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8 results on '"Cong, Feng-Yu"'

2. Effect of trans-1,4-poly(butadiene-co-isoprene) rubber on properties of styrene-butadiene rubber/natural rubber blends for rubber roller

Author

LANG Yue, XU Cong, FENG Yu, HAN Jia-chi, WANG Zhong

Subjects
trans-1, 4-poly(butadiene-co-isoprene) rubber, styrene-butadiene rubber, natural rubber, blend, rubber roller, mechanical property, wear resistance, Organic chemistry, QD241-441, Chemical engineering, TP155-156, Chemicals: Manufacture, use, etc., TP200-248
Abstract

The rubber blends for rubber roller were prepared with natural rubber, styrene-butadiene rubber and trans-1,4-poly(butadiene-co-isoprene) rubber, the effects of trans-1,4-poly(butadiene-co-isoprene) rubber amount on the compatibility, curing characteristics, mechanical properties, wear resistance and aging resistance of the blends were investigated. The results showed that as the trans-1,4-poly(butadiene-co-isoprene) rubber amount increased from 0 to 20 phr (mass), the crosslinking density of the blend increased gradually and curing time shortened, tensile strength increased gradually, hardness increased slightly, compression permanent set showed a downward trend, and wear resistance improved. The compatibility of the three rubbers was good, and the dispersion of filler and aging resistance of the blending rubber were improved.

Published
2023
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3. Double Coupled Canonical Polyadic Decomposition for Joint Blind Source Separation

Author

Gong, Xiao-Feng, Lin, Qiu-Hua, Cong, Feng-Yu, and De Lathauwer, Lieven

Subjects
Statistics - Machine Learning
Abstract

Joint blind source separation (J-BSS) is an emerging data-driven technique for multi-set data-fusion. In this paper, J-BSS is addressed from a tensorial perspective. We show how, by using second-order multi-set statistics in J-BSS, a specific double coupled canonical polyadic decomposition (DC-CPD) problem can be formulated. We propose an algebraic DC-CPD algorithm based on a coupled rank-1 detection mapping. This algorithm converts a possibly underdetermined DC-CPD to a set of overdetermined CPDs. The latter can be solved algebraically via a generalized eigenvalue decomposition based scheme. Therefore, this algorithm is deterministic and returns the exact solution in the noiseless case. In the noisy case, it can be used to effectively initialize optimization based DC-CPD algorithms. In addition, we obtain the determini- stic and generic uniqueness conditions for DC-CPD, which are shown to be more relaxed than their CPD counterpart. Experiment results are given to illustrate the superiority of DC-CPD over standard CPD based BSS methods and several existing J-BSS methods, with regards to uniqueness and accuracy., Comment: Accepted by IEEE Transactions on Signal Processing

Published
2016
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8. Blind Separation of Hybrid Mixture Signals.

Author

JIA Peng, CONG Feng-yu, and SHI Xi-zhi

Abstract

Kernel density estimation (KDS) is a nonparametric density estimation based on random variables probability density functions, and it is not limited in any assumption about the density function model, and it is directly evaluated from the original data without the consideration of the above statistical features. Based on it, the score function was straightly estimated by KDS, and then the performance of algorithms for BSS was obviously improved. Both the theoretical derivation and experiments show that this algorithm succeeds in separating the hybrid mixing signals including the super-Gaussian and sub-Guassian signals, and it paves the way to wider applications of BSS methods to real world signal processing. [ABSTRACT FROM AUTHOR]

Published
2004

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