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The purpose of this paper is to develop an information system which is based on the Internet of things (IoT) and used to support the communication and coordination in a cooperative robot team.

The architecture of the IoT applications for decision-making activities in a complex system is elaborated, the focus lies on the effective implementation of system interactions at the device-level. A case study is provided to verify system performances.

The IoT concept has been introduced in an information system of a football robot team to support the coordination among team players. Various sensors are used to collect data from IoT, and data are processed for the controls of robotic players to achieve the better performance at the system level. The field test has shown the feasibility and effectiveness.

To investigate how IoT can be utilized in an information system for making complex decisions effectively, the authors use the decision-support system for a football robot team to illustrate the approaches in developing data acquisition infrastructure, processing and utilizing real-time data for the communication and coordination of robot players in a dynamic competing environment. While the presented work has shown the feasibility of an IoT-based information system, more work are needed to integrate advanced sensors within the IoT and develop more intelligent algorithms to replace manually remote control for the operations of robot players.

The proposed system is specifically for a football robot team; however, the associated approaches are applicable to any decentralized system for developing an information system to support IoT-based communication and coordination within the system in the real-time mode.

The exploration of IoT applications is still at its early stage, existing relevant work is mostly limited to the development of system architecture, sensor networks, and communication protocols. In this paper, the methods on how to use massive real-time data for decision-making of a decentralized team have been investigated, and the proposed system has its theoretical significance to developing other decentralized wireless sensor networks and decision-making systems.

Traditional adaptive analysis based on a coarse mesh, using finite element method (FEM) analysis, produces the original solution. Then post-processing the result and figuring out the regions should be refined and these regions refined once. Finally, this new mesh is used to get the solution of first refinement. After several iterations of above procedures, we can achieve the last result that is closer to the true solution, which takes time, making adaptive scheme inpractical to engineering application. The paper aims to discuss these issues.

This paper based on FEM proposes a multi-level refinement strategy with a refinement strategy and an indicator. The proposed indicator uses value of the maximum difference of strain energy density among the elements that associated with one node, and divides all nodes into several categories based on the value. A multi-level refinement strategy is proposed according to which category the node belongs to refine different elements to different times rather than whether refine or not.

Multi-level refinement strategy takes full use of the numerical calculation, resulting in the whole adaptive analysis that only need to iterate twice while other schemes must iterate more times. Using much less times of numerical calculation and approaches, more accurate solution, making adaptive analysis more practical to engineering.

Multi-level refinement strategy takes full use of the numerical calculation, resulting in the whole adaptive analysis only need iterate twice while other schemes must iterate more times. using much less times of numerical calculation and approaches more accurate solution, making adaptive analysis more practical to engineering.

The purpose of this paper is to present a visualization platform to control and monitor wireless sensor networks (WSNs) in manufacturing applications.

To make the platform flexible and versatile, a modular framework is adopted in modeling and visualizing WSNs. The Eclipse programming environment is used to maximize the scalability and adaptability of the platform. A set of the core functional modules have been designed and implemented to support the system operation. The platform is validated through a case study simulation.

The platform is capable of accommodating different operating systems such as Windows and Linux. It allows integrating new plug-ins developed in various languages such as Java, C, C++, and Matlab. The Graphic User Interface has been applied to process and visualize the acquired real-time data from a WSN, and the embodied methodologies can be used to predict the behaviors of objects in the network.

The work has shown the feasibility and potential of the proposed platform in improving the real-time performance of WSN. However, the number of the developed functional modules is limited, and additional effort is required to develop sophisticated functional modules or sub-systems for a customized application.

The platform can be applied to monitor and visualize various WSN applications in manufacturing environments such as automated workcells, transportation systems, logistic, and storage systems.

The work is motivated by the scarce research on the development tools for monitoring and visualization of WSNs in manufacturing applications. The proposed platform serves for both of system developers and users. It is modularized with a set of core functional modules; it can be extended to accommodate new functional modules with a minimal effort for a different application.

Owing to the excellent performance, giant magnetostrictive materials (GMMs) are widely used in many engineering fields. The dynamic Jiles–Atherton (J-A) model, derived from physical mechanism, is often used to describe the hysteresis characteristics of GMM. However, this model, despite cited by many different literature studies, seems not to possess unique expressions, which may cause great trouble to the subsequent application. This paper aims to provide the rational expressions of the dynamic J-A model and propose a numerical computation scheme to obtain the model results with high accuracy and fast speed.

This paper analyzes different published papers and provides a reasonable form of the dynamic J-A model based on functional properties and physical explanations. Then, a numerical computation scheme, combining the Newton method and the explicit Adams method, is designed to solve the modified model. In addition, the error source and transmission path of the numerical solution are investigated, and the influence of model parameters on the calculation error is explored. Finally, some attempts are made to study the influence of numerical scheme parameters on the accuracy and time of the computation process. Subsequently, an optimization procedure is proposed.

A rational form of the dynamic J-A model is concluded in this paper. Using the proposed numerical calculation scheme, the maximum calculation error, while computing the modified model, can remain below 2 A/m under different model parameter combinations, and the computation time is always less than 0.5 s. After optimization, the calculation speed can be enhanced with the computation accuracy guaranteed.

To the best of the authors’ knowledge, this paper is the first one trying to provide a rational form of the dynamic J-A model among different citations. No other research studies focus on designing a detailed computation scheme targeting the fast and accurate calculation of this model as well. And the performance of the proposed calculation method is validated in different conditions.

Robotic arms’ interactions with the external environment are growing more intricate, demanding higher control precision. This study aims to enhance control precision by establishing a dynamic model through the identification of the dynamic parameters of a self-designed robotic arm.

This study proposes an improved particle swarm optimization (IPSO) method for parameter identification, which comprehensively improves particle initialization diversity, dynamic adjustment of inertia weight, dynamic adjustment of local and global learning factors and global search capabilities. To reduce the number of particles and improve identification accuracy, a step-by-step dynamic parameter identification method was also proposed. Simultaneously, to fully unleash the dynamic characteristics of a robotic arm, and satisfy boundary conditions, a combination of high-order differentiable natural exponential functions and traditional Fourier series is used to develop an excitation trajectory. Finally, an arbitrary verification trajectory was planned using the IPSO to verify the accuracy of the dynamical parameter identification.

Experiments conducted on a self-designed robotic arm validate the proposed parameter identification method. By comparing it with IPSO1, IPSO2, IPSOd and least-square algorithms using the criteria of torque error and root mean square for each joint, the superiority of the IPSO algorithm in parameter identification becomes evident. In this case, the dynamic parameter results of each link are significantly improved.

A new parameter identification model was proposed and validated. Based on the experimental results, the stability of the identification results was improved, providing more accurate parameter identification for further applications.

The purpose of this investigation was to study a transparent coating based on organic silicone resins prepared by the hydrolysis and condensation of methyltriethoxysilane and tetraethoxysilane.

The coating film was characterized by IR, UV, thermal gravity analysis, scanning electron microscope and an automatic contact angle meter. Some properties of the coating film, such as adhesion, impact resistance and wear-resistance also were evaluated.

These uniform, clear and smooth coating films possessed the high transparent and light transmittance, high density, high hydrophobicity, good adhesion, hardness and anti-corrosion.

The coating may be considered as a protective film for the surfaces of the metals and plastics.

This paper aims to present a combination modeling method using multi-source information in the process to improve the accuracy of the dimension propagation relationship for assembly variation reduction.

Based on a variable weight combination prediction method, the combination model that takes the mechanism model and data-driven model based on inspection data into consideration is established. Furthermore, the combination model is applied to qualification rate prediction for process alarming based on the Monte Carlo simulation and also used in engineering tolerance confirmation in mass production stage.

The combination model of variable weights considers both the static theoretical mechanic variation propagation model and the dynamic variation relationships from the regression model based on data collections, and provides more accurate assembly deviation predictions for process alarming.

A combination modeling method could be used to provide more accurate variation predictions and new engineering tolerance design procedures for the assembly process.

In the post-capture stage, the tumbling target rotates the combined spacecraft system, and the detumbling operation performed by the space robot is required. To save the costly onboard fuel of the space robot, this paper aims to present a novel post-capture detumbling strategy.

Actuated by the joint rotations of the manipulator, the combined system is driven from three-axis tumbling state to uniaxial rotation about its maximum principal axis. Only unidirectional thrust perpendicular to the axis is needed to slow down the uniaxial rotation, thus saving the thruster fuel. The optimization problem of the collision-free detumbling trajectory of the space robot is described, and it is optimized by the particle swarm optimization algorithm.

The numerical simulation results show that along the trajectory planned by the detumbling strategy, the maneuver of the manipulator can precisely drive the combined system to rotate around its maximum principal axis, and the final kinetic energy of the combined system is smaller than the initial. The unidirectional thrust and the lower kinetic energy can ensure the fuel-saving in the subsequent detumbling stage.

This paper presents a post-capture detumbling strategy to drive the combined system from three-axis tumbling state to uniaxial rotation about its maximum principal axis by redistributing the angular momentum of the parts of the combined system. The strategy reduces the thrust torque for detumbling to effectively save the thruster fuel.

The purpose of this paper is to examine whether client‐specific litigation risk affects the audit quality differentiation between Big N and non‐Big N auditors. Specifically, the authors examine whether higher quality audits of Big N auditors relative to non‐Big auditors is more pronounced for clients with high litigation risk than for clients with low litigation risk.

The authors develop the hypothesis based on auditors' potential monetary and reputational losses, collect the data of US listed companies from the Compustat and CRSP databases, and conduct regression analyses.

The authors find that the higher effectiveness of Big N auditors over non‐Big N auditors in constraining earning management is greater for high litigation risk clients than for low litigation risk clients, suggesting that clients' high litigation risk can force big auditors to perform more effectively.

This paper contributes to the literature by providing novel evidence on the effect of client‐specific litigation risk on the audit quality differentiation between Big N and non‐Big N auditors. The authors' findings complement the extant research on the relationship between the audit quality differentiation and country‐level litigation risk.

The purpose of the study was to assess the relationship between child sexual abuse (CSA) and posttraumatic stress disorder (PTSD) in female inmates in China. A total of 471 participants were assessed for CSA, personality traits, coping strategies, and frequency of lifetime PTSD. Results demonstrated that CSA and negative coping were predictive factors for lifetime PTSD among female inmates after personality traits were controlled. The findings suggest that CSA is one potential factor contributing to lifetime PTSD among female inmates. The importance of screening for CSA among female inmates was highly emphasized. Early intervention and prevention programmes based on coping skills may be useful to forestall the development of chronic PTSD in female inmates.