Shift-scheduling calibration of automatic transmission (AT) vehicles is vital for both driving experience and automobile industry. Shifting schedules are usually calibrated with the consideration of fuel economy and drivability while neglecting the individual driving preference. In this work, we propose to exemplify the individual shifting preference by integrating the manual transmission (MT) shifting habit into the AT shift-scheduling calibration, where the habit is reflected as the shifting points and is available in most automated manual transmission (AMT) vehicles. The automated calibration of AT shifting schedules is directed by using the particle swarm optimization (PSO), during the virtual automobile cycle test, i.e., FTP-72. Candidate shifting-schedules are generated in the overlapped zone of MT shifting points and the space around the base map, and are evaluated on both shifting quality and fuel economy. Through iterations, the generated candidate shift schedules are tested and assessed until the overall performance reach the optimum. Experimental results are presented to show the effectiveness of the proposed method, which retains the shifting preferences as well as enhances the performance index by about 5%, 4%, and 2% for the drivers with aggressive, moderate, and mild styles, respectively.

Motion stability is vital for the industrial application of robotic manipulators; therefore, its evaluation becomes an important task for robot training. This paper proposes an evaluation system based on the scorecard model to evaluate the stability performance of manipulators. Firstly, the wavelet modulus maximum filtering method is employed to divide the vibration signal into structural vibration components and motion-generated vibration components. Secondly, the evaluation index (EI) is designed based on the processed data to judge whether the vibration signal of the moving process is stable. Finally, the scorecard model is proposed to evaluate the stability of vibration samples, including the process of attribute extraction, binning, WOE coding, feature selection, logistic regression, and score transformation. Additionally, an endpoint localization method is employed to segment the vibration signal of a continuously moving manipulator into single-step motion segments, making it possible to score the motion subprocesses. Experiments are conducted on physical robotic arms to verify the effectiveness of the proposed scheme, and the results show that the model can reasonably score the motion stability of robotic arms for robot training.