjects
REGULARIZATION parameter, NONDESTRUCTIVE testing, SUPPORT vector machines, PORK, SEARCH algorithms, FEATURE selection
Abstract

(Objective) To realize accurate, rapid, and non-destructive testing of meat freshness. (Methods) Extracting spectral feature information based on a spectral acquisition system, proposed a fast non-destructive detection method for meat freshness (TVB-N) by combining an improved beetle whisker search algorithm with least squares support vector machine. By combining SG smoothing filtering and standard normal variables for data preprocessing, combining window competitive adaptive reweighted sampling and iterative continuous projection for feature selection, regularization parameters and kernel parameters of Least-Square Support Vector Machine were optimized by the Improved Beetle Antennae Search Algorithm, a fast non-destructive detection method for meat freshness (TVB-N) was completed. Analyze the performance of the proposed method through experiments. (Results) The experimental method could achieve accurate, rapid, and non-destructive testing of pork freshness (TVB-N), with high detection accuracy and efficiency, the detection correlation coefficient was 0.978 1, the mean square error was 0.302 1, and the average detection time was 0.031 seconds. (Conclusion) A fast non-destructive testing method for meat freshness (TVB-N) can be achieved by combining spectral detection and intelligent algorithms. [ABSTRACT FROM AUTHOR]

Published
2024
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35. Four-dimensional SAR Imaging Algorithm Based on Iterative Reconstruction of Magnitude and Phase

Author

Ren Xiaozhen and Yang Ruliang

Subjects
Synthetic Aperture Radar (SAR), Four-Dimensional (4D), Complex-valued imaging, Compressive Sensing (CS), Electricity and magnetism, QC501-766
Abstract

Observation data obtained from the Four-Dimensional (4D) Synthetic Aperture Radar (SAR) system is sparse and non-uniform in the baseline-time plane. Hence, the imaging results acquired by traditional Fourier-based methods are limited by high side lobes. Compressive Sensing (CS) is a recently proposed technique that allows for the recovery of an unknown sparse signal with overwhelming probability from very limited samples. However, the standard CS framework has been developed for real-valued signals, and the imaging process for 4D synthetic aperture radar deals with complex-valued data. In this study, we propose a new 4D synthetic aperture radar imaging algorithm based on an iterative reconstruction of magnitude and phase, which transforms the height-velocity imaging problem of 4D synthetic aperture radar into a joint reconstruction problem of the magnitude and phase of the complex-valued scatter coefficient. Using the phase information in the algorithm, the image quality is improved. Simulation results confirm the effectiveness of the proposed method.

Published
2016
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47. Accelerated ISAR Sparse Imaging Based on Tight Frame and Projection Operator

Author

Ren, Xiaozhen, Song, Haoyuan, Cui, Jing, and Tan, Lulu

Abstract

For the actual inverse synthetic aperture radar (ISAR) system, some received pulses may be invalid or lost in the long coherent processing interval (CPI) due to nonideal conditions, such as target noncooperation or noise interference. Hence, it is usually difficult to obtain well-focused images for such sparse aperture ISAR data. To address this issue, a novel general analysis model with a tight frame for sparse ISAR imaging is proposed in this article, which operates directly on the ISAR image instead of the redundant sparse coefficient. Furthermore, with the help of the projection operator and majorization minimization algorithmic framework, a simple and effective accelerated iterative algorithm with general analysis model (AIA-GAM) is designed to solve the proposed ISAR imaging model, which only performs the simple proximal mapping in each iteration. The experimental results of simulation data and real measured data demonstrate that the proposed method has better imaging performance and faster convergence speed.

Published
2023
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