The motions of charged particles in electromagnetic fields composed of two or more laser beams show a variety of forms due to the adjustable properties of electromagnetic fields. In this paper, we consider the periodic laser standing wave field composed of two laser beams with opposite propagating directions. The movement of electrons in the standing wave field shows a periodic behavior, accompanied with the obvious radiation, especially when electrons are captured by the laser standing wave field. This phenomenon has aroused much interest of us. Under the existing experimental conditions, the free electron beam with low energy from an electron gun or the relativistic electron beam generated from laser acceleration can be easily obtained and injected into the periodic standing wave field. In this paper, using the single-electron model and the classical radiation theory of charged particles, we study the motion and radiation processes of low and high energy electrons in the polarized laser standing wave field. The results show that when the direction of incident electrons with low-speed is perpendicular to the direction of the laser standing wave electric field, the one-dimensional nearly periodic motion of electrons evolves into a two-dimensional folded movement by gradually increasing the light intensity of the laser standing wave field, and the strong terahertz radiation at micrometer wavelength is produced. High energy electrons generate the high-frequency radiation with the wavelength at several nanometers when the incident direction of high energy electrons is perpendicular or parallel to the direction of the laser standing wave electric field. In the case of low-energy electron, the motion of electron, frequency and intensity of radiation are affected by the laser intensity. In the case of incident high-energy electrons, the laser intensity affects the intensity of electronic radiation, and the initial electron energy influences radiation frequency. The bigger the incident electrons energy, the higher the frequency of radiation is. #br#We can obtain electron beams with different energies by laser acceleration, and they can be promising small radiation sources for terahertz and X-ray by using the electron beam radiation in a laser standing wave field. These studies also provide a basis for experimental researches and the applications of electron radiation in a laser standing wave field.
Acoustic emission (AE) signal of pipeline leak carries the feature information of structure integrity (the dimension and location of leak source), whereas it is stochastic and uncertain. It belongs to random and non-stationary signals in its statistics. Because of the background noise and the complexity of AE signal transmission, it is very difficult to identify the AE source. Presented in this paper is a method to identify a pipeline leak based on spatial-temporal data fusion model. To elucidate the role of this method, the how-to of completion of hardware and software processing is explained in detail. Fusion of multi-data segments in time and space will decrease uncertainty in the process of identification. Experimental results show that the length of testing pipeline can be up to 87m, and the identification rate can be up to 95% for Φ 1mm and above Φ 1mm pinhole leak.
In manufacturing, automotive, and aerospace industries, there is a need for accurately measuring the geometric parameters of surface defects to give an approximate prediction of the remaining life of the part. For this purpose, we describe a multipose measurement system with a combined laser-and-camera sensor to measure surface defects on rotary metal parts accurately. By using the combined sensor and multipose measuring method, the system can accurately measure the defects of the whole surface with time-saving and the dimensions of the defects can be obtained. We mainly focus on system calibration and defect extraction. First, to eliminate strong correlations among all the parameters, kinematic calibration and camera position calibration are accomplished by using a designed compound calibration artifact. Second, a new defect extraction method based on contourlet transform integrating with active contour model is proposed for precisely and rapidly locating defects. Experimental tests were conducted to verify the capability of the developed system in achieving accurate measurement of surface defects on rotary metal parts. The system can measure defects down to ∼6 μm in height/depth and ∼50 μm in lateral dimension.
Photoacoustic spectroscopy based on the photoacoustic effect is the combination of optical imaging and acoustical imaging, which has become a powerful medical diagnosis tool to distinguish different tissues and components with several different wavelength photoacoustic images. But photoacoustic spectroscopy is limited by the scanning speed, system stability and signal accuracy. To solve these limitation problems, in this paper we propose a new method called photoacoustic double spectrum analysis which can greatly improve the image contrast and identification capability with quantitative analysis of the detected photoacoustic signal frequency. The final experimental results indicate that this method has the feasibility to distinguish different tissues quickly and easily with better contrast, which will be helpful for improving the applications of photoacoustic imaging in various branches of physics, biology, engineering, medicine, etc. We also expect the theoretical and experimental research proposed in this paper to establish the foundation and method for photoacoustic frequency imaging.
For the complexity of calculating and analyzing the guided wave propagation and defect reflection in steel pipes, and the instructional role on studying the characters of T(0,1) mode guided wave to experimental studies, a method associating guided wave theory with numerical solution was applied to simulate T(0,1) mode guided wave propagation and defect reflection in steel pipes by building models, imposing surface loads, and calculating in the ANSYS program, and the characters of T(0,1) mode guided wave were studied. The results of numerical calculation prove that: the T(0,1)mode guided wave was basically non-dispersive in reasonable frequencies, the attenuation trend of amplitude was exponential and the amplitude was basically keeping stable after propagating some distance, the T(0,1) mode guide wave was sensitive to both inner and outer circumferential defects. The reflection coefficient of T(0,1) mode guided wave increases linearly with the increase of circumferential length and depth of defects. When defect depth is not through-thickness, axial length has more influence on reflection coefficient. When defect depth is through-thickness, the influence of axial length to reflection coefficient is basically omitted.
Leaks in gas pipelines cause unnecessary waste of limited resources and produce danger factors, thus leak testing is necessary. Acoustic emission (AE) technology is one of the promising methods for pipeline leak testing. AE signals of pipeline leak carry the feature information of structure integrity (the dimension and location of leak source, etc.), which are stochastic and uncertain, and belongs to non-stationary signals. Because of the noise and the complexity of AE signal transmission, the identification of AE source is very difficult. On the basis of analyzing the characteristics of AE signal and background noise, we established a gas leak identification model for city gas pipeline in this paper. A leak identification method is presented based on spatial-temporal data fusion. The multi-data segments are fused in time and space will decrease incertitude in the process of identification. Experimental result shows that the inspection range can be up to 87m, and the identification rate can be up to 95% for ϕ 1mm pinhole leak.
Wetland vegetation is an important component of wetland ecosystems and plays a crucial role in the ecological functions of wetland environments. Accurate distribution mapping and dynamic change monitoring of vegetation are essential for wetland conservation and restoration. The development of unoccupied aerial vehicles (UAVs) provides an efficient and economic platform for wetland vegetation classification. In this study, we evaluated the feasibility of RGB imagery obtained from the DJI Mavic Pro for wetland vegetation classification at the species level, with a specific application to Honghu, which is listed as a wetland of international importance. A total of ten object-based image analysis (OBIA) scenarios were designed to assess the contribution of five machine learning algorithms to the classification accuracy, including Bayes, K-nearest neighbor (KNN), support vector machine (SVM), decision tree (DT), and random forest (RF), multi-feature combinations and feature selection implemented by the recursive feature elimination algorithm (RFE). The overall accuracy and kappa coefficient were compared to determine the optimal classification method. The main results are as follows: (1) RF showed the best performance among the five machine learning algorithms, with an overall accuracy of 89.76% and kappa coefficient of 0.88 when using 53 features (including spectral features (RGB bands), height information, vegetation indices, texture features, and geometric features) for wetland vegetation classification. (2) The RF model constructed by only spectral features showed poor classification results, with an overall accuracy of 73.66% and kappa coefficient of 0.70. By adding height information, VIs, texture features, and geometric features to construct the RF model layer by layer, the overall accuracy was improved by 8.78%, 3.41%, 2.93%, and 0.98%, respectively, demonstrating the importance of multi-feature combinations. (3) The contribution of different types of features to the RF model was not equal, and the height information was the most important for wetland vegetation classification, followed by the vegetation indices. (4) The RFE algorithm effectively reduced the number of original features from 53 to 36, generating an optimal feature subset for wetland vegetation classification. The RF based on the feature selection result of RFE (RF-RFE) had the best performance in ten scenarios, and provided an overall accuracy of 90.73%, which was 0.97% higher than the RF without feature selection. The results illustrate that the combination of UAV-based RGB imagery and the OBIA approach provides a straightforward, yet powerful, approach for high-precision wetland vegetation classification at the species level, in spite of limited spectral information. Compared with satellite data or UAVs equipped with other types of sensors, UAVs with RGB cameras are more cost efficient and convenient for wetland vegetation monitoring and mapping.
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