Photovoltaic power generation has an important place in the energy sector. In order to accurately quantify the uncertainty and fluctuation range of PV(photovoltaic) power and to improve the comprehensiveness of interval forecasts, a probabilistic prediction method for PV power intervals based on feature mining with improved TCN-BiGRU was proposed. First, the maximum information coefficient and symbolic transfer entropy causal analysis were utilized to screen the meteorological features, remove redundant information, and construct global horizontal radiation trend features, seasonal features, and weather clustering features to provide more effective information. Subsequently, the TCN-BiGRU model was improved by combining the temporal pattern attention mechanism and quantile regression methods to construct a combined model for interval prediction. Finally, the probabilistic prediction results are generated using the KDE method of empirical bandwidth selection with scatter measure semi-polar optimization. The proposed method is analyzed by real PV plant data, which verifies the high reliability and applicability of the proposed method in PV power interval probability prediction.

In the context of large-scale integration of wind and solar power into the grid, power system dispatch strategies faced unprecedented challenges. The volatility and randomness of wind and photovoltaic power generation significantly impacted system stability and controllability. To accurately characterize the spatiotemporal correlation of wind-solar power output and construct a practically valuable scenario set, a method for generating spatiotemporal correlated scenarios for wind-solar complementary systems was proposed, based on a coupled SGMM (seasonal Gaussian mixture model) and MCopula (mixed Copula function). Initially, the SGMM was constructed to capture the temporal correlation among wind-solar output variables. Then, the mixed Copula function was employed to describe the spatial correlation among variables. Based on the comprehensive modeling of spatiotemporal correlations, a series of uncertainty scenario sets reflecting these characteristics was generated using the Copula conditional distribution function and inverse transform sampling technique. The simulation results confirmed the effectiveness and reliability of the proposed method. The generated scenario sets not only reflected the spatiotemporal correlation characteristics and annual variation trends of wind-solar output but also better matched the historical actual sequences in terms of distance, providing strong decision-making support for power system dispatch. New perspectives and tools were offered for quantifying uncertainties in wind-solar complementary systems, which had profound theoretical and practical significance for optimizing power system dispatch strategies, reducing uncertainty risks, promoting the efficient utilization of renewable energy, and advancing the sustainable development of power systems.

Vegetation fires may lead to tripping and shutdown of overhead transmission lines, thus endangering the safe and stable operation of power grids. In order to study the change of flame inclination of vegetation under the influence of ambient wind, a square fir crib with the size of 1 m×1 m×0.6 m was used as the research object, and a 5 m×5 m×5 m outdoor site with ventilation was set up by Pyrosim software, and simulation research was carried out on 10 free burning cases with the wind speed of 1~10 m/s, and the change of the flame pattern with the size of the wind was analysed. The formula for calculating the flame inclination applicable to this wind speed range was fit. The flame inclination calculation formula was based on the flame inclination of this range, and the flame inclination calculation formula was based on the flame inclination of this range. The change of flame shape with the wind speed was analyzed. The formula for calculating the flame inclination angle applicable to the wind speed range was fitted. And the horizontal offset distance of the flame was obtained according to the change of the flame inclination angle. The results show that the larger the wind speed is, the smaller the change of flame inclination and flame horizontal offset distance are. The results of this study can be used as a reference for the prevention of tripping accidents on transmission lines and the cutting distance of vegetation on both sides of transmission line corridors.

When traditional methods were used to evoked the potentials SSVEP (steady-state visual evoked potentials) EEG(electroencephalogram) signals, the accuracy and sufficiency of feature extraction were insufficient, which affected the recognition accuracy of signals. A novel approach was proposed which based on a CNN (convolutional neural network) integrated with a CBAM (convolutional block attention module) and a LSTM (long short-term memory network). By incorporating attention mechanisms, both channel and spatial features were effectively extracted within the CNN framework. Additionally, LSTM was introduced to enhance the extraction of temporal features, enabling accurate recognition of SSVEP signals. The experimental results show that the proposed method can effectively extract hierarchical features and achieves a high recognition accuracy.Compared to canonical correlation analysis (CCA), CNN, CBAM-LSTM, and CNN-CBAM, the proposed model improves the recognition accuracy by 5.3%, 2.95%, 2.27%, and 1.71% respectively. It can be seen that the model has a good performance in the classification and recognition of SSVEP signals.

With the rapid development of the Internet and social platforms, the problem of spammer detection has become a major technical challenge in building a harmonious Internet environment. However, user data collected from social platforms are often subject to issues such as missing information and data noise. Therefore, in graph-based learning models for bot army detection, methods that use point estimation as weights fail to express uncertainty in regions with sparse or missing data. A graph neural network model for bot army detection, VRGAT, integrating variational inference, was proposed. It introduces a probability distribution for the weights and derives a variational approximation of the true posterior. By applying different convolution operations to the mean and variance, the model more accurately captures the variability in the data. Simulations based on the Twibot-20 dataset show that, compared to the best existing benchmark for bot army detection (F₁ = 88.12), VRGAT achieved an improved performance with an F₁ score of 89.64.In robustness experiments, when random noise was added at varying levels, the accuracy drop for VRGAT is significantly slower than for other baseline models, demonstrating its superior noise resistance. The experimental results demonstrate that the introduction of variational inference can enhance the effectiveness of spammer detection and improve the model's robustness against noise.

In order to meet the automatic safety needs of the assembly of fixed threaded hole bolts for underground coal mine roadway pipelines, a flexible operation control algorithm for bolt tightening based on admittance method was studied for the six-degree-of-freedom robotic arm equipped with six-dimensional force/torque sensor. The main contents include the theoretical research of gravity compensation algorithm based on six-point positioning method and admittance-based bolt tightening compliance control algorithm. A bolt fastening experimental system based on the robotic arm was set up, and the assembly experiment of fixed threaded hole bolts was carried out. The results indicate a decreasing trend in the forces and torques experienced during assembly, demonstrating the effectiveness and safety of the compliant operation control algorithm based on the admittance method.

Understanding the evolution law of rail service performance is of great significance for reducing the operation and maintenance costs of heavy-duty railway rails. Due to the complex and variable operating environment of rail tracks, which makes it difficult to construct scientifically effective damage evolution indicators to reflect objective development patterns, a method based on t-SNE(t-distributed stochastic neighbor embedding) was proposed for constructing the evolution law of corrugation damage. Firstly, the time-domain, frequency-domain, statistical, and entropy features were extracted from the original rail corrugation vibration signal. The random forest algorithm was then used to rank the features by importance, and the top-ranked features were selected to construct the feature vector. Dimensionality reduction was performed using t-SNE and other methods, and it is found that t-SNE demonstrates superior performance. The final temporal damage degradation index is obtained through Euclidean distance metric and median filtering for smoothing. The results indicate that this method provides good discrimination, anti-interference capability, and practical applicability for damage stages classification.

In the context of unmanned multi-vehicle formation guided by manned vehicles, a system for vehicle recognition and trajectory tracking control of unmanned vehicles during formation driving was devised and executed. An algorithm for multi-sensor fusion moving target detection was proposed, leveraging data from lidar, camera, and mmWave radar sensors. The algorithm utilizes Euclidean clustering, deep learning, and kinematic reasoning techniques for target detection. Additionally, a fusion methodology was introduced to integrate detection outcomes from various sources for precise identification of vehicles in the vicinity. Paths were anticipated based on the trajectories of preceding vehicles, and a Kalman filter was developed to smooth and filter these paths. A vehicle dynamic model, vehicle road error model, and the robust H∞ controller was established for vehicle trajectory tracking control simulation. Outcomes from simulation and real vehicle validation show as follows. The average recognition accuracy of preceding vehicles in test scenarios exceeds 95%. The mean squared error and average trajectory deviation rate of real-time anticipated paths decrease by 17.3% and 48.6% respectively pre and post filtering. Lateral control position error and yaw angle error decrease by 29% and 41% correspondingly compared to PID control. Vehicle formations attain stable working at speeds of up to 54 km/h.

In order to solve the problems of SHO (seahorse optimization), such as low accuracy, precocity and insufficient global search ability. MSHO (multi-strategy seahorse optimization) algorithm based on nonlinear inertial weight strategy, improved whale encircling strategy and improved sine and cosine strategy was MSHO designed. Firstly, the nonlinear inertia weight was introduced into the motion behavior of SHO algorithm to overcome the shortcoming that the algorithm is prone to premature convergence. Secondly, the improved strategy of whale encircling prey was introduced into the updated equation of seahorse hunting success to reduce the probability of the algorithm falling into the local optimal solution. Then, the improved sine-cosine strategy was introduced into the reproduction behavior of the algorithm to enhance the quality of the hippocampal progeny solution, and further improve the global optimization ability and stability of the algorithm. Finally, in order to evaluate the performance of the proposed MSHO algorithm, SHO algorithm, chaotic SHO algorithm, subtraction average algorithm, gray Wolf algorithm, Seagull algorithm, whale optimization algorithm, particle swarm algorithm and MSHO algorithm were compared on 23 benchmark test functions. The experimental results show that MSHO algorithm shows higher convergence accuracy on 20 functions and stronger stability on 16 functions compared with other 7 algorithms. In addition, in order to test the application ability of MSHO algorithm in engineering problems, the algorithm is applied to solve the design problems of welded beams, cantilever beams and pressure vessels. The experimental results show that MSHO algorithm has better search accuracy in these three kinds of engineering design problems than other 7 different algorithms.

An improved version of the EfficientNetV2 network is presented for garbage image classification to address the limitations of mainstream algorithms, such as poor dataset universality, limited recognition types, and algorithmic constraints in specific environments. The proposed algorithm emphasized both classification speed and accuracy. The EfficientNetV2 network was utilized as the baseline model, and classification speed was enhanced through the incorporation of the SK (selective kernel) attention mechanism. Transfer learning strategies were employed to improve classification accuracy. By leveraging deep learning model frameworks for garbage image processing, the need for manual feature extraction from dataset images was eliminated, and the scope of garbage recognition was expanded. Experimental results demonstrate that the proposed algorithm achieves an accuracy of 99.71% on a self-built dataset, which is an improvement of at least 4.77% compared to other algorithms, such as GoogleNet. Furthermore, in terms of time efficiency, the proposed algorithm outperforms algorithms like VggNet19 by at least 50%. Through the enhancement of the EfficientNetV2 network, accurate and faster garbage classification is enabled, providing a scientific and efficient solution to the growing challenges posed by garbage issues.