臺灣學術機構典藏系統 (Taiwan Academic Institutional Repository, TAIR)

	English \| 正體中文 \| 简体中文 \| Total items :2819016
	Visitors : 28410320 Online Users : 459 Project Commissioned by the Ministry of Education Project Executed by National Taiwan University Library

Scope

Adv. Search

Login

Administrator

臺灣學術機構典藏系統 (Taiwan Academic Institutional Repository, TAIR)

About TAIR
	About TAIR

Browse By
	Institution
	Author
	Title
	Date
	Statistics Chart
	Institution Facts
	Trend

News
	2019臺灣學術機構典藏研討會
	機構典藏系統RC7版本已發佈

Copyright
	Publisher & Journal Policy
	TAIR Copyright Conference Minutes

Related Links
	機構典藏計畫網站
	ROAR
	OpenDOAR
	SHERPA
	OAIster
	JAIRO
	Ira
	DSPACE
	RWWR
	臺大學術典藏NTU Scholars

"shang lin wu"

Return to Browse by Author
Sorting by Title	Sort by Date

Showing items 1-5 of 5 (1 Page(s) Totally)
1
View [10|25|50] records per page

Institution	Date	Title	Author
國立臺灣海洋大學	2017-10	EEG-Based Brain-Computer Interfaces: A Novel Neurotechnology and Computational Intelligence Method	Chin-Teng Lin; Yu-Ting Liu; Shang-Lin Wu; Zehong Cao; Yu-Kai Wang; Chih-Sheng Huang; Jung-Tai King; Shi-An Chen; Shao-Wei Lu; Chun-Hsiang Chuang
國立臺灣海洋大學	2017-02	Fuzzy Integral With Particle Swarm Optimization for a Motor-Imagery-Based Brain–Computer Interface	Shang-Lin Wu; Yu-Ting Liu; Tsung-Yu Hsieh; Yang-Yin Lin; Chih-Yu Chen; Chun-Hsiang Chuang; Chin-Teng Lin
國立臺灣海洋大學	2016-07	A motor imagery based brain-computer interface system via swarm-optimized fuzzy integral and its application	Shang-Lin Wu; Yu-Ting Liu; Kuang-Pen Chou; Yang-Yin Lin; Jie Lu; Guangquan Zhang; Chun-Hsiang Chuang; WenChieh Lin; Chin-Teng Lin
國立臺灣海洋大學	2016-02	Brain Dynamics in Predicting Driving Fatigue Using a Recurrent Self-Evolving Fuzzy Neural Network	Yu-Ting Liu; Yang-Yin Lin; Shang-Lin Wu; Chun-Hsiang Chuang; Chin-Teng Lin
國立臺灣海洋大學	2014-07	EEG-based driving fatigue prediction system using functional-link-based fuzzy neural network	Yu-Ting Liu; Yang-Yin Lin; Shang-Lin Wu; Chun-Hsiang Chuang; Mukesh Prasad; ChinTeng Lin

Showing items 1-5 of 5 (1 Page(s) Totally)
1
View [10|25|50] records per page