scholarly journals Vision-based Deformation Measurement for Pile-soil Testing

2019 ◽  
Vol 275 ◽  
pp. 03009
Author(s):  
Kun Zhou ◽  
Linhua Chen ◽  
Shanshan Yu

Image measurement technology has been widely used in monitoring the deformation of the soil field around the pile with its advantages of no damage, no contact, full-field measurement, no added quality and high sensitivity. But there are few researches on image-based bearing deformation measurement of the pile. Through an indoor pile-soil semi-model test, the rigid body displacement and load-bearing deformation of a new type of prefabricated steel tube pile foundation under horizontal load was measured based on image features. In this study, the concept of optical extensometer is first applied to the measurement of local average strain of a non-uniform deformed structure. Based on an improved feature point tracking algorithm SURF-BRISK, non-contact measurement of tiny strain of pile body is realized. In addition, based on DIC technology, this study also obtained the progressive development of displacement field of soil around pile. The above work fully reflects the non-contact convenience and full-field richness of the optical measurement method compared with the traditional measurement method.

2012 ◽  
Vol 591-593 ◽  
pp. 1089-1093
Author(s):  
Jiong Shiun Hsu ◽  
Bor Jiunn Wen ◽  
Liang Jian Chang

Glass transition temperature is an important thermal property for polymer materials. When its temperature exceeds this temperature, the exhibiting characters dramatically changes. Although the experimental techniques suited to this temperature measurement are well-established, the full-field optical measurement method has not been employed to characterize this property. Therefore, this paper aims to measure the glass transition temperature using optical method.


Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3456
Author(s):  
Hyeon-Sang Hwang ◽  
Eui-Chul Lee

Conventional respiration measurement requires a separate device and/or can cause discomfort, so it is difficult to perform routinely, even for patients with respiratory diseases. The development of contactless respiration measurement technology would reduce discomfort and help detect and prevent fatal diseases. Therefore, we propose a respiration measurement method using a learning-based region-of-interest detector and a clustering-based respiration pixel estimation technique. The proposed method consists of a model for classifying whether a pixel conveys respiration information based on its variance and a method for classifying pixels with clear breathing components using the symmetry of the respiration signals. The proposed method was evaluated with the data of 14 men and women acquired in an actual environment, and it was confirmed that the average error was within approximately 0.1 bpm. In addition, a Bland–Altman analysis confirmed that the measurement result had no error bias, and regression analysis confirmed that the correlation of the results with the reference is high. The proposed method, designed to be inexpensive, fast, and robust to noise, is potentially suitable for practical use in clinical scenarios.


2009 ◽  
Author(s):  
Songbae Moon ◽  
Seong-Yoon Kim ◽  
Gyung-Yoon Bang ◽  
Byung-Gook Kim ◽  
Sang-Gyun Woo ◽  
...  

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