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The purpose of this paper is to describe how the authors designed a small satellite formation ground test bed in order to study the small satellite formation flying technologies, such as autonomous formation control and network communication. As one of the subsystems, the vision detection system is responsible for the pose (position and orientation) detection of the three small satellite simulators, each of which is composed of a wheeled mobile robot and an on‐board micro control unit. In this paper, the rapid vision locating of the three small satellite simulators in the wide field is discussed.

The scene size required by the test bed has exceeded the scope of one camera, thus how to obtain the complete scene becomes a difficulty. On the base of image mosaic, a vision system composed of two cameras is designed to capture the scene simultaneously. After the two overlapped images are rapidly stitched, the real‐time view of the big scene is attained. Second, the new color tag representing the pose of small satellite simulators is designed, which can be easily identified.

A real‐time multiple mobile robots visual locating system is introduced, in which the global search algorithm and track search algorithm are combined together to identify the real‐time pose of multiple mobile robots. The switching strategy between the two algorithms is given to ensure the accuracy and improve retrieval speed.

The paper shows how, without camera calibration, the pose of each small satellite simulator in the world coordinate system can be directly calculated by the coordinate transformation from the image coordinate system to the world coordinate system based on relative measurement. The accuracy and real‐time performance of the vision detection system have been validated by experiments on locating static tags and dynamic tracking three small satellite simulators.

A new system for on‐machine measurement of workpieces is described. The system is based on a fine touch sensor that enables the cutting tool itself to act as a contact probe to inspect the workpiece. The proposed measurement technology combines the Q‐setter with the fine touch sensor. This low cost measurement system is applied to automatic workpiece setup and improving workpiece dimensional accuracy on a CNC turning center. It is shown that using the proposed measurement system results in workpiece setting time decrease and workpiece machining accuracy improvement. The development of the on‐machine measurement system makes the fine touch sensor extremely attractive for CNC machine retrofitting.

In this paper, a high-frequency radar test system was used to collect the data of clean ballast bed and fouled ballast bed of ballasted tracks, respectively, for a quantitative evaluation of the condition of railway ballast bed.

Based on original radar signals, the time–frequency characteristics of radar signals were analyzed, five ballast bed condition characteristic indexes were proposed, including the frequency domain integral area, scanning area, number of intersections with the time axis, number of time-domain inflection points and amplitude envelope obtained by Hilbert transform, and the effectiveness and sensitivity of the indexes were analyzed.

The thickness of ballast bed tested at the sleep bottom by high-frequency radar is up to 55 cm, which meets the requirements of ballast bed detection. Compared with clean ballast bed, the values of the five indexes of fouled ballast bed are larger, and the five indexes could effectively show the condition of the ballast bed. The computational efficiency of amplitude envelope obtained by Hilbert transform is 140 s·km⁻¹, and the computational efficiency of other indexes is 5 s·km⁻¹. The amplitude envelopes obtained by Hilbert transform in the subgrade sections and tunnel sections are the most sensitive, followed by scanning area. The number of intersections with the time axis in the bridge sections was the most sensitive, followed by the scanning area. The scanning area can adapt to different substructures such as subgrade, bridges and tunnels, with high comprehensive sensitivity.

The research can provide appropriate characteristic indexes from the high-frequency radar original signal to quantitatively evaluate ballast bed condition under different substructures.

This study aims to solve the premature failure of the rubber stator due to wear, reduce the frictional resistance moment of the screw pump to solve the problem of a model of Daqing oilfield screw pump oil recovery system shutdown after the difficult start.

For the first time, the rotor surface of a screw pump was treated with dot-matrix texture to study the effect of dot matrix texture on the tribological performance of the stator-rotor friction subsets of screw pump. Reciprocating friction tests with different texture morphologies (S-shape, double tongue) and angular parameters (0°, 45° and 90°) were conducted at 10% of the texture area and pump silicone grease.

When point texture was added to the surface of the rotor sample, the friction coefficient and wear quantity of the sample were lower than those of the surface without texture treatment, and the double tongue 0° combination showed the best tribological properties. At this time, the average coefficient of friction and wear is reduced by 22.8% and 62%, 28.6% and 64.8%.

The introduction of texture can effectively improve the tribological performance of progressive screw pumps, and this paper provides important theoretical and experimental support for the design of progressive screw pumps in practical applications.

Rapid digital transformation underway represents both a risk and an opportunity for both policymakers and firms. This can address the risk and seize the opportunity by leveraging FDI to grow digital capabilities and competitiveness through a three-part strategy. First, launching Digital FDI enabling projects (DEPs) to create “digital friendly” investment climates through enabling policies, regulations, and measures. Second, using a “SMART” test as a heuristic before a full-fledged DEP is launched, which benchmarks their economy’s digital Skills, Market functioning, Access through connectivity, Restrictions, and Trust, and provides tools to tackle limiting factors. Third, reviewing FDI trends in six sectors that are important to grow the digital economy (two of which are proposed as essential, namely Communications and Software & IT services), with graphical evidence that can guide policymakers to prioritize policy reforms and investment promotion where they are relatively weak. Throughout, particular attention should be paid to growing the digital capacity of small and medium-sized enterprises (SMEs). A Sustainable Technology Board – modeled after the Financial Stability Board but oriented to cooperation over new technologies – could further help address techno-competition and other concerns over Digital FDI.

More and more attention has been paid to the financing of small- and medium-sized enterprises (SMEs) and environmental protection. Many literatures have done detailed research on them; yet, few of them studied the interaction between corporate financing behaviors and environmental concerns. This study aims to incorporate the impact of the role of data collection into the mathematical model to explore the optimal financial and ordering strategies when considering environmental protection.

A Stackelberg game modeling and backward induction methods are used to derive the optimal equilibrium solutions, using numerical experiments to further explore the influences of various financing strategies on the green degree of product and ordering policies.

No matter which financing modes the capital-constrained retailer chooses, both the loan interest rate and order quantity considering environmental protection are larger than that without environmental protection concerns. As the retailer's initial capital increases, the optimal loan interest rates under various financing modes are all decreasing. The application of big data technology would promote the environmental protection of enterprises and increase the accuracy of financing decisions.

This paper studies financing activities of a supply chain considering data collection and environmental protection behaviors, which provides meaningful guidance for the financing and environmental decision-making of enterprises.

The purpose of this study is to set up an evaluation model to predict massive open online courses (MOOC) learning performance by analyzing MOOC learners’ online learning behaviors, and comparing three algorithms – multiple linear regression (MLR), multilayer perceptron (MLP) and classification and regression tree (CART).

Through literature review and analysis of data correlation in the original database, a framework of online learning behavior indicators containing 26 behaviors was constructed. The degree of correlation with the final learning performance was analyzed based on learners’ system interaction behavior, resource interaction behavior, social interaction behavior and independent learning behavior. A total of 12 behaviors highly correlated to learning performance were extracted as major indicators, and the MLR method, MLP method and CART method were used as typical algorithms to evaluate learners’ MOOC learning performance.

The behavioral indicator framework constructed in this study can effectively analyze learners’ learning, and the evaluation model constructed using the MLP method (89.91%) and CART method (90.29%) can better achieve the prediction of MOOC learners’ learning performance than using MLR method (83.64%).

This study explores the patterns and characteristics among different learning behaviors and constructs an effective prediction model for MOOC learners’ learning performance, which can help teachers understand learners’ learning status, locate learners with learning difficulties promptly and provide targeted instructional interventions at the right time to improve teaching quality.

In the past several decades, considerable research has been dedicated to the development of mobile systems that can traverse vertical surfaces. For the control of the climbing robot, high-precision sensing of the climbing robot’s heading angle during movement is very important. This paper aims to propose a vision-based scheme for the 2D direction angle detection of wall-climbing robots.

First, the authors proposed a method based on image geometric transformation to transform a camera image into a front view image of the wall, as the position and direction angle of the robot can be detected from the transformed image to eliminate the need for calibration of the camera’s internal and external parameters. Second, the AngleNet model is proposed to detect the 2D direction angle of the wall-climbing robot. Third, a training sample expansion strategy is proposed, which greatly decreased the workload of annotating training samples for AngleNet.

The single image processing time of AngleNet on the GPU is only 1.7 ms, which satisfies the demands of real-time processing. The mean and maximum error of predicted direction angle on the 556 samples of the test set are 1.1° and 3.8°, respectively.

This research offers an effective method for measuring the climbing robot’s direction angle in a complex outdoor environment. Combined with position detection, it can provide high-precision position and direction angle measurement information for the motion control of the climbing robot.

With the increasing importance of e-commerce to the economy and people's lives, user-generated content, such as electronic word-of-mouth (eWOM) represented by online reviews, has exploded. On one hand, it is of great significance for review consumers (readers) to identify high-quality ones from a large number of existing reviews to assist their purchase decision. On the other hand, how to use appropriate strategies to make their published reviews more concerned by others is also important to review generators (reviewers). The purpose of this study is to understand the comprehensive relationship among review characteristics, review helpfulness and receiver attention.

This study uses the online movie reviews obtained from the most popular review platform in China to conduct multiple empirical analyses.

The results show that the review helpfulness plays a mediating role between the emotional characteristics of online reviews and the receiver attention, and such a mediating role is more significant among reviewers with rich review expertise. The reviewer's expertise also moderates the impact of review emotions on review helpfulness.

This work studies eWOM receiver involvement, which can ultimately impact product sales, but seldom be investigated in eWOM domain. Therefore, this research can enrich studies on eWOM and provide valuable practical implications as well.

The aim of the study is to evaluate role of trade war between the United States and China on oil price. For this purpose, global oil price and US trade balance with China are selected as variables. In addition to this issue, monthly data of these variables for the periods between 1990 and 2019 are taken into consideration. In the evaluation process, both Engle–Granger cointegration and Toda–Yamamoto causality analysis are considered. The results of Engle–Granger cointegration analysis indicates that there is a ­relationship between trade war and oil prices. Nevertheless, according to the results of Toda–Yamamoto causality analysis, it is identified that trade war does not cause oil prices. While looking at these results, it is determined that trade war between the United States and China has an influence on the oil price changes. However, it is also understood that it is not the main factor of this volatility. Thus, it is recommended that in order to identify the main indicator of the oil price volatility, some different factors should also be taken into consideration.