Because the more and more complex external environment affects the safe and stable operation of power metering equipment, this paper studies the state evaluation system of power metering equipment based on BP neural network. The hardware of the system includes multi-environment parameter acquisition module, signal processing module, data transmission module and error evaluation module. The software system includes systematically collecting detection data of power metering equipment, selecting ambient temperature, humidity, magnetic field strength and atmospheric pressure as state quantities of power metering equipment, and introducing BP neural network to perform iterative calculation of state quantities to realize automatic state evaluation of power metering equipment. The experimental results show that the shortest detection time of the system is 2s, and the detection results are consistent with the actual results, which verifies that the system has a high efficiency and accuracy of the equipment status detection.
This paper presents a parallel algorithm designed for 1/f noise signal estimation based on Compressed sensing theory on the GPU platform. In the accelerating process, we select parts of the serial program as the object to be speeded up for the execution time of algorithm. Compared with the conventional methods for 1/f noise estimation, our scheme has shown a 20x speedup.
In this paper, we aimed to separate the 1/f noise from the original signal, and analyzed its characteristics of power spectrum. First, an N-level wavelet transform has been applied to the original data signal before the compressed sensing observation for the original signal. Compared with the tradition measurement procession of compressed sensing, the measurement matrix here is replaced with the circulant matrix. This can greatly reduce the measurement number compared with the random Gaussian matrix. To reduce the algorithm time, some zero independent elements are introduced to the circulant matrix. This proposed circulant matrix is then helpful to save 60 percent of algorithm’s reconstruction time.
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