A new cuckoo search and its application of spread spectrum radar polly phase code design

Abstract: The Lévy flight was used in cuckoo search to achieve good optimization performance. Although the optimization performance of cuckoo search algorithm is good based on the Lévy flight some complicated mathematical operations should be used to realize the Lévy flight such as trigonometric function, gamma function and exponential functions, which limited the application of the cuckoo search algorithm especially there is high requirement about computation complex like in the embedded systems. The Lévy flight is replaced by a simple uniform distribution function based on the randomly chosen dimension and a local search method is applied to improve the optimization performance although the general structure of cuckoo search algorithm is not changed. The proposed cuckoo search algorithm is applied to several benchmark functions and the simulation results show the simplified cuckoo search algorithm can achieve better optimization performance than the original cuckoo search algorithm. Finally the simplified cuckoo search algorithm is applied to the spread spectrum radar Polly phase code design

Analysis of the Qi three dimensional chaotic system

Abstract: This paper applies the center manifold theorem to reduce the dimensions of the Qi three-dimensional system. Local bifurcation phenomena are analyzed, including the pitchfork and Hopf bifurcations of the chaotic system. The Poincaré map is also investigated. The analyses demonstrate the rich dynamics of the Qi chaotic system. Finally, the frequency spectral analysis shows that the system has a broad frequency bandwidth, which is desirable for engineering applications such as secure communications

The focusing of electron flow in a bipolar Graphene ribbon with different chiralities

The focusing of electron flow in a symmetric p-n junction (PNJ) of graphene ribbon with different chiralities is studied. Considering the PNJ with the sharp interface, in a armchair ribbon, the electron flow emitting from $(-L,0)$ in n-region can always be focused perfectly at $(L,0)$ in p-region in the whole Dirac fermion regime, i.e. in whole regime $E_0<t$ where $E_0$ is the distance between Dirac-point energy and Fermi energy and $t$ is the nearest hopping energy. For the bipolar ribbon with zigzag edge, however, the incoming electron flow in n-region is perfectly converged in p-region only in a very low energy regime with $E_0<0.05t$. Moreover, for a smooth PNJ, electrons are backscattered near PNJ, which weakens the focusing effect. But the focusing pattern still remains the same as that of the sharp PNJ. In addition, quantum oscillation in charge density occurs due to the interference between forward and backward scattering. Finally, in the presence of weak perpendicular magnetic field, charge carriers are deflected in opposite directions in the p-region and n-region. As a result, the focusing effect is smeared. The lower energy $E_0$, the easier the focusing effect is destroyed. For the high energy $E_0$ (e.g. $E_0=0.9t$), however, the focusing effect can still survive in a moderate magnetic field on order of one Tesla.Comment: 29 pages, 16 figure

Symmetry and transport property of spin current induced spin-Hall effect

We study the spin current induced spin-Hall effect that a longitudinal spin dependent chemical potential $qV_{s=x,y,z}$ induces a transverse spin conductances $G^{ss'}$. A four terminal system with Rashba and Dresselhaus spin-orbit interaction (SOI) in the scattering region is considered. By using Landauer-B$\ddot u$ttiker formula with the aid of the Green function, various spin current induced spin-Hall conductances $G^{ss'}$ are calculated. With the charge chemical potential $qV_c$ or spin chemical potential $qV_{s=x,y,z}$, there are 16 elements for the transverse conductances $G^{\mu \nu}_p=J_{p,\mu}/V_{\nu}$ where $\mu,\nu=x,y,z,c$. Due to the symmetry of our system these elements are not independent. For the system with $C_2$ symmetry half of elements are zero, when the center region only exists the Rashba SOI or Dresselhaus SOI. The numerical results show that of all the conductance elements, the spin current induced spin-Hall conductances $G^{ss'}$ are usually much greater (about one or two orders of magnitude) than the spin Hall conductances $G^{sc}$ and the reciprocal spin Hall conductances $G^{cs}$. So the spin current induced spin-Hall effect is dominating in the present device.Comment: 7 pages, 6 figure

Cask theory based parameter optimization for particle swarm optimization

Abstract: To avoid the bored try and error method of finding a set of parameters of Particle Swarm Optimization (PSO) and achieve good optimization performance, it is desired to get an adaptive optimization method to search a good set of parameters. A nested optimization method is proposed in this paper and it can be used to search the tuned parameters such as inertia weight, acceleration coefficients c1 and c2, and so on. This method considers the cask theory to achieve a better optimization performance. Several famous benchmarks were used to validate the proposed method and the simulation results showed the efficiency of the proposed method.Originally presented at Fourth International Conference on Swarm Intelligence (ICSI 2013), Harbin, China, 12-15, June, 2013

Analysis of a fractional order nonlinear system based on the frequency domain approximation

Abstract: The dynamics of nonlinear system is very complicated especially the fractional nonlinear system since they can be found in many areas of engineering and science. The dynamics of the Lorenz system with fractional derivatives is analysed based on the frequency approximation. For a given range of parameters where the non‐fractional Lorenz system has periodic orbits, it is found that the fractional Lorenz system exhibits chaos and hyperchaos. A striking finding is that the fractional Lorenz system exhibits hyperchaos, although the total system order is less than 3, which is contrary to the well known conclusion that hyperchaos cannot occur in the integer‐order continuous‐time autonomous system of order less than 4. Finally, a reasonable explanation is offered for this complicated dynamical phenomenon

Adaptive optimal digital PID controller

Abstract: It is necessary to change the parameters of PID controller if the parameters of plants change or there are disturbances. Particle swarm optimization algorithm is a powerful optimization algorithm to find the global optimal values in the problem space. In this paper, the particle swarm optimization algorithm is used to identify the model of the plant and the parameter of digital PID controller online. The model of the plant is identified online according to the absolute error of the real system output and the identified model output. The digital PID parameters are tuned based on the identified model and they are adaptive if the model is changed. Simulations are done to validate the proposed method comparing with the classical PID controller.Originally presented at 2014 International Conference on Mechatronics, Automation and Manufacturing (ICMAM 2014), Beijing, October 24-26, 2014

The spectrum of big data analytics

Big data analytics is playing a pivotal role in big data, artificial intelligence, management, governance, and society with the dramatic development of big data, analytics, artificial intelligence. However, what is the spectrum of big data analytics and how to develop the spectrum are still a fundamental issue in the academic community. This article addresses these issues by presenting a big data derived small data approach. It then uses the proposed approach to analyze the top 150 profiles of Google Scholar, including big data analytics as one research field and proposes a spectrum of big data analytics. The spectrum of big data analytics mainly includes data mining, machine learning, data science and systems, artificial intelligence, distributed computing and systems, and cloud computing, taking into account degree of importance. The proposed approach and findings will generalize to other researchers and practitioners of big data analytics, machine learning, artificial intelligence, and data science. © 2019 International Association for Computer Information Systems

Fully connected multi-objective particle swarm optimizer based on neural network

Abstract: In this paper, a new model for multi-objective particle swarm optimization (MOPSO) is proposed. In this model, each particle’s behavior is influenced by the best experience among its neighbors, its own best experience and all its components. The influence among different components of particles is implemented by the on-line training of a multi-input Multi-output back propagation (BP) neural network. The inputs and outputs of the BP neural network are the particle position and its the ’gradient descent’ direction vector to the less objective value according to the definition of no-domination, respectively. Therefore, the new structured MOPSO model is called a fully connected multi-objective particle swarm optimizer (FCMOPSO). Simulation results and comparisons with exiting MOPSOs demonstrate that the proposed FCMOPSO is more stable and can improve the optimization performance.Originally presented at Fourth International Conference on Information and Computing (ICIC 2011), Phuket Island, Thailand 25 – 27 April 2011