Zobrazeno 1 - 10 of 14 pro vyhledávání: '"Xin Hou Wang"'
1
Experimental Investigation into a New Melt-Blowing Die for Dual Rectangular Jets
Autor: Xin Hou Wang, Yu Dong Wang
Publikováno v: Advanced Materials Research. :270-273
In this work, online measurements of turbulent air flow field in the melt-blowing process for the slot die with inner stabilizing pieces and the corresponding blunt die were carried out with a hot-wire anemometer. The experiment results reveal that t
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f4f619b7d1ef09c8ad0d3674fb1f20da
https://doi.org/10.4028/www.scientific.net/amr.945-949.270
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2
Identification of Natural Bamboo Fibers and Flax Fibers
Autor: Xin Hou Wang, Li Hua Peng, Wei Dong Li
Publikováno v: Advanced Materials Research. :153-157
In this paper, the method based on the testing of the Fourier transform near infrared (NIR) spectra is proposed to identify natural bamboo fibers and flax fibers. The discrimination models between natural bamboo fibers and flax fibers are established
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::1284972edb87c83a460c7ec8f1c64a5b
https://doi.org/10.4028/www.scientific.net/amr.821-822.153
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3
Prediction of Yarn Unevenness Based on BP Neural Network
Autor: Xin Hou Wang, Hui Jun Li
Publikováno v: Advanced Materials Research. 627:329-332
The objective of this research is to predict yarn unevenness. The model of predicting yarn unevenness is built based on BP neural network. The BP neural networks are trained with HVI test results of cotton and USTER TENSOJET 5-S400 test results of ya
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::fe4d362a2ccd293ca91046776e5d6705
https://doi.org/10.4028/www.scientific.net/amr.627.329
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4
Investigation into the Characteristic of Air Pressure Field in the Melt Blowing with Dual Slots via Numerical Simulation
Autor: Xin Hou Wang, San Fa Xin
Publikováno v: Advanced Materials Research. :804-809
The characteristic of air pressure field in melt blowing with dual slots was studied. The main research method was the numerical simulation of three dimensional. The results show that the air pressure field is symmetric. There exist two sections: the
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::175befd7866028fe0b4129805147001e
https://doi.org/10.4028/www.scientific.net/amr.538-541.804
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6
Predicting Yarn Unevenness Using Improved BP Neural Network
Autor: Xin Hou Wang, Hui Jun Li
Publikováno v: Advanced Materials Research. 331:219-222
The objective of this research is to predict yarn unevenness. The model of predicting yarn unevenness is built based on improved BP neural network. The improved BP neural networks are trained with HVI test results of cotton and USTER TENSOJET 5-S400
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e9153dd1311bd936e8aaa3b55ad0ccfd
https://doi.org/10.4028/www.scientific.net/amr.331.219
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7
Computer Aided Design of Coat-Hanger Die in Polymer Film Processing
Autor: Qing Guan Chen, Xin Hou Wang, Kai Meng
Publikováno v: Advanced Materials Research. 186:266-270
With the aid of computer simulations, coat-hanger die was designed for more uniform residence time distribution. Analysis of velocity distribution and flow path line indicate that stagnation areas exist in the coat-hanger die, which is caused by the
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::f9f3be53cceee870563ca67c8a11c12e
https://doi.org/10.4028/www.scientific.net/amr.186.266
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8
Prediction of rotor spun yarn strength from cotton fiber properties using adaptive neuro-fuzzy inference system method
Autor: Xin Hou Wang, Deogratias Nurwaha
Publikováno v: Fibers and Polymers. 11:97-100
In this study, we present the application of a hybrid neuro-fuzzy system for the prediction of cotton rotor spun yarn strength from cotton fiber properties. The proposed system possesses the advantages of both artificial neural networks and fuzzy log
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::2a8327a9682817c29b341f8a9dd80d86
https://doi.org/10.1007/s12221-010-0097-6
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9
The use of k-means and artificial neural network to classify cotton lint
Autor: XiuBao Huang, Xin Hou Wang, Josphat Igadwa Mwasiagi
Publikováno v: Fibers and Polymers. 10:379-383
The use of High Volume Instrument (HVI) to measure cotton lint characteristics produces high dimensional data. A model which utilized Kohonen Self Organizing Maps (SOM) to visualize cotton lint HVI data, k-means clustering technique to cluster the da
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::24c1768becd9dc9a2969438996c0a21c
https://doi.org/10.1007/s12221-009-0379-z
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10
Use of input selection techniques to improve the performance of an artificial neural network during the prediction of yarn quality properties
Autor: Josphat Igadwa Mwasiagi, Xin Hou Wang, Xiu Bao Huang
Publikováno v: Journal of Applied Polymer Science. 108:320-327
The performance of an artificial neural net- work (ANN) is affected by the number and types of inputs. The aim of this article is to study the performance of ANN algorithms, used for the prediction of cotton yarn strength, elongation, and evenness, a
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::e699e7c3eac7c4f072171d2c54f99b6d
https://doi.org/10.1002/app.27586
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