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The purpose of this paper is to propose a defect detection method of bare printed circuit boards (PCB) with high accuracy.

First, bilateral filtering of the PCB image was performed in the uniform color space, and the copper-clad areas were segmented according to the color difference among different areas. Then, according to the chaotic characteristics of the spatial distribution and the gradient direction of the edge pixels on the boundary of the defective areas, the feature vector, which evaluates quantitatively the significant degree of the defect characteristics by using the gradient direction information entropy and the uniform local binary patterns, was constructed. Finally, support vector machine classifier was used for the identification and localization of the PCB defects.

Experimental results show that the proposed algorithm can accurately detect typical defects of the bare PCB, such as short circuit, open circuit, scratches and voids.

Considering the limitations of describing all kinds of defects on bare PCB by using single kind of feature, the gradient direction information entropy and the local binary patterns were fused to build a feature vector, which evaluates quantitatively the significant degree of the defect features.

How to realize social capital “exit from virtual to real” has become not only a hot issue that elicited economists' and the practice field's concern but also a key economic structure problem that the government has to solve urgently. The main purpose of this study is to explore effective methods for social capital to “exit from virtual to real”.

The study investigates the realization path of social capital's “exit from virtual to real” by using firm theory and data from the National Bureau of Statistics in China. Provincial panel data are also utilized to empirically test the impact of social capital's de-realization to virtual (or from virtual to real) on economic development and whether the path of social capital “from virtual to real” is valid.

This study analyzes the development status of social funds serving the real economy and the hazards of social funds' “exit from real to virtual,” which are mainly viewed as eroding the development of the real economy and causing operating difficulties. On the basis of firm theory, the internal motivation for why social funds flow to the real economy is explored from the perspectives of the needs of the real economy, price and profit. Moreover, this study designs a path for returning social capital to the real economy.

Overall, expanding aggregate demand while providing an effective supply and implementing a proactive fiscal policy that focuses on structural tax cuts while keeping margins in the virtual economy are appropriate for promoting the competitiveness of the real economy.

This study explores a topic, namely, social capital “exit from virtual to real,” that has received little attention. It provides an in-depth discussion of the following questions. (1) What is the current situation of social capital serving the real economy? (2) What kind of harm can social capital bring to society? What are the inherent barriers to the flow of social capital to the real economy? (3) At this stage, how can the effective transformation of social capital into the real economy be realized? The findings help in understanding the sustainable entrepreneurship concept, particularly in developing countries.

China's New Rural Pension Program (NRPP) has been implemented for a decade, but the factors that facilitate rural residents' participation have received little attention. This study aims to investigate whether financial literacy has an influence on rural residents' behavior of participation in the NRPP. In particular, this study further verifies if high financial literacy is important and whether financial education can enhance the impact of financial literacy on current, long-term and dynamic pension decisions of rural households.

This study investigates the impact of financial literacy on rural residents' participation in China's NRPP using the China Household Financial Survey (CHFS) Data of 2015 and 2017. This study constructs an analytical framework for current, long-term and dynamic impacts and comprehensively analyzes the value of financial literacy in the decision making of the NRPP. This study uses the instrumental variable method to solve the possible endogeneity problem. In addition, the authors also demonstrate the positive role of high financial literacy in household pension decisions. Further analysis reveals gender and regional heterogeneity in the impact of financial literacy on pension decisions. The moderating effect model explores whether financial education has a significant moderating effect on financial literacy and pension decision making of the NRPP.

Financial literacy can improve the participation behavior of households in rural areas (dynamic effect) and promote their current and long-term participation in the NRPP, choosing a higher pension contribution level in the NRPP. However, financial literacy has no significant effect on the change in the contribution amount of the NRPP. Further research finds that high financial literacy has comparative advantages in household pension decision making in rural areas. There are gender and regional differences in the impact of financial literacy on pension decisions. In addition, effective financial literacy education enhances the current, long-term and dynamic impacts of residents' financial literacy on NRPP participation and pension contributions.

This study comprehensively considers the impact of financial literacy on pension decision making behavior from three aspects: current, long-term and dynamic, making up for the dearth in the existing literature that only focuses on the impact of financial literacy on current financial behaviors and bridging the gap between the theoretical framework and experimental results. Our study proposes new policy implications: (1) Governments and financial institutions should pay attention to financial literacy and education levels in rural areas and carry out financial education and training programs to increase social welfare levels by increasing rural residents' participation and pension contribution. (2) The community can strengthen the policy advocacy of the NRPP and make people develop a stronger sense of trust toward it. The government can also subsidize individual accounts through financial support.

This study comprehensively considers the impact of financial literacy on pension decision-making behavior from three aspects: current, long-term and dynamic, making up for the dearth in the existing literature that only focuses on the impact of financial literacy on current financial behaviors and bridging the gap between the theoretical framework and experimental results. Our study proposes new policy implications: (1) Governments and financial institutions should pay attention to financial literacy and education levels in rural areas and carry out financial education and training programs to increase social welfare levels by increasing rural residents' participation and pension contribution. (2) The community can strengthen the policy advocacy of the NRPP and make people develop a stronger sense of trust toward it. The government can also subsidize individual accounts through financial support.

The purpose of this paper is to use numerical simulations to investigate the energy conversion performance and the flow and temperature structures inside horizontal tubes connected to a vertical manifold channel.

The simulations are performed for different flow rates and inlet temperatures using CFD.

In both the “flowing wind mode” and “upwind mode,” the inlet velocity is not infinitely small under the influence of natural convection; however, such small inlet velocities cannot be achieved in practice and are of no practical significance. In the “flowing wind mode,” the appropriate velocity for achieving high efficiency is 0.01-0.02 m/s. In the “upwind mode,” the appropriate velocity for obtaining high efficiency is 0.1-0.2 m/s. A high inlet temperature can lead to high efficiency; therefore, a large temperature difference and a small flow can be used in actual designs.

The energy conversion performance and flow structures inside evacuated tubular collectors were investigated using CFD for different operating conditions, notably in the “following wind mode” and the “upwind mode.”

An optimal solution method based on 2-norm is proposed in this study to solve the inverse kinematics multiple-solution problem caused by a high redundancy. The current research also presents a motion optimization based on the 2-Norm of high-redundant mobile humanoid robots, in which a kinematic model is designed through the entire modeling.

The current study designs a highly redundant humanoid mobile robot with a differential mobile platform. The high-redundancy mobile humanoid robot consists of three modular parts (differential driving platform with two degrees of freedom (DOF), namely, left and right arms with seven DOF, respectively) and has total of 14 DOFs. Given the high redundancy of humanoid mobile robot, a kinematic model is designed through the entire modeling and an optimal solution extraction method based on 2-norm is proposed to solve the inverse kinematics multiple solutions problem. That is, the 2-norm of the angle difference before and after rotation is used as the shortest stroke index to select the optimal solution. The optimal solution of the inverse kinematics equation in the step is obtained by solving the minimum value of the objective function of a step. Through the step-by-step cycle in the entire tracking process, the kinematic optimization of the highly redundant humanoid robot in the entire tracking process is realized.

Compared with the before and after motion optimizations based on the 2-norm algorithm of the robot, its motion after optimization shows minimal fluctuation, improved smoothness, limited energy consumption and short path during the entire mobile tracking and operating process.

In this paper, the whole kinematics model of the highly redundant humanoid mobile robot is established and its motion is optimized based on 2-norm, which provides a theoretical basis for the follow-up research of the service robot.

In this paper, the whole kinematics model of the highly redundant humanoid mobile robot is established and its motion is optimized based on 2-norm, which provides a theoretical basis for the follow-up research of the service robot.

In this paper, the whole kinematics model of the highly redundant humanoid mobile robot is established and its motion is optimized based on 2-norm, which provides a theoretical basis for the follow-up research of the service robot.

Motion optimization based on the 2-norm of a highly redundant humanoid mobile robot with the entire modeling is performed on the basis of the entire modeling. This motion optimization can make the highly redundant humanoid mobile robot’s motion path considerably short, minimize energy loss and shorten time. These researches provide a theoretical basis for the follow-up research of the service robot, including tracking and operating target, etc. Finally, the motion optimization algorithm is verified by the tracking and operating behaviors of the robot and an example.

This paper studies the lateral stability regulation of intelligent electric vehicle (EV) based on model predictive control (MPC) algorithm.

Firstly, the bicycle model is adopted in the system modelling process. To improve the accuracy, the lateral stiffness of front and rear tire is estimated using the real-time yaw rate acceleration and lateral acceleration of the vehicle based on the vehicle dynamics. Then the constraint of input and output in the model predictive controller is designed. Soft constraints on the lateral speed of the vehicle are designed to guarantee the solved persistent feasibility and enforce the vehicle’s sideslip angle within a safety range.

The simulation results show that the proposed lateral stability controller based on the MPC algorithm can improve the handling and stability performance of the vehicle under complex working conditions.

The MPC schema and the objective function are established. The integrated active front steering/direct yaw moments control strategy is simultaneously adopted in the model. The vehicle’s sideslip angle is chosen as the constraint and is controlled in stable range. The online estimation of tire stiffness is performed. The vehicle’s lateral acceleration and the yaw rate acceleration are modelled into the two-degree-of-freedom equation to solve the tire cornering stiffness in real time. This can ensure the accuracy of model.

This paper aims to propose a measurement principle and a calibration method of measurement system integrated with serial robot and 3D camera to identify its parameters conveniently and achieve high measurement accuracy.

A stiffness and kinematic measurement principle of the integrated system is proposed, which considers the influence of robot weight and load weight on measurement accuracy. Then an error model is derived based on the principle that the coordinate of sphere center is invariant, which can simultaneously identify the parameters of joint stiffness, kinematic and hand-eye relationship. Further, considering the errors of the parameters to be calibrated and the measurement error of 3D camera, a method to generate calibration observation data is proposed to validate both calibration accuracy and parameter identification accuracy of calibration method.

Comparative simulations and experiments of conventional kinematic calibration method and the stiffness and kinematic calibration method proposed in this paper are conducted. The results of the simulations show that the proposed method is more accurate, and the identified values of angle parameters in modified Denavit and Hartenberg model are closer to their real values. Compared with the conventional calibration method in experiments, the proposed method decreases the maximum and mean errors by 19.9% and 13.4%, respectively.

A new measurement principle and a novel calibration method are proposed. The proposed method can simultaneously identify joint stiffness, kinematic and hand-eye parameters and obtain not only higher measurement accuracy but also higher parameter identification accuracy, which is suitable for on-site calibration.

At present, electrical heating clothing is widely used to keep ourselves warm at low temperature. The purpose of this paper is to explore the heat transfer performance of electrical heating fabric and the thermal comfort of human skin at low temperature.

The combined model of skin-electrical heating fabric system was established to simulate human skin tissue wearing electrical heating clothing. A series of simulation experiments are designed on the basis of verifying the effectiveness of the combined model. The temperature distribution inside the combined model and on the skin surface under different heating powers is simulated and analyzed. At the same time, the influence of ambient temperature on the thermal performance of electrical heating fabric was explored.

The skin model with blood vessels reflected the temperature change of human skin wearing electrical heating clothing. The higher the heating power of the electrical heating fabric was, the greater the temperature of the skin surface changed, the faster the temperature rose and the longer the time required to reach the stable state would be. After the heating element was electrified, it had the greatest effect on the average temperature of the epidermis and dermis, had smaller effect on the average temperature of subcutaneous layer and had little effect on the temperature of blood vessels. When the heating power was the same, the higher the ambient temperature was, the more obvious the heating effect of electrical heating fabric was. Electrical heating fabrics with different heating powers were suitable for different ambient temperature ranges.

A reasonable and effective evaluation method for the thermal comfort of electrical heating fabric was provided by establishing the skin model and combined model of the skin-electrical heating fabric system. It provides a reference for the design and application of electrical heating clothing.

The heat transfer properties play significant roles in the thermal comfort of the clothing products. The purpose of this paper is to find the relationship between heat transfer properties and fabrics' structure, yarn properties and predict the effective thermal conductivity of single layer woven fabrics by a parametric mathematical model.

First, the weave unit was divided into four types of element regions, including yarn overlap regions, yarn crossing regions, yarn floating regions and pore regions. Second, the number and area proportion of each region were calculated respectively. Some formulas were created to calculate the effective thermal conductivity of each element region based on serial model, parallel model or series–parallel mixing model. Finally, according to the number and area proportion of each region in weave unit, the formulas were established to calculate the fabric overall effective thermal conductivity in thickness direction based on the parallel models.

The influences of yarn spacing, yarn width, fabric thickness, the compressing coefficients of air layers and weave type on the effective thermal conductivity were further discussed respectively. In this model, the relationships between the effective thermal conductivity and each parameter are some polynomial fitting curves with different orders. Weave type affects the change of effective thermal conductivity mainly through the numbers of different elements and their area ratios.

In this model, the formulas were created respectively to calculate the effective thermal conductivity of each element region and whole weave unit. The serial–parallel mixing characteristics of yarn and surrounding air are considered, as well as the compression coefficients of air layers. The results of this study can be further applied to the optimal design of mixture fabrics with different warp and filling yarn densities or different yarn thermal properties.

The purpose of this paper is to present a neural network approach to control performance assessment.

The performance index under study is based on the minimum variance control benchmark, a radial basis function network (RBFN) is used as the pre‐whitening filter to estimate the white noise sequence, and a stable filtering and correlation analysis method is adopted to calculate the performance index by estimating innovations sequence using the RBFN pre‐whitening filter. The new approach is compared with the auto‐regressive moving average model and the Laguerre model methods, for both linear and nonlinear cases.

Simulation results show that the RBFN approach works satisfactorily for both linear and nonlinear examples. In particular, the proposed scheme shows merits in assessing controller performance for nonlinear systems and surpasses the Laguerre model method in parameter selection.

A RBFN approach is proposed for control performance assessment. This new approach, in comparison with some well‐known methods, provides satisfactory performance and potentials for both linear and nonlinear cases.