Driving cycle is an important indicator to evaluate vehicle performance and to measure fuel consumption. Its impact is not only on pollution emissions but also on asset management. Based on the real life detailed data from the city tour buses in Beijing, this paper proposes to couple a genetic algorithm based GA-K-means clustering with the Hidden Markov Model (HMM) to construct a city tour bus urban road driving cycle in Beijing. Compared to the standard driving cycle C-WTVC used for heavy commercial vehicle fuel consumption certification in China, our driving cycle model can reflect the real life situation more accurately. With our model, the influence of driving state and driving behavior on fuel consumption is also analyzed, and the fuel consumption estimation model under the corresponding driving cycle is constructed. To gain deeper insight and better fuel consumption, we further segment and construct the driving sub-cycles of peak and off-peak periods according to the difference in fuel consumption at different time periods in Beijing in September 2019. Through this fine-grain driving cycle, we can further improve the accuracy of the fuel consumption estimation model.

In the field of noncontact human-computer interaction, it is of crucial importance to distinguish different surface electromyography (sEMG) gestures accurately for intelligent prosthetic control. Gesture recognition based on low sampling frequency sEMG signal can extend the application of wearable low-cost EMG sensor (for example, MYO bracelet) in motion control. In this paper, a combination of sEMG gesture recognition consisting of feature extraction, genetic algorithm (GA), and support vector machine (SVM) model is proposed. Particularly, a novel adaptive mutation particle swarm optimization (AMPSO) algorithm is proposed to optimize the parameters of SVM; moreover, a new calculation method of mutation probability is also defined. The AMPSO-SVM model based on combination processing is successfully applied to MYO bracelet dataset, and four gesture classifications are carried out. Furthermore, AMPSO-SVM is compared with PSO-SVM, GS-SVM, and BP. The sEMG gesture recognition rate of AMPSO-SVM is 0.975, PSO-SVM is 0.9463, GS-SVM is 0.9093, and BP is 0.9019. The experimental results show that AMPSO-SVM is effective for low-frequency sEMG signals of different gestures.