Design for Motor Controller in Hybrid Electric Vehicle Based on Vector Frequency Conversion Technology

Motor and its control technology are one of the main components of Hybrid Electric Vehicle (HEV). To meet HEV's fast torque response, vector control algorithm based on rotor flux-oriented and simulation model is concerned and modular designs for controller's hardware and software are presented in the paper in order to build a platform to achieve the vector control of asynchronous induction motor. Analyze the controller's electromagnetic compatibility, introduce the corresponding antijamming measures to assure the normal operation of the electromagnetic sensitive devices such as CAN bus; experiment proves that the measure is practical and feasible. On the basis of the control logic correct, such as improving CAN bus communication reliability, assuring power-on sequence and fault treatment, carry on the motor bench experiment, test its static properties, and adjust the controller parameters. The experimental results show that the designed driving system has the performance of low speed and high torque, a wide range of variable speed and high comprehensive efficiency

A Weakly Supervised Propagation Model for Rumor Verification and Stance Detection with Multiple Instance Learning

The diffusion of rumors on microblogs generally follows a propagation tree structure, that provides valuable clues on how an original message is transmitted and responded by users over time. Recent studies reveal that rumor detection and stance detection are two different but relevant tasks which can jointly enhance each other, e.g., rumors can be debunked by cross-checking the stances conveyed by their relevant microblog posts, and stances are also conditioned on the nature of the rumor. However, most stance detection methods require enormous post-level stance labels for training, which are labor-intensive given a large number of posts. Enlightened by Multiple Instance Learning (MIL) scheme, we first represent the diffusion of claims with bottom-up and top-down trees, then propose two tree-structured weakly supervised frameworks to jointly classify rumors and stances, where only the bag-level labels concerning claim's veracity are needed. Specifically, we convert the multi-class problem into a multiple MIL-based binary classification problem where each binary model focuses on differentiating a target stance or rumor type and other types. Finally, we propose a hierarchical attention mechanism to aggregate the binary predictions, including (1) a bottom-up or top-down tree attention layer to aggregate binary stances into binary veracity; and (2) a discriminative attention layer to aggregate the binary class into finer-grained classes. Extensive experiments conducted on three Twitter-based datasets demonstrate promising performance of our model on both claim-level rumor detection and post-level stance classification compared with state-of-the-art methods.Comment: Accepted by SIGIR 202