Image-based water level measurement is a visual-sensing technique which automatically inspects the reading of the water line via image processing instead of the human eye. It can be realized easily on an existing video surveillance system and has advantages like low cost, non-contact, as well as results that are verifiable. It has the potential to be widely used in flood and waterlogging monitoring, while facing the challenge that water-line detection under complex natural or artificial illumination conditions is quite difficult in field applications. To handle this problem, a method is proposed assuming that the water line is generally located on the row with the largest local change of gray or edge features in the image of the water gauge. The water line is determined by coarse-to-fine detection of the position of the maximum mean difference (MMD) of the horizontal projections of gray and edge images. Image-based flow-level measurement systems were developed at two measurement sites. In situ comparative experiments were conducted with the float-type stage gauge and other image-based methods. The results show that the fusion of gray and edge features can overcome the shortcomings of single feature methods under complex illumination conditions such as dim light, glares, shadows and artificial night lighting. A coarse-to-fine strategy utilizes the periodicity of the surface pattern distribution of the standard bicolor water gauge, which improves the reliability of water-line detection. The resolution and accuracy of water-level measurement are 1 mm and 1 cm, respectively. In particular, the MMD value is efficient at identifying extremely unfavorable conditions and reducing gross errors.

Nowadays, additive manufacturing (AM) is advanced to deliver high-value end-use products rather than individual components. This evolution necessitates integrating multiple manufacturing processes to implement multi-material processing, much more complex structures, and the realization of end-user functionality. One significant product category that benefits from such advanced AM technologies is 3D microelectronics. However, the complexity of the entire manufacturing procedure and the various microstructures of 3D microelectronic products significantly intensified the risk of product failure due to fabrication defects. To respond to this challenge, this work presents a defect detection technology based on deep learning and machine vision for real-time monitoring of the AM fabrication process. We have proposed an enhanced YOLOv8 algorithm to train a defect detection model capable of identifying and evaluating defect images. To assess the feasibility of our approach, we took the extrusion 3D printing process as an application object and tailored a dataset comprising a total of 3550 images across four typical defect categories. Test results demonstrated that the improved YOLOv8 model achieved an impressive mean average precision (mAP50) of 91.7% at a frame rate of 71.9 frames per second.

Hyperspectral remote sensing images (HSIs) have great research and application value. At present, deep learning has become an important method for studying image processing. The Generative Adversarial Network (GAN) model is a typical network of deep learning developed in recent years and the GAN model can also be used to classify HSIs. However, there are still some problems in the classification of HSIs. On the one hand, due to the existence of different objects with the same spectrum phenomenon, if only according to the original GAN model to generate samples from spectral samples, it will produce the wrong detailed characteristic information. On the other hand, the gradient disappears in the original GAN model and the scoring ability of a single discriminator limits the quality of the generated samples. In order to solve the above problems, we introduce the scoring mechanism of multi-discriminator collaboration and complete semi-supervised classification on three hyperspectral data sets. Compared with the original GAN model with a single discriminator, the adjusted criterion is more rigorous and accurate and the generated samples can show more accurate characteristics. Aiming at the pattern collapse and diversity deficiency of the original GAN generated by single discriminator, this paper proposes a multi-discriminator generative adversarial networks (MDGANs) and studies the influence of the number of discriminators on the classification results. The experimental results show that the introduction of multi-discriminator improves the judgment ability of the model, ensures the effect of generating samples, solves the problem of noise in generating spectral samples and can improve the classification effect of HSIs. At the same time, the number of discriminators has different effects on different data sets.

Lattice structures are a group of cellular materials composed of regular repeating unit cells. Due to their extraordinary mechanical properties, such as specific mechanical strength, ultra-low density, negative Poisson’s ratio, etc., lattice structures have been widely applied in the fields of aviation and aerospace, medical devices, architecture, and automobiles. Hybrid additive manufacturing (HAM), an integrated manufacturing technology of 3D printing processes and other complementary processes, is becoming a competent candidate for conveniently delivering lattice structures with multifunctionalities, not just mechanical aspects. This work proposes a HAM technology that combines vat photopolymerization (VPP) and electroless plating process to fabricate smart metal-coated lattice structures. VPP 3D printing process is applied to create a highly precise polymer lattice structure, and thereafter electroless plating is conducted to deposit a thin layer of metal, which could be used as a resistive sensor for monitoring the mechanical loading on the structure. Ni-P layer and copper layer were successfully obtained with the resistivity of $8.2\times {10}^{-7}\mathsf{\Omega }\cdot \mathrm{m}$ and $2.0 \times {10}^{-8} \mathsf{\Omega }\cdot \mathrm{m}$, respectively. Smart lattice structures with force-loading self-sensing functionality are fabricated to prove the feasibility of this HAM technology for fabricating multifunctional polymer-metal lattice composites.

With increasing interest in the rapid development of lattice structures, hybrid additive manufacturing (HAM) technology has become a competent alternative to traditional solutions such as water jet cutting and investment casting. Herein, a HAM technology that combines vat photopolymerization (VPP) and electroless/electroplating processes is developed for the fabrication of multifunctional polymer-metal lattice composites. A VPP 3D printing process is used to deliver complex lattice frameworks, and afterward, electroless plating is employed to deposit a thin layer of nickel-phosphorus (Ni-P) conductive seed layer. With the subsequent electroplating process, the thickness of the copper layer can reach 40 μm within 1 h and the resistivity is around $1.9\times {10}^{-8} \mathsf{\Omega }\cdot \mathrm{m}$, which is quite close to pure copper ($1.7 \times {10}^{-8} \mathsf{\Omega }\cdot \mathrm{m}$). The thick metal shell can largely enhance the mechanical performance of lattice structures, including structural strength, ductility, and stiffness, and meanwhile provide current supply capability for electrical applications. With this technology, the frame arms of unmanned aerial vehicles (UAV) are developed to demonstrate the application potential of this HAM technology for fabricating multifunctional polymer-metal lattice composites.

In recent years, unmanned aerial vehicles (UAVs) have become a popular and cost-effective technology in urban scenarios, encompassing applications such as material transportation, aerial photography, remote sensing, and disaster relief. However, the execution of prolonged tasks poses a heightened challenge owing to the constrained endurance of UAVs. This paper proposes a model to accurately represent urban scenarios and an unmanned system. Restricted zones, no-fly zones, and building obstructions to the detection range are introduced to make sure the model is realistic enough. We also introduced an unmanned ground vehicle (UGV) into the model to solve the endurance of the UAVs in this long-time task scenario. The UGV and UAVs constituted a heterogeneous unmanned system to collaboratively solve the path-planning problem in the model. Building upon this model, this paper designs a Three-stage Alternating Optimization Algorithm (TAOA), involving two crucial steps of prediction and rolling optimization. A three-stage scheme is introduced to rolling optimization to effectively address the complex optimization process for the unmanned system. Finally, the TAOA was experimentally validated in both synthetic scenarios and scenarios modeled based on a real-world location to demonstrate their reliability. The experiments conducted in the synthetic scenarios aimed to assess the algorithm under hypothetical conditions, while the experiments in the scenarios based on real-world locations provided a practical evaluation of the proposed methods in more complex and authentic environments. The consistent performance observed across these experiments underscores the robustness and effectiveness of the proposed approaches, supporting their potential applicability in various real-world scenarios.