Dong Enzeng

A Chaotic Images Encryption Algorithm with the Key Mixing Proportion Factor

The pseudo random property of chaotic sequences is very useful in the field of information security, based on the idea of orbit hopping, a chaotic image encryption algorithm with a key mixing proportion factor is proposed. In the proposed algorithm the position scrambling matrix and the value scrambling matrix are generated by the improved chaotic logistic sequences. A key mixing proportion factor (KMPF) is generated based on the plaintext information. By adding this factor, most of cipher text elements value can be changed by the change of any plaintext element. The Security of the proposed algorithm is improved with this factor, and the ability of the proposed algorithm resisting plaintext attacks is strengthened obviously, and the encryption algorithm can resist the statistical and differential attacks effectively, moreover, the algorithm has a large key space and high encryption speed. The effectiveness of the proposed algorithm is verified by the theoretical analysis and numerical simulations.

A novel four-wing chaotic attractor generated from a three-dimensional quadratic autonomous system ⁄

This paper presents a new 3D quadratic autonomous chaotic system which contains flve system parameters and three quadratic cross-product terms, and the system can generate a single four-wing chaotic attractor with wide parameter ranges. Through theoretical analysis, the Hopf bifurcation processes are proved to arise at certain equilibrium points. Numerical bifurcation analysis shows that the system has many interesting complex dynamical behaviours; the system trajectory can evolve to a chaotic attractor from a periodic orbit or a flxed point as the proper parameter varies. Finally, an analog electronic circuit is designed to physically realize the chaotic system; the existence of four-wing chaotic attractor is verifled by the analog circuit realization.

An Improved Fitness Evaluation Mechanism with Memory in Spatial Prisoner's Dilemma Game on Regular Lattices

To deeply understand the emergence of cooperation in natural, social and economical systems, we present an improved fitness evaluation mechanism with memory in spatial prisoners dilemma game on regular lattices. In our model, the individual fitness is not only determined by the payoff in the current game round, but also by the payoffs in previous round bins. A tunable parameter, termed as the memory strength (μ), which lies between 0 and 1, is introduced into the model to regulate the ratio of payoffs of current and previous game rounds in the individual fitness calculation. When μ = 0, our model is reduced to the standard prisoners dilemma game; while μ = 1 represents the case in which the payoff is totally determined by the initial strategies and thus it is far from the realistic ones. Extensive numerical simulations indicate that the memory effect can substantially promote the evolution of cooperation. For μ < 1, the stronger the memory effect, the higher the cooperation level, but μ = 1 leads to a pathological state of cooperation, but can partially enhance the cooperation in the very large temptation parameter. The current results are of great significance for us to account for the role of memory effect during the evolution of cooperation among selfish players.

Fault detection algorithm of inverter based on singular value decomposition nonlinear filtering

Power electronic devices, power semiconductor devices and control circuits most were vulnerable to failure. In this paper, took the inverter as example, discussed its fault type, and designed a new fault detection algorithm based on singular value decomposition (SVD) nonlinear filtering. Simulation result shown that this algorithm could detect fault conveniently, and using extended Kalman Filter (EKF) based on singular value decomposition (SVD), stability of filter could be evaluated, at the same time, calculated amount was not increase, and filter precision was not decrease. This algorithm could meet the need of fault detection.

An adaptive GPC algorithm for a vectored water-jet-based spherical underwater vehicle

All kinds of interferences have enormous negative impacts on accomplishing the control of underwater vehicle, especially for small one, the influences of turbulences are more fiercely and complicate. This paper proposed a generalized predictive control algorithm that can track position along the X-Y planes by controlling the propulsion vector, which can be used to a small underwater vehicle. The algorithm adopted the online system identification and changing variables over time which were verified based on a designed vectored water-jet-based spherical underwater vehicle. Based on the established objective model, the Simulations show the virtues that can track the position and resist the white noises very well.

NCS flexible experimental system with network load controlled

Networked control system has many different characteristics compared with the traditional control system, so it is necessary to do some researches on strategies of networked control systems. In order to research in this area, establishing NCS simulation research platform is extremely important. A design scheme of NCS flexible researching platform is proposed, in which a switch and three computers are utilized to constitute a typical NCS, and the implementation of the scheme is completed well. Finally, with a typical second-order system model, some experiments under PID algorithm in different network load are carried out. From the experimental results, feasibility and effectiveness of the scheme of NCS research platform proposed in this paper are verified.

Forecast of system marginal price by a neural network with critic mechanism

A RBF neural network considering the critic mechanism is introduced to predict the system marginal price (SMP). The system consists of three elements, which are a predictor, an evaluator and a learning machine. The predictor is used to forecast the future SMP. The estimator is used to evaluate the predictions validity. The explorer is used to determine the predictive step length. And the learning machine is used to keep the predictor self-learning. So the predictor can conform to SMP by self-learning and be in a good forecasting state. The simulation shows the proposed method has higher forecasting accuracy in irregular SMP cases than the conventional method has.

Networked control system with long time delay based on IPFC

Networked control system (NCS) is a kind of closed 1oop control systems where the sensors, the controller and the actuators exchange data through network. Because the networks, in which the band width is limited, are introduced in the systems, some new issues appear unavoidably. At present, the research of networked control system becomes a focus area in control engineering. According to the characteristics of NCS, incremental predictive functional control (IPFC) strategy with random long time delay in NCS is proposed in this paper. Based on the strategy proposed controller design is given, which realized the compensation of NCS time delay. Simulation experiments are carried out, and simulation results confirmed that perfect compensation effect is obtained in the long time delay NCS with the control strategy proposed in this paper.