In the aerospace industry, spacecraft often serve in harsh operating environments, so the design of ultra-lightweight and high-performance structures is a major requirement in aerospace structure design. In this article, a lightweight aerospace bracket considering fatigue performance was designed by topology optimization and manufactured by 3D-printing. Considering the requirements of assembly with a fixture for fatigue testing and avoiding stress concentration, a reconstructed model was presented by CAD software before manufacturing. To improve the fatigue performance of the structure, this article proposes the design idea of abstracting the practiced working condition of the bracket subjected to cycle loads in the vertical direction via a multiple load-case topology optimization problem by minimizing compliance under a variety of asymmetric extreme loading conditions. Parameter sweeping was used to improve the computational efficiency. The mass of the new bracket was reduced by 37% compared to the original structure. Both numerical simulation and the fatigue test were implemented to support the validity of the new bracket. This work indicates that the integration of the proposed topology optimization design method and additive manufacturing can be a powerful tool for the design of lightweight structures considering fatigue performance.

To improve the deficient tracking ability of fully-convolutional Siamese networks (SiamFC) in complex scenes, an object tracking framework with Siamese network and re-detection mechanism (Siam-RM) is proposed. The mechanism adopts the Siamese instance search tracker (SINT) as the re-detection network. When multiple peaks appear on the response map of SiamFC, a more accurate re-detection network can re-determine the location of the object. Meanwhile, for the sake of adapting to various changes in appearance of the object, this paper employs a generative model to construct the templates of SiamFC. Furthermore, a method of template updating with high confidence is also used to prevent the template from being contaminated. Objective evaluation on the popular online tracking benchmark (OTB) shows that the tracking accuracy and the success rate of the proposed framework can reach 79.8% and 63.8%, respectively. Compared to SiamFC, the results of several representative video sequences demonstrate that our framework has higher accuracy and robustness in scenes with fast motion, occlusion, background clutter, and illumination variations.

Temperature variation not only results in changes in refractive index, radius, thickness, and air space, but also leads to surface deformation due to the mismatch in thermal expansion coefficients between glass and mechanical materials. However, existing passive athermal optical design methods cannot optimize thermal-induced surface deformation, and optimization methods usually focus on structural optimization or thermal control rather than optical optimization. Here, we investigate the deterioration in image quality caused by thermal-induced surface deformation and propose a passive athermal optical design method to reduce deterioration. To this end, MATLAB was utilized to jointly call finite element analysis (FEA) software (COMSOL) and optical design software (Code V) to realize the data exchange of an optical–mechanical–thermal integrated analysis for iterative optical optimization. This process makes automatic iterative optimization possible by transforming parametric FEA results into Zernike coefficients in each iteration of optimization. The theoretical and design examples indicate that our method can effectively reduce the degradation in image quality with surface deformation. Our method provides an optical optimization approach for optical designers to work on a passive athermal optical design by considering thermal-induced surface deformation.

Highly accurate measurements of infrared systems cannot be achieved without precise radiometric calibrations. In order to correctly interpret and process infrared images and monitor the performance of infrared cameras, their radiometric calibration is also required periodically. In this paper, an equivalent calibration method is proposed based on an internal blackbody baffle. It is used for the replacement of a large surface-source blackbody covering the aperture for the field calibration of large-aperture equipment. Subsequently, the expressions of the equivalent calibration conversion function (ECCF) are derived based on the grayscale response of the camera and the calibration models of the two methods, and experimental measurements and fits are performed using a cooled mid-wave infrared camera. The results show that the measured functional form is consistent with the physical meaning. Moreover, in the target imaging experiments, the results of the inversion using the equivalent calibration conversion function and the results of the direct calibration of the external blackbody are largely consistent with the average error of 0.198% between the two, and the maximum error is within 1%. The maximum error between the inversion result of radiation brightness and the actual value of the target is 6.29%, and the accuracy fully meets the radiometric measurement requirements.

Biomimetic Soft Robots The combination of photo-thermal effect of Au and the mismatch between thermal expansion coefficients of LDPE film and PI adhesive has enabled the design of sandwich-structure...

An automatic colorization algorithm can convert a grayscale image to a colorful image using regression loss functions or classification loss functions. However, the regression loss function leads to brown results, while the classification loss function leads to the problem of color overflow and the computation of the color categories and balance weights of the ground truth required for the weighted classification loss is too large. In this paper, we propose a new method to compute color categories and balance the weights of color images. In this paper, we propose a new method to compute color categories and balance weights of color images. Furthermore, we propose a U-Net-based colorization network. First, we propose a category conversion module and a category balance module to obtain the color categories and to balance weights, which dramatically reduces the training time. Second, we construct a classification subnetwork to constrain the colorization network with category loss, which improves the colorization accuracy and saturation. Finally, we introduce an asymmetric feature fusion (AFF) module to fuse the multiscale features, which effectively prevents color overflow and improves the colorization effect. The experiments show that our colorization network has peak signal-to-noise ratio (PSNR) and structure similarity index measure (SSIM) metrics of 25.8803 and 0.9368, respectively, for the ImageNet dataset. As compared with existing algorithms, our algorithm produces colorful images with vivid colors, no significant color overflow, and higher saturation.