Public environmental sentiment has always played an important role in public social sentiment and has a certain degree of influence. Adopting a reasonable and effective public environmental sentiment prediction method for the government’s public attention in environmental management, promulgation of local policies, and hosting characteristics activities has important guiding significance. By using VAR (vector autoregressive), the public environmental sentiment level prediction is regarded as a time series prediction problem. This paper studies the development of a mobile “impression ecology” platform to collect time spans in five cities in Lanzhou for one year. In addition, a parameter optimization algorithm, WOA (Whale Optimization Algorithm), is introduced on the basis of the prediction method. It is expected to predict the public environmental sentiment more accurately while predicting the atmospheric environment. This paper compares the decision performance of LSTM (Long Short-Term Memory) and RNN (Recurrent Neural Network) models on the public environment emotional level through experiments, and uses a variety of error assessment methods to quantitatively analyze the prediction results, verifying the LSTM’s performance in prediction performance and level decision-making effectiveness and robustness.

A novel modular-stator outer-rotor flux-switching permanent-magnet (MSOR-FSPM) motor is proposed and studied in this paper. Structure, operation and design principles of the MSOR-FSPM motor are introduced and analyzed. Considering that the combination of different pole number and slot number has a great influence on the motor performance, the optimum rotor pole number for the 12-stator-slot MSOR-FSPM motor is researched to obtain good performance and make full use of the space in the MSOR-FSPM motor. The influences of rotor pole number on cogging torque, torque ripple and electromagnetic torque are analyzed and a 12-slot/10-pole MSOR-FSPM motor was chosen for further study. Then, several main parameters of the 12-slot/10-pole MSOR-FSPM motor were optimized to reduce the torque ripple. Finally, the utilization of permanent magnet (PM) in the MSOR-FSPM motor and a conventional outer-rotor flux-switching permanent-magnet (COR-FSPM) motor are compared and analyzed from the point of view of magnetic flux path, and verified by the finite element method (FEM). The FEM results show that the PM volume of MSOR-FSPM motor is only 54.04% of that in a COR-FSPM motor, but its average electromagnetic torque can reach more than 75% of the torque of COR-FSPM motor.

This paper is motivated by the need for wheels-off time prediction required for improving departure scheduling. It will be possible to estimate wheels-off time with high precision at major U. S. airports where airport surface automation, such as the Surface Management System, is deployed. At other airports, controllers are expected to estimate wheels-off time for departure scheduling. This paper analyzes Dallas-Fort Worth airport state data metrics derived from the Aviation System Performance Metrics database and Surface Management System logs for use as inputs to a neural network for predicting wheels-off time at airports where the Surface Management System will not be available. At airports without Surface Management System, these metrics will be either directly available to controllers or can be computed using combinations of flight-plan data, taxi clearance data, surface surveillance data, airline provided Out-Off-On-In (OOOI) data and airport surface geometry data. Analysis of metrics derived from Aviation System Performance Metrics database and Surface Management System logs show that there is a high degree of correlation between these metrics and gate to wheels-off time. Correlations of these metrics with gate departure delay were found to be quite low, so no attempt was made to predict gate departure delay using these metrics. This study assumed the gate departure time to be known for estimating wheels-off time. Gate to wheels-off time is predicted using the neural network with chosen metrics as inputs. The neural network is trained with six days of data and tested on one day of data. Results show that the trained neural network performance on the test data is as good as on the training data. The main finding of the study is that a neural network trained on several days of airport state data was able to generate gate to wheels-off time predictions within the Call for Release departure compliance window of two-minutes early to one-minute late 59% of the time. This performance can be improved further by removing outliers in the training and test sets. Next, a linear model was created with the same set of metrics as independent variables and gate to wheels-off time as the dependent variable. Six days of data were used with the least-squares method to compute the coefficients. These coefficients were then used with one day of test data to estimate the gate to wheels-off time. Results were found to be comparable to that generated by the neural network based on the Mahalanobis distance metric. Comparison of predictions generated using the neural network and the linear model to the test data provide evidence that the chosen metrics are suitable for predicting gate to wheels-off time.

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This paper presents the comparative studies of two permanent magnet (PM) motors, which are modular-stator outer-rotor flux-switching permanent-magnet (MSOR-FSPM) motor and conventional outer-rotor flux-switching permanent-magnet (COR-FSPM) motor. The differences in structure and design principles between the two types are compared. Then, a 2D finite element method (FEM) is used to analyze the basic electromagnetic performances. The magnetic field distribution, no-load back-EMF, cogging torque, and electromagnetic torque are presented and compared. Furthermore, a comprehensive comparison of the motor performances which are important for electric vehicles is investigated. The theoretical analysis and FEM results predict that the MSOR-FSPM motor has superior capability compared to that of the COR-FSPM motor, including fault tolerance capability, and field weakening capability.INDEX TERMS Electric vehicles (EVs), flux-switching, permanent-magnet motor, modular-stator, outer-rotor.