Lithium-ion batteries(LIBs) have been used in electric vehicles(EVs) because of its high energy density and no pollution. As one of the important parameters of battery management system(BMS), accurately estimating the state-of-charge (SOC) can ensure the energy distribution and safe use of the battery. Therefore, in order to obtain accurate SOC estimation, this paper improves the estimation accuracy of SOC from four aspects. Firstly, to overcome the dependence of the model on the internal parameters of the battery, this paper uses the least squares support vector machine (LSSVM) to establish the battery model. The current, voltage, temperature are used as input vectors to estimate the SOC. Besides, the parameters of LSSVM are determined by a grey wolf optimizer(GWO). The GWO can improve the ability of LSSVM model by finding the global optimal solution. Thirdly, in order to improve the estimation accuracy of SOC, a novel LSSVM model based on the sliding window is proposed. The SOC estimated at the previous time, together with voltage, current and temperature measured at the current time are selected as the input vectors by sliding window method to improve the SOC accuracy. Finally, the effectiveness of the proposed model is verified under different driving conditions at different temperatures by comparing with other estimators. The comparison results indicate that the SOC estimation error(MAE) can be controlled within 1%, the root mean square error (RMSE) decreases from 0.89% to 0.22%, which are verified the effectiveness and robustness of the model.

Accurately estimating the state-of-charge (SOC) of battery is of particular importance for real-time monitoring and safety control in electric vehicles. To obtain better SOC estimation accuracy, a joint modeling method based on adaptive unscented Kalman filter(AUKF) and least-squares support vector machine(LSSVM) is proposed. This paper improves the accuracy of SOC estimation from four aspects. Firstly, the nonlinear relationship between SOC, current, and voltage is established by LSSVM. Secondly, a novel voltage estimation method based on improved LSSVM is proposed. Thirdly, the measurement equation of the novel AUKF is created by the improved LSSVM. Finally, the effectiveness of the proposed model is verified under different driving conditions. The comparison results show that the model can improve the accuracy of voltage and SOC estimation, and the SOC estimation error is controlled within 2%.

Accurate prediction of binary stock price movements remains a challenging task due to the coexistence of short-term noise and medium-term trend dynamics in financial time series. Existing recurrent models typically encode raw price sequences within a single representation stream and aggregate temporal information using softmax-based attention, which often entangles noisy fluctuations with underlying trends and limits nonlinear expressiveness in the final classification stage. In this paper, we propose PULSE-KAN (Price-aware Unified Linear-attention and Smoothed-trend Encoder with Kolmogorov–Arnold Network Head), a modular neural architecture designed to enhance binary stock movement prediction. The proposed framework introduces three plug-and-play components designed for LSTM-based pipelines as demonstrated here within the Adv-ALSTM framework. First, the P-EMA Trend Bridge constructs an explicit smoothed trend representation via a parameterized exponential moving average and fuses it with the raw price stream to improve trend awareness. Second, the Pola Pulse Router performs efficient temporal aggregation using linear-complexity polarized attention combined with local convolutional priors, enabling better capture of multi-scale temporal dependencies. Third, the KAN Signal Refiner replaces the conventional linear prediction head with learnable Chebyshev-polynomial activations, providing enhanced nonlinear modeling capacity for decision boundaries. Extensive experiments on two public benchmark datasets demonstrate that PULSE-KAN consistently outperforms strong recurrent and attention-based baselines in terms of both classification accuracy and the Matthews Correlation Coefficient. Further ablation studies verify that each proposed component contributes independently and significantly to the overall performance improvement.

With the continuous advancement of 3D perception technology, point cloud data has found increasingly widespread application. However, the presence of holes in point cloud data caused by device limitations and environmental interference severely restricts algorithmic performance, making point cloud completion a research topic of high interest. This study observes that most existing mainstream point cloud completion methods primarily focus on capturing global features, while often underrepresenting local structural details. Moreover, the generation process of complete point clouds lacks effective control over fine-grained features, leading to insufficient detail in the completed outputs and reduced data integrity. To address these issues, we propose a Set Combination Multi-Layer Perceptron (SCMP) module that enables the simultaneous extraction of both local and global features, thereby reducing the loss of local detail information. In addition, we introduce the Squeeze Excitation Pooling Network (SEP-Net) module, an informative channel attention mechanism capable of adaptively identifying and enhancing critical channel features, thus improving the overall feature representation capability. Based on these modules, we further design a novel Feature Fusion Point Fractal Network (FFPF-Net), which fuses multi-dimensional point cloud features to enhance representation capacity and progressively refines the missing regions to generate a more complete point cloud. Extensive experiments conducted on the ShapeNet-Part and MVP datasets compared to L-GAN and PCN showed average prediction error improvements of 1.3 and 1.4, respectively. The average completion errors on the ShapeNet-Part and MVP datasets are 0.783 and 0.824, highlighting the improved fine-detail reconstruction capability of our network. These results indicate that the proposed method effectively enhances point cloud completion performance and can further promote the practical application of point cloud data in various real-world scenarios.

Multi-sensor fusion represents a primary approach for enhancing environmental perception in intelligent transportation scenes. Among diverse fusion strategies, Bird’s-Eye View (BEV) perspective-based fusion methods have emerged as a prominent research focus owing to advantages such as unified spatial representation. However, current BEV fusion methods still face challenges with insufficient robustness in cross-modal alignment and weak perception of dynamic objects. To address these challenges, this paper proposes a Bidirectional Complementary Cross-Attention Module (BCCA), which achieves deep fusion of image and point cloud features by adaptively learning cross-modal attention weights, thereby significantly improving cross-modal information interaction. Secondly, we propose a Temporal Adaptive Fusion Module (TAFusion). This module effectively incorporates temporal information within the BEV space and enables efficient fusion of multi-modal features across different frames through a two-stage alignment strategy, substantially enhancing the model’s ability to perceive dynamic objects. Based on the above, we integrate these two modules to propose the Dual Temporal and Transversal Attention Network (DTTANet), a novel camera and LiDAR fusion framework. Comprehensive experiments demonstrate that our proposed method achieves improvements of 1.42% in mAP and 1.26% in NDS on the nuScenes dataset compared to baseline networks, effectively advancing the development of 3D object detection technology for intelligent transportation scenes.