Yu Chen, Yu Sun and Chunping Cao
The purpose of this study is to investigate the hydrodynamic characteristics of journal bearings in a high-speed and heavy-load press system by considering thermal influence and…
Abstract
Purpose
The purpose of this study is to investigate the hydrodynamic characteristics of journal bearings in a high-speed and heavy-load press system by considering thermal influence and cavitation.
Design/methodology/approach
A proper and effectual computational method is presented for steady-state analysis of fluid interaction in a rotor-bearing press system by combining computational fluid dynamics techniques.
Findings
The influences of eccentricity ratio, rotational speed and oil-film thickness on the hydrodynamic behavior of the journal bearing are studied.
Originality/value
The computational method can be used for creating a precise lubrication design for a journal bearing of a lubrication system.
Details
Keywords
Chunping Zhou, Zheng Wei, Huajin Lei, Fangyun Ma and Wei Li
Surrogate models are extensively used to substitute real models which are expensive to evaluate in the time-dependent reliability analysis. Normally, different surrogate models…
Abstract
Purpose
Surrogate models are extensively used to substitute real models which are expensive to evaluate in the time-dependent reliability analysis. Normally, different surrogate models have different scopes of application. However, information is often insufficient for analysts to select the most appropriate surrogate model for a specific application. Thus, the result precited by individual surrogate model tends to be suboptimal or even inaccurate. Ensemble model can effectively deal with the above concern. This work aims to study the application of ensemble model for reliability analysis of time-independent problems.
Design/methodology/approach
In this work, a method of reliability analysis for time-dependent problems based on ensemble learning of surrogate models is developed. The ensemble of surrogate models includes Kriging, radial basis function, and support vector machine. The prediction is approximated by the weighted average model. The ensemble learning of surrogate models is updated by finding and adding the sample points with large prediction errors throughout the entire procedure.
Findings
The effectiveness of the proposed method is verified by several examples. The results show that the ensemble of surrogate models can effectively propagate the uncertainty of time-varying problems, and evaluate the reliability with high prediction accuracy and computational efficiency.
Originality/value
This work proposes an adaptive learning framework for the uncertainty propagation of time-dependent problems based on the ensemble of surrogate models. Compared with individual surrogate models, the ensemble model not only saves the effort of selecting an appropriate surrogate model especially when the knowledge of unknown problem is lacking, but also improves the prediction accuracy and computational efficiency.
Details
Keywords
Zishuo Han, Chunping Wang and Qiang Fu
The purpose of this paper is to use the most popular deep learning algorithm to complete the vehicle detection in the urban area of MiniSAR image, and provide reliable means for…
Abstract
Purpose
The purpose of this paper is to use the most popular deep learning algorithm to complete the vehicle detection in the urban area of MiniSAR image, and provide reliable means for ground monitoring.
Design/methodology/approach
An accurate detector called the rotation region-based convolution neural networks (CNN) with multilayer fusion and multidimensional attention (M2R-Net) is proposed in this paper. Specifically, M2R-Net adopts the multilayer feature fusion strategy to extract feature maps with more extensive information. Next, the authors implement the multidimensional attention network to highlight target areas. Furthermore, a novel balanced sampling strategy for hard and easy positive-negative samples and a global balanced loss function are applied to deal with spatial imbalance and objective imbalance. Finally, rotation anchors are used to predict and calibrate the minimum circumscribed rectangle of vehicles.
Findings
By analyzing many groups of experiments, the validity and universality of the proposed model are verified. More importantly, comparisons with SSD, LRTDet, RFCN, DFPN, CMF-RCNN, R3Det, SCRDet demonstrate that M2R-Net has state-of-the-art detection performance.
Research limitations/implications
The progress in the field of MiniSAR application has been slow due to strong speckle noise, phase error, complex environments and a low signal-to-noise ratio. In addition, four kinds of imbalances, i.e. spatial imbalance, scale imbalance, class imbalance and objective imbalance, in object detection based on the CNN greatly inhibit the optimization of detection performance.
Originality/value
This research can not only enrich the means of daily traffic monitoring but also be used for enemy intelligence reconnaissance in wartime.