Search results

Nylon 6 filaments have weak light and heat resistance in terms of stability, which restrict its application in engineering field. The purpose of this paper is to prepare a new photo-stabilization functional nanocomposite inks by using graphene nanosheet as UV light-resisting functional materials incorporated with polyurethane.

Sunlight-resisting functional nylon filaments were produced by the continuous solution dip coating technology, through which the functional inks was coated on the surface of nylon 6 filament. The surface morphology of the coated filaments was characterized by scanning electron microscopy and the graphene/polyurethane nanocomposite inks as the coating agent was confirmed and well dispersed on the fiber’s surface.

Under UV exposure, the strength loss rate of the graphene-modified nylon filaments was less than 50 percent, while that of the control nylon filament was over 85 percent, which indicated that graphene remarkably enhanced the light-resistant property of nylon. Besides, graphene/polyurethane-coated Nylon 6 filaments exhibited reasonable electrical properties and the electrical conductivity could reach 10–4 S/cm.

Graphene inks was first proposed as the UV photo-stabilization in this paper.

The purpose of this paper is to search the optimal arrangement scheme of random motion accuracy of joints for optimal synthesis of pose repeatability which can make robot design more reasonable and reduce the development cost of robots.

In this paper, a mathematical model of pose repeatability, which includes positioning repeatability and orientation repeatability of robots, is established. According to the ISO 9283 standard, an optimal synthesis method of pose repeatability for collaborative robots is introduced, and three optimization objective functions are proposed. The optimization model is solved by using numerical analysis software, and the optimal arrangement scheme of random motion accuracy of joints is obtained which meets the requirements of pose repeatability of robot.

It is found that, in three optimization objective functions, the single-objective evaluation function of maximization of joint motion error is more suitable for optimal synthesis of pose repeatability. In practice, due to the safety factor, the test results of pose repeatability are better than the results of optimal synthesis of pose repeatability.

This method makes robot design more reasonable and reduces the development cost of robots.

This work is the first time to optimize the orientation repeatability of collaborative robots. Because the pose repeatability of most robots is tested by the ISO 9283 standard, so this method which is based on this standard is more suitable for the performance requirements of robot products.

This paper aims to present a fast, economical and practical attitude control design approach for flight vehicles operating within wide envelopes.

Based on a linear disturbance observer, an enhanced proportional-derivative (PD) control scheme is proposed. Utilizing the data from the onboard gyro, the observer can treat the entire response of the system, with the exception of the control term, as a disturbance, and use the estimation of the disturbance to cancel out this response and thereby to effectively simplify the control channel. Using the stability margin tester, the explicit graphical tuning rules are given in a consistent way for the longitudinal dynamics based on the induction method. Mathematical simulations are performed for a highly maneuverable flight vehicle to test the proposed method, which are compared with the traditional PD and H8 control algorithms.

The proposed strategy for attitude control can be reformulated as a static-dynamic control algorithm and the robust synthesis method can be employed to determine the control parameters according to a specific performance configuration. The remarkable control performance robustness can be achieved as shown in the comparative simulations.

There is a sole parameter, steady gain, needed to be scheduled and it can be estimated with a high accuracy.

This paper applies the linear active disturbance rejection control scheme to flight control scenario. The proposed method can reduce the design and implementation complexity of attitude control for flight vehicles operating within a wide envelope, which originates from diverse time-varying flight dynamics. The new method converts the attitude control problem to a sole parameter gain scheduling problem, and there is no complicated and time-consuming multi-dimension interpolation needed for the control parameters.

The function of cognitive diversity has not yet been studied to a sufficient degree. To address this gap, the current study aims to answer the questions of how and when team cognitive diversity fosters individual creativity by integrating the intellectual capital view and the inclusion literature.

With a paired and time-lagged sample consisting of 368 members and 46 leaders from Chinese high-tech organizations, a multilevel moderated mediation model was developed to test the hypothesized relationships using structural equation modeling.

Team cognitive diversity is positively related to individual creativity via team intellectual capital, but this positive indirect effect is obtained only when the inclusive team climate is high.

Team intellectual capital serves as an alternative mechanism for translating team cognitive diversity into favorable outcomes, and an inclusive team climate plays a pivotal role in harvesting the benefits of team cognitive diversity. Future research could extend our study by adopting a multiwave longitudinal or experimental design, examining the possibility of curvilinearity, considering the changes in patterns over time, and conducting cross-cultural studies.

Managers should take the initiative to assemble a team featuring cognitive diversity when facing creative tasks, and should proactively cultivate an inclusive culture when leading such a team.

This study is among the first to consider the mediating role of team intellectual capital in the cross-level effect of team cognitive diversity on individual creativity and to examine the boundary role of an inclusive team climate with respect to this indirect effect.

The aim of this paper is to present a comprehensive analysis of risk management in East Asia from 1998 to 2021 by using bibliometric methods and tools to explore research trends, hotspots, and directions for future research.

The data source for this paper is the Web of Science Core Collection, and 7,154 publications and related information have been derived. We use recognized bibliometric indicators to evaluate publications and visually analyze them through scientific mapping tools (VOS Viewer and CiteSpace).

The analysis results show that China is the most productive and influential country/region. East Asia countries have strong cooperation with each other and also have cooperation with other countries. The study shows that risk management has been involved in various fields such as credit, supply chain, health emergency and disaster especially in the background of COVID-19. We also found that machine learning, especially deep learning, has been playing an increasingly important role in risk management due to its excellent performance.

This paper focuses on studying risk management in East Asia, exploring its publication's fundamental information, citation and cooperation networks, hotspots, and research trends. It provides some reference value for scholars who are interested or further research in this field.

This paper takes the seven overcapacity industries such as the textile industry, electricity and heat, steel, coal, automobile manufacturing, nonferrous metals and petrochemical industry as research objects and proposes a TOPSIS grey relational projection group decision method with mixed multiattributes, which is used for the ranking of the seven industries with overcapacity and provided relevant departments with a basis for decision-making.

First, an evaluation index system from four aspects is established. Secondly, the attributes of linguistic information are converted into two-dimensional interval numbers and triangular fuzzy numbers, and an evaluation matrix is constructed and normalized. This paper uses the AHP method to determine the subjective weights and uses the coefficient of variation method to determine the objective weights. Moreover, this paper sets up the optimization model with the largest comprehensive evaluation value to determine the combined weights. Finally, the TOPSIS grey relational projection method is proposed to calculate the closeness of grey relational projections and to rank them.

This paper analyzes the problem of overcapacity in seven industries with the TOPSIS grey relational projection method. The results show that the four industries of automobile manufacturing, textile, coal and petrochemical are all in serious overcapacity levels, while the three industries of steel, nonferrous metals and electric power are relatively in weak overcapacity level in the three years of 2016–2018. TOPSIS grey relational projection method ranks the overcapacity degree of the seven major overcapacity industries, making the relative overcapacity degree of each industry more clear and providing a reference for the government to formulate targeted policies and measures for each industry.

By using TOPSIS grey relational projection method to evaluate the overcapacity of the seven major overcapacity industries, on the one hand, it makes the relative overcapacity degree of each industry more clear, on the other hand, it can provides the basis for the government and decision-making departments. This helps them promote better the healthy and orderly economic development of the seven major industries and avoid resource waste caused by overcapacity.

This article solves the single evaluation method caused by the limited indicators in the past, combines TOPSIS and the grey relational projection method and applies it to the overcapacity evaluation of the industry, not only applies it to the evaluation of overcapacity for the first time but also involves novel problems and methods, which expands the scope of application of the model.

The primary purpose of this study is to investigate the relationship between leader bottom-line mentality (BLM) and employee innovative behavior, which may be interpreted by employees’ perceived creativity expectations and moderated by employee time orientation.

A multi-wave and multi-source questionnaire survey with 259 paired Chinese employee–leader dyads provided data to test the theoretical model. Hypotheses were tested with Statistical Package for the Social Sciences (SPSS).

Consistent with hypotheses, leader BLM reduces employees’ perceived creativity expectations and thus inhibits employees’ innovative behavior, and this effect is stronger for employees with short-term orientation.

Our findings highlight the negative influences of leader BLM on innovative behavior and the buffering role of employees’ long-term orientation. Organizations may incorporate BLM in leadership promotion and evaluation and provide corresponding training for leaders to overcome BLM. In addition, long-term orientation can be a valuable indicator in employee recruitment and selection.

This study contributes to a new theoretical perspective of the Pygmalion effects for understanding leader BLM’s influence on employee innovative behavior.

The purpose of this paper is to introduce a method for stiffness modeling, identification and updating of collaborative robots (cobots). This method operates in real-time and with high precision and can eliminate the modeling error between the actual stiffness model and the theoretical stiffness model.

To simultaneously ensure the computational efficiency and modeling accuracy of the stiffness model, this method introduces the finite element substructure method (FESM) into the virtual joint method (VJM). The stiffness model of the cobots is built by integrating several 6-degree of freedom virtual joints that represent the elastic deformation of the cobot modules, and the stiffness matrices of these modules can be identified and obtained by the FESM. A model-updating method is proposed to identify stiffness influence coefficients, which can eliminate the modeling error between the actual prototype model and the theoretical finite element model.

The average relative error and the cycle time of the proposed method are approximately 6.14% and 1.31 ms, respectively. Compared with other stiffness modeling methods, this method not only has high modeling accuracy in high dexterity poses but also in low dexterity poses.

A hybrid stiffness modeling method is introduced to integrate the modeling accuracy of the FESM into the VJM. Stiffness influence coefficients are proposed to eliminate the modeling error between the theoretical and actual stiffness models.

The purpose of this study is to explore a sustainable hierarchical framework for the business environment in smart cities. However, this hierarchical framework must consider unnecessary attributes and interrelationships between criteria to capture the difference between smart cities and traditional cities.

Hence, the fuzzy set theory is used for screening unnecessary attributes, the decision-making and trial evaluation laboratory (DEMATEL) is applied to manage the complex interrelationships among the aspects and attributes and interpretive structural modeling (ISM) is used to divide the hierarchy and construct a hierarchical theoretical framework. Ultimately, this research is applied to develop a sustainable hierarchical framework of the business environment in smart cities.

The results show that traditional social problems are still at the core of business environment development in smart cities, new smart opportunities may be discovered, but they are still limited by traditional social factors, the economy is still the main aspect of the business environment and there are still obstacles to solving social problems with smart technologies.

This theoretical hierarchical framework aims to guide smart cities toward sustainability. This study also proposes creating a predictable business environment by improving administrative efficiency, transparency, social mobility and infrastructure services and cultivating new business opportunities with intelligent technology.

Hydrodynamic parameter estimation is significant for the velocity prediction of unmanned surface vehicles. Considering the field data’s uncertain nonlinearities (environmental disturbances and measurement noise), this paper aims to propose a hybrid adaptive parameter estimation (HAPE) strategy.

First, a rough estimation of hydrodynamic parameters is used by the least squares method. Second, an improved adaptive parameter estimation algorithm is applied to compensate for the influence of uncertain nonlinearities and adjust the parameters within the rough range. Finally, it is proved that the calculated velocity asymptotically converges to the actual value during the parameter estimation procedure.

The numerical simulation and pool experiments are conducted in two scenarios of steady turning and sinusoidal thrust to verify the effectiveness of the proposed HAPE method. The results validate that the accuracy of the predicted velocity using the hydrodynamic model obtained by the HAPE strategy is better than the APE algorithm. In addition, the hydrodynamic parameters estimated with the sinusoidal thrust data are more applicable than the steady turning data.

This study proposes a HAPE strategy that considers the uncertain nonlinearities of the field data. This method provides a more accurate predicted velocity. Besides, as far as we know, it is the first time to analyze the influence of different test conditions on the accuracy of the predicted velocity.