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262 results on '"LU ZiXing"'

1. DYNAMIC CRUSHING OF HONEYCOMBS WITH A NEGATIVE POISSON’S RATIO—A FINITE ELEMENT STUDY

Author

LU ZiXing and LI Kang

Subjects
Honeycomb, Negative Poisson ’s ratio, Cellular materials, Dynamic crushing, Deformation mechanism, Mechanical engineering and machinery, TJ1-1570, Materials of engineering and construction. Mechanics of materials, TA401-492
Abstract

The deformation modes and energy absorption properties of honeycombs with a negative Poisson’s ratio under inplane dynamic crushing were studied by means of finite element simulation in this paper. The results show that behavior mechanism of negative Poisson’s ratio under dynamic crushing is the same with that at static loading of which is the cave-in or rotation,and this explains the necking phenomena of the stagger-arranged triangle and quadratic cells during the in-plane crushing. The results provide us a certain understanding of the dynamic behavior and energy absorption property of twodimensional cellular materials with a negative Poisson’s ratio and lay the foundation of the impact behavior of three-dimensional foam materials with a negative Poisson’s ratio.

Published
2016
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36. Bayesian Personalized Ranking with the Synthesis of Multiple User and Item Classification

Author

Yang Gu Yang Gu, Fei Xiong Yang Gu, Zixing Wang Fei Xiong, Ran Lu Zixing Wang, and Yang Zhang Ran Lu

Subjects
General Computer Science
Abstract

In recent years, rapidly evolving networks have facilitated data interactions between users and goods, and recommender systems have emerged due to the needs of the times. Implicit feedback in recommender systems always is divided into the observed item set and unobserved item set. However, items in the unobserved set often are treated equally, and the items that are of potential interest to users are not sufficiently exploited from unobserved items. In this paper, we come up with a method to solve the problem of the insufficient exploration of unrated items, and we contribute to the additional alleviation of rating sparseness. To do so, we propose a novel Bayesian personalized ranking with the synthesis of multiple users and item classification (BPRS). For each user, we divide the items into three categories, which are positive, interest and negative. We conduct multiple user classification and item classification to exploit the items that are of interest and generate the final ranking. Experiments on three real-world datasets demonstrate the effectiveness of our algorithm for greatly improving the accuracy of recommendation results and alleviating the cold-start problem. &nbsp

Published
2022

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