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This study aims to solve the problem of the existing metal foreign object (MFO) detecting systems, which are not sensitive to the small size MFO in wireless charging region of electric vehicle (EV) because of the extremely complex signal noise in the process of wireless charging of EV.

A method for MFO detection based on the principle that MFOs can cause mistuned resonance of detection coil resonant circuit is proposed. The general scheme of detecting system is proposed. The design method for detection coils which is effective to small MFOs detection in large-area region of wireless charging of EV is presented. The design of time-sharing driving circuit and amplifying circuit of high frequency exciting signal for detection coils is introduced. The design scheme of signal processing circuit (including filter and rectifier) of detection coil terminal voltage is also proposed.

The influence of exciting frequency of detection coils on detecting sensitivity and the anti-noise feature of system are analyzed according to the experiment results.

The experiment of MFO detection indicates that the proposed method can effectively detect the coin-sized small MFO in the process of wireless charging of EV.

This paper aims to propose an identification method based on monocular vision for cylindrical parts in cluttered scene, which solves the issue that iterative closest point (ICP) algorithm fails to obtain global optimal solution, as the deviation from scene point cloud to target CAD model is huge in nature.

The images of the parts are captured at three locations by a camera amounted on a robotic end effector to reconstruct initial scene point cloud. Color signatures of histogram of orientations (C-SHOT) local feature descriptors are extracted from the model and scene point cloud. Random sample consensus (RANSAC) algorithm is used to perform the first initial matching of point sets. Then, the second initial matching is conducted by proposed remote closest point (RCP) algorithm to make the model get close to the scene point cloud. Levenberg Marquardt (LM)-ICP is used to complete fine registration to obtain accurate pose estimation.

The experimental results in bolt-cluttered scene demonstrate that the accuracy of pose estimation obtained by the proposed method is higher than that obtained by two other methods. The position error is less than 0.92 mm and the orientation error is less than 0.86°. The average recognition rate is 96.67 per cent and the identification time of the single bolt does not exceed 3.5 s.

The presented approach can be applied or integrated into automatic sorting production lines in the factories.

The proposed method improves the efficiency and accuracy of the identification and classification of cylindrical parts using a robotic arm.

With respect to the multiple-attribute decision-making problem with subjective preference for a certain attribute whose weight-value range have been given over other attributes whose weight values are unknown, a method based on the mean value of the grey number is proposed to analyse the decision-making problem. This method is used to choose a supply-chain partner under the condition that the decision makers have a preference for a certain attribute of various alternatives. The paper aims to discuss these issues.

First, the middle value of the preferred attribute’s weight-value range is supposed to be its weight value according to the content of the mean value of the grey number. Second, to reflect the decision maker’s subjective preference information, an improved optimisation model that requests the minimum deviation between the actual and expected numerical value of each attribute is constructed to assess the attributes’ weights. Third, the correlated degree and the correlation matrix, which are determined by the weight values of all attributes, are used to rank all the alternatives.

This paper provides a method for making a decision when decision makers have a preference for a certain attribute from an array of various alternatives, and the range of the certain attribute’s weight value is given but the weight value of the other attributes is unknown. When applied to supply-chain partner selection, this method proves feasible and effective.

This method is feasible and effective when applied to supply-chain partner selection, and can be applied to other kinds of decision-making problems. This means it has significant theoretical importance and extensive practical value.

Based on the mean value of the grey number, an optimisation model is built to determine the importance degree of each attribute, then the correlated degree of each alternative is combined to rank all the alternatives. This method can suit the decision makers’ subjective preference for a certain attribute well.

The purpose of this paper is to draw on triadic reciprocal determinism and social exchange theory to examine how “induced-type” and “compulsory-type” union participation influence union commitment and job involvement, and how union participation in the west differs from that in China. It also examines whether the role of both organizational justice and employee participation climate (EPC) functions in the Chinese context.

Cross-sectional data are collected from 694 employees in 46 non-publicly owned enterprises, both Chinese and foreign, in the Pearl River Delta region of China. A multi-level moderated mediation test is used to examine the model of this research.

Union participation is positively related to organizational justice, union commitment and job involvement. In addition, organizational justice acts as the mediator among union participation, union commitment and job involvement. Specifically, the mediating role of organizational justice between union participation and union commitment, and between union participation and job involvement, is stronger in high-EPC contexts than low-EPC contexts.

Instead of examining the impacts of attitudes on union participation, as per most studies in the western context, this research examines the impacts of union participation in the Chinese context on attitudes, including union commitment and job involvement. It also reveals the role of both organizational justice and EPC in the process through which union participation influences union commitment and job involvement.

This paper aims to study a parameter tuning method for the active disturbance rejection control (ADRC) to improve the anti-interference ability and position tracking of the performance of the servo system, and to ensure the stability and accuracy of practical applications.

This study proposes a parameter self-tuning method for ADRC based on an improved glowworm swarm optimization algorithm. The algorithm is improved by using sine and cosine local optimization operators and an adaptive mutation strategy. The improved algorithm is then used for parameter tuning of the ADRC to improve the anti-interference ability of the control system and ensure the accuracy of the controller parameters.

The authors designed an optimization model based on MATLAB, selected examples of simulation and experimental research and compared it with the standard glowworm swarm optimization algorithm, particle swarm algorithm and artificial bee colony algorithm. The results show that the response time of using the improved glowworm swarm optimization algorithm to optimize the auto-disturbance rejection control is short; there is no overshoot; the tracking process is relatively stable; the anti-interference ability is strong; and the optimization effect is better.

The innovation of this study is to improve the glowworm swarm optimization algorithm, propose a sine and cosine, local optimization operator, expand the firefly search space and introduce a new adaptive mutation strategy to adaptively adjust the mutation probability based on the fitness value, improve the global search ability of the algorithm and use the improved algorithm to adjust the parameters of the active disturbance rejection controller.

The purpose of this paper is to identify risk patterns of road and bridge projects in Vietnam, where the construction market is emerging but attractive to construction organizations, especially foreign companies.

Using a questionnaire, experienced practitioners of various contractors were interviewed to collect risk-related data in terms of actual likelihood and impact from road and bridge construction projects in Vietnam. Using the collected data of actual likelihood and impact, the specific probability and impact of risk factors were determined for different types of road and bridge projects, including small and medium type, big type, government-funding type, and other-funding type (e.g. official development assistance funds, public-private partnership).

The results of analysis indicate the specific probability and impact of risk factors in four risk themes, including contractor-related, project-related, owner-related, and external risks. Actual risk patterns for different types of road and bridge projects in Vietnam were identified.

The identification of actual risk patterns could help practitioners to know which risk factors are severe in frequency and/or impact. Hence, they could establish proper strategies to manage risk-related problems of road and bridge projects, in which they are directly involved.

The findings of this study could provide construction companies, especially foreign companies, with a better understanding of real risk panorama in Vietnamese road and bridge construction. Hence, they could make effective improvements on risk management of road and bridge projects in Vietnam.

There is a scarcity of empirical evidence in the literature on the chain of causality involving tax incentives for Research and Development (R&D) activities and their subsequent transformation into innovation. This study aims to assess the influence of R&D tax incentives on the organizational attributes of Portuguese firms to identify how they are converted into innovation.

A structural research model consisting of 339 companies that benefited from the Fiscal Incentive System supporting R&D in Enterprises, during the period from 2013 to 2016, was developed. This was done to assess the role of R&D tax incentives on the organizational attributes that form the innovation capacity. The model was validated using the multivariate statistical technique of structural equation modeling with partial least squares estimation (partial least squares structural equation modeling – PLS-SEM).

The results found suggest that although it is not possible to unequivocally identify the mechanisms used to convert tax incentives into innovation, it is possible to conclude that they play an important spillover effect for the construction and strengthening of organizational attributes. These form the basis of innovation capacity, to the extent that they positively influence the firms’ total assets, equity, liabilities, number of employees and sales. Hence, contributions are brought to both the literature on tax incentives and the general literature on innovation.

For policymakers, the evidence points to the fact that in addition to the incentives provided, novel mechanisms need to be established to help firms develop their absorptive capacity. The objective is to effectively convert the incentives received into innovation through the organizational attributes analyzed. From the firms’ point of view, the results found suggest that tax incentives act as a catalyst for making R&D investments. Additionally, there is an influence on employability, which effectively enhances the chances of innovation in the long run. Tax incentives received by Portuguese firms also have the effect of promoting economic dynamism – by enhancing the following: investments in infrastructure, the hiring of employees and the increasing of sales, generating positive externalities for both firms and society.