Nanjing University

State Grid Jiangsu Electric Power Co., Ltd. Suzhou Branch

Xia Hua

Xun Cao

Xiaowen Li

Jian Xu

Jun Wang

Chong Tong

Bowen Wu

Yunfeng Cai

Siyang Liao

Yuetao Wu

Jianlian Zhang

Liang Wang

Vang Xu

Liudi Fu

Ye Huang

A snapshot hyperspectral imaging method includes the steps of: S, selecting a set of reference wavelengths for calibration, rectifying the shifted positions due to dispersion at each reference wavelength, and selecting a center wavelength; S, estimating relative dispersion at each reconstructed wavelength with respect to the center wavelength; S, generating a dispersion matrix describing the direction of dispersion, and generating a spectral response matrix using a spectral response curve of a sensor; S, capturing images blurred with dispersion; S, deblurring the dispersed images captured in Susing the dispersion matrix and the spectral response matrix generated in Sto obtain spectral data spatially aligned in all spectrums; and S, projecting the aligned spectral data obtained in Sinto color space, extracting a foreground image by a threshold method, sampling the dispersed images obtained in Sas strong prior constraints for the foreground image, and reconstructing accurate spatial hyperspectral data.

Snapshot hyperspectral imaging method with de-blurring dispersed images

A method for removing periodic noise from a reconstructed light field image includes the steps of: acquiring a light field image of a sample; acquiring an optical center position map without the sample; calibrating the imaging centers of the microlenses and performing reconstruction on the light field image; transforming a reconstructed light field image to the frequency domain and generating an image frequency spectrum; preprocessing the image frequency spectrum; generating a low-pass filter; multiplying the low-pass filter with the preprocessed image frequency spectrum, and then setting the frequency spectrum value of the low-frequency component to zero; performing binarization on the reconstructed light field image frequency spectrum to obtain an image mask; removing the high-frequency periodic noise component from the original frequency spectrum of the reconstructed light field image; and transforming the filtered reconstructed light field image frequency spectrum back to the spatial domain to obtain the reconstructed light field image.

Frequency domain-based method for removing periodic noise from reconstructed light field image

A lightning prewarning-based method for active protection against a lightning strike on an important transmission channel includes collecting meteorological element data, and establishing a transmission line-specific lightning strike probability prewarning model; using a maximum probability of a lightning strike prewarning level as a lightning strike prewarning level; performing equivalence calculation on a large-scale regional power system, using a minimum sum of a power loss risk cost and a regulation cost as an optimized objective function, and establishing a power flow transfer optimization model; and comparing a sum of a regulation cost and load power loss risk cost caused after the tie line is disconnected if regulation is performed before a lightning strike, and a load power loss risk cost caused if regulation is not performed, and when the regulation cost is not greater than the load power loss risk cost, performing regulation before the lightning strike.

Lightning prewarning-based method for active protection against lightning strike on important transmission channel

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Xia Hua