The powered parafoil system is obtained by adding the propeller thrust to the unpowered parafoil system, and has coupling and nonlinear characteristics, which make its precise control more difficult than that of the unpowered parafoil system. To achieve the trajectory tracking control of the powered parafoil system in the field of precision airdrop, a mathematical model of the 6-DOF of the powered parafoil system is established first. Then, a new trajectory tracking strategy is proposed, which can overcome the limitations of the traditional guidance-based trajectory tracking strategy. We design lateral, longitudinal, and velocity controllers on the basis of the motion characteristics of the powered parafoil system. Then, we adopt the widely used PID control strategy in engineering. In response to the difficult of the PID controller parameter tuning of the powered parafoil system, we achieve the PID controller parameter tuning with the ecosystem particle swarm optimization (ESPSO) of the swarm intelligence optimization algorithm. The effectiveness of the algorithm is verified by simulation experiments. Results show that the proposed algorithm can obtain high trajectory tracking accuracy even when a deviation in the initial state and a random gust wind disturbance in the outside world occur regardless of the method of the multiphase homing or the optimal control homing adopted. The proposed trajectory tracking strategy also has strong robustness and adaptability.

Positron images generated by positron non-destructive testing technology under rapid detection scenes such as low concentration dose, low exposure time and short imaging time, which have some problems like low-resolution and poor definition. These issues cannot be solved for the being time. To solves these problems, this research super-resolves the low-resolution positron images to generate images with high-resolution and clear details. To make the generated super-resolution images more capable of restoring the features of low- resolution images, this research proposed a positron image super-resolution reconstruction method based on generative adversarial networks. In order to improve the input information utilization rate, long skip connections were added into the generator. In addition, the discriminant model, where composed of an image discriminator and a feature discriminator, can stimulate the generator to generate clearer super-resolution images which contain more details. In attempting to solve the problem of dataset matching, a special positron image super-resolution dataset is constructed for network application scenarios. In the adversarial training stage, perceptual similarity loss and adversarial loss are used to replace the traditional mean squared error loss to improve the images perception quality. Experimental results show that the proposed model can reconstruct low-resolution images by four times super-resolution in 0.16 seconds. The super-resolution images obtained are superior to other algorithms in visual effect, which have clearer detail structure and higher objective performance values. Hence this model can meet the requirements of rapid non-destructive testing of industrial parts.

As a nondestructive testing technology, γ-photon imaging shows immense potential in the industrial field. However, the limitations of γ-photon imaging theory and detection technology result in various problems, such as low image resolution and edge blur. The technology is particularly difficult to apply to industrial detection that requires high imaging speed and high resolution. Therefore, this study proposes a reconstruction algorithm for regions of interest (ROI) in γ-photon images. The proposed algorithm is suitable for fast industrial detection and is based on the reconstruction algorithm for sinusoidal graph data, that is, the ordered subset expectation maximization (OSEM) image reconstruction algorithm. It is an improvement of the traditional point-and-line system matrix (SM) model. In the application of the proposed algorithm, the probability weight of a pixel is determined by the solid angle of the crystal bar at both ends of the line of response (LOR) to the pixel it passes through. In this work, the known contour parameters of industrial parts are used to describe the area of nuclide distribution as the ROI. Only the pixels through which the LOR passed in the ROI are counted, and the probability weights of these pixels are calculated to construct the SM. Gaussian filters are added in each iteration to suppress the clutter of scattered noise inside the image. The effectiveness of the algorithm was verified in two model experiments. A closed cavity detection experiment on industrial hydraulic parts was also conducted to compare the image reconstruction effects before and after the improvement. Results showed that the proposed algorithm can effectively improve image resolution and image edge contours. In the tee pipe model experiment and cavity detection experiment on hydraulic parts, the image reconstruction speed increased by more than 6 and 10 times, respectively. Hence, the proposed algorithm provided a feasible solution for quickly obtaining images with clear edges and high resolution under a large aperture detector ring.

This study investigates the application of positron annihilation techniques to the interior of dense metal cavities for Three-dimensional (3D) imaging and extraction of contour features inside the cavities. A feature extraction algorithm based on a 3D anisotropic convolutional operator is proposed for profile feature extraction in low-resolution, low-contrast, and low-signal-to-noise positron image. First, aiming at the problem of positron image noise caused by inconsistent detectors and metal scattering effects, an image preprocessing algorithm combining filtering and full pixel correction is proposed. A 3D anisotropic convolution operator is then designed to extract contour features. To solve the contour feature discontinuity in the extracted contour feature, a 3D path search algorithm is proposed to obtain the centroid coordinate set of the contour feature, and then the centroid coordinate set is subjected to 3D curve fitting to obtain a smooth and continuous contour feature. The study is carried out on the raw 16-bit Digital Imaging and Communications in Medicine (DICOM) data of the positron image, and the data are processed from a 3D perspective, taking full advantage of the correlation between slices in the 3D positron image. In the actual testing, positron images with different kinds of foreign objects in the cavity are extracted using a 3D anisotropic convolution operator, and the contour feature extraction resolution reaches 2 mm.

The research goal of this article is to imaging the oil distribution in landing gear hydraulic cylinder by Positron Emission Tomography(PET). Considering the flexibility and commercial cost for automation testing, two kinds of Not-Full-Ring sensors arrangement, Uniform-arrangement and Bilateral-arrangement, are proposed. The Full-Ring PET is used as the contrast template of imaging. Firstly, GATE 7.1 is used as the Monte Carlo simulation platform to simulate the sensors arrangement of the two Not-Full-Ring schemes. The results of scattering fraction, sensitivity and photon energy distribution were used to evaluate the feasibility of the two schemes. Then, a set of verification device with rotating platform and variable sensing diameter is constructed. The two kinds of Not-Full-Ring sensors arrangements were verified through the verification device. By imaging the hydraulic oil in the hydraulic cylinder, the results were evaluated and compared by using Signal Noise Ratio(SNR). According to the simulation and verification results, Not-Full-Ring scheme is feasible for imaging the liquid distribution in the hydraulic cavity. Uniform- arrangement has a more better imaging performance than Bilateral-arrangement. Such sensors arrangement scheme is not only suitable for this research, but also suitable for other PET imaging applications which need to consider the flexibility and commercial cost.