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Chi Eung Danforn Lim,Felix Wu Shun Wong,Warren Smith 셀메드 세포교정의약학회 2011 TANG Vol.1 No.1
Bioelectric impedance measurements have been reported to show significant variation between individuals. Different physiological conditions like thickened skin, obesity, and fluid retention can affect the impedance measurement. Therefore, it is important to learn what other factors can affect the measurements of impedance even in healthy individuals. Such information is a prerequisite for understanding the changes in impedance associated with acupuncture treatment. This study investigated the bio-impedance properties of tissue segments in the arms of a number of healthy subjects, so as to define the factors that might influence the variation of the bio-impedance data in acupuncture meridians studies. 51 healthy subjects were recruited through Liverpool Hospital, Sydney. Demographic data was collected from each subject including the age, sex, BMI, and time since most recent meal. Electrodes were applied to the forearms of each test subject. Measurements were done by a purpose-built Bio-Impedance Research Device (BIRD-I) which allowed the determination of core resistance (Rc) and core reactance (Xc) of each of the three meridian tissue segments on the anterior surface of the forearm. No significant difference was found in the core resistance attributable to age group, gender, BMI or meal intake. However, a statistically significant trend in increasing resistance from the radial to ulnar aspect of the forearm (p < 0.001) was found. No significant difference was found in the core resistance of test tissue segments among the 51 healthy subjects measured in this study. However, the trend of increasing core resistance from the radial to ulnar aspects of the arm deserves further investigation.
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Localization and Size Estimation for Breaks in Nuclear Power Plants
Ting-Han Lin,Ching Chen,Shun-Chi Wu,Te-Chuan Wang,Yuh-Ming Ferng 한국원자력학회 2022 Nuclear Engineering and Technology Vol.54 No.1
Several algorithms for nuclear power plant (NPP) break event detection, isolation, localization, and size estimation are proposed. A break event can be promptly detected and isolated after its occurrence by simultaneously monitoring changes in the sensing readings and by employing an interquartile range-based isolation scheme. By considering the multi-sensor data block of a break to be rank-one, it can be located as the position whose lead field vector is most orthogonal to the noise subspace of that data block using the Multiple Signal Classification (MUSIC) algorithm. Owing to the flexibility of deep neural networks in selecting the best regression model for the available data, we can estimate the break size using multiple-sensor recordings of the break regardless of the sensor types. The efficacy of the proposed algorithms was evaluated using the data generated by Maanshan NPP simulator. The experimental results demonstrated that the MUSIC method could distinguish two near breaks. However, if the two breaks were close and of small sizes, the MUSIC method might wrongly locate them. The break sizes estimated by the proposed deep learning model were close to their actual values, but relative errors of more than 8% were seen while estimating small breaks’ sizes.
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