We conducted a crosswell seismic project in April 2002 in the Ma production area of Jianghan oil field in central Hubei Provice, P.R. China. This part of the Jianghan fault basin contains complex faults that dominate the reservoir zone, and they significantly affect flow paths and fluid distribution. Therefore, it is critical to understand the fault structure distribution in this area at the reservoir scale in order to develop and enhance production.
In the past decade, with the development of Unmanned Aerial Vehicles (UAV), research about multi-rotor UAV has become one hot spot in its area. Along with the increasing number of UAV-users, illegal-flying occasions appear more frequently, which will affect citizens' daily life. The regulation of safe and ordered UAV-flying has drawn great attentions from the whole society. Recognizing the remote controller individuals is one crucial part for regulating UAV flight. Due to the subtle signal differences between controller individuals, some traditional identification methods are no longer applicable. This paper proposes two fingerprint-based methods for identifying different remote controllers of UAVs and compares the two identification methods. By extracting key features of the constellation diagrams and bispectrum, then applying five machine learning methods, such as decision tree(Tree), Naive Bayes (NB), Discriminant analysis (Discr), k-nearest neighbor (KNN) and Error Correction Output Coding (ECOC), we can achieve accurate UAV controller recognition. By using constellation diagrams as key features,the experiment results show that five machine learning methods can achieve more than 95% accuracy of recognition for four individuals when the SNR is 10dB. By using bispectrum as key features, the decision tree can obtain more than 95% recognition accuracy when the SNR is 5dB, and discr still achieves more than 95% recognition accuracy when the SNR is 0dB, while the other three methods perform in between.
A novel electrode modified with oligonucleotide and microporous gold was fabricated for the determination of mercury by differential pulse adsorptive stripping voltammetry (DPAdSV). Microporous gold was synthesized by electrochemical reduction using dynamic hydrogen bubble template. The oligonucleotide was immobilized on microporous gold by self-assembly. The prepared electrode exhibited an improved electrochemical response for mercury(II) ion because of the large surface area and excellent electron transfer capacity provided by microporous gold and the specific coordination between mercury ion and thymine bases in oligonucleotides. Under the optimal experiment conditions, the oligonucleotide functionalized microporous gold electrode had a linear relationship between the stripping current and mercury ion concentration in the range from 0.5 to 30 µg/L with a detection limit of 0.021 µg/L. Moreover, the prepared electrode exhibited good selectivity, reproducibility, repeatability and stability. Furthermore, the prepared electrode was applied to detect mercury in tap water with satisfactory results.
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