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In order to explore the structural characteristics of Chinese aviation and high-speed rail integrated transportation network, cities was treated as nodes and established three weighted networks: a high-speed rail sub-network, an aviation sub-network, and a comprehensive air-rail transportation network by connecting cities with flight or high-speed rail services, with flight numbers and highspeed rail numbers as weights. Firstly,mean correlation, triangle betweenness centrality, and weighted harmonic centrality were used to analyze key nodes in different networks. Secondly,clustering coefficients and average shortest path lengths were used to analyze the small-world properties of the three networks. Thridly,the Newman algorithm was used to study the community structure of the three networks. Lastly,weighted network efficiency was used as an indicator to compare the robustness of the three networks. The results show that Beijing, Shanghai, Guangzhou, and Shenzhen ranked high in all three weighted centrality measures and were the most critical nodes in all three networks. All three networks had small-world properties, but only the high-speed rail sub-network had a modularity value greater than 0. 3, indicating significant community structures, which were divided into ten communities. The comprehensive airrail transportation network had a better weighted network efficiency, approximately 0. 32, which was the highest among the three networks. It also had the strongest robustness and the smallest change in weighted efficiency when nodes failed. It is concluded that the comprehensive air-rail transportation network integrates the complementary advantages of the high-speed rail and aviation networks and has better structural characteristics than single transportation networks. [ABSTRACT FROM AUTHOR]

Urban logistics unmanned aerial vehicle(UAV) path planning is a core content of UAV mission planning system. In order to realize the path planning problem of logistics UAVs safely and efficiently, firstly, the environment modeling was carried out by using grid method. Considering the performance limitations of UAVs, the path planning model of multi-constraint logistics UAVs was established by taking the shortest path length, the height variation of UAVs and the minimum grid risk as targets. Secondly, in view of the problems existing in the traditional particle swarm optimization algorithm, Singer mapping was introduced to improve the initial particle distribution, linear adjustment of acceleration coefficients and maximum velocity, new updating strategy of particle position, and dynamic adjustment of inertia weight, and the improved particle swarm optimization algorithm was applied to solve the model. Finally, an example is given for simulation analysis. When the grid size is 5 meters, the path nodes are 5 and the cost function weights are 0. 1, 0. 4 and 0. 5 respectively, compared with the other four algorithms, the total generation value of the proposed algorithm is the best, which is reduced by 44. 5%, 3. 5%, 42. 8% and 30%, respectively. The results show that the model and algorithm in this paper are feasible and effective for UAV path planning. [ABSTRACT FROM AUTHOR]

The degree of mental workload is closely related to the work efficiency and the allocation of human resources and the occurrence of accidents of man-machine operation. Therefore, it is of great significance to study the state of operators􀆳 mental workload. In order to solve the problem that the classification effect of the existing mental workload recognition methods is poor due to the small number of samples in the training set, a cross-subject mental workload classification method based on instance selection for visual and operational task was proposed. Firstly, referring to a small amount of historical data of the target subject, an adaptive source domain training set was obtained by instance selection of the training set data of the electroencephalogram (EEG) data of other subjects, thereby the number of samples and the domain difference between the training sets and test sets was reduced. Secondly, principal component analysis was used to reduce the feature dimension of the adaptive training set and target test set. Finally, the adaptive training set principal component was used to establish a support vector machine classification model to identify the mental workload state of the test set samples. The results show that this method can improve classification efficiency while improving classification accuracy, and achieve fast and accurate mental workload state recognition. [ABSTRACT FROM AUTHOR]

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Recognition of mental workload is considered to be of great significance to improve the work efficiency of operators and reduce human accidents. However, because the electroencephalogram (EEG) signal is collected by a multi-channel EEG cap and distributed in various frequency bands, the high complexity of the subsequent recognition model is attributed to the high dimension features obtained by feature extraction. In this regard, principal component analysis (PCA) is usually used to perform dimensionality reduction processing on high-dimensional feature vectors, but it is difficult to determine the value of the dimensionality reduction dimension. An adaptive dimensional optimization method based on principal component analysis was proposed. The high-dimensional features in the experimental data set were reduced to each dimension within the optimized dimension range using principal component analysis, and the classification accuracy of each dimension was plotted as a dimension-classification accuracy curve. The optimal dimensionality reduction dimension of the experimental dataset was determined by identifying the “elbow” of the curve, and this dimension was applied to the same experimental data set and then the mental workload recognition was performed on it. The results show that the method can accurately identify the optimal dimensionality reduction dimension commonly used in the same experimental data set, and effectively improve the recognition efficiency. [ABSTRACT FROM AUTHOR]