Abstract To achieve precise oil film thickness measurement in enclosed cavities of complex industrial components, this study proposes a double-layer oil film measurement method based on time-of-flight positron annihilation detection. The arrival-time difference of coincident γ photons is used to estimate the annihilation position along the line of response, thereby enabling statistical attribution of response lines to different oil film layers. Because the finite timing resolution of the detector broadens the reconstructed annihilation positions into a Gaussian distribution, a 3 σ interval classification method is first established, in which response lines in the overlap region are excluded to reduce layer misattribution. To improve data utilization and measurement accuracy under overlapping distributions, an improved overlapping distribution curve method for double-layer oil film thickness measurement (IODC-DLOF) is further proposed. Simulation results show that the proposed method can distinguish double-layer oil film response lines, with relative errors remaining within approximately 2.3% for the tested simulation cases. Physical experiments using millimeter-scale oil film models further validate the method, with the IODC-DLOF method achieving a maximum relative error of approximately 5.2% and a maximum coefficient of variation below 0.6% among four selected probe crystal pairs.

Positron tomography technology (PET) can adapt to complex on-site environments, enabling industrial non-destructive testing without disturbance or damage. PET super-resolution reconstruction aims to reduce detection costs and improve accuracy, making it highly valuable for research. In this study, we propose a generative adversarial network (GAN)-based super-resolution model for industrial PET images that incorporates prior knowledge to address issues such as detail loss and artifact distortion in existing algorithms. We design a texture enhancement network to extract detailed features and employ a connection network to fuse texture and super-resolution features, enhancing texture details. Additionally, we introduce texture loss and super-resolution loss to further improve the model’s performance. Experimental results demonstrate that the proposed method enhances super-resolution image quality in both visual and objective evaluation metrics and has been validated in practical industrial detection.

Positron imaging has shown great potential in industrial non-destructive testing due to its high sensitivity and ability to reveal internal structures of complex components. However, reconstructing high-quality images from positron emission data remains challenging, particularly under limited sampling and ill-posed inverse problems, which are common in applications such as closed cavity detection. To address this, we propose an iterative reconstruction method for industrial positron images based on a generative adversarial network (PIIR-GAN). The method integrates a generative adversarial framework with a self-attention mechanism to exploit prior information and improve image quality under low-sample conditions. A key innovation is embedding the neural network model directly into the iterative reconstruction process, enabling end-to-end learning. Furthermore, a likelihood-based constraint is incorporated into the objective function to guide optimization. Experimental results on a GATE simulation dataset show significant improvements in both PSNR and SSIM compared with conventional methods, and real-world industrial defect detection further verifies the effectiveness of the approach.

The flexibility of parafoils introduces hysteresis and issues of insufficient control force in their management. Traditional approaches that employ geometric curves for segmented trajectory planning fail to capture certain flight characteristics of parafoils, complicating the achievement of autonomous control. Meanwhile, complex consideration of the flexibility-induced aerodynamic changes will result in a redundant computational burden, diminishing the real-time performance of the parafoil system, rendering it unsuitable in actual airdrop missions. This study optimizes the six-degree-of-freedom (6-DOF) motion model for parafoils by considering the hysteresis and nonlinear characteristics caused by flexibility and introduces a method for trajectory planning that smoothly transitions based on parafoil flight speed. Analysis of this model revealed hysteresis and nonlinear changes in the three-axis velocity within the parafoil body coordinate system owing to control. The proposed velocity planning method is efficient, optimizing the control energy while providing specific control instructions. Using this method, the parafoil system achieves a landing position accuracy within +/- 0.3 m in simulations and within +/- 2 m in flight tests.

Images from positron emission tomography (PET) for non-destructive testing of industrial cavities have low resolution and blurred edges. This study proposes an algorithm based on depth of interaction information to improve the image edge recognition and restoration. A synchronous iterative filter–maximum likelihood expectation maximisation (SIF–MELM) algorithm is proposed based on the traditional MLEM algorithm to improve the imaging quality. A set of engine blade simulation models is designed to verify the performance of the algorithm. Image quality evaluations are conducted on the images reconstructed by the algorithm before and after improvement. A set of wind tunnel oil flow experiments are designed to verify the effectiveness and superiority of this method. Experimental results show that, the peak signal-to-noise and structural similarity of the reconstructed images increase from 23.99 and 0.60 to 27.37 and 0.73, respectively. Moreover, the oil flow trajectory conforms to the simulation results.

In order to realize the y-photon nondestructive detection of industrial confined pipelines, it is necessary to construct large axial ring y-photon detectors, but with the increase of the axial length of the detectors, the acquisition of response lines will be truncated and missing, resulting in the degradation of the detection imaging quality in large axial space. In this paper, we propose a list-mode data-based imaging algorithm for industrial confined pipe detection, which makes full use of the list-mode to conform to the position and time information in the event and to reasonably assign the weights of the system matrix in the image reconstruction. When calculating the weight contribution of the pixels passing through a response line to that response line, only the pixels within the uncertainty range need to be calculated without the pixels corresponding to the complete response line, and the system matrix containing time-of-flight (TOF) information is obtained, thus effectively suppressing the noise caused by truncated and missing large axial spatial data. In addition, the parallel feature of CUDA is also used to divide the system matrix calculation process into small blocks that are independent of each other to achieve accelerated optimization of the algorithm. Finally, two industrial models are used for simulation experiments, and the experimental results show that the proposed method can significantly improve the resolution and spatial contrast of large axial spatial reconstruction images in industrial confined pipeline inspection, and can meet the demand of y-photon nondestructive inspection of industrial confined pipelines.

Positron imaging technology has shown good practical value in industrial non-destructive testing, but the noise and artifacts generated during the imaging process of flow field images will directly affect the accuracy of industrial fault diagnosis. Therefore, how to obtain high-quality reconstructed images of the positron flow field is a challenging problem. In the existing image denoising methods, the denoising performance of positron images of industrial flow fields in special fields still needs to be strengthened. Considering the characteristics of few sample data and strong regularity of positron flow field image,in this work, we propose a new method for image denoising of positron flow field, which is based on a generative adversarial network with zero-shot learning. This method realizes image denoising under the condition of small sample data, and constrains image generation by constructing the extraction model of image internal features. The experimental results show that the proposed method can reduce the noise while retaining the key information of the image. It has also achieved good performance in the practical application of industrial flow field positron imaging.

The nondestructive characteristics of $\gamma $ -photon imaging technology make it attractive potential in the industry. However, in industrial detection with a large detection range and high resolution, iteration method, the image reconstruction algorithm which is most widely used, faces the challenge of an overly large system matrix, and the current compression algorithms using the geometric symmetry of the positron emission tomography (PET) system have problems of complex pixel division and recovery mode. Therefore, this study proposes a lossless compression and linear recovery algorithm of the system matrix based on a polar adaptive pixel (LCLR-PAP). Based on the structure of the detection ring and rotation of the circle, the detection field of view (FOV) is designed as a cylinder and the circular slice is divided into several sectors. The pixels are adaptively divided within the sector to realize the lossless compression of the system matrix from the structure, and based on which the angle change of pixels can be converted to matrix transformation to achieve linear recovery. A partial pixel partition is optimized to compensate for the unevenness of the pixel size in the center of the adaptive image. Experiments show that the LCLR-PAP algorithm can provide an efficient solution to the large-scale system matrix compression recovery problem, that is, through a simple and convenient adaptive pixel division with matrix sparsity and axial symmetry, the system matrix can be compressed to less than 100,000th of the original, and realize the lossless compression and fast linear recovery.

Positron emission tomography (PET) technique can visualize the working status or fluid flow state inside opaque devices, and how to reconstruct high-quality images from low-count (LC) projection data with short scan time to meet the real-time online inspection remains an important research problem. A direct reconstruction algorithm CED-PET based on gradient-penalized Wasserstein Generative Adversarial Network (WGAN-GP) architecture is proposed. This network combines content loss, perceptual loss, and adversarial loss to achieve fast and high-quality reconstruction of low-count projection data. In addition, a special dataset for obtuse body bypassing was produced by combining Computational Fluid Dynamics (CFD) simulation software and the Geant4 Application for Tomographic Emission (GATE) simulation platform. The results on this dataset show that CED-PET can quickly reconstruct high-quality images with more realistic detail contours.

In $\gamma $ -photon industrial large-space high-resolution full 3-D nondestructive testing imaging, when there is a large increase in the number of detection crystals, the number of response lines, the number of computational tasks, and the storage space of the system matrix all increase dramatically; therefore, reducing computation time and storage space becomes challenging. In this study, we propose a $\gamma $ -photon high-resolution fast 3-D image reconstruction method based on a lossless equivalent system matrix (LESM), which divides the cylindrical effective field of view into multiple equivalent sector blocks, performs polar voxel discretization according to adaptive rules, and accurately calculates the system matrix corresponding to one equivalent sector block by a polar voxel stereo angle model. Furthermore, the system matrix elements corresponding to the remaining sector blocks are obtained by rotational symmetry. Meanwhile, the system matrix elements corresponding to the polar voxels in the equivalent sector block are divided into subsets according to radial and mirror symmetry to further reduce the number of system matrix elements that need to be computed, so as to realize the lossless compression and fast recovery of the 3-D system matrix. To improve the accuracy of the system matrix and effectively suppress noise, the error caused by the depth of interaction (DOI) is further reduced based on the LESM calculation, and the display of the image is completed by precomputing the mapping matrix ${T}$ . Parallel computing is used to accelerate the algorithm. Simulation and experimental results show that compared with the traditional Cartesian voxel method, the proposed method significantly reduces the computational tasks and storage space of the system matrix elements and improves the contrast and spatial resolution of industrial 3-D reconstructed images, thus meeting the demand of $\gamma $ -photon industrial large-space detection imaging.

Dynamic positron emission tomography (PET) imaging has the potential to address technical challenges that persist in the visualization of optically inaccessible flow fields in integrated systems. However, traditional reconstruction algorithms are unable to reconstruct high-quality images from dynamic scan data. In this paper, a neural network structure that can reconstruct high-quality images directly using sinograms as input by combining the filtered back-projection (FBP) algorithm and denoising convolutional neural network (CNN) is proposed, which is named FBP-CNN. Computational fluid dynamics (CFD) software and the Monte Carlo simulation platform are used jointly to generate a dataset for the flow around the bluff body problem. The dataset is then used to train, validate, and test the FBP-CNN network, and the network after completing training is used to reconstruct the real projection data. The results show that FBP-CNN can reconstruct high-quality images from both simulated datasets and real projection data.

PET (Positron Emission Computed Tomography) imaging is a challenge due to the ill-posed nature and the low data of photo response lines. Generative adversarial networks have been widely used in computer vision and made great success recently. In our paper, we trained an adversarial model to improve the industrial positron images quality based on the attention mechanism. The innovation of the proposed method is that we build a memory module that focuses on the contribution of feature details to interested parts of images. We use an encoder to get the hidden vectors from a basic dataset as the prior knowledge and train the nets jointly. We evaluate the quality of the simulation positron images by MS-SSIM and PSNR. At the same time, the real industrial positron images also show a good visual effect.

利用γ光子探测腔体内部动态流场需要快速的图像重建算法,传统处理方式是先采集所有事件、再进行OSEM等算法处理.本文提出了一种按时间流对响应事件进行子集划分的图像重建(T-OSEM)算法.在连续采样数据的同时,按时间段将采样到的数据划分为子采样数据集,对子集进行OSEM迭代实现图像重建.并将上一帧图像作为迭代输入,利用帧间图像相关性,以加快收敛速度.该算法中数据流的采样与上一帧图像的处理同时进行,并通过多线程并行运算加速图像重建过程.研究了最优子集事件数量及相对应采样时间的关系,以实现在尽可能短的采样时间下达到最优的重建效果.实验表明,当采样时间段达到1 s时,T-OSEM算法仍有很好的粒子跟踪效果,粒子轨迹图像结构相似比为0.92,表明T-OSEM算法对于动态图像重建是一个比较好的解决方案.

翼伞在民用、军事、航空领域有广泛的应用,尤其在新冠疫情爆发期间,翼伞在物资投放中发挥了重要的作用.翼伞的组提带张力对其飞行的安全性以及着陆地点的精确性至关重要.为实现翼伞组提带张力的测量,设计一种工字型无线张力传感器结构,可解决现有张力传感器存在的量程不足、会破坏组提带结构等问题.运用SolidWorks设计弹性装配体模型并导入ANSYS Workbench18.0,对其进行静力学分析,选取传感器材料并找到应变片最佳的粘贴位置,对该传感器模型进行静力学仿真和模态分析,分析结果证明该传感器结构的可行性.

正电子发射断层扫描成像(PET)技术作为一种非侵入式成像手段,主要应用于检测工业件内部结构、缺陷等静态特征.而进行检测件内部的结构分割、缺陷定位等操作一般需要PET图像的边缘信息作为参考.为了快速提取PET图像的边缘信息,在FPGA上设计了一种基于中值滤波的多方向Sobel快速边缘检测优化算法.实验结果表明,该算法在提取清晰图像边缘的同时,能提升算法上百倍的执行速度.

γ photon pairs produced by positron annihilation can penetrate metals well; thus, they can be used for nondestructive detection of the inner state of metal pipelines. An experimental device is designed to simulate the working state of lube oil pipelines and inject nuclide into it. A symmetrical structure and a ratio algorithm are proposed considering the effects of various factors. Two sensors with good consistency are used to record the number of γ photon events in liquids with and without impurities. The ratio of the recorded events of reference and impurity sensors is taken as the test result of impurity content. The advantage of the method proposed in this paper is that it can eliminate the environmental error and inconsistency of the sensors by using ratio calculation and improve the measurement accuracy. Experimental results show that the proposed detection scheme and algorithm can well detect impurity content, including metal, in various pipelines. The detection accuracy of matched sensors can exceed 2%. Detected impurities are not limited to metal particles. Thus, the proposed method can be applied to in situ and online detection of impurities in oil piping systems equipped with engines.

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.

传统翼伞系统的航迹规划主要考虑落点精度及逆风着陆等指标,而当空投区域环境较为复杂,在翼伞系统归航路径上存在障碍时,如何规避这些障碍也成为翼伞系统航迹规划所必须要考虑的因素.针对翼伞空投过程有可能遇到高山或者高大建筑物阻碍的问题,提出了一种复杂环境下翼伞系统的组合式航迹规划策略.该方法将翼伞空投的区域分为障碍区和着陆区,在障碍区中采用快速搜索随机树(RRT)算法进行可行路径搜索,考虑到RRT算法生成的轨迹包含棱角,导致路径不够平滑的问题,结合翼伞系统质点模型的运动特性,对其进行了适用性改进,以使规划的航迹满足实际翼伞空投需求.为了解决RRT算法搜索方向随机,难以满足逆风着陆的问题,当翼伞系统进入着陆区后采用分段归航的方式设计航迹,并借助遗传算法(GA)求解目标参数,实现翼伞系统能量控制及逆风着陆.提出的复杂环境下翼伞系统的组合式航迹规划策略求解速度较快,能够同时满足翼伞系统避障、能量控制及逆风着陆要求,得到的参考航迹较为平滑.

As a nondestructive testing technology, $\gamma $ -photon imaging shows immense potential in the industrial field. However, the limitations of $\gamma $ -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 $\gamma $ -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.

Only a few effective methods can detect internal defects and monitor the internal state of complex structural parts. On the basis of the principle of PET (positron emission computed tomography), a new measurement method, using γ photon to detect defects of an inner surface, is proposed. This method has the characteristics of strong penetration, anti-corrosion and anti-interference. With the aim of improving detection accuracy and imaging speed, this study also proposes image reconstruction algorithms, combining the classic FBP (filtered back projection) with MLEM (maximum likelihood expectation Maximization) algorithm. The proposed scheme can reduce the number of iterations required, when imaging, to achieve the same image quality. According to the operational demands of FPGAs (field-programmable gate array), a BPML (back projection maximum likelihood) algorithm is adapted to the structural characteristics of an FPGA, which makes it feasible to test the proposed algorithms therein. Furthermore, edge detection and defect recognition are conducted after reconstructing the inner image. The effectiveness and superiority of the algorithm are verified, and the performance of the FPGA is evaluated by the experiments.