Search

Non-orthogonal multiple access (NOMA)-based device-to-device (D2D) network has attracted widespread attention since it can address the problem of spectrum shortage in the next-generation communication networks. However, the robust resource allocation problem in this network has not been well investigated. In this paper, we aim for maximizing the energy efficiency (EE) of a NOMA-based D2D network with simultaneous wireless information and power transfer technique under imperfect channel state information. The considered problem is modeled as a non-convex optimization problem that considers the maximum tolerable outage probability of each D2D user (DU), the successive interference cancellation decoding order, and the maximum transmit power of base station and DUs, where the transmit power, power splitting factor, and resource block assignment factor are jointly optimized. Since the formulated mixed-integer fractional programming problem with outage probability constraints is non-convex and difficult to solve, we firstly transform it into a non-probabilistic problem through a relaxation approach, and then, transform it into a convex one by using the variable-substitution approach and Dinkelbach’s method. Finally, an EE-based iterative algorithm is proposed to solve this intractable problem. Simulation results show that the proposed algorithm has a fast convergence and low outage probability.

With the rapid development of renewable energy industry, the proton exchange membrane fuel cell (PEMFC) has broad application prospects due to its the advantages of high-power density, zero emission, easy refueling and quick start. Optimizing oxygen excess ratio (OER) control of proton exchange membrane fuel cell (PEMFC) is a key problem to improve their economic efficiency and performance. The work of this paper focuses on the following aspects. Taking OER as the control objective, we proposed a control system based on adaptive fuzzy PID control (AFPID) to improve PEMFC performance by adjusting OER to avoid starvation, reduce parasitic power and obtain more net available power, which proves the stability of the system under various operation conditions. In addition, the control performance of the controller was compared with the feedforward and the conventional PID control. This algorithm used adaptive method to improve compressor efficiency under uncertain conditions. The results shown that the control strategy has fine control effect.

A methodology of the the Equivalent-Input-Disturbance control system based on H-infinity $(H_{\infty})$ is proposed in this paper. This design method aims to solve the problem that the parameters of the control system lack unified standard to set. It converts the controller design problem into a convex optimization problem with LMI constraints. The parameters of the control system are obtained by using the LMI toolbox in Matlab. Firstly, the model of the Equivalent-Input-Disturbance system containing state feedback controller is built; secondly, the controller design problem is transformed into a standard $H_{\infty}$ state-feedback design problem and a mathematical model of the transformed $H_{\infty}$ standard control model is established; finally, under the $H_{\infty}$ formulation, the state feedback gain is obtained by solving the LMI constraint inequations and the stability is considered based on the small gain theorem. Simulation results show that the proposed method is effective and easy to implement.

In this paper, an equivalent distorted Born iteration method (DBIM) is proposed to solve inverse scattering problem based on volume integral equation (VIE) method. For its forward problem, the object region is regarded as the scatterer and free space is considered to be the background, which relates to a free space Green's function. The equivalent DBIM inversion model is constructed base on the forward model which has the same convergent rate with the conventional DBIM. Differently, the integration kernel, the free space Green's function, remains unchanged in the equivalent DBIM while that of the conventional DBIM need to be computed and updated in every iterations. In this way, the computational complexity of the DBIM has been reduced remarkably, and it is easy to be employed in solving three-dimensional (3-D) inverse problem.

An improved high resolution fault diagnostic approach is presented, which contains two parts. First, a new time-frequency analysis named self-commissioning short time zoom matrix pencil method has been proposed. The analysis has high computing efficiency and maintains high spectral resolution, and it is completely free of human maneuver in dealing with nonstationary and quasi-stationary operating conditions. Vast estimates can be generated from a very finite length of measured data. Second, a feature extraction analysis is presented to settle the common problem of high resolution spectral approaches in quasi-stationary operating condition, where noise, fluctuations of loads and rotor speed contribute to judgement discrepancy or failure in diagnosis results. The feature extraction analysis provides approximately unbiased fault-related frequencies and sideband amplitudes by introducing Monte Carlo method and specific distribution fitting. The entire approach has been validated on interior permanent magnet motors with different degrees of rotor eccentricity in experiments.

Gas turbine engine blades are subjected to severe fatigue which is caused by complex in-service environment. In fact, blade crack is one of the costliest sources of damage in gas turbine. Detection of cracks of blades is important to ensure safe and reliable operation of gas turbine engine. A vibration-based damage-detection methodology is presented in this paper to monitor the health condition of gas turbine rotor blade. In order to simulation the real-time dynamic crack propagation of rotor blades, model analysis was performed using a finite element model to calculate the natural frequencies and mode shapes of gas turbine blade, the possible crack initiation location was determined, the natural frequencies and mode shapes changed as the crack growing, indicating the feasibility of the methodology to capture the dynamic health condition of blade on-line. Simulation results have indicated the capability of the methodology in evaluating the changes of blade vibration signals once damage is initiated of growing, and in consistently detecting cracks of damaged blade to monitor the health condition. Additionally, the results indicated that blade natural frequency decays slightly when crack propagating, providing the potential for reliable diagnosis of the gas turbine blades with crack fault, thus the implementation of blade health monitoring system will be the approach to achieve condition-based maintenance.

In this paper, a mathematical model is established to describe the dynamics of the dengue fever with quarantine measures. Firstly, we discuss the existence and the local stability of the equilibria of the model. Secondly, by constructing Lyapunov function, we gain the sufficient conditions for global stability of the equilibria.

A new augmented combined field integral equation formulation to address the low frequency instability issue is presented. The new integral equation formulation is achieved by combing the magnetic field integral operator and the augmented electric field integral equation (A-EFIE). In addition to that, a constraint preconditioning is also introduced to fast up the convergence speed of the system matrix. Numerical examples are given to show the validity and efficiency of the proposed formulation.

A low complexity Maximum Likelihood (ML) method for monostatic MIMO radar has been proposed in this paper. In order to reduce the computational burden and maintain the accuracy of solution, the Beam Space Reduced Complexity ML algorithm (BS-RC ML) is developed for monostatic MIMO radar. The reduced-complexity ML method takes full advantage of the Reduced-dimension transformation and Beam-Space processing to reduce the dimension of the manifold vector from MN X K to B X K (B « MN). As the results, the lower computational cost and same angle estimation accuracy have been obtained. Some simulation results have been given to show the effectiveness of the proposed method. The strategy of reducing complexity in ML method can also be applied to some other angle estimation algorithms for MIMO radar.

Distributed file system has the characteristics of large capacity, good scalability and high reliability, which make it widely used in many areas involving large-scale data storage. It offers simplified, highly-available services for users to access data. However, due to the non-metadata design, the performance of traversal operation on large directories in those non-metadata distributed file systems is poor. With the increasing amount of files, it severely affects the user experience. In this paper, we present a directory prefetching mechanism on the client side to reduce directory traversal operation latency in non-metadata distributed file system. The mechanism, combined with the client's cache, adopts the directory access history to predict future access pattern and fetches the content of the directory without user intervention. Our goal is to reduce the overall access latency in the non-metadata distributed file system in order to better satisfy the user experience.

In this paper, matrix pencil (MP) algorithm is comprehensively treated for the diagnosis of IM rotor faults. It requires a short length of current data and has a strong ability to eliminate noise. However, the main defect of MP is that the threshold in its singular value truncation needs trial and error and is subjected to operating conditions. To solve the problem, an adaptive threshold auto-tuning method based on information entropy is presented. Then an adaptive MP methodology is developed for detecting mixed rotor faults, including bar breakages and rotor eccentricities without frequency leakage. Experimental results have validated the effectiveness under different operating speeds and load conditions. The auto-adjusted truncation threshold is well adapted to different operating conditions and the frequency accuracy of the identified fault characteristics is high at most points of interest. Clear features of IM mixed rotor faults can be revealed by the proposed technique.

This paper is intended to solve the problem of the stochastic disturbance suppression for the heating furnace temperature control existed in the billet heating process. It aims to propose an active compensation control method based on adaptive reset state observer (ARSO) for stochastic disturbance suppression. The structure of the active compensation control system (ACCS) for the stochastic disturbance suppression is presented and an ARSO is designed. In this method, an equivalent input disturbance (EID) compensation is drawn into the input channel of the ACCS to eliminate the influence of the stochastic disturbance by real-time estimation of the outside stochastic disturbance. With consider the strong nonlinear, large time delay, big inertia of the heating furnace, an adaptive parameter is introduced to the state observer, so that the states are reset based on the sector reset condition. For the parameters of the ACCS, the stability of the ACCS is analysed by the Lyapunov function, then yields a set of linear matrix inequality (LMI). The simulation results show that this method not only well suppress the influence of stochastic disturbance in the process of billet heating, but also meet the requirements of stability and accuracy.

This paper deals with the problem of robust variance control design for a class of uncertain nonlinear discrete time-delay systems. The system under consideration involves state time delay, nonlinear, parameter uncertainties and white noise disturbances. The purpose of this problem is to design a static state feedback controller such that not only the resulting closed-loop system is asymptotically stable, but also the steady-state variance of each state is not more than the individual pre-specified value simultaneously. Using the linear matrix inequality approach in this paper, the existence conditions of such controllers are derived. Furthermore, a numerical example is given to illustrate the effectiveness of the proposed approach.

The failure of floating wind turbine can suspend the operation of the offshore wind turbine generator, which will severely affect the generated energy and cause huge economic loss. Hence, the reliability evaluation of floating wind turbine is of vital importance. RPN calculation which adopts the traditional FMEA method carries significant limitations and cannot solve failure sequence in case RPN is the same. Therefore, the paper makes use of fuzzy FMEA method based on Related Weights and TOPSIS Theory to make fuzzy evaluation on each failure mode of floating wind turbine as well as construct a failure matrix which adopts triangular fuzzy number. This paper utilizes Related Weights that come from different experts to different failure modes and TOPSIS Theory to calculate similarity algorithm to replace RPN value to sequence the risks. Results show that fuzzy FMEA method that applies Related Weights and TOPSIS Theory can more accurately judge the degree of risks of floating wind turbine system's failure mode to avoid the effect of subjectivity on evaluation results to the extreme extent. In this way, the more effective maintenance strategy can be formulated so as to improve the system's reliability.

SQL injection exploits the weakness of server, causing damage to database and threating the benefit of both corporations and individuals. SQL injection attack is still one of the most serious security threats. This paper proposes a method with static analysis and dynamic monitor to detect SQL injection attacks. Comparing to the current tools, this tool can compute danger degree of user input and classify user behavior with lower time and space complexity.

The article discussed the product development opportunity and challenges that Chinese small and medium-sized enterprise, which mainly export light industry products, faced under the influence of the financial crisis. And after the product development environments that these enterprises faced were discussed, a mode of spontaneous design management was put forward and analysed briefly. The application of spontaneous design management not only can help the small and medium-sized enterprises pull through this recession but also help them set up big brands and good impression in the international markets. And it also can help China realize the translation from Made in China to Created in China.

A new method for image restoration based on robust error function and BP neural network optimized with particle swarm optimization (PSO) is proposed in this paper. In this technique, BP neural network uses a robust error function as its error function, and then the neural network optimized with PSO. This method can minimize an evaluation function established based on an observed image. The proposed method takes into consideration point spread function (PSF) blurring as well as an additive random noise and obtains restoration image with more preserved image details. Experimental results demonstrate that the proposed new method can have a very high quality both in the visual qualitative performance and the quantitative performance than the traditional algorithms.