Ground segmentation on the 3D point cloud is fundamental to many applications, such as SLAM and object segmentation. As it is usually a preprocessing module of these applications, high efficiency and accuracy are the basic requirements for guaranteeing the whole system’s performance. To this end, we avoid ground fitting and region division on the 3D point cloud. We propose a pixel-wise image-based method named DipG-Seg, which projects the 3D point cloud onto two cylindrical images, horizontal range-and z-images, then segments based on them. To realize fast and accurate ground segmentation, we first introduce innovative designs for image-based features. Specifically, we improve the slope feature with consideration of the LiDAR model and propose combining features with different sizes of receptive fields for better recognition of the ground. Then, based on these features, we devise a pre-segmenting pattern for pixel-wise classification. For fine segmentation, we devise a hierarchical refinement framework integrating a nonlinear filter and majority-vote kernel-based convolution, which is demonstrated to enhance the accuracy by over 7% on the basis of pre-segmenting. Comprehensive experiments were conducted on a real-world platform, SemanticKITTI, and nuScenes datasets. The results have demonstrated that our method can achieve an accuracy of 94.41% and a speed of 127 Hz on 64-beams LiDAR, outperforming the state-of-the-art methods and guaranteeing competitive robustness. Our method will be available at: https://github.com/EEPT-LAB/DipG-Seg.

Power factor correction (PFC) converters with a diode bridge are extensively used in different AC-DC applications. However, the diode bridge has to employ four diodes to complete the AC to DC at the expense of high conduction losses. Thus, many bridgeless PFC converters have been proposed with dual converter cells to minimize the number of conducted diodes for better efficiency. Unfortunately, these dual-converter cell-based bridgeless converters need almost double components. To solve this issue, this paper proposes a true bridgeless buck-type PFC converter, which annihilates the diode bridge completely with only fewer component counts. The proposed converter uses buck and buck-boost cells to obtain the novel bridgeless topology, which features a simple structure and control to achieve high PF and low total harmonics of input current (THDi). Simulations are given to validate the feasibility of the proposed topology and the control method. Comparisons with the conventional buck PFC converter are also given to confirm the better performances of the proposed topology.

Electric vehicles (EVs) can act as virtual energy routers (VERs) in the grid, giving them the flexibility to change the direction of energy flow. Therefore, a day-ahead scheduling for radial microgrids deploying EV-based VERs is proposed. In the day-ahead scheduling, the charging/discharging operation, state of charge (SOC), and available time of EV-based VERs are involved in the social utility maximization problem. With the laxity model of EV-based VERs and the forecasted reference demand, the supply and demand in the microgrid are optimized to minimize generation costs and maximize consumer utility in a whole day. Binary variables, which indicate the charging and discharging choices of the EV-based VERs, exist in the day-ahead scheduling. Therefore, mixed integer nonlinear programming (MINLP) is adopted to solve the optimization problem. The simulation cases are then provided to verify the effectiveness of the suggested day-ahead scheduling approach.

Axial-flux permanent magnet (AFPM) machines are welcomed widely in many applications thanks to the high torque density and compactness. In the AFPM machine, the concentrated winding and distributed winding have different characteristics. In order to increase the winding factor and reduce the winding ends, a novel concentric winding AFPM machine is proposed in the paper. The stator of the proposed machine is divided into three circles from the inner side to the outer side. Then, the three-phase windings are arranged on the three circles, respectively. The phase angle difference is achieved through the mechanical offset between the teeth of three circles. Thus, the winding factor can be kept as 1 with a few winding ends by adjusting the pole-slot combination. In addition, the staggered teeth between the three circles can also reduce the cogging torque. In the optimization, the back EMFs of the three phases are nearly balanced. 3D finite element analysis also shows that the machine has a strong overload capability.

The application of wireless power transfer (WPT) in electric vehicles (EVs) has brought great convenience, safety, and flexibility to EV owners. Traditional EV charging converters can only support wired charging or wireless charging with low integration. In order to realize both wired and wireless charging functions in the same system, excessive power switches will be utilized, resulting in a redundant structure and low power density. To solve this problem, this paper proposes a novel multi-functional converter for EV charging. Using one set of power switches, the proposed converter can output DC voltage for wired charging or high-frequency AC voltage for wireless charging. Circuit topology and control method are discussed and analyzed. Finally, simulations in MATLAB/SIMULINK are conducted to verify the effectiveness of the proposed multi-functional converter.

Axial flux permanent magnet (AFPM) machines are welcomed in industries due to the compactness. In order to improve the fault-tolerant capability and decrease phase voltage level, a dual three-phase dual stator AFPM machine with mechanical offset is proposed. In this paper, a theoretical analysis is conducted for the harmonics of the proposed machine. The mechanical offset mainly affects the amplitudes of harmonics. Then, a 3D finite element analysis is performed to verify the theoretical analysis. The orders of main air-gap flux density harmonics are related to the number of rotor pole pairs and teeth, but the harmonics have little influence in the back electromotive force. As for the axial force density, the orders of main harmonic components are the two times number of stator pole pairs, rotor pole pairs, and teeth. The results are consistent with the theoretical analysis and in-depth discussion.

A k-winner-take-all (kWTA) network is able to find out the k largest numbers from n inputs. Recently, a dual neural network (DNN) approach was proposed to implement the kWTA process. Compared to the conventional approach, the DNN approach has much less number of interconnections. A rough upper bound on the convergence time of the DNN-kWTA model, which is expressed in terms of input variables, was given. This brief derives the exact convergence time of the DNN-kWTA model. With our result, we can study the convergence time without spending excessive time to simulate the network dynamics. We also theoretically study the statistical properties of the convergence time when the inputs are uniformly distributed. Since a nonuniform distribution can be converted into a uniform one and the conversion preserves the ordering of the inputs, our theoretical result is also valid for nonuniformly distributed inputs.