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The purpose of this study is to develop a novel mechanical flux-weakening topology for the switched flux permanent magnet (SFPM) machine to extend the speed range, which will be suitable for the electric vehicles and hybrid electric vehicles.

The 3 dimensional mechanical flux-weakening model with flux adjusters (FAs) in the end-cap is established. Subsequently, the electromagnetic performance is compared between the SFPM machine with FAs in the end-cap and without FAs. The open circuit flux-linkage is calculated by finite element analysis (FEA) to investigate the influence of this mechanical flux-weakening topology together with the d/q-axis inductance. The flux-weakening capability and torque-speed curve is also calculated.

The proposed topology decreases the permanent magnet flux-linkage and increases the d-axis inductance, which improve the flux-weakening capability simultaneously. Subsequently, the speed range and constant power region are much wider than those without FAs. Finally, the prototype is fabricated and the measured result of the open circuit back electromotive force has good agreement with the FEA result.

This paper provides a novel mechanical flux-weakening topology with FAs in the end-cap for the SFPM machine, whose volume is smaller than another mechanical flux-weakening topology with FAs at the stator outside. Thus, higher torque and power density can be obtained compared with the SFPM machine with FAs at the stator outside.

As a core rotating component of power machinery and working machinery, the rotor system is widely used in the fields of machinery, electric power and aviation. When the system operates at high speed, the system stability is of great importance. To enhance the system stability, squeeze film damper (SFD) is being installed in the rotor system to alleviate vibration. The purpose of this paper is to first classify the rotor system into two types, the dual rotor system and the single rotor system, and to comprehensively and specifically mention the method of generating the dynamic model. Next, based on the establishment of a dynamic model with and without SFD in the rotor system, the optimization design of the rotor system with SFD was carried out using a genetic algorithm. Through sensitivity analysis, SFD clearance, shaft stiffness and oil viscosity were determined as design variables of the rotor system, and the objective function was the minimization of the maximum amplitude of the rotor system with SFD within the operation speed range.

In this paper, first, the rotor system was classified into two types, namely, the dual rotor system and the single rotor system, and the method of creating a dynamic model was comprehensively and specifically mentioned. Here, the dynamic model of the rotor system was derived in detail for the single rotor system and the dual rotor system with and without SFD. Next, based on the establishment of a dynamic model with and without SFD in the rotor system, the optimization design of the rotor system with SFD was carried out using a genetic algorithm. The sensitivity analysis of the unbalanced response was carried out to determine the design variables of the optimization design. Through sensitivity analysis, SFD clearance, shaft stiffness and oil viscosity were determined as design variables of the rotor system, and the objective function was the minimization of the maximum amplitude of the rotor system with SFD within the operation speed range.

SFD clearance, shaft stiffness and oil viscosity were determined as design variables of the rotor system through sensitivity analysis of the unbalanced response. These three variables are basic factors affecting the amplitude of the rotor system with SFD.

In the existing studies, only a dynamic model of a single rotor system with SFD was created, and the characteristic values of pure SFD were selected as optimization variables and optimization design was carried out. But in this study, the rotor system was classified into two types, namely, the dual rotor system and the single rotor system, and the method of creating a dynamic model was comprehensively and specifically mentioned. In addition, optimization design variables were selected and optimized design was performed through sensitivity analysis on the unbalanced response of factors affecting the vibration characteristics of the rotor system.

With the development of automation technology, the accuracy, bearing capacity and self-adaptation requirements of wheeled mobile robots are more and more demanding under various complex conditions, which will urge designers such shortcomings as the low accuracy, poor flexibility and weak obstacle crossing ability of traditional heavy haul vehicles and improve the wear resistance and bearing capacity of traditional omnidirectional wheels.

The optimal configuration for heavy payload transportation is obtained by building sliding friction consumption model of traditional wheels with different driving types based on Hertz tangential contact theory. The heavy payload omnidirectional wheel with a double-wheel steering and a coupled differential wheel driving is designed with the optimal configuration. The wheel consists of a differential gear train unit and a nonindependent suspension unit. Kinematics model of the wheel is established and relative parameters are optimized.

The prototype experiments show that the wheel has higher motion accuracy and environment adaptability. The results are consistent with the theoretical calculation, which show that the accuracy is more than 50% higher than that of differential prototype. The motion stability and the accuracy of the coupled differential omnidirectional wheel are better than those of the traditional omnidirectional wheels during the moving and obstacle crossing process under complex conditions, which verifies the correctness and advantages of the design.

Aiming at the specific application of heavy payload omnidirectional transportation, a new omnidirectional mobile mechanism with a two-wheel coupling drive structure and an adaptive mechanism is proposed. The simulation and experimental results show that it can realize the high-precision heavy-load omnidirectional movement, the effective contact with the ground and improve the adaptability to the rugged ground. It is flexible, simple and modular and can be widely applied to transportation, exploration, detection and other related industrial fields.

The quad‐rotor is an under‐actuation, strong coupled nonlinear system with parameters uncertainty, unmodeled disturbance and drive capability boundedness. The purpose of the paper is to design a flight control system to regulate the aircraft track the desired trajectory and keep the attitude angles stable on account of these issues.

Considering the dynamics of a quad‐rotor, the closed‐loop flight control system is divided into two nested loops: the translational outer‐loop and the attitude inner‐loop. In the outer‐loop, the translational controller, which exports the desired attitude angles to the inner‐loop, is designed based on bounded control technique. In consideration of the influence of uncertain rotational inertia and external disturbance, the backstepping sliding mode approach with adaptive gains is used in the inner‐loop. The switching control strategy based on the sign functions of sliding surface is introduced into the design procedure with respect to the input saturation.

The validity of the proposed flight control system was verified through numerical simulation and prototype flight experiment in this paper. Furthermore, with relation to the flying, the motor speed is kept in the predetermined scope.

This article introduces a new flight control system designed for a quad‐rotor.

Traditional Chinese medicine (TCM) prescriptions have always relied on the experience of TCM doctors, and machine learning(ML) provides a technical means for learning these experiences and intelligently assists in prescribing. However, in TCM prescription, there are the main (Jun) herb and the auxiliary (Chen, Zuo and Shi) herb collocations. In a prescription, the types of auxiliary herbs are often more than the main herb and the auxiliary herbs often appear in other prescriptions. This leads to different frequencies of different herbs in prescriptions, namely, imbalanced labels (herbs). As a result, the existing ML algorithms are biased, and it is difficult to predict the main herb with less frequency in the actual prediction and poor performance. In order to solve the impact of this problem, this paper proposes a framework for multi-label traditional Chinese medicine (ML-TCM) based on multi-label resampling.

In this work, a multi-label learning framework is proposed that adopts and compares the multi-label random resampling (MLROS), multi-label synthesized resampling (MLSMOTE) and multi-label synthesized resampling based on local label imbalance (MLSOL), three multi-label oversampling techniques to rebalance the TCM data.

The experimental results show that after resampling, the less frequent but important herbs can be predicted more accurately. The MLSOL method is shown to be the best with over 10% improvements on average because it balances the data by considering both features and labels when resampling.

The authors first systematically analyzed the label imbalance problem of different sampling methods in the field of TCM and provide a solution. And through the experimental results analysis, the authors proved the feasibility of this method, which can improve the performance by 10%−30% compared with the state-of-the-art methods.

The aim of the study is to evaluate role of trade war between the United States and China on oil price. For this purpose, global oil price and US trade balance with China are selected as variables. In addition to this issue, monthly data of these variables for the periods between 1990 and 2019 are taken into consideration. In the evaluation process, both Engle–Granger cointegration and Toda–Yamamoto causality analysis are considered. The results of Engle–Granger cointegration analysis indicates that there is a ­relationship between trade war and oil prices. Nevertheless, according to the results of Toda–Yamamoto causality analysis, it is identified that trade war does not cause oil prices. While looking at these results, it is determined that trade war between the United States and China has an influence on the oil price changes. However, it is also understood that it is not the main factor of this volatility. Thus, it is recommended that in order to identify the main indicator of the oil price volatility, some different factors should also be taken into consideration.

This study aims at providing exploratory insights into the initiative and capabilities of Chinese SMEs to develop and utilize diverse networks to support internationalization. Such network development and utilization efforts are fundamental to the analysis and explanation of Chinese firms’ internationalization patterns and outcomes. Extending from the existing network studies in the Chinese context that generally put emphasis on strong‐tie and ethnic‐oriented networks, this paper investigates and explains explicitly the use and effects of both strong‐ and weak‐tie networks in the international development of Chinese SMEs. Indepth case studies on four rapidly internationalized Chinese SMEs are conducted. The case findings demonstrate that weak‐tie networks are essential to the firms’ business development in foreign markets; and were proactively developed and utilized in the course of the firms’ development. The cases also provide alternative perspectives to the beliefs and values underpinning strong‐tie networks presumed in existing literature. The findings draw attention to the changing business values and approaches of the Chinese firms aiming at developing internationally. Managerial implications concerning the significant influence of effective networking on internationalization are pinpointed.

Fixed mode noise and random mode noise always exist in the image sensor, which affects the imaging quality of the image sensor. The charge diffusion and color mixing between pixels in the photoelectric conversion process belong to fixed mode noise. This study aims to improve the image sensor imaging quality by processing the fixed mode noise.

Through an iterative training of an ergoable long- and short-term memory recurrent neural network model, the authors obtain a neural network model able to compensate for image noise crosstalk. To overcome the lack of differences in the same color pixels on each template of the image sensor under flat-field light, the data before and after compensation were used as a new data set to further train the neural network iteratively.

The comparison of the images compensated by the two sets of neural network models shows that the gray value distribution is more concentrated and uniform. The middle and high frequency components in the spatial spectrum are all increased, indicating that the compensated image edges change faster and are more detailed (Hinton and Salakhutdinov, 2006; LeCun et al., 1998; Mohanty et al., 2016; Zang et al., 2023).

In this paper, the authors use the iterative learning color image pixel crosstalk compensation method to effectively alleviate the incomplete color mixing problem caused by the insufficient filter rate and the electric crosstalk problem caused by the lateral diffusion of the optical charge caused by the adjacent pixel potential trap.

The purpose of this study is to set up an evaluation model to predict massive open online courses (MOOC) learning performance by analyzing MOOC learners’ online learning behaviors, and comparing three algorithms – multiple linear regression (MLR), multilayer perceptron (MLP) and classification and regression tree (CART).

Through literature review and analysis of data correlation in the original database, a framework of online learning behavior indicators containing 26 behaviors was constructed. The degree of correlation with the final learning performance was analyzed based on learners’ system interaction behavior, resource interaction behavior, social interaction behavior and independent learning behavior. A total of 12 behaviors highly correlated to learning performance were extracted as major indicators, and the MLR method, MLP method and CART method were used as typical algorithms to evaluate learners’ MOOC learning performance.

The behavioral indicator framework constructed in this study can effectively analyze learners’ learning, and the evaluation model constructed using the MLP method (89.91%) and CART method (90.29%) can better achieve the prediction of MOOC learners’ learning performance than using MLR method (83.64%).

This study explores the patterns and characteristics among different learning behaviors and constructs an effective prediction model for MOOC learners’ learning performance, which can help teachers understand learners’ learning status, locate learners with learning difficulties promptly and provide targeted instructional interventions at the right time to improve teaching quality.

This study aims to examine the effectiveness of bilingual learning strategies designed to support Chinese undergraduate business students facing significant learning challenges in an Australian university capstone curriculum delivered at their Chinese university. These challenges include the students’ difficulty understanding discipline-specific English terminology, using this terminology to discuss disciplinary concepts with their instructors and stress caused by an abnormally high study load.

In response to these challenges, the project team implemented a suite of bilingual strategies to reduce cognitive load and enhance learning, which included Chinese-English glossaries to build disciplinary-specific vocabularies; a bilingual teaching assistant to enable students to communicate in their language of choice; the use of WeChat to connect students to staff and to provide translanguaging opportunities; and bilateral managerial and academic support for strengthening the institutional cross-cultural relationship through staff exchange and language learning programs. A series of surveys were administered to measure the impact of these strategies on students’ learning, and WeChat logs were analysed to determine students’ linguistic preferences during discussions with staff and students.

The results of this project show strong support for each bilingual strategy, high academic performance amongst the student cohort, the positive contribution to learning and connection provided by social media technology, students’ language of choice preferences and chosen translanguaging styles and the important role of teaching staff in supporting international students’ intercultural learning and adaptation to a foreign university learning system.

This original evidence-based study helps to address the gap in bilingual education in Australian higher education demonstrating a successful strategy for dealing with language and discipline-specific challenges confronting EAL students.