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This article solves the problem of almost sure exponential stabilization via output feedback control for stochastic feedforward nonlinear systems with the unknown growth rate. A novel dynamic gain is introduced not only to cope with system uncertainties but also to ensure exponential convergence. Based on this dynamic gain, an adaptive output feedback controller is explicitly constructed. By introducing an improved stochastic analysis method, we rigorously prove that the closed-loop system has an almost surely unique solution, all the closed-loop signals are bounded almost surely, and the solution of the closed-loop system tends to the equilibrium position exponentially fast.

In this article, we study the adaptive stability for parabolic partial differential equation (PDE)-ordinary differential equation (ODE) cascade systems with actuator dynamics, where the actuator dynamics are nonlinear subject to unknown parameters. Compared with a class of PDE–ODE coupled systems that the control input only acts on the PDE boundary and the linear sandwiched system without uncertainty, the structure of such systems is more complex. First of all, infinite-dimensional backstepping transformation is adopted. The original PDE-ODE cascade system is changed to a new system that is easier to design. On this basis, finite-dimensional backstepping transformation and adaptive compensation technology are combined to develop a state-feedback controller. Then, the boundedness of all the signals in the closed-loop system is proved by the Lyapunov functional analysis. Furthermore, the control law and the original system states eventually converge to zero. Finally, different simulation data are presented to illustrate the validity of the theoretical results.

Dear Editor, This letter deals with the output feedback stabilization of a class of high-order nonlinear time-delay systems with more general low-order and high-order nonlinearities. By constructing reduced-order observer, based on homogeneous domination theory together with the adding a power integrator method, an output feedback controller is developed to guarantee the equilibrium of the closed system globally uniformly asymptotically stable.

Accumulating evidences show that circular RNAs (circRNAs) play an important role in regulating gene expression, and involve in many complex human diseases. Identifying associations of circRNA with disease helps to understand the pathogenesis, treatment and diagnosis of complex diseases. Since inferring circRNA-disease associations by biological experiments is costly and time-consuming, there is an urgently need to develop a computational model to identify the association between them. In this paper, we proposed a novel method named KNN-NMF, which combines $K\ $Knearest neighbors with nonnegative matrix factorization to infer associations between circRNA and disease (KNN-NMF). Frist, we compute the Gaussian Interaction Profile (GIP) kernel similarity of circRNA and disease, the semantic similarity of disease, respectively. Then, the circRNA-disease new interaction profiles are established using weight $K$K nearest neighbors to reduce the false negative association impact on prediction performance. Finally, Nonnegative Matrix Factorization is implemented to predict associations of circRNA with disease. The experiment results indicate that the prediction performance of KNN-NMF outperforms the competing methods under five-fold cross-validation. Moreover, case studies of two common diseases further show that KNN-NMF can identify potential circRNA-disease associations effectively.

Motivated by the thermoelastic coupling effect arising in microelectromechanical systems (MEMS), event-triggered control of second-order ODE-heat systems coupled at intermediate point is investigated. The event-triggered control includes two parts, one is the feedback control signal, and the other is the event-triggered mechanism that decides when to update the control input. First, we design an event-triggered controller by using Zero-Order Hold to a continuous-time controller. Then, a dynamic triggering condition is established. Next, we derive that the existence of a minimal dwell-time, which avoids the occurrence of Zeno behavior. By utilizing Lyapunov-Krasovskii functional method, the global exponential stability of the closed-loop system is demonstrated. Finally, the simulation data based on the thermoelastic coupling system is displayed to demonstrate the availability of the theoretical results.

This article investigates the problem of global neural network (NN) tracking control for uncertain nonlinear systems in output feedback form under disturbances with unknown bounds. Compared with the existing NN control method, the differences of the proposed scheme are as follows. The designed actual controller consists of an NN controller working in the approximate domain and a robust controller working outside the approximate domain, in addition, a new smooth switching function is designed to achieve the smooth switching between the two controllers, in order to ensure the globally uniformly ultimately bounded of all closed-loop signals. The Lyapunov analysis method is used to strictly prove the global stability under the combined action of unmeasured states and system uncertainties, and the output tracking error is guaranteed to converge to an arbitrarily small neighborhood through a reasonable selection of design parameters. A numerical example and a practical example were put forward to verify the effectiveness of the control strategy.

This paper addresses the problem of adaptive neural output-feedback decentralized control for a class of strongly interconnected nonlinear systems suffering stochastic disturbances. An state observer is designed to approximate the unmeasurable state signals. Using the approximation capability of radial basis function neural networks (NNs) and employing classic adaptive control strategy, an observer-based adaptive backstepping decentralized controller is developed. In the control design process, NNs are applied to model the uncertain nonlinear functions, and adaptive control and backstepping are combined to construct the controller. The developed control scheme can guarantee that all signals in the closed-loop systems are semiglobally uniformly ultimately bounded in fourth-moment. The simulation results demonstrate the effectiveness of the presented control scheme.

The selection of an optimal material is an important aspect of design for mechanical, electrical, thermal, chemical or other application. Many factors (attributes) need to be considered in material selection process, and thus material selection problem is a multi-attribute decision making (MADM) problem. This paper proposes a new MADM method for material selection problem. G1 method does not need to test consistency of the judgment matrix. Thus it is better than AHP. In this paper, firstly, we use the G1 method to determine the attribute weight. Then TOPSIS method is used to calculate the closeness of the candidate materials with respect positive solution. A practical material selection case is used to demonstrate the effectiveness and feasibility of the proposed method.

Aiming at some boiler tubescrack in a power plant, the element composition and the valence of elements in corrosion products on the outer surfaces of the boiler tubes were analysed by Scanning Electron Microanalyzer (SEM) and X-ray Photoelectron Spectroscopy (XPS), and the corrosion reason and mechanism of the outer surfaces of the boiler tubes were studied.The results showed as follows. (1)The content of element S in the corrosion products on the outer surfaces of the fire side boiler tubes was bigger than that in the corrosion products on the outer surfaces of the back side boiler tubes and the corrosion of the outer surfaces of the fire side boiler tubes was more serious than that of the outer surfaces of the back side boiler tubes. (2) the element S in the corrosion products existed in the form of sulfate (SO42-), because the valence of S in the corrosion products on the outer surfaces of the fire side boiler tubes and the back side boiler tubes was +6, not-2. (3) the element Fe in the corrosion products on the outer surfaces of the fire side boiler tubes and the back side boiler tubes existed in the form of Fe3O4, Fe2O3 or Fe2(SO4)3. (4) The corrosion of the boiler tubes was induced by sulphate under high temperature, and aggravated with the temperature of the outer surfaces of the boiler tubes elevated.

The definition and the sources of the particulate pollutant in the transformer oil were described in the article. The prevention and elimination of the particulate pollutant were introduced as well.

The mechanism of oil flow electrification in transformers was described in this paper. The mechanism of oil flow electrification in transformers could be understood from the effect of the oil flow’s movement and the electromotive action of AC electric field. The electric polarity of oil flow and the insulating layer were mainly depended on the state of charged cations and anions in oil, while the surface characteristics of solid materials only played a supporting adsorption role.