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RISS 인기검색어
- 검색키워드
- 저자 : Jinwen Hu (검색결과 2 건)
- 무료
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- 유료
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Manipulation of Yeast Cells by a Tapered Fiber Probe and Measurement of Trapping Force
Zhaohui Hu,Jia Wang,Jinwen Liang 한국물리학회 2005 THE JOURNAL OF THE KOREAN PHYSICAL SOCIETY Vol.47 No.1
Optical tweezers including near-field optical trapping have been developed theoretically and experimentally. For a novel method, an optical trapping system with a tapered fiber probe inserted at an angle is built and discussed in this paper. Yeast cells can be trapped and manipulated by moving the sample chamber or the tapered fiber probe in three dimensions. Then, twenty-one yeast cells are manipulated to form a pattern of two letters “TH”. Next, the optical-trapping-force curves as a function of the offset are measured and discussed by reference to static and dynamic methods at various laser powers.
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An Incipient On-Line Anomaly Detection Approach for the Dynamic Rolling Process
Yanjing Ou,Jinwen Hu,Xiang Li,Salah Haridy 한국정밀공학회 2014 International Journal of Precision Engineering and Vol. No.
Over the past decades, various methods have been developed to analysis and monitor the dynamic metal processes, specially theextensively used cold rolling process. However, some limitation still exists for the traditional data analysis tools to be implementedwell for these processes. For example, the performance of many of the traditional data analysis approaches cannot be guaranteedwhen the distribution assumption is violated. Meanwhile, it is still lack of systematic method to make good use of the huge conditionparameters. In this article we develop a viable on-line anomaly incipient detection technique towards the cold rolling process of steelsheets. Based on the condition-based SPC, the proposed approach can monitor the multi condition parameters as well as thecorresponding output characteristic in a real-time manner simultaneously and efficiently. It provides a framework for statisticalprocess monitoring development under such dynamic manufacturing environment in order to improve the detecting Sensitivity andSpecificity. The real data practical application verifies that this proposed approach can have an excellent performance without thenormal distribution assumption, thus it has great potential to be employed in a large application area.
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