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A robotic wheelchair system was designed to assist disabled people with disabilities to walk.

An anticipated sharing control strategy based on topological map is proposed in this paper, which is used to assist robotic wheelchairs to realize interactive navigation. Then, a robotic wheelchair navigation control system based on the brain-computer interface and topological map was designed and implemented.

In the field of robotic wheelchairs, the problems of poor use, narrow application range and low humanization are still not improved.

In the system, the topological map construction is not restricted by the environment structure, which helps to expand the scope of application; the shared control system can predict the users’ intention and replace the users’ decision to realize human-machine interactive navigation, which has higher security, robustness and comfort.

The wavelet neural network (WNN) has the drawbacks of slow convergence speed and easy falling into local optima in data prediction. Although the artificial bee colony (ABC) algorithm has strong global optimization ability and fast convergence speed, it also has the drawbacks of slow speed while finding the optimal solution and weak optimization ability in the later stage.

This article uses an ABC algorithm to optimize the WNN and establishes an ABC-WNN analysis model. Based on the example of the Jinan Yuhan underground tunnel project, the deformation of the surrounding rock of the double-arch tunnel crossing the fault fracture zone is predicted and analyzed, and the analysis results are compared with the actual detection amount.

The comparison results show that the predicted values of the ABC-WNN model have a high degree of fitting with the actual engineering data, with a maximum relative error of only 4.73%. On this basis, the results show that the statistical features of ABC-WNN are the lowest, with the errors at 0.566 and 0.573, compared with the single back propagation (BP) neural network model and WNN model. Therefore, it can be derived that the ABC-WNN model has higher prediction accuracy, better computational stability and faster convergence speed for deformation.

This article uses firstly the ABC-WNN for the deformation analysis of double-arch tunnels. This attempt laid the foundation for artificial intelligence prediction in deformation analysis of multi-arch tunnels and small clearance tunnels. It can provide a new and effective way for deformation prediction in similar projects.

The car body stiffness of express freight sliding side covered wagon decreased for the sliding and the resonance vibration based on the flexible car body has affected the dynamics performances. Dynamic loading will cause fatigue cracks and eventually lead to fatigue failure of the car body. This paper aims to investigate the influence of car body flexibility on the evaluation of the failure.

In this paper, the railway vehicles random analysis procedure (RVRAP) is employed to study the fatigue failure of the rigid-flexibility model. Following the analysis process, the rigid-flexibility model is established and four contrastive schemes for simulation analysis are designed. To verify the results, an experimental test using the real car body structure on the vibration test plant is carried out; the RMS of dynamic stress is obtained and compared with the simulation results.

The results show that the flexibility of the car body has a crucial influence on the fatigue life.

The reliability is verified regarding the use of RVRAP at an appropriate stage on the antifatigue design of the vehicle.

Wheeled robots have been widely used in People’s Daily life. Accurate positioning is the premise of autonomous navigation. In this paper, an optimization-based visual-inertial-wheel odometer tightly coupled system is proposed, which solves the problem of failure of visual inertia initialization due to unobservable scale.The aim of this paper is to achieve robust localization of visually challenging scenes.

During system initialization, the wheel odometer measurement and visual-inertial odometry (VIO) fusion are initialized using maximum a posteriori (MAP). Aiming at the visual challenge scene, a fusion method of wheel odometer and inertial measurement unit (IMU) measurement is proposed, which can still be robust initialization in the scene without visual features. To solve the problem of low track accuracy caused by cumulative errors of VIO, the local and global positioning accuracy is improved by integrating wheel odometer data. The system is validated on a public data set.

The results show that our system performs well in visual challenge scenarios, can achieve robust initialization with high efficiency and improves the state estimation accuracy of wheeled robots.

To realize robust initialization of wheeled robot, wheel odometer measurement and vision-inertia fusion are initialized using MAP. Aiming at the visual challenge scene, a fusion method of wheel odometer and IMU measurement is proposed. To improve the accuracy of state estimation of wheeled robot, wheel encoder measurement and plane constraint information are added to local and global BA, so as to achieve refined scale estimation.

Metamodels are widely used to replace simulation models in engineering design optimization to reduce the computational cost. The purpose of this paper is to develop a novel sequential sampling strategy (weighted accumulative error sampling, WAES) to obtain accurate metamodels and apply it to improve the quality of global optimization.

A sequential single objective formulation is constructed to adaptively select new sample points. In this formulation, the optimization objective is to select a sample point with the maximum weighted accumulative predicted error obtained by analyzing data from previous iterations, and a space-filling criterion is introduced and treated as a constraint to avoid generating clustered sample points. Based on the proposed sequential sampling strategy, a two-step global optimization approach is developed.

The proposed WAES approach and the global optimization approach are tested in several cases. A comparison has been made between the proposed approach and other existing approaches. Results illustrate that WAES approach performs the best in improving metamodel accuracy and the two-step global optimization approach has a great ability to avoid local optimum.

The proposed WAES approach overcomes the shortcomings of some existing approaches. Besides, the two-step global optimization approach can be used for improving the optimization results.

Steam turbine final assembly is a dynamic process, in which various interference events occur frequently. Currently, data transmission relies on oral presentation, while scheduling depends on the manual experience of managers. This mode has low information transmission efficiency and is difficult to timely respond to emergencies. Besides, it is difficult to consider various factors when manually adjusting the plan, which reduces assembly efficiency. The purpose of this paper is to propose a knowledge-based real-time scheduling system under cyber-physical system (CPS) environment which can improve the assembly efficiency of steam turbines.

First, an Internet of Things based CPS framework is proposed to achieve real-time monitoring of turbine assembly and improve the efficiency of information transmission. Second, a knowledge-based real-time scheduling system consisting of three modules is designed to replace manual experience for steam turbine assembly scheduling.

Experiments show that the scheduling results of the knowledge-based scheduling system outperform heuristic algorithms based on priority rules. Compared with manual scheduling, the delay time is reduced by 43.9%.

A knowledge-based real-time scheduling system under CPS environment is proposed to improve the assembly efficiency of steam turbines. This paper provides a reference paradigm for the application of the knowledge-based system and CPS in the assembly control of labor-intensive engineering-to-order products.

Following self-determination theory, the purpose of this paper is to investigate the impact of four motivational profiles (external, introjected, identified regulation, and intrinsic motivation) on work performance (interpersonal, adaptive, task, and dedicative performance). The authors also examined the proposed relations with longitudinal data.

Participants in Studies 1 and 2 were from several companies in China. Employees completed the questionnaires to measure their work motivation, and managers completed the questionnaires to assess the subordinates’ work performance.

In Study 1, the authors found that identified regulation significantly predicted interpersonal performance and adaptive performance. External regulation, introjected regulation, and intrinsic motivation had no significant impacts on interpersonal, adaptive, task, or dedicative performance. In Study 2, the results revealed that identified regulation significantly predicted dedicative and interpersonal performance, but external regulation, introjected regulation, and intrinsic motivation had no significant impacts on the four types of performance. These two studies concluded that only identified regulation strongly predicts work performance.

The study has contributed to the body of knowledge by clarifying that identified regulation is an important type of motivation in the workplace. Managers might therefore focus on supporting employees for identifying with the organizational goals in order to promote better performance.

Media-effect refers to the phenomenon that stocks with no or low media coverage earn higher returns than stocks with high coverage. This paper aims to explore the existence of media-effect in China stock market and tests the two competing hypotheses explaining this phenomenon.

The authors construct a research sample based on a media-coverage event: the publications of lists of the most wealthy Chinese individuals; in addition, they identify the stocks of which listed firms are led by a controller who is recognized on the publicized lists. This paper uses event study methodology to test the existence of media effect in China A-share market. The authors employed propensity score matching (PSM) to construct a control group with same number of non-listed stocks. Then compared the returns of the two portfolios to test the risk premium hypothesis, and the abnormal trading volume and price reaction around the event date is explored to test the over-attention underperformance hypothesis.

Sampled stocks show significantly negative abnormal returns within the event period, but the matched control group formed by PSM shows no significant abnormal return, indicating that the risk premium hypothesis is not supported. Covered stocks show significantly magnified trading volume. The portfolio gains significant positive return before the event date but turns significantly negative afterward, which is consistent with the over-attention underperformance hypothesis.

This paper offers insights into media-effect in China stock market and provides empirical evidence explaining its existence.

The papers in this issue make a significant original contribution which will result in the better understanding of Chinese corporate finance management challenges from both theoretical and practical perspective. The paper aims to discuss these issues.

The papers of this issue have a wide range of coverage related to corporate finance, such as CSR disclosure, organizational structure, earnings management, ownership structure, media impact, corporate accidents, and institution background. Those papers selected are all based on empirical researches with the original designs of their modeling, by the real and fundamental data from the latest Chinese market and enterprises.

The author believes that such in-depth analysis and discussion will enhance the understanding for both researches and practitioners on the behavior of managing corporate finance in China. Those findings will contribute to the establishment of Chinese management theories and to the building of a wide range of Chinese management practices.

Although the angle of each paper varies, they are all trying to have a deep analysis which may unveil the behavior of corporate finance decision. Some of such behaviors will be beneficial to the long-term sustainable growth of the company, while others, though, may have the negative impact on the corporate value creation.

The purpose of this paper is to investigate the role of intrinsic motivation and self-construal in explaining the relationship between work-related identity discrepancy and employee innovation behavior.

Based on a survey of 637 respondents from 15 organizations in southern China, this study examines four hypotheses with regard to the relationship between work-related identity discrepancy and employee innovation behavior through hierarchical regression analysis.

The research results indicate that work-related identity discrepancy is negatively related to employee innovation behavior, but intrinsic motivation mediates their negative relationship, and self-construal moderates this mediating effect further. Specifically, the results demonstrate that perceiving work-related identity discrepancy can lower intrinsic motivation among employees with interdependent self-construal and subsequently reduce their innovation behavior.

Drawn on social cognitive theory, this study reveals the negative effect of work-related identity discrepancy on employee innovation behavior and the moderated mediation effect of intrinsic motivation and self-construal on the negative relationship. The finding expands existing literature on work-related identity discrepancy and employee innovation behavior.