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This paper aims to address the problem of integrating sensor feedback in robotized interior finishing operations. Its motivation is to finally realize automatic operations necessitating no human intervention. A vision-based approach is proposed for monitoring the execution status and changing the action accordingly.

First, a robotic system is proposed which can realize two typical interior finishing operations, namely, putty applying and wall sanding. Second, a new method based on a deep neural network is proposed to process the visual information capturing the execution status of the interior finishing operations. It helps to determine essential parameters on where should be processed and how to execute the corresponding operation. With the proposed method, vision information is embedded into the execution of interior finishing in a closed loop style.

The experiments demonstrate the feasibility of the proposal and reveal problems for further improvement of the autonomous interior finishing robot.

This provides an original insight into robotized interior finishing by addressing an attempt on integrating visual feedback into the manual process.

The study aims to propose reverse processing solution to improve the performance of strapdown inertial navigation system (SINS) initial alignment and SINS-/global positioning system- (GPS) integrated navigation. The proposed scheme can be well applied in the fields of aircraft and aerospace navigation.

For the SINS alignment phase, a fast initial alignment scheme is proposed: the initial value of reverse filter is determined by the final result of forward filter, and then, the reverse filter is carried out using the stored data. Multiple iterations are performed until the accuracy is satisfied. For the SINS-/GPS-integrated phase, a forward–reverse navigation algorithm is proposed: first, the standard forward filter is used, and then, the reverse filter is carried out using the initial value determined by the forward filter, and the final fusion results are achieved by the weighted smoothing of the forward and reverse filtering results.

The simulation and the actual test results show that in the initial alignment stage, the proposed reverse processing method can obviously shorten the SINS alignment time and improve the alignment accuracy. In the SINS-/GPS-integrated navigation data fusion stage, the proposed forward–reverse data fusion processing can, obviously, improve the performance of the navigation solution.

The proposed reverse processing technology has an important application in improving the accuracy of navigation and evaluating the performance of real-time navigation. The proposed scheme can be not only used for SINS-/GPS-integrated system but also applied to other integrated systems for general aviation aircraft.

Compared with the common forward filtering algorithm, the proposed reverse scheme can not only shorten alignment time and improve alignment accuracy but also improve the performance of the integrated navigation.

The purpose of this study is to reveal the influence mechanism of social innovation education (SIE) on sustainability learning outcomes and analyze the roles of intrinsic learning motivation and prosocial motivation.

A sample of 322 undergraduates from one higher education institution in Tianjin was used to test the hypotheses.

This study found that SIE positively affected sustainability learning outcomes, and intrinsic learning motivation mediated the relationship between them. The results showed that prosocial motivation positively moderates the positive effect of SIE on intrinsic learning motivation and the overall mediation model.

The findings have important practical implications for higher education institutions to carry out SIE. Higher education institutions should focus on integrating social innovation and sustainability into top-level design. Furthermore, higher education institutions should focus on stimulating students’ intrinsic learning motivation and cultivating their prosocial motivation.

This study identified the relationship between SIE and sustainability learning outcomes and clarified the influence mechanism of SIE on sustainability learning outcomes. Moreover, this study emphasized the importance of prosocial motivation as a key boundary condition of SIE.

The purpose of this paper is to propose a system with the highest discriminatory power by selecting an indicator system based on the K–S test according to the unique circumstances of small enterprises.

The proposed method relies on calculating the K–S test statistical magnitude of D iteratively to reach a system with the maximum discriminatory power.

The empirical results, demonstrated using 3,045 small businesses from a Chinese bank, show that credit rating system should focus on the indicator system’s discriminatory power rather than a single indicator’s discriminatory power, because the interaction between indicators affects the discriminatory power of the system.

The proposed method creates a credit rating system with the highest discriminatory power, rather than its indicators, which is a more reasonable and novel approach to credit rating.

The approach is unique because the final system will have high discriminatory power and has excellent potential for decision support. The authors believe that this contribution is theoretically and practically relevant because credit rating for small business is especially difficult and complicated.

This research establishes a suitable casting model for magnesium alloy wheel. The casting of the wheel is an element that must be considered in the design of the wheel. Casting is an important basic process and technology in the field of machinery which is widely used in production, transportation, national defense, social life and other aspects. Computer numerical simulation of the casting process can shorten the product manufacturing cycle, reduce product costs, reduce casting defects and ensure product quality. The casting material in this study is AZ91 magnesium alloy used for wheel lightweight.

Lightweight research of automobile is a significant trend, and people are paying attention to the lightweight design of automobiles. Higher requirement was proposed on design and casting performance of the wheel which is an important part of lightweighting vehicle. In order to achieve better quality, the parametric studies of alloy wheel and casting are necessary. This research designs a new model of automobile wheel, to ensure energy efficiency, the wheels must be as lightweight as possible, using magnesium alloy material for lightweight.

Analysis of casting process is a very complex issue. This research based on finite element theory and actual production, designed reasonable casting model, instant filling and solidification data were obtained. Aiming at reducing casting defects, process improvement of casting riser structure was designed. On the basis of the foundation, it has important guiding significance for actual foundry production.

This research establish a suitable casting model for magnesium alloy wheel. Aiming at reducing casting defects, process improvement of casting riser structure were designed.

Metamodeling is an effective method to approximate the relations between input and output parameters when significant efforts of experiments and simulations are required to collect the data to build the relations. This paper aims to develop a new sequential sampling method for adaptive metamodeling by using the data with highly nonlinear relation between input and output parameters.

In this method, the Latin hypercube sampling method is used to sample the initial data, and kriging method is used to construct the metamodel. In this work, input parameter values for collecting the next output data to update the currently achieved metamodel are determined based on qualities of data in both the input and output parameter spaces. Uniformity is used to evaluate data in the input parameter space. Leave-one-out errors and sensitivities are considered to evaluate data in the output parameter space.

This new method has been compared with the existing methods to demonstrate its effectiveness in approximation. This new method has also been compared with the existing methods in solving global optimization problems. An engineering case is used at last to verify the method further.

This paper provides an effective sequential sampling method for adaptive metamodeling to approximate highly nonlinear relations between input and output parameters.

The current bending test method can only test the bending performance of fabric in one direction at a time. It is not possible to directly observe the bending morphology of fabrics in different directions, and it is necessary to cut samples and repeat the test several times, which takes more time. For this situation, a multidirectional visualization of the fabric bending test method is proposed, using which multiple results can be obtained at one time and the fabric bending can be visualized.

About 17 fabrics are tested using a self-designed device. The fabrics are cut into special triangles and multiple sets of results in three directions are obtained at once using the device. The experimental specimens are photographed from the above and the transverse elongation length, bending projection area and circumference are extracted after image processing.

The results show that the correlation coefficients of transverse elongation, bending projected area and circumference are good with the bending length measured by the cantilever method. In which, all three indicators are positively correlated with the bending length. This indicates the good feasibility of the new method.

This method can get the bending index of fabrics in three directions, with five samples in each direction at one time. Meanwhile, it can also visualize the flexural differences between different fabrics and directions of the same fabric. It can provide more efficient testing means for the textile testing field, and the testing efficiency is 15 times of the existing method, which has better theoretical significance and practical values.

Adaptive slicing is a key step in three-dimensional (3D) printing as it is closely related to the building time and the surface quality. This study aims to develop a novel adaptive slicing method based on ameliorative area ratio and accurate cusp height for 3D printing using stereolithography (STL) models.

The proposed method consists of two stages. In the first stage, the STL model is sliced with constant layer thickness, where an improved algorithm for generating active triangular patches, the list is developed to preprocess the model faster. In the second stage, the model is first divided into several blocks according to the number of contours, then an axis-aligned bounding box-based contour matching algorithm and a polygons intersection algorithm are given to compare the geometric information between several successive layers, which will determine whether these layers can be merged to one.

Several benchmarks are applied to verify this new method. Developed method has also been compared with the uniform slicing method and two existing adaptive slicing methods to demonstrate its effectiveness in slicing.

Compared with other methods, the method leads to fewer layers whilst keeping the geometric error within a given threshold. It demonstrates that the proposed slicing method can reach a trade-off between the building time and the surface quality.

This paper aims at building a discharge model for the power cable bellows based on plasma energy deposition and analyzing the discharge ablation problem.

Aiming at the multiphysical mechanism of the discharge ablation process, a multiphysical field model based on plasma energy deposition is established to analyze the discharge characteristics of the power cable bellows. The electrostatic field, plasma characteristics, energy deposition and temperature field are analyzed. The discharge experiment is also carried out for result validation.

The physical mechanism of the bellows ablative effect caused by partial discharge is studied. The results show that the electric field intensity between the aluminum sheath and the buffer layer easily exceeds the pressure resistance value of air breakdown. On the plasma surface of the buffer layer, the electron density is about 4 × 1,019/m³, and the average temperature of electrons is about 3.5 eV. The energy deposition analysis using the Monte Carlo method shows that the electron range in the plasma is very short. The release will complete within 10 nm, and it only takes 0.1 s to increase the maximum temperature of the buffer layer to more than 1,000 K, thus causing various thermal effects.

Its physical process involves the distortion of electric field, formation of plasma, energy deposition of electrons, and abrupt change of temperature field.

The purpose of this paper is to study the effects of short‐sale constraints and differences of opinions on the price premium of dual listed Chinese A‐H shares.

The analysis mainly follows the Miller's model, which indicates that the relaxation of stringent short‐sale constraint could reduce the upward bias in stock prices. Following the literature, the paper uses the idiosyncratic return volatility and monthly turnover rate as two main proxies of differences of opinions.

This study shows that the high level of A‐share differences of opinions will lead to the high price premium of A‐share portfolio with the short‐sale constraint in the A‐share market. However, the high level of H‐share differences of opinions has no effect on the price premium of H‐share portfolio and has also positively contributed to the A‐share price premium. The price premium of shorted A‐share portfolio is declined more significantly than those of non‐shorted ones after the relaxation of short‐sale constraint in the A‐share market.

The findings in this study provide further evidence that dual listed Chinese A‐shares with high level of differences of opinions and short‐sale constraints tend to be overvalued.

This study supports Miller's hypothesis that with the control of short‐sale constraint, the high level of differences of opinions could lead to the high degree of overvaluation of A‐share portfolio. The market capitalization and book‐to‐market ratio of A‐shares also generate significant positive effect to the A‐share price premium. Finally, the introduction of short‐sale mechanism in A‐share market could partially eliminate the mispricing of dual‐listed A‐shares and improve the price efficiency of A‐share market.

This study is mainly focused on the joint effects of differences of opinions and short‐sale constraints on the A‐share price premium. The new short‐sale policy in A‐share market in March 2010 provides us an opportunity to study the effect of relaxation of stringent short‐sale constraint on the A‐share price premium. In the literatures so far, all studies assumed A‐shares are strictly prohibited to be sold short.