In recent years, the rapid development of artificial intelligence, especially deep learning technology, makes machine learning have application scenarios in the fields of power system stability analysis, coordination along with scheduling and load forecasting. This paper designs an emotional deep learning programming controller (EDLPC) for automatic voltage control of power systems. The designed EDLPC contains an emotional deep neural network (EDNN) structure and an artificial emotional Q-learning algorithm. Besides, a specially defined proportional-integral-derivative (PID) controller is added to the deep neural networks (DNNs) structure as the actuator of an EDNN to realize the automatic tuning of PID controller parameters. In terms of control, the controller combines the advantages of the EDNN and PID controller, meanwhile adopts a reinforcement learning algorithm to optimize the parameters. From the perspective of reinforcement learning, embedding prior knowledge into the output instructions of EDNN is helpful to weaken the fitting problem in the training process. Compared with the outputs of the DNN and Q-learning algorithm under the two cases, the EDLPC could gain the highest control performance with smaller voltage deviations. The simulation results verify the feasibility and effectiveness of the proposed method for automatic voltage control of power systems.

Multi-object tracking achieves the acquisition of target location information and identity information through two subtasks, detection and re-identification (ReID). The existing commonly used one-shot framework has speed advantages, but the two subtasks have different feature requirements, which leads to competitive learning in the training and thus weakens the feature quality. We propose a feature decoupling based multi-object tracking framework FDTrack for contradictory feature requirements. Through the mutual inhibition of the two subtasks, the features of the backbone network are decoupled. Then the decoupled features are self-constrained to enhance effective features. Considering the instability of the target state and the different confidence of the detections, a more reasonable association strategy is employed to maximize the matchings between detections, thus recovering low-confidence targets. FDTrack is extensively tested on the MOT17 and MOT20 benchmarks. The experimental results show that FDTrack surpasses the previous state-of-the-art (SOTA) methods and has good anti-interference and real-time performance. Moreover, our proposed modules have good portability and can be applied in other one-shot trackers to achieve performance improvement.

Vision-and-Language Navigation (VLN) has garnered widespread attention and research interest due to its potential applications in real-world scenarios. Despite significant progress in the VLN field in recent years, limitations persist. Many agents struggle to make accurate decisions when faced with similar candidate views during navigation, relying solely on the overall features of these views. This challenge primarily arises from the lack of common-sense knowledge about room layouts. Recognizing that room knowledge can establish relationships between rooms and objects in the environment, we construct room layout knowledge described in natural language by leveraging BLIP-2, including relationships between rooms and individual objects, relationships between objects, attributes of individual objects (such as color), and room types, thus providing comprehensive room layout information to the agent. We propose a Knowledge-Enhanced Scene Understanding (KESU) model to augment the agent's understanding of the environment by leveraging room layout knowledge. The Instruction Augmentation Module (IA) and the Knowledge History Fusion Module (KHF) in KESU respectively provide room layout knowledge for instructions and vision-history features, thereby enhancing the agent's navigation abilities. To more effectively integrate knowledge information with instruction features, we introduce Dynamic Residual Fusion (DRF) in the IA module. Finally, we conduct extensive experiments on the R2R, REVERIE, and SOON datasets, demonstrating the effectiveness of the proposed approach