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780 results on '"Liu, GuoZhu"'

22. Graceful Labeling For bipartite graceful Graphs and related Graphs

Author

Liu, Chunfeng, Wang, Xiuying, and Liu, Guozhu

Subjects
Mathematics - General Mathematics
Abstract

The concept of graceful labels was proposed by Rosa, scholars began to study graceful labels of various graphs and obtained relevant results.Let the graph is a bipartite graceful graph, we have proved some graphs are graceful labeling in this paper., Comment: 8 pages,12 figures

Published
2020

36. Anatase‐reinforced PtZn@Silicalite‐1 structured catalysts boosting propane dehydrogenation.

Author

Xia, Liming, Zhang, Bofeng, Hou, Gang, Zhang, Shuo, Wang, Li, and Liu, Guozhu

Subjects
CATALYST structure, DISCONTINUOUS precipitation, THERMAL stability, BOND strengths, HEAT transfer
Abstract

Structured catalysts exhibit the advantages of high diffusion efficiency and low heat transfer resistance, which have attracted increasing attention to non‐adiabatic gas–solid process. However, the metal‐supported coating catalysts face the problems of weaker bond strength and severe sintering, especially under the conditions of large flow rate and high temperature. Herein, metal@Silicalite‐1 structured catalysts with high adhesion and thermal stability were successfully prepared by hydroxylating the substrate with anatase. Rich surface Ti‐OH significantly strengthened the adhesion stability of the zeolite coating. In propane dehydrogenation reaction, the optimized PtZn@S‐1‐15Ti showed a high specific activity of 49.6 molC3H6·molPt−1·s−1 with propylene selectivity above 99% at 600°C. The introduction of anatase accelerated the aggregation of silicon sources and induced nucleation with growth content of zeolite increased by 3.6 times. It breaks the inherent contradiction between high loading amount and strong binding ability of coated catalysts, which broadens the avenues for industrial applications. [ABSTRACT FROM AUTHOR]

Published
2025
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43. Review of Deep Convolution Applied to Target Detection Algorithms

Author

DONG Wenxuan, LIANG Hongtao, LIU Guozhu, HU Qiang, YU Xu

Subjects
computer vision, deep convolution, target detection, one-stage, two-stage, Electronic computers. Computer science, QA75.5-76.95
Abstract

As one of the most fundamental and challenging tasks in computer vision, target detection aims to find out specific targets in images and to locate and classify them, and is now widely used in many fields such as industrial quality inspection, video surveillance and unmanned vehicles. In recent years, with the breakthroughs in computer hardware resources and depth convolution algorithms in image classification tasks, depth convolution-based target detection algorithms have gradually replaced the traditional target detection algorithms and achieved significant results in terms of accuracy and performance. This paper reviews the current research status of depth convolution-based target detection algorithms and possible future development directions. It introduces the authoritative datasets and evaluation metrics of target detection algorithms with the limitations of traditional target detection algorithms as a guide, and then reviews the research and development history of representative algorithms for depth convolution-based target detection in recent years with time and algorithm architecture as the main research lines. The network structures of one-stage, two-stage and other improved algorithms are compared and analyzed, and the characteristics, advantages and limitations of various target detection algorithms are summarized. Finally, the future trends are prospected in the light of current problems and challenges of target detection.

Published
2022
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