A Predictor-Corrector Finite Element Method for Time-Harmonic Maxwell’s Equations in Polygonal DomainsRead the full article
Mathematical Problems in Engineering is a broad-based journal publishes results of rigorous engineering research across all disciplines, carried out using mathematical tools.
Chief Editor, Professor Guangming Xie, is currently a full professor of dynamics and control with the College of Engineering, Peking University. His research interests include complex system dynamics and control and intelligent and biomimetic robots.
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A Novel Attentive Generative Adversarial Network for Waterdrop Detection and Removal of Rubber Conveyor Belt Image
The lens for monitoring the rubber conveyor belt is easy to adhere to a large number of water droplets, which seriously affects the image quality and then affects the effect of fault monitoring. In this paper, a new method for detecting and removing water droplets on rubber conveyor belts based on the attentive generative adversarial network is proposed to solve this problem. First, the water droplet image of the rubber conveyor belt is input into the generative network composed of a cyclic visual attentive network and an autoencoder with skip connections, and an image of removing water droplets and an attention map for detecting the position of the water droplet are generated. Then, the generated image of removing water droplets is evaluated by the attentive discriminant network to assess the local consistency of the water droplet recovery area. In order to better learn the water droplet regions and the surrounding structures during the training, the image morphology is added to the precise water droplet regions. A dewatered rubber conveyor belt image is generated by increasing the number of circular visual attention network layers and the number of skip connection layers of the autoencoder. Finally, a large number of comparative experiments prove the effectiveness of the water droplet image removal algorithm proposed in this paper, which outperforms of Convolutional Neural Network (CNN), Discriminative Sparse Coding (DSC), Layer Prior (LP), and Attention Generative Adversarial Network (ATTGAN).
A Total Fractional-Order Variation Regularized Reconstruction Method for CT
The total variation (TV) regularized reconstruction methods for computed tomography (CT) may lead to staircase effects in the reconstructed images because of using the TV regularization. This paper develops a total fractional-order variation regularized CT reconstruction method, aiming at overcoming the weakness of the reconstruction methods based on the TV. Specifically, we propose an optimization model for CT reconstruction, including a fidelity term, a regularization term, and a constraint term. Here, the regularization is a total fractional-order variation arising from the fractional derivative of the underlying solution. To address the nondifferentiability of the resulting model, we introduce a fixed-point characterization for its solution through the proximity operators of the nondifferentiable functions. Based on the characterization, we further develop a fixed-point iterative scheme to solve the resulting model and provide convergence analysis of the developed scheme. Numerical experiments are presented to demonstrate that the developed method outperforms the TV regularized reconstruction method in terms of suppressing noise for CT reconstruction.
Research on China’s Ecological Welfare Performance Evaluation and Improvement Path from the Perspective of High-Quality Development
Based on the data of China’s ecological environment from 2006 to 2018, the paper uses the super-efficiency DEA and Malmquist index methods to evaluate China’s ecological welfare performance from a static and dynamic perspective. Based on this, the Theil index is used to analyze the group’s ecological welfare performance. The internal and intergroup differences show that, from the static evaluation results, China’s ecological welfare performance is in a situation of “high in the east, low in the west, and average in the central region.” There is not much difference between the eastern and central regions, while the ecological welfare performance in the western region is low. From the results of dynamic evaluation, the overall level of regional ecological welfare performance in China has improved in recent years, and the average Malmquist index has exceeded 1, indicating that the growth pattern of ecological welfare performance has shifted to high quality, but the degree of increase in each region is different. There is still much room for improvement in ecological welfare performance; from the perspective of intragroup and intergroup differences, the intragroup differences and intergroup differences in the three major regions have generally maintained a continuous downward trend, and the contribution of the differences in ecowelfare performance between group rate has a clear advantage. Finally, corresponding suggestions are put forward based on the empirical results of the paper.
Study on Design and Diamond Turning of Optical Freeform Surface for Progressive Addition Lenses
Optical freeform surface components have attracted much attention due to their high degree of design freedom and small size. However, the design and processing difficulty of such components limit its wide application in optics industry. In recent years, diamond turning has been considered an efficient method for processing optical freeform surfaces, but the research on tool path generation of this processing method is not systematic. Progressive addition lens (PAL) is a typical optical freeform surface and is widely used to correct people’s vision problems. Firstly, this paper introduces a method of designing PAL. Then, an optimized tool path generation method for diamond turning of the optical freeform surface is proposed, the equal angle method is used to select the discrete points, and a tool nose radius compensation method suitable for both slow slide servo (SSS) and fast tool servo (FTS) is adopted. Finally, the turning experiment is carried out with a single point diamond lathe, and a PAL surface with a roughness of 0.087 μm was obtained. The power and astigmatism distributions were measured using a Rotlex freeform verifier to verify the rationality of the optical design.
Finite-Time Speed Control of Marine Diesel Engine Based on ADRC
In this paper, in order to handle the nonlinear system and the sophisticated disturbance in the marine engine, a finite-time convergence control method is proposed for the diesel engine rotating speed control. First, the mean value model is established for the diesel engine, which can represent response of engine fuel injection to engine speed. Then, in order to deal with parameter perturbation and load disturbance of the marine diesel engine, a finite-time convergence active disturbance rejection control (ADRC) is proposed. At the last, simulation experiments are conducted to verify the effectiveness of the proposed controller under the different load disturbances for the 7RT-Flex60C marine diesel engine. The simulation results demonstrate that the proposed control scheme has better control effect and stronger anti-interference ability than the linear ADRC.
Data-Driven Optimal Control for Pulp Washing Process Based on Neural Network
Pulp washing process has the features of multivariate, time delay, nonlinearity. Considering the difficulties of modeling and optimal control in pulp washing process, a data-driven operational-pattern optimization method is proposed to model and optimize the pulp washing process in this paper. The most important quality indexes of pulp washing performance are residual soda in the washed pulp and Baume degree of extracted black liquor. Considering the difficulties of modeling, online measurement of these indexes, two-step neural networks, and multivariate logistic regression are used to establish the prediction models of residual soda and Baume degree. The mathematical model of the washing process can be identified, and the indexes can meet the production requirements. In the target of better product quality, low cost, and low energy consumption, a multiobjective problems is solved by ant colony optimization algorithm based on the optimized operational-pattern database. It shows that the theoretical analyses are correct and the practical applications are feasible, optimization control system has been designed for the pulp washing process, and the practical results show that pulp production increased by 20% and water consumption decreased by nearly 30%. This method is effective in the pulp washing process.