KEYWORDS: Acoustics, Optical fibers, Signal detection, Sensors, Fiber Bragg gratings, Computer simulations, Temperature sensors, Temperature metrology, Inspection, Detector development
In this work, different kinds of detection methods of pipeline leakage were summarized based on the currently reported technology. In the case of different leakage situations, the detection method could be hardware-based or software-based. The working principle, advantages, and disadvantages of the methods were discussed and compared with each other in accuracy, sensitivity, cost, and so on.
KEYWORDS: Unmanned aerial vehicles, Inspection, Electromagnetism, Data centers, Intelligence systems, Safety, Meteorology, Signal processing, Minerals, Digital signal processing
With the development and progress of unmanned aerial vehicle (UAV) technology, UAVs have become the main force in performing various complex tasks, but at present, there is little related research on the application of UAVs in oil pipeline inspection, especially where there are continuous snowy peaks in alpine mountainous areas. Ravines, harsh natural conditions, and complex electromagnetic environments have adverse effects on the launch, recovery, use efficiency, command and control, and maintenance support of UAVs. By analyzing the characteristics of the alpine environment and its impact on UAVs, this paper puts forward solutions from three aspects: strengthening the support guarantee, enhancing the adaptability and innovating the methodology of designing alpine UAVs. It aims to provide a reference for UAVs to carry out the oil pipeline inspection tasks in alpine mountain areas.
KEYWORDS: Radar, Performance modeling, Convolution, Signal to noise ratio, Modulation, Continuous wavelet transforms, Signal processing, Radar signal processing, Detection and tracking algorithms, Time-frequency analysis
For the problem that traditional radar intra-pulse signals identification needs expert knowledge, a method of radar intra-pulse signals identification based on Choi-Williams Distribution (CWD) and Convolutional Neural Network (CNN) is proposed in this work. Firstly, the characteristics of the collected radar intra-pulse signals are acquired. Then, the feature images of these characteristics are preprocessed. Finally, the intelligent identification of radar intra-pulse signals based on CNN is realized. The experimental results show that when the Signal to Noise Ratio (SNR) is 5dB, the identification accuracy of algorithm model proposed in this work based on CWD and CNN can reach 87.95%, while that based on Continuous Wavelet Transform (CWT) and CNN can only reach 72.23%. The significance of this work further optimizes the feature extraction of radar intra-pulse signals and provides an empirical reference for radar intelligent identification in EW.
When the micromotion (MM) target has a large MM amplitude, the bandwidth of the synthetic aperture radar (SAR) azimuth echo will be greater than the pulse repetition frequency (PRF) of SAR. According to Nyquist sampling theorem, spectrum aliasing will occur at this time, and conventional MM target detection and estimation algorithms may lose effect. To solve the problem of large MM target detection with echo bandwidth greater than the PRF, an inverse Radon transform (IRT)-based large MM target detection algorithm is proposed. The study found that the time-frequency (TF) spectrum of large MM target azimuth echo is usually a folded sinusoidal curve. Then we spliced enough identical TF spectrums to restore an intact sinusoidal curve. The intact sinusoidal curve can be mapped into a peak in the parameter space using IRT. Hence, the algorithm can detect large MM target and estimate its parameters accurately. Data processing results prove the effectiveness of the algorithm. At the same time, the performance analysis proves that the operation speed and antinoise performance of the algorithm are better than that of the Hough transform algorithm.
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