To satisfy the growing demand for traffic prediction induced by urbanization, the intelligent transportation system integrated various cutting-edge artificial intelligence technologies, with large language models (LLMs) as a representative, has been developed. However, existing methods are mostly confined by shallow LLMs utilization, where the semantic capacity of LLMs is ignored and the traffic data are directly fed in. Furthermore, the modality diversity of different traffic prediction scenarios (e.g., flow, speed, and demanding) remains to be underexplored, which restricts the model flexibility towards downstream applications. To mitigate these limitations, we propose a Mixture of Semantic and Spatial Experts (SS-MoE) for traffic prediction along with the human-intelligible post-hoc result explanation. Specifically, to enlighten the traffic predictor with abundant semantic information, we design hierarchically coarse- and fine-grained prompts including role assignments, dataset descriptions, and background supplements, which serves as the auxiliary knowledge for downstream prediction. Afterwards, considering the diversity of real-world traffic scenarios, we construct the MoE framework consisting of a spatial expert, a semantic expert, and a general expert, which accounts for the node-level features, the semantic representations, and the overall generalization, respectively. At last, we instruct the LLM to explain and analyze the final prediction, which is able to provide insightful conclusions and support intelligent transportation decisions, forming a unified prediction-explanation pipeline. Extensive experiments on five public traffic datasets demonstrate the superiority of SS-MoE across three traffic prediction tasks. Experimental results indicate that the MAE and RMSE values of SS-MoE are reduced by up to 4.04% and 3.20% compared with that of the runner-up, respectively.

Anomaly detection for flight data is crucial in maintaining the safety and stability of unmanned aerial vehicles (UAVs), making it a topic of significant research and attention. However, existing anomaly detection methods often ignore the random noise of UAV flight data and lack effective parameter selection, resulting in inadequate anomaly detection performance. Furthermore, current methods generally face the problem of insufficient feature extraction capability. In this paper, a spatio-temporal correlation based on one-dimensional convolutional neural network (1D CNN), bidirectional long short-term memory (BiLSTM), and attention mechanism (AM) hybrid neural network with residual filtering (STC-1D CBiAM-RF) data-driven multiple regression framework is proposed for anomaly detection and recovery of UAV flight data. First, a correlation analysis method is used for parameter selection to reduce the dependence on expert knowledge. Second, a multiple regression model fusing attention mechanism is designed. It utilizes 1D CNN-BiLSTM as a feature extractor, guided by the attention mechanism, to enhance the learning of crucial information from UAV flight data. Then, to effectively mitigate the impact of random noise, a residual filtering method is introduced to smooth the residuals, thereby improving anomaly detection performance. Finally, anomaly detection is achieved by comparing the square of the smoothed residuals with the statistical threshold, and data recovery is achieved by replacing the anomalous data with the predicted data. The effectiveness of the proposed method is verified through a series of experiments using real UAV flight data injected with different anomaly types.

High‐performance tracking control is essential for permanent magnet synchronous motors in the perturbed environment. Given this, a new hybrid controller is proposed in this study for a permanent magnet synchronous motor with load disturbances as well as time delays. First, a new prescribed performance method is proposed to achieve the full‐state performance constraints with load disturbances. Second, a time‐varying filter is proposed for the first time to avoid the “complexity explosion” problem of the backstepping method while guaranteeing the convergence of the filtering error. Third, by combining Lyapunov–Krasovskii functionals with adaptive neural networks, the time‐delay disturbance and unknown nonlinear dynamics of the control system have been solved. The stability analysis proves that all signals in the closed‐loop system are bounded. To show the effectiveness of the intelligent controller, the comparison simulations are given to confirm the advantages of the proposed adaptive neural control scheme.

The manufacturing processes of unmanned aerial vehicle (UAV) power systems generate large amounts of data and knowledge. The extraction of useful information or patterns from redundant data and knowledge texts has become a challenge in intelligent manufacturing. Unfortunately, graphics processing unit (GPU)‐based parallel computing is limited, and the inference speeds of the available named entity recognition (NER) models for Chinese text datasets are low because they are mainly based on the long short‐term memory (LSTM) algorithm. Herein, first, the flat‐lattice transformer (FLAT) model was optimized by using a stochastic gradient descent with momentum (SGDM) optimizer and adjusting the model hyperparameters. Compared with the existing NER methods, the proposed optimization algorithm achieved better performance on the available dataset. Then, an NER method named the TL_FLAT model based on transfer learning and the abovementioned optimization model was introduced. Finally, a Chinese text dataset from a UAV power system created by the authors was used to validate the proposed method. The F1 score was 76.26%, the precision value was 76.98%, and the recall value was 75.56%, indicating that the TL_FLAT model was suitable for Chinese text entity recognition for UAV power systems.

In order to improve the comprehensive performance of energy dispatching between different sites, the optimization research of particle swarm optimization (PSO) algorithm and ant colony optimization (ACO) algorithm is carried out. We proposed a new improved PSO‐ACO algorithm based on the idea of hybrid algorithm to solve the problem of poor energy dispatching efficiency between sites. First, the multiobjective performance indicators were introduced to transform the sites’ energy dispatching problem into a multiobjective optimization problem. Second, the vitality factor was introduced into the PSO strategy to solve the local optimal problem, and in the PSO‐ACO fusion strategy, the PSO routes were transformed into the ant colony enhancement pheromone to accelerate the accumulation speed of the ACO initial pheromone. Then, the angle guidance function was introduced into the state transition probability of the ACO strategy to improve the global search capability, and a high‐quality pheromone update rule was proposed to improve the convergence speed of the algorithm. Finally, simulation experiments were carried out on the improved PSO‐ACO algorithm, Min–Max Ant System (MMAS) algorithm, ACO algorithm, PSO algorithm, and PSO update algorithm in a variety of complex site scenarios. The simulation results show that the improved PSO‐ACO algorithm can plan a site energy dispatching route with shorter route, less time‐consuming, and higher security and realize the comprehensive and global optimization of energy dispatching.

The development of object detection networks has reached a high point, and there have been significant improvements in accuracy and detection speed. Object detection is widely used in intelligent robots, self‐driving cars, and other edge‐intelligent terminals. Unfortunately, when a detector is allowed to learn new objects in an unfamiliar environment, it can catastrophically forget the objects it has already learned. In particular, reliable and stable knowledge cannot be extracted from old models. Based on this, a new multinetwork mean distillation loss function for open‐world domain incremental object detection is presented. To better extract reliable and stable knowledge from old models, we enhanced the distillation output of the detector with a ResNet50 backbone and an output RoI head. The distillation output of the intermediate RPN is softened by adaptive distillation. To obtain more stable results, the ResNet50 backbone and RPN on the channel are zero‐averaged. Various incremental steps and stability experiments are performed on two benchmark datasets, PASCAL VOC and MS COCO. The experimental results show the excellent performance of our method in different experimental scenarios, and it is superior to the most advanced methods. For example, in the setting of the batch task, incremental object detection on the PASCAL VOC and MS COCO datasets is improved by 3.4% and 2.1%, respectively.

Fixed‐wing unmanned aerial vehicles (FW‐UAVs) play an essential role in many fields, but the faults of FW‐UAV components lead to severe accidents frequently; so, there is a need to continuously explore more intelligent fault detection methods to improve the safety and reliability of FW‐UAVs. Deep learning provides advanced solution ideas for future UAV fault detection, but the current lack of UAV monitoring data limits the advantages of deep learning in UAV fault detection, which are both a challenge and an opportunity. In this paper, we mainly consider the data availability of deep learning under various practical flight conditions of FW‐UAVs and propose a fault detection framework based on hybrid deep domain adaptation BiLSTM networks and the Hampel filter (HDBNH), the main purpose of which is to learn the knowledge of acquired data for detecting FW‐UAV faults in other unknown operating conditions. HDBNH consists of three modules: feature extractor, domain adaptor, and fault detector. The feature extractor is two BiLSTM networks constructed to extract the past and future state features from the time‐series flight data. The discrepancy of feature distribution between different domains is effectively reduced in the domain adaptor by a hybrid adversarial and the maximum mean discrepancy (MMD) domain adaptation method. The fault detector consists of a fault classification module and a Hampel filter. According to the continuous and dynamic characteristics of FW‐UAV state changes, the Hampel filter is used to detect and correct the predicted values of the fault classification module. Meanwhile, a new state sample preparation strategy is proposed to support the work of HDBNH better. Finally, the effectiveness of HDBNH is confirmed by conducting extensive experiments in real FW‐UAV flight data.