5results about How to "Return to normal structure" patented technology

The present application belongs to the field of catalyst regeneration technology, and discloses a regeneration method of a supported bimetallic alloy catalyst in alkane dehydrogenation. After the supported bimetallic alloy catalyst is calcined by oxygen, the auxiliary metal oxide is physically blended with the supported bimetallic alloy catalyst, and then the reduction treatment is carried out under high temperature using hydrogen atmosphere, so as to restore the activity of the supported bimetallic alloy catalyst. The present application can solve the problems of metal loss and bimetallic alloy structure damage of the bimetallic catalyst in the long-time high-temperature reaction environment and the regeneration process. By adding the oxide of the auxiliary metal, the auxiliary metal atoms are reduced and diffused onto the metal alloy under high-temperature conditions, so as to restore the structure and activity of the bimetallic alloy in the catalyst. The method is simple and convenient to operate, and is suitable for the supported bimetallic alloy prepared by different methods.

This invention relates to a robust image semantic perception adaptive masking transmission method for vehicle-to-everything (V2X) networks, comprising: Step 1: The transmitting end performs semantic analysis on the original input image, extracts high-precision semantic features, and identifies the regions of interest (ROI) and non-ROI regions of interest (NROI); Step 2: A Boolean mask is dynamically generated based on the semantic segmentation result and SNR; Step 3: The ROI features are directly masked based on the Boolean mask; Subsequently, the processed feature map is fed into a semantic encoder to extract multi-scale semantic features; Step 4: Power normalization is performed, dynamic feature encoding is performed, and then the encoded signal is transmitted through a typical analog wireless channel, and noise is superimposed before reception and recovery; Step 5: The receiving end receives the noisy feature signal, performs channel fading compensation and preliminary denoising, and sends the decoded features into a semantic decoder; Through progressive upsampling and multi-level semantic reconstruction, multi-scale features are fused, and finally, a high-quality reconstructed image is recovered and output.

The invention discloses modified graphite, a high-flame-retardant MC nylon material, a lightweight wearing plate and an air spring. The modified graphite material is modified by sequentially oxidizing and reducing a powdery graphite material to form reduced graphite oxide and then introducing an amino flame-retardant group and a phosphate flame-retardant group onto the reduced graphite oxide. The high-flame-retardant MC nylon material comprises caprolactam, a catalyst, an activating agent and a modified graphite material. The light-weight wearing plate comprises a wear-resisting plate which is integrally formed by adopting a high-flame-retardant MC nylon material, and the wear-resisting plate is provided with a T-shaped metal insert, an exhaust groove and a circular ring backstop. The air spring comprises a high-strength high-flame-retardant lightweight wearing plate mounted above an auxiliary spring, a high-strength lightweight composite material upper cover plate mounted at one end of an air bag, and a PAI-Si high-wear-resistant lubricating coating sprayed on a metal plate and / or the wearing plate. The dry friction coefficient of the composite upper cover plate and the high-strength and high-flame-retardant lightweight wearing plate in an oil-free and maintenance-free state meets the requirement of being smaller than 0.1.

The invention discloses a cotton piece filling structure, and belongs to the field of cigarette production equipment, the cotton piece filling structure comprises a mounting rack, an incoming material drum wheel and a feeding drum wheel, the incoming material drum wheel and the feeding drum wheel are both mounted on the mounting rack, and the incoming material drum wheel is arranged above the feeding drum wheel; a shaping wheel disc is arranged below the feeding drum wheel and comprises a shaping cover and a positioning seat; the shaping cover is mounted on the mounting frame through a connecting rod, the shaping cover is located over the positioning seat, a rotating shaft is arranged below the positioning seat, and the shaping cover adjusts the appearance of the cotton piece by adjusting the temperature and the rotating speed of the cotton piece. The fibers are promoted to generate recovery stress through alternate cooling and heating treatment, and ordered and stretched external force guide is provided for the fibers in combination with specific mechanical movement of non-uniform rolling, so that the fluffy structure and the regular form of the fibers are actively recovered.

This invention belongs to the field of deep learning and image processing technology, and discloses a learnable tensor low-rank enhancement method for near-infrared images under low illumination. Based on the estimation results of the local brightness mean and noise variance of the input image, the image is non-uniformly adaptively divided, and a three-dimensional local feature tensor containing spatial height, width, and brightness channels is constructed on each image block using a lightweight convolutional feature extraction network. Subsequently, based on the brightness energy of the image block, the tensor decomposition rank of each block is adaptively determined; factor generation networks and kernel tensor prediction networks are used to generate directional factor matrices and kernel tensors, respectively; threshold suppression is applied to the singular values ​​of the kernel tensor to remove noise subspaces, and scaling of high-frequency directional factors is combined to enhance texture details, effectively restoring degraded edge structures under low illumination conditions. Finally, a weighted fusion method based on minimizing the brightness difference in overlapping regions is used to reconstruct the entire image from the restored image blocks.