An Algorithm for Solution of an Interval Valued EOQ Model
Directory of Open Access Journals (Sweden)
Susovan CHAKRABORTTY
2013-01-01
Full Text Available This paper deals with the problem of determining the economic order quantity (EOQin the interval sense. A purchasing inventory model with shortages and lead time, whose carryingcost, shortage cost, setup cost, demand quantity and lead time are considered as interval numbers,instead of real numbers. First, a brief survey of the existing works on comparing and ranking anytwo interval numbers on the real line is presented. A common algorithm for the optimum productionquantity (Economic lot-size per cycle of a single product (so as to minimize the total average cost isdeveloped which works well on interval number optimization under consideration. A numerical exampleis presented for better understanding the solution procedure. Finally a sensitive analysis of the optimalsolution with respect to the parameters of the model is examined.
A Numerical Algorithm for the Solution of a Phase-Field Model of Polycrystalline Materials
Energy Technology Data Exchange (ETDEWEB)
Dorr, M R; Fattebert, J; Wickett, M E; Belak, J F; Turchi, P A
2008-12-04
We describe an algorithm for the numerical solution of a phase-field model (PFM) of microstructure evolution in polycrystalline materials. The PFM system of equations includes a local order parameter, a quaternion representation of local orientation and a species composition parameter. The algorithm is based on the implicit integration of a semidiscretization of the PFM system using a backward difference formula (BDF) temporal discretization combined with a Newton-Krylov algorithm to solve the nonlinear system at each time step. The BDF algorithm is combined with a coordinate projection method to maintain quaternion unit length, which is related to an important solution invariant. A key element of the Newton-Krylov algorithm is the selection of a preconditioner to accelerate the convergence of the Generalized Minimum Residual algorithm used to solve the Jacobian linear system in each Newton step. Results are presented for the application of the algorithm to 2D and 3D examples.
Energy Technology Data Exchange (ETDEWEB)
Makeechev, V.A. [Industrial Power Company, Krasnopresnenskaya Naberejnaya 12, 123610 Moscow (Russian Federation); Soukhanov, O.A. [Energy Systems Institute, 1 st Yamskogo Polya Street 15, 125040 Moscow (Russian Federation); Sharov, Y.V. [Moscow Power Engineering Institute, Krasnokazarmennaya Street 14, 111250 Moscow (Russian Federation)
2008-07-15
This paper presents foundations of the optimization method intended for solution of power systems operation problems and based on the principles of functional modeling (FM). This paper also presents several types of hierarchical FM algorithms for economic dispatch in these systems derived from this method. According to the FM method a power system is represented by hierarchical model consisting of systems of equations of lower (subsystem) levels and higher level system of connection equations (SCE), in which only boundary variables of subsystems are present. Solution of optimization problem in accordance with the FM method consists of the following operations: (1) solution of optimization problem for each subsystem (values of boundary variables for subsystems should be determined on the higher level of model); (2) calculation of functional characteristic (FC) of each subsystem, pertaining to state of subsystem on current iteration (these two steps are carried out on the lower level of the model); (3) formation and solution of the higher level system of equations (SCE), which gives values of boundary and supplementary boundary variables on current iteration. The key elements in the general structure of the FM method are FCs of subsystems, which represent them on the higher level of the model as ''black boxes''. Important advantage of hierarchical FM algorithms is that results obtained with them on each iteration are identical to those of corresponding basic one level algorithms. (author)
Directory of Open Access Journals (Sweden)
Liying Zhang
2013-11-01
Full Text Available Compared with the conventional control systems, networked control systems (NCSs are more open to the external network. As a result, they are more vulnerable to attacks from disgruntled insiders or malicious cyber-terrorist organizations. Therefore, the security issues of NCSs have been receiving a lot of attention recently. In this brief, we review the existing literature on security issues of NCSs and propose some security solutions for the DC motor networked control system. The typical Data Encryption Standard (DES algorithm is adopted to implement data encryption and decryption. Furthermore, we design a Detection and Reaction Mechanism (DARM on the basis of DES algorithm and the improved grey prediction model. Finally, our proposed security solutions are tested with the established models of deception and DOS attacks. According to the results of numerical experiments, it's clear to see the great feasibility and effectiveness of the proposed solutions above.
Lindegren, Lennart; Hobbs, David; O'Mullane, William; Bastian, Ulrich; Hernández, José
2011-01-01
The Gaia satellite will observe about one billion stars and other point-like sources. The astrometric core solution will determine the astrometric parameters (position, parallax, and proper motion) for a subset of these sources, using a global solution approach which must also include a large number of parameters for the satellite attitude and optical instrument. The accurate and efficient implementation of this solution is an extremely demanding task, but crucial for the outcome of the mission. We provide a comprehensive overview of the mathematical and physical models applicable to this solution, as well as its numerical and algorithmic framework. The astrometric core solution is a simultaneous least-squares estimation of about half a billion parameters, including the astrometric parameters for some 100 million well-behaved so-called primary sources. The global nature of the solution requires an iterative approach, which can be broken down into a small number of distinct processing blocks (source, attitude,...
Application of Harmony Search algorithm to the solution of groundwater management models
Tamer Ayvaz, M.
2009-06-01
This study proposes a groundwater resources management model in which the solution is performed through a combined simulation-optimization model. A modular three-dimensional finite difference groundwater flow model, MODFLOW is used as the simulation model. This model is then combined with a Harmony Search (HS) optimization algorithm which is based on the musical process of searching for a perfect state of harmony. The performance of the proposed HS based management model is tested on three separate groundwater management problems: (i) maximization of total pumping from an aquifer (steady-state); (ii) minimization of the total pumping cost to satisfy the given demand (steady-state); and (iii) minimization of the pumping cost to satisfy the given demand for multiple management periods (transient). The sensitivity of HS algorithm is evaluated by performing a sensitivity analysis which aims to determine the impact of related solution parameters on convergence behavior. The results show that HS yields nearly same or better solutions than the previous solution methods and may be used to solve management problems in groundwater modeling.
Thermodynamically Consistent Algorithms for the Solution of Phase-Field Models
Vignal, Philippe
2016-02-11
Phase-field models are emerging as a promising strategy to simulate interfacial phenomena. Rather than tracking interfaces explicitly as done in sharp interface descriptions, these models use a diffuse order parameter to monitor interfaces implicitly. This implicit description, as well as solid physical and mathematical footings, allow phase-field models to overcome problems found by predecessors. Nonetheless, the method has significant drawbacks. The phase-field framework relies on the solution of high-order, nonlinear partial differential equations. Solving these equations entails a considerable computational cost, so finding efficient strategies to handle them is important. Also, standard discretization strategies can many times lead to incorrect solutions. This happens because, for numerical solutions to phase-field equations to be valid, physical conditions such as mass conservation and free energy monotonicity need to be guaranteed. In this work, we focus on the development of thermodynamically consistent algorithms for time integration of phase-field models. The first part of this thesis focuses on an energy-stable numerical strategy developed for the phase-field crystal equation. This model was put forward to model microstructure evolution. The algorithm developed conserves, guarantees energy stability and is second order accurate in time. The second part of the thesis presents two numerical schemes that generalize literature regarding energy-stable methods for conserved and non-conserved phase-field models. The time discretization strategies can conserve mass if needed, are energy-stable, and second order accurate in time. We also develop an adaptive time-stepping strategy, which can be applied to any second-order accurate scheme. This time-adaptive strategy relies on a backward approximation to give an accurate error estimator. The spatial discretization, in both parts, relies on a mixed finite element formulation and isogeometric analysis. The codes are
Energy Technology Data Exchange (ETDEWEB)
Bagheri, Saman; Nikkar, Ali [University of Tabriz, Tabriz (Iran, Islamic Republic of)
2014-11-15
This paper deals with the determination of approximate solutions for a model of column buckling using two efficient and powerful methods called He's variational approach and variational iteration algorithm-II. These methods are used to find analytical approximate solution of nonlinear dynamic equation of a model for the column buckling. First and second order approximate solutions of the equation of the system are achieved. To validate the solutions, the analytical results have been compared with those resulted from Runge-Kutta 4th order method. A good agreement of the approximate frequencies and periodic solutions with the numerical results and the exact solution shows that the present methods can be easily extended to other nonlinear oscillation problems in engineering. The accuracy and convenience of the proposed methods are also revealed in comparisons with the other solution techniques.
Improved Chaff Solution Algorithm
2009-03-01
Programme de démonstration de technologies (PDT) sur l’intégration de capteurs et de systèmes d’armes embarqués (SISWS), un algorithme a été élaboré...technologies (PDT) sur l’intégration de capteurs et de systèmes d’armes embarqués (SISWS), un algorithme a été élaboré pour déterminer automatiquement...0Z4 2. SECURITY CLASSIFICATION (Overall security classification of the document including special warning terms if applicable .) UNCLASSIFIED
Selçuk K. İşleyen; Ö. Faruk Baykoç
2008-01-01
We define a special case for the vehicle routing problem with stochastic demands (SC-VRPSD) where customer demands are normally distributed. We propose a new linear model for computing the expected length of a tour in SC-VRPSD. The proposed model is based on the integration of the “Traveling Salesman Problem” (TSP) and the Assignment Problem. For large-scale problems, we also use an Iterated Local Search (ILS) algorithm in order to reach an effective solution.
Directory of Open Access Journals (Sweden)
2009-03-01
Full Text Available We define a special case for the vehicle routing problem with stochastic demands (SC-VRPSD where customer demands are normally distributed. We propose a new linear model for computing the expected length of a tour in SC-VRPSD. The proposed model is based on the integration of the “Traveling Salesman Problem” (TSP and the Assignment Problem. For large-scale problems, we also use an Iterated Local Search (ILS algorithm in order to reach an effective solution.
Efficient Solution Algorithms for Factored MDPs
Guestrin, C; Parr, R; Venkataraman, S; 10.1613/jair.1000
2011-01-01
This paper addresses the problem of planning under uncertainty in large Markov Decision Processes (MDPs). Factored MDPs represent a complex state space using state variables and the transition model using a dynamic Bayesian network. This representation often allows an exponential reduction in the representation size of structured MDPs, but the complexity of exact solution algorithms for such MDPs can grow exponentially in the representation size. In this paper, we present two approximate solution algorithms that exploit structure in factored MDPs. Both use an approximate value function represented as a linear combination of basis functions, where each basis function involves only a small subset of the domain variables. A key contribution of this paper is that it shows how the basic operations of both algorithms can be performed efficiently in closed form, by exploiting both additive and context-specific structure in a factored MDP. A central element of our algorithms is a novel linear program decomposition te...
Java Based Computer Algorithms for the Solution of a Business Mathematics Model
Directory of Open Access Journals (Sweden)
A. D. Chinedu
2014-10-01
Full Text Available A novel approach is proposed as a framework for working out uncertainties associated with decisions between the choices of leasing and procurement of capital assets in a manufacturing industry. The mathematical concept of the tool is discussed while the technique adopted is much simpler to implement and initialize. The codes were developed in Java-programming language and text-run and executed on a computer system running on Windows 7 operating system. This was done in order to solve a model that illustrates a case study in actuarial mathematics. Meanwhile the solution obtained proves to be stable and proffers to suit the growing frenzy for software for similar recurring cases in business. In addition, it speeds up the computational results. The results obtained using the empirical method is compared with the output and adjudged excellent in terms of accuracy and adoption.
Discrete Riccati equation solutions: Distributed algorithms
Directory of Open Access Journals (Sweden)
D. G. Lainiotis
1996-01-01
Full Text Available In this paper new distributed algorithms for the solution of the discrete Riccati equation are introduced. The algorithms are used to provide robust and computational efficient solutions to the discrete Riccati equation. The proposed distributed algorithms are theoretically interesting and computationally attractive.
Game tree algorithms and solution trees
W.H.L.M. Pijls (Wim); A. de Bruin (Arie)
1998-01-01
textabstractIn this paper, a theory of game tree algorithms is presented, entirely based upon the concept of solution tree. Two types of solution trees are distinguished: max and min trees. Every game tree algorithm tries to prune nodes as many as possible from the game tree. A cut-off criterion in
Directory of Open Access Journals (Sweden)
Daniel J. Garcia
2015-07-01
Full Text Available The water footprint of energy systems must be considered, as future water scarcity has been identified as a major concern. This work presents a general life cycle network modeling and optimization framework for energy-based products and processes using a functional unit of liters of water consumed in the processing pathway. We analyze and optimize the water-energy nexus over the objectives of water footprint minimization, maximization of economic output per liter of water consumed (economic efficiency of water, and maximization of energy output per liter of water consumed (energy efficiency of water. A mixed integer, multiobjective nonlinear fractional programming (MINLFP model is formulated. A mixed integer linear programing (MILP-based branch and refine algorithm that incorporates both the parametric algorithm and nonlinear programming (NLP subproblems is developed to boost solving efficiency. A case study in bioenergy is presented, and the water footprint is considered from biomass cultivation to biofuel production, providing a novel perspective into the consumption of water throughout the value chain. The case study, optimized successively over the three aforementioned objectives, utilizes a variety of candidate biomass feedstocks to meet primary fuel products demand (ethanol, diesel, and gasoline. A minimum water footprint of 55.1 ML/year was found, economic efficiencies of water range from −$1.31/L to $0.76/L, and energy efficiencies of water ranged from 15.32 MJ/L to 27.98 MJ/L. These results show optimization provides avenues for process improvement, as reported values for the energy efficiency of bioethanol range from 0.62 MJ/L to 3.18 MJ/L. Furthermore, the proposed solution approach was shown to be an order of magnitude more efficient than directly solving the original MINLFP problem with general purpose solvers.
Hasuike, Takashi; Katagiri, Hideki
2010-10-01
This paper focuses on the proposition of a portfolio selection problem considering an investor's subjectivity and the sensitivity analysis for the change of subjectivity. Since this proposed problem is formulated as a random fuzzy programming problem due to both randomness and subjectivity presented by fuzzy numbers, it is not well-defined. Therefore, introducing Sharpe ratio which is one of important performance measures of portfolio models, the main problem is transformed into the standard fuzzy programming problem. Furthermore, using the sensitivity analysis for fuzziness, the analytical optimal portfolio with the sensitivity factor is obtained.
A new algorithm for anisotropic solutions
Indian Academy of Sciences (India)
M Chaisi; S D Maharaj
2006-02-01
We establish a new algorithm that generates a new solution to the Einstein field equations, with an anisotropic matter distribution, from a seed isotropic solution. The new solution is expressed in terms of integrals of an isotropic gravitational potential; and the integration can be completed exactly for particular isotropic seed metrics. A good feature of our approach is that the anisotropic solutions necessarily have an isotropic limit. We find two examples of anisotropic solutions which generalise the isothermal sphere and the Schwarzschild interior sphere. Both examples are expressed in closed form involving elementary functions only.
Chen, Tinggui; Xiao, Renbin
2014-01-01
Due to fierce market competition, how to improve product quality and reduce development cost determines the core competitiveness of enterprises. However, design iteration generally causes increases of product cost and delays of development time as well, so how to identify and model couplings among tasks in product design and development has become an important issue for enterprises to settle. In this paper, the shortcomings existing in WTM model are discussed and tearing approach as well as inner iteration method is used to complement the classic WTM model. In addition, the ABC algorithm is also introduced to find out the optimal decoupling schemes. In this paper, firstly, tearing approach and inner iteration method are analyzed for solving coupled sets. Secondly, a hybrid iteration model combining these two technologies is set up. Thirdly, a high-performance swarm intelligence algorithm, artificial bee colony, is adopted to realize problem-solving. Finally, an engineering design of a chemical processing system is given in order to verify its reasonability and effectiveness.
Complex fluids modeling and algorithms
Saramito, Pierre
2016-01-01
This book presents a comprehensive overview of the modeling of complex fluids, including many common substances, such as toothpaste, hair gel, mayonnaise, liquid foam, cement and blood, which cannot be described by Navier-Stokes equations. It also offers an up-to-date mathematical and numerical analysis of the corresponding equations, as well as several practical numerical algorithms and software solutions for the approximation of the solutions. It discusses industrial (molten plastics, forming process), geophysical (mud flows, volcanic lava, glaciers and snow avalanches), and biological (blood flows, tissues) modeling applications. This book is a valuable resource for undergraduate students and researchers in applied mathematics, mechanical engineering and physics.
Paduszyński, Kamil
2016-08-22
The aim of the paper is to address all the disadvantages of currently available models for calculating infinite dilution activity coefficients (γ(∞)) of molecular solutes in ionic liquids (ILs)-a relevant property from the point of view of many applications of ILs, particularly in separations. Three new models are proposed, each of them based on distinct machine learning algorithm: stepwise multiple linear regression (SWMLR), feed-forward artificial neural network (FFANN), and least-squares support vector machine (LSSVM). The models were established based on the most comprehensive γ(∞) data bank reported so far (>34 000 data points for 188 ILs and 128 solutes). Following the paper published previously [J. Chem. Inf. Model 2014, 54, 1311-1324], the ILs were treated in terms of group contributions, whereas the Abraham solvation parameters were used to quantify an impact of solute structure. Temperature is also included in the input data of the models so that they can be utilized to obtain temperature-dependent data and thus related thermodynamic functions. Both internal and external validation techniques were applied to assess the statistical significance and explanatory power of the final correlations. A comparative study of the overall performance of the investigated SWMLR/FFANN/LSSVM approaches is presented in terms of root-mean-square error and average absolute relative deviation between calculated and experimental γ(∞), evaluated for different families of ILs and solutes, as well as between calculated and experimental infinite dilution selectivity for separation problems benzene from n-hexane and thiophene from n-heptane. LSSVM is shown to be a method with the lowest values of both training and generalization errors. It is finally demonstrated that the established models exhibit an improved accuracy compared to the state-of-the-art model, namely, temperature-dependent group contribution linear solvation energy relationship, published in 2011 [J. Chem
Algebraic dynamics solution and algebraic dynamics algorithm of Burgers equations
Institute of Scientific and Technical Information of China (English)
2008-01-01
Algebraic dynamics solution and algebraic dynamics algorithm of nonlinear partial differential evolution equations in the functional space are applied to Burgers equation. The results indicate that the approach is effective for analytical solutions to Burgers equation, and the algorithm for numerical solutions of Burgers equation is more stable, with higher precision than other existing finite difference algo-rithms.
Decherchi, Sergio; Colmenares, José; Catalano, Chiara Eva; Spagnuolo, Michela; Alexov, Emil; Rocchia, Walter
2013-01-01
The definition of a molecular surface which is physically sound and computationally efficient is a very interesting and long standing problem in the implicit solvent continuum modeling of biomolecular systems as well as in the molecular graphics field. In this work, two molecular surfaces are evaluated with respect to their suitability for electrostatic computation as alternatives to the widely used Connolly-Richards surface: the blobby surface, an implicit Gaussian atom centered surface, and the skin surface. As figures of merit, we considered surface differentiability and surface area continuity with respect to atom positions, and the agreement with explicit solvent simulations. Geometric analysis seems to privilege the skin to the blobby surface, and points to an unexpected relationship between the non connectedness of the surface, caused by interstices in the solute volume, and the surface area dependence on atomic centers. In order to assess the ability to reproduce explicit solvent results, specific software tools have been developed to enable the use of the skin surface in Poisson-Boltzmann calculations with the DelPhi solver. Results indicate that the skin and Connolly surfaces have a comparable performance from this last point of view.
On the multi-level solution algorithm for Markov chains
Energy Technology Data Exchange (ETDEWEB)
Horton, G. [Univ. of Erlangen, Nuernberg (Germany)
1996-12-31
We discuss the recently introduced multi-level algorithm for the steady-state solution of Markov chains. The method is based on the aggregation principle, which is well established in the literature. Recursive application of the aggregation yields a multi-level method which has been shown experimentally to give results significantly faster than the methods currently in use. The algorithm can be reformulated as an algebraic multigrid scheme of Galerkin-full approximation type. The uniqueness of the scheme stems from its solution-dependent prolongation operator which permits significant computational savings in the evaluation of certain terms. This paper describes the modeling of computer systems to derive information on performance, measured typically as job throughput or component utilization, and availability, defined as the proportion of time a system is able to perform a certain function in the presence of component failures and possibly also repairs.
2012-01-01
The definition of a molecular surface which is physically sound and computationally efficient is a very interesting and long standing problem in the implicit solvent continuum modeling of biomolecular systems as well as in the molecular graphics field. In this work, two molecular surfaces are evaluated with respect to their suitability for electrostatic computation as alternatives to the widely used Connolly-Richards surface: the blobby surface, an implicit Gaussian atom centered surface, and...
Institute of Scientific and Technical Information of China (English)
XIA Yuan-yuan; SHAO He-song; LI Shi-xiong; LU Jing-yu
2012-01-01
The essential for microseismic monitoring is fast and accurate calculation of seismic wave source location.The precision of most traditional microseismic monitoring processes of mines,using TDOA location method in two-dimensional space to position the microseismic events,as well as the accuracy of positioning microseismic events,may be reduced by the two-dimensional model and simple method,and ill-conditioned equations produced by TDOA location method will increase the positioning error.This article,based on inversion theory,studies the mathematical model of TDOA location method,polarization analysis location method,and comprehensive difference location method of adding angle factor in the traditional TDOA location method.The feasibility of three methods is verified by numerical simulation and analysis of the positioning error of them.The results show that the comprehensive location method of adding angle difference has strong positioning stability and high positioning accuracy,and it may reduce the impact effectively about ill-conditioned equations to positioning results.Comprehensive location method with the data of actual measure may get better positioning results.
LeBlanc, J. P. F.; Antipov, Andrey E.; Becca, Federico; Bulik, Ireneusz W.; Chan, Garnet Kin-Lic; Chung, Chia-Min; Deng, Youjin; Ferrero, Michel; Henderson, Thomas M.; Jiménez-Hoyos, Carlos A.; Kozik, E.; Liu, Xuan-Wen; Millis, Andrew J.; Prokof'ev, N. V.; Qin, Mingpu; Scuseria, Gustavo E.; Shi, Hao; Svistunov, B. V.; Tocchio, Luca F.; Tupitsyn, I. S.; White, Steven R.; Zhang, Shiwei; Zheng, Bo-Xiao; Zhu, Zhenyue; Gull, Emanuel; Simons Collaboration on the Many-Electron Problem
2015-10-01
Numerical results for ground-state and excited-state properties (energies, double occupancies, and Matsubara-axis self-energies) of the single-orbital Hubbard model on a two-dimensional square lattice are presented, in order to provide an assessment of our ability to compute accurate results in the thermodynamic limit. Many methods are employed, including auxiliary-field quantum Monte Carlo, bare and bold-line diagrammatic Monte Carlo, method of dual fermions, density matrix embedding theory, density matrix renormalization group, dynamical cluster approximation, diffusion Monte Carlo within a fixed-node approximation, unrestricted coupled cluster theory, and multireference projected Hartree-Fock methods. Comparison of results obtained by different methods allows for the identification of uncertainties and systematic errors. The importance of extrapolation to converged thermodynamic-limit values is emphasized. Cases where agreement between different methods is obtained establish benchmark results that may be useful in the validation of new approaches and the improvement of existing methods.
Leblanc, James
In this talk we present numerical results for ground state and excited state properties (energies, double occupancies, and Matsubara-axis self energies) of the single-orbital Hubbard model on a two-dimensional square lattice. In order to provide an assessment of our ability to compute accurate results in the thermodynamic limit we employ numerous methods including auxiliary field quantum Monte Carlo, bare and bold-line diagrammatic Monte Carlo, method of dual fermions, density matrix embedding theory, density matrix renormalization group, dynamical cluster approximation, diffusion Monte Carlo within a fixed node approximation, unrestricted coupled cluster theory, and multireference projected Hartree-Fock. We illustrate cases where agreement between different methods is obtained in order to establish benchmark results that should be useful in the validation of future results.
Biodiversity optimal sampling: an algorithmic solution
Directory of Open Access Journals (Sweden)
Alessandro Ferrarini
2012-03-01
Full Text Available Biodiversity sampling is a very serious task. When biodiversity sampling is not representative of the biodiversity spatial pattern due to few data or uncorrected sampling point locations, successive analyses, models and simulations are inevitably biased. In this work, I propose a new solution to the problem of biodiversity sampling. The proposed approach is proficient for habitats, plant and animal species, in addition it is able to answer the two pivotal questions of biodiversity sampling: 1 how many sampling points and 2 where are the sampling points.
Directory of Open Access Journals (Sweden)
2015-12-01
Full Text Available Numerical results for ground-state and excited-state properties (energies, double occupancies, and Matsubara-axis self-energies of the single-orbital Hubbard model on a two-dimensional square lattice are presented, in order to provide an assessment of our ability to compute accurate results in the thermodynamic limit. Many methods are employed, including auxiliary-field quantum Monte Carlo, bare and bold-line diagrammatic Monte Carlo, method of dual fermions, density matrix embedding theory, density matrix renormalization group, dynamical cluster approximation, diffusion Monte Carlo within a fixed-node approximation, unrestricted coupled cluster theory, and multireference projected Hartree-Fock methods. Comparison of results obtained by different methods allows for the identification of uncertainties and systematic errors. The importance of extrapolation to converged thermodynamic-limit values is emphasized. Cases where agreement between different methods is obtained establish benchmark results that may be useful in the validation of new approaches and the improvement of existing methods.
Graphical model construction based on evolutionary algorithms
Institute of Scientific and Technical Information of China (English)
Youlong YANG; Yan WU; Sanyang LIU
2006-01-01
Using Bayesian networks to model promising solutions from the current population of the evolutionary algorithms can ensure efficiency and intelligence search for the optimum. However, to construct a Bayesian network that fits a given dataset is a NP-hard problem, and it also needs consuming mass computational resources. This paper develops a methodology for constructing a graphical model based on Bayesian Dirichlet metric. Our approach is derived from a set of propositions and theorems by researching the local metric relationship of networks matching dataset. This paper presents the algorithm to construct a tree model from a set of potential solutions using above approach. This method is important not only for evolutionary algorithms based on graphical models, but also for machine learning and data mining.The experimental results show that the exact theoretical results and the approximations match very well.
Models and Algorithm for Stochastic Network Designs
Institute of Scientific and Technical Information of China (English)
Anthony Chen; Juyoung Kim; Seungjae Lee; Jaisung Choi
2009-01-01
The network design problem (NDP) is one of the most difficult and challenging problems in trans-portation. Traditional NDP models are often posed as a deterministic bilevel program assuming that all rele-vant inputs are known with certainty. This paper presents three stochastic models for designing transporta-tion networks with demand uncertainty. These three stochastic NDP models were formulated as the ex-pected value model, chance-constrained model, and dependent-chance model in a bilevel programming framework using different criteria to hedge against demand uncertainty. Solution procedures based on the traffic assignment algorithm, genetic algorithm, and Monte-Cado simulations were developed to solve these stochastic NDP models. The nonlinear and nonconvex nature of the bilevel program was handled by the genetic algorithm and traffic assignment algorithm, whereas the stochastic nature was addressed through simulations. Numerical experiments were conducted to evaluate the applicability of the stochastic NDP models and the solution procedure. Results from the three experiments show that the solution procedures are quite robust to different parameter settings.
Massively Parallel Algorithms for Solution of Schrodinger Equation
Fijany, Amir; Barhen, Jacob; Toomerian, Nikzad
1994-01-01
In this paper massively parallel algorithms for solution of Schrodinger equation are developed. Our results clearly indicate that the Crank-Nicolson method, in addition to its excellent numerical properties, is also highly suitable for massively parallel computation.
SOLUTION OF THE SATELLITE TRANSFER PROBLEM WITH HYBRID MEMETIC ALGORITHM
Directory of Open Access Journals (Sweden)
A. V. Panteleyev
2014-01-01
Full Text Available This paper presents a hybrid memetic algorithm (MA to solve the problem of finding the optimal program control of nonlinear continuous deterministic systems based on the concept of the meme, which is one of the promising solutions obtained in the course of implementing the procedure for searching the extremes. On the basis of the proposed algorithm the software complex is formed in C#. The solution of satellite transfer problem is presented.
Influence Maximization in Social Networks: Towards an Optimal Algorithmic Solution
Borgs, Christian; Chayes, Jennifer; Lucier, Brendan
2012-01-01
Diffusion is a fundamental graph process, underpinning such phenomena as epidemic disease contagion and the spread of innovation by word-of-mouth. We address the algorithmic problem of finding a set of k initial seed nodes in a network so that the expected size of the resulting cascade is maximized, under the standard independent cascade model of network diffusion. Our main result is an algorithm for the influence maximization problem that obtains the near-optimal approximation factor of (1 - 1/e - epsilon), for any epsilon > 0, in time O((m+n)log(n) / epsilon^3) where n and m are the number of vertices and edges in the network. Our algorithm is nearly runtime-optimal (up to a logarithmic factor) as we establish a lower bound of Omega(m+n) on the runtime required to obtain a constant approximation. Our method also allows a provable tradeoff between solution quality and runtime: we obtain an O(1/beta)-approximation in time O(n log^3(n) * a(G) / beta) for any beta > 1, where a(G) denotes the arboricity of the d...
Multiagent scheduling models and algorithms
Agnetis, Alessandro; Gawiejnowicz, Stanisław; Pacciarelli, Dario; Soukhal, Ameur
2014-01-01
This book presents multi-agent scheduling models in which subsets of jobs sharing the same resources are evaluated by different criteria. It discusses complexity results, approximation schemes, heuristics and exact algorithms.
A genetic algorithm for solving supply chain network design model
Firoozi, Z.; Ismail, N.; Ariafar, S. H.; Tang, S. H.; Ariffin, M. K. M. A.
2013-09-01
Network design is by nature costly and optimization models play significant role in reducing the unnecessary cost components of a distribution network. This study proposes a genetic algorithm to solve a distribution network design model. The structure of the chromosome in the proposed algorithm is defined in a novel way that in addition to producing feasible solutions, it also reduces the computational complexity of the algorithm. Computational results are presented to show the algorithm performance.
Applied Integer Programming Modeling and Solution
Chen, Der-San; Dang, Yu
2011-01-01
An accessible treatment of the modeling and solution of integer programming problems, featuring modern applications and software In order to fully comprehend the algorithms associated with integer programming, it is important to understand not only how algorithms work, but also why they work. Applied Integer Programming features a unique emphasis on this point, focusing on problem modeling and solution using commercial software. Taking an application-oriented approach, this book addresses the art and science of mathematical modeling related to the mixed integer programming (MIP) framework and
A solution quality assessment method for swarm intelligence optimization algorithms.
Zhang, Zhaojun; Wang, Gai-Ge; Zou, Kuansheng; Zhang, Jianhua
2014-01-01
Nowadays, swarm intelligence optimization has become an important optimization tool and wildly used in many fields of application. In contrast to many successful applications, the theoretical foundation is rather weak. Therefore, there are still many problems to be solved. One problem is how to quantify the performance of algorithm in finite time, that is, how to evaluate the solution quality got by algorithm for practical problems. It greatly limits the application in practical problems. A solution quality assessment method for intelligent optimization is proposed in this paper. It is an experimental analysis method based on the analysis of search space and characteristic of algorithm itself. Instead of "value performance," the "ordinal performance" is used as evaluation criteria in this method. The feasible solutions were clustered according to distance to divide solution samples into several parts. Then, solution space and "good enough" set can be decomposed based on the clustering results. Last, using relative knowledge of statistics, the evaluation result can be got. To validate the proposed method, some intelligent algorithms such as ant colony optimization (ACO), particle swarm optimization (PSO), and artificial fish swarm algorithm (AFS) were taken to solve traveling salesman problem. Computational results indicate the feasibility of proposed method.
Cashier Problem: a Greedy Algorithm and an optimal Solution
Directory of Open Access Journals (Sweden)
Nicolae GIURGITEANU
2006-01-01
Full Text Available We will remind briefly the cashier problem. A cashier has leeway a range of different fractional coins and has to pay a certain amount using the most reduced number of coins. If we mark the pay-desk monetary with P {p ,..., pn } 1 = , each pi having as denomination di and with A the final sum, the cashier must determine a coins subset { } m S q ,..., q 1 = of P, so that i m i id q A Σ==1. In order to solve this problem, there are several solutions consisting in greedy algorithms. Although there is an optimal solution, in the present article we will highlight a greedy algorithm and an optimal solution for this problem. The solution known at the time being use a lot of memory and, in addition, is difficult to justify, occurring the risk of misunderstanding by the reader. Our solution is simpler and uses little memory
Directory of Open Access Journals (Sweden)
Özgür Başkan
2014-09-01
Full Text Available Differential Evolution algorithm has effectively been used to solve engineering optimization problems recently. The Differential Evolution algorithm, which uses similar principles with Genetic Algorithms, is more robust on obtaining optimal solution than many other heuristic algorithms with its simpler structure. In this study, Differential Evolution algorithm is applied to the transportation network design problems and its effectiveness on the solution is investigated. In this context, Differential Evolution based models are developed using bi-level programming approach for the solution of the transportation network design problem and determination of the on-street parking places in urban road networks. In these models, optimal investment and parking strategies are investigated on the upper level. On the lower level, deterministic traffic assignment problem, which represents drivers' responses, is solved using Frank-Wolfe algorithm and VISUM traffic modeling software. In order to determine the effectiveness of the proposed models, numerical applications are carried out on Sioux-Falls test network. Results showed that the Differential Evolution algorithm may effectively been used for the solution of transportation network design problems.
Institute of Scientific and Technical Information of China (English)
HOU XueLiang; LU Mei
2008-01-01
In order to seek the co-adaptability solution to conflict events in construction en-gineering projects,a new method referred to as segmented hierarchical algorithm is proposed in this paper by means of comparing co-adaptability evolution process of conflict events to the stackelberg model.By this new algorithm,local solutions to the first-order transformation of co-adaptability for conflict events can be ob-tained,based upon which,a global solution to the second-order transformation of co-adaptability for conflict events can also be decided by judging satisfaction de-gree of local solutions.The research results show that this algorithm can be used not only for obtaining co-adaptability solution to conflict events efficiently,but also for other general decision-making problems with multi-layers and multi-subsidi-aries in project management field.
Institute of Scientific and Technical Information of China (English)
2008-01-01
In order to seek the co-adaptability solution to conflict events in construction engineering projects, a new method referred to as segmented hierarchical algorithm is proposed in this paper by means of comparing co-adaptability evolution process of conflict events to the stackelberg model. By this new algorithm, local solutions to the first-order transformation of co-adaptability for conflict events can be obtained, based upon which, a global solution to the second-order transformation of co-adaptability for conflict events can also be decided by judging satisfaction degree of local solutions. The research results show that this algorithm can be used not only for obtaining co-adaptability solution to conflict events efficiently, but also for other general decision-making problems with multi-layers and multi-subsidi-aries in project management field.
Algorithmic Issues in Modeling Motion
DEFF Research Database (Denmark)
Agarwal, P. K; Guibas, L. J; Edelsbrunner, H.
2003-01-01
This article is a survey of research areas in which motion plays a pivotal role. The aim of the article is to review current approaches to modeling motion together with related data structures and algorithms, and to summarize the challenges that lie ahead in producing a more unified theory...
Combinatorial optimization problem solution based on improved genetic algorithm
Zhang, Peng
2017-08-01
Traveling salesman problem (TSP) is a classic combinatorial optimization problem. It is a simplified form of many complex problems. In the process of study and research, it is understood that the parameters that affect the performance of genetic algorithm mainly include the quality of initial population, the population size, and crossover probability and mutation probability values. As a result, an improved genetic algorithm for solving TSP problems is put forward. The population is graded according to individual similarity, and different operations are performed to different levels of individuals. In addition, elitist retention strategy is adopted at each level, and the crossover operator and mutation operator are improved. Several experiments are designed to verify the feasibility of the algorithm. Through the experimental results analysis, it is proved that the improved algorithm can improve the accuracy and efficiency of the solution.
Energy Hole Solution Algorithm in Wireless Sensor Network
Directory of Open Access Journals (Sweden)
Yuting Lu
2014-04-01
Full Text Available Since the sensor nodes near the Sink take more communication load leading to excessive energy consumption and short its life cycle, this paper proposes a new energy hole solution algorithm. The algorithm adds some long chain to the Sink node to relieve energy hole of reducing data forwarding number around the Sink node, so as to prolong the lifecycle of the network. Firstly, The algorithm carries out the analysis of energy consumption to equidistance transmission network and puts forward adopting tactics of small world to alleviate energy hole and analyzes the position and number of long chain’ influence on energy consumption and the network life cycle. Finally, the thesis carries out the simulation experiment. The experimental results show that this algorithm can significantly improve the network lifetime and easy to implement in practice.
Pickl, S.
2002-09-01
This paper is concerned with a mathematical derivation of the nonlinear time-discrete Technology-Emissions Means (TEM-) model. A detailed introduction to the dynamics modelling a Joint Implementation Program concerning Kyoto Protocol is given at the end of the paper. As the nonlinear time-discrete dynamics tends to chaotic behaviour, the necessary introduction of control parameters in the dynamics of the TEM model leads to new results in the field of time-discrete control systems. Furthermore the numerical results give new insights into a Joint-Implementation Program and herewith, they may improve this important economic tool. The iterative solution presented at the end might be a useful orientation to anticipate and support Kyoto Process.
Naturally selecting solutions: the use of genetic algorithms in bioinformatics.
Manning, Timmy; Sleator, Roy D; Walsh, Paul
2013-01-01
For decades, computer scientists have looked to nature for biologically inspired solutions to computational problems; ranging from robotic control to scheduling optimization. Paradoxically, as we move deeper into the post-genomics era, the reverse is occurring, as biologists and bioinformaticians look to computational techniques, to solve a variety of biological problems. One of the most common biologically inspired techniques are genetic algorithms (GAs), which take the Darwinian concept of natural selection as the driving force behind systems for solving real world problems, including those in the bioinformatics domain. Herein, we provide an overview of genetic algorithms and survey some of the most recent applications of this approach to bioinformatics based problems.
Fast Algorithms for Model-Based Diagnosis
Fijany, Amir; Barrett, Anthony; Vatan, Farrokh; Mackey, Ryan
2005-01-01
Two improved new methods for automated diagnosis of complex engineering systems involve the use of novel algorithms that are more efficient than prior algorithms used for the same purpose. Both the recently developed algorithms and the prior algorithms in question are instances of model-based diagnosis, which is based on exploring the logical inconsistency between an observation and a description of a system to be diagnosed. As engineering systems grow more complex and increasingly autonomous in their functions, the need for automated diagnosis increases concomitantly. In model-based diagnosis, the function of each component and the interconnections among all the components of the system to be diagnosed (for example, see figure) are represented as a logical system, called the system description (SD). Hence, the expected behavior of the system is the set of logical consequences of the SD. Faulty components lead to inconsistency between the observed behaviors of the system and the SD. The task of finding the faulty components (diagnosis) reduces to finding the components, the abnormalities of which could explain all the inconsistencies. Of course, the meaningful solution should be a minimal set of faulty components (called a minimal diagnosis), because the trivial solution, in which all components are assumed to be faulty, always explains all inconsistencies. Although the prior algorithms in question implement powerful methods of diagnosis, they are not practical because they essentially require exhaustive searches among all possible combinations of faulty components and therefore entail the amounts of computation that grow exponentially with the number of components of the system.
On the Multilevel Solution Algorithm for Markov Chains
Horton, Graham
1997-01-01
We discuss the recently introduced multilevel algorithm for the steady-state solution of Markov chains. The method is based on an aggregation principle which is well established in the literature and features a multiplicative coarse-level correction. Recursive application of the aggregation principle, which uses an operator-dependent coarsening, yields a multi-level method which has been shown experimentally to give results significantly faster than the typical methods currently in use. When cast as a multigrid-like method, the algorithm is seen to be a Galerkin-Full Approximation Scheme with a solution-dependent prolongation operator. Special properties of this prolongation lead to the cancellation of the computationally intensive terms of the coarse-level equations.
Direct Model Checking Matrix Algorithm
Institute of Scientific and Technical Information of China (English)
Zhi-Hong Tao; Hans Kleine Büning; Li-Fu Wang
2006-01-01
During the last decade, Model Checking has proven its efficacy and power in circuit design, network protocol analysis and bug hunting. Recent research on automatic verification has shown that no single model-checking technique has the edge over all others in all application areas. So, it is very difficult to determine which technique is the most suitable for a given model. It is thus sensible to apply different techniques to the same model. However, this is a very tedious and time-consuming task, for each algorithm uses its own description language. Applying Model Checking in software design and verification has been proved very difficult. Software architectures (SA) are engineering artifacts that provide high-level and abstract descriptions of complex software systems. In this paper a Direct Model Checking (DMC) method based on Kripke Structure and Matrix Algorithm is provided. Combined and integrated with domain specific software architecture description languages (ADLs), DMC can be used for computing consistency and other critical properties.
Energy Technology Data Exchange (ETDEWEB)
Chatterjee, Kausik, E-mail: kausik.chatterjee@aggiemail.usu.edu [Strategic and Military Space Division, Space Dynamics Laboratory, North Logan, UT 84341 (United States); Center for Atmospheric and Space Sciences, Utah State University, Logan, UT 84322 (United States); Roadcap, John R., E-mail: john.roadcap@us.af.mil [Air Force Research Laboratory, Kirtland AFB, NM 87117 (United States); Singh, Surendra, E-mail: surendra-singh@utulsa.edu [Department of Electrical Engineering, The University of Tulsa, Tulsa, OK 74104 (United States)
2014-11-01
The objective of this paper is the exposition of a recently-developed, novel Green's function Monte Carlo (GFMC) algorithm for the solution of nonlinear partial differential equations and its application to the modeling of the plasma sheath region around a cylindrical conducting object, carrying a potential and moving at low speeds through an otherwise neutral medium. The plasma sheath is modeled in equilibrium through the GFMC solution of the nonlinear Poisson–Boltzmann (NPB) equation. The traditional Monte Carlo based approaches for the solution of nonlinear equations are iterative in nature, involving branching stochastic processes which are used to calculate linear functionals of the solution of nonlinear integral equations. Over the last several years, one of the authors of this paper, K. Chatterjee has been developing a philosophically-different approach, where the linearization of the equation of interest is not required and hence there is no need for iteration and the simulation of branching processes. Instead, an approximate expression for the Green's function is obtained using perturbation theory, which is used to formulate the random walk equations within the problem sub-domains where the random walker makes its walks. However, as a trade-off, the dimensions of these sub-domains have to be restricted by the limitations imposed by perturbation theory. The greatest advantage of this approach is the ease and simplicity of parallelization stemming from the lack of the need for iteration, as a result of which the parallelization procedure is identical to the parallelization procedure for the GFMC solution of a linear problem. The application area of interest is in the modeling of the communication breakdown problem during a space vehicle's re-entry into the atmosphere. However, additional application areas are being explored in the modeling of electromagnetic propagation through the atmosphere/ionosphere in UHF/GPS applications.
Models and algorithms for biomolecules and molecular networks
DasGupta, Bhaskar
2016-01-01
By providing expositions to modeling principles, theories, computational solutions, and open problems, this reference presents a full scope on relevant biological phenomena, modeling frameworks, technical challenges, and algorithms. * Up-to-date developments of structures of biomolecules, systems biology, advanced models, and algorithms * Sampling techniques for estimating evolutionary rates and generating molecular structures * Accurate computation of probability landscape of stochastic networks, solving discrete chemical master equations * End-of-chapter exercises
Parallel hybrid algorithm for solution in electrical impedance equation
Ponomaryov, Volodymyr; Robles-Gonzalez, Marco; Bucio-Ramirez, Ariana; Ramirez-Tachiquin, Marco; Ramos-Diaz, Eduardo
2015-02-01
This work is dedicated to the analysis of the forward and the inverse problem to obtain a better approximation to the Electrical Impedance Tomography equation. In this case, we employ for the forward problem the numerical method based on the Taylor series in formal power and for the inverse problem the Finite Element Method. For the analysis of the forward problem, we proposed a novel algorithm, which employs a regularization technique for the stability, additionally the parallel computing is used to obtain the solution faster; this modification permits to obtain an efficient solution of the forward problem. Then, the found solution is used in the inverse problem for the approximation employing the Finite Element Method. The algorithms employed in this work are developed in structural programming paradigm in C++, including parallel processing; the time run analysis is performed only in the forward problem because the Finite Element Method due to their high recursive does not accept parallelism. Some examples are performed for this analysis, in which several conductivity functions are employed for two different cases: for the analytical cases: the exponential and sinusoidal functions are used, and for the geometrical cases the circle at center and five disk structure are revised as conductivity functions. The Lebesgue measure is used as metric for error estimation in the forward problem, meanwhile, in the inverse problem PSNR, SSIM, MSE criteria are applied, to determine the convergence of both methods.
Fireworks algorithm for mean-VaR/CVaR models
Zhang, Tingting; Liu, Zhifeng
2017-10-01
Intelligent algorithms have been widely applied to portfolio optimization problems. In this paper, we introduce a novel intelligent algorithm, named fireworks algorithm, to solve the mean-VaR/CVaR model for the first time. The results show that, compared with the classical genetic algorithm, fireworks algorithm not only improves the optimization accuracy and the optimization speed, but also makes the optimal solution more stable. We repeat our experiments at different confidence levels and different degrees of risk aversion, and the results are robust. It suggests that fireworks algorithm has more advantages than genetic algorithm in solving the portfolio optimization problem, and it is feasible and promising to apply it into this field.
Fan, Mingyi; Li, Tongjun; Hu, Jiwei; Cao, Rensheng; Wei, Xionghui; Shi, Xuedan; Ruan, Wenqian
2017-05-17
Reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) composites were synthesized in the present study by chemical deposition method and were then characterized by various methods, such as Fourier-transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). The nZVI/rGO composites prepared were utilized for Cd(II) removal from aqueous solutions in batch mode at different initial Cd(II) concentrations, initial pH values, contact times, and operating temperatures. Response surface methodology (RSM) and artificial neural network hybridized with genetic algorithm (ANN-GA) were used for modeling the removal efficiency of Cd(II) and optimizing the four removal process variables. The average values of prediction errors for the RSM and ANN-GA models were 6.47% and 1.08%. Although both models were proven to be reliable in terms of predicting the removal efficiency of Cd(II), the ANN-GA model was found to be more accurate than the RSM model. In addition, experimental data were fitted to the Langmuir, Freundlich, and Dubinin-Radushkevich (D-R) isotherms. It was found that the Cd(II) adsorption was best fitted to the Langmuir isotherm. Examination on thermodynamic parameters revealed that the removal process was spontaneous and exothermic in nature. Furthermore, the pseudo-second-order model can better describe the kinetics of Cd(II) removal with a good R² value than the pseudo-first-order model.
Institute of Scientific and Technical Information of China (English)
于克训; 刘小洪
2000-01-01
The solution and algorithm of reverse-current pole-changing on the symmetrical block diagram and GA optimizing model are developed. The detailed mathematical flow is also presented with the corresponding computer software. The computer automation is realized in pole-changing winding design. Its practicability and advantages are proved in the example given.%基于变极绕组设计的对称块图法和遗传优化算法反向法变极的数学模型，提出了具体的求解方法及算法，给出了详细的计算流程图，研制了相应的计算机软件，实现了变极绕组设计的计算机自动化，实例证明了理论及方法的实用性及先进性.
A NEW SOLUTION MODEL OF NONLINEAR DYNAMIC LEAST SQUARE ADJUSTMENT
Institute of Scientific and Technical Information of China (English)
陶华学; 郭金运
2000-01-01
The nonlinear least square adjustment is a head object studied in technology fields. The paper studies on the non-derivative solution to the nonlinear dynamic least square adjustment and puts forward a new algorithm model and its solution model. The method has little calculation load and is simple. This opens up a theoretical method to solve the linear dynamic least square adjustment.
ALGORITHM OF SELECTION EFFECTIVE SOLUTIONS FOR REPROFILING OF INDUSTRIAL BUILDINGS
Directory of Open Access Journals (Sweden)
MENEJLJUK A. I.
2016-08-01
Full Text Available Raising of problem.Non-compliance requirements of today's industrial enterprises, which were built during the Soviet period, as well as significant technical progress, economic reform and transition to market principles of performance evaluation leading to necessity to change their target and functionality. The technical condition of many industrial buildings in Ukraine allows to exploit them for decades.Redesigning manufacturing enterprises allows not only to reduce the cost of construction, but also to obtain new facilities in the city. Despite the large number of industrial buildings that have lost their effectiveness and relevance, as well as a significant investor interest in these objects, the scope of redevelopment in the construction remains unexplored. Analysis researches on the topic. The problem of reconstruction of industrial buildings considered in Topchy D. [3], Travin V. [9], as well as in the work of other scientists. However, there are no rules in regulatory documents and system studies for improving the organization of the reconstruction of buildings at realigning. The purpose of this work is the development an algorithm of actions for selection of effective organizational decisions at the planning stage of a reprofiling project of industrial buildings. The proposed algorithm allows you to select an effective organizational and technological solution for the re-profiling of industrial buildings, taking into account features of the building, its location, its state of structures and existing restrictions. The most effective organizational solution allows realize the reprofiling project of an industrial building in the most possible short terms and with the lowest possible use of material resources, taking into account the available features and restrictions. Conclusion. Each object has a number of unique features that necessary for considering at choosing an effective reprofiling variant. The developed algorithm for selecting
Warehouse Optimization Model Based on Genetic Algorithm
Directory of Open Access Journals (Sweden)
Guofeng Qin
2013-01-01
Full Text Available This paper takes Bao Steel logistics automated warehouse system as an example. The premise is to maintain the focus of the shelf below half of the height of the shelf. As a result, the cost time of getting or putting goods on the shelf is reduced, and the distance of the same kind of goods is also reduced. Construct a multiobjective optimization model, using genetic algorithm to optimize problem. At last, we get a local optimal solution. Before optimization, the average cost time of getting or putting goods is 4.52996 s, and the average distance of the same kinds of goods is 2.35318 m. After optimization, the average cost time is 4.28859 s, and the average distance is 1.97366 m. After analysis, we can draw the conclusion that this model can improve the efficiency of cargo storage.
Application of firefly algorithm to the dynamic model updating problem
Shabbir, Faisal; Omenzetter, Piotr
2015-04-01
Model updating can be considered as a branch of optimization problems in which calibration of the finite element (FE) model is undertaken by comparing the modal properties of the actual structure with these of the FE predictions. The attainment of a global solution in a multi dimensional search space is a challenging problem. The nature-inspired algorithms have gained increasing attention in the previous decade for solving such complex optimization problems. This study applies the novel Firefly Algorithm (FA), a global optimization search technique, to a dynamic model updating problem. This is to the authors' best knowledge the first time FA is applied to model updating. The working of FA is inspired by the flashing characteristics of fireflies. Each firefly represents a randomly generated solution which is assigned brightness according to the value of the objective function. The physical structure under consideration is a full scale cable stayed pedestrian bridge with composite bridge deck. Data from dynamic testing of the bridge was used to correlate and update the initial model by using FA. The algorithm aimed at minimizing the difference between the natural frequencies and mode shapes of the structure. The performance of the algorithm is analyzed in finding the optimal solution in a multi dimensional search space. The paper concludes with an investigation of the efficacy of the algorithm in obtaining a reference finite element model which correctly represents the as-built original structure.
New Model and Algorithm for Hardware/Software Partitioning
Institute of Scientific and Technical Information of China (English)
Ji-Gang Wu; Thambipillai Srikanthan; Guang-Wei Zou
2008-01-01
This paper focuses on the algorithmic aspects for the hardware/software (HW/SW) partitioning which searches a reasonable composition of hardware and software components which not only satisfies the constraint of hardware area but also optimizes the execution time. The computational model is extended so that all possible types of communications can be taken into account for the HW/SW partitioning. Also, a new dynamic programming algorithm is proposed on the basis of the computational model, in which source data, rather than speedup in previous work, of basic scheduling blocks are directly utilized to calculate the optimal solution. The proposed algorithm runs in O(n. A) for n code fragments and the available hardware area A. Simulation results show that the proposed algorithm solves the HW/SW partitioning without increase in running time, compared with the algorithm cited in the literature.
Algebraic dynamics solution to and algebraic dynamics algorithm for nonlinear advection equation
Institute of Scientific and Technical Information of China (English)
2008-01-01
Algebraic dynamics approach and algebraic dynamics algorithm for the solution of nonlinear partial differential equations are applied to the nonlinear advection equa-tion. The results show that the approach is effective for the exact analytical solu-tion and the algorithm has higher precision than other existing algorithms in nu-merical computation for the nonlinear advection equation.
Institute of Scientific and Technical Information of China (English)
WANG Yi-bo; YANG Hai-tian; WU Rui-feng
2005-01-01
By modeling direct transient heat conduction problems via finite element method (FEM) and precise integral algorithm, a new approach is presented to solve transient inverse heat conduction problems with multi-variables. Firstly, the spatial space and temporal domain are discretized by FEM and precise integral algorithm respectively. Then, the high accuracy semi-analytical solution of direct problem can be got. Finally, based on the solution, the computing model of inverse problem and expression of sensitivity analysis are established. Single variable and variables combined identifications including thermal parameters, boundary conditions and source-related terms etc. are given to validate the approach proposed in 1-D and 2-D cases. The effects of noise data and initial guess on the results are investigated. The numerical examples show the effectiveness of this approach.
Modeling and Engineering Algorithms for Mobile Data
DEFF Research Database (Denmark)
Blunck, Henrik; Hinrichs, Klaus; Sondern, Joëlle;
2006-01-01
In this paper, we present an object-oriented approach to modeling mobile data and algorithms operating on such data. Our model is general enough to capture any kind of continuous motion while at the same time allowing for encompassing algorithms optimized for specific types of motion. Such motion...
DiamondTorre Algorithm for High-Performance Wave Modeling
Directory of Open Access Journals (Sweden)
Vadim Levchenko
2016-08-01
Full Text Available Effective algorithms of physical media numerical modeling problems’ solution are discussed. The computation rate of such problems is limited by memory bandwidth if implemented with traditional algorithms. The numerical solution of the wave equation is considered. A finite difference scheme with a cross stencil and a high order of approximation is used. The DiamondTorre algorithm is constructed, with regard to the specifics of the GPGPU’s (general purpose graphical processing unit memory hierarchy and parallelism. The advantages of these algorithms are a high level of data localization, as well as the property of asynchrony, which allows one to effectively utilize all levels of GPGPU parallelism. The computational intensity of the algorithm is greater than the one for the best traditional algorithms with stepwise synchronization. As a consequence, it becomes possible to overcome the above-mentioned limitation. The algorithm is implemented with CUDA. For the scheme with the second order of approximation, the calculation performance of 50 billion cells per second is achieved. This exceeds the result of the best traditional algorithm by a factor of five.
Quantitative Methods in Supply Chain Management Models and Algorithms
Christou, Ioannis T
2012-01-01
Quantitative Methods in Supply Chain Management presents some of the most important methods and tools available for modeling and solving problems arising in the context of supply chain management. In the context of this book, “solving problems” usually means designing efficient algorithms for obtaining high-quality solutions. The first chapter is an extensive optimization review covering continuous unconstrained and constrained linear and nonlinear optimization algorithms, as well as dynamic programming and discrete optimization exact methods and heuristics. The second chapter presents time-series forecasting methods together with prediction market techniques for demand forecasting of new products and services. The third chapter details models and algorithms for planning and scheduling with an emphasis on production planning and personnel scheduling. The fourth chapter presents deterministic and stochastic models for inventory control with a detailed analysis on periodic review systems and algorithmic dev...
Alfonso, Lester; Zamora, Jose; Cruz, Pedro
2015-04-01
The stochastic approach to coagulation considers the coalescence process going in a system of a finite number of particles enclosed in a finite volume. Within this approach, the full description of the system can be obtained from the solution of the multivariate master equation, which models the evolution of the probability distribution of the state vector for the number of particles of a given mass. Unfortunately, due to its complexity, only limited results were obtained for certain type of kernels and monodisperse initial conditions. In this work, a novel numerical algorithm for the solution of the multivariate master equation for stochastic coalescence that works for any type of kernels and initial conditions is introduced. The performance of the method was checked by comparing the numerically calculated particle mass spectrum with analytical solutions obtained for the constant and sum kernels, with an excellent correspondence between the analytical and numerical solutions. In order to increase the speedup of the algorithm, software parallelization techniques with OpenMP standard were used, along with an implementation in order to take advantage of new accelerator technologies. Simulations results show an important speedup of the parallelized algorithms. This study was funded by a grant from Consejo Nacional de Ciencia y Tecnologia de Mexico SEP-CONACYT CB-131879. The authors also thanks LUFAC® Computacion SA de CV for CPU time and all the support provided.
Liqiang Liu; Yuntao Dai; Jinyu Gao
2014-01-01
Ant colony optimization algorithm for continuous domains is a major research direction for ant colony optimization algorithm. In this paper, we propose a distribution model of ant colony foraging, through analysis of the relationship between the position distribution and food source in the process of ant colony foraging. We design a continuous domain optimization algorithm based on the model and give the form of solution for the algorithm, the distribution model of pheromone, the update rules...
Zhu, Chun; Li, Run; Que, Li-Zhi; Zhu, Tuo; Chen, Guo-Qing
2014-07-01
The three-dimensional spectra of mixed solutions of allure red, sunset yellow and brilliant blue were obtained. Then the three synthetic food colors were determined by parallel factor analysis (PARAFAC) and alternating trilinear decomposition (ATLD) algorithms, respectively. The component number of model is three by core-consistency diagnostic. The average recoveries of allure red, sunset yellow and brilliant blue obtained by PARAFAC were 98.75% +/- 8.9%, 97.22% +/- 2.9% and 99.00% +/- 2.9% and those by ATLD algorithm were 99.78% +/- 5.9%, 92.52% +/- 5.5% and 97.23% +/- 5.8%, respectively. Results show that both of the algorithms can be used in direct and rapid determination of multi-components of mixtures. From further comparison, the PARAFAC is more stable and advantageous.
SOFTWARE SOLUTIONS FOR ARDL MODELS
Directory of Open Access Journals (Sweden)
Nicolae-Marius JULA
2015-07-01
Full Text Available VAR type models can be used only for stationary time series. Causality analyses through econometric models need that series to have the same integrated order. Usually, when constraining the series to comply these restrictions (e.g. by differentiating, economic interpretation of the outcomes may become difficult. Recent solution for mitigating these problems is the use of ARDL (autoregressive distributed lag models. We present implementation in E-Views of these models and we test the impact of exchange rate on consumer price index.
Implementing Modifed Burg Algorithms in Multivariate Subset Autoregressive Modeling
Directory of Open Access Journals (Sweden)
A. Alexandre Trindade
2003-02-01
Full Text Available The large number of parameters in subset vector autoregressive models often leads one to procure fast, simple, and efficient alternatives or precursors to maximum likelihood estimation. We present the solution of the multivariate subset Yule-Walker equations as one such alternative. In recent work, Brockwell, Dahlhaus, and Trindade (2002, show that the Yule-Walker estimators can actually be obtained as a special case of a general recursive Burg-type algorithm. We illustrate the structure of this Algorithm, and discuss its implementation in a high-level programming language. Applications of the Algorithm in univariate and bivariate modeling are showcased in examples. Univariate and bivariate versions of the Algorithm written in Fortran 90 are included in the appendix, and their use illustrated.
LCD motion blur: modeling, analysis, and algorithm.
Chan, Stanley H; Nguyen, Truong Q
2011-08-01
Liquid crystal display (LCD) devices are well known for their slow responses due to the physical limitations of liquid crystals. Therefore, fast moving objects in a scene are often perceived as blurred. This effect is known as the LCD motion blur. In order to reduce LCD motion blur, an accurate LCD model and an efficient deblurring algorithm are needed. However, existing LCD motion blur models are insufficient to reflect the limitation of human-eye-tracking system. Also, the spatiotemporal equivalence in LCD motion blur models has not been proven directly in the discrete 2-D spatial domain, although it is widely used. There are three main contributions of this paper: modeling, analysis, and algorithm. First, a comprehensive LCD motion blur model is presented, in which human-eye-tracking limits are taken into consideration. Second, a complete analysis of spatiotemporal equivalence is provided and verified using real video sequences. Third, an LCD motion blur reduction algorithm is proposed. The proposed algorithm solves an l(1)-norm regularized least-squares minimization problem using a subgradient projection method. Numerical results show that the proposed algorithm gives higher peak SNR, lower temporal error, and lower spatial error than motion-compensated inverse filtering and Lucy-Richardson deconvolution algorithm, which are two state-of-the-art LCD deblurring algorithms.
A Topological Model for Parallel Algorithm Design
1991-09-01
New York, 1989. 108. J. Dugundji . Topology . Allen and Bacon, Rockleigh, NJ, 1966. 109. R. Duncan. A Survey of Parallel Computer Architectures. IEEE...Approved for public release; distribition unlimited 4N1f-e AFIT/DS/ENG/91-02 A TOPOLOGICAL MODEL FOR PARALLEL ALGORITHM DESIGN DISSERTATION Presented to...DC 20503. 4. TITLE AND SUBTITLE 5. FUNDING NUMBERS A Topological Model For Parallel Algorithm Design 6. AUTHOR(S) Jeffrey A Simmers, Captain, USAF 7
A new solution for maximal clique problem based sticker model.
Darehmiraki, Majid
2009-02-01
In this paper, we use stickers to construct a solution space of DNA for the maximal clique problem (MCP). Simultaneously, we also apply the DNA operation in the sticker-based model to develop a DNA algorithm. The results of the proposed algorithm show that the MCP is resolved with biological operations in the sticker-based model for the solution space of the sticker. Moreover, this work presents clear evidence of the ability of DNA computing to solve the NP-complete problem. The potential of DNA computing for the MCP is promising given the operational time complexity of O(nxk).
Fast Combinatorial Algorithm for the Solution of Linearly Constrained Least Squares Problems
Van Benthem, Mark H.; Keenan, Michael R.
2008-11-11
A fast combinatorial algorithm can significantly reduce the computational burden when solving general equality and inequality constrained least squares problems with large numbers of observation vectors. The combinatorial algorithm provides a mathematically rigorous solution and operates at great speed by reorganizing the calculations to take advantage of the combinatorial nature of the problems to be solved. The combinatorial algorithm exploits the structure that exists in large-scale problems in order to minimize the number of arithmetic operations required to obtain a solution.
Sopov, E.; Semenkina, O.
2015-01-01
Genetic and distribution building algorithms with binary representation are analyzed. A property of convergence to the optimal solution is discussed. A novel convergence prediction method is proposed and investigated. The method is based on analysis of gene value probabilities distribution dynamics, thus it can predict gene values of the optimal solution to which the algorithm converges. The results of investigations for the optimal prediction algorithm performance are presented.
Carbon export algorithm advancements in models
Çağlar Yumruktepe, Veli; Salihoğlu, Barış
2015-04-01
The rate at which anthropogenic CO2 is absorbed by the oceans remains a critical question under investigation by climate researchers. Construction of a complete carbon budget, requires better understanding of air-sea exchanges and the processes controlling the vertical and horizontal transport of carbon in the ocean, particularly the biological carbon pump. Improved parameterization of carbon sequestration within ecosystem models is vital to better understand and predict changes in the global carbon cycle. Due to the complexity of processes controlling particle aggregation, sinking and decomposition, existing ecosystem models necessarily parameterize carbon sequestration using simple algorithms. Development of improved algorithms describing carbon export and sequestration, suitable for inclusion in numerical models is an ongoing work. Existing unique algorithms used in the state-of-the art ecosystem models and new experimental results obtained from mesocosm experiments and open ocean observations have been inserted into a common 1D pelagic ecosystem model for testing purposes. The model was implemented to the timeseries stations in the North Atlantic (BATS, PAP and ESTOC) and were evaluated with datasets of carbon export. Targetted topics of algorithms were PFT functional types, grazing and vertical movement of zooplankton, and remineralization, aggregation and ballasting dynamics of organic matter. Ultimately it is intended to feed improved algorithms to the 3D modelling community, for inclusion in coupled numerical models.
A simple algorithm for optimization and model fitting: AGA (asexual genetic algorithm)
Cantó, J.; Curiel, S.; Martínez-Gómez, E.
2009-07-01
Context: Mathematical optimization can be used as a computational tool to obtain the optimal solution to a given problem in a systematic and efficient way. For example, in twice-differentiable functions and problems with no constraints, the optimization consists of finding the points where the gradient of the objective function is zero and using the Hessian matrix to classify the type of each point. Sometimes, however it is impossible to compute these derivatives and other type of techniques must be employed such as the steepest descent/ascent method and more sophisticated methods such as those based on the evolutionary algorithms. Aims: We present a simple algorithm based on the idea of genetic algorithms (GA) for optimization. We refer to this algorithm as AGA (asexual genetic algorithm) and apply it to two kinds of problems: the maximization of a function where classical methods fail and model fitting in astronomy. For the latter case, we minimize the chi-square function to estimate the parameters in two examples: the orbits of exoplanets by taking a set of radial velocity data, and the spectral energy distribution (SED) observed towards a YSO (Young Stellar Object). Methods: The algorithm AGA may also be called genetic, although it differs from standard genetic algorithms in two main aspects: a) the initial population is not encoded; and b) the new generations are constructed by asexual reproduction. Results: Applying our algorithm in optimizing some complicated functions, we find the global maxima within a few iterations. For model fitting to the orbits of exoplanets and the SED of a YSO, we estimate the parameters and their associated errors.
An adaptive solution domain algorithm for solving multiphase flow equations
Katyal, A. K.; Parker, J. C.
1992-01-01
An adaptive solution domain (ASD) finite-element model for simulating hydrocarbon spills has been developed that is computationally more efficient than conventional numerical methods. Coupled flow of water and oil with an air phase at constant pressure is considered. In the ASD formulation, the solution domain for water- and oil-flow equations is restricted by eliminating elements from the global matrix assembly which are not experiencing significant changes in fluid saturations or pressures. When any nodes of an element exhibit changes in fluid pressures more than a stipulated tolerance τ, or changes in fluid saturations greater than tolerance τ 2 during the current time step, it is labeled active and included in the computations for the next iteration. This formulation achieves computational efficiency by solving the flow equations for only the part of the domain where changes in fluid pressure or the saturations take place above stipulated tolerances. Examples involving infiltration and redistribution of oil in 1- and 2-D spatial domains are described to illustrate the application of the ASD method and the savings in the processor time achieved by this formulation. Savings in the computational effort up to 84% during infiltration and 63% during redistribution were achieved for the 2-D example problem.
Adaptive Genetic Algorithm Model for Intrusion Detection
Directory of Open Access Journals (Sweden)
K. S. Anil Kumar
2012-09-01
Full Text Available Intrusion detection systems are intelligent systems designed to identify and prevent the misuse of computer networks and systems. Various approaches to Intrusion Detection are currently being used, but they are relatively ineffective. Thus the emerging network security systems need be part of the life system and this ispossible only by embedding knowledge into the network. The Adaptive Genetic Algorithm Model - IDS comprising of K-Means clustering Algorithm, Genetic Algorithm and Neural Network techniques. Thetechnique is tested using multitude of background knowledge sets in DARPA network traffic datasets.
Institute of Scientific and Technical Information of China (English)
李莉; 王俊衡; 孟繁佳; 穆永航; 袁洪波3; 王海华; N. Sigrimis
2016-01-01
封闭式栽培是解决中国日光温室传统栽培连作障碍问题的有效途径之一。营养液调控过程的性能指标是封闭式栽培模式的关键。该文针对一种具有二次混肥特性的营养液调控过程进行动态分析并建立模型，使用PID控制算法实现了营养液调控，并对影响因素进行对比试验。试验结果表明，系统达到稳定的时间随出液流量的减小或营养液浓度之和的增大呈现减小的趋势；系统震荡持续时间与超调量随出液流量的减小或营养液浓度之和的增大呈现增大的趋势。由试验结果与分析可知，该带有二次混肥的封闭式栽培系统中最优的出液流量与营养液浓度之和分别为6 m3/h与200 g/L，在此状态下进行PID控制参数整定，得到系统在最优条件下稳定时间为100 s、超调量为3%、震荡持续时间为25 s。表明该带有二次混肥的封闭式栽培系统的营养液调控过程能使用PID算法进行调控，能使系统实现稳定、高效的运行。该研究为封闭式栽培的营养液调控提供了参考。%Closed cultivation is a new type of cultivation model, which is widely used in solar greenhouse, and it uses EC as the performance index of the regulation process of nutrient solution. In the study, a dynamic analysis was conducted on the regulation process of closed culture nutrient solution with two-step mixed fertilizer characteristics. PID control algorithm was used to control the regulation process of the nutrient solution, and the effects of 6 factors on control process were analyzed through comparative experiment. The results showed that it was difficult to use a precise model to characterize the fertilizer mixing process, since the output liquid flow rate, the fertilizer concentration and the PID parameters had great influence on the control effect of the two-step mixed fertilizer system. Experiments were carried out to find the effect of different output liquid
On Models of Nonlinear Evolution Paths in Adiabatic Quantum Algorithms
Institute of Scientific and Technical Information of China (English)
SUN Jie; LU Song-Feng; Samuel L.Braunstein
2013-01-01
In this paper,we study two different nonlinear interpolating paths in adiabatic evolution algorithms for solving a particular class of quantum search problems where both the initial and final Hamiltonian are one-dimensional projector Hamiltonians on the corresponding ground state.If the overlap between the initial state and final state of the quantum system is not equal to zero,both of these models can provide a constant time speedup over the usual adiabatic algorithms by increasing some another corresponding "complexity".But when the initial state has a zero overlap with the solution state in the problem,the second model leads to an infinite time complexity of the algorithm for whatever interpolating functions being applied while the first one can still provide a constant running time.However,inspired by a related reference,a variant of the first model can be constructed which also fails for the problem when the overlap is exactly equal to zero if we want to make up the "intrinsic" fault of the second model — an increase in energy.Two concrete theorems are given to serve as explanations why neither of these two models can improve the usual adiabatic evolution algorithms for the phenomenon above.These just tell us what should be noted when using certain nonlinear evolution paths in adiabatic quantum algorithms for some special kind of problems.
Directory of Open Access Journals (Sweden)
Nilson C. Roberty
2011-01-01
Full Text Available We introduce algorithms marching over a polygonal mesh with elements consistent with the propagation directions of the particle (radiation flux. The decision for adopting this kind of mesh to solve the one-speed Boltzmann transport equation is due to characteristics of the domain of the transport operator which controls derivatives only in the direction of propagation of the particles (radiation flux in the absorbing and scattering media. This a priori adaptivity has the advantages that it formulates a consistent scheme which makes appropriate the application of the Lax equivalence theorem framework to the problem. In this work, we present the main functional spaces involved in the formalism and a description of the algorithms for the mesh generation and the transport equation solution. Some numerical examples related to the solution of a transmission problem in a high-contrast model with absorption and scattering are presented. Also, a comparison with benchmarks problems for source and reactor criticality simulations shows the compatibility between calculations with the algorithms proposed here and theoretical results.
Genetic algorithm in DNA computing:A solution to the maximal clique problem
Institute of Scientific and Technical Information of China (English)
LI Yuan; FANG Chen; OUYANG Qi
2004-01-01
Genetic algorithm is one of the possible ways to break the limit of brute-force method in DNA computing. Using the idea of Darwinian evolution, we introduce a genetic DNA computing algorithm to solve the maximal clique problem. All the operations in the algorithm are accessible with today's molecular biotechnology. Our computer simulations show that with this new computing algorithm, it is possible to get a solution from a very small initial data pool, avoiding enumerating all candidate solutions. For randomly generated problems, genetic algorithm can give correct solution within a few cycles at high probability. Although the current speed of a DNA computer is slow compared with silicon computers, our simulation indicates that the number of cycles needed in this genetic algorithm is approximately a linear function of the number of vertices in the network. This may make DNA computers more powerfully attacking some hard computational problems.
Model Checking Algorithms for CTMDPs
DEFF Research Database (Denmark)
Buchholz, Peter; Hahn, Ernst Moritz; Hermanns, Holger
2011-01-01
Continuous Stochastic Logic (CSL) can be interpreted over continuoustime Markov decision processes (CTMDPs) to specify quantitative properties of stochastic systems that allow some external control. Model checking CSL formulae over CTMDPs requires then the computation of optimal control strategie...
Model Checking Algorithms for CTMDPs
DEFF Research Database (Denmark)
Buchholz, Peter; Hahn, Ernst Moritz; Hermanns, Holger
2011-01-01
Continuous Stochastic Logic (CSL) can be interpreted over continuoustime Markov decision processes (CTMDPs) to specify quantitative properties of stochastic systems that allow some external control. Model checking CSL formulae over CTMDPs requires then the computation of optimal control strategie...
A LOAD BALANCING MODEL USING FIREFLY ALGORITHM IN CLOUD COMPUTING
Directory of Open Access Journals (Sweden)
A. Paulin Florence
2014-01-01
Full Text Available Cloud computing is a model that points at streamlining the on-demand provisioning of software, hardware and data as services and providing end-users with flexible and scalable services accessible through the Internet. The main objective of the proposed approach is to maximize the resource utilization and provide a good balanced load among all the resources in cloud servers. Initially, a load model of every resource will be derived based on several factors such as, memory usage, processing time and access rate. Based on the newly derived load index, the current load will be computed for all the resources shared in virtual machine of cloud servers. Once the load index is computed for all the resources, load balancing operation will be initiated to effectively use the resources dynamically with the process of assigning resources to the corresponding node to reduce the load value. So, assigning of resources to proper nodes is an optimal distribution problem so that many optimization algorithms such as genetic algorithm and modified genetic algorithm are utilized for load balancing. These algorithms are not much effective in providing the neighbour solutions since it does not overcome exploration and exploration problem. So, utilizing the effective optimization procedure instead of genetic algorithm can lead to better load balancing since it is a traditional and old algorithm. Accordingly, I have planned to utilize a recent optimization algorithm, called firefly algorithm to do the load balancing operation in our proposed work. At first, the index table will be maintained by considering the availability of virtual servers and sequence of request. Then, load index will be computed based on the newly derived formulae. Based on load index, load balancing operation will be carried out using firefly algorithm. The performance analysis produced expected results and thus proved the proposed approach is efficient in optimizing schedules by balancing the
Fast Algorithm of Numerical Solutions for Strong Nonlinear Partial Differential Equations
Directory of Open Access Journals (Sweden)
Tongjing Liu
2014-07-01
Full Text Available Because of a high mobility ratio in the chemical and gas flooding for oil reservoirs, the problems of numerical dispersion and low calculation efficiency also exist in the common methods, such as IMPES and adaptive implicit methods. Therefore, the original calculation process, “one-step calculation for pressure and multistep calculation for saturation,” was improved by introducing a velocity item and using the fractional flow in a direction to calculate the saturation. Based on these developments, a new algorithm of numerical solution for “one-step calculation for pressure, one-step calculation for velocity, and multi-step calculation for fractional flow and saturation” was obtained, and the convergence condition for the calculation of saturation was also proposed. The simulation result of a typical theoretical model shows that the nonconvergence occurred for about 6,000 times in the conventional algorithm of IMPES, and a high fluctuation was observed in the calculation steps. However, the calculation step of the fast algorithm was stabilized for 0.5 d, indicating that the fast algorithm can avoid the nonconvergence caused by the saturation that was directly calculated by pressure. This has an important reference value in the numerical simulations of chemical and gas flooding for oil reservoirs.
Optimized quantum random-walk search algorithm for multi-solution search
Institute of Scientific and Technical Information of China (English)
张宇超; 鲍皖苏; 汪翔; 付向群
2015-01-01
This study investigates the multi-solution search of the optimized quantum random-walk search algorithm on the hypercube. Through generalizing the abstract search algorithm which is a general tool for analyzing the search on the graph to the multi-solution case, it can be applied to analyze the multi-solution case of quantum random-walk search on the graph directly. Thus, the computational complexity of the optimized quantum random-walk search algorithm for the multi-solution search is obtained. Through numerical simulations and analysis, we obtain a critical value of the proportion of solutions q. For a given q, we derive the relationship between the success rate of the algorithm and the number of iterations when q is no longer than the critical value.
Underground water quality model inversion of genetic algorithm
Institute of Scientific and Technical Information of China (English)
MA Ruijie; LI Xin
2009-01-01
The underground water quality model with non-linear inversion problem is ill-posed, and boils down to solving the minimum of nonlinear function. Genetic algorithms are adopted in a number of individuals of groups by iterative search to find the optimal solution of the problem, the encoding strings as its operational objective, and achieving the iterative calculations by the genetic operators. It is an effective method of inverse problems of groundwater, with incomparable advantages and practical significances.
A multi-level solution algorithm for steady-state Markov chains
Horton, Graham; Leutenegger, Scott T.
1993-01-01
A new iterative algorithm, the multi-level algorithm, for the numerical solution of steady state Markov chains is presented. The method utilizes a set of recursively coarsened representations of the original system to achieve accelerated convergence. It is motivated by multigrid methods, which are widely used for fast solution of partial differential equations. Initial results of numerical experiments are reported, showing significant reductions in computation time, often an order of magnitude or more, relative to the Gauss-Seidel and optimal SOR algorithms for a variety of test problems. The multi-level method is compared and contrasted with the iterative aggregation-disaggregation algorithm of Takahashi.
Numerical solution of dynamic equilibrium models under Poisson uncertainty
DEFF Research Database (Denmark)
Posch, Olaf; Trimborn, Timo
2013-01-01
of the retarded type. We apply the Waveform Relaxation algorithm, i.e., we provide a guess of the policy function and solve the resulting system of (deterministic) ordinary differential equations by standard techniques. For parametric restrictions, analytical solutions to the stochastic growth model and a novel...
Institute of Scientific and Technical Information of China (English)
李沛恒; 楼颖燕
2015-01-01
To determine the onset and duration of contraflow evacuation, a multi-objective optimization (MOO) model is proposed to explicitly consider both the total system evacuation time and the operation cost. A solution algorithm that enhances the popular evolutionary algorithm NSGA-II is proposed to solve the model. The algorithm incorporates preliminary results as prior information and includes a meta-model as an alternative to evaluation by simulation. Numerical analysis of a case study suggests that the proposed formulation and solution algorithm are valid, and the enhanced NSGA-II outperforms the original algorithm in both convergence to the true Pareto-optimal set and solution diversity.
Fuzzy audit risk modeling algorithm
Directory of Open Access Journals (Sweden)
Zohreh Hajihaa
2011-07-01
Full Text Available Fuzzy logic has created suitable mathematics for making decisions in uncertain environments including professional judgments. One of the situations is to assess auditee risks. During recent years, risk based audit (RBA has been regarded as one of the main tools to fight against fraud. The main issue in RBA is to determine the overall audit risk an auditor accepts, which impact the efficiency of an audit. The primary objective of this research is to redesign the audit risk model (ARM proposed by auditing standards. The proposed model of this paper uses fuzzy inference systems (FIS based on the judgments of audit experts. The implementation of proposed fuzzy technique uses triangular fuzzy numbers to express the inputs and Mamdani method along with center of gravity are incorporated for defuzzification. The proposed model uses three FISs for audit, inherent and control risks, and there are five levels of linguistic variables for outputs. FISs include 25, 25 and 81 rules of if-then respectively and officials of Iranian audit experts confirm all the rules.
A simple algorithm for optimization and model fitting: AGA (asexual genetic algorithm)
Canto, J; Martinez-Gomez, E; 10.1051/0004-6361/200911740
2009-01-01
Context. Mathematical optimization can be used as a computational tool to obtain the optimal solution to a given problem in a systematic and efficient way. For example, in twice-differentiable functions and problems with no constraints, the optimization consists of finding the points where the gradient of the objective function is zero and using the Hessian matrix to classify the type of each point. Sometimes, however it is impossible to compute these derivatives and other type of techniques must be employed such as the steepest descent/ascent method and more sophisticated methods such as those based on the evolutionary algorithms. Aims. We present a simple algorithm based on the idea of genetic algorithms (GA) for optimization. We refer to this algorithm as AGA (Asexual Genetic Algorithm) and apply it to two kinds of problems: the maximization of a function where classical methods fail and model fitting in astronomy. For the latter case, we minimize the chi-square function to estimate the parameters in two e...
Design of attitude solution algorithm for tail-sitter VTOL UAV
Directory of Open Access Journals (Sweden)
Donghui LIU
2016-02-01
Full Text Available The tail-sitter Vertical Takeoff and Landing (VTOL Unmanned Aerial Vehicle(UAV, flying in a fixed-wing model, overcomes many shortcomings of traditional fixed-wing UAVs, and inherits the advantage of high overall efficiency, which means it has great development potential and very broad application prospects. The attitude of tail-sitter VTOL UAV shows a wide change range in its takeoff and landing stages, and when the attitude sensor changes more than 90 degrees in pitch direction, the Euler angles converted by the Quaternions will have singular points, which means gimbal deadlock appears. From the solution algorithm, this paper provides a method of changing the order of rotation to avoid the appearance of singular points. The results show that this method can be well applied to the attitude solution of the VTOL UAV.
Application layer multicast routing solution based on genetic algorithms
Institute of Scientific and Technical Information of China (English)
Peng CHENG; Qiufeng WU; Qionghai DAI
2009-01-01
Application layer multicast routing is a multi-objective optimization problem.Three routing con-straints,tree's cost,tree's balance and network layer load distribution are analyzed in this paper.The three fitness functions are used to evaluate a multicast tree on the three indexes respectively and one general fitness function is generated.A novel approach based on genetic algorithms is proposed.Numerical simulations show that,compared with geometrical routing rules,the proposed algorithm improve all three indexes,especially on cost and network layer load distribution indexes.
AN EVOLUTIONARY ALGORITHM FOR THE SOLUTION OF WIND THERMAL DISPATCH
Directory of Open Access Journals (Sweden)
K. DHAYALINI
2013-06-01
Full Text Available In this paper, optimal wind and thermal generation dispatch is performed by Evolutionary Algorithm approach. To determine the optimal dispatch scheme that can integrate wind power efficiently and reliably into the conventional system, it is necessary to develop a better wind thermal coordination dispatch. In this paper Evolutionary Algorithm approach is used to coordinate the wind and thermal coordination dispatch. Also to minimize the total production cost in the economic dispatch scheme considering the wind power generation and valve point effect of the thermal units. For numerical simulation ten unit systems incorporating one wind power generation is utilized. Economic dispatch scheme with and without wind power production are simulated.
Numerical algorithm of distributed TOPKAPI model and its application
Institute of Scientific and Technical Information of China (English)
Deng Peng; Li Zhijia; Liu Zhiyu
2008-01-01
The TOPKAPI (TOPographic Kinematic APproximation and Integration) model is a physically based rainfall-runoff model derived from the integration in space of the kinematic wave model. In the TOPKAPI model, rainfall-runoff and runoff routing processes are described by three nonlinear reservoir differential equations that are structurally similar and describe different hydrological and hydraulic processes. Equations are integrated over grid cells that describe the geometry of the catchment, leading to a cascade of nonlinear reservoir equations. For the sake of improving the model's computation precision, this paper provides the general form of these equations and describes the solution by means of a numerical algorithm, the variable-step fourth-order Runge-Kutta algorithm. For the purpose of assessing the quality of the comprehensive numerical algorithm, this paper presents a case study application to the Buliu River Basin, which has an area of 3 310 km2, using a DEM (digital elevation model) grid with a resolution of 1 km. The results show that the variable-step fourth-order Runge-Kutta algorithm for nonlinear reservoir equations is a good approximation of subsurface flow in the soil matrix, overland flow over the slopes, and surface flow in the channel network, allowing us to retain the physical properties of the original equations at scales ranging from a few meters to 1 km.
Adaptive Ant Colony Algorithm for the VRP Solution of Logistics Distribution
Directory of Open Access Journals (Sweden)
Yu-Ping Wang
2013-06-01
Full Text Available In order to conquer the premature convergence problem and lower the cost of computing of the basic Ant Colony Algorithm (ACA, we present an adaptive ant colony algorithm, named AACA, coupled with a Pareto Local Search (PLS algorithm and apply to the Vehicle Routing Problem (VRP and Capacitated VRP (CVRP. By using the information entropy, the algorithm adjusts the pheromone updating strategy adaptively. Experiments on various aspects of the algorithm and computational results for some benchmark problems are reported. We compare our approach with some classic, powerful meta-heuristics and show that the proposed approach can obtain the better quality of the solutions.
Dynamic exponents for potts model cluster algorithms
Coddington, Paul D.; Baillie, Clive F.
We have studied the Swendsen-Wang and Wolff cluster update algorithms for the Ising model in 2, 3 and 4 dimensions. The data indicate simple relations between the specific heat and the Wolff autocorrelations, and between the magnetization and the Swendsen-Wang autocorrelations. This implies that the dynamic critical exponents are related to the static exponents of the Ising model. We also investigate the possibility of similar relationships for the Q-state Potts model.
Solution of optimal power flow using evolutionary-based algorithms
African Journals Online (AJOL)
This paper applies two reliable and efficient evolutionary-based methods named Shuffled Frog Leaping Algorithm ... Grey Wolf Optimizer (GWO) to solve Optimal Power Flow (OPF) problem. OPF is ..... The wolves search for the prey based on the alpha, beta, and delta positions. ..... Energy Conversion and Management, Vol.
A Generic Design Model for Evolutionary Algorithms
Institute of Scientific and Technical Information of China (English)
He Feng; Kang Li-shan; Chen Yu-ping
2003-01-01
A generic design model for evolutionary algo rithms is proposed in this paper. The model, which was described by UML in details, focuses on the key concepts and mechanisms in evolutionary algorithms. The model not only achieves separation of concerns and encapsulation of implementations by classification and abstraction of those concepts,it also has a flexible architecture due to the application of design patterns. As a result, the model is reusable, extendible,easy to understand, easy to use, and easy to test. A large number of experiments applying the model to solve many different problems adequately illustrate the generality and effectivity of the model.
Crime Busting Model Based on Dynamic Ranking Algorithms
Directory of Open Access Journals (Sweden)
Yang Cao
2013-01-01
Full Text Available This paper proposed a crime busting model with two dynamic ranking algorithms to detect the likelihood of a suspect and the possibility of a leader in a complex social network. Signally, in order to obtain the priority list of suspects, an advanced network mining approach with a dynamic cumulative nominating algorithm is adopted to rapidly reduce computational expensiveness than most other topology-based approaches. Our method can also greatly increase the accuracy of solution with the enhancement of semantic learning filtering at the same time. Moreover, another dynamic algorithm of node contraction is also presented to help identify the leader among conspirators. Test results are given to verify the theoretical results, which show the great performance for either small or large datasets.
Institute of Scientific and Technical Information of China (English)
WANG Shunjin; ZHANG Hua
2006-01-01
The problem of preserving fidelity in numerical computation of nonlinear ordinary differential equations is studied in terms of preserving local differential structure and approximating global integration structure of the dynamical system.The ordinary differential equations are lifted to the corresponding partial differential equations in the framework of algebraic dynamics,and a new algorithm-algebraic dynamics algorithm is proposed based on the exact analytical solutions of the ordinary differential equations by the algebraic dynamics method.In the new algorithm,the time evolution of the ordinary differential system is described locally by the time translation operator and globally by the time evolution operator.The exact analytical piece-like solution of the ordinary differential equations is expressd in terms of Taylor series with a local convergent radius,and its finite order truncation leads to the new numerical algorithm with a controllable precision better than Runge Kutta Algorithm and Symplectic Geometric Algorithm.
Directory of Open Access Journals (Sweden)
Khalid. W. Magld
2012-07-01
Full Text Available The Traveling salesman problem (TSP is to find a tour of a given number of cities (visiting each city exactly once where the length of this tour is minimized. Testing every possibility for an N city tour would be N! Math additions. Genetic algorithms (GA and Memetic algorithms (MA are a relatively new optimization technique which can be applied to various problems, including those that are NPhard. The technique does not ensure an optimal solution, however it usually gives good approximations in a reasonable amount of time. They, therefore, would be good algorithms to try on the traveling salesman problem, one of the most famous NP-hard problems. In this paper I have proposed a algorithm to solve TSP using Genetic algorithms (GA and Memetic algorithms (MA with the crossover operator Edge Assembly Crossover (EAX and also analyzed the result on different parameter like group size and mutation percentage and compared the result with other solutions.
Numerical algorithm of distributed TOPKAPI model and its application
Directory of Open Access Journals (Sweden)
Deng Peng
2008-12-01
Full Text Available The TOPKAPI (TOPographic Kinematic APproximation and Integration model is a physically based rainfall-runoff model derived from the integration in space of the kinematic wave model. In the TOPKAPI model, rainfall-runoff and runoff routing processes are described by three nonlinear reservoir differential equations that are structurally similar and describe different hydrological and hydraulic processes. Equations are integrated over grid cells that describe the geometry of the catchment, leading to a cascade of nonlinear reservoir equations. For the sake of improving the model’s computation precision, this paper provides the general form of these equations and describes the solution by means of a numerical algorithm, the variable-step fourth-order Runge-Kutta algorithm. For the purpose of assessing the quality of the comprehensive numerical algorithm, this paper presents a case study application to the Buliu River Basin, which has an area of 3 310 km2, using a DEM (digital elevation model grid with a resolution of 1 km. The results show that the variable-step fourth-order Runge-Kutta algorithm for nonlinear reservoir equations is a good approximation of subsurface flow in the soil matrix, overland flow over the slopes, and surface flow in the channel network, allowing us to retain the physical properties of the original equations at scales ranging from a few meters to 1 km.
IIR Filter Modeling Using an Algorithm Inspired on Electromagnetism
Directory of Open Access Journals (Sweden)
Cuevas-Jiménez E.
2013-01-01
Full Text Available Infinite-impulse-response (IIR filtering provides a powerful approach for solving a variety of problems. However, its design represents a very complicated task, since the error surface of IIR filters is generally multimodal, global optimization techniques are required in order to avoid local minima. In this paper, a new method based on the Electromagnetism-Like Optimization Algorithm (EMO is proposed for IIR filter modeling. EMO originates from the electro-magnetism theory of physics by assuming potential solutions as electrically charged particles which spread around the solution space. The charge of each particle depends on its objective function value. This algorithm employs a collective attraction-repulsion mechanism to move the particles towards optimality. The experimental results confirm the high performance of the proposed method in solving various benchmark identification problems.
Goloviznin, V. M.; Kanaev, A. A.
2012-03-01
The CABARET computational algorithm is generalized to one-dimensional scalar quasilinear hyperbolic partial differential equations with allowance for inequality constraints on the solution. This generalization can be used to analyze seepage of liquid radioactive wastes through the unsaturated zone.
Solution of transient optimization problems by using an algorithm based on nonlinear programming
Teren, F.
1977-01-01
A new algorithm is presented for solution of dynamic optimization problems which are nonlinear in the state variables and linear in the control variables. It is shown that the optimal control is bang-bang. A nominal bang-bang solution is found which satisfies the system equations and constraints, and influence functions are generated which check the optimality of the solution. Nonlinear optimization (gradient search) techniques are used to find the optimal solution. The algorithm is used to find a minimum time acceleration for a turbofan engine.
Institute of Scientific and Technical Information of China (English)
Liu-chuan Zeng
2004-01-01
The purpose of this paper is to investigate the iterative algorithm for finding approximate solutions of a class of mixed variational-like inequalities in a real Hilbert space,where the iterative algorithm is presented by virtue of the auxiliary principle technique.On one hand,the existence of approximate solutions of this class of mixed variational-like inequalities is proven.On the other hand,it is shown that the approximate solutions converge strongly to the exact solution of this class of mixed variational-like inequalities.
Solution of transient optimization problems by using an algorithm based on nonlinear programming
Teren, F.
1977-01-01
A new algorithm is presented for solution of dynamic optimization problems which are nonlinear in the state variables and linear in the control variables. It is shown that the optimal control is bang-bang. A nominal bang-bang solution is found which satisfies the system equations and constraints, and influence functions are generated which check the optimality of the solution. Nonlinear optimization (gradient search) techniques are used to find the optimal solution. The algorithm is used to find a minimum time acceleration for a turbofan engine.
Model based development of engine control algorithms
Dekker, H.J.; Sturm, W.L.
1996-01-01
Model based development of engine control systems has several advantages. The development time and costs are strongly reduced because much of the development and optimization work is carried out by simulating both engine and control system. After optimizing the control algorithm it can be executed b
Worm algorithm for the CPN−1 model
Directory of Open Access Journals (Sweden)
Tobias Rindlisbacher
2017-05-01
Full Text Available The CPN−1 model in 2D is an interesting toy model for 4D QCD as it possesses confinement, asymptotic freedom and a non-trivial vacuum structure. Due to the lower dimensionality and the absence of fermions, the computational cost for simulating 2D CPN−1 on the lattice is much lower than that for simulating 4D QCD. However, to our knowledge, no efficient algorithm for simulating the lattice CPN−1 model for N>2 has been tested so far, which also works at finite density. To this end we propose a new type of worm algorithm which is appropriate to simulate the lattice CPN−1 model in a dual, flux-variables based representation, in which the introduction of a chemical potential does not give rise to any complications. In addition to the usual worm moves where a defect is just moved from one lattice site to the next, our algorithm additionally allows for worm-type moves in the internal variable space of single links, which accelerates the Monte Carlo evolution. We use our algorithm to compare the two popular CPN−1 lattice actions and exhibit marked differences in their approach to the continuum limit.
Algorithms and Models for the Web Graph
Gleich, David F.; Komjathy, Julia; Litvak, Nelly
2015-01-01
This volume contains the papers presented at WAW2015, the 12th Workshop on Algorithms and Models for the Web-Graph held during December 10–11, 2015, in Eindhoven. There were 24 submissions. Each submission was reviewed by at least one, and on average two, Program Committee members. The committee dec
Algorithms and Models for the Web Graph
Gleich, David F.; Komjathy, Julia; Litvak, Nelli
2015-01-01
This volume contains the papers presented at WAW2015, the 12th Workshop on Algorithms and Models for the Web-Graph held during December 10–11, 2015, in Eindhoven. There were 24 submissions. Each submission was reviewed by at least one, and on average two, Program Committee members. The committee
A genetic algorithm for the pareto optimal solution set of multi-objective shortest path problem
Institute of Scientific and Technical Information of China (English)
HU Shi-cheng; XU Xiao-fei; ZHAN De-chen
2005-01-01
Unlike the shortest path problem that has only one optimal solution and can be solved in polynomial time, the multi-objective shortest path problem (MSPP) has a set of pareto optimal solutions and cannot be solved in polynomial time. The present algorithms focused mainly on how to obtain a precisely pareto optimal solution for MSPP resulting in a long time to obtain multiple pareto optimal solutions with them. In order to obtain a set of satisfied solutions for MSPP in reasonable time to meet the demand of a decision maker, a genetic algorithm MSPP-GA is presented to solve the MSPP with typically competing objectives, cost and time, in this paper. The encoding of the solution and the operators such as crossover, mutation and selection are developed.The algorithm introduced pareto domination tournament and sharing based selection operator, which can not only directly search the pareto optimal frontier but also maintain the diversity of populations in the process of evolutionary computation. Experimental results show that MSPP-GA can obtain most efficient solutions distributed all along the pareto frontier in less time than an exact algorithm. The algorithm proposed in this paper provides a new and effective method of how to obtain the set of pareto optimal solutions for other multiple objective optimization problems in a short time.
Optimization in engineering models and algorithms
Sioshansi, Ramteen
2017-01-01
This textbook covers the fundamentals of optimization, including linear, mixed-integer linear, nonlinear, and dynamic optimization techniques, with a clear engineering focus. It carefully describes classical optimization models and algorithms using an engineering problem-solving perspective, and emphasizes modeling issues using many real-world examples related to a variety of application areas. Providing an appropriate blend of practical applications and optimization theory makes the text useful to both practitioners and students, and gives the reader a good sense of the power of optimization and the potential difficulties in applying optimization to modeling real-world systems. The book is intended for undergraduate and graduate-level teaching in industrial engineering and other engineering specialties. It is also of use to industry practitioners, due to the inclusion of real-world applications, opening the door to advanced courses on both modeling and algorithm development within the industrial engineering ...
Weekly Fleet Assignment Model and Algorithm
Institute of Scientific and Technical Information of China (English)
ZHU Xing-hui; ZHU Jin-fu; GONG Zai-wu
2007-01-01
A 0-1 integer programming model for weekly fleet assignment was put forward based on linear network and weekly flight scheduling in China. In this model, the objective function is to maximize the total profit of fleet assignment, subject to the constraints of coverage, aircraft flow balance, fleet size, aircraft availability, aircraft usage, flight restriction, aircraft seat capacity,and stopover. Then the branch-and-bound algorithm based on special ordered set was applied to solve the model. At last, a realworld case study on an airline with 5 fleets, 48 aircrafts and 1 786 flight legs indicated that the profit increase was $1591276 one week and the running time was no more than 4 min, which shows that the model and algorithm are fairly good for domestic airline.
Directory of Open Access Journals (Sweden)
Alkın Yurtkuran
2016-01-01
Full Text Available The artificial bee colony (ABC algorithm is a popular swarm based technique, which is inspired from the intelligent foraging behavior of honeybee swarms. This paper proposes a new variant of ABC algorithm, namely, enhanced ABC with solution acceptance rule and probabilistic multisearch (ABC-SA to address global optimization problems. A new solution acceptance rule is proposed where, instead of greedy selection between old solution and new candidate solution, worse candidate solutions have a probability to be accepted. Additionally, the acceptance probability of worse candidates is nonlinearly decreased throughout the search process adaptively. Moreover, in order to improve the performance of the ABC and balance the intensification and diversification, a probabilistic multisearch strategy is presented. Three different search equations with distinctive characters are employed using predetermined search probabilities. By implementing a new solution acceptance rule and a probabilistic multisearch approach, the intensification and diversification performance of the ABC algorithm is improved. The proposed algorithm has been tested on well-known benchmark functions of varying dimensions by comparing against novel ABC variants, as well as several recent state-of-the-art algorithms. Computational results show that the proposed ABC-SA outperforms other ABC variants and is superior to state-of-the-art algorithms proposed in the literature.
Computational Granular Dynamics Models and Algorithms
Pöschel, Thorsten
2005-01-01
Computer simulations not only belong to the most important methods for the theoretical investigation of granular materials, but also provide the tools that have enabled much of the expanding research by physicists and engineers. The present book is intended to serve as an introduction to the application of numerical methods to systems of granular particles. Accordingly, emphasis is placed on a general understanding of the subject rather than on the presentation of the latest advances in numerical algorithms. Although a basic knowledge of C++ is needed for the understanding of the numerical methods and algorithms in the book, it avoids usage of elegant but complicated algorithms to remain accessible for those who prefer to use a different programming language. While the book focuses more on models than on the physics of granular material, many applications to real systems are presented.
Efficient Algorithms for Parsing the DOP Model
Goodman, J
1996-01-01
Excellent results have been reported for Data-Oriented Parsing (DOP) of natural language texts (Bod, 1993). Unfortunately, existing algorithms are both computationally intensive and difficult to implement. Previous algorithms are expensive due to two factors: the exponential number of rules that must be generated and the use of a Monte Carlo parsing algorithm. In this paper we solve the first problem by a novel reduction of the DOP model to a small, equivalent probabilistic context-free grammar. We solve the second problem by a novel deterministic parsing strategy that maximizes the expected number of correct constituents, rather than the probability of a correct parse tree. Using the optimizations, experiments yield a 97% crossing brackets rate and 88% zero crossing brackets rate. This differs significantly from the results reported by Bod, and is comparable to results from a duplication of Pereira and Schabes's (1992) experiment on the same data. We show that Bod's results are at least partially due to an e...
The Integration of Cooperation Model and Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
In the photogrammetry,some researchers have applied genetic algorithms in aerial image texture classification and reducing hyper-spectrum remote sensing data.Genetic algorithm can rapidly find the solutions which are close to the optimal solution.But it is not easy to find the optimal solution.In order to solve the problem,a cooperative evolution idea integrating genetic algorithm and ant colony algorithm is presented in this paper.On the basis of the advantages of ant colony algorithm,this paper proposes the method integrating genetic algorithms and ant colony algorithm to overcome the drawback of genetic algorithms.Moreover,the paper takes designing texture classification masks of aerial images as an example to illustrate the integration theory and procedures.
Protopopescu, V; Barhen, J
2003-01-01
A constant-time solution of the continuous global optimization problem (GOP) is obtained by using an ensemble algorithm. We show that under certain assumptions, the solution can be guaranteed by mapping the GOP onto a discrete unsorted search problem, whereupon Brueschweiler's ensemble search algorithm is applied. For adequate sensitivities of the measurement technique, the query complexity of the ensemble search algorithm depends linearly on the size of the function's domain. Advantages and limitations of an eventual NMR implementation are discussed. (letter to the editor)
Adjustment Criterion and Algorithm in Adjustment Model with Uncertain
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SONG Yingchun
2015-02-01
Full Text Available Uncertainty often exists in the process of obtaining measurement data, which affects the reliability of parameter estimation. This paper establishes a new adjustment model in which uncertainty is incorporated into the function model as a parameter. A new adjustment criterion and its iterative algorithm are given based on uncertainty propagation law in the residual error, in which the maximum possible uncertainty is minimized. This paper also analyzes, with examples, the different adjustment criteria and features of optimal solutions about the least-squares adjustment, the uncertainty adjustment and total least-squares adjustment. Existing error theory is extended with new observational data processing method about uncertainty.
Directory of Open Access Journals (Sweden)
Ji-Huan He
2012-01-01
Full Text Available This paper applies an ancient Chinese algorithm to differential-difference equations, and a solitary-solution formulation is obtained. The discrete mKdV lattice equation is used as an example to elucidate the solution procedure.
Markov chains models, algorithms and applications
Ching, Wai-Ki; Ng, Michael K; Siu, Tak-Kuen
2013-01-01
This new edition of Markov Chains: Models, Algorithms and Applications has been completely reformatted as a text, complete with end-of-chapter exercises, a new focus on management science, new applications of the models, and new examples with applications in financial risk management and modeling of financial data.This book consists of eight chapters. Chapter 1 gives a brief introduction to the classical theory on both discrete and continuous time Markov chains. The relationship between Markov chains of finite states and matrix theory will also be highlighted. Some classical iterative methods
Genetic Algorithm Based Microscale Vehicle Emissions Modelling
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Sicong Zhu
2015-01-01
Full Text Available There is a need to match emission estimations accuracy with the outputs of transport models. The overall error rate in long-term traffic forecasts resulting from strategic transport models is likely to be significant. Microsimulation models, whilst high-resolution in nature, may have similar measurement errors if they use the outputs of strategic models to obtain traffic demand predictions. At the microlevel, this paper discusses the limitations of existing emissions estimation approaches. Emission models for predicting emission pollutants other than CO2 are proposed. A genetic algorithm approach is adopted to select the predicting variables for the black box model. The approach is capable of solving combinatorial optimization problems. Overall, the emission prediction results reveal that the proposed new models outperform conventional equations in terms of accuracy and robustness.
Efficiency of algorithm for solution of vector radiative transfer equation in turbid medium slab
Budak, V. P.; Efremenko, D. S.; Shagalov, O. V.
2012-06-01
The numerical solution of the vectorial radiative transfer equation (VRTE) is possible only by its discretization, which requires elimination of the solution anisotropic part including all the singularities. Discretized VRTE for the turbid medium slab has the unique analytical solution in the matrix form. Modern packages of matrix (linear) algebra allow only one possible algorithm of VRTE solution by computer. Various realizations of such an algorithm differ by the method of the elimination of the solution anisotropic part. Methods of the solution anisotropic part elimination are analysed in the paper. The codes created by the authors of these methods are analysed in simple situations in order to define its influence on the code efficiency. It is shown that the most effective method is based on the small angle modification of the spherical harmonics method (MSH). The code based on MSH is investigated in details by the influence of different properties of hard and software.
Load-balancing algorithms for climate models
Energy Technology Data Exchange (ETDEWEB)
Foster, I.T.; Toonen, B.R.
1994-06-01
Implementations of climate models on scalable parallel computer systems can suffer from load imbalances due to temporal and spatial variations in the amount of computation required for physical parameterizations such as solar radiation and convective adjustment. We have developed specialized techniques for correcting such imbalances. These techniques are incorporated in a general-purpose, programmable load-balancing library that allows the mapping of computation to processors to be specified as a series of maps generated by a programmer-supplied load-balancing module. The communication required to move from one map to another is performed automatically by the library, without programmer intervention. In this paper, we de scribe the load-balancing problem and the techniques that we have developed to solve it. We also describe specific load-balancing algorithms that we have developed for PCCM2, a scalable parallel implementation of the community Climate Model, and present experimental results that demonstrate the effectiveness of these algorithms on parallel computers.
Load-balancing algorithms for climate models
Foster, I. T.; Toonen, B. R.
Implementations of climate models on scalable parallel computer systems can suffer from load imbalances due to temporal and spatial variations in the amount of computation required for physical parameterizations such as solar radiation and convective adjustment. We have developed specialized techniques for correcting such imbalances. These techniques are incorporated in a general-purpose, programmable load-balancing library that allows the mapping of computation to processors to be specified as a series of maps generated by a programmer-supplied load-balancing module. The communication required to move from one map to another is performed automatically by the library, without programmer intervention. In this paper, we describe the load-balancing problem and the techniques that we have developed to solve it. We also describe specific load-balancing algorithms that we have developed for PCCM2, a scalable parallel implementation of the community climate model, and present experimental results that demonstrate the effectiveness of these algorithms on parallel computers.
Synaptic dynamics: linear model and adaptation algorithm.
Yousefi, Ali; Dibazar, Alireza A; Berger, Theodore W
2014-08-01
In this research, temporal processing in brain neural circuitries is addressed by a dynamic model of synaptic connections in which the synapse model accounts for both pre- and post-synaptic processes determining its temporal dynamics and strength. Neurons, which are excited by the post-synaptic potentials of hundred of the synapses, build the computational engine capable of processing dynamic neural stimuli. Temporal dynamics in neural models with dynamic synapses will be analyzed, and learning algorithms for synaptic adaptation of neural networks with hundreds of synaptic connections are proposed. The paper starts by introducing a linear approximate model for the temporal dynamics of synaptic transmission. The proposed linear model substantially simplifies the analysis and training of spiking neural networks. Furthermore, it is capable of replicating the synaptic response of the non-linear facilitation-depression model with an accuracy better than 92.5%. In the second part of the paper, a supervised spike-in-spike-out learning rule for synaptic adaptation in dynamic synapse neural networks (DSNN) is proposed. The proposed learning rule is a biologically plausible process, and it is capable of simultaneously adjusting both pre- and post-synaptic components of individual synapses. The last section of the paper starts with presenting the rigorous analysis of the learning algorithm in a system identification task with hundreds of synaptic connections which confirms the learning algorithm's accuracy, repeatability and scalability. The DSNN is utilized to predict the spiking activity of cortical neurons and pattern recognition tasks. The DSNN model is demonstrated to be a generative model capable of producing different cortical neuron spiking patterns and CA1 Pyramidal neurons recordings. A single-layer DSNN classifier on a benchmark pattern recognition task outperforms a 2-Layer Neural Network and GMM classifiers while having fewer numbers of free parameters and
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Yi Xu
2013-01-01
Full Text Available We propose a fourth-order total bounded variation regularization model which could reduce undesirable effects effectively. Based on this model, we introduce an improved split Bregman iteration algorithm to obtain the optimum solution. The convergence property of our algorithm is provided. Numerical experiments show the more excellent visual quality of the proposed model compared with the second-order total bounded variation model which is proposed by Liu and Huang (2010.
Liu, Liqiang; Dai, Yuntao; Gao, Jinyu
2014-01-01
Ant colony optimization algorithm for continuous domains is a major research direction for ant colony optimization algorithm. In this paper, we propose a distribution model of ant colony foraging, through analysis of the relationship between the position distribution and food source in the process of ant colony foraging. We design a continuous domain optimization algorithm based on the model and give the form of solution for the algorithm, the distribution model of pheromone, the update rules of ant colony position, and the processing method of constraint condition. Algorithm performance against a set of test trials was unconstrained optimization test functions and a set of optimization test functions, and test results of other algorithms are compared and analyzed to verify the correctness and effectiveness of the proposed algorithm.
Directory of Open Access Journals (Sweden)
Liqiang Liu
2014-01-01
Full Text Available Ant colony optimization algorithm for continuous domains is a major research direction for ant colony optimization algorithm. In this paper, we propose a distribution model of ant colony foraging, through analysis of the relationship between the position distribution and food source in the process of ant colony foraging. We design a continuous domain optimization algorithm based on the model and give the form of solution for the algorithm, the distribution model of pheromone, the update rules of ant colony position, and the processing method of constraint condition. Algorithm performance against a set of test trials was unconstrained optimization test functions and a set of optimization test functions, and test results of other algorithms are compared and analyzed to verify the correctness and effectiveness of the proposed algorithm.
Integration of Finite Element Method with Runge – Kuta Solution Algorithm
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Olawale Simon
2017-05-01
Full Text Available Runge – Kuta (RK method is reasonably simple and robust for numerical solution of differential equations but it requires an intelligent adaptive step-size routine; to achieve this, there is need to develop a good logical computer code. This study develops a finite element code in Java using Runge-Kuta method as a solution algorithm to predict dynamic time response of structural beam under impulse load. The solution obtained using direct integration and the present work is comparable.
Comparative study of fusion algorithms and implementation of new efficient solution
Besrour, Amine; Snoussi, Hichem; Siala, Mohamed; Abdelkefi, Fatma
2014-05-01
High Dynamic Range (HDR) imaging has been the subject of significant researches over the past years, the goal of acquiring the best cinema-quality HDR images of fast-moving scenes using an efficient merging algorithm has not yet been achieved. In fact, through the years, many efficient algorithms have been implemented and developed. However, they are not yet efficient since they don't treat all the situations and they have not enough speed to ensure fast HDR image reconstitution. In this paper, we will present a full comparative analyze and study of the available fusion algorithms. Also, we will implement our personal algorithm which can be more optimized and faster than the existed ones. We will also present our investigated algorithm that has the advantage to be more optimized than the existing ones. This merging algorithm is related to our hardware solution allowing us to obtain four pictures with different exposures.
Exact Solutions in Nonlocal Linear Models
Vernov, S. Yu.
2008-01-01
A general class of cosmological models driven by a nonlocal scalar field inspired by the string field theory is studied. Using the fact that the considering linear nonlocal model is equivalent to an infinite number of local models we have found an exact special solution of the nonlocal Friedmann equations. This solution describes a monotonically increasing Universe with the phantom dark energy.
AN EFFECTIVE CONTINUOUS ALGORITHM FOR APPROXIMATE SOLUTIONS OF LARGE SCALE MAX-CUT PROBLEMS
Institute of Scientific and Technical Information of China (English)
Cheng-xian Xu; Xiao-liang He; Feng-min Xu
2006-01-01
An effective continuous algorithm is proposed to find approximate solutions of NP-hard max-cut problems. The algorithm relaxes the max-cut problem into a continuous nonlinear programming problem by replacing n discrete constraints in the original problem with one single continuous constraint. A feasible direction method is designed to solve the resulting nonlinear programming problem. The method employs only the gradient evaluations of the objective function, and no any matrix calculations and no line searches are required.This greatly reduces the calculation cost of the method, and is suitable for the solution of large size max-cut problems. The convergence properties of the proposed method to KKT points of the nonlinear programming are analyzed. If the solution obtained by the proposed method is a global solution of the nonlinear programming problem, the solution will provide an upper bound on the max-cut value. Then an approximate solution to the max-cut problem is generated from the solution of the nonlinear programming and provides a lower bound on the max-cut value. Numerical experiments and comparisons on some max-cut test problems (small and large size) show that the proposed algorithm is efficient to get the exact solutions for all small test problems and well satisfied solutions for most of the large size test problems with less calculation costs.
COMPARISON OF TDOA LOCATION ALGORITHMS WITH DIRECT SOLUTION METHOD
Institute of Scientific and Technical Information of China (English)
Li Chun; Liu Congfeng; Liao Guisheng
2011-01-01
For Time Difference Of Arrival (TDOA) location based on multi-ground stations scene,two direct solution methods are proposed to solve the target position in TDOA location.Therein,the solving methods are realized in the rectangular and polar coordinates.On the condition of rectangular coordinates,first of all,it solves the radial range between the target and reference station,then calculates the location of the target.In the case of polar coordinates,the azimuth between the target and reference station is solved first,then the radial range between the target and reference station is figured out,finally the location of the target is obtained.Simultaneously,the simulation and comparison analysis are given in detail,and show that the polar solving method has the better fuzzy performance than that of rectangular coordinate.
Evolutionary algorithms in genetic regulatory networks model
Raza, Khalid
2012-01-01
Genetic Regulatory Networks (GRNs) plays a vital role in the understanding of complex biological processes. Modeling GRNs is significantly important in order to reveal fundamental cellular processes, examine gene functions and understanding their complex relationships. Understanding the interactions between genes gives rise to develop better method for drug discovery and diagnosis of the disease since many diseases are characterized by abnormal behaviour of the genes. In this paper we have reviewed various evolutionary algorithms-based approach for modeling GRNs and discussed various opportunities and challenges.
Sparse modeling theory, algorithms, and applications
Rish, Irina
2014-01-01
""A comprehensive, clear, and well-articulated book on sparse modeling. This book will stand as a prime reference to the research community for many years to come.""-Ricardo Vilalta, Department of Computer Science, University of Houston""This book provides a modern introduction to sparse methods for machine learning and signal processing, with a comprehensive treatment of both theory and algorithms. Sparse Modeling is an ideal book for a first-year graduate course.""-Francis Bach, INRIA - École Normale Supřieure, Paris
New exact solutions in standard inflationary models
Chervon, S V; Shchigolev, V K
1997-01-01
The exact solutions in the standard inflationary model based on the self-interacting scalar field minimally coupled to gravity are considered. The shape's freedom of the self-interacting potential $V(\\phi)$ is postulated to obtain a new set of the exact solutions in the framework of Friedmann-Robertson-Walker Universes. The general solution was found in the case of power law inflation. We obtained new solutions and compared them with obtained ones earlir for the exponential type inflation.
Institute of Scientific and Technical Information of China (English)
Xianbin Wen; Hua Zhang; Jianguang Zhang; Xu Jiao; Lei Wang
2009-01-01
A novel method that hybridizes genetic algorithm (GA) and expectation maximization (EM) algorithm for the classification of syn-thetic aperture radar (SAR) imagery is proposed by the finite Gaussian mixtures model (GMM) and multiscale autoregressive (MAR)model. This algorithm is capable of improving the global optimality and consistency of the classification performance. The experiments on the SAR images show that the proposed algorithm outperforms the standard EM method significantly in classification accuracy.
Preliminary Analysis on the Relative Solution Space Sizes for MTSP with Genetic Algorithm
Hao, Junling
It is well known that the chromosome design is pivotal to solve the multiple traveling salesman problems with genetic algorithm. A well-designed chromosome coding can eliminate or reduce the redundant solutions. One chromosome and two chromosome design methods and a recently proposed two-part chromosome design are firstly introduced in this paper. Then the preliminary quantitative comparison analysis of the solution spaces of three different chromosome design methods is presented when the number of cities is linear with the travelers. The concept of relative solution space is proposed in order to compare the relative size of the solution spaces. The solution space of two-part chromosome design is much smaller than those of the traditional chromosome design. The result given in this paper provides a good guideline for the possible algorithmic design and engineering applications.
Multiobjective Route Planning Model and Algorithm for Emergency Management
Directory of Open Access Journals (Sweden)
Wen-mei Gai
2015-01-01
Full Text Available In order to model route planning problem for emergency logistics management taking both route timeliness and safety into account, a multiobjective mathematical model is proposed based on the theories of bounded rationality. The route safety is modeled as the product of safety through arcs included in the path. For solving this model, we convert the multiobjective optimization problem into its equivalent deterministic form. We take uncertainty of the weight coefficient for each objective function in actual multiobjective optimization into account. Finally, we develop an easy-to-implement heuristic in order to gain an efficient and feasible solution and its corresponding appropriate vector of weight coefficients quickly. Simulation results show the effectiveness and feasibility of the models and algorithms presented in this paper.
SOLA-DM: A numerical solution algorithm for transient three-dimensional flows
Energy Technology Data Exchange (ETDEWEB)
Wilson, T.L.; Nichols, B.D.; Hirt, C.W.; Stein, L.R.
1988-02-01
SOLA-DM is a three-dimensional time-explicit, finite-difference, Eulerian, fluid-dynamics computer code for solving the time-dependent incompressible Navier-Stokes equations. The solution algorithm (SOLA) evolved from the marker-and-cell (MAC) method, and the code is highly vectorized for efficient performance on a Cray computer. The computational domain is discretized by a mesh of parallelepiped cells in either cartesian or cylindrical geometry. The primary hydrodynamic variables for approximating the solution of the momentum equations are cell-face-centered velocity components and cell-centered pressures. Spatial accuracy is selected by the user to be first or second order; the time differencing is first-order accurate. The incompressibility condition results in an elliptic equation for pressure that is solved by a conjugate gradient method. Boundary conditions of five general types may be chosen: free-slip, no-slip, continuative, periodic, and specified pressure. In addition, internal mesh specifications to model obstacles and walls are provided. SOLA-DM also solves the equations for discrete particle dynamics, permitting the transport of marker particles or other solid particles through the fluid to be modeled. 7 refs., 7 figs.
A new efficient Cluster Algorithm for the Ising Model
Nyffeler, M; Wiese, U J; Nyfeler, Matthias; Pepe, Michele; Wiese, Uwe-Jens
2005-01-01
Using D-theory we construct a new efficient cluster algorithm for the Ising model. The construction is very different from the standard Swendsen-Wang algorithm and related to worm algorithms. With the new algorithm we have measured the correlation function with high precision over a surprisingly large number of orders of magnitude.
A numerical solution algorithm and its application to studies of pulsed light fields propagation
Banakh, V. A.; Gerasimova, L. O.; Smalikho, I. N.; Falits, A. V.
2016-08-01
A new method for studies of pulsed laser beams propagation in a turbulent atmosphere was proposed. The algorithm of numerical simulation is based on the solution of wave parabolic equation for complex spectral amplitude of wave field using method of splitting into physical factors. Examples of the use of the algorithm in the case the propagation pulsed Laguerre-Gaussian beams of femtosecond duration in the turbulence atmosphere has been shown.
Directory of Open Access Journals (Sweden)
Ion LUNGU
2012-01-01
Full Text Available In this paper, we research, analyze and develop optimization solutions for the parallel reduction function using graphics processing units (GPUs that implement the Compute Unified Device Architecture (CUDA, a modern and novel approach for improving the software performance of data processing applications and algorithms. Many of these applications and algorithms make use of the reduction function in their computational steps. After having designed the function and its algorithmic steps in CUDA, we have progressively developed and implemented optimization solutions for the reduction function. In order to confirm, test and evaluate the solutions' efficiency, we have developed a custom tailored benchmark suite. We have analyzed the obtained experimental results regarding: the comparison of the execution time and bandwidth when using graphic processing units covering the main CUDA architectures (Tesla GT200, Fermi GF100, Kepler GK104 and a central processing unit; the data type influence; the binary operator's influence.
Institute of Scientific and Technical Information of China (English)
闫利军; 申清明; 刘敏; 杨建民
2013-01-01
In view of the complexity of design process planning problem and limitation of existed methods, this paper takes total time and cost of the whole tasks in product development as objectives by considering all kinds of uncertain factors in practical product development and further described design process planning as a simulation based stochastic optimization problem. A new hybrid algorithm is proposed to solve this problem by introducing optimal computing budget allocation technique into genetic algorithm framework to improve algorithm searching efficiency and result reliability. Finally, the development of rotor and bearing system in turbine is adopted as example to validate the effectiveness of proposed method. Results demonstrate effectiveness of modeling method and high efficiency of solving algorithm. The method can be extended to solve all kinds of product development process and is of universality.%针对目前产品设计过程规划研究中存在的不足,在充分考虑实际设计过程中存在的各种不确定因素的基础上,以产品开发过程中的全体任务为规划对象,以设计迭代时间和成本为目标,将设计过程规划问题描述为基于仿真的随机优化问题进行处理.提出一种模型求解的混合遗传算法,该算法引入最优计算量分配技术进行样本分配,极大地提高了算法的搜索效率,有效地改善了遗传算法搜索的可靠性.以汽轮机轴承转子系统的设计为例,对提出的方法的有效性进行了验证,仿真结果表明,该建模方法有效且算法求解效率高.该方法可推广应用于各种产品设计过程的规划,具有普遍意义.
Disorder solutions of lattice spin models
Batchelor, M. T.; van Leeuwen, J. M. J.
1989-01-01
It is shown that disorder solutions, which have been obtained by different methods, follow from a simple decimation method. The method is put in general form and new disorder solutions are constructed for the Blume-Emery-Griffiths model on a triangular lattice and for Potts and Ising models on square and fcc lattices.
Exact Solution of a Drop-Push Model for Percolation
Majumdar, Satya N.; Dean, David S.
2002-08-01
Motivated by a computer science algorithm known as ``linear probing with hashing,'' we study a new type of percolation model whose basic features include a sequential ``dropping'' of particles on a substrate followed by their transport via a ``pushing'' mechanism. Our exact solution in one dimension shows that, unlike the ordinary random percolation model, the drop-push model has nontrivial spatial correlations generated by the dynamics itself. The critical exponents in the drop-push model are also different from those of the ordinary percolation. The relevance of our results to computer science is pointed out.
[A new algorithm for NIR modeling based on manifold learning].
Hong, Ming-Jian; Wen, Zhi-Yu; Zhang, Xiao-Hong; Wen, Quan
2009-07-01
Manifold learning is a new kind of algorithm originating from the field of machine learning to find the intrinsic dimensionality of numerous and complex data and to extract most important information from the raw data to develop a regression or classification model. The basic assumption of the manifold learning is that the high-dimensional data measured from the same object using some devices must reside on a manifold with much lower dimensions determined by a few properties of the object. While NIR spectra are characterized by their high dimensions and complicated band assignment, the authors may assume that the NIR spectra of the same kind of substances with different chemical concentrations should reside on a manifold with much lower dimensions determined by the concentrations, according to the above assumption. As one of the best known algorithms of manifold learning, locally linear embedding (LLE) further assumes that the underlying manifold is locally linear. So, every data point in the manifold should be a linear combination of its neighbors. Based on the above assumptions, the present paper proposes a new algorithm named least square locally weighted regression (LS-LWR), which is a kind of LWR with weights determined by the least squares instead of a predefined function. Then, the NIR spectra of glucose solutions with various concentrations are measured using a NIR spectrometer and LS-LWR is verified by predicting the concentrations of glucose solutions quantitatively. Compared with the existing algorithms such as principal component regression (PCR) and partial least squares regression (PLSR), the LS-LWR has better predictability measured by the standard error of prediction (SEP) and generates an elegant model with good stability and efficiency.
Enhanced hybrid search algorithm for protein structure prediction using the 3D-HP lattice model.
Zhou, Changjun; Hou, Caixia; Zhang, Qiang; Wei, Xiaopeng
2013-09-01
The problem of protein structure prediction in the hydrophobic-polar (HP) lattice model is the prediction of protein tertiary structure. This problem is usually referred to as the protein folding problem. This paper presents a method for the application of an enhanced hybrid search algorithm to the problem of protein folding prediction, using the three dimensional (3D) HP lattice model. The enhanced hybrid search algorithm is a combination of the particle swarm optimizer (PSO) and tabu search (TS) algorithms. Since the PSO algorithm entraps local minimum in later evolution extremely easily, we combined PSO with the TS algorithm, which has properties of global optimization. Since the technologies of crossover and mutation are applied many times to PSO and TS algorithms, so enhanced hybrid search algorithm is called the MCMPSO-TS (multiple crossover and mutation PSO-TS) algorithm. Experimental results show that the MCMPSO-TS algorithm can find the best solutions so far for the listed benchmarks, which will help comparison with any future paper approach. Moreover, real protein sequences and Fibonacci sequences are verified in the 3D HP lattice model for the first time. Compared with the previous evolutionary algorithms, the new hybrid search algorithm is novel, and can be used effectively to predict 3D protein folding structure. With continuous development and changes in amino acids sequences, the new algorithm will also make a contribution to the study of new protein sequences.
Institute of Scientific and Technical Information of China (English)
YAN Zhen-Ya
2004-01-01
A Weierstrass elliptic function expansion method and its algorithm are developed in this paper. The method changes the problem solving nonlinear evolution equations into another one solving the correspondingsystem of nonlinear algebraic equations. With the aid of symbolic computation (e.g. Maple), the method is applied to the combined KdV-mKdV equation and (2+1)-dimensional coupled Davey-Stewartson equation. As a consequence, many new types of doubly periodic solutions are obtained in terms of the Weierstrass elliptic function. Jacobi elliptic function solutions and solitary wave solutions are also given as simple limits of doubly periodic solutions.
Institute of Scientific and Technical Information of China (English)
YANZhen-Ya
2004-01-01
A Weierstrass elliptic function expansion method and its algorithm are developed in this paper. The method changes the problem solving nonlinear evolution equations into another one solving the corresponding system of nonlinear algebraic equations. With the aid of symbolic computation (e.g. Maple), the method is applied to the combined KdV-mKdV equation and (2+1)-dimensional coupled Davey-Stewartson equation. As a consequence, many new types of doubly periodic solutions are obtained in terms of the Weierstrass elliptic function. Jacobi elliptic function solutions and solitary wave solutions are also given as simple limits of doubly periodic solutions.
Lattice Model for water-solute mixtures
Furlan, A. P.; Almarza, N. G.; M. C. Barbosa
2016-01-01
A lattice model for the study of mixtures of associating liquids is proposed. Solvent and solute are modeled by adapting the associating lattice gas (ALG) model. The nature of interaction solute/solvent is controlled by tuning the energy interactions between the patches of ALG model. We have studied three set of parameters, resulting on, hydrophilic, inert and hydrophobic interactions. Extensive Monte Carlo simulations were carried out and the behavior of pure components and the excess proper...
Electromagnetic Model and Image Reconstruction Algorithms Based on EIT System
Institute of Scientific and Technical Information of China (English)
CAO Zhang; WANG Huaxiang
2006-01-01
An intuitive 2 D model of circular electrical impedance tomography ( EIT) sensor with small size electrodes is established based on the theory of analytic functions.The validation of the model is proved using the result from the solution of Laplace equation.Suggestions on to electrode optimization and explanation to the ill-condition property of the sensitivity matrix are provided based on the model,which takes electrode distance into account and can be generalized to the sensor with any simple connected region through a conformal transformation.Image reconstruction algorithms based on the model are implemented to show feasibility of the model using experimental data collected from the EIT system developed in Tianjin University.In the simulation with a human chestlike configuration,electrical conductivity distributions are reconstructed using equi-potential backprojection (EBP) and Tikhonov regularization (TR) based on a conformal transformation of the model.The algorithms based on the model are suitable for online image reconstruction and the reconstructed results are good both in size and position.
Institute of Scientific and Technical Information of China (English)
ZENG Luchuan
2004-01-01
The purpose of this paper is to introduce and study a new class of generalized strongly mixed implicit quasi-variational inequalities in Hilbert spaces, which includes the known class of generalized mixed implicit quasi-variational inequalities as a special case.By applying the auxiliary variational principle technique, the existence of solutions for this class of quasi-variational inequalities is proved. Moreover, a new iterative algorithm for computing approximate solutions is constructed and the convergence criteria for this iterative algorithm are also established.
Link mining models, algorithms, and applications
Yu, Philip S; Faloutsos, Christos
2010-01-01
This book presents in-depth surveys and systematic discussions on models, algorithms and applications for link mining. Link mining is an important field of data mining. Traditional data mining focuses on 'flat' data in which each data object is represented as a fixed-length attribute vector. However, many real-world data sets are much richer in structure, involving objects of multiple types that are related to each other. Hence, recently link mining has become an emerging field of data mining, which has a high impact in various important applications such as text mining, social network analysi
Genetic Algorithms Principles Towards Hidden Markov Model
Directory of Open Access Journals (Sweden)
Nabil M. Hewahi
2011-10-01
Full Text Available In this paper we propose a general approach based on Genetic Algorithms (GAs to evolve Hidden Markov Models (HMM. The problem appears when experts assign probability values for HMM, they use only some limited inputs. The assigned probability values might not be accurate to serve in other cases related to the same domain. We introduce an approach based on GAs to find
out the suitable probability values for the HMM to be mostly correct in more cases than what have been used to assign the probability values.
Models and Algorithms for Tracking Target with Coordinated Turn Motion
Directory of Open Access Journals (Sweden)
Xianghui Yuan
2014-01-01
Full Text Available Tracking target with coordinated turn (CT motion is highly dependent on the models and algorithms. First, the widely used models are compared in this paper—coordinated turn (CT model with known turn rate, augmented coordinated turn (ACT model with Cartesian velocity, ACT model with polar velocity, CT model using a kinematic constraint, and maneuver centered circular motion model. Then, in the single model tracking framework, the tracking algorithms for the last four models are compared and the suggestions on the choice of models for different practical target tracking problems are given. Finally, in the multiple models (MM framework, the algorithm based on expectation maximization (EM algorithm is derived, including both the batch form and the recursive form. Compared with the widely used interacting multiple model (IMM algorithm, the EM algorithm shows its effectiveness.
Investigating multiple solutions in the constrained minimal supersymmetric standard model
Energy Technology Data Exchange (ETDEWEB)
Allanach, B.C. [DAMTP, CMS, University of Cambridge,Wilberforce Road, Cambridge, CB3 0HA (United Kingdom); George, Damien P. [DAMTP, CMS, University of Cambridge,Wilberforce Road, Cambridge, CB3 0HA (United Kingdom); Cavendish Laboratory, University of Cambridge,JJ Thomson Avenue, Cambridge, CB3 0HE (United Kingdom); Nachman, Benjamin [SLAC, Stanford University,2575 Sand Hill Rd, Menlo Park, CA 94025 (United States)
2014-02-07
Recent work has shown that the Constrained Minimal Supersymmetric Standard Model (CMSSM) can possess several distinct solutions for certain values of its parameters. The extra solutions were not previously found by public supersymmetric spectrum generators because fixed point iteration (the algorithm used by the generators) is unstable in the neighbourhood of these solutions. The existence of the additional solutions calls into question the robustness of exclusion limits derived from collider experiments and cosmological observations upon the CMSSM, because limits were only placed on one of the solutions. Here, we map the CMSSM by exploring its multi-dimensional parameter space using the shooting method, which is not subject to the stability issues which can plague fixed point iteration. We are able to find multiple solutions where in all previous literature only one was found. The multiple solutions are of two distinct classes. One class, close to the border of bad electroweak symmetry breaking, is disfavoured by LEP2 searches for neutralinos and charginos. The other class has sparticles that are heavy enough to evade the LEP2 bounds. Chargino masses may differ by up to around 10% between the different solutions, whereas other sparticle masses differ at the sub-percent level. The prediction for the dark matter relic density can vary by a hundred percent or more between the different solutions, so analyses employing the dark matter constraint are incomplete without their inclusion.
A Multiple Model Approach to Modeling Based on LPF Algorithm
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Input-output data fitting methods are often used for unknown-structure nonlinear system modeling. Based on model-on-demand tactics, a multiple model approach to modeling for nonlinear systems is presented. The basic idea is to find out, from vast historical system input-output data sets, some data sets matching with the current working point, then to develop a local model using Local Polynomial Fitting (LPF) algorithm. With the change of working points, multiple local models are built, which realize the exact modeling for the global system. By comparing to other methods, the simulation results show good performance for its simple, effective and reliable estimation.``
Data mining with SPSS modeler theory, exercises and solutions
Wendler, Tilo
2016-01-01
Introducing the IBM SPSS Modeler, this book guides readers through data mining processes and presents relevant statistical methods. There is a special focus on step-by-step tutorials and well-documented examples that help demystify complex mathematical algorithms and computer programs. The variety of exercises and solutions as well as an accompanying website with data sets and SPSS Modeler streams are particularly valuable. While intended for students, the simplicity of the Modeler makes the book useful for anyone wishing to learn about basic and more advanced data mining, and put this knowledge into practice.
Comparison of Nonequilibrium Solution Algorithms Applied to Chemically Stiff Hypersonic Flows
Palmer, Grant; Venkatapathy, Ethiraj
1995-01-01
Three solution algorithms, explicit under-relaxation, point implicit, and lower-upper symmetric Gauss-Seidel, are used to compute nonequilibrium flow around the Apollo 4 return capsule at the 62-km altitude point in its descent trajectory. By varying the Mach number, the efficiency and robustness of the solution algorithms were tested for different levels of chemical stiffness.The performance of the solution algorithms degraded as the Mach number and stiffness of the flow increased. At Mach 15 and 30, the lower-upper symmetric Gauss-Seidel method produces an eight order of magnitude drop in the energy residual in one-third to one-half the Cray C-90 computer time as compared to the point implicit and explicit under-relaxation methods. The explicit under-relaxation algorithm experienced convergence difficulties at Mach 30 and above. At Mach 40 the performance of the lower-upper symmetric Gauss-Seidel algorithm deteriorates to the point that it is out performed by the point implicit method. The effects of the viscous terms are investigated. Grid dependency questions are explored.
Nonlinear model predictive control theory and algorithms
Grüne, Lars
2017-01-01
This book offers readers a thorough and rigorous introduction to nonlinear model predictive control (NMPC) for discrete-time and sampled-data systems. NMPC schemes with and without stabilizing terminal constraints are detailed, and intuitive examples illustrate the performance of different NMPC variants. NMPC is interpreted as an approximation of infinite-horizon optimal control so that important properties like closed-loop stability, inverse optimality and suboptimality can be derived in a uniform manner. These results are complemented by discussions of feasibility and robustness. An introduction to nonlinear optimal control algorithms yields essential insights into how the nonlinear optimization routine—the core of any nonlinear model predictive controller—works. Accompanying software in MATLAB® and C++ (downloadable from extras.springer.com/), together with an explanatory appendix in the book itself, enables readers to perform computer experiments exploring the possibilities and limitations of NMPC. T...
Institute of Scientific and Technical Information of China (English)
WANG Shundin; ZHANG Hua
2008-01-01
Using functional derivative technique In quantum field theory,the algebraic dy-namics approach for solution of ordinary differential evolution equations was gen-eralized to treat partial differential evolution equations.The partial differential evo-lution equations were lifted to the corresponding functional partial differential equations in functional space by Introducing the time translation operator.The functional partial differential evolution equations were solved by algebraic dynam-ics.The algebraic dynamics solutions are analytical In Taylor series In terms of both initial functions and time.Based on the exact analytical solutions,a new nu-merical algorithm-algebraic dynamics algorithm was proposed for partial differ-ential evolution equations.The difficulty of and the way out for the algorithm were discussed.The application of the approach to and computer numerical experi-ments on the nonlinear Burgers equation and meteorological advection equation indicate that the algebraic dynamics approach and algebraic dynamics algorithm are effective to the solution of nonlinear partial differential evolution equations both analytically and numerically.
Gravitational Lens Modeling with Genetic Algorithms and Particle Swarm Optimizers
Rogers, Adam
2011-01-01
Strong gravitational lensing of an extended object is described by a mapping from source to image coordinates that is nonlinear and cannot generally be inverted analytically. Determining the structure of the source intensity distribution also requires a description of the blurring effect due to a point spread function. This initial study uses an iterative gravitational lens modeling scheme based on the semilinear method to determine the linear parameters (source intensity profile) of a strongly lensed system. Our 'matrix-free' approach avoids construction of the lens and blurring operators while retaining the least squares formulation of the problem. The parameters of an analytical lens model are found through nonlinear optimization by an advanced genetic algorithm (GA) and particle swarm optimizer (PSO). These global optimization routines are designed to explore the parameter space thoroughly, mapping model degeneracies in detail. We develop a novel method that determines the L-curve for each solution automa...
Parameter Optimization of Single-Diode Model of Photovoltaic Cell Using Memetic Algorithm
Directory of Open Access Journals (Sweden)
Yourim Yoon
2015-01-01
Full Text Available This study proposes a memetic approach for optimally determining the parameter values of single-diode-equivalent solar cell model. The memetic algorithm, which combines metaheuristic and gradient-based techniques, has the merit of good performance in both global and local searches. First, 10 single algorithms were considered including genetic algorithm, simulated annealing, particle swarm optimization, harmony search, differential evolution, cuckoo search, least squares method, and pattern search; then their final solutions were used as initial vectors for generalized reduced gradient technique. From this memetic approach, we could further improve the accuracy of the estimated solar cell parameters when compared with single algorithm approaches.
Chatfield, David C.; Reeves, Melissa S.; Truhlar, Donald G.; Duneczky, Csilla; Schwenke, David W.
1992-01-01
Complex dense matrices corresponding to the D + H2 and O + HD reactions were solved using a complex generalized minimal residual (GMRes) algorithm described by Saad and Schultz (1986) and Saad (1990). To provide a test case with a different structure, the H + H2 system was also considered. It is shown that the computational effort for solutions with the GMRes algorithm depends on the dimension of the linear system, the total energy of the scattering problem, and the accuracy criterion. In several cases with dimensions in the range 1110-5632, the GMRes algorithm outperformed the LAPACK direct solver, with speedups for the linear equation solution as large as a factor of 23.
Karandashev, Yakov M
2016-01-01
In this paper we propose and realize (the code is publicly available at https://github.com/Thrawn1985/2D-Partition-Function) an algorithm for exact calculation of partition function for planar graph models with binary spins. The complexity of the algorithm is O(N^2). Test experiments shows good agreement with Onsager's analytical solution for two-dimensional Ising model of infinite size.
Adaptive Numerical Algorithms in Space Weather Modeling
Toth, Gabor; vanderHolst, Bart; Sokolov, Igor V.; DeZeeuw, Darren; Gombosi, Tamas I.; Fang, Fang; Manchester, Ward B.; Meng, Xing; Nakib, Dalal; Powell, Kenneth G.; Stout, Quentin F.; Glocer, Alex; Ma, Ying-Juan; Opher, Merav
2010-01-01
Space weather describes the various processes in the Sun-Earth system that present danger to human health and technology. The goal of space weather forecasting is to provide an opportunity to mitigate these negative effects. Physics-based space weather modeling is characterized by disparate temporal and spatial scales as well as by different physics in different domains. A multi-physics system can be modeled by a software framework comprising of several components. Each component corresponds to a physics domain, and each component is represented by one or more numerical models. The publicly available Space Weather Modeling Framework (SWMF) can execute and couple together several components distributed over a parallel machine in a flexible and efficient manner. The framework also allows resolving disparate spatial and temporal scales with independent spatial and temporal discretizations in the various models. Several of the computationally most expensive domains of the framework are modeled by the Block-Adaptive Tree Solar wind Roe Upwind Scheme (BATS-R-US) code that can solve various forms of the magnetohydrodynamics (MHD) equations, including Hall, semi-relativistic, multi-species and multi-fluid MHD, anisotropic pressure, radiative transport and heat conduction. Modeling disparate scales within BATS-R-US is achieved by a block-adaptive mesh both in Cartesian and generalized coordinates. Most recently we have created a new core for BATS-R-US: the Block-Adaptive Tree Library (BATL) that provides a general toolkit for creating, load balancing and message passing in a 1, 2 or 3 dimensional block-adaptive grid. We describe the algorithms of BATL and demonstrate its efficiency and scaling properties for various problems. BATS-R-US uses several time-integration schemes to address multiple time-scales: explicit time stepping with fixed or local time steps, partially steady-state evolution, point-implicit, semi-implicit, explicit/implicit, and fully implicit numerical
Dynamical behavior of the Niedermayer algorithm applied to Potts models
Girardi, D.; Penna, T. J. P.; Branco, N. S.
2012-01-01
In this work we make a numerical study of the dynamic universality class of the Niedermayer algorithm applied to the two-dimensional Potts model with 2, 3, and 4 states. This algorithm updates clusters of spins and has a free parameter, $E_0$, which controls the size of these clusters, such that $E_0=1$ is the Metropolis algorithm and $E_0=0$ regains the Wolff algorithm, for the Potts model. For $-1
Hunziker, J.; Thorbecke, J.; Slob, E. C.
2014-12-01
Commonly, electromagnetic measurements for exploring and monitoring hydrocarbon reservoirs are inverted for the subsurface conductivity distribution by minimizing the difference between the actual data and a forward modeled dataset. The convergence of the inversion process to the correct solution strongly depends on the shape of the solution space. Since this is a non-linear problem, there exist a multitude of minima of which only the global one provides the correct conductivity values. To easily find the global minimum we desire it to have a broad cone of attraction, while it should also feature a very narrow bottom in order to obtain the subsurface conductivity with high resolution. In this study, we aim to determine which combination of input data corresponds to a favorable shape of the solution space. Since the solution space is N-dimensional, with N being the number of unknown subsurface parameters, plotting it is out of the question. In our approach, we use a genetic algorithm (Goldberg, 1989) to probe the solution space. Such algorithms have the advantage that every run of the same problem will end up at a different solution. Most of these solutions are expected to lie close to the global minimum. A situation where only few runs end up in the global minimum indicates that the solution space consists of a lot of local minima or that the cone of attraction of the global minimum is small. If a lot of runs end up with a similar data-misfit but with a large spread of the subsurface medium parameters in one or more direction, it can be concluded that the chosen data-input is not sensitive with respect to that direction. Compared to the study of Hunziker et al. 2014, we allow also to invert for subsurface boundaries and include more combinations of input datasets. The results so far suggest that it is essential to include the magnetic field in the inversion process in order to find the anisotropic conductivity values. ReferencesGoldberg, D. E., 1989. Genetic
Rapid solution of kriging equations, using a banded Gauss elimination algorithm
Energy Technology Data Exchange (ETDEWEB)
Carr, J.R. (University of Nevada, Reno, NV (USA))
1990-12-01
A crucial concern when implementing computer algorithms for geostatistical analyses on microcomputers is speed of execution. Kriging, in particular, typically relies on a Gauss elimination algorithm to solve for weights. Because such an alogrithm is required for each estimate, the solution for weights can result in very slow program execution speed on a microcomputer. One approach to enhancing the efficiency of Gauss elimination is demonstrated herein. The upper triangle plus diagonal of the intersample covariance matrix is used in a modified banded Gauss elimination algorithm. Results show that such an algorithm yields approximately a two-fold reduction in execution time for kriging when the number of nearest neighbours used for estimation is large. 4 refs., 2 figs., 1 tab.
A predictor-corrector algorithm to estimate the fractional flow in oil-water models
Energy Technology Data Exchange (ETDEWEB)
Savioli, Gabriela B [Laboratorio de IngenierIa de Reservorios, IGPUBA and Departamento de IngenierIa Quimica, Facultad de IngenierIa, Universidad de Buenos Aires, Av. Las Heras 2214 Piso 3 C1127AAR Buenos Aires (Argentina); Berdaguer, Elena M Fernandez [Instituto de Calculo, Facultad de Ciencias Exactas y Naturales, UBA-CONICET and Departamento de Matematica, Facultad de IngenierIa, Universidad de Buenos Aires, 1428 Buenos Aires (Argentina)], E-mail: gsavioli@di.fcen.uba.ar, E-mail: efernan@ic.fcen.uba.ar
2008-11-01
We introduce a predictor-corrector algorithm to estimate parameters in a nonlinear hyperbolic problem. It can be used to estimate the oil-fractional flow function from the Buckley-Leverett equation. The forward model is non-linear: the sought- for parameter is a function of the solution of the equation. Traditionally, the estimation of functions requires the selection of a fitting parametric model. The algorithm that we develop does not require a predetermined parameter model. Therefore, the estimation problem is carried out over a set of parameters which are functions. The algorithm is based on the linearization of the parameter-to-output mapping. This technique is new in the field of nonlinear estimation. It has the advantage of laying aside parametric models. The algorithm is iterative and is of predictor-corrector type. We present theoretical results on the inverse problem. We use synthetic data to test the new algorithm.
Directory of Open Access Journals (Sweden)
R. Venkata Rao
2016-03-01
Full Text Available The performance of rapid prototyping (RP processes is often measured in terms of build time, product quality, dimensional accuracy, cost of production, mechanical and tribological properties of the models and energy consumed in the process. The success of any RP process in terms of these performance measures entails selection of the optimum combination of the influential process parameters. Thus, in this work the single-objective and multi-objective optimization problems of a widely used RP process, namely, fused deposition modeling (FDM, are formulated, and the same are solved using the teaching-learning-based optimization (TLBO algorithm and non-dominated Sorting TLBO (NSTLBO algorithm, respectively. The results of the TLBO algorithm are compared with those obtained using genetic algorithm (GA, and quantum behaved particle swarm optimization (QPSO algorithm. The TLBO algorithm showed better performance as compared to GA and QPSO algorithms. The NSTLBO algorithm proposed to solve the multi-objective optimization problems of the FDM process in this work is a posteriori version of the TLBO algorithm. The NSTLBO algorithm is incorporated with non-dominated sorting concept and crowding distance assignment mechanism to obtain a dense set of Pareto optimal solutions in a single simulation run. The results of the NSTLBO algorithm are compared with those obtained using non-dominated sorting genetic algorithm (NSGA-II and the desirability function approach. The Pareto-optimal set of solutions for each problem is obtained and reported. These Pareto-optimal set of solutions will help the decision maker in volatile scenarios and are useful for the FDM process.
Institute of Scientific and Technical Information of China (English)
曾六川
2003-01-01
A new class of general multivalued mixed implicit quasi-variational inequalities in a real Hilbert space was introduced, which includes the known class of generalized mixed implicit quasi-variational inequalities as a special case, introduced and studied by Ding Xieping. The auxiliary variational principle technique was applied to solve this class of general multivalued mixed implicit quasi-variational inequalities. Firstly, a new auxiliary variational inequality with a proper convex, lower semicontinuous, binary functional was defined and a suitable functional was chosen so that its unique minimum point is equivalent to the solution of such an auxiliary variational inequality. Secondly, this auxiliary variational inequality was utilized to construct a new iterative algorithm for computing approximate solutions to general multivalued mixed implicit quasi-variational inequalities. Here, the equivalence guarantees that the algorithm can generate a sequence of approximate solutions.Finally, the existence of solutions and convergence of approximate solutions for general multivalued mixed implicit quasi-variational inequalities are proved. Moreover, the new convergerce criteria for the algorithm were provided. Therefore, the results give an affirmative anwer to the open question raised by M . A. Noor , and extend and improve the earlier and recent results for various variational inequalities and complementarity problems including the corresponding results for mixed variational inequalities, mixed quasi-variational inequalities and quasi-complementarity problems involving the single-valued and set-valued mappings in the recent literature.
Genetic Algorithm Approaches to Prebiobiotic Chemistry Modeling
Lohn, Jason; Colombano, Silvano
1997-01-01
We model an artificial chemistry comprised of interacting polymers by specifying two initial conditions: a distribution of polymers and a fixed set of reversible catalytic reactions. A genetic algorithm is used to find a set of reactions that exhibit a desired dynamical behavior. Such a technique is useful because it allows an investigator to determine whether a specific pattern of dynamics can be produced, and if it can, the reaction network found can be then analyzed. We present our results in the context of studying simplified chemical dynamics in theorized protocells - hypothesized precursors of the first living organisms. Our results show that given a small sample of plausible protocell reaction dynamics, catalytic reaction sets can be found. We present cases where this is not possible and also analyze the evolved reaction sets.
Frolov, Vladimir; Backhaus, Scott; Chertkov, Misha
2014-10-01
In a companion manuscript (Frolov et al 2014 New J. Phys. 16 art. no.) , we developed a novel optimization method for the placement, sizing, and operation of flexible alternating current transmission system (FACTS) devices to relieve transmission network congestion. Specifically, we addressed FACTS that provide series compensation (SC) via modification of line inductance. In this sequel manuscript, this heuristic algorithm and its solutions are explored on a number of test cases: a 30-bus test network and a realistically-sized model of the Polish grid (˜2700 nodes and ˜3300 lines). The results from the 30-bus network are used to study the general properties of the solutions, including nonlocality and sparsity. The Polish grid is used to demonstrate the computational efficiency of the heuristics that leverage sequential linearization of power flow constraints, and cutting plane methods that take advantage of the sparse nature of the SC placement solutions. Using these approaches, we can use the algorithm to solve a Polish transmission grid in tens of seconds. We explore the utility of the algorithm by analyzing transmission networks congested by (i) uniform load growth, (ii) multiple overloaded configurations, and (iii) sequential generator retirements.
Energy Technology Data Exchange (ETDEWEB)
Frolov, Vladimir [Moscow Inst. of Physics and Technology (MIPT), Moscow (Russian Federation); Backhaus, Scott N. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States); Chertkov, Michael [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2014-01-14
In a companion manuscript, we developed a novel optimization method for placement, sizing, and operation of Flexible Alternating Current Transmission System (FACTS) devices to relieve transmission network congestion. Specifically, we addressed FACTS that provide Series Compensation (SC) via modification of line inductance. In this manuscript, this heuristic algorithm and its solutions are explored on a number of test cases: a 30-bus test network and a realistically-sized model of the Polish grid (~2700 nodes and ~3300 lines). The results on the 30-bus network are used to study the general properties of the solutions including non-locality and sparsity. The Polish grid is used as a demonstration of the computational efficiency of the heuristics that leverages sequential linearization of power flow constraints and cutting plane methods that take advantage of the sparse nature of the SC placement solutions. Using these approaches, the algorithm is able to solve an instance of Polish grid in tens of seconds. We explore the utility of the algorithm by analyzing transmission networks congested by (a) uniform load growth, (b) multiple overloaded configurations, and (c) sequential generator retirements
Munhoven, G.
2013-03-01
The total alkalinity-pH equation, which relates total alkalinity and pH for a given set of total concentrations of the acid-base systems that contribute to total alkalinity in a given water sample, is reviewed and its mathematical properties established. We prove that the equation function is strictly monotone and always has exactly one positive root. Different commonly used approximations are discussed and compared. An original method to derive appropriate initial values for the iterative solution of the cubic polynomial equation based upon carbonate-borate-alkalinity is presented. We then review different methods that have been used to solve the total alkalinity-pH equation, with a main focus on biogeochemical models. The shortcomings and limitations of these methods are made out and discussed. We then present two variants of a new, robust and universally convergent algorithm to solve the total alkalinity-pH equation. This algorithm does not require any a priori knowledge of the solution. The iterative procedure is shown to converge from any starting value to the physical solution. The extra computational cost for the convergence security is only 10-15% compared to the fastest algorithm in our test series.
Integer programming model for optimizing bus timetable using genetic algorithm
Wihartiko, F. D.; Buono, A.; Silalahi, B. P.
2017-01-01
Bus timetable gave an information for passengers to ensure the availability of bus services. Timetable optimal condition happened when bus trips frequency could adapt and suit with passenger demand. In the peak time, the number of bus trips would be larger than the off-peak time. If the number of bus trips were more frequent than the optimal condition, it would make a high operating cost for bus operator. Conversely, if the number of trip was less than optimal condition, it would make a bad quality service for passengers. In this paper, the bus timetabling problem would be solved by integer programming model with modified genetic algorithm. Modification was placed in the chromosomes design, initial population recovery technique, chromosomes reconstruction and chromosomes extermination on specific generation. The result of this model gave the optimal solution with accuracy 99.1%.
An Intelligent Model for Pairs Trading Using Genetic Algorithms.
Huang, Chien-Feng; Hsu, Chi-Jen; Chen, Chi-Chung; Chang, Bao Rong; Li, Chen-An
2015-01-01
Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice.
An Intelligent Model for Pairs Trading Using Genetic Algorithms
Hsu, Chi-Jen; Chen, Chi-Chung; Li, Chen-An
2015-01-01
Pairs trading is an important and challenging research area in computational finance, in which pairs of stocks are bought and sold in pair combinations for arbitrage opportunities. Traditional methods that solve this set of problems mostly rely on statistical methods such as regression. In contrast to the statistical approaches, recent advances in computational intelligence (CI) are leading to promising opportunities for solving problems in the financial applications more effectively. In this paper, we present a novel methodology for pairs trading using genetic algorithms (GA). Our results showed that the GA-based models are able to significantly outperform the benchmark and our proposed method is capable of generating robust models to tackle the dynamic characteristics in the financial application studied. Based upon the promising results obtained, we expect this GA-based method to advance the research in computational intelligence for finance and provide an effective solution to pairs trading for investment in practice. PMID:26339236
The quantum Rabi model: solution and dynamics
Xie, Qiongtao; Zhong, Honghua; Batchelor, Murray T.; Lee, Chaohong
2017-03-01
This article presents a review of recent developments on various aspects of the quantum Rabi model. Particular emphasis is given on the exact analytic solution obtained in terms of confluent Heun functions. The analytic solutions for various generalisations of the quantum Rabi model are also discussed. Results are also reviewed on the level statistics and the dynamics of the quantum Rabi model. The article concludes with an introductory overview of several experimental realisations of the quantum Rabi model. An outlook towards future developments is also given.
The quantum Rabi model: solution and dynamics
Xie, Qiongtao; Batchelor, Murray T; Lee, Chaohong
2016-01-01
This article presents a review of recent developments on various aspects of the quantum Rabi model. Particular emphasis is given on the exact analytic solution obtained in terms of confluent Heun functions. The analytic solutions for various generalisations of the quantum Rabi model are also discussed. Results are also reviewed on the level statistics and the dynamics of the quantum Rabi model. The article concludes with an introductory overview of several experimental realisations of the quantum Rabi model. An outlook towards future developments is also given.
Bayesian online algorithms for learning in discrete Hidden Markov Models
Alamino, Roberto C.; Caticha, Nestor
2008-01-01
We propose and analyze two different Bayesian online algorithms for learning in discrete Hidden Markov Models and compare their performance with the already known Baldi-Chauvin Algorithm. Using the Kullback-Leibler divergence as a measure of generalization we draw learning curves in simplified situations for these algorithms and compare their performances.
Bouc–Wen hysteresis model identification using Modified Firefly Algorithm
Energy Technology Data Exchange (ETDEWEB)
Zaman, Mohammad Asif, E-mail: zaman@stanford.edu [Department of Electrical Engineering, Stanford University (United States); Sikder, Urmita [Department of Electrical Engineering and Computer Sciences, University of California, Berkeley (United States)
2015-12-01
The parameters of Bouc–Wen hysteresis model are identified using a Modified Firefly Algorithm. The proposed algorithm uses dynamic process control parameters to improve its performance. The algorithm is used to find the model parameter values that results in the least amount of error between a set of given data points and points obtained from the Bouc–Wen model. The performance of the algorithm is compared with the performance of conventional Firefly Algorithm, Genetic Algorithm and Differential Evolution algorithm in terms of convergence rate and accuracy. Compared to the other three optimization algorithms, the proposed algorithm is found to have good convergence rate with high degree of accuracy in identifying Bouc–Wen model parameters. Finally, the proposed method is used to find the Bouc–Wen model parameters from experimental data. The obtained model is found to be in good agreement with measured data. - Highlights: • We describe a new method to find the Bouc–Wen hysteresis model parameters. • We propose a Modified Firefly Algorithm. • We compare our method with existing methods to find that the proposed method performs better. • We use our model to fit experimental results. Good agreement is found.
Motion Model Employment using interacting Motion Model Algorithm
DEFF Research Database (Denmark)
Hussain, Dil Muhammad Akbar
2006-01-01
The paper presents a simulation study to track a maneuvering target using a selective approach in choosing Interacting Multiple Models (IMM) algorithm to provide a wider coverage to track such targets. Initially, there are two motion models in the system to track a target. Probability of each...... model being correct is computed through a likelihood function for each model. The study presented a simple technique to introduce additional models into the system using deterministic acceleration which basically defines the dynamics of the system. Therefore, based on this value more motion models can...... be employed to increase the coverage. Finally, the combined estimate is obtained using posteriori probabilities from different filter models. The implemented approach provides an adaptive scheme for selecting various number of motion models. Motion model description is important as it defines the kind...
Institute of Scientific and Technical Information of China (English)
2008-01-01
Using functional derivative technique in quantum field theory, the algebraic dy-namics approach for solution of ordinary differential evolution equations was gen-eralized to treat partial differential evolution equations. The partial differential evo-lution equations were lifted to the corresponding functional partial differential equations in functional space by introducing the time translation operator. The functional partial differential evolution equations were solved by algebraic dynam-ics. The algebraic dynamics solutions are analytical in Taylor series in terms of both initial functions and time. Based on the exact analytical solutions, a new nu-merical algorithm—algebraic dynamics algorithm was proposed for partial differ-ential evolution equations. The difficulty of and the way out for the algorithm were discussed. The application of the approach to and computer numerical experi-ments on the nonlinear Burgers equation and meteorological advection equation indicate that the algebraic dynamics approach and algebraic dynamics algorithm are effective to the solution of nonlinear partial differential evolution equations both analytically and numerically.
Routing Flow-Shop with Buffers and Ready Times – Comparison of Selected Solution Algorithms
Directory of Open Access Journals (Sweden)
Józefczyk Jerzy
2014-12-01
Full Text Available This article extends the former results concerning the routing flow-shop problem to minimize the makespan on the case with buffers, non-zero ready times and different speeds of machines. The corresponding combinatorial optimization problem is formulated. The exact as well as four heuristic solution algorithms are presented. The branch and bound approach is applied for the former one. The heuristic algorithms employ known constructive idea proposed for the former version of the problem as well as the Tabu Search metaheuristics. Moreover, the improvement procedure is proposed to enhance the quality of both heuristic algorithms. The conducted simulation experiments allow evaluating all algorithms. Firstly, the heuristic algorithms are compared with the exact one for small instances of the problem in terms of the criterion and execution times. Then, for larger instances, the heuristic algorithms are mutually compared. The case study regarding the maintenance of software products, given in the final part of the paper, illustrates the possibility to apply the results for real-world manufacturing systems.
Kourakos, George; Mantoglou, Aristotelis
2013-02-01
SummaryThe demand for fresh water in coastal areas and islands can be very high due to increased local needs and tourism. A multi-objective optimization methodology is developed, involving minimization of economic and environmental costs while satisfying water demand. The methodology considers desalinization of pumped water and injection of treated water into the aquifer. Variable density aquifer models are computationally intractable when integrated in optimization algorithms. In order to alleviate this problem, a multi-objective optimization algorithm is developed combining surrogate models based on Modular Neural Networks [MOSA(MNNs)]. The surrogate models are trained adaptively during optimization based on a genetic algorithm. In the crossover step, each pair of parents generates a pool of offspring which are evaluated using the fast surrogate model. Then, the most promising offspring are evaluated using the exact numerical model. This procedure eliminates errors in Pareto solution due to imprecise predictions of the surrogate model. The method has important advancements compared to previous methods such as precise evaluation of the Pareto set and alleviation of propagation of errors due to surrogate model approximations. The method is applied to an aquifer in the Greek island of Santorini. The results show that the new MOSA(MNN) algorithm offers significant reduction in computational time compared to previous methods (in the case study it requires only 5% of the time required by other methods). Further, the Pareto solution is better than the solution obtained by alternative algorithms.
Actuator Disc Model Using a Modified Rhie-Chow/SIMPLE Pressure Correction Algorithm
DEFF Research Database (Denmark)
Rethore, Pierre-Elouan; Sørensen, Niels
2008-01-01
An actuator disc model for the flow solver EllipSys (2D&3D) is proposed. It is based on a correction of the Rhie-Chow algorithm for using discreet body forces in collocated variable finite volume CFD code. It is compared with three cases where an analytical solution is known.......An actuator disc model for the flow solver EllipSys (2D&3D) is proposed. It is based on a correction of the Rhie-Chow algorithm for using discreet body forces in collocated variable finite volume CFD code. It is compared with three cases where an analytical solution is known....
Algorithm for Realistic Modeling of Graphitic Systems
Directory of Open Access Journals (Sweden)
A.V. Khomenko
2011-01-01
Full Text Available An algorithm for molecular dynamics simulations of graphitic systems using realistic semiempirical interaction potentials of carbon atoms taking into account both short-range and long-range contributions is proposed. Results of the use of the algorithm for a graphite sample are presented. The scalability of the algorithm depending on the system size and the number of processor cores involved in the calculations is analyzed.
Modeling of higher order systems using artificial bee colony algorithm
Directory of Open Access Journals (Sweden)
Aytekin Bağış
2016-05-01
Full Text Available In this work, modeling of the higher order systems based on the use of the artificial bee colony (ABC algorithm were examined. Proposed model parameters for the sample systems in the literature were obtained by using the algorithm, and its performance was presented comparatively with the other methods. Simulation results show that the ABC algorithm based system modeling approach can be used as an efficient and powerful method for higher order systems.
Chen, Jiaoxuan; Zhang, Maomao; Liu, Yinyan; Chen, Jiaoliao; Li, Yi
2017-03-01
Electrical capacitance tomography (ECT) is a promising technique applied in many fields. However, the solutions for ECT are not unique and highly sensitive to the measurement noise. To remain a good shape of reconstructed object and endure a noisy data, a Rudin–Osher–Fatemi (ROF) model with total variation regularization is applied to image reconstruction in ECT. Two numerical methods, which are simplified augmented Lagrangian (SAL) and accelerated alternating direction method of multipliers (AADMM), are innovatively introduced to try to solve the above mentioned problems in ECT. The effect of the parameters and the number of iterations for different algorithms, and the noise level in capacitance data are discussed. Both simulation and experimental tests were carried out to validate the feasibility of the proposed algorithms, compared to the Landweber iteration (LI) algorithm. The results show that the SAL and AADMM algorithms can handle a high level of noise and the AADMM algorithm outperforms other algorithms in identifying the object from its background.
K—Dimensional Optimal Parallel Algorithm for the Solution of a General Class of Recurrence Equations
Institute of Scientific and Technical Information of China (English)
高庆狮; 刘志勇
1995-01-01
This paper proposes a parallel algorithm,called KDOP (K-Dimensional Optimal Parallel algorithm),to solve a general class of recurrence equations efficiently.The KDOP algorithm partitions the computation into a series of subcomputations,each of which is executed in the fashion that all the processors work simultaneously with each one executing an optimal sequential algorithm to solve a subcomputation task.The algorithm solves the equations in O(N/P) steps in EREW PRAM model (Exclusive Read Exclusive Write Parallel Random Access Machine model) using p≤N1-∈ processors,where N is the size of the problem,and ∈ is a given constant.This is an optimal algorithm (its sepeedup is O(p)) in the case of p≤N1-∈.Such an optimal speedup for this problem was previously achieved only in the case of p≤N0.5.The algorithm can be implemented on machines with multiple processing elements or pipelined vector machines with parallel memory systems.
Scaffolding Mathematical Modelling with a Solution Plan
Schukajlow, Stanislaw; Kolter, Jana; Blum, Werner
2015-01-01
In the study presented in this paper, we examined the possibility to scaffold mathematical modelling with strategies. The strategies were prompted using an instrument called "solution plan" as a scaffold. The effects of this step by step instrument on mathematical modelling competency and on self-reported strategies were tested using…
Directory of Open Access Journals (Sweden)
Uamporn Witthayarat
2012-01-01
Full Text Available The aim of this paper is to introduce an iterative algorithm for finding a common solution of the sets (A+M2−1(0 and (B+M1−1(0, where M is a maximal accretive operator in a Banach space and, by using the proposed algorithm, to establish some strong convergence theorems for common solutions of the two sets above in a uniformly convex and 2-uniformly smooth Banach space. The results obtained in this paper extend and improve the corresponding results of Qin et al. 2011 from Hilbert spaces to Banach spaces and Petrot et al. 2011. Moreover, we also apply our results to some applications for solving convex feasibility problems.
Investigation of ALEGRA shock hydrocode algorithms using an exact free surface jet flow solution.
Energy Technology Data Exchange (ETDEWEB)
Hanks, Bradley Wright.; Robinson, Allen C
2014-01-01
Computational testing of the arbitrary Lagrangian-Eulerian shock physics code, ALEGRA, is presented using an exact solution that is very similar to a shaped charge jet flow. The solution is a steady, isentropic, subsonic free surface flow with significant compression and release and is provided as a steady state initial condition. There should be no shocks and no entropy production throughout the problem. The purpose of this test problem is to present a detailed and challenging computation in order to provide evidence for algorithmic strengths and weaknesses in ALEGRA which should be examined further. The results of this work are intended to be used to guide future algorithmic improvements in the spirit of test-driven development processes.
River network solution for a distributed hydrological model and applications
Jha, Raghunath; Herath, Srikantha; Musiake, Katumi
2000-02-01
A simultaneous solution for one-dimensional unsteady flow routing for a network of rivers has been developed, which can be used either with a complete distributed hydrological model, a simple rainfall-runoff model or as a stand alone river routing model. Either dynamic or kinematic solution schemes can be selected to simulate the river flows. The river network is either generated from the Digital Elevation Model (DEM) or directly input to the model. The model can handle any number of upstream channels and computational points. A sparse matrix solution algorithm is used to solve the 2N×2N matrix resulting from N nodes in the network. A submodule generates the initial water depth and discharge at each computational point from equilibrium discharge in the absence of observed initial conditions. The model is applied in three sub-catchments of the Chao Phraya river basin, Thailand, considering three different conditions. The simulated results show good agreement with observed discharges and provide insight to water level fluctuations, especially where tributaries join the main channel.
Optimal Solution for VLSI Physical Design Automation Using Hybrid Genetic Algorithm
Directory of Open Access Journals (Sweden)
I. Hameem Shanavas
2014-01-01
Full Text Available In Optimization of VLSI Physical Design, area minimization and interconnect length minimization is an important objective in physical design automation of very large scale integration chips. The objective of minimizing the area and interconnect length would scale down the size of integrated chips. To meet the above objective, it is necessary to find an optimal solution for physical design components like partitioning, floorplanning, placement, and routing. This work helps to perform the optimization of the benchmark circuits with the above said components of physical design using hierarchical approach of evolutionary algorithms. The goal of minimizing the delay in partitioning, minimizing the silicon area in floorplanning, minimizing the layout area in placement, minimizing the wirelength in routing has indefinite influence on other criteria like power, clock, speed, cost, and so forth. Hybrid evolutionary algorithm is applied on each of its phases to achieve the objective. Because evolutionary algorithm that includes one or many local search steps within its evolutionary cycles to obtain the minimization of area and interconnect length. This approach combines a hierarchical design like genetic algorithm and simulated annealing to attain the objective. This hybrid approach can quickly produce optimal solutions for the popular benchmarks.
Polynomial search and global modeling: Two algorithms for modeling chaos.
Mangiarotti, S; Coudret, R; Drapeau, L; Jarlan, L
2012-10-01
Global modeling aims to build mathematical models of concise description. Polynomial Model Search (PoMoS) and Global Modeling (GloMo) are two complementary algorithms (freely downloadable at the following address: http://www.cesbio.ups-tlse.fr/us/pomos_et_glomo.html) designed for the modeling of observed dynamical systems based on a small set of time series. Models considered in these algorithms are based on ordinary differential equations built on a polynomial formulation. More specifically, PoMoS aims at finding polynomial formulations from a given set of 1 to N time series, whereas GloMo is designed for single time series and aims to identify the parameters for a selected structure. GloMo also provides basic features to visualize integrated trajectories and to characterize their structure when it is simple enough: One allows for drawing the first return map for a chosen Poincaré section in the reconstructed space; another one computes the Lyapunov exponent along the trajectory. In the present paper, global modeling from single time series is considered. A description of the algorithms is given and three examples are provided. The first example is based on the three variables of the Rössler attractor. The second one comes from an experimental analysis of the copper electrodissolution in phosphoric acid for which a less parsimonious global model was obtained in a previous study. The third example is an exploratory case and concerns the cycle of rainfed wheat under semiarid climatic conditions as observed through a vegetation index derived from a spatial sensor.
Aqueous Solution Vessel Thermal Model Development II
Energy Technology Data Exchange (ETDEWEB)
Buechler, Cynthia Eileen [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
2015-10-28
The work presented in this report is a continuation of the work described in the May 2015 report, “Aqueous Solution Vessel Thermal Model Development”. This computational fluid dynamics (CFD) model aims to predict the temperature and bubble volume fraction in an aqueous solution of uranium. These values affect the reactivity of the fissile solution, so it is important to be able to calculate them and determine their effects on the reaction. Part A of this report describes some of the parameter comparisons performed on the CFD model using Fluent. Part B describes the coupling of the Fluent model with a Monte-Carlo N-Particle (MCNP) neutron transport model. The fuel tank geometry is the same as it was in the May 2015 report, annular with a thickness-to-height ratio of 0.16. An accelerator-driven neutron source provides the excitation for the reaction, and internal and external water cooling channels remove the heat. The model used in this work incorporates the Eulerian multiphase model with lift, wall lubrication, turbulent dispersion and turbulence interaction. The buoyancy-driven flow is modeled using the Boussinesq approximation, and the flow turbulence is determined using the k-ω Shear-Stress-Transport (SST) model. The dispersed turbulence multiphase model is employed to capture the multiphase turbulence effects.
Exact solution of phantom dark energy model
Institute of Scientific and Technical Information of China (English)
Wang Wen-Fu; Shui Zheng-Wei; Tang Bin
2010-01-01
We investigate the phantom dark energy model derived from the scalar field with a negative kinetic term. By assuming a particular relation between the time derivative of the phantom field and the Hubble function, an exact solution of the model is constructed. Absence of the 'big rip' singularity is shown explicitly. We then derive special features of phantom dark energy model and show that its predictions are consistent with all astrophysical observations.
Fast and Parallel Spectral Transform Algorithms for Global Shallow Water Models
Jakob, Ruediger
1993-01-01
This dissertation examines spectral transform algorithms for the solution of the shallow water equations on the sphere and studies their implementation and performance on shared memory vector multiprocessors. Beginning with the standard spectral transform algorithm in vorticity divergence form and its implementation in the Fortran based parallel programming language Force, two modifications are researched. First, the transforms and matrices associated with the meridional derivatives of the associated Legendre functions are replaced by corresponding operations with the spherical harmonic coefficients. Second, based on the fast Fourier transform and the fast multipole method, a lower complexity algorithm is derived that uses fast transformations between Legendre and interior Fourier nodes, fast surface spherical truncation and a fast spherical Helmholtz solver. The first modification is fully implemented, and comparative performance data are obtained for varying resolution and number of processes, showing a significant storage saving and slightly reduced execution time on a Cray Y -MP 8/864. The important performance parameters for the spectral transform algorithm and its implementation on vector multiprocessors are determined and validated with the measured performance data. The second modification is described at the algorithmic level, but only the novel fast surface spherical truncation algorithm is implemented. This new multipole algorithm has lower complexity than the standard algorithm, and requires asymptotically only order N ^2log N operations per time step for a grid with order N^2 points. Because the global shallow water equations are similar to the horizontal dynamical component of general circulation models, the results can be applied to spectral transform numerical weather prediction and climate models. In general, the derived algorithms may speed up the solution of time dependent partial differential equations in spherical geometry. A performance model
Optimizing the Forward Algorithm for Hidden Markov Model on IBM Roadrunner clusters
Directory of Open Access Journals (Sweden)
SOIMAN, S.-I.
2015-05-01
Full Text Available In this paper we present a parallel solution of the Forward Algorithm for Hidden Markov Models. The Forward algorithm compute a probability of a hidden state from Markov model at a certain time, this process being recursively. The whole process requires large computational resources for those models with a large number of states and long observation sequences. Our solution in order to reduce the computational time is a multilevel parallelization of Forward algorithm. Two types of cores were used in our implementation, for each level of parallelization, cores that are graved on the same chip of PowerXCell8i processor. This hybrid architecture of processors permitted us to obtain a speedup factor over 40 relative to the sequential algorithm for a model with 24 states and 25 millions of observable symbols. Experimental results showed that the parallel Forward algorithm can evaluate the probability of an observation sequence on a hidden Markov model 40 times faster than the classic one does. Based on the performance obtained, we demonstrate the applicability of this parallel implementation of Forward algorithm in complex problems such as large vocabulary speech recognition.
Critical dynamics of cluster algorithms in the dilute Ising model
Hennecke, M.; Heyken, U.
1993-08-01
Autocorrelation times for thermodynamic quantities at T C are calculated from Monte Carlo simulations of the site-diluted simple cubic Ising model, using the Swendsen-Wang and Wolff cluster algorithms. Our results show that for these algorithms the autocorrelation times decrease when reducing the concentration of magnetic sites from 100% down to 40%. This is of crucial importance when estimating static properties of the model, since the variances of these estimators increase with autocorrelation time. The dynamical critical exponents are calculated for both algorithms, observing pronounced finite-size effects in the energy autocorrelation data for the algorithm of Wolff. We conclude that, when applied to the dilute Ising model, cluster algorithms become even more effective than local algorithms, for which increasing autocorrelation times are expected.
Stationary solution and parametric estimation for Bilinear model driven by ARCH noises
Institute of Scientific and Technical Information of China (English)
潘家柱; 李国栋; 谢衷洁
2002-01-01
Bilinear model driven by ARCH (1) noises is proposed. Existence, uniqueness and form of sta-tionary solution to this new model are presented. Maximum likelihood estimation of the model is discussedand some simulation results are given to evaluate our algorithm.
A new algorithm of ionospheric tomography——two-step solution
Wen, Debao
The inherent non-ideal geometry of ground-based global navigation satellite system (GNSS) observation stations distribution results in limited-angle tomographic inverse problems that are ill-posed. To cope with the above problem, a new tomographic algorithm, which is called two-step solution (TSS), is presented in this paper. In the new method, the electron density can be estimated by using two steps: 1) Phillips smoothing method (PSM) is first used to resolve the ill-posed problem in ionospheric tomography system; 2) The "coarse" solution of PSM is then input as the initial value of multiplicative algebraic reconstruction technique (MART) and improved by iterative mode. Numerical simulation experiment demonstrates that the two-step solution is feasible to GNSS-based ionospheric tomography and superior to PSM or MART alone.
Performance analysis of FXLMS algorithm with secondary path modeling error
Institute of Scientific and Technical Information of China (English)
SUN Xu; CHEN Duanshi
2003-01-01
Performance analysis of filtered-X LMS (FXLMS) algorithm with secondary path modeling error is carried out in both time and frequency domain. It is shown firstly that the effects of secondary path modeling error on the performance of FXLMS algorithm are determined by the distribution of the relative error of secondary path model along with frequency.In case of that the distribution of relative error is uniform the modeling error of secondary path will have no effects on the performance of the algorithm. In addition, a limitation property of FXLMS algorithm is proved, which implies that the negative effects of secondary path modeling error can be compensated by increasing the adaptive filter length. At last, some insights into the "spillover" phenomenon of FXLMS algorithm are given.
Zhang, Zili; Gao, Chao; Liu, Yuxin; Qian, Tao
2014-09-01
Ant colony optimization (ACO) algorithms often fall into the local optimal solution and have lower search efficiency for solving the travelling salesman problem (TSP). According to these shortcomings, this paper proposes a universal optimization strategy for updating the pheromone matrix in the ACO algorithms. The new optimization strategy takes advantages of the unique feature of critical paths reserved in the process of evolving adaptive networks of the Physarum-inspired mathematical model (PMM). The optimized algorithms, denoted as PMACO algorithms, can enhance the amount of pheromone in the critical paths and promote the exploitation of the optimal solution. Experimental results in synthetic and real networks show that the PMACO algorithms are more efficient and robust than the traditional ACO algorithms, which are adaptable to solve the TSP with single or multiple objectives. Meanwhile, we further analyse the influence of parameters on the performance of the PMACO algorithms. Based on these analyses, the best values of these parameters are worked out for the TSP.
Super-Exponential Solution for a Retrial Supermarket Model
Li, Quan-Lin; Wang, Yang
2011-01-01
In this paper, we provide a new and effective approach for studying super-exponential solution of a retrial supermarket model with Poisson arrivals, exponential service times and exponential retrial times and with two different probing-server numbers. We describe the retrial supermarket model as a system of differential equations by means of density-dependent jump Markov processes, and obtain an iterative algorithm for computing the fixed point of the system of differential equations. Based on the fixed point, we analyze the expected sojourn time that a tagged arriving customer spends in this system, and use numerical examples to indicate different influence of the two probing-server numbers on system performance including the fixed point and the expected sojourn time. Furthermore, we analyze exponential convergence of the current location of the retrial supermarket model to the fixed point, and apply the Kurtz Theorem to study density-dependent jump Markov process given in the retrial supermarket model, whic...
Kriging-approximation simulated annealing algorithm for groundwater modeling
Shen, C. H.
2015-12-01
Optimization algorithms are often applied to search best parameters for complex groundwater models. Running the complex groundwater models to evaluate objective function might be time-consuming. This research proposes a Kriging-approximation simulated annealing algorithm. Kriging is a spatial statistics method used to interpolate unknown variables based on surrounding given data. In the algorithm, Kriging method is used to estimate complicate objective function and is incorporated with simulated annealing. The contribution of the Kriging-approximation simulated annealing algorithm is to reduce calculation time and increase efficiency.
The Evolutionary Algorithm to Find Robust Pareto-Optimal Solutions over Time
Directory of Open Access Journals (Sweden)
Meirong Chen
2015-01-01
Full Text Available In dynamic multiobjective optimization problems, the environmental parameters change over time, which makes the true pareto fronts shifted. So far, most works of research on dynamic multiobjective optimization methods have concentrated on detecting the changed environment and triggering the population based optimization methods so as to track the moving pareto fronts over time. Yet, in many real-world applications, it is not necessary to find the optimal nondominant solutions in each dynamic environment. To solve this weakness, a novel method called robust pareto-optimal solution over time is proposed. It is in fact to replace the optimal pareto front at each time-varying moment with the series of robust pareto-optimal solutions. This means that each robust solution can fit for more than one time-varying moment. Two metrics, including the average survival time and average robust generational distance, are present to measure the robustness of the robust pareto solution set. Another contribution is to construct the algorithm framework searching for robust pareto-optimal solutions over time based on the survival time. Experimental results indicate that this definition is a more practical and time-saving method of addressing dynamic multiobjective optimization problems changing over time.
Directory of Open Access Journals (Sweden)
Wei Yue
2015-01-01
Full Text Available The major issues for mean-variance-skewness models are the errors in estimations that cause corner solutions and low diversity in the portfolio. In this paper, a multiobjective fuzzy portfolio selection model with transaction cost and liquidity is proposed to maintain the diversity of portfolio. In addition, we have designed a multiobjective evolutionary algorithm based on decomposition of the objective space to maintain the diversity of obtained solutions. The algorithm is used to obtain a set of Pareto-optimal portfolios with good diversity and convergence. To demonstrate the effectiveness of the proposed model and algorithm, the performance of the proposed algorithm is compared with the classic MOEA/D and NSGA-II through some numerical examples based on the data of the Shanghai Stock Exchange Market. Simulation results show that our proposed algorithm is able to obtain better diversity and more evenly distributed Pareto front than the other two algorithms and the proposed model can maintain quite well the diversity of portfolio. The purpose of this paper is to deal with portfolio problems in the weighted possibilistic mean-variance-skewness (MVS and possibilistic mean-variance-skewness-entropy (MVS-E frameworks with transaction cost and liquidity and to provide different Pareto-optimal investment strategies as diversified as possible for investors at a time, rather than one strategy for investors at a time.
Adaptation of an Evolutionary Algorithm in Modeling Electric Circuits
Directory of Open Access Journals (Sweden)
J. Hájek
2010-01-01
Full Text Available This paper describes the influence of setting control parameters of a differential evolutionary algorithm (DE and the influence of adapting these parameters on the simulation of electric circuits and their components. Various DE algorithm strategies are investigated, and also the influence of adapting the controlling parameters (Cr, F during simulation and the effect of sample size. Optimizing an equivalent circuit diagram is chosen as a test task. Several strategies and settings of a DE algorithm are evaluated according to their convergence to the right solution.
A solution algorithm for fluid-particle flows across all flow regimes
Kong, Bo; Fox, Rodney O.
2017-09-01
Many fluid-particle flows occurring in nature and in technological applications exhibit large variations in the local particle volume fraction. For example, in circulating fluidized beds there are regions where the particles are close-packed as well as very dilute regions where particle-particle collisions are rare. Thus, in order to simulate such fluid-particle systems, it is necessary to design a flow solver that can accurately treat all flow regimes occurring simultaneously in the same flow domain. In this work, a solution algorithm is proposed for this purpose. The algorithm is based on splitting the free-transport flux solver dynamically and locally in the flow. In close-packed to moderately dense regions, a hydrodynamic solver is employed, while in dilute to very dilute regions a kinetic-based finite-volume solver is used in conjunction with quadrature-based moment methods. To illustrate the accuracy and robustness of the proposed solution algorithm, it is implemented in OpenFOAM for particle velocity moments up to second order, and applied to simulate gravity-driven, gas-particle flows exhibiting cluster-induced turbulence. By varying the average particle volume fraction in the flow domain, it is demonstrated that the flow solver can handle seamlessly all flow regimes present in fluid-particle flows.
Autotuning algorithm of particle swarm PID parameter based on D-Tent chaotic model
Institute of Scientific and Technical Information of China (English)
Min Zhu; Chunling Yang; Weiliang Li
2013-01-01
An improved particle swarm algorithm based on the D-Tent chaotic model is put forward aiming at the standard particle swarm algorithm. The convergence rate of the late of proposed al-gorithm is improved by revising the inertia weight of global optimal particles and the introduction of D-Tent chaotic sequence. Through the test of typical function and the autotuning test of proportional-integral-derivative (PID) parameter, finally a simulation is made to the servo control system of a permanent magnet synchronous motor (PMSM) under double-loop control of rotating speed and current by utilizing the chaotic particle swarm algorithm. Studies show that the proposed algorithm can reduce the iterative times and improve the convergence rate under the condition that the global optimal solution can be got.
Engineering of Algorithms for Hidden Markov models and Tree Distances
DEFF Research Database (Denmark)
Sand, Andreas
grown exponentially because of drastic improvements in the technology behind DNA and RNA sequencing, and focus on the research field has increased due to its potential to expand our knowledge about biological mechanisms and to improve public health. There has therefore been a continuously growing demand...... of the algorithms to exploit the parallel architecture of modern computers. In this PhD dissertation, I present my work with algorithmic optimizations and parallelizations in primarily two areas in algorithmic bioinformatics: algorithms for analyzing hidden Markov models and algorithms for computing distance...... measures between phylogenetic trees. Hidden Markov models is a class of probabilistic models that is used in a number of core applications in bioinformatics such as modeling of proteins, gene finding and reconstruction of species and population histories. I show how a relatively simple parallelization can...
Model and algorithm of optimizing alternate traffic restriction scheme in urban traffic network
Institute of Scientific and Technical Information of China (English)
徐光明; 史峰; 刘冰; 黄合来
2014-01-01
An optimization model and its solution algorithm for alternate traffic restriction (ATR) schemes were introduced in terms of both the restriction districts and the proportion of restricted automobiles. A bi-level programming model was proposed to model the ATR scheme optimization problem by aiming at consumer surplus maximization and overload flow minimization at the upper-level model. At the lower-level model, elastic demand, mode choice and multi-class user equilibrium assignment were synthetically optimized. A genetic algorithm involving prolonging codes was constructed, demonstrating high computing efficiency in that it dynamically includes newly-appearing overload links in the codes so as to reduce the subsequent searching range. Moreover, practical processing approaches were suggested, which may improve the operability of the model-based solutions.
[Determination of Virtual Surgery Mass Point Spring Model Parameters Based on Genetic Algorithms].
Chen, Ying; Hu, Xuyi; Zhu, Qiguang
2015-12-01
Mass point-spring model is one of the commonly used models in virtual surgery. However, its model parameters have no clear physical meaning, and it is hard to set the parameter conveniently. We, therefore, proposed a method based on genetic algorithm to determine the mass-spring model parameters. Computer-aided tomography (CAT) data were used to determine the mass value of the particle, and stiffness and damping coefficient were obtained by genetic algorithm. We used the difference between the reference deformation and virtual deformation as the fitness function to get the approximate optimal solution of the model parameters. Experimental results showed that this method could obtain an approximate optimal solution of spring parameters with lower cost, and could accurately reproduce the effect of the actual deformation model as well.
Application of stochastic weighted algorithms to a multidimensional silica particle model
Energy Technology Data Exchange (ETDEWEB)
Menz, William J. [Department of Chemical Engineering and Biotechnology, University of Cambridge, New Museums Site, Pembroke Street, Cambridge CB2 3RA (United Kingdom); Patterson, Robert I.A.; Wagner, Wolfgang [Weierstrass Institute for Applied Analysis and Stochastics, Mohrenstrasse 39, Berlin 10117 (Germany); Kraft, Markus, E-mail: mk306@cam.ac.uk [Department of Chemical Engineering and Biotechnology, University of Cambridge, New Museums Site, Pembroke Street, Cambridge CB2 3RA (United Kingdom)
2013-09-01
Highlights: •Stochastic weighted algorithms (SWAs) are developed for a detailed silica model. •An implementation of SWAs with the transition kernel is presented. •The SWAs’ solutions converge to the direct simulation algorithm’s (DSA) solution. •The efficiency of SWAs is evaluated for this multidimensional particle model. •It is shown that SWAs can be used for coagulation problems in industrial systems. -- Abstract: This paper presents a detailed study of the numerical behaviour of stochastic weighted algorithms (SWAs) using the transition regime coagulation kernel and a multidimensional silica particle model. The implementation in the SWAs of the transition regime coagulation kernel and associated majorant rates is described. The silica particle model of Shekar et al. [S. Shekar, A.J. Smith, W.J. Menz, M. Sander, M. Kraft, A multidimensional population balance model to describe the aerosol synthesis of silica nanoparticles, Journal of Aerosol Science 44 (2012) 83–98] was used in conjunction with this coagulation kernel to study the convergence properties of SWAs with a multidimensional particle model. High precision solutions were calculated with two SWAs and also with the established direct simulation algorithm. These solutions, which were generated using large number of computational particles, showed close agreement. It was thus demonstrated that SWAs can be successfully used with complex coagulation kernels and high dimensional particle models to simulate real-world systems.
Model-Free Adaptive Control Algorithm with Data Dropout Compensation
Xuhui Bu; Fashan Yu; Zhongsheng Hou; Hongwei Zhang
2012-01-01
The convergence of model-free adaptive control (MFAC) algorithm can be guaranteed when the system is subject to measurement data dropout. The system output convergent speed gets slower as dropout rate increases. This paper proposes a MFAC algorithm with data compensation. The missing data is first estimated using the dynamical linearization method, and then the estimated value is introduced to update control input. The convergence analysis of the proposed MFAC algorithm is given, and the effe...
Performance modeling and prediction for linear algebra algorithms
Iakymchuk, Roman
2012-01-01
This dissertation incorporates two research projects: performance modeling and prediction for dense linear algebra algorithms, and high-performance computing on clouds. The first project is focused on dense matrix computations, which are often used as computational kernels for numerous scientific applications. To solve a particular mathematical operation, linear algebra libraries provide a variety of algorithms. The algorithm of choice depends, obviously, on its performance. Performance of su...
Subdaily Earth rotation model and GPS solutions
Panafidina, Natalia; Hugentobler, Urs; Seitz, Manuela
2014-05-01
In this contribution we study the influence of the subdaily Earth rotation model on the GPS solution including station coordinates, satellite orbits and daily Earth rotation parameters (ERPs). The approach used is based on the transformation of GPS normal equation systems: free daily normal equations containing ERPs with 1-hour resolution are used as input data, in this case the high-frequency ERPs can be transformed into tidal terms which then can be fixed to new a priori values, thus changing implicitly the underlying subdaily Earth rotation model. To study the influence of individual tidal terms on the solution we successively changed a priori values for one tidal term in polar motion and compared the resulting solutions for GPS orbits, station coordinates and daily ERPs for a time interval of 13 years. The comparison reveals periodic changes in all estimated parameters with periods depending on the periods of the changed tidal terms. The dynamical reference frame realized by the GPS orbits is also affected: the whole satellite constellation shows periodic orientation variations, and each individual satellite shows periodic changes in the position of the orbit origin. We present a mechanism showing how errors in the subdaily Earth rotation model are propagated into the dynamical reference frame and the estimated parameters. Our model represents a change in one tidal term over one day as the sum of a prograde diurnal wave, a retrograde diurnal wave and an offset and linear drift in x- and y-pole. We demonstrate that this simple model, in conjunction with appropriate constraints, can explain well the observed variations in a one day GPS solution as well as in daily pole rates caused by changes in the subdaily Earth rotation model.
Energy Technology Data Exchange (ETDEWEB)
Svensson, Urban [Computer-aided Fluid Engineering AB, Norrkoeping (Sweden)
2001-04-01
A particle tracking algorithm, PARTRACK, that simulates transport and dispersion in a sparsely fractured rock is described. The main novel feature of the algorithm is the introduction of multiple particle states. It is demonstrated that the introduction of this feature allows for the simultaneous simulation of Taylor dispersion, sorption and matrix diffusion. A number of test cases are used to verify and demonstrate the features of PARTRACK. It is shown that PARTRACK can simulate the following processes, believed to be important for the problem addressed: the split up of a tracer cloud at a fracture intersection, channeling in a fracture plane, Taylor dispersion and matrix diffusion and sorption. From the results of the test cases, it is concluded that PARTRACK is an adequate framework for simulation of transport and dispersion of a solute in a sparsely fractured rock.
Júdice, Joaquim; Raydan, Marcos; Rosa, Silvério; Santos, Sandra
2008-04-01
This paper is devoted to the eigenvalue complementarity problem (EiCP) with symmetric real matrices. This problem is equivalent to finding a stationary point of a differentiable optimization program involving the Rayleigh quotient on a simplex (Queiroz et al., Math. Comput. 73, 1849-1863, 2004). We discuss a logarithmic function and a quadratic programming formulation to find a complementarity eigenvalue by computing a stationary point of an appropriate merit function on a special convex set. A variant of the spectral projected gradient algorithm with a specially designed line search is introduced to solve the EiCP. Computational experience shows that the application of this algorithm to the logarithmic function formulation is a quite efficient way to find a solution to the symmetric EiCP.
Algorithm Development for the Two-Fluid Plasma Model
2009-02-17
of m=0 sausage instabilities in an axisymmetric Z-pinch", Physics of Plasmas 13, 082310 (2006). • A. Hakim and U. Shumlak, "Two-fluid physics and...accurate as the solution variables. The high-order representation of the solution variables satisfies the accuracy requirement to preserve the...here. [2] It also illustrates the dispersive nature of the waves which makes capturing the effect difficult in MHD algorithms. The electromagnetic
Automatic calibration of urban drainage model using a novel multi-objective genetic algorithm.
di Pierro, F; Djordjević, S; Kapelan, Z; Khu, S T; Savić, D; Walters, G A
2005-01-01
In order to successfully calibrate an urban drainage model, multiple calibration criteria should be considered. This raises the issue of adopting a method for comparing different solutions (parameter sets) according to a set of objectives. Amongst the global optimization techniques that have blossomed in recent years, Multi Objective Genetic Algorithms (MOGA) have proved effective in numerous engineering applications, including sewer network modelling. Most of the techniques rely on the condition of Pareto efficiency to compare different solutions. However, as the number of criteria increases, the ratio of Pareto optimal to feasible solutions increases as well. The pitfalls are twofold: the efficiency of the genetic algorithm search worsens and decision makers are presented with an overwhelming number of equally optimal solutions. This paper proposes a new MOGA, the Preference Ordering Genetic Algorithm, which alleviates the drawbacks of conventional Pareto-based methods. The efficacy of the algorithm is demonstrated on the calibration of a physically-based, distributed sewer network model and the results are compared with those obtained by NSGA-II, a widely used MOGA.
DEVELOPMENT OF 2D HUMAN BODY MODELING USING THINNING ALGORITHM
Directory of Open Access Journals (Sweden)
K. Srinivasan
2010-11-01
Full Text Available Monitoring the behavior and activities of people in Video surveillance has gained more applications in Computer vision. This paper proposes a new approach to model the human body in 2D view for the activity analysis using Thinning algorithm. The first step of this work is Background subtraction which is achieved by the frame differencing algorithm. Thinning algorithm has been used to find the skeleton of the human body. After thinning, the thirteen feature points like terminating points, intersecting points, shoulder, elbow, and knee points have been extracted. Here, this research work attempts to represent the body model in three different ways such as Stick figure model, Patch model and Rectangle body model. The activities of humans have been analyzed with the help of 2D model for the pre-defined poses from the monocular video data. Finally, the time consumption and efficiency of our proposed algorithm have been evaluated.
Structure and aggregation in model tetramethylurea solutions
Energy Technology Data Exchange (ETDEWEB)
Gupta, Rini; Patey, G. N., E-mail: patey@chem.ubc.ca [Department of Chemistry, University of British Columbia, Vancouver, British Columbia V6T 1Z1 (Canada)
2014-08-14
The structure of model aqueous tetramethylurea (TMU) solutions is investigated employing large-scale (32 000, 64 000 particles) molecular dynamics simulations. Results are reported for TMU mole fractions, X{sub t}, ranging from infinite dilution up to 0.07, and for two temperatures, 300 and 330 K. Two existing force fields for TMU-water solutions are considered. These are the GROMOS 53A6 united-atom TMU model combined with SPC/E water [TMU(GROMOS-UA)/W(SPC/E)], and the more frequently employed AMBER03 all-atom force field for TMU combined with the TIP3P water model [TMU(AMBER-AA)/W(TIP3P)]. It is shown that TMU has a tendency towards aggregation for both models considered, but the tendency is significantly stronger for the [TMU(AMBER-AA)/W(TIP3P)] force field. For this model signs of aggregation are detected at X{sub t} = 0.005, aggregation is a well established feature of the solution at X{sub t} = 0.02, and the aggregates increase further in size with increasing concentration. This is in agreement with at least some experimental studies, which report signals of aggregation in the low concentration regime. The TMU aggregates exhibit little structure and are simply loosely ordered, TMU-rich regions of solution. The [TMU(GROMOS-UA)/W(SPC/E)] model shows strong signs of aggregation only at higher concentrations (X{sub t} ≳ 0.04), and the aggregates appear more loosely ordered, and less well-defined than those occurring in the [TMU(AMBER-AA)/W(TIP3P)] system. For both models, TMU aggregation increases when the temperature is increased from 300 to 330 K, consistent with an underlying entropy driven, hydrophobic interaction mechanism. At X{sub t} = 0.07, the extra-molecular correlation length expected for microheterogeneous solutions has become comparable with the size of the simulation cell for both models considered, indicating that even the systems simulated here are sufficiently large only at low concentrations.
Methodology and basic algorithms of the Livermore Economic Modeling System
Energy Technology Data Exchange (ETDEWEB)
Bell, R.B.
1981-03-17
The methodology and the basic pricing algorithms used in the Livermore Economic Modeling System (EMS) are described. The report explains the derivations of the EMS equations in detail; however, it could also serve as a general introduction to the modeling system. A brief but comprehensive explanation of what EMS is and does, and how it does it is presented. The second part examines the basic pricing algorithms currently implemented in EMS. Each algorithm's function is analyzed and a detailed derivation of the actual mathematical expressions used to implement the algorithm is presented. EMS is an evolving modeling system; improvements in existing algorithms are constantly under development and new submodels are being introduced. A snapshot of the standard version of EMS is provided and areas currently under study and development are considered briefly.
求解FJSP的混合遗传一蚁群算法%Hybrid genetic algorithm-ant colony optimization for FJSP solution
Institute of Scientific and Technical Information of China (English)
董蓉; 何卫平
2012-01-01
为更有效地求解柔性作业车间调度问题，综合考虑其中的机器分配与工序排序问题，建立了相关析取图模型，提出一种混合遗传一蚁群算法。该算法首先通过遗传算法获取问题的较优解，据此给出蚁群算法的信息素初始分布；之后充分利用蚁群算法的正反馈性进行求解，采用精英策略对蚁群的信息素进行局部更新；最后借鉴遗传算法交叉算子的邻域搜索特性扩大蚁群算法解的搜索空间，从而改善解的质量。通过3个经典算例的实验仿真，以及与其他算法的比较，验证了所提算法的可行性与有效性。%To solve Flexible Job-Shop Scheduling Problem(FJSP)more effectively, a related disjunctive graph model was built and a hybrid Genetic Algorithm(GA)-Ant Colony Optimization( ACO) was proposed by considering equip- ments arrangement and operation sequencing. In this algorithm, a better solution to the problem was obtained by ge- netic algorithm, and pheromones initial distribution of ACO was provided on this basis. The positive feedback of ACO was used to solve the problem, and the local update of the pheromones were conducted by elitist strategy. The neighborhood searching feature of crossover operator in GA was used to increase the search space of ACO, thus the quality of solution was improved. Through the experimental simulation of 3 classical examples, the feasibility and effectiveness of proposed algorithm were verified.
Genetic Algorithms for a Parameter Estimation of a Fermentation Process Model: A Comparison
Directory of Open Access Journals (Sweden)
Olympia Roeva
2005-12-01
Full Text Available In this paper the problem of a parameter estimation using genetic algorithms is examined. A case study considering the estimation of 6 parameters of a nonlinear dynamic model of E. coli fermentation is presented as a test problem. The parameter estimation problem is stated as a nonlinear programming problem subject to nonlinear differential-algebraic constraints. This problem is known to be frequently ill-conditioned and multimodal. Thus, traditional (gradient-based local optimization methods fail to arrive satisfied solutions. To overcome their limitations, the use of different genetic algorithms as stochastic global optimization methods is explored. These algorithms are proved to be very suitable for the optimization of highly non-linear problems with many variables. Genetic algorithms can guarantee global optimality and robustness. These facts make them advantageous in use for parameter identification of fermentation models. A comparison between simple, modified and multi-population genetic algorithms is presented. The best result is obtained using the modified genetic algorithm. The considered algorithms converged very closely to the cost value but the modified algorithm is in times faster than other two.
Model-Free Adaptive Control Algorithm with Data Dropout Compensation
Directory of Open Access Journals (Sweden)
Xuhui Bu
2012-01-01
Full Text Available The convergence of model-free adaptive control (MFAC algorithm can be guaranteed when the system is subject to measurement data dropout. The system output convergent speed gets slower as dropout rate increases. This paper proposes a MFAC algorithm with data compensation. The missing data is first estimated using the dynamical linearization method, and then the estimated value is introduced to update control input. The convergence analysis of the proposed MFAC algorithm is given, and the effectiveness is also validated by simulations. It is shown that the proposed algorithm can compensate the effect of the data dropout, and the better output performance can be obtained.
Adachi, Kohei
2013-01-01
Rubin and Thayer ("Psychometrika," 47:69-76, 1982) proposed the EM algorithm for exploratory and confirmatory maximum likelihood factor analysis. In this paper, we prove the following fact: the EM algorithm always gives a proper solution with positive unique variances and factor correlations with absolute values that do not exceed one,…
Adachi, Kohei
2013-01-01
Rubin and Thayer ("Psychometrika," 47:69-76, 1982) proposed the EM algorithm for exploratory and confirmatory maximum likelihood factor analysis. In this paper, we prove the following fact: the EM algorithm always gives a proper solution with positive unique variances and factor correlations with absolute values that do not exceed one,…
An analysis dictionary learning algorithm under a noisy data model with orthogonality constraint.
Zhang, Ye; Yu, Tenglong; Wang, Wenwu
2014-01-01
Two common problems are often encountered in analysis dictionary learning (ADL) algorithms. The first one is that the original clean signals for learning the dictionary are assumed to be known, which otherwise need to be estimated from noisy measurements. This, however, renders a computationally slow optimization process and potentially unreliable estimation (if the noise level is high), as represented by the Analysis K-SVD (AK-SVD) algorithm. The other problem is the trivial solution to the dictionary, for example, the null dictionary matrix that may be given by a dictionary learning algorithm, as discussed in the learning overcomplete sparsifying transform (LOST) algorithm. Here we propose a novel optimization model and an iterative algorithm to learn the analysis dictionary, where we directly employ the observed data to compute the approximate analysis sparse representation of the original signals (leading to a fast optimization procedure) and enforce an orthogonality constraint on the optimization criterion to avoid the trivial solutions. Experiments demonstrate the competitive performance of the proposed algorithm as compared with three baselines, namely, the AK-SVD, LOST, and NAAOLA algorithms.
An Analysis Dictionary Learning Algorithm under a Noisy Data Model with Orthogonality Constraint
Directory of Open Access Journals (Sweden)
Ye Zhang
2014-01-01
Full Text Available Two common problems are often encountered in analysis dictionary learning (ADL algorithms. The first one is that the original clean signals for learning the dictionary are assumed to be known, which otherwise need to be estimated from noisy measurements. This, however, renders a computationally slow optimization process and potentially unreliable estimation (if the noise level is high, as represented by the Analysis K-SVD (AK-SVD algorithm. The other problem is the trivial solution to the dictionary, for example, the null dictionary matrix that may be given by a dictionary learning algorithm, as discussed in the learning overcomplete sparsifying transform (LOST algorithm. Here we propose a novel optimization model and an iterative algorithm to learn the analysis dictionary, where we directly employ the observed data to compute the approximate analysis sparse representation of the original signals (leading to a fast optimization procedure and enforce an orthogonality constraint on the optimization criterion to avoid the trivial solutions. Experiments demonstrate the competitive performance of the proposed algorithm as compared with three baselines, namely, the AK-SVD, LOST, and NAAOLA algorithms.
A motion retargeting algorithm based on model simplification
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
A new motion retargeting algorithm is presented, which adapts the motion capture data to a new character. To make the resulting motion realistic, the physically-based optimization method is adopted. However, the optimization process is difficult to converge to the optimal value because of high complexity of the physical human model. In order to address this problem, an appropriate simplified model automatically determined by a motion analysis technique is utilized, and then motion retargeting with this simplified model as an intermediate agent is implemented. The entire motion retargeting algorithm involves three steps of nonlinearly constrained optimization: forward retargeting, motion scaling and inverse retargeting. Experimental results show the validity of this algorithm.
Strong solutions of semilinear matched microstructure models
Escher, Joachim
2011-01-01
The subject of this article is a matched microstructure model for Newtonian fluid flows in fractured porous media. This is a homogenized model which takes the form of two coupled parabolic differential equations with boundary conditions in a given (two-scale) domain in Euclidean space. The main objective is to establish the local well-posedness in the strong sense of the flow. Two main settings are investigated: semi-linear systems with linear boundary conditions and semi-linear systems with nonlinear boundary conditions. With the help of analytic semigoups we establish local well-posedness and investigate the long-time behaviour of the solutions in the first case: we establish global existence and show that solutions converge to zero at an exponential rate.
Quantum Monte Carlo methods algorithms for lattice models
Gubernatis, James; Werner, Philipp
2016-01-01
Featuring detailed explanations of the major algorithms used in quantum Monte Carlo simulations, this is the first textbook of its kind to provide a pedagogical overview of the field and its applications. The book provides a comprehensive introduction to the Monte Carlo method, its use, and its foundations, and examines algorithms for the simulation of quantum many-body lattice problems at finite and zero temperature. These algorithms include continuous-time loop and cluster algorithms for quantum spins, determinant methods for simulating fermions, power methods for computing ground and excited states, and the variational Monte Carlo method. Also discussed are continuous-time algorithms for quantum impurity models and their use within dynamical mean-field theory, along with algorithms for analytically continuing imaginary-time quantum Monte Carlo data. The parallelization of Monte Carlo simulations is also addressed. This is an essential resource for graduate students, teachers, and researchers interested in ...
Modeling Electrolyte Solutions with the extended universal quasichemical (UNIQUAC) Model
DEFF Research Database (Denmark)
Thomsen, Kaj
2005-01-01
The extended universal quasichemical (UNIQUAC) model is a thermodynamic model for solutions containing electrolytes and non-electrolytes. The model is a gibbs excess function consisting of a Debye-Hückel term and a standard UNIQUAC term. The model only requires binary, ion specific interaction...... parameters. A unique choice of standard states makes the model able to reproduce solid-liquid, vapor-liquid, and liquid-liquid phase equilibria as well as thermal properties of electrolyte solutions using one set of parameters....
Modeling Electrolyte Solutions with the extended universal quasichemical (UNIQUAC) Model
DEFF Research Database (Denmark)
Thomsen, Kaj
2005-01-01
The extended universal quasichemical (UNIQUAC) model is a thermodynamic model for solutions containing electrolytes and non-electrolytes. The model is a gibbs excess function consisting of a Debye-Hückel term and a standard UNIQUAC term. The model only requires binary, ion specific interaction...... parameters. A unique choice of standard states makes the model able to reproduce solid-liquid, vapor-liquid, and liquid-liquid phase equilibria as well as thermal properties of electrolyte solutions using one set of parameters....
Directory of Open Access Journals (Sweden)
Eman Ali Hussain
2015-01-01
Full Text Available Absract In this project A new method for solving Stochastic Differential Equations SDEs deriving by Wiener process numerically will be construct and implement using Accelerated Genetic Algorithm AGA. An SDE is a differential equation in which one or more of the terms and hence the solutions itself is a stochastic process. Solving stochastic differential equations requires going away from the recognizable deterministic setting of ordinary and partial differential equations into a world where the evolution of a quantity has an inherent random component and where the expected behavior of this quantity can be described in terms of probability distributions. We applied our method on the Ito formula which is equivalent to the SDE to find approximation solution of the SDEs. Numerical experiments illustrate the behavior of the proposed method.
Introduction of Parallel GPGPU Acceleration Algorithms for the Solution of Radiative Transfer
Godoy, William F.; Liu, Xu
2011-01-01
General-purpose computing on graphics processing units (GPGPU) is a recent technique that allows the parallel graphics processing unit (GPU) to accelerate calculations performed sequentially by the central processing unit (CPU). To introduce GPGPU to radiative transfer, the Gauss-Seidel solution of the well-known expressions for 1-D and 3-D homogeneous, isotropic media is selected as a test case. Different algorithms are introduced to balance memory and GPU-CPU communication, critical aspects of GPGPU. Results show that speed-ups of one to two orders of magnitude are obtained when compared to sequential solutions. The underlying value of GPGPU is its potential extension in radiative solvers (e.g., Monte Carlo, discrete ordinates) at a minimal learning curve.
An Automatic Registration Algorithm for 3D Maxillofacial Model
Qiu, Luwen; Zhou, Zhongwei; Guo, Jixiang; Lv, Jiancheng
2016-09-01
3D image registration aims at aligning two 3D data sets in a common coordinate system, which has been widely used in computer vision, pattern recognition and computer assisted surgery. One challenging problem in 3D registration is that point-wise correspondences between two point sets are often unknown apriori. In this work, we develop an automatic algorithm for 3D maxillofacial models registration including facial surface model and skull model. Our proposed registration algorithm can achieve a good alignment result between partial and whole maxillofacial model in spite of ambiguous matching, which has a potential application in the oral and maxillofacial reparative and reconstructive surgery. The proposed algorithm includes three steps: (1) 3D-SIFT features extraction and FPFH descriptors construction; (2) feature matching using SAC-IA; (3) coarse rigid alignment and refinement by ICP. Experiments on facial surfaces and mandible skull models demonstrate the efficiency and robustness of our algorithm.
A Dynamic Traffic Signal Timing Model and its Algorithm for Junction of Urban Road
DEFF Research Database (Denmark)
Cai, Yanguang; Cai, Hao
2012-01-01
-time and dynamic signal control of junction. To obtain the optimal solution of the model by hybrid chaotic quantum evolutionary algorithm, the model is converted to an easily solvable form. To simplify calculation, we give the expression of the partial derivative and change rate of the objective function...... such that the implementation of the algorithm only involves function assignments and arithmetic operations and thus avoids complex operations such as integral and differential. Simulation results show that the algorithm has less remain vehicles than Webster method, higher convergence rate and convergence speed than quantum......As an important part of Intelligent Transportation System, the scientific traffic signal timing of junction can improve the efficiency of urban transport. This paper presents a novel dynamic traffic signal timing model. According to the characteristics of the model, hybrid chaotic quantum...
Optimization of the K-means algorithm for the solution of high dimensional instances
Pérez, Joaquín; Pazos, Rodolfo; Olivares, Víctor; Hidalgo, Miguel; Ruiz, Jorge; Martínez, Alicia; Almanza, Nelva; González, Moisés
2016-06-01
This paper addresses the problem of clustering instances with a high number of dimensions. In particular, a new heuristic for reducing the complexity of the K-means algorithm is proposed. Traditionally, there are two approaches that deal with the clustering of instances with high dimensionality. The first executes a preprocessing step to remove those attributes of limited importance. The second, called divide and conquer, creates subsets that are clustered separately and later their results are integrated through post-processing. In contrast, this paper proposes a new solution which consists of the reduction of distance calculations from the objects to the centroids at the classification step. This heuristic is derived from the visual observation of the clustering process of K-means, in which it was found that the objects can only migrate to adjacent clusters without crossing distant clusters. Therefore, this heuristic can significantly reduce the number of distance calculations from an object to the centroids of the potential clusters that it may be classified to. To validate the proposed heuristic, it was designed a set of experiments with synthetic and high dimensional instances. One of the most notable results was obtained for an instance of 25,000 objects and 200 dimensions, where its execution time was reduced up to 96.5% and the quality of the solution decreased by only 0.24% when compared to the K-means algorithm.
Management Of Large Scale Osmotic Dehydration Solution Using The Pearsons Square Algorithm
Directory of Open Access Journals (Sweden)
Oladejo Duduyemi
2015-01-01
Full Text Available ABSTRACT Osmotic dehydration is a widely researched and advantageous pre-treatment process in food preservation but has not enjoyed industrial acceptance because if its highly concentrated and voluminous effluent. The Pearsons square algorithm was employed to give a focussed attack to the problem by developing a user-friendly template for reconstituting effluents for recycling purposes using Java script programme. Outflow from a pilot scale plant was reactivated and introduced into a scheme of operation for continuous OD of fruits and vegetables. Screened and re-concentrated effluent were subjected to statistical analysis in comparison to initial concentrations solution at confidence limit of p0.05. The template proven to be an adequate representation of the Pearsons square algorithm it is sufficiently good in reconstituting used osmotic solutions for repetitive usage. This protocol if adopted in the industry is not only environmentally friendly but also promises significant economic improvement of OD process. Application Recycling of non-reacting media and as a template for automation in continuous OD processing.
Chang, Weng-Long; Ren, Ting-Ting; Feng, Mang
2015-01-01
In this paper, it is shown that the proposed quantum algorithm for implementing Boolean circuits generated from the DNA-based algorithm solving the vertex-cover problem of any graph G with m edges and n vertices is the optimal quantum algorithm. Next, it is also demonstrated that mathematical solutions of the same biomolecular solutions are represented in terms of a unit vector in the finite-dimensional Hilbert space. Furthermore, for testing our theory, a nuclear magnetic resonance (NMR) experiment of three quantum bits to solve the simplest vertex-cover problem is completed.
Comparison of parameter estimation algorithms in hydrological modelling
DEFF Research Database (Denmark)
Blasone, Roberta-Serena; Madsen, Henrik; Rosbjerg, Dan
2006-01-01
Local search methods have been applied successfully in calibration of simple groundwater models, but might fail in locating the optimum for models of increased complexity, due to the more complex shape of the response surface. Global search algorithms have been demonstrated to perform well...... for these types of models, although at a more expensive computational cost. The main purpose of this study is to investigate the performance of a global and a local parameter optimization algorithm, respectively, the Shuffled Complex Evolution (SCE) algorithm and the gradient-based Gauss......-Marquardt-Levenberg algorithm (implemented in the PEST software), when applied to a steady-state and a transient groundwater model. The results show that PEST can have severe problems in locating the global optimum and in being trapped in local regions of attractions. The global SCE procedure is, in general, more effective...
A Mining Algorithm for Extracting Decision Process Data Models
Directory of Open Access Journals (Sweden)
Cristina-Claudia DOLEAN
2011-01-01
Full Text Available The paper introduces an algorithm that mines logs of user interaction with simulation software. It outputs a model that explicitly shows the data perspective of the decision process, namely the Decision Data Model (DDM. In the first part of the paper we focus on how the DDM is extracted by our mining algorithm. We introduce it as pseudo-code and, then, provide explanations and examples of how it actually works. In the second part of the paper, we use a series of small case studies to prove the robustness of the mining algorithm and how it deals with the most common patterns we found in real logs.
Efficient Cluster Algorithm for CP(N-1) Models
Beard, B B; Riederer, S; Wiese, U J
2006-01-01
Despite several attempts, no efficient cluster algorithm has been constructed for CP(N-1) models in the standard Wilson formulation of lattice field theory. In fact, there is a no-go theorem that prevents the construction of an efficient Wolff-type embedding algorithm. In this paper, we construct an efficient cluster algorithm for ferromagnetic SU(N)-symmetric quantum spin systems. Such systems provide a regularization for CP(N-1) models in the framework of D-theory. We present detailed studies of the autocorrelations and find a dynamical critical exponent that is consistent with z = 0.
Efficient cluster algorithm for CP(N-1) models
Beard, B. B.; Pepe, M.; Riederer, S.; Wiese, U.-J.
2006-11-01
Despite several attempts, no efficient cluster algorithm has been constructed for CP(N-1) models in the standard Wilson formulation of lattice field theory. In fact, there is a no-go theorem that prevents the construction of an efficient Wolff-type embedding algorithm. In this paper, we construct an efficient cluster algorithm for ferromagnetic SU(N)-symmetric quantum spin systems. Such systems provide a regularization for CP(N-1) models in the framework of D-theory. We present detailed studies of the autocorrelations and find a dynamical critical exponent that is consistent with z=0.
Petri net model for analysis of concurrently processed complex algorithms
Stoughton, John W.; Mielke, Roland R.
1986-01-01
This paper presents a Petri-net model suitable for analyzing the concurrent processing of computationally complex algorithms. The decomposed operations are to be processed in a multiple processor, data driven architecture. Of particular interest is the application of the model to both the description of the data/control flow of a particular algorithm, and to the general specification of the data driven architecture. A candidate architecture is also presented.
Partially dynamic vehicle routing - models and algorithms
DEFF Research Database (Denmark)
Larsen, Allan; Madsen, Oli B.G.; Solomon, M.
2002-01-01
In this paper we propose a framework for dynamic routing systems based on their degree of dynamism. Next, we consider its impact on solution methodology and quality. Specifically, we introduce the Partially Dynamic Travelling Repairman Problem and describe several dynamic policies to minimize rou...
Fsheikh, Ahmed H.
2013-01-01
A nonlinear orthogonal matching pursuit (NOMP) for sparse calibration of reservoir models is presented. Sparse calibration is a challenging problem as the unknowns are both the non-zero components of the solution and their associated weights. NOMP is a greedy algorithm that discovers at each iteration the most correlated components of the basis functions with the residual. The discovered basis (aka support) is augmented across the nonlinear iterations. Once the basis functions are selected from the dictionary, the solution is obtained by applying Tikhonov regularization. The proposed algorithm relies on approximate gradient estimation using an iterative stochastic ensemble method (ISEM). ISEM utilizes an ensemble of directional derivatives to efficiently approximate gradients. In the current study, the search space is parameterized using an overcomplete dictionary of basis functions built using the K-SVD algorithm.
An efficient Cellular Potts Model algorithm that forbids cell fragmentation
Durand, Marc; Guesnet, Etienne
2016-11-01
The Cellular Potts Model (CPM) is a lattice based modeling technique which is widely used for simulating cellular patterns such as foams or biological tissues. Despite its realism and generality, the standard Monte Carlo algorithm used in the scientific literature to evolve this model preserves connectivity of cells on a limited range of simulation temperature only. We present a new algorithm in which cell fragmentation is forbidden for all simulation temperatures. This allows to significantly enhance realism of the simulated patterns. It also increases the computational efficiency compared with the standard CPM algorithm even at same simulation temperature, thanks to the time spared in not doing unrealistic moves. Moreover, our algorithm restores the detailed balance equation, ensuring that the long-term stage is independent of the chosen acceptance rate and chosen path in the temperature space.
Dynamical behavior of the Niedermayer algorithm applied to Potts models
Girardi, D.; Penna, T. J. P.; Branco, N. S.
2012-08-01
In this work, we make a numerical study of the dynamic universality class of the Niedermayer algorithm applied to the two-dimensional Potts model with 2, 3, and 4 states. This algorithm updates clusters of spins and has a free parameter, E0, which controls the size of these clusters, such that E0=1 is the Metropolis algorithm and E0=0 regains the Wolff algorithm, for the Potts model. For -1clusters of equal spins can be formed: we show that the mean size of the clusters of (possibly) turned spins initially grows with the linear size of the lattice, L, but eventually saturates at a given lattice size L˜, which depends on E0. For L≥L˜, the Niedermayer algorithm is in the same dynamic universality class of the Metropolis one, i.e, they have the same dynamic exponent. For E0>0, spins in different states may be added to the cluster but the dynamic behavior is less efficient than for the Wolff algorithm (E0=0). Therefore, our results show that the Wolff algorithm is the best choice for Potts models, when compared to the Niedermayer's generalization.
Gas Emission Prediction Model of Coal Mine Based on CSBP Algorithm
Directory of Open Access Journals (Sweden)
Xiong Yan
2016-01-01
Full Text Available In view of the nonlinear characteristics of gas emission in a coal working face, a prediction method is proposed based on cuckoo search algorithm optimized BP neural network (CSBP. In the CSBP algorithm, the cuckoo search is adopted to optimize weight and threshold parameters of BP network, and obtains the global optimal solutions. Furthermore, the twelve main affecting factors of the gas emission in the coal working face are taken as input vectors of CSBP algorithm, the gas emission is acted as output vector, and then the prediction model of BP neural network with optimal parameters is established. The results show that the CSBP algorithm has batter generalization ability and higher prediction accuracy, and can be utilized effectively in the prediction of coal mine gas emission.
Analytic solutions for seismic travel time and ray path geometry through simple velocity models.
Energy Technology Data Exchange (ETDEWEB)
Ballard, Sanford
2007-12-01
The geometry of ray paths through realistic Earth models can be extremely complex due to the vertical and lateral heterogeneity of the velocity distribution within the models. Calculation of high fidelity ray paths and travel times through these models generally involves sophisticated algorithms that require significant assumptions and approximations. To test such algorithms it is desirable to have available analytic solutions for the geometry and travel time of rays through simpler velocity distributions against which the more complex algorithms can be compared. Also, in situations where computational performance requirements prohibit implementation of full 3D algorithms, it may be necessary to accept the accuracy limitations of analytic solutions in order to compute solutions that satisfy those requirements. Analytic solutions are described for the geometry and travel time of infinite frequency rays through radially symmetric 1D Earth models characterized by an inner sphere where the velocity distribution is given by the function V (r) = A-Br{sup 2}, optionally surrounded by some number of spherical shells of constant velocity. The mathematical basis of the calculations is described, sample calculations are presented, and results are compared to the Taup Toolkit of Crotwell et al. (1999). These solutions are useful for evaluating the fidelity of sophisticated 3D travel time calculators and in situations where performance requirements preclude the use of more computationally intensive calculators. It should be noted that most of the solutions presented are only quasi-analytic. Exact, closed form equations are derived but computation of solutions to specific problems generally require application of numerical integration or root finding techniques, which, while approximations, can be calculated to very high accuracy. Tolerances are set in the numerical algorithms such that computed travel time accuracies are better than 1 microsecond.
Transmission function models of finite population genetic algorithms
Kemenade, C.H.M. van; Kok, J.N.; La Poutré, J.A.; Thierens, D.
1998-01-01
Infinite population models show a deterministic behaviour. Genetic algorithms with finite populations behave non-deterministicly. For small population sizes, the results obtained with these models differ strongly from the results predicted by the infinite population model. When the population size i
Integrating R and Java for Enhancing Interactivity of Algorithmic Data Analysis Software Solutions
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Titus Felix FURTUNĂ
2016-06-01
Full Text Available Conceiving software solutions for statistical processing and algorithmic data analysis involves handling diverse data, fetched from various sources and in different formats, and presenting the results in a suggestive, tailorable manner. Our ongoing research aims to design programming technics for integrating R developing environment with Java programming language for interoperability at a source code level. The goal is to combine the intensive data processing capabilities of R programing language, along with the multitude of statistical function libraries, with the flexibility offered by Java programming language and platform, in terms of graphical user interface and mathematical function libraries. Both developing environments are multiplatform oriented, and can complement each other through interoperability. R is a comprehensive and concise programming language, benefiting from a continuously expanding and evolving set of packages for statistical analysis, developed by the open source community. While is a very efficient environment for statistical data processing, R platform lacks support for developing user friendly, interactive, graphical user interfaces (GUIs. Java on the other hand, is a high level object oriented programming language, which supports designing and developing performant and interactive frameworks for general purpose software solutions, through Java Foundation Classes, JavaFX and various graphical libraries. In this paper we treat both aspects of integration and interoperability that refer to integrating Java code into R applications, and bringing R processing sequences into Java driven software solutions. Our research has been conducted focusing on case studies concerning pattern recognition and cluster analysis.
Energy Technology Data Exchange (ETDEWEB)
Nanda, T.; Bijwe, P.R.; Kothari, D.P.
1982-10-01
This paper presents the development of a highly effective piecewise fast developed load flow algorithm which has a promising potential for practical application. The algorithm requires minimal storage which is almost independent of the sytem size thus enabling power flow solutions of large systems being accomplished on available small size computers and microprocessors. The potential of the suggested algorithm for practical application has been demonstrated by obtaining the load flow results for a few sample systems. It is envisaged that the algorithm would immensely appeal to the utility engineers, since the engineer not only needs the minimum memory for solving the problem but also can develop the program with utmost care and confidence since the algorithm is devoid of such programming complexities like sparsity exploitation and optimal ordering inherent with modern load flow programs. It is believed that the algorithm would find great popularity with the utilities.
Models and algorithms for stochastic online scheduling
Megow, N.; Uetz, Marc Jochen; Vredeveld, T.
We consider a model for scheduling under uncertainty. In this model, we combine the main characteristics of online and stochastic scheduling in a simple and natural way. Job processing times are assumed to be stochastic, but in contrast to traditional stochastic scheduling models, we assume that
Ding, Dawei; Luo, Xiaoshu; Liu, Yuliang
2007-01-01
This paper focuses on the delay induced Hopf bifurcation in a dual model of Internet congestion control algorithms which can be modeled as a time-delay system described by a one-order delay differential equation (DDE). By choosing communication delay as the bifurcation parameter, we demonstrate that the system loses its stability and a Hopf bifurcation occurs when communication delay passes through a critical value. Moreover, the bifurcating periodic solution of system is calculated by means of perturbation methods. Discussion of stability of the periodic solutions involves the computation of Floquet exponents by considering the corresponding Poincare -Lindstedt series expansion. Finally, numerical simulations for verify the theoretical analysis are provided.
A NEW GENETIC SIMULATED ANNEALING ALGORITHM FOR FLOOD ROUTING MODEL
Institute of Scientific and Technical Information of China (English)
KANG Ling; WANG Cheng; JIANG Tie-bing
2004-01-01
In this paper, a new approach, the Genetic Simulated Annealing (GSA), was proposed for optimizing the parameters in the Muskingum routing model. By integrating the simulated annealing method into the genetic algorithm, the hybrid method could avoid some troubles of traditional methods, such as arduous trial-and-error procedure, premature convergence in genetic algorithm and search blindness in simulated annealing. The principle and implementing procedure of this algorithm were described. Numerical experiments show that the GSA can adjust the optimization population, prevent premature convergence and seek the global optimal result.Applications to the Nanyunhe River and Qingjiang River show that the proposed approach is of higher forecast accuracy and practicability.
Solution profiles for some simple combustion models
Energy Technology Data Exchange (ETDEWEB)
Bebernes, J.; Eberly, D.; Fulks, W.
1986-01-01
In this paper, the shape (solution profile) of the solutions of the Gelfand problem and the perturbed Gelfand problem are studied. Both of these models play a fundamental role in the mathematical theory of thermal explosions for finite rigid and gaseous systems. For rigid systems the physical processes are determined by a pointwise balance between chemical heat addition and heat loss by conduction. During the inductive period, with a duration measured by the conduction time scale of the bounding container, the heat released by the chemical reaction is redistributed by thermal conduction. As the temperature of the container increases, the reaction rate grows dramatically. Eventually, the characteristic time for heat release becomes significantly smaller than the conduction time in a well-defined hot spot embedded in the system. Then the heat released is used almost entirely to increase the hot-spot temperature. The purpose of this paper is to show that both models detect this hot-spot development in a very precise manner. This hot-spot development had previously been detected only numerically.
Directory of Open Access Journals (Sweden)
Ambarish Panda
2016-09-01
Full Text Available A new evolutionary hybrid algorithm (HA has been proposed in this work for environmental optimal power flow (EOPF problem. The EOPF problem has been formulated in a nonlinear constrained multi objective optimization framework. Considering the intermittency of available wind power a cost model of the wind and thermal generation system is developed. Suitably formed objective function considering the operational cost, cost of emission, real power loss and cost of installation of FACTS devices for maintaining a stable voltage in the system has been optimized with HA and compared with particle swarm optimization algorithm (PSOA to prove its effectiveness. All the simulations are carried out in MATLAB/SIMULINK environment taking IEEE30 bus as the test system.
The Cosparse Analysis Model and Algorithms
Nam, Sangnam; Elad, Michael; Gribonval, Rémi
2011-01-01
After a decade of extensive study of the sparse representation synthesis model, we can safely say that this is a mature and stable field, with clear theoretical foundations, and appealing applications. Alongside this approach, there is an analysis counterpart model, which, despite its similarity to the synthesis alternative, is markedly different. Surprisingly, the analysis model did not get a similar attention, and its understanding today is shallow and partial. In this paper we take a closer look at the analysis approach, better define it as a generative model for signals, and contrast it with the synthesis one. This work proposes effective pursuit methods that aim to solve inverse problems regularized with the analysis-model prior, accompanied by a preliminary theoretical study of their performance. We demonstrate the effectiveness of the analysis model in several experiments.
A robust algorithm for moving interface of multi-material fluids based on Riemann solutions
Institute of Scientific and Technical Information of China (English)
Xueying Zhang; Ning Zhao
2006-01-01
In the paper,the numerical simulation of interface problems for multiple material fluids is studied.The level set function is designed to capture the location of the material interface.For multi-dimensional and multi-material fluids,the modified ghost fluid method needs a Riemann solution to renew the variable states near the interface.Here we present a new convenient and effective algorithm for solving the Riemann problem in the normal direction.The extrapolated variables are populated by Taylor series expansions in the direction.The anti-diffusive high order WENO difference scheme with the limiter is adopted for the numerical simulation.Finally we implement a series of numerical experiments of multi-material flows.The obtained results are satisfying,compared to those by other methods.
Institute of Scientific and Technical Information of China (English)
YAN Shiliang; WANG Yinling
2007-01-01
Travelling Salesman Problem (TSP) is a classical optimization problem and it is one of a class of NP-Problem. The purposes of this work is to apply data mining methodologies to explore the patterns in data generated by an Ant Colony Algorithm (ACA) performing a searching operation and to develop a rule set searcher which approximates the ACA'S searcher. An attribute-oriented induction methodology was used to explore the relationship between an operations' sequence and its attributes and a set of rules has been developed. At the end of this paper, the experimental results have shown that the proposed approach has good performance with respect to the quality of solution and the speed of computation.
Multiple QoS modeling and algorithm in computational grid
Institute of Scientific and Technical Information of China (English)
Li Chunlin; Feng Meilai; Li Layuan
2007-01-01
Multiple QoS modeling and algorithm in grid system is considered.Grid QoS requirements can be formulated as a utility function for each task as a weighted sum of its each dimensional QoS utility functions.Multiple QoS constraint resource scheduling optimization in computational grid is distributed to two subproblems: optimization of grid user and grid resource provider.Grid QoS scheduling can be achieved by solving sub problems via an iterative algorithm.
Directory of Open Access Journals (Sweden)
Sara Nakhjirkan
2017-09-01
in green supply chain. Vehicle routing between distribution centres and customers has been considered in the model. Establishment place of distribution centres among potential places is determined by the model. The distributors use continuous review policy (r, Q to control the inventory. The proposed model object is to find an optimal supply chain with minimum costs. To validate the proposed model and measure its compliance with real world problems, GAMS IDE/Cplex has been used. In order to measure the efficiency of the proposed model in large scale problems, a genetic algorithm has been used. The results confirm the efficiency of the proposed model as a practical tool for decision makers to solve location-inventory-routing problems in green supply chain. The proposed GA could reduce the solving time by 85% while reaching on the average 97% of optimal solution compared with exact method.
Penenko, Alexey; Antokhin, Pavel
2016-11-01
The performance of a variational data assimilation algorithm for a transport and transformation model of atmospheric chemical composition is studied numerically in the case where the emission inventories are missing while there are additional in situ indirect concentration measurements. The algorithm is based on decomposition and splitting methods with a direct solution of the data assimilation problems at the splitting stages. This design allows avoiding iterative processes and working in real-time. In numerical experiments we study the sensitivity of data assimilation to measurement data quantity and quality.
Musharavati, Farayi; Hamouda, Abdelmagid Salem
2015-01-01
Multiple parts process planning (MPPP) is a hard optimization problem that requires the rigor and intensity of metaheuristic-based algorithms such as simulated annealing and genetic algorithms. In this paper, a solution method for this problem is developed based on genetic algorithms. Genetic algorithms solve problems by exploring a given search space. To do this, a landscape over which the search traverses is constructed based on a number of algorithm choices. Key algorithm choices include (...
Directory of Open Access Journals (Sweden)
Farnaz Barzinpour
2014-01-01
Full Text Available Thousands of victims and millions of affected people are hurt by natural disasters every year. Therefore, it is essential to prepare proper response programs that consider early activities of disaster management. In this paper, a multiobjective model for distribution centers which are located and allocated periodically to the damaged areas in order to distribute relief commodities is offered. The main objectives of this model are minimizing the total costs and maximizing the least rate of the satisfaction in the sense of being fair while distributing the items. The model simultaneously determines the location of relief distribution centers and the allocation of affected areas to relief distribution centers. Furthermore, an efficient solution approach based on genetic algorithm has been developed in order to solve the proposed mathematical model. The results of genetic algorithm are compared with the results provided by simulated annealing algorithm and LINGO software. The computational results show that the proposed genetic algorithm provides relatively good solutions in a reasonable time.
Basic Research on Adaptive Model Algorithmic Control
1985-12-01
Control Conference. Richalet, J., A. Rault, J.L. Testud and J. Papon (1978). Model predictive heuristic control: applications to industrial...pp.977-982. Richalet, J., A. Rault, J. L. Testud and J. Papon (1978). Model predictive heuristic control: applications to industrial processes
Immune System Model Calibration by Genetic Algorithm
Presbitero, A.; Krzhizhanovskaya, V.; Mancini, E.; Brands, R.; Sloot, P.
2016-01-01
We aim to develop a mathematical model of the human immune system for advanced individualized healthcare where medication plan is fine-tuned to fit a patient's conditions through monitored biochemical processes. One of the challenges is calibrating model parameters to satisfy existing experimental
Approximation Algorithms for Model-Based Diagnosis
Feldman, A.B.
2010-01-01
Model-based diagnosis is an area of abductive inference that uses a system model, together with observations about system behavior, to isolate sets of faulty components (diagnoses) that explain the observed behavior, according to some minimality criterion. This thesis presents greedy approximation a
Approximation Algorithms for Model-Based Diagnosis
Feldman, A.B.
2010-01-01
Model-based diagnosis is an area of abductive inference that uses a system model, together with observations about system behavior, to isolate sets of faulty components (diagnoses) that explain the observed behavior, according to some minimality criterion. This thesis presents greedy approximation a
Directory of Open Access Journals (Sweden)
A. Fiol-Zulueta
2009-05-01
Full Text Available El problema de la obtención de secuencias de trabajo en las actividades productivas consiste en ladeterminación de una posible combinación, deseablemente la mejor, de opciones de secuencias detrabajo para cada uno de los elementos que conforman la entidad productiva y por todas ellas, apartir de la elaboración previa de secuencias evaluadas considerando un determinado indicador deeficiencia. En el trabajo se propone un modelo matemático para el caso de un taller de flujo híbridocon tiempos de procesamiento dependientes de la secuencia y de las máquinas, y suimplementación utilizando una meta heurística, con el propósito de ayudar a la toma de decisionescon vistas a mejorar los instrumentos utilizados para lograr la conciliación de las secuencias detrabajo en los talleres de producción, debido a la necesidad de elevar los indicadores de eficienciade dichos talleres.Palabras claves: secuencias de producción en talleres de maquinado, taller de flujo, meta heurística,optimización bajo criterios múltiples.____________________________________________________________________________AbstractThe problem of obtaining work sequences in the productive activities consists on the determinationof a possible combination, desirably the best, of work sequences for each one of the elements ofthe productive entity and for all them starting from the previous elaboration of evaluatedsequences considering a certain efficiency indicator. In the work a mathematical model, for thecase of a hybrid flow shop with times depending of the sequence prosecution and of the machinescharacteristics, and it implementation using a metaheurístic procedure are proposed, with thepurpose of aiding the decisions making process with a view of improving the tools used to achievethe work sequences conciliation of the production shops, due to the necessity of raising theefficiency indicators of these workshops.Key words: work sequences in mechanical industry workshops, flow
An Algorithm for Optimally Fitting a Wiener Model
Directory of Open Access Journals (Sweden)
Lucas P. Beverlin
2011-01-01
Full Text Available The purpose of this work is to present a new methodology for fitting Wiener networks to datasets with a large number of variables. Wiener networks have the ability to model a wide range of data types, and their structures can yield parameters with phenomenological meaning. There are several challenges to fitting such a model: model stiffness, the nonlinear nature of a Wiener network, possible overfitting, and the large number of parameters inherent with large input sets. This work describes a methodology to overcome these challenges by using several iterative algorithms under supervised learning and fitting subsets of the parameters at a time. This methodology is applied to Wiener networks that are used to predict blood glucose concentrations. The predictions of validation sets from models fit to four subjects using this methodology yielded a higher correlation between observed and predicted observations than other algorithms, including the Gauss-Newton and Levenberg-Marquardt algorithms.
Montagnier, Olivier
2011-01-01
This study deals with the optimisation of subcritical and supercritical laminated composite drive shafts, based on a genetic algorithm. The first part focuses on the modelling of a composite drive shaft. Flexural vibrations in a simply supported composite drive shaft mounted on viscoelastic supports, including shear effects are studied. In particular, an analytic stability criterion is developed to ensure the integrity of the system. The torsional strength is then computed with the maximum stress criterion, assuming the coupling effects to be null. Torsional buckling of thin walled composite tubes is modelled using a combination between laminate theory and Fl\\"ugge theory. In the second part, the genetic algorithm is developed. The last part presents a comparative study between various composite materials solutions on a helicopter tail rotor driveline. In particular, hybrid tubes consisting of high modulus and high resistance carbon/epoxy plies are studied. These solutions make it possible to replace the conv...
Distribution Network Design for Fixed Lifetime Perishable Products: A Model and Solution Approach
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Z. Firoozi
2013-01-01
Full Text Available Nowadays, many distribution networks deal with the distribution and storage of perishable products. However, distribution network design models are largely based on assumptions that do not consider time limitations for the storage of products within the network. This study develops a model for the design of a distribution network that considers the short lifetime of perishable products. The model simultaneously determines the network configuration and inventory control decisions of the network. Moreover, as the lifetime is strictly dependent on the storage conditions, the model develops a trade-off between enhancing storage conditions (higher inventory cost to obtain a longer lifetime and selecting those storage conditions that lead to shorter lifetimes (less inventory cost. To solve the model, an efficient Lagrangian relaxation heuristic algorithm is developed. The model and algorithm are validated by sensitivity analysis on some key parameters. Results show that the algorithm finds optimal or near optimal solutions even for large-size cases.
Discrete-time dynamic graphical games:model-free reinforcement learning solution
Institute of Scientific and Technical Information of China (English)
Mohammed I ABOUHEAF; Frank L LEWIS; Magdi S MAHMOUD; Dariusz G MIKULSKI
2015-01-01
This paper introduces a model-free reinforcement learning technique that is used to solve a class of dynamic games known as dynamic graphical games. The graphical game results from multi-agent dynamical systems, where pinning control is used to make all the agents synchronize to the state of a command generator or a leader agent. Novel coupled Bellman equations and Hamiltonian functions are developed for the dynamic graphical games. The Hamiltonian mechanics are used to derive the necessary conditions for optimality. The solution for the dynamic graphical game is given in terms of the solution to a set of coupled Hamilton-Jacobi-Bellman equations developed herein. Nash equilibrium solution for the graphical game is given in terms of the solution to the underlying coupled Hamilton-Jacobi-Bellman equations. An online model-free policy iteration algorithm is developed to learn the Nash solution for the dynamic graphical game. This algorithm does not require any knowledge of the agents’ dynamics. A proof of convergence for this multi-agent learning algorithm is given under mild assumption about the inter-connectivity properties of the graph. A gradient descent technique with critic network structures is used to implement the policy iteration algorithm to solve the graphical game online in real-time.
Institute of Scientific and Technical Information of China (English)
吴剑锋; 朱学愚; 刘建立
1999-01-01
The genetic algorithm (GA) is a global and random search procedure based on the mechanics of natural selection and natural genetics. A new optimization method of the genetic algorithm-based simulated annealing penalty function (GASAPF) is presented to solve groundwater management model. Compared with the traditional gradient-based algorithms, the GA is straightforward and there is no need to calculate derivatives of the objective function. The GA is able to generate both convex and nonconvex points within the feasible region. It can be sure that the GA converges to the global or at least near-global optimal solution to handle the constraints by simulated annealing technique. Maximum pumping example results show that the GASAPF to solve optimization model is very efficient and robust.
Development of Improved Algorithms and Multiscale Modeling Capability with SUNTANS
2015-09-30
High-resolution simulations using nonhydrostatic models like SUNTANS are crucial for understanding multiscale processes that are unresolved, and...1 DISTRIBUTION STATEMENT A. Approved for public release; distribution is unlimited. Development of Improved Algorithms and Multiscale ... Modeling Capability with SUNTANS Oliver B. Fringer 473 Via Ortega, Room 187 Dept. of Civil and Environmental Engineering Stanford University
A general model for matroids and the greedy algorithm
Faigle, U.; Fujishige, Saturo
2009-01-01
We present a general model for set systems to be independence families with respect to set families which determine classes of proper weight functions on a ground set. Within this model, matroids arise from a natural subclass and can be characterized by the optimality of the greedy algorithm. This
Comparison of parameter estimation algorithms in hydrological modelling
DEFF Research Database (Denmark)
Blasone, Roberta-Serena; Madsen, Henrik; Rosbjerg, Dan
2006-01-01
Local search methods have been applied successfully in calibration of simple groundwater models, but might fail in locating the optimum for models of increased complexity, due to the more complex shape of the response surface. Global search algorithms have been demonstrated to perform well for th...
Mathematical models and heuristic solutions for container positioning problems in port terminals
DEFF Research Database (Denmark)
Kallehauge, Louise Sibbesen
2008-01-01
concerning the subject is reviewed. The research presented in this thesis is divided into two main parts: Construction and investigation of new mathematical programming formulations of the CPP and development and implementation of a new event-based heuristic for the problem. The first part presents three...... for analyzing the CPP, demonstrating its complexity, and investigating potentials in model-based exact solution approaches. The models are solved by standard optimization software and the results as well as perspectives for alternative solution methods, making use of the models, are discussed. The second part...... presents an efficient solution algorithm for the CPP. Based on a number of new concepts, an event-based construction heuristic is developed and its ability to solve real-life problem instances is established. The backbone of the algorithm is a list of events, corresponding to a sequence of operations...
Development of Gis Tool for the Solution of Minimum Spanning Tree Problem using Prim's Algorithm
Dutta, S.; Patra, D.; Shankar, H.; Alok Verma, P.
2014-11-01
minimum spanning tree (MST) of a connected, undirected and weighted network is a tree of that network consisting of all its nodes and the sum of weights of all its edges is minimum among all such possible spanning trees of the same network. In this study, we have developed a new GIS tool using most commonly known rudimentary algorithm called Prim's algorithm to construct the minimum spanning tree of a connected, undirected and weighted road network. This algorithm is based on the weight (adjacency) matrix of a weighted network and helps to solve complex network MST problem easily, efficiently and effectively. The selection of the appropriate algorithm is very essential otherwise it will be very hard to get an optimal result. In case of Road Transportation Network, it is very essential to find the optimal results by considering all the necessary points based on cost factor (time or distance). This paper is based on solving the Minimum Spanning Tree (MST) problem of a road network by finding it's minimum span by considering all the important network junction point. GIS technology is usually used to solve the network related problems like the optimal path problem, travelling salesman problem, vehicle routing problems, location-allocation problems etc. Therefore, in this study we have developed a customized GIS tool using Python script in ArcGIS software for the solution of MST problem for a Road Transportation Network of Dehradun city by considering distance and time as the impedance (cost) factors. It has a number of advantages like the users do not need a greater knowledge of the subject as the tool is user-friendly and that allows to access information varied and adapted the needs of the users. This GIS tool for MST can be applied for a nationwide plan called Prime Minister Gram Sadak Yojana in India to provide optimal all weather road connectivity to unconnected villages (points). This tool is also useful for constructing highways or railways spanning several
Hospital Case Cost Estimates Modelling - Algorithm Comparison
Andru, Peter
2008-01-01
Ontario (Canada) Health System stakeholders support the idea and necessity of the integrated source of data that would include both clinical (e.g. diagnosis, intervention, length of stay, case mix group) and financial (e.g. cost per weighted case, cost per diem) characteristics of the Ontario healthcare system activities at the patient-specific level. At present, the actual patient-level case costs in the explicit form are not available in the financial databases for all hospitals. The goal of this research effort is to develop financial models that will assign each clinical case in the patient-specific data warehouse a dollar value, representing the cost incurred by the Ontario health care facility which treated the patient. Five mathematical models have been developed and verified using real dataset. All models can be classified into two groups based on their underlying method: 1. Models based on using relative intensity weights of the cases, and 2. Models based on using cost per diem.
A modified EM algorithm for estimation in generalized mixed models.
Steele, B M
1996-12-01
Application of the EM algorithm for estimation in the generalized mixed model has been largely unsuccessful because the E-step cannot be determined in most instances. The E-step computes the conditional expectation of the complete data log-likelihood and when the random effect distribution is normal, this expectation remains an intractable integral. The problem can be approached by numerical or analytic approximations; however, the computational burden imposed by numerical integration methods and the absence of an accurate analytic approximation have limited the use of the EM algorithm. In this paper, Laplace's method is adapted for analytic approximation within the E-step. The proposed algorithm is computationally straightforward and retains much of the conceptual simplicity of the conventional EM algorithm, although the usual convergence properties are not guaranteed. The proposed algorithm accommodates multiple random factors and random effect distributions besides the normal, e.g., the log-gamma distribution. Parameter estimates obtained for several data sets and through simulation show that this modified EM algorithm compares favorably with other generalized mixed model methods.
Image processing algorithm acceleration using reconfigurable macro processor model
Institute of Scientific and Technical Information of China (English)
孙广富; 陈华明; 卢焕章
2004-01-01
The concept and advantage of reconfigurable technology is introduced. A kind of processor architecture of reconfigurable macro processor (RMP) model based on FPGA array and DSP is put forward and has been implemented.Two image algorithms are developed: template-based automatic target recognition and zone labeling. One is estimating for motion direction in the infrared image background, another is line picking-up algorithm based on image zone labeling and phase grouping technique. It is a kind of "hardware" function that can be called by the DSP in high-level algorithm.It is also a kind of hardware algorithm of the DSP. The results of experiments show the reconfigurable computing technology based on RMP is an ideal accelerating means to deal with the high-speed image processing tasks. High real time performance is obtained in our two applications on RMP.
Multi-level Algorithm for the Anderson Impurity Model
Chandrasekharan, S.; Yoo, J.; Baranger, H. U.
2004-03-01
We develop a new quantum Monte Carlo algorithm to solve the Anderson impurity model. Instead of integrating out the Fermions, we work in the Fermion occupation number basis and thus have direct access to the Fermionic physics. The sign problem that arises in this formulation can be solved by a multi-level technique developed by Luscher and Weisz in the context of lattice QCD [JHEP, 0109 (2001) 010]. We use the directed-loop algorithm to update the degrees of freedom. Further, this algorithm allows us to work directly in the Euclidean time continuum limit for arbitrary values of the interaction strength thus avoiding time discretization errors. We present results for the impurity susceptibility and the properties of the screening cloud obtained using the algorithm.
Analytic solution of simplified Cardan's shaft model
Directory of Open Access Journals (Sweden)
Zajíček M.
2014-12-01
Full Text Available Torsional oscillations and stability assessment of the homokinetic Cardan shaft with a small misalignment angle is described in this paper. The simplified mathematical model of this system leads to the linearized equation of the Mathieu's type. This equation with and without a stationary damping parameter is considered. The solution of the original differential equation is identical with those one of the Fredholm’s integral equation with degenerated kernel assembled by means of a periodic Green's function. The conditions of solvability of such problem enable the identification of the borders between stability and instability regions. These results are presented in the form of stability charts and they are verified using the Floquet theory. The correctness of oscillation results for the system with periodic stiffness is then validated by means of the Runge-Kutta integration method.
Co-clustering models, algorithms and applications
Govaert, Gérard
2013-01-01
Cluster or co-cluster analyses are important tools in a variety of scientific areas. The introduction of this book presents a state of the art of already well-established, as well as more recent methods of co-clustering. The authors mainly deal with the two-mode partitioning under different approaches, but pay particular attention to a probabilistic approach. Chapter 1 concerns clustering in general and the model-based clustering in particular. The authors briefly review the classical clustering methods and focus on the mixture model. They present and discuss the use of different mixture
Munhoven, G.
2013-08-01
The total alkalinity-pH equation, which relates total alkalinity and pH for a given set of total concentrations of the acid-base systems that contribute to total alkalinity in a given water sample, is reviewed and its mathematical properties established. We prove that the equation function is strictly monotone and always has exactly one positive root. Different commonly used approximations are discussed and compared. An original method to derive appropriate initial values for the iterative solution of the cubic polynomial equation based upon carbonate-borate-alkalinity is presented. We then review different methods that have been used to solve the total alkalinity-pH equation, with a main focus on biogeochemical models. The shortcomings and limitations of these methods are made out and discussed. We then present two variants of a new, robust and universally convergent algorithm to solve the total alkalinity-pH equation. This algorithm does not require any a priori knowledge of the solution. SolveSAPHE (Solver Suite for Alkalinity-PH Equations) provides reference implementations of several variants of the new algorithm in Fortran 90, together with new implementations of other, previously published solvers. The new iterative procedure is shown to converge from any starting value to the physical solution. The extra computational cost for the convergence security is only 10-15% compared to the fastest algorithm in our test series.
Institute of Scientific and Technical Information of China (English)
WANG ShunJin; ZHANG Hua
2007-01-01
Based on the exact analytical solution of ordinary differential equations,a truncation of the Taylor series of the exact solution to the Nth order leads to the Nth order algebraic dynamics algorithm.A detailed numerical comparison is presented with Runge-Kutta algorithm and symplectic geometric algorithm for 12 test models.The results show that the algebraic dynamics algorithm can better preserve both geometrical and dynamical fidelity of a dynamical system at a controllable precision,and it can solve the problem of algorithm-induced dissipation for the Runge-Kutta algorithm and the problem of algorithm-induced phase shift for the symplectic geometric algorithm.
Institute of Scientific and Technical Information of China (English)
2007-01-01
Based on the exact analytical solution of ordinary differential equations, a truncation of the Taylor series of the exact solution to the Nth order leads to the Nth order algebraic dynamics algorithm. A detailed numerical comparison is presented with Runge-Kutta algorithm and symplectic geometric algorithm for 12 test models. The results show that the algebraic dynamics algorithm can better preserve both geometrical and dynamical fidelity of a dynamical system at a controllable precision, and it can solve the problem of algorithm-induced dissipation for the Runge-Kutta algorithm and the problem of algorithm-induced phase shift for the symplectic geometric algorithm.
Energy Technology Data Exchange (ETDEWEB)
Nanda, J.; Bijwe, P.R.; Kothari, D.P.
1982-10-01
A highly effective piecewise fast developed load flow algorithm has been developed which has a promising potential for practical application. The algorithm requires minimal storage which is almost independent of the systems size thus enabling power flow solutions of large systems being accomplished on available small size computers and microprocessors. The potential of the suggested algorithm for practical applications has been demonstrated by obtaining the load flow results for a few sample systems. It is envisaged that the algorithm would immensely appeal to utility engineers who not only need the minimum memory for solving the problem but also can develop the program with utmost care and confidence since the algorithm i devoid of such programming complexities like sparsity exploitation and optimal ordering inherent with modern load programs.
Leutenegger, Scott T.; Horton, Graham
1994-01-01
Recently the Multi-Level algorithm was introduced as a general purpose solver for the solution of steady state Markov chains. In this paper, we consider the performance of the Multi-Level algorithm for solving Nearly Completely Decomposable (NCD) Markov chains, for which special-purpose iteractive aggregation/disaggregation algorithms such as the Koury-McAllister-Stewart (KMS) method have been developed that can exploit the decomposability of the the Markov chain. We present experimental results indicating that the general-purpose Multi-Level algorithm is competitive, and can be significantly faster than the special-purpose KMS algorithm when Gauss-Seidel and Gaussian Elimination are used for solving the individual blocks.
Impulsive Neural Networks Algorithm Based on the Artificial Genome Model
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Yuan Gao
2014-05-01
Full Text Available To describe gene regulatory networks, this article takes the framework of the artificial genome model and proposes impulsive neural networks algorithm based on the artificial genome model. Firstly, the gene expression and the cell division tree are applied to generate spiking neurons with specific attributes, neural network structure, connection weights and specific learning rules of each neuron. Next, the gene segment duplications and divergence model are applied to design the evolutionary algorithm of impulsive neural networks at the level of the artificial genome. The dynamic changes of developmental gene regulatory networks are controlled during the whole evolutionary process. Finally, the behavior of collecting food for autonomous intelligent agent is simulated, which is driven by nerves. Experimental results demonstrate that the algorithm in this article has the evolutionary ability on large-scale impulsive neural networks
Differential Evolution algorithm applied to FSW model calibration
Idagawa, H. S.; Santos, T. F. A.; Ramirez, A. J.
2014-03-01
Friction Stir Welding (FSW) is a solid state welding process that can be modelled using a Computational Fluid Dynamics (CFD) approach. These models use adjustable parameters to control the heat transfer and the heat input to the weld. These parameters are used to calibrate the model and they are generally determined using the conventional trial and error approach. Since this method is not very efficient, we used the Differential Evolution (DE) algorithm to successfully determine these parameters. In order to improve the success rate and to reduce the computational cost of the method, this work studied different characteristics of the DE algorithm, such as the evolution strategy, the objective function, the mutation scaling factor and the crossover rate. The DE algorithm was tested using a friction stir weld performed on a UNS S32205 Duplex Stainless Steel.
Software Model Checking for Verifying Distributed Algorithms
2014-10-28
Verification procedure is an intelligent exhaustive search of the state space of the design Model Checking 6 Verifying Synchronous Distributed App...Distributed App Sagar Chaki, June 11, 2014 © 2014 Carnegie Mellon University Tool Usage Project webpage (http://mcda.googlecode.com) • Tutorial
Economic Models and Algorithms for Distributed Systems
Neumann, Dirk; Altmann, Jorn; Rana, Omer F
2009-01-01
Distributed computing models for sharing resources such as Grids, Peer-to-Peer systems, or voluntary computing are becoming increasingly popular. This book intends to discover fresh avenues of research and amendments to existing technologies, aiming at the successful deployment of commercial distributed systems
A tractable algorithm for the wellfounded model
Jonker, C.M.; Renardel de Lavalette, G.R.
In the area of general logic programming (negated atoms allowed in the bodies of rules) and reason maintenance systems, the wellfounded model (first defined by Van Gelder, Ross and Schlipf in 1988) is generally considered to be the declarative semantics of the program. In this paper we present
Directory of Open Access Journals (Sweden)
Y. Tang
2006-01-01
Full Text Available This study provides a comprehensive assessment of state-of-the-art evolutionary multiobjective optimization (EMO tools' relative effectiveness in calibrating hydrologic models. The relative computational efficiency, accuracy, and ease-of-use of the following EMO algorithms are tested: Epsilon Dominance Nondominated Sorted Genetic Algorithm-II (ε-NSGAII, the Multiobjective Shuffled Complex Evolution Metropolis algorithm (MOSCEM-UA, and the Strength Pareto Evolutionary Algorithm 2 (SPEA2. This study uses three test cases to compare the algorithms' performances: (1 a standardized test function suite from the computer science literature, (2 a benchmark hydrologic calibration test case for the Leaf River near Collins, Mississippi, and (3 a computationally intensive integrated surface-subsurface model application in the Shale Hills watershed in Pennsylvania. One challenge and contribution of this work is the development of a methodology for comprehensively comparing EMO algorithms that have different search operators and randomization techniques. Overall, SPEA2 attained competitive to superior results for most of the problems tested in this study. The primary strengths of the SPEA2 algorithm lie in its search reliability and its diversity preservation operator. The biggest challenge in maximizing the performance of SPEA2 lies in specifying an effective archive size without a priori knowledge of the Pareto set. In practice, this would require significant trial-and-error analysis, which is problematic for more complex, computationally intensive calibration applications. ε-NSGAII appears to be superior to MOSCEM-UA and competitive with SPEA2 for hydrologic model calibration. ε-NSGAII's primary strength lies in its ease-of-use due to its dynamic population sizing and archiving which lead to rapid convergence to very high quality solutions with minimal user input. MOSCEM-UA is best suited for hydrologic model calibration applications that have small
Data mining concepts models methods and algorithms
Kantardzic, Mehmed
2011-01-01
This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making. The goal of this book is to provide a single introductory source, organized in a systematic way, in which we could direct the readers in analysis of large data sets, through the explanation of basic concepts, models and methodologies developed in recent decades.
Efficiency of Evolutionary Algorithms for Calibration of Watershed Models
Ahmadi, M.; Arabi, M.
2009-12-01
Since the promulgation of the Clean Water Act in the U.S. and other similar legislations around the world over the past three decades, watershed management programs have focused on the nexus of pollution prevention and mitigation. In this context, hydrologic/water quality models have been increasingly embedded in the decision making process. Simulation models are now commonly used to investigate the hydrologic response of watershed systems under varying climatic and land use conditions, and also to study the fate and transport of contaminants at various spatiotemporal scales. Adequate calibration and corroboration of models for various outputs at varying scales is an essential component of watershed modeling. The parameter estimation process could be challenging when multiple objectives are important. For example, improving streamflow predictions of the model at a stream location may result in degradation of model predictions for sediments and/or nutrient at the same location or other outlets. This paper aims to evaluate the applicability and efficiency of single and multi objective evolutionary algorithms for parameter estimation of complex watershed models. To this end, the Shuffled Complex Evolution (SCE-UA) algorithm, a single-objective genetic algorithm (GA), and a multi-objective genetic algorithm (i.e., NSGA-II) were reconciled with the Soil and Water Assessment Tool (SWAT) to calibrate the model at various locations within the Wildcat Creek Watershed, Indiana. The efficiency of these methods were investigated using different error statistics including root mean square error, coefficient of determination and Nash-Sutcliffe efficiency coefficient for the output variables as well as the baseflow component of the stream discharge. A sensitivity analysis was carried out to screening model parameters that bear significant uncertainties. Results indicated that while flow processes can be reasonably ascertained, parameterization of nutrient and pesticide processes
van der Lee, J H; Svrcek, W Y; Young, B R
2008-01-01
Model Predictive Control is a valuable tool for the process control engineer in a wide variety of applications. Because of this the structure of an MPC can vary dramatically from application to application. There have been a number of works dedicated to MPC tuning for specific cases. Since MPCs can differ significantly, this means that these tuning methods become inapplicable and a trial and error tuning approach must be used. This can be quite time consuming and can result in non-optimum tuning. In an attempt to resolve this, a generalized automated tuning algorithm for MPCs was developed. This approach is numerically based and combines a genetic algorithm with multi-objective fuzzy decision-making. The key advantages to this approach are that genetic algorithms are not problem specific and only need to be adapted to account for the number and ranges of tuning parameters for a given MPC. As well, multi-objective fuzzy decision-making can handle qualitative statements of what optimum control is, in addition to being able to use multiple inputs to determine tuning parameters that best match the desired results. This is particularly useful for multi-input, multi-output (MIMO) cases where the definition of "optimum" control is subject to the opinion of the control engineer tuning the system. A case study will be presented in order to illustrate the use of the tuning algorithm. This will include how different definitions of "optimum" control can arise, and how they are accounted for in the multi-objective decision making algorithm. The resulting tuning parameters from each of the definition sets will be compared, and in doing so show that the tuning parameters vary in order to meet each definition of optimum control, thus showing the generalized automated tuning algorithm approach for tuning MPCs is feasible.
Genetic Algorithm Modeling with GPU Parallel Computing Technology
Cavuoti, Stefano; Brescia, Massimo; Pescapé, Antonio; Longo, Giuseppe; Ventre, Giorgio
2012-01-01
We present a multi-purpose genetic algorithm, designed and implemented with GPGPU / CUDA parallel computing technology. The model was derived from a multi-core CPU serial implementation, named GAME, already scientifically successfully tested and validated on astrophysical massive data classification problems, through a web application resource (DAMEWARE), specialized in data mining based on Machine Learning paradigms. Since genetic algorithms are inherently parallel, the GPGPU computing paradigm has provided an exploit of the internal training features of the model, permitting a strong optimization in terms of processing performances and scalability.
An Extended Clustering Algorithm for Statistical Language Models
Ueberla, J P
1994-01-01
Statistical language models frequently suffer from a lack of training data. This problem can be alleviated by clustering, because it reduces the number of free parameters that need to be trained. However, clustered models have the following drawback: if there is ``enough'' data to train an unclustered model, then the clustered variant may perform worse. On currently used language modeling corpora, e.g. the Wall Street Journal corpus, how do the performances of a clustered and an unclustered model compare? While trying to address this question, we develop the following two ideas. First, to get a clustering algorithm with potentially high performance, an existing algorithm is extended to deal with higher order N-grams. Second, to make it possible to cluster large amounts of training data more efficiently, a heuristic to speed up the algorithm is presented. The resulting clustering algorithm can be used to cluster trigrams on the Wall Street Journal corpus and the language models it produces can compete with exi...
Study on Fleet Assignment Problem Model and Algorithm
Directory of Open Access Journals (Sweden)
Yaohua Li
2013-01-01
Full Text Available The Fleet Assignment Problem (FAP of aircraft scheduling in airlines is studied, and the optimization model of FAP is proposed. The objective function of this model is revenue maximization, and it considers comprehensively the difference of scheduled flights and aircraft models in flight areas and mean passenger flows. In order to solve the model, a self-adapting genetic algorithm is supposed to solve the model, which uses natural number coding, adjusts dynamically crossover and mutation operator probability, and adopts intelligent heuristic adjusting to quicken optimization pace. The simulation with production data of an airline shows that the model and algorithms suggested in this paper are feasible and have a good application value.
A dynamic model reduction algorithm for atmospheric chemistry models
Santillana, Mauricio; Le Sager, Philippe; Jacob, Daniel J.; Brenner, Michael
2010-05-01
Understanding the dynamics of the chemical composition of our atmosphere is essential to address a wide range of environmental issues from air quality to climate change. Current models solve a very large and stiff system of nonlinear advection-reaction coupled partial differential equations in order to calculate the time evolution of the concentration of over a hundred chemical species. The numerical solution of this system of equations is difficult and the development of efficient and accurate techniques to achieve this has inspired research for the past four decades. In this work, we propose an adaptive method that dynamically adjusts the chemical mechanism to be solved to the local environment and we show that the use of our approach leads to accurate results and considerable computational savings. Our strategy consists of partitioning the computational domain in active and inactive regions for each chemical species at every time step. In a given grid-box, the concentration of active species is calculated using an accurate numerical scheme, whereas the concentration of inactive species is calculated using a simple and computationally inexpensive formula. We demonstrate the performance of the method by application to the GEOS-Chem global chemical transport model.
Hadia, Sarman K.; Thakker, R. A.; Bhatt, Kirit R.
2016-05-01
The study proposes an application of evolutionary algorithms, specifically an artificial bee colony (ABC), variant ABC and particle swarm optimisation (PSO), to extract the parameters of metal oxide semiconductor field effect transistor (MOSFET) model. These algorithms are applied for the MOSFET parameter extraction problem using a Pennsylvania surface potential model. MOSFET parameter extraction procedures involve reducing the error between measured and modelled data. This study shows that ABC algorithm optimises the parameter values based on intelligent activities of honey bee swarms. Some modifications have also been applied to the basic ABC algorithm. Particle swarm optimisation is a population-based stochastic optimisation method that is based on bird flocking activities. The performances of these algorithms are compared with respect to the quality of the solutions. The simulation results of this study show that the PSO algorithm performs better than the variant ABC and basic ABC algorithm for the parameter extraction of the MOSFET model; also the implementation of the ABC algorithm is shown to be simpler than that of the PSO algorithm.
Efficient Parallel Implementation of Active Appearance Model Fitting Algorithm on GPU
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Jinwei Wang
2014-01-01
Full Text Available The active appearance model (AAM is one of the most powerful model-based object detecting and tracking methods which has been widely used in various situations. However, the high-dimensional texture representation causes very time-consuming computations, which makes the AAM difficult to apply to real-time systems. The emergence of modern graphics processing units (GPUs that feature a many-core, fine-grained parallel architecture provides new and promising solutions to overcome the computational challenge. In this paper, we propose an efficient parallel implementation of the AAM fitting algorithm on GPUs. Our design idea is fine grain parallelism in which we distribute the texture data of the AAM, in pixels, to thousands of parallel GPU threads for processing, which makes the algorithm fit better into the GPU architecture. We implement our algorithm using the compute unified device architecture (CUDA on the Nvidia’s GTX 650 GPU, which has the latest Kepler architecture. To compare the performance of our algorithm with different data sizes, we built sixteen face AAM models of different dimensional textures. The experiment results show that our parallel AAM fitting algorithm can achieve real-time performance for videos even on very high-dimensional textures.
Efficient parallel implementation of active appearance model fitting algorithm on GPU.
Wang, Jinwei; Ma, Xirong; Zhu, Yuanping; Sun, Jizhou
2014-01-01
The active appearance model (AAM) is one of the most powerful model-based object detecting and tracking methods which has been widely used in various situations. However, the high-dimensional texture representation causes very time-consuming computations, which makes the AAM difficult to apply to real-time systems. The emergence of modern graphics processing units (GPUs) that feature a many-core, fine-grained parallel architecture provides new and promising solutions to overcome the computational challenge. In this paper, we propose an efficient parallel implementation of the AAM fitting algorithm on GPUs. Our design idea is fine grain parallelism in which we distribute the texture data of the AAM, in pixels, to thousands of parallel GPU threads for processing, which makes the algorithm fit better into the GPU architecture. We implement our algorithm using the compute unified device architecture (CUDA) on the Nvidia's GTX 650 GPU, which has the latest Kepler architecture. To compare the performance of our algorithm with different data sizes, we built sixteen face AAM models of different dimensional textures. The experiment results show that our parallel AAM fitting algorithm can achieve real-time performance for videos even on very high-dimensional textures.
Sherer, Eric A; Sale, Mark E; Pollock, Bruce G; Belani, Chandra P; Egorin, Merrill J; Ivy, Percy S; Lieberman, Jeffrey A; Manuck, Stephen B; Marder, Stephen R; Muldoon, Matthew F; Scher, Howard I; Solit, David B; Bies, Robert R
2012-08-01
compounds. The root mean squared error and absolute mean prediction error of the best single-objective hybrid genetic algorithm candidates were a median of 0.2 points higher (range of 38.9 point decrease to 27.3 point increase) and 0.02 points lower (range of 0.98 point decrease to 0.74 point increase), respectively, than that of the final stepwise models. In addition, the best single-objective, hybrid genetic algorithm candidate models had successful convergence and covariance steps for each compound, used the same compartment structure as the manual stepwise approach for 6 of 7 (86 %) compounds, and identified 54 % (7 of 13) of covariates included by the manual stepwise approach and 16 covariate relationships not included by manual stepwise models. The model parameter values between the final manual stepwise and best single-objective, hybrid genetic algorithm models differed by a median of 26.7 % (q₁ = 4.9 % and q₃ = 57.1 %). Finally, the single-objective, hybrid genetic algorithm approach was able to identify models capable of estimating absorption rate parameters for four compounds that the manual stepwise approach did not identify. The single-objective, hybrid genetic algorithm represents a general pharmacokinetic model building methodology whose ability to rapidly search the feasible solution space leads to nearly equivalent or superior model fits to pharmacokinetic data.
Financial Data Modeling by Using Asynchronous Parallel Evolutionary Algorithms
Institute of Scientific and Technical Information of China (English)
Wang Chun; Li Qiao-yun
2003-01-01
In this paper, the high-level knowledge of financial data modeled by ordinary differential equations (ODEs) is discovered in dynamic data by using an asynchronous parallel evolutionary modeling algorithm (APHEMA). A numerical example of Nasdaq index analysis is used to demonstrate the potential of APHEMA. The results show that the dynamic models automatically discovered in dynamic data by computer can be used to predict the financial trends.
Novel Genetic Algorithm Based Solutions for Optimal Power Flow under Contingency Conditions
Directory of Open Access Journals (Sweden)
S. V. Durga Bhavani,
2014-06-01
Full Text Available Power system throughout the world is undergoing tremendous changes and developments due to rapid Restructuring, Deregulation and Open-access policies. Greater liberalization, larger market and increasing dependency on the electricity lead to the system operators to work on limited spinning reserve and to operate on vicinities to maximize the economy compromising on the reliability and security of the system for greater profits, which lead to establishment of a monitoring authority and accurate electronic system to prevent any untoward incidents like Blackouts. In any power system, unexpected outages of lines or transformers occur due to faults or other disturbances. These events may cause significant overloading of transmission lines or transformers, which in turn may lead to a viability crisis of the power system. The main role of power system control is to maintain a secure system state, i.e., to prevent the power system, moving from secure state into emergency state over the widest range of operating conditions. Security Constrained Optimal Power Flow (SCOPF is major tool used to improve the security of the system. In this work, Genetic algorithm has been used to solve the OPF and SCOPF problems. As initial effort conventional GA (binary coded based OPF and SCOPF is going to be attempted. The difficulties of binary coded GA in handling continuous search space lead to the evolution of real coded GA‟s. Solutions obtained using both the algorithms are compared. Case studies are made on the IEEE30 bus test system to demonstrate the ability of real coded GA in solving the OPF and SCOPF problems.
Zheng, Ying; Yeh, Chen-Wei; Yang, Chi-Da; Jang, Shi-Shang; Chu, I-Ming
2007-08-31
Biological information generated by high-throughput technology has made systems approach feasible for many biological problems. By this approach, optimization of metabolic pathway has been successfully applied in the amino acid production. However, in this technique, gene modifications of metabolic control architecture as well as enzyme expression levels are coupled and result in a mixed integer nonlinear programming problem. Furthermore, the stoichiometric complexity of metabolic pathway, along with strong nonlinear behaviour of the regulatory kinetic models, directs a highly rugged contour in the whole optimization problem. There may exist local optimal solutions wherein the same level of production through different flux distributions compared with global optimum. The purpose of this work is to develop a novel stochastic optimization approach-information guided genetic algorithm (IGA) to discover the local optima with different levels of modification of the regulatory loop and production rates. The novelties of this work include the information theory, local search, and clustering analysis to discover the local optima which have physical meaning among the qualified solutions.
Directory of Open Access Journals (Sweden)
Chu-Liangyong
2013-06-01
Full Text Available The network of Chinese Waterborne Petroleum Logistics (CWPL is so complex that reasonably disposing and choosing Chinese Waterborne Petroleum Logistics Distribution Center (CWPLDC take on the important theory value and the practical significance. In the study, the network construct of CWPL distribution is provided and the corresponding mathematical model for locating CWPLDC is established, which is a nonlinear mixed interger model. In view of the nonlinerar programming characteristic of model, the genetic algorithm as the solution strategy is put forward here, the strategies of hybrid coding, constraint elimination , fitness function and genetic operator are given followed the algorithm. The result indicates that this model is effective and reliable. This method could also be applicable for other types of large-scale logistics distribution center optimization.
Directory of Open Access Journals (Sweden)
Xing-cai Liu
2014-01-01
Full Text Available The railway freight center stations location and wagon flow organization in railway transport are interconnected, and each of them is complicated in a large-scale rail network. In this paper, a two-stage method is proposed to optimize railway freight center stations location and wagon flow organization together. The location model is present with the objective to minimize the operation cost and fixed construction cost. Then, the second model of wagon flow organization is proposed to decide the optimal train service between different freight center stations. The location of the stations is the output of the first model. A heuristic algorithm that combined tabu search (TS with adaptive clonal selection algorithm (ACSA is proposed to solve those two models. The numerical results show the proposed solution method is effective.
Methodology, models and algorithms in thermographic diagnostics
Živčák, Jozef; Madarász, Ladislav; Rudas, Imre J
2013-01-01
This book presents the methodology and techniques of thermographic applications with focus primarily on medical thermography implemented for parametrizing the diagnostics of the human body. The first part of the book describes the basics of infrared thermography, the possibilities of thermographic diagnostics and the physical nature of thermography. The second half includes tools of intelligent engineering applied for the solving of selected applications and projects. Thermographic diagnostics was applied to problematics of paraplegia and tetraplegia and carpal tunnel syndrome (CTS). The results of the research activities were created with the cooperation of the four projects within the Ministry of Education, Science, Research and Sport of the Slovak Republic entitled Digital control of complex systems with two degrees of freedom, Progressive methods of education in the area of control and modeling of complex object oriented systems on aircraft turbocompressor engines, Center for research of control of te...
Computational modeling of red blood cells: A symplectic integration algorithm
Schiller, Ulf D.; Ladd, Anthony J. C.
2010-03-01
Red blood cells can undergo shape transformations that impact the rheological properties of blood. Computational models have to account for the deformability and red blood cells are often modeled as elastically deformable objects. We present a symplectic integration algorithm for deformable objects. The surface is represented by a set of marker points obtained by surface triangulation, along with a set of fiber vectors that describe the orientation of the material plane. The various elastic energies are formulated in terms of these variables and the equations of motion are obtained by exact differentiation of a discretized Hamiltonian. The integration algorithm preserves the Hamiltonian structure and leads to highly accurate energy conservation, hence he method is expected to be more stable than conventional finite element methods. We apply the algorithm to simulate the shape dynamics of red blood cells.
An Efficient Cluster Algorithm for CP(N-1) Models
Beard, B B; Riederer, S; Wiese, U J
2005-01-01
We construct an efficient cluster algorithm for ferromagnetic SU(N)-symmetric quantum spin systems. Such systems provide a new regularization for CP(N-1) models in the framework of D-theory, which is an alternative non-perturbative approach to quantum field theory formulated in terms of discrete quantum variables instead of classical fields. Despite several attempts, no efficient cluster algorithm has been constructed for CP(N-1) models in the standard formulation of lattice field theory. In fact, there is even a no-go theorem that prevents the construction of an efficient Wolff-type embedding algorithm. We present various simulations for different correlation lengths, couplings and lattice sizes. We have simulated correlation lengths up to 250 lattice spacings on lattices as large as 640x640 and we detect no evidence for critical slowing down.
Calibration of microscopic traffic simulation models using metaheuristic algorithms
Directory of Open Access Journals (Sweden)
Miao Yu
2017-06-01
Full Text Available This paper presents several metaheuristic algorithms to calibrate a microscopic traffic simulation model. The genetic algorithm (GA, Tabu Search (TS, and a combination of the GA and TS (i.e., warmed GA and warmed TS are implemented and compared. A set of traffic data collected from the I-5 Freeway, Los Angles, California, is used. Objective functions are defined to minimize the difference between simulated and field traffic data which are built based on the flow and speed. Several car-following parameters in VISSIM, which can significantly affect the simulation outputs, are selected to calibrate. A better match to the field measurements is reached with the GA, TS, and warmed GA and TS when comparing with that only using the default parameters in VISSIM. Overall, TS performs very well and can be used to calibrate parameters. Combining metaheuristic algorithms clearly performs better and therefore is highly recommended for calibrating microscopic traffic simulation models.
Walker, Joel W
2014-01-01
The MT2 or "s-transverse mass", statistic was developed to cope with the difficulty of associating a parent mass scale with a missing transverse energy signature, given that models of new physics generally predict production of escaping particles in pairs, while collider experiments are sensitive to just a single vector sum over all sources of missing transverse momentum. This document focuses on the generalized extension of that statistic to asymmetric one- and two-step decay chains, with arbitrary child particle masses and upstream missing transverse momentum. It provides a unified theoretical formulation, complete solution classification, taxonomy of critical points, and technical algorithmic prescription for treatment of the MT2 event scale. An implementation of the described algorithm is available for download, and is also a deployable component of the author's fully-featured selection cut software package AEACuS (Algorithmic Event Arbiter and Cut Selector).
Continuum fusion solutions for replacement target models in electro-optic detection.
Schaum, Alan
2014-05-01
The additive target model is used routinely in the statistical detection of opaque targets, despite its phenomenological inaccuracy. The more appropriate replacement target model is seldom used, because the standard method for producing a detection algorithm from it proves to be intractable, unless narrow restrictions are imposed. Now, the recently developed continuum fusion (CF) methodology allows an expanded solution set to the general replacement target problem. It also provides a mechanism for producing approximate solutions for the standard approach. We illustrate the principles of CF by using them to generate both types of answers for the correct detection model.
Directory of Open Access Journals (Sweden)
A.V. Gusynin
2005-02-01
Full Text Available The approach to simulation of flight dynamics and numerically-analytical method of airship control algorithms are offered. It’s based on differential transformations of initial mathematical model of airship motion. The given approach allows for elimination of viewing time function for their differential spectra in the image field. It gives the possibility to reduce a problem of closed algorithm synthesis of vehicle control to the solution of non-linear equation system concerning control variable.
Real Time Optima Tracking Using Harvesting Models of the Genetic Algorithm
Baskaran, Subbiah; Noever, D.
1999-01-01
Tracking optima in real time propulsion control, particularly for non-stationary optimization problems is a challenging task. Several approaches have been put forward for such a study including the numerical method called the genetic algorithm. In brief, this approach is built upon Darwinian-style competition between numerical alternatives displayed in the form of binary strings, or by analogy to 'pseudogenes'. Breeding of improved solution is an often cited parallel to natural selection in.evolutionary or soft computing. In this report we present our results of applying a novel model of a genetic algorithm for tracking optima in propulsion engineering and in real time control. We specialize the algorithm to mission profiling and planning optimizations, both to select reduced propulsion needs through trajectory planning and to explore time or fuel conservation strategies.
Palmer, Grant; Venkatapathy, Ethiraj
1993-01-01
Three solution algorithms, explicit underrelaxation, point implicit, and lower upper symmetric Gauss-Seidel (LUSGS), are used to compute nonequilibrium flow around the Apollo 4 return capsule at 62 km altitude. By varying the Mach number, the efficiency and robustness of the solution algorithms were tested for different levels of chemical stiffness. The performance of the solution algorithms degraded as the Mach number and stiffness of the flow increased. At Mach 15, 23, and 30, the LUSGS method produces an eight order of magnitude drop in the L2 norm of the energy residual in 1/3 to 1/2 the Cray C-90 computer time as compared to the point implicit and explicit under-relaxation methods. The explicit under-relaxation algorithm experienced convergence difficulties at Mach 23 and above. At Mach 40 the performance of the LUSGS algorithm deteriorates to the point it is out-performed by the point implicit method. The effects of the viscous terms are investigated. Grid dependency questions are explored.
Domínguez, Luis F.
2012-06-25
An algorithm for the solution of convex multiparametric mixed-integer nonlinear programming problems arising in process engineering problems under uncertainty is introduced. The proposed algorithm iterates between a multiparametric nonlinear programming subproblem and a mixed-integer nonlinear programming subproblem to provide a series of parametric upper and lower bounds. The primal subproblem is formulated by fixing the integer variables and solved through a series of multiparametric quadratic programming (mp-QP) problems based on quadratic approximations of the objective function, while the deterministic master subproblem is formulated so as to provide feasible integer solutions for the next primal subproblem. To reduce the computational effort when infeasibilities are encountered at the vertices of the critical regions (CRs) generated by the primal subproblem, a simplicial approximation approach is used to obtain CRs that are feasible at each of their vertices. The algorithm terminates when there does not exist an integer solution that is better than the one previously used by the primal problem. Through a series of examples, the proposed algorithm is compared with a multiparametric mixed-integer outer approximation (mp-MIOA) algorithm to demonstrate its computational advantages. © 2012 American Institute of Chemical Engineers (AIChE).
Thickness determination in textile material design: dynamic modeling and numerical algorithms
Xu, Dinghua; Ge, Meibao
2012-03-01
Textile material design is of paramount importance in the study of functional clothing design. It is therefore important to determine the dynamic heat and moisture transfer characteristics in the human body-clothing-environment system, which directly determine the heat-moisture comfort level of the human body. Based on a model of dynamic heat and moisture transfer with condensation in porous fabric at low temperature, this paper presents a new inverse problem of textile thickness determination (IPTTD). Adopting the idea of the least-squares method, we formulate the IPTTD into a function minimization problem. By means of the finite-difference method, quasi-solution method and direct search method for one-dimensional minimization problems, we construct iterative algorithms of the approximated solution for the IPTTD. Numerical simulation results validate the formulation of the IPTTD and demonstrate the effectiveness of the proposed numerical algorithms.
Exact Solutions in Modified Gravity Models
Directory of Open Access Journals (Sweden)
Valery V. Obukhov
2012-06-01
Full Text Available We review the exact solutions in modified gravity. It is one of the main problems of mathematical physics for the gravity theory. One can obtain an exact solution if the field equations reduce to a system of ordinary differential equations. In this paper we consider a number of exact solutions obtained by the method of separation of variables. Some applications to Cosmology and BH entropy are briefly mentioned.
Exact Solutions in Modified Gravity Models
Makarenko, Andrey N
2012-01-01
We review the exact solutions in modified gravity. It is one of the main problems of mathematical physics for the gravity theory. One can obtain an exact solution if the field equations reduce to a system of ordinary differential equations. In this paper we consider a number of exact solutions obtained by the method of separation of variables. Some applications to Cosmology and BH entropy are briefly mentioned.
Directory of Open Access Journals (Sweden)
A. Norozi
2010-01-01
Full Text Available Problem statement: In the area of globalization the degree of competition in the market increased and many companies attempted to manufacture the products efficiently to overcome the challenges faced. Approach: Mixed model assembly line was able to provide continuous flow of material and flexibility with regard to model change. The problem under study attempted to describe the mathematical programming limitation for minimizing the overall make-span and balancing objective for set of parallel lines. Results: A proposed mixed-integer model only able to find the best job sequence in each line to meet the problem objectives for the given number of job allotted to each line. Hence using the proposed mathematical model for large size problem was time consuming and inefficient as so many job allocation values should be checked. This study presented an intelligence based genetic algorithm approach to optimize the considered problem objectives through reducing the problem complexity. A heuristic algorithm was introduced to generate the initial population for intelligence based genetic algorithm. Then, it started to find the best sequence of jobs for each line based on the generated population by heuristic algorithm. By this means, intelligence based genetic algorithm only concentrated on those initial populations that produce better solutions instead of probing the entire search space. Conclusion/Recommendations: The results obtained from intelligence based genetic algorithm were used as an initial point for fine-tuning by simulated annealing to increase the quality of solution. In order to check the capability of proposed algorithm, several experimentations on the set of problems were done. As the total objective values in most of problems could not be improved by simulated algorithm, it proved the well performing of proposed intelligence based genetic algorithm in reaching the near optimal solutions.
Epidemic Processes on Complex Networks: Modelling, Simulation and Algorithms
Van de Bovenkamp, R.
2015-01-01
Local interactions on a graph will lead to global dynamic behaviour. In this thesis we focus on two types of dynamic processes on graphs: the Susceptible-Infected-Susceptilbe (SIS) virus spreading model, and gossip style epidemic algorithms. The largest part of this thesis is devoted to the SIS mode
Worm Algorithm for CP(N-1) Model
Rindlisbacher, Tobias
2017-01-01
The CP(N-1) model in 2D is an interesting toy model for 4D QCD as it possesses confinement, asymptotic freedom and a non-trivial vacuum structure. Due to the lower dimensionality and the absence of fermions, the computational cost for simulating 2D CP(N-1) on the lattice is much lower than that for simulating 4D QCD. However, to our knowledge, no efficient algorithm for simulating the lattice CP(N-1) model has been tested so far, which also works at finite density. To this end we propose a new type of worm algorithm which is appropriate to simulate the lattice CP(N-1) model in a dual, flux-variables based representation, in which the introduction of a chemical potential does not give rise to any complications. In addition to the usual worm moves where a defect is just moved from one lattice site to the next, our algorithm additionally allows for worm-type moves in the internal variable space of single links, which accelerates the Monte Carlo evolution. We use our algorithm to compare the two popular CP(N-1) l...
Worm algorithm for the CP N - 1 model
Rindlisbacher, Tobias; de Forcrand, Philippe
2017-05-01
The CP N - 1 model in 2D is an interesting toy model for 4D QCD as it possesses confinement, asymptotic freedom and a non-trivial vacuum structure. Due to the lower dimensionality and the absence of fermions, the computational cost for simulating 2D CP N - 1 on the lattice is much lower than that for simulating 4D QCD. However, to our knowledge, no efficient algorithm for simulating the lattice CP N - 1 model for N > 2 has been tested so far, which also works at finite density. To this end we propose a new type of worm algorithm which is appropriate to simulate the lattice CP N - 1 model in a dual, flux-variables based representation, in which the introduction of a chemical potential does not give rise to any complications. In addition to the usual worm moves where a defect is just moved from one lattice site to the next, our algorithm additionally allows for worm-type moves in the internal variable space of single links, which accelerates the Monte Carlo evolution. We use our algorithm to compare the two popular CP N - 1 lattice actions and exhibit marked differences in their approach to the continuum limit.
Evolving the Topology of Hidden Markov Models using Evolutionary Algorithms
DEFF Research Database (Denmark)
Thomsen, Réne
2002-01-01
Hidden Markov models (HMM) are widely used for speech recognition and have recently gained a lot of attention in the bioinformatics community, because of their ability to capture the information buried in biological sequences. Usually, heuristic algorithms such as Baum-Welch are used to estimate...
Institute of Scientific and Technical Information of China (English)
WU Jian-sheng; JIN Long
2009-01-01
Because of the difficulty in deciding on the structure of BP neural network in operational meteorological application and the tendency tbr the network to transform to an issue of local solution,a hybrid Particle Swarm Optimization Algorithm based on Artificial Neural Network (PSO-BP) model is proposed for monthly mean rainfall of the whole area of Guangxi. It combines Particle Swarm Optimization (PSO) with BP,that is,the number of hidden nodes and connection weights are optimized by the implementation of PSO operation. The method produces a better network architecture and initial connection weights,trains the traditional backward propagation again by training samples. The ensemble strategy is carried out for the linear programming to calculate the best weights based on the "east sum of the error absolute value" as the optimal rule. The weighted coefficient of each ensemble individual is obtained. The results show that the method can effectively improve learning and generalization ability of the neural network.
B-Spline with Symplectic Algorithm Method for Solution of Time-Dependent Schr(o)dinger Equations
Institute of Scientific and Technical Information of China (English)
BIAN Xue-Bin; QIAO Hao-Xue; SHI Ting-Yun
2006-01-01
@@ A B-spline with the symplectic algorithm method for the solution of time-dependent Schr(o)dinger equations(TDSEs) is introduced. The spatial part of the wavefunction is expanded by B-spline and the time evolution is given in a symplectic scheme.
Kulasiri, Don
2002-01-01
Most of the natural and biological phenomena such as solute transport in porous media exhibit variability which can not be modeled by using deterministic approaches. There is evidence in natural phenomena to suggest that some of the observations can not be explained by using the models which give deterministic solutions. Stochastic processes have a rich repository of objects which can be used to express the randomness inherent in the system and the evolution of the system over time. The attractiveness of the stochastic differential equations (SDE) and stochastic partial differential equations (SPDE) come from the fact that we can integrate the variability of the system along with the scientific knowledge pertaining to the system. One of the aims of this book is to explaim some useufl concepts in stochastic dynamics so that the scientists and engineers with a background in undergraduate differential calculus could appreciate the applicability and appropriateness of these developments in mathematics. The ideas ...
Directory of Open Access Journals (Sweden)
Tarek Bouktir
2012-06-01
Full Text Available This paper presents solution of optimal power flow (OPF problem of a power system via Differential Evolution (DE algorithm. The purpose of an electric power system is to deliver real power to the greatest number of users at the lowest possible cost all the time. So the objective is to minimize the total fuel cost of the generating units and also maintaining an acceptable system performance in terms of limits on generator reactive power outputs, bus voltages, Static VAR Compensator (SVC parameters and overload in transmission lines. CPU times can be reduced by decomposing the problem in two subproblems, the first subproblem minimize the fuel cost of generation and the second subproblem is a reactive power dispatch so optimum bus voltages can be determined and reduce the losses by controlling tap changes of the transformers and the static Var Compensators (SVC. To verify the proposed approach and for comparison purposes, we perform simulations on the Algerian network with 114 buses, 175 branches (lines and transformers and 15 generators. The obtained results indicate that DE is an easy to use, fast, robust and powerful optimization technique compared to the other global optimization methods such as PSO and GA.
Directory of Open Access Journals (Sweden)
Ari Muzakir
2017-05-01
Full Text Available Ease of deployment of digital image through the internet has positive and negative sides, especially for owners of the original digital image. The positive side of the ease of rapid deployment is the owner of that image deploys digital image files to various sites in the world address. While the downside is that if there is no copyright that serves as protector of the image it will be very easily recognized ownership by other parties. Watermarking is one solution to protect the copyright and know the results of the digital image. With Digital Image Watermarking, copyright resulting digital image will be protected through the insertion of additional information such as owner information and the authenticity of the digital image. The least significant bit (LSB is one of the algorithm is simple and easy to understand. The results of the simulations carried out using android smartphone shows that the LSB watermarking technique is not able to be seen by naked human eye, meaning there is no significant difference in the image of the original files with images that have been inserted watermarking. The resulting image has dimensions of 640x480 with a bit depth of 32 bits. In addition, to determine the function of the ability of the device (smartphone in processing the image using this application used black box testing.
A combined model reduction algorithm for controlled biochemical systems.
Snowden, Thomas J; van der Graaf, Piet H; Tindall, Marcus J
2017-02-13
Systems Biology continues to produce increasingly large models of complex biochemical reaction networks. In applications requiring, for example, parameter estimation, the use of agent-based modelling approaches, or real-time simulation, this growing model complexity can present a significant hurdle. Often, however, not all portions of a model are of equal interest in a given setting. In such situations methods of model reduction offer one possible approach for addressing the issue of complexity by seeking to eliminate those portions of a pathway that can be shown to have the least effect upon the properties of interest. In this paper a model reduction algorithm bringing together the complementary aspects of proper lumping and empirical balanced truncation is presented. Additional contributions include the development of a criterion for the selection of state-variable elimination via conservation analysis and use of an 'averaged' lumping inverse. This combined algorithm is highly automatable and of particular applicability in the context of 'controlled' biochemical networks. The algorithm is demonstrated here via application to two examples; an 11 dimensional model of bacterial chemotaxis in Escherichia coli and a 99 dimensional model of extracellular regulatory kinase activation (ERK) mediated via the epidermal growth factor (EGF) and nerve growth factor (NGF) receptor pathways. In the case of the chemotaxis model the algorithm was able to reduce the model to 2 state-variables producing a maximal relative error between the dynamics of the original and reduced models of only 2.8% whilst yielding a 26 fold speed up in simulation time. For the ERK activation model the algorithm was able to reduce the system to 7 state-variables, incurring a maximal relative error of 4.8%, and producing an approximately 10 fold speed up in the rate of simulation. Indices of controllability and observability are additionally developed and demonstrated throughout the paper. These provide
Evaluation of Genetic Algorithm Concepts Using Model Problems. Part 2; Multi-Objective Optimization
Holst, Terry L.; Pulliam, Thomas H.
2003-01-01
A genetic algorithm approach suitable for solving multi-objective optimization problems is described and evaluated using a series of simple model problems. Several new features including a binning selection algorithm and a gene-space transformation procedure are included. The genetic algorithm is suitable for finding pareto optimal solutions in search spaces that are defined by any number of genes and that contain any number of local extrema. Results indicate that the genetic algorithm optimization approach is flexible in application and extremely reliable, providing optimal results for all optimization problems attempted. The binning algorithm generally provides pareto front quality enhancements and moderate convergence efficiency improvements for most of the model problems. The gene-space transformation procedure provides a large convergence efficiency enhancement for problems with non-convoluted pareto fronts and a degradation in efficiency for problems with convoluted pareto fronts. The most difficult problems --multi-mode search spaces with a large number of genes and convoluted pareto fronts-- require a large number of function evaluations for GA convergence, but always converge.
Mitavskiy, Boris; Cannings, Chris
2009-01-01
The evolutionary algorithm stochastic process is well-known to be Markovian. These have been under investigation in much of the theoretical evolutionary computing research. When the mutation rate is positive, the Markov chain modeling of an evolutionary algorithm is irreducible and, therefore, has a unique stationary distribution. Rather little is known about the stationary distribution. In fact, the only quantitative facts established so far tell us that the stationary distributions of Markov chains modeling evolutionary algorithms concentrate on uniform populations (i.e., those populations consisting of a repeated copy of the same individual). At the same time, knowing the stationary distribution may provide some information about the expected time it takes for the algorithm to reach a certain solution, assessment of the biases due to recombination and selection, and is of importance in population genetics to assess what is called a "genetic load" (see the introduction for more details). In the recent joint works of the first author, some bounds have been established on the rates at which the stationary distribution concentrates on the uniform populations. The primary tool used in these papers is the "quotient construction" method. It turns out that the quotient construction method can be exploited to derive much more informative bounds on ratios of the stationary distribution values of various subsets of the state space. In fact, some of the bounds obtained in the current work are expressed in terms of the parameters involved in all the three main stages of an evolutionary algorithm: namely, selection, recombination, and mutation.
Study on model and algorithm of inventory routing problem
Wan, Fengjiao
Vehicle routing problem(VRP) is one of important research in the logistics system. Nowadays, there are many researches on the VRP, but their don't consider the cost of inventory. Thus, the conclusion doesn't meet reality. This paper studies on the inventory routing problem (IRP)and uses one target function to describe these two conflicting problems, which are very important in the logistics optimization. The paper establishes the model of single client and many clients' inventory routing problem. An optimizing iterative algorithm is presented to solve the model. According to the model we can confirm the best quantity, efficiency and route of delivery. Finally, an example is given to illustrate the efficiency of model and algorithm.
Solutions to a nonlinear drift-diffusion model for semiconductors
Directory of Open Access Journals (Sweden)
Weifu Fang
1999-05-01
Full Text Available A nonlinear drift-diffusion model for semiconductors is analyzed to show the existence of non-vacuum global solutions and stationary solutions. The long time behavior of the solutions is studied by establishing the existence of an absorbing set and a compact attractor of the dynamical system. Parallel results on vacuum solutions are also obtained under weaker conditions on model parameters.
Liu, Jianzhou; Zhang, Juan
2011-08-01
In this article, applying the properties of M-matrix and non-negative matrix, utilising eigenvalue inequalities of matrix's sum and product, we firstly develop new upper and lower matrix bounds of the solution for discrete coupled algebraic Riccati equation (DCARE). Secondly, we discuss the solution existence uniqueness condition of the DCARE using the developed upper and lower matrix bounds and a fixed point theorem. Thirdly, a new fixed iterative algorithm of the solution for the DCARE is shown. Finally, the corresponding numerical examples are given to illustrate the effectiveness of the developed results.
Model-based Bayesian signal extraction algorithm for peripheral nerves
Eggers, Thomas E.; Dweiri, Yazan M.; McCallum, Grant A.; Durand, Dominique M.
2017-10-01
Objective. Multi-channel cuff electrodes have recently been investigated for extracting fascicular-level motor commands from mixed neural recordings. Such signals could provide volitional, intuitive control over a robotic prosthesis for amputee patients. Recent work has demonstrated success in extracting these signals in acute and chronic preparations using spatial filtering techniques. These extracted signals, however, had low signal-to-noise ratios and thus limited their utility to binary classification. In this work a new algorithm is proposed which combines previous source localization approaches to create a model based method which operates in real time. Approach. To validate this algorithm, a saline benchtop setup was created to allow the precise placement of artificial sources within a cuff and interference sources outside the cuff. The artificial source was taken from five seconds of chronic neural activity to replicate realistic recordings. The proposed algorithm, hybrid Bayesian signal extraction (HBSE), is then compared to previous algorithms, beamforming and a Bayesian spatial filtering method, on this test data. An example chronic neural recording is also analyzed with all three algorithms. Main results. The proposed algorithm improved the signal to noise and signal to interference ratio of extracted test signals two to three fold, as well as increased the correlation coefficient between the original and recovered signals by 10-20%. These improvements translated to the chronic recording example and increased the calculated bit rate between the recovered signals and the recorded motor activity. Significance. HBSE significantly outperforms previous algorithms in extracting realistic neural signals, even in the presence of external noise sources. These results demonstrate the feasibility of extracting dynamic motor signals from a multi-fascicled intact nerve trunk, which in turn could extract motor command signals from an amputee for the end goal of
Query Optimization Using Genetic Algorithms in the Vector Space Model
Mashagba, Eman Al; Nassar, Mohammad Othman
2011-01-01
In information retrieval research; Genetic Algorithms (GA) can be used to find global solutions in many difficult problems. This study used different similarity measures (Dice, Inner Product) in the VSM, for each similarity measure we compared ten different GA approaches based on different fitness functions, different mutations and different crossover strategies to find the best strategy and fitness function that can be used when the data collection is the Arabic language. Our results shows that the GA approach which uses one-point crossover operator, point mutation and Inner Product similarity as a fitness function is the best IR system in VSM.
The production-distribution problem with order acceptance and package delivery: models and algorithm
Directory of Open Access Journals (Sweden)
Khalili Majid
2016-01-01
Full Text Available The production planning and distribution are among the most important decisions in the supply chain. Classically, in this problem, it is assumed that all orders have to produced and separately delivered; while, in practice, an order may be rejected if the cost that it brings to the supply chain exceeds its revenue. Moreover, orders can be delivered in a batch to reduce the related costs. This paper considers the production planning and distribution problem with order acceptance and package delivery to maximize the profit. At first, a new mathematical model based on mixed integer linear programming is developed. Using commercial optimization software, the model can optimally solve small or even medium sized instances. For large instances, a solution method, based on imperialist competitive algorithms, is also proposed. Using numerical experiments, the proposed model and algorithm are evaluated.
The mathematical model realization algorithm of high voltage cable
2006-01-01
At mathematical model realization algorithm is very important to know the account order of necessary relations and how it presents. Depending of loads or signal sources connection in selected points of mathematical model its very important to know as to make the equations in this point that it was possible to determine all unknown variables in this point. The number of equations which describe this point must to coincide with number of unknown variables, and matrix which describes factor...
Optimisation-Based Solution Methods for Set Partitioning Models
DEFF Research Database (Denmark)
Rasmussen, Matias Sevel
The scheduling of crew, i.e. the construction of work schedules for crew members, is often not a trivial task, but a complex puzzle. The task is complicated by rules, restrictions, and preferences. Therefore, manual solutions as well as solutions from standard software packages are not always su......_cient with respect to solution quality and solution time. Enhancement of the overall solution quality as well as the solution time can be of vital importance to many organisations. The _elds of operations research and mathematical optimisation deal with mathematical modelling of di_cult scheduling problems (among...... other topics). The _elds also deal with the development of sophisticated solution methods for these mathematical models. This thesis describes the set partitioning model which has been widely used for modelling crew scheduling problems. Integer properties for the set partitioning model are shown...
Threat Modeling-Oriented Attack Path Evaluating Algorithm
Institute of Scientific and Technical Information of China (English)
LI Xiaohong; LIU Ran; FENG Zhiyong; HE Ke
2009-01-01
In order to evaluate all attack paths in a threat tree,based on threat modeling theory,a weight distribution algorithm of the root node in a threat tree is designed,which computes threat coefficients of leaf nodes in two ways including threat occurring possibility and the degree of damage.Besides,an algorithm of searching attack path was also obtained in accordence with its definition.Finally,an attack path evaluation system was implemented which can output the threat coefficients of the leaf nodes in a target threat tree,the weight distribution information,and the attack paths.An example threat tree is given to verify the effectiveness of the algorithms.
Gray Cerebrovascular Image Skeleton Extraction Algorithm Using Level Set Model
Directory of Open Access Journals (Sweden)
Jian Wu
2010-06-01
Full Text Available The ambiguity and complexity of medical cerebrovascular image makes the skeleton gained by conventional skeleton algorithm discontinuous, which is sensitive at the weak edges, with poor robustness and too many burrs. This paper proposes a cerebrovascular image skeleton extraction algorithm based on Level Set model, using Euclidean distance field and improved gradient vector flow to obtain two different energy functions. The first energy function controls the obtain of topological nodes for the beginning of skeleton curve. The second energy function controls the extraction of skeleton surface. This algorithm avoids the locating and classifying of the skeleton connection points which guide the skeleton extraction. Because all its parameters are gotten by the analysis and reasoning, no artificial interference is needed.
Time-Based Dynamic Trust Model Using Ant Colony Algorithm
Institute of Scientific and Technical Information of China (English)
TANG Zhuo; LU Zhengding; LI Kai
2006-01-01
The trust in distributed environment is uncertain, which is variation for various factors. This paper introduces TDTM, a model for time-based dynamic trust. Every entity in the distribute environment is endowed with a trust-vector, which figures the trust intensity between this entity and the others. The trust intensity is dynamic due to the time and the inter-operation between two entities, a method is proposed to quantify this change based on the mind of ant colony algorithm and then an algorithm for the transfer of trust relation is also proposed. Furthermore, this paper analyses the influence to the trust intensity among all entities that is aroused by the change of trust intensity between the two entities, and presents an algorithm to resolve the problem. Finally, we show the process of the trusts'change that is aroused by the time' lapse and the inter-operation through an instance.
A family of exact solutions for unpolarized Gowdy models
Obregón, O; Obregon, Octavio; Ryan, Michael P.
1998-01-01
Unpolarized Gowdy models are inhomogeneous cosmological models that depend on time and one spatial variable and have complicated nonlinear equations of motion. There are two topologies associated with these models, a three-torus and a one-sphere cross a two-sphere. The three-torus models have been used for numerical studies because it seems difficult to find analytic solutions to their nonlinear Einstein equations. The one-sphere cross tow-sphere models have even more complicated equations, but at least one family of analytic solutions can be given as a reinterpretation of known solutions. Various properties of this family of solutions are studied.
Reconstruction of cylindrically layered media using an iterative algorithm with a stable solution
Institute of Scientific and Technical Information of China (English)
CHENG Ji-zhen; NIU Zuo-yuan; CHENG Chong-hu
2007-01-01
The reconstruction of cylindrically layered media is investigated in this article. The inverse problem is modeled using a source-type integral equation with a series of cylindrical waves as incidences, and a conventional Born iterative procedure is modified for solving the integral equation. In the modified iterative procedure, a conventional single-point approximation for the calculation of the field inside media is replaced by a multi-points approximation to improve the numerical stability of its solution. Numerical simulations for different permittivity distributions are demon- strated in terms of artificial scattering data with the procedure. The result shows that the procedure enjoys both accuracy and stability in the numerical computation.
Fast sweeping algorithm for accurate solution of the TTI eikonal equation using factorization
bin Waheed, Umair
2017-06-10
Traveltime computation is essential for many seismic data processing applications and velocity analysis tools. High-resolution seismic imaging requires eikonal solvers to account for anisotropy whenever it significantly affects the seismic wave kinematics. Moreover, computation of auxiliary quantities, such as amplitude and take-off angle, rely on highly accurate traveltime solutions. However, the finite-difference based eikonal solution for a point-source initial condition has an upwind source-singularity at the source position, since the wavefront curvature is large near the source point. Therefore, all finite-difference solvers, even the high-order ones, show inaccuracies since the errors due to source-singularity spread from the source point to the whole computational domain. We address the source-singularity problem for tilted transversely isotropic (TTI) eikonal solvers using factorization. We solve a sequence of factored tilted elliptically anisotropic (TEA) eikonal equations iteratively, each time by updating the right hand side function. At each iteration, we factor the unknown TEA traveltime into two factors. One of the factors is specified analytically, such that the other factor is smooth in the source neighborhood. Therefore, through the iterative procedure we obtain accurate solution to the TTI eikonal equation. Numerical tests show significant improvement in accuracy due to factorization. The idea can be easily extended to compute accurate traveltimes for models with lower anisotropic symmetries, such as orthorhombic, monoclinic or even triclinic media.
Modelling and genetic algorithm based optimisation of inverse supply chain
Bányai, T.
2009-04-01
(Recycling of household appliances with emphasis on reuse options). The purpose of this paper is the presentation of a possible method for avoiding the unnecessary environmental risk and landscape use through unprovoked large supply chain of collection systems of recycling processes. In the first part of the paper the author presents the mathematical model of recycling related collection systems (applied especially for wastes of electric and electronic products) and in the second part of the work a genetic algorithm based optimisation method will be demonstrated, by the aid of which it is possible to determine the optimal structure of the inverse supply chain from the point of view economical, ecological and logistic objective functions. The model of the inverse supply chain is based on a multi-level, hierarchical collection system. In case of this static model it is assumed that technical conditions are permanent. The total costs consist of three parts: total infrastructure costs, total material handling costs and environmental risk costs. The infrastructure-related costs are dependent only on the specific fixed costs and the specific unit costs of the operation points (collection, pre-treatment, treatment, recycling and reuse plants). The costs of warehousing and transportation are represented by the material handling related costs. The most important factors determining the level of environmental risk cost are the number of out of time recycled (treated or reused) products, the number of supply chain objects and the length of transportation routes. The objective function is the minimization of the total cost taking into consideration the constraints. However a lot of research work discussed the design of supply chain [8], but most of them concentrate on linear cost functions. In the case of this model non-linear cost functions were used. The non-linear cost functions and the possible high number of objects of the inverse supply chain leaded to the problem of choosing a
Modeling Solute Reactivity in a Phreatic Solution Conduit Penetrating a Karst Aquifer
Field, M.
2014-12-01
A two-dimensional model for solute migration, transformation, and sorption in a phreatic solution conduit penetrating a karst aquifer is presented in which the solute is anthropogenic to the natural system. Transformation of a reacting solute in a solution conduit has generally been accepted as likely occurring but actual physical measurements and mathematical analyses of the suspected process are lacking, primarily because of the logistics of sample collection and the complexities associated with solute transport through solution conduits. The model demonstrates how a reacting solute might be converted to a product solute some of which then diffuses to the solution conduit wall where it may become adsorbed. Model effects vary for laminar flow and turbulent flow in the axial direction. Laminar and turbulent diffusion in the radial direction also exhibits marked differences. In addition to single reaction zones simulations considered multiple adjacent and nonadjacent reaction zones, both with varying reaction rates. Reaction zones were found to enhance subsequent reactions due to some overlap resulting from the hydrodynamic dispersion caused by the axial flow. The simulations showed that varying the reaction rate coefficient strongly affects solute reactions, but that varying deposition coefficients had only minimal impacts. Application of the model to a tracer test that used the tracer dye, Rhodamine WT which readily converts to deaminoalkylated Rhodamine WT after release, illustrates how the model may be used to suggest a possible cause for less than 100% tracer mass recovery. In terms of pollutants in a karst aquifer the model suggests a possible explanation for pollutant transformation in a solution conduit.
Periodic solutions of nonautonomous differential systems modeling obesity population
Energy Technology Data Exchange (ETDEWEB)
Arenas, Abraham J. [Departamento de Matematicas y Estadistica, Universidad de Cordoba Monteria (Colombia)], E-mail: aarenas@sinu.unicordoba.edu.co; Gonzalez-Parra, Gilberto [Departamento de Calculo, Universidad de los Andes, Merida (Venezuela, Bolivarian Republic of)], E-mail: gcarlos@ula.ve; Jodar, Lucas [Instituto de Matematica Multidisciplinar, Universidad Politecnica de Valencia Edificio 8G, 2o, 46022 Valencia (Spain)], E-mail: ljodar@imm.upv.es
2009-10-30
In this paper we study the periodic behaviour of the solutions of a nonautonomous model for obesity population. The mathematical model represented by a nonautonomous system of nonlinear ordinary differential equations is used to model the dynamics of obese populations. Numerical simulations suggest periodic behaviour of subpopulations solutions. Sufficient conditions which guarantee the existence of a periodic positive solution are obtained using a continuation theorem based on coincidence degree theory.
A PISO-like algorithm to simulate superfluid helium flow with the two-fluid model
Soulaine, Cyprien; Allain, Hervé; Baudouy, Bertrand; Van Weelderen, Rob
2015-01-01
This paper presents a segregated algorithm to solve numerically the superfluid helium (He II) equations using the two-fluid model. In order to validate the resulting code and illustrate its potential, different simulations have been performed. First, the flow through a capillary filled with He II with a heated area on one side is simulated and results are compared to analytical solutions in both Landau and Gorter–Mellink flow regimes. Then, transient heat transfer of a forced flow of He II is investigated. Finally, some two-dimensional simulations in a porous medium model are carried out.
Model and algorithm for optimization of rescue center location of emergent catastrophe
Institute of Scientific and Technical Information of China (English)
WANG Ding-wei; ZHANG Guo-xiang
2006-01-01
The location of rescue centers is a key problem in optimal resource allocation and logistics in emergency response.We propose a mathematical model for rescue center location with the considerations of emergency occurrence probability,catastrophe diffusion function and rescue function.Because the catastrophe diffusion and rescue functions are both nonlinear and time-variable,it cannot be solved by common mathematical programming methods.We develop a heuristic embedded genetic algorithm for the special model solution.The computation based on a large number of examples with practical data has shown us satisfactory results.
Convection in a Single Column -- Modelling, Algorithm and Analysis
Bokhove, Onno; Dedner, Andreas; Esler, Gavin; Norbury, John; Turner, Matthew R; Vanneste, Jacques; Cullen, Mike
2016-01-01
The group focused on a model problem of idealised moist air convection in a single column of atmosphere. Height, temperature and moisture variables were chosen to simplify the mathematical representation (along the lines of the Boussinesq approximation in a height variable defined in terms of pressure). This allowed exact simple solutions of the numerical and partial differential equation problems to be found. By examining these, we identify column behaviour, stability issues and explore the feasibility of a more general solution process.
A Software Pattern of the Genetic Algorithm -a Study on Reusable Object Model of Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
The Genetic Algorithm (GA) has been a pop research field, butthere is little concern on GA in view of Software Engineering and this result in a serie s of problems. In this paper, we extract a GA's software pattern, draw a model d iagram of the reusable objects, analyze the advantages and disadvantages of the pattern, and give a sample code at the end. We are then able to improve the reus ability and expansibility of GA. The results make it easier to program a new GA code by using some existing successful operators, thereby reducing the difficult ies and workload of programming a GA's code, and facilitate the GA application.
Improving permafrost distribution modelling using feature selection algorithms
Deluigi, Nicola; Lambiel, Christophe; Kanevski, Mikhail
2016-04-01
The availability of an increasing number of spatial data on the occurrence of mountain permafrost allows the employment of machine learning (ML) classification algorithms for modelling the distribution of the phenomenon. One of the major problems when dealing with high-dimensional dataset is the number of input features (variables) involved. Application of ML classification algorithms to this large number of variables leads to the risk of overfitting, with the consequence of a poor generalization/prediction. For this reason, applying feature selection (FS) techniques helps simplifying the amount of factors required and improves the knowledge on adopted features and their relation with the studied phenomenon. Moreover, taking away irrelevant or redundant variables from the dataset effectively improves the quality of the ML prediction. This research deals with a comparative analysis of permafrost distribution models supported by FS variable importance assessment. The input dataset (dimension = 20-25, 10 m spatial resolution) was constructed using landcover maps, climate data and DEM derived variables (altitude, aspect, slope, terrain curvature, solar radiation, etc.). It was completed with permafrost evidences (geophysical and thermal data and rock glacier inventories) that serve as training permafrost data. Used FS algorithms informed about variables that appeared less statistically important for permafrost presence/absence. Three different algorithms were compared: Information Gain (IG), Correlation-based Feature Selection (CFS) and Random Forest (RF). IG is a filter technique that evaluates the worth of a predictor by measuring the information gain with respect to the permafrost presence/absence. Conversely, CFS is a wrapper technique that evaluates the worth of a subset of predictors by considering the individual predictive ability of each variable along with the degree of redundancy between them. Finally, RF is a ML algorithm that performs FS as part of its
Multidimensional Scaling and Genetic Algorithms : A Solution Approach to Avoid Local Minima
Etschberger, Stefan; Hilbert, Andreas
2002-01-01
Multidimensional scaling is very common in exploratory data analysis. It is mainly used to represent sets of objects with respect to their proximities in a low dimensional Euclidean space. Widely used optimization algorithms try to improve the representation via shifting its coordinates in direction of the negative gradient of a corresponding fit function. Depending on the initial configuration, the chosen algorithm and its parameter settings there is a possibility for the algorithm to termin...
Nouizi, F.; Erkol, H.; Luk, A.; Marks, M.; Unlu, M. B.; Gulsen, G.
2016-10-01
We previously introduced photo-magnetic imaging (PMI), an imaging technique that illuminates the medium under investigation with near-infrared light and measures the induced temperature increase using magnetic resonance thermometry (MRT). Using a multiphysics solver combining photon migration and heat diffusion, PMI models the spatiotemporal distribution of temperature variation and recovers high resolution optical absorption images using these temperature maps. In this paper, we present a new fast non-iterative reconstruction algorithm for PMI. This new algorithm uses analytic methods during the resolution of the forward problem and the assembly of the sensitivity matrix. We validate our new analytic-based algorithm with the first generation finite element method (FEM) based reconstruction algorithm previously developed by our team. The validation is performed using, first synthetic data and afterwards, real MRT measured temperature maps. Our new method accelerates the reconstruction process 30-fold when compared to a single iteration of the FEM-based algorithm.
Improved Marquardt Algorithm for Training Neural Networks for Chemical Process Modeling
Institute of Scientific and Technical Information of China (English)
吴建昱; 何小荣
2002-01-01
Back-propagation (BP) artificial neural networks have been widely used to model chemical processes. BP networks are often trained using the generalized delta-rule (GDR) algorithm but application of such networks is limited because of the low convergent speed of the algorithm. This paper presents a new algorithm incorporating the Marquardt algorithm into the BP algorithm for training feedforward BP neural networks. The new algorithm was tested with several case studies and used to model the Reid vapor pressure (RVP) of stabilizer gasoline. The new algorithm has faster convergence and is much more efficient than the GDR algorithm.
Directory of Open Access Journals (Sweden)
Anan Mungwattana
2016-06-01
Full Text Available This paper deals with the vehicle routing problem with time windows (VRPTW. The VRPTW routes a set of vehicles to service customers having two-sided time windows, i.e. earliest and latest start of service times. The demand requests are served by capacitated vehicles with limited travel times to return to the depot. The purpose of this paper is to develop a hybrid algorithm that uses the modified push forward insertion heuristic (MPFIH, a λ-interchange local search descent method (λ-LSD and a genetic algorithm to solve the VRPTW with two objectives. The first objective aims to determine the minimum number of vehicles required and the second is to find the solution that minimizes the total travel time. A set of well-known benchmark problems are used to compare the quality of solutions. The results show that the proposed algorithm provides effective solutions compared with best found solutions and better than another heuristic used for comparison.
An Algebraic Solution for the Kermack-McKendrick Model
Carvalho, Alexsandro M
2016-01-01
We present an algebraic solution for the Susceptible-Infective-Removed (SIR) model originally presented by Kermack-McKendrick in 1927. Starting from the differential equation for the removed subjects presented by them in the original paper, we re-write it in a slightly different form in order to derive formally the solution, unless one integration. Then, using algebraic techniques and some well justified numerical assumptions we obtain an analytic solution for the integral. Finally, we compare the numerical solution of the differential equations of the SIR model with the analytically solution here proposed, showing an excellent agreement.
Directory of Open Access Journals (Sweden)
H. Vazquez-Leal
2014-01-01
Full Text Available We present a homotopy continuation method (HCM for finding multiple operating points of nonlinear circuits composed of devices modelled by using piecewise linear (PWL representations. We propose an adaptation of the modified spheres path tracking algorithm to trace the homotopy trajectories of PWL circuits. In order to assess the benefits of this proposal, four nonlinear circuits composed of piecewise linear modelled devices are analysed to determine their multiple operating points. The results show that HCM can find multiple solutions within a single homotopy trajectory. Furthermore, we take advantage of the fact that homotopy trajectories are PWL curves meant to replace the multidimensional interpolation and fine tuning stages of the path tracking algorithm with a simple and highly accurate procedure based on the parametric straight line equation.
Acikmese, Ahmet Behcet; Carson, John M., III
2006-01-01
A robustly stabilizing MPC (model predictive control) algorithm for uncertain nonlinear systems is developed that guarantees resolvability. With resolvability, initial feasibility of the finite-horizon optimal control problem implies future feasibility in a receding-horizon framework. The control consists of two components; (i) feed-forward, and (ii) feedback part. Feed-forward control is obtained by online solution of a finite-horizon optimal control problem for the nominal system dynamics. The feedback control policy is designed off-line based on a bound on the uncertainty in the system model. The entire controller is shown to be robustly stabilizing with a region of attraction composed of initial states for which the finite-horizon optimal control problem is feasible. The controller design for this algorithm is demonstrated on a class of systems with uncertain nonlinear terms that have norm-bounded derivatives and derivatives in polytopes. An illustrative numerical example is also provided.
Modeling and Algorithmic Approaches to Constitutively-Complex, Microstructured Fluids
Energy Technology Data Exchange (ETDEWEB)
Miller, Gregory H. [Univ. of California, Davis, CA (United States); Forest, Gregory [Univ. of California, Davis, CA (United States)
2014-05-01
We present a new multiscale model for complex fluids based on three scales: microscopic, kinetic, and continuum. We choose the microscopic level as Kramers' bead-rod model for polymers, which we describe as a system of stochastic differential equations with an implicit constraint formulation. The associated Fokker-Planck equation is then derived, and adiabatic elimination removes the fast momentum coordinates. Approached in this way, the kinetic level reduces to a dispersive drift equation. The continuum level is modeled with a finite volume Godunov-projection algorithm. We demonstrate computation of viscoelastic stress divergence using this multiscale approach.
Solutions of two-factor models with variable interest rates
Li, Jinglu; Clemons, C. B.; Young, G. W.; Zhu, J.
2008-12-01
The focus of this work is on numerical solutions to two-factor option pricing partial differential equations with variable interest rates. Two interest rate models, the Vasicek model and the Cox-Ingersoll-Ross model (CIR), are considered. Emphasis is placed on the definition and implementation of boundary conditions for different portfolio models, and on appropriate truncation of the computational domain. An exact solution to the Vasicek model and an exact solution for the price of bonds convertible to stock at expiration under a stochastic interest rate are derived. The exact solutions are used to evaluate the accuracy of the numerical simulation schemes. For the numerical simulations the pricing solution is analyzed as the market completeness decreases from the ideal complete level to one with higher volatility of the interest rate and a slower mean-reverting environment. Simulations indicate that the CIR model yields more reasonable results than the Vasicek model in a less complete market.
Bouallègue, Fayçal Ben; Crouzet, Jean-François; Comtat, Claude; Fourcade, Marjolaine; Mohammadi, Bijan; Mariano-Goulart, Denis
2007-07-01
This paper presents an extended 3-D exact rebinning formula in the Fourier space that leads to an iterative reprojection algorithm (iterative FOREPROJ), which enables the estimation of unmeasured oblique projection data on the basis of the whole set of measured data. In first approximation, this analytical formula also leads to an extended Fourier rebinning equation that is the basis for an approximate reprojection algorithm (extended FORE). These algorithms were evaluated on numerically simulated 3-D positron emission tomography (PET) data for the solution of the truncation problem, i.e., the estimation of the missing portions in the oblique projection data, before the application of algorithms that require complete projection data such as some rebinning methods (FOREX) or 3-D reconstruction algorithms (3DRP or direct Fourier methods). By taking advantage of all the 3-D data statistics, the iterative FOREPROJ reprojection provides a reliable alternative to the classical FOREPROJ method, which only exploits the low-statistics nonoblique data. It significantly improves the quality of the external reconstructed slices without loss of spatial resolution. As for the approximate extended FORE algorithm, it clearly exhibits limitations due to axial interpolations, but will require clinical studies with more realistic measured data in order to decide on its pertinence.
Hybrid algorithm: A cost efficient solution for ONU placement in Fiber-Wireless (FiWi) network
Bhatt, Uma Rathore; Chouhan, Nitin; Upadhyay, Raksha
2015-03-01
Fiber-Wireless (FiWi) network is a promising access technology as it integrates the technical merits of optical and wireless access networks. FiWi provides large bandwidth and high stability of optical network and lower cost of wireless network respectively. Therefore, FiWi gives users to access broadband services in an "anywhere-anytime" way. One of the key issues in FiWi network is its deployment cost, which depends on the number of ONUs in the network. Therefore optimal placement of ONUs is desirable to design a cost effective network. In this paper, we propose an algorithm for optimal placement of ONUs. First we place an ONU in the center of each grid then we form a set of wireless routers associated with each ONU according to wireless hop number. The number of ONUs are minimized in such a way, that all the wireless routers can communicate to at least one of the ONUs. The number of ONUs in the network further reduced by using genetic algorithm. The effectiveness of the proposed algorithm is tested by considering Internet traffic as well as peer-to-peer (p2p) traffic in the network, which is a current need. Simulation results show that the proposed algorithm is better than existing algorithms in minimizing number of ONUs in the network for both types of traffics. Hence proposed algorithm offers cost effective solution to design the FiWi network.
Toward a Mesoscale Model for the Dynamics of Polymer Solutions
Energy Technology Data Exchange (ETDEWEB)
Miller, G H; Trebotich, D
2006-10-02
To model entire microfluidic systems containing solvated polymers we argue that it is necessary to have a numerical stability constraint governed only by the advective CFL condition. Advancements in the treatment of Kramers bead-rod polymer models are presented to enable tightly-coupled fluid-particle algorithms in the context of system-level modeling.
DR-model-based estimation algorithm for NCS
Institute of Scientific and Technical Information of China (English)
HUANG Si-niu; CHEN Zong-ji; WEI Chen
2006-01-01
A novel estimation scheme based on dead reckoning (DR) model for networked control system (NCS)is proposed in this paper.Both the detailed DR estimation algorithm and the stability analysis of the system are given.By using DR estimation of the state,the effect of communication delays is overcome.This makes a controller designed without considering delays still applicable in NCS Moreover,the scheme can effectively solve the problem of data packet loss or timeout.
Directory of Open Access Journals (Sweden)
Yanhua Jiang
2014-09-01
Full Text Available This paper presents a monocular visual odometry algorithm that incorporates a wheeled vehicle model for ground vehicles. The main innovation of this algorithm is to use the single-track bicycle model to interpret the relationship between the yaw rate and side slip angle, which are the two most important parameters that describe the motion of a wheeled vehicle. Additionally, the pitch angle is also considered since the planar-motion hypothesis often fails due to the dynamic characteristics of wheel suspensions and tires in real-world environments. Linearization is used to calculate a closed-form solution of the motion parameters that works as a hypothesis generator in a RAndom SAmple Consensus (RANSAC scheme to reduce the complexity in solving equations involving trigonometric. All inliers found are used to refine the winner solution through minimizing the reprojection error. Finally, the algorithm is applied to real-time on-board visual localization applications. Its performance is evaluated by comparing against the state-of-the-art monocular visual odometry methods using both synthetic data and publicly available datasets over several kilometers in dynamic outdoor environments.
Modelling Agro-Met Station Observations Using Genetic Algorithm
Directory of Open Access Journals (Sweden)
Prashant Kumar
2014-01-01
Full Text Available The present work discusses the development of a nonlinear data-fitting technique based on genetic algorithm (GA for the prediction of routine weather parameters using observations from Agro-Met Stations (AMS. The algorithm produces the equations that best describe the temporal evolutions of daily minimum and maximum near-surface (at 2.5-meter height air temperature and relative humidity and daily averaged wind speed (at 10-meter height at selected AMS locations. These enable the forecasts of these weather parameters, which could have possible use in crop forecast models. The forecast equations developed in the present study use only the past observations of the above-mentioned parameters. This approach, unlike other prediction methods, provides explicit analytical forecast equation for each parameter. The predictions up to 3 days in advance have been validated using independent datasets, unknown to the training algorithm, with impressive results. The power of the algorithm has also been demonstrated by its superiority over persistence forecast used as a benchmark.
Evaluating Multicore Algorithms on the Unified Memory Model
Directory of Open Access Journals (Sweden)
John E. Savage
2009-01-01
Full Text Available One of the challenges to achieving good performance on multicore architectures is the effective utilization of the underlying memory hierarchy. While this is an issue for single-core architectures, it is a critical problem for multicore chips. In this paper, we formulate the unified multicore model (UMM to help understand the fundamental limits on cache performance on these architectures. The UMM seamlessly handles different types of multiple-core processors with varying degrees of cache sharing at different levels. We demonstrate that our model can be used to study a variety of multicore architectures on a variety of applications. In particular, we use it to analyze an option pricing problem using the trinomial model and develop an algorithm for it that has near-optimal memory traffic between cache levels. We have implemented the algorithm on a two Quad-Core Intel Xeon 5310 1.6 GHz processors (8 cores. It achieves a peak performance of 19.5 GFLOPs, which is 38% of the theoretical peak of the multicore system. We demonstrate that our algorithm outperforms compiler-optimized and auto-parallelized code by a factor of up to 7.5.
Quantum algorithms for biomolecular solutions of the satisfiability problem on a quantum machine.
Chang, Weng-Long; Ren, Ting-Ting; Luo, Jun; Feng, Mang; Guo, Minyi; Weicheng Lin, Kawuu
2008-09-01
In this paper, we demonstrate that the logic computation performed by the DNA-based algorithm for solving general cases of the satisfiability problem can be implemented more efficiently by our proposed quantum algorithm on the quantum machine proposed by Deutsch. To test our theory, we carry out a three-quantum bit nuclear magnetic resonance experiment for solving the simplest satisfiability problem.
Combinatorial model of solute transport in porous media
Institute of Scientific and Technical Information of China (English)
张妙仙; 张丽萍
2004-01-01
Modeling of solute transport is a key issue in the area of soil physics and hydrogeology. The most common approach (the convection-dispersion equation) considers an average convection flow rate and Fickian-like dispersion. Here,we propose a solute transport model in porous media of continuously expanding scale, according to the combinatorics principle. The model supposed actual porous media as a combinative body of many basic segments. First, we studied the solute transport process in each basic segment body, and then deduced the distribution of pore velocity in each basic segment body by difference approximation, finally assembled the solute transport process of each basic segment body into one of the combinative body. The simulation result coincided with the solute transport process observed in test. The model provides useful insight into the solute transport process of the non-Fickian dispersion in continuously expanding scale.
Experiments in Model-Checking Optimistic Replication Algorithms
Boucheneb, Hanifa
2008-01-01
This paper describes a series of model-checking experiments to verify optimistic replication algorithms based on Operational Transformation (OT) approach used for supporting collaborative edition. We formally define, using tool UPPAAL, the behavior and the main consistency requirement (i.e. convergence property) of the collaborative editing systems, as well as the abstract behavior of the environment where these systems are supposed to operate. Due to data replication and the unpredictable nature of user interactions, such systems have infinitely many states. So, we show how to exploit some features of the UPPAAL specification language to attenuate the severe state explosion problem. Two models are proposed. The first one, called concrete model, is very close to the system implementation but runs up against a severe explosion of states. The second model, called symbolic model, aims to overcome the limitation of the concrete model by delaying the effective selection and execution of editing operations until th...
Global identifiability of linear compartmental models--a computer algebra algorithm.
Audoly, S; D'Angiò, L; Saccomani, M P; Cobelli, C
1998-01-01
A priori global identifiability deals with the uniqueness of the solution for the unknown parameters of a model and is, thus, a prerequisite for parameter estimation of biological dynamic models. Global identifiability is however difficult to test, since it requires solving a system of algebraic nonlinear equations which increases both in nonlinearity degree and number of terms and unknowns with increasing model order. In this paper, a computer algebra tool, GLOBI (GLOBal Identifiability) is presented, which combines the topological transfer function method with the Buchberger algorithm, to test global identifiability of linear compartmental models. GLOBI allows for the automatic testing of a priori global identifiability of general structure compartmental models from general multi input-multi output experiments. Examples of usage of GLOBI to analyze a priori global identifiability of some complex biological compartmental models are provided.
A new model and simple algorithms for multi-label mumford-shah problems
Hong, Byungwoo
2013-06-01
In this work, we address the multi-label Mumford-Shah problem, i.e., the problem of jointly estimating a partitioning of the domain of the image, and functions defined within regions of the partition. We create algorithms that are efficient, robust to undesirable local minima, and are easy-to-implement. Our algorithms are formulated by slightly modifying the underlying statistical model from which the multi-label Mumford-Shah functional is derived. The advantage of this statistical model is that the underlying variables: the labels and the functions are less coupled than in the original formulation, and the labels can be computed from the functions with more global updates. The resulting algorithms can be tuned to the desired level of locality of the solution: from fully global updates to more local updates. We demonstrate our algorithm on two applications: joint multi-label segmentation and denoising, and joint multi-label motion segmentation and flow estimation. We compare to the state-of-the-art in multi-label Mumford-Shah problems and show that we achieve more promising results. © 2013 IEEE.
Analytical modeling of bargaining solutions for multicast cellular services
Directory of Open Access Journals (Sweden)
Giuseppe Araniti
2013-07-01
Full Text Available Nowadays, the growing demand for group-oriented services over mobile devices has lead to the definition of new communication standards and multimedia applications in cellular systems. In this article we study the use of game theoretic solutions for these services to model and perform a trade-off analysis between fairness and efficiency in the resources allocation. More precisely, we model bargaining solutions for the multicast data services provisioning and introduce the analytical resolution for the proposed solutions.
Institute of Scientific and Technical Information of China (English)
YaoZhijian
2005-01-01
In this paper, a two-species nonautonomous competitive model with stage structure and harvesting is considered. Sufficient conditions for the existence, uniqueness, global attractivity of positive periodic solution and the existence, uniform asvmntotic stability of almost neriodic solution are obtained.
Orazbayev, B. B.; Orazbayeva, K. N.; Kurmangaziyeva, L. T.; Makhatova, V.E.
2015-01-01
Mathematical equations for the multi-criteria task of the optimisation of chemical engineering systems, for example for the optimisation of working regimes for industrial installations for benzene production, have been formulated and developed, and based on fuzzy mathematical methods, algorithms for their solution have been developed. Since the chemical engineering system, which is being researched, is characterised by multiple criteria and often functions in conditions of uncertainty, the presenting problem is formulated in the form of multi-criteria equations for fuzzy mathematical programming. New mathematical formulations for the problems being solved in a fuzzy environment and heuristic algorithms for their solution have been developed by the modification of various optimisation principles based on fuzzy mathematical methods.
Orazbayev, B B; Orazbayeva, K N; Kurmangaziyeva, L T; Makhatova, V E
2015-01-01
Mathematical equations for the multi-criteria task of the optimisation of chemical engineering systems, for example for the optimisation of working regimes for industrial installations for benzene production, have been formulated and developed, and based on fuzzy mathematical methods, algorithms for their solution have been developed. Since the chemical engineering system, which is being researched, is characterised by multiple criteria and often functions in conditions of uncertainty, the presenting problem is formulated in the form of multi-criteria equations for fuzzy mathematical programming. New mathematical formulations for the problems being solved in a fuzzy environment and heuristic algorithms for their solution have been developed by the modification of various optimisation principles based on fuzzy mathematical methods.
Motion Model Employment using interacting Motion Model Algorithm
DEFF Research Database (Denmark)
Hussain, Dil Muhammad Akbar
2006-01-01
model being correct is computed through a likelihood function for each model. The study presented a simple technique to introduce additional models into the system using deterministic acceleration which basically defines the dynamics of the system. Therefore, based on this value more motion models can...... be employed to increase the coverage. Finally, the combined estimate is obtained using posteriori probabilities from different filter models. The implemented approach provides an adaptive scheme for selecting various number of motion models. Motion model description is important as it defines the kind...
Institute of Scientific and Technical Information of China (English)
Bian Xue-Bin; Qiao Hao-Xue; Shi Ting-Yun
2007-01-01
A pseudospectral method with symplectic algorithm for the solution of time-dependent Schr(o)dinger equations(TDSE) is introduced. The spatial part of the wavefunction is discretized into sparse grid by pseudospectral method and the time evolution is given in symplectic scheme. This method allows us to obtain a highly accurate and stable solution of TDSE. The effectiveness and efficiency of this method is demonstrated by the high-order harmonic spectra of one-dimensional atom in strong laser field as compared with previously published work. The influence of the additional static electric field is also investigated.
Routine Discovery of Complex Genetic Models using Genetic Algorithms.
Moore, Jason H; Hahn, Lance W; Ritchie, Marylyn D; Thornton, Tricia A; White, Bill C
2004-02-01
Simulation studies are useful in various disciplines for a number of reasons including the development and evaluation of new computational and statistical methods. This is particularly true in human genetics and genetic epidemiology where new analytical methods are needed for the detection and characterization of disease susceptibility genes whose effects are complex, nonlinear, and partially or solely dependent on the effects of other genes (i.e. epistasis or gene-gene interaction). Despite this need, the development of complex genetic models that can be used to simulate data is not always intuitive. In fact, only a few such models have been published. We have previously developed a genetic algorithm approach to discovering complex genetic models in which two single nucleotide polymorphisms (SNPs) influence disease risk solely through nonlinear interactions. In this paper, we extend this approach for the discovery of high-order epistasis models involving three to five SNPs. We demonstrate that the genetic algorithm is capable of routinely discovering interesting high-order epistasis models in which each SNP influences risk of disease only through interactions with the other SNPs in the model. This study opens the door for routine simulation of complex gene-gene interactions among SNPs for the development and evaluation of new statistical and computational approaches for identifying common, complex multifactorial disease susceptibility genes.
Novel Hierarchical Fall Detection Algorithm Using a Multiphase Fall Model
Hsieh, Chia-Yeh; Liu, Kai-Chun; Huang, Chih-Ning; Chu, Woei-Chyn; Chan, Chia-Tai
2017-01-01
Falls are the primary cause of accidents for the elderly in the living environment. Reducing hazards in the living environment and performing exercises for training balance and muscles are the common strategies for fall prevention. However, falls cannot be avoided completely; fall detection provides an alarm that can decrease injuries or death caused by the lack of rescue. The automatic fall detection system has opportunities to provide real-time emergency alarms for improving the safety and quality of home healthcare services. Two common technical challenges are also tackled in order to provide a reliable fall detection algorithm, including variability and ambiguity. We propose a novel hierarchical fall detection algorithm involving threshold-based and knowledge-based approaches to detect a fall event. The threshold-based approach efficiently supports the detection and identification of fall events from continuous sensor data. A multiphase fall model is utilized, including free fall, impact, and rest phases for the knowledge-based approach, which identifies fall events and has the potential to deal with the aforementioned technical challenges of a fall detection system. Seven kinds of falls and seven types of daily activities arranged in an experiment are used to explore the performance of the proposed fall detection algorithm. The overall performances of the sensitivity, specificity, precision, and accuracy using a knowledge-based algorithm are 99.79%, 98.74%, 99.05% and 99.33%, respectively. The results show that the proposed novel hierarchical fall detection algorithm can cope with the variability and ambiguity of the technical challenges and fulfill the reliability, adaptability, and flexibility requirements of an automatic fall detection system with respect to the individual differences. PMID:28208694
TDH solution of the Suzuki model of nuclear monopole oscillation
Skalski, J.
1987-09-01
The exact time-dependent Hartree solution of the schematic model describing nuclear monopole oscillation — the Suzuki model — is presented. The energies of vibrational states are quantized according to the gauge-invariant periodic quantization prescription.
The PRIME model: a management solution in academic medicine ...
African Journals Online (AJOL)
The PRIME model: a management solution in academic medicine. ... School of Medicine, measured against the background of good management practices. ... the principles of the PRIME model in other Medical Schools in South Africa in order ...
Linguistically motivated statistical machine translation models and algorithms
Xiong, Deyi
2015-01-01
This book provides a wide variety of algorithms and models to integrate linguistic knowledge into Statistical Machine Translation (SMT). It helps advance conventional SMT to linguistically motivated SMT by enhancing the following three essential components: translation, reordering and bracketing models. It also serves the purpose of promoting the in-depth study of the impacts of linguistic knowledge on machine translation. Finally it provides a systematic introduction of Bracketing Transduction Grammar (BTG) based SMT, one of the state-of-the-art SMT formalisms, as well as a case study of linguistically motivated SMT on a BTG-based platform.
The Model Identification for Small Unmanned Aerial Rotorcraft Based on Adaptive Ant Colony Algorithm
Institute of Scientific and Technical Information of China (English)
Xusheng Lei; Kexin Guo
2012-01-01
This paper proposes a model identification method to get high performance dynamic model of a small unmanned aerial rotorcraft.With the analysis of flight characteristics,a linear dynamic model is constructed by the small perturbation theory.Using the micro guidance navigation and control module,the system can record the control signals of servos,the state information of attitude and velocity information in sequence.After the data preprocessing,an adaptive ant colony algorithm is proposed to get optimal parameters of the dynamic model.With the adaptive adjustment of the pheromone in the selection process,the proposed model identification method can escape from local minima traps and get the optimal solution quickly.Performance analysis and experiments are conducted to validate the effectiveness of the identified dynamic model.Compared with real flight data,the identified model generated by the proposed method has a better performance than the model generated by the adaptive genetic algorithm.Based on the identified dynamic model,the small unmanned aerial rotorcraft can generate suitable control parameters to realize stable hovering,turning,and straight flight.
Gas kinetic algorithm for flows in Poiseuille-like microchannels using Boltzmann model equation
Institute of Scientific and Technical Information of China (English)
LI; Zhihui; ZHANG; Hanxin; FU; Song
2005-01-01
The gas-kinetic unified algorithm using Boltzmann model equation have been extended and developed to solve the micro-scale gas flows in Poiseuille-like micro-channels from Micro-Electro-Mechanical Systems (MEMS). The numerical modeling of the gas kinetic boundary conditions suitable for micro-scale gas flows is presented. To test the present method, the classical Couette flows with various Knudsen numbers, the gas flows from short microchannels like plane Poiseuille and the pressure-driven gas flows in two-dimensional short microchannels have been simulated and compared with the approximate solutions of the Boltzmann equation, the related DSMC results, the modified N-S solutions with slip-flow boundary theory, the gas-kinetic BGK-Burnett solutions and the experimental data. The comparisons show that the present gas-kinetic numerical algorithm using the mesoscopic Boltzmann simplified velocity distribution function equation can effectively simulate and reveal the gas flows in microchannels. The numerical experience indicates that this method may be a powerful tool in the numerical simulation of micro-scale gas flows from MEMS.
Comparison of evolutionary algorithms in gene regulatory network model inference.
LENUS (Irish Health Repository)
2010-01-01
ABSTRACT: BACKGROUND: The evolution of high throughput technologies that measure gene expression levels has created a data base for inferring GRNs (a process also known as reverse engineering of GRNs). However, the nature of these data has made this process very difficult. At the moment, several methods of discovering qualitative causal relationships between genes with high accuracy from microarray data exist, but large scale quantitative analysis on real biological datasets cannot be performed, to date, as existing approaches are not suitable for real microarray data which are noisy and insufficient. RESULTS: This paper performs an analysis of several existing evolutionary algorithms for quantitative gene regulatory network modelling. The aim is to present the techniques used and offer a comprehensive comparison of approaches, under a common framework. Algorithms are applied to both synthetic and real gene expression data from DNA microarrays, and ability to reproduce biological behaviour, scalability and robustness to noise are assessed and compared. CONCLUSIONS: Presented is a comparison framework for assessment of evolutionary algorithms, used to infer gene regulatory networks. Promising methods are identified and a platform for development of appropriate model formalisms is established.
An adaptive correspondence algorithm for modeling scenes with strong interreflections.
Xu, Yi; Aliaga, Daniel G
2009-01-01
Modeling real-world scenes, beyond diffuse objects, plays an important role in computer graphics, virtual reality, and other commercial applications. One active approach is projecting binary patterns in order to obtain correspondence and reconstruct a densely sampled 3D model. In such structured-light systems, determining whether a pixel is directly illuminated by the projector is essential to decoding the patterns. When a scene has abundant indirect light, this process is especially difficult. In this paper, we present a robust pixel classification algorithm for this purpose. Our method correctly establishes the lower and upper bounds of the possible intensity values of an illuminated pixel and of a non-illuminated pixel. Based on the two intervals, our method classifies a pixel by determining whether its intensity is within one interval but not in the other. Our method performs better than standard method due to the fact that it avoids gross errors during decoding process caused by strong inter-reflections. For the remaining uncertain pixels, we apply an iterative algorithm to reduce the inter-reflection within the scene. Thus, more points can be decoded and reconstructed after each iteration. Moreover, the iterative algorithm is carried out in an adaptive fashion for fast convergence.
Renormalized New Solutions for the Massless Thirring Model
Casana, R.
We present a nonperturbative study of the (1+1)-dimensional massless Thirring model by using path integral methods. The regularization ambiguities — coming from the computation of the fermionic determinant — allow to find new solution types for the model. At quantum level the Ward identity for the 1PI 2-point function for the fermionic current separates such solutions in two phases or sectors, the first one has a local gauge symmetry that is implemented at quantum level and the other one without this symmetry. The symmetric phase is a new solution which is unrelated to the previous studies of the model and, in the nonsymmetric phase there are solutions that for some values of the ambiguity parameter are related to well-known solutions of the model. We construct the Schwinger-Dyson equations and the Ward identities. We make a detailed analysis of their UV divergence structure and, after, we perform a nonperturbative regularization and renormalization of the model.
Hierarchical Stochastic Simulation Algorithm for SBML Models of Genetic Circuits
Directory of Open Access Journals (Sweden)
Leandro eWatanabe
2014-11-01
Full Text Available This paper describes a hierarchical stochastic simulation algorithm which has been implemented within iBioSim, a tool used to model, analyze, and visualize genetic circuits. Many biological analysis tools flatten out hierarchy before simulation, but there are many disadvantages associated with this approach. First, the memory required to represent the model can quickly expand in the process. Second, the flattening process is computationally expensive. Finally, when modeling a dynamic cellular population within iBioSim, inlining the hierarchy of the model is inefficient since models must grow dynamically over time. This paper discusses a new approach to handle hierarchy on the fly to make the tool faster and more memory-efficient. This approach yields significant performance improvements as compared to the former flat analysis method.
The Distance Field Model and Distance Constrained MAP Adaptation Algorithm
Institute of Scientific and Technical Information of China (English)
YUPeng; WANGZuoying
2003-01-01
Spatial structure information, i.e., the rel-ative position information of phonetic states in the feature space, is long to be carefully researched yet. In this pa-per, a new model named “Distance Field” is proposed to describe the spatial structure information. Based on this model, a modified MAP adaptation algorithm named dis-tance constrained maximum a poateriori (DCMAP) is in-troduced. The distance field model gives large penalty when the spatial structure is destroyed. As a result the DCMAP reserves the spatial structure information in adaptation process. Experiments show the Distance Field Model improves the performance of MAP adapta-tion. Further results show DCMAP has strong cross-state estimation ability, which is used to train a well-performed speaker-dependent model by data from only part of pho-
Institute of Scientific and Technical Information of China (English)
李春景; 顾传青
2003-01-01
Two efficient recursive algorithms epsilon- algorithm and eta-algorithm are approximants were used to accelerate the convergence of the power series with functionvalued coefficients and to estimate characteristic value of the integral equations. Famous two algorithms.
High speed railway track dynamics models, algorithms and applications
Lei, Xiaoyan
2017-01-01
This book systematically summarizes the latest research findings on high-speed railway track dynamics, made by the author and his research team over the past decade. It explores cutting-edge issues concerning the basic theory of high-speed railways, covering the dynamic theories, models, algorithms and engineering applications of the high-speed train and track coupling system. Presenting original concepts, systematic theories and advanced algorithms, the book places great emphasis on the precision and completeness of its content. The chapters are interrelated yet largely self-contained, allowing readers to either read through the book as a whole or focus on specific topics. It also combines theories with practice to effectively introduce readers to the latest research findings and developments in high-speed railway track dynamics. It offers a valuable resource for researchers, postgraduates and engineers in the fields of civil engineering, transportation, highway & railway engineering.
Lin, Youzuo; O'Malley, Daniel; Vesselinov, Velimir V.
2016-09-01
Inverse modeling seeks model parameters given a set of observations. However, for practical problems because the number of measurements is often large and the model parameters are also numerous, conventional methods for inverse modeling can be computationally expensive. We have developed a new, computationally efficient parallel Levenberg-Marquardt method for solving inverse modeling problems with a highly parameterized model space. Levenberg-Marquardt methods require the solution of a linear system of equations which can be prohibitively expensive to compute for moderate to large-scale problems. Our novel method projects the original linear problem down to a Krylov subspace such that the dimensionality of the problem can be significantly reduced. Furthermore, we store the Krylov subspace computed when using the first damping parameter and recycle the subspace for the subsequent damping parameters. The efficiency of our new inverse modeling algorithm is significantly improved using these computational techniques. We apply this new inverse modeling method to invert for random transmissivity fields in 2-D and a random hydraulic conductivity field in 3-D. Our algorithm is fast enough to solve for the distributed model parameters (transmissivity) in the model domain. The algorithm is coded in Julia and implemented in the MADS computational framework (http://mads.lanl.gov). By comparing with Levenberg-Marquardt methods using standard linear inversion techniques such as QR or SVD methods, our Levenberg-Marquardt method yields a speed-up ratio on the order of ˜101 to ˜102 in a multicore computational environment. Therefore, our new inverse modeling method is a powerful tool for characterizing subsurface heterogeneity for moderate to large-scale problems.
Multiobjective Optimal Algorithm for Automatic Calibration of Daily Streamflow Forecasting Model
Directory of Open Access Journals (Sweden)
Yi Liu
2016-01-01
Full Text Available Single-objection function cannot describe the characteristics of the complicated hydrologic system. Consequently, it stands to reason that multiobjective functions are needed for calibration of hydrologic model. The multiobjective algorithms based on the theory of nondominate are employed to solve this multiobjective optimal problem. In this paper, a novel multiobjective optimization method based on differential evolution with adaptive Cauchy mutation and Chaos searching (MODE-CMCS is proposed to optimize the daily streamflow forecasting model. Besides, to enhance the diversity performance of Pareto solutions, a more precise crowd distance assigner is presented in this paper. Furthermore, the traditional generalized spread metric (SP is sensitive with the size of Pareto set. A novel diversity performance metric, which is independent of Pareto set size, is put forward in this research. The efficacy of the new algorithm MODE-CMCS is compared with the nondominated sorting genetic algorithm II (NSGA-II on a daily streamflow forecasting model based on support vector machine (SVM. The results verify that the performance of MODE-CMCS is superior to the NSGA-II for automatic calibration of hydrologic model.
Groebner basis methods for stationary solutions of a low-dimensional model for a shear flow
Pausch, Marina; Eckhardt, Bruno; Romanovski, Valery G
2014-01-01
We use Groebner basis methods to extract all stationary solutions for the 9-mode shear flow model that is described in Moehlis et al, New J. Phys. 6, 54 (2004). Using rational approximations to irrational wave numbers and algebraic manipulation techniques we reduce the problem of determining all stationary states to finding roots of a polynomial of order 30. The coefficients differ by 30 powers of 10 so that algorithms for extended precision are needed to extract the roots reliably. We find that there are eight stationary solutions consisting of two distinct states that each appear in four symmetry-related phases. We discuss extensions of these results for other flows.
A self-organizing algorithm for modeling protein loops.
Directory of Open Access Journals (Sweden)
Pu Liu
2009-08-01
Full Text Available Protein loops, the flexible short segments connecting two stable secondary structural units in proteins, play a critical role in protein structure and function. Constructing chemically sensible conformations of protein loops that seamlessly bridge the gap between the anchor points without introducing any steric collisions remains an open challenge. A variety of algorithms have been developed to tackle the loop closure problem, ranging from inverse kinematics to knowledge-based approaches that utilize pre-existing fragments extracted from known protein structures. However, many of these approaches focus on the generation of conformations that mainly satisfy the fixed end point condition, leaving the steric constraints to be resolved in subsequent post-processing steps. In the present work, we describe a simple solution that simultaneously satisfies not only the end point and steric conditions, but also chirality and planarity constraints. Starting from random initial atomic coordinates, each individual conformation is generated independently by using a simple alternating scheme of pairwise distance adjustments of randomly chosen atoms, followed by fast geometric matching of the conformationally rigid components of the constituent amino acids. The method is conceptually simple, numerically stable and computationally efficient. Very importantly, additional constraints, such as those derived from NMR experiments, hydrogen bonds or salt bridges, can be incorporated into the algorithm in a straightforward and inexpensive way, making the method ideal for solving more complex multi-loop problems. The remarkable performance and robustness of the algorithm are demonstrated on a set of protein loops of length 4, 8, and 12 that have been used in previous studies.
Solution for integer linear bilevel programming problems using orthogonal genetic algorithm
Institute of Scientific and Technical Information of China (English)
Hong Li; Li Zhang; Yongchang Jiao
2014-01-01
An integer linear bilevel programming problem is firstly transformed into a binary linear bilevel programming problem, and then converted into a single-level binary implicit program-ming. An orthogonal genetic algorithm is developed for solving the binary linear implicit programming problem based on the ortho-gonal design. The orthogonal design with the factor analysis, an experimental design method is applied to the genetic algorithm to make the algorithm more robust, statistical y sound and quickly convergent. A crossover operator formed by the orthogonal array and the factor analysis is presented. First, this crossover operator can generate a smal but representative sample of points as off-spring. After al of the better genes of these offspring are selected, a best combination among these offspring is then generated. The simulation results show the effectiveness of the proposed algo-rithm.
Cutting path as a Rural Postman Problem: solutions by Memetic Algorithms
Directory of Open Access Journals (Sweden)
Ana Maria Rodrigues
2012-01-01
Full Text Available The Rural Postman Problem (RPP is a particular Arc Routing Problem (ARP which consists of determining a minimum cost circuit on a graph so that a given subset of required edges is traversed. The RPP is an NP-hard problem with significant real-life applications. This paper introduces an original approach based on Memetic Algorithms - the MARP algorithm - to solve the RPP and, also deals with an interesting Industrial Application, which focuses on the path optimization for component cutting operations. Memetic Algorithms are a class of Metaheuristics which may be seen as a population strategy that involves cooperation and competition processes between population elements and integrates “social knowledge”, using a local search procedure. The MARP algorithm is tested with different groups of instances and the results are compared with those gathered from other publications. MARP is also used in the context of various real-life applications.
Deb, Kalyanmoy; Mohan, Manikanth; Mishra, Shikhar
2005-01-01
Since the suggestion of a computing procedure of multiple Pareto-optimal solutions in multi-objective optimization problems in the early Nineties, researchers have been on the look out for a procedure which is computationally fast and simultaneously capable of finding a well-converged and well-distributed set of solutions. Most multi-objective evolutionary algorithms (MOEAs) developed in the past decade are either good for achieving a well-distributed solutions at the expense of a large computational effort or computationally fast at the expense of achieving a not-so-good distribution of solutions. For example, although the Strength Pareto Evolutionary Algorithm or SPEA (Zitzler and Thiele, 1999) produces a much better distribution compared to the elitist non-dominated sorting GA or NSGA-II (Deb et al., 2002a), the computational time needed to run SPEA is much greater. In this paper, we evaluate a recently-proposed steady-state MOEA (Deb et al., 2003) which was developed based on the epsilon-dominance concept introduced earlier(Laumanns et al., 2002) and using efficient parent and archive update strategies for achieving a well-distributed and well-converged set of solutions quickly. Based on an extensive comparative study with four other state-of-the-art MOEAs on a number of two, three, and four objective test problems, it is observed that the steady-state MOEA is a good compromise in terms of convergence near to the Pareto-optimal front, diversity of solutions, and computational time. Moreover, the epsilon-MOEA is a step closer towards making MOEAs pragmatic, particularly allowing a decision-maker to control the achievable accuracy in the obtained Pareto-optimal solutions.
Solute based Lagrangian scheme in modeling the drying process of soft matter solutions.
Meng, Fanlong; Luo, Ling; Doi, Masao; Ouyang, Zhongcan
2016-02-01
We develop a new dynamical model to study the drying process of a droplet of soft matter solutions. The model includes the processes of solute diffusion, gel-layer formation and cavity creation. A new scheme is proposed to handle the diffusion dynamics taking place in such processes. In this scheme, the dynamics is described by the motion of material points taken on solute. It is convenient to apply this scheme to solve problems that involve moving boundaries and phase changes. As an example, we show results of a numerical calculation for a drying spherical droplet, and discuss how initial concentration and evaporation rate affect the structural evolution of the droplet.
Stochastic time-dependent vehicle routing problem: Mathematical models and ant colony algorithm
Directory of Open Access Journals (Sweden)
Zhengyu Duan
2015-11-01
Full Text Available This article addresses the stochastic time-dependent vehicle routing problem. Two mathematical models named robust optimal schedule time model and minimum expected schedule time model are proposed for stochastic time-dependent vehicle routing problem, which can guarantee delivery within the time windows of customers. The robust optimal schedule time model only requires the variation range of link travel time, which can be conveniently derived from historical traffic data. In addition, the robust optimal schedule time model based on robust optimization method can be converted into a time-dependent vehicle routing problem. Moreover, an ant colony optimization algorithm is designed to solve stochastic time-dependent vehicle routing problem. As the improvements in initial solution and transition probability, ant colony optimization algorithm has a good performance in convergence. Through computational instances and Monte Carlo simulation tests, robust optimal schedule time model is proved to be better than minimum expected schedule time model in computational efficiency and coping with the travel time fluctuations. Therefore, robust optimal schedule time model is applicable in real road network.
Zemlyanaya, E. V.; Bashashin, M. V.; Rahmonov, I. R.; Shukrinov, Yu. M.; Atanasova, P. Kh.; Volokhova, A. V.
2016-10-01
We consider a model of system of long Josephson junctions (LJJ) with inductive and capacitive coupling. Corresponding system of nonlinear partial differential equations is solved by means of the standard three-point finite-difference approximation in the spatial coordinate and utilizing the Runge-Kutta method for solution of the resulting Cauchy problem. A parallel algorithm is developed and implemented on a basis of the MPI (Message Passing Interface) technology. Effect of the coupling between the JJs on the properties of LJJ system is demonstrated. Numerical results are discussed from the viewpoint of effectiveness of parallel implementation.
Rocha, Frederico AE; Lourenço, Nuno CC; Horta, Nuno CG
2013-01-01
This book applies to the scientific area of electronic design automation (EDA) and addresses the automatic sizing of analog integrated circuits (ICs). Particularly, this book presents an approach to enhance a state-of-the-art layout-aware circuit-level optimizer (GENOM-POF), by embedding statistical knowledge from an automatically generated gradient model into the multi-objective multi-constraint optimization kernel based on the NSGA-II algorithm. The results showed allow the designer to explore the different trade-offs of the solution space, both through the achieved device sizes, or the resp
Two-way nesting in split-explicit ocean models: Algorithms, implementation and validation
Debreu, Laurent; Marchesiello, Patrick; Penven, Pierrick; Cambon, Gildas
2012-06-01
A full two-way nesting approach for split-explicit, free surface ocean models is presented. It is novel in three main respects: the treatment of grid refinement at the fast mode (barotropic) level; the use of scale selective update schemes; the conservation of both volume and tracer contents via refluxing. An idealized application to vortex propagation on a β plane shows agreement between nested and high resolution solutions. A realistic application to the California Current System then confirm these results in a complex configuration. The selected algorithm is now part of ROMS_AGRIF. It is fully consistent with ROMS parallel capabilities on both shared and distributed memory architectures. The nesting implementation authorizes several nesting levels and several grids at any particular level. This operational capability, combined with the inner qualities of our two-way nesting algorithm and generally high-order accuracy of ROMS numerics, allow for realistic simulation of coastal and ocean dynamics at multiple, interacting scales.
Modelling and control algorithms of the cross conveyors line with multiengine variable speed drives
Cheremushkina, M. S.; Baburin, S. V.
2017-02-01
The paper deals with the actual problem of developing the control algorithm that meets the technical requirements of the mine belt conveyors, and enables energy and resource savings taking into account a random sort of traffic. The most effective method of solution of these tasks is the construction of control systems with the use of variable speed drives for asynchronous motors. The authors designed the mathematical model of the system ‘variable speed multiengine drive – conveyor – control system of conveyors’ that takes into account the dynamic processes occurring in the elements of the transport system, provides an assessment of the energy efficiency of application the developed algorithms, which allows one to reduce the dynamic overload in the belt to 15-20%.
Directory of Open Access Journals (Sweden)
Pacuraru Raluca
2011-04-01
Full Text Available The goal of a Virtual Organization is to find the most appropriate partners in terms of expertise, cost wise, quick response, and environment. In this study we propose a model and a solution approach to a partner selection problem considering three main evaluation criteria: cost, time and risk. This multiobjective problem is solved by an improved genetic algorithm (GA that includes meiosis specific characteristics and step-size adaptation for the mutation operator. The algorithm performs strong exploration initially and exploitation in later generations. It has a high global search ability and a fast convergence rate and also avoids premature convergence. On the basis of the numerical investigations, the incorporation of the proposed enhancements has been successfully proved.
Ternary interaction parameters in calphad solution models
Energy Technology Data Exchange (ETDEWEB)
Eleno, Luiz T.F., E-mail: luizeleno@usp.br [Universidade de Sao Paulo (USP), SP (Brazil). Instituto de Fisica; Schön, Claudio G., E-mail: schoen@usp.br [Universidade de Sao Paulo (USP), SP (Brazil). Computational Materials Science Laboratory. Department of Metallurgical and Materials Engineering
2014-07-01
For random, diluted, multicomponent solutions, the excess chemical potentials can be expanded in power series of the composition, with coefficients that are pressure- and temperature-dependent. For a binary system, this approach is equivalent to using polynomial truncated expansions, such as the Redlich-Kister series for describing integral thermodynamic quantities. For ternary systems, an equivalent expansion of the excess chemical potentials clearly justifies the inclusion of ternary interaction parameters, which arise naturally in the form of correction terms in higher-order power expansions. To demonstrate this, we carry out truncated polynomial expansions of the excess chemical potential up to the sixth power of the composition variables. (author)
Institute of Scientific and Technical Information of China (English)
田廓
2013-01-01
Large-scale grid-integration of new energy sources such as wind power generation and so on leads to new problems in secure and stable operation of traditional power grids. For a hybrid power grid containing thermal power plants, wind farms and energy storage equipments, by means of constructing a unit commitment model and the stochastic property of wind power output uncertainty is simulated by scenario tree. Leading chaos embedded particle swarm optimization (CEPSO) into scenario reduction algorithms (SRA) the results of stochastic simulation and the ability to search the optimal solution are improved. Taking a hybrid power system composed of a wind farm and a 10-machine system as simulation example, simulation results show that the obtained unit commitment scheme can dispatch as many wind power units as possible and the operational cost of thermal generation units can be reduced to suit to the demand of energy conservation and emission reduction.% 风电等新能源发电机组的大规模并网，对传统电力系统的安全稳定运行带来了新的问题。研究了一种含有风−火−储联合运行的混合电力系统，通过构建机组组合问题模型，利用情景树方法模拟风电出力的不确定性的随机特性，将混沌群粒子优化算法引入情景约简算法，改善随机模拟结果和提高最优解的搜寻能力。算例分析结果表明，得到的机组组合方案能够尽量多调度风电机组，降低火电机组的运行成本，适应节能减排工作需要。
Modeling supercritical fluid extraction process involving solute-solid interaction
Energy Technology Data Exchange (ETDEWEB)
Goto, M.; Roy, B. Kodama, A.; Hirose, T. [Kumamoto Univ., Kumamoto (Japan)
1998-04-01
Extraction or leaching of solute from natural solid material is a mass transfer process involving dissolution or release of solutes from a solid matrix. Interaction between the solute and solid matrix often influences the supercritical fluid extraction process. A model accounting for the solute-solid interaction as well as mass transfer is developed. The BET equation is used to incorporate the interaction and the solubility of solutes into the local equilibrium in the model. Experimental data for the supercritical extraction of essential oil and cuticular wax from peppermint leaves are successfully analyzed by the model. The effects of parameters on the extraction behavior are demonstrated to illustrate the concept of the model. 18 refs., 5 figs., 1 tab.
Model reduction using the genetic algorithm and routh approximations
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
A new method of model reduction combining the genetic algorithm(GA) with the Routh approximation method is presented. It is suggested that a high-order system can be approximated by a low-order model with a time delay. The denominator parameters of the reduced-order model are determined by the Routh approximation method, then the numerator parameters and time delay are identified by the GA. The reduced-order models obtained by the proposed method will always be stable if the original system is stable and produce a good approximation to the original system in both the frequency domain and time domain. Two numerical examples show that the method is computationally simple and efficient.
Exploration Of Deep Learning Algorithms Using Openacc Parallel Programming Model
Hamam, Alwaleed A.
2017-03-13
Deep learning is based on a set of algorithms that attempt to model high level abstractions in data. Specifically, RBM is a deep learning algorithm that used in the project to increase it\\'s time performance using some efficient parallel implementation by OpenACC tool with best possible optimizations on RBM to harness the massively parallel power of NVIDIA GPUs. GPUs development in the last few years has contributed to growing the concept of deep learning. OpenACC is a directive based ap-proach for computing where directives provide compiler hints to accelerate code. The traditional Restricted Boltzmann Ma-chine is a stochastic neural network that essentially perform a binary version of factor analysis. RBM is a useful neural net-work basis for larger modern deep learning model, such as Deep Belief Network. RBM parameters are estimated using an efficient training method that called Contrastive Divergence. Parallel implementation of RBM is available using different models such as OpenMP, and CUDA. But this project has been the first attempt to apply OpenACC model on RBM.
A hybrid multiview stereo algorithm for modeling urban scenes.
Lafarge, Florent; Keriven, Renaud; Brédif, Mathieu; Vu, Hoang-Hiep
2013-01-01
We present an original multiview stereo reconstruction algorithm which allows the 3D-modeling of urban scenes as a combination of meshes and geometric primitives. The method provides a compact model while preserving details: Irregular elements such as statues and ornaments are described by meshes, whereas regular structures such as columns and walls are described by primitives (planes, spheres, cylinders, cones, and tori). We adopt a two-step strategy consisting first in segmenting the initial meshbased surface using a multilabel Markov Random Field-based model and second in sampling primitive and mesh components simultaneously on the obtained partition by a Jump-Diffusion process. The quality of a reconstruction is measured by a multi-object energy model which takes into account both photo-consistency and semantic considerations (i.e., geometry and shape layout). The segmentation and sampling steps are embedded into an iterative refinement procedure which provides an increasingly accurate hybrid representation. Experimental results on complex urban structures and large scenes are presented and compared to state-of-the-art multiview stereo meshing algorithms.
A nonlinear regression model-based predictive control algorithm.
Dubay, R; Abu-Ayyad, M; Hernandez, J M
2009-04-01
This paper presents a unique approach for designing a nonlinear regression model-based predictive controller (NRPC) for single-input-single-output (SISO) and multi-input-multi-output (MIMO) processes that are common in industrial applications. The innovation of this strategy is that the controller structure allows nonlinear open-loop modeling to be conducted while closed-loop control is executed every sampling instant. Consequently, the system matrix is regenerated every sampling instant using a continuous function providing a more accurate prediction of the plant. Computer simulations are carried out on nonlinear plants, demonstrating that the new approach is easily implemented and provides tight control. Also, the proposed algorithm is implemented on two real time SISO applications; a DC motor, a plastic injection molding machine and a nonlinear MIMO thermal system comprising three temperature zones to be controlled with interacting effects. The experimental closed-loop responses of the proposed algorithm were compared to a multi-model dynamic matrix controller (MPC) with improved results for various set point trajectories. Good disturbance rejection was attained, resulting in improved tracking of multi-set point profiles in comparison to multi-model MPC.
A quasilinear model for solute transport under unsaturated flow
Energy Technology Data Exchange (ETDEWEB)
Houseworth, J.E.; Leem, J.
2009-05-15
We developed an analytical solution for solute transport under steady-state, two-dimensional, unsaturated flow and transport conditions for the investigation of high-level radioactive waste disposal. The two-dimensional, unsaturated flow problem is treated using the quasilinear flow method for a system with homogeneous material properties. Dispersion is modeled as isotropic and is proportional to the effective hydraulic conductivity. This leads to a quasilinear form for the transport problem in terms of a scalar potential that is analogous to the Kirchhoff potential for quasilinear flow. The solutions for both flow and transport scalar potentials take the form of Fourier series. The particular solution given here is for two sources of flow, with one source containing a dissolved solute. The solution method may easily be extended, however, for any combination of flow and solute sources under steady-state conditions. The analytical results for multidimensional solute transport problems, which previously could only be solved numerically, also offer an additional way to benchmark numerical solutions. An analytical solution for two-dimensional, steady-state solute transport under unsaturated flow conditions is presented. A specific case with two sources is solved but may be generalized to any combination of sources. The analytical results complement numerical solutions, which were previously required to solve this class of problems.
A Building Model Framework for a Genetic Algorithm Multi-objective Model Predictive Control
DEFF Research Database (Denmark)
Arendt, Krzysztof; Ionesi, Ana; Jradi, Muhyiddine
2016-01-01
implemented only in few buildings. The following difficulties hinder the widespread usage of MPC: (1) significant model development time, (2) limited portability of models, (3) model computational demand. In the present study a new model development framework for an MPC system based on a Genetic Algorithm (GA...
Multi-objective calibration of a distributed hydrological model (WetSpa using a genetic algorithm
Directory of Open Access Journals (Sweden)
M. Shafii
2009-01-01
Full Text Available A multi-objective genetic algorithm, NSGA-II, is applied to calibrate a distributed hydrological model (WetSpa for predicting river discharge. The evaluation criteria considered are the model bias (mass balance, the model efficiency (Nash-Sutcliffe efficiency, and a logarithmic transformed model efficiency (to emphasize low-flow values. The concept of Pareto dominance is used to solve the multi-objective optimization problem and derive Pareto-optimal parameter sets. In order to analyze the applicability of the approach, a comparison is made with another calibration routine using the parameter estimator PEST to minimize the model efficiency. The two approaches are evaluated by applying the WetSpa model to the Hornad River (Slovakia for which observations of daily precipitation, temperature, potential evapotranspiration, and discharge are available for a 10 year period (1991–2000. The first 5 years of the data series are used for model calibration, while the second 5 years for model validation. The results revealed that the quality of the solutions obtained with NSGA-II is comparable or even better to what can be obtained with PEST, considering the same assumptions. Hence, NSGA-II is capable of locating Pareto optimal solutions in the parameter search space and the results obtained prove the excellent performance of the multi-objective model calibration methodology.
Regularity of solutions of a phase field model
Amler, Thomas
2013-01-01
Phase field models are widely-used for modelling phase transition processes such as solidification, freezing or CO2 sequestration. In this paper, a phase field model proposed by G. Caginalp is considered. The existence and uniqueness of solutions are proved in the case of nonsmooth initial data. Continuity of solutions with respect to time is established. In particular, it is shown that the governing initial boundary value problem can be considered as a dynamical system. © 2013 International Press.
Modelling environmental dynamics. Advances in goematic solutions
Energy Technology Data Exchange (ETDEWEB)
Paegelow, Martin [Toulouse-2 Univ., 31 (France). GEODE UMR 5602 CNRS; Camacho Olmedo, Maria Teresa (eds.) [Granada Univ (Spain). Dpto. de Analisis Geografico Regional y Geografia Fisica
2008-07-01
Modelling environmental dynamics is critical to understanding and predicting the evolution of the environment in response to the large number of influences including urbanisation, climate change and deforestation. Simulation and modelling provide support for decision making in environmental management. The first chapter introduces terminology and provides an overview of methodological modelling approaches which may be applied to environmental and complex dynamics. Based on this introduction this book illustrates various models applied to a large variety of themes: deforestation in tropical regions, fire risk, natural reforestation in European mountains, agriculture, biodiversity, urbanism, climate change and land management for decision support, etc. These case studies, provided by a large international spectrum of researchers and presented in a uniform structure, focus particularly on methods and model validation so that this book is not only aimed at researchers and graduates but also at professionals. (orig.)
Analytical solutions for the Rabi model
Yu, Lixian; Liang, Qifeng; Chen, Gang; Jia, Suotang
2012-01-01
The Rabi model that describes the fundamental interaction between a two-level system with a quantized harmonic oscillator is one of the simplest and most ubiquitous models in modern physics. However, this model has not been solved exactly because it is hard to find a second conserved quantity besides the energy. Here we present a unitary transformation to map this unsolvable Rabi model into a solvable Jaynes-Cummings-like model by choosing a proper variation parameter. As a result, the analytical energy spectrums and wavefunctions including both the ground and the excited states can be obtained easily. Moreover, these explicit results agree well with the direct numerical simulations in a wide range of the experimental parameters. In addition, based on our obtained energy spectrums, the recent experimental observation of Bloch-Siegert in the circuit quantum electrodynamics with the ultrastrong coupling can be explained perfectly. Our results have the potential application in the solid-state quantum information...
The WITCH Model. Structure, Baseline, Solutions.
Energy Technology Data Exchange (ETDEWEB)
Bosetti, V.; Massetti, E.; Tavoni, M.
2007-07-01
WITCH - World Induced Technical Change Hybrid - is a regionally disaggregated hard link hybrid global model with a neoclassical optimal growth structure (top down) and an energy input detail (bottom up). The model endogenously accounts for technological change, both through learning curves affecting prices of new vintages of capital and through R and D investments. The model features the main economic and environmental policies in each world region as the outcome of a dynamic game. WITCH belongs to the class of Integrated Assessment Models as it possesses a climate module that feeds climate changes back into the economy. In this paper we provide a thorough discussion of the model structure and baseline projections. We report detailed information on the evolution of energy demand, technology and CO2 emissions. Finally, we explicitly quantifiy the role of free riding in determining the emissions scenarios. (auth)
Optimizing ion channel models using a parallel genetic algorithm on graphical processors.
Ben-Shalom, Roy; Aviv, Amit; Razon, Benjamin; Korngreen, Alon
2012-01-01
We have recently shown that we can semi-automatically constrain models of voltage-gated ion channels by combining a stochastic search algorithm with ionic currents measured using multiple voltage-clamp protocols. Although numerically successful, this approach is highly demanding computationally, with optimization on a high performance Linux cluster typically lasting several days. To solve this computational bottleneck we converted our optimization algorithm for work on a graphical processing unit (GPU) using NVIDIA's CUDA. Parallelizing the process on a Fermi graphic computing engine from NVIDIA increased the speed ∼180 times over an application running on an 80 node Linux cluster, considerably reducing simulation times. This application allows users to optimize models for ion channel kinetics on a single, inexpensive, desktop "super computer," greatly reducing the time and cost of building models relevant to neuronal physiology. We also demonstrate that the point of algorithm parallelization is crucial to its performance. We substantially reduced computing time by solving the ODEs (Ordinary Differential Equations) so as to massively reduce memory transfers to and from the GPU. This approach may be applied to speed up other data intensive applications requiring iterative solutions of ODEs.
A new Gibbs sampling based algorithm for Bayesian model updating with incomplete complex modal data
Cheung, Sai Hung; Bansal, Sahil
2017-08-01
Model updating using measured system dynamic response has a wide range of applications in system response evaluation and control, health monitoring, or reliability and risk assessment. In this paper, we are interested in model updating of a linear dynamic system with non-classical damping based on incomplete modal data including modal frequencies, damping ratios and partial complex mode shapes of some of the dominant modes. In the proposed algorithm, the identification model is based on a linear structural model where the mass and stiffness matrix are represented as a linear sum of contribution of the corresponding mass and stiffness matrices from the individual prescribed substructures, and the damping matrix is represented as a sum of individual substructures in the case of viscous damping, in terms of mass and stiffness matrices in the case of Rayleigh damping or a combination of the former. To quantify the uncertainties and plausibility of the model parameters, a Bayesian approach is developed. A new Gibbs-sampling based algorithm is proposed that allows for an efficient update of the probability distribution of the model parameters. In addition to the model parameters, the probability distribution of complete mode shapes is also updated. Convergence issues and numerical issues arising in the case of high-dimensionality of the problem are addressed and solutions to tackle these problems are proposed. The effectiveness and efficiency of the proposed method are illustrated by numerical examples with complex modes.
Original analytic solution of a half-bridge modelled as a statically indeterminate system
Oanta, Emil M.; Panait, Cornel; Raicu, Alexandra; Barhalescu, Mihaela
2016-12-01
The paper presents an original computer based analytical model of a half-bridge belonging to a circular settling tank. The primary unknown is computed using the force method, the coefficients of the canonical equation being calculated using either the discretization of the bending moment diagram in trapezoids, or using the relations specific to the polygons. A second algorithm based on the method of initial parameters is also presented. Analyzing the new solution we came to the conclusion that most of the computer code developed for other model may be reused. The results are useful to evaluate the behavior of the structure and to compare with the results of the finite element models.
Bayesian Reliability Modeling and Assessment Solution for NC Machine Tools under Small-sample Data
Institute of Scientific and Technical Information of China (English)
YANG Zhaojun; KAN Yingnan; CHEN Fei; XU Binbin; CHEN Chuanhai; YANG Chuangui
2015-01-01
Although Markov chain Monte Carlo(MCMC) algorithms are accurate, many factors may cause instability when they are utilized in reliability analysis; such instability makes these algorithms unsuitable for widespread engineering applications. Thus, a reliability modeling and assessment solution aimed at small-sample data of numerical control(NC) machine tools is proposed on the basis of Bayes theories. An expert-judgment process of fusing multi-source prior information is developed to obtain the Weibull parameters’ prior distributions and reduce the subjective bias of usual expert-judgment methods. The grid approximation method is applied to two-parameter Weibull distribution to derive the formulas for the parameters’ posterior distributions and solve the calculation difficulty of high-dimensional integration. The method is then applied to the real data of a type of NC machine tool to implement a reliability assessment and obtain the mean time between failures(MTBF). The relative error of the proposed method is 5.8020×10-4 compared with the MTBF obtained by the MCMC algorithm. This result indicates that the proposed method is as accurate as MCMC. The newly developed solution for reliability modeling and assessment of NC machine tools under small-sample data is easy, practical, and highly suitable for widespread application in the engineering field; in addition, the solution does not reduce accuracy.
Modeling the Swift BAT Trigger Algorithm with Machine Learning
Graff, Philip B.; Lien, Amy Y.; Baker, John G.; Sakamoto, Takanori
2015-01-01
To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift BAT triggering algorithm for long GRBs, a computationally expensive procedure, and models it using machine learning algorithms. A large sample of simulated GRBs from Lien et al. (2014) is used to train various models: random forests, boosted decision trees (with AdaBoost), support vector machines, and artificial neural networks. The best models have accuracies of approximately greater than 97% (approximately less than 3% error), which is a significant improvement on a cut in GRB flux which has an accuracy of 89:6% (10:4% error). These models are then used to measure the detection efficiency of Swift as a function of redshift z, which is used to perform Bayesian parameter estimation on the GRB rate distribution. We find a local GRB rate density of eta(sub 0) approximately 0.48(+0.41/-0.23) Gpc(exp -3) yr(exp -1) with power-law indices of eta(sub 1) approximately 1.7(+0.6/-0.5) and eta(sub 2) approximately -5.9(+5.7/-0.1) for GRBs above and below a break point of z(sub 1) approximately 6.8(+2.8/-3.2). This methodology is able to improve upon earlier studies by more accurately modeling Swift detection and using this for fully Bayesian model fitting. The code used in this is analysis is publicly available online.
Modeling the Swift Bat Trigger Algorithm with Machine Learning
Graff, Philip B.; Lien, Amy Y.; Baker, John G.; Sakamoto, Takanori
2016-01-01
To draw inferences about gamma-ray burst (GRB) source populations based on Swift observations, it is essential to understand the detection efficiency of the Swift burst alert telescope (BAT). This study considers the problem of modeling the Swift / BAT triggering algorithm for long GRBs, a computationally expensive procedure, and models it using machine learning algorithms. A large sample of simulated GRBs from Lien et al. is used to train various models: random forests, boosted decision trees (with AdaBoost), support vector machines, and artificial neural networks. The best models have accuracies of greater than or equal to 97 percent (less than or equal to 3 percent error), which is a significant improvement on a cut in GRB flux, which has an accuracy of 89.6 percent (10.4 percent error). These models are then used to measure the detection efficiency of Swift as a function of redshift z, which is used to perform Bayesian parameter estimation on the GRB rate distribution. We find a local GRB rate density of n (sub 0) approaching 0.48 (sup plus 0.41) (sub minus 0.23) per cubic gigaparsecs per year with power-law indices of n (sub 1) approaching 1.7 (sup plus 0.6) (sub minus 0.5) and n (sub 2) approaching minus 5.9 (sup plus 5.7) (sub minus 0.1) for GRBs above and below a break point of z (redshift) (sub 1) approaching 6.8 (sup plus 2.8) (sub minus 3.2). This methodology is able to improve upon earlier studies by more accurately modeling Swift detection and using this for fully Bayesian model fitting.
Eikonal solutions to optical model coupled-channel equations
Cucinotta, Francis A.; Khandelwal, Govind S.; Maung, Khin M.; Townsend, Lawrence W.; Wilson, John W.
1988-01-01
Methods of solution are presented for the Eikonal form of the nucleus-nucleus coupled-channel scattering amplitudes. Analytic solutions are obtained for the second-order optical potential for elastic scattering. A numerical comparison is made between the first and second order optical model solutions for elastic and inelastic scattering of H-1 and He-4 on C-12. The effects of bound-state excitations on total and reaction cross sections are also estimated.
Stochastic geometry, spatial statistics and random fields models and algorithms
2015-01-01
Providing a graduate level introduction to various aspects of stochastic geometry, spatial statistics and random fields, this volume places a special emphasis on fundamental classes of models and algorithms as well as on their applications, for example in materials science, biology and genetics. This book has a strong focus on simulations and includes extensive codes in Matlab and R, which are widely used in the mathematical community. It can be regarded as a continuation of the recent volume 2068 of Lecture Notes in Mathematics, where other issues of stochastic geometry, spatial statistics and random fields were considered, with a focus on asymptotic methods.
Space resection model calculation based on Random Sample Consensus algorithm
Liu, Xinzhu; Kang, Zhizhong
2016-03-01
Resection has been one of the most important content in photogrammetry. It aims at the position and attitude information of camera at the shooting point. However in some cases, the observed values for calculating are with gross errors. This paper presents a robust algorithm that using RANSAC method with DLT model can effectually avoiding the difficulties to determine initial values when using co-linear equation. The results also show that our strategies can exclude crude handicap and lead to an accurate and efficient way to gain elements of exterior orientation.
Load-balancing algorithms for the parallel community climate model
Energy Technology Data Exchange (ETDEWEB)
Foster, I.T.; Toonen, B.R.
1995-01-01
Implementations of climate models on scalable parallel computer systems can suffer from load imbalances resulting from temporal and spatial variations in the amount of computation required for physical parameterizations such as solar radiation and convective adjustment. We have developed specialized techniques for correcting such imbalances. These techniques are incorporated in a general-purpose, programmable load-balancing library that allows the mapping of computation to processors to be specified as a series of maps generated by a programmer-supplied load-balancing module. The communication required to move from one map to another is performed automatically by the library, without programmer intervention. In this paper, we describe the load-balancing problem and the techniques that we have developed to solve it. We also describe specific load-balancing algorithms that we have developed for PCCM2, a scalable parallel implementation of the Community Climate Model, and present experimental results that demonstrate the effectiveness of these algorithms on parallel computers. The load-balancing library developed in this work is available for use in other climate models.
Solutions manual to accompany finite mathematics models and applications
Morris, Carla C
2015-01-01
A solutions manual to accompany Finite Mathematics: Models and Applications In order to emphasize the main concepts of each chapter, Finite Mathematics: Models and Applications features plentiful pedagogical elements throughout such as special exercises, end notes, hints, select solutions, biographies of key mathematicians, boxed key principles, a glossary of important terms and topics, and an overview of use of technology. The book encourages the modeling of linear programs and their solutions and uses common computer software programs such as LINDO. In addition to extensive chapters on pr
Nonpertubative Solutions of Massless Gauged Thirring Model
Bufalo, R.; Casana, R.; Pimentel, B. M.
2010-11-01
We present a nonperturbative quantization of the two-dimensional massless gauged Thirring model by using the path-integral approach. First, we will study the constraint structure of model via the Dirac's formalism and by using the Faddeev-Senjanovic method we calculate the vacuum-vacuum transition amplitude in a Rξ-gauge, then we compute the Green's functions in a nonperturbative framework.
Librino, Federico; Zorzi, Michele
2012-01-01
A novel iterative algorithm for the efficient computation of the intersection areas of an arbitrary number of circles is presented. The algorithm, based on a trellis-structure, hinges on two geometric results which allow the existence-check and the computation of the area of the intersection regions generated by more than three circles by simple algebraic manipulations of the intersection areas of a smaller number of circles. The presented algorithm is a powerful tool for the performance analysis of wireless networks, and finds many applications, ranging from sensor to cellular networks. As an example of practical application, an insightful study of the uplink outage probability of in a wireless network with cooperative access points as a function of the transmission power and access point density is presented.
Current algorithmic solutions for peptide-based proteomics data generation and identification.
Hoopmann, Michael R; Moritz, Robert L
2013-02-01
Peptide-based proteomic data sets are ever increasing in size and complexity. These data sets provide computational challenges when attempting to quickly analyze spectra and obtain correct protein identifications. Database search and de novo algorithms must consider high-resolution MS/MS spectra and alternative fragmentation methods. Protein inference is a tricky problem when analyzing large data sets of degenerate peptide identifications. Combining multiple algorithms for improved peptide identification puts significant strain on computational systems when investigating large data sets. This review highlights some of the recent developments in peptide and protein identification algorithms for analyzing shotgun mass spectrometry data when encountering the aforementioned hurdles. Also explored are the roles that analytical pipelines, public spectral libraries, and cloud computing play in the evolution of peptide-based proteomics.
Directory of Open Access Journals (Sweden)
Chunhua Ju
2012-01-01
Full Text Available Managing multiple project is a complex task involving the unrelenting pressures of time and cost. Many studies have proposed various tools and techniques for single-project scheduling; however, the literature further considering multimode or multiproject issues occurring in the real world is rather scarce. In this paper, design structure matrix (DSM and an improved artificial immune network algorithm (aiNet are developed to solve a multi-mode resource-constrained scheduling problem. Firstly, the DSM is used to simplify the mathematic model of multi-project scheduling problem. Subsequently, aiNet algorithm comprised of clonal selection, negative selection, and network suppression is adopted to realize the local searching and global searching, which will assure that it has a powerful searching ability and also avoids the possible combinatorial explosion. Finally, the approach is tested on a set of randomly cases generated from ProGen. The computational results validate the effectiveness of the proposed algorithm comparing with other famous metaheuristic algorithms such as genetic algorithm (GA, simulated annealing algorithm (SA, and ant colony optimization (ACO.
Directory of Open Access Journals (Sweden)
Issa Ahmed Abed
2016-12-01
Full Text Available This paper present a method to enhance the firefly algorithm by coupling with a local search. The constructed technique is applied to identify the solar parameters model where the method has been proved its ability to obtain the photovoltaic parameters model. Standard firefly algorithm (FA, electromagnetism-like (EM algorithm, and electromagnetism-like without local (EMW search algorithm all are compared with the suggested method to test its capability to solve this model.
Analytical solutions of the lattice Boltzmann BGK model
Zou, Q; Doolen, G D; Zou, Qisu; Hou, Shuling; Doolen, Gary D.
1995-01-01
Abstract: Analytical solutions of the two dimensional triangular and square lattice Boltzmann BGK models have been obtained for the plain Poiseuille flow and the plain Couette flow. The analytical solutions are written in terms of the characteristic velocity of the flow, the single relaxation time representation of these two flows without any approximation.
Positive Solutions for a Competition Model with an Inhibitor Involved
Institute of Scientific and Technical Information of China (English)
Bin Chen
2008-01-01
In the paper, we study the positive solutions of a diffusive competition model with an inhibitor involved subject to the homogeneous Dirichlet boundary condition. The existence, uniqueness, stability and multiplicity of positive solutions are discussed. This is mainly done by using the local and global bifurcation theory.
SOLUTION OF NONLINEAR PROBLEMS IN WATER RESOURCES SYSTEMS BY GENETIC ALGORITHM
Directory of Open Access Journals (Sweden)
Ahmet BAYLAR
1998-03-01
Full Text Available Genetic Algorithm methodology is a genetic process treated on computer which is considering evolution process in the nature. The genetic operations takes place within the chromosomes stored in computer memory. By means of various operators, the genetic knowledge in chromosomes change continuously and success of the community progressively increases as a result of these operations. The primary purpose of this study is calculation of nonlinear programming problems in water resources systems by Genetic Algorithm. For this purpose a Genetic Algoritm based optimization program were developed. It can be concluded that the results obtained from the genetic search based method give the precise results.
A Sequential Quadratic Programming Algorithm Using An Incomplete Solution of the Subproblem
1993-05-01
Research Stanford University tDept. de Estadistica y Econometria Universidad Carlos III de Madrid Abstract We analyze sequential quadratic programming...xEK- NP s.t. c(x) > 0, where F : R " --+ R and c : R1 --+ Rm. Since we shall not assume second derivatives are known, computing x*, a point satisfying...algorithm We first present an outline of the algorithm. Given Ho positive definite, z0 and A0, select P-1 0 O, 0 < a < r 1,/7 < > jc-(xo)lo,, _ Ž IIAoll
A sequential quadratic programming algorithm using an incomplete solution of the subproblem
Energy Technology Data Exchange (ETDEWEB)
Murray, W. [Stanford Univ., CA (United States). Systems Optimization Lab.; Prieto, F.J. [Universidad `Carlos III` de Madrid (Spain). Dept. de Estadistica y Econometria
1993-05-01
We analyze sequential quadratic programming (SQP) methods to solve nonlinear constrained optimization problems that are more flexible in their definition than standard SQP methods. The type of flexibility introduced is motivated by the necessity to deviate from the standard approach when solving large problems. Specifically we no longer require a minimizer of the QP subproblem to be determined or particular Lagrange multiplier estimates to be used. Our main focus is on an SQP algorithm that uses a particular augmented Lagrangian merit function. New results are derived for this algorithm under weaker conditions than previously assumed; in particular, it is not assumed that the iterates lie on a compact set.
Manufactured analytical solutions for isothermal full-Stokes ice sheet models
Directory of Open Access Journals (Sweden)
A. Sargent
2010-04-01
Full Text Available We present the detailed construction of an exact solution to time-dependent and steady-state isothermal full-Stokes ice sheet problems. The solutions are constructed for two-dimensional flowline and three-dimensional full-Stokes ice sheet models with variable viscosity. The construction is done by choosing for the specified ice surface and bed a velocity distribution that satisfies both mass conservation and the kinematic boundary conditions. Then a compensatory stress term in the conservation of momentum equations and their boundary conditions is calculated to make the chosen velocity distributions as well as the chosen pressure field into exact solutions. By substituting different ice surface and bed geometry formulas into the derived solution formulas, analytical solutions for different geometries can be constructed.
The boundary conditions can be specified as essential Dirichlet conditions or as periodic boundary conditions. By changing a parameter value, the analytical solutions allow investigation of algorithms for a different range of aspect ratios as well as for different, frozen or sliding, basal conditions. The analytical solutions can also be used to estimate the numerical error of the method in the case when the effects of the boundary conditions are eliminated, that is, when the exact solution values are specified as inflow and outflow boundary conditions.
Manufactured analytical solutions for isothermal full-Stokes ice sheet models
Directory of Open Access Journals (Sweden)
A. Sargent
2010-08-01
Full Text Available We present the detailed construction of a manufactured analytical solution to time-dependent and steady-state isothermal full-Stokes ice sheet problems. The solutions are constructed for two-dimensional flowline and three-dimensional full-Stokes ice sheet models with variable viscosity. The construction is done by choosing for the specified ice surface and bed a velocity distribution that satisfies both mass conservation and the kinematic boundary conditions. Then a compensatory stress term in the conservation of momentum equations and their boundary conditions is calculated to make the chosen velocity distributions as well as the chosen pressure field into exact solutions. By substituting different ice surface and bed geometry formulas into the derived solution formulas, analytical solutions for different geometries can be constructed.
The boundary conditions can be specified as essential Dirichlet conditions or as periodic boundary conditions. By changing a parameter value, the analytical solutions allow investigation of algorithms for a different range of aspect ratios as well as for different, frozen or sliding, basal conditions. The analytical solutions can also be used to estimate the numerical error of the method in the case when the effects of the boundary conditions are eliminated, that is, when the exact solution values are specified as inflow and outflow boundary conditions.
El-Ajou, Ahmad; Arqub, Omar Abu; Momani, Shaher
2015-07-01
In this paper, explicit and approximate solutions of the nonlinear fractional KdV-Burgers equation with time-space-fractional derivatives are presented and discussed. The solutions of our equation are calculated in the form of rabidly convergent series with easily computable components. The utilized method is a numerical technique based on the generalized Taylor series formula which constructs an analytical solution in the form of a convergent series. Five illustrative applications are given to demonstrate the effectiveness and the leverage of the present method. Graphical results and series formulas are utilized and discussed quantitatively to illustrate the solution. The results reveal that the method is very effective and simple in determination of solution of the fractional KdV-Burgers equation.
Some Standard model problems and possible solutions
Barranco, J.
2016-10-01
Three problems of the standard model of elementary particles are studied from a phenomenological approach. (i) It is shown that the Dirac or the Majorana nature of the neutrino can be studied by looking for differences in the v-electron scattering if the polarization of the neutrino is considered. (ii) The absolute scale of the neutrino mass can be set if a four zero mass matrix texture is considered for the leptons. It is found that m ν3 ∼⃒ 0.05 eV. (iii) It is shown that it is possible -within a certain class of two Higgs model extensions of the standard model- to have a cancelation of the quadratic divergences to the mass of physical Higgs boson.
Model-checking mean-field models: algorithms & applications
Kolesnichenko, Anna Victorovna
2014-01-01
Large systems of interacting objects are highly prevalent in today's world. In this thesis we primarily address such large systems in computer science. We model such large systems using mean-field approximation, which allows to compute the limiting behaviour of an infinite population of identical o
Huang, Junqi; Goltz, Mark N.
2017-06-01
To greatly simplify their solution, the equations describing radial advective/dispersive transport to an extraction well in a porous medium typically neglect molecular diffusion. While this simplification is appropriate to simulate transport in the saturated zone, it can result in significant errors when modeling gas phase transport in the vadose zone, as might be applied when simulating a soil vapor extraction (SVE) system to remediate vadose zone contamination. A new analytical solution for the equations describing radial gas phase transport of a sorbing contaminant to an extraction well is presented. The equations model advection, dispersion (including both mechanical dispersion and molecular diffusion), and rate-limited mass transfer of dissolved, separate phase, and sorbed contaminants into the gas phase. The model equations are analytically solved by using the Laplace transform with respect to time. The solutions are represented by confluent hypergeometric functions in the Laplace domain. The Laplace domain solutions are then evaluated using a numerical Laplace inversion algorithm. The solutions can be used to simulate the spatial distribution and the temporal evolution of contaminant concentrations during operation of a soil vapor extraction well. Results of model simulations show that the effect of gas phase molecular diffusion upon concentrations at the extraction well is relatively small, although the effect upon the distribution of concentrations in space is significant. This study provides a tool that can be useful in designing SVE remediation strategies, as well as verifying numerical models used to simulate SVE system performance.
D'onorio de Meo, Marco; Oh, Suhk Kun
1992-07-01
The problem of applying Wolff's cluster algorithm to anisotropic classical spin models is resolved by modifying a part of the Wolff algorithm. To test the effectiveness of our modified algorithm, the spin-van der Waals model is investigated in detail. Our estimate of the dynamical exponent of the model is z=0.19+/-0.04.
Periodic Solutions of a Model of Mitosis in Frog Eggs
Institute of Scientific and Technical Information of China (English)
Bei-ye Feng; Zuo-huan Zheng
2002-01-01
In this paper,we discuss a simplified model of mitosis in frog eggs proposed by M.T. Borisuk and J.J.Tyson in [1]. By using rigorous qualitative analysis, we prove the existence of the periodic solutions on a large scale and present the space region of the periodic solutions and the parameter region coresponding to the periodic solution. We also present the space region and the parameter region where there are no periodic solutions. The results are in accordance with the numerical results in [1] up to the qualitative property.
Epidemic Modelling by Ripple-Spreading Network and Genetic Algorithm
Directory of Open Access Journals (Sweden)
Jian-Qin Liao
2013-01-01
Full Text Available Mathematical analysis and modelling is central to infectious disease epidemiology. This paper, inspired by the natural ripple-spreading phenomenon, proposes a novel ripple-spreading network model for the study of infectious disease transmission. The new epidemic model naturally has good potential for capturing many spatial and temporal features observed in the outbreak of plagues. In particular, using a stochastic ripple-spreading process simulates the effect of random contacts and movements of individuals on the probability of infection well, which is usually a challenging issue in epidemic modeling. Some ripple-spreading related parameters such as threshold and amplifying factor of nodes are ideal to describe the importance of individuals’ physical fitness and immunity. The new model is rich in parameters to incorporate many real factors such as public health service and policies, and it is highly flexible to modifications. A genetic algorithm is used to tune the parameters of the model by referring to historic data of an epidemic. The well-tuned model can then be used for analyzing and forecasting purposes. The effectiveness of the proposed method is illustrated by simulation results.
A MATLAB GUI based algorithm for modelling Magnetotelluric data
Timur, Emre; Onsen, Funda
2016-04-01
The magnetotelluric method is an electromagnetic survey technique that images the electrical resistivity distribution of layers in subsurface depths. Magnetotelluric method measures simultaneously total electromagnetic field components such as both time-varying magnetic field B(t) and induced electric field E(t). At the same time, forward modeling of magnetotelluric method is so beneficial for survey planning purpose, for comprehending the method, especially for students, and as part of an iteration process in inverting measured data. The MTINV program can be used to model and to interpret geophysical electromagnetic (EM) magnetotelluric (MT) measurements using a horizontally layered earth model. This program uses either the apparent resistivity and phase components of the MT data together or the apparent resistivity data alone. Parameter optimization, which is based on linearized inversion method, can be utilized in 1D interpretations. In this study, a new MATLAB GUI based algorithm has been written for the 1D-forward modeling of magnetotelluric response function for multiple layers to use in educational studies. The code also includes an automatic Gaussian noise option for a demanded ratio value. Numerous applications were carried out and presented for 2,3 and 4 layer models and obtained theoretical data were interpreted using MTINV, in order to evaluate the initial parameters and effect of noise. Keywords: Education, Forward Modelling, Inverse Modelling, Magnetotelluric
"Updates to Model Algorithms & Inputs for the Biogenic ...
We have developed new canopy emission algorithms and land use data for BEIS. Simulations with BEIS v3.4 and these updates in CMAQ v5.0.2 are compared these changes to the Model of Emissions of Gases and Aerosols from Nature (MEGAN) and evaluated the simulations against observations. This has resulted in improvements in model evaluations of modeled isoprene, NOx, and O3. The National Exposure Research Laboratory (NERL) Atmospheric Modeling and Analysis Division (AMAD) conducts research in support of EPA mission to protect human health and the environment. AMAD research program is engaged in developing and evaluating predictive atmospheric models on all spatial and temporal scales for forecasting the air quality and for assessing changes in air quality and air pollutant exposures, as affected by changes in ecosystem management and regulatory decisions. AMAD is responsible for providing a sound scientific and technical basis for regulatory policies based on air quality models to improve ambient air quality. The models developed by AMAD are being used by EPA, NOAA, and the air pollution community in understanding and forecasting not only the magnitude of the air pollution problem, but also in developing emission control policies and regulations for air quality improvements.
Eavesdropping in a quantum secret sharing protocol based on Grover algorithm and its solution
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
A detailed analysis has showed that the quantum secret sharing protocol based on the Grover algorithm (Phys Rev A, 2003, 68: 022306) is insecure. A dishonest receiver may obtain the full information without being detected. A quantum secret-sharing protocol is presents here, which mends the security loophole of the original secret-sharing protocol, and doubles the information capacity.
Solution to the problem of ant being stuck by ant colony routing algorithm
Institute of Scientific and Technical Information of China (English)
ZHAO Jing; TONG Wei-ming
2009-01-01
Many ant colony routing (ACR) algorithms have been presented in recent years, but few have studied the problem that ants will get stuck with probability in any terminal host when they are searching paths to route packets around a network. The problem has to be faced when designing and implementing the ACR algorithm. This article analyzes in detail the differences between the ACR and the ant colony optimization (ACO). Besides, particular restrictions on the ACR are pointed out and the three causes of ant being-stuck problem are obtained. Furthermore, this article proposes a new ant searching mechanism through dual path-checking and online routing loop removing by every intermediate node an ant visited and the destination host respectively, to solve the problem of ant being stuck and routing loop simultaneously. The result of numerical simulation is abstracted from one real network. Compared with existing two typical ACR algorithms, it shows that the proposed algorithm can settle the problem of ant being stuck and achieve more effective searching outcome for optimization path.
Local Existence of Smooth Solutions to the FENE Dumbbell Model
Institute of Scientific and Technical Information of China (English)
Ge YANG
2012-01-01
The author proves the local existence of smooth solutions to the finite extensible nonlinear elasticity (FENE) dumbbell model of polymeric flows in some weighted spaces if the non-dimensional parameter b ＞ 2.
Melanoma prognostic model using tissue microarrays and genetic algorithms.
Gould Rothberg, Bonnie E; Berger, Aaron J; Molinaro, Annette M; Subtil, Antonio; Krauthammer, Michael O; Camp, Robert L; Bradley, William R; Ariyan, Stephan; Kluger, Harriet M; Rimm, David L
2009-12-01
As a result of the questionable risk-to-benefit ratio of adjuvant therapies, stage II melanoma is currently managed by observation because available clinicopathologic parameters cannot identify the 20% to 60% of such patients likely to develop metastatic disease. Here, we propose a multimarker molecular prognostic assay that can help triage patients at increased risk of recurrence. Protein expression for 38 candidates relevant to melanoma oncogenesis was evaluated using the automated quantitative analysis (AQUA) method for immunofluorescence-based immunohistochemistry in formalin-fixed, paraffin-embedded specimens from a cohort of 192 primary melanomas collected during 1959 to 1994. The prognostic assay was built using a genetic algorithm and validated on an independent cohort of 246 serial primary melanomas collected from 1997 to 2004. Multiple iterations of the genetic algorithm yielded a consistent five-marker solution. A favorable prognosis was predicted by ATF2 ln(non-nuclear/nuclear AQUA score ratio) of more than -0.052, p21(WAF1) nuclear compartment AQUA score of more than 12.98, p16(INK4A) ln(non-nuclear/nuclear AQUA score ratio) of < or = -0.083, beta-catenin total AQUA score of more than 38.68, and fibronectin total AQUA score of < or = 57.93. Primary tumors that met at least four of these five conditions were considered a low-risk group, and those that met three or fewer conditions formed a high-risk group (log-rank P < .0001). Multivariable proportional hazards analysis adjusting for clinicopathologic parameters shows that the high-risk group has significantly reduced survival on both the discovery (hazard ratio = 2.84; 95% CI, 1.46 to 5.49; P = .002) and validation (hazard ratio = 2.72; 95% CI, 1.12 to 6.58; P = .027) cohorts. This multimarker prognostic assay, an independent determinant of melanoma survival, might be beneficial in improving the selection of stage II patients for adjuvant therapy.
Institute of Scientific and Technical Information of China (English)
Xie Ping DING
2012-01-01
Some classes of mixed equilibrium problems and bilevel mixed equilibrium problems are introduced and studied in reflexive Banach spaces.First,by using a minimax inequality,some new existence results of solutious and the behavior of solution set for the mixed equilibrium problems and the bilevel mixed equilibrium problems are proved under suitable assumptions without the coercive conditions.Next,by using auxiliary principle technique,some new iterative algorithms for solving the mixed equilibrium problems and the bilevel mixed equilibrium problems are suggested and analyzed.The strong convergence of the iterative sequences generated by the proposed algorithms is proved under suitable assumptions without the coercive conditions.These results are new and generalize some recent results in this field.
Model Algorithm Research on Cooling Path Control of Hot-rolled Dual-phase Steel
Institute of Scientific and Technical Information of China (English)
Xiao-qing XU; Xiao-dong HAO; Shi-guang ZHOU; Chang-sheng LIU; Qi-fu ZHANG
2016-01-01
With the development of advanced high strength steel,especially for dual-phase steel,the model algorithm for cooling control after hot rolling has to achieve the targeted coiling temperature control at the location of downcoiler whilst maintaining the cooling path control based on strip microstructure along the whole cooling section.A cooling path control algorithm was proposed for the laminar cooling process as a solution to practical difficulties associated with the realization of the thermal cycle during cooling process.The heat conduction equation coupled with the carbon diffusion equation with moving boundary was employed in order to simulate temperature change and phase transfor-mation kinetics,making it possible to observe the temperature field and the phase fraction of the strip in real time. On this basis,an optimization method was utilized for valve settings to ensure the minimum deviations between the predicted and actual cooling path of the strip,taking into account the constraints of the cooling equipment′s specific capacity,cooling line length,etc.Results showed that the model algorithm was able to achieve the online cooling path control for dual-phase steel.
Institute of Scientific and Technical Information of China (English)
Liuhong Zhu; Gang Guo
2012-01-01
This study tested an improved fiber tracking algorithm, which was based on fiber assignment using a continuous tracking algorithm and a two-tensor model. Different models and tracking decisions were used by judging the type of estimation of each voxel. This method should solve the cross-track problem. This study included eight healthy subjects, two axonal injury patients and seven demyelinating disease patients. This new algorithm clearly exhibited a difference in nerve fiber direction between axonal injury and demyelinating disease patients and healthy control subjects. Compared with fiber assignment with a continuous tracking algorithm, our novel method can track more and longer nerve fibers, and also can solve the fiber crossing problem.
On black hole solutions in model with anisotropic fluid
Dehnen, H; Melnikov, V N
2003-01-01
A family of spherically symmetric solutions in the model with 1-component anisotropic fluid is considered. The metric of the solution depends on a parameter q > 0 relating radial pressure and the density and contains n -1 parameters corresponding to Ricci-flat ``internal space'' metrics. For q = 1 and certain equations of state the metric coincides with the metric of black brane solutions in the model with antisymmetric form. A family of black hole solutions corresponding to natural numbers q = 1,2, ... is singled out. Certain examples of solutions (e.g. containing for q =1 Reissner-Nordstr\\"{o}m, M2 and M5 black brane metrics) are considered. The post-Newtonian parameters beta and gamma corresponding to the 4-dimensional section of the metric are calculated.
NONSMOOTH MODEL FOR PLASTIC LIMIT ANALYSIS AND ITS SMOOTHING ALGORITHM
Institute of Scientific and Technical Information of China (English)
LI Jian-yu; PAN Shao-hua; LI Xing-si
2006-01-01
By means of Lagrange duality theory of the convex program, a dual problem of Hill's maximum plastic work principle under Mises' yield condition has been derived and whereby a non-differentiable convex optimization model for the limit analysis is developed. With this model, it is not necessary to linearize the yield condition and its discrete form becomes a minimization problem of the sum of Euclidean norms subject to linear constraints. Aimed at resolving the non-differentiability of Euclidean norms, a smoothing algorithm for the limit analysis of perfect-plastic continuum media is proposed.Its efficiency is demonstrated by computing the limit load factor and the collapse state for some plane stress and plain strain problems.
Viscosity solutions for a polymer crystal growth model
Cardaliaguet, Pierre; Monteillet, Aurélien
2010-01-01
We prove existence of a solution for a polymer crystal growth model describing the movement of a front $(\\Gamma(t))$ evolving with a nonlocal velocity. In this model the nonlocal velocity is linked to the solution of a heat equation with source $\\delta_\\Gamma$. The proof relies on new regularity results for the eikonal equation, in which the velocity is positive but merely measurable in time and with H\\"{o}lder bounds in space. From this result, we deduce \\textit{a priori} regularity for the front. On the other hand, under this regularity assumption, we prove bounds and regularity estimates for the solution of the heat equation.
The Solution Construction of Heterotic Super-Liouville Model
Institute of Scientific and Technical Information of China (English)
YANG Zhan-Ying; ZHEN Yi
2001-01-01
We investigate the heterotic super-Liouville model on the base of the basic Lie super-algebra Osp(1|2).Using the super extension of Leznov-Saveliev analysis and Drinfeld Sokolov linear system, we construct the explicit solution of the heterotic super-Liouville system in component form. We also show that the solutions are local and periodic by calculating the exchange relation of the solution. Finally starting from the action of heterotic super-Liou ville model, we obtain the conserved current and conserved charge which possessed the BR ST properties.
Exact travelling wave solutions for some important nonlinear physical models
Indian Academy of Sciences (India)
Jonu Lee; Rathinasamy Sakthivel
2013-05-01
The two-dimensional nonlinear physical models and coupled nonlinear systems such as Maccari equations, Higgs equations and Schrödinger–KdV equations have been widely applied in many branches of physics. So, finding exact travelling wave solutions of such equations are very helpful in the theories and numerical studies. In this paper, the Kudryashov method is used to seek exact travelling wave solutions of such physical models. Further, three-dimensional plots of some of the solutions are also given to visualize the dynamics of the equations. The results reveal that the method is a very effective and powerful tool for solving nonlinear partial differential equations arising in mathematical physics.
Identification of Hammerstein Model Based on Quantum Genetic Algorithm
Directory of Open Access Journals (Sweden)
Zhang Hai Li
2013-07-01
Full Text Available Nonlinear system identification is a main topic of modern identification. A new method for nonlinear system identification is presented by using Quantum Genetic Algorithm(QGA.The problems of nonlinear system identification are cast as function optimization overprameter space，and the Quantum Genetic Algorithm is adopted to solve the optimization problem. Simulation experiments show that: compared with the genetic algorithm, quantum genetic algorithm is an effective swarm intelligence algorithm, its salient features of the algorithm parameters, small population size, and the use of Quantum gate update populations, greatly improving the recognition in the optimization of speed and accuracy. Simulation results show the effectiveness of the proposed method.
Kennedy, K.D.; Vries, E.T. de; Koorevaar, P.
1998-01-01
This paper presents results obtained from two different Dynamic Channel Allocation (DCA) algorithms, namely the Timid and Persistent Polite Aggressive (PPA) algorithms, simulated under both static homogeneous and dynamic inhomogeneous traffic. The dynamic inhomogeneous traffic is modelled upon real
Physics Based Model for Cryogenic Chilldown and Loading. Part I: Algorithm
Luchinsky, Dmitry G.; Smelyanskiy, Vadim N.; Brown, Barbara
2014-01-01
We report the progress in the development of the physics based model for cryogenic chilldown and loading. The chilldown and loading is model as fully separated non-equilibrium two-phase flow of cryogenic fluid thermally coupled to the pipe walls. The solution follow closely nearly-implicit and semi-implicit algorithms developed for autonomous control of thermal-hydraulic systems developed by Idaho National Laboratory. A special attention is paid to the treatment of instabilities. The model is applied to the analysis of chilldown in rapid loading system developed at NASA-Kennedy Space Center. The nontrivial characteristic feature of the analyzed chilldown regime is its active control by dump valves. The numerical predictions are in reasonable agreement with the experimental time traces. The obtained results pave the way to the development of autonomous loading operation on the ground and space.
Another solution of 2D Ising model
Vergeles, S. N.
2009-04-01
The partition function of the Ising model on a two-dimensional regular lattice is calculated by using the matrix representation of a Clifford algebra (the Dirac algebra), with number of generators equal to the number of lattice sites. It is shown that the partition function over all loops in a 2D lattice including self-intersecting ones is the trace of a polynomial in terms of Dirac matrices. The polynomial is an element of the rotation group in the spinor representation. Thus, the partition function is a function of a character on an orthogonal group of a high degree in the spinor representation.
Energy Technology Data Exchange (ETDEWEB)
Leimbach, Marian [Potsdam-Institut fuer Klimafolgenforschung e.V., Potsdam (Germany); Eisenack, Klaus [Oldenburg Univ. (Germany). Dept. of Economics and Statistics
2008-11-15
In this paper we present an algorithm that deals with trade interactions within a multi-region model. In contrast to traditional approaches this algorithm is able to handle spillover externalities. Technological spillovers are expected to foster the diffusion of new technologies, which helps to lower the cost of climate change mitigation. We focus on technological spillovers which are due to capital trade. The algorithm of finding a pareto-optimal solution in an intertemporal framework is embedded in a decomposed optimization process. The paper analyzes convergence and equilibrium properties of this algorithm. In the final part of the paper, we apply the algorithm to investigate possible impacts of technological spillovers. While benefits of technological spillovers are significant for the capital-importing region, benefits for the capital-exporting region depend on the type of regional disparities and the resulting specialization and terms-of-trade effects. (orig.)
Directory of Open Access Journals (Sweden)
A. S. M. Zahid Kausar
2014-01-01
Full Text Available Although ray tracing based propagation prediction models are popular for indoor radio wave propagation characterization, most of them do not provide an integrated approach for achieving the goal of optimum coverage, which is a key part in designing wireless network. In this paper, an accelerated technique of three-dimensional ray tracing is presented, where rough surface scattering is included for making a more accurate ray tracing technique. Here, the rough surface scattering is represented by microfacets, for which it becomes possible to compute the scattering field in all possible directions. New optimization techniques, like dual quadrant skipping (DQS and closest object finder (COF, are implemented for fast characterization of wireless communications and making the ray tracing technique more efficient. In conjunction with the ray tracing technique, probability based coverage optimization algorithm is accumulated with the ray tracing technique to make a compact solution for indoor propagation prediction. The proposed technique decreases the ray tracing time by omitting the unnecessary objects for ray tracing using the DQS technique and by decreasing the ray-object intersection time using the COF technique. On the other hand, the coverage optimization algorithm is based on probability theory, which finds out the minimum number of transmitters and their corresponding positions in order to achieve optimal indoor wireless coverage. Both of the space and time complexities of the proposed algorithm surpass the existing algorithms. For the verification of the proposed ray tracing technique and coverage algorithm, detailed simulation results for different scattering factors, different antenna types, and different operating frequencies are presented. Furthermore, the proposed technique is verified by the experimental results.
DEFF Research Database (Denmark)
Sousa, Tiago; Morais, Hugo; Castro, Rui
2016-01-01
. The proposed algorithms have been developed as modules to be more flexible their use by other metaheuristics than just simulated annealing. The simulated annealing with different initial solution algorithms has been tested in a 37-bus distribution network with distributed resources, especially electric...
Analysis and modeling of alkali halide aqueous solutions
DEFF Research Database (Denmark)
Kim, Sun Hyung; Anantpinijwatna, Amata; Kang, Jeong Won;
2016-01-01
A new model is proposed for correlation and prediction of thermodynamic properties of electrolyte solutions. In the proposed model, terms of a second virial coefficient-type and of a KT-UNIFAC model are used to account for a contribution of binary interactions between ion and ion, and water and ion...... on calculations for various electrolyte properties of alkali halide aqueous solutions such as mean ionic activity coefficients, osmotic coefficients, and salt solubilities. The model covers highly nonideal electrolyte systems such as lithium chloride, lithium bromide and lithium iodide, that is, systems...