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In hazy weather, due to the low visibility of traffic roads, the video images collected are degraded and the image information is fuzzy. Considering the problem of low real-time processing of the traditional system, an image defogging system based on the ZYNQ platform is designed and applied to the intelligent driving recorder system. First, aiming at the problems of the traditional dark channel defogging algorithm in the sky region, a sky region segmentation strategy is proposed to correct the image restoration parameters. Then, in order to solve the problem that the pixel ordering of the whole image consumes a lot of resources when calculating the global atmospheric light value, a frame iteration method is proposed to optimize the atmospheric light value by making the advantage of the parallel operation on the FPGA platform, and at the same time, the optimization of guided filtering is realized. Finally, using dual-channel high definition multimedia interface (HDMI) resources, a real-time traffic image video processing experimental platform is established by using one channel of the dual HDMI resources as video input and the other as video processing output, and experimental simulation of the algorithm in this paper is conducted. The experimental results show that the system has a good defogging performance for the traffic video in hazy weather, especially in solving the problem of defogging distortion in the sky area. When the traffic video with a resolution of 1 280×720 is defogged, the processing speed can reach 30 frame/s meeting the real-time requirements.

Aimed at the problems of insufficient search range and optimization performance in 3D path planning of unmanned aerial vehicles (UAVs), and the lack of optimization accuracy of the existing slime mould algorithm (SMA), which is easy to fall into local optimization, a 3D path planning method for UAV based on adaptive slime mould algorithm optimization is proposed. First, according to the actual environment that the UAV passes through, the 3D terrain, the threat source and the constraints of the AUV were established. Next, for the problem of insufficient search range, an improved Logistic chaotic map is designed to increase the diversity of the population and expand the search range, which improves the global search ability of SMA. Then, a nonlinear adaptive inertia weight factor is designed to change the linear convergence method into nonlinear convergence, and the weight value is used to update the position of the slime mould, which improves the convergence speed. Finally, in the later stage of the algorithm, the adaptive cauchy mutation is designed, which increases the search space of the slime mould and improves the optimization accuracy. The experimental results show that GSMA has a shorter and smoother path, a faster convergence, a higher optimization accuracy, and a lower energy consumption compared with the gray wolf optimizer (GWO) algorithm, the SMA, and the seagull algorithm (SOA), which further improves the path planning capability of the UAV.

Aimed at the problems of low resolution, low contrast,and dark color of images recovered by traditional dehazing processing, an improved images dehazing method is proposed and applied to the unmanned aerial vehicle (UAV) aerial building image processing. First, to solve the problem that the value of global atmospheric light is easily affected by the scene objects, a method of atmospheric light with minimum variance of color attenuation prior projection is proposed. The difference image of brightness and saturation is constructed to solve the region where the minimum variance occurres, and the estimation of global atmospheric light is determined. Then, the regional atmospheric light is fused with the global atmospheric light by using the depth information of the image scene, and a new atmospheric light image is obtained. Finally, the haze line based on the non-local information prior theory in view of the transmittance is optimized. Moreover, this paper proposes a method based on the theory of haze line and guide relative to the total variation regularization algorithm. The transmission rate is fixed through calculating transmittance reliability function. A large amount of useless texture information existing in the image is eliminated, which enhances the precision of transmission rate estimation. It effectively improves the image quality of thick haze and abrupt depth-of-field in UAV aerial shooting scene. The experimental results show that, compared with other algorithms, the average gradient, contrast, haze aware density evaluator, and blur coefficient of the recovered images are improved by 12.2%, 7.0%, 11.9%, and 12.5%, respectively. The operation time of the proposed algorithm is shorter than that of some other algorithms, and the processed aerial images are clearer, which are more consistent with the visual perception of human eyes.

Aimed at the problem that the existing K-means clustering (KMC) algorithm is greatly affected by initialization, and the randomly generated clustering center can easily make the clustering result fall into local optimum and stop iterating, resulting in low clustering accuracy and poor robustness, a K-means hybrid iterative clustering algorithm based on memory transfer sailfish optimization (MTSFO-HIKMC) is proposed. First, learning from the existing improvement ideas, the maximum and minimum distance product is introduced to initialize the KMC cluster center, to avoid the uncertainty caused by random initialization. At the same time, in the iterative process, the current optimal solution is made to locally perform adaptive memory transfer correction to solve the problem of poor global optimization ability and insufficient search accuracy caused by the single search path of the sailfish algorithm. Using the Iris, Seeds, CMC and Wine international standard data sets, the MTSFO-HIKMC, the sailfish optimized K-means hybrid iterative clustering (SFO-KMC) algorithm, the introduction of the improved Moth-to-fire K-means cross iterative clustering (IMFO-KMC) algorithm, the KMC algorithm, and the fuzzy C-means (FCM) algorithm are compared and tested. From the obtained convergence curves and performance indicators, it can be seen that the MTSFO-HIKMC algorithm proposed in this paper has a faster convergence speed than IMFO-KMC. Compared with the IMFO-KMC algorithm, the dimensional space has a higher search accuracy. Compared with the KMC algorithm and FCM, it has a higher search accuracy. Compared with the SFO-KMC algorithm, its convergence speed and search accuracy are significantly improved, especially in high-dimensional data sets.

In order to solve the problem of low accuracy and the poor global searching ability of the moth-flame optimization algorithm, an improved adaptive interpolation moth-flame optimization algorithm is proposed, which is embedded into multi-feature particle filter to optimize. Besides, a multi-feature particle filter vehicle tracking algorithm based on adaptive interpolation moth-flame optimization is constructed. First, adaptive weights are added to the moths’ position updating mechanism to improve the global searching ability of the proposed algorithm. Next, the adaptive interpolation moth-flame optimization algorithm is used to optimize the sampling process. Then, in combination with the multi-feature adaptive fusion particle filter vehicle tracking algorithm, the particle distribution according to the latest observation information is continuously adjusted, so that the particles in the low weight layer can move to the area with higher weight to enhance the particle quality and avoid sample degradation. The experimental results show that the proposed algorithm can effectively reduce the number of sample particles required for state prediction, improve the tracking performance of the algorithm, and track the target vehicle accurately and stably under the interferences of occlusion, illumination, attitude, and scale changes.

High-permittivity Ba(Eu1/5Sm1/5Nd1/5Pr1/5La1/5)2Ti4O12 (BESNPLT) high-entropy ceramics (HECs) were synthesized via a solid-state route. The microstructure, sintering behavior, phase structure, vibration modes, and microwave dielectric characteristics of the BESNPLT HECs were thoroughly investigated. The phase structure of the BESNPLT HECs was confirmed to be a single-phase orthorhombic tungsten-bronze-type structure of Pnma space group. Permittivity (εr) was primarily influenced by polarizability and relative density. The quality factor (Q×f) exhibited a significant correlation with packing fraction, whereas the temperature coefficient (TCF) of the BESNPLT HECs closely depended on the tolerance factor and bond valence of B-site. The BESNPLT HECs sintered at 1400 °C, demonstrating high relative density (>97%) and optimum microwave dielectric characteristics with TCF = +38.9 ppm/°C, Q×f = 8069 GHz (@6.1 GHz), and εr = 87.26. This study indicates that high-entropy strategy was an efficient route in modifying the dielectric characteristics of tungsten-bronze-type microwave ceramics. [ABSTRACT FROM AUTHOR]

Rapidly and accurately assessing the geometric characteristics of coarse aggregate particles is crucial for ensuring pavement performance in highway engineering. This article introduces an innovative system for the three-dimensional (3D) surface reconstruction of coarse aggregate particles using occlusion-free multi-view imaging. The system captures synchronized images of particles in free fall, employing a matte sphere and a nonlinear optimization approach to estimate the camera projection matrices. A pre-trained segmentation model is utilized to eliminate the background of the images. The Shape from Silhouettes (SfS) algorithm is then applied to generate 3D voxel data, followed by the Marching Cubes algorithm to construct the 3D surface contour. Validation against standard parts and diverse coarse aggregate particles confirms the method's high accuracy, with an average measurement precision of 0.434 mm and a significant increase in scanning and reconstruction efficiency. [ABSTRACT FROM AUTHOR]

Xinzhuang Coal Mine is a typical kilometer-deep rockburst mine in the Ningzheng mining area of China Huaneng Group, which is still in the capital construction period. At present, there are a few studies on the rockburst mechanism and prevention of this mine. In order to explore the occurrence mechanism and main influencing factors of rockburst under the complex geological conditions of large buried depth, thick topsoil, and hard overburden in Xinzhuang Coal Mine, this paper, based on the analysis of the basic geological data of the mine, deeply explores the comprehensive disaster factors of Xinzhuang Coal Mine; then, uses the analytic hierarchy process to carry out quantitative analysis of each disaster factor, and finally, it obtains the impact type and occurrence mechanism of the mine. Through research, it is found that the main disaster factors of Xinzhuang Coal Mine are mining depth, coal seam thickness, coal seam thickness change, tectonic stress field, hard overburden, roadway layout, bottom coal reservation, and tunneling activities. The key to the process of the rockburst disaster in Xinzhuang Coal Mine is the superimposed effect of static and dynamic loads on the roadway surrounding the rock system. The potential rockburst type during excavation and mining is the “high static and dynamic load disturbance” type.

Objective To construct an integrative pre-hospital emergency care course in a certain Traditional Chinese Medicine university. Methods The expert consultation form was designed based on training materials of pre-hospital emergency care and literature review. Suggestions and comments were collected from twenty clinicians and faculty members with expertise in pre-hospital emergency care training. Results Forty-two items were selected as the teaching content by the two-round consultation. The credit hours, teaching methods and the selection criteria for clinical teachers were also finalized. Conclusion The integrative pre-hospital emergency care course constructed by the two-round expert consultation is scientifically sound. It can serve as a reference for other universities when constructing a similar course. (目的 构建高等中医药院校中西医结合院前急救公共选修课。方法 基于院前急救培训素材及文献回顾设计专家咨询表, 方便选取从事急救临床与护理的医务人员及高等院校从事急救相关教学的教师20人进行专家咨询。结果 通过两轮咨询, 最终确定课程内容包括基础生命支持、常见急症及中医急救三个方面共42个条目。同时确定了课时数、理论课与操作课的教学方法、临床师资的遴选标准等课程设置相关内容。结论 本研究通过专家咨询法构建的院前急救中西医结合公共选修课程具有科学性, 可为兄弟院校开设类似课程提供参考。)