Enhanced CNN for image denoising

Owing to flexible architectures of deep convolutional neural networks (CNNs), CNNs are successfully used for image denoising. However, they suffer from the following drawbacks: (i) deep network architecture is very difficult to train. (ii) Deeper networks face the challenge of performance saturation. In this study, the authors propose a novel method called enhanced convolutional neural denoising network (ECNDNet). Specifically, they use residual learning and batch normalisation techniques to address the problem of training difficulties and accelerate the convergence of the network. In addition, dilated convolutions are used in the proposed network to enlarge the context information and reduce the computational cost. Extensive experiments demonstrate that the ECNDNet outperforms the state-of-the-art methods for image denoising.Comment: CAAI Transactions on Intelligence Technology[J], 201

Improvement of peptide identification with considering the abundance of mRNA and peptide

Scripts used for data analysis in this study. (DOCX 35 kb

Research on reconfigurable control for a hovering PVTOL aircraft

This paper presents a novel reconfigurable control method for the planar vertical take-off and landing (PVTOL) aircraft when actuator faults occur. According to the position subsystem within the multivariable coupling, and the series between subsystems of position and attitude, an active disturbance rejection controller (ADRC) is used to counteract the adverse effects when actuator faults occur. The controller is cascade and ensures the input value of the controlled system can be tracked accurately. The coordinate transformation method is used for model decoupling due to the severe coupling. In addition, the Taylor differentiator is designed to improve the control precision based on the detailed research for tracking differentiator. The stability and safety of the aircraft is much improved in the event of actuator faults. Finally, the simulation results are given to show the effectiveness and performance of the developed method

Two-sided jumps risk model with proportional investment and random observation periods

In this paper, we consider a two-sided jumps risk model with proportional investments and random observation periods. The downward jumps represent the claim while the upward jumps represent the random returns. Suppose an insurance company invests all of their surplus in risk-free and risky investments in proportion. In real life, corporate boards regularly review their accounts rather than continuously monitoring them. Therefore, we assume that insurers regularly observe surplus levels to determine whether they will ruin and that the random observation periods are exponentially distributed. Our goal is to study the Gerber-Shiu function (i.e., the expected discounted penalty function) of the two-sided jumps risk model under random observation. First, we derive the integral differential equations (IDEs) satisfied by the Gerber-Shiu function. Due to the difficulty in obtaining explicit solutions for the IDEs, we utilize the sinc approximation method to obtain the approximate solution. Second, we analyze the error between the approximate and explicit solutions and find the upper bound of the error. Finally, we discuss examples of sensitivity analysis