Aiming at the problems of excessive fluctuation and long adjustment time when LLC resonant converter encounters load mutation in traditional control strategy, which seriously affects the normal use, an improved self-disturbance control strategy based on sliding mode control is proposed. The mathematical modeling of LLC resonant converter is carried out by using the method of extended description function to obtain the transfer function of LLC resonant converter. On the basis of linear self-disturbance control, sliding mode control is introduced as the control rate. Through simulation, the steady-state characteristics and dynamic characteristics of LLC resonant converter under three different control strategies of PI (proportional integral controller), LADRC (linear active disturbance rejection control), and SM_ADRC (sliding mode active disturbance rejection control) are compared and analyzed. The simulation results show that the sliding mode active disturbance rejection control is superior to the other two control strategies in improving the response speed and anti-interference ability of LLC resonant converter, with a response time of only 2ms and a ripple of only 0.04V. A small power experimental platform with an output power of 192W is built. The experimental results show that LLC resonant converter can be restored to steady state in a short time by using sliding mode active disturbance rejection control, which effectively improves the dynamic characteristics of the system.

When using pulsed ultra‐wideband radar (UWB) noncontact detection technology to detect vital signs, weak vital signs echo signals are often covered by various noises, making human targets unable to identify and locate. To solve this problem, a new method for vital sign detection is proposed which is based on impulse ultra‐wideband (UWB) radar. The range is determined based on the continuous wavelet transform (CWT) of the variance of the received signals. In addition, the TVF‐EMD method is used to obtain the information of respiration and heartbeat frequency. Fifteen sets of experiments were carried out, and the echo radar signals of 5 volunteers at 3 different distances were collected. The analysis results of the measured data showed that the proposed algorithm can accurately and effectively extract the distance to the target human and its vital signs information, which shows vast prospects in research and application.

Ultra-wideband (UWB) radar enables non-invasive detection of human vital signals without any electrodes or sensors touching the human body. In order to eliminate and reduce the clutter interference in the vital signal when performing life search and rescue, a UWB radar vital sign extraction method based on the combination of permutation entropy (PE) and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is proposed for this problem. Based on the slow time series echo characteristics of the received signal echo matrix, the distance gates are determined using PE values; in addition, the selected slow time series are reconstructed and denoised using ICEEMDAN, and the frequencies of the reconstructed respiratory and heartbeat signals are extracted using fast fourier transform (FFT); finally, a total of 15 sets of experimental data at three distances are collected from five volunteers respectively for experimental validation, and the experimental analysis The results showed that the proposed algorithm detected an average error of 0.0093 m for distance, 0.0127 Hz for respiration frequency and 0.036 Hz for heartbeat frequency.The proposed algorithm has the advantages of being resistant to environmental interference, adaptive and reliable, and can accurately locate the target location and extract the frequencies of respiration and heartbeat signals.

Road vector data are an important part of geographic information databases and play a leading role in social and economic development. Among them, the accuracy and current situation of vector data are the key values of their application. In recent years, with the development of remote sensing technology, remote sensing images contain increasingly more road information, which provides a large number of available features for the establishment of vector correction models. Therefore, the vector-to-image vector correction method based on remote sensing images has become an important means to ensure the accuracy and current situation of vector data. However, in actual scenes, there are great differences in the design structure and design index between urban roads and rural roads, which is reflected in the serious spatial heterogeneity between them in the remote sensing images. Therefore, when the existing methods use remote sensing images for vector correction, the universality is limited by the change of regional scenes, so it is difficult to realize the simultaneous correction of urban road and rural road vectors, with poor applicability and low compatibility. To solve this problem, this paper proposes a vector data partition correction method supported by deep learning. First, the U²-net model and line segment sequence method are used to generate the feature set. Second, according to the characteristics of the poor quality of urban road extraction results, the regular geometric form of vector data and the poor structural information of road images, the methods of vector line decomposition, subvector line correction, subvector line centralization and vector line synthesis are proposed to correct the vector lines of urban roads. Finally, according to the characteristics of high-quality rural road extraction, the irregular geometric form of vector data and the strong information of road image structures, this paper proposes road edge extraction, a road centreline reasoning model and a vector line connection method to complete the correction of rural vector road data. The quantitative analysis of the experimental results shows that compared with other methods, the urban road vector correction method in this paper is much better than the comparison methods and has achieved better correction results in rural areas. This method has the advantages of compatibility and better accuracy for roads in different areas.

The deformation and fracture evolution of coal and rock under unloading are prone to sudden instability or dynamic damage. To solve the problem, this paper combines interdisciplinary theories such as damage mechanics and electromagnetic field theory. The mathematical model of multiphysics coupling during loading and unloading of composite coal‐rock is deduced. In addition, numerical simulations along with experimental verification are carried out to study multi‐physical field variation and coupling mechanisms. The composite coal‐rock deforms and ruptures under unloading, and the brittle failure of the rock body becomes more sudden when the confining pressure is unloaded. Macroscopically, many microcracks are generated and expanded during the loading and unloading of composite coal‐rock. Microscopically, the internal old molecular chains are broken to form new molecular chains by the force. Simulation results show that, during the loading and unloading process, the three physical fields of the composite coal‐rock all change regularly. During the unloading of coal and rock, there is a transition period in which the temperature increases sharply and reaches the maximum. Then, the temperature decreases due to the gradual decrease of its bearing capacity. Besides, the electromagnetic field is strongest on the surface of the coal body, and its propagation in the air decays exponentially. There are small fluctuations that appear at the junction of the coal body and the air. The experimental results show that the internal infrared radiation temperature of the composite coal‐rock decreases during the initial stage of loading and unloading due to the discharge of internal gas. In the first stage of “loading and unloading,” it increases with the increase in stress, and the temperature suddenly increases in a short time after unloading. The electromagnetic radiation fluctuates in small amplitudes at the initial stage. When the stress is about to reach the peak, the electromagnetic radiation intensity increases and reaches the peak suddenly. Then, the coal‐rock ruptures, the stress decreases, and the electromagnetic radiation weakens. The experiment and simulation results are consistent. The multiphysics coupling model is used to study the characteristics of coal and rock unloading under complex conditions, providing a theoretical basis and new method for the prediction and forecast of coal and rock mining dynamic disasters.