Due to outstanding feature extraction ability, neural networks have recently achieved great success in sentiment analysis. However, one of the remaining challenges of sentiment analysis is to model long texts to consider the intrinsic relations between two sentences in the semantic meaning of a document. Moreover, most existing methods are not powerful enough to differentiate the importance of different document features. To address these problems, this paper proposes a new neural network model: AttBiLSTM-2DCNN, which entails two perspectives. First, a two-layer, bidirectional long short-term memory (BiLSTM) network is utilized to obtain the sentiment semantics of a document. The first BiLSTM layer learns the sentiment semantic representation from both directions of a sentence, and the second BiLSTM layer is used to encode the intrinsic relations of sentences into the document matrix representation with a feature dimension and a time-step dimension. Second, a two-dimensional convolutional neural network (2DCNN) is employed to obtain more sentiment dependencies between two sentences. Third, we utilize a two-layer attention mechanism to distinguish the importance of words and sentences in the document. Last, to validate the model, we perform an experiment on two public review datasets that are derived from Yelp2015 and IMDB. Accuracy, F1-Measure, and MSE are used as evaluation metrics. The experimental results show that our model can not only capture sentimental relations but also outperform certain state-of-the-art models.

The most significant assumption of conventional analytical methods (e.g., the subdomain technique) is that the stator and rotor iron cores are infinitely permeable. The magnetic saturation is ignored, resulting in an overestimation of the flux density. This paper presents an analytical model of permanent magnet (PM) machines with segmented Halbach arrays (PMMSHA). It considers the finite magnetic permeabilities of the stator teeth and tooth tips (STTT) and can study the tooth saturation effect. The permeability variation in the slots and teeth is solved via Cauchy’s product theorem. Based on magnetic vector potential and the Maxwell-Fourier method, Poisson’s and Laplace’s equations in the stator teeth/slots, tooth tips/slot openings, air gap, and PMs regions are solved. Consequently, the electromagnetic performance, including the magnetic field distribution in all regions, exciting force density, cogging torque, and electromagnetic torque, is calculated. The results of finite element analysis (FEA) show that the analytical model is highly accurate. Furthermore, the influences of the permeability of the STTT and the number of blocks per pole (NBPP) on the performance of the PMMSHA are studied.

Accurate calculation of magnetic field distribution is a prerequisite for motor design and optimization. This paper develops an analytical subdomain model of external rotor in-wheel motor with surface-inset magnets (ERIWMSIM). Besides, to weaken its torque ripple, an improved topology by integrating magnetic flux modulation slots (MFMS) into tooth tips is proposed. First, the entire field problem is divided into five subdomains, viz., magnet rotor slots, air gap, stator slots, stator slot openings, and MFMSs. Then, this study solves Laplace’s or Poisson’s equation of magnetic vector potential in each subdomain according to the type of excitation source and use the boundary conditions of adjacent subdomains to determine the magnetic field solution. Consequently, the electromagnetic performances such as air gap flux density, back-electromotive (EMF), cogging torque, and on-load output torque are accurately predicted. The analytical results are shown in line with those obtained from finite element analysis (FEA) and experimental results. Furthermore, the influence of MFMS parameters on the torque is further investigated. When double MFMSs are integrated into each tooth tip, the depth of the MFMS is 0.7 mm, and the width is 1.05 times the slot opening; the results demonstrate that the torque ripple can be essentially weakened compared with other topologies.

Lateral driving behavior analysis is the foundation of freeway cross-section design and the focus of road safety research. However, the factors that influence vehicle lateral driving behavior have not been clearly explained. The dataset of the natural driving trajectory of freeways is used in this study to analyze vehicle lateral driving behavior and trajectory characteristics. As vehicle trajectory characteristic indicators, parameters such as preferred trajectory deviation and standard deviation are extracted. The effects of lane position, speed, road safety facilities, and vehicle types on freeway trajectory behavior are investigated. The results show that lane width and lane position significantly impact vehicle trajectory distribution. As driving speed increases, the lateral distance between vehicles in the inner lane and the guardrail tends to increase. In contrast, vehicles in the outside lane will stay away from the road edge line, and vehicles in the middle lane will stay away from the right lane dividing line when the speed increases. Statistical analysis shows that the preferred trajectory distribution of the same vehicle type in different lane positions is significantly different among groups (Cohen’s d > 0.7). In the same lane, the lateral position characteristics of the center of mass of different vehicle types are basically the same (Cohen’s d < 0.35). This work aims to explain what variables cause trajectory deviation behaviors and how to design traffic safety facilities (guardrail and shoulder) and lane width to accommodate various vehicle types and design speeds.

Many small-spacing interchanges (SSI) appear when the density of the expressway interchanges increases. However, the characteristics of traffic accidents in SSI have not been explained clearly. Therefore, this paper systematically takes the G3001 expressway in Xi’an as the research object to explore the accident characteristics of SSI. Firstly, the expressway is divided into four sections. Furthermore, their safety can be evaluated by the number of accidents per unit distance of 100 million vehicles (NAP). Subsequently, eight indexes, such as mean spacing distance (MSD), are selected to explain the cause affecting expressway safety by developing the least square support vector machine (LSSVM). Secondly, the difference between SSI and normal-spacing interchanges (NSI) is clarified by statistical analysis. Finally, LSSVM, random forest, and logistic regression models are built using 12 indicators, such as the time spent exploring the causes of serious accidents. The results show that the inner ring NAP in Sections I and II with SSI is 27.2 and 33.7, higher than in other sections. The density, annual average daily traffic, and MSD adversely affect expressway traffic safety. The road condition mainly influences the serious traffic accidents in the SSI. This study can provide the theoretical basis for traffic management and accident prevention in the SSI of the expressway.

The permeability of the iron is usually assumed to be infinite by the linear subdomain technique, leading to the neglect of magnet saturation. This study presents an analytical model of a segmented Halbach array permanent magnet machine by considering the iron B‐H curve and saturation effects. Firstly, the entire field problem is divided into six regions in the radial direction according to the excitation source. Then, Poisson's and Laplace's equations are solved in each region to obtain the magnetic vector potential. Specifically, in our approach, the teeth and slots' permeability variation is solved by Cauchy's product theorem. An iterative algorithm incorporates the nonlinearity of the iron. Subsequently, the electromagnetic performance, such as air‐gap magnetic flux density, electromagnetic torque, and unbalanced magnetic force, is predicted by the analytical method. The analytical results are shown in line with those obtained from finite element analysis, and a prototype was fabricated to further verify the accuracy of the analytical method. Besides, the proposed method can also be used to evaluate the performance of the Halbach array machines with any number of segmented magnets.