Jian Chen

Core loss is governed by coupled factors, strong nonlinear behavior, and excitation waveform; traditional empirical models often fail to deliver high accuracy under complex operating conditions. Data heterogeneity across waveforms also limits the generalization ability of data-driven models. To address these issues, this paper proposes a waveform-classified Bayesian-optimized artificial neural network (WC-BOANN) core loss prediction model, which combines Random Forest waveform classification with a Bayesian-optimized artificial neural network. Features including form factor, skewness, total harmonic distortion, and differential variance are first extracted from magnetic flux density time series. The Random Forest waveform classification then identifies sinusoidal, triangular, and trapezoidal waveforms automatically. Separate artificial neural network models are built for each excitation waveform, and Optuna-based Bayesian optimization is used to tune the network structure and hyperparameters. The results show that the unclassified Bayesian-optimized artificial neural network (UC-BOANN) performs unevenly across materials, with MAPE values of 9.46%~19.65% and R² values of 0.9737~0.9857. By contrast, WC-BOANN improves prediction accuracy: on the test sets of four magnetic materials, all R² values exceed 0.99, all MAPE values are below 3%, the 95-percentile prediction errors are below 7.5%, and the ±10% prediction accuracy (±10% PA) exceeds 99%.

In mobile robot path planning, the conventional A* algorithm often suffers from redundant node expansion and excessive turning points, whereas the Dynamic Window Approach (DWA) is prone to local optima and deviations from the global path in dynamic environments. To address these issues, this paper proposes a hybrid algorithm, termed A*-GA-DWA, which combines an improved A* algorithm with a GA-optimized DWA method. In the global planning stage, a directional six-neighborhood search strategy, an obstacle-aware adaptive heuristic function, and a turning-point smoothing method are introduced to improve path quality and reduce redundant node expansion. In the local planning stage, genetic algorithm optimization is applied to the DWA evaluation weights to enhance obstacle avoidance adaptability in dynamic environments. In addition, key nodes extracted from the global path are used as sub-goals to strengthen the coordination between global guidance and local replanning. Simulation results on a 30 × 30 map with dynamic obstacles show that, compared with conventional A*-DWA, the proposed method reduces the path length by 14.07% and the navigation execution time by 45.98%; compared with M-A*-DWA, the path length and navigation execution time are further reduced by 0.32% and 21.23%, respectively. Additional experiments on a ROS-based mobile robot platform were conducted to further validate the deployability and obstacle-avoidance capability of the proposed framework. These results provide an effective solution for mobile robot path planning tasks.

Predicting core loss under high-frequency non-sinusoidal excitation is crucial for power electronics equipment design. Temperature significantly affects core loss, and traditional core loss prediction models typically incorporate temperature corrections to enable accurate loss estimation across varying temperatures. Based on the Modified Steinmetz Equation (nonT-MSE) model, this study considers the temperature effect by employing a combination of the Tanh function and a linear term to modify the three empirical parameters, with the Tanh function capturing the nonlinear saturation of the loss coefficient k with increasing temperature. This leads to the establishment of the temperature-corrected non-TMSE (T-MSE) model for predicting magnetic core loss under high-frequency non-sinusoidal excitation. During model derivation, training data undergo logarithmic transformation processing. Subsequently, with T-MSE empirical parameters as variables and the minimum mean squared error between T-MSE predicted values and experimental values as the objective function, a single-objective optimization model is established. Finally, the empirical parameters of T-MSE are calculated using the training data and the single-objective optimization model. Comparing the core loss experimental results of the four materials, the average MSE values for the T-MSE model, the nonT-MSE model, and the square-root temperature-corrected non-TMSE model proposed by Zeng et al. (Zeng) are 0.0082, 0.0459, and 0.0110, respectively; with average MAPE of 1.57%, 1.87%, and 2.17%, respectively; and average R² of 0.9862, 0.9807, and 0.9731. Compared to the nonT-MSE model and the Zeng model, the T-MSE model demonstrated higher prediction accuracy.

Flight safety, as the core of civil aviation transportation industry, has attracted much attention from society in recent years. Based on the concept of aviation safety and data-driven, this paper innovatively proposes a direct multi-step prediction model and early warning strategy based on QAR real-time flight parameter data. First, based on the data pre-processing, COG NORM ACCEL parameter was used as the prediction index, Random Forest algorithm was used to analyze the importance of the remaining 29 flight parameters, and 6 important flight parameters were selected for real-time prediction. Then, based on a dynamic weight allocation mechanism, an end-to-end two-layer fusion prediction model was constructed. The model employed LSTM, Transformer, and TCN as parallel base models, where complementarity among multiple models was achieved through feature-level first-layer fusion and prediction-level second-layer fusion. Numerical experimental results demonstrated that the proposed fusion strategy not only achieved hierarchical improvement in prediction performance but also enhanced the interpretability of prediction results. Finally, an early warning strategy based on the real value and sliding window volatility was established, and its effectiveness is verified by the simulation of CAP CLM 1 POSN. In conclusion, this study provides a new data-driven approach for aviation safety management, which helps to identify and deal with potential flight risks in advance, and provides a strong reference for the automated early warning system of airlines.

Autonomous valet parking technology refers to a vehicle’s use of onboard sensors and line-controlled chassis to carry out a fully automatic valet parking function, which can greatly improve the driver’s experience. This study focuses on autonomous parking, employing environmental modeling and vehicle kinematics models. Innovatively applying the PSBi-RRT algorithm to path planning in autonomous parking systems constitutes this research’s contribution to this field. Firstly, the environment is modelled by the raster method; then, the PSBi-RRT algorithm is used for path planning, and a B-spline curve is used for path optimization. Speed and acceleration are smoothed at the same time, and finally, a smooth and obstacle-avoiding path planning scheme is obtained. The results show that an autonomous parking system based on the PSBi-RRT algorithm performs path planning from the vehicle to the parking space. Compared to RRT and Bi-RRT, PSBi-RRT generates shorter planning paths, smoother heading angle changes, shorter planning times, fewer nodes, and higher success rates. This research provides theoretical support for the development of autonomous parking technology.

Fatigued driving threatens the safety of people’s lives and property. Scent countermeasures offer minimal disruption and high efficacy, making them a promising approach. The aim of this study was to explore the application of scent countermeasures in alleviating fatigued driving. This study explored changes in EEG frequency bands (alpha, beta, theta, and gamma) and the activity of EEG metrics (R_(α/β), R_θ/(α+β) and R_{(α+θ)/(α+β)}) in the temporal lobe during driving tasks, selected fatigued driving identifiers, and aided validation by investigating subjective fatigue with the Karolinska Sleepiness Scale (KSS). The EEG indicators all increased, with a significant increase in R_(α/β). R_(α/β) was combined with the KSS to explore the effects of three scents, peppermint, grapefruit, and lavender, on driving fatigue. The subjective questionnaire results indicated that all three scents significantly improved driving fatigue, with significantly lower levels of driving fatigue compared to the control group. The analysis of EEG signals revealed a significant decrease in R_(α/β) after the implementation of scent countermeasures. Moreover, R_(α/β) was found to be lower in all three odor intervention groups compared to the control group. All three scents were found to significantly alleviate driving fatigue. The grapefruit scent had a better timely effect in relieving driving fatigue and the lavender scent had a longer effectiveness. This study provides further exploration for the application of odor interventions to alleviate driving fatigue. This study provides a practical reference for drivers to use odors to avoid fatigue in order to improve road safety.

The utilization of tempered blast‐furnace slag through the direct fiber forming process to produce high‐value thermal insulation materials offers a dual benefit: it efficiently utilizes the latent heat in the unused slag and significantly increases the value of blast‐furnace slag utilization. However, measuring the melting properties of iron slag at high temperatures is challenging. In this study, the melting behavior of SiO₂ in a high‐temperature molten pool was investigated. We employ dynamic visual data (video stream) captured via a non‐contact charge coupled device video recording system to extract SiO₂ contours through image processing. The change in image centroid characteristics is used to establish a convolution function relationship, and MATLAB's traversal search algorithm determines the centroid position of SiO₂. Given that SiO₂ is proportionate to crucible pixels, the area of the SiO₂ is calculated through pixel statistics within these contours. A new indirect method is then proposed to process image information to obtain SiO₂ volume and mass at different time points. An exponential fitting yields the melting rate function of SiO₂. Finally, this indirect method has been compared with shape from shading, quantitative characterization, and dimensional analysis techniques. Besides, the strengths and limitations of each method have been discussed. Our findings reveal that the indirect solution method presented here boasts straightforward calculation steps and imposes minimal image format requirements, which provides theoretical and technical support for the direct fiber forming process of blast‐furnace slag.

With the booming development of door-to-door housekeeping service, the platform faces the problem of order assignment. Improving the matching mechanism between orders and housekeepers based on a dynamic programming (DP) algorithm can not only achieve flexible order allocation but can also improve the service efficiency and service quality. In this paper, a single objective nonlinear programming model is established, which takes the maximum total weight value as the objective function to study the order allocation problem under offline and online conditions. Under the offline condition, the number of housekeepers is taken as the decision variable. The status of order and housekeeper, order time, and action trajectory are taken as constraints. For online assignment, the order backlog status is treated as the decision variable. The reliability of the model was verified using real data from 20 groups of housekeepers and 50 groups of orders. Finally, the effect of order backlog on online allocation is discussed and the optimal threshold and maximum weight are found. The online order assignment model is compared with the nearest distance assignment model. The results show that the online assignment model with a total weighted score of 1045.14 is better than the nearest distance assignment model with a score of 810.25.