The driving cycle is a speed-to-time curve, a fundamental technique in the automotive industry, and also a basis to set standards for fuel consumption and emissions of vehicles. A driving cycle is developed based on firsthand driving data collected from fieldwork. First, bad data in the original dataset are preprocessed, the time-series standard smoothing algorithm is used to smoothen the data, and Lagrange’s interpolation is used to realize data interpolation. Next, the rules for kinematic fragment extraction are set to divide the data into kinematic fragments. Last, an evaluation system of kinematic fragment feature parameters is built. On that basis, the K-means clustering method is used to cluster the dimensionally reduced data, and the adaptive mutation particle swarm optimization (AMPSO) algorithm is employed to select the optimal fragments from candidate fragments to develop a driving cycle. The experiment result shows that the developed driving cycle can represent the kinematic features of the experiment car and provides a basis for the development of a driving cycle for Fuzhou.

In this work, we present a novel method to intention recognition, based on electroencephalogram (EEG) and eye movement in human-computer interaction(HCI). The fusion of EEG and eye movement will allow the utmost of the advantages of the two physiological signals. Signals of EEG and eye movement were collected for feature extraction, recognition network of machine learning pattern was input for intent recognition, final recognition result was attained by decision-level fusion.We compare the results of the Intention Recognition Algorithms to those of an experiment involving the intention recognition of the operator in a simulated flight mission. In most every case, results show that the intention recognition algorithms performed better than solely rely on single signal.

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There is a strong correlation between the composition of Deinagkistrodon acutus venom proteins and their potential pharmacological effects. The proteomic analysis revealed 103 proteins identified through label-free proteomics from 30 different snake venom families. Phospholipase A2 (30.0%), snaclec (21.0%), antithrombin (17.8%), thrombin (8.1%) and metalloproteinases (4.2%) were the most abundant proteins. The main toxicity of Deinagkistrodon acutus venom is hematotoxicity and neurotoxicity, and it acts on the lung. Deinagkistrodon acutus venom may have anticoagulant and antithrombotic effects. In summary, the protein profile and related toxicity and pharmacological activity of Deinagkistrodon acutus venom from southwest China were put forward for the first time. In addition, we revealed the relationship between the main toxicity, pharmacological effects, and the protein components of snake venom.

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