752 publications from this institution
This chapter introduces some of the mathematical foundations so as to help gain in-depth insight into the algorithms and their algorithmic structures in later chapters. It includes convexity, complexity, entropy, norms, probability, sampling, Monte Carlo, and others.
Biology-derived algorithms are an important part of computational sciences, which are essential to many scientific disciplines and engineering applications. Many computational methods are derived from or based on the analogy to natural evolution and biological activities, and these biologically inspired computations include genetic algorithms, neural networks, cellular automata, and other algorithms.
A common but challenging task in modelling geophysical and geological processes is to handle massive data and to minimize certain objectives. This can essentially be considered as an optimization problem, and thus many new efficient metaheuristic optimization algorithms can be used. In this paper, we will introduce some modern metaheuristic optimization algorithms such as genetic algorithms, harmony search, firefly algorithm, particle swarm optimization and simulated annealing. We will also discuss how these algorithms can be applied to various applications in earth sciences, including nonlinear least-squares, support vector machine, Kriging, inverse finite element analysis, and data-mining. We will present a few examples to show how different problems can be reformulated as optimization. Finally, we will make some recommendations for choosing various algorithms to suit various problems.
The logistic finance is the important component supporting factor of modern logistics, which represents one of the most important development direction of value -added in logistics industry.This paper firstly analyzes vital significance and theoretical foundation of the development of modern logistics financial services, then takes Ningbo City as an example and explains the comparative advantages of Ningbo City for the development of logistics financial services from the perspective of owning the Ningbo Port, favorable logistics industry, good financial environment.Next, some feasible development modes of logistics financial services in Ningbo City are put forward.At last, this paper points out some good countermeasures for the development of modern logistics financial services in Ningbo City.
This book gathers high-quality research papers presented at the Eighth International Congress on Information and Communication Technology (ICICT 2023).
The data visualization system of the sea-crossing bridge is an intuitive observation and evolution analysis of the bridge and the seabed topography, landform, and strata at the top of the tunnel around the bridge, assisting maintenance personnel to carry out operations and providing support information for decision makers. Through the fusion of multi-source seabed topography data, providing two-dimensional, three-dimensional, and infographic visualization of these data is a key link in building a visualization system. This paper mainly introduces three types of submarine terrain detection technologies, namely multiple beams, side-scan sonar, and shallow stratigraphic profile, as well as the key algorithms in the process of data post-processing, and visualizes the surrounding terrain of the Hong Kong-Zhuhai-Macao Bridge through data fusion.
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.
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.
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.
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>
