Ship targets in remote sensing images have multi-scale characteristics, changeable backgrounds, and complex meteorological characteristics, which lead to low accuracy, false detection, and missed detection of small target ships. In response to the above situation, an improved small-target ship detection model based on YOLOv5s was proposed. First, in order to solve the problems of scale changes and background variability in ship detection, the ASFF(adaptive spatial feature fusion) module was introduced. Secondly, in order to reduce the calculation amount and parameter amount of the detection network, the BoTNet attention mechanism was introduced, and then in order to improve the overall network to improve the detection accuracy, the EIoU border loss function was used, and finally the Slim-neck network was introduced to ensure the overall lightweight of the network. Experiments show that on the main data set LEVIR-Ship, compared with the benchmark YOLOv5s, mAP@0.5 increased by 7.1% to 81.3%, the number of parameters is reduced by 0.44 M, the calculation amount is reduced by 0.6GFLOPs, and the weight was reduced by 0.9 M. The proposed method performs better in various key indicators and achieves high-precision small target ship detection in complex environments. Comparative experiments are conducted on the verification data set McShips. The experiments show that the proposed method still performs better, verifying the universal applicability of the proposed method.

With the increasing penetration of distributed power sources in the distribution network, active distribution has become the mainstream direction of the power grid in the future. Microgrids and active stations that are slightly smaller than their scale will gradually increase. Whether these subsystems can be used as energy to schedule becomes the key to improving the economy and stability of power grid operation. Therefore, aiming at the overall optimal scheduling of microgrid and active station area with distributed power supply, a scheduling model solution method based on improved target cascade method was proposed, which mainly takes the optimal benefits generated by different stakeholders as the final scheduling target, and adopts the opportunity constraint description for the processing of uncertain factors of wind and solar. The upper layer is the distribution network and the optimal target of the distribution network, and the lower layer is the microgrid and active station area with the ability to participate in the scheduling. Based on the modeling of distribution network and microgrid, the improved target cascade method was introduced, and the interactive power was used as a shared variable to equivalent the generator and virtual load, so as to realize the decoupling and independent optimization of the upper and lower layers. The comparison of the experimental results shows that the target cascade method with the balance coefficient can obtain better results in the number of iterations, convergence performance, anti-interference performance and overall economic evaluation.

In order to improve the accuracy and efficiency of inventory counting in the process of monitoring and auditing biological assets, a biological asset detection model YOLOSC incorporating the attention mechanism and loss function optimization was proposed. Firstly, the SENet attention mechanism was introduced into the backbone network of the YOLOv5s model to enhance the ability of extracting the key features in the pictures of the biological assets. Secondly, the CIoU was adopted as the regression of the detection frames with the loss function to enhance the regression speed and localization accuracy of the detection frame during the training process. Finally, a biological asset datasets was constructed for targeted training of the proposed model to enhance the model detection effect. The experimental results show that compared with the YOLOv5model, the precision, recall, F₁ value and AP of YOLOSC are improved by 2.3%, 2.1%, 2.7% and 1.6%, respectively, which proves the effectiveness of the proposed biological asset detection model YOLOSC.

In response to the low detection accuracy and high model complexity of existing road damage detection algorithms in complex environments, a lightweight road damage detection algorithm named LDC-YOLOv5 (lightweight deformable convolution YOLOv5) was proposed based on YOLOv5.To address the complexity of real road surface damages, a lightweight feature extraction module was designed using Deformable Conv (deformable convolution) and Depthwise Conv (depthwise convolution) to replace the C3 module in the original network backbone, enabling convolutional kernels to focus on irregular crack damages and enhancing feature extraction for damage detection. To reduce algorithm complexity in the feature fusion stage, a lightweight feature fusion module was constructed using GhostConv to replace the C3 module in the original network neck, lowering network parameters and complexity. Additionally, to prevent missed detections caused by uneven lighting and shadow obstruction, a lightweight attention mechanism, TripletAttention, was introduced in the backbone network to improve the algorithm's understanding of damage information and context. Experiments conducted on the IEEE open dataset RDD2022 and the Kaggle open dataset Road Damage demonstrate that, compared to YOLOv5s, the proposed LDC-YOLOv5 achieves a 1.4% and 4.2% improvement in mAP50 on the two datasets, respectively, with only 67.6% of the model parameters of YOLOv5s.

Machine learning models, widely applied in landslide susceptibility assessment due to their powerful feature extraction capabilities, are continuously evolving in their algorithms to address the common issue of low accuracy. The GCNN (group convolutional neural network) model was introduced into landslide susceptibility assessment, and its results were compared with those of various common machine learning models to comprehensively evaluate the adaptability of these models in this field. Taking Hebei Province as the research area, 16 influencing factors were selected from three aspects: triggering factors, pregnant disaster environment, and susceptible bodies. GCNN model and other common machine learning models—CNN (convolutional neural network), Logistic (logistic regression), RF (random forest), and SVM (support vector machine)—were constructed to build corresponding susceptibility assessment models. The research area is divided into four categories of landslide susceptibility zones, and the accuracy of the zoning is comprehensively evaluated. The study indicates that compared with the other four machine learning models, the GCNN model achieves higher scores in various confusion matrix indicators and is more suitable for landslide susceptibility zoning. The resulting zoning of landslide susceptibility is consistent with the actual occurrence of landslide points, indicating a more accurate delineation of landslide-prone areas.

Aerodynamic principles and the assumption of axial inextensibility of a two-dimensional flexible plate were used to derive a nonlinear theoretical model of flag flutter, investigate and analyze the coupled motion characteristics of flexible flag and airflow in nature and wind energy collection fields, and examine the effects of length, mass ratio, and wind speed on its motion characteristics. The flag oscillation process in the wind was numerically simulated using the bidirectional fluid-structure coupling method and the overlapping mesh methodology, from which the features of the surrounding flow field and the motion behavior of the flag inside it were determined. The findings indicate that while swing displacement increases and subsequently declines with wind speed, the crucial flutter wind speed lowers as flag length increases. The chirp frequency decreases as the mass ratio increases, and the Strahl number is less affected. With the predefined dimensions of the flag, at low wind speeds, both the displacement and frequency of the swing are low. However, when the wind speed exceeds the critical vibration threshold, a significant vibration phenomenon occurs. Changes in surrounding pressure and velocity are caused by the flag-encircling vortex as it progresses through phases of formation, shedding, and disappearing. Numerical simulation techniques based on the overlapping mesh methodology successfully address the deformation problem of flexible flags. Theoretical and numerical simulations can be verified and analyzed with this method.

Pavement runoff could enter the karst aquifer system through sinkholes, karst windows, karst pools in karst areas, which could influence the karst water environment quality. Consequently, it is necessary to study the hydrochemical environment characteristics of pavement runoff in karst areas. Pavement runoff of Yaji, Qingshuiqiao and Baizhujing were sampled, characteristics of hydrochemical compounds and their influencing factors, hydrochemical environment quality were analyzed using multiple statistical method, Nemerow index method and comprehensive pollution index method. External influencing factor has small influences on the common hydrochemical ions, while has great influences on these trace elements. The compounds influencing the water environment of pavement runoff were nutrient compounds (NH₃-N, TP, COD_Mn), landscape compound (suspended solids) and metal compounds (Mn, Hg and TFe) by analyzing the concentrations of hydrochemical compounds of pavement runoff. These compounds have close relationships with pavement behaviors, surrounding vegetations, traffic flow and came from fuel, lube, slop oil, gasoline, worn tyre and vegetations. Four main factors with the cumulative variance contribution rate of 97.99% were extracted from the monitoring dataset using the factor analysis method. It could be known from the four main factors that carbonates weathering was the main source of hydrochemical compounds of pavement runoff, the second was the particles of atmospheric and pavement influencing the SS of pavement runoff, the third was the human activities including pavement behaviors and protective measures of surrounding vegetations. Hydrochemical environment quality of Qingshuiqiao, Yaji and Baizhujing decreased in turn by using the Nemerow index method and comprehensive pollution index method. Hydrochemical environment quality of Baizhujing were poorest, which had potential risks for the water ecological environment, the pavement runoff could be reused for the surrounding vegetations through reasonable measurements. The results could not only provide scientific instructions for the treatment measures of pavement runoff, but also provide scientific evidences for the reasonable exploitation and utilization of karst water resources.

It will raise their feedwater temperature and then improve their operating economy to add No.0 HPH (high-pressure heater) in thermal power units. A steam-water distribution model for a thermal system coupled with No.0 HPH was established based on the mass and energy conservation laws. And a variable-condition calculation method for its heat economics was also proposed. Furthermore, a derivative characterization approach was developed to represent the heat economics impact of No.0 HPH extracted steam flowrate, which realize the quantitative evaluation before and after coupling with No.0 HPH. Taking a 600 MW unit for example to carry out calculations and analysis, it indicates that the index calculation error of the method proposed in this paper is less than 0.15%, which has high precision and can be used for thermal economic evaluation of large thermal power units coupled with No. 0 HPH. Furthermore, after coupling No.0 HPH, the feedwater temperature can be lifted by 19.1~32.3 ℃, the cycle heat efficiency raised by 0.18%~0.2%, and the heat consumption rate decreased by 29.3~34.6 kJ/(kW·h). Moreover, the sacrificial internal work will be even smaller, and the heat consumption decrease be even larger if the turbine load was smaller.

Aiming at the shortcomings of low-quality face recognition algorithms based on unified feature space, such as poor robustness to low-quality faces and limited feature representation capability, a low-quality face image recognition algorithm based on knowledge distillation was proposed. First, the ResNeXt network was used as the backbone feature extraction network, and the two-channel attention module was introduced to construct a teacher-student knowledge distillation framework with an attention mechanism. Secondly, the output features of the teacher network were adopted as labeled knowledge, and the effective recognition features were passed to the student network. And the attention graph features were adopted as the intermediate layer knowledge to solve the lack of single knowledge information in the output layer, and the feature knowledge was enriched by combining two kinds of knowledge distillation to ensure the diversity of knowledge information in the teacher network model. Then, the weighted average of labeled knowledge distillation loss, attention graph distillation loss, and recognition loss were fused as the total network loss function to ensure that the student network model has a better learning ability. Finally, tested under different quality images in AgeDB-30 and CPLFW test sets, the results of the ablation experiments show that compared to the generic face recognition model without distillation, the model with two types of knowledge distillation gains 2.25%, 11.33%, 24.64% and 2.8%, 10.58%, 27.85% improvement in recognition accuracy, respectively. Comparative experiments show that the algorithm proposed in this paper also obtains different degrees of improvement in accuracy compared to other mainstream algorithms.

In order to simulate the seismic damage process of RC (reinforced concrete) columns precisely and effectively, the method of seismic damage analysis of reinforced concrete members was established by combining the uniaxial damage constitutive model of concrete and steel bar with the flexibility-based fiber beam-column element model. The quasi-static test of reinforced concrete column was simulated and analyzed. It is found that the simulation model can simulate the degradation process of stiffness and bearing capacity of component with good accuracy. The shaking table test of reinforced concrete column under biaxial loading was simulated and analyzed. The results show that the simulation model can simulate the nonlinear dynamic behavior and damage distribution of component with good accuracy. Furthermore, the established model can effectively simulate the evolution process of seismic damage of components, and describe weak parts of components, and has high computational efficiency and solution accuracy, which can be used to analyze the collapse process of buildings and bridge structures under earthquake action.