Yanjian Liao

Dr. Yanjian Liao

Chongqing University
orcid

Research Focus & Metrics

Academic Career Length

16Years (2011-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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20 July 2020 in Micromachines
Verified
Journal Article
Open access
PDF file
LCLi Chen
Li Chen
XLXing Liu
Xing Liu
XZXiaolin Zheng
Xiaolin Zheng
XZXiaoling Zhang
Xiaoling Zhang
Jun YangJun Yang
Jun Yang
Yanjian LiaoYanjian Liao
Yanjian Liao
Integrating three-dimensional (3D) microelectrodes on microfluidic chips based on polydimethylsiloxane (PDMS) has been a challenge. This paper introduces a composite 3D electrode composed of Ag powder (particle size of 10 nm) and PDMS. Ethyl acetate is added as an auxiliary dispersant during the compounding process. A micromachining technique for processing 3D microelectrodes of any shape and size was developed to allow the electrodes to be firmly bonded to the PDMS chip. Through theoretical calculations, numerical simulations, and experimental verification, the role of the composite 3D microelectrodes in separating polystyrene particles of three different sizes via dielectrophoresis was systematically studied. This microfluidic device separated 20-, 10-, and 5-μm polystyrene particles nondestructively, efficiently, and accurately.

1 August 2016
Verified
Conference Paper
Closed access
YZYao Zhang
Yao Zhang
WHWensheng Hou
Wensheng Hou
HLHongyan Luo
Hongyan Luo
Xiaoying WuXiaoying Wu
Xiaoying Wu
Yanjian LiaoYanjian Liao
Yanjian Liao
XZXiaolin Zheng
Xiaolin Zheng
This paper is aimed to study the impact of sEMG feature weight on the recognition of similar grasping gesture, of which the classification performance is hindered by their alike underlying muscle activation pattern. The sEMG were collected from six forearm hand muscles (EPB, EPI, FDS, PL, MB, ED) when subjects conducted a 4-second different grasping gestures. Then empirical mode decomposition (EMD) was employed to represent the sEMG as IMF components, of which the time-domain features (ZC and WAMP) were obtained. Then PCA was used to reduce the dimension of feature set. The optimal training set of similar grasping gestures was determined by adjusting the weight of sEMG feature in the feature space. SVM classifier and Genetic-Algorithm (GA) were used to classify the patterns of the gestures. Experimental results showed that the classification rate of a gesture was improved with the increasing proportion of its sEMG feature in training set, and different gesture exhibited different sensitivity for gesture recognition. The present work suggested that the proportion of candidate gesture should not be equal, and the contribution of different gesture should be considered to optimize the training set.

20 August 2011 in JOURNAL OF ELECTRONIC MEASUREMENT AND INSTRUMENT
Verified
Journal Article
Closed access
Yanjian LiaoYanjian Liao
Yanjian Liao
HLHongyan Luo
Hongyan Luo