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The purposes of this paper are to review the progress of and conclude the trend for paint thickness simulation for painting robot trajectory planning.

This paper compares the explicit function-based method and computational fluid dynamics (CFD)-based method used for paint thickness simulation. Previous research is considered, and conclusions with the outlook are drawn.

The CFD-based paint deposition simulation is the trend for paint thickness simulation for painting robot trajectory planning. However, the calculation of paint thickness resulting from dynamically painting complex surface remains to be researched, which needs to build an appropriate CFD model, study approaches to dynamic painting simulation and investigate the simulation with continuously changing painting parameters.

This paper illustrates that the CFD-based method is the trend for the paint thickness simulation for painting robot trajectory planning. Current studies have been analyzed, and techniques of CFD modeling have also been summarized, which is vital for future study.

This paper aims to propose and establish a set of new benchmark models to investigate and confidently validate the modeling and prediction of total stray-field loss inside magnetic and non-magnetic components under harmonics-direct current (HDC) hybrid excitations. As a new member-set (P21^e) of the testing electromagnetic analysis methods Problem 21 Family, the focus is on efficient analysis methods and accurate material property modeling under complex excitations.

This P21^e-based benchmarking covers the design of new benchmark models with magnetic flux compensation, the establishment of a new benchmark measurement system with HDC hybrid excitation, the formulation of the testing program (such as defined Cases I–V) and the measurement and prediction of material properties under HDC hybrid excitations, to test electromagnetic analysis methods and finite element (FE) computation models and investigate the electromagnetic behavior of typical magnetic and electromagnetic shields in electrical equipment.

The updated Problem 21 Family (V.2021) can now be used to investigate and validate the total power loss and the different shielding performance of magnetic and electromagnetic shields under various HDC hybrid excitations, including the different spatial distributions of the same excitation parameters. The new member-set (P21^e) with magnetic flux compensation can experimentally determine the total power loss inside the load-component, which helps to validate the numerical modeling and simulation with confidence. The additional iron loss inside the laminated sheets caused by the magnetic flux normal to the laminations must be correctly modeled and predicted during the design and analysis. It is also observed that the magnetic properties (B27R090) measured in the rolling and transverse directions with different direct current (DC) biasing magnetic field are quite different from each other.

The future benchmarking target is to study the effects of stronger HDC hybrid excitations on the internal loss behavior and the microstructure of magnetic load components.

This paper proposes a new extension of Problem 21 Family (1993–2021) with the upgraded excitation, involving multi-harmonics and DC bias. The alternating current (AC) and DC excitation can be applied at the two sides of the model’s load-component to avoid the adverse impact on the AC and DC power supply and investigate the effect of different AC and DC hybrid patterns on the total loss inside the load-component. The overall effectiveness of numerical modeling and simulation is highlighted and achieved via combining the efficient electromagnetic analysis methods and solvers, the reliable material property modeling and prediction under complex excitations and the precise FE computation model using partition processing. The outcome of this project will be beneficial to large-scale and high-performance numerical modeling.

Based on virtual maintenance, this paper aims to propose a time prediction method of assembly and disassembly (A&D) actions of product maintenance process to enhance existing methods’ prediction accuracy, applicability and efficiency.

First, a framework of A&D time prediction model is constructed, which describes the time prediction process in detail. Then, basic maintenance motions which can comprise a whole A&D process are classified into five categories: body movement, working posture change, upper limb movement, operation and grasp/placement. A standard posture library is developed based on the classification. Next, according to motion characteristics, different time prediction methods for each motion category are proposed based on virtual maintenance simulation, modular arrangement of predetermined time standard theory and the statistics acquired from motion experiment. Finally, time correction based on the quantitative evaluation method of motion time influence factors is studied so that A&D time could be predicted with more accuracy.

Case study of time prediction of products’ various A&D processes is conducted by implementing the proposed method. The prediction process of diesel cooling fan disassemble time is presented in detail. Through comparison, the advantages and effectiveness of the method are demonstrated.

This paper proposes a more accurate, efficient and applicable product A&D time prediction method. It can help designers predict A&D time of a product maintenance accurately in early design phases without a physical prototype. It can also provide basis for the verification of maintainability, the balance of the design of product structure and system layout.

The purpose of this paper is to design a special automatic redundant robot painting system (RRPS), which can automatically navigate and paint in the long non-regular duct.

The RRPS is designed with three subsystems: a redundant robot, a spraying system and a control and safety system. Based on the modular design theory, the robot falls naturally into a mobile platform, a 4-DOF location mechanism and a 10-DOF manipulator. The restriction of the distance between the links and the duct axis is used to plan the trajectory of the manipulator so that it would not collide with the duct. The restriction model is constructed by minimizing the sum of the weighed distances between the duct axis and the special points.

A fully working prototype system has been developed. Test results show that the minimal distance between the robot joints and duct is 18 mm, and it can finish painting long non-regular ducts at the speed of 12.5 cm/s and the spraying distance of 16 cm. The quality of coating layers is good.

The RRPS was used to paint non-regular rectangular ducts, cylindrical ducts and long non-regular ducts. The feasibility of painting long non-regular duct is proved with the prototype implementation and successful test results.

The RRPS shows a novel solution that is based on the 14-DOF redundant robot design for painting long non-regular ducts which is used in airplane.

This paper aims to share some experiences and practical activities related to the use and management of usage data in the Digital Resource Acquisition Alliance of Chinese Academic Libraries (DRAA) as a reference for library consortia engaged in providing usage statistics services of e-resources to member libraries.

A literature review and online survey are used to analyze the research and practice of e-resources usage statistics conducted internationally. The case of DRAA is introduced to present how DRAA develops usage statistics services and promotes the implementation of the Standardized Usage Statistics Harvesting Initiative (SUSHI) to deliver usage statistics to member libraries. Future developments and enhancements are also described.

The main actions taken by DRAA to develop usage statistics services are as follows: development of the DRAA Usage Statistics Portal to provide a single point of access to usage statistics from participating publishers on behalf of member libraries; development of a SUSHI client, taking the lead in promoting SUSHI implementation to automatically obtain usage statistics in Chinese academic libraries; establishment of a working group on usage statistics and the China Academic Library and Information System/DRAA Standards and Recommended Practices Research Task Group to form a long-term mechanism for monitoring and gathering usage statistics; and strengthening of the understanding and application of standards and best practices for libraries and vendors in China. Scheduled enhancements in the future include a deep analysis and utilization of usage statistics, the promotion of Counting Online Usage of NeTworked Electronic Resources and SUSHI to Chinese academic resource publishers and raising awareness about normalizing usage statistics.

This paper has pertinence and wider implications for library consortia engaged in providing e-resources usage statistics services to member libraries.

This study aims to explain the state-of-the-art machine learning models that are used in the intrusion detection problem for human-being understandable and study the relationship between the explainability and the performance of the models.

The authors study a recent intrusion data set collected from real-world scenarios and use state-of-the-art machine learning algorithms to detect the intrusion. The authors apply several novel techniques to explain the models, then evaluate manually the explanation. The authors then compare the performance of model post- and prior-explainability-based feature selection.

The authors confirm our hypothesis above and claim that by forcing the explainability, the model becomes more robust, requires less computational power but achieves a better predictive performance.

The authors draw our conclusions based on their own research and experimental works.

The purpose of this paper is to address and overcome the problem that a single prediction model cannot accurately fit a data sequence with large fluctuations.

Initially, the grey linear regression combination model was put forward. The Discrete Grey Model (DGM)(1,1) model and the multiple linear regression model were then combined using the entropy weight method. The grain yield from 2010 to 2015 was forecasted using DGM(1,1), a multiple linear regression model, the combined model and a GM(1,N) model. The predicted values were then compared against the actual values.

The results reveal that the combination model used in this paper offers greater simulation precision. The combination model can be applied to the series with fluctuations and the weights of influencing factors in the model can be objectively evaluated. The simulation accuracy of GM(1,N) model fluctuates greatly in this prediction.

The combined model adopted in this paper can be applied to grain forecasting to improve the accuracy of grain prediction. This is important as data on grain yield are typically characterised by large fluctuation and some information is often missed.

This paper puts the grey linear regression combination model which combines the DGM(1,1) model and the multiple linear regression model using the entropy weight method to determine the results weighting of the two models. It is intended that prediction accuracy can be improved through the combination of models used within this paper.

The unilateral axle counting sensor is an important railway signal device that detects a train. For efficient and stable detection, the amplitude of induced electromotive force and its changes must be big enough. Therefore, the purpose of this study is to find a way to design and optimize the sensor structure quickly and accurately.

With the help of extensive electromagnetic field calculations, the study puts forward a modified model based on the finite element method, establishes an independent air domain around the sensor, wheel and the railway and adopts a unique grid division method. It offers a design optimization method of the induction coil angles and its spatial location with respect to the excitation coil by using the combination weighting algorithm.

The modified modeling method can greatly reduce the number of finite element mesh and the operation time, and the method can also be applied to other areas. The combination weighting algorithm can optimize the structure of the sensor quickly and accurately.

This study provides a way to design and optimize the structure of the sensor and a theoretical basis for the development. The results can improve and expand the technology of the axle counting sensor.

This paper therefore aims at systematically estimating the agricultural trade induced farm employment effects in China.

Using detailed agricultural trade and production data during 1994‐2009, the authors estimate the “labor contents” of agricultural trade flows and use these estimates to compute the farm employment effects.

The authors find that China's agricultural trade has indeed generally developed along its widely believed comparative advantages and disadvantages; however, the farm employment “creation” effect due to labor‐intensive exports has actually been dominated by the employment “substitution” effect due to increased land‐intensive imports, thereby mostly resulting in negative net farm employment in the post‐WTO accession era.

Findings from this first systematic attempt to estimate the trade‐induced farm employment effects do not lend support to the popular notion that increased agricultural trade would help increase farm employment and have important implications for evaluating current and future trade policy in China and elsewhere.