Flux Reversal Machine Design

Flux reversal permanent magnet machines (FRPMMs) have a simple reluctance rotor and a stator with armature windings and permanent magnets (PMs). Due to the high torque density and high efficiency of FRPMMs, they have been widely used in many applications such as electric vehicle, wind power generation, etc. However, the general design method of FRPMMs has not been established in books. Therefore, this chapter will focus on introducing an analytical design method, which allows for fast design of FRPMMs. First of all, the analytical sizing equations are deduced based on a magneto motive force (MMF)-permeance model. After that, the effects of some key performances including average torque, pulsating torque, power factor, and PM demagnetization are analyzed. Moreover, the feasible slot-pole combinations are summarized and the corresponding winding type of each combination is recommended in order to maximize the output torque. Besides, the detailed geometric design of stator and rotor are presented. Finally, a case study is presented to help readers better understand the introduced design methodology

Compliments to Accomplishments: The Effect of Compliments by Digital Platforms on Consumer Behavior

When shopping online, consumers sometimes hesitate, for example, because they are uncertain about product quality, or they do not know whether the price is reasonable. In the offline shopping context, sellers can encourage purchases by complimenting consumers. This study aims to explore how digital platforms can adopt the compliment tactic to catalyze consumers’ purchase decisions. We hypothesize that online compliments, like offline compliments, can effectively reduce consumers’ uncertainties in online shopping and thus encourage purchases. We plan to run a lab experiment to test the hypothesis. This study enhances previous research on offline compliments and contributes to e-commerce research by providing causal evidence of how digital platforms can use compliments to influence consumer behavior

Passive detection of moving aerial target based on multiple collaborative GPS satellites

Passive localization is an important part of intelligent surveillance in security and emergency applications. Nowadays, Global Navigation Satellite Systems (GNSSs) have been widely deployed. As a result, the satellite signal receiver may receive multiple GPS signals simultaneously, incurring echo signal detection failure. Therefore, in this paper, a passive method leveraging signals from multiple GPS satellites is proposed for moving aerial target detection. In passive detection, the first challenge is the interference caused by multiple GPS signals transmitted upon the same spectrum resources. To address this issue, successive interference cancellation (SIC) is utilized to separate and reconstruct multiple GPS signals on the reference channel. Moreover, on the monitoring channel, direct wave and multi-path interference are eliminated by extensive cancellation algorithm (ECA). After interference from multiple GPS signals is suppressed, the cycle cross ambiguity function (CCAF) of the signal on the monitoring channel is calculated and coordinate transformation method is adopted to map multiple groups of different time delay-Doppler spectrum into the distance−velocity spectrum. The detection statistics are calculated by the superposition of multiple groups of distance-velocity spectrum. Finally, the echo signal is detected based on a properly defined adaptive detection threshold. Simulation results demonstrate the effectiveness of our proposed method. They show that the detection probability of our proposed method can reach 99%, when the echo signal signal-to-noise ratio (SNR) is only −64 dB. Moreover, our proposed method can achieve 5 dB improvement over the detection method using a single GPS satellite

Fused Text Segmentation Networks for Multi-oriented Scene Text Detection

In this paper, we introduce a novel end-end framework for multi-oriented scene text detection from an instance-aware semantic segmentation perspective. We present Fused Text Segmentation Networks, which combine multi-level features during the feature extracting as text instance may rely on finer feature expression compared to general objects. It detects and segments the text instance jointly and simultaneously, leveraging merits from both semantic segmentation task and region proposal based object detection task. Not involving any extra pipelines, our approach surpasses the current state of the art on multi-oriented scene text detection benchmarks: ICDAR2015 Incidental Scene Text and MSRA-TD500 reaching Hmean 84.1% and 82.0% respectively. Morever, we report a baseline on total-text containing curved text which suggests effectiveness of the proposed approach.Comment: Accepted by ICPR201