Piezoelectric energy harvesting (PEH) systems convert ambient vibration energy into useful electricity. An interface circuit intervenes the electromechanical energy conversion; it has a significant effect on the electromechanical joint dynamics and harvested power. Among the existing interface circuits, the synchronized electric charge extraction (SECE) solution was known for its unique feature of load independence. However, the actual harvested power was usually shown to be lower than the previous theoretical predictions. The reason is that the energy dissipation in power conditioning, e.g. the diode dissipation in the rectifier and the switching dissipation in each energy extraction, have not received sufficient consideration. This paper revisits the joint dynamics and harvested power of PEH systems with a SECE interface circuit by using the energy flow analysis and impedance modeling. By qualitatively scrutinizing the energy cycle of SECE, the electrically induced dynamic characteristics are broken down into three components: the equivalent capacitance, dissipative resistance, and harvesting resistance, which have the same effects but different values, like those in other PEH interface circuits. The three components are equivalent to an additional stiffness, a dissipative damper, and a regenerative damper in the mechanical domain. The theoretical harvested power, which is estimated based on the impedance modeling, shows good agreement with the experimental results under different loading conditions and operating frequencies. Owing to its modular way of thinking, the impedance modeling technique once again shows its effectiveness and efficiency towards the analyses of joint dynamics and harvested power in PEH systems using different power conditioning interface circuits.
Local feature description is a fundamental yet challenging task in 3D computer vision. This paper proposes a novel descriptor, named Statistic of Deviation Angles on Subdivided Space (SDASS), of encoding geometrical and spatial information of local surface on Local Reference Axis (LRA). In terms of encoding geometrical information, considering that surface normals, which are usually used for encoding geometrical information of local surface, are vulnerable to various nuisances (e.g., noise, varying mesh resolutions etc.), we propose a robust geometrical attribute, called Local Minimum Axis (LMA), to replace the normals for generating the geometrical feature in our SDASS descriptor. For encoding spatial information, we use two spatial features for fully encoding the spatial information of a local surface based on LRA which usually presents high overall repeatability than Local Reference Axis (LRF). Besides, an improved LRA is proposed for increasing the robustness of our SDASS to noise and varying mesh resolutions. The performance of the SDASS descriptor is rigorously tested on four popular datasets. The results show that our descriptor has a high descriptiveness and strong robustness, and its performance outperform existing algorithms by a large margin. Finally, the proposed descriptor is applied to 3D registration. The accurate result further confirms the effectiveness of our SDASS method.
Large-scale surfaces are prevalent in advanced manufacturing industries, and 3D profilometry of these surfaces plays a pivotal role for quality control. This paper proposes a novel and flexible large-scale 3D scanning system assembled by combining a robot, a binocular structured light scanner and a laser tracker. The measurement principle and system construction of the integrated system are introduced. A mathematical model is established for the global data fusion. Subsequently, a robust method is introduced for the establishment of the end coordinate system. As for hand-eye calibration, the calibration ball is observed by the scanner and the laser tracker simultaneously. With this data, the hand-eye relationship is solved, and then an algorithm is built to get the transformation matrix between the end coordinate system and the world coordinate system. A validation experiment is designed to verify the proposed algorithms. Firstly, a hand-eye calibration experiment is implemented and the computation of the transformation matrix is done. Then a car body rear is measured 22 times in order to verify the global data fusion algorithm. The 3D shape of the rear is reconstructed successfully. To evaluate the precision of the proposed method, a metric tool is built and the results are presented.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.