With the depletion of fossil fuels and the emergence of biofuels, ethanol-hydrogen fuel as a new generation of clean renewable fuel has attracted widespread attention. It is necessary to study the effects of ethanol-hydrogen air premixed flame combustion characteristics. Based on the constant volume combustion system, the laminar combustion characteristics of ethanol/hydrogen/air premixed flame were studied under the conditions of initial temperature of${400}\mathrm{\;K}$, hydrogen ratio of${20}\%$, equivalent ratio of${0.7}\sim {1.4}$and initial pressure of$2 \times {10}^{5},3 \times {10}^{5}$and$4 \times {10}^{5}\mathrm{\;{Pa}}$. Based on the mechanism of ethanol oxidation of Marinov and experimental data, the laminar combustion rate of Marinov was studied and the influencing factors were analyzed. Based on Chemkin-Pro software, the chemical reaction kinetics and numerical study were carried out. The results show that the laminar combustion rate of mixed fuel slows down with the increase of pressure.$\mathrm{H}$group is the main pathway of ethanol consumption, and$\mathrm{H},\mathrm{O}$, and$\mathrm{{OH}}$radicals play a leading role in the reaction of${\mathrm{{SC}}}_{2}{\mathrm{H}}_{4}\mathrm{{OH}}$and${\mathrm{{PC}}}_{2}{\mathrm{H}}_{4}\mathrm{{OH}}$from$\mathrm{H}$extracted by ethanol.$\mathrm{R}1 :\mathrm{H}+ {\mathrm{O}}_{2}\rightleftharpoons \mathrm{O}+ \mathrm{{OH}}$has the most positive effect on laminar combustion speed. The peak molar fraction of the active radical pool composed of active free radicals(H, OandOH) has a good correlation with the laminar combustion rate of ethanol in the whole equivalent ratio range, and the influence is huge. Further exploring this correlation, it is found that there is an approximate linear relationship, and the expression of laminar combustion rate with the peak molar fraction of$\mathrm{H}+ \mathrm{{OH}}+{\mathrm{{CH}}}_{3}$and$\mathrm{H}+\mathrm{{OH}}$is fitted.

At present, China's offshore floating photovoltaic is in its exploratory stage, which ensures the dynamic stability of floating photovoltaic foundation under varying environmental loads becoming a key research priority. In order to more effectively solve the above-mentioned difficulties and the shortage of land resources in the photovoltaic industry, a floating photovoltaic PE floating block foundation structure design, hydrodynamic calculation and optimization were completed through numerical simulation calculation based on the environmental conditions of a sea area in Rushan City, Weihai. The results indicate that wave height, wave period, and wave incidence angle have varying degrees of influence on the motion response and internal force values of the structure. Based on the analysis, practical applications of the new floating foundation in specific projects are guided, showing promising results that verify the feasibility and stability of this foundation structure's application.

With the construction of a new type of power system with new energy as the main body under the "double carbon" target, the challenges of natural disasters such as wildfires, icing and typhoons in the production, transmission and distribution of electric energy are more severe. Therefore, a fire point identification algorithm based on the world's first operational dawn-dusk orbit meteorological satellite Fengyun-3E satellite (FY-3E) was proposed, which was suitable for mountain fire monitoring in transmission line corridors, the adverse effects of large solar zenith angle observation conditions and satellite perspective differences on the accurate acquisition of infrared channel detection data were eliminated. Cloud information extraction and cloud pixel fire point extraction under complex atmospheric observation conditions were realized, which reduced fire point false alarms and missed alarms. The analysis of mixed pixel linear spectrum separation method shows that the fire point detection sensitivity of FY-3E mid-infrared channel is 4 times higher than that of geostationary meteorological satellite. The effectiveness of the proposed algorithm and the superiority of FY-3E in fire detection sensitivity, spatial range accuracy and positioning accuracy were verified. Compared with geostationary meteorological satellites, the fire location accuracy can be increased by more than one time, and it can effectively detect the fire time in advance by the transmission line operation and maintenance department, and guide the relevant departments to take timely measures to reduce the impact of wildfires on power grid operation.

Given the prevalent issue of aging assets within power grid companies and the subpar management of aging equipment, a decision-making approach was introduced for optimizing the decommissioning of aging assets, taking into account the influence of transmission and distribution tariffs. Firstly, in accordance with the policy framework for reforming transmission and distribution tariffs, an accounting model for transmission and distribution tariffs for provincial power grids operating at varying voltage levels was developed. Secondly, the decommissioning planning model for over-age assets was established with the optimization objective of achieving the highest return throughout the investment cycle. The decision variables of time and scale for decommissioning were utilized in this model. Through a process of rolling optimization and feedback correction, the model ensured that the scale of decommissioning for overage assets in each stage aligns with the desired value, thereby meeting the premise of system reliability. Finally, the efficacy of the model and its ability to enhance investment efficiency were demonstrated through the examination of the${110}\mathrm{{kV}}$voltage level grid in a specific province.

Synthetic aperture radar (SAR) target recognition method based on deep networks requires a large amount of training data, and in practical applications, it is extremely difficult for SAR imaging systems to obtain sufficient and evenly distributed target data. One way to solve the small sample problem in SAR target recognition, is to use electromagnetic simulation technology to generate a large amount of SAR simulation data. However, there are still significant differences between simulated images and measured SAR images, so using simulation data directly cannot bring significant performance improvement for target recognition. A simulation data optimization method based on SAR target characteristic constraints was proposed to address the above issues. On the basis of analyzing the characteristics of SAR targets, a texture structure cycle-consistent generative adversarial network (TS-CycleGAN) based on texture structure and cycle consistency was constructed, in which the structural similarity measure was used to constrain the generation process of CycleGAN. This method can reduce the difference between simulation data and measured data, and can improve the usability of simulation data. The experimental results on the SAR SAMPLE dataset show that, compared to other simulation data optimization methods, the proposed method achieves significant improvements in image quality evaluation and classification performance.

Capsule networks can encode the properties and spatial relationships of skin cancer image features, thereby overcoming the disadvantage of information loss in the pooling process of convolutional neural networks. Aiming at the problem that only shallow features can be extracted and the convergence performance of the squash function in capsule networks, a ResNeXt cascaded with capsule networks was proposed for Rs-Capsnet networks. Firstly, the complex features of the image were learned using the ResNeXt network. The Inception module and the residual connection were used to extract the deep features, and the weights of the feature map were adjusted and delivered to the capsule module through the CBAM attention module. Then, an improved squash function capsule network was used to complete the classification. Finally, the improved network was compared with mainstream models. The results show that Rs-Capsnet exhibits better performance in skin cancer image classification.

Given the practical application background of installing underground pipelines in coal mine tunnels and the actual environmental conditions underground, a jointed tunnel pipeline installation robot was designed. The detailed design of the robotic arm structure was completed, along with its 3D modeling. The kinematic model of the robot was established, and MATLAB was employed to verify the forward and inverse kinematics of the robotic arm. Based on the established kinematic model, a trajectory planning algorithm for the Cartesian space of the robotic arm was designed, and MATLAB and ADAMS software were used to verify the robotic arm through simulation experiments. The simulation demonstrates that the structural design of the robotic arm is reasonable, and the trajectory planning scheme for the robotic arm is feasible.

In order to quickly identify the location of the leakage point and the leak aperture in the coal mine, a model was proposed for identifying and locating the leak aperture by using the pressure and flow signals generated when the water supply pipeline leaked. Modal energy entropy and genetic algorithm combined with envelope entropy were used to optimize the parameters of variational mode decomposition (VMD), and then VMD was used to denoise the pressure signal. Convolutional neural network (CNN) was used to extract the deep feature sequence of pressure and flow signal, and the long short-term memory network (LSTM) was used to extract the time sequence of deep feature sequence to identify and locate the leak aperture. The experimental results show that compared with Kalman filter, mean value filter and low-pass filter, the variational modal decomposition with optimized parameters has higher root-mean-square error (RMSE), mean absolute error (MAE), signal-to-noise ratio (SNR) and normalized cross correlation (NCC), which indicates that it can effectively reduce noise components and retain effective signals. Compared with LSTM, the MAE, mean absolute percentage error (MAPE) and RMSE of CNN-LSTM in leak location decrease by 65.97%, 61.22% and 59.11%. In the identification of leak aperture, MAE decreases by 12.04%, MAPE decreases by 22.45%, and RMSE decreases by 3.29%, which proves that CNN-LSTM can make full use of the spatial and temporal characteristics of pipeline pressure and flow signals to identify the leak location and aperture, and its detection effect is more accurate and stable than LSTM.

The bridge health monitoring system based on sensor data acquisition has become standard for new bridge construction. However, this scenario presents challenges due to the massive volume of monitoring data that is difficult to store. Therefore, focusing on the time-series characteristics of bridge monitoring data, compression schemes were explored for bridge monitoring data. Differential compression was investigated based on the arithmetic progression properties of bridge monitoring timestamps and floating-point exclusive OR(XOR) compression based on the low frequency of changes in monitoring value data. Compared to the Gorilla time series database algorithm, the XOR compression method added control bits to avoid degradation of compression results. Experimental analysis reveals that both algorithms exhibit varying degrees of superiority over common compressors. The differential compression of timestamp sequences demonstrates superior compression rates compared to common compressors, achieving a compression rate of 0.015 6 for timestamp sequences that conform to arithmetic progression characteristics, approaching the compression limit value. Compression and decompression speeds are above average, and the method is insensitive to monitoring type. On the other hand, the XOR compression method performs well on datasets with low frequency of change, achieving compression rates of 0. 302 8 for bridge data and 0. 662 8 for non-bridge data, indicating sensitivity of the XOR compression method to monitoring type. In practical applications of bridge monitoring, suitable compression storage schemes can be selected based on the characteristics of the bridge monitoring dataset.

Aiming at the prominent problems of ignoring the unnatural connection relationship and interaction relationship between human bodies in two-person interaction recognition algorithm, a two-person interaction recognition network based on improved spatial temporal graph convolutional model was proposed. Firstly, the edge features of joint point data were aggregated by edge convolution to capture the unnatural connectivity relations inherent in the human body. Secondly, the interaction relationship graph between two people was constructed by using the improved relationship network. Furthermore, the branch of edge convolution and the interaction relationship graph were embedded into the spatial temporal graph convolutional network block, which were constructed as an edge-graph convolutional block and interaction relation graph convolutional block. Finally, an improved spatial temporal graph convolution algorithm was proposed to capture both the unnatural connection relationship and the interaction relationship, so as to realized the recognition of two-person interaction behavior. To verify the effectiveness of the network, it was tested on the international public large-scale standard dataset NTU RGB + D. The experimental results show that the network obtain a recognition accuracy of 97.77%, which is an improvement of 4. 28 percentage points compared to the baseline spatial temporal graph convolutional network. It improves the expressiveness of two-person interaction behavioral features, and achieves a better recognition effect than the existing state-of-the-art network models.