Due to the influence of many factors in monocular vision pose measurement, the reliability of pose measurement results needs to be evaluated by uncertainty. The difficulties of the traditional guide to the expression of uncertainty in measurement (GUM) method in actual pose evaluation are analyzed, and the adaptive Monte Carlo method (AMCM) is used to evaluate the uncertainty of pose results. According to the mathematical model of pose measurement, the factors affecting pose results are analyzed in detail. The uncertainty evaluation model is established by the error traceability method and the distribution of uncertainty components is analyzed reasonably. Both the Monte Carlo method (MCM) and AMCM are used to evaluate the uncertainty of pose results. The feasibility and effectiveness of the AMCM evaluation method are verified. The experimental results show that MCM and AMCM, as the supplement of the GUM method, can well solve the problem of pose evaluation in the actual measurement system. However, AMCM is more convenient and efficient than MCM to solve the problem of too large or too small sampling times.

An improved strain-transfer model was developed for surface-bonded sapphire-derived fiber Bragg grating sensors. In the model, the core and cladding of the fiber are separated into individual layers, unlike in conventional treatment that regards the fiber as a unitive structure. The separation is because large shear deformation occurs in the cladding when the core of the sapphire-derived fiber is heavily doped with alumina, a material with a high Young’s modulus. Thus, the model was established to have four layers, namely, a core, a cladding, an adhesive, and a host material. A three-layer model could also be obtained from the regressed four-layer model when the core’s radius increased to that of the cladding, which treated the fiber as if it were still homogeneous material. The accuracy of both the four- and three-layer models was verified using a finite-element model and a tensile-strain experiment. Experiment results indicated that a larger core diameter and a higher alumina content resulted in a lower average strain-transfer rate. Error percentages were less than 1.8% when the four- and three-layer models were used to predict the transfer rates of sensors with high and low alumina content, respectively.

This study explores the potential effects of the aerodynamic parameters on the performance of the galloping piezoelectric energy harvester. By considering the geometric configurations, a bluff body cross-sectional shape evolution approach is proposed using Boolean operations on the polygons and forty-eight different cross-sectional shapes with the protruding and depressed features are considered. Computational fluid dynamics is employed to perform a time-varying simulation of the aerodynamic characteristics. The effects of the aerodynamic parameters on performance are investigated computationally using a distributed parameter electromechanical coupling model. The critical wind speed, maximum output power, and the slope of the power versus wind speed curve are introduced as the performance evaluation parameters. The results show that the rear-side protruding feature and the top-side and bottom-side depressed feature have significant potential to enhance the performance. Furthermore, a symmetrical structure of the cross-sectional shape in the downstream direction should be prioritized over asymmetric designs.

The arterial blood sample collector produced in large quantities often fails to meet the requirements due to missing components in the packaging bag, and traditional manual detection methods are both inefficient and inaccurate. To solve this problem, a PyCharm-integrated development environment was used to study image processing and recognition algorithms for identifying components inside the packaging bag of the HN-3 arterial blood sample collector. The machine vision system was used to capture images of the packaging bags of the HN-3 Arterial blood sample collector. Template matching was employed to extract the packaging ROI, and the threshold segmentation method in the HSV color model was used to extract material features based on the packaging ROI. Morphological processing algorithms such as dilation or erosion were used to enhance the connectivity of the extracted features. The existence of components was determined by setting thresholds for the connected domain area or length. The results of the recognition experiment show that the false detection rate is 0.2%, the missed detection rate is 0%, and the average image processing time per product is no more than 39 ms. Compared with manual recognition methods, the efficiency and accuracy have been improved by 36.5 times and 2.3%, respectively. The experimental results confirm the effectiveness of the image processing algorithm.

This study explores the potential effect of a cross-sectional shape with an arcuate protruding and depressed features on the performance. The geometric configurations include two feature types (protruding and depressed), each with six distinct perimeter arrangements and three depths per arrangement, yielding thirty-six different cross-sectional shapes for systematic evaluation. The aerodynamic characteristics and electrical performance are numerically analyzed, using a computational fluid dynamics model and a distributed parameter electromechanical coupling model, respectively. A smooth protruding feature on the front, top, or bottom side suppresses the electrical output; however, when located on the rear side, it significantly increases the slope of the power versus wind speed curve. Depressed features on the rear, top, or bottom side only reduce the critical wind speed and the power enhancement positively correlates with the feature depth. Compared to a square, a harvester with depressed feature on both top and bottom sides exhibits a significant jump in power at the critical wind speed, greatly improving the power. These findings provide important design guidelines for structural optimization of galloping piezoelectric energy harvesters, enabling them to match the wind energy distribution characteristics of specific regions with optimal performance.