Sportsman’s Mental State Evaluation and Early Warning Method Using Decision Tree

Aiming at the intelligent needs of psychological state assessment of university students, the text information-based psychological problem identification approach is investigated in the paper. This approach uses the text of student forums within universities as the database and introduces the convolutional neural network (CNN) model in deep learning, which contains a convolutional layer, a pooling layer, and a fully connected layer. After the convolution is completed, the convolution result is de-linearized by the activation function, and then pooling is performed to improve the fitting ability of the network for nonlinearities. For data processing, behavioral features attribute features, content features, and social relationship features are extracted from text information as the input of the CNN by using the decision tree. The psychological lexicon of expertise (LIWC) is used to enhance the efficiency of text word frequency statistics when performing text content extraction. To evaluate the performance of the proposed method, simulations are performed in the open dataset of CLPsyh2017 ReachOut Forum, and the FastText method is used as a comparison. The results show that the CNN model achieves an accuracy of 0.71 in the full-sample domain, which is significantly higher than that of the FastText model at 0.64. In the early warning evaluation of mental states, the CNN performance is better than that of FastText.

State evaluation of some Hydrangea macrophylla (Thunb.) varieties after winter hardiness in the agrometeorological conditions of central Russia

Hydrangea macrophylla (Thunb.) Ser. is one of the most popular ornamental crops, used in landscape design. This article represents the study of the variety resistance of species and influence their of climatic change. In that sense the effective technology of plant shelter for the winter hardiness can be considered as a successful important factor for the promotion of this crop in northern area. Wherefore we consider in a comparative analysis of H. macrophylla varieties Forever & Ever Blue and Peppermint, which we carried out after winter hardiness in the conditions of central Russia. The most reliable and proven way to shelter for winter of hydrangea is the shelter of lutrasil, which contributed to an increase in the viability of shoots and the amount awakened buds, and also provided the better growth of shoots during the month. The highest percentage of overwintered shoots (77,8%), the amount of vegetative and generative buds (19,3±1,3 сm и 9,6±0,7 сm., respectively) and the total growth of shoots in spring (10,2 cm) were characteristic for variety Forever & Ever Blue for using lutrasil as a shelter. Keywords: HYDRANGEA MACROPHYLLA, VARIETIES, WINTER HARDINESS, FROST RESISTANCE, VIABILITY, SHELTER

A Principal Component Analysis Control Chart Method for Catenary Status Evaluation and Diagnosis

To make accurate and comprehensive evaluation of the catenary and diagnose the causes of the catenary fault, a method of catenary state evaluation and diagnosis based on the principal component analysis control chart was proposed, which can make full use of the multidimensional detection parameters of the catenary. The principal component analysis was used to reduce the dimension of catenary parameters, the principal component T2 control chart was calculated to show the change of principal component of catenary state data, the residual SPE control chart was calculated to show the change of their correlation, and the contribution rate control chart was calculated to show the cause of abnormal state data. The method can not only transform the multidimensional detection parameters of the catenary into a statistic to realize the simple and intuitive evaluation of the catenary state but also can accurately determine the cause of the abnormal state, so as to provide technical support for the targeted condition-based maintenance of the catenary.

A Decision-Making Model for Self-Driving Vehicles Based on Overtaking Frequency

The driving state of a self-driving vehicle represents an important component in the self-driving decision system. To ensure the safe and efficient driving state of a self-driving vehicle, the driving state of the self-driving vehicle needs to be evaluated quantitatively. In this paper, a driving state assessment method for the decision system of self-driving vehicles is proposed. First, a self-driving vehicle and surrounding vehicles are compared in terms of the overtaking frequency (OTF), and an OTF-based driving state evaluation algorithm is proposed considering the future driving efficiency. Next, a decision model based on the deep deterministic policy gradient (DDPG) algorithm and the proposed method is designed, and the driving state assessment method is integrated with the existing time-to-collision (TTC) and minimum safe distance. In addition, the reward function and multiple driving scenarios are designed so that the most efficient driving strategy at the current moment can be determined by optimal search under the condition of ensuring safety. Finally, the proposed decision model is verified by simulations in four three-lane highway scenarios. The simulation results show that the proposed decision model that integrates the self-driving vehicle driving state assessment method can help self-driving vehicles to drive safely and to maintain good maneuverability.

Study on the safety speed of vehicle turning with considering the driving safety evaluation

Safety speeds estimation, as an indispensable link in the development of the aided or automatic drive, receives wide attention recently. Due to uncertain disturbances of vehicle driving, it is a challenging task to estimate the safety speed credibly. This work proposes a modified estimation approach to predict the turning safety speed by combining the static drive safety evaluation with the dynamic vehicle speed calculation. First, the driving safety state is evaluated considering the coupling of driver-vehicle-road-environment, where a comprehensive evaluation is obtained by combing the analytic hierarchy process and entropy weight analysis method. Then, a turning critical speed is calculated based on the vehicle driving dynamics considering both sideslip and rollover. The estimation of turning safety speed is achieved by modifying the critical speed with a safety correction factor obtained from the driving safety state evaluation. Finally, cases discussion on driving safety states evaluation, as well as the critical speed verification and safety speed analysis, are carried out. The results verify the validity of the driving safety state evaluation and critical speed calculation. The safety speeds have a reasonable safety margin according to the driving safety state evaluation. The maximum differences between the safety speeds and critical speeds are about 26.45% for buses and 26.39% for cars under low adhesion conditions, showing sufficient reliability for safety speed estimation.