This passage elucidates a research endeavor focused on addressing electromagnetic vibration challenges within permanent magnet synchronous motors. The narrative commences with an analytical derivation of the radial electromagnetic force expression in permanent magnet synchronous motors, employing the Maxwell tensor method. A comprehensive summary detailing the orders and frequencies of the electromagnetic force is presented. Subsequently, a spatiotemporal order table is introduced, encapsulating the principal electromagnetic forces in an 8-pole 48-slot motor. The research methodology involves a meticulous simulation analysis on an internally configured permanent magnet synchronous motor featuring an 8-pole 48-slot design. Various magnetic barrier structures are systematically modeled utilizing finite element analysis. The ensuing investigation explores the influence of diverse magnetic barrier structures on the amplitudes of radial electromagnetic forces, specifically focusing on (0th, 6f0) and (0th, 12f0) frequencies. In comparison to the prototype motor, the enhanced motor demonstrates significant improvements in performance. Specifically, at the rated speed of 3000 rpm, there is a 7.96% reduction in the 10f0 noise and a 16.293% reduction in the 12f0 noise. At a speed of 6000 rpm, the 6f0 noise decreases by 2.243%, and the 14f0 noise decreases by 17.661%. Furthermore, the torque pulsation coefficient experiences a substantial reduction of 33.76%.
In this paper, we propose a new method for driving style recognition on the basis of SAX and bitmap technology. This method converts the sensor signal into a discrete symbol sequence and uses a bitmap for feature extraction. The weight matrix is generated based on the vector space model (VSM) to characterize the driving style fingerprint. Different from the traditional driving feature representation method, we propose a new concept of using bitmap to represent driving fingerprints. Given the sensor data captured by the vehicle during natural driving, we regard driving style recognition as a time series classification task and divides it into three classifications: normal driving, aggressive driving and drowsy driving. We evaluate our proposed model on the open natural driving behavior classification dataset UAH-DriveSet. Compared with traditional classification methods, our proposed model achieves the most advanced results on UAH-DriveSet, achieving 83% and 95% F1-measure scores in motorway and secondary road, which is more than 20% higher than the closest comparison method.
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