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This paper aims to present a complete relative pose error model for robot calibration, considering both the relative distance error and the relative rotation error of the robot end-effector, which can improve calibration accuracy.

In this paper, the relative distance error model and the relative rotation error model of robot calibration are derived by ignoring high-order nonlinear errors, and the two models form into a complete relative pose error model. Besides, mathematical expectation of the nonlinear errors is calculated, indicating that they have little influence on calibration accuracy.

Comparative experiments have indicated that the proposed complete relative pose error model does better in robot calibration than only the distance error model.

The main contribution of this paper lies in the derivation of the relative rotation error model, which helps to form a complete relative pose error model for calibration. The proposed method improves calibration accuracy, with avoiding identifying the transformation matrix between the measurement system frame and the robot base frame.

Smog seriously affects the ecological environment and poses a threat to public health. Therefore, smog control has become a key task in China, which requires reliable prediction.

This paper establishes a novel time-lag GM(1,N) model based on interval grey number sequences. Firstly, calculating kernel and degree of greyness of the interval grey number sequence respectively. Then, establishing the time-lag GM(1,N) model of kernel and degree of greyness sequences respectively to obtain their values after determining the time-lag parameters of two models. Finally, the upper and lower bounds of interval grey number sequences are obtained by restoring the values of kernel and degree of greyness.

In order to verify the validity and practicability of the model, the monthly concentrations of PM2.5, SO2 and NO2 in Beijing during August 2017 to September 2018 are selected to establish the time-lag GM(1,3) model for kernel and degree of greyness sequences respectively. Compared with three existing models, the proposed model in this paper has better simulation accuracy. Therefore, the novel model is applied to forecast monthly PM2.5 concentration for October to December 2018 in Beijing and provides a reference basis for the government to formulate smog control policies.

The proposed model can simulate and forecast system characteristic data with the time-lag effect more accurately, which shows that the time-lag GM(1,N) model proposed in this paper is practical and effective.

Based on interval grey number sequences, the traditional GM(1,N) model neglects the time-lag effect of driving terms, hence this paper introduces the time-lag parameters into driving terms of the traditional GM(1,N) model and proposes a novel time-lag GM(1,N) model.

The purpose of this paper is to establish a random simulation method to compare the forecasting performance between grey prediction models, and between grey model and other kinds of prediction models. Then, the different performance of three grey models and linear regression prediction model is studied, based on the proposed method.

A random simulation method was proposed to test the modelling accuracy of grey prediction model. This method was enlightened by Monte Carlo simulation method. It regarded a class of sequences as population, and selected a large sample from population though random sampling. Then, sample sequences were modeled by grey prediction model. Through modeling error calculation, the average error of grey model for the sample was obtained. Finally, the grey model accuracy for this kind of problem was acquired by statistical inference testing model. Through the statistical significant test method, the modeling accuracy of grey models for the same problem can be compared. Also, accuracy difference between grey prediction model and regression analysis, support vector machine, neural network, and other forecasting methods can be also compared.

Though random simulation experiments, the following conclusion was obtained. First, grey model can be applied to the long sequence whose growth rate was less than 20 per cent, and the short sequence whose growth rate was less than 50 per cent. Second, GM(1,1) cannot be applied to a long sequence with high growth. Third, growth rate was a more important factor than growth length on modeling accuracy of GM(1,1). Fourth, when the sequence length was short, accuracy of GM(1,1) model was higher than linear regression. While the length of the sequence was more than 15, and the growth rate in [0‐10 per cent], two kinds of modeling error was not significantly different.

The method proposed in the paper can be used to compare the performance of different prediction models, and to select appropriate model for a prediction problem.

The paper succeeded in establishing an accuracy test method for grey models and other prediction models. It will standardize the grey modelling and contribute to application of grey models.

Large size of aircraft assembly tooling structure and complex measurement environment exist. The laid enhanced reference points (ERS) are subject to a combination of nonuniform temperature fields and measurement errors, resulting in increased measurement registration errors. In view of the nonuniform temperature field and measurement errors affecting the ERS point registration problem, the purpose of this paper is to propose a neural network-based ERS point registration compensation method for large-size measurement fields under a nonuniform temperature field.

The approach is to collect ERS point information and temperature data, normalize the collected data to complete the data structure design and complete the construction of the neural network prediction model by data training. The data learning is performed to complete the prediction model construction, and the prediction model is used to complete the compensation analysis of ERS points. Finally, the algorithm is verified through experiments and engineering practice.

Experimental results show that the proposed neural network-based ERS point prediction and compensation method for nonuniform temperature fields effectively predicts ERS point deformation under nonuniform temperature fields compared with the conventional method. After the compensation analysis, the registration error is effectively reduced to improve registration accuracy. Reducing the combined effect of environmental nonuniform temperature field and measurement error has apparent advantages.

The method reduces the registration error caused by combining a nonuniform temperature field and measurement error. It can be used for aircraft assembly site prediction and registration error compensation analysis, which is essential to improve measurement accuracy further.

The purpose of this study is to model steam condensing flows through steam turbine blades and find the most suitable condensation model to predict the condensation phenomenon.

To find the most suitable condensation model, five nucleation equations and four droplet growth equations are combined, and 20 cases are considered for modelling the wet steam flow through steam turbine blades. Finally, by the comparison between the numerical results and experiments, the most suitable case is proposed. To find out whether the proposed case is also valid for other boundary conditions and geometries, it is used to simulate wet steam flows in de Laval nozzles.

The results indicate that among all the cases, combining the Hale nucleation equation with the Gyarmathy droplet growth equation results in the smallest error in the simulation of wet steam flows through steam turbine blades. Compared with experimental data, the proposed model’s relative error for the static pressure distribution on the blade suction and pressure sides is 2.7% and 2.3%, respectively, and for the liquid droplet radius distribution it totals to 1%. This case is also reliable for simulating condensing steam flows in de Laval nozzles.

The selection of an appropriate condensation model plays a vital role in the simulation of wet steam flows. Considering that the results of numerical studies on condensation models in recent years have not been completely consistent with the experiments and that there are still uncertainties in this field, further studies aiming to improve condensation models are of particular importance. As condensation models play an important role in simulating the condensation phenomenon, this research can help other researchers to better understand the purpose and importance of choosing a suitable condensation model in improving the results. This study is a significant step to improve the existing condensation models and it can help other researchers to gain a revealing insight into choosing an appropriate condensation model for their simulations.

In order to improve the accuracy of project cost prediction, considering the limitations of existing models, the construction cost prediction model based on SVM (Standard Support Vector Machine) and LSSVM (Least Squares Support Vector Machine) is put forward.

In the competitive growth and industries 4.0, the prediction in the cost plays a key role.

At the same time, the original data is dimensionality reduced. The processed data are imported into the SVM and LSSVM models for training and prediction respectively, and the prediction results are compared and analyzed and a more reasonable prediction model is selected.

The prediction result is further optimized by parameter optimization. The relative error of the prediction model is within 7%, and the prediction accuracy is high and the result is stable.

In order to find a more accurate prediction method for the track of an aerial target, this paper aims to establish a track prediction model by the theory of grey system based some track data of the target.

Aimed at the purpose, the authors have modified a model GM(1,1) with corrected residual error had presented in the literature, but it modeling the data with initial condition by the first data x(1), it is better to use the new information for the prediction, this means that the authors can use the last date x(n) as the initial conditions to modeling the track of air target to build a new model.

A modified model GM(1,1) with corrected residual error and initial conditions data x(n) is presented in this paper. The example demonstrates that the model is good fit for the prediction of the target tracks, especially for the case of that difficult to get accurate and reliable data when the data are limited.

And the presented GM model can set up online to improve prediction precision. Further research is a need to do for actual engineering application although the example demonstrates that the model is good fit for the prediction of the target tracks as the tag pointed out. When the target is moving in a uniform velocity along a line, the GM prediction has a good accuracy due to the orderliness of the data.

As the example, here data are given in the case of variable accelerate for this is more fit the factual applications. The precision of GM(1,1) could be enhanced with the boundary condition at the terminus, and so much as the residual error revising could be omitted.

The modified model effectually improved the veracity of track prediction of an aerial target. It is shown that the grey forecasting model can be used in the prediction of an air target tracks at least. Combining some other techniques could gain some further results.

The purpose of this paper is to propose a pre-defined performance robust control method for pre-assembly configuration establishment of in-space assembly missions, and collision avoidance is considered during the configuration establishment process.

First, six-degrees-of-freedom error kinematic and dynamic models of relative translational and rotational motion between transportation systems are developed. Second, the prescribed transient-state performance bounds of tracking errors are designed. In addition, based on the backstepping, combining the pre-defined performance control method with a robust control method, a pre-defined performance robust controller is designed.

By designing prescribed transient-state performance bounds of tracking errors to guarantee that there is no overshoot, collision-avoidance can be achieved. Combining the pre-defined performance control method with a robust control method, robustness to disturbance is guaranteed.

This paper proposed a pre-defined performance robust control method. Simulation results demonstrate that the proposed controller can achieve a pre-assembly configuration establishment with collision avoidance in the existence of external disturbances.

This study investigates the time‐series properties of operating cash flows per share and earnings per share for all manufacturing firms on the Compustat Quarterly Industrial tape for which sufficient data are available. Both individually‐identified and “premier” models are compared on the basis of their relative fit and forecasting accuracy. The empirical results suggest that for both accounting variables the individually‐identified models outperform the premier models, although this advantage is larger for earnings, and for forecast horizons beyond one quarter ahead. A major conclusion of the study is that the time‐series properties of cash flows are quite different than those of earnings. In particular, the cash flow series are considerably less predictable, as shown by their relatively high incidence of white‐noise series and relatively large forecast errors.

This paper aims to design an optimal shape for an annular S-duct, considering both energy losses and exit flow uniformity, starting from a given baseline design. Moreover, this paper seeks to identify the design factors that affect the optimal annular S-duct designs.

The author has carried out computational fluid dynamic (CFD)-based shape optimization relative to five distinct numerical objectives, to understand their interrelations in optimal designs. Starting from a given baseline S-duct design, they have applied control node-induced shape deformations and high-order polynomial response surfaces for modeling the functional relationships between the shape variables and the numerical objectives. A statistical correlation analysis is carried out across the optimal designs.

The author has shown by single-objective optimization that the two typical goals in S-duct design, energy loss minimization and exit flow uniformity, are mutually contradictory. He has presented a multi-objective solution for an optimal shape, reducing the total pressure loss by 15.6 per cent and the normalized absolute radial exit velocity by 34.2 per cent relative to a baseline design. For each of the five numerical objectives, the best optimization results are obtained by using high-order polynomial models.

The methodology is applicable to axisymmetric two-dimensional geometry models.

This paper applies a recently introduced shape optimization methodology to annular S-ducts, and, it is, to the author’s knowledge, the first paper to point out that the two widely studied design objectives for annular S-ducts are contradictory. This paper also addresses the value of using high-order polynomial response surface models in CFD-based shape optimization.