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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.
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.
Cheng, Wei-Hong,Kao, Chen-Yi,Hung, Yu-Shin,Su, Po-Jung,Hsieh, Chia-Hsun,Chen, Jen-Shi,Wang, Hung-Ming,Chou, Wen-Chi Asian Pacific Journal of Cancer Prevention 2012 Asian Pacific journal of cancer prevention Vol.13 No.6
Background: The aim of our study was to assess the practical utility of the palliative prognostic index (PPI) as a prognostic tool used by nurse specialists in a hospice consultation setting in Taiwan. Methods: In total, 623 terminal cancer patients under hospice consultation care from one medical center in northern Taiwan were enrolled between January 1 and June 30, 2011. PPI was assessed by a nurse specialist at first hospice consultation and patients categorized into groups by prognosis (good, intermediate, poor). Patient survival was analyzed retrospectively to determine significance of between-group differences. Results: By PPI sum score, 37.2% of patients were in the good prognosis group, 18% in the intermediate prognosis group and 44.8% in the poor prognosis group. The death rates were 56%, 81.2% and 89.6% and median survivals were 76, 18 and 7 days, respectively. The hazard ratio was 0.19 (95% confidence interval [CI] 0.10-0.24, p<0.001) for the poor versus good prognosis group and 0.54 (95% CI 0.43-0.69, p<0.001) for the poor versus intermediate prognosis group. The sensitivity and specificity for the poor prognosis group was 66% and 71%; the positive predictive value and negative predictive value were 81% and 52%, respectively, to predict patient death within 21 days (area under the curve of the receiver operating characteristic was 0.68). Conclusions: Assessment by PPI can accurately predict survival of terminal cancer patients receiving hospice consultation care. PPI is a simple tool and can be administered by nurse members of hospice consultation teams.
FDG PET or PET/CT in Evaluation of Renal Angiomyolipoma
Chun-Yi Lin,Hui-Yi Chen,Hueisch-Jy Ding,Kuo-Yang Yen,Chia-Hung Kao 대한영상의학회 2013 Korean Journal of Radiology Vol.14 No.2
Objective: Angiomyolipoma is the most common benign kidney tumor. However, literature describing FDG PET findings on renal angiomyolipoma (AML) is limited. This study reports the FDG PET and PET/CT findings of 21 cases of renal AML. Materials and Methods: The study reviews FDG PET and PET/CT images of 21 patients diagnosed with renal AML. The diagnosis is based on the classical appearance of an AML on CT scan with active surveillance for 6 months. The study is focused on the observation of clinical and radiographic features. Results: Six men and 15 women were included in our study. The mean age of the patients was 57.14 ± 9.67 years old. The mean diameter of 21 renal AML on CT scans was 1.76 ± 1.00 cm (Min: 0.6 cm; Max: 4.4 cm). CT scans illustrated renal masses typical of AMLs, and the corresponding FDG PET scans showed minimal FDG activities in the area of the tumors. None of the 21 AMLs showed a maximum standardized uptake value (SUVmax) greater than 1.98. No statistically significant correlation was present between SUVmax and tumor size. Conclusion: Renal AMLs demonstrate very low to low uptake on FDG PET and PET/CT imaging in this study. When a fatcontaining tumor in the kidney is found on a CT scan, it is critical to differentiate an AML from a malignant tumor including an RCC, liposarcoma, and Wilms tumor. This study suggests that FDG PET or PET/CT imaging is useful for differentiating a renal AML from a fat-containing malignant tumor.