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The purpose of this paper is to introduce a co‐simulation method to study the ground maneuvers of aircraft anti‐skid braking and steering.

A virtual prototype of aircraft is established in the multibody system dynamics software MSC.ADAMS/Aircraft. The anti‐skid braking control model, which adopts the multi‐threshold PID control method with a slip‐velocity‐controlled, pressure‐bias‐modulated (PBM) system, is established in MATLAB/Simulink. EASY5 is used to establish the hydraulic system of nose wheel steering. The ADAMS model is connected to block diagrams of the anti‐skid braking control model in MATLAB/Simulink, and is also connected to the block diagrams of nose wheel steering system model in EASY5, so that the ground maneuvers of aircraft anti‐skid braking and steering are simulated separately.

Results are presented to investigate the performance of anti‐skid braking system in aircraft anti‐skid simulation. In aircraft steering simulation, the influence of two important parameters on the forces acting on the tires is discussed in detail, and the safe area to prevent aircraft sideslip is obtained.

This paper presents an advanced method to study the ground maneuvers of aircraft anti‐skid braking and steering, and establishes an integrated aircraft model of airframe, landing gear, steering system, and anti‐skid braking system to investigate the interaction of each subsystem via simulation.

This paper aims to effectively achieve endmembers and relative abundances simultaneously in hyperspectral image unmixing yield. Hyperspectral unmixing, which is an important step before image classification and recognition, is a challenging issue because of the limited resolution of image sensors and the complex diversity of nature. Unmixing can be performed using different methods, such as blind source separation and semi-supervised spectral unmixing. However, these methods have disadvantages such as inaccurate results or the need for the spectral library to be known a priori.

This paper proposes a novel method for hyperspectral unmixing called fuzzy c-means unmixing, which achieves endmembers and relative abundance through repeated iteration analysis at the same time.

Experimental results demonstrate that the proposed method can effectively implement hyperspectral unmixing with high accuracy.

The proposed method present an effective framework for the challenging field of hyperspectral image unmixing.

A time-varying grey Fourier model (TVGFM(1,1,N)) is proposed for the simulation of variable amplitude seasonal fluctuation time series, as the performance of traditional grey models can't catch the time-varying trend well.

The proposed model couples Fourier series and linear time-varying terms as the grey action, to describe the characteristics of variable amplitude and seasonality. The truncated Fourier order N is preselected from the alternative order set by Nyquist-Shannon sampling theorem and the principle of simplicity, then the optimal Fourier order is determined by hold-out method to improve the robustness of the proposed model. Initial value correction and the multiple transformation are also studied to improve the precision.

The new model has a broader applicability range as a result of the new grey action, attaining higher fitting and forecasting accuracy. The numerical experiment of a generated monthly time series indicates the proposed model can accurately fit the variable amplitude seasonal sequence, in which the mean absolute percentage error (MAPE) is only 0.01%, and the complex simulations based on Monte-Carlo method testify the validity of the proposed model. The results of monthly electricity consumption in China's primary industry, demonstrate the proposed model catches the time-varying trend and has good performances, where MAPE_F and MAPE_T are below 5%. Moreover, the proposed TVGFM(1,1,N) model is superior to the benchmark models, grey polynomial model (GMP(1,1,N)), grey Fourier model (GFM(1,1,N)), seasonal grey model (SGM(1,1)), seasonal ARIMA model seasonal autoregressive integrated moving average model (SARIMA) and support vector regression (SVR).

The parameter estimates and forecasting of the new proposed TVGFM are studied, and the good fitting and forecasting accuracy of time-varying amplitude seasonal fluctuation series are testified by numerical simulations and a case study.

The purpose of this study is to analyze the intelligent fault diagnosis method of rolling bearing.

The vibration signal data of rolling bearing has long time series and strong noise interference, which brings great difficulties for the accurate diagnosis of bearing faults. To solve those problems, an intelligent fault diagnosis model based on stacked denoising autoencoder (SDAE) and convolutional neural network (CNN) is proposed in this paper. The SDAE is used to process the time series data with multiple dimensions and noise interference. Then the dimension-reduced samples can be put into CNN model, and the fault classification results can be obtained by convolution and pooling operations of hidden layers in CNN.

The effectiveness of the proposed method is validated through experimental verification and comparative experimental analysis. The results demonstrate that the proposed model can achieve an average classification accuracy of 96.5% under three noise levels, which is 3-13% higher than the traditional models and single deep-learning models.

The combined SDAE–CNN model proposed in this paper can denoise and reduce dimensions of raw vibration signal data, and extract the in-depth features in image samples of rolling bearing. Consequently, the proposed model has more accurate fault diagnosis results for the rolling bearing vibration signal data with long time series and noise interference.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2019-0496/

We consider a joint optimization problem of product platform design and scheduling on unrelated additive/subtractive hybrid machines, and seek to find efficient solution approaches to solve such problem.

We propose a mathematical formulation for the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines, and develop a simulated annealing-based hyper-heuristic algorithm with adjustable operator sequence length to solve the problem.

The simulated annealing-based hyper-heuristic algorithm with adjustable operator sequence length (SAHH-osla) that we proposed can be quite efficient in solving the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines.

To the best of our knowledge, we are one of the first to consider both cost-related and time-related criteria for the problem of simultaneous product platform design and scheduling on unrelated additive/subtractive hybrid machines.

The purpose of this paper is to explore how consumers’ trust is enhanced by e-commerce-based agribusiness companies. It also aims to shed light on the role of social commerce in improving consumers’ trust.

To achieve the research purpose, an in-depth multiple case study is performed. In this study, three cases in short food supply chain (SFSC) in China are selected, and they are all e-commerce agribusiness companies. They adopted common ways to build up, maintain and reinforce consumers’ trust.

It is revealed that the companies innovatively adopted social commerce, both online and offline, to overcome the trust problems usually faced by e-commerce companies. It is also shown that offline contact with potential consumers is an important first step for agribusiness e-commerce entrepreneurs to build up trust with consumers.

By adopting a multiple case study method, the research has limited generalizability to other types of SFSCs. Since the findings are from Chinese agribusiness e-commerce companies, the generalization to other sectors must be done with caution.

Some managerial implications are given as follows: first, offline contact with consumers could be realized through different channels. Taking advantage of existing social network or trying to find consumers in urban communities might be effective ways. Second, trust building with consumers is not an easy task, managers need to emphasize trust building, trust maintaining, as well as trust reinforcing with consumers. In agri-food sector, managers might need to specifically address the importance of food safety and quality so as to not lose consumer trust in one night.

The study has mainly two contributions: first, it has managerial implications for agribusiness e-commerce entrepreneurs, addressing the important role of social presence in building up consumer trust. Second, it contributes to social presence and social relations literature by providing new empirical evidence from e-commerce in agri-food sector and in developing countries.