Search

Your search keyword '"Ma, Shi-Ping"' showing total 179 results
Start Over Author "Ma, Shi-Ping"
179 results on '"Ma, Shi-Ping"'

2. A Novel Self-adaptive Defog Method Based on Bionic

Author

Ma, Shi-ping, Li, Quan-He, Bi, Du-Yan, Dong, Ya-Yun, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Zhao, Yao, editor, Kong, Xiangwei, editor, and Taubman, David, editor

Published
2017
Full Text
View/download PDF

35. Development of Supermolecular Structure of a Thermoplastic Polyimide during Rolling

Author

MA Shi-Ping, Kensuke Sakurai, Takashi Sasaki, and Toshisada Takahashi

Subjects
Thermoplastic polyimide, Crystallography, Materials science, Polymers and Plastics, Small-angle X-ray scattering, Materials Science (miscellaneous), Chemical Engineering (miscellaneous), Development (differential geometry), Composite material, General Environmental Science
Abstract

圧延した全芳香族ポリイミド (TPI) の高次構造を研究した. 溶融押出し後熱処理したTPI膜を圧延比 (λ) 4.2まで手動で圧延した. 275℃ (>Tg) で圧延したTPI (λ=1.5) の3枚のWAXD図 (Through-, Edge-, End-View) は (100) 面が膜面に対し約24°の角度に配向しているが, c軸は圧延面内でランダムに配向していることを示した. 圧延比が1.5を超えると, c軸はしだいに圧延方向に向いていき, また (100) 面は圧延面に平行に配向していった. TPI膜 (λ=1.5～2.0) は4個の散乱極大が楕円環上にのっているSAXS図 (Edge View) を与えた. 一方, TPI膜 (λ=3.5～4.2) のSAXS図 (Edge View) には散漫なストリークとして層線上に広がる4点像が現れた. 圧延中 (λ=1.0～4.2) におけるTPI結晶の変形挙動をTPI結晶の構造モデルに基づいて定性的に議論した. 結晶性のTPI膜を200℃ (

Published
1995

37. Research on Particle Swarm Optimization strategy for forest fire detection system based on Wireless Sensor Networks

Author

Ma Shi-ping, Tao Zuo-ying, and Lin Zhu-liang

Subjects
Mathematical optimization, Engineering, Rate of convergence, Fire detection, business.industry, Convergence (routing), Particle swarm optimization, Network performance, Maximization, business, Wireless sensor network, Electronic mail
Abstract

In order to improve the network performance, to increase network coverage rate, to achieve the maximization of network coverage and extend the network of life in the forest fire detection system, the present research has proposed a Wireless Sensor Networks (WSNs) coverage Particle Swarm Optimization (PSO) strategy on the basis of probability measuring model. Through the PSO, the strategy achieves coverage control for the optimization objectives of network coverage rate and analyzes the effect of coverage performance about sensing radius. The coverage rate has increased and convergence rate has speeded up with perceived radius increased gradually. The simulation shows that effective coverage has reached 85.63 percent. Compared with the Conventional Genetic Algorithms (CGA) about the optimization effectiveness, the coverage rate has increased by 0.64 percent and the convergence rate has increased 13.7 percent. Therefore, the PSO strategy has a better coverage optimization results than CGA.

Published
2009

Catalog

Books, media, physical & digital resources
See catalog results