The magnetic flux leakage detection technology has been widely used in the field of ferromagnetic material defect detection, in which the magnetic dipole method is currently the most widely used mathematical method for predicting magnetic flux leakage from structural defects. The magnetic dipole method is primarily employed for predicting the magnetic flux leakage signal of regular defects. The unit magnetic dipole band superposition model can be used to predict the magnetic flux leakage signal of complex defects. The magnetic charge density of complex defects needs to be calculated when the model is used for prediction. However, the magnetic charge density distribution of complex defects is inhomogeneous, and the calculation is complex. Therefore, a calculation method of discrete magnetic charge density field was proposed for calculating the magnetic charge density of three-dimensional irregular defects. The computational complexity of the model was reduced by using this method and the magnetic flux leakage signal of three-dimensional irregular defects can be quickly and exactly obtained. Comparison between the signal predicted by the unit magnetic dipole band superposition model based on discrete magnetic charge density field and the signal simulated by COMSOL software demonstrates the feasibility of the method. Experimental results show that the prediction performance of the model has been significantly improved by using this method, the maximum prediction error is reduced by 90.08%, and the calculation time is reduced by 97.43%, thus a fast and effective solution for the calculation of the magnetic charge distribution of three-dimensional irregular defects is provided.

A geomagnetic storm is a periodic natural disaster in which the changing geomagnetic field can induce an induced geoelectric field. A geomagnetic induced current (GIC) loop is formed between the transmission line and the earth conductor through the neutral points of grounding transformers. GIC seriously threatens the safe and stable operation of extra-high and ultra-high voltage AC transmission systems. There are many types of terrain and complex structures in our country, which makes the influence of geological landforms on induced geoelectric fields very significant. A finite element calculation method for GIC was proposed based on a three-dimensional earth conductivity model to address the difficulties in modeling and calculating GIC. Firstly, a three-dimensional earth conductivity model was established considering the anisotropy of geological structures. Meanwhile, a calculation model for electromagnetic field penetration depth under multi-layer geological conditions was given. Secondly, a mathematical model based on time-varying electromagnetic fields was established. Combined with the topology of the power grid, an equivalent calculation model for the power grid GIC was derived. Finally, taking the Shache-Turpan 750 kV transmission line in Xinjiang area as an example, a corresponding physical model was built in COMSOL Multiphysics finite element simulation software. The three-dimensional distribution of the induced ground electric field in the power grid was obtained through geometric modeling, boundary condition setting, grid division, and iterative solution. Furthermore, the GIC flowing through the neutral point of the 750 kV transformer was obtained. The research results indicatethat the overall level of GIC obtained by the 3D model is higher than that of the 2D model. Besides, the 3D model considers the geometric angle between the transmission line and different terrains, which can provide a more detailed distribution of induced geoelectric fields. The research results verify the effectiveness of the method proposed, which provides a reference basis for scientific planning of ultra-high and ultra-high voltage transmission corridors.

In order to solve the influence of intermittent and fluctuating wind energy on the economy and reliability of the system, a day-ahead and real-time energy management strategy for islanded wind power hydrogen production system with energy storage battery was proposed. In the day-ahead energy management stage, the energy management strategy of alkaline electrolytic cell power classification was adopted, and the economic energy management model of isolated wind power hydrogen production system was established with the maximum daily profit of the system as the objective function. An improved grey wolf optimization algorithm was proposed to solve the system energy management model, optimize the system energy flow and improve the system economy. In the real-time energy management stage, in order to ensure that the system can maintain stable operation when unplanned power fluctuations occur, a real-time energy management strategy for island wind power hydrogen production system based on condition identification was designed, which can adjust the system operation status in time according to the real-time operating conditions of the system. The experimental results show that the day-ahead energy management based on the improved grey wolf optimization algorithm effectively increases the daily revenue of the system and improves the economy of the system. The real-time stage energy management based on condition identification can maintain the system power balance when the wind power fluctuates under the day-ahead plan, and realize the stable hydrogen production of the electrolytic cell throughout the day.

With the transition from traditional centralized power systems to distributed energy systems, harmonics are produced due to the penetration of renewable energy, resulting in additional losses. Distribution network loss accounts for more than half of the total system loss and is the focus of network loss reduction. A network reconstruction method based on improved binary particle swarm optimization algorithm was proposed to reduce distribution network loss. Firstly, considering the influence of harmonic wave on network loss and the harmonic effect of the line under high frequency current, the line impedance was modified. Then, the total loss of the network was calculated using the corrected impedance, and a probabilistic reverse learning approach suitable for binary algorithms was creatively proposed, integrating the theory of the good point set to obtain uniform and diverse initial particles. Finally, taking the modified 33 node power system as an example, the optimization objective was to minimize the total loss and obtain the optimal topology structure of the distribution network. The experimental results show that the distribution network reconstruction considering harmonic factors has played a good role in reducing the loss of distribution network.

A method for optimizing the control parameters of the sample point distribution state within the framework of the unscented transform (UT) for the unscented Kalman filter (UKF) was introduced. The issue of abnormal filtering performance arising from the state of sample point distributions was addressed by this method. A multi-strategy improved sparrow search algorithm(ISSA) was employed to finely tune the control parameters. The goal is to enhance the distribution of Sigma points, thereby improving the effectiveness of nonlinear approximations and ultimately enhancing the accuracy of filtering estimations. To address the shortcomings of traditional sparrow search algorithms, several refinements were implemented. Initially, a Cubic chaotic mapping was applied to diversify the initial population. Furthermore, during the exploration phase, a nonlinear adaptive convergence factor was introduced to balance the algorithm’s capacity for global exploration and local exploitation. Additionally, a wavelet mutation strategy was integrated into the follower phase to prevent blind adherence to specific paths and mitigate the risk of becoming trapped in local optima. Lastly, an adaptive t-distribution perturbation capability was introduced to strengthen the algorithm’s ability to perform wide-ranging global searches. The efficacy of the proposed ISSA was demonstrated through simulation experiments conducted on various test functions. The results consistently show that ISSA outperforms other methods in terms of convergence and solution accuracy. Furthermore, the benefits of ISSA are extended to the optimization of parameters within the UKF algorithm. Experimental outcomes indicate that the ISSA-UKF algorithm reduces the root mean square error (RMSE) of position by 52.2% and the RMSE of velocity by 21.9%, thus affirming the viability and effectiveness of the proposed enhancements.

The path planning problem of electric vertical takeoff and landing (eVTOL) aircraft in urban scenarios was studied. Firstly, a three-dimensional urban space model was constructed using the hazard grid method. Considering the selected model of eVTOL, with range, operational risk, and altitude variation as objective functions, a path planning model for manned eVTOL with multiple constraints was developed, taking into account the characteristics of the aircraft and environmental limitations. Subsequently, an improved artificial electric field algorithm (IAEFA) was proposed, which enhances the traditional artificial electric field algorithm (AEFA) by introducing an adaptive Coulomb parameter and incorporating a decreasing coefficient in the Coulomb constant calculation for simulation-based solution. Experimental results demonstrate that the constructed model achieves the expected outcomes. The solution effectiveness of path planning using the improved algorithm surpasses that of traditional particle swarm optimization and artificial electric field methods, resulting in shorter range, minimal altitude variation, and enhanced safety during operations. Finally, based on comparative experiments, the value of the decreasing coefficient was determined. The optimal solution effectiveness of the improved algorithm is achieved when the decreasing coefficient is set to 1.5.

Continuum robots have been widely used in natural cavity intervention therapy, but the current flexible continuum robots have poor positioning accuracy and lack of accurate navigation means. In response, a multi-segment continuum robot shape reconstruction method based on electromagnetic positioning information was proposed, aimed at achieving accurate end navigation and positioning. An efficient two-stage active structure shape reconstruction method based on third-order Bessel curve was proposed. The solution of Bessel curve was optimized to nonlinear least squares problem, which was solved quickly and accurately by particle swarm algorithm with linear decreasing weights. The proposed method can accurately fit the shape of the two-segment continuum and has good real-time performance. The results show that within the bending angle process, the average root-mean-square error of shape reconstruction is 0.48 mm, which is 0.8% of the length of the continuum bending segment. The accuracy and reliability of the proposed shape reconstruction method for intracavitary interventions using micro-continuum robots are demonstrated through detailed modeling and validation results.

Multivariate time series classification is a key problem in many fields, but the current research on multivariate time series classification is faced with some problems, such as high dimensionality of original data, low accuracy, and lack of interpretability, which limits the performance improvement of models and makes it difficult to meet the actual requirements. Aiming at above problem, a multivariate time series classification method based on Shapelets was proposed. Firstly, unsupervised Shapelet learning of adaptive neighbors was used to automatically learn significant multivariate Shapelets by combining Shapelets transform and adaptive weights. Then, the method was combined with Shapelet similarity and class label constraint to enhance the interpretability and classification accuracy of the model. Finally, the optimization strategy of the model was proposed to obtain the best Shapelets to further improve the classification accuracy of the model. Three different types of 11 algorithms were compared on 11 public data sets, and the experimental results show that the proposed algorithm has high classification accuracy.

In order to improve the recognition accuracy of high-altitude nuts and reduce the false detection rate of bolts and nuts, a high-altitude nut recognition model based on improved YOLOv5 was proposed. Firstly, a new attention mechanism efficient multi-scale attention(EMA) was added to the backbone network to integrate more information. Secondly, in order to enhance the network’s feature extraction capability, bidirectional feature pyramid network(BiFPN) was used to replace the PANet of the neck network. Finally, structured intersection over union(SIoU) was used to replace the original loss function complete intersection-over-union(CIoU) to accelerate the convergence of the model and improve its classification accuracy. The results show that the improved model has better performance than the original YOLOv5 model. The accuracy of the improved model increases by 0.92%. The recall increases by 0.16%. The average precision 1 (mAP_0.5:0.5) increases by 0.53%. And the average precision 2 (mAP:0.95) increases by 2.26%. An actual recognition comparison experiment between the improved model and the original YOLOv5 model was carried out. The experimental results show that the improved model has better recognition performance, which reduces the missed detection rate and the false detection rate, and improves the actual recognition rate. The improved model can well meet the recognition and image data acquisition of high-altitude nuts. And it also provide a data foundation for subsequent nut maintenance.

Based on the sand-mud interlayer core of a block in Ordos Basin, denoising neural network based on wavelet transformation (DWTNet) was used to denoise the core image. The evaluation of this method was carried out by comparing the peak signal-to-noise ratio (PSNR) and the post-denoising image outcomes. The investigation reveals that by applying the DWTNet denoising algorithm to the test sets YX1 and YX2, and contrasting it with other denoising algorithms such as EGDNet, the PSNR values at noise levels of 25, 50, and 75 dB are respectively 0.527, 0.418, and 1.1 dB higher than those achieved by the EGDNet algorithm. The proposed algorithm surpasses others in terms of metrics including peak signal to noise ratio(PSNR), and visually, the resulting images processed by it exhibit enhanced clarity. The introduction of this method holds substantial significance for the calculation of parameters like porosity, mean specific surface area, mean curvature, among other rock properties, thereby advancing the capabilities in digital core technology, CT scanning analysis, and understanding of rock characteristics.