Naeem, Huma; Hussain, Mukhtar; Khan, Shoab A
2009-01-01
Surveillance control and reporting (SCR) system for air threats play an important role in the defense of a country. SCR system corresponds to air and ground situation management/processing along with information fusion, communication, coordination, simulation and other critical defense oriented tasks. Threat Evaluation and Weapon Assignment (TEWA) sits at the core of SCR system. In such a system, maximal or near maximal utilization of constrained resources is of extreme importance. Manual TEWA systems cannot provide optimality because of different limitations e.g.surface to air missile (SAM) can fire from a distance of 5Km, but manual TEWA systems are constrained by human vision range and other constraints. Current TEWA systems usually work on target-by-target basis using some type of greedy algorithm thus affecting the optimality of the solution and failing in multi-target scenario. his paper relates to a novel two-staged flexible dynamic decision support based optimal threat evaluation and weapon assignment...
Symplectic algebraic dynamics algorithm
Institute of Scientific and Technical Information of China (English)
2007-01-01
Based on the algebraic dynamics solution of ordinary differential equations andintegration of ,the symplectic algebraic dynamics algorithm sn is designed,which preserves the local symplectic geometric structure of a Hamiltonian systemand possesses the same precision of the na ve algebraic dynamics algorithm n.Computer experiments for the 4th order algorithms are made for five test modelsand the numerical results are compared with the conventional symplectic geometric algorithm,indicating that sn has higher precision,the algorithm-inducedphase shift of the conventional symplectic geometric algorithm can be reduced,and the dynamical fidelity can be improved by one order of magnitude.
Next Generation Suspension Dynamics Algorithms
Energy Technology Data Exchange (ETDEWEB)
Schunk, Peter Randall [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Higdon, Jonathon [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Chen, Steven [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
2014-12-01
This research project has the objective to extend the range of application, improve the efficiency and conduct simulations with the Fast Lubrication Dynamics (FLD) algorithm for concentrated particle suspensions in a Newtonian fluid solvent. The research involves a combination of mathematical development, new computational algorithms, and application to processing flows of relevance in materials processing. The mathematical developments clarify the underlying theory, facilitate verification against classic monographs in the field and provide the framework for a novel parallel implementation optimized for an OpenMP shared memory environment. The project considered application to consolidation flows of major interest in high throughput materials processing and identified hitherto unforeseen challenges in the use of FLD in these applications. Extensions to the algorithm have been developed to improve its accuracy in these applications.
Parallel algorithms for robot dynamics
Energy Technology Data Exchange (ETDEWEB)
Barhen, J.; Babcock, S.M.
1984-01-01
The Department of Energy recently established a Center for Engineering Systems Advanced Research (CESAR) at the Oak Ridge National Laboratory (ORNL). The Center's charter is to conduct long-range energy-related research in intelligent control systems. This paper reports initial results in developing parallel algorithms for efficiency enhancement in real-time solutions of manipulator dynamics equations. Two approaches to the solution of the inverse dynamics problem are discussed. The first is concerned with the implementation of Newton-Euler equations in multiprocessor architecture with emphasis on asynchronous algorithms and interprocess communication. The alternative approach is based on an explicit state description of the manipulator dynamics, obtained using computer-assisted analytic simplifications of the symbolic Lagrange-Euler equations. Multicomputer and multiprocessor implementations are discussed. The construction of a compact knowledge-base in terms of associative memories is also suggested, to allow solutions of the inverse dynamics based on similarity. Future directions are also outlined. This research is an integral part of a large systems integration effort with complementary tasks in strategy planning, sensor fusion, etc.
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.
Naeem, Huma; Masood, Asif; Hussain, Mukhtar; Shoab A. Khan
2009-01-01
Surveillance control and reporting (SCR) system for air threats play an important role in the defense of a country. SCR system corresponds to air and ground situation management/processing along with information fusion, communication, coordination, simulation and other critical defense oriented tasks. Threat Evaluation and Weapon Assignment (TEWA) sits at the core of SCR system. In such a system, maximal or near maximal utilization of constrained resources is of extreme importance. Manual TEW...
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.
An empirical study of dynamic graph algorithms
Energy Technology Data Exchange (ETDEWEB)
Alberts, D. [Freie Universitaet Berlin (Germany); Cattaneo, G. [Universita di Salerno (Italy); Italiano, G.F. [Universita Ca Forscari di Venezia (Italy)
1996-12-31
We conduct an empirical study on some dynamic graph algorithms which where developed recently. The following implementations were tested and compared with simple algorithms: dynamic connectivity, and dynamic minimum 1 spanning tree based on sparsification by Eppstein et al.; dynamic connectivity based on a very recent paper by Henzinger and King. In our experiments, we considered both random and non-random inputs. Moreover, we present a simplified variant of the algorithm by Henzinger and King, which for random inputs was always faster than the original implementation. Indeed, this variant was among the fastest implementations for random inputs. For non-random inputs, sparsification was the fastest algorithm for small sequences of updates; for medium and large sequences of updates, the original algorithm by Henzinger and King was faster. Perhaps one of the main practical results of this paper is that our implementations of the sophisticated dynamic graph algorithms were faster than simpler algorithms for most practical values of the graph parameters, and competitive with simpler algorithms even in case of very small graphs (say graphs with less than a dozen vertices and edges). From the theoretical point of view, we analyze the average case running time of sparsification and prove that the logarithmic overhead for simple sparsification vanishes for dynamic random graphs.
Efficient Algorithms for Langevin and DPD Dynamics.
Goga, N; Rzepiela, A J; de Vries, A H; Marrink, S J; Berendsen, H J C
2012-10-09
In this article, we present several algorithms for stochastic dynamics, including Langevin dynamics and different variants of Dissipative Particle Dynamics (DPD), applicable to systems with or without constraints. The algorithms are based on the impulsive application of friction and noise, thus avoiding the computational complexity of algorithms that apply continuous friction and noise. Simulation results on thermostat strength and diffusion properties for ideal gas, coarse-grained (MARTINI) water, and constrained atomic (SPC/E) water systems are discussed. We show that the measured thermal relaxation rates agree well with theoretical predictions. The influence of various parameters on the diffusion coefficient is discussed.
Dynamic Shortest Path Algorithms for Hypergraphs
2012-01-01
Performance comparison of algorithms for the dynamic shortest path problem,” IEICE Transactions on Fundamentals of Electronics , Communications and...computation,” IEEE/ACM Transactions on Networking, vol. 8, no. 6, pp. 734–746, 2000. [8] G. Ramalingam and T. Reps, “An incremental algorithm for a...multihop performance,” IEEE Transactions on Mobile Computing, pp. 337–348, 2003. [17] S. Chachulski, M. Jennings, S. Katti, and D. Katabli, “Trading
Dynamic algorithms for the Dyck languages
DEFF Research Database (Denmark)
Frandsen, Gudmund Skovbjerg; Husfeldt, Thore; Miltersen, Peter Bro;
1995-01-01
We study Dynamic Membership problems for the Dyck languages, the class of strings of properly balanced parentheses. We also study the Dynamic Word problem for the free group. We present deterministic algorithms and data structures which maintain a string under replacements of symbols, insertions......, and deletions of symbols, and language membership queries. Updates and queries are handled in polylogarithmic time. We also give both Las Vegas- and Monte Carlo-type randomised algorithms to achieve better running times, and present lower bounds on the complexity for variants of the problems....
Intelligent control algorithm for ship dynamic positioning
Directory of Open Access Journals (Sweden)
Meng Wang
2014-12-01
Full Text Available Ship motion in the sea is a complex nonlinear kinematics. The hydrodynamic coefficients of ship model are very difficult to accurately determine. Establishing accurate mathematical model of ship motion is difficult because of changing random factors in the marine environment. Aiming at seeking a method of control to realize ship positioning, intelligent control algorithms are adopt utilizing operator's experience. Fuzzy controller and the neural network controller are respectively designed. Through simulations and experiments, intelligent control algorithm can deal with the complex nonlinear motion, and has good robustness. The ship dynamic positioning system with neural network control has high positioning accuracy and performance.
Nonequilibrium molecular dynamics theory, algorithms and applications
Todd, Billy D
2017-01-01
Written by two specialists with over twenty-five years of experience in the field, this valuable text presents a wide range of topics within the growing field of nonequilibrium molecular dynamics (NEMD). It introduces theories which are fundamental to the field - namely, nonequilibrium statistical mechanics and nonequilibrium thermodynamics - and provides state-of-the-art algorithms and advice for designing reliable NEMD code, as well as examining applications for both atomic and molecular fluids. It discusses homogenous and inhomogenous flows and pays considerable attention to highly confined fluids, such as nanofluidics. In addition to statistical mechanics and thermodynamics, the book covers the themes of temperature and thermodynamic fluxes and their computation, the theory and algorithms for homogenous shear and elongational flows, response theory and its applications, heat and mass transport algorithms, applications in molecular rheology, highly confined fluids (nanofluidics), the phenomenon of slip and...
An Improved Dynamic Bandwidth Allocation Algorithm for Ethernet PON
Institute of Scientific and Technical Information of China (English)
无
2003-01-01
This paper proposes an improved Dynamic Bandwidth Allocation (DBA) algorithm for EPON, which combines static and traditional dynamic allocation schemes. Simulation result shows that the proposed algorithm may effectively improve the performance of packet delay.
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.
Dynamic programming algorithms for biological sequence comparison.
Pearson, W R; Miller, W
1992-01-01
Efficient dynamic programming algorithms are available for a broad class of protein and DNA sequence comparison problems. These algorithms require computer time proportional to the product of the lengths of the two sequences being compared [O(N2)] but require memory space proportional only to the sum of these lengths [O(N)]. Although the requirement for O(N2) time limits use of the algorithms to the largest computers when searching protein and DNA sequence databases, many other applications of these algorithms, such as calculation of distances for evolutionary trees and comparison of a new sequence to a library of sequence profiles, are well within the capabilities of desktop computers. In particular, the results of library searches with rapid searching programs, such as FASTA or BLAST, should be confirmed by performing a rigorous optimal alignment. Whereas rapid methods do not overlook significant sequence similarities, FASTA limits the number of gaps that can be inserted into an alignment, so that a rigorous alignment may extend the alignment substantially in some cases. BLAST does not allow gaps in the local regions that it reports; a calculation that allows gaps is very likely to extend the alignment substantially. Although a Monte Carlo evaluation of the statistical significance of a similarity score with a rigorous algorithm is much slower than the heuristic approach used by the RDF2 program, the dynamic programming approach should take less than 1 hr on a 386-based PC or desktop Unix workstation. For descriptive purposes, we have limited our discussion to methods for calculating similarity scores and distances that use gap penalties of the form g = rk. Nevertheless, programs for the more general case (g = q+rk) are readily available. Versions of these programs that run either on Unix workstations, IBM-PC class computers, or the Macintosh can be obtained from either of the authors.
Fundamental algorithms in computational fluid dynamics
Pulliam, Thomas H
2014-01-01
Intended as a textbook for courses in computational fluid dynamics at the senior undergraduate or graduate level, this book is a follow-up to the book Fundamentals of Computational Fluid Dynamics by the same authors, which was published in the series Scientific Computation in 2001. Whereas the earlier book concentrated on the analysis of numerical methods applied to model equations, this new book concentrates on algorithms for the numerical solution of the Euler and Navier-Stokes equations. It focuses on some classical algorithms as well as the underlying ideas based on the latest methods. A key feature of the book is the inclusion of programming exercises at the end of each chapter based on the numerical solution of the quasi-one-dimensional Euler equations and the shock-tube problem. These exercises can be included in the context of a typical course, and sample solutions are provided in each chapter, so readers can confirm that they have coded the algorithms correctly.
An improved genetic algorithm with dynamic topology
Cai, Kai-Quan; Tang, Yan-Wu; Zhang, Xue-Jun; Guan, Xiang-Min
2016-12-01
The genetic algorithm (GA) is a nature-inspired evolutionary algorithm to find optima in search space via the interaction of individuals. Recently, researchers demonstrated that the interaction topology plays an important role in information exchange among individuals of evolutionary algorithm. In this paper, we investigate the effect of different network topologies adopted to represent the interaction structures. It is found that GA with a high-density topology ends up more likely with an unsatisfactory solution, contrarily, a low-density topology can impede convergence. Consequently, we propose an improved GA with dynamic topology, named DT-GA, in which the topology structure varies dynamically along with the fitness evolution. Several experiments executed with 15 well-known test functions have illustrated that DT-GA outperforms other test GAs for making a balance of convergence speed and optimum quality. Our work may have implications in the combination of complex networks and computational intelligence. Project supported by the National Natural Science Foundation for Young Scientists of China (Grant No. 61401011), the National Key Technologies R & D Program of China (Grant No. 2015BAG15B01), and the National Natural Science Foundation of China (Grant No. U1533119).
Enhanced Dynamic Algorithm of Genome Sequence Alignments
Directory of Open Access Journals (Sweden)
Arabi E. keshk
2014-05-01
Full Text Available The merging of biology and computer science has created a new field called computational biology that explore the capacities of computers to gain knowledge from biological data, bioinformatics. Computational biology is rooted in life sciences as well as computers, information sciences, and technologies. The main problem in computational biology is sequence alignment that is a way of arranging the sequences of DNA, RNA or protein to identify the region of similarity and relationship between sequences. This paper introduces an enhancement of dynamic algorithm of genome sequence alignment, which called EDAGSA. It is filling the three main diagonals without filling the entire matrix by the unused data. It gets the optimal solution with decreasing the execution time and therefore the performance is increased. To illustrate the effectiveness of optimizing the performance of the proposed algorithm, it is compared with the traditional methods such as Needleman-Wunsch, Smith-Waterman and longest common subsequence algorithms. Also, database is implemented for using the algorithm in multi-sequence alignments for searching the optimal sequence that matches the given sequence.
An Improved Artificial Immune Algorithm with a Dynamic Threshold
Institute of Scientific and Technical Information of China (English)
Zhang Qiao; Xu Xu; Liang Yan-chun
2006-01-01
An improved artificial immune algorithm with a dynamic threshold is presented. The calculation for the affinity function in the real-valued coding artificial immune algorithm is modified through considering the antibody's fitness and setting the dynamic threshold value. Numerical experiments show that compared with the genetic algorithm and the originally real-valued coding artificial immune algorithm, the improved algorithm possesses high speed of convergence and good performance for preventing premature convergence.
Domain decomposition algorithms and computational fluid dynamics
Chan, Tony F.
1988-01-01
Some of the new domain decomposition algorithms are applied to two model problems in computational fluid dynamics: the two-dimensional convection-diffusion problem and the incompressible driven cavity flow problem. First, a brief introduction to the various approaches of domain decomposition is given, and a survey of domain decomposition preconditioners for the operator on the interface separating the subdomains is then presented. For the convection-diffusion problem, the effect of the convection term and its discretization on the performance of some of the preconditioners is discussed. For the driven cavity problem, the effectiveness of a class of boundary probe preconditioners is examined.
A LEAP-FROG ALGORITHM FOR STOCHASTIC DYNAMICS
Van Gunsteren, W. F.; Berendsen, H. J. C.
1988-01-01
A third-order algorithm for stochastic dynamics (SD) simulations is proposed, identical to the powerful molecular dynamics leapfrog algorithm in the limit of infinitely small friction coefficient gamma. It belongs to the class of SD algorithms, in which the integration time step Delta t is not limit
Dynamic Correction Algorithm of Rolling Force in Plate Rolling
Institute of Scientific and Technical Information of China (English)
QIU Hong-lei; WANG Jun; HU Xian-lei; WANG Zhao-dong; WANG Guo-dong
2005-01-01
Based on the Shougang plat mill project, an on-line dynamic correction algorithm was analyzed. This algorithm can adjust model coefficients better because the reasonable correction is based on the measured and calculated rolling force. The results of application on site show that this on-line dynamic correction algorithm is effective.
Dynamic Data Updating Algorithm for Image Superresolution Reconstruction
Institute of Scientific and Technical Information of China (English)
TAN Bing; XU Qing; ZHANG Yan; XING Shuai
2006-01-01
A dynamic data updating algorithm for image superesolution is proposed. On the basis of Delaunay triangulation and its local updating property, this algorithm can update the changed region directly under the circumstances that only a part of the source images has been changed. For its high efficiency and adaptability, this algorithm can serve as a fast algorithm for image superesolution reconstruction.
Successful Community Midwives in Pakistan: An Asset-Based Approach.
Mumtaz, Zubia; Levay, Adrienne V; Bhatti, Afshan
2015-01-01
In response to the low levels of skilled birth attendance in rural Pakistan, the government introduced a new cadre of community midwives (CMWs) in 2006. Assessments to-date have found that these CMWs have yet to emerge as significant providers for a number of sociocultural, geographic and financial reasons. However, a small number of CMWs have managed to establish functional practices in the private sector in conservative, infrastructure-challenged rural contexts. With an objective to highlight "what are the successful CMWs doing right given their context?" this paper adopts an asset-based approach to explore the experiences of the Pakistani CMWs who have managed to overcome the barriers and practice. We drew upon ethnographic data that was collected as part of a larger mixed methods study conducted in 2011-2012 in districts Jhelum and Layyah, Pakistan. Thirty eight CMWs, 45 other health care providers, 20 policymakers, 78 women, 35 husbands and 23 older women were interviewed. CMW clinics and practices were observed. Our data showed that only eight 8 out of 38 CMWs sampled were active providers. Poverty as a push factor to work and intrinsic individual-level characteristics that enabled the CMWs to respond successfully to the demands of the midwifery profession in the private sector emerged as the two key themes. Household poverty pushed the CMWs to work in this perceived low-status occupation. Their families supported them since they became the breadwinners. The successful CMWs also had an intrinsic sense of what was required to establish a private practice; they exhibited professionalism, had strong business sense and provided respectful maternity care. The study provides insight into how the program might improve its functioning by adapting its recruitment criteria to ensure selection of right candidates.
Successful Community Midwives in Pakistan: An Asset-Based Approach.
Directory of Open Access Journals (Sweden)
Zubia Mumtaz
Full Text Available In response to the low levels of skilled birth attendance in rural Pakistan, the government introduced a new cadre of community midwives (CMWs in 2006. Assessments to-date have found that these CMWs have yet to emerge as significant providers for a number of sociocultural, geographic and financial reasons. However, a small number of CMWs have managed to establish functional practices in the private sector in conservative, infrastructure-challenged rural contexts. With an objective to highlight "what are the successful CMWs doing right given their context?" this paper adopts an asset-based approach to explore the experiences of the Pakistani CMWs who have managed to overcome the barriers and practice. We drew upon ethnographic data that was collected as part of a larger mixed methods study conducted in 2011-2012 in districts Jhelum and Layyah, Pakistan. Thirty eight CMWs, 45 other health care providers, 20 policymakers, 78 women, 35 husbands and 23 older women were interviewed. CMW clinics and practices were observed. Our data showed that only eight 8 out of 38 CMWs sampled were active providers. Poverty as a push factor to work and intrinsic individual-level characteristics that enabled the CMWs to respond successfully to the demands of the midwifery profession in the private sector emerged as the two key themes. Household poverty pushed the CMWs to work in this perceived low-status occupation. Their families supported them since they became the breadwinners. The successful CMWs also had an intrinsic sense of what was required to establish a private practice; they exhibited professionalism, had strong business sense and provided respectful maternity care. The study provides insight into how the program might improve its functioning by adapting its recruitment criteria to ensure selection of right candidates.
Domain decomposition algorithms and computation fluid dynamics
Chan, Tony F.
1988-01-01
In the past several years, domain decomposition was a very popular topic, partly motivated by the potential of parallelization. While a large body of theory and algorithms were developed for model elliptic problems, they are only recently starting to be tested on realistic applications. The application of some of these methods to two model problems in computational fluid dynamics are investigated. Some examples are two dimensional convection-diffusion problems and the incompressible driven cavity flow problem. The construction and analysis of efficient preconditioners for the interface operator to be used in the iterative solution of the interface solution is described. For the convection-diffusion problems, the effect of the convection term and its discretization on the performance of some of the preconditioners is discussed. For the driven cavity problem, the effectiveness of a class of boundary probe preconditioners is discussed.
Optimized Dynamical Decoupling via Genetic Algorithms
Quiroz, Gregory
2013-01-01
We utilize genetic algorithms to find optimal dynamical decoupling (DD) sequences for a single-qubit system subjected to a general decoherence model under a variety of control pulse conditions. We focus on the case of sequences with equal pulse-intervals and perform the optimization with respect to pulse type and order. In this manner we obtain robust DD sequences, first in the limit of ideal pulses, then when including pulse imperfections such as finite pulse duration and qubit rotation (flip-angle) errors. Although our optimization is numerical, we identify a deterministic structure underlies the top-performing sequences. We use this structure to devise DD sequences which outperform previously designed concatenated DD (CDD) and quadratic DD (QDD) sequences in the presence of pulse errors. We explain our findings using time-dependent perturbation theory and provide a detailed scaling analysis of the optimal sequences.
Online Assignment Algorithms for Dynamic Bipartite Graphs
Sahai, Ankur
2011-01-01
This paper analyzes the problem of assigning weights to edges incrementally in a dynamic complete bipartite graph consisting of producer and consumer nodes. The objective is to minimize the overall cost while satisfying certain constraints. The cost and constraints are functions of attributes of the edges, nodes and online service requests. Novelty of this work is that it models real-time distributed resource allocation using an approach to solve this theoretical problem. This paper studies variants of this assignment problem where the edges, producers and consumers can disappear and reappear or their attributes can change over time. Primal-Dual algorithms are used for solving these problems and their competitive ratios are evaluated.
Optimized dynamical decoupling via genetic algorithms
Quiroz, Gregory; Lidar, Daniel A.
2013-11-01
We utilize genetic algorithms aided by simulated annealing to find optimal dynamical decoupling (DD) sequences for a single-qubit system subjected to a general decoherence model under a variety of control pulse conditions. We focus on the case of sequences with equal pulse intervals and perform the optimization with respect to pulse type and order. In this manner, we obtain robust DD sequences, first in the limit of ideal pulses, then when including pulse imperfections such as finite-pulse duration and qubit rotation (flip-angle) errors. Although our optimization is numerical, we identify a deterministic structure that underlies the top-performing sequences. We use this structure to devise DD sequences which outperform previously designed concatenated DD (CDD) and quadratic DD (QDD) sequences in the presence of pulse errors. We explain our findings using time-dependent perturbation theory and provide a detailed scaling analysis of the optimal sequences.
An efficient dynamic load balancing algorithm
Lagaros, Nikos D.
2014-01-01
In engineering problems, randomness and uncertainties are inherent. Robust design procedures, formulated in the framework of multi-objective optimization, have been proposed in order to take into account sources of randomness and uncertainty. These design procedures require orders of magnitude more computational effort than conventional analysis or optimum design processes since a very large number of finite element analyses is required to be dealt. It is therefore an imperative need to exploit the capabilities of computing resources in order to deal with this kind of problems. In particular, parallel computing can be implemented at the level of metaheuristic optimization, by exploiting the physical parallelization feature of the nondominated sorting evolution strategies method, as well as at the level of repeated structural analyses required for assessing the behavioural constraints and for calculating the objective functions. In this study an efficient dynamic load balancing algorithm for optimum exploitation of available computing resources is proposed and, without loss of generality, is applied for computing the desired Pareto front. In such problems the computation of the complete Pareto front with feasible designs only, constitutes a very challenging task. The proposed algorithm achieves linear speedup factors and almost 100% speedup factor values with reference to the sequential procedure.
Genetic Algorithms in Dynamical Systems Optimisation and Adaptation
Reus, N.M. de; Visser, E.K.; Bruggeman, B.
1998-01-01
Both in the design of dynamical systems, ranging from control systems to state estimators as in the adaptation of these systems the use of genetic algorithms is worth studying. This paper presents some approaches for using genetic algorithms in dynamical systems. The layouts and specific uses are di
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
Distributed multicast routing algorithm with dynamic performance in multimedia networks
Institute of Scientific and Technical Information of China (English)
Zhu Baoping; Zhang Kun
2009-01-01
Tbe delay and DVBMT problem is known to be NP-complete. In this paper, an efficient distributed dynamic multicast muting algorithm was proposed to produce muting trees with delay and delay variation constraints. The pro-posed algorithm is fully distributed, and supports the dynamic reorganizing of the muhicast tree in response to changes for the destination. Simulations demonstrate that our algorithm is better in terms of tree delay and muting success ratio as compared with other existing algorithms, and performs excellently in delay variation performance under lower time complexity, which ensures it to support the requirements of real-time multimedia communications more effectively.
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.
A New Dynamical Evolutionary Algorithm Based on Statistical Mechanics
Institute of Scientific and Technical Information of China (English)
LI YuanXiang(李元香); ZOU XiuFen(邹秀芬); KANG LiShan(康立山); Zbigniew Michalewicz
2003-01-01
In this paper, a new dynamical evolutionary algorithm (DEA) is presented basedon the theory of statistical mechanics. The novelty of this kind of dynamical evolutionary algorithmis that all individuals in a population (called particles in a dynamical system) are running andsearching with their population evolving driven by a nev selecting mechanism. This mechanismsimulates the principle of molecular dynamics, which is easy to design and implement. A basictheoretical analysis for the dynamical evolutionary algorithm is given and as a consequence twostopping criteria of the algorithm are derived from the principle of energy minimization and the lawof entropy increasing. In order to verify the effectiveness of the scheme, DEA is applied to solvingsome typical numerical function minimization problems which are poorly solved by traditionalevolutionary algorithms. The experimental results show that DEA is fast and reliable.
DYNAMIC LABELING BASED FPGA DELAY OPTIMIZATION ALGORITHM
Institute of Scientific and Technical Information of China (English)
吕宗伟; 林争辉; 张镭
2001-01-01
DAG-MAP is an FPGA technology mapping algorithm for delay optimization and the labeling phase is the algorithm's kernel. This paper studied the labeling phase and presented an improved labeling method. It is shown through the experimental results on MCNC benchmarks that the improved method is more effective than the original method while the computation time is almost the same.
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...
Congested Link Inference Algorithms in Dynamic Routing IP Network
Directory of Open Access Journals (Sweden)
Yu Chen
2017-01-01
Full Text Available The performance descending of current congested link inference algorithms is obviously in dynamic routing IP network, such as the most classical algorithm CLINK. To overcome this problem, based on the assumptions of Markov property and time homogeneity, we build a kind of Variable Structure Discrete Dynamic Bayesian (VSDDB network simplified model of dynamic routing IP network. Under the simplified VSDDB model, based on the Bayesian Maximum A Posteriori (BMAP and Rest Bayesian Network Model (RBNM, we proposed an Improved CLINK (ICLINK algorithm. Considering the concurrent phenomenon of multiple link congestion usually happens, we also proposed algorithm CLILRS (Congested Link Inference algorithm based on Lagrangian Relaxation Subgradient to infer the set of congested links. We validated our results by the experiments of analogy, simulation, and actual Internet.
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.
Research of Steward Dynamic Platform Simulation Numerical Algorithm
Institute of Scientific and Technical Information of China (English)
Wang Mingwei; Hu Deji
2015-01-01
In order to achieve attitude control of the six degrees of freedom Steward dynamic platform, as well as the real time simulation cockpit attitude, Washout Filtering method was adopted in this paper as the simulation algorithm to derive Washout Filter high-pass, low-pass filter transfer function into a differential equation algorithm and longitudinal acceleration tilt strategy, pitching strategies etc. Experimental examples are used to verify correctness of the algorithm.
Finding Global Minima with a New Dynamical Evolutionary Algorithm
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
A new dynamical evolutionary algorithm (DEA) based on the theory of statistical mechanics is presented. This algorithm is very different from the traditional evolutionary algorithm and the two novel fe-atures are the unique of selecting strategy and the determination of individuals that are selected to crossover and mutate. We use DEA to solve a lot of global optimization problems that are nonlinear, multimodal and multidimensional and obtain satisfactory results.
Dynamic Obfuscation Algorithm based on Demand-Driven Symbolic Execution
Directory of Open Access Journals (Sweden)
Yubo Yang
2014-06-01
Full Text Available Dynamic code obfuscation technique increases the difficulty of dynamically reverse by the runtime confusion. Path explosion directly affects the efficiency and accuracy of dynamic symbolic analysis. Because of the defect, this paper presents a novel algorithm DDD (Demand-Driven Dynamic Obfuscation Algorithm by using the demand-driven theory of symbolic analysis. First, create a large number of invalid paths to mislead the result of symbolic analysis. Second, according to the demand-driven theory, create a specific execution path to protect the security of software. The design and implementation of the algorithm is based on the current popular and mature SMT (satisfiability model theory, and the experimental effects are tested by Z3 - the SMT solver and Pex - the symbolic execution test tools. The experimental results prove that the algorithm enhance the security of the program.
Dynamic Uniform Scaling for Multiobjective Genetic Algorithms
DEFF Research Database (Denmark)
Pedersen, Gerulf; Goldberg, David E.
2004-01-01
Before Multiobjective Evolutionary Algorithms (MOEAs) can be used as a widespread tool for solving arbitrary real world problems there are some salient issues which require further investigation. One of these issues is how a uniform distribution of solutions along the Pareto non-dominated front can......, the issue of obtaining a diverse set of solutions for badly scaled objective functions will be investigated and proposed solutions will be implemented using the NSGA-II algorithm....
Dynamic Uniform Scaling for Multiobjective Genetic Algorithms
DEFF Research Database (Denmark)
Pedersen, Gerulf; Goldberg, D.E.
2004-01-01
Before Multiobjective Evolutionary Algorithms (MOEAs) can be used as a widespread tool for solving arbitrary real world problems there are some salient issues which require further investigation. One of these issues is how a uniform distribution of solutions along the Pareto non-dominated front c......, the issue of obtaining a diverse set of solutions for badly scaled objective functions will be investigated and proposed solutions will be implemented using the NSGA-II algorithm....
New MPPT algorithm based on hybrid dynamical theory
Elmetennani, Shahrazed
2014-11-01
This paper presents a new maximum power point tracking algorithm based on the hybrid dynamical theory. A multiceli converter has been considered as an adaptation stage for the photovoltaic chain. The proposed algorithm is a hybrid automata switching between eight different operating modes, which has been validated by simulation tests under different working conditions. © 2014 IEEE.
Scale invariance of entanglement dynamics in Grover's quantum search algorithm
Rossi, M; Macchiavello, C
2012-01-01
We calculate the amount of entanglement of the multiqubit quantum states employed in the Grover algorithm, by following its dynamics at each step of the computation. We show that genuine multipartite entanglement is always present. Remarkably, the dynamics of any type of entanglement as well as of genuine multipartite entanglement is independent of the number $n$ of qubits for large $n$, thus exhibiting a scale invariance property. We also investigate criteria for efficient simulatability in the context of Grover's algorithm.
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.
A New Parallel Algorithm in Power Flow Calculation: Dynamic Asynchronous Parallel Algorithm
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
Based on the general methods in power flow calculation of power system and onconceptions and classifications of parallel algorithm, a new approach named DynamicAsynchronous Parallel Algorithm that applies to the online analysis and real-time dispatching and controlling of large-scale power network was put forward in this paper. Its performances of high speed and dynamic following have been verified on IEEE-14 bus system.
Using Genetic Algorithms for Navigation Planning in Dynamic Environments
Directory of Open Access Journals (Sweden)
Ferhat Uçan
2012-01-01
Full Text Available Navigation planning can be considered as a combination of searching and executing the most convenient flight path from an initial waypoint to a destination waypoint. Generally the aim is to follow the flight path, which provides minimum fuel consumption for the air vehicle. For dynamic environments, constraints change dynamically during flight. This is a special case of dynamic path planning. As the main concern of this paper is flight planning, the conditions and objectives that are most probable to be used in navigation problem are considered. In this paper, the genetic algorithm solution of the dynamic flight planning problem is explained. The evolutionary dynamic navigation planning algorithm is developed for compensating the existing deficiencies of the other approaches. The existing fully dynamic algorithms process unit changes to topology one modification at a time, but when there are several such operations occurring in the environment simultaneously, the algorithms are quite inefficient. The proposed algorithm may respond to the concurrent constraint updates in a shorter time for dynamic environment. The most secure navigation of the air vehicle is planned and executed so that the fuel consumption is minimum.
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.
A multicast dynamic wavelength assignment algorithm based on matching degree
Institute of Scientific and Technical Information of China (English)
WU Qi-wu; ZHOU Xian-wei; WANG Jian-ping; YIN Zhi-hong; ZHANG Long
2009-01-01
The wavelength assignment with multiple multicast requests in fixed routing WDM network is studied. A new multicast dynamic wavelength assignment algorithm is presented based on matching degree. First, the wavelength matching degree between available wavelengths and multicast routing trees is introduced into the algorithm. Then, the wavelength assign-ment is translated into the maximum weight matching in bipartite graph, and this matching problem is solved by using an extended Kuhn-Munkres algorithm. The simulation results prove that the overall optimal wavelength assignment scheme is obtained in polynomial time. At the same time, the proposed algorithm can reduce the connecting blocking probability and improve the system resource utilization.
Dynamic gate algorithm for multimode fiber Bragg grating sensor systems
DEFF Research Database (Denmark)
Ganziy, Denis; Jespersen, O.; Woyessa, Getinet
2015-01-01
We propose a novel dynamic gate algorithm (DGA) for precise and accurate peak detection. The algorithm uses a threshold-determined detection window and center of gravity algorithm with bias compensation. We analyze the wavelength fit resolution of the DGA for different values of the signal......-to-noise ratio and different peak shapes. Our simulations and experiments demonstrate that the DGA method is fast and robust with better stability and accuracy than conventional algorithms. This makes it very attractive for future implementation in sensing systems, especially based on multimode fiber Bragg...
Performance Comparision of Dynamic Load Balancing Algorithm in Cloud Computing
Directory of Open Access Journals (Sweden)
Yogita kaushik
2016-08-01
Full Text Available Cloud computing as a distributed paradigm, it has the latent to make over a large part of the Cooperative industry. In cloud computing it’s automatically describe more technologies like distributed computing, virtualization, software, web services and networking. We review the new cloud computing technologies, and indicate the main challenges for their development in future, among which load balancing problem stands out and attracts our attention Concept of load balancing in networking and in cloud environment both are widely different. Load balancing in networking its complete concern to avoid the problem of overloading and under loading in any sever networking cloud computing its complete different its involves different elements metrics such as security, reliability, throughput, tolerance, on demand services, cost etc. Through these elements we avoiding various node problem of distributing system where many services waiting for request and others are heavily loaded and through these its increase response time and degraded performance optimization. In this paper first we classify algorithms in static and dynamic. Then we analyzed the dynamic algorithms applied in dynamics environments in cloud. Through this paper we have been show compression of various dynamics algorithm in which we include honey bee algorithm, throttled algorithm, Biased random algorithm with different elements and describe how and which is best in cloud environment with different metrics mainly used elements are performance, resource utilization and minimum cost. Our main focus of paper is in the analyze various load balancing algorithms and their applicability in cloud environment.
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.
Study of the dynamic behavior of Niedermayer's algorithm
Girardi, Daniel; Branco, Nilton
2010-03-01
We calculate the dynamic exponent for the Niedermayer algorithm applied to the two-dimensional Ising and XY models, for various values of the free parameter E0. For E0=-1 we reobtain the Metropolis algorithm and for E0=1 we regain the Wolff algorithm. For -1L, the Niedermayer algorithm is equivalent to the Metropolis one, i.e, they have the same dynamic exponent. For a given size L, the correlation time is always greater for the Niedermayer algorithm than for Wolff's. For E0>1, the mean size of the islands of turned spins grows faster than a power of L and the correlation time is always greater than for the Wolff algorithm. Therefore, we show that the best choice of cluster algorithm is the Wolff one, when compared to the Nierdermayer generalization. We also obtain the dynamic behavior of the Wolff algorithm: although not conclusive, we propose a scaling law for the dependence of the correlation time on L.
Effective multicasting algorithm for dynamic membership with delay constraint
Institute of Scientific and Technical Information of China (English)
CHEN Lin; XU Zheng-quan
2006-01-01
This paper proposes an effective heuristic algorithm for dynamic multicast routing with delay-constrained DDMR.The tree constructed by DDMR has the following characteristics: (1) multicast tree changes with the dynamic memberships; (2)the cost of the tree is as small as possible at each node addition/removal event; (3) all of the path delay meet a fixed delay constraint;(4) minimal perturbation to an existing tree. The proposed algorithm is based on "damage" and "usefulness" concepts proposed in previous work, and has a new parameter bf(Balancing Factor) for judging whether or not to rearrange a tree region when membership changes. Mutation operation in Genetic Algorithm (GA) is also employed to find an attached node for a new adding node.Simulation showed that our algorithm performs well and is better than static heuristic algorithms, in term of cost especially.
A Computational Fluid Dynamics Algorithm on a Massively Parallel Computer
Jespersen, Dennis C.; Levit, Creon
1989-01-01
The discipline of computational fluid dynamics is demanding ever-increasing computational power to deal with complex fluid flow problems. We investigate the performance of a finite-difference computational fluid dynamics algorithm on a massively parallel computer, the Connection Machine. Of special interest is an implicit time-stepping algorithm; to obtain maximum performance from the Connection Machine, it is necessary to use a nonstandard algorithm to solve the linear systems that arise in the implicit algorithm. We find that the Connection Machine ran achieve very high computation rates on both explicit and implicit algorithms. The performance of the Connection Machine puts it in the same class as today's most powerful conventional supercomputers.
Dynamic Algorithms for Graphs with Treewidth 2
Bodlaender, H.L.
1993-01-01
In this paper, we consider algorithms for maintaining tree-decompositions with constant bounded treewith under edge and vertex insertions and deletions for graphs with treewith at most 2 (also called: partial 2-trees, or series-parallel graphs), and for almost trees with parameter k. Each operation
A Hybrid Immigrants Scheme for Genetic Algorithms in Dynamic Environments
Institute of Scientific and Technical Information of China (English)
Shengxiang Yang; Renato Tinós
2007-01-01
Dynamic optimization problems are a kind of optimization problems that involve changes over time. They pose a serious challenge to traditional optimization methods as well as conventional genetic algorithms since the goal is no longer to search for the optimal solution(s) of a fixed problem but to track the moving optimum over time. Dynamic optimization problems have attracted a growing interest from the genetic algorithm community in recent years. Several approaches have been developed to enhance the performance of genetic algorithms in dynamic environments. One approach is to maintain the diversity of the population via random immigrants. This paper proposes a hybrid immigrants scheme that combines the concepts of elitism, dualism and random immigrants for genetic algorithms to address dynamic optimization problems. In this hybrid scheme, the best individual, i.e., the elite, from the previous generation and its dual individual are retrieved as the bases to create immigrants via traditional mutation scheme. These elitism-based and dualism-based immigrants together with some random immigrants are substituted into the current population, replacing the worst individuals in the population. These three kinds of immigrants aim to address environmental changes of slight, medium and significant degrees respectively and hence efficiently adapt genetic algorithms to dynamic environments that are subject to different severities of changes. Based on a series of systematically constructed dynamic test problems, experiments are carried out to investigate the performance of genetic algorithms with the hybrid immigrants scheme and traditional random immigrants scheme. Experimental results validate the efficiency of the proposed hybrid immigrants scheme for improving the performance of genetic algorithms in dynamic environments.
Dynamic programming algorithm for detecting dim infrared moving targets
He, Lisha; Mao, Liangjing; Xie, Lijun
2009-10-01
Infrared (IR) target detection is a key part of airborne infrared weapon system, especially the detection of poor dim moving IR target embedded in complex context. This paper presents an improved Dynamic Programming (DP) algorithm in allusion to low Signal to Noise Ratio (SNR) infrared dim moving targets under cluttered context. The algorithm brings the dim target to prominence by accumulating the energy of pixels in the image sequence, after suppressing the background noise with a mathematical morphology preprocessor. As considering the continuity and stabilization of target's energy and forward direction, this algorithm has well solved the energy scattering problem that exists in the original DP algorithm. An effective energy segmentation threshold is given by a Contrast-Limited Adaptive Histogram Equalization (CLAHE) filter with a regional peak extraction algorithm. Simulation results show that the improved DP tracking algorithm performs well in detecting poor dim targets.
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.
Quantum Dynamical Entropies and Gács Algorithmic Entropy
Directory of Open Access Journals (Sweden)
Fabio Benatti
2012-07-01
Full Text Available Several quantum dynamical entropies have been proposed that extend the classical Kolmogorov–Sinai (dynamical entropy. The same scenario appears in relation to the extension of algorithmic complexity theory to the quantum realm. A theorem of Brudno establishes that the complexity per unit time step along typical trajectories of a classical ergodic system equals the KS-entropy. In the following, we establish a similar relation between the Connes–Narnhofer–Thirring quantum dynamical entropy for the shift on quantum spin chains and the Gács algorithmic entropy. We further provide, for the same system, a weaker linkage between the latter algorithmic complexity and a different quantum dynamical entropy proposed by Alicki and Fannes.
Dynamic Shortest Path Algorithms for Hypergraphs
2014-01-01
hypergraphs, energy efficient routing in multichannel multiradio networks, and the Enron email data set. The experiment with the Enron email data set...efficient routing inmultichannel multiradio networks, and the Enron email data set. The experiment with the Enron email data set illustrates the application...FOR HYPERGRAPHS 3 of each actor. In Section VII, we apply the proposed shortest hy- perpath algorithms to the Enron e-mail data set. We propose a
Heuristic Scheduling Algorithm Oriented Dynamic Tasks for Imaging Satellites
Directory of Open Access Journals (Sweden)
Maocai Wang
2014-01-01
Full Text Available Imaging satellite scheduling is an NP-hard problem with many complex constraints. This paper researches the scheduling problem for dynamic tasks oriented to some emergency cases. After the dynamic properties of satellite scheduling were analyzed, the optimization model is proposed in this paper. Based on the model, two heuristic algorithms are proposed to solve the problem. The first heuristic algorithm arranges new tasks by inserting or deleting them, then inserting them repeatedly according to the priority from low to high, which is named IDI algorithm. The second one called ISDR adopts four steps: insert directly, insert by shifting, insert by deleting, and reinsert the tasks deleted. Moreover, two heuristic factors, congestion degree of a time window and the overlapping degree of a task, are employed to improve the algorithm’s performance. Finally, a case is given to test the algorithms. The results show that the IDI algorithm is better than ISDR from the running time point of view while ISDR algorithm with heuristic factors is more effective with regard to algorithm performance. Moreover, the results also show that our method has good performance for the larger size of the dynamic tasks in comparison with the other two methods.
Optimization of Algorithms Using Extensions of Dynamic Programming
AbouEisha, Hassan M.
2017-04-09
We study and answer questions related to the complexity of various important problems such as: multi-frontal solvers of hp-adaptive finite element method, sorting and majority. We advocate the use of dynamic programming as a viable tool to study optimal algorithms for these problems. The main approach used to attack these problems is modeling classes of algorithms that may solve this problem using a discrete model of computation then defining cost functions on this discrete structure that reflect different complexity measures of the represented algorithms. As a last step, dynamic programming algorithms are designed and used to optimize those models (algorithms) and to obtain exact results on the complexity of the studied problems. The first part of the thesis presents a novel model of computation (element partition tree) that represents a class of algorithms for multi-frontal solvers along with cost functions reflecting various complexity measures such as: time and space. It then introduces dynamic programming algorithms for multi-stage and bi-criteria optimization of element partition trees. In addition, it presents results based on optimal element partition trees for famous benchmark meshes such as: meshes with point and edge singularities. New improved heuristics for those benchmark meshes were ob- tained based on insights of the optimal results found by our algorithms. The second part of the thesis starts by introducing a general problem where different problems can be reduced to and show how to use a decision table to model such problem. We describe how decision trees and decision tests for this table correspond to adaptive and non-adaptive algorithms for the original problem. We present exact bounds on the average time complexity of adaptive algorithms for the eight elements sorting problem. Then bounds on adaptive and non-adaptive algorithms for a variant of the majority problem are introduced. Adaptive algorithms are modeled as decision trees whose depth
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.
Ssewamala, Fred M.; Wang, Julia Shu-Huah; Karimli, Leyla; Nabunya, Proscovia
2011-01-01
This paper is divided into three complementary parts. First, we examine the challenges to the Universal Primary Education (UPE) policy in Uganda, including insufficient instructional materials and family-level poverty. Second, guided by asset theory, and based on a systematic review of studies on asset-based development programs and interventions,…
An improved HMM/SVM dynamic hand gesture recognition algorithm
Zhang, Yi; Yao, Yuanyuan; Luo, Yuan
2015-10-01
In order to improve the recognition rate and stability of dynamic hand gesture recognition, for the low accuracy rate of the classical HMM algorithm in train the B parameter, this paper proposed an improved HMM/SVM dynamic gesture recognition algorithm. In the calculation of the B parameter of HMM model, this paper introduced the SVM algorithm which has the strong ability of classification. Through the sigmoid function converted the state output of the SVM into the probability and treat this probability as the observation state transition probability of the HMM model. After this, it optimized the B parameter of HMM model and improved the recognition rate of the system. At the same time, it also enhanced the accuracy and the real-time performance of the human-computer interaction. Experiments show that this algorithm has a strong robustness under the complex background environment and the varying illumination environment. The average recognition rate increased from 86.4% to 97.55%.
Dynamic Algorithm for LQGPC Predictive Control
DEFF Research Database (Denmark)
Hangstrup, M.; Ordys, A.W.; Grimble, M.J.
1998-01-01
In this paper the optimal control law is derived for a multi-variable state space Linear Quadratic Gaussian Predictive Controller (LQGPC). A dynamic performance index is utilized resulting in an optimal steady state controller. Knowledge of future reference values is incorporated into the control...
An enhanced dynamic hash TRIE algorithm for lexicon search
Yang, Lai; Xu, Lida; Shi, Zhongzhi
2012-11-01
Information retrieval (IR) is essential to enterprise systems along with growing orders, customers and materials. In this article, an enhanced dynamic hash TRIE (eDH-TRIE) algorithm is proposed that can be used in a lexicon search in Chinese, Japanese and Korean (CJK) segmentation and in URL identification. In particular, the eDH-TRIE algorithm is suitable for Unicode retrieval. The Auto-Array algorithm and Hash-Array algorithm are proposed to handle the auxiliary memory allocation; the former changes its size on demand without redundant restructuring, and the latter replaces linked lists with arrays, saving the overhead of memory. Comparative experiments show that the Auto-Array algorithm and Hash-Array algorithm have better spatial performance; they can be used in a multitude of situations. The eDH-TRIE is evaluated for both speed and storage and compared with the naïve DH-TRIE algorithms. The experiments show that the eDH-TRIE algorithm performs better. These algorithms reduce memory overheads and speed up IR.
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.
An Algorithm of Sensor Management Based on Dynamic Target Detection
Institute of Scientific and Technical Information of China (English)
LIUXianxing; ZHOULin; JINYong
2005-01-01
The probability density of stationary target is only evolved at measurement update, but the probability density of dynamic target is evolved not only at measurement update but also during measurements, this paper researches an algorithm of dynamic targets detection. Firstly, it presents the evolution of probability density at measurement update by Bayes' rule and the evolution of probability density during measurements by Fokker-Planck differential equations, respectively. Secondly, the method of obtaining information entropy by the probability density is given and sensor resources are distributed based on the evolution of information entropy viz. the maximization of information gain. Simulation results show that compared with the algorithm of serial search, this algorithm is feasible and effective when it is used to detect dynamic target.
An improved harmony search algorithm with dynamically varying bandwidth
Kalivarapu, J.; Jain, S.; Bag, S.
2016-07-01
The present work demonstrates a new variant of the harmony search (HS) algorithm where bandwidth (BW) is one of the deciding factors for the time complexity and the performance of the algorithm. The BW needs to have both explorative and exploitative characteristics. The ideology is to use a large BW to search in the full domain and to adjust the BW dynamically closer to the optimal solution. After trying a series of approaches, a methodology inspired by the functioning of a low-pass filter showed satisfactory results. This approach was implemented in the self-adaptive improved harmony search (SIHS) algorithm and tested on several benchmark functions. Compared to the existing HS algorithm and its variants, SIHS showed better performance on most of the test functions. Thereafter, the algorithm was applied to geometric parameter optimization of a friction stir welding tool.
Lorentz Covariant Canonical Symplectic Algorithms for Dynamics of Charged Particles
Wang, Yulei; Qin, Hong
2016-01-01
In this paper, the Lorentz covariance of algorithms is introduced. Under Lorentz transformation, both the form and performance of a Lorentz covariant algorithm are invariant. To acquire the advantages of symplectic algorithms and Lorentz covariance, a general procedure for constructing Lorentz covariant canonical symplectic algorithms (LCCSA) is provided, based on which an explicit LCCSA for dynamics of relativistic charged particles is built. LCCSA possesses Lorentz invariance as well as long-term numerical accuracy and stability, due to the preservation of discrete symplectic structure and Lorentz symmetry of the system. For situations with time-dependent electromagnetic fields, which is difficult to handle in traditional construction procedures of symplectic algorithms, LCCSA provides a perfect explicit canonical symplectic solution by implementing the discretization in 4-spacetime. We also show that LCCSA has built-in energy-based adaptive time steps, which can optimize the computation performance when th...
Novel algorithm for distributed replicas management based on dynamic programming
Institute of Scientific and Technical Information of China (English)
Wang Tao; Lu Xianliang; Hou Mengshu
2006-01-01
Replicas can improve the data reliability in distributed system. However, the traditional algorithms for replica management are based on the assumption that all replicas have the uniform reliability, which is inaccurate in some actual systems. To address such problem, a novel algorithm is proposed based on dynamic programming to manage the number and distribution of replicas in different nodes. By using Markov model, replicas management is organized as a multi-phase process, and the recursion equations are provided. In this algorithm, the heterogeneity of nodes, the expense for maintaining replicas and the engaged space have been considered. Under these restricted conditions, this algorithm realizes high data reliability in a distributed system. The results of case analysis prove the feasibility of the algorithm.
Online Assignment Algorithms for Dynamic Bipartite Graphs
Sahai, Ankur
2011-01-01
This paper analyzes the problem of assigning weights to edges incrementally in a dynamic complete bipartite graph consisting of producer and consumer nodes. The objective is to minimize the overall cost while satisfying certain constraints. The cost and constraints are functions of attributes of the edges, nodes and online service requests. Novelty of this work is that it models real-time distributed resource allocation using an approach to solve this theoretical problem. This paper studies v...
A Dynamic Hashing Algorithm Suitable for Embedded System
Directory of Open Access Journals (Sweden)
Li Jianwei
2013-06-01
Full Text Available With the increasing of the data numbers, the linear hashing will be a lot of overflow blocks result from Data skew and the index size of extendible hash will surge so as to waste too much memory. This lead to the above two Typical Dynamic hashing algorithm don’t suitable for embedded system that need certain real-time requirements and memory resources are very scarce. To solve this problem, this paper was proposed a dynamic hashing algorithm suitable for embedded system combining with the characteristic of extendible hashing and linear hashing.it is no overflow buckets and the index size is proportional to the adjustment number.
Dynamics of Quantum Adiabatic Evolution Algorithm for Number Partitioning
Smelyanskiy, Vadius; vonToussaint, Udo V.; Timucin, Dogan A.; Clancy, Daniel (Technical Monitor)
2002-01-01
We have developed a general technique to study the dynamics of the quantum adiabatic evolution algorithm applied to random combinatorial optimization problems in the asymptotic limit of large problem size n. We use as an example the NP-complete Number Partitioning problem and map the algorithm dynamics to that of an auxiliary quantum spin glass system with the slowly varying Hamiltonian. We use a Green function method to obtain the adiabatic eigenstates and the minimum exitation gap, gmin = O(n2(sup -n/2)), corresponding to the exponential complexity of the algorithm for Number Partitioning. The key element of the analysis is the conditional energy distribution computed for the set of all spin configurations generated from a given (ancestor) configuration by simultaneous flipping of a fixed number of spins. For the problem in question this distribution is shown to depend on the ancestor spin configuration only via a certain parameter related to the energy of the configuration. As the result, the algorithm dynamics can be described in terms of one-dimensional quantum diffusion in the energy space. This effect provides a general limitation of a quantum adiabatic computation in random optimization problems. Analytical results are in agreement with the numerical simulation of the algorithm.
Node-Dependence-Based Dynamic Incentive Algorithm in Opportunistic Networks
Directory of Open Access Journals (Sweden)
Ruiyun Yu
2014-01-01
Full Text Available Opportunistic networks lack end-to-end paths between source nodes and destination nodes, so the communications are mainly carried out by the “store-carry-forward” strategy. Selfish behaviors of rejecting packet relay requests will severely worsen the network performance. Incentive is an efficient way to reduce selfish behaviors and hence improves the reliability and robustness of the networks. In this paper, we propose the node-dependence-based dynamic gaming incentive (NDI algorithm, which exploits the dynamic repeated gaming to motivate nodes relaying packets for other nodes. The NDI algorithm presents a mechanism of tolerating selfish behaviors of nodes. Reward and punishment methods are also designed based on the node dependence degree. Simulation results show that the NDI algorithm is effective in increasing the delivery ratio and decreasing average latency when there are a lot of selfish nodes in the opportunistic networks.
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.
Dynamical transitions in the evolution of learning algorithms by selection
Neirotti, J P; Neirotti, Juan Pablo; Caticha, Nestor
2002-01-01
We study the evolution of artificial learning systems by means of selection. Genetic programming is used to generate a sequence of populations of algorithms which can be used by neural networks for supervised learning of a rule that generates examples. In opposition to concentrating on final results, which would be the natural aim while designing good learning algorithms, we study the evolution process and pay particular attention to the temporal order of appearance of functional structures responsible for the improvements in the learning process, as measured by the generalization capabilities of the resulting algorithms. The effect of such appearances can be described as dynamical phase transitions. The concepts of phenotypic and genotypic entropies, which serve to describe the distribution of fitness in the population and the distribution of symbols respectively, are used to monitor the dynamics. In different runs the phase transitions might be present or not, with the system finding out good solutions, or ...
Multiscale reaction-diffusion algorithms: PDE-assisted Brownian dynamics
Franz, Benjamin; Chapman, S Jonathan; Erban, Radek
2012-01-01
Two algorithms that combine Brownian dynamics (BD) simulations with mean-field partial differential equations (PDEs) are presented. This PDE-assisted Brownian dynamics (PBD) methodology provides exact particle tracking data in parts of the domain, whilst making use of a mean-field reaction-diffusion PDE description elsewhere. The first PBD algorithm couples BD simulations with PDEs by randomly creating new particles close to the interface which partitions the domain and by reincorporating particles into the continuum PDE-description when they cross the interface. The second PBD algorithm introduces an overlap region, where both descriptions exist in parallel. It is shown that to accurately compute variances using the PBD simulation requires the overlap region. Advantages of both PBD approaches are discussed and illustrative numerical examples are presented.
A NEW ALGORITHM OF TIME STEPPING IN DYNAMIC VISCOELASTIC PROBLEMS
Institute of Scientific and Technical Information of China (English)
杨海天; 高强; 郭杏林; 邬瑞锋
2001-01-01
A new scheme of time stepping for solving the dynamic viscoelastic problems are presented. By expanding variables at a discrete time interval, FEM based recurrent formulae are derived. A self-adaptive algorithm for different sizes of time steps can be carried out to improve computing accuracy. Numerical validation shows satisfactory performance.
DYNAMIC REQUEST DISPATCHING ALGORITHM FOR WEB SERVER CLUSTER
Institute of Scientific and Technical Information of China (English)
Yang Zhenjiang; Zhang Deyun; Sun Qindong; Sun Qing
2006-01-01
Distributed architectures support increased load on popular web sites by dispatching client requests transparently among multiple servers in a cluster. Packet Single-Rewriting technology and client address hashing algorithm in ONE-IP technology which can ensure application-session-keep have been analyzed, an improved request dispatching algorithm which is simple, effective and supports dynamic load balance has been proposed. In this algorithm, dispatcher evaluates which server node will process request by applying a hash function to the client IP address and comparing the result with its assigned identifier subset; it adjusts the size of the subset according to the performance and current load of each server, so as to utilize all servers' resource effectively. Simulation shows that the improved algorithm has better performance than the original one.
Decentralized Control of Dynamic Routing with a Neural Network Algorithm
Institute of Scientific and Technical Information of China (English)
无
2000-01-01
A state-dependent routing algorithm based on the neural network model, which takes advantage of other dynamic routing algorithm for circuit-switched network, is given in [1]. But, the Algorithm in [1] is a centralized control model with complex O (N7), therefore, is difficult to realize by hardware. A simplified algorithm is put forward in this paper, in which routing can be controlled decentralizedly, and its complexity is reduced to O (10N3). Computer simulations are made in a fully connected test network with eight nodes. The results show that the centralized control model has very effective performance that can match RTNR, and the centralized control model is not as good as the centralized one but better than DAR-1.
A New Fuzzy Approach for Dynamic Load Balancing Algorithm
Karimi, Abbas; Jantan, Adznan b; Ramli, A R; Saripan, M Iqbal b
2009-01-01
Load balancing is the process of improving the Performance of a parallel and distributed system through is distribution of load among the processors [1-2]. Most of the previous work in load balancing and distributed decision making in general, do not effectively take into account the uncertainty and inconsistency in state information but in fuzzy logic, we have advantage of using crisps inputs. In this paper, we present a new approach for implementing dynamic load balancing algorithm with fuzzy logic, which can face to uncertainty and inconsistency of previous algorithms, further more our algorithm shows better response time than round robin and randomize algorithm respectively 30.84 percent and 45.45 percent.
A New Fuzzy Approach for Dynamic Load Balancing Algorithm
Directory of Open Access Journals (Sweden)
Abbas Karimi
2009-10-01
Full Text Available Load balancing is the process of improving the Performance of a parallel and distributed system through is distribution of load among the processors [1-2]. Most of the previous work in load balancing and distributed decision making in general, do not effectively take into account the uncertainty and inconsistency in state information but in fuzzy logic, we have advantage of using crisps inputs. In this paper,we present a new approach for implementing dynamic load balancing algorithm with fuzzy logic, which can face to uncertainty and inconsistency of previous algorithms, further more our algorithm shows better response time than round robin and randomize algorithm respectively 30.84% and 45.45%.
A Dynamical Reliability Prediction Algorithm for Composite Service
Directory of Open Access Journals (Sweden)
Chunli Xie
2014-01-01
Full Text Available Dynamic selection and dynamic binding and rebinding at runtime are new characters of composite services. The traditional static reliability prediction models are unsuitable to dynamic composite services. A new reliability predicting algorithm for composite services is proposed in this paper. Firstly, a composite service is decomposed into composition unites (executing path, composite module and atomic service according to their constituents. Consequently, a hierarchical graph of all composite units is constructed. Lastly, a new dynamic reliability prediction algorithm is presented. Comparing with the traditional reliability model, the new dynamic reliability approach is more flexible, which does not recompute reliability for all composite units and only computes the reliability of the effected composite units. In addition, an example to show how to measure the reliability based on our algorithm is designed. The experimental results show our proposed methods can give an accurate estimation of reliability. Furthermore, a more flexible sensitivity analysis is performed to determine which service component has the most significant impact on the improvement of composite service reliability.
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.
An eddy tracking algorithm based on dynamical systems theory
Conti, Daniel; Orfila, Alejandro; Mason, Evan; Sayol, Juan Manuel; Simarro, Gonzalo; Balle, Salvador
2016-11-01
This work introduces a new method for ocean eddy detection that applies concepts from stationary dynamical systems theory. The method is composed of three steps: first, the centers of eddies are obtained from fixed points and their linear stability analysis; second, the size of the eddies is estimated from the vorticity between the eddy center and its neighboring fixed points, and, third, a tracking algorithm connects the different time frames. The tracking algorithm has been designed to avoid mismatching connections between eddies at different frames. Eddies are detected for the period between 1992 and 2012 using geostrophic velocities derived from AVISO altimetry and a new database is provided for the global ocean.
Multiscale Reaction-Diffusion Algorithms: PDE-Assisted Brownian Dynamics
Franz, Benjamin
2013-06-19
Two algorithms that combine Brownian dynami cs (BD) simulations with mean-field partial differential equations (PDEs) are presented. This PDE-assisted Brownian dynamics (PBD) methodology provides exact particle tracking data in parts of the domain, whilst making use of a mean-field reaction-diffusion PDE description elsewhere. The first PBD algorithm couples BD simulations with PDEs by randomly creating new particles close to the interface, which partitions the domain, and by reincorporating particles into the continuum PDE-description when they cross the interface. The second PBD algorithm introduces an overlap region, where both descriptions exist in parallel. It is shown that the overlap region is required to accurately compute variances using PBD simulations. Advantages of both PBD approaches are discussed and illustrative numerical examples are presented. © 2013 Society for Industrial and Applied Mathematics.
Iterative Dynamic Diversity Evolutionary Algorithm for Constrained Optimization
Institute of Scientific and Technical Information of China (English)
GAO Wei-Shang; SHAO Cheng
2014-01-01
Evolutionary algorithms (EAs) were shown to be effective for complex constrained optimization problems. However, inflexible exploration in general EAs would lead to losing the global optimum nearby the ill-convergence regions. In this paper, we propose an iterative dynamic diversity evolutionary algorithm (IDDEA) with contractive subregions guiding exploitation through local extrema to the global optimum in suitable steps. In IDDEA, a novel optimum estimation strategy with multi-agents evolving diversely is suggested to eﬃciently compute dominance trend and establish a subregion. In addition, a subregion converging iteration is designed to redistrict a smaller subregion in current subregion for next iteration, which is based on a special dominance estimation scheme. Meanwhile, an infimum penalty function is embedded into IDDEA to judge agents and penalize adaptively the unfeasible agents with the lowest fitness of feasible agents. Furthermore, several engineering design optimization problems taken from the specialized literature are successfully solved by the present algorithm with high reliable solutions.
A dynamic fuzzy clustering method based on genetic algorithm
Institute of Scientific and Technical Information of China (English)
ZHENG Yan; ZHOU Chunguang; LIANG Yanchun; GUO Dongwei
2003-01-01
A dynamic fuzzy clustering method is presented based on the genetic algorithm. By calculating the fuzzy dissimilarity between samples the essential associations among samples are modeled factually. The fuzzy dissimilarity between two samples is mapped into their Euclidean distance, that is, the high dimensional samples are mapped into the two-dimensional plane. The mapping is optimized globally by the genetic algorithm, which adjusts the coordinates of each sample, and thus the Euclidean distance, to approximate to the fuzzy dissimilarity between samples gradually. A key advantage of the proposed method is that the clustering is independent of the space distribution of input samples, which improves the flexibility and visualization. This method possesses characteristics of a faster convergence rate and more exact clustering than some typical clustering algorithms. Simulated experiments show the feasibility and availability of the proposed method.
Dynamic Routing Algorithm for Increasing Robustness in Satellite Networks
Institute of Scientific and Technical Information of China (English)
LI Dong-ni; ZHANG Da-kun
2008-01-01
In low earth orbit(LEO)and medium earth orbit(MEO)satellite networks,the network topology changes rapidly because of the high relative speed movement of satellites.When some inter-satellite links (ISLs)fail,they can not be repaired in a short time.In order to increase the robustness for LEO/MEO satellite networks,an effective dynamic routing algorithm is proposed.All the routes to a certain node are found by constructing a destination oriented acyclic directed graph(DOADG)with the node as the destination.In this algorithm,multiple routes are provided,loop-free is guaranteed,and as long as the DOADG maintains,it is not necessary to reroute even if some ISLs fail.Simulation results show that comparing to the conventional routing algorithms,it is more efficient and reliable,costs less transmission overhead and converges faster.
Automated Guide Vehicles Dynamic Scheduling Based on Annealing Genetic Algorithm
Directory of Open Access Journals (Sweden)
Zou Gan
2013-05-01
Full Text Available Dispatching automated guided vehicles (AGVs is the common approach for AGVs scheduling in practice, the information about load arrivals in advance was not used to optimize the performance of the automated guided vehicles system (AGVsS. According to the characteristics of the AGVsS, the mathematical model of AGVs scheduling was established. A heuristic algorithm called Annealing Genetic Algorithm (AGA was presented to deal with the AGVs scheduling problem,and applied the algorithm dynamically by using it repeatedly under a combined rolling optimization strategy. the performance of the proposed approach for AGVs scheduling was compared with the dispatching rules by simulation. Results showed that the approach performs significantly better than the dispatching rules and proved that it is really effective for AGVsS.
A novel dynamical community detection algorithm based on weighting scheme
Li, Ju; Yu, Kai; Hu, Ke
2015-12-01
Network dynamics plays an important role in analyzing the correlation between the function properties and the topological structure. In this paper, we propose a novel dynamical iteration (DI) algorithm, which incorporates the iterative process of membership vector with weighting scheme, i.e. weighting W and tightness T. These new elements can be used to adjust the link strength and the node compactness for improving the speed and accuracy of community structure detection. To estimate the optimal stop time of iteration, we utilize a new stability measure which is defined as the Markov random walk auto-covariance. We do not need to specify the number of communities in advance. It naturally supports the overlapping communities by associating each node with a membership vector describing the node's involvement in each community. Theoretical analysis and experiments show that the algorithm can uncover communities effectively and efficiently.
An automated algorithm for the generation of dynamically reconstructed trajectories
Komalapriya, C.; Romano, M. C.; Thiel, M.; Marwan, N.; Kurths, J.; Kiss, I. Z.; Hudson, J. L.
2010-03-01
The lack of long enough data sets is a major problem in the study of many real world systems. As it has been recently shown [C. Komalapriya, M. Thiel, M. C. Romano, N. Marwan, U. Schwarz, and J. Kurths, Phys. Rev. E 78, 066217 (2008)], this problem can be overcome in the case of ergodic systems if an ensemble of short trajectories is available, from which dynamically reconstructed trajectories can be generated. However, this method has some disadvantages which hinder its applicability, such as the need for estimation of optimal parameters. Here, we propose a substantially improved algorithm that overcomes the problems encountered by the former one, allowing its automatic application. Furthermore, we show that the new algorithm not only reproduces the short term but also the long term dynamics of the system under study, in contrast to the former algorithm. To exemplify the potential of the new algorithm, we apply it to experimental data from electrochemical oscillators and also to analyze the well-known problem of transient chaotic trajectories.
Dynamic and static properties of the invaded cluster algorithm
Moriarty, K.; Machta, J.; Chayes, L. Y.
1999-02-01
Simulations of the two-dimensional Ising and three-state Potts models at their critical points are performed using the invaded cluster (IC) algorithm. It is argued that observables measured on a sublattice of size l should exhibit a crossover to Swendsen-Wang (SW) behavior for l sufficiently less than the lattice size L, and a scaling form is proposed to describe the crossover phenomenon. It is found that the energy autocorrelation time τɛ(l,L) for an l×l sublattice attains a maximum in the crossover region, and a dynamic exponent zIC for the IC algorithm is defined according to τɛ,max~LzIC. Simulation results for the three-state model yield zIC=0.346+/-0.002, which is smaller than values of the dynamic exponent found for the SW and Wolff algorithms and also less than the Li-Sokal bound. The results are less conclusive for the Ising model, but it appears that zICWolff algorithms.
A SYMPLECTIC ALGORITHM FOR DYNAMICS OF RIGID BODY
Institute of Scientific and Technical Information of China (English)
LU Ying-jie; REN Ge-xue
2006-01-01
For the dynamics of a rigid body with a fixed point based on the quaternion and the corresponding generalized momenta, a displacement-based symplectic integration scheme for differential-algebraic equations is proposed and applied to the Lagrange's equations based on dependent generalized momenta. Numerical experiments show that the algorithm possesses such characters as high precision and preserving system invariants.More importantly, the generalized momenta based Lagrange's equations show unique advantages over the traditional Lagrange's equations in symplectic integrations.
SENSITIVITY ANALYSIS BASED ON LANCZOS ALGORITHM IN STRUCTURAL DYNAMICS
Institute of Scientific and Technical Information of China (English)
李书; 王波; 胡继忠
2003-01-01
The sensitivity calculating formulas in structural dynamics was developed byutilizing the mathematical theorem and new definitions of sensitivities. So the singularityproblem of sensitivity with repeated eigenvalues is solved completely. To improve thecomputational efficiency, the reduction system is obtained based on Lanczos vectors. Afterincorporating the mathematical theory with the Lanczos algorithm, the approximatesensitivity solution can be obtained. A numerical example is presented to illustrate theperformance of the method.
Computational Fluid Dynamics. [numerical methods and algorithm development
1992-01-01
This collection of papers was presented at the Computational Fluid Dynamics (CFD) Conference held at Ames Research Center in California on March 12 through 14, 1991. It is an overview of CFD activities at NASA Lewis Research Center. The main thrust of computational work at Lewis is aimed at propulsion systems. Specific issues related to propulsion CFD and associated modeling will also be presented. Examples of results obtained with the most recent algorithm development will also be presented.
Cone Algorithm of Spinning Vehicles under Dynamic Coning Environment
Directory of Open Access Journals (Sweden)
Shuang-biao Zhang
2015-01-01
Full Text Available Due to the fact that attitude error of vehicles has an intense trend of divergence when vehicles undergo worsening coning environment, in this paper, the model of dynamic coning environment is derived firstly. Then, through investigation of the effect on Euler attitude algorithm for the equivalency of traditional attitude algorithm, it is found that attitude error is actually the roll angle error including drifting error and oscillating error, which is induced directly by dynamic coning environment and further affects the pitch angle and yaw angle through transferring. Based on definition of the cone frame and cone attitude, a cone algorithm is proposed by rotation relationship to calculate cone attitude, and the relationship between cone attitude and Euler attitude of spinning vehicle is established. Through numerical simulations with different conditions of dynamic coning environment, it is shown that the induced error of Euler attitude fluctuates by the variation of precession and nutation, especially by that of nutation, and the oscillating frequency of roll angle error is twice that of pitch angle error and yaw angle error. In addition, the rotation angle is more competent to describe the spinning process of vehicles under coning environment than Euler angle gamma, and the real pitch angle and yaw angle are calculated finally.
Dynamic airspace configuration algorithms for next generation air transportation system
Wei, Jian
The National Airspace System (NAS) is under great pressure to safely and efficiently handle the record-high air traffic volume nowadays, and will face even greater challenge to keep pace with the steady increase of future air travel demand, since the air travel demand is projected to increase to two to three times the current level by 2025. The inefficiency of traffic flow management initiatives causes severe airspace congestion and frequent flight delays, which cost billions of economic losses every year. To address the increasingly severe airspace congestion and delays, the Next Generation Air Transportation System (NextGen) is proposed to transform the current static and rigid radar based system to a dynamic and flexible satellite based system. New operational concepts such as Dynamic Airspace Configuration (DAC) have been under development to allow more flexibility required to mitigate the demand-capacity imbalances in order to increase the throughput of the entire NAS. In this dissertation, we address the DAC problem in the en route and terminal airspace under the framework of NextGen. We develop a series of algorithms to facilitate the implementation of innovative concepts relevant with DAC in both the en route and terminal airspace. We also develop a performance evaluation framework for comprehensive benefit analyses on different aspects of future sector design algorithms. First, we complete a graph based sectorization algorithm for DAC in the en route airspace, which models the underlying air route network with a weighted graph, converts the sectorization problem into the graph partition problem, partitions the weighted graph with an iterative spectral bipartition method, and constructs the sectors from the partitioned graph. The algorithm uses a graph model to accurately capture the complex traffic patterns of the real flights, and generates sectors with high efficiency while evenly distributing the workload among the generated sectors. We further improve
Application of Zigbee in Smart Home with Dynamic Routing Algorithm
Directory of Open Access Journals (Sweden)
Li Zhongqin
2013-07-01
Full Text Available In order to avoid damaging the walls and bore a hole through the walls., also to save the cost of rewiring , low voltage power line can be used to implement the smart home .However ,several difficult problem must be solved at the same time , the most important are finding a technology method to suppress the noise interference and resist the weaker signal. The smart home system is introduced in the paper. During the design of Smart Home System adopting power line signal carrier, a dynamic routine algorithm based on the idea of Genetic Algorithm is proposed to cope with the time-varying and random feature of power line channel. Thus the system can find the transmission path in real time and dispatch the signal carrier to transmit among the nodes. With this algorithm, the communication between a certain group-controller and its terminal nodes or between the main-controller and the group-controllers is realized successfully. In the proposed algorithm all nodes but the controller adopt same driver, therefore the plug and play is realized for all nodes.
A Dynamic Job Scheduling Algorithm for Parallel System
Institute of Scientific and Technical Information of China (English)
张建; 陆鑫达; 加力
2003-01-01
One of the fundamental problems in parallel and distributed systems is deciding how to allocate jobs toprocessors. The goals of job scheduling in a parallel environment are to minimize the parallel execution time of ajob and try to balance the user's desire with the system's desire. The users always want their jobs be completed asquickly as possible, while the system wants to service as many jobs as possible. In this paper, a dynamic job-scheduling algorithm was introduced. This algorithm tries to utilize the information of a practical system to allo-cate the jobs more evenly. The communication time between the processor and scheduler is overlapped with thecomputation time of the processor. So the communication overhead can be little. The principle of scheduling thejob is based on the desirability of each processor. The scheduler would not allocate a new job to a processor that isalready fully utilized. The execution efficiency of the system will be increased. This algorithm also can be reused inother complex algorithms.
Dynamic programming and graph algorithms in computer vision.
Felzenszwalb, Pedro F; Zabih, Ramin
2011-04-01
Optimization is a powerful paradigm for expressing and solving problems in a wide range of areas, and has been successfully applied to many vision problems. Discrete optimization techniques are especially interesting since, by carefully exploiting problem structure, they often provide nontrivial guarantees concerning solution quality. In this paper, we review dynamic programming and graph algorithms, and discuss representative examples of how these discrete optimization techniques have been applied to some classical vision problems. We focus on the low-level vision problem of stereo, the mid-level problem of interactive object segmentation, and the high-level problem of model-based recognition.
Structure-Preserving Algorithms for a Class of Dynamical Systems
Institute of Scientific and Technical Information of China (English)
Ling-shu Wang; Guang-hui Feng
2007-01-01
In this paper, we study structure-preserving algorithms for dynamical systems defined by ordinary differential equations in Rn. The equations are assumed to be of the form y = A(y) + D(y) + R(y), where A(y)of damping and expanding; R(y) reflects strange phenomenon of the system. It is shown that the numerical approximations to the exact ones, and these methods can describe the structural properties of the quadratic energy for these systems. Some numerical experiments and backward error analysis also show that these methods are better than other methods including the general algebraically stable Runge-Kutta(RK)methods.
Control of Chaotic Regimes in Encryption Algorithm Based on Dynamic Chaos
Sidorenko, V.; Mulyarchik, K. S.
2013-01-01
Chaotic regime of a dynamic system is a necessary condition determining cryptographic security of an encryption algorithm. A chaotic dynamic regime control method is proposed which uses parameters of nonlinear dynamics regime for an analysis of encrypted data.
Tsai, Ming-Chi; Tsui, Fu-Chiang; Wagner, Michael M
2007-10-11
Performing fast data analysis to detect disease outbreaks plays a critical role in real-time biosurveillance. In this paper, we described and evaluated an Algorithm Distribution Manager Service (ADMS) based on grid technologies, which dynamically partition and distribute detection algorithms across multiple computers. We compared the execution time to perform the analysis on a single computer and on a grid network (3 computing nodes) with and without using dynamic algorithm distribution. We found that algorithms with long runtime completed approximately three times earlier in distributed environment than in a single computer while short runtime algorithms performed worse in distributed environment. A dynamic algorithm distribution approach also performed better than static algorithm distribution approach. This pilot study shows a great potential to reduce lengthy analysis time through dynamic algorithm partitioning and parallel processing, and provides the opportunity of distributing algorithms from a client to remote computers in a grid network.
GBT Dynamic Scheduling System: Algorithms, Metrics, and Simulations
Balser, D. S.; Bignell, C.; Braatz, J.; Clark, M.; Condon, J.; Harnett, J.; O'Neil, K.; Maddalena, R.; Marganian, P.; McCarty, M.; Sessoms, E.; Shelton, A.
2009-09-01
We discuss the scoring algorithm of the Robert C. Byrd Green Bank Telescope (GBT) Dynamic Scheduling System (DSS). Since the GBT is located in a continental, mid-latitude region where weather is dominated by water vapor and small-scale effects, the weather plays an important role in optimizing the observing efficiency of the GBT. We score observing sessions as a product of many factors. Some are continuous functions while others are binary limits taking values of 0 or 1, any one of which can eliminate a candidate session by forcing the score to zero. Others reflect management decisions to expedite observations by visiting observers, ensure the timely completion of projects, etc. Simulations indicate that dynamic scheduling can increase the effective observing time at frequencies higher than 10 GHz by about 50% over one full year. Beta tests of the DSS during Summer 2008 revealed the significance of various scheduling constraints and telescope overhead time to the overall observing efficiency.
Inverse problem of HIV cell dynamics using Genetic Algorithms
González, J. A.; Guzmán, F. S.
2017-01-01
In order to describe the cell dynamics of T-cells in a patient infected with HIV, we use a flavour of Perelson's model. This is a non-linear system of Ordinary Differential Equations that describes the evolution of healthy, latently infected, infected T-cell concentrations and the free viral cells. Different parameters in the equations give different dynamics. Considering the concentration of these types of cells is known for a particular patient, the inverse problem consists in estimating the parameters in the model. We solve this inverse problem using a Genetic Algorithm (GA) that minimizes the error between the solutions of the model and the data from the patient. These errors depend on the parameters of the GA, like mutation rate and population, although a detailed analysis of this dependence will be described elsewhere.
Multiagent Flight Control in Dynamic Environments with Cooperative Coevolutionary Algorithms
Knudson, Matthew D.; Colby, Mitchell; Tumer, Kagan
2014-01-01
Dynamic flight environments in which objectives and environmental features change with respect to time pose a difficult problem with regards to planning optimal flight paths. Path planning methods are typically computationally expensive, and are often difficult to implement in real time if system objectives are changed. This computational problem is compounded when multiple agents are present in the system, as the state and action space grows exponentially. In this work, we use cooperative coevolutionary algorithms in order to develop policies which control agent motion in a dynamic multiagent unmanned aerial system environment such that goals and perceptions change, while ensuring safety constraints are not violated. Rather than replanning new paths when the environment changes, we develop a policy which can map the new environmental features to a trajectory for the agent while ensuring safe and reliable operation, while providing 92% of the theoretically optimal performance
Bidirectional Dynamic Diversity Evolutionary Algorithm for Constrained Optimization
Directory of Open Access Journals (Sweden)
Weishang Gao
2013-01-01
Full Text Available Evolutionary algorithms (EAs were shown to be effective for complex constrained optimization problems. However, inflexible exploration-exploitation and improper penalty in EAs with penalty function would lead to losing the global optimum nearby or on the constrained boundary. To determine an appropriate penalty coefficient is also difficult in most studies. In this paper, we propose a bidirectional dynamic diversity evolutionary algorithm (Bi-DDEA with multiagents guiding exploration-exploitation through local extrema to the global optimum in suitable steps. In Bi-DDEA potential advantage is detected by three kinds of agents. The scale and the density of agents will change dynamically according to the emerging of potential optimal area, which play an important role of flexible exploration-exploitation. Meanwhile, a novel double optimum estimation strategy with objective fitness and penalty fitness is suggested to compute, respectively, the dominance trend of agents in feasible region and forbidden region. This bidirectional evolving with multiagents can not only effectively avoid the problem of determining penalty coefficient but also quickly converge to the global optimum nearby or on the constrained boundary. By examining the rapidity and veracity of Bi-DDEA across benchmark functions, the proposed method is shown to be effective.
Integrated dynamic shared protection algorithm for GMPLS networks
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
The path protection approach is widely investigated as a survivability solution for GMPLS networks, which has the advantage of efficient capacity utilization. However, there is a problem of the path protection approach that searching a disjoint backup path for a primary path is often unsuccessful. In order to resolve this problem, an integrated dynamic shared protection (IDSP) algorithm is proposed. The main idea of the proposed algorithm is that the path protection approach is first used to establish a backup path for the primary path; if the establishment is unsuccessful, then the primary path is dynamically divided into segments whose hop count are not fixed but not more than the limitation calculated by the equations introduced. In this proposal, backup bandwidth sharing is allowed to improve the capacity utilization ratio, which makes the link cost function quite different from previous ones. Simulation experiments are presented to demonstrate the efficiency of the proposed method compared with previous methods. Numerical results show that IDSP can not only achieve low protection failure probability but can also gain a better tradeoff between the protection overbuild and the average recovery time.
2013-01-01
Tone mapping algorithms are used to adapt captured wide dynamic range (WDR) scenes to the limited dynamic range of available display devices. Although there are several tone mapping algorithms available, most of them require manual tuning of their rendering parameters. In addition, the high complexities of some of these algorithms make it difficult to implement efficient real-time hardware systems. In this work, a real-time hardware implementation of an exponent-based tone mapping algorithm i...
A hybrid algorithm for parallel molecular dynamics simulations
Mangiardi, Chris M
2016-01-01
This article describes an algorithm for hybrid parallelization and SIMD vectorization of molecular dynamics simulations with short-ranged forces. The parallelization method combines domain decomposition with a thread-based parallelization approach. The goal of the work is to enable efficient simulations of very large (tens of millions of atoms) and inhomogeneous systems on many-core processors with hundreds or thousands of cores and SIMD units with large vector sizes. In order to test the efficiency of the method, simulations of a variety of configurations with up to 74 million atoms have been performed. Results are shown that were obtained on multi-core systems with AVX and AVX-2 processors as well as Xeon-Phi co-processors.
VST telescope dynamic analisys and position control algorithms
Schipani, P
2001-01-01
The VST (VLT Survey Telescope) is a 2.6 m class Alt-Az telescope to be installed on Cerro Paranal in the Atacama desert, Northern Chile, in the European Southern Observatory (ESO) site. The VST is a wide-field imaging facility planned to supply databases for the ESO Very Large Telescope (VLT) science and carry out stand-alone observations in the UV to I spectral range. So far no telescope has been dedicated entirely to surveys; the VST will be the first survey telescope to start the operation, as a powerful survey facility for the VLT observatory. This paper will focus on the axes motion control system. The dynamic model of the telescope will be analyzed, as well as the effect of the wind disturbance on the telescope performance. Some algorithms for the telescope position control will be briefly discussed.
Efficient Dynamic Replication Algorithm Using Agent for Data Grid
Directory of Open Access Journals (Sweden)
Priyanka Vashisht
2014-01-01
Full Text Available In data grids scientific and business applications produce huge volume of data which needs to be transferred among the distributed and heterogeneous nodes of data grids. Data replication provides a solution for managing data files efficiently in large grids. The data replication helps in enhancing the data availability which reduces the overall access time of the file. In this paper an algorithm, namely, EDRA using agents for data grid, has been proposed and implemented. EDRA consists of dynamic replication of hierarchical structure taken into account for the selection of best replica. Decision for selecting the best replica is based on scheduling parameters. The scheduling parameters are bandwidth, load gauge, and computing capacity of the node. The scheduling in data grid helps in reducing the data access time. The distribution of the load on the nodes of data grid is done evenly by considering scheduling parameters. EDRA is implemented using data grid simulator, namely, OptorSim. European Data Grid CMS test bed topology is used in this experiment. The simulation results are obtained by comparing BHR, LRU, No Replication, and EDRA. The result shows the efficiency of EDRA algorithm in terms of mean job execution time, network usage, and storage usage of node.
Parallel Genetic Algorithms with Dynamic Topology using Cluster Computing
Directory of Open Access Journals (Sweden)
ADAR, N.
2016-08-01
Full Text Available A parallel genetic algorithm (PGA conducts a distributed meta-heuristic search by employing genetic algorithms on more than one subpopulation simultaneously. PGAs migrate a number of individuals between subpopulations over generations. The layout that facilitates the interactions of the subpopulations is called the topology. Static migration topologies have been widely incorporated into PGAs. In this article, a PGA with a dynamic migration topology (D-PGA is proposed. D-PGA generates a new migration topology in every epoch based on the average fitness values of the subpopulations. The D-PGA has been tested against ring and fully connected migration topologies in a Beowulf Cluster. The D-PGA has outperformed the ring migration topology with comparable communication cost and has provided competitive or better results than a fully connected migration topology with significantly lower communication cost. PGA convergence behaviors have been analyzed in terms of the diversities within and between subpopulations. Conventional diversity can be considered as the diversity within a subpopulation. A new concept of permeability has been introduced to measure the diversity between subpopulations. It is shown that the success of the proposed D-PGA can be attributed to maintaining a high level of permeability while preserving diversity within subpopulations.
AN ADVANCED DYNAMIC FEEDBACK AND RANDOM DISPATCHING LOAD-BALANCING ALGORITHM FOR GMLC IN 3G
Institute of Scientific and Technical Information of China (English)
Liao Jianxin; Zhang Hao; Zhu Xiaomin
2006-01-01
Based on the system architecture and software structure of GMLC (Gateway Mobile Location Center) in 3G (third generation), a new dynamic load-balancing algorithm is proposed. It bases on dynamic feedback and imports the increment for admitting new request into the load forecast. It dynamically adjusts the dispatching probability according to the remainder process capability of each node. Experiments on the performance of algorithm have been carried out in GMLC and the algorithm is compared with Pick-KX algorithm and DFB (Dynamic FeedBack) algorithm in average throughput and average response time. Experiments results show that the average throughput of the proposed algorithm is about five percents higher than that of the other two algorithms and the average response time is four percents higher under high system loading condition.
Ab initio multiple cloning algorithm for quantum nonadiabatic molecular dynamics.
Makhov, Dmitry V; Glover, William J; Martinez, Todd J; Shalashilin, Dmitrii V
2014-08-07
We present a new algorithm for ab initio quantum nonadiabatic molecular dynamics that combines the best features of ab initio Multiple Spawning (AIMS) and Multiconfigurational Ehrenfest (MCE) methods. In this new method, ab initio multiple cloning (AIMC), the individual trajectory basis functions (TBFs) follow Ehrenfest equations of motion (as in MCE). However, the basis set is expanded (as in AIMS) when these TBFs become sufficiently mixed, preventing prolonged evolution on an averaged potential energy surface. We refer to the expansion of the basis set as "cloning," in analogy to the "spawning" procedure in AIMS. This synthesis of AIMS and MCE allows us to leverage the benefits of mean-field evolution during periods of strong nonadiabatic coupling while simultaneously avoiding mean-field artifacts in Ehrenfest dynamics. We explore the use of time-displaced basis sets, "trains," as a means of expanding the basis set for little cost. We also introduce a new bra-ket averaged Taylor expansion (BAT) to approximate the necessary potential energy and nonadiabatic coupling matrix elements. The BAT approximation avoids the necessity of computing electronic structure information at intermediate points between TBFs, as is usually done in saddle-point approximations used in AIMS. The efficiency of AIMC is demonstrated on the nonradiative decay of the first excited state of ethylene. The AIMC method has been implemented within the AIMS-MOLPRO package, which was extended to include Ehrenfest basis functions.
A Bayesian Algorithm for Functional Mapping of Dynamic Complex Traits
Directory of Open Access Journals (Sweden)
Rongling Wu
2009-04-01
Full Text Available Functional mapping of dynamic traits measured in a longitudinal study was originally derived within the maximum likelihood (ML context and implemented with the EM algorithm. Although ML-based functional mapping possesses many favorable statistical properties in parameter estimation, it may be computationally intractable for analyzing longitudinal data with high dimensions and high measurement errors. In this article, we derive a general functional mapping framework for quantitative trait locus mapping of dynamic traits within the Bayesian paradigm. Markov chain Monte Carlo techniques were implemented for functional mapping to estimate biologically and statistically sensible parameters that model the structures of time-dependent genetic effects and covariance matrix. The Bayesian approach is useful to handle difficulties in constructing confidence intervals as well as the identifiability problem, enhancing the statistical inference of functional mapping. We have undertaken simulation studies to investigate the statistical behavior of Bayesian-based functional mapping and used a real example with F2 mice to validate the utilization and usefulness of the model.
Ab initio multiple cloning algorithm for quantum nonadiabatic molecular dynamics
Energy Technology Data Exchange (ETDEWEB)
Makhov, Dmitry V.; Shalashilin, Dmitrii V. [Department of Chemistry, University of Leeds, Leeds LS2 9JT (United Kingdom); Glover, William J.; Martinez, Todd J. [Department of Chemistry and The PULSE Institute, Stanford University, Stanford, California 94305, USA and SLAC National Accelerator Laboratory, Menlo Park, California 94025 (United States)
2014-08-07
We present a new algorithm for ab initio quantum nonadiabatic molecular dynamics that combines the best features of ab initio Multiple Spawning (AIMS) and Multiconfigurational Ehrenfest (MCE) methods. In this new method, ab initio multiple cloning (AIMC), the individual trajectory basis functions (TBFs) follow Ehrenfest equations of motion (as in MCE). However, the basis set is expanded (as in AIMS) when these TBFs become sufficiently mixed, preventing prolonged evolution on an averaged potential energy surface. We refer to the expansion of the basis set as “cloning,” in analogy to the “spawning” procedure in AIMS. This synthesis of AIMS and MCE allows us to leverage the benefits of mean-field evolution during periods of strong nonadiabatic coupling while simultaneously avoiding mean-field artifacts in Ehrenfest dynamics. We explore the use of time-displaced basis sets, “trains,” as a means of expanding the basis set for little cost. We also introduce a new bra-ket averaged Taylor expansion (BAT) to approximate the necessary potential energy and nonadiabatic coupling matrix elements. The BAT approximation avoids the necessity of computing electronic structure information at intermediate points between TBFs, as is usually done in saddle-point approximations used in AIMS. The efficiency of AIMC is demonstrated on the nonradiative decay of the first excited state of ethylene. The AIMC method has been implemented within the AIMS-MOLPRO package, which was extended to include Ehrenfest basis functions.
Dynamic recurrent Elman neural network based on immune clonal selection algorithm
Wang, Limin; Han, Xuming; Li, Ming; Sun, Haibo; Li, Qingzhao
2012-04-01
Owing to the immune clonal selection algorithm introduced into dynamic threshold strategy has better advantage on optimizing multi-parameters, therefore a novel approach that the immune clonal selection algorithm introduced into dynamic threshold strategy, is used to optimize the dynamic recursion Elman neural network is proposed in the paper. The concrete structure of the recursion neural network, the connect weight and the initial values of the contact units etc. are done by evolving training and learning automatically. Thus it could realize to construct and design for dynamic recursion Elman neural networks. It could provide a new effective approach for immune clonal selection algorithm optimizing dynamic recursion neural networks.
Identification of Dynamic Parameters Based on Pseudo-Parallel Ant Colony Optimization Algorithm
Institute of Scientific and Technical Information of China (English)
ZHAO Feng-yao; MA Zhen-yue; ZHANG Yun-liang
2007-01-01
For the parameter identification of dynamic problems, a pseudo-parallel ant colony optimization (PPACO) algorithm based on graph-based ant system (AS) was introduced. On the platform of ANSYS dynamic analysis, the PPACO algorithm was applied to the identification of dynamic parameters successfully. Using simulated data of forces and displacements, elastic modulus E and damping ratio ξ was identified for a designed 3D finite element model, and the detailed identification step was given. Mathematical example and simulation example show that the proposed method has higher precision, faster convergence speed and stronger antinoise ability compared with the standard genetic algorithm and the ant colony optimization (ACO) algorithms.
Critical Dynamics Behavior of the Wolff Algorithm in the Site-Bond-Correlated Ising Model
Campos, P. R. A.; Onody, R. N.
Here we apply the Wolff single-cluster algorithm to the site-bond-correlated Ising model and study its critical dynamical behavior. We have verified that the autocorrelation time diminishes in the presence of dilution and correlation, showing that the Wolff algorithm performs even better in such situations. The critical dynamical exponents are also estimated.
Analysis of Ant Colony Optimization and Population-Based Evolutionary Algorithms on Dynamic Problems
DEFF Research Database (Denmark)
Lissovoi, Andrei
This thesis presents new running time analyses of nature-inspired algorithms on various dynamic problems. It aims to identify and analyse the features of algorithms and problem classes which allow efficient optimization to occur in the presence of dynamic behaviour. We consider the following...
Dynamic Vehicle Routing for Robotic Networks: Models, Fundamental Limitations and Algorithms
2010-04-16
partitions. SIAM Review, January 2010. Submitted Francesco Bullo (UCSB) Dynamic Vehicle Routing 16apr10 @ ARL 31 / 34 Gossip partitioning policy: sample...Control Conference, Hollywood, CA, October 2009 Francesco Bullo (UCSB) Dynamic Vehicle Routing 16apr10 @ ARL 32 / 34 Gossip partitioning policy: analysis...Dynamic Vehicle Routing for Robotic Networks: Models, Fundamental Limitations and Algorithms Francesco Bullo Center for Control, Dynamical Systems
Application of Symplectic Algebraic Dynamics Algorithm to Circular Restricted Three-Body Problem
Institute of Scientific and Technical Information of China (English)
LU Wei-Tao; ZHANG Hua; WANG Shun-Jin
2008-01-01
Symplectic algebraic dynamics algorithm (SADA) for ordinary differential equations is applied to solve numerically the circular restricted three-body problem (CR3BP) in dynamical astronomy for both stable motion and chaotic motion. The result is compared with those of Runge-Kutta algorithm and symplectic algorithm under the fourth order, which shows that SADA has higher accuracy than the others in the long-term calculations of the CR3BP.
Derandomization of Online Assignment Algorithms for Dynamic Graphs
Sahai, Ankur
2011-01-01
This paper analyzes different online algorithms for the problem of assigning weights to edges in a fully-connected bipartite graph that minimizes the overall cost while satisfying constraints. Edges in this graph may disappear and reappear over time. Performance of these algorithms is measured using simulations. This paper also attempts to derandomize the randomized online algorithm for this problem.
A DYNAMICAL SYSTEM ALGORITHM FOR SOLVING A LEAST SQUARES PROBLEM WITH ORTHOGONALITY CONSTRAINTS
Institute of Scientific and Technical Information of China (English)
黄建国; 叶中行; 徐雷
2001-01-01
This paper introduced a dynamical system (neural networks) algorithm for solving a least squares problem with orthogonality constraints, which has wide applications in computer vision and signal processing. A rigorous analysis for the convergence and stability of the algorithm was provided. Moreover, a so called zero-extension technique was presented to keep the algorithm always convergent to the needed result for any randomly chosen initial data. Numerical experiments illustrate the effectiveness and efficiency of the algorithm.
Prof. Rakesh Mohanty; Prof H.S Behera; Khusbu Patwari; Monisha Dash; M. Lakshmi Prasanna
2011-01-01
In this paper, a new variant of Round Robin (RR) algorithm is proposed which is suitable for soft real time systems. RR algorithm performs optimally in timeshared systems, but it is not suitable for soft real time systems. Because it gives more number of context switches, larger waiting time and larger response time. We have proposed a novel algorithm, known as Priority Based Dynamic Round Robin Algorithm(PBDRR), which calculates intelligent time slice for individual processes and changes aft...
Development of new flux splitting schemes. [computational fluid dynamics algorithms
Liou, Meng-Sing; Steffen, Christopher J., Jr.
1992-01-01
Maximizing both accuracy and efficiency has been the primary objective in designing a numerical algorithm for computational fluid dynamics (CFD). This is especially important for solutions of complex three dimensional systems of Navier-Stokes equations which often include turbulence modeling and chemistry effects. Recently, upwind schemes have been well received for their capability in resolving discontinuities. With this in mind, presented are two new flux splitting techniques for upwind differencing. The first method is based on High-Order Polynomial Expansions (HOPE) of the mass flux vector. The second new flux splitting is based on the Advection Upwind Splitting Method (AUSM). The calculation of the hypersonic conical flow demonstrates the accuracy of the splitting in resolving the flow in the presence of strong gradients. A second series of tests involving the two dimensional inviscid flow over a NACA 0012 airfoil demonstrates the ability of the AUSM to resolve the shock discontinuity at transonic speed. A third case calculates a series of supersonic flows over a circular cylinder. Finally, the fourth case deals with tests of a two dimensional shock wave/boundary layer interaction.
Maintenance of Process Control Algorithms based on Dynamic Program Slicing
DEFF Research Database (Denmark)
Hansen, Ole Fink; Andersen, Nils Axel; Ravn, Ole
2010-01-01
Today’s industrial control systems gradually lose performance after installation and must be regularly maintained by means of adjusting parameters and modifying the control algorithm, in order to regain high performance. Industrial control algorithms are complex software systems, and it is partic......Today’s industrial control systems gradually lose performance after installation and must be regularly maintained by means of adjusting parameters and modifying the control algorithm, in order to regain high performance. Industrial control algorithms are complex software systems...... the behavior of a control algorithm, enables maintenance personnel to focus on only relevant parts of the algorithm and semi-automatically locate the part of the algorithm that is responsible for the reduced performance. The solution is tuning-free and can be applied to installed and running systems without...
Numerical simulation study of the dynamical behavior of the Niedermayer algorithm
Girardi, D
2010-01-01
We calculate the dynamic critical exponent for the Niedermayer algorithm applied to the two-dimensional Ising and XY models, for various values of the free parameter $E_0$. For $E_0=-1$ we regain the Metropolis algorithm and for $E_0=1$ we regain the Wolff algorithm. For $-1\\widetilde{L}$, the Niedermayer algorithm is equivalent to the Metropolis one, i.e, they have the same dynamic exponent. For $E_0>1$, the autocorrelation time is always greater than for $E_0=1$ (Wolff) and, more important, it also grows faster than a power of $L$. Therefore, we show that the best choice of cluster algorithm is the Wolff one, when compared to the Nierdermayer generalization. We also obtain the dynamic behavior of the Wolff algorithm: although not conclusive, we propose a scaling law for the dependence of the autocorrelation time on $L$.
Explicit symplectic algorithms based on generating functions for charged particle dynamics
Zhang, Ruili; Tang, Yifa; Liu, Jian; He, Yang; Xiao, Jianyuan
2016-01-01
Dynamics of a charged particle in the canonical coordinates is a Hamiltonian system, and the well-known symplectic algorithm has been regarded as the de facto method for numerical integration of Hamiltonian systems due to its long-term accuracy and fidelity. For long-term simulations with high efficiency, explicit symplectic algorithms are desirable. However, it is widely accepted that explicit symplectic algorithms are only available for sum-separable Hamiltonians, and that this restriction severely limits the application of explicit symplectic algorithms to charged particle dynamics. To overcome this difficulty, we combine the familiar sum-split method and a generating function method to construct second and third order explicit symplectic algorithms for dynamics of charged particle. The generating function method is designed to generate explicit symplectic algorithms for product-separable Hamiltonian with form of $H(\\mathbf{p},\\mathbf{q})=\\mathbf{p}_{i}f(\\mathbf{q})$ or $H(\\mathbf{p},\\mathbf{q})=\\mathbf{q}...
Qi, Wei; Zhang, Chi; Fu, Guangtao; Zhou, Huicheng
2016-02-01
It is widely recognized that optimization algorithm parameters have significant impacts on algorithm performance, but quantifying the influence is very complex and difficult due to high computational demands and dynamic nature of search parameters. The overall aim of this paper is to develop a global sensitivity analysis based framework to dynamically quantify the individual and interactive influence of algorithm parameters on algorithm performance. A variance decomposition sensitivity analysis method, Analysis of Variance (ANOVA), is used for sensitivity quantification, because it is capable of handling small samples and more computationally efficient compared with other approaches. The Shuffled Complex Evolution method developed at the University of Arizona algorithm (SCE-UA) is selected as an optimization algorithm for investigation, and two criteria, i.e., convergence speed and success rate, are used to measure the performance of SCE-UA. Results show the proposed framework can effectively reveal the dynamic sensitivity of algorithm parameters in the search processes, including individual influences of parameters and their interactive impacts. Interactions between algorithm parameters have significant impacts on SCE-UA performance, which has not been reported in previous research. The proposed framework provides a means to understand the dynamics of algorithm parameter influence, and highlights the significance of considering interactive parameter influence to improve algorithm performance in the search processes.
Directory of Open Access Journals (Sweden)
Prof. Rakesh Mohanty
2011-03-01
Full Text Available In this paper, a new variant of Round Robin (RR algorithm is proposed which is suitable for soft real time systems. RR algorithm performs optimally in timeshared systems, but it is not suitable for soft real time systems. Because it gives more number of context switches, larger waiting time and larger response time. We have proposed a novel algorithm, known as Priority Based Dynamic Round Robin Algorithm(PBDRR, which calculates intelligent time slice for individual processes and changes after every round of execution. The proposed scheduling algorithm is developed by taking dynamic time quantum concept into account. Our experimental results show that our proposed algorithm performs better than algorithm in [8] in terms of reducing the number of context switches, average waiting time and average turnaround time.
Mohanty, Rakesh; Patwari, Khusbu; Dash, Monisha; Prasanna, M Lakshmi
2011-01-01
In this paper, a new variant of Round Robin (RR) algorithm is proposed which is suitable for soft real time systems. RR algorithm performs optimally in timeshared systems, but it is not suitable for soft real time systems. Because it gives more number of context switches, larger waiting time and larger response time. We have proposed a novel algorithm, known as Priority Based Dynamic Round Robin Algorithm(PBDRR),which calculates intelligent time slice for individual processes and changes after every round of execution. The proposed scheduling algorithm is developed by taking dynamic time quantum concept into account. Our experimental results show that our proposed algorithm performs better than algorithm in [8] in terms of reducing the number of context switches, average waiting time and average turnaround time.
Event-chain algorithm for the Heisenberg model: Evidence for z≃1 dynamic scaling.
Nishikawa, Yoshihiko; Michel, Manon; Krauth, Werner; Hukushima, Koji
2015-12-01
We apply the event-chain Monte Carlo algorithm to the three-dimensional ferromagnetic Heisenberg model. The algorithm is rejection-free and also realizes an irreversible Markov chain that satisfies global balance. The autocorrelation functions of the magnetic susceptibility and the energy indicate a dynamical critical exponent z≈1 at the critical temperature, while that of the magnetization does not measure the performance of the algorithm. We show that the event-chain Monte Carlo algorithm substantially reduces the dynamical critical exponent from the conventional value of z≃2.
Event-chain algorithm for the Heisenberg model: Evidence for z ≃1 dynamic scaling
Nishikawa, Yoshihiko; Michel, Manon; Krauth, Werner; Hukushima, Koji
2015-12-01
We apply the event-chain Monte Carlo algorithm to the three-dimensional ferromagnetic Heisenberg model. The algorithm is rejection-free and also realizes an irreversible Markov chain that satisfies global balance. The autocorrelation functions of the magnetic susceptibility and the energy indicate a dynamical critical exponent z ≈1 at the critical temperature, while that of the magnetization does not measure the performance of the algorithm. We show that the event-chain Monte Carlo algorithm substantially reduces the dynamical critical exponent from the conventional value of z ≃2 .
Du, Jianqing; Zheng, Bo; Wang, Jian-Sheng
2006-05-01
Using a nonequilibrium relaxation method, we calculate the dynamic critical exponent z of the two-dimensional Ising model for the Swendsen-Wang and Wolff algorithms. We examine dynamic relaxation processes following a quench from a disordered or an ordered initial state to the critical temperature Tc, and measure the exponential relaxation time of the system energy. For the Swendsen-Wang algorithm with an ordered or a disordered initial state, and for the Wolff algorithm with an ordered initial state, the exponential relaxation time fits well to a logarithmic size dependence up to a lattice size L = 8192. For the Wolff algorithm with a disordered initial state, we obtain an effective dynamic exponent zexp = 1.19(2) up to L = 2048. For comparison, we also compute the effective dynamic exponents through the integrated correlation times. In addition, an exact result of the Swendsen-Wang dynamic spectrum of a one-dimensional Ising chain is derived.
A HYBRID GRANULARITY PARALLEL ALGORITHM FOR PRECISE INTEGRATION OF STRUCTURAL DYNAMIC RESPONSES
Institute of Scientific and Technical Information of China (English)
Yuanyin Li; Xianlong Jin; Genguo Li
2008-01-01
Precise integration methods to solve structural dynamic responses and the corre-sponding time integration formula are composed of two parts: the multiplication of an exponential matrix with a vector and the integration term. The second term can be solved by the series solu-tion. Two hybrid granularity parallel algorithms are designed, that is, the exponential matrix and the first term are computed by the fine-grained parallel algorithm and the second term is com-puted by the coarse-grained parallel algorithm. Numerical examples show that these two hybrid granularity parallel algorithms obtain higher speedup and parallel efficiency than two existing parallel algorithms.
Page, Andrew J; Keane, Thomas M; Naughton, Thomas J
2010-07-01
We present a multi-heuristic evolutionary task allocation algorithm to dynamically map tasks to processors in a heterogeneous distributed system. It utilizes a genetic algorithm, combined with eight common heuristics, in an effort to minimize the total execution time. It operates on batches of unmapped tasks and can preemptively remap tasks to processors. The algorithm has been implemented on a Java distributed system and evaluated with a set of six problems from the areas of bioinformatics, biomedical engineering, computer science and cryptography. Experiments using up to 150 heterogeneous processors show that the algorithm achieves better efficiency than other state-of-the-art heuristic algorithms.
A New Recommendation Algorithm Based on User’s Dynamic Information in Complex Social Network
Directory of Open Access Journals (Sweden)
Jiujun Cheng
2015-01-01
Full Text Available The development of recommendation system comes with the research of data sparsity, cold start, scalability, and privacy protection problems. Even though many papers proposed different improved recommendation algorithms to solve those problems, there is still plenty of room for improvement. In the complex social network, we can take full advantage of dynamic information such as user’s hobby, social relationship, and historical log to improve the performance of recommendation system. In this paper, we proposed a new recommendation algorithm which is based on social user’s dynamic information to solve the cold start problem of traditional collaborative filtering algorithm and also considered the dynamic factors. The algorithm takes user’s response information, dynamic interest, and the classic similar measurement of collaborative filtering algorithm into account. Then, we compared the new proposed recommendation algorithm with the traditional user based collaborative filtering algorithm and also presented some of the findings from experiment. The results of experiment demonstrate that the new proposed algorithm has a better recommended performance than the collaborative filtering algorithm in cold start scenario.
Zhang, Zhihua; Sheng, Zheng; Shi, Hanqing; Fan, Zhiqiang
2016-01-01
Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS) algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO). Rechenberg's 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter.
Directory of Open Access Journals (Sweden)
Zhihua Zhang
2016-01-01
Full Text Available Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO. Rechenberg’s 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter.
Institute of Scientific and Technical Information of China (English)
DU Mao-Kang; HE Bo; WANG Yong
2011-01-01
Recently, the cryptosystem based on chaos has attracted much attention. Wang and Yu (Commun. Nonlin. Sci. Numer. Simulat. 14(2009)574) proposed a block encryption algorithm based on dynamic sequences of multiple chaotic systems. We analyze the potential Saws in the algorithm. Then, a chosen-plaintext attack is presented. Some remedial measures are suggested to avoid the flaws effectively. Furthermore, an improved encryption algorithm is proposed to resist the attacks and to keep all the merits of the original cryptosystem.
NONLINEAR FILTER METHOD OF GPS DYNAMIC POSITIONING BASED ON BANCROFT ALGORITHM
Institute of Scientific and Technical Information of China (English)
ZHANGQin; TAOBen-zao; ZHAOChao-ying; WANGLi
2005-01-01
Because of the ignored items after linearization, the extended Kalman filter (EKF) becomes a form of suboptimal gradient descent algorithm. The emanative tendency exists in GPS solution when the filter equations are ill-posed. The deviation in the estimation cannot be avoided. Furthermore, the true solution may be lost in pseudorange positioning because the linearized pseudorange equations are partial solutions. To solve the above problems in GPS dynamic positioning by using EKF, a closed-form Kalman filter method called the two-stage algorithm is presented for the nonlinear algebraic solution of GPS dynamic positioning based on the global nonlinear least squares closed algorithm--Bancroft numerical algorithm of American. The method separates the spatial parts from temporal parts during processing the GPS filter problems, and solves the nonlinear GPS dynamic positioning, thus getting stable and reliable dynamic positioning solutions.
Genetic algorithms with memory- and elitism-based immigrants in dynamic environments.
Yang, Shengxiang
2008-01-01
In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.
Force-Based Incremental Algorithm for Mining Community Structure in Dynamic Network
Institute of Scientific and Technical Information of China (English)
Bo Yang; Da-You Liu
2006-01-01
Community structure is an important property of network. Being able to identify communities can provide invaluable help in exploiting and understanding both social and non-social networks. Several algorithms have been developed up till now. However, all these algorithms can work well only with small or moderate networks with vertexes of order 104.Besides, all the existing algorithms are off-line and cannot work well with highly dynamic networks such as web, in which web pages are updated frequently. When an already clustered network is updated, the entire network including original and incremental parts has to be recalculated, even though only slight changes are involved. To address this problem, an incremental algorithm is proposed, which allows for mining community structure in large-scale and dynamic networks. Based on the community structure detected previously, the algorithm takes little time to reclassify the entire network including both the original and incremental parts. Furthermore, the algorithm is faster than most of the existing algorithms such as Girvan and Newman's algorithm and its improved versions. Also, the algorithm can help to visualize these community structures in network and provide a new approach to research on the evolving process of dynamic networks.
Dynamical Consensus Algorithm for Second-Order Multi-Agent Systems Subjected to Communication Delay
Institute of Scientific and Technical Information of China (English)
LIU Cheng-Lin; LIU Fei
2013-01-01
To solve the dynamical consensus problem of second-order multi-agent systems with communication delay,delay-dependent compensations are added into the normal asynchronously-coupled consensus algorithm so as to make the agents achieve a dynamical consensus.Based on frequency-domain analysis,sufficient conditions are gained for second-order multi-agent systems with communication delay under leaderless and leader-following consensus algorithms respectively.Simulation illustrates the correctness of the results.
Extracting quantum dynamics from genetic learning algorithms through principal component analysis
White, J L; Bucksbaum, P H
2004-01-01
Genetic learning algorithms are widely used to control ultrafast optical pulse shapes for photo-induced quantum control of atoms and molecules. An outstanding issue is how to use the solutions found by these algorithms to learn about the system's quantum dynamics. We propose a simple method based on principal component analysis of the control space, which can reveal the degrees of freedom responsible for control, and aid in the construction of an effective Hamiltonian for the dynamics.
Behera, H S; Sahu, Sabyasachi; Bhoi, Sourav Kumar
2011-01-01
CPU being considered a primary computer resource, its scheduling is central to operating-system design. A thorough performance evaluation of various scheduling algorithms manifests that Round Robin Algorithm is considered as optimal in time shared environment because the static time is equally shared among the processes. We have proposed an efficient technique in the process scheduling algorithm by using dynamic time quantum in Round Robin. Our approach is based on the calculation of time quantum twice in single round robin cycle. Taking into consideration the arrival time, we implement the algorithm. Experimental analysis shows better performance of this improved algorithm over the Round Robin algorithm and the Shortest Remaining Burst Round Robin algorithm. It minimizes the overall number of context switches, average waiting time and average turn-around time. Consequently the throughput and CPU utilization is better.
Dynamic route guidance algorithm based algorithm based on artificial immune system
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
To improve the performance of the K-shortest paths search in intelligent traffic guidance systems,this paper proposes an optimal search algorithm based on the intelligent optimization search theory and the memphor mechanism of vertebrate immune systems.This algorithm,applied to the urban traffic network model established by the node-expanding method,can expediently realize K-shortest paths search in the urban traffic guidance systems.Because of the immune memory and global parallel search ability from artificial immune systems,K shortest paths can be found without any repeat,which indicates evidently the superiority of the algorithm to the conventional ones.Not only does it perform a better parallelism,the algorithm also prevents premature phenomenon that often occurs in genetic algorithms.Thus,it is especially suitable for real-time requirement of the traffic guidance system and other engineering optimal applications.A case study verifies the efficiency and the practicability of the algorithm aforementioned.
Battiste, Vernol; Lawton, George; Lachter, Joel; Brandt, Summer; Koteskey, Robert; Dao, Arik-Quang; Kraut, Josh; Ligda, Sarah; Johnson, Walter W.
2012-01-01
Managing the interval between arrival aircraft is a major part of the en route and TRACON controller s job. In an effort to reduce controller workload and low altitude vectoring, algorithms have been developed to allow pilots to take responsibility for, achieve and maintain proper spacing. Additionally, algorithms have been developed to create dynamic weather-free arrival routes in the presence of convective weather. In a recent study we examined an algorithm to handle dynamic re-routing in the presence of convective weather and two distinct spacing algorithms. The spacing algorithms originated from different core algorithms; both were enhanced with trajectory intent data for the study. These two algorithms were used simultaneously in a human-in-the-loop (HITL) simulation where pilots performed weather-impacted arrival operations into Louisville International Airport while also performing interval management (IM) on some trials. The controllers retained responsibility for separation and for managing the en route airspace and some trials managing IM. The goal was a stress test of dynamic arrival algorithms with ground and airborne spacing concepts. The flight deck spacing algorithms or controller managed spacing not only had to be robust to the dynamic nature of aircraft re-routing around weather but also had to be compatible with two alternative algorithms for achieving the spacing goal. Flight deck interval management spacing in this simulation provided a clear reduction in controller workload relative to when controllers were responsible for spacing the aircraft. At the same time, spacing was much less variable with the flight deck automated spacing. Even though the approaches taken by the two spacing algorithms to achieve the interval management goals were slightly different they seem to be simpatico in achieving the interval management goal of 130 sec by the TRACON boundary.
Enhanced Precedence Scheduling Algorithm with Dynamic Time Quantum (EPSADTQ
Directory of Open Access Journals (Sweden)
G. Siva Nageswara Rao
2015-07-01
Full Text Available This study proposes a new algorithm which is a logical extension of the popular Round Robin CPU scheduling algorithm. The Round Robin algorithm can be effective only if the time quantum is chosen accurately. Even by taking mean average of burst times as time quantum, the performance of the RR cannot be improved beyond a certain point. However, the novel method proposed here, suggests that a priority be assigned to each process based on balanced precedence factor. The novel method also uses mean average as a time quantum. Experiments are conducted in order to measure the effectiveness of this novel method. The results clearly showed that EPSADTQ is superior to RR and PSMTQ and its variants. EPSADTQ resulted in a significant reduction of the no. of context switches, average waiting time and average turnaround time.
Adaptive-mesh algorithms for computational fluid dynamics
Powell, Kenneth G.; Roe, Philip L.; Quirk, James
1993-01-01
The basic goal of adaptive-mesh algorithms is to distribute computational resources wisely by increasing the resolution of 'important' regions of the flow and decreasing the resolution of regions that are less important. While this goal is one that is worthwhile, implementing schemes that have this degree of sophistication remains more of an art than a science. In this paper, the basic pieces of adaptive-mesh algorithms are described and some of the possible ways to implement them are discussed and compared. These basic pieces are the data structure to be used, the generation of an initial mesh, the criterion to be used to adapt the mesh to the solution, and the flow-solver algorithm on the resulting mesh. Each of these is discussed, with particular emphasis on methods suitable for the computation of compressible flows.
Information dynamics algorithm for detecting communities in networks
Massaro, E; Bagnoli, F; Liò, P
2011-01-01
The problem of community detection is relevant in many scientific disciplines, from social science to statistical physics. Given the impact of community detection in many areas, such as psychology and social sciences, we have addressed the issue of modifying existing well performing algorithms by incorporating elements of the domain application fields, i.e. domain-inspired. We have focused on a psychology and social network - inspired approach which may be useful for further strengthening the link between social network studies and mathematics of community detection. Here we introduce a community-detection algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method by considering networks' nodes as agents capable to take decisions. In this framework we have introduced a memory factor to mimic a typical human behavior such as the oblivion effect. The method is based on information diffusion and it includes a non-linear processing phase. We test our method on two classical community benchmark ...
Positive role of glassy dynamics in finite-time optimization by threshold algorithms
Hasegawa, M.
2011-01-01
The optimization mechanism of threshold algorithms is investigated in the solving process of a random Euclidean traveling salesman problem. A series of computer experiments previously designed for simulated annealing is conducted with algorithms such as generalized simulated annealing. The results show that if the threshold function decays fast enough, a previous positive view of slow relaxation dynamics in finite-time optimization by simulated annealing is still applicable regardless of the algorithm. These dynamics work effectively as an optimizer at around an intermediate temperature, which can be identified by using the Deborah number.
Numerical simulation study of the dynamical behavior of the Niedermayer algorithm
Girardi, D.; Branco, N. S.
2010-01-01
We calculate the dynamic critical exponent for the Niedermayer algorithm applied to the two-dimensional Ising and XY models, for various values of the free parameter $E_0$. For $E_0=-1$ we regain the Metropolis algorithm and for $E_0=1$ we regain the Wolff algorithm. For $-11$, the autocorrelation time is always greater than for $E_0=1$ (Wolff) and, more important, it also grows faster than a power of $L$. Therefore, we show that the best choice of cluster algorithm is the Wolff one, when com...
DEFF Research Database (Denmark)
Rong, Aiying; Hakonen, Henri; Lahdelma, Risto
2008-01-01
in the system, the number of periods over the planning horizon and the time for solving a single-period economic dispatch problem. We have compared the DP-RSC1 algorithm with realistic power plants against the unit decommitment algorithm and the traditional priority listing method. The results show that the DP...... introduce in this paper the DP-RSC1 algorithm, which is a variant of the dynamic programming (DP) algorithm based on linear relaxation of the ON/OFF states of the units and sequential commitment of units one by one. The time complexity of DP-RSC1 is proportional to the number of generating units...
Directory of Open Access Journals (Sweden)
Arild Helseth
2015-12-01
Full Text Available Stochastic dual dynamic programming (SDDP has become a popular algorithm used in practical long-term scheduling of hydropower systems. The SDDP algorithm is computationally demanding, but can be designed to take advantage of parallel processing. This paper presents a novel parallel scheme for the SDDP algorithm, where the stage-wise synchronization point traditionally used in the backward iteration of the SDDP algorithm is partially relaxed. The proposed scheme was tested on a realistic model of a Norwegian water course, proving that the synchronization point relaxation significantly improves parallel efficiency.
An Algorithm for the Stochastic Simulation of Gene Expression and Heterogeneous Population Dynamics
Charlebois, Daniel A; Fraser, Dawn; Kaern, Mads
2011-01-01
We present an algorithm for the stochastic simulation of gene expression and heterogeneous population dynamics. The algorithm combines an exact method to simulate molecular-level fluctuations in single cells and a constant-number Monte Carlo method to simulate time-dependent statistical characteristics of growing cell populations. To benchmark performance, we compare simulation results with steadystate and time-dependent analytical solutions for several scenarios, including steadystate and time-dependent gene expression, and the effects on population heterogeneity of cell growth, division, and DNA replication. This comparison demonstrates that the algorithm provides an efficient and accurate approach to simulate how complex biological features influence gene expression. We also use the algorithm to model gene expression dynamics within "bet-hedging" cell populations during their adaption to environmental stress. These simulations indicate that the algorithm provides a framework suitable for simulating and ana...
Li, Jun-Qing; Pan, Quan-Ke; Duan, Pei-Yong
2016-06-01
In this paper, we propose an improved discrete artificial bee colony (DABC) algorithm to solve the hybrid flexible flowshop scheduling problem with dynamic operation skipping features in molten iron systems. First, each solution is represented by a two-vector-based solution representation, and a dynamic encoding mechanism is developed. Second, a flexible decoding strategy is designed. Next, a right-shift strategy considering the problem characteristics is developed, which can clearly improve the solution quality. In addition, several skipping and scheduling neighborhood structures are presented to balance the exploration and exploitation ability. Finally, an enhanced local search is embedded in the proposed algorithm to further improve the exploitation ability. The proposed algorithm is tested on sets of the instances that are generated based on the realistic production. Through comprehensive computational comparisons and statistical analysis, the highly effective performance of the proposed DABC algorithm is favorably compared against several presented algorithms, both in solution quality and efficiency.
Evolutionary decision-makings for the dynamic weapon-target assignment problem
Institute of Scientific and Technical Information of China (English)
CHEN Jie; XIN Bin; PENG ZhiHong; DOU LiHua; ZHANG Juan
2009-01-01
The dynamic weapon-target assignment (DWTA) problem is an important issue In the field of military command and control.An asset-based DWTA optimization model was proposed with four kinds of constraints considered,including capability constraints,strategy constraints,resource constraints and engagement feasibility constraints.A general "virtual"representation of decisions was presented to facilitate the generation of feasible decisions.The representation is in essence the permutation of all assignment pairs.A construction procedure converts the permutations into real feasible decisions.In order to solve this problem,three evolutionary decision-making algorithms,Including a genetic algorithm and two memeitc algorithms,were developed.Experimental results show that the memetic algorithm based on greedy local search can generate obviously better DWTA decisions,especially for large-scale problems,than the genetic algorithm and the memetic algorithm based on steepest local search.
Multi-constraint quality of service routing algorithm for dynamic topology networks
Institute of Scientific and Technical Information of China (English)
无
2008-01-01
An adaptive multi-QoS routing algorithm called AMQRA is proposed for dynamic topology networks,such as satellite networks and Ad-hoc networks.The AMQRA is a distributed and mobile-agents-based routing algorithm,which combines ant quantity system (AQS) with ant colony optimization (ACO) that is used in AntNet routing algorithm.In dynamic topology networks,the AMQRA achieves timely optimization for concave metric QoS constraint and fast convergence.The proposed routing algorithm is simulated in Iridium satellite constellation on OPNET.The results show that AMQRA not only outperforms the AntNet in convergence rate in dynamic topology networks but also can optimize concave metric QoS constraint and reasonably allot bandwidth to the load to avoid networks congestion.
Institute of Scientific and Technical Information of China (English)
Liu Jie; Shi Shu-Ting; Zhao Jun-Chan
2013-01-01
The three most widely used methods for reconstructing the underlying time series via the recurrence plots (RPs) of a dynamical system are compared with each other in this paper.We aim to reconstruct a toy series,a periodical series,a random series,and a chaotic series to compare the effectiveness of the most widely used typical methods in terms of signal correlation analysis.The application of the most effective algorithm to the typical chaotic Lorenz system verifies the correctness of such an effective algorithm.It is verified that,based on the unthresholded RPs,one can reconstruct the original attractor by choosing different RP thresholds based on the Hirata algorithm.It is shown that,in real applications,it is possible to reconstruct the underlying dynamics by using quite little information from observations of real dynamical systems.Moreover,rules of the threshold chosen in the algorithm are also suggested.
Directory of Open Access Journals (Sweden)
Tsai Chi-Yi
2011-01-01
Full Text Available Abstract This article addresses the problem of low dynamic range image enhancement for commercial digital cameras. A novel simultaneous dynamic range compression and local contrast enhancement algorithm (SDRCLCE is presented to resolve this problem in a single-stage procedure. The proposed SDRCLCE algorithm is able to combine with many existent intensity transfer functions, which greatly increases the applicability of the proposed method. An adaptive intensity transfer function is also proposed to combine with SDRCLCE algorithm that provides the capability to adjustably control the level of overall lightness and contrast achieved at the enhanced output. Moreover, the proposed method is amenable to parallel processing implementation that allows us to improve the processing speed of SDRCLCE algorithm. Experimental results show that the performance of the proposed method outperforms three state-of-the-art methods in terms of dynamic range compression and local contrast enhancement.
Probabilistic dynamic deployment of wireless sensor networks by artificial bee colony algorithm.
Ozturk, Celal; Karaboga, Dervis; Gorkemli, Beyza
2011-01-01
As the usage and development of wireless sensor networks are increasing, the problems related to these networks are being realized. Dynamic deployment is one of the main topics that directly affect the performance of the wireless sensor networks. In this paper, the artificial bee colony algorithm is applied to the dynamic deployment of stationary and mobile sensor networks to achieve better performance by trying to increase the coverage area of the network. A probabilistic detection model is considered to obtain more realistic results while computing the effectively covered area. Performance of the algorithm is compared with that of the particle swarm optimization algorithm, which is also a swarm based optimization technique and formerly used in wireless sensor network deployment. Results show artificial bee colony algorithm can be preferable in the dynamic deployment of wireless sensor networks.
Igeta, Hideki; Hasegawa, Mikio
Chaotic dynamics have been effectively applied to improve various heuristic algorithms for combinatorial optimization problems in many studies. Currently, the most used chaotic optimization scheme is to drive heuristic solution search algorithms applicable to large-scale problems by chaotic neurodynamics including the tabu effect of the tabu search. Alternatively, meta-heuristic algorithms are used for combinatorial optimization by combining a neighboring solution search algorithm, such as tabu, gradient, or other search method, with a global search algorithm, such as genetic algorithms (GA), ant colony optimization (ACO), or others. In these hybrid approaches, the ACO has effectively optimized the solution of many benchmark problems in the quadratic assignment problem library. In this paper, we propose a novel hybrid method that combines the effective chaotic search algorithm that has better performance than the tabu search and global search algorithms such as ACO and GA. Our results show that the proposed chaotic hybrid algorithm has better performance than the conventional chaotic search and conventional hybrid algorithms. In addition, we show that chaotic search algorithm combined with ACO has better performance than when combined with GA.
PACE: A dynamic programming algorithm for hardware/software partitioning
DEFF Research Database (Denmark)
Knudsen, Peter Voigt; Madsen, Jan
1996-01-01
with a hardware area constraint and the problem of minimizing hardware area with a system execution time constraint. The target architecture consists of a single microprocessor and a single hardware chip (ASIC, FPGA, etc.) which are connected by a communication channel. The algorithm incorporates a realistic...
A hierarchic collision detection algorithm for simple Brownian dynamics.
Katsimitsoulia, Zoe; Taylor, William R
2010-04-01
We describe an algorithm to avoid steric violation (bumps) between bodies arranged in a hierarchy. The algorithm recursively directs the focus of a bump-detector towards the interactions of children whose parents are in collision. This has the effect of concentrating available computer resources towards maintaining good steric interactions in the region where bodies are colliding. The algorithm was implemented and tested under two programming environments: a graphical environment, OpenGL under Java3D, and a non-graphical environment in "C". The former used a built-in collision detection system whereas the latter used a simple algorithm devised previously for the interaction of "soft" bodies. This simpler system was found to run much faster (by 50-fold) even after allowing for time spent on graphical activity and was also better at preventing steric violations. With a hierarchy of three levels of 100, the non-graphical implementation was able to simulate a million atomic bodies for 100,000 steps in 12h on a laptop computer.
Advanced Dynamically Adaptive Algorithms for Stochastic Simulations on Extreme Scales
Energy Technology Data Exchange (ETDEWEB)
Xiu, Dongbin [Univ. of Utah, Salt Lake City, UT (United States)
2017-03-03
The focus of the project is the development of mathematical methods and high-performance computational tools for stochastic simulations, with a particular emphasis on computations on extreme scales. The core of the project revolves around the design of highly efficient and scalable numerical algorithms that can adaptively and accurately, in high dimensional spaces, resolve stochastic problems with limited smoothness, even containing discontinuities.
Optimization in Quaternion Dynamic Systems: Gradient, Hessian, and Learning Algorithms.
Xu, Dongpo; Xia, Yili; Mandic, Danilo P
2016-02-01
The optimization of real scalar functions of quaternion variables, such as the mean square error or array output power, underpins many practical applications. Solutions typically require the calculation of the gradient and Hessian. However, real functions of quaternion variables are essentially nonanalytic, which are prohibitive to the development of quaternion-valued learning systems. To address this issue, we propose new definitions of quaternion gradient and Hessian, based on the novel generalized Hamilton-real (GHR) calculus, thus making a possible efficient derivation of general optimization algorithms directly in the quaternion field, rather than using the isomorphism with the real domain, as is current practice. In addition, unlike the existing quaternion gradients, the GHR calculus allows for the product and chain rule, and for a one-to-one correspondence of the novel quaternion gradient and Hessian with their real counterparts. Properties of the quaternion gradient and Hessian relevant to numerical applications are also introduced, opening a new avenue of research in quaternion optimization and greatly simplified the derivations of learning algorithms. The proposed GHR calculus is shown to yield the same generic algorithm forms as the corresponding real- and complex-valued algorithms. Advantages of the proposed framework are illuminated over illustrative simulations in quaternion signal processing and neural networks.
EVOLUTIONARY NEURAL NETWORKS ALGORITHM FOR THE DYNAMIC FREQUENCY ASSIGNMENT PROBLEM
Directory of Open Access Journals (Sweden)
Jamal Elhachmi
2011-06-01
Full Text Available Wireless communication is used in many different situations such as mobile telephony, radio and TV broadcasting, satellite communication, wireless LANs, and military operations. In each of these situations a frequency assignment problem arises with application-specific characteristics. Researchers have developed different modelling ideas for each of the features of the problem, such as the handling of interference among radio signals, the availability of frequencies, and the optimization criterion. This paper presents a new approach for solving the problem of frequency allocation based on using initially a partial solution respecting all constraints according to a greedy algorithm. This partial solution is then used for the construction of our stimulation in the form of a neural network. In a second step, the approach will use searching techniques used in conjunction with iterative algorithms for theoptimization of the parameters and topology of the network. The iterative algorithms used are named hierarchical genetic algorithms (HGA. Our approach has been tested on standard benchmark problems called Philadelphia problems of frequency assignment. The results obtained are equivalent to those of current methods. Moreover, our approach shows more efficiency in terms of flexibility and autonomy.
Directory of Open Access Journals (Sweden)
Orly Yadid-Pecht
2013-10-01
Full Text Available Tone mapping algorithms are used to adapt captured wide dynamic range (WDR scenes to the limited dynamic range of available display devices. Although there are several tone mapping algorithms available, most of them require manual tuning of their rendering parameters. In addition, the high complexities of some of these algorithms make it difficult to implement efficient real-time hardware systems. In this work, a real-time hardware implementation of an exponent-based tone mapping algorithm is presented. The algorithm performs a mixture of both global and local compression on colored WDR images. An automatic parameter selector has been proposed for the tone mapping algorithm in order to achieve good tone-mapped images without manual reconfiguration of the algorithm for each WDR image. Both algorithms are described in Verilog and synthesized for a field programmable gate array (FPGA. The hardware architecture employs a combination of parallelism and system pipelining, so as to achieve a high performance in power consumption, hardware resources usage and processing speed. Results show that the hardware architecture produces images of good visual quality that can be compared to software-based tone mapping algorithms. High peak signal-to-noise ratio (PSNR and structural similarity (SSIM scores were obtained when the results were compared with output images obtained from software simulations using MATLAB.
Dynamic task scheduling algorithm with load balancing for heterogeneous computing system
Directory of Open Access Journals (Sweden)
Doaa M. Abdelkader
2012-07-01
Full Text Available In parallel computation, the scheduling and mapping tasks is considered the most critical problem which needs High Performance Computing (HPC to solve it by breaking the problem into subtasks and working on those subtasks at the same time. The application sub tasks are assigned to underline machines and ordered for execution according to its proceeding to grantee efficient use of available resources such as minimize execution time and satisfy load balance between processors of the underline machine. The underline infrastructure may be homogeneous or heterogeneous. Homogeneous infrastructure could use the same machines power and performance. While heterogeneous infrastructure include machines differ in its performance, speed, and interconnection. According to work in this paper a new dynamic task scheduling algorithm for Heterogeneous called a Clustering Based HEFT with Duplication (CBHD have been developed. The CBHD algorithm is considered an amalgamation between the most two important task scheduling in Heterogeneous machine, The Heterogeneous Earliest Finish Time (HEFT and the Triplet Clustering algorithms. In the CBHD algorithm the duplication is required to improve the performance of algorithm. A comparative study among the developed CBHD, the HEFT, and the Triplet Cluster algorithms has been done. According to the comparative results, it is found that the developed CBHD algorithm satisfies better execution time than both HEFT algorithm and Triplet Cluster algorithm, and in the same time, it achieves the load balancing which considered one of the main performance factors in the dynamic environment.
Resource Matchmaking Algorithm using Dynamic Rough Set in Grid Environment
Ataollahi, Iraj
2009-01-01
Grid environment is a service oriented infrastructure in which many heterogeneous resources participate to provide the high performance computation. One of the bug issues in the grid environment is the vagueness and uncertainty between advertised resources and requested resources. Furthermore, in an environment such as grid dynamicity is considered as a crucial issue which must be dealt with. Classical rough set have been used to deal with the uncertainty and vagueness. But it can just be used on the static systems and can not support dynamicity in a system. In this work we propose a solution, called Dynamic Rough Set Resource Discovery (DRSRD), for dealing with cases of vagueness and uncertainty problems based on Dynamic rough set theory which considers dynamic features in this environment. In this way, requested resource properties have a weight as priority according to which resource matchmaking and ranking process is done. We also report the result of the solution obtained from the simulation in GridSim s...
DEFF Research Database (Denmark)
Rong, Aiying; Hakonen, Henri; Lahdelma, Risto
2009-01-01
This paper addresses the unit commitment (UC) in multi-period combined heat and power (CHP) production planning under the deregulated power market. In CHP plants (units), generation of heat and power follows joint characteristics, which implies that it is difficult to determine the relative cost...... efficiency of the plants. We introduce in this paper the DRDP-RSC algorithm, which is a dynamic regrouping based dynamic programming (DP) algorithm based on linear relaxation of the ON/OFF states of the units, sequential commitment of units in small groups. Relaxed states of the plants are used to reduce...... the dimension of the UC problem and dynamic regrouping is used to improve the solution quality. Numerical results based on real-life data sets show that this algorithm is efficient and optimal or near-optimal solutions with very small optimality gap are obtained....
A Novel Dynamic Clustering Algorithm Based on Immune Network and Tabu Search
Institute of Scientific and Technical Information of China (English)
ZHONGJiang; WUZhongfu; WUKaigui; YANGQiang
2005-01-01
It's difficult to indicate the rational number of partitions in the data set before clustering usually.The problem can't be solved by traditional clustering algorithm, such as k-means or its variations. This paper proposes a novel Dynamic clustering algorithm based on the artificial immune network and tabu search (DCBIT). It optimizes the number and the location of the clusters at the same time. The algorithm includes two phases, it begins by running immune network algorithm to find a Clustering feasible solution (CFS), then it employs tabu search to get the optimum cluster number and cluster centers on the CFS. Also, the probabilities acquiring the CFS through immune network algorithm have been discussed in this paper. Some experimental results show that new algorithm has satisfied convergent probability and convergent speed.
Performance analysis of dynamic load balancing algorithm for multiprocessor interconnection network
Directory of Open Access Journals (Sweden)
M.U. Bokhari
2016-09-01
Full Text Available Multiprocessor interconnection network have become powerful parallel computing system for real-time applications. Nowadays the many researchers posses studies on the dynamic load balancing in multiprocessor system. Load balancing is the method of dividing the total load among the processors of the distributed system to progress task's response time as well as resource utilization whereas ignoring a condition where few processors are overloaded or underloaded or moderately loaded. However, in dynamic load balancing algorithm presumes no priori information about behaviour of tasks or the global state of the system. There are numerous issues while designing an efficient dynamic load balancing algorithm that involves utilization of system, amount of information transferred among processors, selection of tasks for migration, load evaluation, comparison of load levels and many more. This paper enlightens the performance analysis on dynamic load balancing strategy (DLBS algorithm, used for hypercube network in multiprocessor system.
Molecular dynamics algorithm enforcing energy conservation for microcanonical simulations.
Salueña, Clara; Avalos, Josep Bonet
2014-05-01
A reversible algorithm [enforced energy conservation (EEC)] that enforces total energy conservation for microcanonical simulations is presented. The key point is the introduction of the discrete-gradient method to define the forces from the conservative potentials, instead of the direct use of the force field at the actual position of the particle. We have studied the performance and accuracy of the EEC in two cases, namely Lennard-Jones fluid and a simple electrolyte model. Truncated potentials that usually induce inaccuracies in energy conservation are used. In particular, the reaction field approach is used in the latter. The EEC is able to preserve energy conservation for a long time, and, in addition, it performs better than the Verlet algorithm for these kinds of simulations.
Advanced Dynamically Adaptive Algorithms for Stochastic Simulations on Extreme Scales
Energy Technology Data Exchange (ETDEWEB)
Xiu, Dongbin [Purdue Univ., West Lafayette, IN (United States)
2016-06-21
The focus of the project is the development of mathematical methods and high-performance com- putational tools for stochastic simulations, with a particular emphasis on computations on extreme scales. The core of the project revolves around the design of highly e cient and scalable numer- ical algorithms that can adaptively and accurately, in high dimensional spaces, resolve stochastic problems with limited smoothness, even containing discontinuities.
ALGORITHM FOR DYNAMIC SCALING RELATIONAL DATABASE IN CLOUDS
Directory of Open Access Journals (Sweden)
Alexander V. Boichenko
2014-01-01
Full Text Available This article analyzes the main methods of scalingdatabases (replication, sharding and their supportat the popular relational databases and NoSQLsolutions with different data models: document-oriented, key-value, column-oriented and graph.The article presents an algorithm for the dynamicscaling of a relational database (DB, that takesinto account the speciﬁcs of the different types of logic database model. This article was prepared with the support of RFBR (grant № 13-07-00749.
Information dynamics algorithm for detecting communities in networks
Massaro, Emanuele; Bagnoli, Franco; Guazzini, Andrea; Lió, Pietro
2012-11-01
The problem of community detection is relevant in many scientific disciplines, from social science to statistical physics. Given the impact of community detection in many areas, such as psychology and social sciences, we have addressed the issue of modifying existing well performing algorithms by incorporating elements of the domain application fields, i.e. domain-inspired. We have focused on a psychology and social network-inspired approach which may be useful for further strengthening the link between social network studies and mathematics of community detection. Here we introduce a community-detection algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method [4] by considering networks' nodes as agents capable to take decisions. In this framework we have introduced a memory factor to mimic a typical human behavior such as the oblivion effect. The method is based on information diffusion and it includes a non-linear processing phase. We test our method on two classical community benchmark and on computer generated networks with known community structure. Our approach has three important features: the capacity of detecting overlapping communities, the capability of identifying communities from an individual point of view and the fine tuning the community detectability with respect to prior knowledge of the data. Finally we discuss how to use a Shannon entropy measure for parameter estimation in complex networks.
Indian Academy of Sciences (India)
A Rama Mohan Rao; T V S R Appa Rao; B Dattaguru
2004-02-01
The work reported in this paper is motivated by the need to develop portable parallel processing algorithms and codes which can run on a variety of hardware platforms without any modiﬁcations. The prime aim of the research work reported here is to test the portability of the parallel algorithms and also to study and understand the comparative efﬁciencies of three parallel algorithms developed for implicit time integration technique. The standard message passing interface (MPI) is used to develop parallel algorithms for computing nonlinear dynamic response of large structures employing implicit time-marching scheme. The parallel algorithms presented in this paper are developed under the broad framework of non-overlapped domain decomposition technique. Numerical studies indicate that the parallel algorithm devised employing the conventional form of Newmark time integration algorithm is faster than the predictor–corrector form. It is also accurate and highly adaptive to ﬁne grain computations. The group implicit algorithm is found to be extremely superior in performance when compared to the other two parallel algorithms. This algorithm is better suited for large size problems on coarse grain environment as the resulting submeshes will obviously be large and thus permit larger time steps without losing accuracy.
A New Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Complex Networks
Directory of Open Access Journals (Sweden)
Guoqiang Chen
2013-01-01
Full Text Available Community detection in dynamic networks is an important research topic and has received an enormous amount of attention in recent years. Modularity is selected as a measure to quantify the quality of the community partition in previous detection methods. But, the modularity has been exposed to resolution limits. In this paper, we propose a novel multiobjective evolutionary algorithm for dynamic networks community detection based on the framework of nondominated sorting genetic algorithm. Modularity density which can address the limitations of modularity function is adopted to measure the snapshot cost, and normalized mutual information is selected to measure temporal cost, respectively. The characteristics knowledge of the problem is used in designing the genetic operators. Furthermore, a local search operator was designed, which can improve the effectiveness and efficiency of community detection. Experimental studies based on synthetic datasets show that the proposed algorithm can obtain better performance than the compared algorithms.
An FDTD algorithm for simulating light propagation in anisotropic dynamic gain media
Al-Jabr, A. A.
2014-05-02
Simulating light propagation in anisotropic dynamic gain media such as semiconductors and solid-state lasers using the finite difference time-domain FDTD technique is a tedious process, as many variables need to be evaluated in the same instant of time. The algorithm has to take care of the laser dynamic gain, rate equations, anisotropy and dispersion. In this paper, to the best of our knowledge, we present the first algorithm that solves this problem. The algorithm is based on separating calculations into independent layers and hence solving each problem in a layer of calculations. The anisotropic gain medium is presented and tested using a one-dimensional set-up. The algorithm is then used for the analysis of a two-dimensional problem.
DBSR: Dynamic base station Repositioning using Genetic algorithm in wireless sensor network
Mollanejad, Amir; Zeynali, Mohammad
2010-01-01
Wireless sensor networks (WSNs) are commonly used in various ubiquitous and pervasive applications. Due to limited power resources, the optimal dynamic base station (BS) replacement could be Prolong the sensor network lifetime. In this paper we'll present a dynamic optimum method for base station replacement so that can save energy in sensors and increases network lifetime. Because positioning problem is a NPhard problem [1], therefore we'll use genetic algorithm to solve positioning problem. We've considered energy and distance parameters for finding BS optimized position. In our represented algorithm base station position is fixed just during each round and its positioning is done at the start of next round then it'll be placed in optimized position. Evaluating our proposed algorithm, we'll execute DBSR algorithm on LEACH & HEED Protocols.
Detectability thresholds and optimal algorithms for community structure in dynamic networks
Ghasemian, Amir; Clauset, Aaron; Moore, Cristopher; Peel, Leto
2015-01-01
We study the fundamental limits on learning latent community structure in dynamic networks. Specifically, we study dynamic stochastic block models where nodes change their community membership over time, but where edges are generated independently at each time step. In this setting (which is a special case of several existing models), we are able to derive the detectability threshold exactly, as a function of the rate of change and the strength of the communities. Below this threshold, we claim that no algorithm can identify the communities better than chance. We then give two algorithms that are optimal in the sense that they succeed all the way down to this limit. The first uses belief propagation (BP), which gives asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm, based on linearizing the BP equations. We verify our analytic and algorithmic results via numerical simulation, and close with a brief discussion of extensions and open questions.
Mohanty, Rakesh; Das, Manas; Prasanna, M. Lakshmi; Sudhashree
2011-01-01
In this paper, we have proposed a new variant of Round Robin scheduling algorithm by executing the processes according to the new calculated Fit Factor f and using the concept of dynamic time quantum. We have compared the performance of our proposed Fittest Job First Dynamic Round Robin(FJFDRR) algorithm with the Priority Based Static Round Robin(PBSRR) algorithm. Experimental results show that our proposed algorithm performs better than PBSRR in terms of reducing the number of context switch...
A genetic algorithm for dynamic parameters reverse deduction of integrated anchorage system
Institute of Scientific and Technical Information of China (English)
无
2006-01-01
In the analysis of the system of anchoring bar and wall rock in small strain and longitudinal vibration dynamic response, the influence of the cement grouting as well as the rock layer on the anchor bar can be evaluated as the two kinds of parameters: the dynamic stiffness and the damp, which are the vital reference of the anchorage quality. Based on the analytic solution to the dynamic equation of the integrated anchor bar, the new approach which combines genetic algorithm and the toolbox of Matlab is applied to solve the problem of multi-parameters reverse deduction for integrated anchorage system in dynamic testing. Using the traits of the self-organizing, self-adapting and the fast convergence speed of the genetic algorithm, the optimum of all possible solutions to dynamic parameters is obtained by calculating the project instances. Examples show that the method presented in this paper is effective and reliable.
Capacitated Dynamic Facility Location Problem Based on Tabu Search Algorithm
Institute of Scientific and Technical Information of China (English)
KUANG Yi-jun; ZHU Ke-jun
2007-01-01
Facility location problem is a kind of NP-Hard combinational problem. Considering ever-changing demand sites, demand quantity and releasing cost, we formulate a model combining tabu search and FCM (fuzzy clustering method) to solve the eapacitated dynamic facility location problem. Some results are achieved and they show that the proposed method is effective.
A Dynamic Programming Algorithm on Project-Gang Investment Decision-Making
Institute of Scientific and Technical Information of China (English)
无
2002-01-01
The investment decision-making of Project-Gang, the projects that are associated with one another on economy and technique, is studied. In order to find out the best Scheme that can make the maximum profit, a dynamic programming algorithm on the investment decision-making of Project-Gang is brought forward, and this algorithm can find out the best Scheme of distributing the m resources to the n Items in the time of O(m2n).
Solving chemical dynamic optimization problems with ranking-based differential evolution algorithms
Institute of Scientific and Technical Information of China (English)
Xu Chen; Wenli Du; Feng Qian
2016-01-01
Dynamic optimization problems (DOPs) described by differential equations are often encountered in chemical engineering. Deterministic techniques based on mathematic programming become invalid when the models are non-differentiable or explicit mathematical descriptions do not exist. Recently, evolutionary algorithms are gaining popularity for DOPs as they can be used as robust alternatives when the deterministic techniques are in-valid. In this article, a technology named ranking-based mutation operator (RMO) is presented to enhance the previous differential evolution (DE) algorithms to solve DOPs using control vector parameterization. In the RMO, better individuals have higher probabilities to produce offspring, which is helpful for the performance enhancement of DE algorithms. Three DE-RMO algorithms are designed by incorporating the RMO. The three DE-RMO algorithms and their three original DE algorithms are applied to solve four constrained DOPs from the literature. Our simulation results indicate that DE-RMO algorithms exhibit better performance than previous non-ranking DE algorithms and other four evolutionary algorithms.
Influence of Topological Features on Spatially-Structured Evolutionary Algorithms Dynamics
DeFelice, Matteo; Panzieri, Stefano
2012-01-01
In the last decades, complex networks theory significantly influenced other disciplines on the modeling of both static and dynamic aspects of systems observed in nature. This work aims to investigate the effects of networks' topological features on the dynamics of an evolutionary algorithm, considering in particular the ability to find a large number of optima on multi-modal problems. We introduce a novel spatially-structured evolutionary algorithm and we apply it on two combinatorial problems: ONEMAX and the multi-modal NMAX. Considering three different network models we investigate the relationships between their features, algorithm's convergence and its ability to find multiple optima (for the multi-modal problem). In order to perform a deeper analysis we investigate the introduction of weighted graphs with time-varying weights. The results show that networks with a large Average Path Length lead to an higher number of optima and a consequent slow exploration dynamics (i.e. low First Hitting Time). Further...
Application of the dynamic ant colony algorithm on the optimal operation of cascade reservoirs
Tong, X. X.; Xu, W. S.; Wang, Y. F.; Zhang, Y. W.; Zhang, P. C.
2016-08-01
Due to the lack of dynamic adjustments between global searches and local optimization, it is difficult to maintain high diversity and overcome local optimum problems for Ant Colony Algorithms (ACA). Therefore, this paper proposes an improved ACA, Dynamic Ant Colony Algorithm (DACA). DACA applies dynamic adjustments on heuristic factor changes to balance global searches and local optimization in ACA, which decreases cosines. At the same time, by utilizing the randomness and ergodicity of the chaotic search, DACA implements the chaos disturbance on the path found in each ACA iteration to improve the algorithm's ability to jump out of the local optimum and avoid premature convergence. We conducted a case study with DACA for optimal joint operation of the Dadu River cascade reservoirs. The simulation results were compared with the results of the gradual optimization method and the standard ACA, which demonstrated the advantages of DACA in speed and precision.
Energy Technology Data Exchange (ETDEWEB)
Trudnowski, Daniel J.; Pierre, John W.; Zhou, Ning; Hauer, John F.; Parashar, Manu
2008-05-31
The frequency and damping of electromechanical modes offer considerable insight into the dynamic stability properties of a power system. The performance properties of three block-processing algorithms from the perspective of near real-time automated stability assessment are demonstrated and examined. The algorithms are: the extended modified Yule Walker (YW); extended modified Yule Walker with Spectral analysis (YWS); and numerical state-space subspace system identification(N4SID) algorithm. The YW and N4SID have been introduced in previous publications while the YWS is introduced here. Issues addressed include: stability assessment requirements; automated subset selecting identified modes; using algorithms in an automated format; data assumptions and quality; and expected algorithm estimation performance.
Multi-Parameter Signal Sorting Algorithm Based on Dynamic Distance Clustering
Institute of Scientific and Technical Information of China (English)
Ai-Ling He; De-Guo Zeng; Jun Wang; Bin Tang
2009-01-01
A multi-parameter signal sorting algo- rithm for interleaved radar pulses in dense emitter environment is presented. The algorithm includes two parts, pulse classification and pulse repetition interval (PRI) analysis. Firstly, we propose the dynamic distance clustering (DDC) for classification. In the clustering algorithm, the multi-dimension features of radar pulse are used for reliable classification. The similarity threshold estimation method in DDC is derived, which contributes to the efficiency of the algorithm. However, DDC has large computation with many signal pulses. Then, in order to sort radar signals in real time, the improved DDC (IDDC) algorithm is proposed. Finally, PRI analysis is adopted to complete the process of sorting. The simulation experiments and hardware implementations show both algorithms are effective.
A study on the dynamic tie points ASI algorithm in the Arctic Ocean
Institute of Scientific and Technical Information of China (English)
HAO Guanghua; SU Jie
2015-01-01
Sea ice concentration is an important parameter for polar sea ice monitoring. Based on 89 GHz AMSR-E (Advanced Microwave Scanning Radiometer for Earth Observing System) data, a gridded high-resolution passive microwave sea ice concentration product can be obtained using the ASI (the Arctic Radiation And Turbulence Interaction Study (ARTIST) Sea Ice) retrieval algorithm. Instead of using fixed-point values, we developed ASI algorithm based on daily changed tie points, called as the dynamic tie point ASI algorithm in this study. Here the tie points are expressed as the brightness temperature polarization difference of open water and 100% sea ice. In 2010, the yearly-averaged tie points of open water and sea ice in Arctic are estimated to be 50.8 K and 7.8 K, respectively. It is confirmed that the sea ice concentrations retrieved by the dynamic tie point ASI algorithm can increase (decrease) the sea ice concentrations in low-value (high-value) areas. This improved the sea ice concentrations by present retrieval algorithm from microwave data to some extent. Comparing with the products using fixed tie points, the sea ice concentrations retrieved from AMSR-E data by using the dynamic tie point ASI algorithm are closer to those obtained from MODIS (Moderate-resolution Imaging Spectroradiometer) data. In 40 selected cloud-free sample regions, 95% of our results have smaller mean differences and 75% of our results have lower root mean square (RMS) differences compare with those by the fixed tie points.
A Dynamic Multistage Hybrid Swarm Intelligence Optimization Algorithm for Function Optimization
Directory of Open Access Journals (Sweden)
Daqing Wu
2012-01-01
Full Text Available A novel dynamic multistage hybrid swarm intelligence optimization algorithm is introduced, which is abbreviated as DM-PSO-ABC. The DM-PSO-ABC combined the exploration capabilities of the dynamic multiswarm particle swarm optimizer (PSO and the stochastic exploitation of the cooperative artificial bee colony algorithm (CABC for solving the function optimization. In the proposed hybrid algorithm, the whole process is divided into three stages. In the first stage, a dynamic multiswarm PSO is constructed to maintain the population diversity. In the second stage, the parallel, positive feedback of CABC was implemented in each small swarm. In the third stage, we make use of the particle swarm optimization global model, which has a faster convergence speed to enhance the global convergence in solving the whole problem. To verify the effectiveness and efficiency of the proposed hybrid algorithm, various scale benchmark problems are tested to demonstrate the potential of the proposed multistage hybrid swarm intelligence optimization algorithm. The results show that DM-PSO-ABC is better in the search precision, and convergence property and has strong ability to escape from the local suboptima when compared with several other peer algorithms.
Directory of Open Access Journals (Sweden)
Deepika Saxena
2016-02-01
Full Text Available Cloud computing has become buzzword today. It is a digital service where dynamically scalable and virtualized resources are provided as a service over internet. Task scheduling is premier research topic in cloud computing. It is always a challenging task to map variety of complex task on various available heterogenous resources in scalable and efficient way. The very objective of this paper is to dynamically optimize task scheduling at system level as well as user level. This paper relates benefit-fairness algorithm based on weighted-fair Queuing model which is much more efficient than simple priority queuing. In proposed algorithm, we have classified and grouped all tasks as deadline based and minimum cost based constraints and after dynamic optimization, priority of fairness is applied. Here different priority queue (high, mid, low are implemented in round-robin fashion as per weights assign to them .We recompile the CloudSim and simulate the proposed algorithm and results of this algorithm is compared with sequential task scheduling and simple constraints (cost and deadline based task scheduling algorithm. The experimental results indicates that proposed algorithm is, not only beneficial to user and service provider, but also provides better efficiency and fairness at priority level, i.e. benefit at system level.
Numerical simulation study of the dynamical behavior of the Niedermayer algorithm
Girardi, D.; Branco, N. S.
2010-04-01
We calculate the dynamic critical exponent for the Niedermayer algorithm applied to the two-dimensional Ising and XY models, for various values of the free parameter E0. For E0 = - 1 we regain the Metropolis algorithm and for E0 = 1 we regain the Wolff algorithm. For - 1 clusters of (possibly) turned spins initially grows with the linear size of the lattice, L, but eventually saturates at a given lattice size \\widetilde {L} , which depends on E0. For L\\gt \\widetilde {L} , the Niedermayer algorithm is equivalent to the Metropolis one, i.e., they have the same dynamic exponent. For E0 > 1, the autocorrelation time is always greater than for E0 = 1 (Wolff) and, more important, it also grows faster than a power of L. Therefore, we show that the best choice of cluster algorithm is the Wolff one, when comparing against the Niedermayer generalization. We also obtain the dynamic behavior of the Wolff algorithm: although not conclusively, we propose a scaling law for the dependence of the autocorrelation time on L.
An Approach for State Observation in Dynamical Systems Based on the Twisting Algorithm
DEFF Research Database (Denmark)
Schmidt, Lasse; Andersen, Torben Ole; Pedersen, Henrik C.
2013-01-01
This paper discusses a novel approach for state estimation in dynamical systems, with the special focus on hydraulic valve-cylinder drives. The proposed observer structure is based on the framework of the so-called twisting algorithm. This algorithm utilizes the sign of the state being the target...... for the observation, not directly implying its applicability for state observation. However it is shown that in the discrete case this approach may be applied to obtain a real time derivative using an intermediate integral process. Furthermore, different types of gains for the algorithm are suggested and analyzed via...
Institute of Scientific and Technical Information of China (English)
鄢田云; 张翠芳; 靳蕃
2003-01-01
Identification simulation for dynamical system which is based on genetic algorithm (GA) and recurrent multilayer neural network (RMNN) is presented. In order to reduce the inputs of the model, RMNN which can remember and store some previous parameters is used for identifier. And for its high efficiency and optimization, genetic algorithm is introduced into training RMNN. Simulation results show the effectiveness of the proposed scheme. Under the same training algorithm, the identification performance of RMNN is superior to that of nonrecurrent multilayer neural network (NRMNN).
Empirical relations between static and dynamic exponents for Ising model cluster algorithms
Energy Technology Data Exchange (ETDEWEB)
Coddington, P.D. (Department of Physics, Syracuse University, Syracuse, New York 13244 (United States)); Baillie, C.F. (Department of Physics, University of Colorado, Boulder, Colorado 80309 (United States))
1992-02-17
We have measured the autocorrelations for the Swendsen-Wang and the Wolff cluster update algorithms for the Ising model in two, three, and four dimensions. The data for the Wolff algorithm suggest that the autocorrelations are linearly related to the specific heat, in which case the dynamic critical exponent is {ital z}{sub int,}{ital E}{sup W}={alpha}/{nu}. For the Swendsen-Wang algorithm, scaling the autocorrelations by the average maximum cluster size gives either a constant or a logarithm, which implies that {ital z}{sub int,}{ital E}{sup SW}={beta}/{nu} for the Ising model.
A Novel Dynamic Bandwidth Assignment Algorithm for Multi-Services EPONs
Institute of Scientific and Technical Information of China (English)
CHEN Xue; ZHANG Yang; HUANG Xiang; DENG Yu; SUN Shu-he
2005-01-01
In this paper we propose a novel Dynamic Bandwidth Assignment (DBA) algorithm for Ethernet-based Passive Optical Networks (EPON) which offers multiple kinds of services. To satisfy crucial Quality of Service (QoS) requirement for Time Division Multiplexing (TDM) service and achieve fair and high bandwidth utilization simultaneously, the algorithm integrates periodic, for TDM service, and polling granting for Ethernet service. Detailed simulation shows that the algorithm guarantees carrier-grade QoS for TDM service, high bandwidth utilization and good fairness of bandwidth assignment among Optical Network Units (ONU).
Empirical relations between static and dynamic exponents for Ising model cluster algorithms
Coddington, Paul D.; Baillie, Clive F.
1992-02-01
We have measured the autocorrelations for the Swendsen-Wang and the Wolff cluster update algorithms for the Ising model in two, three, and four dimensions. The data for the Wolff algorithm suggest that the autocorrelations are linearly related to the specific heat, in which case the dynamic critical exponent is zint,EW=α/ν. For the Swendsen-Wang algorithm, scaling the autocorrelations by the average maximum cluster size gives either a constant or a logarithm, which implies that zint,ESW=β/ν for the Ising model.
Adaptive Dynamic Programming for Control Algorithms and Stability
Zhang, Huaguang; Luo, Yanhong; Wang, Ding
2013-01-01
There are many methods of stable controller design for nonlinear systems. In seeking to go beyond the minimum requirement of stability, Adaptive Dynamic Programming for Control approaches the challenging topic of optimal control for nonlinear systems using the tools of adaptive dynamic programming (ADP). The range of systems treated is extensive; affine, switched, singularly perturbed and time-delay nonlinear systems are discussed as are the uses of neural networks and techniques of value and policy iteration. The text features three main aspects of ADP in which the methods proposed for stabilization and for tracking and games benefit from the incorporation of optimal control methods: • infinite-horizon control for which the difficulty of solving partial differential Hamilton–Jacobi–Bellman equations directly is overcome, and proof provided that the iterative value function updating sequence converges to the infimum of all the value functions obtained by admissible control law sequences; • finite-...
Fuzzy Dynamic Discrimination Algorithms for Distributed Knowledge Management Systems
Directory of Open Access Journals (Sweden)
Vasile MAZILESCU
2010-12-01
Full Text Available A reduction of the algorithmic complexity of the fuzzy inference engine has the following property: the inputs (the fuzzy rules and the fuzzy facts can be divided in two parts, one being relatively constant for a long a time (the fuzzy rule or the knowledge model when it is compared to the second part (the fuzzy facts for every inference cycle. The occurrence of certain transformations over the constant part makes sense, in order to decrease the solution procurement time, in the case that the second part varies, but it is known at certain moments in time. The transformations attained in advance are called pre-processing or knowledge compilation. The use of variables in a Business Rule Management System knowledge representation allows factorising knowledge, like in classical knowledge based systems. The language of the first-degree predicates facilitates the formulation of complex knowledge in a rigorous way, imposing appropriate reasoning techniques. It is, thus, necessary to define the description method of fuzzy knowledge, to justify the knowledge exploiting efficiency when the compiling technique is used, to present the inference engine and highlight the functional features of the pattern matching and the state space processes. This paper presents the main results of our project PR356 for designing a compiler for fuzzy knowledge, like Rete compiler, that comprises two main components: a static fuzzy discrimination structure (Fuzzy Unification Tree and the Fuzzy Variables Linking Network. There are also presented the features of the elementary pattern matching process that is based on the compiled structure of fuzzy knowledge. We developed fuzzy discrimination algorithms for Distributed Knowledge Management Systems (DKMSs. The implementations have been elaborated in a prototype system FRCOM (Fuzzy Rule COMpiler.
A Dynamic Programming Algorithm for the k-Haplotyping Problem
Institute of Scientific and Technical Information of China (English)
Zhen-ping Li; Ling-yun Wu; Yu-ying Zhao; Xiang-sun Zhang
2006-01-01
The Minimum Fragments Removal (MFR) problem is one of the haplotyping problems: given a set of fragments, remove the minimum number of fragments so that the resulting fragments can be partitioned into k classes of non-conflicting subsets. In this paper, we formulate the k-MFR problem as an integer linear programming problem, and develop a dynamic programming approach to solve the k-MFR problem for both the gapless and gap cases.
Recognition algorithm of seabed pipeline defect inspection based on dynamic WBF neural networks
Institute of Scientific and Technical Information of China (English)
Jin Tao; Que Peiwen; Tao Zhengshu
2005-01-01
This paper describes a magnetic flux leak (MFL) model of pipeline defect inspection, and presents a recognition algorithm based on dynamic wavelet basis function (WBF) neural network. The dynamic network utilizes multiscale and multiresolution orthogonal wavelet, through signals backwards propagation, has more significant advantages than BP or other neural networks used in MFL inspection. It also can control the accuracy of the predicted defect profiles, high-speed convergence possessing and well approaching feature. The performance applying the algorithm based on the network to predict defect profile from experimental MFL signals is presented.
The generation algorithm of arbitrary polygon animation based on dynamic correction
Directory of Open Access Journals (Sweden)
Hou Ya Wei
2016-01-01
Full Text Available This paper, based on the key-frame polygon sequence, proposes a method that makes use of dynamic correction to develop continuous animation. Firstly we use quadratic Bezier curve to interpolate the corresponding sides vector of polygon sequence consecutive frame and realize the continuity of animation sequences. And then, according to Bezier curve characteristic, we conduct dynamic regulation to interpolation parameters and implement the changing smoothness. Meanwhile, we take use of Lagrange Multiplier Method to correct the polygon and close it. Finally, we provide the concrete algorithm flow and present numerical experiment results. The experiment results show that the algorithm acquires excellent effect.
A novel algorithm for SLAM in dynamic environments using landscape theory of aggregation
Institute of Scientific and Technical Information of China (English)
华承昊; 窦丽华; 方浩; 付浩
2016-01-01
To tackle the problem of simultaneous localization and mapping (SLAM) in dynamic environments, a novel algorithm using landscape theory of aggregation is presented. By exploiting the coherent explanation how actors form alignments in a game provided by the landscape theory of aggregation, the algorithm is able to explicitly deal with the ever-changing relationship between the static objects and the moving objects without any prior models of the moving objects. The effectiveness of the method has been validated by experiments in two representative dynamic environments: the campus road and the urban road.
An elementary singularity-free Rotational Brownian Dynamics algorithm for anisotropic particles
Energy Technology Data Exchange (ETDEWEB)
Ilie, Ioana M.; Briels, Wim J. [Computational Biophysics, Faculty of Science and Technology, University of Twente, P.O. Box 217, 7500 AE Enschede (Netherlands); MESA+ Institute for Nanotechnology, University of Twente, P.O. Box 217, 7500 AE Enschede (Netherlands); Otter, Wouter K. den, E-mail: w.k.denotter@utwente.nl [Computational Biophysics, Faculty of Science and Technology, University of Twente, P.O. Box 217, 7500 AE Enschede (Netherlands); MESA+ Institute for Nanotechnology, University of Twente, P.O. Box 217, 7500 AE Enschede (Netherlands); Multi Scale Mechanics, Faculty of Engineering Technology, University of Twente, P.O. Box 217, 7500 AE Enschede (Netherlands)
2015-03-21
Brownian Dynamics is the designated technique to simulate the collective dynamics of colloidal particles suspended in a solution, e.g., the self-assembly of patchy particles. Simulating the rotational dynamics of anisotropic particles by a first-order Langevin equation, however, gives rise to a number of complications, ranging from singularities when using a set of three rotational coordinates to subtle metric and drift corrections. Here, we derive and numerically validate a quaternion-based Rotational Brownian Dynamics algorithm that handles these complications in a simple and elegant way. The extension to hydrodynamic interactions is also discussed.
A Scheduling Algorithm for Cloud Computing System Based on the Driver of Dynamic Essential Path.
Xie, Zhiqiang; Shao, Xia; Xin, Yu
2016-01-01
To solve the problem of task scheduling in the cloud computing system, this paper proposes a scheduling algorithm for cloud computing based on the driver of dynamic essential path (DDEP). This algorithm applies a predecessor-task layer priority strategy to solve the problem of constraint relations among task nodes. The strategy assigns different priority values to every task node based on the scheduling order of task node as affected by the constraint relations among task nodes, and the task node list is generated by the different priority value. To address the scheduling order problem in which task nodes have the same priority value, the dynamic essential long path strategy is proposed. This strategy computes the dynamic essential path of the pre-scheduling task nodes based on the actual computation cost and communication cost of task node in the scheduling process. The task node that has the longest dynamic essential path is scheduled first as the completion time of task graph is indirectly influenced by the finishing time of task nodes in the longest dynamic essential path. Finally, we demonstrate the proposed algorithm via simulation experiments using Matlab tools. The experimental results indicate that the proposed algorithm can effectively reduce the task Makespan in most cases and meet a high quality performance objective.
Sciandra, Vincent
The National Airspace System (NAS) is the vast network of systems enabling safe and efficient air travel in the United States. It consists of a set of static sectors, each controlled by one or more air traffic controllers. Air traffic control is tasked with ensuring that all flights can depart and arrive on time and in a safe and efficient matter. However, skyrocketing demand will only increase the stress on an already inefficient system, causing massive delays. The current, static configuration of the NAS cannot possibly handle the future demand on the system safely and efficiently, especially since it is projected to triple by 2025. To overcome these issues, the Next Generation of Air Transportation System (NextGen) is being enacted to increase the flexibility of the NAS. A major objective of NextGen is to implement Adaptable Dynamic Airspace Configuration (ADAC) which will dynamically allocate the sectors to best fit the traffic in the area. Dynamically allocating sectors will allow resources such as controllers to be better distributed to meet traffic demands. Currently, most DAC research has involved the en route airspace. This leaves the terminal airspace, which accounts for a large amount of the overall NAS complexity, in need of work. Using a combination of methods used in en route sectorization, this thesis has developed an algorithm for the dynamic allocation of sectors in the terminal airspace. This algorithm will be evaluated using metrics common in the evaluation of dynamic density, which is adapted for the unique challenges of the terminal airspace, and used to measure workload on air traffic controllers. These metrics give a better view of the controller workload than the number of aircraft alone. By comparing the test results with sectors currently used in the NAS using real traffic data, the algorithm xv generated sectors can be quantitatively evaluated for improvement of the current sectorizations. This will be accomplished by testing the
Explicit K-symplectic algorithms for charged particle dynamics
He, Yang; Zhou, Zhaoqi; Sun, Yajuan; Liu, Jian; Qin, Hong
2017-02-01
We study the Lorentz force equation of charged particle dynamics by considering its K-symplectic structure. As the Hamiltonian of the system can be decomposed as four parts, we are able to construct the numerical methods that preserve the K-symplectic structure based on Hamiltonian splitting technique. The newly derived numerical methods are explicit, and are shown in numerical experiments to be stable over long-term simulation. The error convergency as well as the long term energy conservation of the numerical solutions is also analyzed by means of the Darboux transformation.
Mousse, Mikaël A.; Motamed, Cina; Ezin, Eugène C.
2016-11-01
The detection of moving objects in a video sequence is the first step in an automatic video surveillance system. This work proposes an enhancement of a codebook-based algorithm for moving objects extraction. The proposed algorithm used a perceptual-based approach to optimize foreground information extraction complexity by using a modified codebook algorithm. The purpose of the adaptive strategy is to reduce the computational complexity of the foreground detection algorithm while maintaining its global accuracy. In this algorithm, we use a superpixels segmentation approach to model the spatial dependencies between pixels. The processing of the superpixels is controlled to focus it on the superpixels that are near to the possible location of foreground objects. The performance of the proposed algorithm is evaluated and compared to other algorithms of the state of the art using a public dataset that proposes sequences with a dynamic background. Experimental results prove that the proposed algorithm obtained the best the frame processing rate during the foreground detection.
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.
Lu, Jianfeng
2016-01-01
In the spirit of the fewest switches surface hopping, the frozen Gaussian approximation with surface hopping (FGA-SH) method samples a path integral representation of the non-adiabatic dynamics in the semiclassical regime. An improved sampling scheme is developed in this work for FGA-SH based on birth and death branching processes. The algorithm is validated for the standard test examples of non-adiabatic dynamics.
Donev, A; Stillinger, F H; Donev, Aleksandar; Torquato, Salvatore; Stillinger, Frank H.
2004-01-01
In the first part of a series of two papers, we present in considerable detail a collision-driven molecular dynamics algorithm for a system of nonspherical particles, within a parallelepiped simulation domain, under both periodic or hard-wall boundary conditions. The algorithm extends previous event-driven molecular dynamics algorithms for spheres. We present a novel partial-update near-neighbor list (NNL) algorithm that is superior to previous algorithms at high densities, without compromising the correctness of the algorithm. This efficiency of the algorithm is further increased for systems of very aspherical particles by using bounding sphere complexes (BSC). In the second part of this series of papers we apply the algorithm presented in the first part of this series of papers to systems of hard ellipses and ellipsoids. The theoretical machinery needed to treat such particles, including the overlap potentials, is developed in full detail. We describe an algorithm for predicting the time of collision for tw...
Community Detection in Dynamic Social Networks Based on Multiobjective Immune Algorithm
Institute of Scientific and Technical Information of China (English)
Mao-Guo Gong; Ling-Jun Zhang; Jing-Jing Ma; Li-Cheng Jiao
2012-01-01
Community structure is one of the most important properties in social networks,and community detection has received an enormous amount of attention in recent years.In dynamic networks,the communities may evolve over time so that pose more challenging tasks than in static ones.Community detection in dynamic networks is a problem which can naturally be formulated with two contradictory objectives and consequently be solved by multiobjective optimization algorithms.In this paper,a novel multiobjective immune algorithm is proposed to solve the community detection problem in dynamic networks.It employs the framework of nondominated neighbor immune algorithm to simultaneously optimize the modularity and normalized mutual information,which quantitatively measure the quality of the community partitions and temporal cost,respectively.The problem-specific knowledge is incorporated in genetic operators and local search to improve the effectiveness and efficiency of our method.Experimental studies based on four synthetic datasets and two real-world social networks demonstrate that our algorithm can not only find community structure and capture community evolution more accurately but also be more steadily than the state-of-the-art algorithms.
Institute of Scientific and Technical Information of China (English)
SUN Fan; DU Wenli; QI Rongbin; QIAN Feng; ZHONG Weimin
2013-01-01
The solutions of dynamic optimization problems are usually very difficult due to their highly nonlinear and multidimensional nature.Genetic algorithm(GA)has been proved to be a feasible method when the gradient is difficult to calculate.Its advantage is that the control profiles at all time stages are optimized simultaneously,but its convergence is very slow in the later period of evolution and it is easily trapped in the local optimum.In this study,a hybrid improved genetic algorithm(HIGA)for solving dynamic optimization problems is proposed to overcome these defects.Simplex method(SM)is used to perform the local search in the neighborhood of the optimal solution.By using SM,the ideal searching direction of global optimal solution could be found as soon as possible and the convergence speed of the algorithm is improved.The hybrid algorithm presents some improvements,such as protecting the best individual,accepting immigrations,as well as employing adaptive crossover and Gaussian mutation operators.The efficiency of the proposed algorithm is demonstrated by solving several dynamic optimization problems.At last,HIGA is applied to the optimal production of secreted protein in a fed batch reactor and the optimal feed-rate found by HIGA is effective and relatively stable.
Scheduling Algorithm: Tasks Scheduling Algorithm for Multiple Processors with Dynamic Reassignment
Directory of Open Access Journals (Sweden)
Pradeep Kumar Yadav
2008-01-01
Full Text Available Distributed computing systems [DCSs] offer the potential for improved performance and resource sharing. To make the best use of the computational power available, it is essential to assign the tasks dynamically to that processor whose characteristics are most appropriate for the execution of the tasks in distributed processing system. We have developed a mathematical model for allocating “M” tasks of distributed program to “N” multiple processors (M>N that minimizes the total cost of the program. Relocating the tasks from one processor to another at certain points during the course of execution of the program that contributes to the total cost of the running program has been taken into account. Phasewise execution cost [EC], intertask communication cost [ITCT], residence cost [RC] of each task on different processors, and relocation cost [REC] for each task have been considered while preparing a dynamic tasks allocation model. The present model is suitable for arbitrary number of phases and processors with random program structure.
Chimeric alignment by dynamic programming: Algorithm and biological uses
Energy Technology Data Exchange (ETDEWEB)
Komatsoulis, G.A.; Waterman, M.S. [Univ. of Southern California, Los Angeles, CA (United States)
1997-12-01
A new nearest-neighbor method for detecting chimeric 16S rRNA artifacts generated during PCR amplification from mixed populations has been developed. The method uses dynamic programming to generate an optimal chimeric alignment, defined as the highest scoring alignment between a query and a concatenation of a 5{prime} and a 3{prime} segment from two separate entries from a database of related sequences. Chimeras are detected by studying the scores and form of the chimeric and global sequence alignments. The chimeric alignment method was found to be marginally more effective than k-tuple based nearest-neighbor methods in simulation studies, but its most effective use is in concert with k-tuple methods. 15 refs., 3 figs., 1 tab.
Algorithm to illustrate context using dynamic lighting effects
John, Roshy M.; Balasubramanian, T.
2007-09-01
With the invention of Ultra-Bright LED, solid state lighting has come to something which is much more efficient and energy saving when compared to conventional incandescent or fluorescent lighting. With the use of proper driver electronics now a days it is possible to install solid state lighting systems with the cost same as that of any other lighting technology. This paper is a part of the research project we are doing in our lab, which deals with using ultra bright LEDs of different colors for lighting applications. The driver electronics are made in such a way that, the color and brightness of the lights will change according to context. For instance, if one of the users is reading a story or listening to music in a Personal Computer or in a hand held device such as a PDA, the lighting systems and the HVAC (Heating Ventilation Air-conditioning) systems will change dramatically according to the content of the story or the music. The vulnerability of solid-state lighting helps to accomplish such an effect. Such a type of system will help the reader to feel the story mentally and physically as well. We developed complete driver electronics for the system using multiple microcomputers and a full software suite which uses complex algorithms to decode the context from text or music and synchronize it to lighting and HVAC information. The paper also presents some case-study statistics which shows the advantage of using the system to teach kindergarten children, deaf and dumb children and for language learning classes.
Dynamic load balance scheme for the DSMC algorithm
Energy Technology Data Exchange (ETDEWEB)
Li, Jin; Geng, Xiangren; Jiang, Dingwu; Chen, Jianqiang [Computational Aerodynamics Institute of China Aerodynamics Research and Development Center, Mianyang, Sichuan, 621000 (China)
2014-12-09
The direct simulation Monte Carlo (DSMC) algorithm, devised by Bird, has been used over a wide range of various rarified flow problems in the past 40 years. While the DSMC is suitable for the parallel implementation on powerful multi-processor architecture, it also introduces a large load imbalance across the processor array, even for small examples. The load imposed on a processor by a DSMC calculation is determined to a large extent by the total of simulator particles upon it. Since most flows are impulsively started with initial distribution of particles which is surely quite different from the steady state, the total of simulator particles will change dramatically. The load balance based upon an initial distribution of particles will break down as the steady state of flow is reached. The load imbalance and huge computational cost of DSMC has limited its application to rarefied or simple transitional flows. In this paper, by taking advantage of METIS, a software for partitioning unstructured graphs, and taking the total of simulator particles in each cell as a weight information, the repartitioning based upon the principle that each processor handles approximately the equal total of simulator particles has been achieved. The computation must pause several times to renew the total of simulator particles in each processor and repartition the whole domain again. Thus the load balance across the processors array holds in the duration of computation. The parallel efficiency can be improved effectively. The benchmark solution of a cylinder submerged in hypersonic flow has been simulated numerically. Besides, hypersonic flow past around a complex wing-body configuration has also been simulated. The results have displayed that, for both of cases, the computational time can be reduced by about 50%.
Dynamic load balance scheme for the DSMC algorithm
Li, Jin; Geng, Xiangren; Jiang, Dingwu; Chen, Jianqiang
2014-12-01
The direct simulation Monte Carlo (DSMC) algorithm, devised by Bird, has been used over a wide range of various rarified flow problems in the past 40 years. While the DSMC is suitable for the parallel implementation on powerful multi-processor architecture, it also introduces a large load imbalance across the processor array, even for small examples. The load imposed on a processor by a DSMC calculation is determined to a large extent by the total of simulator particles upon it. Since most flows are impulsively started with initial distribution of particles which is surely quite different from the steady state, the total of simulator particles will change dramatically. The load balance based upon an initial distribution of particles will break down as the steady state of flow is reached. The load imbalance and huge computational cost of DSMC has limited its application to rarefied or simple transitional flows. In this paper, by taking advantage of METIS, a software for partitioning unstructured graphs, and taking the total of simulator particles in each cell as a weight information, the repartitioning based upon the principle that each processor handles approximately the equal total of simulator particles has been achieved. The computation must pause several times to renew the total of simulator particles in each processor and repartition the whole domain again. Thus the load balance across the processors array holds in the duration of computation. The parallel efficiency can be improved effectively. The benchmark solution of a cylinder submerged in hypersonic flow has been simulated numerically. Besides, hypersonic flow past around a complex wing-body configuration has also been simulated. The results have displayed that, for both of cases, the computational time can be reduced by about 50%.
Hierarchical adaptive stereo matching algorithm for obstacle detection with dynamic programming
Institute of Scientific and Technical Information of China (English)
Ming BAI; Yan ZHUANG; Wei WANG
2009-01-01
An adaptive weighted stereo matching algorithm with multilevel and bidirectional dynamic programming based on ground control points(GCPs) is presented.To decrease time complexity without losing matching precision,using a multilevel search scheme,the coarse matching is processed in typical disparity space image,while the fine matching is processed in disparity-offset space image.In the upper level,GCPs are obtained by enhanced volumetric iterative algorithm enforcing the mutual constraint and the threshold constraint.Under the supervision of the highly reliable GCPs,bidirec-tional dynamic programming framework is employed to solve the inconsistency in the optimization path.In the lower level,to reduce running time,disparity-offset space is proposed to efficiently achieve the dense disparity image.In addition,an adaptive dual support-weight strategy is presented to aggregate matching cost,which considers photometric and geomet-ric information.Further,post-processing algorithm can ameliorate disparity results in areas with depth discontinuities and related by occlusions using dual threshold algorithm,where missing stereo information is substituted from surrounding re-gions.To demonstrate the effectiveness of the algorithm,we present the two groups of experimental results for four widely used standard stereo data sets,including discussion on performance and comparison with other methods,which show that the algorithm has not only a fast speed,but also significantly improves the efficiency of holistic optimization.
DOUBLE FOUR-BAR CRANK-SLIDER MECHANISM DYNAMIC BALANCING BY META-HEURISTIC ALGORITHMS
Directory of Open Access Journals (Sweden)
Habib Emdadi
2013-09-01
Full Text Available In this paper, a new method for dynamic balancing of double four-bar crank slider mechanism by metaheuristic-based optimization algorithms is proposed. For this purpose, a proper objective function which is necessary for balancing of this mechanism and corresponding constraints has been obtained by dynamic modeling of the mechanism. Then PSO, ABC, BGA and HGAPSO algorithms have been applied for minimizing the defined cost function in optimization step. The optimization results have been studied completely by extracting the cost function, fitness, convergence speed and runtime values of applied algorithms. It has been shown that PSO and ABC are more efficient than BGA and HGAPSO in terms of convergence speed and result quality. Also, a laboratory scale experimental doublefour-bar crank-slider mechanism was provided for validating the proposed balancing method practically.
Double Four-Bar Crank-Slider Mechanism Dynamic Balancing by Meta-Heuristic Algorithms
Directory of Open Access Journals (Sweden)
Habib Emdadi
2013-09-01
Full Text Available In this paper, a new method for dynamic balancing of double four-bar crank slider mechanism by meta-heuristic-based optimization algorithms is proposed. For this purpose, a proper objective function which is necessary for balancing of this mechanism and corresponding constraints has been obtained by dynamic modeling of the mechanism.Then PSO, ABC, BGA and HGAPSO algorithms have been applied for minimizing the defined cost function in optimization step. The optimization results have been studied completely by extracting the cost function, fitness, convergence speed and run time values of applied algorithms. It has been shown that PSO and ABC are more efficient than BGA and HGAPSO in terms of convergence speed and result quality. Also, a laboratory scale experimental double four-bar crank-slider mechanism was provided for validating the proposedbalancing method practicall
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...
Adaptation Algorithm of Geometric Graphs for Robot Motion Planning in Dynamic Environments
Directory of Open Access Journals (Sweden)
Jae-Han Park
2016-01-01
Full Text Available This study proposes an adaptive graph algorithm for collision-free motion planning of articulated robots in dynamic environments. For this purpose, deformations of the configuration space were analyzed according to the changes of the workspace using various simulations. Subsequently, we adopted the principles of gas motion dynamics in our adaptation algorithm to address the issue of the deformation of the configuration space. The proposed algorithm has an adaptation mechanism based on expansive repulsion and sensory repulsion, and it can be performed to provide the entire adaptation using distributed processing. The simulation results confirmed that the proposed method allows the adaptation of the roadmap graph to changes of the configuration space.
Novotny, M.A.
2010-02-01
The efficiency of dynamic Monte Carlo algorithms for off-lattice systems composed of particles is studied for the case of a single impurity particle. The theoretical efficiencies of the rejection-free method and of the Monte Carlo with Absorbing Markov Chains method are given. Simulation results are presented to confirm the theoretical efficiencies. © 2010.
An Optimal Algorithm towards Successive Location Privacy in Sensor Networks with Dynamic Programming
Zhao, Baokang; Wang, Dan; Shao, Zili; Cao, Jiannong; Chan, Keith C. C.; Su, Jinshu
In wireless sensor networks, preserving location privacy under successive inference attacks is extremely critical. Although this problem is NP-complete in general cases, we propose a dynamic programming based algorithm and prove it is optimal in special cases where the correlation only exists between p immediate adjacent observations.
A Fast Component-Tree Algorithm for High Dynamic-Range Images and Second Generation Connectivity
Wilkinson, Michael H.F.
2011-01-01
Component trees are important data structures for computation of connected attribute filters. Though some of the available algorithms are suitable for high-dynamic range, and in particular floating point data, none are suitable for computation of component trees for so-called second-generation, and
DEFF Research Database (Denmark)
Sokoler, Leo Emil; Standardi, Laura; Edlund, Kristian
2014-01-01
This paper presents a warm-started Dantzig–Wolfe decomposition algorithm tailored to economic model predictive control of dynamically decoupled subsystems. We formulate the constrained optimal control problem solved at each sampling instant as a linear program with state space constraints, input...
DEFF Research Database (Denmark)
Meng, Lexuan; Dragicevic, Tomislav; Guerrero, Josep M.;
2014-01-01
. Accordingly, this paper proposes a dynamic consensus algorithm based distributed optimization method aiming at improving the system efficiency while offering higher expandability and flexibility when compared to centralized control. Hardware-in-the-loop (HIL) results are shown to demonstrate the effectiveness...
Construction of a Parallel Algorithm to Solve the Multiphase Gas Dynamics Problem
Directory of Open Access Journals (Sweden)
B. Rybakin
1995-11-01
Full Text Available This paper considers questions of an effective use of multiprocessor computing system to implement a parallel algorithm solving the multiphase gas dynamics problem. A technique is offered to parallelize the two-dimensional explicit differential scheme to implement it on multiprocessor systems with distributed memory (MIMD architecture.
A simple and accurate algorithm for path integral molecular dynamics with the Langevin thermostat.
Liu, Jian; Li, Dezhang; Liu, Xinzijian
2016-07-14
We introduce a novel simple algorithm for thermostatting path integral molecular dynamics (PIMD) with the Langevin equation. The staging transformation of path integral beads is employed for demonstration. The optimum friction coefficients for the staging modes in the free particle limit are used for all systems. In comparison to the path integral Langevin equation thermostat, the new algorithm exploits a different order of splitting for the phase space propagator associated to the Langevin equation. While the error analysis is made for both algorithms, they are also employed in the PIMD simulations of three realistic systems (the H2O molecule, liquid para-hydrogen, and liquid water) for comparison. It is shown that the new thermostat increases the time interval of PIMD by a factor of 4-6 or more for achieving the same accuracy. In addition, the supplementary material shows the error analysis made for the algorithms when the normal-mode transformation of path integral beads is used.
A simple and accurate algorithm for path integral molecular dynamics with the Langevin thermostat
Liu, Jian; Li, Dezhang; Liu, Xinzijian
2016-07-01
We introduce a novel simple algorithm for thermostatting path integral molecular dynamics (PIMD) with the Langevin equation. The staging transformation of path integral beads is employed for demonstration. The optimum friction coefficients for the staging modes in the free particle limit are used for all systems. In comparison to the path integral Langevin equation thermostat, the new algorithm exploits a different order of splitting for the phase space propagator associated to the Langevin equation. While the error analysis is made for both algorithms, they are also employed in the PIMD simulations of three realistic systems (the H2O molecule, liquid para-hydrogen, and liquid water) for comparison. It is shown that the new thermostat increases the time interval of PIMD by a factor of 4-6 or more for achieving the same accuracy. In addition, the supplementary material shows the error analysis made for the algorithms when the normal-mode transformation of path integral beads is used.
Dynamically Predicting the Quality of Service: Batch, Online, and Hybrid Algorithms
Directory of Open Access Journals (Sweden)
Ya Chen
2017-01-01
Full Text Available This paper studies the problem of dynamically modeling the quality of web service. The philosophy of designing practical web service recommender systems is delivered in this paper. A general system architecture for such systems continuously collects the user-service invocation records and includes both an online training module and an offline training module for quality prediction. In addition, we introduce matrix factorization-based online and offline training algorithms based on the gradient descent algorithms and demonstrate the fitness of this online/offline algorithm framework to the proposed architecture. The superiority of the proposed model is confirmed by empirical studies on a real-life quality of web service data set and comparisons with existing web service recommendation algorithms.
Liu, Derong; Li, Hongliang; Wang, Ding
2015-06-01
In this paper, we establish error bounds of adaptive dynamic programming algorithms for solving undiscounted infinite-horizon optimal control problems of discrete-time deterministic nonlinear systems. We consider approximation errors in the update equations of both value function and control policy. We utilize a new assumption instead of the contraction assumption in discounted optimal control problems. We establish the error bounds for approximate value iteration based on a new error condition. Furthermore, we also establish the error bounds for approximate policy iteration and approximate optimistic policy iteration algorithms. It is shown that the iterative approximate value function can converge to a finite neighborhood of the optimal value function under some conditions. To implement the developed algorithms, critic and action neural networks are used to approximate the value function and control policy, respectively. Finally, a simulation example is given to demonstrate the effectiveness of the developed algorithms.
New dynamic routing algorithm based on MANET in LEO/MEO satellite network
Institute of Scientific and Technical Information of China (English)
LI Zhe; LI Dong-ni; WANG Guang-xing
2006-01-01
The features of low earth orbit/medium earth orbit (LEO/MEO) satellite networks routing algorithm based on inter-satellite link are analyzed and the similarities between satellite networks and mobile Ad Hoc network (MANET) are pointed out.The similar parts in MANET routing protocol are used in the satellite network for reference.A new dynamic routing algorithm based on MANET in LEO/MEO satellite networks,which fits for the LEO/MEO satellite communication system,is proposed.At the same time,the model of the algorithm is simulated and features are analyzed.It is shown that the algorithm has strong adaptability.It can give the network high autonomy,perfect function,low system overhead and great compatibility.
Directory of Open Access Journals (Sweden)
Ziyi Fu
2012-10-01
Full Text Available According to the upstream TDM in the system of Ethernet passive optical network (EPON, this paper proposes a novel dynamic bandwidth allocation algorithm which supports the mechanism with correction-based the multiple services estimation. To improve the real-time performance of the bandwidth allocation, this algorithm forecasts the traffic of high priority services, and then pre-allocate bandwidth for various priority services is corrected according to Gaussian distribution characteristics, which will make traffic prediction closer to the real traffic. The simulation results show that proposed algorithm is better than the existing DBA algorithm. Not only can it meet the delay requirement of high priority services, but also control the delay abnormity of low priority services. In addition, with rectification scheme, it obviously improves the bandwidth utilization.
Dynamic Programming and Genetic Algorithm for Business Processes Optimisation
Directory of Open Access Journals (Sweden)
Mateusz Wibig
2012-12-01
Full Text Available There are many business process modelling techniques, which allow to capture features of those processes, but graphical, diagrammatic models seems to be used most in companies and organizations. Although the modelling notations are more and more mature and can be used not only to visualise the process idea but also to implement it in the workflow solution and although modern software allows us to gather a lot of data for analysis purposes, there is still not much commercial used business process optimisation methods. In this paper the scheduling / optimisation method for automatic task scheduling in business processes models is described. The Petri Net model is used, but it can be easily applied to any other modelling notation, where the process is presented as a set of tasks, i.e. BPMN (Business Process Modelling Notation. The method uses Petri Nets’, business processes’ scalability and dynamic programming concept to reduce the necessary computations, by revising only those parts of the model, to which the change was applied.
An effective dynamic reconfiguration algorithm for IP over WDM networks
Yu, Hongfang; Zhou, Tao; Wang, Sheng; Li, Lemin
2005-02-01
WDM (Wavelength Division Multiplexing) technology can provide multiple wavelengths on a fiber. IP directly over WDM (or IP over WDM) has become the hot topic of industry. A promising approach for building an IP over WDM network is that a logical network consisting of the wavelength channels (lightpaths) is built on the physical WDM network. Then, IP traffic is carried on the logical topology, by utilizing the MPLS (Multiple Protocol Label Switching) or GMPLS (Generalized MPLS). When the traffic demand pattern changes in the IP layer, the network performance may become poor. In order to improve the network performance, the virtual topology can be reconfigured to suit the changing traffic patterns. In this paper, dynamic slowly-adaptation scheme (e.g. tearing down a lightpath that is lightly loaded or setting up a new lightpath when congestion occurs) is adopted. How to select the source and the destination nodes of the new lightpath to be added and the underutilized lightpath to be deleted if it is necessary to do so is our key issue. Four selection ways are developed. These ways are evaluated through detail simulations and various performances are investigated.
A time integral formulation and algorithm for structural dynamics with nonlinear stiffness
Institute of Scientific and Technical Information of China (English)
Kaiping Yu; Jie Zhao
2006-01-01
A newly-developed numerical algorithm, which is called the new Generalized-α(G-α)method, is presented for solving structural dynamics problems with nonlinear stiffness. The traditional G-α method has undesired overshoot properties as for a class of α-method. In the present work, seven independent parameters are introduced into the single-step three-stage algorithmic formulations and the nonlinear internal force at every time interval is approximated by means of the generalized trapezoidal rule, and then the algorithm is implemented based on the finite difference theory. An analysis on the stability, accuracy, energy and overshoot properties of the proposed scheme is performed in the nonlinear regime. The values or the ranges of values of the seven independent parameters are determined in the analysis process. The computational results obtained by the new algorithm show that the displacement accuracy is of order two, and the acceleration can also be improved to a second order accuracy by a suitable choice of parameters. Obviously, the present algorithm is zerostable, and the energy conservation or energy decay can be realized in the high-frequency range, which can be regarded as stable in an energy sense. The algorithmic overshoot can be completely avoided by using the new algorithm without any constraints with respect to the damping force and initial conditions.
Pluchino, Alessandro; Latora, Vito
2008-01-01
We have recently introduced an efficient method for the detection and identification of modules in complex networks, based on the de-synchronization properties (dynamical clustering) of phase oscillators. In this paper we apply the dynamical clustering tecnique to the identification of communities of marine organisms living in the Chesapeake Bay food web. We show that our algorithm is able to perform a very reliable classification of the real communities existing in this ecosystem by using different kinds of dynamical oscillators. We compare also our results with those of other methods for the detection of community structures in complex networks.
A new extension of classical molecular dynamics: An electron transfer algorithm.
Raskovalov, Anton
2017-05-05
The molecular dynamics is one of the most widely used methods for the simulation of the properties corresponding to ionic motion. Unfortunately, classical molecular dynamics cannot be applied for electron transfer simulation. Suggested modification of the molecular dynamics allows performing the electron transfer from one particle to another during simulation runtime. All additional data structure and the corresponding algorithms are presented in this article. The method can be applied to the systems with pair Van der Waals and Coulomb interactions. Moreover, it may be extended for many-bodied interatomic interactions. In addition, an algorithm of transference numbers calculation has been designed. This extension is not an independent method but it can be useful for simulating the systems with high concentration of electron donors and acceptors. © 2017 Wiley Periodicals, Inc.
Institute of Scientific and Technical Information of China (English)
ZhangLiangjie; LiYanda; 等
1997-01-01
In this paper,a dynamic bandwidth allocation technique based on fuzz neural networks(FNNs) and genetic algorithm(GA)is proposed for preventive congestion control in ATM network.The traffic model based on FNN does not need the descriptive traffic parameters in detail,which greatly depend on the user's terminal.Genetic algorithm is used to predict the equivalent bandwidth of the accepted traffic in real-time.Thus,the proposed scheme can estimate the dynamic bandwidth of the network in the time scale from the call arrival to the call admission/rejection due to the fuzzy-tech and GA hardware implementation.Simulation results show that the scheme can perform accurate dynamic bandwidth allocation to DN/OFF bursty traffic in accordance with the required quality of service(QOS),and the bandwidth utilization is improved from the overall point of view.
Lin, Yuan; Samei, Ehsan
2016-07-01
Dynamic perfusion imaging can provide the morphologic details of the scanned organs as well as the dynamic information of blood perfusion. However, due to the polyenergetic property of the x-ray spectra, beam hardening effect results in undesirable artifacts and inaccurate CT values. To address this problem, this study proposes a segmentation-free polyenergetic dynamic perfusion imaging algorithm (pDP) to provide superior perfusion imaging. Dynamic perfusion usually is composed of two phases, i.e., a precontrast phase and a postcontrast phase. In the precontrast phase, the attenuation properties of diverse base materials (e.g., in a thorax perfusion exam, base materials can include lung, fat, breast, soft tissue, bone, and metal implants) can be incorporated to reconstruct artifact-free precontrast images. If patient motions are negligible or can be corrected by registration, the precontrast images can then be employed as a priori information to derive linearized iodine projections from the postcontrast images. With the linearized iodine projections, iodine perfusion maps can be reconstructed directly without the influence of various influential factors, such as iodine location, patient size, x-ray spectrum, and background tissue type. A series of simulations were conducted on a dynamic iodine calibration phantom and a dynamic anthropomorphic thorax phantom to validate the proposed algorithm. The simulations with the dynamic iodine calibration phantom showed that the proposed algorithm could effectively eliminate the beam hardening effect and enable quantitative iodine map reconstruction across various influential factors. The error range of the iodine concentration factors ([Formula: see text]) was reduced from [Formula: see text] for filtered back-projection (FBP) to [Formula: see text] for pDP. The quantitative results of the simulations with the dynamic anthropomorphic thorax phantom indicated that the maximum error of iodine concentrations can be reduced from
Directory of Open Access Journals (Sweden)
Jingjing Ma
2014-01-01
Full Text Available Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms.
Ma, Jingjing; Liu, Jie; Ma, Wenping; Gong, Maoguo; Jiao, Licheng
2014-01-01
Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms.
An Event-Driven Hybrid Molecular Dynamics and Direct Simulation Monte Carlo Algorithm
Energy Technology Data Exchange (ETDEWEB)
Donev, A; Garcia, A L; Alder, B J
2007-07-30
A novel algorithm is developed for the simulation of polymer chains suspended in a solvent. The polymers are represented as chains of hard spheres tethered by square wells and interact with the solvent particles with hard core potentials. The algorithm uses event-driven molecular dynamics (MD) for the simulation of the polymer chain and the interactions between the chain beads and the surrounding solvent particles. The interactions between the solvent particles themselves are not treated deterministically as in event-driven algorithms, rather, the momentum and energy exchange in the solvent is determined stochastically using the Direct Simulation Monte Carlo (DSMC) method. The coupling between the solvent and the solute is consistently represented at the particle level, however, unlike full MD simulations of both the solvent and the solute, the spatial structure of the solvent is ignored. The algorithm is described in detail and applied to the study of the dynamics of a polymer chain tethered to a hard wall subjected to uniform shear. The algorithm closely reproduces full MD simulations with two orders of magnitude greater efficiency. Results do not confirm the existence of periodic (cycling) motion of the polymer chain.
A dynamic material discrimination algorithm for dual MV energy X-ray digital radiography.
Li, Liang; Li, Ruizhe; Zhang, Siyuan; Zhao, Tiao; Chen, Zhiqiang
2016-08-01
Dual-energy X-ray radiography has become a well-established technique in medical, industrial, and security applications, because of its material or tissue discrimination capability. The main difficulty of this technique is dealing with the materials overlapping problem. When there are two or more materials along the X-ray beam path, its material discrimination performance will be affected. In order to solve this problem, a new dynamic material discrimination algorithm is proposed for dual-energy X-ray digital radiography, which can also be extended to multi-energy X-ray situations. The algorithm has three steps: α-curve-based pre-classification, decomposition of overlapped materials, and the final material recognition. The key of the algorithm is to establish a dual-energy radiograph database of both pure basis materials and pair combinations of them. After the pre-classification results, original dual-energy projections of overlapped materials can be dynamically decomposed into two sets of dual-energy radiographs of each pure material by the algorithm. Thus, more accurate discrimination results can be provided even with the existence of the overlapping problem. Both numerical and experimental results that prove the validity and effectiveness of the algorithm are presented.
A structural dynamic factor model for the effects of monetary policy estimated by the EM algorithm
DEFF Research Database (Denmark)
Bork, Lasse
This paper applies the maximum likelihood based EM algorithm to a large-dimensional factor analysis of US monetary policy. Specifically, economy-wide effects of shocks to the US federal funds rate are estimated in a structural dynamic factor model in which 100+ US macroeconomic and financial time...... series are driven by the joint dynamics of the federal funds rate and a few correlated dynamic factors. This paper contains a number of methodological contributions to the existing literature on data-rich monetary policy analysis. Firstly, the identification scheme allows for correlated factor dynamics...... as opposed to the orthogonal factors resulting from the popular principal component approach to structural factor models. Correlated factors are economically more sensible and important for a richer monetary policy transmission mechanism. Secondly, I consider both static factor loadings as well as dynamic...
Dynamics Assessment of Grid-Synchronization Algorithms for Single-Phase Grid-Connected Converters
DEFF Research Database (Denmark)
Han, Yang; Luo, Mingyu; Guerrero, Josep M.
2015-01-01
Several advanced phase-lock-loop (PLL) algorithms have been proposed for single-phase power electronic systems. Among these algorithms, the orthogonal signal generators (OSGs) are widely utilized to generate a set of in-quadrature signals, owing to its benefit of simple digital implementation......, and the performance comparison of transient response and disturbance rejection capabilities are presented. Finally, the moving average filter (MAF) is applied to enhance steady state and dynamic response of the delayed-type PLL, derivator-based PLL and the complex-coefficient filter (CCF-PLL) under grid frequency...
Dynamic learning rates algorithm for BPNN to forecast time series of dam security
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Time series data of dam security have a large number of observed values and should be forecasted accurately in time.Neural networks have the powerful approach ablilities of arbitrary functions and have been broadly utilized in many domains.In this paper,a dynamic learning rate training algorithm of hack-propagation neural networks for time series forecasting is proposed and the networks with this algorithm are built to forecast time series of dam security.The application results demonostrate the efficiency of modelling and the effictiveness of forecasting.
DEFF Research Database (Denmark)
Hou, Peng; Hu, Weihao; Chen, Zhe
2015-01-01
Anew approach, Dynamic Minimal Spanning Tree (DMST) algorithm, whichisbased on the MST algorithm isproposed in this paper to optimizethe cable connectionlayout for large scale offshore wind farm collection system. The current carrying capacity of the cable is considered as the main constraint....... Thedynamic changing of the cable capacity, therefore, the cost during the searching process is presented in this work. Twowind farms arechosen as the studied case and the final results showthat the proposed methodcan save the investment on cables 1.07% and 6.10% respectively compared with MST method...
A Dynamic Programming Algorithm For (1,2)-Exemplar Breakpoint Distance.
Wei, Zhexue; Zhu, Daming; Wang, Lusheng
2015-07-01
The exemplar breakpoint distance problem is motivated by finding conserved sets of genes between two genomes. It asks to find respective exemplars in two genomes to minimize the breakpoint distance between them. If one genome has no repeated gene (called trivial genome) and the other has genes repeating at most twice, it is referred to as the (1, 2)-exemplar breakpoint distance problem, EBD(1, 2) for short. Little has been done on algorithm design for this problem by now. In this article, we propose a parameter to describe the maximum physical span between two copies of a gene in a genome, and based on it, design a fixed-parameter algorithm for EBD(1, 2). Using a dynamic programming approach, our algorithm can take O(4(s)n(2)) time and O(4(s)n) space to solve an EBD(1, 2) instance that has two genomes of n genes where the second genome has each two copies of a gene spanning at most s copies of the genes. Our algorithm can also be used to compute the maximum adjacencies between two genomes. The algorithm has been implemented in C++. Simulations on randomly generated data have verified the effectiveness of our algorithm. The software package is available from the authors.
Dynamic Routing Algorithm Based on the Channel Quality Control for Farmland Sensor Networks
Directory of Open Access Journals (Sweden)
Dongfeng Xu
2014-04-01
Full Text Available This article reports a Dynamic Routing Algorithm for Farmland Sensor Networks (DRA-FSN based on channel quality control to improve energy efficiency, which combines the distance and communication characteristics of farmland wireless sensor network. The functional architecture of the DRA-FSN algorithm, routing establish the mechanisms, the communication transmission mechanism, the global routing beacon return mechanism, abnormal node handling mechanism and sensor networks timing control mechanisms were designed in detail in this article. This article also evaluates and simulated the performance of DRA-FSN algorithm in different conditions from energy efficiency, packet energy consumption and packet distribution balance by comparing DRA-FSN algorithm with DSDV, EAP algorithm. Simulations showed that the DRA-FSN was more energy efficient than EAP and DSDV, the DRA-FSN algorithm overcame the shortcoming that capacity and bandwidth of the routing table correspondingly increase as more and more nodes joining the network. It has better performance in scalability and network loading balance
Soner Yorgun, M; Rood, Richard B
2016-12-01
An object-based evaluation method using a pattern recognition algorithm (i.e., classification trees) is applied to the simulated orographic precipitation for idealized experimental setups using the National Center of Atmospheric Research (NCAR) Community Atmosphere Model (CAM) with the finite volume (FV) and the Eulerian spectral transform dynamical cores with varying resolutions. Daily simulations were analyzed and three different types of precipitation features were identified by the classification tree algorithm. The statistical characteristics of these features (i.e., maximum value, mean value, and variance) were calculated to quantify the difference between the dynamical cores and changing resolutions. Even with the simple and smooth topography in the idealized setups, complexity in the precipitation fields simulated by the models develops quickly. The classification tree algorithm using objective thresholding successfully detected different types of precipitation features even as the complexity of the precipitation field increased. The results show that the complexity and the bias introduced in small-scale phenomena due to the spectral transform method of CAM Eulerian spectral dynamical core is prominent, and is an important reason for its dissimilarity from the FV dynamical core. The resolvable scales, both in horizontal and vertical dimensions, have significant effect on the simulation of precipitation. The results of this study also suggest that an efficient and informative study about the biases produced by GCMs should involve daily (or even hourly) output (rather than monthly mean) analysis over local scales.
Koch, Caleb; Winfrey, Leigh
2014-10-01
Natural Gas is a major energy source in Europe, yet political instabilities have the potential to disrupt access and supply. Energy resilience is an increasingly essential construct and begins with transmission network design. This study proposes a new way of thinking about modelling natural gas flow. Rather than relying on classical economic models, this problem is cast into a time-dependent Hamiltonian dynamics discussion. Traditional Natural Gas constraints, including inelastic demand and maximum/minimum pipe flows, are portrayed as energy functions and built into the dynamics of each pipe flow. Doing so allows the constraints to be built into the dynamics of each pipeline. As time progresses in the model, natural gas flow rates find the minimum energy, thus the optimal gas flow rates. The most important result of this study is using dynamical principles to ensure the output of natural gas at demand nodes remains constant, which is important for country to country natural gas transmission. Another important step in this study is building the dynamics of each flow in a decentralized algorithm format. Decentralized regulation has solved congestion problems for internet data flow, traffic flow, epidemiology, and as demonstrated in this study can solve the problem of Natural Gas congestion. A mathematical description is provided for how decentralized regulation leads to globally optimized network flow. Furthermore, the dynamical principles and decentralized algorithm are applied to a case study of the Fluxys Belgium Natural Gas Network.
Weeks, Cindy Lou
1986-01-01
Experiments were conducted at NASA Ames Research Center to define multi-tasking software requirements for multiple-instruction, multiple-data stream (MIMD) computer architectures. The focus was on specifying solutions for algorithms in the field of computational fluid dynamics (CFD). The program objectives were to allow researchers to produce usable parallel application software as soon as possible after acquiring MIMD computer equipment, to provide researchers with an easy-to-learn and easy-to-use parallel software language which could be implemented on several different MIMD machines, and to enable researchers to list preferred design specifications for future MIMD computer architectures. Analysis of CFD algorithms indicated that extensions of an existing programming language, adaptable to new computer architectures, provided the best solution to meeting program objectives. The CoFORTRAN Language was written in response to these objectives and to provide researchers a means to experiment with parallel software solutions to CFD algorithms on machines with parallel architectures.
Noon, Abbas; Kadry, Seifedine
2011-01-01
Round Robin, considered as the most widely adopted CPU scheduling algorithm, undergoes severe problems directly related to quantum size. If time quantum chosen is too large, the response time of the processes is considered too high. On the other hand, if this quantum is too small, it increases the overhead of the CPU. In this paper, we propose a new algorithm, called AN, based on a new approach called dynamic-time-quantum; the idea of this approach is to make the operating systems adjusts the time quantum according to the burst time of the set of waiting processes in the ready queue. Based on the simulations and experiments, we show that the new proposed algorithm solves the fixed time quantum problem and increases the performance of Round Robin.
Incremental Density-Based Link Clustering Algorithm for Community Detection in Dynamic Networks
Directory of Open Access Journals (Sweden)
Fanrong Meng
2016-01-01
Full Text Available Community detection in complex networks has become a research hotspot in recent years. However, most of the existing community detection algorithms are designed for the static networks; namely, the connections between the nodes are invariable. In this paper, we propose an incremental density-based link clustering algorithm for community detection in dynamic networks, iDBLINK. This algorithm is an extended version of DBLINK which is proposed in our previous work. It can update the local link community structure in the current moment through the change of similarity between the edges at the adjacent moments, which includes the creation, growth, merging, deletion, contraction, and division of link communities. Extensive experimental results demonstrate that iDBLINK not only has a great time efficiency, but also maintains a high quality community detection performance when the network topology is changing.
Directory of Open Access Journals (Sweden)
Diamantidis A. C.
2004-01-01
Full Text Available In this study, the buffer allocation problem (BAP in homogeneous, asymptotically reliable serial production lines is considered. A known aggregation method, given by Lim, Meerkov, and Top (1990, for the performance evaluation (i.e., estimation of throughput of this type of production lines when the buffer allocation is known, is used as an evaluative method in conjunction with a newly developed dynamic programming (DP algorithm for the BAP. The proposed algorithm is applied to production lines where the number of machines is varying from four up to a hundred machines. The proposed algorithm is fast because it reduces the volume of computations by rejecting allocations that do not lead to maximization of the line's throughput. Numerical results are also given for large production lines.
An $\\dot{f}(f)$-frequency dynamics algorithm for gravitational waves
Van Putten, M H P M; Putten, Maurice H.P.M. van; Sarkar, Abhinanda
2000-01-01
Coalescence of low mass compact binaries of neutron stars and black holes are primary burst sources for LIGO and VIRGO.Of importance in the early stages of observations will be the classification of candidate detections by source-type. The diversity in source parameters and serendipity in any new window of observations suggest to consider model-independent detection algorithms. Here a frequency dynamics algorithm is described which extracts a trajectory in the $\\dot{f}(f)$-plane from the noisy signal. The algorithm is studied in simulated binary coalescence. Robust results are obtained with experimental noise data. Experiments show the method to be superior to matched filtering in the presence of model imperfections.
Evaluation of a new parallel numerical parameter optimization algorithm for a dynamical system
Duran, Ahmet; Tuncel, Mehmet
2016-10-01
It is important to have a scalable parallel numerical parameter optimization algorithm for a dynamical system used in financial applications where time limitation is crucial. We use Message Passing Interface parallel programming and present such a new parallel algorithm for parameter estimation. For example, we apply the algorithm to the asset flow differential equations that have been developed and analyzed since 1989 (see [3-6] and references contained therein). We achieved speed-up for some time series to run up to 512 cores (see [10]). Unlike [10], we consider more extensive financial market situations, for example, in presence of low volatility, high volatility and stock market price at a discount/premium to its net asset value with varying magnitude, in this work. Moreover, we evaluated the convergence of the model parameter vector, the nonlinear least squares error and maximum improvement factor to quantify the success of the optimization process depending on the number of initial parameter vectors.
Survey on Dynamic Optimization Algorithms%动态优化算法综述
Institute of Scientific and Technical Information of China (English)
陈莉; 丁立新
2011-01-01
Research on Algorithms dealing with dynamic optimization problems has been one of the hotspots in the optimization algorithms＇ area.The population-based optimization algorithms are grouped into five categories： increasing diversity after environment changes,keeping diversity during the run,using memory schemes,multi-population and prediction-based approaches.The keys of these methods are keeping the balance of the exploration and exploitation in research space.The algorithms can not only find the optimum but also track the changing optimum.At last this paper points out the problems in the dynamic optimization area needed to research deeply in the future.Dynamic optimization algorithms need researches on algorithm design,building near-real-world dynamic optimization problem model and algorithm theory for dynamic optimization in the future.%动态优化算法的研究已成为优化算法领域研究的一个热点.对于基于种群的优化算法而言,它主要可以分为环境变化后增加多样性的方法、运行过程中始终保持多样性的方法、基于记忆机制的方法、多种群方法和基于预测机制方法5类.动态优化算法的关键是在搜索过程中始终保持搜索空间开发和探索之间平衡.该类算法不仅能发现最优个体,而且能在动态环境中跟踪变化了的最优个体.在今后的动态优化研究中,重点应放在动态优化算法理论方面和算法设计、构建上,使它更接近现实问题.
Express company’s vehicle routing optimization by multiple-dynamic saving algorithm
Directory of Open Access Journals (Sweden)
Junchao Liu
2014-05-01
Full Text Available Purpose: According to the disorder in circulation commuting and crossover commuting of SF company which is the China’s largest private express delivery service provider, the study uses the Saving Algorithm to make the vehicle routing and resources optimized, on this basis, proposes innovative improvements with Saving Algorithm and then applies it in every distribution center of SF forming a "multi-dynamic" type of Saving Algorithm to ensure both cost savings and timeliness. This method can be generalized for all express company to get the vehicle routing optimized.Design/methodology/approach: As the special transportation requirements of express companies, this study optimizes the vehicle route based on Saving Algorithm, uses multiple-dynamic Saving Algorithm, and considers the timeliness requirements of the express company to achieve a balance of cost and timeliness.Findings: The main finding is that a new method proposed which there can be a balance improvement for both cost and timeliness to optimize the vehicle route of express company. Calculation example validates the effectiveness of the model established and solving method.Practical implications: It is a widespread practice that by setting up model and parameters for the objectives, express company can maintain the balances between cost and timeliness and achieve the optimized vehicle route.Originality/value: It proposes innovative improvements, takes SF express company as an example, with Saving Algorithm which can be applied in every distribution center of express company to ensure the balance improvement for both cost and timeliness, and has a great practical significance to the transportation network and path optimization of express companies.
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.
Directory of Open Access Journals (Sweden)
Bruno H. Dias
2010-01-01
Full Text Available This paper presents a new approach for the expected cost-to-go functions modeling used in the stochastic dynamic programming (SDP algorithm. The SDP technique is applied to the long-term operation planning of electrical power systems. Using state space discretization, the Convex Hull algorithm is used for constructing a series of hyperplanes that composes a convex set. These planes represent a piecewise linear approximation for the expected cost-to-go functions. The mean operational costs for using the proposed methodology were compared with those from the deterministic dual dynamic problem in a case study, considering a single inflow scenario. This sensitivity analysis shows the convergence of both methods and is used to determine the minimum discretization level. Additionally, the applicability of the proposed methodology for two hydroplants in a cascade is demonstrated. With proper adaptations, this work can be extended to a complete hydrothermal system.
Tracking and Following Algorithms of Mobile Robots for Service Activities in Dynamic Environments
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Feng-Li Lian
2015-02-01
Full Text Available By providing the capability of following a human target in an appropriate manner, the robot can assist people in various ways under different environments. One of the main difficulties when performing human tracking and following is the occlusion problem caused by static as well as dynamic obstacles. The aim of the paper is to tackle the occlusion problem by planning a robotic trajectory of maximizing target visibility and following the moving target. Initially, a laser range finder is used to detect the human target and then robustly track the target using the Kalman filter. Afterward, a human following algorithm based on a look-ahead algorithm, DWA*, is implemented to pursue the target while avoiding any static or dynamic obstacles. Fundamental experiments have been extensively tested to evaluate robot maneuvers and several field tests are conducted in more complex environments such as student cafeteria, computer center, and university library.
Study on algorithm of dynamic uncalibrated eye-in-hand visual servoing system
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
Currently, most visual servoing system must be calibrated, while it is impossible to calibrate cameras and robot models precisely in industrial practice, so a novel dynamic uncalibrated eye-in-hand visual servoing system of tracking a moving target is proposed. The method does not require calibration of camera and robot kinematic models. Vision guided algorithm for tracking dynamic image is developed through minimizing nonlinear objective function. For the large residual has not been approximated in dynamic environment and the change of composite image Jacobian with time increment has not been computed in visual servoing system now,large residuals are dynamic approximated and the change of composite image Jacobian at each iterative step is computed.Simulation results demonstrate the validity of these approaches.
Dynamic Fuzzy Controlled RWA Algorithm for IP/GMPLS over WDM Networks
Institute of Scientific and Technical Information of China (English)
I-Shyan Hwang; I-Feng Huang; Shin-Cheng Yu
2005-01-01
This paper proposes a dynamic RWA scheme using fuzzy logic control on IP/GMPLS over WDM networks to achieve the best quality of network transmission. The proposed algorithm dynamically allocates network resources and reserves partial bandwidth based on the current network status, which includes the request bandwidth, average utilization for each wavelength and its coefficient of variance (C.V.) of data traffic, to determine whether the connection can be set up. Five fuzzy sets for request bandwidth, average rate and C.V. of data traffic are used to divide the variable space: very large (LP), large (SP), normal (ZE), small (SN), and very small (LN). Setting the fuzzy limit is a key part in the proposed algorithm. The simulation of scenarios in this paper has two steps. In the first step, the adaptive fuzzy limits are evaluated based on average transmission cost pertaining to ten network statuses. The second step is to compare the proposed algorithm with periodic measurement of traffic (PMT) in ATM networks in six network situations to show that the proposed FC-RWA algorithm can provide better network transmission.
Lelu, Alain; Cuxac, Pascal
2008-01-01
We address here two major challenges presented by dynamic data mining: 1) the stability challenge: we have implemented a rigorous incremental density-based clustering algorithm, independent from any initial conditions and ordering of the data-vectors stream, 2) the cognitive challenge: we have implemented a stringent selection process of association rules between clusters at time t-1 and time t for directly generating the main conclusions about the dynamics of a data-stream. We illustrate these points with an application to a two years and 2600 documents scientific information database.
Institute of Scientific and Technical Information of China (English)
LIU Xiao; WANG Cheng-en
2005-01-01
This paper addresses a single item dynamic lot-sizing model with inventory capacity and out-sourcing. The goal is to minimize the total costs of production, setup, inventory holding and out-sourcing. Two versions of an out-sourcing model with time-varying costs are considered: stock out case and conservation case. Zero Inventory Order property has been found and some new properties are obtained in an optimal solution. Dynamic programming algorithms are developed to solve the problem in strongly polynomial time respectively. Furthermore, some numerical results demonstrate that the approach proposed is efficient and applicable.
Minary, Peter; Martyna, Glenn J.; Tuckerman, Mark E.
2003-02-01
In this paper (Paper I) and a companion paper (Paper II), novel new algorithms and applications of the isokinetic ensemble as generated by Gauss' principle of least constraint, pioneered for use with molecular dynamics 20 years ago, are presented for biophysical, path integral, and Car-Parrinello based ab initio molecular dynamics. In Paper I, a new "extended system" version of the isokinetic equations of motion that overcomes the ergodicity problems inherent in the standard approach, is developed using a new theory of non-Hamiltonian phase space analysis [M. E. Tuckerman et al., Europhys. Lett. 45, 149 (1999); J. Chem. Phys. 115, 1678 (2001)]. Reversible multiple time step integrations schemes for the isokinetic methods, first presented by Zhang [J. Chem. Phys. 106, 6102 (1997)] are reviewed. Next, holonomic constraints are incorporated into the isokinetic methodology for use in fast efficient biomolecular simulation studies. Model and realistic examples are presented in order to evaluate, critically, the performance of the new isokinetic molecular dynamic schemes. Comparisons are made to the, now standard, canonical dynamics method, Nosé-Hoover chain dynamics [G. J. Martyna et al., J. Chem. Phys. 97, 2635 (1992)]. The new isokinetic techniques are found to yield more efficient sampling than the Nosé-Hoover chain method in both path integral molecular dynamics and biophysical molecular dynamics calculations. In Paper II, the use of isokinetic methods in Car-Parrinello based ab initio molecular dynamics calculations is presented.
Mohanty, Rakesh; Prasanna, M Lakshmi; Sudhashree,
2011-01-01
In this paper, we have proposed a new variant of Round Robin scheduling algorithm by executing the processes according to the new calculated Fit Factor f and using the concept of dynamic time quantum. We have compared the performance of our proposed Fittest Job First Dynamic Round Robin(FJFDRR) algorithm with the Priority Based Static Round Robin(PBSRR) algorithm. Experimental results show that our proposed algorithm performs better than PBSRR in terms of reducing the number of context switches, average waiting time and average turnaround time.
Institute of Scientific and Technical Information of China (English)
Lin Jiang; Ruolin Wu∗and Zhichao Zhu
2015-01-01
The dynamic characteristics of hydraulic self servo swing cylinder were analyzed according to the hydraulic system natural frequency formula. Based on that, a method of the hydraulic self servo swing cylinder structure optimization based on genetic algorithm was proposed in this paper. By analyzing the four parameters that affect the dynamic characteristics, we had to optimize the structure to obtain as larger the Dm ( displacement) as possible under the condition with the purpose of improving the dynamic characteristics of hydraulic self servo swing cylinder. So three state equations were established in this paper. The paper analyzed the effect of the four parameters in hydraulic self servo swing cylinder natural frequency equation and used the genetic algorithm to obtain the optimal solution of structure parameters. The model was simulated by substituting the parameters and initial value to the simulink model. Simulation results show that: using self servo hydraulic swing cylinder natural frequency equation to study its dynamic response characteristics is very effective. Compared with no optimization, the overall system dynamic response speed is significantly improved.
Institute of Scientific and Technical Information of China (English)
LI Qiang; WU Jianxin; SUN Yan
2009-01-01
Dynamic optimization of electromechanical coupling system is a significant engineering problem in the field of mechatronics. The performance improvement of electromechanical equipment depends on the system design parameters. Aiming at the spindle unit of refitted machine tool for solid rocket, the vibration acceleration of tool is taken as objective function, and the electromechanical system design parameters are appointed as design variables. Dynamic optimization model is set up by adopting Lagrange-Maxwell equations, Park transform and electromechanical system energy equations. In the procedure of seeking high efficient optimization method, exponential function is adopted to be the weight function of particle swarm optimization algorithm. Exponential inertia weight particle swarm algorithm(EPSA), is formed and applied to solve the dynamic optimization problem of electromechanical system. The probability density function of EPSA is presented and used to perform convergence analysis. After calculation, the optimized design parameters of the spindle unit are obtained in limited time period. The vibration acceleration of the tool has been decreased greatly by the optimized design parameters. The research job in the paper reveals that the problem of dynamic optimization of electromechanical system can be solved by the method of combining system dynamic analysis with reformed swarm particle optimization. Such kind of method can be applied in the design of robots, NC machine, and other electromechanical equipments.
A New Recommendation Algorithm Based on User’s Dynamic Information in Complex Social Network
Jiujun Cheng; Yingbo Liu; Huiting Zhang; Xiao Wu; Fuzhen Chen
2015-01-01
The development of recommendation system comes with the research of data sparsity, cold start, scalability, and privacy protection problems. Even though many papers proposed different improved recommendation algorithms to solve those problems, there is still plenty of room for improvement. In the complex social network, we can take full advantage of dynamic information such as user’s hobby, social relationship, and historical log to improve the performance of recommendation system. In this pa...
Detectability Thresholds and Optimal Algorithms for Community Structure in Dynamic Networks
Ghasemian, Amir; Zhang, Pan; Clauset, Aaron; Moore, Cristopher; Peel, Leto
2016-07-01
The detection of communities within a dynamic network is a common means for obtaining a coarse-grained view of a complex system and for investigating its underlying processes. While a number of methods have been proposed in the machine learning and physics literature, we lack a theoretical analysis of their strengths and weaknesses, or of the ultimate limits on when communities can be detected. Here, we study the fundamental limits of detecting community structure in dynamic networks. Specifically, we analyze the limits of detectability for a dynamic stochastic block model where nodes change their community memberships over time, but where edges are generated independently at each time step. Using the cavity method, we derive a precise detectability threshold as a function of the rate of change and the strength of the communities. Below this sharp threshold, we claim that no efficient algorithm can identify the communities better than chance. We then give two algorithms that are optimal in the sense that they succeed all the way down to this threshold. The first uses belief propagation, which gives asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm, based on linearizing the belief propagation equations. These results extend our understanding of the limits of community detection in an important direction, and introduce new mathematical tools for similar extensions to networks with other types of auxiliary information.
DYNAMIC K-MEANS ALGORITHM FOR OPTIMIZED ROUTING IN MOBILE AD HOC NETWORKS
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Zahra Zandieh Shirazi
2016-04-01
Full Text Available In this paper, a dynamic K-means algorithm to improve the routing process in Mobile Ad-Hoc networks (MANETs is presented. Mobile ad-hoc networks are a collocation of mobile wireless nodes that can operate without using focal access points, pre-existing infrastructures, or a centralized management point. In MANETs, the quick motion of nodes modifies the topology of network. This feature of MANETS is lead to various problems in the routing process such as increase of the overhead massages and inefficient routing between nodes of network. A large variety of clustering methods have been developed for establishing an efficient routing process in MANETs. Routing is one of the crucial topics which are having significant impact on MANETs performance. The K-means algorithm is one of the effective clustering methods aimed to reduce routing difficulties related to bandwidth, throughput and power consumption. This paper proposed a new K-means clustering algorithm to find out optimal path from source node to destinations node in MANETs. The main goal of proposed approach which is called the dynamic K-means clustering methods is to solve the limitation of basic K-means method like permanent cluster head and fixed cluster members. The experimental results demonstrate that using dynamic K-means scheme enhance the performance of routing process in Mobile ad-hoc networks.
Obara, Shin'ya
A micro-grid with the capacity for sustainable energy is expected to be a distributed energy system that exhibits quite a small environmental impact. In an independent micro-grid, “green energy,” which is typically thought of as unstable, can be utilized effectively by introducing a battery. In the past study, the production-of-electricity prediction algorithm (PAS) of the solar cell was developed. In PAS, a layered neural network is made to learn based on past weather data and the operation plan of the compound system of a solar cell and other energy systems was examined using this prediction algorithm. In this paper, a dynamic operational scheduling algorithm is developed using a neural network (PAS) and a genetic algorithm (GA) to provide predictions for solar cell power output. We also do a case study analysis in which we use this algorithm to plan the operation of a system that connects nine houses in Sapporo to a micro-grid composed of power equipment and a polycrystalline silicon solar cell. In this work, the relationship between the accuracy of output prediction of the solar cell and the operation plan of the micro-grid was clarified. Moreover, we found that operating the micro-grid according to the plan derived with PAS was far superior, in terms of equipment hours of operation, to that using past average weather data.
A new second-order integration algorithm for simulating mechanical dynamic systems
Howe, R. M.
1989-01-01
A new integration algorithm which has the simplicity of Euler integration but exhibits second-order accuracy is described. In fixed-step numerical integration of differential equations for mechanical dynamic systems the method represents displacement and acceleration variables at integer step times and velocity variables at half-integer step times. Asymptotic accuracy of the algorithm is twice that of trapezoidal integration and ten times that of second-order Adams-Bashforth integration. The algorithm is also compatible with real-time inputs when used for a real-time simulation. It can be used to produce simulation outputs at double the integration frame rate, i.e., at both half-integer and integer frame times, even though it requires only one evaluation of state-variable derivatives per integration step. The new algorithm is shown to be especially effective in the simulation of lightly-damped structural modes. Both time-domain and frequency-domain accuracy comparisons with traditional integration methods are presented. Stability of the new algorithm is also examined.
Institute of Scientific and Technical Information of China (English)
Mohammed A.M. Ibrahim; Lu Xinda; M. SaifMokbel
2005-01-01
The rapid growth of interconnected high performance workstations has produced a new computing paradigm called clustered of workstations computing. In these systems load balance problem is a serious impediment to achieve good performance. The main concern of this paper is the implementation of dynamic load balancing algorithm,asynchronous Round Robin (ARR), for balancing workload of parallel tree computation depth-first-search algorithm on Cluster of Heterogeneous Workstations (COW) Many algorithms in artificial intelligence and other areas of computer science are based on depth first search in implicitty defined trees. For these algorithms a loadbalancing scheme is required, which is able to evenly distribute parts of an irregularly shaped tree over the workstations with minimal interprocessor communication and without prior knowledge of the tree's shape. For the( ARR ) algorithm only minimal interpreeessor communication is needed when necessary and it runs under the MPI (Message passing interface) that allows parallel execution on heterogeneous SUN cluster of workstation platform. The program code is written in C language and executed under UNIX operating system (Solaris version).
Gündüç, Semra; Dilaver, Mehmet; Aydın, Meral; Gündüç, Yiğit
2005-02-01
In this work we have studied the dynamic scaling behavior of two scaling functions and we have shown that scaling functions obey the dynamic finite size scaling rules. Dynamic finite size scaling of scaling functions opens possibilities for a wide range of applications. As an application we have calculated the dynamic critical exponent (z) of Wolff's cluster algorithm for 2-, 3- and 4-dimensional Ising models. Configurations with vanishing initial magnetization are chosen in order to avoid complications due to initial magnetization. The observed dynamic finite size scaling behavior during early stages of the Monte Carlo simulation yields z for Wolff's cluster algorithm for 2-, 3- and 4-dimensional Ising models with vanishing values which are consistent with the values obtained from the autocorrelations. Especially, the vanishing dynamic critical exponent we obtained for d=3 implies that the Wolff algorithm is more efficient in eliminating critical slowing down in Monte Carlo simulations than previously reported.
Dynamic Air-Route Adjustments - Model,Algorithm,and Sensitivity Analysis
Institute of Scientific and Technical Information of China (English)
GENG Rui; CHENG Peng; CUI Deguang
2009-01-01
Dynamic airspace management (DAM) is an important approach to extend limited air space resources by using them more efficiently and flexibly.This paper analyzes the use of the dynamic air-route adjustment (DARA) method as a core procedure in DAM systems.DARA method makes dynamic decisions on when and how to adjust the current air-route network with the minimum cost.This model differs from the air traffic flow management (ATFM) problem because it considers dynamic opening and closing of air-route segments instead of only arranging flights on a given air traffic network and it takes into account several new constraints,such as the shortest opening time constraint.The DARA problem is solved using a two-step heuristic algorithm.The sensitivities of important coefficients in the model are analyzed to determine proper values for these coefficients.The computational results based on practical data from the Beijing ATC region show that the two-step heuristic algorithm gives as good results as the CPLEX in less or equal time in most cases.
Toh, H
1997-08-01
Two approximations were introduced into the double dynamic programming algorithm, in order to reduce the computational time for structural alignment. One of them was the so-called distance cut-off, which approximately describes the structural environment of each residue by its local environment. In the approximation, a sphere with a given radius is placed at the center of the side chain of each residue. The local environment of a residue is constituted only by the residues with side chain centers that are present within the sphere, which is expressed by a set of center-to-center distances from the side chain of the residue to those of all the other constituent residues. The residues outside the sphere are neglected from the local environment. Another approximation is associated with the distance cut-off, which is referred to here as the delta N cut-off. If two local environments are similar to each other, the numbers of residues constituting the environments are expected to be similar. The delta N cut-off was introduced based on the idea. If the difference between the numbers of the constituent residues of two local environments is greater than a given threshold value, delta N, the evaluation of the similarity between the local environments is skipped. The introduction of the two approximations dramatically reduced the computational time for structural alignment by the double dynamic programming algorithm. However, the approximations also decreased the accuracy of the alignment. To improve the accuracy with the approximations, a program with a two-step alignment algorithm was constructed. At first, an alignment was roughly constructed with the approximations. Then, the epsilon-suboptimal region for the alignment was determined. Finally, the double dynamic programming algorithm with full structural environments was applied to the residue pairs within the epsilon-suboptimal region to produce an improved alignment.
Analysis of limit forces on the vehicle wheels using an algorithm of Dynamic Square Method
Brukalski, M.
2016-09-01
This article presents a method named as Dynamic Square Method (DSM) used for dynamic analysis of a vehicle equipped with a four wheel drive system. This method allows determination of maximum (limit) forces acting on the wheels. Here, the maximum longitudinal forces acting on the wheels are assumed and then used to predict whether they can be achieved by a specific dynamic motion or whether the actual friction forces under a given wheel is large enough to transfer lateral forces. For the analysis of DSM a four wheel vehicle model is used. On the basis of this characteristic it is possible to determine the maximum longitudinal force acting on the wheels of the given axle depending on the lateral acceleration of the vehicle. The results of this analysis may be useful in the development of a control algorithm used for example in active differentials.
Nair, T R Gopalakrishnan; Yashoda, M B
2011-01-01
In Internet Routing, the static shortest path (SP) problem has been addressed using well known intelligent optimization techniques like artificial neural networks, genetic algorithms (GAs) and particle swarm optimization. Advancement in wireless communication lead more and more mobile wireless networks, such as mobile networks [mobile ad hoc networks (MANETs)] and wireless sensor networks. Dynamic nature of the network is the main characteristic of MANET. Therefore, the SP routing problem in MANET turns into dynamic optimization problem (DOP). Here the nodes ae made aware of the environmental condition, thereby making it intelligent, which goes as the input for GA. The implementation then uses GAs with immigrants and memory schemes to solve the dynamic SP routing problem (DSPRP) in MANETS. In our paper, once the network topology changes, the optimal solutions in the new environment can be searched using the new immigrants or the useful information stored in the memory. Results shows GA with new immigrants sho...
Pluchino, A.; Rapisarda, A.; Latora, V.
2008-10-01
We have recently introduced [Phys. Rev. E 75, 045102(R) (2007); AIP Conference Proceedings 965, 2007, p. 323] an efficient method for the detection and identification of modules in complex networks, based on the de-synchronization properties (dynamical clustering) of phase oscillators. In this paper we apply the dynamical clustering tecnique to the identification of communities of marine organisms living in the Chesapeake Bay food web. We show that our algorithm is able to perform a very reliable classification of the real communities existing in this ecosystem by using different kinds of dynamical oscillators. We compare also our results with those of other methods for the detection of community structures in complex networks.
Teo, P. T.; Crow, R.; Van Nest, S.; Sasaki, D.; Pistorius, S.
2013-07-01
This paper investigates the feasibility and accuracy of using a computer vision algorithm and electronic portal images to track the motion of a tumour-like target from a breathing phantom. A multi-resolution optical flow algorithm that incorporates weighting based on the differences between frames was used to obtain a set of vectors corresponding to the motion between two frames. A global value representing the average motion was obtained by computing the average weighted mean from the set of vectors. The tracking accuracy of the optical flow algorithm as a function of the breathing rate and target visibility was investigated. Synthetic images with different contrast-to-noise ratios (CNR) were created, and motions were tracked. The accuracy of the proposed algorithm was compared against potentiometer measurements giving average position errors of 0.6 ± 0.2 mm, 0.2 ± 0.2 mm and 0.1 ± 0.1 mm with average velocity errors of 0.2 ± 0.2 mm s-1, 0.4 ± 0.3 mm s-1 and 0.6 ± 0.5 mm s-1 for 6, 12 and 16 breaths min-1 motions, respectively. The cumulative average position error reduces more rapidly with the greater number of breathing cycles present in higher breathing rates. As the CNR increases from 4.27 to 5.6, the average relative error approaches zero and the errors are less dependent on the velocity. When tracking a tumour on a patient's digitally reconstructed radiograph images, a high correlation was obtained between the dynamically weighted optical flow algorithm, a manual delineation process and a centroid tracking algorithm. While the accuracy of our approach is similar to that of other methods, the benefits are that it does not require manual delineation of the target and can therefore provide accurate real-time motion estimation during treatment.
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.
Staged optimization algorithms based MAC dynamic bandwidth allocation for OFDMA-PON
Liu, Yafan; Qian, Chen; Cao, Bingyao; Dun, Han; Shi, Yan; Zou, Junni; Lin, Rujian; Wang, Min
2016-06-01
Orthogonal frequency division multiple access passive optical network (OFDMA-PON) has being considered as a promising solution for next generation PONs due to its high spectral efficiency and flexible bandwidth allocation scheme. In order to take full advantage of these merits of OFDMA-PON, a high-efficiency medium access control (MAC) dynamic bandwidth allocation (DBA) scheme is needed. In this paper, we propose two DBA algorithms which can act on two different stages of a resource allocation process. To achieve higher bandwidth utilization and ensure the equity of ONUs, we propose a DBA algorithm based on frame structure for the stage of physical layer mapping. Targeting the global quality of service (QoS) of OFDMA-PON, we propose a full-range DBA algorithm with service level agreement (SLA) and class of service (CoS) for the stage of bandwidth allocation arbitration. The performance of the proposed MAC DBA scheme containing these two algorithms is evaluated using numerical simulations. Simulations of a 15 Gbps network with 1024 sub-carriers and 32 ONUs demonstrate the maximum network throughput of 14.87 Gbps and the maximum packet delay of 1.45 ms for the highest priority CoS under high load condition.
Ng, Hok K.; Grabbe, Shon; Mukherjee, Avijit
2010-01-01
The optimization of traffic flows in congested airspace with varying convective weather is a challenging problem. One approach is to generate shortest routes between origins and destinations while meeting airspace capacity constraint in the presence of uncertainties, such as weather and airspace demand. This study focuses on development of an optimal flight path search algorithm that optimizes national airspace system throughput and efficiency in the presence of uncertainties. The algorithm is based on dynamic programming and utilizes the predicted probability that an aircraft will deviate around convective weather. It is shown that the running time of the algorithm increases linearly with the total number of links between all stages. The optimal routes minimize a combination of fuel cost and expected cost of route deviation due to convective weather. They are considered as alternatives to the set of coded departure routes which are predefined by FAA to reroute pre-departure flights around weather or air traffic constraints. A formula, which calculates predicted probability of deviation from a given flight path, is also derived. The predicted probability of deviation is calculated for all path candidates. Routes with the best probability are selected as optimal. The predicted probability of deviation serves as a computable measure of reliability in pre-departure rerouting. The algorithm can also be extended to automatically adjust its design parameters to satisfy the desired level of reliability.
Dias, Maluge Pubuduni Imali; Wong, Elaine
2013-04-22
In this work, we present a comparative study of two just-in-time (JIT) dynamic bandwidth allocation algorithms (DBAs), designed to improve the energy-efficiency of the 10 Gbps Ethernet passive optical networks (10G-EPONs). The algorithms, termed just-in-time with varying polling cycle times (JIT) and just-in-time with fixed polling cycle times (J-FIT), are designed to achieve energy-savings when the idle time of an optical network unit (ONU) is less than the sleep-to-active transition time. This is made possible by a vertical-cavity surface-emitting laser (VCSEL) ONU that can transit into sleep or doze modes during its idle time. We evaluate the performance of the algorithms in terms of polling cycle time, power consumption, percentage of energy-savings, and average delay. The energy-efficiency of a VCSEL ONU that can transition into sleep or doze mode is compared to an always-ON distributed feedback (DFB) laser ONU. Simulation results indicate that both JIT and J-FIT DBA algorithms result in improved energy-efficiency whilst J-FIT performs better in terms of energy-savings at low network loads. The J-FIT DBA however, results in increased average delay in comparison to the JIT DBA. Nonetheless, this increase in average delay is within the acceptable range to support the quality of service (QoS) requirements of the next-generation access networks.
QoS-aware dynamic bandwidth allocation algorithm for Gigabit-capable PONS
Institute of Scientific and Technical Information of China (English)
WANG Xin; ZHAO Yang; GU Wanyi
2007-01-01
The Gigabit-capable passive optical network (GPON)technology is being considered as a promising solution for the next-generation broadband access network.Since the network topology of the GPON is point-to-multipoint,a media access control called dynamic bandwidth allocation(DBA)algorithm is an important factor for determining the performance of the GPON.In this paper,we propose a new DBA algorithm to effectively and fairly allocate bandwidths among end users.This DBA algorithm supports difierentiated services-a cmcial requirement for a converged broadband access network with heterogeneous traffic.In this article we first reviewed the signaling and configuration of the DBA,and then proposed a new DBA scheme that implemented QoS-based priority for this need to maximally satisfy the requirements of all optical network units(ONUs)and provide difierentiated services.Analyses and simulation results show that the new algorithm can improve the bandwidth utilization and realize the fairness for both different ONUs and services.
The Dynamic Enterprise Network Composition Algorithm for Efficient Operation in Cloud Manufacturing
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Gilseung Ahn
2016-11-01
Full Text Available As a service oriented and networked model, cloud manufacturing (CM has been proposed recently for solving a variety of manufacturing problems, including diverse requirements from customers. In CM, on-demand manufacturing services are provided by a temporary production network composed of several enterprises participating within an enterprise network. In other words, the production network is the main agent of production and a subset of an enterprise network. Therefore, it is essential to compose the enterprise network in a way that can respond to demands properly. A properly-composed enterprise network means the network can handle demands that arrive at the CM, with minimal costs, such as network composition and operation costs, such as participation contract costs, system maintenance costs, and so forth. Due to trade-offs among costs (e.g., contract cost and opportunity cost of production, it is a non-trivial problem to find the optimal network enterprise composition. In addition, this includes probabilistic constraints, such as forecasted demand. In this paper, we propose an algorithm, named the dynamic enterprise network composition algorithm (DENCA, based on a genetic algorithm to solve the enterprise network composition problem. A numerical simulation result is provided to demonstrate the performance of the proposed algorithm.
Kodali, Anuradha
In this thesis, we develop dynamic multiple fault diagnosis (DMFD) algorithms to diagnose faults that are sporadic and coupled. Firstly, we formulate a coupled factorial hidden Markov model-based (CFHMM) framework to diagnose dependent faults occurring over time (dynamic case). Here, we implement a mixed memory Markov coupling model to determine the most likely sequence of (dependent) fault states, the one that best explains the observed test outcomes over time. An iterative Gauss-Seidel coordinate ascent optimization method is proposed for solving the problem. A soft Viterbi algorithm is also implemented within the framework for decoding dependent fault states over time. We demonstrate the algorithm on simulated and real-world systems with coupled faults; the results show that this approach improves the correct isolation rate as compared to the formulation where independent fault states are assumed. Secondly, we formulate a generalization of set-covering, termed dynamic set-covering (DSC), which involves a series of coupled set-covering problems over time. The objective of the DSC problem is to infer the most probable time sequence of a parsimonious set of failure sources that explains the observed test outcomes over time. The DSC problem is NP-hard and intractable due to the fault-test dependency matrix that couples the failed tests and faults via the constraint matrix, and the temporal dependence of failure sources over time. Here, the DSC problem is motivated from the viewpoint of a dynamic multiple fault diagnosis problem, but it has wide applications in operations research, for e.g., facility location problem. Thus, we also formulated the DSC problem in the context of a dynamically evolving facility location problem. Here, a facility can be opened, closed, or can be temporarily unavailable at any time for a given requirement of demand points. These activities are associated with costs or penalties, viz., phase-in or phase-out for the opening or closing of a
Directory of Open Access Journals (Sweden)
Hatm Alkadeki
2015-12-01
Full Text Available The IEEE 802.11 backoff algorithm is very important for controlling system throughput over contentionbased wireless networks. For this reason, there are many studies on wireless network performance focus on developing backoff algorithms. However, most existing models are based on saturated traffic loads, which are not a real representation of actual network conditions. In this paper, a dynamic control backoff time algorithm is proposed to enhance both delay and throughput performance of the IEEE 802.11 distributed coordination function. This algorithm considers the distinction between high and low traffic loads in order to deal with unsaturated traffic load conditions. In particular, the equilibrium point analysis model is used to represent the algorithm under various traffic load conditions. Results of extensive simulation experiments illustrate that the proposed algorithm yields better performance throughput and a better average transmission packet delay than related algorithms.
A DYNAMIC FAULT TOLERANT ALGORITHM FOR IMPROVISING PERFORMANCE OF MULTIMEDIA SERVICES
Institute of Scientific and Technical Information of China (English)
无
2005-01-01
Multimedia Services has drawn much attention from both industrial and academic researchers due to the emerging consumer market, how to provide High-Availability service is one of most important issues to take into account. In this paper, a dynamic fault tolerant algorithm is presented for highly available distributed multimedia service, then by introducing SLB(server load balancing) into fault tolerance and switching servers in different ways according to their functions, the proposed schema can preserve reliability and real-time of the system .The analysis and experiments indicate that resuming server's faulty by this method is smooth and transparent to the client The proposed algorithm is effectively improving the reliability of the multimedia service.
A Wavelet Analysis-Based Dynamic Prediction Algorithm to Network Traffic
Directory of Open Access Journals (Sweden)
Meng Fan-Bo
2016-01-01
Full Text Available Network traffic is a significantly important parameter for network traffic engineering, while it holds highly dynamic nature in the network. Accordingly, it is difficult and impossible to directly predict traffic amount of end-to-end flows. This paper proposes a new prediction algorithm to network traffic using the wavelet analysis. Firstly, network traffic is converted into the time-frequency domain to capture time-frequency feature of network traffic. Secondly, in different frequency components, we model network traffic in the time-frequency domain. Finally, we build the prediction model about network traffic. At the same time, the corresponding prediction algorithm is presented to attain network traffic prediction. Simulation results indicates that our approach is promising.
Kim, Byung Soo; Lee, Woon-Seek; Koh, Shiegheun
2012-07-01
This article considers an inbound ordering and outbound dispatching problem for a single product in a third-party warehouse, where the demands are dynamic over a discrete and finite time horizon, and moreover, each demand has a time window in which it must be satisfied. Replenishing orders are shipped in containers and the freight cost is proportional to the number of containers used. The problem is classified into two cases, i.e. non-split demand case and split demand case, and a mathematical model for each case is presented. An in-depth analysis of the models shows that they are very complicated and difficult to find optimal solutions as the problem size becomes large. Therefore, genetic algorithm (GA) based heuristic approaches are designed to solve the problems in a reasonable time. To validate and evaluate the algorithms, finally, some computational experiments are conducted.
Directory of Open Access Journals (Sweden)
Eskandar Gholipour
2013-02-01
Full Text Available Power-system dynamic stability improvement by a static synchronous series compensator (SSSC based damping controller is thoroughly investigated in this paper. In order to design the optimal parameters of the controller, Imperialist Competitive Algorithm (ICA is employed to search for the optimal controller parameters. Both local and remote signals are considered in the present study and the performance of the proposed controllers with variations in the signal transmission delays has been investigated. The performances of the proposed controllers are evaluated under different disturbances for both single-machine-infinite-bus and multi-machine power systems. Finally, the results of ICA method are compared with the results of Genetic Algorithm (GA.
Speed improvement of B-snake algorithm using dynamic programming optimization.
Charfi, Maher; Zrida, Jalel
2011-10-01
This paper presents a novel approach to contour approximation carried out by means of the B-snake algorithm and the dynamic programming (DP) optimization technique. Using the proposed strategy for contour point search procedure, computing complexity is reduced to O(N×M(2)), whereas the standard DP method has an O(N×M(4)) complexity, with N being the number of contour sample points and M being the number of candidates in the search space. The storage requirement was also decreased from N×M(3) to N×M memory elements. Some experiments on noise corrupted synthetic image, magnetic resonance, and computer tomography medical images have shown that the proposed approach results are equivalent to those obtained by the standard DP algorithm.
Bhole, Gaurav; Anjusha, V. S.; Mahesh, T. S.
2016-04-01
A robust control over quantum dynamics is of paramount importance for quantum technologies. Many of the existing control techniques are based on smooth Hamiltonian modulations involving repeated calculations of basic unitaries resulting in time complexities scaling rapidly with the length of the control sequence. Here we show that bang-bang controls need one-time calculation of basic unitaries and hence scale much more efficiently. By employing a global optimization routine such as the genetic algorithm, it is possible to synthesize not only highly intricate unitaries, but also certain nonunitary operations. We demonstrate the unitary control through the implementation of the optimal fixed-point quantum search algorithm in a three-qubit nuclear magnetic resonance (NMR) system. Moreover, by combining the bang-bang pulses with the crusher gradients, we also demonstrate nonunitary transformations of thermal equilibrium states into effective pure states in three- as well as five-qubit NMR systems.
Li, Y.; Kirchengast, G.; Scherllin-Pirscher, B.; Norman, R.; Yuan, Y. B.; Fritzer, J.; Schwaerz, M.; Zhang, K.
2015-01-01
We introduce a new dynamic statistical optimization algorithm to initialize ionosphere-corrected bending angles of Global Navigation Satellite System (GNSS) based radio occultation (RO) measurements. The new algorithm estimates background and observation error covariance matrices with geographically-varying uncertainty profiles and realistic global-mean correlation matrices. The error covariance matrices estimated by the new approach are more accurate and realistic than in simplified existing approaches and can therefore be used in statistical optimization to provide optimal bending angle profiles for high-altitude initialization of the subsequent Abel transform retrieval of refractivity. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.6 (OPSv5.6) algorithm, using simulated data on two test days from January and July 2008 and real observed CHAMP and COSMIC measurements from the complete months of January and July 2008. The following is achieved for the new method's performance compared to OPSv5.6: (1) significant reduction in random errors (standard deviations) of optimized bending angles down to about two-thirds of their size or more; (2) reduction of the systematic differences in optimized bending angles for simulated MetOp data; (3) improved retrieval of refractivity and temperature profiles; (4) produces realistically estimated global-mean correlation matrices and realistic uncertainty fields for the background and observations. Overall the results indicate high suitability for employing the new dynamic approach in the processing of long-term RO data into a reference climate record, leading to well characterized and high-quality atmospheric profiles over the entire stratosphere.
Li, Y.; Kirchengast, G.; Scherllin-Pirscher, B.; Norman, R.; Yuan, Y. B.; Fritzer, J.; Schwaerz, M.; Zhang, K.
2015-08-01
We introduce a new dynamic statistical optimization algorithm to initialize ionosphere-corrected bending angles of Global Navigation Satellite System (GNSS)-based radio occultation (RO) measurements. The new algorithm estimates background and observation error covariance matrices with geographically varying uncertainty profiles and realistic global-mean correlation matrices. The error covariance matrices estimated by the new approach are more accurate and realistic than in simplified existing approaches and can therefore be used in statistical optimization to provide optimal bending angle profiles for high-altitude initialization of the subsequent Abel transform retrieval of refractivity. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.6 (OPSv5.6) algorithm, using simulated data on two test days from January and July 2008 and real observed CHAllenging Minisatellite Payload (CHAMP) and Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) measurements from the complete months of January and July 2008. The following is achieved for the new method's performance compared to OPSv5.6: (1) significant reduction of random errors (standard deviations) of optimized bending angles down to about half of their size or more; (2) reduction of the systematic differences in optimized bending angles for simulated MetOp data; (3) improved retrieval of refractivity and temperature profiles; and (4) realistically estimated global-mean correlation matrices and realistic uncertainty fields for the background and observations. Overall the results indicate high suitability for employing the new dynamic approach in the processing of long-term RO data into a reference climate record, leading to well-characterized and high-quality atmospheric profiles over the entire stratosphere.
Directory of Open Access Journals (Sweden)
Y. Li
2015-01-01
Full Text Available We introduce a new dynamic statistical optimization algorithm to initialize ionosphere-corrected bending angles of Global Navigation Satellite System (GNSS based radio occultation (RO measurements. The new algorithm estimates background and observation error covariance matrices with geographically-varying uncertainty profiles and realistic global-mean correlation matrices. The error covariance matrices estimated by the new approach are more accurate and realistic than in simplified existing approaches and can therefore be used in statistical optimization to provide optimal bending angle profiles for high-altitude initialization of the subsequent Abel transform retrieval of refractivity. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.6 (OPSv5.6 algorithm, using simulated data on two test days from January and July 2008 and real observed CHAMP and COSMIC measurements from the complete months of January and July 2008. The following is achieved for the new method's performance compared to OPSv5.6: (1 significant reduction in random errors (standard deviations of optimized bending angles down to about two-thirds of their size or more; (2 reduction of the systematic differences in optimized bending angles for simulated MetOp data; (3 improved retrieval of refractivity and temperature profiles; (4 produces realistically estimated global-mean correlation matrices and realistic uncertainty fields for the background and observations. Overall the results indicate high suitability for employing the new dynamic approach in the processing of long-term RO data into a reference climate record, leading to well characterized and high-quality atmospheric profiles over the entire stratosphere.
Enhanced high dynamic range 3D shape measurement based on generalized phase-shifting algorithm
Wang, Minmin; Du, Guangliang; Zhou, Canlin; Zhang, Chaorui; Si, Shuchun; Li, Hui; Lei, Zhenkun; Li, YanJie
2017-02-01
Measuring objects with large reflectivity variations across their surface is one of the open challenges in phase measurement profilometry (PMP). Saturated or dark pixels in the deformed fringe patterns captured by the camera will lead to phase fluctuations and errors. Jiang et al. proposed a high dynamic range real-time three-dimensional (3D) shape measurement method (Jiang et al., 2016) [17] that does not require changing camera exposures. Three inverted phase-shifted fringe patterns are used to complement three regular phase-shifted fringe patterns for phase retrieval whenever any of the regular fringe patterns are saturated. Nonetheless, Jiang's method has some drawbacks: (1) the phases of saturated pixels are estimated by different formulas on a case by case basis; in other words, the method lacks a universal formula; (2) it cannot be extended to the four-step phase-shifting algorithm, because inverted fringe patterns are the repetition of regular fringe patterns; (3) for every pixel in the fringe patterns, only three unsaturated intensity values can be chosen for phase demodulation, leaving the other unsaturated ones idle. We propose a method to enhance high dynamic range 3D shape measurement based on a generalized phase-shifting algorithm, which combines the complementary techniques of inverted and regular fringe patterns with a generalized phase-shifting algorithm. Firstly, two sets of complementary phase-shifted fringe patterns, namely the regular and the inverted fringe patterns, are projected and collected. Then, all unsaturated intensity values at the same camera pixel from two sets of fringe patterns are selected and employed to retrieve the phase using a generalized phase-shifting algorithm. Finally, simulations and experiments are conducted to prove the validity of the proposed method. The results are analyzed and compared with those of Jiang's method, demonstrating that our method not only expands the scope of Jiang's method, but also improves
A graph based algorithm for adaptable dynamic airspace configuration for NextGen
Savai, Mehernaz P.
The National Airspace System (NAS) is a complicated large-scale aviation network, consisting of many static sectors wherein each sector is controlled by one or more controllers. The main purpose of the NAS is to enable safe and prompt air travel in the U.S. However, such static configuration of sectors will not be able to handle the continued growth of air travel which is projected to be more than double the current traffic by 2025. Under the initiative of the Next Generation of Air Transportation system (NextGen), the main objective of Adaptable Dynamic Airspace Configuration (ADAC) is that the sectors should change to the changing traffic so as to reduce the controller workload variance with time while increasing the throughput. Change in the resectorization should be such that there is a minimal increase in exchange of air traffic among controllers. The benefit of a new design (improvement in workload balance, etc.) should sufficiently exceed the transition cost, in order to deserve a change. This leads to the analysis of the concept of transition workload which is the cost associated with a transition from one sectorization to another. Given two airspace configurations, a transition workload metric which considers the air traffic as well as the geometry of the airspace is proposed. A solution to reduce this transition workload is also discussed. The algorithm is specifically designed to be implemented for the Dynamic Airspace Configuration (DAC) Algorithm. A graph model which accurately represents the air route structure and air traffic in the NAS is used to formulate the airspace configuration problem. In addition, a multilevel graph partitioning algorithm is developed for Dynamic Airspace Configuration which partitions the graph model of airspace with given user defined constraints and hence provides the user more flexibility and control over various partitions. In terms of air traffic management, vertices represent airports and waypoints. Some of the major
DEFF Research Database (Denmark)
Hou, Peng; Hu, Weihao; Chen, Cong
2016-01-01
The approach in this paper hads been developed to optimize the cable connection layout of large scale offshore wind farms. The objective is to minimize the Levelised Production Cost (LPC) og an offshore wind farm by optimizing the cable connection configuration. Based on the minimum spanning tree...... (MST) algorithm, an improved algorithm, the Dynamic Minimum Spanning Tree (DMST) algorithm is proposed. The current carrying capacity of the cable is considered to be the main constraint and the cable sectional area is changed dynamically. An irregular shaped wind farm is chosen as the studie case...
Distributed Algorithm for Computing the Vehicle Launch Dynamics under Interaction with the Medium
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G. A. Shcheglov
2015-01-01
Full Text Available The paper describes a distributed algorithm and a structure of the software package for its implementation in which a program for computing the vehicle launch dynamics under interaction with the medium flow is complemented with a program to determine the unsteady hydrodynamic loads by the vortex element method.A distinctive feature of the developed system is that its local (running on a single computing core LEAVING program to calculate the launch dynamics runs together with concurrent (running on multiple computing cores MDVDD program to compute the unsteady vortex flow and hydrodynamic loads. The LEAVING program is the main one. It is launched app and then runs the MDVDD program in concurrent mode on the specified number of cores. Using MPI technology allows you to use a multiprocessor PC or a local network of multiple PCs to perform calculations. The equations of launcher spring-mass model dynamics and equations of vortex elements parameters evolution are integrated with the same time step. The interprogram communiaction in the step is provided asynchronously using the OS Windows Event mechanism (Events. Interfacing between LEAVING and MDVDD programs is built using the OS Windows FileMapping technology, which allows a specified data structure to be displayed and read to the fixed memory area.The paper provides analysis of acceleration achieved with parallel processing on different numbers of cores, and defines a parallelization degree of various operations. It shows that the parallelization efficiency of the developed algorithm is slower than in case of calculation of the rigid body flow. The causes of reduced efficiency are discussed.It is shown that the developed algorithm can be effectively used to solve problems on a small number of cores, e.g. on PC based on one or two quad-core processors.
A novel graph-based partitioning algorithm for large-scale dynamical systems
Kamelian, Saeed; Salahshoor, Karim
2015-01-01
In this paper, a novel graph-based system partitioning approach is proposed to facilitate the design of distributed or decentralised control in large-scale dynamical systems. In large-scale dynamical systems, a decomposition method is required to determine a suitable set of distributed subsystems and their relevant variables. In the proposed approach, a decomposition algorithm starts to generate an overall graph representation of the system model in the form of a new weighted digraph on the basis of a sensitivity analysis concept to quantify the coupling strengths among the system variables in terms of graph edge weights. The produced weighted digraph and its structural information are then used to partition the system model. All the potential system control inputs are first characterised as the main graph vertices, representing fixed subsystems centres. Then, the remaining vertices, representing system states or outputs, are assigned to the created subgraphs. Once the initial grouping is accordingly formed, a merging routine is automatically conducted to merge the small subgraphs in other subgraphs in an iterative searching way to find the smaller cut sizes. Each time a merging occurs, the total cost of the merged configuration, being defined in terms of an averaged linear quadratic regulator (LQR) metric, is used as a novel dynamic performance metric versus total group number reduction to terminate the algorithm for the best grouping result. A chemical industrial process plant is used as a benchmark to assess performance of the proposed methodology to fulfil the system partitioning objective. The output result of the algorithm is then comparatively used for a decentralised non-linear model-based predictive control methodology to demonstrate its ultimate merits.
A Based-Bottleneck Multiple Vehicle Type Dynamic Marginal Cost Model and Algorithm
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Shuguang Li
2012-09-01
Full Text Available Single vehicle type dynamic marginal cost model is extended to multiple vehicle type dynamic one based on time-dependent multiple vehicle type queue analysis at a bottleneck. A dynamic link model is presented to model interactions between cars and trucks, given the link consists of two distinct segments. The first segment is the running segment on which cars (trucks run at their free-flow speeds and the second segment is the exit queue segment. A car or a truck is assumed to be a point without length. The class-specific pi parameter is used to transform the effect of truck into passenger car equivalents, so the exit flow of cars and trucks can be calculated according to the exit capacity of a bottleneck. The analytic expression of multiple vehicle type dynamic marginal cost function is deduced under congested and uncongested conditions. Then a heuristic algorithm is presented in solving multiple vehicle type dynamic queues, tolls under system optimum and user equilibrium conditions. The numerical example illustrates the simplicity and applicability of the proposed approach.
Genetic algorithm-fuzzy based dynamic motion planning approach for a mobile robot
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Presents the mobile robots dynamic motion planning problem with a task to find an obstacle-free route that requires minimum travel time from the start point to the destination point in a changing environment, due to the obstacle's moving. An Genetic Algorithm fuzzy(GA-Fuzzy)based optimal approach proposed to find any obstacle-free path and the GA used to select the optimal one, points ont that using this learned knowledge off line, a mobile robot can navigate to its goal point when it faces new scenario on-line. Concludes with the opti mal rule base given and the simulation results showing its effectiveness.
Penalty Dynamic Programming Algorithm for Dim Targets Detection in Sensor Systems
2012-01-01
In order to detect and track multiple maneuvering dim targets in sensor systems, an improved dynamic programming track-before-detect algorithm (DP-TBD) called penalty DP-TBD (PDP-TBD) is proposed. The performances of tracking techniques are used as a feedback to the detection part. The feedback is constructed by a penalty term in the merit function, and the penalty term is a function of the possible target state estimation, which can be obtained by the tracking methods. With this feedback, th...
Dynamic Bandwidth Allocation Algorithm with Fairness in 1G/10G Coexistence EPON System
Tanaka, Masaki; Takemoto, Michiya; Takahashi, Akira; Shimokasa, Kiyoshi
Gigabit Ethernet Passive Optical Networks (GE-PON) systems have been deployed widely as a broadband access solution for the optical access network, the so-called FTTx networks. 10 Gigabit EPON (10G-EPON) is about to be standardizing by a task force (802.3av). To provide the next FTTx solution with 10G-EPON systems, one of the key technologies is how to migrate from 1G-based to 10G-based. In this paper, we present Dynamic Bandwidth Allocation (DBA) algorithm which considered a fair policy for 1G/10G coexistence EPON system to achieve a smooth migration.
An efficient dissipative particle dynamics-based algorithm for simulating electrolyte solutions
Medina, Stefan; Wang, Zhen-Gang; Schmid, Friederike
2014-01-01
We propose an efficient simulation algorithm based on the dissipative particle dynamics (DPD) method for studying electrohydrodynamic phenomena in electrolyte fluids. The fluid flow is mimicked with DPD particles while the evolution of the concentration of the ionic species is described using Brownian pseudo particles. The method is designed especially for systems with high salt concentrations, as explicit treatment of the salt ions becomes computationally expensive. For illustration, we apply the method to electro-osmotic flow over patterned, superhydrophobic surfaces. The results are in good agreement with recent theoretical predictions.
Institute of Scientific and Technical Information of China (English)
LIN Xiangguo; LIANG Yong
2005-01-01
The processing of nonlinear data was one of hot topics in surveying and mapping field in recent years.As a result, many linear methods and nonlinear methods have been developed.But the methods for processing generalized nonlinear surveying and mapping data, especially for different data types and including unknown parameters with random or nonrandom, are seldom noticed.A new algorithm model is presented in this paper for processing nonlinear dynamic multiple-period and multiple-accuracy data derived from deformation monitoring network.
Janzing, Dominik; Chaves, Rafael; Schölkopf, Bernhard
2016-09-01
We postulate a principle stating that the initial condition of a physical system is typically algorithmically independent of the dynamical law. We discuss the implications of this principle and argue that they link thermodynamics and causal inference. On the one hand, they entail behavior that is similar to the usual arrow of time. On the other hand, they motivate a statistical asymmetry between cause and effect that has recently been postulated in the field of causal inference, namely, that the probability distribution {P}{{cause}} contains no information about the conditional distribution {P}{{effect}| {{cause}}} and vice versa, while {P}{{effect}} may contain information about {P}{{cause}| {{effect}}}.
EZDCP:A new static task scheduling algorithm with edge-zeroing based on dynamic critical paths
Institute of Scientific and Technical Information of China (English)
陈志刚; 华强胜
2003-01-01
A new static task scheduling algorithm named edge-zeroing based on dynamic critical paths is proposed.The main ideas of the algorithm are as follows: firstly suppose that all of the tasks are in different clusters; secondly, select one of the critical paths of the partially clustered directed acyclic graph; thirdly, try to zero one of graph communication edges; fourthly, repeat above three processes until all edges are zeroed; finally, check the generated clusters to see if some of them can be further merged without increasing the parallel time. Comparisons of the previous algorithms with edge-zeroing based on dynamic critical paths show that the new algorithm has not only a low complexity but also a desired performance comparable or even better on average to much higher complexity heuristic algorithms.
Inchworm Monte Carlo for exact non-adiabatic dynamics I. Theory and algorithms
Chen, Hsing-Ta; Reichman, David R
2016-01-01
In this paper we provide a detailed description of the inchworm Monte Carlo formalism for the exact study of real-time non-adiabatic dynamics. This method optimally recycles Monte Carlo information from earlier times to greatly suppress the dynamical sign problem. Using the example of the spin-boson model, we formulate the inchworm expansion in two distinct ways: The first with respect to an expansion in the system-bath coupling and the second as an expansion in the diabatic coupling. The latter approach motivates the development of a cumulant version of the inchworm Monte Carlo method, which has the benefit of improved scaling. This paper deals completely with methodology, while the companion paper provides a comprehensive comparison of the performance of the inchworm Monte Carlo algorithms to other exact methodologies as well as a discussion of the relative advantages and disadvantages of each.
Institute of Scientific and Technical Information of China (English)
Li Yuan-xiang; Liu Dong-mei
2003-01-01
With the frequent information accesses from users to the Internet, it is important to organize and allocate information resources properly on different web servers. This paper considers the following protblem: Due to the capacity limitation of each single web server, it is impossible to put all information resources on one web server. Hence it is an important problem to put them on several different servers suchas: (1) the amount of information resources assigned on any server is less than its capacity; (2) the access bottleneck can be avoided. In order to solve the problem in which the access frequency is variable, this paper proposes a dynamic optimal modeling. Based on the computational complexity results, the paper further focuses on the genetic algorithm for solving the dynamic problem. Finally we give the simulation results and conclusions.
Dynamic Task-Scheduling in Grid Computing using Prioritized Round Robin Algorithm
Directory of Open Access Journals (Sweden)
Sunita Bansal
2011-03-01
Full Text Available Over the years, grid computing has emerged as one of the most viable and scalable alternatives to high performance supercomputing, tapping into computing power of the order of Gigaflops. However, the inherent dynamicity in grid computing has made it extremely difficult to come up with near-optimal solutions to efficiently schedule tasks in grids. The present paper proposes a novel grid-scheduling heuristic that adaptively and dynamically schedules tasks without requiring any prior information on the workload of incoming tasks. The approach models the grid system in the form of a state-transition diagram, employing a prioritized round-robin algorithm with task replication to optimally schedule tasks, using prediction information on processor utilization of individual nodes. Simulations, comparing the proposed approach with the round-robin heuristic, have shown the given heuristic to be more effective in scheduling tasks as compared to the latter.
Inchworm Monte Carlo for exact non-adiabatic dynamics. I. Theory and algorithms
Chen, Hsing-Ta; Cohen, Guy; Reichman, David R.
2017-02-01
In this paper, we provide a detailed description of the inchworm Monte Carlo formalism for the exact study of real-time non-adiabatic dynamics. This method optimally recycles Monte Carlo information from earlier times to greatly suppress the dynamical sign problem. Using the example of the spin-boson model, we formulate the inchworm expansion in two distinct ways: The first with respect to an expansion in the system-bath coupling and the second as an expansion in the diabatic coupling. The latter approach motivates the development of a cumulant version of the inchworm Monte Carlo method, which has the benefit of improved scaling. This paper deals completely with methodology, while Paper II provides a comprehensive comparison of the performance of the inchworm Monte Carlo algorithms to other exact methodologies as well as a discussion of the relative advantages and disadvantages of each.
Directory of Open Access Journals (Sweden)
Mohammad Abaee Shoushtary
2014-01-01
Full Text Available This paper describes a new hybrid algorithm extracted from honey bee mating optimization (HBMO algorithm (for robot travelling distance minimization and tabu list technique (for obstacle avoidance for team robot system. This algorithm was implemented in a C++ programming language on a Pentium computer and simulated on simple cylindrical robots in a simulation software. The environment in this simulation was dynamic with moving obstacles and goals. The results of simulation have shown validity and reliability of new algorithm. The outcomes of simulation have shown better performance than ACO and PSO algorithm (society, nature algorithms with respect to two well-known metrics included, ATPD (average total path deviation and AUTD (average uncovered target distance.
Directory of Open Access Journals (Sweden)
P.Nirmala
2014-08-01
Full Text Available In this paper, an optimal design of FIR filter is carried out using a “Dynamic Regional Harmony Search algorithm (DRHS with Opposition and Local Learning”. The Harmony Search (HS is a robust optimization algorithm which mimics the musician’s improvisation method and has been used by many researchers for solving and optimizing various real-world optimization problems and numerical solutions. For optimizing the functionality of the FIR filter, DRHS algorithm which is an enhanced variant of the HS algorithm is adopted to avoid pre-mature convergence and stagnation. BY adopting DRHS algorithm the low pass, high pass, band pass and band stop FIR filters are constructed and their performances are evaluated and compared with the other existing optimization techniques. A comparison of the DRHS with other optimization algorithms for constructing FIR filter clearly shows the DRHS finds the optimal solution and the convergence is clearly guaranteed.
Hasegawa, Taisuke
2016-11-07
We propose a novel molecular dynamics (MD) algorithm for approximately dealing with a nuclear quantum dynamics in a real-time MD simulation. We have found that real-time dynamics of the ensemble of classical particles acquires quantum nature by introducing a constant quantum mechanical uncertainty constraint on its classical dynamics. The constant uncertainty constraint is handled by the Lagrange multiplier method and implemented into a conventional MD algorithm. The resulting constant uncertainty molecular dynamics (CUMD) is applied to the calculation of quantum position autocorrelation functions on quartic and Morse potentials. The test calculations show that CUMD gives better performance than ring-polymer MD because of the inclusion of the quantum zero-point energy during real-time evolution as well as the quantum imaginary-time statistical effect stored in an initial condition. The CUMD approach will be a possible starting point for new real-time quantum dynamics simulation in condensed phase.
Hasegawa, Taisuke
2016-11-01
We propose a novel molecular dynamics (MD) algorithm for approximately dealing with a nuclear quantum dynamics in a real-time MD simulation. We have found that real-time dynamics of the ensemble of classical particles acquires quantum nature by introducing a constant quantum mechanical uncertainty constraint on its classical dynamics. The constant uncertainty constraint is handled by the Lagrange multiplier method and implemented into a conventional MD algorithm. The resulting constant uncertainty molecular dynamics (CUMD) is applied to the calculation of quantum position autocorrelation functions on quartic and Morse potentials. The test calculations show that CUMD gives better performance than ring-polymer MD because of the inclusion of the quantum zero-point energy during real-time evolution as well as the quantum imaginary-time statistical effect stored in an initial condition. The CUMD approach will be a possible starting point for new real-time quantum dynamics simulation in condensed phase.
An Analytical Measuring Rectification Algorithm of Monocular Systems in Dynamic Environment
Directory of Open Access Journals (Sweden)
Deshi Li
2016-01-01
Full Text Available Range estimation is crucial for maintaining a safe distance, in particular for vision navigation and localization. Monocular autonomous vehicles are appropriate for outdoor environment due to their mobility and operability. However, accurate range estimation using vision system is challenging because of the nonholonomic dynamics and susceptibility of vehicles. In this paper, a measuring rectification algorithm for range estimation under shaking conditions is designed. The proposed method focuses on how to estimate range using monocular vision when a shake occurs and the algorithm only requires the pose variations of the camera to be acquired. Simultaneously, it solves the problem of how to assimilate results from different kinds of sensors. To eliminate measuring errors by shakes, we establish a pose-range variation model. Afterwards, the algebraic relation between distance increment and a camera’s poses variation is formulated. The pose variations are presented in the form of roll, pitch, and yaw angle changes to evaluate the pixel coordinate incensement. To demonstrate the superiority of our proposed algorithm, the approach is validated in a laboratory environment using Pioneer 3-DX robots. The experimental results demonstrate that the proposed approach improves in the range accuracy significantly.
Computational fluid dynamics based bulbous bow optimization using a genetic algorithm
Mahmood, Shahid; Huang, Debo
2012-09-01
Computational fluid dynamics (CFD) plays a major role in predicting the flow behavior of a ship. With the development of fast computers and robust CFD software, CFD has become an important tool for designers and engineers in the ship industry. In this paper, the hull form of a ship was optimized for total resistance using CFD as a calculation tool and a genetic algorithm as an optimization tool. CFD based optimization consists of major steps involving automatic generation of geometry based on design parameters, automatic generation of mesh, automatic analysis of fluid flow to calculate the required objective/cost function, and finally an optimization tool to evaluate the cost for optimization. In this paper, integration of a genetic algorithm program, written in MATLAB, was carried out with the geometry and meshing software GAMBIT and CFD analysis software FLUENT. Different geometries of additive bulbous bow were incorporated in the original hull based on design parameters. These design variables were optimized to achieve a minimum cost function of "total resistance". Integration of a genetic algorithm with CFD tools proves to be effective for hull form optimization.
Computational Fluid Dynamics Based Bulbous Bow Optimization Using a Genetic Algorithm
Institute of Scientific and Technical Information of China (English)
Shahid Mahmood; Debo Huang
2012-01-01
Computational fluid dynamics (CFD) plays a major role in predicting the flow behavior of a ship.With the development of fast computers and robust CFD software,CFD has become an important tool for designers and engineers in the ship industry.In this paper,the hull form of a ship was optimized for total resistance using CFD as a calculation tool and a genetic algorithm as an optimization tool.CFD based optimization consists of major steps involving automatic generation of geometry based on design parameters,automatic generation of mesh,automatic analysis of fluid flow to calculate the required objective/cost function,and finally an optimization tool to evaluate the cost for optimization.In this paper,integration of a genetic algorithm program,written in MATLAB,was carried out with the geometry and meshing software GAMBIT and CFD analysis software FLUENT.Different geometries of additive bulbous bow were incorporated in the original hull based on design parameters.These design variables were optimized to achieve a minimum cost function of “total resistance”.Integration of a genetic algorithm with CFD tools proves to be effective for hull form optimization.
Andrade, Andre; Costa, Marcelo; Paolucci, Leopoldo; Braga, Antônio; Pires, Flavio; Ugrinowitsch, Herbert; Menzel, Hans-Joachim
2015-01-01
The aim of this study was to present a new training algorithm using artificial neural networks called multi-objective least absolute shrinkage and selection operator (MOBJ-LASSO) applied to the classification of dynamic gait patterns. The movement pattern is identified by 20 characteristics from the three components of the ground reaction force which are used as input information for the neural networks in gender-specific gait classification. The classification performance between MOBJ-LASSO (97.4%) and multi-objective algorithm (MOBJ) (97.1%) is similar, but the MOBJ-LASSO algorithm achieved more improved results than the MOBJ because it is able to eliminate the inputs and automatically select the parameters of the neural network. Thus, it is an effective tool for data mining using neural networks. From 20 inputs used for training, MOBJ-LASSO selected the first and second peaks of the vertical force and the force peak in the antero-posterior direction as the variables that classify the gait patterns of the different genders.
A matrix product algorithm for stochastic dynamics on locally tree-like graphs
Barthel, Thomas; de Bacco, Caterina; Franz, Silvio
In this talk, I describe a novel algorithm for the efficient simulation of generic stochastic dynamics of classical degrees of freedom defined on the vertices of locally tree-like graphs. Such models correspond for example to spin-glass systems, Boolean networks, neural networks, or other technological, biological, and social networks. Building upon the cavity method and ideas from quantum many-body theory, the algorithm is based on a matrix product approximation of the so-called edge messages - conditional probabilities of vertex variable trajectories. The matrix product edge messages (MPEM) are constructed recursively. Computation costs and accuracy can be tuned by controlling the matrix dimensions of the MPEM in truncations. In contrast to Monte Carlo simulations, the approach has a better error scaling and works for both, single instances as well as the thermodynamic limit. Due to the absence of cancellation effects, observables with small expectation values can be evaluated accurately, allowing for the study of decay processes and temporal correlations with unprecedented accuracy. The method is demonstrated for the prototypical non-equilibrium Glauber dynamics of an Ising spin system. Reference: arXiv:1508.03295.
New MPPT algorithm for PV applications based on hybrid dynamical approach
Elmetennani, S.
2016-10-24
This paper proposes a new Maximum Power Point Tracking (MPPT) algorithm for photovoltaic applications using the multicellular converter as a stage of power adaptation. The proposed MPPT technique has been designed using a hybrid dynamical approach to model the photovoltaic generator. The hybrid dynamical theory has been applied taking advantage of the particular topology of the multicellular converter. Then, a hybrid automata has been established to optimize the power production. The maximization of the produced solar energy is achieved by switching between the different operative modes of the hybrid automata, which is conditioned by some invariance and transition conditions. These conditions have been validated by simulation tests under different conditions of temperature and irradiance. Moreover, the performance of the proposed algorithm has been then evaluated by comparison with standard MPPT techniques numerically and by experimental tests under varying external working conditions. The results have shown the interesting features that the hybrid MPPT technique presents in terms of performance and simplicity for real time implementation.
Khan, Mohammad Ibrahim; Kamal, Md Sarwar
2015-03-01
Markov Chain is very effective in prediction basically in long data set. In DNA sequencing it is always very important to find the existence of certain nucleotides based on the previous history of the data set. We imposed the Chapman Kolmogorov equation to accomplish the task of Markov Chain. Chapman Kolmogorov equation is the key to help the address the proper places of the DNA chain and this is very powerful tools in mathematics as well as in any other prediction based research. It incorporates the score of DNA sequences calculated by various techniques. Our research utilize the fundamentals of Warshall Algorithm (WA) and Dynamic Programming (DP) to measures the score of DNA segments. The outcomes of the experiment are that Warshall Algorithm is good for small DNA sequences on the other hand Dynamic Programming are good for long DNA sequences. On the top of above findings, it is very important to measure the risk factors of local sequencing during the matching of local sequence alignments whatever the length.
Chen, Yunjie; Kale, Seyit; Weare, Jonathan; Dinner, Aaron R; Roux, Benoît
2016-04-12
A multiple time-step integrator based on a dual Hamiltonian and a hybrid method combining molecular dynamics (MD) and Monte Carlo (MC) is proposed to sample systems in the canonical ensemble. The Dual Hamiltonian Multiple Time-Step (DHMTS) algorithm is based on two similar Hamiltonians: a computationally expensive one that serves as a reference and a computationally inexpensive one to which the workload is shifted. The central assumption is that the difference between the two Hamiltonians is slowly varying. Earlier work has shown that such dual Hamiltonian multiple time-step schemes effectively precondition nonlinear differential equations for dynamics by reformulating them into a recursive root finding problem that can be solved by propagating a correction term through an internal loop, analogous to RESPA. Of special interest in the present context, a hybrid MD-MC version of the DHMTS algorithm is introduced to enforce detailed balance via a Metropolis acceptance criterion and ensure consistency with the Boltzmann distribution. The Metropolis criterion suppresses the discretization errors normally associated with the propagation according to the computationally inexpensive Hamiltonian, treating the discretization error as an external work. Illustrative tests are carried out to demonstrate the effectiveness of the method.
Renison, C Alicia; Fernandes, Kyle D; Naidoo, Kevin J
2015-07-05
This article describes an extension of the quantum supercharger library (QSL) to perform quantum mechanical (QM) gradient and optimization calculations as well as hybrid QM and molecular mechanical (QM/MM) molecular dynamics simulations. The integral derivatives are, after the two-electron integrals, the most computationally expensive part of the aforementioned calculations/simulations. Algorithms are presented for accelerating the one- and two-electron integral derivatives on a graphical processing unit (GPU). It is shown that a Hartree-Fock ab initio gradient calculation is up to 9.3X faster on a single GPU compared with a single central processing unit running an optimized serial version of GAMESS-UK, which uses the efficient Schlegel method for s- and l-orbitals. Benchmark QM and QM/MM molecular dynamics simulations are performed on cellobiose in vacuo and in a 39 Å water sphere (45 QM atoms and 24843 point charges, respectively) using the 6-31G basis set. The QSL can perform 9.7 ps/day of ab initio QM dynamics and 6.4 ps/day of QM/MM dynamics on a single GPU in full double precision. © 2015 Wiley Periodicals, Inc.
A Dynamic Health Assessment Approach for Shearer Based on Artificial Immune Algorithm
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Zhongbin Wang
2016-01-01
Full Text Available In order to accurately identify the dynamic health of shearer, reducing operating trouble and production accident of shearer and improving coal production efficiency further, a dynamic health assessment approach for shearer based on artificial immune algorithm was proposed. The key technologies such as system framework, selecting the indicators for shearer dynamic health assessment, and health assessment model were provided, and the flowchart of the proposed approach was designed. A simulation example, with an accuracy of 96%, based on the collected data from industrial production scene was provided. Furthermore, the comparison demonstrated that the proposed method exhibited higher classification accuracy than the classifiers based on back propagation-neural network (BP-NN and support vector machine (SVM methods. Finally, the proposed approach was applied in an engineering problem of shearer dynamic health assessment. The industrial application results showed that the paper research achievements could be used combining with shearer automation control system in fully mechanized coal face. The simulation and the application results indicated that the proposed method was feasible and outperforming others.
Al-Jabr, Ahmad Ali
2013-01-01
This paper presents methods of simulating gain media in the finite difference time-domain (FDTD) algorithm utilizing a generalized polarization formulation. The gain can be static or dynamic. For static gain, Lorentzian and non-Lorentzian models are presented and tested. For the dynamic gain, rate equations for two-level and four-level models are incorporated in the FDTD scheme. The simulation results conform with the expected behavior of wave amplification and dynamic population inversion.
Shimojo, Fuyuki; Hattori, Shinnosuke; Kalia, Rajiv K.; Kunaseth, Manaschai; Mou, Weiwei; Nakano, Aiichiro; Nomura, Ken-ichi; Ohmura, Satoshi; Rajak, Pankaj; Shimamura, Kohei; Vashishta, Priya
2014-05-01
We introduce an extension of the divide-and-conquer (DC) algorithmic paradigm called divide-conquer-recombine (DCR) to perform large quantum molecular dynamics (QMD) simulations on massively parallel supercomputers, in which interatomic forces are computed quantum mechanically in the framework of density functional theory (DFT). In DCR, the DC phase constructs globally informed, overlapping local-domain solutions, which in the recombine phase are synthesized into a global solution encompassing large spatiotemporal scales. For the DC phase, we design a lean divide-and-conquer (LDC) DFT algorithm, which significantly reduces the prefactor of the O(N) computational cost for N electrons by applying a density-adaptive boundary condition at the peripheries of the DC domains. Our globally scalable and locally efficient solver is based on a hybrid real-reciprocal space approach that combines: (1) a highly scalable real-space multigrid to represent the global charge density; and (2) a numerically efficient plane-wave basis for local electronic wave functions and charge density within each domain. Hybrid space-band decomposition is used to implement the LDC-DFT algorithm on parallel computers. A benchmark test on an IBM Blue Gene/Q computer exhibits an isogranular parallel efficiency of 0.984 on 786 432 cores for a 50.3 × 106-atom SiC system. As a test of production runs, LDC-DFT-based QMD simulation involving 16 661 atoms is performed on the Blue Gene/Q to study on-demand production of hydrogen gas from water using LiAl alloy particles. As an example of the recombine phase, LDC-DFT electronic structures are used as a basis set to describe global photoexcitation dynamics with nonadiabatic QMD (NAQMD) and kinetic Monte Carlo (KMC) methods. The NAQMD simulations are based on the linear response time-dependent density functional theory to describe electronic excited states and a surface-hopping approach to describe transitions between the excited states. A series of techniques
Energy Technology Data Exchange (ETDEWEB)
Shimojo, Fuyuki; Hattori, Shinnosuke [Collaboratory for Advanced Computing and Simulations, Department of Physics and Astronomy, Department of Computer Science, and Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089-0242 (United States); Department of Physics, Kumamoto University, Kumamoto 860-8555 (Japan); Kalia, Rajiv K.; Mou, Weiwei; Nakano, Aiichiro; Nomura, Ken-ichi; Rajak, Pankaj; Vashishta, Priya [Collaboratory for Advanced Computing and Simulations, Department of Physics and Astronomy, Department of Computer Science, and Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089-0242 (United States); Kunaseth, Manaschai [Collaboratory for Advanced Computing and Simulations, Department of Physics and Astronomy, Department of Computer Science, and Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089-0242 (United States); National Nanotechnology Center, Pathumthani 12120 (Thailand); Ohmura, Satoshi [Collaboratory for Advanced Computing and Simulations, Department of Physics and Astronomy, Department of Computer Science, and Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089-0242 (United States); Department of Physics, Kumamoto University, Kumamoto 860-8555 (Japan); Department of Physics, Kyoto University, Kyoto 606-8502 (Japan); Shimamura, Kohei [Collaboratory for Advanced Computing and Simulations, Department of Physics and Astronomy, Department of Computer Science, and Department of Chemical Engineering and Materials Science, University of Southern California, Los Angeles, California 90089-0242 (United States); Department of Physics, Kumamoto University, Kumamoto 860-8555 (Japan); Department of Applied Quantum Physics and Nuclear Engineering, Kyushu University, Fukuoka 819-0395 (Japan)
2014-05-14
We introduce an extension of the divide-and-conquer (DC) algorithmic paradigm called divide-conquer-recombine (DCR) to perform large quantum molecular dynamics (QMD) simulations on massively parallel supercomputers, in which interatomic forces are computed quantum mechanically in the framework of density functional theory (DFT). In DCR, the DC phase constructs globally informed, overlapping local-domain solutions, which in the recombine phase are synthesized into a global solution encompassing large spatiotemporal scales. For the DC phase, we design a lean divide-and-conquer (LDC) DFT algorithm, which significantly reduces the prefactor of the O(N) computational cost for N electrons by applying a density-adaptive boundary condition at the peripheries of the DC domains. Our globally scalable and locally efficient solver is based on a hybrid real-reciprocal space approach that combines: (1) a highly scalable real-space multigrid to represent the global charge density; and (2) a numerically efficient plane-wave basis for local electronic wave functions and charge density within each domain. Hybrid space-band decomposition is used to implement the LDC-DFT algorithm on parallel computers. A benchmark test on an IBM Blue Gene/Q computer exhibits an isogranular parallel efficiency of 0.984 on 786 432 cores for a 50.3 × 10{sup 6}-atom SiC system. As a test of production runs, LDC-DFT-based QMD simulation involving 16 661 atoms is performed on the Blue Gene/Q to study on-demand production of hydrogen gas from water using LiAl alloy particles. As an example of the recombine phase, LDC-DFT electronic structures are used as a basis set to describe global photoexcitation dynamics with nonadiabatic QMD (NAQMD) and kinetic Monte Carlo (KMC) methods. The NAQMD simulations are based on the linear response time-dependent density functional theory to describe electronic excited states and a surface-hopping approach to describe transitions between the excited states. A series of
Directory of Open Access Journals (Sweden)
H.Z. Igamberdiyev
2014-07-01
Full Text Available Dynamic systems condition estimation regularization algorithms in the conditions of signals and hindrances statistical characteristics aprioristic uncertainty are offered. Regular iterative algorithms of strengthening matrix factor elements of the Kalman filter, allowing to adapt the filter to changing hindrance-alarm conditions are developed. Steady adaptive estimation algorithms of a condition vector in the aprioristic uncertainty conditions of covariance matrixes of object noise and the measurements hindrances providing a certain roughness of filtration process in relation to changing statistical characteristics of signals information parameters are offered. Offered practical realization results of the dynamic systems condition estimation algorithms are given at the adaptive management systems synthesis problems solution by technological processes of granulation drying of an ammophos pulp and receiving ammonia.
Designs and Algorithms to Map Eye Tracking Data with Dynamic Multielement Moving Objects
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Ziho Kang
2016-01-01
Full Text Available Design concepts and algorithms were developed to address the eye tracking analysis issues that arise when (1 participants interrogate dynamic multielement objects that can overlap on the display and (2 visual angle error of the eye trackers is incapable of providing exact eye fixation coordinates. These issues were addressed by (1 developing dynamic areas of interests (AOIs in the form of either convex or rectangular shapes to represent the moving and shape-changing multielement objects, (2 introducing the concept of AOI gap tolerance (AGT that controls the size of the AOIs to address the overlapping and visual angle error issues, and (3 finding a near optimal AGT value. The approach was tested in the context of air traffic control (ATC operations where air traffic controller specialists (ATCSs interrogated multiple moving aircraft on a radar display to detect and control the aircraft for the purpose of maintaining safe and expeditious air transportation. In addition, we show how eye tracking analysis results can differ based on how we define dynamic AOIs to determine eye fixations on moving objects. The results serve as a framework to more accurately analyze eye tracking data and to better support the analysis of human performance.
Designs and Algorithms to Map Eye Tracking Data with Dynamic Multielement Moving Objects
Mandal, Saptarshi
2016-01-01
Design concepts and algorithms were developed to address the eye tracking analysis issues that arise when (1) participants interrogate dynamic multielement objects that can overlap on the display and (2) visual angle error of the eye trackers is incapable of providing exact eye fixation coordinates. These issues were addressed by (1) developing dynamic areas of interests (AOIs) in the form of either convex or rectangular shapes to represent the moving and shape-changing multielement objects, (2) introducing the concept of AOI gap tolerance (AGT) that controls the size of the AOIs to address the overlapping and visual angle error issues, and (3) finding a near optimal AGT value. The approach was tested in the context of air traffic control (ATC) operations where air traffic controller specialists (ATCSs) interrogated multiple moving aircraft on a radar display to detect and control the aircraft for the purpose of maintaining safe and expeditious air transportation. In addition, we show how eye tracking analysis results can differ based on how we define dynamic AOIs to determine eye fixations on moving objects. The results serve as a framework to more accurately analyze eye tracking data and to better support the analysis of human performance. PMID:27725830
Energy Technology Data Exchange (ETDEWEB)
Madduri, Kamesh; Bader, David A.
2009-02-15
Graph-theoretic abstractions are extensively used to analyze massive data sets. Temporal data streams from socioeconomic interactions, social networking web sites, communication traffic, and scientific computing can be intuitively modeled as graphs. We present the first study of novel high-performance combinatorial techniques for analyzing large-scale information networks, encapsulating dynamic interaction data in the order of billions of entities. We present new data structures to represent dynamic interaction networks, and discuss algorithms for processing parallel insertions and deletions of edges in small-world networks. With these new approaches, we achieve an average performance rate of 25 million structural updates per second and a parallel speedup of nearly28 on a 64-way Sun UltraSPARC T2 multicore processor, for insertions and deletions to a small-world network of 33.5 million vertices and 268 million edges. We also design parallel implementations of fundamental dynamic graph kernels related to connectivity and centrality queries. Our implementations are freely distributed as part of the open-source SNAP (Small-world Network Analysis and Partitioning) complex network analysis framework.
DEFF Research Database (Denmark)
Schmidt, Lasse; Andersen, Torben O.
2016-01-01
consideration are applied for position tracking control of a hydraulic valve-cylinder drive exhibiting strong variations in inertia- and gravitational loads, and furthermore suffer from profound valve dynamics. Results demonstrate that both the twisting- and super twisting algorithms may be successfully applied......The application of second order sliding mode algorithms for output feedback control in hydraulic valve-cylinder drives appear attractive due to their simple realization and parametrization, and strong robustness toward bounded parameter variations and uncertainties. However, intrinsic nonlinear...
Kizilkaya, Elif A.; Gupta, Surendra M.
2005-11-01
In this paper, we compare the impact of different disassembly line balancing (DLB) algorithms on the performance of our recently introduced Dynamic Kanban System for Disassembly Line (DKSDL) to accommodate the vagaries of uncertainties associated with disassembly and remanufacturing processing. We consider a case study to illustrate the impact of various DLB algorithms on the DKSDL. The approach to the solution, scenario settings, results and the discussions of the results are included.
Siberia snow depth climatology derived from SSM/I data using a combined dynamic and static algorithm
Grippa, M.; Mognard, N.; Le, Toan T.; Josberger, E.G.
2004-01-01
One of the major challenges in determining snow depth (SD) from passive microwave measurements is to take into account the spatiotemporal variations of the snow grain size. Static algorithms based on a constant snow grain size cannot provide accurate estimates of snow pack thickness, particularly over large regions where the snow pack is subjected to big spatial temperature variations. A recent dynamic algorithm that accounts for the dependence of the microwave scattering on the snow grain size has been developed to estimate snow depth from the Special Sensor Microwave/Imager (SSM/I) over the Northern Great Plains (NGP) in the US. In this paper, we develop a combined dynamic and static algorithm to estimate snow depth from 13 years of SSM/I observations over Central Siberia. This region is characterised by extremely cold surface air temperatures and by the presence of permafrost that significantly affects the ground temperature. The dynamic algorithm is implemented to take into account these effects and it yields accurate snow depths early in the winter, when thin snowpacks combine with cold air temperatures to generate rapid crystal growth. However, it is not applicable later in the winter when the grain size growth slows. Combining the dynamic algorithm to a static algorithm, with a temporally constant but spatially varying coefficient, we obtain reasonable snow depth estimates throughout the entire snow season. Validation is carried out by comparing the satellite snow depth monthly averages to monthly climatological data. We show that the location of the snow depth maxima and minima is improved when applying the combined algorithm, since its dynamic portion explicitly incorporate the thermal gradient through the snowpack. The results obtained are presented and evaluated for five different vegetation zones of Central Siberia. Comparison with in situ measurements is also shown and discussed. ?? 2004 Elsevier Inc. All rights reserved.
Maes, K.; Iliopoulos, A.; Weijtjens, W.; Devriendt, C.; Lombaert, G.
2016-08-01
Offshore wind turbines are exposed to continuous wind and wave excitation. The monitoring of high periodic strains at critical locations is important to assess the remaining lifetime of the structure. At some critical locations below the water level, direct measurements of the strains are not feasible. Response estimation techniques can then be used to estimate the strains from a limited set of response measurements and a system model. This paper compares a Kalman filtering algorithm, a joint input-state estimation algorithm, and a modal expansion algorithm, for the estimation of dynamic strains in the tower of an offshore monopile wind turbine. The algorithms make use of a model of the structure and a limited number of response measurements for the prediction of the strain responses. The strain signals obtained from the response estimation algorithms are compared to the actual measured strains in the tower.
AbouEisha, Hassan M.
2014-06-06
In this paper we present a dynamic programming algorithm for finding optimal elimination trees for computational grids refined towards point or edge singularities. The elimination tree is utilized to guide the multi-frontal direct solver algorithm. Thus, the criterion for the optimization of the elimination tree is the computational cost associated with the multi-frontal solver algorithm executed over such tree. We illustrate the paper with several examples of optimal trees found for grids with point, isotropic edge and anisotropic edge mixed with point singularity. We show the comparison of the execution time of the multi-frontal solver algorithm with results of MUMPS solver with METIS library, implementing the nested dissection algorithm.
A Fair Dynamic Quantum Algorithm%一种公平的动态轮转算法
Institute of Scientific and Technical Information of China (English)
胡赛; 赵碧海; 熊慧军
2012-01-01
时间片轮转算法作为一种经典的调度算法得到了广泛的应用.针对时间片轮转算法的调度策略和时间片长度的选取等问题开展深入的研究,提出了一种改进的动态轮转算法,算法是短作业优先算法、多级队列算法和时间片轮转算法的综合和发展.利用生灭过程理论建立了时间片轮转算法和动态轮转算法的性能模型,分析了两种算法的平均等待时间和平均周转时间,引入性能提高百分比的概念对比两种算法的差异.实验结果和理论分析均表明改进算法的性能优于传统的时间片轮转算法.%As a classic scheduling algorithm, round robin algorithm has been widely applied. According to the scheduling policy and the selection of the length of time slice for the round robin algorithm, a large number of in-depth studies are carried out. An improved dynamic quantum algorithm is proposed. The algorithm is the development and integration of shortest job first algorithm, multi-level queue scheduling algorithm and round robin algorithm. The performance models of the round robin algorithm and the dynamic quantum algorithm are built by using the theory of birth-death process, including average waiting time and average turnaround time of the two algorithms. The concept of performance improvement percentage is introduced in order to show the differences between the two algorithms. Both theoretical analysis and experimental results indicate that the performance of the improved algorithm is better than the traditional algorithm. The advantages of the improved algorithm become more obviously when the number of tasks increase and the rate of service improves.
Directory of Open Access Journals (Sweden)
Xiaochen Zhang
2015-01-01
Full Text Available To evaluate the performance of ball screw, screw performance degradation assessment technology based on quantum genetic algorithm (QGA and dynamic fuzzy neural network (DFNN is studied. The ball screw of the CINCINNATIV5-3000 machining center is treated as the study object. Two Kistler 8704B100M1 accelerometers and a Kistler 8765A250M5 three-way accelerometer are installed to monitor the degradation trend of screw performance. First, screw vibration signal features are extracted both in time domain and frequency domain. Then the feature vectors can be obtained by principal component analysis (PCA. Second, the initialization parameters of the DFNN are optimized by means of QGA. Finally, the feature vectors are inputted to DFNN for training and then get the screw performance degradation model. The experiment results show that the screw performance degradation model could effectively evaluate the performance of NC machine screw.
Multi-robot path planning in a dynamic environment using improved gravitational search algorithm
Directory of Open Access Journals (Sweden)
P.K. Das
2016-09-01
Full Text Available This paper proposes a new methodology to optimize trajectory of the path for multi-robots using improved gravitational search algorithm (IGSA in a dynamic environment. GSA is improved based on memory information, social, cognitive factor of PSO (particle swarm optimization and then, population for next generation is decided by the greedy strategy. A path planning scheme has been developed using IGSA to optimally obtain the succeeding positions of the robots from the existing position. Finally, the analytical and experimental results of the multi-robot path planning have been compared with those obtained by IGSA, GSA and PSO in a similar environment. The simulation and the Khepera environmental results outperform IGSA as compared to GSA and PSO with respect to performance matrix.
Comprehensive proton dose algorithm using pencil beam redefinition and recursive dynamic splitting
Gottschalk, Bernard
2016-01-01
We compute, from first principles, the absolute dose or fluence distribution per incident proton charge in a known heterogeneous terrain exposed to known proton beams. The algorithm is equally amenable to scattered or scanned beams. All objects in the terrain (including collimators) are sliced into slabs, of any convenient thickness, perpendicular to the nominal beam direction. Transport is by standard Fermi-Eyges theory. Transverse heterogeneities are handled by breaking up pencil beams (PBs) either by conventional redefinition or a new form of 2D recursive dynamic splitting: the mother PB is replaced, conserving emittance and charge, by seven daughters of equal transverse size. One has 1/4 the charge and travels in the mother's direction and six have 1/8 the charge, are arranged hexagonally and radiate from the mother's virtual point source. The longitudinal (energy-like) variable is pv (proton momentum times speed). Each material encountered is treated on its own merits, not referenced to water. Slowing do...
An Evolutionary Algorithm Approach to Link Prediction in Dynamic Social Networks
Bliss, Catherine A; Danforth, Christopher M; Dodds, Peter Sheridan
2013-01-01
Many real world, complex phenomena have underlying structures of evolving networks where nodes and links are added and removed over time. A central scientific challenge is the description and explanation of network dynamics, with a key test being the prediction of short and long term changes. For the problem of short-term link prediction, existing methods attempt to determine neighborhood metrics that correlate with the appearance of a link in the next observation period. Recent work has suggested that the incorporation of user-specific metadata and usage patterns can improve link prediction, however methodologies for doing so in a systematic way are largely unexplored in the literature. Here, we provide an approach to predicting future links by applying an evolutionary algorithm to weights which are used in a linear combination of sixteen neighborhood and node similarity indices. We examine Twitter reciprocal reply networks constructed at the time scale of weeks, both as a test of our general method and as a...
Tang, Yu-Hang
2013-01-01
We present a scalable dissipative particle dynamics simulation code, fully implemented on the Graphics Processing Units (GPUs) using a hybrid CUDA/MPI programming model, which achieves 10-30 times speedup on a single GPU over 16 CPU cores and almost linear weak scaling across a thousand nodes. A unified framework is developed within which the efficient generation of the neighbor list and maintaining particle data locality are addressed. Our algorithm generates strictly ordered neighbor lists in parallel, while the construction is deterministic and makes no use of atomic operations or sorting. Such neighbor list leads to optimal data loading efficiency when combined with a two-level particle reordering scheme. A faster in situ generation scheme for Gaussian random numbers is proposed using precomputed binary signatures. We designed custom transcendental functions that are fast and accurate for evaluating the pairwise interaction. The correctness and accuracy of the code is verified through a set of test cases ...
Muda, Lindasalwa; Elamvazuthi, I
2010-01-01
Digital processing of speech signal and voice recognition algorithm is very important for fast and accurate automatic voice recognition technology. The voice is a signal of infinite information. A direct analysis and synthesizing the complex voice signal is due to too much information contained in the signal. Therefore the digital signal processes such as Feature Extraction and Feature Matching are introduced to represent the voice signal. Several methods such as Liner Predictive Predictive Coding (LPC), Hidden Markov Model (HMM), Artificial Neural Network (ANN) and etc are evaluated with a view to identify a straight forward and effective method for voice signal. The extraction and matching process is implemented right after the Pre Processing or filtering signal is performed. The non-parametric method for modelling the human auditory perception system, Mel Frequency Cepstral Coefficients (MFCCs) are utilize as extraction techniques. The non linear sequence alignment known as Dynamic Time Warping (DTW) intro...
Metric-tensor flexible-cell algorithm for isothermal-isobaric molecular dynamics simulations
Hernández, E
2001-01-01
An extended Hamiltonian approach to conduct isothermal-isobaric molecular dynamics simulations with full cell flexibility is presented. The components of the metric tensor are used as the fictitious degrees of freedom for the cell, thus avoiding the problem of spurious cell rotations and artificial symmetry breaking effects present in the original Parrinello-Rahman scheme. This is complemented by the Nose-Poincare approach for isothermal sampling. The combination of these two approaches leads to equations of motion that are Hamiltonian in structure, and which can therefore be solved numerically using recently developed powerful symplectic integrators. One such integrator, the generalised leap-frog, is employed to provide a numerical algorithm for integrating the isothermal-isobaric equations of motion obtained.
A novel dynamic frame rate control algorithm for H.264 low-bit-rate video coding
Institute of Scientific and Technical Information of China (English)
Yang Jing; Fang Xiangzhong
2007-01-01
The goal of this paper is to improve human visual perceptual quality as well as coding efficiency of H.264 video at low bit rate conditions by adaptively adjusting the number of skipped frames. The encoding frames ale selected according to the motion activity of each frame and the motion accumulation of successive frames. The motion activity analysis is based on the statistics of motion vectors and with consideration of the characteristics of H. 264 coding standard. A prediction model of motion accumulation is proposed to reduce complex computation of motion estimation. The dynamic encoding frame rate control algorithm is applied to both the frame level and the GOB (Group of Macroblocks) level. Simulation is done to compare the performance of JM76 with the proposed frame level scheme and GOB level scheme.
A novel chaotic block image encryption algorithm based on dynamic random growth technique
Wang, Xingyuan; Liu, Lintao; Zhang, Yingqian
2015-03-01
This paper proposes a new block image encryption scheme based on hybrid chaotic maps and dynamic random growth technique. Since cat map is periodic and can be easily cracked by chosen plaintext attack, we use cat map in another securer way, which can completely eliminate the cyclical phenomenon and resist chosen plaintext attack. In the diffusion process, an intermediate parameter is calculated according to the image block. The intermediate parameter is used as the initial parameter of chaotic map to generate random data stream. In this way, the generated key streams are dependent on the plaintext image, which can resist the chosen plaintext attack. The experiment results prove that the proposed encryption algorithm is secure enough to be used in image transmission systems.
Dynamic Protection-at-Lightpath Algorithms in Traffic-Grooming WDM Mesh Networks
Institute of Scientific and Technical Information of China (English)
HERongxi; WENHaibo; LILemin
2005-01-01
Under the constraints of the number of transceivers per node and wavelength continuity, we investigate the problem of provisioning Protection-at-lightpath (PAL) level for connections with different bandwidth granularities in traffic-grooming WDM mesh networks. We first develop a new Protection graph model (PGM) to represent current state of the network, i.e., the information about transceivers, wavelengths, protected lightpath, bandwidth, etc. We then present two grooming policies for PAL, which are called Minimal wavelength-link policy (MWP) and Minimal transceiver policy (MTP). Based on the PGM, we propose two dynamic PAL algorithms with MWP and MTP respectively, which are called Minimal wavelength-link method (MWM) and Minimal transceiver method (MTM). At last we compare the performance of MWM and MTM with other methods utilizing different grooming policies presented in previous literatures via simulations. Our results show that MWM and MTM outperform other methods significantly and MWM has a better performance than MTM.
The neighbor list algorithm for a parallelepiped box in molecular dynamics simulations
Institute of Scientific and Technical Information of China (English)
CUI ZhiWei; SUN Yi; QU JianMin
2009-01-01
In classic molecular dynamics (MD) simulations, the conventional Verlet table, cell linked list and many other techniques have been adopted to increase the computational efficiency. However, these methods are only applicable in cubic systems. In this work, the above techniques along with the metric-tensor method are extended to handle NP ensembles, so that MD simulations can be carried out under the most general loading conditions. In order to do so, a particular spatial Cartesian reference frame is proposed to determine the scaling matrix. Also, a combination method, taking the advantages of the improved Verlet table and cell linked list, is established to identify the neighbor atoms very quickly in a parallelepiped box. An example using Lennard-Jones potential is presented to verify the validity of the proposed algorithm.
Directory of Open Access Journals (Sweden)
Saraiva J. T.
2012-10-01
Full Text Available The basic objective of Transmission Expansion Planning (TEP is to schedule a number of transmission projects along an extended planning horizon minimizing the network construction and operational costs while satisfying the requirement of delivering power safely and reliably to load centres along the horizon. This principle is quite simple, but the complexity of the problem and the impact on society transforms TEP on a challenging issue. This paper describes a new approach to solve the dynamic TEP problem, based on an improved discrete integer version of the Evolutionary Particle Swarm Optimization (EPSO meta-heuristic algorithm. The paper includes sections describing in detail the EPSO enhanced approach, the mathematical formulation of the TEP problem, including the objective function and the constraints, and a section devoted to the application of the developed approach to this problem. Finally, the use of the developed approach is illustrated using a case study based on the IEEE 24 bus 38 branch test system.
A Dynamic Algorithm for Facilitated Charging of Plug-In Electric Vehicles
Taheri, Nicole; Ye, Yinyu
2011-01-01
Plug-in Electric Vehicles (PEVs) are a rapidly developing technology that can reduce greenhouse gas emissions and change the way vehicles obtain power. PEV charging stations will most likely be available at home and at work, and occasionally be publicly available, offering flexible charging options. Ideally, each vehicle will charge during periods when electricity prices are relatively low, to minimize the cost to the consumer and maximize societal benefits. A Demand Response (DR) service for a fleet of PEVs could yield such charging schedules by regulating consumer electricity use during certain time periods, in order to meet an obligation to the market. We construct an automated DR mechanism for a fleet of PEVs that facilitates vehicle charging to ensure the demands of the vehicles and the market are met. Our dynamic algorithm depends only on the knowledge of a few hundred driving behaviors from a previous similar day, and uses a simple adjusted pricing scheme to instantly assign feasible and satisfactory c...
Directory of Open Access Journals (Sweden)
Javid Jouzdani
2016-01-01
Full Text Available With the constantly increasing pressure of the competitive environment, supply chain (SC decision makers are forced to consider several aspects of business climate. More specifically, they should take into account the endogenous features (e.g., available means of transportation, and the variety of products and exogenous criteria (e.g., the environmental uncertainty, and transportation system conditions. In this paper, a mixed integer nonlinear programming (MINLP model for dynamic design of a supply chain network is proposed. In this model, multiple products and multiple transportation modes, the time value of money, traffic congestion, and both supply-side and demand-side uncertainties are considered. Due to the complexity of such models, conventional solution methods are not applicable; therefore, two hybrid Electromagnetism-Like Algorithms (EMA are designed and discussed for tackling the problem. The numerical results show the applicability of the proposed model and the capabilities of the solution approaches to the MINLP problem.
Setiawan, A.; Wangsaputra, R.; Martawirya, Y. Y.; Halim, A. H.
2016-02-01
This paper deals with Flexible Manufacturing System (FMS) production rescheduling due to unavailability of cutting tools caused either of cutting tool failure or life time limit. The FMS consists of parallel identical machines integrated with an automatic material handling system and it runs fully automatically. Each machine has a same cutting tool configuration that consists of different geometrical cutting tool types on each tool magazine. The job usually takes two stages. Each stage has sequential operations allocated to machines considering the cutting tool life. In the real situation, the cutting tool can fail before the cutting tool life is reached. The objective in this paper is to develop a dynamic scheduling algorithm when a cutting tool is broken during unmanned and a rescheduling needed. The algorithm consists of four steps. The first step is generating initial schedule, the second step is determination the cutting tool failure time, the third step is determination of system status at cutting tool failure time and the fourth step is the rescheduling for unfinished jobs. The approaches to solve the problem are complete-reactive scheduling and robust-proactive scheduling. The new schedules result differences starting time and completion time of each operations from the initial schedule.
Hybrid SOA-SQP algorithm for dynamic economic dispatch with valve-point effects
Energy Technology Data Exchange (ETDEWEB)
Sivasubramani, S.; Swarup, K.S. [Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai 600036 (India)
2010-12-15
This paper proposes a hybrid technique combining a new heuristic algorithm named seeker optimization algorithm (SOA) and sequential quadratic programming (SQP) method for solving dynamic economic dispatch problem with valve-point effects. The SOA is based on the concept of simulating the act of human searching, where the search direction is based on the empirical gradient (EG) by evaluating the response to the position changes and the step length is based on uncertainty reasoning by using a simple fuzzy rule. In this paper, SOA is used as a base level search, which can give a good direction to the optimal global region and SQP as a local search to fine tune the solution obtained from SOA. Thus SQP guides SOA to find optimal or near optimal solution in the complex search space. Two test systems i.e., 5 unit with losses and 10 unit without losses, have been taken to validate the efficiency of the proposed hybrid method. Simulation results clearly show that the proposed method outperforms the existing method in terms of solution quality. (author)
A Novel Dynamic Algorithm for IT Outsourcing Risk Assessment Based on Transaction Cost Theory
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Guodong Cong
2015-01-01
Full Text Available With the great risk exposed in IT outsourcing, how to assess IT outsourcing risk becomes a critical issue. However, most of approaches to date need to further adapt to the particular complexity of IT outsourcing risk for either falling short in subjective bias, inaccuracy, or efficiency. This paper proposes a dynamic algorithm of risk assessment. It initially forwards extended three layers (risk factors, risks, and risk consequences of transferring mechanism based on transaction cost theory (TCT as the framework of risk analysis, which bridges the interconnection of components in three layers with preset transferring probability and impact. Then, it establishes an equation group between risk factors and risk consequences, which assures the “attribution” more precisely to track the specific sources that lead to certain loss. Namely, in each phase of the outsourcing lifecycle, both the likelihood and the loss of each risk factor and those of each risk are acquired through solving equation group with real data of risk consequences collected. In this “reverse” way, risk assessment becomes a responsive and interactive process with real data instead of subjective estimation, which improves the accuracy and alleviates bias in risk assessment. The numerical case proves the effectiveness of the algorithm compared with the approach forwarded by other references.
Image Encryption Algorithm Based on Dynamic DNA Coding and Chen’s Hyperchaotic System
Directory of Open Access Journals (Sweden)
Jian Zhang
2016-01-01
Full Text Available With the development of national information processes, specific image information from secret departments or individuals is often required to be confidentially transmitted. Numerous image encryption methods exist, especially since the initial value sensitivity and other characteristics of chaos theory and chaos theory-based encryption have become increasingly important in recent years. At present, DNA coding constitutes a new research direction of image encryption that uses the four base pairs of DNA code and image pixel values to establish a special correspondence, in order to achieve pixel diffusion. There are eight DNA encoding rules, and current methods of selecting the DNA encoding rules are largely fixed. Thus, the security of encoded data is not high. In this paper, we use the Lorenz chaotic system, Chen’s hyperchaotic system, and the DNA encoding combination and present a new image encryption algorithm that can dynamically select eight types of DNA encoding rules and eight types of DNA addition and subtraction rules, with significant improvements in security. Through simulation experiments and histograms, correlations, and NPCR analyses, we have determined that the algorithm possesses numerous desirable features, including good encryption effects and antishear and antinoise performances.
A Novel Dynamic Adjusting Algorithm for Load Balancing and Handover Co-Optimization in LTE SON
Institute of Scientific and Technical Information of China (English)
Wen-Yu Li; Xiang Zhang; Shu-Cong Jia; Xin-Yu Gu; Lin Zhang; Xiao-Yu Duan; Jia-Ru Lin
2013-01-01
With the development of mobile internet and multi-media service,advanced techniques need to be applied in wireless network to improve user experience.Long term evolution (LTE) systems,which can offer up to 100Mbps downlink date rates,have been deployed in USA and Korea.However,because plenty of complex physical layer algorithms are utilized,network planning and optimization become heavy burdens for LTE network operators.Self-organizing network (SON) is a promising method to overcome this problem by automatically selecting and adjusting key parameters in LTE systems.In this paper,we present a dynamic adjusting algorithm to improve both handover and load balancing performance by introducing a weighted co-satisfaction factor (CSF).Analysis and system level simulation are conducted to exhibit the performance improvement of the proposed scheme.Results show that the proposed method outperforms the conventional solutions in terms of the network handover success ratio and load balancing gains significantly.
DYNAMIC RELOCATION OF PLANT/WAREHOUSE FACILITIES:A FAST COMPACT GENETIC ALGORITHM APPROACH
Institute of Scientific and Technical Information of China (English)
Li Shugang; Wu Zhiming; Pang Xiaohong
2004-01-01
The problem of dynamic relocation and phase-out of combined manufacturing plant and warehousing facilities in the supply chain are concerned.A multiple time/multiple objective model is proposed to maximize total profit during the time horizon, minimize total access time from the plant/warehouse facilities to its suppliers and customers and maximize aggregated local incentives during the time horizon.The relocation problem keeps the feature of NP-hard and with the traditional method the optimal result cannot be got easily.So a compact genetic algorithm (CGA) is introduced to solve the problem.In order to accelerate the convergence speed of the CGA, the least square approach is introduced and a fast compact genetic algorithm (fCGA) is proposed.Finally, simulation results with the fCGA are compared with the CGA and classical integer programming (IP).The results show that the fCGA proposed is of high efficiency for Pareto optimality problem.
Itoh, Yoshiaki; Tanaka, Kazuyo
2004-08-01
Word frequency in a document has often been utilized in text searching and summarization. Similarly, identifying frequent words or phrases in a speech data set for searching and summarization would also be meaningful. However, obtaining word frequency in a speech data set is difficult, because frequent words are often special terms in the speech and cannot be recognized by a general speech recognizer. This paper proposes another approach that is effective for automatic extraction of such frequent word sections in a speech data set. The proposed method is applicable to any domain of monologue speech, because no language models or specific terms are required in advance. The extracted sections can be regarded as speech labels of some kind or a digest of the speech presentation. The frequent word sections are determined by detecting similar sections, which are sections of audio data that represent the same word or phrase. The similar sections are detected by an efficient algorithm, called Shift Continuous Dynamic Programming (Shift CDP), which realizes fast matching between arbitrary sections in the reference speech pattern and those in the input speech, and enables frame-synchronous extraction of similar sections. In experiments, the algorithm is applied to extract the repeated sections in oral presentation speeches recorded in academic conferences in Japan. The results show that Shift CDP successfully detects similar sections and identifies the frequent word sections in individual presentation speeches, without prior domain knowledge, such as language models and terms.
Energy Technology Data Exchange (ETDEWEB)
Chandrasekhar Potluri,; Madhavi Anugolu; Marco P. Schoen; D. Subbaram Naidu
2013-08-01
In this work, an array of three surface Electrography (sEMG) sensors are used to acquired muscle extension and contraction signals for 18 healthy test subjects. The skeletal muscle force is estimated using the acquired sEMG signals and a Non-linear Wiener Hammerstein model, relating the two signals in a dynamic fashion. The model is obtained from using System Identification (SI) algorithm. The obtained force models for each sensor are fused using a proposed fuzzy logic concept with the intent to improve the force estimation accuracy and resilience to sensor failure or misalignment. For the fuzzy logic inference system, the sEMG entropy, the relative error, and the correlation of the force signals are considered for defining the membership functions. The proposed fusion algorithm yields an average of 92.49% correlation between the actual force and the overall estimated force output. In addition, the proposed fusionbased approach is implemented on a test platform. Experiments indicate an improvement in finger/hand force estimation.
Zhang, Ruili; He, Yang; Xiao, Jianyuan; Liu, Jian; Qin, Hong; Tang, Yifa
2016-01-01
Relativistic dynamics of a charged particle in time-dependent electromagnetic fields has theoretical significance and a wide range of applications. It is often multi-scale and requires accurate long-term numerical simulations using symplectic integrators. For modern large-scale particle simulations in complex, time-dependent electromagnetic field, explicit symplectic algorithms are much more preferable. In this paper, we treat the relativistic dynamics of a particle as a Hamiltonian system on the cotangent space of the space-time, and construct for the first time explicit symplectic algorithms for relativistic charged particles of order 2 and 3 using the sum-split technique and generating functions.
Dynamic composition of medical support services in the ICU: Platform and algorithm design details.
Hristoskova, Anna; Moeyersoon, Dieter; Van Hoecke, Sofie; Verstichel, Stijn; Decruyenaere, Johan; De Turck, Filip
2010-12-01
The Intensive Care Unit (ICU) is an extremely data-intensive environment where each patient needs to be monitored 24/7. Bedside monitors continuously register vital patient values (such as serum creatinine, systolic blood pressure) which are recorded frequently in the hospital database (e.g. every 2 min in the ICU of the Ghent University Hospital), laboratories generate hundreds of results of blood and urine samples, and nurses measure blood pressure and temperature up to 4 times an hour. The processing of such large amount of data requires an automated system to support the physicians' daily work. The Intensive Care Service Platform (ICSP) offers the needed support through the development of medical support services for processing and monitoring patients' data. With an increased deployment of these medical support services, reusing existing services as building blocks to create new services offers flexibility to the developer and accelerates the design process. This paper presents a new addition to the ICSP, the Dynamic Composer for Web services. Based on a semantic description of the medical support services, this Composer enables a service to be executed by creating a composition of medical services that provide the needed calculations. The composition is achieved using various algorithms satisfying certain quality of service (QoS) constraints and requirements. In addition to the automatic composition the paper also proposes a recovery mechanism in case of unavailable services. When executing the composition of medical services, unavailable services are dynamically replaced by equivalent services or a new composition achieving the same result. The presented platform and QoS algorithms are put through extensive performance and scalability tests for typical ICU scenarios, in which basic medical services are composed to a complex patient monitoring service.
Institute of Scientific and Technical Information of China (English)
ZHANG Ren; HONG Mei; SUN Zhao-bo; NIU Sheng-jie; ZHU Wei-jun; MIN Jin-zhong; WAN Qi-lin
2006-01-01
Aiming at the difficulty of accurately constructing the dynamic model of subtropical high, based on the potential height field time series over 500 hPa layer of T106 numerical forecast products, by using EOF(empirical orthogonal function) temporal-spatial separation technique, the disassembled EOF time coefficients series were regarded as dynamical model variables, and dynamic system retrieval idea as well as genetic algorithm were introduced to make dynamical model parameters optimization search, then, a reasonable non-linear dynamic model of EOF time-coefficients was established. By dynamic model integral and EOF temporal-spatial components assembly, a mid-/long-term forecast of subtropical high was carried out. The experimental results show that the forecast results of dynamic model are superior to that of general numerical model forecast results.A new modeling idea and forecast technique is presented for diagnosing and forecasting such complicated weathers as subtropical high.
Sun, Lin; Wei, Jing; Wang, Jian; Mi, Xueting; Guo, Yamin; Lv, Yang; Yang, Yikun; Gan, Ping; Zhou, Xueying; Jia, Chen; Tian, Xinpeng
2016-06-01
Conventional cloud detection methods are easily affected by mixed pixels, complex surface structures, and atmospheric factors, resulting in poor cloud detection results. To minimize these problems, a new Universal Dynamic Threshold Cloud Detection Algorithm (UDTCDA) supported by a priori surface reflectance database is proposed in this paper. A monthly surface reflectance database is constructed using long-time-sequenced MODerate resolution Imaging Spectroradiometer surface reflectance product (MOD09A1) to provide the surface reflectance of the underlying surfaces. The relationships between the apparent reflectance changes and the surface reflectance are simulated under different observation and atmospheric conditions with the 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) model, and the dynamic threshold cloud detection models are developed. Two typical remote sensing data with important application significance and different sensor parameters, MODIS and Landsat 8, are selected for cloud detection experiments. The results were validated against the visual interpretation of clouds and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation cloud measurements. The results showed that the UDTCDA can obtain a high precision in cloud detection, correctly identifying cloudy pixels and clear-sky pixels at rates greater than 80% with error rate and missing rate of less than 20%. The UDTCDA cloud product overall shows less estimation uncertainty than the current MODIS cloud mask products. Moreover, the UDTCDA can effectively reduce the effects of atmospheric factors and mixed pixels and can be applied to different satellite sensors to realize long-term, large-scale cloud detection operations.
Elenchezhiyan, M; Prakash, J
2015-09-01
In this work, state estimation schemes for non-linear hybrid dynamic systems subjected to stochastic state disturbances and random errors in measurements using interacting multiple-model (IMM) algorithms are formulated. In order to compute both discrete modes and continuous state estimates of a hybrid dynamic system either an IMM extended Kalman filter (IMM-EKF) or an IMM based derivative-free Kalman filters is proposed in this study. The efficacy of the proposed IMM based state estimation schemes is demonstrated by conducting Monte-Carlo simulation studies on the two-tank hybrid system and switched non-isothermal continuous stirred tank reactor system. Extensive simulation studies reveal that the proposed IMM based state estimation schemes are able to generate fairly accurate continuous state estimates and discrete modes. In the presence and absence of sensor bias, the simulation studies reveal that the proposed IMM unscented Kalman filter (IMM-UKF) based simultaneous state and parameter estimation scheme outperforms multiple-model UKF (MM-UKF) based simultaneous state and parameter estimation scheme.
A QoS-Based Dynamic Queue Length Scheduling Algorithm in Multiantenna Heterogeneous Systems
Directory of Open Access Journals (Sweden)
Verikoukis Christos
2010-01-01
Full Text Available The use of real-time delay-sensitive applications in wireless systems has significantly grown during the last years. Therefore the designers of wireless systems have faced a challenging issue to guarantee the required Quality of Service (QoS. On the other hand, the recent advances and the extensive use of multiple antennas have already been included in several commercial standards, where the multibeam opportunistic transmission beamforming strategies have been proposed to improve the performance of the wireless systems. A cross-layer-based dynamically tuned queue length scheduler is presented in this paper, for the Downlink of multiuser and multiantenna WLAN systems with heterogeneous traffic requirements. To align with modern wireless systems transmission strategies, an opportunistic scheduling algorithm is employed, while a priority to the different traffic classes is applied. A tradeoff between the maximization of the throughput of the system and the guarantee of the maximum allowed delay is obtained. Therefore, the length of the queue is dynamically adjusted to select the appropriate conditions based on the operator requirements.
A unified pore-network algorithm for dynamic two-phase flow
Sheng, Qiang; Thompson, Karsten
2016-09-01
This paper describes recent work on image-based network modeling of multiphase flow. The algorithm expands the range of flow scenarios and boundary conditions that can be implemented using dynamic network modeling, the most significant advance being the ability to model simultaneous injection of immiscible fluids under either transient or steady-state conditions using non-periodic domains. Pore-scale saturation distributions are solved rigorously from two-phase mass conservation equations simultaneously within each pore. Results show that simulations using a periodic network fail to track saturation history because periodic domains limit how the bulk saturation can evolve over time. In contrast, simulations using a non-periodic network with fractional flow as the boundary condition can account for behavior associated with both hysteresis and saturation history, and can capture phenomena such as the long pressure and saturation tails that are observed during dynamic drainage processes. Results include a sensitivity analysis of relative permeability to different model variables, which may provide insight into mechanisms for a variety of transient, viscous dominated flow processes.
Generalized scalable multiple copy algorithms for molecular dynamics simulations in NAMD
Jiang, Wei; Phillips, James C.; Huang, Lei; Fajer, Mikolai; Meng, Yilin; Gumbart, James C.; Luo, Yun; Schulten, Klaus; Roux, Benoît
2014-03-01
Computational methodologies that couple the dynamical evolution of a set of replicated copies of a system of interest offer powerful and flexible approaches to characterize complex molecular processes. Such multiple copy algorithms (MCAs) can be used to enhance sampling, compute reversible work and free energies, as well as refine transition pathways. Widely used examples of MCAs include temperature and Hamiltonian-tempering replica-exchange molecular dynamics (T-REMD and H-REMD), alchemical free energy perturbation with lambda replica-exchange (FEP/λ-REMD), umbrella sampling with Hamiltonian replica exchange (US/H-REMD), and string method with swarms-of-trajectories conformational transition pathways. Here, we report a robust and general implementation of MCAs for molecular dynamics (MD) simulations in the highly scalable program NAMD built upon the parallel programming system Charm++. Multiple concurrent NAMD instances are launched with internal partitions of Charm++ and located continuously within a single communication world. Messages between NAMD instances are passed by low-level point-to-point communication functions, which are accessible through NAMD’s Tcl scripting interface. The communication-enabled Tcl scripting provides a sustainable application interface for end users to realize generalized MCAs without modifying the source code. Illustrative applications of MCAs with fine-grained inter-copy communication structure, including global lambda exchange in FEP/λ-REMD, window swapping US/H-REMD in multidimensional order parameter space, and string method with swarms-of-trajectories were carried out on IBM Blue Gene/Q to demonstrate the versatility and massive scalability of the present implementation.
Directory of Open Access Journals (Sweden)
Luman Zhao
2015-01-01
Full Text Available A thrust allocation method was proposed based on a hybrid optimization algorithm to efficiently and dynamically position a semisubmersible drilling rig. That is, the thrust allocation was optimized to produce the generalized forces and moment required while at the same time minimizing the total power consumption under the premise that forbidden zones should be taken into account. An optimization problem was mathematically formulated to provide the optimal thrust allocation by introducing the corresponding design variables, objective function, and constraints. A hybrid optimization algorithm consisting of a genetic algorithm and a sequential quadratic programming (SQP algorithm was selected and used to solve this problem. The proposed method was evaluated by applying it to a thrust allocation problem for a semisubmersible drilling rig. The results indicate that the proposed method can be used as part of a cost-effective strategy for thrust allocation of the rig.
Directory of Open Access Journals (Sweden)
Syed Mahamud Hossein
2013-09-01
Full Text Available Storing, transmitting and security of DNA sequences are well known research challenge. The problem has got magnified with increasing discovery and availability of DNA sequences. We have represent DNA sequence compression algorithm based on Dynamic Look Up Table (DLUT and modified Huffman technique. DLUT consists of 43(64 bases that are 64 sub-stings, each sub-string is of 3 bases long. Each sub-string are individually coded by single ASCII code from 33(! to 96(` and vice versa. Encode depends on encryption key choose by user from four base pair {a,t.g and c}and decode also require decryption key provide by the encoded user. Decoding must require authenticate input for encode the data. The sub-strings are combined into a Dynamic Look up Table based pre-coding routine. This algorithm is tested on reverse; complement & reverse complement the DNA sequences and also test on artificial DNA sequences of equivalent length. Speed of encryption and security levels are two important measurements for evaluating any encryption system. Due to proliferate of ubiquitous computing system, where digital contents are accessible through resource constraint biological database security concern is very important issue. A lot of research has been made to find an encryption system which can be run effectively in those biological databases. Information security is the most challenging question to protect the data from unauthorized user. The proposed method may protect the data from hackers. It can provide the three tier security, in tier one is ASCII code, in tier two is nucleotide (a,t,g and c choice by user and tier three is change of label or change of node position in Huffman Tree. Compression of the genome sequences will help to increase the efficiency of their use. The greatest advantage of this algorithm is fast execution, small memory occupation and easy implementation. Since the program to implement the technique have been written originally in the C language
A DiffServ Supporting And Distributed Dynamic Dual Round Robin Scheduling Algorithm on DS-CICQ
Xiuqin, Li; Luoheng, Yan; Julong, Lan
A distributed dynamic dual round robin scheduling algorithm (DSDRR) that can support differential services is presented. For each scheduler, the strategy of double pointer and round robin is adopted and scheduling complexity is substantially reduced. Its quantum function, related to the status of input queue and priority, is a traffic-adaptive dynamic scheduling mechanism and is rather sensitive to the changing network; therefore, it has the traits of dynamics and fairness. It can better support differential services and guarantee the realization of QoS.
Xu, Z N; Wang, S Y
2015-02-01
To improve the accuracy in the calculation of dynamic contact angle for drops on the inclined surface, a significant number of numerical drop profiles on the inclined surface with different inclination angles, drop volumes, and contact angles are generated based on the finite difference method, a least-squares ellipse-fitting algorithm is used to calculate the dynamic contact angle. The influences of the above three factors are systematically investigated. The results reveal that the dynamic contact angle errors, including the errors of the left and right contact angles, evaluated by the ellipse-fitting algorithm tend to increase with inclination angle/drop volume/contact angle. If the drop volume and the solid substrate are fixed, the errors of the left and right contact angles increase with inclination angle. After performing a tremendous amount of computation, the critical dimensionless drop volumes corresponding to the critical contact angle error are obtained. Based on the values of the critical volumes, a highly accurate dynamic contact angle algorithm is proposed and fully validated. Within nearly the whole hydrophobicity range, it can decrease the dynamic contact angle error in the inclined plane method to less than a certain value even for different types of liquids.
The Evaluation of Dynamic Airport Competitiveness Based on IDCQGA-BP Algorithm
Directory of Open Access Journals (Sweden)
Qiang Cui
2013-01-01
Full Text Available Aimed at the multidimensional and complex characteristic of airport competitiveness, a new algorithm is proposed in which BP neural network is optimized by improved double chains quantum genetic algorithm (IDCQGA-BP. The new algorithm is better than existing algorithms in convergence and the diversity of quantum chromosomes. The empirical data of eight airports in Yangtze River Delta in 2011 and 2012 is applied to verify the feasibility of the new algorithm, and then the competitiveness of the eight airports from 2013 to 2015 is gotten through the algorithm. The results show the following. (1 The new algorithm is better than the existing optimization algorithms in the aspects of error accuracy and run time. (2 The gaps of the airports in Yangtze River Delta are narrowing; the competition and cooperation are getting stronger and stronger. (3 The main increase reason of airport competitiveness is the increase of own investment.
Zhang, Dashan; Guo, Jie; Lei, Xiujun; Zhu, Changan
2016-04-22
The development of image sensor and optics enables the application of vision-based techniques to the non-contact dynamic vibration analysis of large-scale structures. As an emerging technology, a vision-based approach allows for remote measuring and does not bring any additional mass to the measuring object compared with traditional contact measurements. In this study, a high-speed vision-based sensor system is developed to extract structure vibration signals in real time. A fast motion extraction algorithm is required for this system because the maximum sampling frequency of the charge-coupled device (CCD) sensor can reach up to 1000 Hz. Two efficient subpixel level motion extraction algorithms, namely the modified Taylor approximation refinement algorithm and the localization refinement algorithm, are integrated into the proposed vision sensor. Quantitative analysis shows that both of the two modified algorithms are at least five times faster than conventional upsampled cross-correlation approaches and achieve satisfactory error performance. The practicability of the developed sensor is evaluated by an experiment in a laboratory environment and a field test. Experimental results indicate that the developed high-speed vision-based sensor system can extract accurate dynamic structure vibration signals by tracking either artificial targets or natural features.
A Scalable O(N) Algorithm for Large-Scale Parallel First-Principles Molecular Dynamics Simulations
Energy Technology Data Exchange (ETDEWEB)
Osei-Kuffuor, Daniel [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Fattebert, Jean-Luc [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2014-01-01
Traditional algorithms for first-principles molecular dynamics (FPMD) simulations only gain a modest capability increase from current petascale computers, due to their O(N^{3}) complexity and their heavy use of global communications. To address this issue, we are developing a truly scalable O(N) complexity FPMD algorithm, based on density functional theory (DFT), which avoids global communications. The computational model uses a general nonorthogonal orbital formulation for the DFT energy functional, which requires knowledge of selected elements of the inverse of the associated overlap matrix. We present a scalable algorithm for approximately computing selected entries of the inverse of the overlap matrix, based on an approximate inverse technique, by inverting local blocks corresponding to principal submatrices of the global overlap matrix. The new FPMD algorithm exploits sparsity and uses nearest neighbor communication to provide a computational scheme capable of extreme scalability. Accuracy is controlled by the mesh spacing of the finite difference discretization, the size of the localization regions in which the electronic orbitals are confined, and a cutoff beyond which the entries of the overlap matrix can be omitted when computing selected entries of its inverse. We demonstrate the algorithm's excellent parallel scaling for up to O(100K) atoms on O(100K) processors, with a wall-clock time of O(1) minute per molecular dynamics time step.
A genetic algorithm for a bi-objective mathematical model for dynamic virtual cell formation problem
Moradgholi, Mostafa; Paydar, Mohammad Mahdi; Mahdavi, Iraj; Jouzdani, Javid
2016-05-01
Nowadays, with the increasing pressure of the competitive business environment and demand for diverse products, manufacturers are force to seek for solutions that reduce production costs and rise product quality. Cellular manufacturing system (CMS), as a means to this end, has been a point of attraction to both researchers and practitioners. Limitations of cell formation problem (CFP), as one of important topics in CMS, have led to the introduction of virtual CMS (VCMS). This research addresses a bi-objective dynamic virtual cell formation problem (DVCFP) with the objective of finding the optimal formation of cells, considering the material handling costs, fixed machine installation costs and variable production costs of machines and workforce. Furthermore, we consider different skills on different machines in workforce assignment in a multi-period planning horizon. The bi-objective model is transformed to a single-objective fuzzy goal programming model and to show its performance; numerical examples are solved using the LINGO software. In addition, genetic algorithm (GA) is customized to tackle large-scale instances of the problems to show the performance of the solution method.
Policy iteration adaptive dynamic programming algorithm for discrete-time nonlinear systems.
Liu, Derong; Wei, Qinglai
2014-03-01
This paper is concerned with a new discrete-time policy iteration adaptive dynamic programming (ADP) method for solving the infinite horizon optimal control problem of nonlinear systems. The idea is to use an iterative ADP technique to obtain the iterative control law, which optimizes the iterative performance index function. The main contribution of this paper is to analyze the convergence and stability properties of policy iteration method for discrete-time nonlinear systems for the first time. It shows that the iterative performance index function is nonincreasingly convergent to the optimal solution of the Hamilton-Jacobi-Bellman equation. It is also proven that any of the iterative control laws can stabilize the nonlinear systems. Neural networks are used to approximate the performance index function and compute the optimal control law, respectively, for facilitating the implementation of the iterative ADP algorithm, where the convergence of the weight matrices is analyzed. Finally, the numerical results and analysis are presented to illustrate the performance of the developed method.
Mathews, David H.; Disney, Matthew D.; Childs, Jessica L.; Schroeder, Susan J.; Zuker, Michael; Turner, Douglas H.
2004-01-01
A dynamic programming algorithm for prediction of RNA secondary structure has been revised to accommodate folding constraints determined by chemical modification and to include free energy increments for coaxial stacking of helices when they are either adjacent or separated by a single mismatch. Furthermore, free energy parameters are revised to account for recent experimental results for terminal mismatches and hairpin, bulge, internal, and multibranch loops. To demonstrate the applicability of this method, in vivo modification was performed on 5S rRNA in both Escherichia coli and Candida albicans with 1-cyclohexyl-3-(2-morpholinoethyl) carbodiimide metho-p-toluene sulfonate, dimethyl sulfate, and kethoxal. The percentage of known base pairs in the predicted structure increased from 26.3% to 86.8% for the E. coli sequence by using modification constraints. For C. albicans, the accuracy remained 87.5% both with and without modification data. On average, for these sequences and a set of 14 sequences with known secondary structure and chemical modification data taken from the literature, accuracy improves from 67% to 76%. This enhancement primarily reflects improvement for three sequences that are predicted with <40% accuracy on the basis of energetics alone. For these sequences, inclusion of chemical modification constraints improves the average accuracy from 28% to 78%. For the 11 sequences with <6% pseudoknotted base pairs, structures predicted with constraints from chemical modification contain on average 84% of known canonical base pairs. PMID:15123812
Enhanced high dynamic range 3D shape measurement based on generalized phase-shifting algorithm
Wang, Minmin; Zhou, Canlin; Zhang, Chaorui; Si, Shuchun; Li, Hui; Lei, Zhenkun; Li, YanJie
2016-01-01
It is a challenge for Phase Measurement Profilometry (PMP) to measure objects with a large range of reflectivity variation across the surface. Saturated or dark pixels in the deformed fringe patterns captured by the camera will lead to phase fluctuations and errors. Jiang et al. proposed a high dynamic range real-time 3D shape measurement method without changing camera exposures. Three inverted phase-shifted fringe patterns are used to complement three regular phase-shifted fringe patterns for phase retrieval when any of the regular fringe patterns are saturated. But Jiang's method still has some drawbacks: (1) The phases in saturated pixels are respectively estimated by different formulas for different cases. It is shortage of an universal formula; (2) it cannot be extended to four-step phase-shifting algorithm because inverted fringe patterns are the repetition of regular fringe patterns; (3) only three unsaturated intensity values at every pixel of fringe patterns are chosen for phase demodulation, lying i...
[A quick algorithm of dynamic spectrum photoelectric pulse wave detection based on LabVIEW].
Lin, Ling; Li, Na; Li, Gang
2010-02-01
Dynamic spectrum (DS) detection is attractive among the numerous noninvasive blood component detection methods because of the elimination of the main interference of the individual discrepancy and measure conditions. DS is a kind of spectrum extracted from the photoelectric pulse wave and closely relative to the artery blood. It can be used in a noninvasive blood component concentration examination. The key issues in DS detection are high detection precision and high operation speed. The precision of measure can be advanced by making use of over-sampling and lock-in amplifying on the pick-up of photoelectric pulse wave in DS detection. In the present paper, the theory expression formula of the over-sampling and lock-in amplifying method was deduced firstly. Then in order to overcome the problems of great data and excessive operation brought on by this technology, a quick algorithm based on LabVIEW and a method of using external C code applied in the pick-up of photoelectric pulse wave were presented. Experimental verification was conducted in the environment of LabVIEW. The results show that by the method pres ented, the speed of operation was promoted rapidly and the data memory was reduced largely.
Directory of Open Access Journals (Sweden)
E.Kayalvizhi
2015-08-01
Full Text Available Mitigation of global warming gases from burning gasoline for transportation in vehicles is one of the biggest and most complex issues the world has ever faced. In an intention to eradicate the environmental crisis caused due to global warming, electric vehicles were been introduced that are powered by electric motor which works on the energy stored in a battery pack. Inspired by the research on power management in electric vehicles, this paper focuses on the development of an energy management system for electric vehicles (EMSEV to optimally balance the energy from battery pack. The proposed methodology uses firefly optimization algorithm to optimize the power consumption of the devices like electric motor, power steering, air conditioner, power window, automatic door locks, radio, speaker, horn, wiper, GPS, internal and external lights etc., from the battery in electric vehicles. Depending upon the distance to cover and the battery availability, the devices are made to switch down automatically through dynamic EDF scheduling. CAN protocol is used for effective communication between the devices and the controller. Simulation results are obtained using MATLAB.
Institute of Scientific and Technical Information of China (English)
无
2007-01-01
A global optimization approach to turbine blade design based on hierarchical fair competition genetic algorithms with dynamic niche (HFCDN-GAs) coupled with Reynolds-averaged Navier-Stokes (RANS) equation is presented. In order to meet the search theory of GAs and the aerodynamic performances of turbine, Bezier curve is adopted to parameterize the turbine blade profile, and a fitness function pertaining to optimization is designed. The design variables are the control points' ordinates of characteristic polygon of Bezier curve representing the turbine blade profile. The object function is the maximum lift-drag ratio of the turbine blade. The constraint conditions take into account the leading and trailing edge metal angle, and the strength and aerodynamic performances of turbine blade. And the treatment method of the constraint conditions is the flexible penalty function. The convergence history of test function indicates that HFCDN-GAs can locate the global optimum within a few search steps and have high robustness. The lift-drag ratio of the optimized blade is 8.3% higher than that of the original one. The results show that the proposed global optimization approach is effective for turbine blade.
Comparison of parametric FBP and OS-EM reconstruction algorithm images for PET dynamic study
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Oda, Keiichi; Uemura, Koji; Kimura, Yuichi; Senda, Michio [Tokyo Metropolitan Inst. of Gerontology (Japan). Positron Medical Center; Toyama, Hinako; Ikoma, Yoko
2001-10-01
An ordered subsets expectation maximization (OS-EM) algorithm is used for image reconstruction to suppress image noise and to make non-negative value images. We have applied OS-EM to a digital brain phantom and to human brain {sup 18}F-FDG PET kinetic studies to generate parametric images. A 45 min dynamic scan was performed starting injection of FDG with a 2D PET scanner. The images were reconstructed with OS-EM (6 iterations, 16 subsets) and with filtered backprojection (FBP), and K1, k2 and k3 images were created by the Marquardt non-linear least squares method based on the 3-parameter kinetic model. Although the OS-EM activity images correlated fairly well with those obtained by FBP, the pixel correlations were poor for the k2 and k3 parametric images, but the plots were scattered along the line of identity and the mean values for K1, k2 and k3 obtained by OS-EM were almost equal to those by FBP. The kinetic fitting error for OS-EM was no smaller than that for FBP. The results suggest that OS-EM is not necessarily superior to FBP for creating parametric images. (author)
Nonomura, Yoshihiko
2014-11-01
Nonequilibrium relaxation behaviors in the Ising model on a square lattice based on the Wolff algorithm are totally different from those based on local-update algorithms. In particular, the critical relaxation is described by the stretched-exponential decay. We propose a novel scaling procedure to connect nonequilibrium and equilibrium behaviors continuously, and find that the stretched-exponential scaling region in the Wolff algorithm is as wide as the power-law scaling region in local-update algorithms. We also find that relaxation to the spontaneous magnetization in the ordered phase is characterized by the exponential decay, not the stretched-exponential decay based on local-update algorithms.
Institute of Scientific and Technical Information of China (English)
ZHANG Hong-lie; ZHANG Guo-yin; YAO Ai-hong
2010-01-01
This paper presents an algorithm that combines the chaos optimization algorithm with the maximum entropy(COA-ME)by using entropy model based on chaos algorithm,in which the maximum entropy is used as the second method of searching the excellent solution.The search direction is improved by chaos optimization algorithm and realizes the selective acceptance of wrong solution.The experimental result shows that the presented algorithm can be used in the partitioning of hardware/software of reconfigurable system.It effectively reduces the local extremum problem,and search speed as well as performance of partitioning is improved.
Beccaria, M.; Presilla, C.; DeAngelis, G. F.; Jona-Lasinio, G.
1999-11-01
We present a simple derivation of a Feynman-Kac type formula to study fermionic systems. In this approach the real time or the imaginary time dynamics is expressed in terms of the evolution of a collection of Poisson processes. This formula leads to a family of algorithms parametrized by the values of the jump rates of the Poisson processes. From these an optimal algorithm can be chosen which coincides with the Green Function Monte Carlo method in the limit when the latter becomes exact.
Wang, Hong; Wang, Xicheng; Li, Zheng; Li, Keqiu
2016-01-01
The metabolic network model allows for an in-depth insight into the molecular mechanism of a particular organism. Because most parameters of the metabolic network cannot be directly measured, they must be estimated by using optimization algorithms. However, three characteristics of the metabolic network model, i.e., high nonlinearity, large amount parameters, and huge variation scopes of parameters, restrict the application of many traditional optimization algorithms. As a result, there is a growing demand to develop efficient optimization approaches to address this complex problem. In this paper, a Kriging-based algorithm aiming at parameter estimation is presented for constructing the metabolic networks. In the algorithm, a new infill sampling criterion, named expected improvement and mutual information (EI&MI), is adopted to improve the modeling accuracy by selecting multiple new sample points at each cycle, and the domain decomposition strategy based on the principal component analysis is introduced to save computing time. Meanwhile, the convergence speed is accelerated by combining a single-dimensional optimization method with the dynamic coordinate perturbation strategy when determining the new sample points. Finally, the algorithm is applied to the arachidonic acid metabolic network to estimate its parameters. The obtained results demonstrate the effectiveness of the proposed algorithm in getting precise parameter values under a limited number of iterations.
Dynamic Routing Algorithm for Satellite Network Based on Ant Colony Algorithm%基于蚁群算法的卫星网动态路由算法
Institute of Scientific and Technical Information of China (English)
马海滨; 王汝传; 饶元
2011-01-01
卫星网络路由应当具有使用较小的通信开销和处理能力计算出最优路径,并能够适应卫星网络拓扑结构动态变化等特点,这与蚁群算法的特征相匹配,能很好地解决这一问题.以此为背景,提出了一种新型的基于蚁群算法的卫星网动态路由算法(DRAS-ACA),并在NS2网络仿真平台上实现了该路由算法,使用gnuplot分析了仿真结果.%Satellite network routing should have the use of smaller capacity and communication overhead to calculate the optimal path, and be able to adapt to the satellite network topology changes, and other characteristics. The Ant Colony Algorithm should be a good appraach to solve this problem. In this paper, a dynamic routing algorithm for satellite network based on ant colony algorithm (DRAS-ACA) was presented and simulated on NS2 platform,and we also analyzed the simulation results with gnuplot.
Valentini, Paolo; Schwartzentruber, Thomas E.
2009-12-01
A novel combined Event-Driven/Time-Driven (ED/TD) algorithm to speed-up the Molecular Dynamics simulation of rarefied gases using realistic spherically symmetric soft potentials is presented. Due to the low density regime, the proposed method correctly identifies the time that must elapse before the next interaction occurs, similarly to Event-Driven Molecular Dynamics. However, each interaction is treated using Time-Driven Molecular Dynamics, thereby integrating Newton's Second Law using the sufficiently small time step needed to correctly resolve the atomic motion. Although infrequent, many-body interactions are also accounted for with a small approximation. The combined ED/TD method is shown to correctly reproduce translational relaxation in argon, described using the Lennard-Jones potential. For densities between ρ=10-4 kg/m and ρ=10-1 kg/m, comparisons with kinetic theory, Direct Simulation Monte Carlo, and pure Time-Driven Molecular Dynamics demonstrate that the ED/TD algorithm correctly reproduces the proper collision rates and the evolution toward thermal equilibrium. Finally, the combined ED/TD algorithm is applied to the simulation of a Mach 9 shock wave in rarefied argon. Density and temperature profiles as well as molecular velocity distributions accurately match DSMC results, and the shock thickness is within the experimental uncertainty. For the problems considered, the ED/TD algorithm ranged from several hundred to several thousand times faster than conventional Time-Driven MD. Moreover, the force calculation to integrate the molecular trajectories is found to contribute a negligible amount to the overall ED/TD simulation time. Therefore, this method could pave the way for the application of much more refined and expensive interatomic potentials, either classical or first-principles, to Molecular Dynamics simulations of shock waves in rarefied gases, involving vibrational nonequilibrium and chemical reactivity.
Directory of Open Access Journals (Sweden)
Balen Julie
2010-09-01
Full Text Available Abstract Background There are growing concerns regarding inequities in health, with poverty being an important determinant of health as well as a product of health status. Within the People's Republic of China (P.R. China, disparities in socio-economic position are apparent, with the rural-urban gap of particular concern. Our aim was to compare direct and proxy methods of estimating household wealth in a rural and a peri-urban setting of Hunan province, P.R. China. Methods We collected data on ownership of household durable assets, housing characteristics, and utility and sanitation variables in two village-wide surveys in Hunan province. We employed principal components analysis (PCA and principal axis factoring (PAF to generate household asset-based proxy wealth indices. Households were grouped into quartiles, from 'most wealthy' to 'most poor'. We compared the estimated household wealth for each approach. Asset-based proxy wealth indices were compared to those based on self-reported average annual income and savings at the household level. Results Spearman's rank correlation analysis revealed that PCA and PAF yielded similar results, indicating that either approach may be used for estimating household wealth. In both settings investigated, the two indices were significantly associated with self-reported average annual income and combined income and savings, but not with savings alone. However, low correlation coefficients between the proxy and direct measures of wealth indicated that they are not complementary. We found wide disparities in ownership of household durable assets, and utility and sanitation variables, within and between settings. Conclusion PCA and PAF yielded almost identical results and generated robust proxy wealth indices and categories. Pooled data from the rural and peri-urban settings highlighted structural differences in wealth, most likely a result of localized urbanization and modernization. Further research is needed
Li, Liang
2016-01-01
In coherent detection employing digital signal processing, chromatic dispersion (CD) can be compensated effectively in the electrical domain. In practical optical transport networks, the signal lightpaths between two terminal nodes can be different due to current network conditions. Accordingly, the transmission distance and the accumulated dispersion in the lightpath cannot be predicted. Therefore, the adaptive compensation of dynamic dispersion such as the use of least-mean-square (LMS) algorithm is necessary in such optical fiber networks to enable a flexible routing and switching. In this paper, we present a detailed analysis on the adaptive dispersion compensation using the LMS algorithms in coherent optical transmission networks. Numerical simulations have been carried out accordingly. It can be found that the variable-step-size LMS equalizer can achieve the adaptive CD equalization with a lower complexity, compared to the traditional LMS algorithm.
Behera, H S; Nayak, Debashree
2011-01-01
Scheduling is the central concept used frequently in Operating System. It helps in choosing the processes for execution. Round Robin (RR) is one of the most widely used CPU scheduling algorithm. But, its performance degrades with respect to context switching, which is an overhead and it occurs during each scheduling. Overall performance of the system depends on choice of an optimal time quantum, so that context switching can be reduced. In this paper, we have proposed a new variant of RR scheduling algorithm, known as Dynamic Quantum with Readjusted Round Robin (DQRRR) algorithm. We have experimentally shown that performance of DQRRR is better than RR by reducing number of context switching, average waiting time and average turn around time.
Directory of Open Access Journals (Sweden)
Abbas Noon
2011-05-01
Full Text Available Round Robin, considered as the most widely adopted CPU scheduling algorithm, undergoes severe problems directly related to quantum size. If time quantum chosen is too large, the response time of the processes is considered too high. On the other hand, if this quantum is too small, it increases the overhead of the CPU. In this paper, we propose a new algorithm, called AN, based on a new approach called dynamic-time-quantum; the idea of this approach is to make the operating systems adjusts the time quantum according to the burst time of the set of waiting processes in the ready queue. Based on the simulations and experiments, we show that the new proposed algorithm solves the fixed time quantum problem and increases the performance of Round Robin.
An Adaptive Algorithm for Dynamic Priority Based Virtual Machine Scheduling in Cloud
Directory of Open Access Journals (Sweden)
Subramanian S
2012-11-01
Full Text Available Cloud computing, a relatively new technology, has been gaining immense popularity over the last few years. The number of cloud users has been growing exponentially and apparently scheduling of virtual machines in the cloud becomes an important issue to analyze. This paper throws light on the various scheduling algorithms present for scheduling virtual machines and also proposes a new algorithm that combines the advantages of all the existing algorithms and overcomes their disadvantages.
Jou, Jonathan D; Jain, Swati; Georgiev, Ivelin S; Donald, Bruce R
2016-06-01
Sparse energy functions that ignore long range interactions between residue pairs are frequently used by protein design algorithms to reduce computational cost. Current dynamic programming algorithms that fully exploit the optimal substructure produced by these energy functions only compute the GMEC. This disproportionately favors the sequence of a single, static conformation and overlooks better binding sequences with multiple low-energy conformations. Provable, ensemble-based algorithms such as A* avoid this problem, but A* cannot guarantee better performance than exhaustive enumeration. We propose a novel, provable, dynamic programming algorithm called Branch-Width Minimization* (BWM*) to enumerate a gap-free ensemble of conformations in order of increasing energy. Given a branch-decomposition of branch-width w for an n-residue protein design with at most q discrete side-chain conformations per residue, BWM* returns the sparse GMEC in O([Formula: see text]) time and enumerates each additional conformation in merely O([Formula: see text]) time. We define a new measure, Total Effective Search Space (TESS), which can be computed efficiently a priori before BWM* or A* is run. We ran BWM* on 67 protein design problems and found that TESS discriminated between BWM*-efficient and A*-efficient cases with 100% accuracy. As predicted by TESS and validated experimentally, BWM* outperforms A* in 73% of the cases and computes the full ensemble or a close approximation faster than A*, enumerating each additional conformation in milliseconds. Unlike A*, the performance of BWM* can be predicted in polynomial time before running the algorithm, which gives protein designers the power to choose the most efficient algorithm for their particular design problem.
Acharya, Ayan; Konar, Amit; Janarthanan, Ramadoss
2008-01-01
Ant Colony Optimization (ACO) is a metaheuristic for solving difficult discrete optimization problems. This paper presents a deterministic model based on differential equation to analyze the dynamics of basic Ant System algorithm. Traditionally, the deposition of pheromone on different parts of the tour of a particular ant is always kept unvarying. Thus the pheromone concentration remains uniform throughout the entire path of an ant. This article introduces an exponentially increasing pheromone deposition approach by artificial ants to improve the performance of basic Ant System algorithm. The idea here is to introduce an additional attracting force to guide the ants towards destination more easily by constructing an artificial potential field identified by increasing pheromone concentration towards the goal. Apart from carrying out analysis of Ant System dynamics with both traditional and the newly proposed deposition rules, the paper presents an exhaustive set of experiments performed to find out suitable p...
Energy Technology Data Exchange (ETDEWEB)
Osei-Kuffuor, Daniel [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Fattebert, Jean-Luc [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2014-01-01
We present the first truly scalable first-principles molecular dynamics algorithm with O(N) complexity and controllable accuracy, capable of simulating systems with finite band gaps of sizes that were previously impossible with this degree of accuracy. By avoiding global communications, we provide a practical computational scheme capable of extreme scalability. Accuracy is controlled by the mesh spacing of the finite difference discretization, the size of the localization regions in which the electronic wave functions are confined, and a cutoff beyond which the components of the overlap matrix can be omitted when computing selected elements of its inverse. We demonstrate the algorithm's excellent parallel scaling for up to 101 952 atoms on 23 328 processors, with a wall-clock time of the order of 1 min per molecular dynamics time step and numerical error on the forces of less than 7x10^{-4} Ha/Bohr.
Osei-Kuffuor, Daniel; Fattebert, Jean-Luc
2014-01-31
We present the first truly scalable first-principles molecular dynamics algorithm with O(N) complexity and controllable accuracy, capable of simulating systems with finite band gaps of sizes that were previously impossible with this degree of accuracy. By avoiding global communications, we provide a practical computational scheme capable of extreme scalability. Accuracy is controlled by the mesh spacing of the finite difference discretization, the size of the localization regions in which the electronic wave functions are confined, and a cutoff beyond which the components of the overlap matrix can be omitted when computing selected elements of its inverse. We demonstrate the algorithm's excellent parallel scaling for up to 101,952 atoms on 23,328 processors, with a wall-clock time of the order of 1 min per molecular dynamics time step and numerical error on the forces of less than 7×10(-4) Ha/Bohr.
Study on Contact Algorithm of Dynamic Explicit FEM for Sheet Forming Simulation
Institute of Scientific and Technical Information of China (English)
无
2001-01-01
Based on existing algorithms, a newly developed contact search algorithm is proposed. The new algorithm consists of global search, local searching, local tracking and penetration calculation processes. It requires no iteration steps. It can deal with not only general tool surfaces with vertical walls, but also tool surfaces meshed with elements having very poor aspect ratios. It is demonstrated that the FE code employing this new contact search algorithm becomes more reliable, efficient and accurate for sheet metal forming sim ulation than conventional ones.
Fast time-reversible algorithms for molecular dynamics of rigid-body systems
Kajima, Yasuhiro; Hiyama, Miyabi; Ogata, Shuji; Kobayashi, Ryo; Tamura, Tomoyuki
2012-06-01
In this paper, we present time-reversible simulation algorithms for rigid bodies in the quaternion representation. By advancing a time-reversible algorithm [Y. Kajima, M. Hiyama, S. Ogata, and T. Tamura, J. Phys. Soc. Jpn. 80, 114002 (2011), 10.1143/JPSJ.80.114002] that requires iterations in calculating the angular velocity at each time step, we propose two kinds of iteration-free fast time-reversible algorithms. They are easily implemented in codes. The codes are compared with that of existing algorithms through demonstrative simulation of a nanometer-sized water droplet to find their stability of the total energy and computation speeds.
Directory of Open Access Journals (Sweden)
mahmood Joorabian
2013-02-01
Full Text Available This study presents a dynamically adapted bacterial foraging algorithm (BFA to solve the economic dispatch (ED problem considering valve-point effects and power losses. In addition, wind power is included in the problem formulation. Renewable sources and wind energy in particular have recently been getting more interest because of various environmental and economical considerations. The original BFA is a recently developed evolutionary optimisation technique inspired by the foraging behaviour of the Escherichia coli bacteria. The basic BFA has been successfully implemented to solve small optimisation problems; however, it shows poor convergence characteristics for larger constrained problems. To deal with the complexity and highdimensioned search space of the ED problem, essential modifications are introduced to enhance the performance of the algorithm. The basic chemotactic step is adjusted to have a dynamic non-linear behavior in order to improve balancing the global and local search. The stopping criterion of the original BFA is also modified to be adaptive depending on the solution improvement instead of the preset maximum number of iterations. The proposed algorithm is validated using several test systems. The results are compared with those obtained by other algorithms previously applied to solve the problem considering valve-point effects and power losses in addition to wind power.
When Algorithms Shape Collective Action: Social Media and the Dynamics of Cloud Protesting
Directory of Open Access Journals (Sweden)
Stefania Milan
2015-12-01
Full Text Available How does the algorithmically mediated environment of social media restructure social action? This article combines social movement studies and science and technology studies to explore the role of social media in the organization, unfolding, and diffusion of contemporary protests. In particular, it examines how activists leverage the technical properties of social media to develop a joint narrative and a collective identity. To this end, it offers the notion of cloud protesting as a theoretical approach and framework for empirical analysis. Cloud protesting indicates a specific type of mobilization that is grounded on, modeled around, and enabled by social media platforms and mobile devices and the virtual universes they identify. The notion emphasizes both the productive mediation of social and mobile media and the importance of activists’ sense-making activities. It also acknowledges that social media set in motion a process that is sociotechnical in nature rather than merely sociological or communicative, and thus can be understood only by intersecting the material and the symbolic dimensions of contemporary digitally mediated collective action. The article shows how the specific materiality of social media intervenes in the actors’ meaning work by fostering four mechanisms—namely performance, interpellation, temporality, and reproducibility—which concur to create a “politics of visibility” that alters traditional identity dynamics. In addition, it exposes the connection between organizational patterns and the role of individuals, explaining how the politics of visibility is the result of a process that originates and ends within the individual—which ultimately creates individuals-in-the-group rather than groups.
Energy Technology Data Exchange (ETDEWEB)
Yin, Jiandong; Yang, Jiawen; Guo, Qiyong [Shengjing Hospital of China Medical University, Department of Radiology, Shenyang (China)
2015-05-01
Arterial input function (AIF) plays an important role in the quantification of cerebral hemodynamics. The purpose of this study was to select the best reproducible clustering method for AIF detection by comparing three algorithms reported previously in terms of detection accuracy and computational complexity. First, three reproducible clustering methods, normalized cut (Ncut), hierarchy (HIER), and fast affine propagation (FastAP), were applied independently to simulated data which contained the true AIF. Next, a clinical verification was performed where 42 subjects participated in dynamic susceptibility contrast MRI (DSC-MRI) scanning. The manual AIF and AIFs based on the different algorithms were obtained. The performance of each algorithm was evaluated based on shape parameters of the estimated AIFs and the true or manual AIF. Moreover, the execution time of each algorithm was recorded to determine the algorithm that operated more rapidly in clinical practice. In terms of the detection accuracy, Ncut and HIER method produced similar AIF detection results, which were closer to the expected AIF and more accurate than those obtained using FastAP method; in terms of the computational efficiency, the Ncut method required the shortest execution time. Ncut clustering appears promising because it facilitates the automatic and robust determination of AIF with high accuracy and efficiency. (orig.)
Dynamic Critical Behaviour of Wolff's Algorithm for $RP^N$ $\\sigma$-Models
Caracciolo, Sergio; Pelissetto, A; Sokal, A D
1992-01-01
We study the performance of a Wolff-type embedding algorithm for $RP^N$ $\\sigma$-models. We find that the algorithm in which we update the embedded Ising model \\`a la Swendsen-Wang has critical slowing-down as $z_\\chi \\approx 1$. If instead we update the Ising spins with a perfect algorithm which at every iteration produces a new independent configuration, we obtain $z_\\chi \\approx 0$. This shows that the Ising embedding encodes well the collective modes of the system, and that the behaviour of the first algorithm is connected to the poor performance of the Swendsen-Wang algorithm in dealing with a frustrated Ising model.
HIGHLY DYNAMIC RANGE IMAGE ENHANCEMENT ALGORITHM%高动态范围图像增强算法
Institute of Scientific and Technical Information of China (English)
席志红; 赵蓝飞
2014-01-01
Due to display devices cannot restore the true effects of highly dynamic range images,the article puts forward an image en-hancement algorithm based on hierarchical tone mapping.Firstly the algorithm applies Retinex model and fast bilateral filtering algorithm to estimate the illumination images.Secondly it applies Gaussian mixture model and expectation maximization algorithm to split the illumination image probability into hierarchical Gaussian models mixed together.Thirdly by iteration it acquires the optimal gamma coefficients correspond-ing to each Gaussian probability hierarchies.At last the hierarchies are layered to obtain the enhanced illumination image.Experiment verifies that the algorithm realizes the compression of image dynamic range while its image enhancement effects are obvious.%针对显示设备无法还原高动态范围图像的真实效果，提出一种基于分层色阶映射的图像增强算法。该算法首先通过Retinex模型与快速双边滤波算法估计光照图像。然后利用高斯混合模型和期望最大算法将光照图像概率分布分割为多层高斯模型混合而成。并通过迭代获得各高斯概率层对应的最优校正系数。最后叠加各层并获得增强后的光照图像。实验证明，该算法在整体上实现了图像动态范围的压缩，图像增强效果明显。
Dynamic Critical Behaviour of Wolff's Algorithm for $RP^N$ $\\sigma$-Models
Caracciolo, S.; Edwards, R. G.; Pelissetto, A.; Sokal, A. D.
1992-01-01
We study the performance of a Wolff-type embedding algorithm for $RP^N$ $\\sigma$-models. We find that the algorithm in which we update the embedded Ising model \\`a la Swendsen-Wang has critical slowing-down as $z_\\chi \\approx 1$. If instead we update the Ising spins with a perfect algorithm which at every iteration produces a new independent configuration, we obtain $z_\\chi \\approx 0$. This shows that the Ising embedding encodes well the collective modes of the system, and that the behaviour ...
Lee, Kyeong-sang; Choi, Sungwon; Seo, Minji; Lee, Chang suk; Seong, Noh-hun; Han, Kyung-Soo
2016-10-01
Snow cover is biggest single component of cryosphere. The Snow is covering the ground in the Northern Hemisphere approximately 50% in winter season and is one of climate factors that affects Earth's energy budget because it has higher reflectance than other land types. Also, snow cover has an important role about hydrological modeling and water resource management. For this reason, accurate detection of snow cover acts as an essential element for regional water resource management. Snow cover detection using satellite-based data have some advantages such as obtaining wide spatial range data and time-series observations periodically. In the case of snow cover detection using satellite data, the discrimination of snow and cloud is very important. Typically, Misclassified cloud and snow pixel can lead directly to error factor for retrieval of satellite-based surface products. However, classification of snow and cloud is difficult because cloud and snow have similar optical characteristics and are composed of water or ice. But cloud and snow has different reflectance in 1.5 1.7 μm wavelength because cloud has lower grain size and moisture content than snow. So, cloud and snow shows difference reflectance patterns change according to wavelength. Therefore, in this study, we perform algorithm for classifying snow cover and cloud with satellite-based data using Dynamic Time Warping (DTW) method which is one of commonly used pattern analysis such as speech and fingerprint recognitions and reflectance spectral library of snow and cloud. Reflectance spectral library is constructed in advance using MOD21km (MODIS Level1 swath 1km) data that their reflectance is six channels including 3 (0.466μm), 4 (0.554μm), 1 (0.647μm), 2 (0.857μm), 26 (1.382μm) and 6 (1.629μm). We validate our result using MODIS RGB image and MOD10 L2 swath (MODIS swath snow cover product). And we use PA (Producer's Accuracy), UA (User's Accuracy) and CI (Comparison Index) as validation criteria
Halder, Amiya
2012-01-01
This paper proposes a Genetic Algorithm based segmentation method that can automatically segment gray-scale images. The proposed method mainly consists of spatial unsupervised grayscale image segmentation that divides an image into regions. The aim of this algorithm is to produce precise segmentation of images using intensity information along with neighborhood relationships. In this paper, Fuzzy Hopfield Neural Network (FHNN) clustering helps in generating the population of Genetic algorithm which there by automatically segments the image. This technique is a powerful method for image segmentation and works for both single and multiple-feature data with spatial information. Validity index has been utilized for introducing a robust technique for finding the optimum number of components in an image. Experimental results shown that the algorithm generates good quality segmented image.
A monolithic homotopy continuation algorithm with application to computational fluid dynamics
Brown, David A.; Zingg, David W.
2016-09-01
A new class of homotopy continuation methods is developed suitable for globalizing quasi-Newton methods for large sparse nonlinear systems of equations. The new continuation methods, described as monolithic homotopy continuation, differ from the classical predictor-corrector algorithm in that the predictor and corrector phases are replaced with a single phase which includes both a predictor and corrector component. Conditional convergence and stability are proved analytically. Using a Laplacian-like operator to construct the homotopy, the new algorithm is shown to be more efficient than the predictor-corrector homotopy continuation algorithm as well as an implementation of the widely-used pseudo-transient continuation algorithm for some inviscid and turbulent, subsonic and transonic external aerodynamic flows over the ONERA M6 wing and the NACA 0012 airfoil using a parallel implicit Newton-Krylov finite-difference flow solver.
Energy Technology Data Exchange (ETDEWEB)
Yoon, Jai-Woong; Sawant, Amit; Suh, Yelin; Cho, Byung-Chul; Suh, Tae-Suk; Keall, Paul [Department of Biomedical Engineering, College of Medicine, Catholic University of Korea, Seoul, Korea 131-700 and Research Institute of Biomedical Engineering, Catholic University of Korea, Seoul, 131-700 (Korea, Republic of); Department of Radiation Oncology, Stanford University, Stanford, California 94305 (United States); Department of Radiation Oncology, Stanford University, Stanford, California 94305 (United States) and Department of Radiation Oncology, Asan Medical Center, Seoul, 138-736 (Korea, Republic of); Department of Biomedical Engineering, College of Medicine, Catholic University of Korea, Seoul, 131-700 and Research Institute of Biomedical Engineering, Catholic University of Korea, Seoul, 131-700 (Korea, Republic of); Department of Radiation Oncology, Stanford University, Stanford, California 94305 (United States) and Radiation Physics Laboratory, Sydney Medical School, University of Sydney, 2006 (Australia)
2011-07-15
Purpose: In dynamic multileaf collimator (MLC) motion tracking with complex intensity-modulated radiation therapy (IMRT) fields, target motion perpendicular to the MLC leaf travel direction can cause beam holds, which increase beam delivery time by up to a factor of 4. As a means to balance delivery efficiency and accuracy, a moving average algorithm was incorporated into a dynamic MLC motion tracking system (i.e., moving average tracking) to account for target motion perpendicular to the MLC leaf travel direction. The experimental investigation of the moving average algorithm compared with real-time tracking and no compensation beam delivery is described. Methods: The properties of the moving average algorithm were measured and compared with those of real-time tracking (dynamic MLC motion tracking accounting for both target motion parallel and perpendicular to the leaf travel direction) and no compensation beam delivery. The algorithm was investigated using a synthetic motion trace with a baseline drift and four patient-measured 3D tumor motion traces representing regular and irregular motions with varying baseline drifts. Each motion trace was reproduced by a moving platform. The delivery efficiency, geometric accuracy, and dosimetric accuracy were evaluated for conformal, step-and-shoot IMRT, and dynamic sliding window IMRT treatment plans using the synthetic and patient motion traces. The dosimetric accuracy was quantified via a {gamma}-test with a 3%/3 mm criterion. Results: The delivery efficiency ranged from 89 to 100% for moving average tracking, 26%-100% for real-time tracking, and 100% (by definition) for no compensation. The root-mean-square geometric error ranged from 3.2 to 4.0 mm for moving average tracking, 0.7-1.1 mm for real-time tracking, and 3.7-7.2 mm for no compensation. The percentage of dosimetric points failing the {gamma}-test ranged from 4 to 30% for moving average tracking, 0%-23% for real-time tracking, and 10%-47% for no compensation
Directory of Open Access Journals (Sweden)
2015-03-01
Full Text Available The dynamic economic load dispatch is one of the main problems of power systems generation and operation. The objective is to schedule power generation for units over a certain period of time, while satisfying operating constraints and load demand in each interval. Wind farms, as renewable energy resources are playing an increasing role in electricity generation. In this paper, a computational framework is presented to solve the dynamic economic emission dispatch problem with inclusion of wind farms considering their associated constraints. An optimization algorithm called modified co-evolutionary particle swarm optimization (MCPSO is proposed to solve the problem. In the proposed algorithm, two kinds of swarms evolve interactively where one of them is used to calculate the penalty factors (constraints handling and the other is used for searching good solutions (optimization process. In addition, some modifications such as using an inertia weight that decreases linearly during the simulation are made to improve the performance of the algorithm. Finally, the validity and superiority of the proposed method are demonstrated by simulation results on a modiﬁed IEEE benchmark system including six thermal units and two wind farms.
Shivakumar, R
2010-01-01
The Application of Bio Inspired Algorithms to complicated Power System Stability Problems has recently attracted the researchers in the field of Artificial Intelligence. Low frequency oscillations after a disturbance in a Power system, if not sufficiently damped, can drive the system unstable. This paper provides a systematic procedure to damp the low frequency oscillations based on Bio Inspired Genetic (GA) and Particle Swarm Optimization (PSO) algorithms. The proposed controller design is based on formulating a System Damping ratio enhancement based Optimization criterion to compute the optimal controller parameters for better stability. The Novel and contrasting feature of this work is the mathematical modeling and simulation of the Synchronous generator model including the Steam Governor Turbine (GT) dynamics. To show the robustness of the proposed controller, Non linear Time domain simulations have been carried out under various system operating conditions. Also, a detailed Comparative study has been don...
Biswas, Abhishek; Ranjan, Desh; Zubair, Mohammad; He, Jing
2015-09-01
The determination of secondary structure topology is a critical step in deriving the atomic structures from the protein density maps obtained from electron cryomicroscopy technique. This step often relies on matching the secondary structure traces detected from the protein density map to the secondary structure sequence segments predicted from the amino acid sequence. Due to inaccuracies in both sources of information, a pool of possible secondary structure positions needs to be sampled. One way to approach the problem is to first derive a small number of possible topologies using existing matching algorithms, and then find the optimal placement for each possible topology. We present a dynamic programming method of Θ(Nq(2)h) to find the optimal placement for a secondary structure topology. We show that our algorithm requires significantly less computational time than the brute force method that is in the order of Θ(q(N) h).
Directory of Open Access Journals (Sweden)
Jianhua Ma
2013-01-01
Full Text Available The objective of vehicle routing problem is usually to minimize the total traveling distance or cost. But in practice, there are a lot of problems needed to minimize the fastest completion time. The milk-run vehicle routing problem (MRVRP is widely used in milk-run distribution. The mutation ACO is given to solve MRVRP with fastest completion time in this paper. The milk-run VRP with fastest completion time is introduced first, and then the customer division method based on dynamic optimization and split algorithm is given to transform this problem into finding the optimal customer order. At last the mutation ACO is given and the numerical examples verify the effectiveness of the algorithm.
Institute of Scientific and Technical Information of China (English)
SUN Fan; ZHONG Weimin; CHENG Hui; QIAN Feng
2013-01-01
Two general approaches are adopted in solving dynamic optimization problems in chemical processes,namely,the analytical and numerical methods.The numerical method,which is based on heuristic algorithms,has been widely used.An approach that combines differential evolution (DE) algorithm and control vector parameterization (CVP) is proposed in this paper.In the proposed CVP,control variables are approximated with polynomials based on state variables and time in the entire time interval.Region reduction strategy is used in DE to reduce the width of the search region,which improves the computing efficiency.The results of the case studies demonstrate the feasibility and efficiency of the proposed methods.
Wu, Meihong; Liao, Lifang; Luo, Xin; Ye, Xiaoquan; Yao, Yuchen; Chen, Pinnan; Shi, Lei; Huang, Hui; Wu, Yunfeng
2016-01-01
Measuring stride variability and dynamics in children is useful for the quantitative study of gait maturation and neuromotor development in childhood and adolescence. In this paper, we computed the sample entropy (SampEn) and average stride interval (ASI) parameters to quantify the stride series of 50 gender-matched children participants in three age groups. We also normalized the SampEn and ASI values by leg length and body mass for each participant, respectively. Results show that the original and normalized SampEn values consistently decrease over the significance level of the Mann-Whitney U test (p algorithms were used to effectively distinguish the children's gait patterns. These ensemble learning algorithms both provided excellent gait classification results in terms of overall accuracy (≥90%), recall (≥0.8), and precision (≥0.8077).
Directory of Open Access Journals (Sweden)
Xiaoyan Lei
2016-01-01
Full Text Available A model for dynamic analysis of the vehicle-track nonlinear coupling system is established by the finite element method. The whole system is divided into two subsystems: the vehicle subsystem and the track subsystem. Coupling of the two subsystems is achieved by equilibrium conditions for wheel-to-rail nonlinear contact forces and geometrical compatibility conditions. To solve the nonlinear dynamics equations for the vehicle-track coupling system, a cross iteration algorithm and a relaxation technique are presented. Examples of vibration analysis of the vehicle and slab track coupling system induced by China’s high speed train CRH3 are given. In the computation, the influences of linear and nonlinear wheel-to-rail contact models and different train speeds are considered. It is found that the cross iteration algorithm and the relaxation technique have the following advantages: simple programming; fast convergence; shorter computation time; and greater accuracy. The analyzed dynamic responses for the vehicle and the track with the wheel-to-rail linear contact model are greater than those with the wheel-to-rail nonlinear contact model, where the increasing range of the displacement and the acceleration is about 10%, and the increasing range of the wheel-to-rail contact force is less than 5%.
An Improved Fast Flocking Algorithm with Obstacle Avoidance for Multiagent Dynamic Systems
Directory of Open Access Journals (Sweden)
Jialiang Wang
2014-01-01
Full Text Available Flocking behavior is a common phenomenon in nature, such as flocks of birds and groups of fish. In order to make the agents effectively avoid obstacles and fast form flocking towards the direction of destination point, this paper proposes a fast multiagent obstacle avoidance (FMOA algorithm. FMOA is illustrated based on the status of whether the flocking has formed. If flocking has not formed, agents should avoid the obstacles toward the direction of target. If otherwise, these agents have reached the state of lattice and then these agents only need to avoid the obstacles and ignore the direction of target. The experimental results show that the proposed FMOA algorithm has better performance in terms of flocking path length. Furthermore, the proposed FMOA algorithm is applied to the formation flying of quad-rotor helicopters. Compared with other technologies to perform the localization of quad-rotor helicopter, this paper innovatively constructs a smart environment by deploying some wireless sensor network (WSN nodes using the proposed localization algorithm. Finally, the proposed FMOA algorithm is used to conduct the formation flying of these quad-rotor helicopters in the smart environment.
QoS-Based Dynamic Multicast Routing Design Using Genetic Algorithms
Institute of Scientific and Technical Information of China (English)
YUANYouwei; YANLamei
2004-01-01
This paper addresses the problem of determining minimum cost paths to nodes in a Multicast group satisfying delay bounds and delay variation bounds. This study explores the use of Genetic algorithms (GAs) for solving the multicast routing problems when multiple Quality of services (QoS) requirements are presented. Our simulation results indicate that it is critical to select a suitable representation method and a set of appropriate parameters in order to obtain good performance. For a medium network, the probability from 0.02 to 0.2 seems to work better than those of too small or too large. As compared with the other optimal algorithm, the proposed algorithm gives better performance in terms of the success rate, the tree cost, the number of exchanged messages and the convergence time.
Energy Technology Data Exchange (ETDEWEB)
Silveira, L.M.; Kamon, M.; Elfadel, I.; White, J. [Massachusetts Inst. of Technology, Cambridge, MA (United States)
1996-12-31
Model order reduction based on Krylov subspace iterative methods has recently emerged as a major tool for compressing the number of states in linear models used for simulating very large physical systems (VLSI circuits, electromagnetic interactions). There are currently two main methods for accomplishing such a compression: one is based on the nonsymmetric look-ahead Lanczos algorithm that gives a numerically stable procedure for finding Pade approximations, while the other is based on a less well characterized Arnoldi algorithm. In this paper, we show that for certain classes of generalized state-space systems, the reduced-order models produced by a coordinate-transformed Arnoldi algorithm inherit the stability of the original system. Complete Proofs of our results will be given in the final paper.
A Bee Optimization Algorithm for Scheduling a Job Dynamically in Grid Environment
Directory of Open Access Journals (Sweden)
P. Rajeswari M. Prakash
2011-12-01
Full Text Available Grid computing is based on large scale resources sharing in a widely connected network. Grid scheduling is defined as the process of making scheduling decisions involving allocating jobs to resources over multiple administrative domains. Scheduling is the one of the key issues in the research. Matchmaking is a key aspect in the grid environment. Matching a job with available suitable resources has to satisfy certain constraints. Resource discovery is one of the key issues for job scheduling in the grid environment. The proposed Bee optimization algorithm is to analyze Quality of Service (QoS metrics such as service class, job type in the heterogeneous grid environment. QoS parameters play a major role in selecting grid resources and optimizing resources effectively and efficiently. The output of the proposed algorithm is compared with max-min and min-min algorithm.
Parallel algorithm of trigonometric collocation method in nonlinear dynamics of rotors
Directory of Open Access Journals (Sweden)
Musil T.
2007-11-01
Full Text Available A parallel algorithm of a numeric procedure based on a method of trigonometric collocation is presented for investigating an unbalance response of a rotor supported by journal bearings. After a condensation process the trigonometric collocation method results in a set of nonlinear algebraic equations which is solved by the Newton-Raphson method. The order of the set is proportional to the number of nonlinear bearing coordinates and terms of the finite Fourier series. The algorithm, realized in the MATLAB parallel computing environment (DCT/DCE, uses message passing technique for interacting among processes on nodes of a parallel computer. This technique enables portability of the source code both on parallel computers with distributed and shared memory. Tests, made on a Beowulf cluster and a symmetric multiprocessor, have revealed very good speed-up and scalability of this algorithm.
Institute of Scientific and Technical Information of China (English)
Huang; Ling; Liu; Yang; Xu; Jianfeng
2015-01-01
With the transformation of the Chinese economy from an extensive growth to intensive development, city development is also gradually turning from incremental construction to stock management. Community, as a basic unit of human settlements, is an important platform to build and improve the social governance capability. In 2013, Shiyoulu Jiedao Offi ce of Yuzhong District led the 1st urban community development planning, which was a milestone of Chongqing’s city regeneration and governance innovation. This paper focuses on two key issues: how to understand the community values and make the community development planning based on the above, and how to integrate with the local forces so that the community development planning can be integrated into the action plan. Combined with the practice of Minlecun Community Development Planning, using the concept of asset-based community development, a comprehensive survey is conducted on community assets(including three aspects of physical, human, and social capital), and a community comprehensive planning strategy is formulated which covers two parts: the optimization of community spaces and the upgrading of community governance. The paper explores the local-based community planning theories and methods from such aspects as value attitude, public participation, role transformation of urban planners, and others.
Tang, Yu-Hang; Karniadakis, George Em
2014-11-01
We present a scalable dissipative particle dynamics simulation code, fully implemented on the Graphics Processing Units (GPUs) using a hybrid CUDA/MPI programming model, which achieves 10-30 times speedup on a single GPU over 16 CPU cores and almost linear weak scaling across a thousand nodes. A unified framework is developed within which the efficient generation of the neighbor list and maintaining particle data locality are addressed. Our algorithm generates strictly ordered neighbor lists in parallel, while the construction is deterministic and makes no use of atomic operations or sorting. Such neighbor list leads to optimal data loading efficiency when combined with a two-level particle reordering scheme. A faster in situ generation scheme for Gaussian random numbers is proposed using precomputed binary signatures. We designed custom transcendental functions that are fast and accurate for evaluating the pairwise interaction. The correctness and accuracy of the code is verified through a set of test cases simulating Poiseuille flow and spontaneous vesicle formation. Computer benchmarks demonstrate the speedup of our implementation over the CPU implementation as well as strong and weak scalability. A large-scale simulation of spontaneous vesicle formation consisting of 128 million particles was conducted to further illustrate the practicality of our code in real-world applications. Catalogue identifier: AETN_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AETN_v1_0.html Program obtainable from: CPC Program Library, Queen’s University, Belfast, N. Ireland Licensing provisions: GNU General Public License, version 3 No. of lines in distributed program, including test data, etc.: 1 602 716 No. of bytes in distributed program, including test data, etc.: 26 489 166 Distribution format: tar.gz Programming language: C/C++, CUDA C/C++, MPI. Computer: Any computers having nVidia GPGPUs with compute capability 3.0. Operating system: Linux. Has the code been
Stable algorithm for event detection in event-driven particle dynamics: logical states
Strobl, Severin; Bannerman, Marcus N.; Pöschel, Thorsten
2016-07-01
Following the recent development of a stable event-detection algorithm for hard-sphere systems, the implications of more complex interaction models are examined. The relative location of particles leads to ambiguity when it is used to determine the interaction state of a particle in stepped potentials, such as the square-well model. To correctly predict the next event in these systems, the concept of an additional state that is tracked separately from the particle position is introduced and integrated into the stable algorithm for event detection.
Stable algorithm for event detection in event-driven particle dynamics: Logical states
Strobl, Severin; Poeschel, Thorsten
2015-01-01
Following the recent development of a stable event-detection algorithm for hard-sphere systems, the implications of more complex interaction models are examined. The relative location of particles leads to ambiguity when it is used to determine the interaction state of a particle in stepped potentials, such as the square-well model. To correctly predict the next event in these systems, the concept of an additional state that is tracked separately from the particle position is introduced and integrated into the stable algorithm for event detection.
A generalized gamma(s)-family of self-starting algorithms for computational structural dynamics
Namburu, Raju R.; Tamma, Kumar K.
1992-01-01
A generalized gamma(s)-family of self-starting single-step formulations are presented in order to provide simplified yet effective dynamic attributes to include features towards eliminating the need to involve accelerations in the computational process for structural dynamic problems. By appropriately selecting the parameters pertaining to gamma(s)(s = 1, 2, 3), both explicit and implicit formulations are obtained. The stability and accuracy characteristics of the gamma(s)-family of representations are presented to validate the robustness of the formulations for structural dynamic problems. Numerous illustrative examples are described and the results are in excellent agreement and validate the applicability of these formulations for structural dynamic computations.
Qiu, J. P.; Niu, D. X.
Micro-grid is one of the key technologies of the future energy supplies. Take economic planning. reliability, and environmental protection of micro grid as a basis for the analysis of multi-strategy objective programming problems for micro grid which contains wind power, solar power, and battery and micro gas turbine. Establish the mathematical model of each power generation characteristics and energy dissipation. and change micro grid planning multi-objective function under different operating strategies to a single objective model based on AHP method. Example analysis shows that in combination with dynamic ant mixed genetic algorithm can get the optimal power output of this model.
DEFF Research Database (Denmark)
Zhao, Xin; Meng, Lexuan; Savaghebi, Mehdi
2016-01-01
of the converters cannot be equally shared if no extra current balancing loop is added. Accordingly, a dynamic consensus algorithm (DCA) based negative/positive sequence current sharing scheme is proposed in this paper. Finally, a lab-scale AC microgrid was designed and tested in the lab to validate the feasibility...... control based voltage support strategy has been proposed to aid MGs riding through three phase asymmetrical voltage sags. However, since the line impedance from each converter to the point of common coupling (PCC) is not identical, both positive sequence and negative sequence output current...
DEFF Research Database (Denmark)
Neumann, Frank; Witt, Carsten
2015-01-01
combinatorial optimization problem, namely makespan scheduling. We study the model of a strong adversary which is allowed to change one job at regular intervals. Furthermore, we investigate the setting of random changes. Our results show that randomized local search and a simple evolutionary algorithm are very...
Worm Algorithm simulations of the hole dynamics in the t-J model
Prokof'ev, Nikolai; Ruebenacker, Oliver
2001-03-01
In the limit of small J 0.4t there is an ongoing argument that at smaller J spin-charge separation is still possible. Worm algorithm Monte Carlo simulations of the hole Green function for 0.1 hole spectral function in the thermodynamic limit.
Kumar, Vivek; Horio, Brant M.; DeCicco, Anthony H.; Hasan, Shahab; Stouffer, Virginia L.; Smith, Jeremy C.; Guerreiro, Nelson M.
2015-01-01
This paper presents a search algorithm based framework to calibrate origin-destination (O-D) market specific airline ticket demands and prices for the Air Transportation System (ATS). This framework is used for calibrating an agent based model of the air ticket buy-sell process - Airline Evolutionary Simulation (Airline EVOS) -that has fidelity of detail that accounts for airline and consumer behaviors and the interdependencies they share between themselves and the NAS. More specificially, this algorithm simultaneous calibrates demand and airfares for each O-D market, to within specified threshold of a pre-specified target value. The proposed algorithm is illustrated with market data targets provided by the Transportation System Analysis Model (TSAM) and Airline Origin and Destination Survey (DB1B). Although we specify these models and datasources for this calibration exercise, the methods described in this paper are applicable to calibrating any low-level model of the ATS to some other demand forecast model-based data. We argue that using a calibration algorithm such as the one we present here to synchronize ATS models with specialized forecast demand models, is a powerful tool for establishing credible baseline conditions in experiments analyzing the effects of proposed policy changes to the ATS.
AN EFFICIENT, BOX SHAPE INDEPENDENT NONBONDED FORCE AND VIRIAL ALGORITHM FOR MOLECULAR-DYNAMICS
Bekker, H.; Dijkstra, E.J; Renardus, M.K.R.; Berendsen, H.J.C.
1995-01-01
A notation is introduced and used to transform a conventional specification of the non-bonded force and virial algorithm in the case of periodic boundary conditions into an alternative specification. The implementation of the transformed specification is simpler and typically a factor of 1.5 faster
When algorithms shape collective action: Social media and the dynamics of cloud protesting
Milan, S.
2015-01-01
How does the algorithmically mediated environment of social media restructure social action? This article combines social movement studies and science and technology studies to explore the role of social media in the organization, unfolding, and diffusion of contemporary protests. In particular, it
NIC: a robust background extraction algorithm for foreground detection in dynamic scenes
Huynh-The, Thien; Banos, Oresti; Lee, Sungyoung; Kang, Byeong Ho; Kim, Eun-Soo; Le-Tien, Thuong
2016-01-01
This paper presents a robust foreground detection method capable of adapting to different motion speeds in scenes. A key contribution of this paper is the background estimation using a proposed novel algorithm, neighbor-based intensity correction (NIC), that identifies and modifies the motion pixels
Directory of Open Access Journals (Sweden)
Mohameed Sarhan Al_Duais
2015-01-01
Full Text Available The drawback of the Back Propagation (BP algorithm is slow training and easily convergence to the local minimum and suffers from saturation training. To overcome those problems, we created a new dynamic function for each training rate and momentum term. In this study, we presented the (BPDRM algorithm, which training with dynamic training rate and momentum term. Also in this study, a new strategy is proposed, which consists of multiple steps to avoid inflation in the gross weight when adding each training rate and momentum term as a dynamic function. In this proposed strategy, fitting is done by making a relationship between the dynamic training rate and the dynamic momentum. As a result, this study placed an implicit dynamic momentum term in the dynamic training rate. This αdmic = f(1/&etadmic . This procedure kept the weights as moderate as possible (not to small or too large. The 2-dimensional XOR problem and buba data were used as benchmarks for testing the effects of the ‘new strategy’. All experiments were performed on Matlab software (2012a. From the experiment’s results, it is evident that the dynamic BPDRM algorithm provides a superior performance in terms of training and it provides faster training compared to the (BP algorithm at same limited error.
Directory of Open Access Journals (Sweden)
Aidin Delgoshaei
2016-09-01
Full Text Available Purpose: The issue resource over-allocating is a big concern for project engineers in the process of scheduling project activities. Resource over-allocating drawback is frequently seen after scheduling of a project in practice which causes a schedule to be useless. Modifying an over-allocated schedule is very complicated and needs a lot of efforts and time. In this paper, a new and fast tracking method is proposed to schedule large scale projects which can help project engineers to schedule the project rapidly and with more confidence. Design/methodology/approach: In this article, a forward approach for maximizing net present value (NPV in multi-mode resource constrained project scheduling problem while assuming discounted positive cash flows (MRCPSP-DCF is proposed. The progress payment method is used and all resources are considered as pre-emptible. The proposed approach maximizes NPV using unscheduled resources through resource calendar in forward mode. For this purpose, a Genetic Algorithm is applied to solve. Findings: The findings show that the proposed method is an effective way to maximize NPV in MRCPSP-DCF problems while activity splitting is allowed. The proposed algorithm is very fast and can schedule experimental cases with 1000 variables and 100 resources in few seconds. The results are then compared with branch and bound method and simulated annealing algorithm and it is found the proposed genetic algorithm can provide results with better quality. Then algorithm is then applied for scheduling a hospital in practice. Originality/value: The method can be used alone or as a macro in Microsoft Office Project® Software to schedule MRCPSP-DCF problems or to modify resource over-allocated activities after scheduling a project. This can help project engineers to schedule project activities rapidly with more accuracy in practice.
Pallab Banerjee; Talat Zabin; ShwetaKumai; Pushpa Kumari
2015-01-01
Round Robin Scheduling algorithm is designed especially for Real Time Operating system (RTOS). It is a preemptive CPU scheduling algorithm which switches between the processes when static time Quantum expires .Existing Round Robin CPU scheduling algorithm cannot be implemented in real time operating system due to their high context switch rates, large waiting time, large response time, large turnaround time and less throughput . In this paper a new algorithm is presented called Best Performan...
基于动态图的软件水印算法%Software Watermarking Algorithm based on Dynamic Graph
Institute of Scientific and Technical Information of China (English)
雷敏; 杨榆; 胡若翔; 谭逢亮
2015-01-01
The identity of the software copyright owner can be proved by software watermarking.This paper proposes a new coding algorithm called DGSW which is based on the PPCT (Planted Plane Cubic Tree),Permutation Graph and the Chinese remainder theorem,by researching the CT dynamic graph software watermarking algorithm and the advantages and disadvantages of its coding schemes.Experiments and analysis indicate that DGSW has a relatively good data embedding rate.Moreover,using DGSW coding scheme improves the ability to resist attacks of the CT watermarking algorithm.%软件水印可以证明软件的版权所有者.研究基于已有的CT动态图软件水印算法,通过研究现有的编码方案的优缺点,提出一种新的基于PPCT(Planted Plane Cubic Tree)、排序图和中国剩余定理的编码算法DGSW.实验分析表明,使用DGSW编码方案的CT软件水印算法在抗攻击能力方面有所提高,同时具有较为出色的数据嵌入率.
Directory of Open Access Journals (Sweden)
Yong Wang
Full Text Available This paper proposes a two-stage algorithm to simultaneously estimate origin-destination (OD matrix, link choice proportion, and dispersion parameter using partial traffic counts in a congested network. A non-linear optimization model is developed which incorporates a dynamic dispersion parameter, followed by a two-stage algorithm in which Generalized Least Squares (GLS estimation and a Stochastic User Equilibrium (SUE assignment model are iteratively applied until the convergence is reached. To evaluate the performance of the algorithm, the proposed approach is implemented in a hypothetical network using input data with high error, and tested under a range of variation coefficients. The root mean squared error (RMSE of the estimated OD demand and link flows are used to evaluate the model estimation results. The results indicate that the estimated dispersion parameter theta is insensitive to the choice of variation coefficients. The proposed approach is shown to outperform two established OD estimation methods and produce parameter estimates that are close to the ground truth. In addition, the proposed approach is applied to an empirical network in Seattle, WA to validate the robustness and practicality of this methodology. In summary, this study proposes and evaluates an innovative computational approach to accurately estimate OD matrices using link-level traffic flow data, and provides useful insight for optimal parameter selection in modeling travelers' route choice behavior.
Directory of Open Access Journals (Sweden)
Kwang Cheol Shin
2009-02-01
Full Text Available In the fields of production, manufacturing and supply chain management, Radio Frequency Identification (RFID is regarded as one of the most important technologies. Nowadays, Mobile RFID, which is often installed in carts or forklift trucks, is increasingly being applied to the search for and checkout of items in warehouses, supermarkets, libraries and other industrial fields. In using Mobile RFID, since the readers are continuously moving, they can interfere with each other when they attempt to read the tags. In this study, we suggest a Time Division Multiple Access (TDMA based anti-collision algorithm for Mobile RFID readers. Our algorithm automatically adjusts the frame size of each reader without using manual parameters by adopting the dynamic frame size adjustment strategy when collisions occur at a reader. Through experiments on a simulated environment for Mobile RFID readers, we show that the proposed method improves the number of successful transmissions by about 228% on average, compared with Colorwave, a representative TDMA based anti-collision algorithm.
Shin, Kwang Cheol; Park, Seung Bo; Jo, Geun Sik
2009-01-01
In the fields of production, manufacturing and supply chain management, Radio Frequency Identification (RFID) is regarded as one of the most important technologies. Nowadays, Mobile RFID, which is often installed in carts or forklift trucks, is increasingly being applied to the search for and checkout of items in warehouses, supermarkets, libraries and other industrial fields. In using Mobile RFID, since the readers are continuously moving, they can interfere with each other when they attempt to read the tags. In this study, we suggest a Time Division Multiple Access (TDMA) based anti-collision algorithm for Mobile RFID readers. Our algorithm automatically adjusts the frame size of each reader without using manual parameters by adopting the dynamic frame size adjustment strategy when collisions occur at a reader. Through experiments on a simulated environment for Mobile RFID readers, we show that the proposed method improves the number of successful transmissions by about 228% on average, compared with Colorwave, a representative TDMA based anti-collision algorithm. PMID:22399942
Institute of Scientific and Technical Information of China (English)
WANG Chong; LI Jun; JING Ning; WANG Jun; CHEN Hao
2011-01-01
Traditionally,heuristic re-planning algorithms are used to tackle the problem of dynamic task planning for multiple satellites.However,the traditional heuristic strategies depend on the concrete tasks,which often affect the result's optimality.Noticing that the historical information of cooperative task planning will impact the latter planning results,we propose a hybrid learning algorithrn for dynamic multi-satellite task planning,which is based on the multi-agent reinforcement learning of policy iteration and the transfer learning.The reinforcement learning strategy of each satellite is described with neural networks.The policy neural network individuals with the best topological structure and weights are found by applying co-evolutionary search iteratively.To avoid the failure of the historical learning caused by the randomly occurring observation requests,a novel approach is proposed to balance the quality and efficiency of the task planning,which converts the historical leaming strategy to the current initial learning strategy by applying the transfer learning algorithm.The simulations and analysis show the feasibility and adaptability of the proposed approach especially for the situation with randomly occurring observation requests.
Nagarajan, Balaji; Sankaranarayanan, Nehru Viji; Patel, Bhaumik B.
2017-01-01
Glycosaminoglycans (GAGs) are key natural biopolymers that exhibit a range of biological functions including growth and differentiation. Despite this multiplicity of function, natural GAG sequences have not yielded drugs because of problems of heterogeneity and synthesis. Recently, several homogenous non-saccharide glycosaminoglycan mimetics (NSGMs) have been reported as agents displaying major therapeutic promise. Yet, it remains unclear whether sulfated NSGMs structurally mimic sulfated GAGs. To address this, we developed a three-step molecular dynamics (MD)-based algorithm to compare sulfated NSGMs with GAGs. In the first step of this algorithm, parameters related to the range of conformations sampled by the two highly sulfated molecules as free entities in water were compared. The second step compared identity of binding site geometries and the final step evaluated comparable dynamics and interactions in the protein-bound state. Using a test case of interactions with fibroblast growth factor-related proteins, we show that this three-step algorithm effectively predicts the GAG structure mimicking property of NSGMs. Specifically, we show that two unique dimeric NSGMs mimic hexameric GAG sequences in the protein-bound state. In contrast, closely related monomeric and trimeric NSGMs do not mimic GAG in either the free or bound states. These results correspond well with the functional properties of NSGMs. The results show for the first time that appropriately designed sulfated NSGMs can be good structural mimetics of GAGs and the incorporation of a MD-based strategy at the NSGM library screening stage can identify promising mimetics of targeted GAG sequences. PMID:28182755
A framework for dynamic traffic diversion during non-recurrent congestion :models and algorithms
Özbay, Kaan
1996-01-01
Real-time control of traffic diversion during non-recurrent congestion continues to be a challenging topic. Especially, with the advent of Intelligent Transportation Systems (ITS), the need for models and algorithms that will control the diversion in real-time, responding to the current traffic conditions has become evident. Several researchers have tried to solve this on-line control problem by adopting different approaches such as, expert systems, feedback control, and mathem...
Dynamic acoustics for the STAR-100. [computer algorithms for time dependent sound waves in jet
Bayliss, A.; Turkel, E.
1979-01-01
An algorithm is described to compute time dependent acoustic waves in a jet. The method differs from previous methods in that no harmonic time dependence is assumed, thus permitting the study of nonharmonic acoustical behavior. Large grids are required to resolve the acoustic waves. Since the problem is nonstiff, explicit high order schemes can be used. These have been adapted to the STAR-100 with great efficiencies and permitted the efficient solution of problems which would not be feasible on a scalar machine.
一种动态个人最优交通分配的启发式方法%A Heuristic Algorithm for an Optimized Dynamic User Traffic Assignment
Institute of Scientific and Technical Information of China (English)
徐天泽; 黄德镛
2004-01-01
给出了一种基于动态最短路的动态个人最优交通分配的启发式方法.同时提出了与动态个人最优交通分配相关的一些定义.%A heuristic algorithm based on dynamic shortest paths for an optimized dynamic user traffic assignment is suggested. Several definitions related to the optimized dynamic user traffic assignment are also given.
Adaptive Inverse Hyperbolic Tangent Algorithm for Dynamic Contrast Adjustment in Displaying Scenes
Directory of Open Access Journals (Sweden)
Chein-I Chang
2010-01-01
Full Text Available Contrast has a great influence on the quality of an image in human visual perception. A poorly illuminated environment can significantly affect the contrast ratio, producing an unexpected image. This paper proposes an Adaptive Inverse Hyperbolic Tangent (AIHT algorithm to improve the display quality and contrast of a scene. Because digital cameras must maintain the shadow in a middle range of luminance that includes a main object such as a face, a gamma function is generally used for this purpose. However, this function has a severe weakness in that it decreases highlight contrast. To mitigate this problem, contrast enhancement algorithms have been designed to adjust contrast to tune human visual perception. The proposed AIHT determines the contrast levels of an original image as well as parameter space for different contrast types so that not only the original histogram shape features can be preserved, but also the contrast can be enhanced effectively. Experimental results show that the proposed algorithm is capable of enhancing the global contrast of the original image adaptively while extruding the details of objects simultaneously.
Adaptive Inverse Hyperbolic Tangent Algorithm for Dynamic Contrast Adjustment in Displaying Scenes
Directory of Open Access Journals (Sweden)
Wang Chuin-Mu
2010-01-01
Full Text Available Abstract Contrast has a great influence on the quality of an image in human visual perception. A poorly illuminated environment can significantly affect the contrast ratio, producing an unexpected image. This paper proposes an Adaptive Inverse Hyperbolic Tangent (AIHT algorithm to improve the display quality and contrast of a scene. Because digital cameras must maintain the shadow in a middle range of luminance that includes a main object such as a face, a gamma function is generally used for this purpose. However, this function has a severe weakness in that it decreases highlight contrast. To mitigate this problem, contrast enhancement algorithms have been designed to adjust contrast to tune human visual perception. The proposed AIHT determines the contrast levels of an original image as well as parameter space for different contrast types so that not only the original histogram shape features can be preserved, but also the contrast can be enhanced effectively. Experimental results show that the proposed algorithm is capable of enhancing the global contrast of the original image adaptively while extruding the details of objects simultaneously.
Dynamic Feasible Region Genetic Algorithm for Optimal Operation of a Multi-Reservoir System
Directory of Open Access Journals (Sweden)
Bin Xu
2012-08-01
Full Text Available Seeking the optimal strategy of a multi-reservoir system is an important approach to develop hydropower energy, in which the Genetic Algorithm (GA is commonly used as an effective tool. However, when the traditional GA is applied in solving the problem, the constraints of water balance equation, hydraulic continuity relationship and power system load demand might be violated by the crossover and mutation operator, which decreases the efficiency of the algorithm in searching for a feasible region or even leads to a convergence on an infeasible chromosome within the expected generations. A modified GA taking stochastic operators within the feasible region of variables is proposed. When determining the feasible region of constraints, the progressive optimal approach is applied to transform constraints imposed on reservoirs into a singular-reservoir constraint, and a joint solution with consideration of adjacent periods at crossover or mutation points is used to turn the singular-reservoir constraints into singular variable constraints. Some statistic indexes are suggested to evaluate the performances of the algorithms. The experimental results show that compared to GA adopting a penalty function or pair-wise comparison in constraint handling, the proposed modified GA improves the refinement of the quality of a solution in a more efficient and robust way.
Energy Technology Data Exchange (ETDEWEB)
Williams, P. T. [Univ. of Tennessee, Knoxville, TN (United States)
1993-09-01
As the field of computational fluid dynamics (CFD) continues to mature, algorithms are required to exploit the most recent advances in approximation theory, numerical mathematics, computing architectures, and hardware. Meeting this requirement is particularly challenging in incompressible fluid mechanics, where primitive-variable CFD formulations that are robust, while also accurate and efficient in three dimensions, remain an elusive goal. This dissertation asserts that one key to accomplishing this goal is recognition of the dual role assumed by the pressure, i.e., a mechanism for instantaneously enforcing conservation of mass and a force in the mechanical balance law for conservation of momentum. Proving this assertion has motivated the development of a new, primitive-variable, incompressible, CFD algorithm called the Continuity Constraint Method (CCM). The theoretical basis for the CCM consists of a finite-element spatial semi-discretization of a Galerkin weak statement, equal-order interpolation for all state-variables, a 0-implicit time-integration scheme, and a quasi-Newton iterative procedure extended by a Taylor Weak Statement (TWS) formulation for dispersion error control. Original contributions to algorithmic theory include: (a) formulation of the unsteady evolution of the divergence error, (b) investigation of the role of non-smoothness in the discretized continuity-constraint function, (c) development of a uniformly H^{1} Galerkin weak statement for the Reynolds-averaged Navier-Stokes pressure Poisson equation, (d) derivation of physically and numerically well-posed boundary conditions, and (e) investigation of sparse data structures and iterative methods for solving the matrix algebra statements generated by the algorithm.
Energy Technology Data Exchange (ETDEWEB)
Williams, P.T.
1993-09-01
As the field of computational fluid dynamics (CFD) continues to mature, algorithms are required to exploit the most recent advances in approximation theory, numerical mathematics, computing architectures, and hardware. Meeting this requirement is particularly challenging in incompressible fluid mechanics, where primitive-variable CFD formulations that are robust, while also accurate and efficient in three dimensions, remain an elusive goal. This dissertation asserts that one key to accomplishing this goal is recognition of the dual role assumed by the pressure, i.e., a mechanism for instantaneously enforcing conservation of mass and a force in the mechanical balance law for conservation of momentum. Proving this assertion has motivated the development of a new, primitive-variable, incompressible, CFD algorithm called the Continuity Constraint Method (CCM). The theoretical basis for the CCM consists of a finite-element spatial semi-discretization of a Galerkin weak statement, equal-order interpolation for all state-variables, a 0-implicit time-integration scheme, and a quasi-Newton iterative procedure extended by a Taylor Weak Statement (TWS) formulation for dispersion error control. Original contributions to algorithmic theory include: (a) formulation of the unsteady evolution of the divergence error, (b) investigation of the role of non-smoothness in the discretized continuity-constraint function, (c) development of a uniformly H{sup 1} Galerkin weak statement for the Reynolds-averaged Navier-Stokes pressure Poisson equation, (d) derivation of physically and numerically well-posed boundary conditions, and (e) investigation of sparse data structures and iterative methods for solving the matrix algebra statements generated by the algorithm.
Cognitive algorithms: dynamic logic, working of the mind, evolution of consciousness and cultures
Perlovsky, Leonid I.
2007-04-01
The paper discusses evolution of consciousness driven by the knowledge instinct, a fundamental mechanism of the mind which determines its higher cognitive functions. Dynamic logic mathematically describes the knowledge instinct. It overcomes past mathematical difficulties encountered in modeling intelligence and relates it to mechanisms of concepts, emotions, instincts, consciousness and unconscious. The two main aspects of the knowledge instinct are differentiation and synthesis. Differentiation is driven by dynamic logic and proceeds from vague and unconscious states to more crisp and conscious states, from less knowledge to more knowledge at each hierarchical level of the mind. Synthesis is driven by dynamic logic operating in a hierarchical organization of the mind; it strives to achieve unity and meaning of knowledge: every concept finds its deeper and more general meaning at a higher level. These mechanisms are in complex relationship of symbiosis and opposition, which leads to complex dynamics of evolution of consciousness and cultures. Modeling this dynamics in a population leads to predictions for the evolution of consciousness, and cultures. Cultural predictive models can be compared to experimental data and used for improvement of human conditions. We discuss existing evidence and future research directions.
Solano, Carlos J F; Pothula, Karunakar R; Prajapati, Jigneshkumar D; De Biase, Pablo M; Noskov, Sergei Yu; Kleinekathöfer, Ulrich
2016-05-10
All-atom molecular dynamics simulations have a long history of applications studying ion and substrate permeation across biological and artificial pores. While offering unprecedented insights into the underpinning transport processes, MD simulations are limited in time-scales and ability to simulate physiological membrane potentials or asymmetric salt solutions and require substantial computational power. While several approaches to circumvent all of these limitations were developed, Brownian dynamics simulations remain an attractive option to the field. The main limitation, however, is an apparent lack of protein flexibility important for the accurate description of permeation events. In the present contribution, we report an extension of the Brownian dynamics scheme which includes conformational dynamics. To achieve this goal, the dynamics of amino-acid residues was incorporated into the many-body potential of mean force and into the Langevin equations of motion. The developed software solution, called BROMOCEA, was applied to ion transport through OmpC as a test case. Compared to fully atomistic simulations, the results show a clear improvement in the ratio of permeating anions and cations. The present tests strongly indicate that pore flexibility can enhance permeation properties which will become even more important in future applications to substrate translocation.
Larsen, Ross E; Bedard-Hearn, Michael J; Schwartz, Benjamin J
2006-10-12
Mixed quantum/classical (MQC) molecular dynamics simulation has become the method of choice for simulating the dynamics of quantum mechanical objects that interact with condensed-phase systems. There are many MQC algorithms available, however, and in cases where nonadiabatic coupling is important, different algorithms may lead to different results. Thus, it has been difficult to reach definitive conclusions about relaxation dynamics using nonadiabatic MQC methods because one is never certain whether any given algorithm includes enough of the necessary physics. In this paper, we explore the physics underlying different nonadiabatic MQC algorithms by comparing and contrasting the excited-state relaxation dynamics of the prototypical condensed-phase MQC system, the hydrated electron, calculated using different algorithms, including: fewest-switches surface hopping, stationary-phase surface hopping, and mean-field dynamics with surface hopping. We also describe in detail how a new nonadiabatic algorithm, mean-field dynamics with stochastic decoherence (MF-SD), is to be implemented for condensed-phase problems, and we apply MF-SD to the excited-state relaxation of the hydrated electron. Our discussion emphasizes the different ways quantum decoherence is treated in each algorithm and the resulting implications for hydrated-electron relaxation dynamics. We find that for three MQC methods that use Tully's fewest-switches criterion to determine surface hopping probabilities, the excited-state lifetime of the electron is the same. Moreover, the nonequilibrium solvent response function of the excited hydrated electron is the same with all of the nonadiabatic MQC algorithms discussed here, so that all of the algorithms would produce similar agreement with experiment. Despite the identical solvent response predicted by each MQC algorithm, we find that MF-SD allows much more mixing of multiple basis states into the quantum wave function than do other methods. This leads to an
An Algorithm to Develop Lumped Model for Gunn-Diode Dynamics
Umesh Kumar
1998-01-01
A nonlinear lumped model can be developed for Gunn-Diodes to describe the diffusion effects as the domain travels from cathode to anode of a Gunn-Diode. The model describes the domain extinction and nucleation phenomena. It allows the user to specify arbitrary nonlinear drift velocity V(E) and nonlinear diffusion D(E).The model simulates arbitrary Gunn-Diode circuits operating in any matured high field domain or in the LSA mode.Here we have constructed an algorithm to lead to development of t...
An improved method using factor division algorithm for reducing the order of linear dynamical system
Indian Academy of Sciences (India)
SHARAD KUMAR TIWARI; GAGANDEEP KAUR
2016-06-01
An improved method is proposed to determine the reduced order model of large scale linear time invariant system. The dominant poles of the low order system are calculated by clustering method. The selection of pole to the cluster point is based on the contributions of each pole in redefining time moment and redefiningMarkov parameters. The coefficients of the numerator polynomial for reduced model are obtained using a factor division algorithm. This method is computationally efficient and keeps up the stability and input output characteristic of the original arrangement
Dissipative Particle Dynamics at Isoenergetic Conditions Using Shardlow-Like Splitting Algorithms
2013-09-01
model by Abu- Nada (7, 8) required 00002.0t to 0.00005, i.e., values of t that are several orders of magnitude smaller than for constant...for much larger time steps for DPD-E simulations. Comparing with the recent study of Abu- Nada that used the standard velocity-Verlet algorithm for DPD...Atomistic and Mesoscopic Simulation. J. Chem. Phys. 1997, 107, 4423. 7. Abu- Nada , E. Natural Convection Heat Transfer Simulation Using Energy
A hybrid algorithm for coupling partial differential equation and compartment-based dynamics.
Harrison, Jonathan U; Yates, Christian A
2016-09-01
Stochastic simulation methods can be applied successfully to model exact spatio-temporally resolved reaction-diffusion systems. However, in many cases, these methods can quickly become extremely computationally intensive with increasing particle numbers. An alternative description of many of these systems can be derived in the diffusive limit as a deterministic, continuum system of partial differential equations (PDEs). Although the numerical solution of such PDEs is, in general, much more efficient than the full stochastic simulation, the deterministic continuum description is generally not valid when copy numbers are low and stochastic effects dominate. Therefore, to take advantage of the benefits of both of these types of models, each of which may be appropriate in different parts of a spatial domain, we have developed an algorithm that can be used to couple these two types of model together. This hybrid coupling algorithm uses an overlap region between the two modelling regimes. By coupling fluxes at one end of the interface and using a concentration-matching condition at the other end, we ensure that mass is appropriately transferred between PDE- and compartment-based regimes. Our methodology gives notable reductions in simulation time in comparison with using a fully stochastic model, while maintaining the important stochastic features of the system and providing detail in appropriate areas of the domain. We test our hybrid methodology robustly by applying it to several biologically motivated problems including diffusion and morphogen gradient formation. Our analysis shows that the resulting error is small, unbiased and does not grow over time.
The adaptive dynamic community detection algorithm based on the non-homogeneous random walking
Xin, Yu; Xie, Zhi-Qiang; Yang, Jing
2016-05-01
With the changing of the habit and custom, people's social activity tends to be changeable. It is required to have a community evolution analyzing method to mine the dynamic information in social network. For that, we design the random walking possibility function and the topology gain function to calculate the global influence matrix of the nodes. By the analysis of the global influence matrix, the clustering directions of the nodes can be obtained, thus the NRW (Non-Homogeneous Random Walk) method for detecting the static overlapping communities can be established. We design the ANRW (Adaptive Non-Homogeneous Random Walk) method via adapting the nodes impacted by the dynamic events based on the NRW. The ANRW combines the local community detection with dynamic adaptive adjustment to decrease the computational cost for ANRW. Furthermore, the ANRW treats the node as the calculating unity, thus the running manner of the ANRW is suitable to the parallel computing, which could meet the requirement of large dataset mining. Finally, by the experiment analysis, the efficiency of ANRW on dynamic community detection is verified.
A Component Mode Synthesis Algorithm for Multibody Dynamics of Wind Turbines
DEFF Research Database (Denmark)
Holm-Jørgensen, Kristian; Nielsen, Søren R.K.
2009-01-01
A system reduction scheme related to a multibody formulation of wind turbine dynamics is devised. Each substructure is described in its own frame of reference, which is moving freely in the vicinity of the moving substructure, in principle without any constraints to the rigid body part of the mot...
Xiao, Li; Cai, Qin; Li, Zhilin; Zhao, Hongkai; Luo, Ray
2014-01-01
A multi-scale framework is proposed for more realistic molecular dynamics simulations in continuum solvent models by coupling a molecular mechanics treatment of solute with a fluid mechanics treatment of solvent. This article reports our initial efforts to formulate the physical concepts necessary for coupling the two mechanics and develop a 3D numerical algorithm to simulate the solvent fluid via the Navier-Stokes equation. The numerical algorithm was validated with multiple test cases. The ...
Rural Poverty Dynamics in Kenya: Structural Declines and Stochastic Escapes
Radeny, M.A.O.; Berg, van den M.M.; Schipper, R.A.
2012-01-01
We use panel survey data and household event-histories to explore patterns of rural poverty dynamics in Kenya over the period 2000–2009. We find substantial mobility across poverty categories using economic transition matrices. Drawing on asset-based approaches, we distinguish stochastic from struct
Xiao, Li; Cai, Qin; Li, Zhilin; Zhao, Hongkai; Luo, Ray
2014-11-25
A multi-scale framework is proposed for more realistic molecular dynamics simulations in continuum solvent models by coupling a molecular mechanics treatment of solute with a fluid mechanics treatment of solvent. This article reports our initial efforts to formulate the physical concepts necessary for coupling the two mechanics and develop a 3D numerical algorithm to simulate the solvent fluid via the Navier-Stokes equation. The numerical algorithm was validated with multiple test cases. The validation shows that the algorithm is effective and stable, with observed accuracy consistent with our design.
Moghani, Mahdy Malekzadeh; Khomami, Bamin
2017-02-01
The computational efficiency of Brownian dynamics (BD) simulation of the constrained model of a polymeric chain (bead-rod) with n beads and in the presence of hydrodynamic interaction (HI) is reduced to the order of n2 via an efficient algorithm which utilizes the conjugate-gradient (CG) method within a Picard iteration scheme. Moreover, the utility of the Barnes and Hut (BH) multipole method in BD simulation of polymeric solutions in the presence of HI, with regard to computational cost, scaling, and accuracy, is discussed. Overall, it is determined that this approach leads to a scaling of O (n1.2) . Furthermore, a stress algorithm is developed which accurately captures the transient stress growth in the startup of flow for the bead-rod model with HI and excluded volume (EV) interaction. Rheological properties of the chains up to n =350 in the presence of EV and HI are computed via the former algorithm. The result depicts qualitative differences in shear thinning behavior of the polymeric solutions in the intermediate values of the Weissenburg number (10
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.
Kazachenko, Sergey; Giovinazzo, Mark; Hall, Kyle Wm; Cann, Natalie M
2015-09-15
A custom code for molecular dynamics simulations has been designed to run on CUDA-enabled NVIDIA graphics processing units (GPUs). The double-precision code simulates multicomponent fluids, with intramolecular and intermolecular forces, coarse-grained and atomistic models, holonomic constraints, Nosé-Hoover thermostats, and the generation of distribution functions. Algorithms to compute Lennard-Jones and Gay-Berne interactions, and the electrostatic force using Ewald summations, are discussed. A neighbor list is introduced to improve scaling with respect to system size. Three test systems are examined: SPC/E water; an n-hexane/2-propanol mixture; and a liquid crystal mesogen, 2-(4-butyloxyphenyl)-5-octyloxypyrimidine. Code performance is analyzed for each system. With one GPU, a 33-119 fold increase in performance is achieved compared with the serial code while the use of two GPUs leads to a 69-287 fold improvement and three GPUs yield a 101-377 fold speedup.
Institute of Scientific and Technical Information of China (English)
无
2010-01-01
We give an equivalent construction of the infinitesimal time translation operator for partial differential evolution equation in the algebraic dynamics algorithm proposed by Shun-Jin Wang and his students. Our construction involves only simple partial differentials and avoids the derivative terms of δ function which appear in the course of computation by means of Wang-Zhang operator. We prove Wang’s equivalent theorem which says that our construction and Wang-Zhang’s are equivalent. We use our construction to deal with several typical equations such as nonlinear advection equation, Burgers equation, nonlinear Schrodinger equation, KdV equation and sine-Gordon equation, and obtain at least second order approximate solutions to them. These equations include the cases of real and complex field variables and the cases of the first and the second order time derivatives.
A DYNAMIC FOOD CHAIN MODEL FOR HONG KONG BASED ON RADFOOD MODEL AND BIRCHALL—JAMES ALGORITHM
Institute of Scientific and Technical Information of China (English)
余君岳; 吴国斌; 等
1995-01-01
In this paper a dynamic food chain model for Hong Kong which simulates the transfer of radioactive substances from a fallout deposition via the food chain into the human bodies is built.The model is based on the RADFOOD model and the Birchall-James algorithm.The radionuclides 131I and 90Sr representing the short-term and long-term risk situations have been studied as sample cases.Various types of crops,and the dietary pattern of the public have been considered.The resulting internal radiation doses have been calculated.The results are obtained for food consumption starting at various time after the fallout deposition and for different consumption durations.
Implementation of the Algorithm for Congestion control in the Dynamic Circuit Network (DCN)
Nalamwar, H. S.; Ivanov, M. A.; Buddhawar, G. U.
2017-01-01
Transport Control Protocol (TCP) incast congestion happens when a number of senders work in parallel with the same server where the high bandwidth and low latency network problem occurs. For many data center network applications such as a search engine, heavy traffic is present on such a server. Incast congestion degrades the entire performance as packets are lost at a server side due to buffer overflow, and as a result, the response time becomes longer. In this work, we focus on TCP throughput, round-trip time (RTT), receive window and retransmission. Our method is based on the proactive adjust of the TCP receive window before the packet loss occurs. We aim to avoid the wastage of the bandwidth by adjusting its size as per the number of packets. To avoid the packet loss, the ICTCP algorithm has been implemented in the data center network (ToR).
Dynamic Consensus Algorithm based Distributed Voltage Harmonic Compensation in Islanded Microgrids
DEFF Research Database (Denmark)
Meng, Lexuan; Tang, Fen; Firoozabadi, Mehdi Savaghebi
2015-01-01
In islanded microgrids, the existence of nonlinear electric loads may cause voltage distortion and affect the performance of power quality sensitive equipment. Thanks to the prevalent utilization of interfacing power electronic devices and information/communication technologies, distributed...... generators can be employed as compensators to enhance the power quality on consumer side. However, conventional centralized control is facing obstacles because of the distributed fashion of generation and consumption. Accordingly, this paper proposes a consensus algorithm based distributed hierarchical...... control to realize voltage harmonic compensation and accurate current sharing in multi-bus islanded microgrids. Low order harmonic components are considered as examples in this paper. Harmonic current sharing is also realized among distributed generators by applying the proposed methods. Plug...
Pulliam, T. H.; Steger, J. L.
1985-01-01
In 1977 and 1978, general purpose centrally space differenced implicit finite difference codes in two and three dimensions have been introduced. These codes, now called ARC2D and ARC3D, can run either in inviscid or viscous mode for steady or unsteady flow. Since the introduction of the ARC2D and ARC3D codes, overall computational efficiency could be improved by making use of a number of algorithmic changes. These changes are related to the use of a spatially varying time step, the use of a sequence of mesh refinements to establish approximate solutions, implementation of various ways to reduce inversion work, improved numerical dissipation terms, and more implicit treatment of terms. The present investigation has the objective to describe the considered improvements and to quantify advantages and disadvantages. It is found that using established and simple procedures, a computer code can be maintained which is competitive with specialized codes.
Dynamic Head Cluster Election Algorithm for Clustered Ad-Hoc Networks
Directory of Open Access Journals (Sweden)
Arwa Zabian
2008-01-01
Full Text Available In distributed system, the concept of clustering consists on dividing the geographical area covered by a set of nodes into small zones. In mobile network, the clustering mechanism varied due to the mobility of the nodes any time in any direction. That causes the partitioning of the network or the joining of nodes. Several existing centralized or globalized algorithm have been proposed for clustering technique, in a manner that no one node becomes isolated and no cluster becomes overloaded. A particular node called head cluster or leader is elected, has the role to organize the distribution of nodes in clusters. We propose a distributed clustering and leader election mechanism for Ad-Hoc mobile networks, in which the leader is a mobile node. Our results show that, in the case of leader mobility the time needed to elect a new leader is smaller than the time needed a significant topological change in the network is happens.
A Data Transmission Algorithm Based on Dynamic Grid Division for Coal Goaf Temperature Monitoring
Directory of Open Access Journals (Sweden)
Qingsong Hu
2014-01-01
Full Text Available WSN (wireless sensor network is a perfect tool of temperature monitoring in coal goaf. Based on the three-zone theory of goaf, the GtmWSN model is proposed, and its dynamic features are analyzed. Accordingly, a data transmission scheme, named DTDGD, is worked out. Firstly, sink nodes conduct dynamic grid division on the GtmWSN according to virtual semicircle. Secondly, each node will confirm to which grid it belongs based on grid number. Finally, data will be delivered to sink nodes with greedy forward and hole avoidance. Simulation results and field data showed that the GtmWSN and DTDGD satisfied the lifetime need of goaf temperature monitoring.
Yang, Dan; Liu, Juan; Zhang, Yingxi; Li, Xin; Wang, Yongtian
2016-10-01
Holographic display has been considered as a promising display technology. Currently, low-speed generation of holograms with big holographic data is one of crucial bottlenecks for three dimensional (3D) dynamic holographic display. To solve this problem, the acceleration method computation platform is presented based on look-up table point source method. The computer generated holograms (CGHs) acquisition is sped up by offline file loading and inline calculation optimization, where a pure phase CGH with gigabyte data is encoded to record an object with 10 MB sampling data. Both numerical simulation and optical experiment demonstrate that the CGHs with 1920×1080 resolution by the proposed method can be applied to the 3D objects reconstruction with high quality successfully. It is believed that the CGHs with huge data can be generated by the proposed method with high speed for 3D dynamic holographic display in near future.
Institute of Scientific and Technical Information of China (English)
ZHANG Li-wei; FENG Xiao-bo; WANG Chang-de
2005-01-01
On the basis of analysis the governing process of downstream water level gates AVIO and AVIS, a mathematical model for simulation of dynamic operation process of hydraulically automated irrigation canals installed with AVIO and AVIS gates is presented. the main point of this mathematical model is firstly applying a set of unsteady flow equations (St. Venant equations here) and treating the condition of gate movement as its dynamic boundary, and then decoupling this interaction of gate movement with the change of canal flow. In this process, it is necessary to give the gates' open-loop transfer function whose input is water level deviation and output is gate discharge. The result of this simulation for a practical reach has shown it has satisfactory accuracy.
Dynamic Topology Re-Configuration in Multihop Cellular Networks Using Sequential Genetic Algorithm
Directory of Open Access Journals (Sweden)
B.Shantha Kumari
2014-10-01
Full Text Available Cellular communications has experienced explosive growth in the past two decades. Today millions of people around the world use cellular phones. Cellular phones allow a person to make or receive a call from almost anywhere. Likewise, a person is allowed to continue the phone conversation while on the move. Cellular communications is supported by an infrastructure called a cellular network, which integrates cellular phones into the public switched telephone network. The cellular network has gone through three generations.The first generation of cellular networks is analog in nature. To accommodate more cellular phone subscribers, digital TDMA (time division multiple access and CDMA (code division multiple access technologies are used in the second generation (2G to increase the network capacity. With digital technologies, digitized voice can be coded and encrypted. Therefore, the 2G cellular network is also more secure. The third generation (3G integrates cellular phones into the Internet world by providing highspeed packet-switching data transmission in addition to circuit-switching voice transmission. The 3G cellular networks have been deployed in some parts of Asia, Europe, and the United States since 2002 and will be widely deployed in the coming years. The high increase in traffic and data rate for future generations of mobile communication systems, with simultaneous requirement for reduced power consumption, makes Multihop Cellular Networks (MCNs an attractive technology. To exploit the potentials of MCNs a new network paradigm is proposed in this paper. In addition, a novel sequential genetic algorithm (SGA is proposed as a heuristic approximation to reconfigure the optimum relaying topology as the network traffic changes. Network coding is used to combine the uplink and downlink transmissions, and incorporate it into the optimum bidirectional relaying with ICI awareness. Numerical results have shown that the algorithms suggested in this