This paper numerically investigates the effects of airfoil leading edge radius on the aerodynamic characteristics of H-rotor Darrieus vertical axis wind turbine (VAWT). 10 modified airfoils are generated by changing the leading edge radius of the base NACA 0015 airfoil from 1%c to 9%c, respectively. A 2D unsteady Computational Fluid Dynamics (CFD) model is established and validated with the previously published experimental data. The power, torque, and flow field characteristics of the rotors are analyzed. The results indicate that the maximum and minimum power coefficient at the optimum tip speed ratio (TSR) are obtained for the LE-5%c and LE-1%c model, respectively. The best aerodynamic characteristics are determined by the LE-5%c model below the optimum TSR and the LE-3%c model beyond the optimum TSR. The torque characteristics and pressure distribution for the single blades with different airfoil leading edge radius show an obvious difference in the upwind region and a very small difference in the downwind region. Moreover, the airfoil leading edge radius influences the strength, region, and diffusion rate of the vortices, being the main reason for the observed differences in instantaneous torque coefficient and power coefficient. The vortices of the LE-1%c model are stronger, larger, and diffuse slower than those of the LE-2%c and LE-5%c model at the optimum TSR.

State-of-charge (SoC) estimation is one of the core functions of battery energy management systems. An accurate SoC estimation can guarantee the safe and reliable operation of the batteries system. In order to overcome the practical problems of low accuracy, noise uncertainty, poor robustness, and adaptability in parameter identification and SoC estimation of lithium-ion batteries, this paper proposes a joint estimation method based on the adaptive extended Kalman filter (AEKF) algorithm and the adaptive unscented Kalman filter (AUKF) algorithm in multiple time scales for 18,650 ternary lithium-ion batteries. Based on the slowly varying characteristics of lithium-ion batteries’ parameters and the quickly varying characteristics of the SoC parameter, firstly, the AEKF algorithm was used to online identify the parameters of the model of batteries with a macroscopic time scale. Secondly, the identified parameters were applied to the AUKF algorithm for SoC estimation of lithium-ion batteries with a microscopic time scale. Finally, the comparative simulation experiments were implemented, and the experimental results show the proposed joint algorithm has higher accuracy, adaptivity, robustness, and self-correction capability compared with the conventional algorithm.

The point clouds registration is a key step in data processing for the 3D laser scanner to obtain complete information of the object surface, and there are many algorithms. In order to overcome the disadvantages of slow calculation speed and low accuracy of existing point clouds registration algorithms, a fast point clouds registration algorithm based on the improved voxel filter and ISS-USC feature is proposed. Firstly, the improved voxel filter is used for down-sampling to reduce the size of the original point clouds data. Secondly, the intrinsic shape signature (ISS) feature point detection algorithm is used to extra feature points from the down-sampled point clouds data, and then the unique shape context (USC) descriptor is calculated to describe the extracted feature points. Next, the improved random sampling consensus (RANSAC) algorithm is used for coarse registration to obtain the initial position. Finally, the iterative closest point (ICP) algorithm based on KD tree is used for fine registration, which realizes the transform from the point clouds scanned by the 3D laser scanner at different angles to the same coordinate system. Through comparing with other algorithms and the registration experiment of the VGA connector for monitor, the experimental results verify the effectiveness and feasibility of the proposed algorithm, and it has fastest registration speed while maintaining high registration accuracy.

In order to improve robust operating performance and enhance bus voltage stability, a learning observer-based fault-tolerant control strategy is proposed for the distributed generation in islanded microgrid with sensor faults and uncertain disturbances. Firstly, the output feedback control theory and the linear matrix inequality method are used to design closed-loop controller for the voltage source inverter of distributed generation; secondly, a fault-tolerant model and control structure of the distributed generation in an islanded microgrid with sensor faults is analyzed. By employing the fault output signal conversion filter and proportional derivative type learning observer, the online estimation and real-time compensation of the sensor fault signal are realized. Thirdly, the system synthesis of output feedback control and fault-tolerant control is completed. Finally, the multi-scenario sensor fault scheme simulation experiment verifies that the proposed control strategy has strong sensor fault tolerance and adaptability.

This paper presents a novel unsupervised learning framework for estimating scene depth and camera pose from video sequences, fundamental to many high-level tasks such as 3D reconstruction, visual navigation, and augmented reality. Although existing unsupervised methods have achieved promising results, their performance suffers in challenging scenes such as those with dynamic objects and occluded regions. As a result, multiple mask technologies and geometric consistency constraints are adopted in this research to mitigate their negative impacts. Firstly, multiple mask technologies are used to identify numerous outliers in the scene, which are excluded from the loss computation. In addition, the identified outliers are employed as a supervised signal to train a mask estimation network. The estimated mask is then utilized to preprocess the input to the pose estimation network, mitigating the potential adverse effects of challenging scenes on pose estimation. Furthermore, we propose geometric consistency constraints to reduce the sensitivity of illumination changes, which act as additional supervised signals to train the network. Experimental results on the KITTI dataset demonstrate that our proposed strategies can effectively enhance the model’s performance, outperforming other unsupervised methods.

Deep convolutional neural networks (CNNs) have significantly enhanced image classification in the past decade. This paper proposes Multi-branch Cascade Receptive Field Residual Networks (MCRF-ResNets) based on the original Residual Network (ResNet) architecture for classification and object detection. MCRF-ResNets incorporate multiple branches with different receptive field (RF) sizes to improve image classification performance. Each MCRF residual block contains a 5 × 5 and a 3 × 3 RF-sized convolution block. The 5 × 5 and 3 × 3 RF-sized blocks recognize larger and smaller objects, respectively. Group convolutions are used to reduce redundancy and balance feature extraction and parameter usage in the multiple branches. The proposed model approach demonstrates significant improvement in model performance on CIFAR-10 and CIFAR-100 datasets and a subset of the ImageNet 2012 dataset. The model achieves a 2.8% increase in top-1 accuracy compared with the baseline ResNet-50 model with a similar number of parameters on the subset of the ImageNet 2012 dataset. In addition, the proposed method shows good object detection on Pascal VOC and MS COCO datasets.