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- Jixiang Wan, Shuangnian Hu, Qianrong Tan, JIXIANG WAN
- 2017

Let e and n be positive integers and S = {x1, . . . , xn} be a set of n distinct positive integers. The n × n matrix having eth power [xi, xj ] of the least common multiple of xi and xj as its (i, j)-entry is called the eth power least common multiple (LCM) matrix on S, denoted by ([S]). The set S is said to be gcd closed (respectively, lcm closed) if (xi,… (More)

- Qingwen Qu, Jixiang Wan, Xiujun Sun
- 2007 IEEE International Conference on Robotics…
- 2007

A new method for smooth trajectory planning of a 6-DOF robot in joint space is described in this paper. By the researching processes of concrete analysis of trajectory planning on robot's manipulator arm, imitation of trajectory based on kinematics and optimization of trajectory in the joint space, an one-input-six-output RBF neural network model is built… (More)

- Yilin Xu, Haojian Yu, +7 authors Xiaoyuan Chen
- Nanoscale research letters
- 2017

Graphene is a promising candidate for the replacement of the typical transparent electrode indium tin oxide in optoelectronic devices. Currently, the application of polycrystalline graphene films grown by chemical vapor deposition is limited for their low electrical conductivity due to the poor transfer technique. In this work, we developed a new method of… (More)

- JIXIANG WAN
- 2014

Let e and n be positive integers and S = {x 1 ,. .. , xn} be a set of n distinct positive integers. The n × n matrix having eth power [x i , x j ] e of the least common multiple of x i and x j as its (i, j)-entry is called the eth power least common multiple (LCM) matrix on S, denoted by ([S] e). The set S is said to be gcd closed (respectively, lcm closed)… (More)

- Yilin Xu, Xiang Wei, +7 authors Xiaoyuan Chen
- Scientific reports
- 2017

Silver nanowires (AgNWs) networks are promising candidates for the replacement of indium tin oxide (ITO). However, the surface roughness of the AgNWs network is still too high for its application in optoelectronic devices. In this work, we have reduced the surface roughness of the AgNWs networks to 6.4 nm, compared to 33.9 nm of the as-deposited AgNWs… (More)

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