Search results

The purpose of this study is to develop a robotic hand–arm system for on-orbit servicing missions at the Tiangong-2 (TG-2) Space Laboratory.

The hand–arm system is mainly composed of a lightweight arm, a dexterous hand, an electrical cabinet, a global camera, a hand–eye camera and some human–machine interfaces. The 6-DOF lightweight arm and the 15-DOF dexterous hand adopt the modular design philosophy that greatly reduces the design cycle and cost. To reduce the computational burden on the central controller and simplify system maintenance, an electrical system which has a hierarchical structure is introduced.

The prototypical operating experiments completed in TG-2 space laboratory demonstrate the performance of the hand–arm system and lay foundations for the future applications of space manipulators.

The main contributions of this paper are as follows a robotic hand–arm system which can perform on-orbit servicing missions such as grasping the electric drill, screwing the bolt, unscrewing J599 electrical connector has been developed; a variable time step motion plan method is proposed to adjust the trajectories of the lightweight arm to reduce or eliminate the collision force; and a dexterous hand uses the coordinated grasp control based on the object Cartesian stiffness to realize stable grasp. To solve the kinematic mapping from the cyber glove commands to the dexterous hand, a fingertip-position-based method is proposed to acquire precise solutions.

This study aims to propose a novel method based on model learning with sparsity inducing norms for estimating dynamic gravity terms of the serial manipulators. This method is realized by operating the robot, acquiring data and filtering the features in signal acquisition to adapt to the dynamic gravity parameters.

The core principle of the method is to analyze the dictionary composition of the basis function of the model based on the dynamic equation and the Jacobian matrix of an arm. According to the structure of the basis function and the sparsity of the features, combined with joint-angle and driving-torque data acquisition, the effective features of dynamic gravity parameters are screened out using L1-norm optimization and learning algorithms.

The theoretical analysis revealed that training data obtained based on joint angles and driving torques could rapidly update dynamic gravity parameters. The simulation experiment was carried out by using the publicly available robot model and compared with the previous disassembly method to evaluate the feasibility and performance. The real 7-degree of freedom (DOF) industrial manipulator was used to further discuss the effects of the feature selection. The results show that this estimation method can be fully operational and efficient in industrial applications.

This approach is applicable to most serial robots with multi-DOF and the dynamic gravity parameters of the robot are estimated through learning and optimization. The method does not require prior knowledge of the robot arm structure and only requires joint-angle and driving-torque data acquisition under low-speed motion. Furthermore, as it is a data-driven-based method, it can be applied to gravity parameters updating.

Different from previous general robot dynamic modelling methods, the sparsity of the analytical form of dynamic equations was exploited and model learning was formulated as a convex optimization problem to achieve effective gravity parameters screening. The novelty of this estimation approach is that the method does not only require any prior knowledge but also does not require a specifically designed trajectory. Thus, this method can avoid the laborious work of parameter calibration and the induced modelling errors. By using a data-driven learning approach, the new parameter updating process can be completed conveniently when the robot carries additional mass or the end-effector changes for different tasks.

The purpose of this paper is to consider that the model of volatility characteristics is more reasonable and the description of volatility is more explanatory.

This paper analyzes the basic characteristics of market yield volatility based on the five-minute trading data of the Chinese CSI300 stock index futures from 2012 to 2017 by Hurst index and GPH test, A-J and J-O Jumping test and Realized-EGARCH model, respectively. The results show that the yield fluctuation rate of CSI300 stock index futures market has obvious non-linear characteristics including long memory, jumpy and asymmetry.

This paper finds that the LHAR-RV-CJ model has a better prediction effect on the volatility of CSI300 stock index futures. The research shows that CSI300 stock index futures market is heterogeneous, means that long-term investors are focused on long-term market fluctuations rather than short-term fluctuations; the influence of the short-term jumping component on the market volatility is limited, and the long jump has a greater negative influence on market fluctuation; the negative impact of long-period yield is limited to short-term market fluctuation, while, with the period extending, the negative influence of long-period impact is gradually increased.

This paper has research limitations in variable measurement and data selection.

This study is based on the high-frequency data or the application number of financial modeling analysis, especially in the study of asset price volatility. It makes full use of all kinds of information contained in high-frequency data, compared to low-frequency data such as day, weekly or monthly data. High-frequency data can be more accurate, better guide financial asset pricing and risk management, and result in effective configuration.

The existing research on the futures market volatility of high frequency data, mainly focus on single feature analysis, and the comprehensive comparative analysis on the volatility characteristics of study is less, at the same time in setting up the model for the forecast of volatility, based on the model research on the basic characteristics is less, so the construction of a model is relatively subjective, in this paper, considering the fluctuation characteristics of the model is more reasonable, characterization of volatility will also be more explanatory power. The difference between this paper and the existing literature lies in that this paper establishes a prediction model based on the basic characteristics of market return volatility, and conducts a description and prediction study on volatility.

The paper examines the effects of individual differences on the key motivational factors affecting Chinese study abroad students.

The current study adopts a quantitative survey approach. Students are recruited through the largest online survey provider in China. Using 335 completed questionnaires and factor analysis, the key factors influencing Chinese students are identified. Subsequently, regressions are employed to analyse the impact of age, gender, socio-economic status, previous study or travel abroad experience, degree level and location on factor scores.

This research sheds new light on the decision-making process of Chinese study abroad students. It is found that Chinese students are influenced by three key factors, social, cultural and economic environment, non-personal and personal recommendations. The results reveal that male students from the lowest socio-economic group rely on non-personal information to decide destination where they can significantly upgrade their socio-economic status. Personal recommendations are used by young persons aged between 18 and 24 and those without previous overseas travel or study experience.

Although prior studies explore the motivations of Chinese study abroad students, very few focus on a large sample of students both in China and abroad and identify key factors using the statistical tool factor analysis. No research has been carried out to understand the impact of personal characteristics such as age, gender, prior study or travel abroad experience, degree level and location on significant factor scores. Such analysis is crucial for the financial stability of the international higher education market, particularly during the current COVID-19 crisis.

Constructing porous wind barriers is one of the most effective approaches to increase the running safety of trains on viaducts in crosswinds. This paper aims to further improve the wind-sheltering performance of the porous wind barriers.

Improved delayed detached eddy simulations based on the k-ω turbulence model were carried out, and the results were validated with wind tunnel tests. The effects of the hole diameter on the flow characteristics and wind-sheltering performance were studied by comparing the wind barriers with the porosity of 21.6% and the hole diameters of 60 mm–360 mm. The flow characteristics above the windward and leeward tracks were analyzed, and the wind-sheltering performance of the wind barriers was assessed using the wind speed reduction coefficients.

The hole diameters affected the jet behind the wind barriers and the recirculation region above the tracks. Below the top of the wind barriers, the time-averaged velocity first decreased and then increased with the increase in the hole diameter. The wind barrier with the hole diameter of 120 mm had the best wind-sheltering performance for the windward track, but such barrier might lead to overprotection on the leeward track. The wind-sheltering performance of the wind barriers with the hole diameters of 240 mm and 360 mm was significantly degraded, especially above the windward track.

The effects of the hole diameters on the wake and wind-sheltering performance of the wind barriers were studied, by which the theoretical basis is provided for a better design of the porous wind barrier.

The purpose of this study is to use polybenzoxazine (Pbz) functionalized ZrO₂ nanoparticles to synthesize polyurethane (PU)-PbZ/ZrO₂ nanocomposite. The results derived from the electrochemical impedance spectroscopy (EIS) and polarization studies indicated the superior anticorrosive activity of PU-Pbz/ZrO₂ nanocomposite coatings compared to those of plain PU coatings. The decreased corrosion current was detected on the scratch of the PU-Pbz/ZrO₂ nanocomposite-coated mild steel surface by scanning electrochemical microscopy (SECM) compared to other studied coatings. The superior anticorrosive and mechanical properties of the proposed nanocomposite coatings provide a new horizon in the development of high-performance anticorrosive coatings for various industries.

The Pbz functionalized ZrO₂ nanoparticles were characterized by Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), scanning electron microscopy (SEM) with energy dispersive X-ray analysis (EDX) and thermogravimetric analysis (TGA) in terms of the structural, morphological and thermal properties of these coatings. A different formulation of coatings such as PU, PU-Pbz, PU-ZrO₂ and PU-Pbz/ZrO₂ were prepared and investigated for their corrosion protection performance on mild steel in natural seawater by electrochemical techniques. The surface morphological studies were done by SEM/EDX and XRD analysis.

The superior anticorrosive property of the proposed nanocomposite coatings provides a new horizon in the development of high-performance anticorrosive coatings for various industries. Addition of Pbz wrapped ZrO₂ nanoparticles into the PU coating resulted in the blockage of charge transfer at the metal/electrolyte interface, which reduced the dissolution of mild steel. It was revealed from the SEM/EDX analysis that the formation of the corrosion products at the metal/electrolyte interface behaved as the passive layer which reduced the dissolution of steel.

The inclusion of polybenzoxazine functionalized ZrO₂ nanoparticles to the polyurethane coating reinforces the barrier and mechanical properties of PU-Pbz/ZrO₂ nanocomposite, which is due to the synergistic effect of ZrO₂ and Pbz.

Combining technology acceptance model and the theory of planned behavior, this study aims to analyze influencing factors on intention of sharing heritage tourism experience in social media from technological, psychological and experience perspectives. The moderating effects of age and gender are also tested.

This study applies a quantitative method using online questionnaires. A total number of 323 questionnaires are collected. The data are analyzed using partial least squares-structural equation modeling to assess measurement and structural models and multi-group analysis to compare differences among age and gender groups.

The results of this study indicate that subjective norm, perceived behavioral control and tourism experience significantly and positively influence the intention of sharing heritage tourism experience in social media, while the effects of perceived usefulness, perceived ease of use and attitude are non-significant. Results reveal the differences in antecedents of sharing heritage tourism experience in social media between males and females and between younger and older tourists.

Developing a model integrating technology acceptance model and theory of planned behavior, this paper analyzes the intention of sharing heritage tourism experience in social media through technological, psychological and tourism experience perspectives. Besides, heterogeneous comparisons between younger and older tourists and between males and females offer significant academic and practical insights.

本研究结合技术接受模型（TAM）和计划行为理论（TPB）, 旨在从技术、心理和体验的角度分析在社交媒体上分享文化遗产旅游体验意愿的影响因素, 同时还测试了年龄和性别的调节效应。

本研究采用定量方法, 使用在线问卷进行调查。共收集到323份问卷。使用PLS-SEM来评估测量和结构模型, 使用多组分析（MGA）来比较不同年龄和性别组之间的差异。

结果表明, 主观规范、感知行为控制和旅游体验对在社交媒体上分享文化遗产旅游体验的意愿有显著的正向影响, 而感知有用性、感知易用性和态度的影响是不显著的。结果揭示了男性和女性以及年轻游客和老年游客之间在社交媒体上分享文化遗产旅游体验前因的差异。

本文开发了一个整合了TAM和TPB的模型, 通过技术、心理和旅游体验的视角分析了在社交媒体上分享文化遗产旅游体验的意愿。此外, 对年轻游客和老年游客以及男性和女性之间的异质比较提供了重要的学术和实践见解。