Wind and photovoltaic (PV) power forecasting are crucial for improving the operational efficiency of power systems and building smart power systems. However, the uncertainty and instability of factors affecting renewable power generation pose challenges to power system operations. To address this, this paper proposes a digital twin-based method for predicting wind and PV power. By utilizing digital twin technology, this approach provides a highly realistic simulation environment that enables accurate monitoring, optimal control, and decision support for power system operations. Furthermore, a digital twin platform for the AI (Artificial Intelligence) Grid is established, allowing real-time monitoring, and ensuring the safe, reliable, and stable operation of the grid. Additionally, a deep learning-based model WPNet is developed to predict wind and PV power at specific future time points. Four datasets are constructed based on weather conditions and historical wind and PV power data from the Flanders and Wallonia regions. The prediction models presented in this paper demonstrate excellent performance on these datasets, achieving mean square error (MSE) values of 0.001399, 0.001833, 0.000704, and 0.002708; mean absolute error (MAE) values of 0.025164, 0.027854, 0.018592, and 0.033501; and root mean square error (RMSE) values of 0.037409, 0.042808, 0.026541, and 0.052042, respectively.

In low-light environments, the presence of numerous small, dense, and occluded objects challenges the effectiveness of conventional object detection methods, failing to achieve desirable results. To address this, this paper proposes an efficient object detection network, YOLO_GD, which is designed for precise detection of targets in low-light scenarios. This algorithm, based on the foundational framework of YOLOv5s, implements a cross-layer feature fusion method founded on an information gathering and distribution mechanism. This method mitigates the issue of information loss during inter-layer feature exchange and, building on this, constructs a Bi-level routing spatial attention module to reduce computational redundancy caused by the self-attention mechanism, thereby enhancing the model’s detection accuracy for small objects. Furthermore, through the introduction of a novel deformable convolution, a cross-stage local feature fusion module is established, enabling the model to capture the complex features of input data more accurately and improve detection precision for dense objects. Lastly, the introduction of a probabilistic distance metric in the bounding box regression loss function enhances the network model’s generalization capability, further increasing detection accuracy in occluded scenarios. Experimental results on the ExDark dataset demonstrate that compared to YOLOv5, there is a 5.97% improvement in mean average precision (mAP), effectively enhancing object detection performance in low-light conditions.

This study is dedicated to developing an innovative method for evaluating spoken English by integrating large language models (LLMs) with effective space learning, focusing on the analysis and evaluation of emotional features in spoken language. Addressing the limitation of current spoken English evaluation software that primarily focuses on acoustic features of speech (such as pronunciation, frequency, and prosody) while neglecting emotional expression, this paper proposes a method capable of deeply recognizing and evaluating emotional features in speech. The core of the method comprises three main parts: (1) the creation of a comprehensive spoken English emotion evaluation dataset combining emotionally rich speech data synthesized using LLMs with the IEMOCAP dataset and student spoken audio; (2) an emotion feature encoding network based on transformer architecture, dedicated to extracting effective spatial features from audio; (3) an emotion evaluation network for the spoken English language that accurately identifies emotions expressed by Chinese students by analyzing different audio characteristics. By decoupling emotional features from other sound characteristics in spoken English, this study achieves automated emotional evaluation. This method not only provides Chinese students with the opportunity to improve their ability to express emotions in spoken English but also opens new research directions in the fields of spoken English teaching and emotional expression evaluation.

In GNSS-deprived settings, such as indoor and underground environments, research on simultaneous localization and mapping (SLAM) technology remains a focal point. Addressing the influence of dynamic variables on positional precision and constructing a persistent map comprising solely static elements are pivotal objectives in visual SLAM for dynamic scenes. This paper introduces optical flow motion segmentation-based SLAM(OS-SLAM), a dynamic environment SLAM system that incorporates optical flow motion segmentation for enhanced robustness. Initially, a lightweight multi-scale optical flow network is developed and optimized using multi-scale feature extraction and update modules to enhance motion segmentation accuracy with rigid masks while maintaining real-time performance. Subsequently, a novel fusion approach combining the YOLO-fastest method and Rigidmask fusion is proposed to mitigate mis-segmentation errors of static backgrounds caused by non-rigid moving objects. Finally, a static dense point cloud map is generated by filtering out abnormal point clouds. OS-SLAM integrates optical flow estimation with motion segmentation to effectively reduce the impact of dynamic objects. Experimental findings from the Technical University of Munich (TUM) dataset demonstrate that the proposed method significantly outperforms ORB-SLAM3 in handling high dynamic sequences, achieving a reduction of 91.2% in absolute position error (APE) and 45.1% in relative position error (RPE) on average.

With the vigorous development of deep learning technology, lane detection tasks have achieved phased results. However, existing lane detection models do not consider the unique geometric and visual features of lanes when dealing with some challenging scenarios, resulting in many difficulties and limitations. To this end, we propose a lane detection network based on full-dimensional convolutional Transformer (OTVLD-Net) to improve the adaptability of the model under extreme road conditions and better handle complex lane topology. In order to extract richer contextual features, we designed ODVT-Net, which uses full-dimensional dynamic convolution combined with improved feature flip fusion layer and non-local network layer, and aggregates lane symmetry features by utilizing the horizontal symmetry of lanes. A feature weight generation mechanism based on Transformer is designed, and a cross-attention mechanism between feature maps and lane requests is added in the decoding stage to enable the network to aggregate global feature information. At the same time, a vanishing point detection module is introduced, and a joint weighted loss function is designed to be trained in coordination with the lane detection task to improve the generalization ability of the lane detection model. Experimental results on the OpenLane and CurveLanes datasets show that the detection effect of the OTVLD-Net model has reached the current advanced level. In particular, the accuracy on the OpenLane dataset is 6.4% higher than the F1 score of the second-ranked model, and the average performance in different challenging scenarios is also improved by 8.9%. At the same time, when ResNet-18 is used as the template feature extraction network, the model achieves a speed of 103FPS and a computing power of 14.2 GFlops, achieving good performance while ensuring real-time performance.

This study aims to develop an emotion evaluation method for second language learners, utilizing multimodal information to comprehensively evaluate students’ emotional expressions. Addressing the limitations of existing emotion evaluation methods, which primarily focus on the acoustic features of speech (e.g., pronunciation, frequency, and rhythm) and often neglect the emotional expressions conveyed through voice and facial videos, this paper proposes an emotion evaluation method based on multimodal information. The method includes the following three main parts: (1) generating virtual data using a Large Language Model (LLM) and audio-driven facial video synthesis, as well as integrating the IEMOCAP dataset with self-recorded student videos and audios containing teacher ratings to construct a multimodal emotion evaluation dataset; (2) a graph convolution-based emotion feature encoding network to extract emotion features from multimodal information; and (3) an emotion evaluation network based on Kolmogorov–Arnold Networks (KAN) to compare students’ emotion features with standard synthetic data for precise evaluation. The emotion recognition method achieves an unweighted accuracy (UA) of 68.02% and an F1 score of 67.11% in experiments with the IEMOCAP dataset and TTS data. The emotion evaluation model, using the KAN network, outperforms the MLP network, with a mean squared error (MSE) of 0.811 compared to 0.943, providing a reliable tool for evaluating language learners’ emotional expressions.