The obstacle avoidance flight of unmanned parafoil formations in complex terrains and under wind disturbances is essential for the success of airdrop missions. This study develops a comprehensive dynamic model of the parafoil and constructs a three-dimensional mountainous simulation environment. An obstacle avoidance algorithm based on the spatial velocity vector method is proposed, which dynamically regulates the parafoil’s motion through traction, avoidance, and guidance velocity vectors, thereby achieving efficient obstacle avoidance in complex wind fields. By incorporating a consensus-based leader-follower formation control strategy, the parafoil formation maintains both geometric configuration and heading stability during obstacle avoidance maneuvers. Simulation results demonstrate that the proposed method effectively enables safe obstacle avoidance and stable formation flight under wind disturbances, confirming the effectiveness of the algorithm.

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.

To address the problem of sudden failures during multi-parafoil formation transportation, a new fault formation reconstruction method based on the leader-following algorithm is proposed. First, monitoring of parafoil failures is established using an event-trigger mechanism within a numerical simulation framework. If a parafoil fails, the latest detachment time of a replacement parafoil is calculated based on its glide ratio to determine whether the altitude of the replacement parafoil meets the task reconstruction requirements. If it does, the formation is reconstructed using a switching control law for the replacement parafoil, enabling it to join the formation of the failed parafoil. Then, the leader-following algorithm is applied to reconstruct the formation, allowing the new multi-parafoil system to reorganize and complete the task in an orderly manner, with the replacement parafoil stably reaching the new target point as part of the reconstructed formation. Under this method, high-priority airdrop transportation tasks are ensured to be prioritized in the event of sudden failures during multi-parafoil formation operations. Lyapunov's theory demonstrates the stability of this method. Simulation results validate the effectiveness of the framework, showing that the algorithm can successfully handle sudden failures of individual parafoils during multi-parafoil formation operations.

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.

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.

In this work, novel ternary composite ZIF-67/Ag NPs/NaYF4:Yb,Er is synthesized by solvothermal method. The photocatalytic activity of the composite is evaluated by sulfadiazine (SDZ) degradation under simulated sunlight. High elimination efficiency of the composite is 95.4% in 180 min with good reusability and stability. The active species (h(+), O-2(-) and OH) are identified. The attack sites and degradation process of SDZ are deeply investigated based on theoretical calculation and liquid chromatography-mass spectrometry analysis. The upconversion mechanism study shows that favorable photocatalytic effectiveness is attributed to the full utilization of sunlight through the energy transfer upconversion process and fluorescence resonance energy transfer. Additionally, the composite is endowed with outstanding light-absorbing qualities and effective photogenerated electron-hole pair separation thanks to the localized surface plasmon resonance effect of Ag nanoparticles. This work can motivate further design of novel photocatalysts with upconversion luminescence performance, which are applied to the removal of sulphonamide antibiotics in the environment.

High-resolution biological tissue slice images provide opportunities for more precise observation and analysis of histopathological features. These images typically consist of enclosed contours, and Bendlets transform is an effective means to approximate such structures. In this study, we leverage Bendlet transform to incorporate multi-level image data, thereby establishing a multi-scale pyramid feature set. This approach aids in achieving a sparser representation of image textures and structures. We apply Bendlets transform to extract multi-frequency wavelet subband features and formulate structured dictionaries for both high and low resolutions based on these features. After creating these dictionaries, we compute sparse representation coefficients using the low-resolution dictionary and subsequently integrate them with the high-resolution dictionary to generate high-resolution subbands. Ultimately, through the process of inverse wavelet transformation, we completed the reconstruction of high-resolution images. This method not only significantly enhances the restoration of image details and clarity but also effectively preserves the overall structural characteristics of the original images.

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.

As a flow display technique, the silk thread method can clearly show the state of the flow field. However, displaying the state of the flow field in industrial closed metal cavities is impossible, and results of the traditional silk thread method fail to present depth information. To address these limitations, we propose a display method based on γ-photon silk thread 3D imaging. Firstly, a flow field visualization experimental platform applicable for industrial closed metal cavity was designed. Then, aiming at the shortcomings of insufficient γ-photon scanning data and limited imaging quality in flow field visualization, an adaptive sinogram interpolation-iterative filtering reconstruction algorithm was proposed to expand the data via the adaptive interpolation processing of the sinogram in image reconstruction. Beltrami filtering was also embedded in the iterative algorithm to effectively reduce noise and artifacts in the imaging image. Finally, the internal flow field inspection of the NACA0018 airfoil and the industrial confinement turbine were used as examples to visualise the wing upper surface flow and turbine blade passage secondary flow, respectively, and the results were consistent with those of the numerical simulation analysis. As the angle of attack of the wing increased, laminar flow separation occurred, and the silk thread structure state changed to the point where the aircraft stalled. After the imaging map analysis, the stall angle ranged between (15°,19°), which is consistent with the simulation results. In the turbine blade passage secondary flow detection, the imaging map formed a horseshoe vortex with airflow separation at the central saddle point and silk threads clearly presenting the structural state of the flow field along different directions.

Semiconductor photocatalysis is one of the most useful methods to solve environmental pollution problems. Herein, nanomaterial PtCu/MIL-101(Cr) was produced by an improved hydrothermal technique. The composite exhibited excellent photocatalytic performance with 99.7 % BPA degradation under 100 min visible light irradiation. First, the PtCu alloy has the effect of the local surface plasmon resonance effect, which can convert the photons to hot electrons and increase the reaction temperature. The synergistic effect (1+1>2) between photocatalysis and thermocatalysis significantly improved the photocatalytic efficiency. Second, the bimetallic alloying lowered the metals' work function and reduced the Schottky barrier between the PtCu alloy and MIL101(Cr), which effectively promoted the carrier transfer. The center dot O-2(-) , center dot OH and h(+) were dominant reactive substances in the degradation process. The intermediates and degradation pathways of BPA were analyzed by 3D-EEM. The reaction mechanism was proposed based on theoretical calculations and experimental analysis. This work provides new directions for designing photothermal synergistic green catalysts and purifying the environment.

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.

Fractional order partial differential equations have a wide range of applications in image processing. The solution of partial differential equations is generally obtained using the finite difference method, which still requires improvement in terms of efficiency and effectiveness. In this work, a multi‐scale interpolative wavelet operator is constructed by means of Shannon‐Cosine wavelets with interpolation, smoothness, compact support and other excellent properties. We use the wavelet operator instead of the finite difference operator to solve fractional‐order partial differential equations. The proposed method can limit the artefacts and remove the noises appeared in the processed images effectively. In addition, for the loss of texture in the denoised image, we enhance the image by employing fractional order differential equations to improve the quality of the image. Finally, the biological sections images are taken as the examples to illustrate the effectiveness of the proposed method.

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.

The noble metal nanoparticles (NMNPs) in different chemical environments have different catalytic activities. The photocatalytic degradation ability of NMNPs was improved obviously by the combination with metal-organic frameworks (MOFs). By adjusting the metal center, the MOFs have the different chemical state and properties, further influence the photodegradation of the NMNPs. In this work, Pt/MIL-53(Al, Cr or Fe) composites were synthesized by a deposition method. The morphology, structure and photoelectric properties of the materials were systematically characterized and compared. The degradation experiments of ketoprofen showed that the catalytic activity was Pt/MIL-53(Al) > Pt/MIL-53(Cr) > Pt/MIL-53(Fe). The degradation pathways and intermediates of ketoprofen were analyzed by density functional theory (DFT) calculations and liquid chromatography-mass spectrometry (LC-MS). Through energy band structure and trap experiments, the synergistic effect of Schottky junctions and localized surface plasmon resonance (LSPR) was found. Under simulated sunlight, Pt NPs can both inject hot electrons into MIL-53 and capture electrons transferred from MIL-53. In addition, the structure-activity relationship of materials was firstly discussed via textural and optoelectronic properties. The full utilization of active sites and e−-h+ pairs is the key to improving the photocatalytic activity of the materials.

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.

The accuracy of traffic flow prediction is significantly degraded by data anomalies and data noise. To solve this problem, a hybrid traffic flow prediction model based on iForest-VMD-GRU is proposed in this paper. Firstly, we detect the outlier anomalies in the original traffic speed sequence through iForest and use interpolation to complete the normal traffic speed sequence. Then, to reduce the interference of noisy data and improve the model prediction accuracy, we decompose the normal traffic speed sequence into basic trend components and multiple random fluctuation components through VMD. Finally, GRU is used to predict each subsequence, and the predicted values of each subsequence are combined into the final prediction results. The empirical analysis shows that the proposed model in this paper can significantly improve the prediction performance.