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Sensorless interior permanent magnet in-wheel motor (IPMIWM), as an exemplar of modular automation system, has attracted considerable interests in recent years. This paper aims to investigate a novel hybrid control approach for the sensorless IPMIWM from a cyber-physical systems (CPS) perspective.

The control approach is presented based on the hybrid dynamical theory. In the standstill-low (S-L) speed, the rotor position/speed signal is estimated by the method of the high frequency (HF) voltage signal injection. The least square support vector machine (LS-SVM) is used to acquire the rotor position/speed signal in medium-high (M-H) speed operation. Hybrid automata model of the IPMIWM is established due to its hybrid dynamic characteristics in wide speed range. A hybrid state observer (HSO), including a discrete state observer (DSO) and a continuous state observer (CSO), is designed for rotor position/speed estimation of the IPMIWM.

The hardware-in-the-loop testing based on dSPACE is carried out on the test bench. Experimental investigations demonstrate the hybrid control approach can not only identify the rotor position/speed signal with a certain load but also be able to reject the load disturbance. The reliability and the effectiveness of the proposed hybrid control approach were verified.

The proposed hybrid control approach for the sensorless IPMIWM promotes the deep combination and coordination of sensorless IPMIWM drive system. It also theoretically supports and extends the development of the hybrid control of the highly integrated modular automation system.

Given the fact that the income disparity has become extremely severe in rural China, the purpose of this paper is to examine heterogeneity in food consumption among various income classes and to investigate the impact of changes in income distribution patterns on food demand in rural China.

In this study, the authors partition the households into five income classes according to the distribution of household per capita net income. Using household data drawn from the China Health and Nutrition Survey in 2011, a two-stage demand model is applied to estimate a food demand system for each of the income classes. After obtaining the estimated income elasticities of eight studied food groups for each income class, the authors then examine the responsiveness of food demand to the changes in income distribution by means of four scenarios with varying income distribution.

The empirical results indicate that substantial differences in food consumption exist across various income classes. Specifically, the lowest-income households are more sensitive to price and income changes for most studied food groups than the highest-income households are. In general, income responsiveness is higher for meats, aquatic products and dairy products. Based on estimated income elasticities, the projected food consumption under different income distribution patterns shows that changes in income distribution have significant influences on food consumption. In addition, the authors conclude that a more equal distribution of income would be associated with a higher demand for food in rural China.

This paper employs a two-stage demand model to estimate food demand in rural China by income classes. The results imply substantial differences in food demand for various income classes. Therefore, income distribution should be taken into account instead of an average estimation for the population as a whole when investigating food demand in rural China. Given the significant changes in income distribution in rural China, this study provides several important policy implications to alleviate income inequality and poverty, as well as to improve nutrition for lower-income classes.

Historic districts are faced with inadequate features protection and landscape destruction in the process of accelerating urban construction, urban design focuses on how places function in addition to how they look, this theory is suitable for the protection of historical districts. At the present stage, the traditional urban design of historical districts has a series of problems, such as uncomprehensive research about characteristics, incomplete workflow and the lack of transformation route from design scheme to implementation, the main reason is that there is a mismatch between the setting goal and planning implementation. Urban design based on “Action planning” has the characteristics of comprehensive research contents, specific implementation plans and complete workflow, it arranges the workflow with four stages, including design preparation, design planning, design implementation and design evaluation, which is a new exploration and attempt to turn traditional blueprint-oriented into action-oriented. Urban design of North Shuncheng Lane Historic Districts in Xi'an city used action planning as a concept and established an framework including “identify development status and problems—construct action target and strategy—determine action projects and specific plans—improve and evaluate the results”, to implement design ideas into practice and to guide the protection of historic districts. Above all, action-oriented planning is considered the key to the development of historic districts.

This study aims to fabricate and manipulate ensemble spin of negative nitrogen-vacancy (NV⁻) centres optimally for future solid atomic magnetometers/gyroscope. Parameters for sample preparation most related to magnetometers/gyroscope are, in particular, the concentration and homogeneity of the NV⁻ centres, the parameters’ microwave antenna of resonance frequency and the strength of the microwave on NV⁻ centres. Besides, the abundance of other impurities such as neutral NV centres (NV⁰) and substitutional nitrogen in the lattice also plays a critical role in magnetic sensing.

The authors succeeded in fabricating the assembly of NV centres in diamond and they determined its concentration of (2-3) × 10¹⁶ cm⁻³ with irradiation followed by annealing under a high temperature condition. They explored a novel magnetic resonance approach to detect the weak magnetic fields that takes advantage of the solid-state electron ensemble spin of NV⁻ centres in diamond. In particular, the authors set up a magnetic sensor on the basis of the assembly of NV centres. They succeeded in fabricating the assembly of NV centres in diamond and determined its concentration. They also clarified the magnetic field intensity measured at different positions along the antenna with different lengths, and they found the optimal position where the signal of the magnetic field reaches the maximum.

The authors mainly reported preparation, initialization, manipulation and measurement of the ensemble spin of the NV centres in diamond using optical excitation and microwave radiation methods with variation of the external magnetic field. They determined the optimal parameters of irradiation and annealing to generate the ensemble NV centres, and a concentration of NV⁻ centres as high as 10¹⁶ cm⁻³ in diamond was obtained. In addition, they found that sensitivity of the magnetometer using this method can reach as low as 5.22 µT/Hz currently.

This research can shed light on the development of an atomic magnetometer and a gyroscope on the basis of the ensemble spin of NV centres in diamond.

High concentration spin of NV⁻ in diamond is one of the advantages compared with that of the atomic vapor cells, because it can obtain a higher concentration. When increasing the spin concentration, the spin signal is easy to detect, and macro-atomic spin magnetometer become possible. This research is the first step for solid atomic magnetometers with high spin density and high sensitivity potentially with further optimization. It has a wide range of applications from fundamental physics tests, sensor applications and navigation to detection of NMR signals.

As has been pointed out, in this research, the authors mainly worked on fabricating NV⁻ centres with high concentration (10¹⁵-10¹⁶ cm⁻³) in diamond by using optimal irradiation and annealing processes, and they quantitatively defined the NV⁻ concentration, which is important for the design of higher concentration processes in the magnetometer and gyroscope. Until now, few groups can directly define the NV⁻ concentration. Besides, the authors optimized the microwave antenna parameters experimentally and explored the dependence between the splitting of the magnetic resonance and the magnetic fields, which dictated the minimum detectable magnetic field.

This paper aims to introduce a novel four-dimensional hyper-chaotic system with different hyper-chaotic attractors as certain parameters vary. The typical dynamical behaviors of the new hyper-chaotic system are discussed in detail. The control problem of these hyper-chaotic attractors is also investigated analytically and numerically. Then, two novel electronic circuits of the proposed hyper-chaotic system with different parameters are presented and realized using physical components.

The adaptive control method is derived to achieve chaotic synchronization and anti-synchronization of the novel hyper-chaotic system with unknown parameters by making the synchronization and anti-synchronization error systems asymptotically stable at the origin based on Lyapunov stability theory. Then, two novel electronic circuits of the proposed hyper-chaotic system with different parameters are presented and realized using physical components. Multisim simulations and electronic circuit experiments are consistent with MATLAB simulation results and they verify the existence of these hyper-chaotic attractors.

Comparisons among MATLAB simulations, Multisim simulation results and physical experimental results show that they are consistent with each other and demonstrate that changing attractors of the hyper-chaotic system exist.

The goal of this paper is to construct a new four-dimensional hyper-chaotic system with different attractors as certain parameters vary. The adaptive synchronization and anti-synchronization laws of the novel hyper-chaotic system are established based on Lyapunov stability theory. The corresponding electronic circuits for the novel hyper-chaotic system with different attractors are also implemented to illustrate the accuracy and efficiency of chaotic circuit design.

The purpose of this paper is to detect the existence of price bubbles and examine the possible contributing factors that associate with price bubble occurrences in China agricultural commodity markets.

Using recently developed rolling window right-side augmented Dickey–Fuller test, we first detect the dates of price bubbles in China's two important agricultural commodity markets, namely corn and soybeans. Then, we use a penalized maximum likelihood estimation of a multinomial logistic model to estimate the contributing factors of price bubbles in both markets, respectively.

Results from the bubble detection indicate that price bubbles account for 5.48% (3.91%) of the studied periods for corn (soybeans). More importantly, we find that market liquidity and speculation have opposite effects on the occurrences of bubbles in the corn and soybeans market. World stocks-to-use and exchange rates affect the occurrences of bubbles in a different way for each commodity, as well. Price bubbles are more likely associated with strong economic activity, high interest rates and low inflation levels.

This is the first study considering commodity-specific features into the formation of price bubbles. Through accurately identifying the bubble dates and fixing the estimation bias of rare events models, this study enables us to obtain robust results for each commodity. The results imply that China's corn and soybeans market respond differently to the speculative activity and external shocks from international markets. Therefore, future policy regulations on commodity markets should focus on more commodity-specific factors when aiming at avoiding bubble occurrences.

The operating wagon records were produced from distinct railway information systems, which resulted in the wagon routing record with the same oriental destination (OD) was different. This phenomenon has brought considerable difficulties to the railway wagon flow forecast. Some were because of poor data quality, which misled the actual prediction, while others were because of the existence of another actual wagon routings. This paper aims at finding all the wagon routing locus patterns from the history records, and thus puts forward an intelligent recognition method for the actual routing locus pattern of railway wagon flow based on SST algorithm.

Based on the big data of railway wagon flow records, the routing metadata model is constructed, and the historical data and real-time data are fused to improve the reliability of the path forecast results in the work of railway wagon flow forecast. Based on the division of spatial characteristics and the reduction of dimension in the distributary station, the improved Simhash algorithm is used to calculate the routing fingerprint. Combined with Squared Error Adjacency Matrix Clustering algorithm and Tarjan algorithm, the fingerprint similarity is calculated, the spatial characteristics are clustering and identified, the routing locus mode is formed and then the intelligent recognition of the actual wagon flow routing locus is realized.

This paper puts forward a more realistic method of railway wagon routing pattern recognition algorithm. The problem of traditional railway wagon routing planning is converted into the routing locus pattern recognition problem, and the wagon routing pattern of all OD streams is excavated from the historical data results. The analysis is carried out from three aspects: routing metadata, routing locus fingerprint and routing locus pattern. Then, the intelligent recognition SST-based algorithm of railway wagon routing locus pattern is proposed, which combines the history data and instant data to improve the reliability of the wagon routing selection result. Finally, railway wagon routing locus could be found out accurately, and the case study tests the validity of the algorithm.

Before the forecasting work of railway wagon flow, it needs to know how many kinds of wagon routing locus exist in a certain OD. Mining all the OD routing locus patterns from the railway wagon operating records is helpful to forecast the future routing combined with the wagon characteristics. The work of this paper is the basis of the railway wagon routing forecast.

As the basis of the railway wagon routing forecast, this research not only improves the accuracy and efficiency for the railway wagon routing forecast but also provides the further support of decision-making for the railway freight transportation organization.

This paper aims to improve some properties of poly (methyl methacrylate) by copolymerization with butyl acrylate (BA) monomer along with the incorporation of the stable and economical synthesized silicone-containing monomer hexamethyldisilazanomethacryloxyphenyl ketone (HDMK) into the copolymer matrix.

For this target solution copolymerization of methyl methacrylate (MMA), BA and HDMK were carried out using a 250 mL four-necked round-bottom flask. Before solution polymerization start-up, the reaction vessel was first charged with 34.8 mL toluene and heated to 170 °C with stirring and reflux cooling. A monomer mixture of 25.86 g (260 mmol) MMA, 26.40 g (200 mmol) BA, 3.00 g (8.60 mmol) HDMK and 0.45 g (2.00 mmol) dibenzoyl peroxide was added continuously from the dropping funnel over a period of 4 h.

The HDMK was successfully synthesized and the water resistance of acrylic resins was improved because of the existence of HDMK.

The materials that were used in this research paper had a reasonably low cost. Also, the procedures for synthesis of monomers and polymers were extremely easy because there was no need for high pressure or temperature and no dangerous solvents were used.

The acrylic resin that contained HDMK was used to synthesis a white architectural paint for exterior coating. Examining the paint characteristics has shown acceptable washing and abrasion resistance, good brushing, excellent storage stability and great surface coating.

HDMK was synthesized for the first time.

The purpose of this paper is to present an algorithm for detecting communities in social networks.

The majority of existing methods of community detection in social networks are based on structural information, and they neglect the content information. In this paper, the authors propose a novel approach that combines the content and structure information to discover more meaningful communities in social networks. To integrate the content information in the process of community detection, the authors propose to exploit the texts involved in social networks to identify the users’ topics of interest. These topics are detected based on the statistical and semantic measures, which allow us to divide the users into different groups so that each group represents a distinct topic. Then, the authors perform links analysis in each group to discover the users who are highly interconnected (communities).

To validate the performance of the approach, the authors carried out a set of experiments on four real life data sets, and they compared their method with classical methods that ignore the content information.

The experimental results demonstrate that the quality of community structure is improved when we take into account the content and structure information during the procedure of community detection.