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Your search keyword '"Xia, Chunming"' showing total 88 results
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88 results on '"Xia, Chunming"'

55. Technology Modification Practice on the Energy Saving of Yarn Spinning Production.

Author

JI Yijun, SHAO Guodong, CUI Yihuai, JI Yuanqing, and XIA Chunming

Subjects
SPUN yarns, POWER resources, WATER, TECHNOLOGY, WATER use
Abstract

The technology modification practice problems on the energy saving of yarn spinning production were discussed. The status of wasting energy and resources for the yarn spinning enterprises on the design of related device was analyzed. The procedure and effect of the improvement were introduced including collective doffing system improvement for short spinning frame, cotton suction system improvement for long spinning frame, cotton suction draught fan improvement for short spinning frame technology improvement of using surface water rom winter irrigation in summer, energy saving impeller improvement for air condition draught fan, technology improvement of power supply and distribution system and so on. The practice proved that energy saving effect of ach modification is better. It is considered that energy saving for cotton spinning enterprises is a systematic, technology and long-term project. By practically carrying out each kind of energy saving improvement, significant results can be achieved and higher quality development of enterprises can be assisted. [ABSTRACT FROM AUTHOR]

Published
2019

56. Improved Performance of $d_{31}$ -Mode Needle-Actuating Transducer With PMN-PT Piezocrystal.

Author

Jiang, Tingyi, Xia, Chunming, Cochran, Sandy, and Huang, Zhihong

Subjects
*NEEDLE biopsy, *LEAD zirconate titanate, *ULTRASONIC imaging, *TRANSDUCERS, *PIEZOELECTRIC ceramics
Abstract

Prototypes of a PZT-based ultrasound needle-actuating device have shown the ability to reduce needle penetration force and enhance needle visibility with color Doppler imaging during needle insertion for tissue biopsy and regional anesthesia. However, the demand for smaller, lighter devices and the need for high performance transducers have motivated investigation of a different configuration of needle-actuation transducer, utilizing the $d_{31}$ -mode of PZT4 piezoceramic, and exploration of further improvement in its performance using relaxor-type piezocrystal. This paper outlines the development of the $d_{31}$ -mode needle actuation transducer design from simulation to fabrication and demonstration. Full characterization was performed on transducers for performance comparison. The performance of the proposed smaller, lighter $d_{31}$ -mode transducer is comparable with that of previous $d_{33}$ -mode transducers. Furthermore, it has been found to be much more efficient when using PMN-PT piezocrystal rather than piezoceramic. [ABSTRACT FROM AUTHOR]

Published
2018
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88. Pattern recognition of head movement based on mechanomyography and its application.

Author

Gu X, Wu Q, Zhang Y, Zhong H, Zhang S, Xia C, and Yu J

Subjects
Algorithms, Discriminant Analysis, Humans, Muscle, Skeletal physiology, Support Vector Machine, Head Movements physiology
Abstract

The first part of this study investigated pattern recognition of head movements based on mechanomyography (MMG) signals. Four channel MMG signals were collected from the sternocleidomastoid (SCM) muscles and the splenius capitis (SPL) muscles in the subjects' neck when they bowed the head, raised the head, side-bent to left, side-bent to right, turned to left and turned to right. The MMG signals were then filtered, normalized and divided using an unequal length segmentation algorithm into a single action frame. After extracting the energy features of the wavelet packet coefficients and the feature of the principal diagonal slices of the bispectrum, the dimension of the energy features were reduced by the Fisher linear discriminant analysis (FLDA). Finally, all the features were classified through the support vector machine (SVM) classifier. The recognition rate was up to 95.92%. On this basis, the second part of this study used the head movements to control a car model for simulating the control of a wheelchair, and the success rate was 85.74%.

Published
2020
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