752 publications from this institution
No abstract is provided for this article.
Cuckoo search (CS) is among the most widely used nature-inspired algorithms. CS is based on the co-evolution of cuckoo species and their interactions with host species. This chapter introduces the basic principle of the CS and its variants. A demo implementation is also provided.
No abstract is provided for this article.
There exists a paradox in dip moveout (DMO) in seismic data processing. The paradox is why Notfors and Godfrey's approximate time log-stretched DMO can produce better impulse responses than the full log DMO, and why Hale's f-k DMO is correct although it was based on two inaccurate assumptions for the midpoint repositioning and the DMO time relationship? Based on the asymptotic analysis of the DMO algorithms, we find that any form of correctly formulated DMO must handle both space and time coordinates properly in order to deal with all dips accurately. The surprising improvement of Notfors and Godfrey's log DMO on Bale and Jakubowicz's full log DMO was due to the equivalent midpoint repositioning by transforming the time-related phase shift to the space-related phase shift. The explanation of why Hale's f-k DMO is correct although it was based on two inaccurate assumptions is that the two approximations exactly cancel each other in the f-k domain to give the correct final result.
Permeability is an important material property of porous and granular materials such as rocks, sands, and soils. The accurate measurement or estimations are very important in modeling the flow through porous media as its relevance to hydrology, soil mechanics, oil industry, and environment protections. There are two fundamental ways to obtain the estimation of permeability: experimental measurement and estimation via theoretical modeling. The experimental method measures the flow through a small sample in a laboratory, then the permeability is calculated. The permeability obtained this way is the averaged result from the representative volume and is not generally applicable in real large flow simulations. The upscaling is to try to bridge the gap between laboratory results and the field parameters. The theoretical estimation of permeability provides a good approximation by using an idealized networks of tubes/pipes through which the fluid flows, and the flow in the pipes is considered as 1-D flow with prescribed pressure or boundary conditions. The networks are generally treated as regular although recent studies begin focus on the fractal structure or statistical features. However, it is very difficult to model the detail flow patterns in random media where different sizes of particles and different porosities are presented. The common approach is to use an averaged representative volume with regular averaged particle size and uniform porosity. Clearly, this is far from the reality in the random porous media. This paper presents a new discrete element approach to the permeability modeling using spherical particles saturated in viscous fluids and treating the interactions among the particles and between fluid and particles in a discrete element manner. For a given particle size distribution, the porosity is calculated. By using the proper boundary conditions, the liquid flux is computed, and the permeability is then estimated. This will in turn give a relationship between permeability and porosity. The simulated results via discrete element method will be compared with experimental results or well-known Kozeny-Carman equations.
Many problems in science and engineering can be modeled in terms of differential equations and thus it is important to introduce the fundamentals of differential equations and related solution techniques.
Test functions are important to validate new optimization algorithms and to compare the performance of various algorithms. There are many test functions in the literature, but there is no standard list or set of test functions one has to follow. New optimization algorithms should be tested using at least a subset of functions with diverse properties so as to make sure whether or not the tested algorithm can solve certain type of optimization efficiently. Here we provide a selected list of test problems for unconstrained optimization.
No abstract is provided for this article.
Swarm intelligence (SI) and bio-inspired computing in general have attracted great interest in almost every area of science, engineering, and industry over the last two decades. In this chapter, we provide an overview of some of the most widely used bio-inspired algorithms, especially those based on SI such as cuckoo search, firefly algorithm, and particle swarm optimization. We also analyze the essence of algorithms and their connections to self-organization. Furthermore, we highlight the main challenging issues associated with these metaheuristic algorithms with in-depth discussions. Finally, we provide some key, open problems that need to be addressed in the next decade.
A survey of various conjugate gradient (CG) algorithms is presented for the minimum/maximum eigen-problems of a fixed symmetric matrix. The CG algorithms are compared to a commonly used conventional method found in IMSL. It is concluded that the CG algorithms are more flexible and efficient than some of the conventional methods used in adaptive spectrum analysis and signal processing.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
No abstract is provided for this article.
A new resident virus propagation model with graded infection rate is established based on the properties of two important stages undergoing by resident virus,which are the latent phase,in which resident virus cannot infect other hosts because it has not yet been loaded into memory and the active phase,in which resident virus resides in memory and infects any suitable program that is executed on the computer.Two computer compartments with different infection rate are established.Furthermore,the dynamic behaviors of this model are investigated by stability theory and numerical simulations.It is found that the dynamical properties of this model are determined by basic reproductive rate.Specifically,virus-free equilibrium is globally asymptotically stable if basic reproductive rate is less than or equal to one,whereas the local asymptotical stability of the viral equilibrium is guaranteed if basic reproductive rate is bigger than one,followed by a conjecture on its global stability.Then the sensitivity analysis of basic reproductive rate to the system parameters is investigated and a collection of policies is advised to control the spread of computer virus over the Internet.
No abstract is provided for this article.
The small-world networks recently introduced by Watts and Strogatz [Nature 393 (1998) 440–442] have attracted much interest in studying the interesting properties of the networks without time-delay. However, a signal or influence travelling on the small-world networks often associated with time-delay features which are very common in biological and physical networks. We develop an analytical approach as well as numerical simulations to try to characterise the effect of time-delay on the properties of small-world networks. An analytical expression of the fractal dimension of the small-world networks is given and thus compared with the results from numerical simulations. Analysis shows that small-world networks with time-delay generally have the multifractals property.
