This paper investigates design, modelling, and test issues related to piezoelectric energy transducer. The model analyzes a rail-borne "seismic" energy harvester that is designed to generate electrical energy from local variations in rail acceleration. The energy harvester analyzed in this model consists of a piezoelectric PZT film clamped at one end to the rail with a tip mass mounted on its other end. It includes two sub-models in this paper: a vehicle-track interaction model considering vehicle travelling load; and a cantilevered piezoelectric beam model for the visualization of voltage and power profile and frequency response. Four rail irregularities (American 6th grade track spectrum, Chinese track spectrum, German high and low-disturbance track spectrum) are compared and implemented into the calculation script. The calculated results indicate a rail displacement of 0.2 mm to 0.8 mm. Vibration tests of the proposed rail-borne device are conducted; a hydraulic driven system with excitation force up to 140 kN is exploited to generate the realistic wheel-rail interaction force. The proposed rail-borne energy harvester is capable of energy harvesting at low-frequency (5 Hz to 7 Hz) and small railway vibration (0.2 mm to 0.4 mm rail displacement). The output power of 4.9 mW with a load impedance of 100 kOhm is achieved. The open circuit peak-peak voltage reaches 24.4 V at 0.2 mm/7 Hz/5 g wheel-rail excitation. A DC-DC buck converter is designed, which works at the resonance frequency of 23 Hz/5 g on a lab vibration rig, providing a 3.3 VDC output.

Abstract. Recently, a neural oscillator network based on biologically framework named LEGION (Locally Excitatory Globally Inhibitory Oscillator Network), which each oscillator has excitatory lateral connections to the oscillators in its local neighbourhood as well as a connection with a global inhibitor, has been applied to segmentation field. The extended LEGION approach is constructed to extract buildings digital surface model (DSM) generated from LiDAR data. This approach is with no assumption about the underlying structures in DSM data and no prior knowledge regarding the number of regions. Instead of using lateral potential to find a major oscillator block in original way, Gray Level Co-occurence Matrix (GLCM) homogeneity measuring DSM height texture is applied to distinguish buildings from trees and assist to find LEGION leaders in building targets. Alongside the DSM height texture attribure, extended LEGION can extract buildings close to trees automatically. Then a solution of least squares with perpendicularity constraints is put forward to determine regularized rectilinear building boundaries, after tracing and connection the rough building boundaries. In general, the paper presents the concept, algorithms and procedures of the approach. It also gives experimental result of Vaihingen A2 region by the ISPRS text project and another result based on a DSM data of suburban area. The experiment result showed that the proposed method can effectively produce more accurate buildings boundary extraction.

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ABSTRACT:Feature selection and description is a key factor in classification of Earth observation data. In this paper a classification method based on tensor decomposition is proposed. First, multiple features are extracted from raw LiDAR point cloud, and raster LiDAR images are derived by accumulating features or the "raw" data attributes. Then, the feature rasters of LiDAR data are stored as a tensor, and tensor decomposition is used to select component features. This tensor representation could keep the initial spatial structure and insure the consideration of the neighborhood. Based on a small number of component features a k nearest neighborhood classification is applied.