1,583 research outputs found

    Frequency-dependent AVO attribute: theory and example

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    Fluid-saturated rocks generally have seismic velocities that depend upon frequency. Exploring this property may help us discriminate different fluids from seismic data. In this paper, we introduce a scheme to calculate a frequency-dependent AVO attribute in order to estimate seismic dispersion from pre-stack data, and apply it to North Sea data. The scheme essentially combines the two-term approximation of Smith and Gidlow (1987) with the method of spectral decomposition based on the Wigner-Ville distribution, which is used to achieve high resolution. The result suggests the potential of this method for detection of seismic dispersion due to fluid saturation

    Feature Based Calibration of a Network of Kinect Sensors

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    The availability of affordable depth sensors in conjunction with common RGB cameras, such as the Microsoft Kinect, can provide robots with a complete and instantaneous representation of the current surrounding environment. However, in the problem of calibrating multiple camera systems, traditional methods bear some drawbacks, such as requiring human intervention. In this thesis, we propose an automatic and reliable calibration framework that can easily estimate the extrinsic parameters of a Kinect sensor network. Our framework includes feature extraction, Random Sample Consensus and camera pose estimation from high accuracy correspondences. We also implement a robustness analysis of position estimation algorithms. The result shows that our system could provide precise data under certain amount noise. Keywords Kinect, Multiple Camera Calibration, Feature Points Extraction, Correspondence, RANSA

    Optimizing Wirelessly Powered Crowd Sensing: Trading energy for data

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    To overcome the limited coverage in traditional wireless sensor networks, \emph{mobile crowd sensing} (MCS) has emerged as a new sensing paradigm. To achieve longer battery lives of user devices and incentive human involvement, this paper presents a novel approach that seamlessly integrates MCS with wireless power transfer, called \emph{wirelessly powered crowd sensing} (WPCS), for supporting crowd sensing with energy consumption and offering rewards as incentives. The optimization problem is formulated to simultaneously maximize the data utility and minimize the energy consumption for service operator, by jointly controlling wireless-power allocation at the \emph{access point} (AP) as well as sensing-data size, compression ratio, and sensor-transmission duration at \emph{mobile sensor} (MS). Given the fixed compression ratios, the optimal power allocation policy is shown to have a \emph{threshold}-based structure with respect to a defined \emph{crowd-sensing priority} function for each MS. Given fixed sensing-data utilities, the compression policy achieves the optimal compression ratio. Extensive simulations are also presented to verify the efficiency of the contributed mechanisms.Comment: arXiv admin note: text overlap with arXiv:1711.0206

    A multi-sensor based online tool condition monitoring system for milling process

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    Tool condition monitoring has been considered as one of the key enabling technologies for manufacturing optimization. Due to the high cost and limited system openness, the relevant developed systems have not been widely adopted by industries, especially Small and Medium-sized Enterprises. In this research, a cost-effective, wireless communication enabled, multi-sensor based tool condition monitoring system has been developed. Various sensor data, such as vibration, cutting force and power data, as well as actual machining parameters, have been collected to support efficient tool condition monitoring and life estimation. The effectiveness of the developed system has been validated via machining cases. The system can be extended to wide manufacturing applications

    Offshoring Pollution while Offshoring Production?

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    Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/138846/1/smj2656.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/138846/2/smj2656_am.pd

    Interspecific Variation and Phylogenic Architecture of Pinus densata and the Hybrid of Pinus tabuliformis×Pinus Yunnanensis in the Pinus densata Habitat: an Electrical Impedance Spectra Perspective

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    We evaluated a novel and non-destructive method of the electrical impedance spectroscopy (EIS) to elucidate the genetic and evolutionary relationship of homoploid hybrid conifer of Pinus densata (P.d) and its parental species Pinus tabuliformis (P.t) and Pinus yunnanensis (P.y), as well as the artificial hybrids of the P.t and P.y. Field common garden tests of96 trees sampled from 760 seedlings and 480 EIS records of 1,440 needles assessed the interspecific variation of the P.d, P.t, P.y and the artificial hybrids. We found that (1) EIS at different frequencies diverged significantly among germplasms; P.y was the highest, P.t was the lowest, and their artificial hybrids were within the range of P.t and P.y; (2) maternal species effect of EIS magnitudes in the hybrids and P.d was stronger than the paternal species characteristics; (3)EIS of the artificial hybrid confirmed the mid-parent and partial maternal species characteristics;(4) unified exponential model of EIS for the interspecific and hybrids can be constructed as |Z|=Af -B; (5) cluster analysis for species and hybrid combinations in total corroborated with the previous hybrid model of Pinus densata. Our non-destructive EIS method complemented the previous finding that Pinus densata was originated from P.t and P.y. We conclude that the impedance would be a viable indicator to investigate the interspecific genetic variations of conifers

    Estimating seismic dispersion from prestack data using frequency-dependent AVO analysis

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    Recent laboratory measurement studies have suggested a growing consensus that fluid saturated rocks can have frequency-dependent properties within the seismic bandwidth. It is appealing to try to use these properties for the discrimination of fluid saturation from seismic data. In this paper, we develop a frequency-dependent AVO (FAVO) attribute to measure magnitude of dispersion from pre-stack data. The scheme essentially extends the Smith and Gidlow (1987)’s two-term AVO approximation to be frequency-dependent, and then linearize the frequency-dependent approximation with Taylor series expansion. The magnitude of dispersion can be estimated with least-square inversion. A high-resolution spectral decomposition method is of vital importance during the implementation of the FAVO attribute calculation. We discuss the resolution of three typical spectral decomposition techniques: the short term Fourier transform (STFT), continuous wavelet transform (CWT) and Wigner-Vill Distribution (WVD) based methods. The smoothed pseudo Wigner-Ville Distribution (SPWVD) method, which uses smooth windows in time and frequency domain to suppress cross-terms, provides higher resolution than that of STFT and CWT. We use SPWVD in the FAVO attribute to calculate the frequency-dependent spectral amplitudes from pre-stack data. We test our attribute on forward models with different time scales and crack densities to understand wave-scatter induced dispersion at the interface between an elastic shale and a dispersive sandstone. The FAVO attribute can determine the maximum magnitude of P-wave dispersion for dispersive partial gas saturation case; higher crack density gives rise to stronger magnitude of P-wave dispersion. Finally, the FAVO attribute was applied to real seismic data from the North Sea. The result suggests the potential of this method for detection of seismic dispersion due to fluid saturation
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