Yanwei Wang

As a crucial fossil fuel, coal will remain a core pillar for safeguarding national energy security over the long term. Coal gangue is the primary waste generated during coal mining and washing proc...

The dynamic characteristics of event cameras demonstrate unique advantages in extreme environments; however, their inherent spatiotemporal asynchrony and noise sensitivity instead become serious drawbacks during extreme image reconstruction of coal mine substations. To overcome the problems of nonconstant noise coupling and existing static convolutional networks in dealing with vibration‐induced motion artifacts, an event‐based spatiotemporal disentangled aggregated image reconstruction (ESDAR) method is proposed here. ESDAR is a method to enhance FireNet. The proposed method integrates three innovations. First, it introduces an asymptotic denoising approach inspired by the diffusion model, which effectively decouples high‐frequency noise and low‐frequency textures. Second, it employs a deformable convolutional mechanism to achieve spatiotemporal alignment between event streams and fuzzy grayscale images. Third, the recurrent unit is substituted with a time convolution network enhanced with deformable convolution (DF‐TCN), thereby achieving a significant reduction in the number of parameters, with a decrease of 48.32%. The effectiveness of this method was evaluated via 1000 synthetic sequences and 1670 real frames (captured at a coal mine substation). The findings of this study demonstrate that ESDAR is markedly superior to FireNet with respect to performance. The results revealed a 39.5% reduction in the mean square error (MSE), a 4.2 dB increase in the peak signal‐to‐noise ratio (PSNR), a 26% reduction in the local perceptual image similarity metric (LPIPS), and a 3.39% increase in the structural similarity index (SSIM). This method effectively achieves a balance between noise suppression and detail preservation under hardware constraints, providing a feasible solution to address image reconstruction in high‐noise, vibration‐intensive industrial environments.

Surface microstructures significantly reduce fluid resistance by regulating solid-liquid boundary slip characteristics, making them an essential approach for drag reduction. This study aims to reveal the influence of hydrophobic surface structures on boundary slip length and explore the regulation of boundary slip by different micro/nanostructures. By systematically adjusting the shape, size, and arrangement of surface micro/nanostructures, the colloidal probe contact mode of atomic force microscopy is employed to measure the slip length in ionized water under varying microstructure dimensions and spacings. For square pillars with side lengths of 3, 4, 5, and 6 μm, the boundary slip lengths are 956, 1161, 1288, and 1341 nm, respectively. For cylindrical pillars with diameters of 3, 4, 5, and 6 μm, the slip lengths are 2574, 3206, 3441, and 3791 nm, respectively. Under a constant microstructure size, the slip lengths for square pillars with spacings of 2, 3, and 4 μm are 956, 2212, and 2324 nm, while for cylindrical pillars, they are 2574, 2786, and 3449 nm. The experimental results demonstrate that the shape, size, and spacing of surface microstructures have a significant effect on boundary slip length. Specifically, as the microstructure size and spacing increase, the slip length increases accordingly. Furthermore, cylindrical structures exhibit significantly higher slip lengths than square pillars under the same size and spacing conditions. These findings indicate that through rational design and optimization of microstructure geometric parameters, fluid slip behavior can be effectively controlled in microfluidic and nanofluidic applications, thereby enhancing flow efficiency and interfacial performance.

Accurate acquisition of two-dimensional digital maps of disaster sites is crucial for rapid and effective emergency response. The construction of two-dimensional digital maps using unmanned aerial vehicle (UAV) aerial images is not affected by factors such as signal interference, terrain, or complex building structures, which are common issues with methods like single-soldier image transmission or satellite imagery. Therefore, this paper investigates a method for modeling two-dimensional digital maps based on UAV aerial images. The proposed Canny edge-enhanced Speeded-Up Robust Features (C-SURF) algorithm in this method is designed to enhance the number of feature extractions and the accuracy of image registration. Compared to the SIFT and SURF algorithms, the number of feature points increased by approximately 44%, and the registration accuracy improved by about 16%, laying a solid foundation for feature-based image stitching. Additionally, a novel image stitching method based on the novel energy function is introduced, effectively addressing issues such as color discrepancies, ghosting, and misalignment in the fused image sequences. Experimental results demonstrate that the signal-to-noise ratio (SNR) of the fused images based on the novel energy function can reach an average of 36 dB.

Network technology has become an inseparable part of daily life, which is easily attacked by some criminals. Therefore, ensuring the security of network becomes an urgent problem. In the past few years, various deep learning techniques have been put forward for identifying network intrusions. However, the majority of these approaches overlook the non-Euclidean nature of relationships within network intrusion data. In this paper, graph neural network (GNN) is used to learn the relationship between data. The sample balancing module is to address the significant disparity between the number of intrusive samples and normal samples. The results obtained from the experiments indicate that the suggested approach can enhance the effectiveness of the intrusion detection system.

Nanobubbles represent a special colloidal system, as they have high stability and large specific surface areas. The preparation of nanobubbles is currently a hot research topic, as it crucial to investigate their characteristics and expand their applications. This article explains the mechanism of generating nanobubbles based on chemical and physical methods, introduces their basic composition’s structure and properties, summarizes the methods of preparing bulk nanobubbles (BNBs) and surface nanobubbles (SNBs), and clarifies the preparation principles and techniques. Seven practical applications of nanobubbles are cited in this paper, including their use as ultrasonic contrast agents in medical imaging, drug delivery systems in drug transportation, promoters of plant growth by affecting plant respiration and water absorption at the roots, tools to remove dirt from surfaces by generating energy during nanobubble bursting, producers of high-density negative ions and free radicals to react with pollutants in wastewater, tools to reduce the resistance of the fluid flow through channels by lowering the internal friction, and means of improving the mineral flotation recovery rate by enhancing the absorption capacity of bubbles to minerals. Finally, the future development of nanobubble preparation technology is discussed, including their roles in optimizing equipment and preparation methods; improving the quantity, efficiency, stability, controllability, and homogeneity of nanobubble generation; and promoting the industrial production of nanobubbles.

Considering the potential risk of X-ray to patients, denoising of low-dose X-ray medical images is imperative. Inspired by deep learning, a convolutional autoencoder method for X-ray breast image denoising is proposed in this paper. First, image symmetry and flip are used to increase the number of images in the public dataset; second, the number of samples is increased further by image cropping segmentation, adding simulated noise, and producing the dataset. Finally, a convolutional autoencoder neural network model is constructed, and clean and noisy images are fed into it to complete the training. The results show that this method effectively removes noise while retaining image details in X-ray breast images, yielding higher peak signal-to-noise ratio and structural similarity index values than classical and novel denoising methods.

To address the features of strong noise, blurred boundaries, and poor imaging quality in breast ultrasound images, we propose a method for segmenting breast ultrasound images using adaptive region growing and variation level sets. First, this method builds a template layer from the difference between the marked image and the original image. Second, the Otsu algorithm is used to measure the target and background using the maximum class variance method to set the threshold. Finally, through the level set of the pixel neighborhood, the boundary points of the adaptive region growth are specified by the level set of the pixel neighborhood, and it is therefore possible to accurately determine the contour perimeter and area of the lesion region. The results demonstrate that the value of Jaccard and Dice for benign tumors is greater than 0.99. Therefore, the segmentation effect of breast images can be achieved by utilizing a breast ultrasound image segmentation approach that uses adaptive region growth and variation level sets.