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RISS 인기검색어
- 검색키워드
- 저자 : Chia-Hung Dylan Tsai (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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Kao, Imin,Li, Xiaolin,Tsai, Chia-Hung Dylan Techno-Press 2009 Smart Structures and Systems, An International Jou Vol.5 No.2
In this paper, the analytical fault detection and diagnosis (FDD) is presented using model-based and signal-based methodology with wavelet analysis on signals obtained from sensors and sensor networks. In the model-based FDD, we present the modeling of contact interface found in soft materials, including the biomedical contacts. Fingerprint analysis and signal-based FDD are also presented with an experimental framework consisting of a mechanical pneumatic system typically found in manufacturing automation. This diagnosis system focuses on the signal-based approach which employs multi-resolution wavelet decomposition of various sensor signals such as pressure, flow rate, etc., to determine leak configuration. Pattern recognition technique and analytical vectorized maps are developed to diagnose an unknown leakage based on the established FDD information using the affine mapping. Experimental studies and analysis are presented to illustrate the FDD methodology. Both model-based and wavelet-based FDD applied in contact interface and manufacturing automation have implication towards better quality of life by applying theory and practice to understand how effective diagnosis can be made using intelligent FDD. As an illustration, a model-based contact surface technology an benefit the diabetes with the detection of abnormal contact patterns that may result in ulceration if not detected and treated in time, thus, improving the quality of life of the patients. Ultimately, effective diagnosis using FDD with wavelet analysis, whether it is employed in biomedical applications or manufacturing automation, can have impacts on improving our quality of life.
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Imin Kao,Xiaolin Li,Chia-Hung Dylan Tsai 국제구조공학회 2009 Smart Structures and Systems, An International Jou Vol.5 No.2
In this paper, the analytical fault detection and diagnosis (FDD) is presented using model-based and signal-based methodology with wavelet analysis on signals obtained from sensors and sensor networks. In the model-based FDD, we present the modeling of contact interface found in soft materials, including the biomedical contacts. Fingerprint analysis and signal-based FDD are also presented with an experimental framework consisting of a mechanical pneumatic system typically found in manufacturing automation. This diagnosis system focuses on the signal-based approach which employs multi-resolution wavelet decomposition of various sensor signals such as pressure, flow rate, etc., to determine leak configuration. Pattern recognition technique and analytical vectorized maps are developed to diagnose an unknown leakage based on the established FDD information using the affine mapping. Experimental studies and analysis are presented to illustrate the FDD methodology. Both model-based and wavelet-based FDD applied in contact interface and manufacturing automation have implication towards better quality of life by applying theory and practice to understand how effective diagnosis can be made using intelligent FDD. As an illustration, a model-based contact surface technology an benefit the diabetes with the detection of abnormal contact patterns that may result in ulceration if not detected and treated in time, thus, improving the quality of life of the patients. Ultimately, effective diagnosis using FDD with wavelet analysis, whether it is employed in biomedical applications or manufacturing automation, can have impacts on improving our quality of life.
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Lim, Jiwon,Choi, Andrew,Kim, Hyung Woo,Yoon, Hyungjun,Park, Sang Min,Tsai, Chia-Hung Dylan,Kaneko, Makoto,Kim, Dong Sung American Chemical Society 2018 ACS APPLIED MATERIALS & INTERFACES Vol.10 No.17
<P>Cell migration is crucial in physiological and pathological processes such as embryonic development and wound healing; such migration is strongly guided by the surrounding nanostructured extracellular matrix. Previous studies have extensively studied the cell migration on anisotropic nanotopographic surfaces; however, only a few studies have reported cell migration on isotropic nanotopographic surfaces. We herein, for the first time, propose a novel concept of adherable area on cell migration using isotropic nanopore surfaces with sufficient nanopore depth by adopting a high aspect ratio. As the pore size of the nanopore surface was controlled to 200, 300, and 400 nm in a fixed center-to-center distance of 480 nm, it produced 86, 68, and 36% of adherable area, respectively, on the fabricated surface. A meticulous investigation of the cell migration in response to changes in the constrained adherable area of the nanotopographic surface showed 1.4-, 1.5-, and 1.6-fold increase in migration speeds and a 1.4-, 2-, and 2.5-fold decrease in the number of focal adhesions as the adherable area was decreased to 86, 68, and 36%, respectively. Furthermore, a strong activation of FAK/Rac1 signaling was observed to be involved in the promoted cell migration. These results suggest that the reduced adherable area promotes cell migration through decreasing the FA formation, which in turn upregulates FAK/Rac1 activation. The findings in this study can be utilized to control the cell migration behaviors, which is a powerful tool in the research fields involving cell migration such as promoting wound healing and tissue repair.</P> [FIG OMISSION]</BR>
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