Sample records for algorithms simulated annealing

  1. Recursive Branching Simulated Annealing Algorithm

    NASA Technical Reports Server (NTRS)

    Bolcar, Matthew; Smith, J. Scott; Aronstein, David

    2012-01-01

    This innovation is a variation of a simulated-annealing optimization algorithm that uses a recursive-branching structure to parallelize the search of a parameter space for the globally optimal solution to an objective. The algorithm has been demonstrated to be more effective at searching a parameter space than traditional simulated-annealing methods for a particular problem of interest, and it can readily be applied to a wide variety of optimization problems, including those with a parameter space having both discrete-value parameters (combinatorial) and continuous-variable parameters. It can take the place of a conventional simulated- annealing, Monte-Carlo, or random- walk algorithm. In a conventional simulated-annealing (SA) algorithm, a starting configuration is randomly selected within the parameter space. The algorithm randomly selects another configuration from the parameter space and evaluates the objective function for that configuration. If the objective function value is better than the previous value, the new configuration is adopted as the new point of interest in the parameter space. If the objective function value is worse than the previous value, the new configuration may be adopted, with a probability determined by a temperature parameter, used in analogy to annealing in metals. As the optimization continues, the region of the parameter space from which new configurations can be selected shrinks, and in conjunction with lowering the annealing temperature (and thus lowering the probability for adopting configurations in parameter space with worse objective functions), the algorithm can converge on the globally optimal configuration. The Recursive Branching Simulated Annealing (RBSA) algorithm shares some features with the SA algorithm, notably including the basic principles that a starting configuration is randomly selected from within the parameter space, the algorithm tests other configurations with the goal of finding the globally optimal

  2. A theoretical comparison of evolutionary algorithms and simulated annealing

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Hart, W.E.

    1995-08-28

    This paper theoretically compares the performance of simulated annealing and evolutionary algorithms. Our main result is that under mild conditions a wide variety of evolutionary algorithms can be shown to have greater performance than simulated annealing after a sufficiently large number of function evaluations. This class of EAs includes variants of evolutionary strategie and evolutionary programming, the canonical genetic algorithm, as well as a variety of genetic algorithms that have been applied to combinatorial optimization problems. The proof of this result is based on a performance analysis of a very general class of stochastic optimization algorithms, which has implications formore » the performance of a variety of other optimization algorithm.« less

  3. Image reconstruction through thin scattering media by simulated annealing algorithm

    NASA Astrophysics Data System (ADS)

    Fang, Longjie; Zuo, Haoyi; Pang, Lin; Yang, Zuogang; Zhang, Xicheng; Zhu, Jianhua

    2018-07-01

    An idea for reconstructing the image of an object behind thin scattering media is proposed by phase modulation. The optimized phase mask is achieved by modulating the scattered light using simulated annealing algorithm. The correlation coefficient is exploited as a fitness function to evaluate the quality of reconstructed image. The reconstructed images optimized from simulated annealing algorithm and genetic algorithm are compared in detail. The experimental results show that our proposed method has better definition and higher speed than genetic algorithm.

  4. List-Based Simulated Annealing Algorithm for Traveling Salesman Problem

    PubMed Central

    Zhan, Shi-hua; Lin, Juan; Zhang, Ze-jun

    2016-01-01

    Simulated annealing (SA) algorithm is a popular intelligent optimization algorithm which has been successfully applied in many fields. Parameters' setting is a key factor for its performance, but it is also a tedious work. To simplify parameters setting, we present a list-based simulated annealing (LBSA) algorithm to solve traveling salesman problem (TSP). LBSA algorithm uses a novel list-based cooling schedule to control the decrease of temperature. Specifically, a list of temperatures is created first, and then the maximum temperature in list is used by Metropolis acceptance criterion to decide whether to accept a candidate solution. The temperature list is adapted iteratively according to the topology of the solution space of the problem. The effectiveness and the parameter sensitivity of the list-based cooling schedule are illustrated through benchmark TSP problems. The LBSA algorithm, whose performance is robust on a wide range of parameter values, shows competitive performance compared with some other state-of-the-art algorithms. PMID:27034650

  5. List-Based Simulated Annealing Algorithm for Traveling Salesman Problem.

    PubMed

    Zhan, Shi-hua; Lin, Juan; Zhang, Ze-jun; Zhong, Yi-wen

    2016-01-01

    Simulated annealing (SA) algorithm is a popular intelligent optimization algorithm which has been successfully applied in many fields. Parameters' setting is a key factor for its performance, but it is also a tedious work. To simplify parameters setting, we present a list-based simulated annealing (LBSA) algorithm to solve traveling salesman problem (TSP). LBSA algorithm uses a novel list-based cooling schedule to control the decrease of temperature. Specifically, a list of temperatures is created first, and then the maximum temperature in list is used by Metropolis acceptance criterion to decide whether to accept a candidate solution. The temperature list is adapted iteratively according to the topology of the solution space of the problem. The effectiveness and the parameter sensitivity of the list-based cooling schedule are illustrated through benchmark TSP problems. The LBSA algorithm, whose performance is robust on a wide range of parameter values, shows competitive performance compared with some other state-of-the-art algorithms.

  6. Experiences with serial and parallel algorithms for channel routing using simulated annealing

    NASA Technical Reports Server (NTRS)

    Brouwer, Randall Jay

    1988-01-01

    Two algorithms for channel routing using simulated annealing are presented. Simulated annealing is an optimization methodology which allows the solution process to back up out of local minima that may be encountered by inappropriate selections. By properly controlling the annealing process, it is very likely that the optimal solution to an NP-complete problem such as channel routing may be found. The algorithm presented proposes very relaxed restrictions on the types of allowable transformations, including overlapping nets. By freeing that restriction and controlling overlap situations with an appropriate cost function, the algorithm becomes very flexible and can be applied to many extensions of channel routing. The selection of the transformation utilizes a number of heuristics, still retaining the pseudorandom nature of simulated annealing. The algorithm was implemented as a serial program for a workstation, and a parallel program designed for a hypercube computer. The details of the serial implementation are presented, including many of the heuristics used and some of the resulting solutions.

  7. An Improved SoC Test Scheduling Method Based on Simulated Annealing Algorithm

    NASA Astrophysics Data System (ADS)

    Zheng, Jingjing; Shen, Zhihang; Gao, Huaien; Chen, Bianna; Zheng, Weida; Xiong, Xiaoming

    2017-02-01

    In this paper, we propose an improved SoC test scheduling method based on simulated annealing algorithm (SA). It is our first to disorganize IP core assignment for each TAM to produce a new solution for SA, allocate TAM width for each TAM using greedy algorithm and calculate corresponding testing time. And accepting the core assignment according to the principle of simulated annealing algorithm and finally attain the optimum solution. Simultaneously, we run the test scheduling experiment with the international reference circuits provided by International Test Conference 2002(ITC’02) and the result shows that our algorithm is superior to the conventional integer linear programming algorithm (ILP), simulated annealing algorithm (SA) and genetic algorithm(GA). When TAM width reaches to 48,56 and 64, the testing time based on our algorithm is lesser than the classic methods and the optimization rates are 30.74%, 3.32%, 16.13% respectively. Moreover, the testing time based on our algorithm is very close to that of improved genetic algorithm (IGA), which is state-of-the-art at present.

  8. Stochastic search in structural optimization - Genetic algorithms and simulated annealing

    NASA Technical Reports Server (NTRS)

    Hajela, Prabhat

    1993-01-01

    An account is given of illustrative applications of genetic algorithms and simulated annealing methods in structural optimization. The advantages of such stochastic search methods over traditional mathematical programming strategies are emphasized; it is noted that these methods offer a significantly higher probability of locating the global optimum in a multimodal design space. Both genetic-search and simulated annealing can be effectively used in problems with a mix of continuous, discrete, and integer design variables.

  9. A parallel simulated annealing algorithm for standard cell placement on a hypercube computer

    NASA Technical Reports Server (NTRS)

    Jones, Mark Howard

    1987-01-01

    A parallel version of a simulated annealing algorithm is presented which is targeted to run on a hypercube computer. A strategy for mapping the cells in a two dimensional area of a chip onto processors in an n-dimensional hypercube is proposed such that both small and large distance moves can be applied. Two types of moves are allowed: cell exchanges and cell displacements. The computation of the cost function in parallel among all the processors in the hypercube is described along with a distributed data structure that needs to be stored in the hypercube to support parallel cost evaluation. A novel tree broadcasting strategy is used extensively in the algorithm for updating cell locations in the parallel environment. Studies on the performance of the algorithm on example industrial circuits show that it is faster and gives better final placement results than the uniprocessor simulated annealing algorithms. An improved uniprocessor algorithm is proposed which is based on the improved results obtained from parallelization of the simulated annealing algorithm.

  10. Parameter estimation for chaotic systems using a hybrid adaptive cuckoo search with simulated annealing algorithm

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Sheng, Zheng, E-mail: 19994035@sina.com; Wang, Jun; Zhou, Bihua

    2014-03-15

    This paper introduces a novel hybrid optimization algorithm to establish the parameters of chaotic systems. In order to deal with the weaknesses of the traditional cuckoo search algorithm, the proposed adaptive cuckoo search with simulated annealing algorithm is presented, which incorporates the adaptive parameters adjusting operation and the simulated annealing operation in the cuckoo search algorithm. Normally, the parameters of the cuckoo search algorithm are kept constant that may result in decreasing the efficiency of the algorithm. For the purpose of balancing and enhancing the accuracy and convergence rate of the cuckoo search algorithm, the adaptive operation is presented tomore » tune the parameters properly. Besides, the local search capability of cuckoo search algorithm is relatively weak that may decrease the quality of optimization. So the simulated annealing operation is merged into the cuckoo search algorithm to enhance the local search ability and improve the accuracy and reliability of the results. The functionality of the proposed hybrid algorithm is investigated through the Lorenz chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the method can estimate parameters efficiently and accurately in the noiseless and noise condition. Finally, the results are compared with the traditional cuckoo search algorithm, genetic algorithm, and particle swarm optimization algorithm. Simulation results demonstrate the effectiveness and superior performance of the proposed algorithm.« less

  11. Simulated annealing algorithm for solving chambering student-case assignment problem

    NASA Astrophysics Data System (ADS)

    Ghazali, Saadiah; Abdul-Rahman, Syariza

    2015-12-01

    The problem related to project assignment problem is one of popular practical problem that appear nowadays. The challenge of solving the problem raise whenever the complexity related to preferences, the existence of real-world constraints and problem size increased. This study focuses on solving a chambering student-case assignment problem by using a simulated annealing algorithm where this problem is classified under project assignment problem. The project assignment problem is considered as hard combinatorial optimization problem and solving it using a metaheuristic approach is an advantage because it could return a good solution in a reasonable time. The problem of assigning chambering students to cases has never been addressed in the literature before. For the proposed problem, it is essential for law graduates to peruse in chambers before they are qualified to become legal counselor. Thus, assigning the chambering students to cases is a critically needed especially when involving many preferences. Hence, this study presents a preliminary study of the proposed project assignment problem. The objective of the study is to minimize the total completion time for all students in solving the given cases. This study employed a minimum cost greedy heuristic in order to construct a feasible initial solution. The search then is preceded with a simulated annealing algorithm for further improvement of solution quality. The analysis of the obtained result has shown that the proposed simulated annealing algorithm has greatly improved the solution constructed by the minimum cost greedy heuristic. Hence, this research has demonstrated the advantages of solving project assignment problem by using metaheuristic techniques.

  12. Modified Backtracking Search Optimization Algorithm Inspired by Simulated Annealing for Constrained Engineering Optimization Problems

    PubMed Central

    Wang, Hailong; Sun, Yuqiu; Su, Qinghua; Xia, Xuewen

    2018-01-01

    The backtracking search optimization algorithm (BSA) is a population-based evolutionary algorithm for numerical optimization problems. BSA has a powerful global exploration capacity while its local exploitation capability is relatively poor. This affects the convergence speed of the algorithm. In this paper, we propose a modified BSA inspired by simulated annealing (BSAISA) to overcome the deficiency of BSA. In the BSAISA, the amplitude control factor (F) is modified based on the Metropolis criterion in simulated annealing. The redesigned F could be adaptively decreased as the number of iterations increases and it does not introduce extra parameters. A self-adaptive ε-constrained method is used to handle the strict constraints. We compared the performance of the proposed BSAISA with BSA and other well-known algorithms when solving thirteen constrained benchmarks and five engineering design problems. The simulation results demonstrated that BSAISA is more effective than BSA and more competitive with other well-known algorithms in terms of convergence speed. PMID:29666635

  13. Deterministic quantum annealing expectation-maximization algorithm

    NASA Astrophysics Data System (ADS)

    Miyahara, Hideyuki; Tsumura, Koji; Sughiyama, Yuki

    2017-11-01

    Maximum likelihood estimation (MLE) is one of the most important methods in machine learning, and the expectation-maximization (EM) algorithm is often used to obtain maximum likelihood estimates. However, EM heavily depends on initial configurations and fails to find the global optimum. On the other hand, in the field of physics, quantum annealing (QA) was proposed as a novel optimization approach. Motivated by QA, we propose a quantum annealing extension of EM, which we call the deterministic quantum annealing expectation-maximization (DQAEM) algorithm. We also discuss its advantage in terms of the path integral formulation. Furthermore, by employing numerical simulations, we illustrate how DQAEM works in MLE and show that DQAEM moderate the problem of local optima in EM.

  14. EIT image regularization by a new Multi-Objective Simulated Annealing algorithm.

    PubMed

    Castro Martins, Thiago; Sales Guerra Tsuzuki, Marcos

    2015-01-01

    Multi-Objective Optimization can be used to produce regularized Electrical Impedance Tomography (EIT) images where the weight of the regularization term is not known a priori. This paper proposes a novel Multi-Objective Optimization algorithm based on Simulated Annealing tailored for EIT image reconstruction. Images are reconstructed from experimental data and compared with images from other Multi and Single Objective optimization methods. A significant performance enhancement from traditional techniques can be inferred from the results.

  15. Forecasting nonlinear chaotic time series with function expression method based on an improved genetic-simulated annealing algorithm.

    PubMed

    Wang, Jun; Zhou, Bi-hua; Zhou, Shu-dao; Sheng, Zheng

    2015-01-01

    The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior.

  16. Application of Simulated Annealing and Related Algorithms to TWTA Design

    NASA Technical Reports Server (NTRS)

    Radke, Eric M.

    2004-01-01

    Simulated Annealing (SA) is a stochastic optimization algorithm used to search for global minima in complex design surfaces where exhaustive searches are not computationally feasible. The algorithm is derived by simulating the annealing process, whereby a solid is heated to a liquid state and then cooled slowly to reach thermodynamic equilibrium at each temperature. The idea is that atoms in the solid continually bond and re-bond at various quantum energy levels, and with sufficient cooling time they will rearrange at the minimum energy state to form a perfect crystal. The distribution of energy levels is given by the Boltzmann distribution: as temperature drops, the probability of the presence of high-energy bonds decreases. In searching for an optimal design, local minima and discontinuities are often present in a design surface. SA presents a distinct advantage over other optimization algorithms in its ability to escape from these local minima. Just as high-energy atomic configurations are visited in the actual annealing process in order to eventually reach the minimum energy state, in SA highly non-optimal configurations are visited in order to find otherwise inaccessible global minima. The SA algorithm produces a Markov chain of points in the design space at each temperature, with a monotonically decreasing temperature. A random point is started upon, and the objective function is evaluated at that point. A stochastic perturbation is then made to the parameters of the point to arrive at a proposed new point in the design space, at which the objection function is evaluated as well. If the change in objective function values (Delta)E is negative, the proposed new point is accepted. If (Delta)E is positive, the proposed new point is accepted according to the Metropolis criterion: rho((Delta)f) = exp((-Delta)E/T), where T is the temperature for the current Markov chain. The process then repeats for the remainder of the Markov chain, after which the temperature is

  17. Forecasting Nonlinear Chaotic Time Series with Function Expression Method Based on an Improved Genetic-Simulated Annealing Algorithm

    PubMed Central

    Wang, Jun; Zhou, Bi-hua; Zhou, Shu-dao; Sheng, Zheng

    2015-01-01

    The paper proposes a novel function expression method to forecast chaotic time series, using an improved genetic-simulated annealing (IGSA) algorithm to establish the optimum function expression that describes the behavior of time series. In order to deal with the weakness associated with the genetic algorithm, the proposed algorithm incorporates the simulated annealing operation which has the strong local search ability into the genetic algorithm to enhance the performance of optimization; besides, the fitness function and genetic operators are also improved. Finally, the method is applied to the chaotic time series of Quadratic and Rossler maps for validation. The effect of noise in the chaotic time series is also studied numerically. The numerical results verify that the method can forecast chaotic time series with high precision and effectiveness, and the forecasting precision with certain noise is also satisfactory. It can be concluded that the IGSA algorithm is energy-efficient and superior. PMID:26000011

  18. Memoryless cooperative graph search based on the simulated annealing algorithm

    NASA Astrophysics Data System (ADS)

    Hou, Jian; Yan, Gang-Feng; Fan, Zhen

    2011-04-01

    We have studied the problem of reaching a globally optimal segment for a graph-like environment with a single or a group of autonomous mobile agents. Firstly, two efficient simulated-annealing-like algorithms are given for a single agent to solve the problem in a partially known environment and an unknown environment, respectively. It shows that under both proposed control strategies, the agent will eventually converge to a globally optimal segment with probability 1. Secondly, we use multi-agent searching to simultaneously reduce the computation complexity and accelerate convergence based on the algorithms we have given for a single agent. By exploiting graph partition, a gossip-consensus method based scheme is presented to update the key parameter—radius of the graph, ensuring that the agents spend much less time finding a globally optimal segment.

  19. A Simulated Annealing Algorithm for the Optimization of Multistage Depressed Collector Efficiency

    NASA Technical Reports Server (NTRS)

    Vaden, Karl R.; Wilson, Jeffrey D.; Bulson, Brian A.

    2002-01-01

    The microwave traveling wave tube amplifier (TWTA) is widely used as a high-power transmitting source for space and airborne communications. One critical factor in designing a TWTA is the overall efficiency. However, overall efficiency is highly dependent upon collector efficiency; so collector design is critical to the performance of a TWTA. Therefore, NASA Glenn Research Center has developed an optimization algorithm based on Simulated Annealing to quickly design highly efficient multi-stage depressed collectors (MDC).

  20. Hybrid annealing: Coupling a quantum simulator to a classical computer

    NASA Astrophysics Data System (ADS)

    Graß, Tobias; Lewenstein, Maciej

    2017-05-01

    Finding the global minimum in a rugged potential landscape is a computationally hard task, often equivalent to relevant optimization problems. Annealing strategies, either classical or quantum, explore the configuration space by evolving the system under the influence of thermal or quantum fluctuations. The thermal annealing dynamics can rapidly freeze the system into a low-energy configuration, and it can be simulated well on a classical computer, but it easily gets stuck in local minima. Quantum annealing, on the other hand, can be guaranteed to find the true ground state and can be implemented in modern quantum simulators; however, quantum adiabatic schemes become prohibitively slow in the presence of quasidegeneracies. Here, we propose a strategy which combines ideas from simulated annealing and quantum annealing. In such a hybrid algorithm, the outcome of a quantum simulator is processed on a classical device. While the quantum simulator explores the configuration space by repeatedly applying quantum fluctuations and performing projective measurements, the classical computer evaluates each configuration and enforces a lowering of the energy. We have simulated this algorithm for small instances of the random energy model, showing that it potentially outperforms both simulated thermal annealing and adiabatic quantum annealing. It becomes most efficient for problems involving many quasidegenerate ground states.

  1. Simulated Stochastic Approximation Annealing for Global Optimization with a Square-Root Cooling Schedule

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Liang, Faming; Cheng, Yichen; Lin, Guang

    2014-06-13

    Simulated annealing has been widely used in the solution of optimization problems. As known by many researchers, the global optima cannot be guaranteed to be located by simulated annealing unless a logarithmic cooling schedule is used. However, the logarithmic cooling schedule is so slow that no one can afford to have such a long CPU time. This paper proposes a new stochastic optimization algorithm, the so-called simulated stochastic approximation annealing algorithm, which is a combination of simulated annealing and the stochastic approximation Monte Carlo algorithm. Under the framework of stochastic approximation Markov chain Monte Carlo, it is shown that themore » new algorithm can work with a cooling schedule in which the temperature can decrease much faster than in the logarithmic cooling schedule, e.g., a square-root cooling schedule, while guaranteeing the global optima to be reached when the temperature tends to zero. The new algorithm has been tested on a few benchmark optimization problems, including feed-forward neural network training and protein-folding. The numerical results indicate that the new algorithm can significantly outperform simulated annealing and other competitors.« less

  2. On simulated annealing phase transitions in phylogeny reconstruction.

    PubMed

    Strobl, Maximilian A R; Barker, Daniel

    2016-08-01

    Phylogeny reconstruction with global criteria is NP-complete or NP-hard, hence in general requires a heuristic search. We investigate the powerful, physically inspired, general-purpose heuristic simulated annealing, applied to phylogeny reconstruction. Simulated annealing mimics the physical process of annealing, where a liquid is gently cooled to form a crystal. During the search, periods of elevated specific heat occur, analogous to physical phase transitions. These simulated annealing phase transitions play a crucial role in the outcome of the search. Nevertheless, they have received comparably little attention, for phylogeny or other optimisation problems. We analyse simulated annealing phase transitions during searches for the optimal phylogenetic tree for 34 real-world multiple alignments. In the same way in which melting temperatures differ between materials, we observe distinct specific heat profiles for each input file. We propose this reflects differences in the search landscape and can serve as a measure for problem difficulty and for suitability of the algorithm's parameters. We discuss application in algorithmic optimisation and as a diagnostic to assess parameterisation before computationally costly, large phylogeny reconstructions are launched. Whilst the focus here lies on phylogeny reconstruction under maximum parsimony, it is plausible that our results are more widely applicable to optimisation procedures in science and industry. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  3. Population annealing simulations of a binary hard-sphere mixture

    NASA Astrophysics Data System (ADS)

    Callaham, Jared; Machta, Jonathan

    2017-06-01

    Population annealing is a sequential Monte Carlo scheme well suited to simulating equilibrium states of systems with rough free energy landscapes. Here we use population annealing to study a binary mixture of hard spheres. Population annealing is a parallel version of simulated annealing with an extra resampling step that ensures that a population of replicas of the system represents the equilibrium ensemble at every packing fraction in an annealing schedule. The algorithm and its equilibration properties are described, and results are presented for a glass-forming fluid composed of a 50/50 mixture of hard spheres with diameter ratio of 1.4:1. For this system, we obtain precise results for the equation of state in the glassy regime up to packing fractions φ ≈0.60 and study deviations from the Boublik-Mansoori-Carnahan-Starling-Leland equation of state. For higher packing fractions, the algorithm falls out of equilibrium and a free volume fit predicts jamming at packing fraction φ ≈0.667 . We conclude that population annealing is an effective tool for studying equilibrium glassy fluids and the jamming transition.

  4. Annealed Importance Sampling Reversible Jump MCMC algorithms

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Karagiannis, Georgios; Andrieu, Christophe

    2013-03-20

    It will soon be 20 years since reversible jump Markov chain Monte Carlo (RJ-MCMC) algorithms have been proposed. They have significantly extended the scope of Markov chain Monte Carlo simulation methods, offering the promise to be able to routinely tackle transdimensional sampling problems, as encountered in Bayesian model selection problems for example, in a principled and flexible fashion. Their practical efficient implementation, however, still remains a challenge. A particular difficulty encountered in practice is in the choice of the dimension matching variables (both their nature and their distribution) and the reversible transformations which allow one to define the one-to-one mappingsmore » underpinning the design of these algorithms. Indeed, even seemingly sensible choices can lead to algorithms with very poor performance. The focus of this paper is the development and performance evaluation of a method, annealed importance sampling RJ-MCMC (aisRJ), which addresses this problem by mitigating the sensitivity of RJ-MCMC algorithms to the aforementioned poor design. As we shall see the algorithm can be understood as being an “exact approximation” of an idealized MCMC algorithm that would sample from the model probabilities directly in a model selection set-up. Such an idealized algorithm may have good theoretical convergence properties, but typically cannot be implemented, and our algorithms can approximate the performance of such idealized algorithms to an arbitrary degree while not introducing any bias for any degree of approximation. Our approach combines the dimension matching ideas of RJ-MCMC with annealed importance sampling and its Markov chain Monte Carlo implementation. We illustrate the performance of the algorithm with numerical simulations which indicate that, although the approach may at first appear computationally involved, it is in fact competitive.« less

  5. GPU accelerated population annealing algorithm

    NASA Astrophysics Data System (ADS)

    Barash, Lev Yu.; Weigel, Martin; Borovský, Michal; Janke, Wolfhard; Shchur, Lev N.

    2017-11-01

    Population annealing is a promising recent approach for Monte Carlo simulations in statistical physics, in particular for the simulation of systems with complex free-energy landscapes. It is a hybrid method, combining importance sampling through Markov chains with elements of sequential Monte Carlo in the form of population control. While it appears to provide algorithmic capabilities for the simulation of such systems that are roughly comparable to those of more established approaches such as parallel tempering, it is intrinsically much more suitable for massively parallel computing. Here, we tap into this structural advantage and present a highly optimized implementation of the population annealing algorithm on GPUs that promises speed-ups of several orders of magnitude as compared to a serial implementation on CPUs. While the sample code is for simulations of the 2D ferromagnetic Ising model, it should be easily adapted for simulations of other spin models, including disordered systems. Our code includes implementations of some advanced algorithmic features that have only recently been suggested, namely the automatic adaptation of temperature steps and a multi-histogram analysis of the data at different temperatures. Program Files doi:http://dx.doi.org/10.17632/sgzt4b7b3m.1 Licensing provisions: Creative Commons Attribution license (CC BY 4.0) Programming language: C, CUDA External routines/libraries: NVIDIA CUDA Toolkit 6.5 or newer Nature of problem: The program calculates the internal energy, specific heat, several magnetization moments, entropy and free energy of the 2D Ising model on square lattices of edge length L with periodic boundary conditions as a function of inverse temperature β. Solution method: The code uses population annealing, a hybrid method combining Markov chain updates with population control. The code is implemented for NVIDIA GPUs using the CUDA language and employs advanced techniques such as multi-spin coding, adaptive temperature

  6. Improving Simulated Annealing by Recasting it as a Non-Cooperative Game

    NASA Technical Reports Server (NTRS)

    Wolpert, David; Bandari, Esfandiar; Tumer, Kagan

    2001-01-01

    The game-theoretic field of COllective INtelligence (COIN) concerns the design of computer-based players engaged in a non-cooperative game so that as those players pursue their self-interests, a pre-specified global goal for the collective computational system is achieved "as a side-effect". Previous implementations of COIN algorithms have outperformed conventional techniques by up to several orders of magnitude, on domains ranging from telecommunications control to optimization in congestion problems. Recent mathematical developments have revealed that these previously developed game-theory-motivated algorithms were based on only two of the three factors determining performance. Consideration of only the third factor would instead lead to conventional optimization techniques like simulated annealing that have little to do with non-cooperative games. In this paper we present an algorithm based on all three terms at once. This algorithm can be viewed as a way to modify simulated annealing by recasting it as a non-cooperative game, with each variable replaced by a player. This recasting allows us to leverage the intelligent behavior of the individual players to substantially improve the exploration step of the simulated annealing. Experiments are presented demonstrating that this recasting improves simulated annealing by several orders of magnitude for spin glass relaxation and bin-packing.

  7. SAGRAD: A Program for Neural Network Training with Simulated Annealing and the Conjugate Gradient Method

    PubMed Central

    Bernal, Javier; Torres-Jimenez, Jose

    2015-01-01

    SAGRAD (Simulated Annealing GRADient), a Fortran 77 program for computing neural networks for classification using batch learning, is discussed. Neural network training in SAGRAD is based on a combination of simulated annealing and Møller’s scaled conjugate gradient algorithm, the latter a variation of the traditional conjugate gradient method, better suited for the nonquadratic nature of neural networks. Different aspects of the implementation of the training process in SAGRAD are discussed, such as the efficient computation of gradients and multiplication of vectors by Hessian matrices that are required by Møller’s algorithm; the (re)initialization of weights with simulated annealing required to (re)start Møller’s algorithm the first time and each time thereafter that it shows insufficient progress in reaching a possibly local minimum; and the use of simulated annealing when Møller’s algorithm, after possibly making considerable progress, becomes stuck at a local minimum or flat area of weight space. Outlines of the scaled conjugate gradient algorithm, the simulated annealing procedure and the training process used in SAGRAD are presented together with results from running SAGRAD on two examples of training data. PMID:26958442

  8. SAGRAD: A Program for Neural Network Training with Simulated Annealing and the Conjugate Gradient Method.

    PubMed

    Bernal, Javier; Torres-Jimenez, Jose

    2015-01-01

    SAGRAD (Simulated Annealing GRADient), a Fortran 77 program for computing neural networks for classification using batch learning, is discussed. Neural network training in SAGRAD is based on a combination of simulated annealing and Møller's scaled conjugate gradient algorithm, the latter a variation of the traditional conjugate gradient method, better suited for the nonquadratic nature of neural networks. Different aspects of the implementation of the training process in SAGRAD are discussed, such as the efficient computation of gradients and multiplication of vectors by Hessian matrices that are required by Møller's algorithm; the (re)initialization of weights with simulated annealing required to (re)start Møller's algorithm the first time and each time thereafter that it shows insufficient progress in reaching a possibly local minimum; and the use of simulated annealing when Møller's algorithm, after possibly making considerable progress, becomes stuck at a local minimum or flat area of weight space. Outlines of the scaled conjugate gradient algorithm, the simulated annealing procedure and the training process used in SAGRAD are presented together with results from running SAGRAD on two examples of training data.

  9. Simulated parallel annealing within a neighborhood for optimization of biomechanical systems.

    PubMed

    Higginson, J S; Neptune, R R; Anderson, F C

    2005-09-01

    Optimization problems for biomechanical systems have become extremely complex. Simulated annealing (SA) algorithms have performed well in a variety of test problems and biomechanical applications; however, despite advances in computer speed, convergence to optimal solutions for systems of even moderate complexity has remained prohibitive. The objective of this study was to develop a portable parallel version of a SA algorithm for solving optimization problems in biomechanics. The algorithm for simulated parallel annealing within a neighborhood (SPAN) was designed to minimize interprocessor communication time and closely retain the heuristics of the serial SA algorithm. The computational speed of the SPAN algorithm scaled linearly with the number of processors on different computer platforms for a simple quadratic test problem and for a more complex forward dynamic simulation of human pedaling.

  10. Thin-film designs by simulated annealing

    NASA Astrophysics Data System (ADS)

    Boudet, T.; Chaton, P.; Herault, L.; Gonon, G.; Jouanet, L.; Keller, P.

    1996-11-01

    With the increasing power of computers, new methods in synthesis of optical multilayer systems have appeared. Among these, the simulated-annealing algorithm has proved its efficiency in several fields of physics. We propose to show its performances in the field of optical multilayer systems through different filter designs.

  11. Prediction of Flood Warning in Taiwan Using Nonlinear SVM with Simulated Annealing Algorithm

    NASA Astrophysics Data System (ADS)

    Lee, C.

    2013-12-01

    The issue of the floods is important in Taiwan. It is because the narrow and high topography of the island make lots of rivers steep in Taiwan. The tropical depression likes typhoon always causes rivers to flood. Prediction of river flow under the extreme rainfall circumstances is important for government to announce the warning of flood. Every time typhoon passed through Taiwan, there were always floods along some rivers. The warning is classified to three levels according to the warning water levels in Taiwan. The propose of this study is to predict the level of floods warning from the information of precipitation, rainfall duration and slope of riverbed. To classify the level of floods warning by the above-mentioned information and modeling the problems, a machine learning model, nonlinear Support vector machine (SVM), is formulated to classify the level of floods warning. In addition, simulated annealing (SA), a probabilistic heuristic algorithm, is used to determine the optimal parameter of the SVM model. A case study of flooding-trend rivers of different gradients in Taiwan is conducted. The contribution of this SVM model with simulated annealing is capable of making efficient announcement for flood warning and keeping the danger of flood from residents along the rivers.

  12. Optimal design of minimum mean-square error noise reduction algorithms using the simulated annealing technique.

    PubMed

    Bai, Mingsian R; Hsieh, Ping-Ju; Hur, Kur-Nan

    2009-02-01

    The performance of the minimum mean-square error noise reduction (MMSE-NR) algorithm in conjunction with time-recursive averaging (TRA) for noise estimation is found to be very sensitive to the choice of two recursion parameters. To address this problem in a more systematic manner, this paper proposes an optimization method to efficiently search the optimal parameters of the MMSE-TRA-NR algorithms. The objective function is based on a regression model, whereas the optimization process is carried out with the simulated annealing algorithm that is well suited for problems with many local optima. Another NR algorithm proposed in the paper employs linear prediction coding as a preprocessor for extracting the correlated portion of human speech. Objective and subjective tests were undertaken to compare the optimized MMSE-TRA-NR algorithm with several conventional NR algorithms. The results of subjective tests were processed by using analysis of variance to justify the statistic significance. A post hoc test, Tukey's Honestly Significant Difference, was conducted to further assess the pairwise difference between the NR algorithms.

  13. An Improved Simulated Annealing Technique for Enhanced Mobility in Smart Cities

    PubMed Central

    Amer, Hayder; Salman, Naveed; Hawes, Matthew; Chaqfeh, Moumena; Mihaylova, Lyudmila; Mayfield, Martin

    2016-01-01

    Vehicular traffic congestion is a significant problem that arises in many cities. This is due to the increasing number of vehicles that are driving on city roads of limited capacity. The vehicular congestion significantly impacts travel distance, travel time, fuel consumption and air pollution. Avoidance of traffic congestion and providing drivers with optimal paths are not trivial tasks. The key contribution of this work consists of the developed approach for dynamic calculation of optimal traffic routes. Two attributes (the average travel speed of the traffic and the roads’ length) are utilized by the proposed method to find the optimal paths. The average travel speed values can be obtained from the sensors deployed in smart cities and communicated to vehicles via the Internet of Vehicles and roadside communication units. The performance of the proposed algorithm is compared to three other algorithms: the simulated annealing weighted sum, the simulated annealing technique for order preference by similarity to the ideal solution and the Dijkstra algorithm. The weighted sum and technique for order preference by similarity to the ideal solution methods are used to formulate different attributes in the simulated annealing cost function. According to the Sheffield scenario, simulation results show that the improved simulated annealing technique for order preference by similarity to the ideal solution method improves the traffic performance in the presence of congestion by an overall average of 19.22% in terms of travel time, fuel consumption and CO2 emissions as compared to other algorithms; also, similar performance patterns were achieved for the Birmingham test scenario. PMID:27376289

  14. An Improved Simulated Annealing Technique for Enhanced Mobility in Smart Cities.

    PubMed

    Amer, Hayder; Salman, Naveed; Hawes, Matthew; Chaqfeh, Moumena; Mihaylova, Lyudmila; Mayfield, Martin

    2016-06-30

    Vehicular traffic congestion is a significant problem that arises in many cities. This is due to the increasing number of vehicles that are driving on city roads of limited capacity. The vehicular congestion significantly impacts travel distance, travel time, fuel consumption and air pollution. Avoidance of traffic congestion and providing drivers with optimal paths are not trivial tasks. The key contribution of this work consists of the developed approach for dynamic calculation of optimal traffic routes. Two attributes (the average travel speed of the traffic and the roads' length) are utilized by the proposed method to find the optimal paths. The average travel speed values can be obtained from the sensors deployed in smart cities and communicated to vehicles via the Internet of Vehicles and roadside communication units. The performance of the proposed algorithm is compared to three other algorithms: the simulated annealing weighted sum, the simulated annealing technique for order preference by similarity to the ideal solution and the Dijkstra algorithm. The weighted sum and technique for order preference by similarity to the ideal solution methods are used to formulate different attributes in the simulated annealing cost function. According to the Sheffield scenario, simulation results show that the improved simulated annealing technique for order preference by similarity to the ideal solution method improves the traffic performance in the presence of congestion by an overall average of 19.22% in terms of travel time, fuel consumption and CO₂ emissions as compared to other algorithms; also, similar performance patterns were achieved for the Birmingham test scenario.

  15. Towards global optimization with adaptive simulated annealing

    NASA Astrophysics Data System (ADS)

    Forbes, Gregory W.; Jones, Andrew E.

    1991-01-01

    The structure of the simulated annealing algorithm is presented and its rationale is discussed. A unifying heuristic is then introduced which serves as a guide in the design of all of the sub-components of the algorithm. Simply put this heuristic principle states that at every cycle in the algorithm the occupation density should be kept as close as possible to the equilibrium distribution. This heuristic has been used as a guide to develop novel step generation and temperature control methods intended to improve the efficiency of the simulated annealing algorithm. The resulting algorithm has been used in attempts to locate good solutions for one of the lens design problems associated with this conference viz. the " monochromatic quartet" and a sample of the results is presented. 1 Global optimization in the context oflens design Whatever the context optimization algorithms relate to problems that take the following form: Given some configuration space with coordinates r (x1 . . x) and a merit function written asffr) find the point r whereftr) takes it lowest value. That is find the global minimum. In many cases there is also a set of auxiliary constraints that must be met so the problem statement becomes: Find the global minimum of the merit function within the region defined by E. (r) 0 j 1 2 . . . p and 0 j 1 2 . . . q.

  16. Improving Simulated Annealing by Replacing Its Variables with Game-Theoretic Utility Maximizers

    NASA Technical Reports Server (NTRS)

    Wolpert, David H.; Bandari, Esfandiar; Tumer, Kagan

    2001-01-01

    The game-theory field of Collective INtelligence (COIN) concerns the design of computer-based players engaged in a non-cooperative game so that as those players pursue their self-interests, a pre-specified global goal for the collective computational system is achieved as a side-effect. Previous implementations of COIN algorithms have outperformed conventional techniques by up to several orders of magnitude, on domains ranging from telecommunications control to optimization in congestion problems. Recent mathematical developments have revealed that these previously developed algorithms were based on only two of the three factors determining performance. Consideration of only the third factor would instead lead to conventional optimization techniques like simulated annealing that have little to do with non-cooperative games. In this paper we present an algorithm based on all three terms at once. This algorithm can be viewed as a way to modify simulated annealing by recasting it as a non-cooperative game, with each variable replaced by a player. This recasting allows us to leverage the intelligent behavior of the individual players to substantially improve the exploration step of the simulated annealing. Experiments are presented demonstrating that this recasting significantly improves simulated annealing for a model of an economic process run over an underlying small-worlds topology. Furthermore, these experiments reveal novel small-worlds phenomena, and highlight the shortcomings of conventional mechanism design in bounded rationality domains.

  17. Functionality limit of classical simulated annealing

    NASA Astrophysics Data System (ADS)

    Hasegawa, M.

    2015-09-01

    By analyzing the system dynamics in the landscape paradigm, optimization function of classical simulated annealing is reviewed on the random traveling salesman problems. The properly functioning region of the algorithm is experimentally determined in the size-time plane and the influence of its boundary on the scalability test is examined in the standard framework of this method. From both results, an empirical choice of temperature length is plausibly explained as a minimum requirement that the algorithm maintains its scalability within its functionality limit. The study exemplifies the applicability of computational physics analysis to the optimization algorithm research.

  18. Automatic Clustering Using Multi-objective Particle Swarm and Simulated Annealing

    PubMed Central

    Abubaker, Ahmad; Baharum, Adam; Alrefaei, Mahmoud

    2015-01-01

    This paper puts forward a new automatic clustering algorithm based on Multi-Objective Particle Swarm Optimization and Simulated Annealing, “MOPSOSA”. The proposed algorithm is capable of automatic clustering which is appropriate for partitioning datasets to a suitable number of clusters. MOPSOSA combines the features of the multi-objective based particle swarm optimization (PSO) and the Multi-Objective Simulated Annealing (MOSA). Three cluster validity indices were optimized simultaneously to establish the suitable number of clusters and the appropriate clustering for a dataset. The first cluster validity index is centred on Euclidean distance, the second on the point symmetry distance, and the last cluster validity index is based on short distance. A number of algorithms have been compared with the MOPSOSA algorithm in resolving clustering problems by determining the actual number of clusters and optimal clustering. Computational experiments were carried out to study fourteen artificial and five real life datasets. PMID:26132309

  19. Adaptive multiple super fast simulated annealing for stochastic microstructure reconstruction

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Ryu, Seun; Lin, Guang; Sun, Xin

    2013-01-01

    Fast image reconstruction from statistical information is critical in image fusion from multimodality chemical imaging instrumentation to create high resolution image with large domain. Stochastic methods have been used widely in image reconstruction from two point correlation function. The main challenge is to increase the efficiency of reconstruction. A novel simulated annealing method is proposed for fast solution of image reconstruction. Combining the advantage of very fast cooling schedules, dynamic adaption and parallelization, the new simulation annealing algorithm increases the efficiencies by several orders of magnitude, making the large domain image fusion feasible.

  20. Differential evolution-simulated annealing for multiple sequence alignment

    NASA Astrophysics Data System (ADS)

    Addawe, R. C.; Addawe, J. M.; Sueño, M. R. K.; Magadia, J. C.

    2017-10-01

    Multiple sequence alignments (MSA) are used in the analysis of molecular evolution and sequence structure relationships. In this paper, a hybrid algorithm, Differential Evolution - Simulated Annealing (DESA) is applied in optimizing multiple sequence alignments (MSAs) based on structural information, non-gaps percentage and totally conserved columns. DESA is a robust algorithm characterized by self-organization, mutation, crossover, and SA-like selection scheme of the strategy parameters. Here, the MSA problem is treated as a multi-objective optimization problem of the hybrid evolutionary algorithm, DESA. Thus, we name the algorithm as DESA-MSA. Simulated sequences and alignments were generated to evaluate the accuracy and efficiency of DESA-MSA using different indel sizes, sequence lengths, deletion rates and insertion rates. The proposed hybrid algorithm obtained acceptable solutions particularly for the MSA problem evaluated based on the three objectives.

  1. Stochastic Simulation of Actin Dynamics Reveals the Role of Annealing and Fragmentation

    PubMed Central

    Fass, Joseph; Pak, Chi; Bamburg, James; Mogilner, Alex

    2008-01-01

    Recent observations of F-actin dynamics call for theoretical models to interpret and understand the quantitative data. A number of existing models rely on simplifications and do not take into account F-actin fragmentation and annealing. We use Gillespie’s algorithm for stochastic simulations of the F-actin dynamics including fragmentation and annealing. The simulations vividly illustrate that fragmentation and annealing have little influence on the shape of the polymerization curve and on nucleotide profiles within filaments but drastically affect the F-actin length distribution, making it exponential. We find that recent surprising measurements of high length diffusivity at the critical concentration cannot be explained by fragmentation and annealing events unless both fragmentation rates and frequency of undetected fragmentation and annealing events are greater than previously thought. The simulations compare well with experimentally measured actin polymerization data and lend additional support to a number of existing theoretical models. PMID:18279896

  2. Combined Simulated Annealing and Genetic Algorithm Approach to Bus Network Design

    NASA Astrophysics Data System (ADS)

    Liu, Li; Olszewski, Piotr; Goh, Pong-Chai

    A new method - combined simulated annealing (SA) and genetic algorithm (GA) approach is proposed to solve the problem of bus route design and frequency setting for a given road network with fixed bus stop locations and fixed travel demand. The method involves two steps: a set of candidate routes is generated first and then the best subset of these routes is selected by the combined SA and GA procedure. SA is the main process to search for a better solution to minimize the total system cost, comprising user and operator costs. GA is used as a sub-process to generate new solutions. Bus demand assignment on two alternative paths is performed at the solution evaluation stage. The method was implemented on four theoretical grid networks of different size and a benchmark network. Several GA operators (crossover and mutation) were utilized and tested for their effectiveness. The results show that the proposed method can efficiently converge to the optimal solution on a small network but computation time increases significantly with network size. The method can also be used for other transport operation management problems.

  3. A self-organizing neural network for the traveling salesman problem that is competitive with simulated annealing.

    PubMed

    Budinich, M

    1996-02-15

    Unsupervised learning applied to an unstructured neural network can give approximate solutions to the traveling salesman problem. For 50 cities in the plane this algorithm performs like the elastic net of Durbin and Willshaw (1987) and it improves when increasing the number of cities to get better than simulated annealing for problems with more than 500 cities. In all the tests this algorithm requires a fraction of the time taken by simulated annealing.

  4. [The utility boiler low NOx combustion optimization based on ANN and simulated annealing algorithm].

    PubMed

    Zhou, Hao; Qian, Xinping; Zheng, Ligang; Weng, Anxin; Cen, Kefa

    2003-11-01

    With the developing restrict environmental protection demand, more attention was paid on the low NOx combustion optimizing technology for its cheap and easy property. In this work, field experiments on the NOx emissions characteristics of a 600 MW coal-fired boiler were carried out, on the base of the artificial neural network (ANN) modeling, the simulated annealing (SA) algorithm was employed to optimize the boiler combustion to achieve a low NOx emissions concentration, and the combustion scheme was obtained. Two sets of SA parameters were adopted to find a better SA scheme, the result show that the parameters of T0 = 50 K, alpha = 0.6 can lead to a better optimizing process. This work can give the foundation of the boiler low NOx combustion on-line control technology.

  5. A cross-disciplinary introduction to quantum annealing-based algorithms

    NASA Astrophysics Data System (ADS)

    Venegas-Andraca, Salvador E.; Cruz-Santos, William; McGeoch, Catherine; Lanzagorta, Marco

    2018-04-01

    A central goal in quantum computing is the development of quantum hardware and quantum algorithms in order to analyse challenging scientific and engineering problems. Research in quantum computation involves contributions from both physics and computer science; hence this article presents a concise introduction to basic concepts from both fields that are used in annealing-based quantum computation, an alternative to the more familiar quantum gate model. We introduce some concepts from computer science required to define difficult computational problems and to realise the potential relevance of quantum algorithms to find novel solutions to those problems. We introduce the structure of quantum annealing-based algorithms as well as two examples of this kind of algorithms for solving instances of the max-SAT and Minimum Multicut problems. An overview of the quantum annealing systems manufactured by D-Wave Systems is also presented.

  6. Sparse approximation problem: how rapid simulated annealing succeeds and fails

    NASA Astrophysics Data System (ADS)

    Obuchi, Tomoyuki; Kabashima, Yoshiyuki

    2016-03-01

    Information processing techniques based on sparseness have been actively studied in several disciplines. Among them, a mathematical framework to approximately express a given dataset by a combination of a small number of basis vectors of an overcomplete basis is termed the sparse approximation. In this paper, we apply simulated annealing, a metaheuristic algorithm for general optimization problems, to sparse approximation in the situation where the given data have a planted sparse representation and noise is present. The result in the noiseless case shows that our simulated annealing works well in a reasonable parameter region: the planted solution is found fairly rapidly. This is true even in the case where a common relaxation of the sparse approximation problem, the G-relaxation, is ineffective. On the other hand, when the dimensionality of the data is close to the number of non-zero components, another metastable state emerges, and our algorithm fails to find the planted solution. This phenomenon is associated with a first-order phase transition. In the case of very strong noise, it is no longer meaningful to search for the planted solution. In this situation, our algorithm determines a solution with close-to-minimum distortion fairly quickly.

  7. Solving the patient zero inverse problem by using generalized simulated annealing

    NASA Astrophysics Data System (ADS)

    Menin, Olavo H.; Bauch, Chris T.

    2018-01-01

    Identifying patient zero - the initially infected source of a given outbreak - is an important step in epidemiological investigations of both existing and emerging infectious diseases. Here, the use of the Generalized Simulated Annealing algorithm (GSA) to solve the inverse problem of finding the source of an outbreak is studied. The classical disease natural histories susceptible-infected (SI), susceptible-infected-susceptible (SIS), susceptible-infected-recovered (SIR) and susceptible-infected-recovered-susceptible (SIRS) in a regular lattice are addressed. Both the position of patient zero and its time of infection are considered unknown. The algorithm performance with respect to the generalization parameter q˜v and the fraction ρ of infected nodes for whom infection was ascertained is assessed. Numerical experiments show the algorithm is able to retrieve the epidemic source with good accuracy, even when ρ is small, but present no evidence to support that GSA performs better than its classical version. Our results suggest that simulated annealing could be a helpful tool for identifying patient zero in an outbreak where not all cases can be ascertained.

  8. A memory structure adapted simulated annealing algorithm for a green vehicle routing problem.

    PubMed

    Küçükoğlu, İlker; Ene, Seval; Aksoy, Aslı; Öztürk, Nursel

    2015-03-01

    Currently, reduction of carbon dioxide (CO2) emissions and fuel consumption has become a critical environmental problem and has attracted the attention of both academia and the industrial sector. Government regulations and customer demands are making environmental responsibility an increasingly important factor in overall supply chain operations. Within these operations, transportation has the most hazardous effects on the environment, i.e., CO2 emissions, fuel consumption, noise and toxic effects on the ecosystem. This study aims to construct vehicle routes with time windows that minimize the total fuel consumption and CO2 emissions. The green vehicle routing problem with time windows (G-VRPTW) is formulated using a mixed integer linear programming model. A memory structure adapted simulated annealing (MSA-SA) meta-heuristic algorithm is constructed due to the high complexity of the proposed problem and long solution times for practical applications. The proposed models are integrated with a fuel consumption and CO2 emissions calculation algorithm that considers the vehicle technical specifications, vehicle load, and transportation distance in a green supply chain environment. The proposed models are validated using well-known instances with different numbers of customers. The computational results indicate that the MSA-SA heuristic is capable of obtaining good G-VRPTW solutions within a reasonable amount of time by providing reductions in fuel consumption and CO2 emissions.

  9. An adaptive approach to the physical annealing strategy for simulated annealing

    NASA Astrophysics Data System (ADS)

    Hasegawa, M.

    2013-02-01

    A new and reasonable method for adaptive implementation of simulated annealing (SA) is studied on two types of random traveling salesman problems. The idea is based on the previous finding on the search characteristics of the threshold algorithms, that is, the primary role of the relaxation dynamics in their finite-time optimization process. It is shown that the effective temperature for optimization can be predicted from the system's behavior analogous to the stabilization phenomenon occurring in the heating process starting from a quenched solution. The subsequent slow cooling near the predicted point draws out the inherent optimizing ability of finite-time SA in more straightforward manner than the conventional adaptive approach.

  10. Instantons in Quantum Annealing: Thermally Assisted Tunneling Vs Quantum Monte Carlo Simulations

    NASA Technical Reports Server (NTRS)

    Jiang, Zhang; Smelyanskiy, Vadim N.; Boixo, Sergio; Isakov, Sergei V.; Neven, Hartmut; Mazzola, Guglielmo; Troyer, Matthias

    2015-01-01

    Recent numerical result (arXiv:1512.02206) from Google suggested that the D-Wave quantum annealer may have an asymptotic speed-up than simulated annealing, however, the asymptotic advantage disappears when it is compared to quantum Monte Carlo (a classical algorithm despite its name). We show analytically that the asymptotic scaling of quantum tunneling is exactly the same as the escape rate in quantum Monte Carlo for a class of problems. Thus, the Google result might be explained in our framework. We also found that the transition state in quantum Monte Carlo corresponds to the instanton solution in quantum tunneling problems, which is observed in numerical simulations.

  11. Discrete-State Simulated Annealing For Traveling-Wave Tube Slow-Wave Circuit Optimization

    NASA Technical Reports Server (NTRS)

    Wilson, Jeffrey D.; Bulson, Brian A.; Kory, Carol L.; Williams, W. Dan (Technical Monitor)

    2001-01-01

    Algorithms based on the global optimization technique of simulated annealing (SA) have proven useful in designing traveling-wave tube (TWT) slow-wave circuits for high RF power efficiency. The characteristic of SA that enables it to determine a globally optimized solution is its ability to accept non-improving moves in a controlled manner. In the initial stages of the optimization, the algorithm moves freely through configuration space, accepting most of the proposed designs. This freedom of movement allows non-intuitive designs to be explored rather than restricting the optimization to local improvement upon the initial configuration. As the optimization proceeds, the rate of acceptance of non-improving moves is gradually reduced until the algorithm converges to the optimized solution. The rate at which the freedom of movement is decreased is known as the annealing or cooling schedule of the SA algorithm. The main disadvantage of SA is that there is not a rigorous theoretical foundation for determining the parameters of the cooling schedule. The choice of these parameters is highly problem dependent and the designer needs to experiment in order to determine values that will provide a good optimization in a reasonable amount of computational time. This experimentation can absorb a large amount of time especially when the algorithm is being applied to a new type of design. In order to eliminate this disadvantage, a variation of SA known as discrete-state simulated annealing (DSSA), was recently developed. DSSA provides the theoretical foundation for a generic cooling schedule which is problem independent, Results of similar quality to SA can be obtained, but without the extra computational time required to tune the cooling parameters. Two algorithm variations based on DSSA were developed and programmed into a Microsoft Excel spreadsheet graphical user interface (GUI) to the two-dimensional nonlinear multisignal helix traveling-wave amplifier analysis program TWA3

  12. An Introduction to Simulated Annealing

    ERIC Educational Resources Information Center

    Albright, Brian

    2007-01-01

    An attempt to model the physical process of annealing lead to the development of a type of combinatorial optimization algorithm that takes on the problem of getting trapped in a local minimum. The author presents a Microsoft Excel spreadsheet that illustrates how this works.

  13. Analysis and optimization of population annealing

    NASA Astrophysics Data System (ADS)

    Amey, Christopher; Machta, Jonathan

    2018-03-01

    Population annealing is an easily parallelizable sequential Monte Carlo algorithm that is well suited for simulating the equilibrium properties of systems with rough free-energy landscapes. In this work we seek to understand and improve the performance of population annealing. We derive several useful relations between quantities that describe the performance of population annealing and use these relations to suggest methods to optimize the algorithm. These optimization methods were tested by performing large-scale simulations of the three-dimensional (3D) Edwards-Anderson (Ising) spin glass and measuring several observables. The optimization methods were found to substantially decrease the amount of computational work necessary as compared to previously used, unoptimized versions of population annealing. We also obtain more accurate values of several important observables for the 3D Edwards-Anderson model.

  14. Conformational Space Annealing explained: A general optimization algorithm, with diverse applications

    NASA Astrophysics Data System (ADS)

    Joung, InSuk; Kim, Jong Yun; Gross, Steven P.; Joo, Keehyoung; Lee, Jooyoung

    2018-02-01

    Many problems in science and engineering can be formulated as optimization problems. One way to solve these problems is to develop tailored problem-specific approaches. As such development is challenging, an alternative is to develop good generally-applicable algorithms. Such algorithms are easy to apply, typically function robustly, and reduce development time. Here we provide a description for one such algorithm called Conformational Space Annealing (CSA) along with its python version, PyCSA. We previously applied it to many optimization problems including protein structure prediction and graph community detection. To demonstrate its utility, we have applied PyCSA to two continuous test functions, namely Ackley and Eggholder functions. In addition, in order to provide complete generality of PyCSA to any types of an objective function, we demonstrate the way PyCSA can be applied to a discrete objective function, namely a parameter optimization problem. Based on the benchmarking results of the three problems, the performance of CSA is shown to be better than or similar to the most popular optimization method, simulated annealing. For continuous objective functions, we found that, L-BFGS-B was the best performing local optimization method, while for a discrete objective function Nelder-Mead was the best. The current version of PyCSA can be run in parallel at the coarse grained level by calculating multiple independent local optimizations separately. The source code of PyCSA is available from http://lee.kias.re.kr.

  15. Optimization of seasonal ARIMA models using differential evolution - simulated annealing (DESA) algorithm in forecasting dengue cases in Baguio City

    NASA Astrophysics Data System (ADS)

    Addawe, Rizavel C.; Addawe, Joel M.; Magadia, Joselito C.

    2016-10-01

    Accurate forecasting of dengue cases would significantly improve epidemic prevention and control capabilities. This paper attempts to provide useful models in forecasting dengue epidemic specific to the young and adult population of Baguio City. To capture the seasonal variations in dengue incidence, this paper develops a robust modeling approach to identify and estimate seasonal autoregressive integrated moving average (SARIMA) models in the presence of additive outliers. Since the least squares estimators are not robust in the presence of outliers, we suggest a robust estimation based on winsorized and reweighted least squares estimators. A hybrid algorithm, Differential Evolution - Simulated Annealing (DESA), is used to identify and estimate the parameters of the optimal SARIMA model. The method is applied to the monthly reported dengue cases in Baguio City, Philippines.

  16. Adaptive MANET multipath routing algorithm based on the simulated annealing approach.

    PubMed

    Kim, Sungwook

    2014-01-01

    Mobile ad hoc network represents a system of wireless mobile nodes that can freely and dynamically self-organize network topologies without any preexisting communication infrastructure. Due to characteristics like temporary topology and absence of centralized authority, routing is one of the major issues in ad hoc networks. In this paper, a new multipath routing scheme is proposed by employing simulated annealing approach. The proposed metaheuristic approach can achieve greater and reciprocal advantages in a hostile dynamic real world network situation. Therefore, the proposed routing scheme is a powerful method for finding an effective solution into the conflict mobile ad hoc network routing problem. Simulation results indicate that the proposed paradigm adapts best to the variation of dynamic network situations. The average remaining energy, network throughput, packet loss probability, and traffic load distribution are improved by about 10%, 10%, 5%, and 10%, respectively, more than the existing schemes.

  17. A hybrid genetic-simulated annealing algorithm for the location-inventory-routing problem considering returns under e-supply chain environment.

    PubMed

    Li, Yanhui; Guo, Hao; Wang, Lin; Fu, Jing

    2013-01-01

    Facility location, inventory control, and vehicle routes scheduling are critical and highly related problems in the design of logistics system for e-business. Meanwhile, the return ratio in Internet sales was significantly higher than in the traditional business. Many of returned merchandise have no quality defects, which can reenter sales channels just after a simple repackaging process. Focusing on the existing problem in e-commerce logistics system, we formulate a location-inventory-routing problem model with no quality defects returns. To solve this NP-hard problem, an effective hybrid genetic simulated annealing algorithm (HGSAA) is proposed. Results of numerical examples show that HGSAA outperforms GA on computing time, optimal solution, and computing stability. The proposed model is very useful to help managers make the right decisions under e-supply chain environment.

  18. A Hybrid Genetic-Simulated Annealing Algorithm for the Location-Inventory-Routing Problem Considering Returns under E-Supply Chain Environment

    PubMed Central

    Guo, Hao; Fu, Jing

    2013-01-01

    Facility location, inventory control, and vehicle routes scheduling are critical and highly related problems in the design of logistics system for e-business. Meanwhile, the return ratio in Internet sales was significantly higher than in the traditional business. Many of returned merchandise have no quality defects, which can reenter sales channels just after a simple repackaging process. Focusing on the existing problem in e-commerce logistics system, we formulate a location-inventory-routing problem model with no quality defects returns. To solve this NP-hard problem, an effective hybrid genetic simulated annealing algorithm (HGSAA) is proposed. Results of numerical examples show that HGSAA outperforms GA on computing time, optimal solution, and computing stability. The proposed model is very useful to help managers make the right decisions under e-supply chain environment. PMID:24489489

  19. Multiphase Simulated Annealing Based on Boltzmann and Bose-Einstein Distribution Applied to Protein Folding Problem.

    PubMed

    Frausto-Solis, Juan; Liñán-García, Ernesto; Sánchez-Hernández, Juan Paulo; González-Barbosa, J Javier; González-Flores, Carlos; Castilla-Valdez, Guadalupe

    2016-01-01

    A new hybrid Multiphase Simulated Annealing Algorithm using Boltzmann and Bose-Einstein distributions (MPSABBE) is proposed. MPSABBE was designed for solving the Protein Folding Problem (PFP) instances. This new approach has four phases: (i) Multiquenching Phase (MQP), (ii) Boltzmann Annealing Phase (BAP), (iii) Bose-Einstein Annealing Phase (BEAP), and (iv) Dynamical Equilibrium Phase (DEP). BAP and BEAP are simulated annealing searching procedures based on Boltzmann and Bose-Einstein distributions, respectively. DEP is also a simulated annealing search procedure, which is applied at the final temperature of the fourth phase, which can be seen as a second Bose-Einstein phase. MQP is a search process that ranges from extremely high to high temperatures, applying a very fast cooling process, and is not very restrictive to accept new solutions. However, BAP and BEAP range from high to low and from low to very low temperatures, respectively. They are more restrictive for accepting new solutions. DEP uses a particular heuristic to detect the stochastic equilibrium by applying a least squares method during its execution. MPSABBE parameters are tuned with an analytical method, which considers the maximal and minimal deterioration of problem instances. MPSABBE was tested with several instances of PFP, showing that the use of both distributions is better than using only the Boltzmann distribution on the classical SA.

  20. Simulated annealing two-point ray tracing

    NASA Astrophysics Data System (ADS)

    Velis, Danilo R.; Ulrych, Tadeusz J.

    We present a new method for solving the two-point seismic ray tracing problem based on Fermat's principle. The algorithm overcomes some well known difficulties that arise in standard ray shooting and bending methods. Problems related to: (1) the selection of new take-off angles, and (2) local minima in multipathing cases, are overcome by using an efficient simulated annealing (SA) algorithm. At each iteration, the ray is propagated from the source by solving a standard initial value problem. The last portion of the raypath is then forced to pass through the receiver. Using SA, the total traveltime is then globally minimized by obtaining the initial conditions that produce the absolute minimum path. The procedure is suitable for tracing rays through 2D complex structures, although it can be extended to deal with 3D velocity media. Not only direct waves, but also reflected and head-waves can be incorporated in the scheme. One important advantage is its simplicity, in as much as any available or user-preferred initial value solver system can be used. A number of clarifying examples of multipathing in 2D media are examined.

  1. Medical image diagnoses by artificial neural networks with image correlation, wavelet transform, simulated annealing

    NASA Astrophysics Data System (ADS)

    Szu, Harold H.

    1993-09-01

    Classical artificial neural networks (ANN) and neurocomputing are reviewed for implementing a real time medical image diagnosis. An algorithm known as the self-reference matched filter that emulates the spatio-temporal integration ability of the human visual system might be utilized for multi-frame processing of medical imaging data. A Cauchy machine, implementing a fast simulated annealing schedule, can determine the degree of abnormality by the degree of orthogonality between the patient imagery and the class of features of healthy persons. An automatic inspection process based on multiple modality image sequences is simulated by incorporating the following new developments: (1) 1-D space-filling Peano curves to preserve the 2-D neighborhood pixels' relationship; (2) fast simulated Cauchy annealing for the global optimization of self-feature extraction; and (3) a mini-max energy function for the intra-inter cluster-segregation respectively useful for top-down ANN designs.

  2. Population Annealing Monte Carlo for Frustrated Systems

    NASA Astrophysics Data System (ADS)

    Amey, Christopher; Machta, Jonathan

    Population annealing is a sequential Monte Carlo algorithm that efficiently simulates equilibrium systems with rough free energy landscapes such as spin glasses and glassy fluids. A large population of configurations is initially thermalized at high temperature and then cooled to low temperature according to an annealing schedule. The population is kept in thermal equilibrium at every annealing step via resampling configurations according to their Boltzmann weights. Population annealing is comparable to parallel tempering in terms of efficiency, but has several distinct and useful features. In this talk I will give an introduction to population annealing and present recent progress in understanding its equilibration properties and optimizing it for spin glasses. Results from large-scale population annealing simulations for the Ising spin glass in 3D and 4D will be presented. NSF Grant DMR-1507506.

  3. Optimization of cladding parameters for resisting corrosion on low carbon steels using simulated annealing algorithm

    NASA Astrophysics Data System (ADS)

    Balan, A. V.; Shivasankaran, N.; Magibalan, S.

    2018-04-01

    Low carbon steels used in chemical industries are frequently affected by corrosion. Cladding is a surfacing process used for depositing a thick layer of filler metal in a highly corrosive materials to achieve corrosion resistance. Flux cored arc welding (FCAW) is preferred in cladding process due to its augmented efficiency and higher deposition rate. In this cladding process, the effect of corrosion can be minimized by controlling the output responses such as minimizing dilution, penetration and maximizing bead width, reinforcement and ferrite number. This paper deals with the multi-objective optimization of flux cored arc welding responses by controlling the process parameters such as wire feed rate, welding speed, Nozzle to plate distance, welding gun angle for super duplex stainless steel material using simulated annealing technique. Regression equation has been developed and validated using ANOVA technique. The multi-objective optimization of weld bead parameters was carried out using simulated annealing to obtain optimum bead geometry for reducing corrosion. The potentiodynamic polarization test reveals the balanced formation of fine particles of ferrite and autenite content with desensitized nature of the microstructure in the optimized clad bead.

  4. High-dose-rate prostate brachytherapy inverse planning on dose-volume criteria by simulated annealing.

    PubMed

    Deist, T M; Gorissen, B L

    2016-02-07

    High-dose-rate brachytherapy is a tumor treatment method where a highly radioactive source is brought in close proximity to the tumor. In this paper we develop a simulated annealing algorithm to optimize the dwell times at preselected dwell positions to maximize tumor coverage under dose-volume constraints on the organs at risk. Compared to existing algorithms, our algorithm has advantages in terms of speed and objective value and does not require an expensive general purpose solver. Its success mainly depends on exploiting the efficiency of matrix multiplication and a careful selection of the neighboring states. In this paper we outline its details and make an in-depth comparison with existing methods using real patient data.

  5. An adaptive evolutionary multi-objective approach based on simulated annealing.

    PubMed

    Li, H; Landa-Silva, D

    2011-01-01

    A multi-objective optimization problem can be solved by decomposing it into one or more single objective subproblems in some multi-objective metaheuristic algorithms. Each subproblem corresponds to one weighted aggregation function. For example, MOEA/D is an evolutionary multi-objective optimization (EMO) algorithm that attempts to optimize multiple subproblems simultaneously by evolving a population of solutions. However, the performance of MOEA/D highly depends on the initial setting and diversity of the weight vectors. In this paper, we present an improved version of MOEA/D, called EMOSA, which incorporates an advanced local search technique (simulated annealing) and adapts the search directions (weight vectors) corresponding to various subproblems. In EMOSA, the weight vector of each subproblem is adaptively modified at the lowest temperature in order to diversify the search toward the unexplored parts of the Pareto-optimal front. Our computational results show that EMOSA outperforms six other well established multi-objective metaheuristic algorithms on both the (constrained) multi-objective knapsack problem and the (unconstrained) multi-objective traveling salesman problem. Moreover, the effects of the main algorithmic components and parameter sensitivities on the search performance of EMOSA are experimentally investigated.

  6. Parameter discovery in stochastic biological models using simulated annealing and statistical model checking.

    PubMed

    Hussain, Faraz; Jha, Sumit K; Jha, Susmit; Langmead, Christopher J

    2014-01-01

    Stochastic models are increasingly used to study the behaviour of biochemical systems. While the structure of such models is often readily available from first principles, unknown quantitative features of the model are incorporated into the model as parameters. Algorithmic discovery of parameter values from experimentally observed facts remains a challenge for the computational systems biology community. We present a new parameter discovery algorithm that uses simulated annealing, sequential hypothesis testing, and statistical model checking to learn the parameters in a stochastic model. We apply our technique to a model of glucose and insulin metabolism used for in-silico validation of artificial pancreata and demonstrate its effectiveness by developing parallel CUDA-based implementation for parameter synthesis in this model.

  7. A Simulated Annealing based Optimization Algorithm for Automatic Variogram Model Fitting

    NASA Astrophysics Data System (ADS)

    Soltani-Mohammadi, Saeed; Safa, Mohammad

    2016-09-01

    Fitting a theoretical model to an experimental variogram is an important issue in geostatistical studies because if the variogram model parameters are tainted with uncertainty, the latter will spread in the results of estimations and simulations. Although the most popular fitting method is fitting by eye, in some cases use is made of the automatic fitting method on the basis of putting together the geostatistical principles and optimization techniques to: 1) provide a basic model to improve fitting by eye, 2) fit a model to a large number of experimental variograms in a short time, and 3) incorporate the variogram related uncertainty in the model fitting. Effort has been made in this paper to improve the quality of the fitted model by improving the popular objective function (weighted least squares) in the automatic fitting. Also, since the variogram model function (£) and number of structures (m) too affect the model quality, a program has been provided in the MATLAB software that can present optimum nested variogram models using the simulated annealing method. Finally, to select the most desirable model from among the single/multi-structured fitted models, use has been made of the cross-validation method, and the best model has been introduced to the user as the output. In order to check the capability of the proposed objective function and the procedure, 3 case studies have been presented.

  8. a New Multimodal Multi-Criteria Route Planning Model by Integrating a Fuzzy-Ahp Weighting Method and a Simulated Annealing Algorithm

    NASA Astrophysics Data System (ADS)

    Ghaderi, F.; Pahlavani, P.

    2015-12-01

    A multimodal multi-criteria route planning (MMRP) system provides an optimal multimodal route from an origin point to a destination point considering two or more criteria in a way this route can be a combination of public and private transportation modes. In this paper, the simulate annealing (SA) and the fuzzy analytical hierarchy process (fuzzy AHP) were combined in order to find this route. In this regard, firstly, the effective criteria that are significant for users in their trip were determined. Then the weight of each criterion was calculated using the fuzzy AHP weighting method. The most important characteristic of this weighting method is the use of fuzzy numbers that aids the users to consider their uncertainty in pairwise comparison of criteria. After determining the criteria weights, the proposed SA algorithm were used for determining an optimal route from an origin to a destination. One of the most important problems in a meta-heuristic algorithm is trapping in local minima. In this study, five transportation modes, including subway, bus rapid transit (BRT), taxi, walking, and bus were considered for moving between nodes. Also, the fare, the time, the user's bother, and the length of the path were considered as effective criteria for solving the problem. The proposed model was implemented in an area in centre of Tehran in a GUI MATLAB programming language. The results showed a high efficiency and speed of the proposed algorithm that support our analyses.

  9. Simulated annealing in orbital flight planning

    NASA Technical Reports Server (NTRS)

    Soller, Jeffrey

    1990-01-01

    Simulated annealing is used to solve a minimum fuel trajectory problem in the space station environment. The environment is unique because the space station will define the first true multivehicle environment in space. The optimization yields surfaces which are potentially complex, with multiple local minima. Because of the likelihood of these local minima, descent techniques are unable to offer robust solutions. Other deterministic optimization techniques were explored without success. The simulated annealing optimization is capable of identifying a minimum-fuel, two-burn trajectory subject to four constraints. Furthermore, the computational efforts involved in the optimization are such that missions could be planned on board the space station. Potential applications could include the on-site planning of rendezvous with a target craft of the emergency rescue of an astronaut. Future research will include multiwaypoint maneuvers, using a knowledge base to guide the optimization.

  10. Quantum versus simulated annealing in wireless interference network optimization.

    PubMed

    Wang, Chi; Chen, Huo; Jonckheere, Edmond

    2016-05-16

    Quantum annealing (QA) serves as a specialized optimizer that is able to solve many NP-hard problems and that is believed to have a theoretical advantage over simulated annealing (SA) via quantum tunneling. With the introduction of the D-Wave programmable quantum annealer, a considerable amount of effort has been devoted to detect and quantify quantum speedup. While the debate over speedup remains inconclusive as of now, instead of attempting to show general quantum advantage, here, we focus on a novel real-world application of D-Wave in wireless networking-more specifically, the scheduling of the activation of the air-links for maximum throughput subject to interference avoidance near network nodes. In addition, D-Wave implementation is made error insensitive by a novel Hamiltonian extra penalty weight adjustment that enlarges the gap and substantially reduces the occurrence of interference violations resulting from inevitable spin bias and coupling errors. The major result of this paper is that quantum annealing benefits more than simulated annealing from this gap expansion process, both in terms of ST99 speedup and network queue occupancy. It is the hope that this could become a real-word application niche where potential benefits of quantum annealing could be objectively assessed.

  11. Quantum versus simulated annealing in wireless interference network optimization

    PubMed Central

    Wang, Chi; Chen, Huo; Jonckheere, Edmond

    2016-01-01

    Quantum annealing (QA) serves as a specialized optimizer that is able to solve many NP-hard problems and that is believed to have a theoretical advantage over simulated annealing (SA) via quantum tunneling. With the introduction of the D-Wave programmable quantum annealer, a considerable amount of effort has been devoted to detect and quantify quantum speedup. While the debate over speedup remains inconclusive as of now, instead of attempting to show general quantum advantage, here, we focus on a novel real-world application of D-Wave in wireless networking—more specifically, the scheduling of the activation of the air-links for maximum throughput subject to interference avoidance near network nodes. In addition, D-Wave implementation is made error insensitive by a novel Hamiltonian extra penalty weight adjustment that enlarges the gap and substantially reduces the occurrence of interference violations resulting from inevitable spin bias and coupling errors. The major result of this paper is that quantum annealing benefits more than simulated annealing from this gap expansion process, both in terms of ST99 speedup and network queue occupancy. It is the hope that this could become a real-word application niche where potential benefits of quantum annealing could be objectively assessed. PMID:27181056

  12. Quantum versus simulated annealing in wireless interference network optimization

    NASA Astrophysics Data System (ADS)

    Wang, Chi; Chen, Huo; Jonckheere, Edmond

    2016-05-01

    Quantum annealing (QA) serves as a specialized optimizer that is able to solve many NP-hard problems and that is believed to have a theoretical advantage over simulated annealing (SA) via quantum tunneling. With the introduction of the D-Wave programmable quantum annealer, a considerable amount of effort has been devoted to detect and quantify quantum speedup. While the debate over speedup remains inconclusive as of now, instead of attempting to show general quantum advantage, here, we focus on a novel real-world application of D-Wave in wireless networking—more specifically, the scheduling of the activation of the air-links for maximum throughput subject to interference avoidance near network nodes. In addition, D-Wave implementation is made error insensitive by a novel Hamiltonian extra penalty weight adjustment that enlarges the gap and substantially reduces the occurrence of interference violations resulting from inevitable spin bias and coupling errors. The major result of this paper is that quantum annealing benefits more than simulated annealing from this gap expansion process, both in terms of ST99 speedup and network queue occupancy. It is the hope that this could become a real-word application niche where potential benefits of quantum annealing could be objectively assessed.

  13. Comparison of optimization algorithms in intensity-modulated radiation therapy planning

    NASA Astrophysics Data System (ADS)

    Kendrick, Rachel

    Intensity-modulated radiation therapy is used to better conform the radiation dose to the target, which includes avoiding healthy tissue. Planning programs employ optimization methods to search for the best fluence of each photon beam, and therefore to create the best treatment plan. The Computational Environment for Radiotherapy Research (CERR), a program written in MATLAB, was used to examine some commonly-used algorithms for one 5-beam plan. Algorithms include the genetic algorithm, quadratic programming, pattern search, constrained nonlinear optimization, simulated annealing, the optimization method used in Varian EclipseTM, and some hybrids of these. Quadratic programing, simulated annealing, and a quadratic/simulated annealing hybrid were also separately compared using different prescription doses. The results of each dose-volume histogram as well as the visual dose color wash were used to compare the plans. CERR's built-in quadratic programming provided the best overall plan, but avoidance of the organ-at-risk was rivaled by other programs. Hybrids of quadratic programming with some of these algorithms seems to suggest the possibility of better planning programs, as shown by the improved quadratic/simulated annealing plan when compared to the simulated annealing algorithm alone. Further experimentation will be done to improve cost functions and computational time.

  14. Simulated annealing with restart strategy for the blood pickup routing problem

    NASA Astrophysics Data System (ADS)

    Yu, V. F.; Iswari, T.; Normasari, N. M. E.; Asih, A. M. S.; Ting, H.

    2018-04-01

    This study develops a simulated annealing heuristic with restart strategy (SA_RS) for solving the blood pickup routing problem (BPRP). BPRP minimizes the total length of the routes for blood bag collection between a blood bank and a set of donation sites, each associated with a time window constraint that must be observed. The proposed SA_RS is implemented in C++ and tested on benchmark instances of the vehicle routing problem with time windows to verify its performance. The algorithm is then tested on some newly generated BPRP instances and the results are compared with those obtained by CPLEX. Experimental results show that the proposed SA_RS heuristic effectively solves BPRP.

  15. Hybrid simulated annealing and its application to optimization of hidden Markov models for visual speech recognition.

    PubMed

    Lee, Jong-Seok; Park, Cheol Hoon

    2010-08-01

    We propose a novel stochastic optimization algorithm, hybrid simulated annealing (SA), to train hidden Markov models (HMMs) for visual speech recognition. In our algorithm, SA is combined with a local optimization operator that substitutes a better solution for the current one to improve the convergence speed and the quality of solutions. We mathematically prove that the sequence of the objective values converges in probability to the global optimum in the algorithm. The algorithm is applied to train HMMs that are used as visual speech recognizers. While the popular training method of HMMs, the expectation-maximization algorithm, achieves only local optima in the parameter space, the proposed method can perform global optimization of the parameters of HMMs and thereby obtain solutions yielding improved recognition performance. The superiority of the proposed algorithm to the conventional ones is demonstrated via isolated word recognition experiments.

  16. Simulated annealing model of acupuncture

    NASA Astrophysics Data System (ADS)

    Shang, Charles; Szu, Harold

    2015-05-01

    The growth control singularity model suggests that acupuncture points (acupoints) originate from organizers in embryogenesis. Organizers are singular points in growth control. Acupuncture can cause perturbation of a system with effects similar to simulated annealing. In clinical trial, the goal of a treatment is to relieve certain disorder which corresponds to reaching certain local optimum in simulated annealing. The self-organizing effect of the system is limited and related to the person's general health and age. Perturbation at acupoints can lead a stronger local excitation (analogous to higher annealing temperature) compared to perturbation at non-singular points (placebo control points). Such difference diminishes as the number of perturbed points increases due to the wider distribution of the limited self-organizing activity. This model explains the following facts from systematic reviews of acupuncture trials: 1. Properly chosen single acupoint treatment for certain disorder can lead to highly repeatable efficacy above placebo 2. When multiple acupoints are used, the result can be highly repeatable if the patients are relatively healthy and young but are usually mixed if the patients are old, frail and have multiple disorders at the same time as the number of local optima or comorbidities increases. 3. As number of acupoints used increases, the efficacy difference between sham and real acupuncture often diminishes. It predicted that the efficacy of acupuncture is negatively correlated to the disease chronicity, severity and patient's age. This is the first biological - physical model of acupuncture which can predict and guide clinical acupuncture research.

  17. Vehicle routing problem with time windows using natural inspired algorithms

    NASA Astrophysics Data System (ADS)

    Pratiwi, A. B.; Pratama, A.; Sa’diyah, I.; Suprajitno, H.

    2018-03-01

    Process of distribution of goods needs a strategy to make the total cost spent for operational activities minimized. But there are several constrains have to be satisfied which are the capacity of the vehicles and the service time of the customers. This Vehicle Routing Problem with Time Windows (VRPTW) gives complex constrains problem. This paper proposes natural inspired algorithms for dealing with constrains of VRPTW which involves Bat Algorithm and Cat Swarm Optimization. Bat Algorithm is being hybrid with Simulated Annealing, the worst solution of Bat Algorithm is replaced by the solution from Simulated Annealing. Algorithm which is based on behavior of cats, Cat Swarm Optimization, is improved using Crow Search Algorithm to make simplier and faster convergence. From the computational result, these algorithms give good performances in finding the minimized total distance. Higher number of population causes better computational performance. The improved Cat Swarm Optimization with Crow Search gives better performance than the hybridization of Bat Algorithm and Simulated Annealing in dealing with big data.

  18. Angular filter refractometry analysis using simulated annealing.

    PubMed

    Angland, P; Haberberger, D; Ivancic, S T; Froula, D H

    2017-10-01

    Angular filter refractometry (AFR) is a novel technique used to characterize the density profiles of laser-produced, long-scale-length plasmas [Haberberger et al., Phys. Plasmas 21, 056304 (2014)]. A new method of analysis for AFR images was developed using an annealing algorithm to iteratively converge upon a solution. A synthetic AFR image is constructed by a user-defined density profile described by eight parameters, and the algorithm systematically alters the parameters until the comparison is optimized. The optimization and statistical uncertainty calculation is based on the minimization of the χ 2 test statistic. The algorithm was successfully applied to experimental data of plasma expanding from a flat, laser-irradiated target, resulting in an average uncertainty in the density profile of 5%-20% in the region of interest.

  19. Simulated annealing with probabilistic analysis for solving traveling salesman problems

    NASA Astrophysics Data System (ADS)

    Hong, Pei-Yee; Lim, Yai-Fung; Ramli, Razamin; Khalid, Ruzelan

    2013-09-01

    Simulated Annealing (SA) is a widely used meta-heuristic that was inspired from the annealing process of recrystallization of metals. Therefore, the efficiency of SA is highly affected by the annealing schedule. As a result, in this paper, we presented an empirical work to provide a comparable annealing schedule to solve symmetric traveling salesman problems (TSP). Randomized complete block design is also used in this study. The results show that different parameters do affect the efficiency of SA and thus, we propose the best found annealing schedule based on the Post Hoc test. SA was tested on seven selected benchmarked problems of symmetric TSP with the proposed annealing schedule. The performance of SA was evaluated empirically alongside with benchmark solutions and simple analysis to validate the quality of solutions. Computational results show that the proposed annealing schedule provides a good quality of solution.

  20. Minimizing distortion and internal forces in truss structures by simulated annealing

    NASA Technical Reports Server (NTRS)

    Kincaid, Rex K.

    1989-01-01

    Inaccuracies in the length of members and the diameters of joints of large truss reflector backup structures may produce unacceptable levels of surface distortion and member forces. However, if the member lengths and joint diameters can be measured accurately it is possible to configure the members and joints so that root-mean-square (rms) surface error and/or rms member forces is minimized. Following Greene and Haftka (1989) it is assumed that the force vector f is linearly proportional to the member length errors e(sub M) of dimension NMEMB (the number of members) and joint errors e(sub J) of dimension NJOINT (the number of joints), and that the best-fit displacement vector d is a linear function of f. Let NNODES denote the number of positions on the surface of the truss where error influences are measured. The solution of the problem is discussed. To classify, this problem was compared to a similar combinatorial optimization problem. In particular, when only the member length errors are considered, minimizing d(sup 2)(sub rms) is equivalent to the quadratic assignment problem. The quadratic assignment problem is a well known NP-complete problem in operations research literature. Hence minimizing d(sup 2)(sub rms) is is also an NP-complete problem. The focus of the research is the development of a simulated annealing algorithm to reduce d(sup 2)(sub rms). The plausibility of this technique is its recent success on a variety of NP-complete combinatorial optimization problems including the quadratic assignment problem. A physical analogy for simulated annealing is the way liquids freeze and crystallize. All computational experiments were done on a MicroVAX. The two interchange heuristic is very fast but produces widely varying results. The two and three interchange heuristic provides less variability in the final objective function values but runs much more slowly. Simulated annealing produced the best objective function values for every starting configuration and

  1. The GIS map coloring support decision-making system based on case-based reasoning and simulated annealing algorithm

    NASA Astrophysics Data System (ADS)

    Deng, Shuang; Xiang, Wenting; Tian, Yangge

    2009-10-01

    Map coloring is a hard task even to the experienced map experts. In the GIS project, usually need to color map according to the customer, which make the work more complex. With the development of GIS, more and more programmers join the project team, which lack the training of cartology, their coloring map are harder to meet the requirements of customer. From the experience, customers with similar background usually have similar tastes for coloring map. So, we developed a GIS color scheme decision-making system which can select color schemes of similar customers from case base for customers to select and adjust. The system is a BS/CS mixed system, the client side use JSP and make it possible for the system developers to go on remote calling of the colors scheme cases in the database server and communicate with customers. Different with general case-based reasoning, even the customers are very similar, their selection may have difference, it is hard to provide a "best" option. So, we select the Simulated Annealing Algorithm (SAA) to arrange the emergence order of different color schemes. Customers can also dynamically adjust certain features colors based on existing case. The result shows that the system can facilitate the communication between the designers and the customers and improve the quality and efficiency of coloring map.

  2. A simulated annealing approach for redesigning a warehouse network problem

    NASA Astrophysics Data System (ADS)

    Khairuddin, Rozieana; Marlizawati Zainuddin, Zaitul; Jiun, Gan Jia

    2017-09-01

    Now a day, several companies consider downsizing their distribution networks in ways that involve consolidation or phase-out of some of their current warehousing facilities due to the increasing competition, mounting cost pressure and taking advantage on the economies of scale. Consequently, the changes on economic situation after a certain period of time require an adjustment on the network model in order to get the optimal cost under the current economic conditions. This paper aimed to develop a mixed-integer linear programming model for a two-echelon warehouse network redesign problem with capacitated plant and uncapacitated warehouses. The main contribution of this study is considering capacity constraint for existing warehouses. A Simulated Annealing algorithm is proposed to tackle with the proposed model. The numerical solution showed the model and method of solution proposed was practical.

  3. Shape optimization of road tunnel cross-section by simulated annealing

    NASA Astrophysics Data System (ADS)

    Sobótka, Maciej; Pachnicz, Michał

    2016-06-01

    The paper concerns shape optimization of a tunnel excavation cross-section. The study incorporates optimization procedure of the simulated annealing (SA). The form of a cost function derives from the energetic optimality condition, formulated in the authors' previous papers. The utilized algorithm takes advantage of the optimization procedure already published by the authors. Unlike other approaches presented in literature, the one introduced in this paper takes into consideration a practical requirement of preserving fixed clearance gauge. Itasca Flac software is utilized in numerical examples. The optimal excavation shapes are determined for five different in situ stress ratios. This factor significantly affects the optimal topology of excavation. The resulting shapes are elongated in the direction of a principal stress greater value. Moreover, the obtained optimal shapes have smooth contours circumscribing the gauge.

  4. A positional misalignment correction method for Fourier ptychographic microscopy based on simulated annealing

    NASA Astrophysics Data System (ADS)

    Sun, Jiasong; Zhang, Yuzhen; Chen, Qian; Zuo, Chao

    2017-02-01

    Fourier ptychographic microscopy (FPM) is a newly developed super-resolution technique, which employs angularly varying illuminations and a phase retrieval algorithm to surpass the diffraction limit of a low numerical aperture (NA) objective lens. In current FPM imaging platforms, accurate knowledge of LED matrix's position is critical to achieve good recovery quality. Furthermore, considering such a wide field-of-view (FOV) in FPM, different regions in the FOV have different sensitivity of LED positional misalignment. In this work, we introduce an iterative method to correct position errors based on the simulated annealing (SA) algorithm. To improve the efficiency of this correcting process, large number of iterations for several images with low illumination NAs are firstly implemented to estimate the initial values of the global positional misalignment model through non-linear regression. Simulation and experimental results are presented to evaluate the performance of the proposed method and it is demonstrated that this method can both improve the quality of the recovered object image and relax the LED elements' position accuracy requirement while aligning the FPM imaging platforms.

  5. Simulated Annealing-based Optimal Proportional-Integral-Derivative (PID) Controller Design: A Case Study on Nonlinear Quadcopter Dynamics

    NASA Astrophysics Data System (ADS)

    Nemirsky, Kristofer Kevin

    In this thesis, the history and evolution of rotor aircraft with simulated annealing-based PID application were reviewed and quadcopter dynamics are presented. The dynamics of a quadcopter were then modeled, analyzed, and linearized. A cascaded loop architecture with PID controllers was used to stabilize the plant dynamics, which was improved upon through the application of simulated annealing (SA). A Simulink model was developed to test the controllers and verify the functionality of the proposed control system design. In addition, the data that the Simulink model provided were compared with flight data to present the validity of derived dynamics as a proper mathematical model representing the true dynamics of the quadcopter system. Then, the SA-based global optimization procedure was applied to obtain optimized PID parameters. It was observed that the tuned gains through the SA algorithm produced a better performing PID controller than the original manually tuned one. Next, we investigated the uncertain dynamics of the quadcopter setup. After adding uncertainty to the gyroscopic effects associated with pitch-and-roll rate dynamics, the controllers were shown to be robust against the added uncertainty. A discussion follows to summarize SA-based algorithm PID controller design and performance outcomes. Lastly, future work on SA application on multi-input-multi-output (MIMO) systems is briefly discussed.

  6. Comparison of particle swarm optimization and simulated annealing for locating additional boreholes considering combined variance minimization

    NASA Astrophysics Data System (ADS)

    Soltani-Mohammadi, Saeed; Safa, Mohammad; Mokhtari, Hadi

    2016-10-01

    One of the most important stages in complementary exploration is optimal designing the additional drilling pattern or defining the optimum number and location of additional boreholes. Quite a lot research has been carried out in this regard in which for most of the proposed algorithms, kriging variance minimization as a criterion for uncertainty assessment is defined as objective function and the problem could be solved through optimization methods. Although kriging variance implementation is known to have many advantages in objective function definition, it is not sensitive to local variability. As a result, the only factors evaluated for locating the additional boreholes are initial data configuration and variogram model parameters and the effects of local variability are omitted. In this paper, with the goal of considering the local variability in boundaries uncertainty assessment, the application of combined variance is investigated to define the objective function. Thus in order to verify the applicability of the proposed objective function, it is used to locate the additional boreholes in Esfordi phosphate mine through the implementation of metaheuristic optimization methods such as simulated annealing and particle swarm optimization. Comparison of results from the proposed objective function and conventional methods indicates that the new changes imposed on the objective function has caused the algorithm output to be sensitive to the variations of grade, domain's boundaries and the thickness of mineralization domain. The comparison between the results of different optimization algorithms proved that for the presented case the application of particle swarm optimization is more appropriate than simulated annealing.

  7. Molecular dynamics simulations and photoluminescence measurements of annealed ZnO surfaces

    NASA Astrophysics Data System (ADS)

    Min, Tjun Kit; Yoon, Tiem Leong; Ling, Chuo Ann; Mahmud, Shahrom; Lim, Thong Leng; Saw, Kim Guan

    2017-06-01

    The effect of thermal annealing on wurtzite ZnO, terminated by two surfaces, (000 1 bar) (which is oxygen-terminated) and (0 0 0 1) (which is Zn-terminated), is investigated via molecular dynamics simulation using reactive force field (ReaxFF). As a result of annealing at a threshold temperature range of 700 K simulations. Our simulations have also revealed the formation of oxygen dimers on the surface and evolution of partial charge distribution during the annealing process. Our MD simulation based on the ReaxFF is consistent with experimental observations.

  8. Enhanced intelligent water drops algorithm for multi-depot vehicle routing problem

    PubMed Central

    Akutsah, Francis; Olusanya, Micheal O.; Adewumi, Aderemi O.

    2018-01-01

    The intelligent water drop algorithm is a swarm-based metaheuristic algorithm, inspired by the characteristics of water drops in the river and the environmental changes resulting from the action of the flowing river. Since its appearance as an alternative stochastic optimization method, the algorithm has found applications in solving a wide range of combinatorial and functional optimization problems. This paper presents an improved intelligent water drop algorithm for solving multi-depot vehicle routing problems. A simulated annealing algorithm was introduced into the proposed algorithm as a local search metaheuristic to prevent the intelligent water drop algorithm from getting trapped into local minima and also improve its solution quality. In addition, some of the potential problematic issues associated with using simulated annealing that include high computational runtime and exponential calculation of the probability of acceptance criteria, are investigated. The exponential calculation of the probability of acceptance criteria for the simulated annealing based techniques is computationally expensive. Therefore, in order to maximize the performance of the intelligent water drop algorithm using simulated annealing, a better way of calculating the probability of acceptance criteria is considered. The performance of the proposed hybrid algorithm is evaluated by using 33 standard test problems, with the results obtained compared with the solutions offered by four well-known techniques from the subject literature. Experimental results and statistical tests show that the new method possesses outstanding performance in terms of solution quality and runtime consumed. In addition, the proposed algorithm is suitable for solving large-scale problems. PMID:29554662

  9. Enhanced intelligent water drops algorithm for multi-depot vehicle routing problem.

    PubMed

    Ezugwu, Absalom E; Akutsah, Francis; Olusanya, Micheal O; Adewumi, Aderemi O

    2018-01-01

    The intelligent water drop algorithm is a swarm-based metaheuristic algorithm, inspired by the characteristics of water drops in the river and the environmental changes resulting from the action of the flowing river. Since its appearance as an alternative stochastic optimization method, the algorithm has found applications in solving a wide range of combinatorial and functional optimization problems. This paper presents an improved intelligent water drop algorithm for solving multi-depot vehicle routing problems. A simulated annealing algorithm was introduced into the proposed algorithm as a local search metaheuristic to prevent the intelligent water drop algorithm from getting trapped into local minima and also improve its solution quality. In addition, some of the potential problematic issues associated with using simulated annealing that include high computational runtime and exponential calculation of the probability of acceptance criteria, are investigated. The exponential calculation of the probability of acceptance criteria for the simulated annealing based techniques is computationally expensive. Therefore, in order to maximize the performance of the intelligent water drop algorithm using simulated annealing, a better way of calculating the probability of acceptance criteria is considered. The performance of the proposed hybrid algorithm is evaluated by using 33 standard test problems, with the results obtained compared with the solutions offered by four well-known techniques from the subject literature. Experimental results and statistical tests show that the new method possesses outstanding performance in terms of solution quality and runtime consumed. In addition, the proposed algorithm is suitable for solving large-scale problems.

  10. spsann - optimization of sample patterns using spatial simulated annealing

    NASA Astrophysics Data System (ADS)

    Samuel-Rosa, Alessandro; Heuvelink, Gerard; Vasques, Gustavo; Anjos, Lúcia

    2015-04-01

    There are many algorithms and computer programs to optimize sample patterns, some private and others publicly available. A few have only been presented in scientific articles and text books. This dispersion and somewhat poor availability is holds back to their wider adoption and further development. We introduce spsann, a new R-package for the optimization of sample patterns using spatial simulated annealing. R is the most popular environment for data processing and analysis. Spatial simulated annealing is a well known method with widespread use to solve optimization problems in the soil and geo-sciences. This is mainly due to its robustness against local optima and easiness of implementation. spsann offers many optimizing criteria for sampling for variogram estimation (number of points or point-pairs per lag distance class - PPL), trend estimation (association/correlation and marginal distribution of the covariates - ACDC), and spatial interpolation (mean squared shortest distance - MSSD). spsann also includes the mean or maximum universal kriging variance (MUKV) as an optimizing criterion, which is used when the model of spatial variation is known. PPL, ACDC and MSSD were combined (PAN) for sampling when we are ignorant about the model of spatial variation. spsann solves this multi-objective optimization problem scaling the objective function values using their maximum absolute value or the mean value computed over 1000 random samples. Scaled values are aggregated using the weighted sum method. A graphical display allows to follow how the sample pattern is being perturbed during the optimization, as well as the evolution of its energy state. It is possible to start perturbing many points and exponentially reduce the number of perturbed points. The maximum perturbation distance reduces linearly with the number of iterations. The acceptance probability also reduces exponentially with the number of iterations. R is memory hungry and spatial simulated annealing is a

  11. Experimental demonstration of a quantum annealing algorithm for the traveling salesman problem in a nuclear-magnetic-resonance quantum simulator

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Chen Hongwei; High Magnetic Field Laboratory, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031; Kong Xi

    The method of quantum annealing (QA) is a promising way for solving many optimization problems in both classical and quantum information theory. The main advantage of this approach, compared with the gate model, is the robustness of the operations against errors originated from both external controls and the environment. In this work, we succeed in demonstrating experimentally an application of the method of QA to a simplified version of the traveling salesman problem by simulating the corresponding Schroedinger evolution with a NMR quantum simulator. The experimental results unambiguously yielded the optimal traveling route, in good agreement with the theoretical prediction.

  12. Performance of quantum annealing on random Ising problems implemented using the D-Wave Two

    NASA Astrophysics Data System (ADS)

    Wang, Zhihui; Job, Joshua; Rønnow, Troels F.; Troyer, Matthias; Lidar, Daniel A.; USC Collaboration; ETH Collaboration

    2014-03-01

    Detecting a possible speedup of quantum annealing compared to classical algorithms is a pressing task in experimental adiabatic quantum computing. In this talk, we discuss the performance of the D-Wave Two quantum annealing device on Ising spin glass problems. The expected time to solution for the device to solve random instances with up to 503 spins and with specified coupling ranges is evaluated while carefully addressing the issue of statistical errors. We perform a systematic comparison of the expected time to solution between the D-Wave Two and classical stochastic solvers, specifically simulated annealing, and simulated quantum annealing based on quantum Monte Carlo, and discuss the question of speedup.

  13. Improved mapping of the travelling salesman problem for quantum annealing

    NASA Astrophysics Data System (ADS)

    Troyer, Matthias; Heim, Bettina; Brown, Ethan; Wecker, David

    2015-03-01

    We consider the quantum adiabatic algorithm as applied to the travelling salesman problem (TSP). We introduce a novel mapping of TSP to an Ising spin glass Hamiltonian and compare it to previous known mappings. Through direct perturbative analysis, unitary evolution, and simulated quantum annealing, we show this new mapping to be significantly superior. We discuss how this advantage can translate to actual physical implementations of TSP on quantum annealers.

  14. Ideal versus real: simulated annealing of experimentally derived and geometric platinum nanoparticles

    NASA Astrophysics Data System (ADS)

    Ellaby, Tom; Aarons, Jolyon; Varambhia, Aakash; Jones, Lewys; Nellist, Peter; Ozkaya, Dogan; Sarwar, Misbah; Thompsett, David; Skylaris, Chris-Kriton

    2018-04-01

    Platinum nanoparticles find significant use as catalysts in industrial applications such as fuel cells. Research into their design has focussed heavily on nanoparticle size and shape as they greatly influence activity. Using high throughput, high precision electron microscopy, the structures of commercially available Pt catalysts have been determined, and we have used classical and quantum atomistic simulations to examine and compare them with geometric cuboctahedral and truncated octahedral structures. A simulated annealing procedure was used both to explore the potential energy surface at different temperatures, and also to assess the effect on catalytic activity that annealing would have on nanoparticles with different geometries and sizes. The differences in response to annealing between the real and geometric nanoparticles are discussed in terms of thermal stability, coordination number and the proportion of optimal binding sites on the surface of the nanoparticles. We find that annealing both experimental and geometric nanoparticles results in structures that appear similar in shape and predicted activity, using oxygen adsorption as a measure. Annealing is predicted to increase the catalytic activity in all cases except the truncated octahedra, where it has the opposite effect. As our simulations have been performed with a classical force field, we also assess its suitability to describe the potential energy of such nanoparticles by comparing with large scale density functional theory calculations.

  15. Joint Optimization of Vertical Component Gravity and Seismic P-wave First Arrivals by Simulated Annealing

    NASA Astrophysics Data System (ADS)

    Louie, J. N.; Basler-Reeder, K.; Kent, G. M.; Pullammanappallil, S. K.

    2015-12-01

    Simultaneous joint seismic-gravity optimization improves P-wave velocity models in areas with sharp lateral velocity contrasts. Optimization is achieved using simulated annealing, a metaheuristic global optimization algorithm that does not require an accurate initial model. Balancing the seismic-gravity objective function is accomplished by a novel approach based on analysis of Pareto charts. Gravity modeling uses a newly developed convolution algorithm, while seismic modeling utilizes the highly efficient Vidale eikonal equation traveltime generation technique. Synthetic tests show that joint optimization improves velocity model accuracy and provides velocity control below the deepest headwave raypath. Detailed first arrival picking followed by trial velocity modeling remediates inconsistent data. We use a set of highly refined first arrival picks to compare results of a convergent joint seismic-gravity optimization to the Plotrefa™ and SeisOpt® Pro™ velocity modeling packages. Plotrefa™ uses a nonlinear least squares approach that is initial model dependent and produces shallow velocity artifacts. SeisOpt® Pro™ utilizes the simulated annealing algorithm and is limited to depths above the deepest raypath. Joint optimization increases the depth of constrained velocities, improving reflector coherency at depth. Kirchoff prestack depth migrations reveal that joint optimization ameliorates shallow velocity artifacts caused by limitations in refraction ray coverage. Seismic and gravity data from the San Emidio Geothermal field of the northwest Basin and Range province demonstrate that joint optimization changes interpretation outcomes. The prior shallow-valley interpretation gives way to a deep valley model, while shallow antiformal reflectors that could have been interpreted as antiformal folds are flattened. Furthermore, joint optimization provides a clearer image of the rangefront fault. This technique can readily be applied to existing datasets and could

  16. Optimization for high-dose-rate brachytherapy of cervical cancer with adaptive simulated annealing and gradient descent.

    PubMed

    Yao, Rui; Templeton, Alistair K; Liao, Yixiang; Turian, Julius V; Kiel, Krystyna D; Chu, James C H

    2014-01-01

    To validate an in-house optimization program that uses adaptive simulated annealing (ASA) and gradient descent (GD) algorithms and investigate features of physical dose and generalized equivalent uniform dose (gEUD)-based objective functions in high-dose-rate (HDR) brachytherapy for cervical cancer. Eight Syed/Neblett template-based cervical cancer HDR interstitial brachytherapy cases were used for this study. Brachytherapy treatment plans were first generated using inverse planning simulated annealing (IPSA). Using the same dwell positions designated in IPSA, plans were then optimized with both physical dose and gEUD-based objective functions, using both ASA and GD algorithms. Comparisons were made between plans both qualitatively and based on dose-volume parameters, evaluating each optimization method and objective function. A hybrid objective function was also designed and implemented in the in-house program. The ASA plans are higher on bladder V75% and D2cc (p=0.034) and lower on rectum V75% and D2cc (p=0.034) than the IPSA plans. The ASA and GD plans are not significantly different. The gEUD-based plans have higher homogeneity index (p=0.034), lower overdose index (p=0.005), and lower rectum gEUD and normal tissue complication probability (p=0.005) than the physical dose-based plans. The hybrid function can produce a plan with dosimetric parameters between the physical dose-based and gEUD-based plans. The optimized plans with the same objective value and dose-volume histogram could have different dose distributions. Our optimization program based on ASA and GD algorithms is flexible on objective functions, optimization parameters, and can generate optimized plans comparable with IPSA. Copyright © 2014 American Brachytherapy Society. Published by Elsevier Inc. All rights reserved.

  17. A noisy chaotic neural network for solving combinatorial optimization problems: stochastic chaotic simulated annealing.

    PubMed

    Wang, Lipo; Li, Sa; Tian, Fuyu; Fu, Xiuju

    2004-10-01

    Recently Chen and Aihara have demonstrated both experimentally and mathematically that their chaotic simulated annealing (CSA) has better search ability for solving combinatorial optimization problems compared to both the Hopfield-Tank approach and stochastic simulated annealing (SSA). However, CSA may not find a globally optimal solution no matter how slowly annealing is carried out, because the chaotic dynamics are completely deterministic. In contrast, SSA tends to settle down to a global optimum if the temperature is reduced sufficiently slowly. Here we combine the best features of both SSA and CSA, thereby proposing a new approach for solving optimization problems, i.e., stochastic chaotic simulated annealing, by using a noisy chaotic neural network. We show the effectiveness of this new approach with two difficult combinatorial optimization problems, i.e., a traveling salesman problem and a channel assignment problem for cellular mobile communications.

  18. a Comparison of Simulated Annealing, Genetic Algorithm and Particle Swarm Optimization in Optimal First-Order Design of Indoor Tls Networks

    NASA Astrophysics Data System (ADS)

    Jia, F.; Lichti, D.

    2017-09-01

    The optimal network design problem has been well addressed in geodesy and photogrammetry but has not received the same attention for terrestrial laser scanner (TLS) networks. The goal of this research is to develop a complete design system that can automatically provide an optimal plan for high-accuracy, large-volume scanning networks. The aim in this paper is to use three heuristic optimization methods, simulated annealing (SA), genetic algorithm (GA) and particle swarm optimization (PSO), to solve the first-order design (FOD) problem for a small-volume indoor network and make a comparison of their performances. The room is simplified as discretized wall segments and possible viewpoints. Each possible viewpoint is evaluated with a score table representing the wall segments visible from each viewpoint based on scanning geometry constraints. The goal is to find a minimum number of viewpoints that can obtain complete coverage of all wall segments with a minimal sum of incidence angles. The different methods have been implemented and compared in terms of the quality of the solutions, runtime and repeatability. The experiment environment was simulated from a room located on University of Calgary campus where multiple scans are required due to occlusions from interior walls. The results obtained in this research show that PSO and GA provide similar solutions while SA doesn't guarantee an optimal solution within limited iterations. Overall, GA is considered as the best choice for this problem based on its capability of providing an optimal solution and fewer parameters to tune.

  19. Hybrid algorithms for fuzzy reverse supply chain network design.

    PubMed

    Che, Z H; Chiang, Tzu-An; Kuo, Y C; Cui, Zhihua

    2014-01-01

    In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods.

  20. Hybrid Algorithms for Fuzzy Reverse Supply Chain Network Design

    PubMed Central

    Che, Z. H.; Chiang, Tzu-An; Kuo, Y. C.

    2014-01-01

    In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods. PMID:24892057

  1. Distributed genetic algorithms for the floorplan design problem

    NASA Technical Reports Server (NTRS)

    Cohoon, James P.; Hegde, Shailesh U.; Martin, Worthy N.; Richards, Dana S.

    1991-01-01

    Designing a VLSI floorplan calls for arranging a given set of modules in the plane to minimize the weighted sum of area and wire-length measures. A method of solving the floorplan design problem using distributed genetic algorithms is presented. Distributed genetic algorithms, based on the paleontological theory of punctuated equilibria, offer a conceptual modification to the traditional genetic algorithms. Experimental results on several problem instances demonstrate the efficacy of this method and indicate the advantages of this method over other methods, such as simulated annealing. The method has performed better than the simulated annealing approach, both in terms of the average cost of the solutions found and the best-found solution, in almost all the problem instances tried.

  2. Population annealing with weighted averages: A Monte Carlo method for rough free-energy landscapes

    NASA Astrophysics Data System (ADS)

    Machta, J.

    2010-08-01

    The population annealing algorithm introduced by Hukushima and Iba is described. Population annealing combines simulated annealing and Boltzmann weighted differential reproduction within a population of replicas to sample equilibrium states. Population annealing gives direct access to the free energy. It is shown that unbiased measurements of observables can be obtained by weighted averages over many runs with weight factors related to the free-energy estimate from the run. Population annealing is well suited to parallelization and may be a useful alternative to parallel tempering for systems with rough free-energy landscapes such as spin glasses. The method is demonstrated for spin glasses.

  3. Quantum annealing for combinatorial clustering

    NASA Astrophysics Data System (ADS)

    Kumar, Vaibhaw; Bass, Gideon; Tomlin, Casey; Dulny, Joseph

    2018-02-01

    Clustering is a powerful machine learning technique that groups "similar" data points based on their characteristics. Many clustering algorithms work by approximating the minimization of an objective function, namely the sum of within-the-cluster distances between points. The straightforward approach involves examining all the possible assignments of points to each of the clusters. This approach guarantees the solution will be a global minimum; however, the number of possible assignments scales quickly with the number of data points and becomes computationally intractable even for very small datasets. In order to circumvent this issue, cost function minima are found using popular local search-based heuristic approaches such as k-means and hierarchical clustering. Due to their greedy nature, such techniques do not guarantee that a global minimum will be found and can lead to sub-optimal clustering assignments. Other classes of global search-based techniques, such as simulated annealing, tabu search, and genetic algorithms, may offer better quality results but can be too time-consuming to implement. In this work, we describe how quantum annealing can be used to carry out clustering. We map the clustering objective to a quadratic binary optimization problem and discuss two clustering algorithms which are then implemented on commercially available quantum annealing hardware, as well as on a purely classical solver "qbsolv." The first algorithm assigns N data points to K clusters, and the second one can be used to perform binary clustering in a hierarchical manner. We present our results in the form of benchmarks against well-known k-means clustering and discuss the advantages and disadvantages of the proposed techniques.

  4. Scheduling Earth Observing Satellites with Evolutionary Algorithms

    NASA Technical Reports Server (NTRS)

    Globus, Al; Crawford, James; Lohn, Jason; Pryor, Anna

    2003-01-01

    We hypothesize that evolutionary algorithms can effectively schedule coordinated fleets of Earth observing satellites. The constraints are complex and the bottlenecks are not well understood, a condition where evolutionary algorithms are often effective. This is, in part, because evolutionary algorithms require only that one can represent solutions, modify solutions, and evaluate solution fitness. To test the hypothesis we have developed a representative set of problems, produced optimization software (in Java) to solve them, and run experiments comparing techniques. This paper presents initial results of a comparison of several evolutionary and other optimization techniques; namely the genetic algorithm, simulated annealing, squeaky wheel optimization, and stochastic hill climbing. We also compare separate satellite vs. integrated scheduling of a two satellite constellation. While the results are not definitive, tests to date suggest that simulated annealing is the best search technique and integrated scheduling is superior.

  5. Satisfiability Test with Synchronous Simulated Annealing on the Fujitsu AP1000 Massively-Parallel Multiprocessor

    NASA Technical Reports Server (NTRS)

    Sohn, Andrew; Biswas, Rupak

    1996-01-01

    Solving the hard Satisfiability Problem is time consuming even for modest-sized problem instances. Solving the Random L-SAT Problem is especially difficult due to the ratio of clauses to variables. This report presents a parallel synchronous simulated annealing method for solving the Random L-SAT Problem on a large-scale distributed-memory multiprocessor. In particular, we use a parallel synchronous simulated annealing procedure, called Generalized Speculative Computation, which guarantees the same decision sequence as sequential simulated annealing. To demonstrate the performance of the parallel method, we have selected problem instances varying in size from 100-variables/425-clauses to 5000-variables/21,250-clauses. Experimental results on the AP1000 multiprocessor indicate that our approach can satisfy 99.9 percent of the clauses while giving almost a 70-fold speedup on 500 processors.

  6. Free Energy Minimization by Simulated Annealing with Applications to Lithospheric Slabs and Mantle Plumes

    NASA Astrophysics Data System (ADS)

    Bina, C. R.

    An optimization algorithm based upon the method of simulated annealing is of utility in calculating equilibrium phase assemblages as functions of pressure, temperature, and chemical composi tion. Operating by analogy to the statistical mechanics of the chemical system, it is applicable both to problems of strict chemical equilibrium and to problems involving metastability. The method reproduces known phase diagrams and illustrates the expected thermal deflection of phase transitions in thermal models of subducting lithospheric slabs and buoyant mantle plumes. It reveals temperature-induced changes in phase transition sharpness and the stability of Fe-rich γ phase within an α+γ field in cold slab thermal models, and it suggests that transitions such as the possible breakdown of silicate perovskite to mixed oxides can amplify velocity anomalies.

  7. Metric optimisation for analogue forecasting by simulated annealing

    NASA Astrophysics Data System (ADS)

    Bliefernicht, J.; Bárdossy, A.

    2009-04-01

    It is well known that weather patterns tend to recur from time to time. This property of the atmosphere is used by analogue forecasting techniques. They have a long history in weather forecasting and there are many applications predicting hydrological variables at the local scale for different lead times. The basic idea of the technique is to identify past weather situations which are similar (analogue) to the predicted one and to take the local conditions of the analogues as forecast. But the forecast performance of the analogue method depends on user-defined criteria like the choice of the distance function and the size of the predictor domain. In this study we propose a new methodology of optimising both criteria by minimising the forecast error with simulated annealing. The performance of the methodology is demonstrated for the probability forecast of daily areal precipitation. It is compared with a traditional analogue forecasting algorithm, which is used operational as an element of a hydrological forecasting system. The study is performed for several meso-scale catchments located in the Rhine basin in Germany. The methodology is validated by a jack-knife method in a perfect prognosis framework for a period of 48 years (1958-2005). The predictor variables are derived from the NCEP/NCAR reanalysis data set. The Brier skill score and the economic value are determined to evaluate the forecast skill and value of the technique. In this presentation we will present the concept of the optimisation algorithm and the outcome of the comparison. It will be also demonstrated how a decision maker should apply a probability forecast to maximise the economic benefit from it.

  8. The Local Minima Problem in Hierarchical Classes Analysis: An Evaluation of a Simulated Annealing Algorithm and Various Multistart Procedures

    ERIC Educational Resources Information Center

    Ceulemans, Eva; Van Mechelen, Iven; Leenen, Iwin

    2007-01-01

    Hierarchical classes models are quasi-order retaining Boolean decomposition models for N-way N-mode binary data. To fit these models to data, rationally started alternating least squares (or, equivalently, alternating least absolute deviations) algorithms have been proposed. Extensive simulation studies showed that these algorithms succeed quite…

  9. Exploring first-order phase transitions with population annealing

    NASA Astrophysics Data System (ADS)

    Barash, Lev Yu.; Weigel, Martin; Shchur, Lev N.; Janke, Wolfhard

    2017-03-01

    Population annealing is a hybrid of sequential and Markov chain Monte Carlo methods geared towards the efficient parallel simulation of systems with complex free-energy landscapes. Systems with first-order phase transitions are among the problems in computational physics that are difficult to tackle with standard methods such as local-update simulations in the canonical ensemble, for example with the Metropolis algorithm. It is hence interesting to see whether such transitions can be more easily studied using population annealing. We report here our preliminary observations from population annealing runs for the two-dimensional Potts model with q > 4, where it undergoes a first-order transition.

  10. Re'class'ification of 'quant'ified classical simulated annealing

    NASA Astrophysics Data System (ADS)

    Tanaka, Toshiyuki

    2009-12-01

    We discuss a classical reinterpretation of quantum-mechanics-based analysis of classical Markov chains with detailed balance, that is based on the quantum-classical correspondence. The classical reinterpretation is then used to demonstrate that it successfully reproduces a sufficient condition for cooling schedule in classical simulated annealing, which has the inverse-logarithmic scaling.

  11. Siting and sizing of distributed generators based on improved simulated annealing particle swarm optimization.

    PubMed

    Su, Hongsheng

    2017-12-18

    Distributed power grids generally contain multiple diverse types of distributed generators (DGs). Traditional particle swarm optimization (PSO) and simulated annealing PSO (SA-PSO) algorithms have some deficiencies in site selection and capacity determination of DGs, such as slow convergence speed and easily falling into local trap. In this paper, an improved SA-PSO (ISA-PSO) algorithm is proposed by introducing crossover and mutation operators of genetic algorithm (GA) into SA-PSO, so that the capabilities of the algorithm are well embodied in global searching and local exploration. In addition, diverse types of DGs are made equivalent to four types of nodes in flow calculation by the backward or forward sweep method, and reactive power sharing principles and allocation theory are applied to determine initial reactive power value and execute subsequent correction, thus providing the algorithm a better start to speed up the convergence. Finally, a mathematical model of the minimum economic cost is established for the siting and sizing of DGs under the location and capacity uncertainties of each single DG. Its objective function considers investment and operation cost of DGs, grid loss cost, annual purchase electricity cost, and environmental pollution cost, and the constraints include power flow, bus voltage, conductor current, and DG capacity. Through applications in an IEEE33-node distributed system, it is found that the proposed method can achieve desirable economic efficiency and safer voltage level relative to traditional PSO and SA-PSO algorithms, and is a more effective planning method for the siting and sizing of DGs in distributed power grids.

  12. Finding Maximum Cliques on the D-Wave Quantum Annealer

    DOE PAGES

    Chapuis, Guillaume; Djidjev, Hristo; Hahn, Georg; ...

    2018-05-03

    This work assesses the performance of the D-Wave 2X (DW) quantum annealer for finding a maximum clique in a graph, one of the most fundamental and important NP-hard problems. Because the size of the largest graphs DW can directly solve is quite small (usually around 45 vertices), we also consider decomposition algorithms intended for larger graphs and analyze their performance. For smaller graphs that fit DW, we provide formulations of the maximum clique problem as a quadratic unconstrained binary optimization (QUBO) problem, which is one of the two input types (together with the Ising model) acceptable by the machine, andmore » compare several quantum implementations to current classical algorithms such as simulated annealing, Gurobi, and third-party clique finding heuristics. We further estimate the contributions of the quantum phase of the quantum annealer and the classical post-processing phase typically used to enhance each solution returned by DW. We demonstrate that on random graphs that fit DW, no quantum speedup can be observed compared with the classical algorithms. On the other hand, for instances specifically designed to fit well the DW qubit interconnection network, we observe substantial speed-ups in computing time over classical approaches.« less

  13. Finding Maximum Cliques on the D-Wave Quantum Annealer

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Chapuis, Guillaume; Djidjev, Hristo; Hahn, Georg

    This work assesses the performance of the D-Wave 2X (DW) quantum annealer for finding a maximum clique in a graph, one of the most fundamental and important NP-hard problems. Because the size of the largest graphs DW can directly solve is quite small (usually around 45 vertices), we also consider decomposition algorithms intended for larger graphs and analyze their performance. For smaller graphs that fit DW, we provide formulations of the maximum clique problem as a quadratic unconstrained binary optimization (QUBO) problem, which is one of the two input types (together with the Ising model) acceptable by the machine, andmore » compare several quantum implementations to current classical algorithms such as simulated annealing, Gurobi, and third-party clique finding heuristics. We further estimate the contributions of the quantum phase of the quantum annealer and the classical post-processing phase typically used to enhance each solution returned by DW. We demonstrate that on random graphs that fit DW, no quantum speedup can be observed compared with the classical algorithms. On the other hand, for instances specifically designed to fit well the DW qubit interconnection network, we observe substantial speed-ups in computing time over classical approaches.« less

  14. Fast simulated annealing inversion of surface waves on pavement using phase-velocity spectra

    USGS Publications Warehouse

    Ryden, N.; Park, C.B.

    2006-01-01

    The conventional inversion of surface waves depends on modal identification of measured dispersion curves, which can be ambiguous. It is possible to avoid mode-number identification and extraction by inverting the complete phase-velocity spectrum obtained from a multichannel record. We use the fast simulated annealing (FSA) global search algorithm to minimize the difference between the measured phase-velocity spectrum and that calculated from a theoretical layer model, including the field setup geometry. Results show that this algorithm can help one avoid getting trapped in local minima while searching for the best-matching layer model. The entire procedure is demonstrated on synthetic and field data for asphalt pavement. The viscoelastic properties of the top asphalt layer are taken into account, and the inverted asphalt stiffness as a function of frequency compares well with laboratory tests on core samples. The thickness and shear-wave velocity of the deeper embedded layers are resolved within 10% deviation from those values measured separately during pavement construction. The proposed method may be equally applicable to normal soil site investigation and in the field of ultrasonic testing of materials. ?? 2006 Society of Exploration Geophysicists.

  15. Stochastic annealing simulations of defect interactions among subcascades

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Heinisch, H.L.; Singh, B.N.

    1997-04-01

    The effects of the subcascade structure of high energy cascades on the temperature dependencies of annihilation, clustering and free defect production are investigated. The subcascade structure is simulated by closely spaced groups of lower energy MD cascades. The simulation results illustrate the strong influence of the defect configuration existing in the primary damage state on subsequent intracascade evolution. Other significant factors affecting the evolution of the defect distribution are the large differences in mobility and stability of vacancy and interstitial defects and the rapid one-dimensional diffusion of small, glissile interstitial loops produced directly in cascades. Annealing simulations are also performedmore » on high-energy, subcascade-producing cascades generated with the binary collision approximation and calibrated to MD results.« less

  16. Fractal Landscape Algorithms for Environmental Simulations

    NASA Astrophysics Data System (ADS)

    Mao, H.; Moran, S.

    2014-12-01

    Natural science and geographical research are now able to take advantage of environmental simulations that more accurately test experimental hypotheses, resulting in deeper understanding. Experiments affected by the natural environment can benefit from 3D landscape simulations capable of simulating a variety of terrains and environmental phenomena. Such simulations can employ random terrain generation algorithms that dynamically simulate environments to test specific models against a variety of factors. Through the use of noise functions such as Perlin noise, Simplex noise, and diamond square algorithms, computers can generate simulations that model a variety of landscapes and ecosystems. This study shows how these algorithms work together to create realistic landscapes. By seeding values into the diamond square algorithm, one can control the shape of landscape. Perlin noise and Simplex noise are also used to simulate moisture and temperature. The smooth gradient created by coherent noise allows more realistic landscapes to be simulated. Terrain generation algorithms can be used in environmental studies and physics simulations. Potential studies that would benefit from simulations include the geophysical impact of flash floods or drought on a particular region and regional impacts on low lying area due to global warming and rising sea levels. Furthermore, terrain generation algorithms also serve as aesthetic tools to display landscapes (Google Earth), and simulate planetary landscapes. Hence, it can be used as a tool to assist science education. Algorithms used to generate these natural phenomena provide scientists a different approach in analyzing our world. The random algorithms used in terrain generation not only contribute to the generating the terrains themselves, but are also capable of simulating weather patterns.

  17. A clustering method of Chinese medicine prescriptions based on modified firefly algorithm.

    PubMed

    Yuan, Feng; Liu, Hong; Chen, Shou-Qiang; Xu, Liang

    2016-12-01

    This paper is aimed to study the clustering method for Chinese medicine (CM) medical cases. The traditional K-means clustering algorithm had shortcomings such as dependence of results on the selection of initial value, trapping in local optimum when processing prescriptions form CM medical cases. Therefore, a new clustering method based on the collaboration of firefly algorithm and simulated annealing algorithm was proposed. This algorithm dynamically determined the iteration of firefly algorithm and simulates sampling of annealing algorithm by fitness changes, and increased the diversity of swarm through expansion of the scope of the sudden jump, thereby effectively avoiding premature problem. The results from confirmatory experiments for CM medical cases suggested that, comparing with traditional K-means clustering algorithms, this method was greatly improved in the individual diversity and the obtained clustering results, the computing results from this method had a certain reference value for cluster analysis on CM prescriptions.

  18. Exploration of DGVM Parameter Solution Space Using Simulated Annealing: Implications for Forecast Uncertainties

    NASA Astrophysics Data System (ADS)

    Wells, J. R.; Kim, J. B.

    2011-12-01

    Parameters in dynamic global vegetation models (DGVMs) are thought to be weakly constrained and can be a significant source of errors and uncertainties. DGVMs use between 5 and 26 plant functional types (PFTs) to represent the average plant life form in each simulated plot, and each PFT typically has a dozen or more parameters that define the way it uses resource and responds to the simulated growing environment. Sensitivity analysis explores how varying parameters affects the output, but does not do a full exploration of the parameter solution space. The solution space for DGVM parameter values are thought to be complex and non-linear; and multiple sets of acceptable parameters may exist. In published studies, PFT parameters are estimated from published literature, and often a parameter value is estimated from a single published value. Further, the parameters are "tuned" using somewhat arbitrary, "trial-and-error" methods. BIOMAP is a new DGVM created by fusing MAPSS biogeography model with Biome-BGC. It represents the vegetation of North America using 26 PFTs. We are using simulated annealing, a global search method, to systematically and objectively explore the solution space for the BIOMAP PFTs and system parameters important for plant water use. We defined the boundaries of the solution space by obtaining maximum and minimum values from published literature, and where those were not available, using +/-20% of current values. We used stratified random sampling to select a set of grid cells representing the vegetation of the conterminous USA. Simulated annealing algorithm is applied to the parameters for spin-up and a transient run during the historical period 1961-1990. A set of parameter values is considered acceptable if the associated simulation run produces a modern potential vegetation distribution map that is as accurate as one produced by trial-and-error calibration. We expect to confirm that the solution space is non-linear and complex, and that

  19. Effective optimization using sample persistence: A case study on quantum annealers and various Monte Carlo optimization methods

    NASA Astrophysics Data System (ADS)

    Karimi, Hamed; Rosenberg, Gili; Katzgraber, Helmut G.

    2017-10-01

    We present and apply a general-purpose, multistart algorithm for improving the performance of low-energy samplers used for solving optimization problems. The algorithm iteratively fixes the value of a large portion of the variables to values that have a high probability of being optimal. The resulting problems are smaller and less connected, and samplers tend to give better low-energy samples for these problems. The algorithm is trivially parallelizable since each start in the multistart algorithm is independent, and could be applied to any heuristic solver that can be run multiple times to give a sample. We present results for several classes of hard problems solved using simulated annealing, path-integral quantum Monte Carlo, parallel tempering with isoenergetic cluster moves, and a quantum annealer, and show that the success metrics and the scaling are improved substantially. When combined with this algorithm, the quantum annealer's scaling was substantially improved for native Chimera graph problems. In addition, with this algorithm the scaling of the time to solution of the quantum annealer is comparable to the Hamze-de Freitas-Selby algorithm on the weak-strong cluster problems introduced by Boixo et al. Parallel tempering with isoenergetic cluster moves was able to consistently solve three-dimensional spin glass problems with 8000 variables when combined with our method, whereas without our method it could not solve any.

  20. Selected-node stochastic simulation algorithm

    NASA Astrophysics Data System (ADS)

    Duso, Lorenzo; Zechner, Christoph

    2018-04-01

    Stochastic simulations of biochemical networks are of vital importance for understanding complex dynamics in cells and tissues. However, existing methods to perform such simulations are associated with computational difficulties and addressing those remains a daunting challenge to the present. Here we introduce the selected-node stochastic simulation algorithm (snSSA), which allows us to exclusively simulate an arbitrary, selected subset of molecular species of a possibly large and complex reaction network. The algorithm is based on an analytical elimination of chemical species, thereby avoiding explicit simulation of the associated chemical events. These species are instead described continuously in terms of statistical moments derived from a stochastic filtering equation, resulting in a substantial speedup when compared to Gillespie's stochastic simulation algorithm (SSA). Moreover, we show that statistics obtained via snSSA profit from a variance reduction, which can significantly lower the number of Monte Carlo samples needed to achieve a certain performance. We demonstrate the algorithm using several biological case studies for which the simulation time could be reduced by orders of magnitude.

  1. Non-stoquastic Hamiltonians in quantum annealing via geometric phases

    NASA Astrophysics Data System (ADS)

    Vinci, Walter; Lidar, Daniel A.

    2017-09-01

    We argue that a complete description of quantum annealing implemented with continuous variables must take into account the non-adiabatic Aharonov-Anandan geometric phase that arises when the system Hamiltonian changes during the anneal. We show that this geometric effect leads to the appearance of non-stoquasticity in the effective quantum Ising Hamiltonians that are typically used to describe quantum annealing with flux qubits. We explicitly demonstrate the effect of this geometric non-stoquasticity when quantum annealing is performed with a system of one and two coupled flux qubits. The realization of non-stoquastic Hamiltonians has important implications from a computational complexity perspective, since it is believed that in many cases quantum annealing with stoquastic Hamiltonians can be efficiently simulated via classical algorithms such as Quantum Monte Carlo. It is well known that the direct implementation of non-stoquastic Hamiltonians with flux qubits is particularly challenging. Our results suggest an alternative path for the implementation of non-stoquasticity via geometric phases that can be exploited for computational purposes.

  2. Jovian vortices by simulated annealing

    NASA Astrophysics Data System (ADS)

    Morrison, P. J.; Flierl, G. R.; Swaminathan, R. V.

    2017-11-01

    We explore the conditions required for isolated vortices to exist in sheared zonal flows and the stability of the underlying zonal winds. This is done using the standard 2-layer quasigeostrophic model with the lower layer depth becoming infinite; however, this model differs from the usual layer model because the lower layer is not assumed to be motionless but has a steady configuration of alternating zonal flows. Steady state vortices are obtained by a simulated annealing computational method introduced in, generalized and applied in in fluid flow, and used in the context of magnetohydrodynamics in. Various cases of vortices with a constant potential vorticity anomaly atop zonal winds and the stability of the underlying winds are considered using a mix of computational and analytical techniques. U.S. Department of Energy Contract DE-FG05-80ET-53088.

  3. Stochastic Evolutionary Algorithms for Planning Robot Paths

    NASA Technical Reports Server (NTRS)

    Fink, Wolfgang; Aghazarian, Hrand; Huntsberger, Terrance; Terrile, Richard

    2006-01-01

    A computer program implements stochastic evolutionary algorithms for planning and optimizing collision-free paths for robots and their jointed limbs. Stochastic evolutionary algorithms can be made to produce acceptably close approximations to exact, optimal solutions for path-planning problems while often demanding much less computation than do exhaustive-search and deterministic inverse-kinematics algorithms that have been used previously for this purpose. Hence, the present software is better suited for application aboard robots having limited computing capabilities (see figure). The stochastic aspect lies in the use of simulated annealing to (1) prevent trapping of an optimization algorithm in local minima of an energy-like error measure by which the fitness of a trial solution is evaluated while (2) ensuring that the entire multidimensional configuration and parameter space of the path-planning problem is sampled efficiently with respect to both robot joint angles and computation time. Simulated annealing is an established technique for avoiding local minima in multidimensional optimization problems, but has not, until now, been applied to planning collision-free robot paths by use of low-power computers.

  4. Displacement cascades and defect annealing in tungsten, Part II: Object kinetic Monte Carlo Simulation of Tungsten Cascade Aging

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Nandipati, Giridhar; Setyawan, Wahyu; Heinisch, Howard L.

    2015-07-01

    The results of object kinetic Monte Carlo (OKMC) simulations of the annealing of primary cascade damage in bulk tungsten using a comprehensive database of cascades obtained from molecular dynamics (Setyawan et al.) are described as a function of primary knock-on atom (PKA) energy at temperatures of 300, 1025 and 2050 K. An increase in SIA clustering coupled with a decrease in vacancy clustering with increasing temperature, in addition to the disparate mobilities of SIAs versus vacancies, causes an interesting effect of temperature on cascade annealing. The annealing efficiency (the ratio of the number of defects after and before annealing) exhibitsmore » an inverse U-shape curve as a function of temperature. The capabilities of the newly developed OKMC code KSOME (kinetic simulations of microstructure evolution) used to carry out these simulations are described.« less

  5. Hybrid General Pattern Search and Simulated Annealing for Industrail Production Planning Problems

    NASA Astrophysics Data System (ADS)

    Vasant, P.; Barsoum, N.

    2010-06-01

    In this paper, the hybridization of GPS (General Pattern Search) method and SA (Simulated Annealing) incorporated in the optimization process in order to look for the global optimal solution for the fitness function and decision variables as well as minimum computational CPU time. The real strength of SA approach been tested in this case study problem of industrial production planning. This is due to the great advantage of SA for being easily escaping from trapped in local minima by accepting up-hill move through a probabilistic procedure in the final stages of optimization process. Vasant [1] in his Ph. D thesis has provided 16 different techniques of heuristic and meta-heuristic in solving industrial production problems with non-linear cubic objective functions, eight decision variables and 29 constraints. In this paper, fuzzy technological problems have been solved using hybrid techniques of general pattern search and simulated annealing. The simulated and computational results are compared to other various evolutionary techniques.

  6. Bayesian Parameter Inference and Model Selection by Population Annealing in Systems Biology

    PubMed Central

    Murakami, Yohei

    2014-01-01

    Parameter inference and model selection are very important for mathematical modeling in systems biology. Bayesian statistics can be used to conduct both parameter inference and model selection. Especially, the framework named approximate Bayesian computation is often used for parameter inference and model selection in systems biology. However, Monte Carlo methods needs to be used to compute Bayesian posterior distributions. In addition, the posterior distributions of parameters are sometimes almost uniform or very similar to their prior distributions. In such cases, it is difficult to choose one specific value of parameter with high credibility as the representative value of the distribution. To overcome the problems, we introduced one of the population Monte Carlo algorithms, population annealing. Although population annealing is usually used in statistical mechanics, we showed that population annealing can be used to compute Bayesian posterior distributions in the approximate Bayesian computation framework. To deal with un-identifiability of the representative values of parameters, we proposed to run the simulations with the parameter ensemble sampled from the posterior distribution, named “posterior parameter ensemble”. We showed that population annealing is an efficient and convenient algorithm to generate posterior parameter ensemble. We also showed that the simulations with the posterior parameter ensemble can, not only reproduce the data used for parameter inference, but also capture and predict the data which was not used for parameter inference. Lastly, we introduced the marginal likelihood in the approximate Bayesian computation framework for Bayesian model selection. We showed that population annealing enables us to compute the marginal likelihood in the approximate Bayesian computation framework and conduct model selection depending on the Bayes factor. PMID:25089832

  7. Analysis of temporal gene expression profiles: clustering by simulated annealing and determining the optimal number of clusters.

    PubMed

    Lukashin, A V; Fuchs, R

    2001-05-01

    Cluster analysis of genome-wide expression data from DNA microarray hybridization studies has proved to be a useful tool for identifying biologically relevant groupings of genes and samples. In the present paper, we focus on several important issues related to clustering algorithms that have not yet been fully studied. We describe a simple and robust algorithm for the clustering of temporal gene expression profiles that is based on the simulated annealing procedure. In general, this algorithm guarantees to eventually find the globally optimal distribution of genes over clusters. We introduce an iterative scheme that serves to evaluate quantitatively the optimal number of clusters for each specific data set. The scheme is based on standard approaches used in regular statistical tests. The basic idea is to organize the search of the optimal number of clusters simultaneously with the optimization of the distribution of genes over clusters. The efficiency of the proposed algorithm has been evaluated by means of a reverse engineering experiment, that is, a situation in which the correct distribution of genes over clusters is known a priori. The employment of this statistically rigorous test has shown that our algorithm places greater than 90% genes into correct clusters. Finally, the algorithm has been tested on real gene expression data (expression changes during yeast cell cycle) for which the fundamental patterns of gene expression and the assignment of genes to clusters are well understood from numerous previous studies.

  8. Inversion of sonobuoy data from shallow-water sites with simulated annealing.

    PubMed

    Lindwall, Dennis; Brozena, John

    2005-02-01

    An enhanced simulated annealing algorithm is used to invert sparsely sampled seismic data collected with sonobuoys to obtain seafloor geoacoustic properties at two littoral marine environments as well as for a synthetic data set. Inversion of field data from a 750-m water-depth site using a water-gun sound source found a good solution which included a pronounced subbottom reflector after 6483 iterations over seven variables. Field data from a 250-m water-depth site using an air-gun source required 35,421 iterations for a good inversion solution because 30 variables had to be solved for, including the shot-to-receiver offsets. The sonobuoy derived compressional wave velocity-depth (Vp-Z) models compare favorably with Vp-Z models derived from nearby, high-quality, multichannel seismic data. There are, however, substantial differences between seafloor reflection coefficients calculated from field models and seafloor reflection coefficients based on commonly used Vp regression curves (gradients). Reflection loss is higher at one field site and lower at the other than predicted from commonly used Vp gradients for terrigenous sediments. In addition, there are strong effects on reflection loss due to the subseafloor interfaces that are also not predicted by Vp gradients.

  9. Optimization of parameter values for complex pulse sequences by simulated annealing: application to 3D MP-RAGE imaging of the brain.

    PubMed

    Epstein, F H; Mugler, J P; Brookeman, J R

    1994-02-01

    A number of pulse sequence techniques, including magnetization-prepared gradient echo (MP-GRE), segmented GRE, and hybrid RARE, employ a relatively large number of variable pulse sequence parameters and acquire the image data during a transient signal evolution. These sequences have recently been proposed and/or used for clinical applications in the brain, spine, liver, and coronary arteries. Thus, the need for a method of deriving optimal pulse sequence parameter values for this class of sequences now exists. Due to the complexity of these sequences, conventional optimization approaches, such as applying differential calculus to signal difference equations, are inadequate. We have developed a general framework for adapting the simulated annealing algorithm to pulse sequence parameter value optimization, and applied this framework to the specific case of optimizing the white matter-gray matter signal difference for a T1-weighted variable flip angle 3D MP-RAGE sequence. Using our algorithm, the values of 35 sequence parameters, including the magnetization-preparation RF pulse flip angle and delay time, 32 flip angles in the variable flip angle gradient-echo acquisition sequence, and the magnetization recovery time, were derived. Optimized 3D MP-RAGE achieved up to a 130% increase in white matter-gray matter signal difference compared with optimized 3D RF-spoiled FLASH with the same total acquisition time. The simulated annealing approach was effective at deriving optimal parameter values for a specific 3D MP-RAGE imaging objective, and may be useful for other imaging objectives and sequences in this general class.

  10. Field development planning using simulated annealing - optimal economic well scheduling and placement

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Beckner, B.L.; Xong, X.

    1995-12-31

    A method for optimizing the net present value of a full field development by varying the placement and sequence of production wells is presented. This approach is automated and combines an economics package and Mobil`s in-house simulator, PEGASUS, within a simulated annealing optimization engine. A novel framing of the well placement and scheduling problem as a classic {open_quotes}travelling salesman problem{close_quotes} is required before optimization via simulated annealing can be applied practically. An example of a full field development using this technique shows that non-uniform well spacings are optimal (from an NPV standpoint) when the effects of well interference and variablemore » reservoir properties are considered. Examples of optimizing field NPV with variable well costs also show that non-uniform wells spacings are optimal. Project NPV increases of 25 to 30 million dollars were shown using the optimal, nonuniform development versus reasonable, uniform developments. The ability of this technology to deduce these non-uniform well spacings opens up many potential applications that should materially impact the economic performance of field developments.« less

  11. Using an improved association rules mining optimization algorithm in web-based mobile-learning system

    NASA Astrophysics Data System (ADS)

    Huang, Yin; Chen, Jianhua; Xiong, Shaojun

    2009-07-01

    Mobile-Learning (M-learning) makes many learners get the advantages of both traditional learning and E-learning. Currently, Web-based Mobile-Learning Systems have created many new ways and defined new relationships between educators and learners. Association rule mining is one of the most important fields in data mining and knowledge discovery in databases. Rules explosion is a serious problem which causes great concerns, as conventional mining algorithms often produce too many rules for decision makers to digest. Since Web-based Mobile-Learning System collects vast amounts of student profile data, data mining and knowledge discovery techniques can be applied to find interesting relationships between attributes of learners, assessments, the solution strategies adopted by learners and so on. Therefore ,this paper focus on a new data-mining algorithm, combined with the advantages of genetic algorithm and simulated annealing algorithm , called ARGSA(Association rules based on an improved Genetic Simulated Annealing Algorithm), to mine the association rules. This paper first takes advantage of the Parallel Genetic Algorithm and Simulated Algorithm designed specifically for discovering association rules. Moreover, the analysis and experiment are also made to show the proposed method is superior to the Apriori algorithm in this Mobile-Learning system.

  12. Minimizing the Discrepancy between Simulated and Historical Failures in Turbine Engines: A Simulation-Based Optimization Method (Postprint)

    DTIC Science & Technology

    2015-01-01

    Procedure. The simulated annealing (SA) algorithm is a well-known local search metaheuristic used to address discrete, continuous, and multiobjective...design of experiments (DOE) to tune the parameters of the optimiza- tion algorithm . Section 5 shows the results of the case study. Finally, concluding... metaheuristic . The proposed method is broken down into two phases. Phase I consists of a Monte Carlo simulation to obtain the simulated percentage of failure

  13. Optimization of Sample Points for Monitoring Arable Land Quality by Simulated Annealing while Considering Spatial Variations

    PubMed Central

    Wang, Junxiao; Wang, Xiaorui; Zhou, Shenglu; Wu, Shaohua; Zhu, Yan; Lu, Chunfeng

    2016-01-01

    With China’s rapid economic development, the reduction in arable land has emerged as one of the most prominent problems in the nation. The long-term dynamic monitoring of arable land quality is important for protecting arable land resources. An efficient practice is to select optimal sample points while obtaining accurate predictions. To this end, the selection of effective points from a dense set of soil sample points is an urgent problem. In this study, data were collected from Donghai County, Jiangsu Province, China. The number and layout of soil sample points are optimized by considering the spatial variations in soil properties and by using an improved simulated annealing (SA) algorithm. The conclusions are as follows: (1) Optimization results in the retention of more sample points in the moderate- and high-variation partitions of the study area; (2) The number of optimal sample points obtained with the improved SA algorithm is markedly reduced, while the accuracy of the predicted soil properties is improved by approximately 5% compared with the raw data; (3) With regard to the monitoring of arable land quality, a dense distribution of sample points is needed to monitor the granularity. PMID:27706051

  14. Identifying fracture‐zone geometry using simulated annealing and hydraulic‐connection data

    USGS Publications Warehouse

    Day-Lewis, Frederick D.; Hsieh, Paul A.; Gorelick, Steven M.

    2000-01-01

    A new approach is presented to condition geostatistical simulation of high‐permeability zones in fractured rock to hydraulic‐connection data. A simulated‐annealing algorithm generates three‐dimensional (3‐D) realizations conditioned to borehole data, inferred hydraulic connections between packer‐isolated borehole intervals, and an indicator (fracture zone or background‐K bedrock) variogram model of spatial variability. We apply the method to data from the U.S. Geological Survey Mirror Lake Site in New Hampshire, where connected high‐permeability fracture zones exert a strong control on fluid flow at the hundred‐meter scale. Single‐well hydraulic‐packer tests indicate where permeable fracture zones intersect boreholes, and multiple‐well pumping tests indicate the degree of hydraulic connection between boreholes. Borehole intervals connected by a fracture zone exhibit similar hydraulic responses, whereas intervals not connected by a fracture zone exhibit different responses. Our approach yields valuable insights into the 3‐D geometry of fracture zones at Mirror Lake. Statistical analysis of the realizations yields maps of the probabilities of intersecting specific fracture zones with additional wells. Inverse flow modeling based on the assumption of equivalent porous media is used to estimate hydraulic conductivity and specific storage and to identify those fracture‐zone geometries that are consistent with hydraulic test data.

  15. Computer-Assisted Scheduling of Army Unit Training: An Application of Simulated Annealing.

    ERIC Educational Resources Information Center

    Hart, Roland J.; Goehring, Dwight J.

    This report of an ongoing research project intended to provide computer assistance to Army units for the scheduling of training focuses on the feasibility of simulated annealing, a heuristic approach for solving scheduling problems. Following an executive summary and brief introduction, the document is divided into three sections. First, the Army…

  16. Seafloor Topography Estimation from Gravity Gradient Using Simulated Annealing

    NASA Astrophysics Data System (ADS)

    Yang, J.; Jekeli, C.; Liu, L.

    2017-12-01

    Inferring seafloor topography from gravimetry is an indirect yet proven and efficient means to map the ocean floor. Standard techniques rely on an approximate, linear relationship (Parker's formula) between topography and gravity. It has been reported that in the very rugged areas the discrepancies between prediction and ship soundings are very large, partly because the linear term of Parker's infinite series is dominant only in areas where the local topography is small compared with the regional topography. The validity of the linear approximation is therefore in need of analysis. In this study the nonlinear effects caused by terrain are quantified by both numerical tests and an algorithmic approach called coherency. It is shown that the nonlinear effects are more significant at higher frequencies, which suggests that estimation algorithms with nonlinear approximation in the modeled relationship between gravity gradient and topography should be developed in preparation for future high-resolution gravity gradient missions. The simulated annealing (SA) method is such an optimization technique that can process nonlinear inverse problems, and is used to estimate the seafloor topography parameters in a forward model by minimizing the difference between the observed and forward-computed vertical gravity gradients. Careful treatments like choosing suitable truncation distance, padding the vicinity of the study area with a known topography model, and using the relative cost function, are considered to improve the estimation accuracy. This study uses the gravity gradient, which is more sensitive to topography at short wavelengths than gravity anomaly. The gravity gradient data are derived from satellite altimetry, but the SA has no restrictions on data distribution, as required in Parker's infinite series model, thus enabling the use of airborne gravity gradient data, whose survey trajectories are irregular. The SA method is tested in an area of Guyots (E 156°-158° in

  17. Microstructure based simulations for prediction of flow curves and selection of process parameters for inter-critical annealing in DP steel

    NASA Astrophysics Data System (ADS)

    Deepu, M. J.; Farivar, H.; Prahl, U.; Phanikumar, G.

    2017-04-01

    Dual phase steels are versatile advanced high strength steels that are being used for sheet metal applications in automotive industry. It also has the potential for application in bulk components like gear. The inter-critical annealing in dual phase steels is one of the crucial steps that determine the mechanical properties of the material. Selection of the process parameters for inter-critical annealing, in particular, the inter-critical annealing temperature and time is important as it plays a major role in determining the volume fractions of ferrite and martensite, which in turn determines the mechanical properties. Selection of these process parameters to obtain a particular required mechanical property requires large number of experimental trials. Simulation of microstructure evolution and virtual compression/tensile testing can help in reducing the number of such experimental trials. In the present work, phase field modeling implemented in the commercial software Micress® is used to predict the microstructure evolution during inter-critical annealing. Virtual compression tests are performed on the simulated microstructure using finite element method implemented in the commercial software, to obtain the effective flow curve of the macroscopic material. The flow curves obtained by simulation are experimentally validated with physical simulation in Gleeble® and compared with that obtained using linear rule of mixture. The methodology could be used in determining the inter-critical annealing process parameters required for achieving a particular flow curve.

  18. Experimental and Numerical Simulations of Phase Transformations Occurring During Continuous Annealing of DP Steel Strips

    NASA Astrophysics Data System (ADS)

    Wrożyna, Andrzej; Pernach, Monika; Kuziak, Roman; Pietrzyk, Maciej

    2016-04-01

    Due to their exceptional strength properties combined with good workability the Advanced High-Strength Steels (AHSS) are commonly used in automotive industry. Manufacturing of these steels is a complex process which requires precise control of technological parameters during thermo-mechanical treatment. Design of these processes can be significantly improved by the numerical models of phase transformations. Evaluation of predictive capabilities of models, as far as their applicability in simulation of thermal cycles thermal cycles for AHSS is considered, was the objective of the paper. Two models were considered. The former was upgrade of the JMAK equation while the latter was an upgrade of the Leblond model. The models can be applied to any AHSS though the examples quoted in the paper refer to the Dual Phase (DP) steel. Three series of experimental simulations were performed. The first included various thermal cycles going beyond limitations of the continuous annealing lines. The objective was to validate models behavior in more complex cooling conditions. The second set of tests included experimental simulations of the thermal cycle characteristic for the continuous annealing lines. Capability of the models to describe properly phase transformations in this process was evaluated. The third set included data from the industrial continuous annealing line. Validation and verification of models confirmed their good predictive capabilities. Since it does not require application of the additivity rule, the upgrade of the Leblond model was selected as the better one for simulation of industrial processes in AHSS production.

  19. Duality quantum algorithm efficiently simulates open quantum systems

    PubMed Central

    Wei, Shi-Jie; Ruan, Dong; Long, Gui-Lu

    2016-01-01

    Because of inevitable coupling with the environment, nearly all practical quantum systems are open system, where the evolution is not necessarily unitary. In this paper, we propose a duality quantum algorithm for simulating Hamiltonian evolution of an open quantum system. In contrast to unitary evolution in a usual quantum computer, the evolution operator in a duality quantum computer is a linear combination of unitary operators. In this duality quantum algorithm, the time evolution of the open quantum system is realized by using Kraus operators which is naturally implemented in duality quantum computer. This duality quantum algorithm has two distinct advantages compared to existing quantum simulation algorithms with unitary evolution operations. Firstly, the query complexity of the algorithm is O(d3) in contrast to O(d4) in existing unitary simulation algorithm, where d is the dimension of the open quantum system. Secondly, By using a truncated Taylor series of the evolution operators, this duality quantum algorithm provides an exponential improvement in precision compared with previous unitary simulation algorithm. PMID:27464855

  20. Iterative simulated quenching for designing irregular-spot-array generators.

    PubMed

    Gillet, J N; Sheng, Y

    2000-07-10

    We propose a novel, to our knowledge, algorithm of iterative simulated quenching with temperature rescaling for designing diffractive optical elements, based on an analogy between simulated annealing and statistical thermodynamics. The temperature is iteratively rescaled at the end of each quenching process according to ensemble statistics to bring the system back from a frozen imperfect state with a local minimum of energy to a dynamic state in a Boltzmann heat bath in thermal equilibrium at the rescaled temperature. The new algorithm achieves much lower cost function and reconstruction error and higher diffraction efficiency than conventional simulated annealing with a fast exponential cooling schedule and is easy to program. The algorithm is used to design binary-phase generators of large irregular spot arrays. The diffractive phase elements have trapezoidal apertures of varying heights, which fit ideal arbitrary-shaped apertures better than do trapezoidal apertures of fixed heights.

  1. PID controller tuning using metaheuristic optimization algorithms for benchmark problems

    NASA Astrophysics Data System (ADS)

    Gholap, Vishal; Naik Dessai, Chaitali; Bagyaveereswaran, V.

    2017-11-01

    This paper contributes to find the optimal PID controller parameters using particle swarm optimization (PSO), Genetic Algorithm (GA) and Simulated Annealing (SA) algorithm. The algorithms were developed through simulation of chemical process and electrical system and the PID controller is tuned. Here, two different fitness functions such as Integral Time Absolute Error and Time domain Specifications were chosen and applied on PSO, GA and SA while tuning the controller. The proposed Algorithms are implemented on two benchmark problems of coupled tank system and DC motor. Finally, comparative study has been done with different algorithms based on best cost, number of iterations and different objective functions. The closed loop process response for each set of tuned parameters is plotted for each system with each fitness function.

  2. Improve earthquake hypocenter using adaptive simulated annealing inversion in regional tectonic, volcano tectonic, and geothermal observation

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Ry, Rexha Verdhora, E-mail: rexha.vry@gmail.com; Nugraha, Andri Dian, E-mail: nugraha@gf.itb.ac.id

    Observation of earthquakes is routinely used widely in tectonic activity observation, and also in local scale such as volcano tectonic and geothermal activity observation. It is necessary for determining the location of precise hypocenter which the process involves finding a hypocenter location that has minimum error between the observed and the calculated travel times. When solving this nonlinear inverse problem, simulated annealing inversion method can be applied to such global optimization problems, which the convergence of its solution is independent of the initial model. In this study, we developed own program codeby applying adaptive simulated annealing inversion in Matlab environment.more » We applied this method to determine earthquake hypocenter using several data cases which are regional tectonic, volcano tectonic, and geothermal field. The travel times were calculated using ray tracing shooting method. We then compared its results with the results using Geiger’s method to analyze its reliability. Our results show hypocenter location has smaller RMS error compared to the Geiger’s result that can be statistically associated with better solution. The hypocenter of earthquakes also well correlated with geological structure in the study area. Werecommend using adaptive simulated annealing inversion to relocate hypocenter location in purpose to get precise and accurate earthquake location.« less

  3. The multinomial simulation algorithm for discrete stochastic simulation of reaction-diffusion systems.

    PubMed

    Lampoudi, Sotiria; Gillespie, Dan T; Petzold, Linda R

    2009-03-07

    The Inhomogeneous Stochastic Simulation Algorithm (ISSA) is a variant of the stochastic simulation algorithm in which the spatially inhomogeneous volume of the system is divided into homogeneous subvolumes, and the chemical reactions in those subvolumes are augmented by diffusive transfers of molecules between adjacent subvolumes. The ISSA can be prohibitively slow when the system is such that diffusive transfers occur much more frequently than chemical reactions. In this paper we present the Multinomial Simulation Algorithm (MSA), which is designed to, on the one hand, outperform the ISSA when diffusive transfer events outnumber reaction events, and on the other, to handle small reactant populations with greater accuracy than deterministic-stochastic hybrid algorithms. The MSA treats reactions in the usual ISSA fashion, but uses appropriately conditioned binomial random variables for representing the net numbers of molecules diffusing from any given subvolume to a neighbor within a prescribed distance. Simulation results illustrate the benefits of the algorithm.

  4. Algorithm for Simulating Atmospheric Turbulence and Aeroelastic Effects on Simulator Motion Systems

    NASA Technical Reports Server (NTRS)

    Ercole, Anthony V.; Cardullo, Frank M.; Kelly, Lon C.; Houck, Jacob A.

    2012-01-01

    Atmospheric turbulence produces high frequency accelerations in aircraft, typically greater than the response to pilot input. Motion system equipped flight simulators must present cues representative of the aircraft response to turbulence in order to maintain the integrity of the simulation. Currently, turbulence motion cueing produced by flight simulator motion systems has been less than satisfactory because the turbulence profiles have been attenuated by the motion cueing algorithms. This report presents a new turbulence motion cueing algorithm, referred to as the augmented turbulence channel. Like the previous turbulence algorithms, the output of the channel only augments the vertical degree of freedom of motion. This algorithm employs a parallel aircraft model and an optional high bandwidth cueing filter. Simulation of aeroelastic effects is also an area where frequency content must be preserved by the cueing algorithm. The current aeroelastic implementation uses a similar secondary channel that supplements the primary motion cue. Two studies were conducted using the NASA Langley Visual Motion Simulator and Cockpit Motion Facility to evaluate the effect of the turbulence channel and aeroelastic model on pilot control input. Results indicate that the pilot is better correlated with the aircraft response, when the augmented channel is in place.

  5. Quantum Spin Glasses, Annealing and Computation

    NASA Astrophysics Data System (ADS)

    Chakrabarti, Bikas K.; Inoue, Jun-ichi; Tamura, Ryo; Tanaka, Shu

    2017-05-01

    List of tables; List of figures, Preface; 1. Introduction; Part I. Quantum Spin Glass, Annealing and Computation: 2. Classical spin models from ferromagnetic spin systems to spin glasses; 3. Simulated annealing; 4. Quantum spin glass; 5. Quantum dynamics; 6. Quantum annealing; Part II. Additional Notes: 7. Notes on adiabatic quantum computers; 8. Quantum information and quenching dynamics; 9. A brief historical note on the studies of quantum glass, annealing and computation.

  6. A hybrid intelligent algorithm for portfolio selection problem with fuzzy returns

    NASA Astrophysics Data System (ADS)

    Li, Xiang; Zhang, Yang; Wong, Hau-San; Qin, Zhongfeng

    2009-11-01

    Portfolio selection theory with fuzzy returns has been well developed and widely applied. Within the framework of credibility theory, several fuzzy portfolio selection models have been proposed such as mean-variance model, entropy optimization model, chance constrained programming model and so on. In order to solve these nonlinear optimization models, a hybrid intelligent algorithm is designed by integrating simulated annealing algorithm, neural network and fuzzy simulation techniques, where the neural network is used to approximate the expected value and variance for fuzzy returns and the fuzzy simulation is used to generate the training data for neural network. Since these models are used to be solved by genetic algorithm, some comparisons between the hybrid intelligent algorithm and genetic algorithm are given in terms of numerical examples, which imply that the hybrid intelligent algorithm is robust and more effective. In particular, it reduces the running time significantly for large size problems.

  7. Minimizing distortion and internal forces in truss structures by simulated annealing

    NASA Technical Reports Server (NTRS)

    Kincaid, Rex K.; Padula, Sharon L.

    1990-01-01

    Inaccuracies in the length of members and the diameters of joints of large space structures may produce unacceptable levels of surface distortion and internal forces. Here, two discrete optimization problems are formulated, one to minimize surface distortion (DSQRMS) and the other to minimize internal forces (FSQRMS). Both of these problems are based on the influence matrices generated by a small-deformation linear analysis. Good solutions are obtained for DSQRMS and FSQRMS through the use of a simulated annealing heuristic.

  8. Vectorized algorithms for spiking neural network simulation.

    PubMed

    Brette, Romain; Goodman, Dan F M

    2011-06-01

    High-level languages (Matlab, Python) are popular in neuroscience because they are flexible and accelerate development. However, for simulating spiking neural networks, the cost of interpretation is a bottleneck. We describe a set of algorithms to simulate large spiking neural networks efficiently with high-level languages using vector-based operations. These algorithms constitute the core of Brian, a spiking neural network simulator written in the Python language. Vectorized simulation makes it possible to combine the flexibility of high-level languages with the computational efficiency usually associated with compiled languages.

  9. Automatic Phase Picker for Local and Teleseismic Events Using Wavelet Transform and Simulated Annealing

    NASA Astrophysics Data System (ADS)

    Gaillot, P.; Bardaine, T.; Lyon-Caen, H.

    2004-12-01

    Since recent years, various automatic phase pickers based on the wavelet transform have been developed. The main motivation for using wavelet transform is that they are excellent at finding the characteristics of transient signals, they have good time resolution at all periods, and they are easy to program for fast execution. Thus, the time-scale properties and flexibility of the wavelets allow detection of P and S phases in a broad frequency range making their utilization possible in various context. However, the direct application of an automatic picking program in a different context/network than the one for which it has been initially developed is quickly tedious. In fact, independently of the strategy involved in automatic picking algorithms (window average, autoregressive, beamforming, optimization filtering, neuronal network), all developed algorithms use different parameters that depend on the objective of the seismological study, the region and the seismological network. Classically, these parameters are manually defined by trial-error or calibrated learning stage. In order to facilitate this laborious process, we have developed an automated method that provide optimal parameters for the picking programs. The set of parameters can be explored using simulated annealing which is a generic name for a family of optimization algorithms based on the principle of stochastic relaxation. The optimization process amounts to systematically modifying an initial realization so as to decrease the value of the objective function, getting the realization acceptably close to the target statistics. Different formulations of the optimization problem (objective function) are discussed using (1) world seismicity data recorded by the French national seismic monitoring network (ReNass), (2) regional seismicity data recorded in the framework of the Corinth Rift Laboratory (CRL) experiment, (3) induced seismicity data from the gas field of Lacq (Western Pyrenees), and (4) micro

  10. Annealing Ant Colony Optimization with Mutation Operator for Solving TSP

    PubMed Central

    2016-01-01

    Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality. PMID:27999590

  11. Annealing Ant Colony Optimization with Mutation Operator for Solving TSP.

    PubMed

    Mohsen, Abdulqader M

    2016-01-01

    Ant Colony Optimization (ACO) has been successfully applied to solve a wide range of combinatorial optimization problems such as minimum spanning tree, traveling salesman problem, and quadratic assignment problem. Basic ACO has drawbacks of trapping into local minimum and low convergence rate. Simulated annealing (SA) and mutation operator have the jumping ability and global convergence; and local search has the ability to speed up the convergence. Therefore, this paper proposed a hybrid ACO algorithm integrating the advantages of ACO, SA, mutation operator, and local search procedure to solve the traveling salesman problem. The core of algorithm is based on the ACO. SA and mutation operator were used to increase the ants population diversity from time to time and the local search was used to exploit the current search area efficiently. The comparative experiments, using 24 TSP instances from TSPLIB, show that the proposed algorithm outperformed some well-known algorithms in the literature in terms of solution quality.

  12. Close packing in curved space by simulated annealing

    NASA Astrophysics Data System (ADS)

    Wille, L. T.

    1987-12-01

    The problem of packing spheres of a maximum radius on the surface of a four-dimensional hypersphere is considered. It is shown how near-optimal solutions can be obtained by packing soft spheres, modelled as classical particles interacting under an inverse power potential, followed by a subsequent hardening of the interaction. In order to avoid trapping in high-lying local minima, the simulated annealing method is used to optimise the soft-sphere packing. Several improvements over other work (based on local optimisation of random initial configurations of hard spheres) have been found. The freezing behaviour of this system is discussed as a function of particle number, softness of the potential and cooling rate. Apart from their geometric interest, these results are useful in the study of topological frustration, metallic glasses and quasicrystals.

  13. Unraveling Quantum Annealers using Classical Hardness

    PubMed Central

    Martin-Mayor, Victor; Hen, Itay

    2015-01-01

    Recent advances in quantum technology have led to the development and manufacturing of experimental programmable quantum annealing optimizers that contain hundreds of quantum bits. These optimizers, commonly referred to as ‘D-Wave’ chips, promise to solve practical optimization problems potentially faster than conventional ‘classical’ computers. Attempts to quantify the quantum nature of these chips have been met with both excitement and skepticism but have also brought up numerous fundamental questions pertaining to the distinguishability of experimental quantum annealers from their classical thermal counterparts. Inspired by recent results in spin-glass theory that recognize ‘temperature chaos’ as the underlying mechanism responsible for the computational intractability of hard optimization problems, we devise a general method to quantify the performance of quantum annealers on optimization problems suffering from varying degrees of temperature chaos: A superior performance of quantum annealers over classical algorithms on these may allude to the role that quantum effects play in providing speedup. We utilize our method to experimentally study the D-Wave Two chip on different temperature-chaotic problems and find, surprisingly, that its performance scales unfavorably as compared to several analogous classical algorithms. We detect, quantify and discuss several purely classical effects that possibly mask the quantum behavior of the chip. PMID:26483257

  14. Multi-Algorithm Particle Simulations with Spatiocyte.

    PubMed

    Arjunan, Satya N V; Takahashi, Koichi

    2017-01-01

    As quantitative biologists get more measurements of spatially regulated systems such as cell division and polarization, simulation of reaction and diffusion of proteins using the data is becoming increasingly relevant to uncover the mechanisms underlying the systems. Spatiocyte is a lattice-based stochastic particle simulator for biochemical reaction and diffusion processes. Simulations can be performed at single molecule and compartment spatial scales simultaneously. Molecules can diffuse and react in 1D (filament), 2D (membrane), and 3D (cytosol) compartments. The implications of crowded regions in the cell can be investigated because each diffusing molecule has spatial dimensions. Spatiocyte adopts multi-algorithm and multi-timescale frameworks to simulate models that simultaneously employ deterministic, stochastic, and particle reaction-diffusion algorithms. Comparison of light microscopy images to simulation snapshots is supported by Spatiocyte microscopy visualization and molecule tagging features. Spatiocyte is open-source software and is freely available at http://spatiocyte.org .

  15. Crosshole Tomography, Waveform Inversion, and Anisotropy: A Combined Approach Using Simulated Annealing

    NASA Astrophysics Data System (ADS)

    Afanasiev, M.; Pratt, R. G.; Kamei, R.; McDowell, G.

    2012-12-01

    Crosshole seismic tomography has been used by Vale to provide geophysical images of mineralized massive sulfides in the Eastern Deeps deposit at Voisey's Bay, Labrador, Canada. To date, these data have been processed using traveltime tomography, and we seek to improve the resolution of these images by applying acoustic Waveform Tomography. Due to the computational cost of acoustic waveform modelling, local descent algorithms are employed in Waveform Tomography; due to non-linearity an initial model is required which predicts first-arrival traveltimes to within a half-cycle of the lowest frequency used. Because seismic velocity anisotropy can be significant in hardrock settings, the initial model must quantify the anisotropy in order to meet the half-cycle criterion. In our case study, significant velocity contrasts between the target massive sulfides and the surrounding country rock led to difficulties in generating an accurate anisotropy model through traveltime tomography, and our starting model for Waveform Tomography failed the half-cycle criterion at large offsets. We formulate a new, semi-global approach for finding the best-fit 1-D elliptical anisotropy model using simulated annealing. Through random perturbations to Thompson's ɛ parameter, we explore the L2 norm of the frequency-domain phase residuals in the space of potential anisotropy models: If a perturbation decreases the residuals, it is always accepted, but if a perturbation increases the residuals, it is accepted with the probability P = exp(-(Ei-E)/T). This is the Metropolis criterion, where Ei is the value of the residuals at the current iteration, E is the value of the residuals for the previously accepted model, and T is a probability control parameter, which is decreased over the course of the simulation via a preselected cooling schedule. Convergence to the global minimum of the residuals is guaranteed only for infinitely slow cooling, but in practice good results are obtained from a variety

  16. Annealed importance sampling with constant cooling rate

    NASA Astrophysics Data System (ADS)

    Giovannelli, Edoardo; Cardini, Gianni; Gellini, Cristina; Pietraperzia, Giangaetano; Chelli, Riccardo

    2015-02-01

    Annealed importance sampling is a simulation method devised by Neal [Stat. Comput. 11, 125 (2001)] to assign weights to configurations generated by simulated annealing trajectories. In particular, the equilibrium average of a generic physical quantity can be computed by a weighted average exploiting weights and estimates of this quantity associated to the final configurations of the annealed trajectories. Here, we review annealed importance sampling from the perspective of nonequilibrium path-ensemble averages [G. E. Crooks, Phys. Rev. E 61, 2361 (2000)]. The equivalence of Neal's and Crooks' treatments highlights the generality of the method, which goes beyond the mere thermal-based protocols. Furthermore, we show that a temperature schedule based on a constant cooling rate outperforms stepwise cooling schedules and that, for a given elapsed computer time, performances of annealed importance sampling are, in general, improved by increasing the number of intermediate temperatures.

  17. Bio-inspired algorithms applied to molecular docking simulations.

    PubMed

    Heberlé, G; de Azevedo, W F

    2011-01-01

    Nature as a source of inspiration has been shown to have a great beneficial impact on the development of new computational methodologies. In this scenario, analyses of the interactions between a protein target and a ligand can be simulated by biologically inspired algorithms (BIAs). These algorithms mimic biological systems to create new paradigms for computation, such as neural networks, evolutionary computing, and swarm intelligence. This review provides a description of the main concepts behind BIAs applied to molecular docking simulations. Special attention is devoted to evolutionary algorithms, guided-directed evolutionary algorithms, and Lamarckian genetic algorithms. Recent applications of these methodologies to protein targets identified in the Mycobacterium tuberculosis genome are described.

  18. SU-F-BRD-13: Quantum Annealing Applied to IMRT Beamlet Intensity Optimization

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Nazareth, D; Spaans, J

    Purpose: We report on the first application of quantum annealing (QA) to the process of beamlet intensity optimization for IMRT. QA is a new technology, which employs novel hardware and software techniques to address various discrete optimization problems in many fields. Methods: We apply the D-Wave Inc. proprietary hardware, which natively exploits quantum mechanical effects for improved optimization. The new QA algorithm, running on this hardware, is most similar to simulated annealing, but relies on natural processes to directly minimize the free energy of a system. A simple quantum system is slowly evolved into a classical system, representing the objectivemore » function. To apply QA to IMRT-type optimization, two prostate cases were considered. A reduced number of beamlets were employed, due to the current QA hardware limitation of ∼500 binary variables. The beamlet dose matrices were computed using CERR, and an objective function was defined based on typical clinical constraints, including dose-volume objectives. The objective function was discretized, and the QA method was compared to two standard optimization Methods: simulated annealing and Tabu search, run on a conventional computing cluster. Results: Based on several runs, the average final objective function value achieved by the QA was 16.9 for the first patient, compared with 10.0 for Tabu and 6.7 for the SA. For the second patient, the values were 70.7 for the QA, 120.0 for Tabu, and 22.9 for the SA. The QA algorithm required 27–38% of the time required by the other two methods. Conclusion: In terms of objective function value, the QA performance was similar to Tabu but less effective than the SA. However, its speed was 3–4 times faster than the other two methods. This initial experiment suggests that QA-based heuristics may offer significant speedup over conventional clinical optimization methods, as quantum annealing hardware scales to larger sizes.« less

  19. Hydrogeophysical Assessment of Aquifer Uncertainty Using Simulated Annealing driven MRF-Based Stochastic Joint Inversion

    NASA Astrophysics Data System (ADS)

    Oware, E. K.

    2017-12-01

    Geophysical quantification of hydrogeological parameters typically involve limited noisy measurements coupled with inadequate understanding of the target phenomenon. Hence, a deterministic solution is unrealistic in light of the largely uncertain inputs. Stochastic imaging (SI), in contrast, provides multiple equiprobable realizations that enable probabilistic assessment of aquifer properties in a realistic manner. Generation of geologically realistic prior models is central to SI frameworks. Higher-order statistics for representing prior geological features in SI are, however, usually borrowed from training images (TIs), which may produce undesirable outcomes if the TIs are unpresentatitve of the target structures. The Markov random field (MRF)-based SI strategy provides a data-driven alternative to TI-based SI algorithms. In the MRF-based method, the simulation of spatial features is guided by Gibbs energy (GE) minimization. Local configurations with smaller GEs have higher likelihood of occurrence and vice versa. The parameters of the Gibbs distribution for computing the GE are estimated from the hydrogeophysical data, thereby enabling the generation of site-specific structures in the absence of reliable TIs. In Metropolis-like SI methods, the variance of the transition probability controls the jump-size. The procedure is a standard Markov chain Monte Carlo (McMC) method when a constant variance is assumed, and becomes simulated annealing (SA) when the variance (cooling temperature) is allowed to decrease gradually with time. We observe that in certain problems, the large variance typically employed at the beginning to hasten burn-in may be unideal for sampling at the equilibrium state. The powerfulness of SA stems from its flexibility to adaptively scale the variance at different stages of the sampling. Degeneration of results were reported in a previous implementation of the MRF-based SI strategy based on a constant variance. Here, we present an updated

  20. 2-D Ultrasound Sparse Arrays Multidepth Radiation Optimization Using Simulated Annealing and Spiral-Array Inspired Energy Functions.

    PubMed

    Roux, Emmanuel; Ramalli, Alessandro; Tortoli, Piero; Cachard, Christian; Robini, Marc C; Liebgott, Herve

    2016-12-01

    Full matrix arrays are excellent tools for 3-D ultrasound imaging, but the required number of active elements is too high to be individually controlled by an equal number of scanner channels. The number of active elements is significantly reduced by the sparse array techniques, but the position of the remaining elements must be carefully optimized. This issue is faced here by introducing novel energy functions in the simulated annealing (SA) algorithm. At each iteration step of the optimization process, one element is freely translated and the associated radiated pattern is simulated. To control the pressure field behavior at multiple depths, three energy functions inspired by the pressure field radiated by a Blackman-tapered spiral array are introduced. Such energy functions aim at limiting the main lobe width while lowering the side lobe and grating lobe levels at multiple depths. Numerical optimization results illustrate the influence of the number of iterations, pressure measurement points, and depths, as well as the influence of the energy function definition on the optimized layout. It is also shown that performance close to or even better than the one provided by a spiral array, here assumed as reference, may be obtained. The finite-time convergence properties of SA allow the duration of the optimization process to be set in advance.

  1. Hydrological Modelling and Sensitivity Analysis Using Topmodel and Simulated Annealing Techniques.application To The Haute-mentue Catchment(switzerland).

    NASA Astrophysics Data System (ADS)

    Balin Talamba, D.; Higy, C.; Joerin, C.; Musy, A.

    The paper presents an application concerning the hydrological modelling for the Haute-Mentue catchment, located in western Switzerland. A simplified version of Topmodel, developed in a Labview programming environment, was applied in the aim of modelling the hydrological processes on this catchment. Previous researches car- ried out in this region outlined the importance of the environmental tracers in studying the hydrological behaviour and an important knowledge has been accumulated dur- ing this period concerning the mechanisms responsible for runoff generation. In con- formity with the theoretical constraints, Topmodel was applied for an Haute-Mentue sub-catchment where tracing experiments showed constantly low contributions of the soil water during the flood events. The model was applied for two humid periods in 1998. First, the model calibration was done in order to provide the best estimations for the total runoff. Instead, the simulated components (groundwater and rapid flow) showed far deviations from the reality indicated by the tracing experiments. Thus, a new calibration was performed including additional information given by the environ- mental tracing. The calibration of the model was done by using simulated annealing (SA) techniques, which are easy to implement and statistically allow for converging to a global minimum. The only problem is that the method is time and computer consum- ing. To improve this, a version of SA was used which is known as very fast-simulated annealing (VFSA). The principles are the same as for the SA technique. The random search is guided by certain probability distribution and the acceptance criterion is the same as for SA but the VFSA allows for better taking into account the ranges of vari- ation of each parameter. Practice with Topmodel showed that the energy function has different sensitivities along different dimensions of the parameter space. The VFSA algorithm allows differentiated search in relation with the

  2. The systems biology simulation core algorithm

    PubMed Central

    2013-01-01

    Background With the increasing availability of high dimensional time course data for metabolites, genes, and fluxes, the mathematical description of dynamical systems has become an essential aspect of research in systems biology. Models are often encoded in formats such as SBML, whose structure is very complex and difficult to evaluate due to many special cases. Results This article describes an efficient algorithm to solve SBML models that are interpreted in terms of ordinary differential equations. We begin our consideration with a formal representation of the mathematical form of the models and explain all parts of the algorithm in detail, including several preprocessing steps. We provide a flexible reference implementation as part of the Systems Biology Simulation Core Library, a community-driven project providing a large collection of numerical solvers and a sophisticated interface hierarchy for the definition of custom differential equation systems. To demonstrate the capabilities of the new algorithm, it has been tested with the entire SBML Test Suite and all models of BioModels Database. Conclusions The formal description of the mathematics behind the SBML format facilitates the implementation of the algorithm within specifically tailored programs. The reference implementation can be used as a simulation backend for Java™-based programs. Source code, binaries, and documentation can be freely obtained under the terms of the LGPL version 3 from http://simulation-core.sourceforge.net. Feature requests, bug reports, contributions, or any further discussion can be directed to the mailing list simulation-core-development@lists.sourceforge.net. PMID:23826941

  3. A hierarchical exact accelerated stochastic simulation algorithm

    NASA Astrophysics Data System (ADS)

    Orendorff, David; Mjolsness, Eric

    2012-12-01

    A new algorithm, "HiER-leap" (hierarchical exact reaction-leaping), is derived which improves on the computational properties of the ER-leap algorithm for exact accelerated simulation of stochastic chemical kinetics. Unlike ER-leap, HiER-leap utilizes a hierarchical or divide-and-conquer organization of reaction channels into tightly coupled "blocks" and is thereby able to speed up systems with many reaction channels. Like ER-leap, HiER-leap is based on the use of upper and lower bounds on the reaction propensities to define a rejection sampling algorithm with inexpensive early rejection and acceptance steps. But in HiER-leap, large portions of intra-block sampling may be done in parallel. An accept/reject step is used to synchronize across blocks. This method scales well when many reaction channels are present and has desirable asymptotic properties. The algorithm is exact, parallelizable and achieves a significant speedup over the stochastic simulation algorithm and ER-leap on certain problems. This algorithm offers a potentially important step towards efficient in silico modeling of entire organisms.

  4. X-ray simulation algorithms used in ISP

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Sullivan, John P.

    ISP is a simulation code which is sometimes used in the USNDS program. ISP is maintained by Sandia National Lab. However, the X-ray simulation algorithm used by ISP was written by scientists at LANL – mainly by Ed Fenimore with some contributions from John Sullivan and George Neuschaefer and probably others. In email to John Sullivan on July 25, 2016, Jill Rivera, ISP project lead, said “ISP uses the function xdosemeters_sim from the xgen library.” The is a fortran subroutine which is also used to simulate the X-ray response in consim (a descendant of xgen). Therefore, no separate documentation ofmore » the X-ray simulation algorithms in ISP have been written – the documentation for the consim simulation can be used.« less

  5. De novo protein structure prediction by dynamic fragment assembly and conformational space annealing.

    PubMed

    Lee, Juyong; Lee, Jinhyuk; Sasaki, Takeshi N; Sasai, Masaki; Seok, Chaok; Lee, Jooyoung

    2011-08-01

    Ab initio protein structure prediction is a challenging problem that requires both an accurate energetic representation of a protein structure and an efficient conformational sampling method for successful protein modeling. In this article, we present an ab initio structure prediction method which combines a recently suggested novel way of fragment assembly, dynamic fragment assembly (DFA) and conformational space annealing (CSA) algorithm. In DFA, model structures are scored by continuous functions constructed based on short- and long-range structural restraint information from a fragment library. Here, DFA is represented by the full-atom model by CHARMM with the addition of the empirical potential of DFIRE. The relative contributions between various energy terms are optimized using linear programming. The conformational sampling was carried out with CSA algorithm, which can find low energy conformations more efficiently than simulated annealing used in the existing DFA study. The newly introduced DFA energy function and CSA sampling algorithm are implemented into CHARMM. Test results on 30 small single-domain proteins and 13 template-free modeling targets of the 8th Critical Assessment of protein Structure Prediction show that the current method provides comparable and complementary prediction results to existing top methods. Copyright © 2011 Wiley-Liss, Inc.

  6. Workflow of the Grover algorithm simulation incorporating CUDA and GPGPU

    NASA Astrophysics Data System (ADS)

    Lu, Xiangwen; Yuan, Jiabin; Zhang, Weiwei

    2013-09-01

    The Grover quantum search algorithm, one of only a few representative quantum algorithms, can speed up many classical algorithms that use search heuristics. No true quantum computer has yet been developed. For the present, simulation is one effective means of verifying the search algorithm. In this work, we focus on the simulation workflow using a compute unified device architecture (CUDA). Two simulation workflow schemes are proposed. These schemes combine the characteristics of the Grover algorithm and the parallelism of general-purpose computing on graphics processing units (GPGPU). We also analyzed the optimization of memory space and memory access from this perspective. We implemented four programs on CUDA to evaluate the performance of schemes and optimization. Through experimentation, we analyzed the organization of threads suited to Grover algorithm simulations, compared the storage costs of the four programs, and validated the effectiveness of optimization. Experimental results also showed that the distinguished program on CUDA outperformed the serial program of libquantum on a CPU with a speedup of up to 23 times (12 times on average), depending on the scale of the simulation.

  7. Epidemic spreading in annealed directed networks: susceptible-infected-susceptible model and contact process.

    PubMed

    Kwon, Sungchul; Kim, Yup

    2013-01-01

    We investigate epidemic spreading in annealed directed scale-free networks with the in-degree (k) distribution P(in)(k)~k(-γ(in)) and the out-degree (ℓ) distribution, P(out)(ℓ)~ℓ(-γ(out)). The correlation of each node on the networks is controlled by the probability r(0≤r≤1) in two different algorithms, the so-called k and ℓ algorithms. For r=1, the k algorithm gives =, whereas the ℓ algorithm gives =<ℓ(2)>. For r=0, =<ℓ> for both algorithms. As the prototype of epidemic spreading, the susceptible-infected-susceptible model and contact process on the networks are analyzed using the heterogeneous mean-field theory and Monte Carlo simulations. The directedness of links and the correlation of the network are found to play important roles in the spreading, so that critical behaviors of both models are distinct from those on undirected scale-free networks.

  8. A Coulomb collision algorithm for weighted particle simulations

    NASA Technical Reports Server (NTRS)

    Miller, Ronald H.; Combi, Michael R.

    1994-01-01

    A binary Coulomb collision algorithm is developed for weighted particle simulations employing Monte Carlo techniques. Charged particles within a given spatial grid cell are pair-wise scattered, explicitly conserving momentum and implicitly conserving energy. A similar algorithm developed by Takizuka and Abe (1977) conserves momentum and energy provided the particles are unweighted (each particle representing equal fractions of the total particle density). If applied as is to simulations incorporating weighted particles, the plasma temperatures equilibrate to an incorrect temperature, as compared to theory. Using the appropriate pairing statistics, a Coulomb collision algorithm is developed for weighted particles. The algorithm conserves energy and momentum and produces the appropriate relaxation time scales as compared to theoretical predictions. Such an algorithm is necessary for future work studying self-consistent multi-species kinetic transport.

  9. Spin-the-bottle Sort and Annealing Sort: Oblivious Sorting via Round-robin Random Comparisons

    PubMed Central

    Goodrich, Michael T.

    2013-01-01

    We study sorting algorithms based on randomized round-robin comparisons. Specifically, we study Spin-the-bottle sort, where comparisons are unrestricted, and Annealing sort, where comparisons are restricted to a distance bounded by a temperature parameter. Both algorithms are simple, randomized, data-oblivious sorting algorithms, which are useful in privacy-preserving computations, but, as we show, Annealing sort is much more efficient. We show that there is an input permutation that causes Spin-the-bottle sort to require Ω(n2 log n) expected time in order to succeed, and that in O(n2 log n) time this algorithm succeeds with high probability for any input. We also show there is a specification of Annealing sort that runs in O(n log n) time and succeeds with very high probability. PMID:24550575

  10. LAWS simulation: Sampling strategies and wind computation algorithms

    NASA Technical Reports Server (NTRS)

    Emmitt, G. D. A.; Wood, S. A.; Houston, S. H.

    1989-01-01

    In general, work has continued on developing and evaluating algorithms designed to manage the Laser Atmospheric Wind Sounder (LAWS) lidar pulses and to compute the horizontal wind vectors from the line-of-sight (LOS) measurements. These efforts fall into three categories: Improvements to the shot management and multi-pair algorithms (SMA/MPA); observing system simulation experiments; and ground-based simulations of LAWS.

  11. Stochastic and Deterministic Crystal Structure Solution Methods in GSAS-II: Monte Carlo/Simulated Annealing Versus Charge Flipping

    DOE PAGES

    Von Dreele, Robert

    2017-08-29

    One of the goals in developing GSAS-II was to expand from the capabilities of the original General Structure Analysis System (GSAS) which largely encompassed just structure refinement and post refinement analysis. GSAS-II has been written almost entirely in Python loaded with graphics, GUI and mathematical packages (matplotlib, pyOpenGL, wxpython, numpy and scipy). Thus, GSAS-II has a fully developed modern GUI as well as extensive graphical display of data and results. However, the structure and operation of Python has required new approaches to many of the algorithms used in crystal structure analysis. The extensions beyond GSAS include image calibration/integration as wellmore » as peak fitting and unit cell indexing for powder data which are precursors for structure solution. Structure solution within GSAS-II begins with either Pawley or LeBail extracted structure factors from powder data or those measured in a single crystal experiment. Both charge flipping and Monte Carlo-Simulated Annealing techniques are available; the former can be applied to (3+1) incommensurate structures as well as conventional 3D structures.« less

  12. An Evaluation of the Sniffer Global Optimization Algorithm Using Standard Test Functions

    NASA Astrophysics Data System (ADS)

    Butler, Roger A. R.; Slaminka, Edward E.

    1992-03-01

    The performance of Sniffer—a new global optimization algorithm—is compared with that of Simulated Annealing. Using the number of function evaluations as a measure of efficiency, the new algorithm is shown to be significantly better at finding the global minimum of seven standard test functions. Several of the test functions used have many local minima and very steep walls surrounding the global minimum. Such functions are intended to thwart global minimization algorithms.

  13. First-order design of geodetic networks using the simulated annealing method

    NASA Astrophysics Data System (ADS)

    Berné, J. L.; Baselga, S.

    2004-09-01

    The general problem of the optimal design for a geodetic network subject to any extrinsic factors, namely the first-order design problem, can be dealt with as a numeric optimization problem. The classic theory of this problem and the optimization methods are revised. Then the innovative use of the simulated annealing method, which has been successfully applied in other fields, is presented for this classical geodetic problem. This method, belonging to iterative heuristic techniques in operational research, uses a thermodynamical analogy to crystalline networks to offer a solution that converges probabilistically to the global optimum. Basic formulation and some examples are studied.

  14. Two hybrid compaction algorithms for the layout optimization problem.

    PubMed

    Xiao, Ren-Bin; Xu, Yi-Chun; Amos, Martyn

    2007-01-01

    In this paper we present two new algorithms for the layout optimization problem: this concerns the placement of circular, weighted objects inside a circular container, the two objectives being to minimize imbalance of mass and to minimize the radius of the container. This problem carries real practical significance in industrial applications (such as the design of satellites), as well as being of significant theoretical interest. We present two nature-inspired algorithms for this problem, the first based on simulated annealing, and the second on particle swarm optimization. We compare our algorithms with the existing best-known algorithm, and show that our approaches out-perform it in terms of both solution quality and execution time.

  15. Temperature Scaling Law for Quantum Annealing Optimizers.

    PubMed

    Albash, Tameem; Martin-Mayor, Victor; Hen, Itay

    2017-09-15

    Physical implementations of quantum annealing unavoidably operate at finite temperatures. We point to a fundamental limitation of fixed finite temperature quantum annealers that prevents them from functioning as competitive scalable optimizers and show that to serve as optimizers annealer temperatures must be appropriately scaled down with problem size. We derive a temperature scaling law dictating that temperature must drop at the very least in a logarithmic manner but also possibly as a power law with problem size. We corroborate our results by experiment and simulations and discuss the implications of these to practical annealers.

  16. Improved non-LTE simulation algorithm

    NASA Astrophysics Data System (ADS)

    Busquet, Michel; Klapisch, Marcel; Colombant, Denis; Fyfe, David; Gardner, John

    2008-11-01

    The RAdiation Dependent Ionization Model (RADIOM)- a.k.a Busquet's model-[1] has proven its success in simulating non --LTE effects in laser fusion plasmas [2]. This improved algorithm can take into account Auger effect by a new parameter fitted to SCROLL [3] results. It is independent of the photon binning thanks to a projection on a standard grid. It guarantees smoother convergence to LTE. This algorithm has been implemented in a new way in the hydro-code FASTnD. Hydro simulations on the recent subMJ targets[4], with and without non-LTE corrections will be shown. [1] M. Busquet, Phys. Fluids B 5, 4191(1993). [2] D.G. Colombant et al, Phys. Plas. 7,2046 (2000). [3] A. Bar-Shalom, J. Oreg M. Klapisch, J. Quant. Spectr. Rad. Transf. 65 ,43 (2000). [4] S. P. Obenschain, D. G. Colombant, A. J. Schmitt et al., Phys. Plasmas 13, 056320 (2006).

  17. Using and comparing metaheuristic algorithms for optimizing bidding strategy viewpoint of profit maximization of generators

    NASA Astrophysics Data System (ADS)

    Mousavi, Seyed Hosein; Nazemi, Ali; Hafezalkotob, Ashkan

    2015-03-01

    With the formation of the competitive electricity markets in the world, optimization of bidding strategies has become one of the main discussions in studies related to market designing. Market design is challenged by multiple objectives that need to be satisfied. The solution of those multi-objective problems is searched often over the combined strategy space, and thus requires the simultaneous optimization of multiple parameters. The problem is formulated analytically using the Nash equilibrium concept for games composed of large numbers of players having discrete and large strategy spaces. The solution methodology is based on a characterization of Nash equilibrium in terms of minima of a function and relies on a metaheuristic optimization approach to find these minima. This paper presents some metaheuristic algorithms to simulate how generators bid in the spot electricity market viewpoint of their profit maximization according to the other generators' strategies, such as genetic algorithm (GA), simulated annealing (SA) and hybrid simulated annealing genetic algorithm (HSAGA) and compares their results. As both GA and SA are generic search methods, HSAGA is also a generic search method. The model based on the actual data is implemented in a peak hour of Tehran's wholesale spot market in 2012. The results of the simulations show that GA outperforms SA and HSAGA on computing time, number of function evaluation and computing stability, as well as the results of calculated Nash equilibriums by GA are less various and different from each other than the other algorithms.

  18. Neighbourhood generation mechanism applied in simulated annealing to job shop scheduling problems

    NASA Astrophysics Data System (ADS)

    Cruz-Chávez, Marco Antonio

    2015-11-01

    This paper presents a neighbourhood generation mechanism for the job shop scheduling problems (JSSPs). In order to obtain a feasible neighbour with the generation mechanism, it is only necessary to generate a permutation of an adjacent pair of operations in a scheduling of the JSSP. If there is no slack time between the adjacent pair of operations that is permuted, then it is proven, through theory and experimentation, that the new neighbour (schedule) generated is feasible. It is demonstrated that the neighbourhood generation mechanism is very efficient and effective in a simulated annealing.

  19. A fast image simulation algorithm for scanning transmission electron microscopy.

    PubMed

    Ophus, Colin

    2017-01-01

    Image simulation for scanning transmission electron microscopy at atomic resolution for samples with realistic dimensions can require very large computation times using existing simulation algorithms. We present a new algorithm named PRISM that combines features of the two most commonly used algorithms, namely the Bloch wave and multislice methods. PRISM uses a Fourier interpolation factor f that has typical values of 4-20 for atomic resolution simulations. We show that in many cases PRISM can provide a speedup that scales with f 4 compared to multislice simulations, with a negligible loss of accuracy. We demonstrate the usefulness of this method with large-scale scanning transmission electron microscopy image simulations of a crystalline nanoparticle on an amorphous carbon substrate.

  20. Optical network unit placement in Fiber-Wireless (FiWi) access network by Moth-Flame optimization algorithm

    NASA Astrophysics Data System (ADS)

    Singh, Puja; Prakash, Shashi

    2017-07-01

    Hybrid wireless-optical broadband access network (WOBAN) or Fiber-Wireless (FiWi) is the integration of wireless access network and optical network. This hybrid multi-domain network adopts the advantages of wireless and optical domains and serves the demand of technology savvy users. FiWi exhibits the properties of cost effectiveness, robustness, flexibility, high capacity, reliability and is self organized. Optical Network Unit (ONU) placement problem in FiWi contributes in simplifying the network design and enhances the performance in terms of cost efficiency and increased throughput. Several individual-based algorithms, such as Simulated Annealing (SA), Tabu Search, etc. have been suggested for ONU placement, but these algorithms suffer from premature convergence (trapping in a local optima). The present research work undertakes the deployment of FiWi and proposes a novel nature-inspired heuristic paradigm called Moth-Flame optimization (MFO) algorithm for multiple optical network units' placement. MFO is a population based algorithm. Population-based algorithms are better in handling local optima avoidance. The simulation results are compared with the existing Greedy and Simulated Annealing algorithms to optimize the position of ONUs. To the best of our knowledge, MFO algorithm has been used for the first time in this domain, moreover it has been able to provide very promising and competitive results. The performance of MFO algorithm has been analyzed by varying the 'b' parameter. MFO algorithm results in faster convergence than the existing strategies of Greedy and SA and returns a lower value of overall cost function. The results exhibit the dependence of the objective function on the distribution of wireless users also.

  1. Synchronization Algorithms for Co-Simulation of Power Grid and Communication Networks

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Ciraci, Selim; Daily, Jeffrey A.; Agarwal, Khushbu

    2014-09-11

    The ongoing modernization of power grids consists of integrating them with communication networks in order to achieve robust and resilient control of grid operations. To understand the operation of the new smart grid, one approach is to use simulation software. Unfortunately, current power grid simulators at best utilize inadequate approximations to simulate communication networks, if at all. Cooperative simulation of specialized power grid and communication network simulators promises to more accurately reproduce the interactions of real smart grid deployments. However, co-simulation is a challenging problem. A co-simulation must manage the exchange of informa- tion, including the synchronization of simulator clocks,more » between all simulators while maintaining adequate computational perfor- mance. This paper describes two new conservative algorithms for reducing the overhead of time synchronization, namely Active Set Conservative and Reactive Conservative. We provide a detailed analysis of their performance characteristics with respect to the current state of the art including both conservative and optimistic synchronization algorithms. In addition, we provide guidelines for selecting the appropriate synchronization algorithm based on the requirements of the co-simulation. The newly proposed algorithms are shown to achieve as much as 14% and 63% im- provement, respectively, over the existing conservative algorithm.« less

  2. Delay Times From Clustered Multi-Channel Cross Correlation and Simulated Annealing

    NASA Astrophysics Data System (ADS)

    Creager, K. C.; Sambridge, M. S.

    2004-12-01

    Several techniques exist to estimate relative delay times of seismic phases based on the assumption that the waveforms observed at several stations can be expressed as a common waveform that has been time shifted and distorted by random uncorrelated noise. We explore the more general problem of estimating the relative delay times for regional or even global distributions of seismometers in cases where waveforms vary systematically across the array. The estimation of relative delay times is formulated as a global optimization of the weighted sum of squares of cross correlations of each seismogram pair evaluated at the corresponding difference in their relative delay times. As there are many local minima in this penalty function, a simulated annealing algorithm is used to obtain a solution. The weights depend strongly on the separation distance among seismogram pairs as well as a measure of the similarity of waveforms. Thus, seismograph pairs that are physically close to each other and have similar waveforms are expected to be well aligned while those with dissimilar waveforms or large separation distances are severely down-weighted and thus need not be well aligned. As a result noisy seismograms, which are not similar to other seismograms, are down-weighted so they do not adversely effect the relative delay times of other seismograms. Finally, natural clusters of seismograms are determined from the weight matrix. Examples of aligning a few hundred P and PKP waveforms from a broadband global array and from a mixed broadband and short-period continental-scale array will be shown. While this method has applications in many situations, it may be especially useful for arrays such as the EarthScope Bigfoot Array.

  3. A fast recursive algorithm for molecular dynamics simulation

    NASA Technical Reports Server (NTRS)

    Jain, A.; Vaidehi, N.; Rodriguez, G.

    1993-01-01

    The present recursive algorithm for solving molecular systems' dynamical equations of motion employs internal variable models that reduce such simulations' computation time by an order of magnitude, relative to Cartesian models. Extensive use is made of spatial operator methods recently developed for analysis and simulation of the dynamics of multibody systems. A factor-of-450 speedup over the conventional O(N-cubed) algorithm is demonstrated for the case of a polypeptide molecule with 400 residues.

  4. Multiple Simulated Annealing-Molecular Dynamics (MSA-MD) for Conformational Space Search of Peptide and Miniprotein

    PubMed Central

    Hao, Ge-Fei; Xu, Wei-Fang; Yang, Sheng-Gang; Yang, Guang-Fu

    2015-01-01

    Protein and peptide structure predictions are of paramount importance for understanding their functions, as well as the interactions with other molecules. However, the use of molecular simulation techniques to directly predict the peptide structure from the primary amino acid sequence is always hindered by the rough topology of the conformational space and the limited simulation time scale. We developed here a new strategy, named Multiple Simulated Annealing-Molecular Dynamics (MSA-MD) to identify the native states of a peptide and miniprotein. A cluster of near native structures could be obtained by using the MSA-MD method, which turned out to be significantly more efficient in reaching the native structure compared to continuous MD and conventional SA-MD simulation. PMID:26492886

  5. A fast image simulation algorithm for scanning transmission electron microscopy

    DOE PAGES

    Ophus, Colin

    2017-05-10

    Image simulation for scanning transmission electron microscopy at atomic resolution for samples with realistic dimensions can require very large computation times using existing simulation algorithms. Here, we present a new algorithm named PRISM that combines features of the two most commonly used algorithms, namely the Bloch wave and multislice methods. PRISM uses a Fourier interpolation factor f that has typical values of 4-20 for atomic resolution simulations. We show that in many cases PRISM can provide a speedup that scales with f 4 compared to multislice simulations, with a negligible loss of accuracy. We demonstrate the usefulness of this methodmore » with large-scale scanning transmission electron microscopy image simulations of a crystalline nanoparticle on an amorphous carbon substrate.« less

  6. An Improved Cuckoo Search Optimization Algorithm for the Problem of Chaotic Systems Parameter Estimation

    PubMed Central

    Wang, Jun; Zhou, Bihua; Zhou, Shudao

    2016-01-01

    This paper proposes an improved cuckoo search (ICS) algorithm to establish the parameters of chaotic systems. In order to improve the optimization capability of the basic cuckoo search (CS) algorithm, the orthogonal design and simulated annealing operation are incorporated in the CS algorithm to enhance the exploitation search ability. Then the proposed algorithm is used to establish parameters of the Lorenz chaotic system and Chen chaotic system under the noiseless and noise condition, respectively. The numerical results demonstrate that the algorithm can estimate parameters with high accuracy and reliability. Finally, the results are compared with the CS algorithm, genetic algorithm, and particle swarm optimization algorithm, and the compared results demonstrate the method is energy-efficient and superior. PMID:26880874

  7. Empirical study of parallel LRU simulation algorithms

    NASA Technical Reports Server (NTRS)

    Carr, Eric; Nicol, David M.

    1994-01-01

    This paper reports on the performance of five parallel algorithms for simulating a fully associative cache operating under the LRU (Least-Recently-Used) replacement policy. Three of the algorithms are SIMD, and are implemented on the MasPar MP-2 architecture. Two other algorithms are parallelizations of an efficient serial algorithm on the Intel Paragon. One SIMD algorithm is quite simple, but its cost is linear in the cache size. The two other SIMD algorithm are more complex, but have costs that are independent on the cache size. Both the second and third SIMD algorithms compute all stack distances; the second SIMD algorithm is completely general, whereas the third SIMD algorithm presumes and takes advantage of bounds on the range of reference tags. Both MIMD algorithm implemented on the Paragon are general and compute all stack distances; they differ in one step that may affect their respective scalability. We assess the strengths and weaknesses of these algorithms as a function of problem size and characteristics, and compare their performance on traces derived from execution of three SPEC benchmark programs.

  8. Parallelization of sequential Gaussian, indicator and direct simulation algorithms

    NASA Astrophysics Data System (ADS)

    Nunes, Ruben; Almeida, José A.

    2010-08-01

    Improving the performance and robustness of algorithms on new high-performance parallel computing architectures is a key issue in efficiently performing 2D and 3D studies with large amount of data. In geostatistics, sequential simulation algorithms are good candidates for parallelization. When compared with other computational applications in geosciences (such as fluid flow simulators), sequential simulation software is not extremely computationally intensive, but parallelization can make it more efficient and creates alternatives for its integration in inverse modelling approaches. This paper describes the implementation and benchmarking of a parallel version of the three classic sequential simulation algorithms: direct sequential simulation (DSS), sequential indicator simulation (SIS) and sequential Gaussian simulation (SGS). For this purpose, the source used was GSLIB, but the entire code was extensively modified to take into account the parallelization approach and was also rewritten in the C programming language. The paper also explains in detail the parallelization strategy and the main modifications. Regarding the integration of secondary information, the DSS algorithm is able to perform simple kriging with local means, kriging with an external drift and collocated cokriging with both local and global correlations. SIS includes a local correction of probabilities. Finally, a brief comparison is presented of simulation results using one, two and four processors. All performance tests were carried out on 2D soil data samples. The source code is completely open source and easy to read. It should be noted that the code is only fully compatible with Microsoft Visual C and should be adapted for other systems/compilers.

  9. Capacity improvement using simulation optimization approaches: A case study in the thermotechnology industry

    NASA Astrophysics Data System (ADS)

    Yelkenci Köse, Simge; Demir, Leyla; Tunalı, Semra; Türsel Eliiyi, Deniz

    2015-02-01

    In manufacturing systems, optimal buffer allocation has a considerable impact on capacity improvement. This study presents a simulation optimization procedure to solve the buffer allocation problem in a heat exchanger production plant so as to improve the capacity of the system. For optimization, three metaheuristic-based search algorithms, i.e. a binary-genetic algorithm (B-GA), a binary-simulated annealing algorithm (B-SA) and a binary-tabu search algorithm (B-TS), are proposed. These algorithms are integrated with the simulation model of the production line. The simulation model, which captures the stochastic and dynamic nature of the production line, is used as an evaluation function for the proposed metaheuristics. The experimental study with benchmark problem instances from the literature and the real-life problem show that the proposed B-TS algorithm outperforms B-GA and B-SA in terms of solution quality.

  10. Fast stochastic algorithm for simulating evolutionary population dynamics

    NASA Astrophysics Data System (ADS)

    Tsimring, Lev; Hasty, Jeff; Mather, William

    2012-02-01

    Evolution and co-evolution of ecological communities are stochastic processes often characterized by vastly different rates of reproduction and mutation and a coexistence of very large and very small sub-populations of co-evolving species. This creates serious difficulties for accurate statistical modeling of evolutionary dynamics. In this talk, we introduce a new exact algorithm for fast fully stochastic simulations of birth/death/mutation processes. It produces a significant speedup compared to the direct stochastic simulation algorithm in a typical case when the total population size is large and the mutation rates are much smaller than birth/death rates. We illustrate the performance of the algorithm on several representative examples: evolution on a smooth fitness landscape, NK model, and stochastic predator-prey system.

  11. Study on Temperature and Synthetic Compensation of Piezo-Resistive Differential Pressure Sensors by Coupled Simulated Annealing and Simplex Optimized Kernel Extreme Learning Machine

    PubMed Central

    Li, Ji; Hu, Guoqing; Zhou, Yonghong; Zou, Chong; Peng, Wei; Alam SM, Jahangir

    2017-01-01

    As a high performance-cost ratio solution for differential pressure measurement, piezo-resistive differential pressure sensors are widely used in engineering processes. However, their performance is severely affected by the environmental temperature and the static pressure applied to them. In order to modify the non-linear measuring characteristics of the piezo-resistive differential pressure sensor, compensation actions should synthetically consider these two aspects. Advantages such as nonlinear approximation capability, highly desirable generalization ability and computational efficiency make the kernel extreme learning machine (KELM) a practical approach for this critical task. Since the KELM model is intrinsically sensitive to the regularization parameter and the kernel parameter, a searching scheme combining the coupled simulated annealing (CSA) algorithm and the Nelder-Mead simplex algorithm is adopted to find an optimal KLEM parameter set. A calibration experiment at different working pressure levels was conducted within the temperature range to assess the proposed method. In comparison with other compensation models such as the back-propagation neural network (BP), radius basis neural network (RBF), particle swarm optimization optimized support vector machine (PSO-SVM), particle swarm optimization optimized least squares support vector machine (PSO-LSSVM) and extreme learning machine (ELM), the compensation results show that the presented compensation algorithm exhibits a more satisfactory performance with respect to temperature compensation and synthetic compensation problems. PMID:28422080

  12. Study on Temperature and Synthetic Compensation of Piezo-Resistive Differential Pressure Sensors by Coupled Simulated Annealing and Simplex Optimized Kernel Extreme Learning Machine.

    PubMed

    Li, Ji; Hu, Guoqing; Zhou, Yonghong; Zou, Chong; Peng, Wei; Alam Sm, Jahangir

    2017-04-19

    As a high performance-cost ratio solution for differential pressure measurement, piezo-resistive differential pressure sensors are widely used in engineering processes. However, their performance is severely affected by the environmental temperature and the static pressure applied to them. In order to modify the non-linear measuring characteristics of the piezo-resistive differential pressure sensor, compensation actions should synthetically consider these two aspects. Advantages such as nonlinear approximation capability, highly desirable generalization ability and computational efficiency make the kernel extreme learning machine (KELM) a practical approach for this critical task. Since the KELM model is intrinsically sensitive to the regularization parameter and the kernel parameter, a searching scheme combining the coupled simulated annealing (CSA) algorithm and the Nelder-Mead simplex algorithm is adopted to find an optimal KLEM parameter set. A calibration experiment at different working pressure levels was conducted within the temperature range to assess the proposed method. In comparison with other compensation models such as the back-propagation neural network (BP), radius basis neural network (RBF), particle swarm optimization optimized support vector machine (PSO-SVM), particle swarm optimization optimized least squares support vector machine (PSO-LSSVM) and extreme learning machine (ELM), the compensation results show that the presented compensation algorithm exhibits a more satisfactory performance with respect to temperature compensation and synthetic compensation problems.

  13. Quantum annealing of the traveling-salesman problem.

    PubMed

    Martonák, Roman; Santoro, Giuseppe E; Tosatti, Erio

    2004-11-01

    We propose a path-integral Monte Carlo quantum annealing scheme for the symmetric traveling-salesman problem, based on a highly constrained Ising-like representation, and we compare its performance against standard thermal simulated annealing. The Monte Carlo moves implemented are standard, and consist in restructuring a tour by exchanging two links (two-opt moves). The quantum annealing scheme, even with a drastically simple form of kinetic energy, appears definitely superior to the classical one, when tested on a 1002-city instance of the standard TSPLIB.

  14. High-temperature annealing of graphite: A molecular dynamics study

    NASA Astrophysics Data System (ADS)

    Petersen, Andrew; Gillette, Victor

    2018-05-01

    A modified AIREBO potential was developed to simulate the effects of thermal annealing on the structure and physical properties of damaged graphite. AIREBO parameter modifications were made to reproduce Density Functional Theory interstitial results. These changes to the potential resulted in high-temperature annealing of the model, as measured by stored-energy reduction. These results show some resemblance to experimental high-temperature annealing results, and show promise that annealing effects in graphite are accessible with molecular dynamics and reactive potentials.

  15. Investigating the enhanced Best Performance Algorithm for Annual Crop Planning problem based on economic factors

    PubMed Central

    2017-01-01

    The Annual Crop Planning (ACP) problem was a recently introduced problem in the literature. This study further expounds on this problem by presenting a new mathematical formulation, which is based on market economic factors. To determine solutions, a new local search metaheuristic algorithm is investigated which is called the enhanced Best Performance Algorithm (eBPA). eBPA’s results are compared against two well-known local search metaheuristic algorithms; these include Tabu Search and Simulated Annealing. The results show the potential of the eBPA for continuous optimization problems. PMID:28792495

  16. Investigating the enhanced Best Performance Algorithm for Annual Crop Planning problem based on economic factors.

    PubMed

    Adewumi, Aderemi Oluyinka; Chetty, Sivashan

    2017-01-01

    The Annual Crop Planning (ACP) problem was a recently introduced problem in the literature. This study further expounds on this problem by presenting a new mathematical formulation, which is based on market economic factors. To determine solutions, a new local search metaheuristic algorithm is investigated which is called the enhanced Best Performance Algorithm (eBPA). eBPA's results are compared against two well-known local search metaheuristic algorithms; these include Tabu Search and Simulated Annealing. The results show the potential of the eBPA for continuous optimization problems.

  17. Angular filter refractometry analysis using simulated annealing [An improved method for characterizing plasma density profiles using angular filter refractometry

    DOE PAGES

    Angland, P.; Haberberger, D.; Ivancic, S. T.; ...

    2017-10-30

    Here, a new method of analysis for angular filter refractometry images was developed to characterize laser-produced, long-scale-length plasmas using an annealing algorithm to iterative converge upon a solution. Angular filter refractometry (AFR) is a novel technique used to characterize the density pro files of laser-produced, long-scale-length plasmas. A synthetic AFR image is constructed by a user-defined density profile described by eight parameters, and the algorithm systematically alters the parameters until the comparison is optimized. The optimization and statistical uncertainty calculation is based on a minimization of themore » $$\\chi$$2 test statistic. The algorithm was successfully applied to experimental data of plasma expanding from a flat, laser-irradiated target, resulting in average uncertainty in the density profile of 5-10% in the region of interest.« less

  18. Angular filter refractometry analysis using simulated annealing [An improved method for characterizing plasma density profiles using angular filter refractometry

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Angland, P.; Haberberger, D.; Ivancic, S. T.

    Here, a new method of analysis for angular filter refractometry images was developed to characterize laser-produced, long-scale-length plasmas using an annealing algorithm to iterative converge upon a solution. Angular filter refractometry (AFR) is a novel technique used to characterize the density pro files of laser-produced, long-scale-length plasmas. A synthetic AFR image is constructed by a user-defined density profile described by eight parameters, and the algorithm systematically alters the parameters until the comparison is optimized. The optimization and statistical uncertainty calculation is based on a minimization of themore » $$\\chi$$2 test statistic. The algorithm was successfully applied to experimental data of plasma expanding from a flat, laser-irradiated target, resulting in average uncertainty in the density profile of 5-10% in the region of interest.« less

  19. Analysis of optimisation method for a two-stroke piston ring using the Finite Element Method and the Simulated Annealing Method

    NASA Astrophysics Data System (ADS)

    Kaliszewski, M.; Mazuro, P.

    2016-09-01

    Simulated Annealing Method of optimisation for the sealing piston ring geometry is tested. The aim of optimisation is to develop ring geometry which would exert demanded pressure on a cylinder just while being bended to fit the cylinder. Method of FEM analysis of an arbitrary piston ring geometry is applied in an ANSYS software. The demanded pressure function (basing on formulae presented by A. Iskra) as well as objective function are introduced. Geometry definition constructed by polynomials in radial coordinate system is delivered and discussed. Possible application of Simulated Annealing Method in a piston ring optimisation task is proposed and visualised. Difficulties leading to possible lack of convergence of optimisation are presented. An example of an unsuccessful optimisation performed in APDL is discussed. Possible line of further optimisation improvement is proposed.

  20. An algorithm for simulating fracture of cohesive-frictional materials

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Nukala, Phani K; Sampath, Rahul S; Barai, Pallab

    Fracture of disordered frictional granular materials is dominated by interfacial failure response that is characterized by de-cohesion followed by frictional sliding response. To capture such an interfacial failure response, we introduce a cohesive-friction random fuse model (CFRFM), wherein the cohesive response of the interface is represented by a linear stress-strain response until a failure threshold, which is then followed by a constant response at a threshold lower than the initial failure threshold to represent the interfacial frictional sliding mechanism. This paper presents an efficient algorithm for simulating fracture of such disordered frictional granular materials using the CFRFM. We note that,more » when applied to perfectly plastic disordered materials, our algorithm is both theoretically and numerically equivalent to the traditional tangent algorithm (Roux and Hansen 1992 J. Physique II 2 1007) used for such simulations. However, the algorithm is general and is capable of modeling discontinuous interfacial response. Our numerical simulations using the algorithm indicate that the local and global roughness exponents ({zeta}{sub loc} and {zeta}, respectively) of the fracture surface are equal to each other, and the two-dimensional crack roughness exponent is estimated to be {zeta}{sub loc} = {zeta} = 0.69 {+-} 0.03.« less

  1. A fast parallel clustering algorithm for molecular simulation trajectories.

    PubMed

    Zhao, Yutong; Sheong, Fu Kit; Sun, Jian; Sander, Pedro; Huang, Xuhui

    2013-01-15

    We implemented a GPU-powered parallel k-centers algorithm to perform clustering on the conformations of molecular dynamics (MD) simulations. The algorithm is up to two orders of magnitude faster than the CPU implementation. We tested our algorithm on four protein MD simulation datasets ranging from the small Alanine Dipeptide to a 370-residue Maltose Binding Protein (MBP). It is capable of grouping 250,000 conformations of the MBP into 4000 clusters within 40 seconds. To achieve this, we effectively parallelized the code on the GPU and utilize the triangle inequality of metric spaces. Furthermore, the algorithm's running time is linear with respect to the number of cluster centers. In addition, we found the triangle inequality to be less effective in higher dimensions and provide a mathematical rationale. Finally, using Alanine Dipeptide as an example, we show a strong correlation between cluster populations resulting from the k-centers algorithm and the underlying density. © 2012 Wiley Periodicals, Inc. Copyright © 2012 Wiley Periodicals, Inc.

  2. Temporal Gillespie Algorithm: Fast Simulation of Contagion Processes on Time-Varying Networks

    PubMed Central

    Vestergaard, Christian L.; Génois, Mathieu

    2015-01-01

    Stochastic simulations are one of the cornerstones of the analysis of dynamical processes on complex networks, and are often the only accessible way to explore their behavior. The development of fast algorithms is paramount to allow large-scale simulations. The Gillespie algorithm can be used for fast simulation of stochastic processes, and variants of it have been applied to simulate dynamical processes on static networks. However, its adaptation to temporal networks remains non-trivial. We here present a temporal Gillespie algorithm that solves this problem. Our method is applicable to general Poisson (constant-rate) processes on temporal networks, stochastically exact, and up to multiple orders of magnitude faster than traditional simulation schemes based on rejection sampling. We also show how it can be extended to simulate non-Markovian processes. The algorithm is easily applicable in practice, and as an illustration we detail how to simulate both Poissonian and non-Markovian models of epidemic spreading. Namely, we provide pseudocode and its implementation in C++ for simulating the paradigmatic Susceptible-Infected-Susceptible and Susceptible-Infected-Recovered models and a Susceptible-Infected-Recovered model with non-constant recovery rates. For empirical networks, the temporal Gillespie algorithm is here typically from 10 to 100 times faster than rejection sampling. PMID:26517860

  3. Temporal Gillespie Algorithm: Fast Simulation of Contagion Processes on Time-Varying Networks.

    PubMed

    Vestergaard, Christian L; Génois, Mathieu

    2015-10-01

    Stochastic simulations are one of the cornerstones of the analysis of dynamical processes on complex networks, and are often the only accessible way to explore their behavior. The development of fast algorithms is paramount to allow large-scale simulations. The Gillespie algorithm can be used for fast simulation of stochastic processes, and variants of it have been applied to simulate dynamical processes on static networks. However, its adaptation to temporal networks remains non-trivial. We here present a temporal Gillespie algorithm that solves this problem. Our method is applicable to general Poisson (constant-rate) processes on temporal networks, stochastically exact, and up to multiple orders of magnitude faster than traditional simulation schemes based on rejection sampling. We also show how it can be extended to simulate non-Markovian processes. The algorithm is easily applicable in practice, and as an illustration we detail how to simulate both Poissonian and non-Markovian models of epidemic spreading. Namely, we provide pseudocode and its implementation in C++ for simulating the paradigmatic Susceptible-Infected-Susceptible and Susceptible-Infected-Recovered models and a Susceptible-Infected-Recovered model with non-constant recovery rates. For empirical networks, the temporal Gillespie algorithm is here typically from 10 to 100 times faster than rejection sampling.

  4. General simulation algorithm for autocorrelated binary processes.

    PubMed

    Serinaldi, Francesco; Lombardo, Federico

    2017-02-01

    The apparent ubiquity of binary random processes in physics and many other fields has attracted considerable attention from the modeling community. However, generation of binary sequences with prescribed autocorrelation is a challenging task owing to the discrete nature of the marginal distributions, which makes the application of classical spectral techniques problematic. We show that such methods can effectively be used if we focus on the parent continuous process of beta distributed transition probabilities rather than on the target binary process. This change of paradigm results in a simulation procedure effectively embedding a spectrum-based iterative amplitude-adjusted Fourier transform method devised for continuous processes. The proposed algorithm is fully general, requires minimal assumptions, and can easily simulate binary signals with power-law and exponentially decaying autocorrelation functions corresponding, for instance, to Hurst-Kolmogorov and Markov processes. An application to rainfall intermittency shows that the proposed algorithm can also simulate surrogate data preserving the empirical autocorrelation.

  5. Computational algorithms for simulations in atmospheric optics.

    PubMed

    Konyaev, P A; Lukin, V P

    2016-04-20

    A computer simulation technique for atmospheric and adaptive optics based on parallel programing is discussed. A parallel propagation algorithm is designed and a modified spectral-phase method for computer generation of 2D time-variant random fields is developed. Temporal power spectra of Laguerre-Gaussian beam fluctuations are considered as an example to illustrate the applications discussed. Implementation of the proposed algorithms using Intel MKL and IPP libraries and NVIDIA CUDA technology is shown to be very fast and accurate. The hardware system for the computer simulation is an off-the-shelf desktop with an Intel Core i7-4790K CPU operating at a turbo-speed frequency up to 5 GHz and an NVIDIA GeForce GTX-960 graphics accelerator with 1024 1.5 GHz processors.

  6. Model Predictive Control Based Motion Drive Algorithm for a Driving Simulator

    NASA Astrophysics Data System (ADS)

    Rehmatullah, Faizan

    In this research, we develop a model predictive control based motion drive algorithm for the driving simulator at Toronto Rehabilitation Institute. Motion drive algorithms exploit the limitations of the human vestibular system to formulate a perception of motion within the constrained workspace of a simulator. In the absence of visual cues, the human perception system is unable to distinguish between acceleration and the force of gravity. The motion drive algorithm determines control inputs to displace the simulator platform, and by using the resulting inertial forces and angular rates, creates the perception of motion. By using model predictive control, we can optimize the use of simulator workspace for every maneuver while simulating the vehicle perception. With the ability to handle nonlinear constraints, the model predictive control allows us to incorporate workspace limitations.

  7. Efficient Monte Carlo Methods for Biomolecular Simulations.

    NASA Astrophysics Data System (ADS)

    Bouzida, Djamal

    A new approach to efficient Monte Carlo simulations of biological molecules is presented. By relaxing the usual restriction to Markov processes, we are able to optimize performance while dealing directly with the inhomogeneity and anisotropy inherent in these systems. The advantage of this approach is that we can introduce a wide variety of Monte Carlo moves to deal with complicated motions of the molecule, while maintaining full optimization at every step. This enables the use of a variety of collective rotational moves that relax long-wavelength modes. We were able to show by explicit simulations that the resulting algorithms substantially increase the speed of the simulation while reproducing the correct equilibrium behavior. This approach is particularly intended for simulations of macromolecules, although we expect it to be useful in other situations. The dynamic optimization of the new Monte Carlo methods makes them very suitable for simulated annealing experiments on all systems whose state space is continuous in general, and to the protein folding problem in particular. We introduce an efficient annealing schedule using preferential bias moves. Our simulated annealing experiments yield structures whose free energies were lower than the equilibrated X-ray structure, which leads us to believe that the empirical energy function used does not fully represent the interatomic interactions. Furthermore, we believe that the largest discrepancies involve the solvent effects in particular.

  8. Improving Search Algorithms by Using Intelligent Coordinates

    NASA Technical Reports Server (NTRS)

    Wolpert, David H.; Tumer, Kagan; Bandari, Esfandiar

    2004-01-01

    We consider algorithms that maximize a global function G in a distributed manner, using a different adaptive computational agent to set each variable of the underlying space. Each agent eta is self-interested; it sets its variable to maximize its own function g (sub eta). Three factors govern such a distributed algorithm's performance, related to exploration/exploitation, game theory, and machine learning. We demonstrate how to exploit alI three factors by modifying a search algorithm's exploration stage: rather than random exploration, each coordinate of the search space is now controlled by a separate machine-learning-based player engaged in a noncooperative game. Experiments demonstrate that this modification improves simulated annealing (SA) by up to an order of magnitude for bin packing and for a model of an economic process run over an underlying network. These experiments also reveal interesting small-world phenomena.

  9. A multilevel-skin neighbor list algorithm for molecular dynamics simulation

    NASA Astrophysics Data System (ADS)

    Zhang, Chenglong; Zhao, Mingcan; Hou, Chaofeng; Ge, Wei

    2018-01-01

    Searching of the interaction pairs and organization of the interaction processes are important steps in molecular dynamics (MD) algorithms and are critical to the overall efficiency of the simulation. Neighbor lists are widely used for these steps, where thicker skin can reduce the frequency of list updating but is discounted by more computation in distance check for the particle pairs. In this paper, we propose a new neighbor-list-based algorithm with a precisely designed multilevel skin which can reduce unnecessary computation on inter-particle distances. The performance advantages over traditional methods are then analyzed against the main simulation parameters on Intel CPUs and MICs (many integrated cores), and are clearly demonstrated. The algorithm can be generalized for various discrete simulations using neighbor lists.

  10. Multiobjective generalized extremal optimization algorithm for simulation of daylight illuminants

    NASA Astrophysics Data System (ADS)

    Kumar, Srividya Ravindra; Kurian, Ciji Pearl; Gomes-Borges, Marcos Eduardo

    2017-10-01

    Daylight illuminants are widely used as references for color quality testing and optical vision testing applications. Presently used daylight simulators make use of fluorescent bulbs that are not tunable and occupy more space inside the quality testing chambers. By designing a spectrally tunable LED light source with an optimal number of LEDs, cost, space, and energy can be saved. This paper describes an application of the generalized extremal optimization (GEO) algorithm for selection of the appropriate quantity and quality of LEDs that compose the light source. The multiobjective approach of this algorithm tries to get the best spectral simulation with minimum fitness error toward the target spectrum, correlated color temperature (CCT) the same as the target spectrum, high color rendering index (CRI), and luminous flux as required for testing applications. GEO is a global search algorithm based on phenomena of natural evolution and is especially designed to be used in complex optimization problems. Several simulations have been conducted to validate the performance of the algorithm. The methodology applied to model the LEDs, together with the theoretical basis for CCT and CRI calculation, is presented in this paper. A comparative result analysis of M-GEO evolutionary algorithm with the Levenberg-Marquardt conventional deterministic algorithm is also presented.

  11. Image-Based Airborne Sensors: A Combined Approach for Spectral Signatures Classification through Deterministic Simulated Annealing

    PubMed Central

    Guijarro, María; Pajares, Gonzalo; Herrera, P. Javier

    2009-01-01

    The increasing technology of high-resolution image airborne sensors, including those on board Unmanned Aerial Vehicles, demands automatic solutions for processing, either on-line or off-line, the huge amountds of image data sensed during the flights. The classification of natural spectral signatures in images is one potential application. The actual tendency in classification is oriented towards the combination of simple classifiers. In this paper we propose a combined strategy based on the Deterministic Simulated Annealing (DSA) framework. The simple classifiers used are the well tested supervised parametric Bayesian estimator and the Fuzzy Clustering. The DSA is an optimization approach, which minimizes an energy function. The main contribution of DSA is its ability to avoid local minima during the optimization process thanks to the annealing scheme. It outperforms simple classifiers used for the combination and some combined strategies, including a scheme based on the fuzzy cognitive maps and an optimization approach based on the Hopfield neural network paradigm. PMID:22399989

  12. A Non-Intrusive Algorithm for Sensitivity Analysis of Chaotic Flow Simulations

    NASA Technical Reports Server (NTRS)

    Blonigan, Patrick J.; Wang, Qiqi; Nielsen, Eric J.; Diskin, Boris

    2017-01-01

    We demonstrate a novel algorithm for computing the sensitivity of statistics in chaotic flow simulations to parameter perturbations. The algorithm is non-intrusive but requires exposing an interface. Based on the principle of shadowing in dynamical systems, this algorithm is designed to reduce the effect of the sampling error in computing sensitivity of statistics in chaotic simulations. We compare the effectiveness of this method to that of the conventional finite difference method.

  13. Shape optimization of electrostatically driven microcantilevers using simulated annealing to enhance static travel range

    NASA Astrophysics Data System (ADS)

    Trivedi, R. R.; Joglekar, M. M.; Shimpi, R. P.; Pawaskar, D. N.

    2013-12-01

    The objective of this paper is to present a systematic development of the generic shape optimization of elec- trostatically actuated microcantilever beams for extending their static travel range. Electrostatic actuators are widely used in micro electro mechanical system (MEMS) devices because of low power density and ease of fab- rication. However, their useful travel range is often restricted by a phenomenon known as pull-in instability. The Rayleigh- Ritz energy method is used for computation of pull-in parameters which includes electrostatic potential and fringing field effect. Appropriate width function and linear thickness functions are employed along the length of the non-prismatic beam to achieve enhanced travel range. Parameters used for varying the thick- ness and width functions are optimized using simulated annealing with pattern search method towards the end to refine the results. Appropriate penalties are imposed on the violation of volume, width, thickness and area constraints. Nine test cases are considered for demonstration of the said optimization method. Our results indicate that around 26% increase in the travel range of a non-prismatic beam can be achieved after optimiza- tion compared to that in a prismatic beam having the same volume. Our results also show an improvement in the pull-in displacement of around 5% compared to that of a variable width constant thickness actuator. We show that simulated annealing is an effective and flexible method to carry out design optimization of structural elements under electrostatic loading.

  14. Optical simulation of quantum algorithms using programmable liquid-crystal displays

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Puentes, Graciana; La Mela, Cecilia; Ledesma, Silvia

    2004-04-01

    We present a scheme to perform an all optical simulation of quantum algorithms and maps. The main components are lenses to efficiently implement the Fourier transform and programmable liquid-crystal displays to introduce space dependent phase changes on a classical optical beam. We show how to simulate Deutsch-Jozsa and Grover's quantum algorithms using essentially the same optical array programmed in two different ways.

  15. Microstructure engineering of Pt-Al alloy thin films through Monte Carlo simulations

    NASA Astrophysics Data System (ADS)

    Harris, R. A.; Terblans, J. J.; Swart, H. C.

    2014-06-01

    A kinetic algorithm, based on the regular solution model, was used in conjunction with the Monte Carlo method to simulate the evolution of a micro-scaled thin film system during exposure to a high temperature environment. Pt-Al thin films were prepared via electron beam physical vapor deposition (EB-PVD) with an atomic concentration ratio of Pt63:Al37. These films were heat treated at an annealing temperature of 400 °C for 16 and 49 minutes. Scanning Auger Microscopy (SAM) (PHI 700) was used to obtain elemental maps while sputtering through the thin films. Simulations were run for the same annealing temperatures and thin-film composition. From these simulations theoretical depth profiles and simulated microstructures were obtained. These were compared to the experimentally measured depth profiles and elemental maps.

  16. Annealing effect on thermodynamic and physical properties of mesoporous silicon: A simulation and nitrogen sorption study

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Kumar, Pushpendra, E-mail: pkumar-iitd@yahoo.com; Huber, Patrick, E-mail: patrick.huber@tuhh.de

    Discovery of porous silicon formation in silicon substrate in 1956 while electro-polishing crystalline Si in hydrofluoric acid (HF), has triggered large scale investigations of porous silicon formation and their changes in physical and chemical properties with thermal and chemical treatment. A nitrogen sorption study is used to investigate the effect of thermal annealing on electrochemically etched mesoporous silicon (PS). The PS was thermally annealed from 200°C to 800°C for 1 hr in the presence of air. It was shown that the pore diameter and porosity of PS vary with annealing temperature. The experimentally obtained adsorption / desorption isotherms show hysteresis typicalmore » for capillary condensation in porous materials. A simulation study based on Saam and Cole model was performed and compared with experimentally observed sorption isotherms to study the physics behind of hysteresis formation. We discuss the shape of the hysteresis loops in the framework of the morphology of the layers. The different behavior of adsorption and desorption of nitrogen in PS with pore diameter was discussed in terms of concave menisci formation inside the pore space, which was shown to related with the induced pressure in varying the pore diameter from 7.2 nm to 3.4 nm.« less

  17. Selecting materialized views using random algorithm

    NASA Astrophysics Data System (ADS)

    Zhou, Lijuan; Hao, Zhongxiao; Liu, Chi

    2007-04-01

    The data warehouse is a repository of information collected from multiple possibly heterogeneous autonomous distributed databases. The information stored at the data warehouse is in form of views referred to as materialized views. The selection of the materialized views is one of the most important decisions in designing a data warehouse. Materialized views are stored in the data warehouse for the purpose of efficiently implementing on-line analytical processing queries. The first issue for the user to consider is query response time. So in this paper, we develop algorithms to select a set of views to materialize in data warehouse in order to minimize the total view maintenance cost under the constraint of a given query response time. We call it query_cost view_ selection problem. First, cost graph and cost model of query_cost view_ selection problem are presented. Second, the methods for selecting materialized views by using random algorithms are presented. The genetic algorithm is applied to the materialized views selection problem. But with the development of genetic process, the legal solution produced become more and more difficult, so a lot of solutions are eliminated and producing time of the solutions is lengthened in genetic algorithm. Therefore, improved algorithm has been presented in this paper, which is the combination of simulated annealing algorithm and genetic algorithm for the purpose of solving the query cost view selection problem. Finally, in order to test the function and efficiency of our algorithms experiment simulation is adopted. The experiments show that the given methods can provide near-optimal solutions in limited time and works better in practical cases. Randomized algorithms will become invaluable tools for data warehouse evolution.

  18. Multipole Algorithms for Molecular Dynamics Simulation on High Performance Computers.

    NASA Astrophysics Data System (ADS)

    Elliott, William Dewey

    1995-01-01

    A fundamental problem in modeling large molecular systems with molecular dynamics (MD) simulations is the underlying N-body problem of computing the interactions between all pairs of N atoms. The simplest algorithm to compute pair-wise atomic interactions scales in runtime {cal O}(N^2), making it impractical for interesting biomolecular systems, which can contain millions of atoms. Recently, several algorithms have become available that solve the N-body problem by computing the effects of all pair-wise interactions while scaling in runtime less than {cal O}(N^2). One algorithm, which scales {cal O}(N) for a uniform distribution of particles, is called the Greengard-Rokhlin Fast Multipole Algorithm (FMA). This work describes an FMA-like algorithm called the Molecular Dynamics Multipole Algorithm (MDMA). The algorithm contains several features that are new to N-body algorithms. MDMA uses new, efficient series expansion equations to compute general 1/r^{n } potentials to arbitrary accuracy. In particular, the 1/r Coulomb potential and the 1/r^6 portion of the Lennard-Jones potential are implemented. The new equations are based on multivariate Taylor series expansions. In addition, MDMA uses a cell-to-cell interaction region of cells that is closely tied to worst case error bounds. The worst case error bounds for MDMA are derived in this work also. These bounds apply to other multipole algorithms as well. Several implementation enhancements are described which apply to MDMA as well as other N-body algorithms such as FMA and tree codes. The mathematics of the cell -to-cell interactions are converted to the Fourier domain for reduced operation count and faster computation. A relative indexing scheme was devised to locate cells in the interaction region which allows efficient pre-computation of redundant information and prestorage of much of the cell-to-cell interaction. Also, MDMA was integrated into the MD program SIgMA to demonstrate the performance of the program over

  19. Efficient Constant-Time Complexity Algorithm for Stochastic Simulation of Large Reaction Networks.

    PubMed

    Thanh, Vo Hong; Zunino, Roberto; Priami, Corrado

    2017-01-01

    Exact stochastic simulation is an indispensable tool for a quantitative study of biochemical reaction networks. The simulation realizes the time evolution of the model by randomly choosing a reaction to fire and update the system state according to a probability that is proportional to the reaction propensity. Two computationally expensive tasks in simulating large biochemical networks are the selection of next reaction firings and the update of reaction propensities due to state changes. We present in this work a new exact algorithm to optimize both of these simulation bottlenecks. Our algorithm employs the composition-rejection on the propensity bounds of reactions to select the next reaction firing. The selection of next reaction firings is independent of the number reactions while the update of propensities is skipped and performed only when necessary. It therefore provides a favorable scaling for the computational complexity in simulating large reaction networks. We benchmark our new algorithm with the state of the art algorithms available in literature to demonstrate its applicability and efficiency.

  20. Simple Simulation Algorithms and Sample Applications

    NASA Astrophysics Data System (ADS)

    Kröger, Martin

    This section offers basic recipes and sample applications which allow the reader to immediately start his/her own simulation project on topics we dealt with in this book. Concerning molecular dynamics and Monte Carlo simulation there are, of course, several useful books already available which describe the ‘art of simulation‘ [141, 156, 256] in an exhaustive way. The reason we print some simple codes is that we skipped algorithmic details in the foregoing chapters. Simulations are always performed using dimensionless numbers, and all dimensional quantities can be expressed in terms of reduced units, cf. Sect. 4.3 for conventional Lennard Jones units. In this chapter, we concentrate on the necessary, and skip anything more sophisticated. Codes have been used in classrooms, they are obviously open for modifications and extensions, and offer not only an executable, but all necessary formulas for doing simulations in the correct (which is often essential) order. The overall spirit is as follows: codes are short, run without changes, demonstrate the main principle in a modular fashion, and are thus in particular open regarding efficiency issues and extensions. Algorithms are presented in the MatlabTM language, which is mostly directly portable to programming languages like fortran, c, or MathematicaTM. For an introduction we refer to [423]. Additional commands needed to visualize the results are given in the figure title for each application. Simulation codes, in a less modular fashion, are also available online at www.complexfluids.ethz.ch. Functions are shared over sections, for that reason we begin with an alphabetic list of all (nonbuiltin) functions in this chapter.

  1. A Contextual Fire Detection Algorithm for Simulated HJ-1B Imagery.

    PubMed

    Qian, Yonggang; Yan, Guangjian; Duan, Sibo; Kong, Xiangsheng

    2009-01-01

    The HJ-1B satellite, which was launched on September 6, 2008, is one of the small ones placed in the constellation for disaster prediction and monitoring. HJ-1B imagery was simulated in this paper, which contains fires of various sizes and temperatures in a wide range of terrestrial biomes and climates, including RED, NIR, MIR and TIR channels. Based on the MODIS version 4 contextual algorithm and the characteristics of HJ-1B sensor, a contextual fire detection algorithm was proposed and tested using simulated HJ-1B data. It was evaluated by the probability of fire detection and false alarm as functions of fire temperature and fire area. Results indicate that when the simulated fire area is larger than 45 m(2) and the simulated fire temperature is larger than 800 K, the algorithm has a higher probability of detection. But if the simulated fire area is smaller than 10 m(2), only when the simulated fire temperature is larger than 900 K, may the fire be detected. For fire areas about 100 m(2), the proposed algorithm has a higher detection probability than that of the MODIS product. Finally, the omission and commission error were evaluated which are important factors to affect the performance of this algorithm. It has been demonstrated that HJ-1B satellite data are much sensitive to smaller and cooler fires than MODIS or AVHRR data and the improved capabilities of HJ-1B data will offer a fine opportunity for the fire detection.

  2. A Contextual Fire Detection Algorithm for Simulated HJ-1B Imagery

    PubMed Central

    Qian, Yonggang; Yan, Guangjian; Duan, Sibo; Kong, Xiangsheng

    2009-01-01

    The HJ-1B satellite, which was launched on September 6, 2008, is one of the small ones placed in the constellation for disaster prediction and monitoring. HJ-1B imagery was simulated in this paper, which contains fires of various sizes and temperatures in a wide range of terrestrial biomes and climates, including RED, NIR, MIR and TIR channels. Based on the MODIS version 4 contextual algorithm and the characteristics of HJ-1B sensor, a contextual fire detection algorithm was proposed and tested using simulated HJ-1B data. It was evaluated by the probability of fire detection and false alarm as functions of fire temperature and fire area. Results indicate that when the simulated fire area is larger than 45 m2 and the simulated fire temperature is larger than 800 K, the algorithm has a higher probability of detection. But if the simulated fire area is smaller than 10 m2, only when the simulated fire temperature is larger than 900 K, may the fire be detected. For fire areas about 100 m2, the proposed algorithm has a higher detection probability than that of the MODIS product. Finally, the omission and commission error were evaluated which are important factors to affect the performance of this algorithm. It has been demonstrated that HJ-1B satellite data are much sensitive to smaller and cooler fires than MODIS or AVHRR data and the improved capabilities of HJ-1B data will offer a fine opportunity for the fire detection. PMID:22399950

  3. Solving a Higgs optimization problem with quantum annealing for machine learning.

    PubMed

    Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria

    2017-10-18

    The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.

  4. Solving a Higgs optimization problem with quantum annealing for machine learning

    NASA Astrophysics Data System (ADS)

    Mott, Alex; Job, Joshua; Vlimant, Jean-Roch; Lidar, Daniel; Spiropulu, Maria

    2017-10-01

    The discovery of Higgs-boson decays in a background of standard-model processes was assisted by machine learning methods. The classifiers used to separate signals such as these from background are trained using highly unerring but not completely perfect simulations of the physical processes involved, often resulting in incorrect labelling of background processes or signals (label noise) and systematic errors. Here we use quantum and classical annealing (probabilistic techniques for approximating the global maximum or minimum of a given function) to solve a Higgs-signal-versus-background machine learning optimization problem, mapped to a problem of finding the ground state of a corresponding Ising spin model. We build a set of weak classifiers based on the kinematic observables of the Higgs decay photons, which we then use to construct a strong classifier. This strong classifier is highly resilient against overtraining and against errors in the correlations of the physical observables in the training data. We show that the resulting quantum and classical annealing-based classifier systems perform comparably to the state-of-the-art machine learning methods that are currently used in particle physics. However, in contrast to these methods, the annealing-based classifiers are simple functions of directly interpretable experimental parameters with clear physical meaning. The annealer-trained classifiers use the excited states in the vicinity of the ground state and demonstrate some advantage over traditional machine learning methods for small training datasets. Given the relative simplicity of the algorithm and its robustness to error, this technique may find application in other areas of experimental particle physics, such as real-time decision making in event-selection problems and classification in neutrino physics.

  5. Simulating large atmospheric phase screens using a woofer-tweeter algorithm.

    PubMed

    Buscher, David F

    2016-10-03

    We describe an algorithm for simulating atmospheric wavefront perturbations over ranges of spatial and temporal scales spanning more than 4 orders of magnitude. An open-source implementation of the algorithm written in Python can simulate the evolution of the perturbations more than an order-of-magnitude faster than real time. Testing of the implementation using metrics appropriate to adaptive optics systems and long-baseline interferometers show accuracies at the few percent level or better.

  6. Optimizing pulsed Nd:YAG laser beam welding process parameters to attain maximum ultimate tensile strength for thin AISI316L sheet using response surface methodology and simulated annealing algorithm

    NASA Astrophysics Data System (ADS)

    Torabi, Amir; Kolahan, Farhad

    2018-07-01

    Pulsed laser welding is a powerful technique especially suitable for joining thin sheet metals. In this study, based on experimental data, pulsed laser welding of thin AISI316L austenitic stainless steel sheet has been modeled and optimized. The experimental data required for modeling are gathered as per Central Composite Design matrix in Response Surface Methodology (RSM) with full replication of 31 runs. Ultimate Tensile Strength (UTS) is considered as the main quality measure in laser welding. Furthermore, the important process parameters including peak power, pulse duration, pulse frequency and welding speed are selected as input process parameters. The relation between input parameters and the output response is established via full quadratic response surface regression with confidence level of 95%. The adequacy of the regression model was verified using Analysis of Variance technique results. The main effects of each factor and the interactions effects with other factors were analyzed graphically in contour and surface plot. Next, to maximum joint UTS, the best combinations of parameters levels were specified using RSM. Moreover, the mathematical model is implanted into a Simulated Annealing (SA) optimization algorithm to determine the optimal values of process parameters. The results obtained by both SA and RSM optimization techniques are in good agreement. The optimal parameters settings for peak power of 1800 W, pulse duration of 4.5 ms, frequency of 4.2 Hz and welding speed of 0.5 mm/s would result in a welded joint with 96% of the base metal UTS. Computational results clearly demonstrate that the proposed modeling and optimization procedures perform quite well for pulsed laser welding process.

  7. Acoustic simulation in architecture with parallel algorithm

    NASA Astrophysics Data System (ADS)

    Li, Xiaohong; Zhang, Xinrong; Li, Dan

    2004-03-01

    In allusion to complexity of architecture environment and Real-time simulation of architecture acoustics, a parallel radiosity algorithm was developed. The distribution of sound energy in scene is solved with this method. And then the impulse response between sources and receivers at frequency segment, which are calculated with multi-process, are combined into whole frequency response. The numerical experiment shows that parallel arithmetic can improve the acoustic simulating efficiency of complex scene.

  8. An adaptive multi-level simulation algorithm for stochastic biological systems

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Lester, C., E-mail: lesterc@maths.ox.ac.uk; Giles, M. B.; Baker, R. E.

    2015-01-14

    Discrete-state, continuous-time Markov models are widely used in the modeling of biochemical reaction networks. Their complexity often precludes analytic solution, and we rely on stochastic simulation algorithms (SSA) to estimate system statistics. The Gillespie algorithm is exact, but computationally costly as it simulates every single reaction. As such, approximate stochastic simulation algorithms such as the tau-leap algorithm are often used. Potentially computationally more efficient, the system statistics generated suffer from significant bias unless tau is relatively small, in which case the computational time can be comparable to that of the Gillespie algorithm. The multi-level method [Anderson and Higham, “Multi-level Montemore » Carlo for continuous time Markov chains, with applications in biochemical kinetics,” SIAM Multiscale Model. Simul. 10(1), 146–179 (2012)] tackles this problem. A base estimator is computed using many (cheap) sample paths at low accuracy. The bias inherent in this estimator is then reduced using a number of corrections. Each correction term is estimated using a collection of paired sample paths where one path of each pair is generated at a higher accuracy compared to the other (and so more expensive). By sharing random variables between these paired paths, the variance of each correction estimator can be reduced. This renders the multi-level method very efficient as only a relatively small number of paired paths are required to calculate each correction term. In the original multi-level method, each sample path is simulated using the tau-leap algorithm with a fixed value of τ. This approach can result in poor performance when the reaction activity of a system changes substantially over the timescale of interest. By introducing a novel adaptive time-stepping approach where τ is chosen according to the stochastic behaviour of each sample path, we extend the applicability of the multi-level method to such cases. We

  9. A Parallel, Finite-Volume Algorithm for Large-Eddy Simulation of Turbulent Flows

    NASA Technical Reports Server (NTRS)

    Bui, Trong T.

    1999-01-01

    A parallel, finite-volume algorithm has been developed for large-eddy simulation (LES) of compressible turbulent flows. This algorithm includes piecewise linear least-square reconstruction, trilinear finite-element interpolation, Roe flux-difference splitting, and second-order MacCormack time marching. Parallel implementation is done using the message-passing programming model. In this paper, the numerical algorithm is described. To validate the numerical method for turbulence simulation, LES of fully developed turbulent flow in a square duct is performed for a Reynolds number of 320 based on the average friction velocity and the hydraulic diameter of the duct. Direct numerical simulation (DNS) results are available for this test case, and the accuracy of this algorithm for turbulence simulations can be ascertained by comparing the LES solutions with the DNS results. The effects of grid resolution, upwind numerical dissipation, and subgrid-scale dissipation on the accuracy of the LES are examined. Comparison with DNS results shows that the standard Roe flux-difference splitting dissipation adversely affects the accuracy of the turbulence simulation. For accurate turbulence simulations, only 3-5 percent of the standard Roe flux-difference splitting dissipation is needed.

  10. Inferring Gene Regulatory Networks by Singular Value Decomposition and Gravitation Field Algorithm

    PubMed Central

    Zheng, Ming; Wu, Jia-nan; Huang, Yan-xin; Liu, Gui-xia; Zhou, You; Zhou, Chun-guang

    2012-01-01

    Reconstruction of gene regulatory networks (GRNs) is of utmost interest and has become a challenge computational problem in system biology. However, every existing inference algorithm from gene expression profiles has its own advantages and disadvantages. In particular, the effectiveness and efficiency of every previous algorithm is not high enough. In this work, we proposed a novel inference algorithm from gene expression data based on differential equation model. In this algorithm, two methods were included for inferring GRNs. Before reconstructing GRNs, singular value decomposition method was used to decompose gene expression data, determine the algorithm solution space, and get all candidate solutions of GRNs. In these generated family of candidate solutions, gravitation field algorithm was modified to infer GRNs, used to optimize the criteria of differential equation model, and search the best network structure result. The proposed algorithm is validated on both the simulated scale-free network and real benchmark gene regulatory network in networks database. Both the Bayesian method and the traditional differential equation model were also used to infer GRNs, and the results were used to compare with the proposed algorithm in our work. And genetic algorithm and simulated annealing were also used to evaluate gravitation field algorithm. The cross-validation results confirmed the effectiveness of our algorithm, which outperforms significantly other previous algorithms. PMID:23226565

  11. Formation Algorithms and Simulation Testbed

    NASA Technical Reports Server (NTRS)

    Wette, Matthew; Sohl, Garett; Scharf, Daniel; Benowitz, Edward

    2004-01-01

    Formation flying for spacecraft is a rapidly developing field that will enable a new era of space science. For one of its missions, the Terrestrial Planet Finder (TPF) project has selected a formation flying interferometer design to detect earth-like planets orbiting distant stars. In order to advance technology needed for the TPF formation flying interferometer, the TPF project has been developing a distributed real-time testbed to demonstrate end-to-end operation of formation flying with TPF-like functionality and precision. This is the Formation Algorithms and Simulation Testbed (FAST) . This FAST was conceived to bring out issues in timing, data fusion, inter-spacecraft communication, inter-spacecraft sensing and system-wide formation robustness. In this paper we describe the FAST and show results from a two-spacecraft formation scenario. The two-spacecraft simulation is the first time that precision end-to-end formation flying operation has been demonstrated in a distributed real-time simulation environment.

  12. Computational plasticity algorithm for particle dynamics simulations

    NASA Astrophysics Data System (ADS)

    Krabbenhoft, K.; Lyamin, A. V.; Vignes, C.

    2018-01-01

    The problem of particle dynamics simulation is interpreted in the framework of computational plasticity leading to an algorithm which is mathematically indistinguishable from the common implicit scheme widely used in the finite element analysis of elastoplastic boundary value problems. This algorithm provides somewhat of a unification of two particle methods, the discrete element method and the contact dynamics method, which usually are thought of as being quite disparate. In particular, it is shown that the former appears as the special case where the time stepping is explicit while the use of implicit time stepping leads to the kind of schemes usually labelled contact dynamics methods. The framing of particle dynamics simulation within computational plasticity paves the way for new approaches similar (or identical) to those frequently employed in nonlinear finite element analysis. These include mixed implicit-explicit time stepping, dynamic relaxation and domain decomposition schemes.

  13. GROMACS 4:  Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation.

    PubMed

    Hess, Berk; Kutzner, Carsten; van der Spoel, David; Lindahl, Erik

    2008-03-01

    Molecular simulation is an extremely useful, but computationally very expensive tool for studies of chemical and biomolecular systems. Here, we present a new implementation of our molecular simulation toolkit GROMACS which now both achieves extremely high performance on single processors from algorithmic optimizations and hand-coded routines and simultaneously scales very well on parallel machines. The code encompasses a minimal-communication domain decomposition algorithm, full dynamic load balancing, a state-of-the-art parallel constraint solver, and efficient virtual site algorithms that allow removal of hydrogen atom degrees of freedom to enable integration time steps up to 5 fs for atomistic simulations also in parallel. To improve the scaling properties of the common particle mesh Ewald electrostatics algorithms, we have in addition used a Multiple-Program, Multiple-Data approach, with separate node domains responsible for direct and reciprocal space interactions. Not only does this combination of algorithms enable extremely long simulations of large systems but also it provides that simulation performance on quite modest numbers of standard cluster nodes.

  14. A parallel algorithm for switch-level timing simulation on a hypercube multiprocessor

    NASA Technical Reports Server (NTRS)

    Rao, Hariprasad Nannapaneni

    1989-01-01

    The parallel approach to speeding up simulation is studied, specifically the simulation of digital LSI MOS circuitry on the Intel iPSC/2 hypercube. The simulation algorithm is based on RSIM, an event driven switch-level simulator that incorporates a linear transistor model for simulating digital MOS circuits. Parallel processing techniques based on the concepts of Virtual Time and rollback are utilized so that portions of the circuit may be simulated on separate processors, in parallel for as large an increase in speed as possible. A partitioning algorithm is also developed in order to subdivide the circuit for parallel processing.

  15. Deterministic annealing for density estimation by multivariate normal mixtures

    NASA Astrophysics Data System (ADS)

    Kloppenburg, Martin; Tavan, Paul

    1997-03-01

    An approach to maximum-likelihood density estimation by mixtures of multivariate normal distributions for large high-dimensional data sets is presented. Conventionally that problem is tackled by notoriously unstable expectation-maximization (EM) algorithms. We remove these instabilities by the introduction of soft constraints, enabling deterministic annealing. Our developments are motivated by the proof that algorithmically stable fuzzy clustering methods that are derived from statistical physics analogs are special cases of EM procedures.

  16. SimBA: simulation algorithm to fit extant-population distributions.

    PubMed

    Parida, Laxmi; Haiminen, Niina

    2015-03-14

    Simulation of populations with specified characteristics such as allele frequencies, linkage disequilibrium etc., is an integral component of many studies, including in-silico breeding optimization. Since the accuracy and sensitivity of population simulation is critical to the quality of the output of the applications that use them, accurate algorithms are required to provide a strong foundation to the methods in these studies. In this paper we present SimBA (Simulation using Best-fit Algorithm) a non-generative approach, based on a combination of stochastic techniques and discrete methods. We optimize a hill climbing algorithm and extend the framework to include multiple subpopulation structures. Additionally, we show that SimBA is very sensitive to the input specifications, i.e., very similar but distinct input characteristics result in distinct outputs with high fidelity to the specified distributions. This property of the simulation is not explicitly modeled or studied by previous methods. We show that SimBA outperforms the existing population simulation methods, both in terms of accuracy as well as time-efficiency. Not only does it construct populations that meet the input specifications more stringently than other published methods, SimBA is also easy to use. It does not require explicit parameter adaptations or calibrations. Also, it can work with input specified as distributions, without an exemplar matrix or population as required by some methods. SimBA is available at http://researcher.ibm.com/project/5669 .

  17. Understanding Quantum Tunneling through Quantum Monte Carlo Simulations.

    PubMed

    Isakov, Sergei V; Mazzola, Guglielmo; Smelyanskiy, Vadim N; Jiang, Zhang; Boixo, Sergio; Neven, Hartmut; Troyer, Matthias

    2016-10-28

    The tunneling between the two ground states of an Ising ferromagnet is a typical example of many-body tunneling processes between two local minima, as they occur during quantum annealing. Performing quantum Monte Carlo (QMC) simulations we find that the QMC tunneling rate displays the same scaling with system size, as the rate of incoherent tunneling. The scaling in both cases is O(Δ^{2}), where Δ is the tunneling splitting (or equivalently the minimum spectral gap). An important consequence is that QMC simulations can be used to predict the performance of a quantum annealer for tunneling through a barrier. Furthermore, by using open instead of periodic boundary conditions in imaginary time, equivalent to a projector QMC algorithm, we obtain a quadratic speedup for QMC simulations, and achieve linear scaling in Δ. We provide a physical understanding of these results and their range of applicability based on an instanton picture.

  18. Simulated annealing in networks for computing possible arrangements for red and green cones

    NASA Technical Reports Server (NTRS)

    Ahumada, Albert J., Jr.

    1987-01-01

    Attention is given to network models in which each of the cones of the retina is given a provisional color at random, and then the cones are allowed to determine the colors of their neighbors through an iterative process. A symmetric-structure spin-glass model has allowed arrays to be generated from completely random arrangements of red and green to arrays with approximately as much disorder as the parafoveal cones. Simulated annealing has also been added to the process in an attempt to generate color arrangements with greater regularity and hence more revealing moirepatterns than than the arrangements yielded by quenched spin-glass processes. Attention is given to the perceptual implications of these results.

  19. Massively parallel algorithms for trace-driven cache simulations

    NASA Technical Reports Server (NTRS)

    Nicol, David M.; Greenberg, Albert G.; Lubachevsky, Boris D.

    1991-01-01

    Trace driven cache simulation is central to computer design. A trace is a very long sequence of reference lines from main memory. At the t(exp th) instant, reference x sub t is hashed into a set of cache locations, the contents of which are then compared with x sub t. If at the t sup th instant x sub t is not present in the cache, then it is said to be a miss, and is loaded into the cache set, possibly forcing the replacement of some other memory line, and making x sub t present for the (t+1) sup st instant. The problem of parallel simulation of a subtrace of N references directed to a C line cache set is considered, with the aim of determining which references are misses and related statistics. A simulation method is presented for the Least Recently Used (LRU) policy, which regradless of the set size C runs in time O(log N) using N processors on the exclusive read, exclusive write (EREW) parallel model. A simpler LRU simulation algorithm is given that runs in O(C log N) time using N/log N processors. Timings are presented of the second algorithm's implementation on the MasPar MP-1, a machine with 16384 processors. A broad class of reference based line replacement policies are considered, which includes LRU as well as the Least Frequently Used and Random replacement policies. A simulation method is presented for any such policy that on any trace of length N directed to a C line set runs in the O(C log N) time with high probability using N processors on the EREW model. The algorithms are simple, have very little space overhead, and are well suited for SIMD implementation.

  20. Heart murmur detection based on wavelet transformation and a synergy between artificial neural network and modified neighbor annealing methods.

    PubMed

    Eslamizadeh, Gholamhossein; Barati, Ramin

    2017-05-01

    Early recognition of heart disease plays a vital role in saving lives. Heart murmurs are one of the common heart problems. In this study, Artificial Neural Network (ANN) is trained with Modified Neighbor Annealing (MNA) to classify heart cycles into normal and murmur classes. Heart cycles are separated from heart sounds using wavelet transformer. The network inputs are features extracted from individual heart cycles, and two classification outputs. Classification accuracy of the proposed model is compared with five multilayer perceptron trained with Levenberg-Marquardt, Extreme-learning-machine, back-propagation, simulated-annealing, and neighbor-annealing algorithms. It is also compared with a Self-Organizing Map (SOM) ANN. The proposed model is trained and tested using real heart sounds available in the Pascal database to show the applicability of the proposed scheme. Also, a device to record real heart sounds has been developed and used for comparison purposes too. Based on the results of this study, MNA can be used to produce considerable results as a heart cycle classifier. Copyright © 2017 Elsevier B.V. All rights reserved.

  1. Simulation of anaerobic digestion processes using stochastic algorithm.

    PubMed

    Palanichamy, Jegathambal; Palani, Sundarambal

    2014-01-01

    The Anaerobic Digestion (AD) processes involve numerous complex biological and chemical reactions occurring simultaneously. Appropriate and efficient models are to be developed for simulation of anaerobic digestion systems. Although several models have been developed, mostly they suffer from lack of knowledge on constants, complexity and weak generalization. The basis of the deterministic approach for modelling the physico and bio-chemical reactions occurring in the AD system is the law of mass action, which gives the simple relationship between the reaction rates and the species concentrations. The assumptions made in the deterministic models are not hold true for the reactions involving chemical species of low concentration. The stochastic behaviour of the physicochemical processes can be modeled at mesoscopic level by application of the stochastic algorithms. In this paper a stochastic algorithm (Gillespie Tau Leap Method) developed in MATLAB was applied to predict the concentration of glucose, acids and methane formation at different time intervals. By this the performance of the digester system can be controlled. The processes given by ADM1 (Anaerobic Digestion Model 1) were taken for verification of the model. The proposed model was verified by comparing the results of Gillespie's algorithms with the deterministic solution for conversion of glucose into methane through degraders. At higher value of 'τ' (timestep), the computational time required for reaching the steady state is more since the number of chosen reactions is less. When the simulation time step is reduced, the results are similar to ODE solver. It was concluded that the stochastic algorithm is a suitable approach for the simulation of complex anaerobic digestion processes. The accuracy of the results depends on the optimum selection of tau value.

  2. A novel hybrid algorithm for the design of the phase diffractive optical elements for beam shaping

    NASA Astrophysics Data System (ADS)

    Jiang, Wenbo; Wang, Jun; Dong, Xiucheng

    2013-02-01

    In this paper, a novel hybrid algorithm for the design of a phase diffractive optical elements (PDOE) is proposed. It combines the genetic algorithm (GA) with the transformable scale BFGS (Broyden, Fletcher, Goldfarb, Shanno) algorithm, the penalty function was used in the cost function definition. The novel hybrid algorithm has the global merits of the genetic algorithm as well as the local improvement capabilities of the transformable scale BFGS algorithm. We designed the PDOE using the conventional simulated annealing algorithm and the novel hybrid algorithm. To compare the performance of two algorithms, three indexes of the diffractive efficiency, uniformity error and the signal-to-noise ratio are considered in numerical simulation. The results show that the novel hybrid algorithm has good convergence property and good stability. As an application example, the PDOE was used for the Gaussian beam shaping; high diffractive efficiency, low uniformity error and high signal-to-noise were obtained. The PDOE can be used for high quality beam shaping such as inertial confinement fusion (ICF), excimer laser lithography, fiber coupling laser diode array, laser welding, etc. It shows wide application value.

  3. Ensemble of hybrid genetic algorithm for two-dimensional phase unwrapping

    NASA Astrophysics Data System (ADS)

    Balakrishnan, D.; Quan, C.; Tay, C. J.

    2013-06-01

    The phase unwrapping is the final and trickiest step in any phase retrieval technique. Phase unwrapping by artificial intelligence methods (optimization algorithms) such as hybrid genetic algorithm, reverse simulated annealing, particle swarm optimization, minimum cost matching showed better results than conventional phase unwrapping methods. In this paper, Ensemble of hybrid genetic algorithm with parallel populations is proposed to solve the branch-cut phase unwrapping problem. In a single populated hybrid genetic algorithm, the selection, cross-over and mutation operators are applied to obtain new population in every generation. The parameters and choice of operators will affect the performance of the hybrid genetic algorithm. The ensemble of hybrid genetic algorithm will facilitate to have different parameters set and different choice of operators simultaneously. Each population will use different set of parameters and the offspring of each population will compete against the offspring of all other populations, which use different set of parameters. The effectiveness of proposed algorithm is demonstrated by phase unwrapping examples and advantages of the proposed method are discussed.

  4. Solving Set Cover with Pairs Problem using Quantum Annealing

    NASA Astrophysics Data System (ADS)

    Cao, Yudong; Jiang, Shuxian; Perouli, Debbie; Kais, Sabre

    2016-09-01

    Here we consider using quantum annealing to solve Set Cover with Pairs (SCP), an NP-hard combinatorial optimization problem that plays an important role in networking, computational biology, and biochemistry. We show an explicit construction of Ising Hamiltonians whose ground states encode the solution of SCP instances. We numerically simulate the time-dependent Schrödinger equation in order to test the performance of quantum annealing for random instances and compare with that of simulated annealing. We also discuss explicit embedding strategies for realizing our Hamiltonian construction on the D-wave type restricted Ising Hamiltonian based on Chimera graphs. Our embedding on the Chimera graph preserves the structure of the original SCP instance and in particular, the embedding for general complete bipartite graphs and logical disjunctions may be of broader use than that the specific problem we deal with.

  5. A Simplified Algorithm for Statistical Investigation of Damage Spreading

    NASA Astrophysics Data System (ADS)

    Gecow, Andrzej

    2009-04-01

    On the way to simulating adaptive evolution of complex system describing a living object or human developed project, a fitness should be defined on node states or network external outputs. Feedbacks lead to circular attractors of these states or outputs which make it difficult to define a fitness. The main statistical effects of adaptive condition are the result of small change tendency and to appear, they only need a statistically correct size of damage initiated by evolutionary change of system. This observation allows to cut loops of feedbacks and in effect to obtain a particular statistically correct state instead of a long circular attractor which in the quenched model is expected for chaotic network with feedback. Defining fitness on such states is simple. We calculate only damaged nodes and only once. Such an algorithm is optimal for investigation of damage spreading i.e. statistical connections of structural parameters of initial change with the size of effected damage. It is a reversed-annealed method—function and states (signals) may be randomly substituted but connections are important and are preserved. The small damages important for adaptive evolution are correctly depicted in comparison to Derrida annealed approximation which expects equilibrium levels for large networks. The algorithm indicates these levels correctly. The relevant program in Pascal, which executes the algorithm for a wide range of parameters, can be obtained from the author.

  6. An improved immune algorithm for optimizing the pulse width modulation control sequence of inverters

    NASA Astrophysics Data System (ADS)

    Sheng, L.; Qian, S. Q.; Ye, Y. Q.; Wu, Y. H.

    2017-09-01

    In this article, an improved immune algorithm (IIA), based on the fundamental principles of the biological immune system, is proposed for optimizing the pulse width modulation (PWM) control sequence of a single-phase full-bridge inverter. The IIA takes advantage of the receptor editing and adaptive mutation mechanisms of the immune system to develop two operations that enhance the population diversity and convergence of the proposed algorithm. To verify the effectiveness and examine the performance of the IIA, 17 cases are considered, including fixed and disturbed resistances. Simulation results show that the IIA is able to obtain an effective PWM control sequence. Furthermore, when compared with existing immune algorithms (IAs), genetic algorithms (GAs), a non-traditional GA, simplified simulated annealing, and a generalized Hopfield neural network method, the IIA can achieve small total harmonic distortion (THD) and large magnitude. Meanwhile, a non-parametric test indicates that the IIA is significantly better than most comparison algorithms. Supplemental data for this article can be accessed at http://dx.doi.org/10.1080/0305215X.2016.1250894.

  7. Quantum annealing with all-to-all connected nonlinear oscillators

    PubMed Central

    Puri, Shruti; Andersen, Christian Kraglund; Grimsmo, Arne L.; Blais, Alexandre

    2017-01-01

    Quantum annealing aims at solving combinatorial optimization problems mapped to Ising interactions between quantum spins. Here, with the objective of developing a noise-resilient annealer, we propose a paradigm for quantum annealing with a scalable network of two-photon-driven Kerr-nonlinear resonators. Each resonator encodes an Ising spin in a robust degenerate subspace formed by two coherent states of opposite phases. A fully connected optimization problem is mapped to local fields driving the resonators, which are connected with only local four-body interactions. We describe an adiabatic annealing protocol in this system and analyse its performance in the presence of photon loss. Numerical simulations indicate substantial resilience to this noise channel, leading to a high success probability for quantum annealing. Finally, we propose a realistic circuit QED implementation of this promising platform for implementing a large-scale quantum Ising machine. PMID:28593952

  8. Efficiency of quantum vs. classical annealing in nonconvex learning problems

    PubMed Central

    Zecchina, Riccardo

    2018-01-01

    Quantum annealers aim at solving nonconvex optimization problems by exploiting cooperative tunneling effects to escape local minima. The underlying idea consists of designing a classical energy function whose ground states are the sought optimal solutions of the original optimization problem and add a controllable quantum transverse field to generate tunneling processes. A key challenge is to identify classes of nonconvex optimization problems for which quantum annealing remains efficient while thermal annealing fails. We show that this happens for a wide class of problems which are central to machine learning. Their energy landscapes are dominated by local minima that cause exponential slowdown of classical thermal annealers while simulated quantum annealing converges efficiently to rare dense regions of optimal solutions. PMID:29382764

  9. Parallel Algorithms for Monte Carlo Particle Transport Simulation on Exascale Computing Architectures

    NASA Astrophysics Data System (ADS)

    Romano, Paul Kollath

    Monte Carlo particle transport methods are being considered as a viable option for high-fidelity simulation of nuclear reactors. While Monte Carlo methods offer several potential advantages over deterministic methods, there are a number of algorithmic shortcomings that would prevent their immediate adoption for full-core analyses. In this thesis, algorithms are proposed both to ameliorate the degradation in parallel efficiency typically observed for large numbers of processors and to offer a means of decomposing large tally data that will be needed for reactor analysis. A nearest-neighbor fission bank algorithm was proposed and subsequently implemented in the OpenMC Monte Carlo code. A theoretical analysis of the communication pattern shows that the expected cost is O( N ) whereas traditional fission bank algorithms are O(N) at best. The algorithm was tested on two supercomputers, the Intrepid Blue Gene/P and the Titan Cray XK7, and demonstrated nearly linear parallel scaling up to 163,840 processor cores on a full-core benchmark problem. An algorithm for reducing network communication arising from tally reduction was analyzed and implemented in OpenMC. The proposed algorithm groups only particle histories on a single processor into batches for tally purposes---in doing so it prevents all network communication for tallies until the very end of the simulation. The algorithm was tested, again on a full-core benchmark, and shown to reduce network communication substantially. A model was developed to predict the impact of load imbalances on the performance of domain decomposed simulations. The analysis demonstrated that load imbalances in domain decomposed simulations arise from two distinct phenomena: non-uniform particle densities and non-uniform spatial leakage. The dominant performance penalty for domain decomposition was shown to come from these physical effects rather than insufficient network bandwidth or high latency. The model predictions were verified with

  10. Developments in Human Centered Cueing Algorithms for Control of Flight Simulator Motion Systems

    NASA Technical Reports Server (NTRS)

    Houck, Jacob A.; Telban, Robert J.; Cardullo, Frank M.

    1997-01-01

    The authors conducted further research with cueing algorithms for control of flight simulator motion systems. A variation of the so-called optimal algorithm was formulated using simulated aircraft angular velocity input as a basis. Models of the human vestibular sensation system, i.e. the semicircular canals and otoliths, are incorporated within the algorithm. Comparisons of angular velocity cueing responses showed a significant improvement over a formulation using angular acceleration input. Results also compared favorably with the coordinated adaptive washout algorithm, yielding similar results for angular velocity cues while eliminating false cues and reducing the tilt rate for longitudinal cues. These results were confirmed in piloted tests on the current motion system at NASA-Langley, the Visual Motion Simulator (VMS). Proposed future developments by the authors in cueing algorithms are revealed. The new motion system, the Cockpit Motion Facility (CMF), where the final evaluation of the cueing algorithms will be conducted, is also described.

  11. A parallel row-based algorithm with error control for standard-cell replacement on a hypercube multiprocessor

    NASA Technical Reports Server (NTRS)

    Sargent, Jeff Scott

    1988-01-01

    A new row-based parallel algorithm for standard-cell placement targeted for execution on a hypercube multiprocessor is presented. Key features of this implementation include a dynamic simulated-annealing schedule, row-partitioning of the VLSI chip image, and two novel new approaches to controlling error in parallel cell-placement algorithms; Heuristic Cell-Coloring and Adaptive (Parallel Move) Sequence Control. Heuristic Cell-Coloring identifies sets of noninteracting cells that can be moved repeatedly, and in parallel, with no buildup of error in the placement cost. Adaptive Sequence Control allows multiple parallel cell moves to take place between global cell-position updates. This feedback mechanism is based on an error bound derived analytically from the traditional annealing move-acceptance profile. Placement results are presented for real industry circuits and the performance is summarized of an implementation on the Intel iPSC/2 Hypercube. The runtime of this algorithm is 5 to 16 times faster than a previous program developed for the Hypercube, while producing equivalent quality placement. An integrated place and route program for the Intel iPSC/2 Hypercube is currently being developed.

  12. On the modeling of breath-by-breath oxygen uptake kinetics at the onset of high-intensity exercises: simulated annealing vs. GRG2 method.

    PubMed

    Bernard, Olivier; Alata, Olivier; Francaux, Marc

    2006-03-01

    Modeling in the time domain, the non-steady-state O2 uptake on-kinetics of high-intensity exercises with empirical models is commonly performed with gradient-descent-based methods. However, these procedures may impair the confidence of the parameter estimation when the modeling functions are not continuously differentiable and when the estimation corresponds to an ill-posed problem. To cope with these problems, an implementation of simulated annealing (SA) methods was compared with the GRG2 algorithm (a gradient-descent method known for its robustness). Forty simulated Vo2 on-responses were generated to mimic the real time course for transitions from light- to high-intensity exercises, with a signal-to-noise ratio equal to 20 dB. They were modeled twice with a discontinuous double-exponential function using both estimation methods. GRG2 significantly biased two estimated kinetic parameters of the first exponential (the time delay td1 and the time constant tau1) and impaired the precision (i.e., standard deviation) of the baseline A0, td1, and tau1 compared with SA. SA significantly improved the precision of the three parameters of the second exponential (the asymptotic increment A2, the time delay td2, and the time constant tau2). Nevertheless, td2 was significantly biased by both procedures, and the large confidence intervals of the whole second component parameters limit their interpretation. To compare both algorithms on experimental data, 26 subjects each performed two transitions from 80 W to 80% maximal O2 uptake on a cycle ergometer and O2 uptake was measured breath by breath. More than 88% of the kinetic parameter estimations done with the SA algorithm produced the lowest residual sum of squares between the experimental data points and the model. Repeatability coefficients were better with GRG2 for A1 although better with SA for A2 and tau2. Our results demonstrate that the implementation of SA improves significantly the estimation of most of these kinetic

  13. Quantum Algorithms to Simulate Many-Body Physics of Correlated Fermions

    NASA Astrophysics Data System (ADS)

    Jiang, Zhang; Sung, Kevin J.; Kechedzhi, Kostyantyn; Smelyanskiy, Vadim N.; Boixo, Sergio

    2018-04-01

    Simulating strongly correlated fermionic systems is notoriously hard on classical computers. An alternative approach, as proposed by Feynman, is to use a quantum computer. We discuss simulating strongly correlated fermionic systems using near-term quantum devices. We focus specifically on two-dimensional (2D) or linear geometry with nearest-neighbor qubit-qubit couplings, typical for superconducting transmon qubit arrays. We improve an existing algorithm to prepare an arbitrary Slater determinant by exploiting a unitary symmetry. We also present a quantum algorithm to prepare an arbitrary fermionic Gaussian state with O (N2) gates and O (N ) circuit depth. Both algorithms are optimal in the sense that the numbers of parameters in the quantum circuits are equal to those describing the quantum states. Furthermore, we propose an algorithm to implement the 2D fermionic Fourier transformation on a 2D qubit array with only O (N1.5) gates and O (√{N }) circuit depth, which is the minimum depth required for quantum information to travel across the qubit array. We also present methods to simulate each time step in the evolution of the 2D Fermi-Hubbard model—again on a 2D qubit array—with O (N ) gates and O (√{N }) circuit depth. Finally, we discuss how these algorithms can be used to determine the ground-state properties and phase diagrams of strongly correlated quantum systems using the Hubbard model as an example.

  14. An improved grey wolf optimizer algorithm for the inversion of geoelectrical data

    NASA Astrophysics Data System (ADS)

    Li, Si-Yu; Wang, Shu-Ming; Wang, Peng-Fei; Su, Xiao-Lu; Zhang, Xin-Song; Dong, Zhi-Hui

    2018-05-01

    The grey wolf optimizer (GWO) is a novel bionics algorithm inspired by the social rank and prey-seeking behaviors of grey wolves. The GWO algorithm is easy to implement because of its basic concept, simple formula, and small number of parameters. This paper develops a GWO algorithm with a nonlinear convergence factor and an adaptive location updating strategy and applies this improved grey wolf optimizer (improved grey wolf optimizer, IGWO) algorithm to geophysical inversion problems using magnetotelluric (MT), DC resistivity and induced polarization (IP) methods. Numerical tests in MATLAB 2010b for the forward modeling data and the observed data show that the IGWO algorithm can find the global minimum and rarely sinks to the local minima. For further study, inverted results using the IGWO are contrasted with particle swarm optimization (PSO) and the simulated annealing (SA) algorithm. The outcomes of the comparison reveal that the IGWO and PSO similarly perform better in counterpoising exploration and exploitation with a given number of iterations than the SA.

  15. Efficient classical simulation of the Deutsch-Jozsa and Simon's algorithms

    NASA Astrophysics Data System (ADS)

    Johansson, Niklas; Larsson, Jan-Åke

    2017-09-01

    A long-standing aim of quantum information research is to understand what gives quantum computers their advantage. This requires separating problems that need genuinely quantum resources from those for which classical resources are enough. Two examples of quantum speed-up are the Deutsch-Jozsa and Simon's problem, both efficiently solvable on a quantum Turing machine, and both believed to lack efficient classical solutions. Here we present a framework that can simulate both quantum algorithms efficiently, solving the Deutsch-Jozsa problem with probability 1 using only one oracle query, and Simon's problem using linearly many oracle queries, just as expected of an ideal quantum computer. The presented simulation framework is in turn efficiently simulatable in a classical probabilistic Turing machine. This shows that the Deutsch-Jozsa and Simon's problem do not require any genuinely quantum resources, and that the quantum algorithms show no speed-up when compared with their corresponding classical simulation. Finally, this gives insight into what properties are needed in the two algorithms and calls for further study of oracle separation between quantum and classical computation.

  16. Compatible topologies and parameters for NMR structure determination of carbohydrates by simulated annealing.

    PubMed

    Feng, Yingang

    2017-01-01

    The use of NMR methods to determine the three-dimensional structures of carbohydrates and glycoproteins is still challenging, in part because of the lack of standard protocols. In order to increase the convenience of structure determination, the topology and parameter files for carbohydrates in the program Crystallography & NMR System (CNS) were investigated and new files were developed to be compatible with the standard simulated annealing protocols for proteins and nucleic acids. Recalculating the published structures of protein-carbohydrate complexes and glycosylated proteins demonstrates that the results are comparable to the published structures which employed more complex procedures for structure calculation. Integrating the new carbohydrate parameters into the standard structure calculation protocol will facilitate three-dimensional structural study of carbohydrates and glycosylated proteins by NMR spectroscopy.

  17. Compatible topologies and parameters for NMR structure determination of carbohydrates by simulated annealing

    PubMed Central

    2017-01-01

    The use of NMR methods to determine the three-dimensional structures of carbohydrates and glycoproteins is still challenging, in part because of the lack of standard protocols. In order to increase the convenience of structure determination, the topology and parameter files for carbohydrates in the program Crystallography & NMR System (CNS) were investigated and new files were developed to be compatible with the standard simulated annealing protocols for proteins and nucleic acids. Recalculating the published structures of protein-carbohydrate complexes and glycosylated proteins demonstrates that the results are comparable to the published structures which employed more complex procedures for structure calculation. Integrating the new carbohydrate parameters into the standard structure calculation protocol will facilitate three-dimensional structural study of carbohydrates and glycosylated proteins by NMR spectroscopy. PMID:29232406

  18. Advanced time integration algorithms for dislocation dynamics simulations of work hardening

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Sills, Ryan B.; Aghaei, Amin; Cai, Wei

    Efficient time integration is a necessity for dislocation dynamics simulations of work hardening to achieve experimentally relevant strains. In this work, an efficient time integration scheme using a high order explicit method with time step subcycling and a newly-developed collision detection algorithm are evaluated. First, time integrator performance is examined for an annihilating Frank–Read source, showing the effects of dislocation line collision. The integrator with subcycling is found to significantly out-perform other integration schemes. The performance of the time integration and collision detection algorithms is then tested in a work hardening simulation. The new algorithms show a 100-fold speed-up relativemore » to traditional schemes. As a result, subcycling is shown to improve efficiency significantly while maintaining an accurate solution, and the new collision algorithm allows an arbitrarily large time step size without missing collisions.« less

  19. Advanced time integration algorithms for dislocation dynamics simulations of work hardening

    DOE PAGES

    Sills, Ryan B.; Aghaei, Amin; Cai, Wei

    2016-04-25

    Efficient time integration is a necessity for dislocation dynamics simulations of work hardening to achieve experimentally relevant strains. In this work, an efficient time integration scheme using a high order explicit method with time step subcycling and a newly-developed collision detection algorithm are evaluated. First, time integrator performance is examined for an annihilating Frank–Read source, showing the effects of dislocation line collision. The integrator with subcycling is found to significantly out-perform other integration schemes. The performance of the time integration and collision detection algorithms is then tested in a work hardening simulation. The new algorithms show a 100-fold speed-up relativemore » to traditional schemes. As a result, subcycling is shown to improve efficiency significantly while maintaining an accurate solution, and the new collision algorithm allows an arbitrarily large time step size without missing collisions.« less

  20. A novel metaheuristic for continuous optimization problems: Virus optimization algorithm

    NASA Astrophysics Data System (ADS)

    Liang, Yun-Chia; Rodolfo Cuevas Juarez, Josue

    2016-01-01

    A novel metaheuristic for continuous optimization problems, named the virus optimization algorithm (VOA), is introduced and investigated. VOA is an iteratively population-based method that imitates the behaviour of viruses attacking a living cell. The number of viruses grows at each replication and is controlled by an immune system (a so-called 'antivirus') to prevent the explosive growth of the virus population. The viruses are divided into two classes (strong and common) to balance the exploitation and exploration effects. The performance of the VOA is validated through a set of eight benchmark functions, which are also subject to rotation and shifting effects to test its robustness. Extensive comparisons were conducted with over 40 well-known metaheuristic algorithms and their variations, such as artificial bee colony, artificial immune system, differential evolution, evolutionary programming, evolutionary strategy, genetic algorithm, harmony search, invasive weed optimization, memetic algorithm, particle swarm optimization and simulated annealing. The results showed that the VOA is a viable solution for continuous optimization.

  1. Three-dimensional Stochastic Estimation of Porosity Distribution: Benefits of Using Ground-penetrating Radar Velocity Tomograms in Simulated-annealing-based or Bayesian Sequential Simulation Approaches

    DTIC Science & Technology

    2012-05-30

    annealing-based or Bayesian sequential simulation approaches B. Dafflon1,2 and W. Barrash1 Received 13 May 2011; revised 12 March 2012; accepted 17 April 2012...the withheld porosity log are also withheld for this estimation process. For both cases we do this for two wells having locally variable stratigraphy ...borehole location is given at the bottom of each log comparison panel. For comparison with stratigraphy at the BHRS, contacts between Units 1 to 4

  2. Verification and rectification of the physical analogy of simulated annealing for the solution of the traveling salesman problem.

    PubMed

    Hasegawa, M

    2011-03-01

    The aim of the present study is to elucidate how simulated annealing (SA) works in its finite-time implementation by starting from the verification of its conventional optimization scenario based on equilibrium statistical mechanics. Two and one supplementary experiments, the design of which is inspired by concepts and methods developed for studies on liquid and glass, are performed on two types of random traveling salesman problems. In the first experiment, a newly parameterized temperature schedule is introduced to simulate a quasistatic process along the scenario and a parametric study is conducted to investigate the optimization characteristics of this adaptive cooling. In the second experiment, the search trajectory of the Metropolis algorithm (constant-temperature SA) is analyzed in the landscape paradigm in the hope of drawing a precise physical analogy by comparison with the corresponding dynamics of glass-forming molecular systems. These two experiments indicate that the effectiveness of finite-time SA comes not from equilibrium sampling at low temperature but from downward interbasin dynamics occurring before equilibrium. These dynamics work most effectively at an intermediate temperature varying with the total search time and thus this effective temperature is identified using the Deborah number. To test directly the role of these relaxation dynamics in the process of cooling, a supplementary experiment is performed using another parameterized temperature schedule with a piecewise variable cooling rate and the effect of this biased cooling is examined systematically. The results show that the optimization performance is not only dependent on but also sensitive to cooling in the vicinity of the above effec-tive temperature and that this feature is interpreted as a consequence of the presence or absence of the workable interbasin dynamics. It is confirmed for the present instances that the effectiveness of finite-time SA derives from the glassy relaxation

  3. Nonlinear relaxation algorithms for circuit simulation

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Saleh, R.A.

    Circuit simulation is an important Computer-Aided Design (CAD) tool in the design of Integrated Circuits (IC). However, the standard techniques used in programs such as SPICE result in very long computer-run times when applied to large problems. In order to reduce the overall run time, a number of new approaches to circuit simulation were developed and are described. These methods are based on nonlinear relaxation techniques and exploit the relative inactivity of large circuits. Simple waveform-processing techniques are described to determine the maximum possible speed improvement that can be obtained by exploiting this property of large circuits. Three simulation algorithmsmore » are described, two of which are based on the Iterated Timing Analysis (ITA) method and a third based on the Waveform-Relaxation Newton (WRN) method. New programs that incorporate these techniques were developed and used to simulate a variety of industrial circuits. The results from these simulations are provided. The techniques are shown to be much faster than the standard approach. In addition, a number of parallel aspects of these algorithms are described, and a general space-time model of parallel-task scheduling is developed.« less

  4. Parallel conjugate gradient algorithms for manipulator dynamic simulation

    NASA Technical Reports Server (NTRS)

    Fijany, Amir; Scheld, Robert E.

    1989-01-01

    Parallel conjugate gradient algorithms for the computation of multibody dynamics are developed for the specialized case of a robot manipulator. For an n-dimensional positive-definite linear system, the Classical Conjugate Gradient (CCG) algorithms are guaranteed to converge in n iterations, each with a computation cost of O(n); this leads to a total computational cost of O(n sq) on a serial processor. A conjugate gradient algorithms is presented that provide greater efficiency using a preconditioner, which reduces the number of iterations required, and by exploiting parallelism, which reduces the cost of each iteration. Two Preconditioned Conjugate Gradient (PCG) algorithms are proposed which respectively use a diagonal and a tridiagonal matrix, composed of the diagonal and tridiagonal elements of the mass matrix, as preconditioners. Parallel algorithms are developed to compute the preconditioners and their inversions in O(log sub 2 n) steps using n processors. A parallel algorithm is also presented which, on the same architecture, achieves the computational time of O(log sub 2 n) for each iteration. Simulation results for a seven degree-of-freedom manipulator are presented. Variants of the proposed algorithms are also developed which can be efficiently implemented on the Robot Mathematics Processor (RMP).

  5. Quantum annealing versus classical machine learning applied to a simplified computational biology problem

    PubMed Central

    Li, Richard Y.; Di Felice, Rosa; Rohs, Remo; Lidar, Daniel A.

    2018-01-01

    Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to predict binding specificity. Using simplified datasets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified datasets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems. PMID:29652405

  6. Quantum annealing versus classical machine learning applied to a simplified computational biology problem

    NASA Astrophysics Data System (ADS)

    Li, Richard Y.; Di Felice, Rosa; Rohs, Remo; Lidar, Daniel A.

    2018-03-01

    Transcription factors regulate gene expression, but how these proteins recognize and specifically bind to their DNA targets is still debated. Machine learning models are effective means to reveal interaction mechanisms. Here we studied the ability of a quantum machine learning approach to classify and rank binding affinities. Using simplified data sets of a small number of DNA sequences derived from actual binding affinity experiments, we trained a commercially available quantum annealer to classify and rank transcription factor binding. The results were compared to state-of-the-art classical approaches for the same simplified data sets, including simulated annealing, simulated quantum annealing, multiple linear regression, LASSO, and extreme gradient boosting. Despite technological limitations, we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets. Thus, we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems.

  7. Splitting algorithm for numerical simulation of Li-ion battery electrochemical processes

    NASA Astrophysics Data System (ADS)

    Iliev, Oleg; Nikiforova, Marina A.; Semenov, Yuri V.; Zakharov, Petr E.

    2017-11-01

    In this paper we present a splitting algorithm for a numerical simulation of Li-ion battery electrochemical processes. Liion battery consists of three domains: anode, cathode and electrolyte. Mathematical model of electrochemical processes is described on a microscopic scale, and contains nonlinear equations for concentration and potential in each domain. On the interface of electrodes and electrolyte there are the Lithium ions intercalation and deintercalation processes, which are described by Butler-Volmer nonlinear equation. To approximate in spatial coordinates we use finite element methods with discontinues Galerkin elements. To simplify numerical simulations we develop the splitting algorithm, which split the original problem into three independent subproblems. We investigate the numerical convergence of the algorithm on 2D model problem.

  8. An assessment of 'shuffle algorithm' collision mechanics for particle simulations

    NASA Technical Reports Server (NTRS)

    Feiereisen, William J.; Boyd, Iain D.

    1991-01-01

    Among the algorithms for collision mechanics used at present, the 'shuffle algorithm' of Baganoff (McDonald and Baganoff, 1988; Baganoff and McDonald, 1990) not only allows efficient vectorization, but also discretizes the possible outcomes of a collision. To assess the applicability of the shuffle algorithm, a simulation was performed of flows in monoatomic gases and the calculated characteristics of shock waves was compared with those obtained using a commonly employed isotropic scattering law. It is shown that, in general, the shuffle algorithm adequately represents the collision mechanics in cases when the goal of calculations are mean profiles of density and temperature.

  9. Efficient Parallel Algorithm For Direct Numerical Simulation of Turbulent Flows

    NASA Technical Reports Server (NTRS)

    Moitra, Stuti; Gatski, Thomas B.

    1997-01-01

    A distributed algorithm for a high-order-accurate finite-difference approach to the direct numerical simulation (DNS) of transition and turbulence in compressible flows is described. This work has two major objectives. The first objective is to demonstrate that parallel and distributed-memory machines can be successfully and efficiently used to solve computationally intensive and input/output intensive algorithms of the DNS class. The second objective is to show that the computational complexity involved in solving the tridiagonal systems inherent in the DNS algorithm can be reduced by algorithm innovations that obviate the need to use a parallelized tridiagonal solver.

  10. Parameterization of annealing kinetics in pharmaceutical glasses.

    PubMed

    Hodge, Ian M

    2013-07-01

    Numerical simulations indicate that neglecting the canonical nonlinearity of glassy-state annealing kinetics in pharmaceutical (and other) glasses leads to good KWW fits to the dependence of enthalpy on annealing time, but with spurious KWW parameters that are affected by nonlinearity. A simplified treatment of nonlinearity that uses the Struik shift factor is found to be a useful approximation for these analyses, and can account for previously reported differences between linear and nonlinear KWW parameters (Kawakami K, Pikal MJ. 2005. J Pharm Sci 94:948-965). Copyright © 2013 Wiley Periodicals, Inc.

  11. Composition dependent thermal annealing behaviour of ion tracks in apatite

    NASA Astrophysics Data System (ADS)

    Nadzri, A.; Schauries, D.; Mota-Santiago, P.; Muradoglu, S.; Trautmann, C.; Gleadow, A. J. W.; Hawley, A.; Kluth, P.

    2016-07-01

    Natural apatite samples with different F/Cl content from a variety of geological locations (Durango, Mexico; Mud Tank, Australia; and Snarum, Norway) were irradiated with swift heavy ions to simulate fission tracks. The annealing kinetics of the resulting ion tracks was investigated using synchrotron-based small-angle X-ray scattering (SAXS) combined with ex situ annealing. The activation energies for track recrystallization were extracted and consistent with previous studies using track-etching, tracks in the chlorine-rich Snarum apatite are more resistant to annealing than in the other compositions.

  12. Effect of Annealing Treatment on Mechanical Properties of Nanocrystalline α-iron: an Atomistic Study

    PubMed Central

    Tong, Xuhang; Zhang, Hao; Li, D. Y.

    2015-01-01

    Claims are often found in the literature that metallic materials can be nanocrystallized by severe plastic deformation (SPD). However, SPD does not generate a well-defined nanocrystalline (NC) material, which can be achieved by subsequent annealing/recovery treatment. In this study, molecular dynamics (MD) simulation is employed to study the effect of annealing on structure and mechanical properties of cyclic deformed NC α-iron, which simulates SPD-processed α-iron. It is demonstrated that grain boundaries in the deformed NC α-iron evolve to a more equilibrium state during annealing, eliminating or minimizing the residual stress. The annealing treatment increases the system's strength by reducing dislocation emission sources, and improves material ductility through strengthening grain boundaries' resistance to intergranular cracks. The results indicate that the annealing treatment is an essential process for obtaining a well-defined NC structure with superior mechanical properties. PMID:25675978

  13. Robotic space simulation integration of vision algorithms into an orbital operations simulation

    NASA Technical Reports Server (NTRS)

    Bochsler, Daniel C.

    1987-01-01

    In order to successfully plan and analyze future space activities, computer-based simulations of activities in low earth orbit will be required to model and integrate vision and robotic operations with vehicle dynamics and proximity operations procedures. The orbital operations simulation (OOS) is configured and enhanced as a testbed for robotic space operations. Vision integration algorithms are being developed in three areas: preprocessing, recognition, and attitude/attitude rates. The vision program (Rice University) was modified for use in the OOS. Systems integration testing is now in progress.

  14. Enhanced sampling techniques in molecular dynamics simulations of biological systems.

    PubMed

    Bernardi, Rafael C; Melo, Marcelo C R; Schulten, Klaus

    2015-05-01

    Molecular dynamics has emerged as an important research methodology covering systems to the level of millions of atoms. However, insufficient sampling often limits its application. The limitation is due to rough energy landscapes, with many local minima separated by high-energy barriers, which govern the biomolecular motion. In the past few decades methods have been developed that address the sampling problem, such as replica-exchange molecular dynamics, metadynamics and simulated annealing. Here we present an overview over theses sampling methods in an attempt to shed light on which should be selected depending on the type of system property studied. Enhanced sampling methods have been employed for a broad range of biological systems and the choice of a suitable method is connected to biological and physical characteristics of the system, in particular system size. While metadynamics and replica-exchange molecular dynamics are the most adopted sampling methods to study biomolecular dynamics, simulated annealing is well suited to characterize very flexible systems. The use of annealing methods for a long time was restricted to simulation of small proteins; however, a variant of the method, generalized simulated annealing, can be employed at a relatively low computational cost to large macromolecular complexes. Molecular dynamics trajectories frequently do not reach all relevant conformational substates, for example those connected with biological function, a problem that can be addressed by employing enhanced sampling algorithms. This article is part of a Special Issue entitled Recent developments of molecular dynamics. Copyright © 2014 Elsevier B.V. All rights reserved.

  15. Extended Information Ratio for Portfolio Optimization Using Simulated Annealing with Constrained Neighborhood

    NASA Astrophysics Data System (ADS)

    Orito, Yukiko; Yamamoto, Hisashi; Tsujimura, Yasuhiro; Kambayashi, Yasushi

    The portfolio optimizations are to determine the proportion-weighted combination in the portfolio in order to achieve investment targets. This optimization is one of the multi-dimensional combinatorial optimizations and it is difficult for the portfolio constructed in the past period to keep its performance in the future period. In order to keep the good performances of portfolios, we propose the extended information ratio as an objective function, using the information ratio, beta, prime beta, or correlation coefficient in this paper. We apply the simulated annealing (SA) to optimize the portfolio employing the proposed ratio. For the SA, we make the neighbor by the operation that changes the structure of the weights in the portfolio. In the numerical experiments, we show that our portfolios keep the good performances when the market trend of the future period becomes different from that of the past period.

  16. Efficient mapping algorithms for scheduling robot inverse dynamics computation on a multiprocessor system

    NASA Technical Reports Server (NTRS)

    Lee, C. S. G.; Chen, C. L.

    1989-01-01

    Two efficient mapping algorithms for scheduling the robot inverse dynamics computation consisting of m computational modules with precedence relationship to be executed on a multiprocessor system consisting of p identical homogeneous processors with processor and communication costs to achieve minimum computation time are presented. An objective function is defined in terms of the sum of the processor finishing time and the interprocessor communication time. The minimax optimization is performed on the objective function to obtain the best mapping. This mapping problem can be formulated as a combination of the graph partitioning and the scheduling problems; both have been known to be NP-complete. Thus, to speed up the searching for a solution, two heuristic algorithms were proposed to obtain fast but suboptimal mapping solutions. The first algorithm utilizes the level and the communication intensity of the task modules to construct an ordered priority list of ready modules and the module assignment is performed by a weighted bipartite matching algorithm. For a near-optimal mapping solution, the problem can be solved by the heuristic algorithm with simulated annealing. These proposed optimization algorithms can solve various large-scale problems within a reasonable time. Computer simulations were performed to evaluate and verify the performance and the validity of the proposed mapping algorithms. Finally, experiments for computing the inverse dynamics of a six-jointed PUMA-like manipulator based on the Newton-Euler dynamic equations were implemented on an NCUBE/ten hypercube computer to verify the proposed mapping algorithms. Computer simulation and experimental results are compared and discussed.

  17. Simulated tempering based on global balance or detailed balance conditions: Suwa-Todo, heat bath, and Metropolis algorithms.

    PubMed

    Mori, Yoshiharu; Okumura, Hisashi

    2015-12-05

    Simulated tempering (ST) is a useful method to enhance sampling of molecular simulations. When ST is used, the Metropolis algorithm, which satisfies the detailed balance condition, is usually applied to calculate the transition probability. Recently, an alternative method that satisfies the global balance condition instead of the detailed balance condition has been proposed by Suwa and Todo. In this study, ST method with the Suwa-Todo algorithm is proposed. Molecular dynamics simulations with ST are performed with three algorithms (the Metropolis, heat bath, and Suwa-Todo algorithms) to calculate the transition probability. Among the three algorithms, the Suwa-Todo algorithm yields the highest acceptance ratio and the shortest autocorrelation time. These suggest that sampling by a ST simulation with the Suwa-Todo algorithm is most efficient. In addition, because the acceptance ratio of the Suwa-Todo algorithm is higher than that of the Metropolis algorithm, the number of temperature states can be reduced by 25% for the Suwa-Todo algorithm when compared with the Metropolis algorithm. © 2015 Wiley Periodicals, Inc.

  18. Voidage correction algorithm for unresolved Euler-Lagrange simulations

    NASA Astrophysics Data System (ADS)

    Askarishahi, Maryam; Salehi, Mohammad-Sadegh; Radl, Stefan

    2018-04-01

    The effect of grid coarsening on the predicted total drag force and heat exchange rate in dense gas-particle flows is investigated using Euler-Lagrange (EL) approach. We demonstrate that grid coarsening may reduce the predicted total drag force and exchange rate. Surprisingly, exchange coefficients predicted by the EL approach deviate more significantly from the exact value compared to results of Euler-Euler (EE)-based calculations. The voidage gradient is identified as the root cause of this peculiar behavior. Consequently, we propose a correction algorithm based on a sigmoidal function to predict the voidage experienced by individual particles. Our correction algorithm can significantly improve the prediction of exchange coefficients in EL models, which is tested for simulations involving Euler grid cell sizes between 2d_p and 12d_p . It is most relevant in simulations of dense polydisperse particle suspensions featuring steep voidage profiles. For these suspensions, classical approaches may result in an error of the total exchange rate of up to 30%.

  19. A hybrid meta-heuristic algorithm for the vehicle routing problem with stochastic travel times considering the driver's satisfaction

    NASA Astrophysics Data System (ADS)

    Tavakkoli-Moghaddam, Reza; Alinaghian, Mehdi; Salamat-Bakhsh, Alireza; Norouzi, Narges

    2012-05-01

    A vehicle routing problem is a significant problem that has attracted great attention from researchers in recent years. The main objectives of the vehicle routing problem are to minimize the traveled distance, total traveling time, number of vehicles and cost function of transportation. Reducing these variables leads to decreasing the total cost and increasing the driver's satisfaction level. On the other hand, this satisfaction, which will decrease by increasing the service time, is considered as an important logistic problem for a company. The stochastic time dominated by a probability variable leads to variation of the service time, while it is ignored in classical routing problems. This paper investigates the problem of the increasing service time by using the stochastic time for each tour such that the total traveling time of the vehicles is limited to a specific limit based on a defined probability. Since exact solutions of the vehicle routing problem that belong to the category of NP-hard problems are not practical in a large scale, a hybrid algorithm based on simulated annealing with genetic operators was proposed to obtain an efficient solution with reasonable computational cost and time. Finally, for some small cases, the related results of the proposed algorithm were compared with results obtained by the Lingo 8 software. The obtained results indicate the efficiency of the proposed hybrid simulated annealing algorithm.

  20. Displacement cascades and defect annealing in tungsten, Part III: The sensitivity of cascade annealing in tungsten to the values of kinetic parameters

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Nandipati, Giridhar; Setyawan, Wahyu; Heinisch, Howard L.

    2015-07-01

    Object kinetic Monte Carlo (OKMC) simulations have been performed to investigate various aspects of cascade aging in bulk tungsten and to determine the sensitivity of the results to the kinetic parameters. The primary focus is on how the kinetic parameters affect the initial recombination of defects in the first few ns of a simulation. The simulations were carried out using the object kinetic Monte Carlo (OKMC) code KSOME (kinetic simulations of microstructure evolution), using a database of cascades obtained from results of molecular dynamics (MD) simulations at various primary knock-on atom (PKA) energies and directions at temperatures of 300, 1025more » and 2050 K. The OKMC model was parameterized using defect migration barriers and binding energies from ab initio calculations. Results indicate that, due to the disparate mobilities of SIA and vacancy clusters in tungsten, annealing is dominated by SIA migration even at temperatures as high as 2050 K. For 100 keV cascades initiated at 300 K recombination is dominated by annihilation of large defect clusters. But for all other PKA energies and temperatures most of the recombination is due to the migration and rotation of small SIA clusters, while all the large SIA clusters escape the cubic simulation cell. The inverse U-shape behavior exhibited by the annealing efficiency as a function of temperature curve, especially for cascades of large PKA energies, is due to asymmetry in SIA and vacancy clustering assisted by the large difference in mobilities of SIAs and vacancies. This annealing behavior is unaffected by the dimensionality of SIA migration persists over a broad range of relative mobilities of SIAs and vacancies.« less

  1. Design of continuous surface-relief phase plates by surface-based simulated annealing to achieve control of focal-plane irradiance

    NASA Astrophysics Data System (ADS)

    Lin, Y.; Kessler, T. J.; Lawrence, G. N.

    1996-10-01

    High-performance phase plates are of vital concern for controlling the far-field irradiance of laser-fusion systems. Several designs for solving this difficult problem have been reported in Optics Letters [e. g., S. N. Dixit et al., Opt. Lett. 19, 417 (1994)]. We report a surface-based form of simulated annealing that significantly improves the irradiance control while eliminating the high-scatter problems that have plagued other methods.

  2. A sweep algorithm for massively parallel simulation of circuit-switched networks

    NASA Technical Reports Server (NTRS)

    Gaujal, Bruno; Greenberg, Albert G.; Nicol, David M.

    1992-01-01

    A new massively parallel algorithm is presented for simulating large asymmetric circuit-switched networks, controlled by a randomized-routing policy that includes trunk-reservation. A single instruction multiple data (SIMD) implementation is described, and corresponding experiments on a 16384 processor MasPar parallel computer are reported. A multiple instruction multiple data (MIMD) implementation is also described, and corresponding experiments on an Intel IPSC/860 parallel computer, using 16 processors, are reported. By exploiting parallelism, our algorithm increases the possible execution rate of such complex simulations by as much as an order of magnitude.

  3. Development of an algorithm to plan and simulate a new interventional procedure.

    PubMed

    Fujita, Buntaro; Kütting, Maximilian; Scholtz, Smita; Utzenrath, Marc; Hakim-Meibodi, Kavous; Paluszkiewicz, Lech; Schmitz, Christoph; Börgermann, Jochen; Gummert, Jan; Steinseifer, Ulrich; Ensminger, Stephan

    2015-07-01

    The number of implanted biological valves for treatment of valvular heart disease is growing and a percentage of these patients will eventually undergo a transcatheter valve-in-valve (ViV) procedure. Some of these patients will represent challenging cases. The aim of this study was to develop a feasible algorithm to plan and in vitro simulate a new interventional procedure to improve patient outcome. In addition to standard diagnostic routine, our algorithm includes 3D printing of the annulus, hydrodynamic measurements and high-speed analysis of leaflet kinematics after simulation of the procedure in different prosthesis positions as well as X-ray imaging of the most suitable valve position to create a 'blueprint' for the patient procedure. This algorithm was developed for a patient with a degenerated Perceval aortic sutureless prosthesis requiring a ViV procedure. Different ViV procedures were assessed in the algorithm and based on these results the best option for the patient was chosen. The actual procedure went exactly as planned with help of this algorithm. Here we have developed a new technically feasible algorithm simulating important aspects of a novel interventional procedure prior to the actual procedure. This algorithm can be applied to virtually all patients requiring a novel interventional procedure to help identify risks and find optimal parameters for prosthesis selection and placement in order to maximize safety for the patient. © The Author 2015. Published by Oxford University Press on behalf of the European Association for Cardio-Thoracic Surgery. All rights reserved.

  4. Fast computation algorithms for speckle pattern simulation

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Nascov, Victor; Samoilă, Cornel; Ursuţiu, Doru

    2013-11-13

    We present our development of a series of efficient computation algorithms, generally usable to calculate light diffraction and particularly for speckle pattern simulation. We use mainly the scalar diffraction theory in the form of Rayleigh-Sommerfeld diffraction formula and its Fresnel approximation. Our algorithms are based on a special form of the convolution theorem and the Fast Fourier Transform. They are able to evaluate the diffraction formula much faster than by direct computation and we have circumvented the restrictions regarding the relative sizes of the input and output domains, met on commonly used procedures. Moreover, the input and output planes canmore » be tilted each to other and the output domain can be off-axis shifted.« less

  5. GENESIS: a hybrid-parallel and multi-scale molecular dynamics simulator with enhanced sampling algorithms for biomolecular and cellular simulations

    PubMed Central

    Jung, Jaewoon; Mori, Takaharu; Kobayashi, Chigusa; Matsunaga, Yasuhiro; Yoda, Takao; Feig, Michael; Sugita, Yuji

    2015-01-01

    GENESIS (Generalized-Ensemble Simulation System) is a new software package for molecular dynamics (MD) simulations of macromolecules. It has two MD simulators, called ATDYN and SPDYN. ATDYN is parallelized based on an atomic decomposition algorithm for the simulations of all-atom force-field models as well as coarse-grained Go-like models. SPDYN is highly parallelized based on a domain decomposition scheme, allowing large-scale MD simulations on supercomputers. Hybrid schemes combining OpenMP and MPI are used in both simulators to target modern multicore computer architectures. Key advantages of GENESIS are (1) the highly parallel performance of SPDYN for very large biological systems consisting of more than one million atoms and (2) the availability of various REMD algorithms (T-REMD, REUS, multi-dimensional REMD for both all-atom and Go-like models under the NVT, NPT, NPAT, and NPγT ensembles). The former is achieved by a combination of the midpoint cell method and the efficient three-dimensional Fast Fourier Transform algorithm, where the domain decomposition space is shared in real-space and reciprocal-space calculations. Other features in SPDYN, such as avoiding concurrent memory access, reducing communication times, and usage of parallel input/output files, also contribute to the performance. We show the REMD simulation results of a mixed (POPC/DMPC) lipid bilayer as a real application using GENESIS. GENESIS is released as free software under the GPLv2 licence and can be easily modified for the development of new algorithms and molecular models. WIREs Comput Mol Sci 2015, 5:310–323. doi: 10.1002/wcms.1220 PMID:26753008

  6. GENESIS: a hybrid-parallel and multi-scale molecular dynamics simulator with enhanced sampling algorithms for biomolecular and cellular simulations.

    PubMed

    Jung, Jaewoon; Mori, Takaharu; Kobayashi, Chigusa; Matsunaga, Yasuhiro; Yoda, Takao; Feig, Michael; Sugita, Yuji

    2015-07-01

    GENESIS (Generalized-Ensemble Simulation System) is a new software package for molecular dynamics (MD) simulations of macromolecules. It has two MD simulators, called ATDYN and SPDYN. ATDYN is parallelized based on an atomic decomposition algorithm for the simulations of all-atom force-field models as well as coarse-grained Go-like models. SPDYN is highly parallelized based on a domain decomposition scheme, allowing large-scale MD simulations on supercomputers. Hybrid schemes combining OpenMP and MPI are used in both simulators to target modern multicore computer architectures. Key advantages of GENESIS are (1) the highly parallel performance of SPDYN for very large biological systems consisting of more than one million atoms and (2) the availability of various REMD algorithms (T-REMD, REUS, multi-dimensional REMD for both all-atom and Go-like models under the NVT, NPT, NPAT, and NPγT ensembles). The former is achieved by a combination of the midpoint cell method and the efficient three-dimensional Fast Fourier Transform algorithm, where the domain decomposition space is shared in real-space and reciprocal-space calculations. Other features in SPDYN, such as avoiding concurrent memory access, reducing communication times, and usage of parallel input/output files, also contribute to the performance. We show the REMD simulation results of a mixed (POPC/DMPC) lipid bilayer as a real application using GENESIS. GENESIS is released as free software under the GPLv2 licence and can be easily modified for the development of new algorithms and molecular models. WIREs Comput Mol Sci 2015, 5:310-323. doi: 10.1002/wcms.1220.

  7. A Bootstrap Metropolis-Hastings Algorithm for Bayesian Analysis of Big Data.

    PubMed

    Liang, Faming; Kim, Jinsu; Song, Qifan

    2016-01-01

    Markov chain Monte Carlo (MCMC) methods have proven to be a very powerful tool for analyzing data of complex structures. However, their computer-intensive nature, which typically require a large number of iterations and a complete scan of the full dataset for each iteration, precludes their use for big data analysis. In this paper, we propose the so-called bootstrap Metropolis-Hastings (BMH) algorithm, which provides a general framework for how to tame powerful MCMC methods to be used for big data analysis; that is to replace the full data log-likelihood by a Monte Carlo average of the log-likelihoods that are calculated in parallel from multiple bootstrap samples. The BMH algorithm possesses an embarrassingly parallel structure and avoids repeated scans of the full dataset in iterations, and is thus feasible for big data problems. Compared to the popular divide-and-combine method, BMH can be generally more efficient as it can asymptotically integrate the whole data information into a single simulation run. The BMH algorithm is very flexible. Like the Metropolis-Hastings algorithm, it can serve as a basic building block for developing advanced MCMC algorithms that are feasible for big data problems. This is illustrated in the paper by the tempering BMH algorithm, which can be viewed as a combination of parallel tempering and the BMH algorithm. BMH can also be used for model selection and optimization by combining with reversible jump MCMC and simulated annealing, respectively.

  8. A Bootstrap Metropolis–Hastings Algorithm for Bayesian Analysis of Big Data

    PubMed Central

    Kim, Jinsu; Song, Qifan

    2016-01-01

    Markov chain Monte Carlo (MCMC) methods have proven to be a very powerful tool for analyzing data of complex structures. However, their computer-intensive nature, which typically require a large number of iterations and a complete scan of the full dataset for each iteration, precludes their use for big data analysis. In this paper, we propose the so-called bootstrap Metropolis-Hastings (BMH) algorithm, which provides a general framework for how to tame powerful MCMC methods to be used for big data analysis; that is to replace the full data log-likelihood by a Monte Carlo average of the log-likelihoods that are calculated in parallel from multiple bootstrap samples. The BMH algorithm possesses an embarrassingly parallel structure and avoids repeated scans of the full dataset in iterations, and is thus feasible for big data problems. Compared to the popular divide-and-combine method, BMH can be generally more efficient as it can asymptotically integrate the whole data information into a single simulation run. The BMH algorithm is very flexible. Like the Metropolis-Hastings algorithm, it can serve as a basic building block for developing advanced MCMC algorithms that are feasible for big data problems. This is illustrated in the paper by the tempering BMH algorithm, which can be viewed as a combination of parallel tempering and the BMH algorithm. BMH can also be used for model selection and optimization by combining with reversible jump MCMC and simulated annealing, respectively. PMID:29033469

  9. Determining residual reduction algorithm kinematic tracking weights for a sidestep cut via numerical optimization.

    PubMed

    Samaan, Michael A; Weinhandl, Joshua T; Bawab, Sebastian Y; Ringleb, Stacie I

    2016-12-01

    Musculoskeletal modeling allows for the determination of various parameters during dynamic maneuvers by using in vivo kinematic and ground reaction force (GRF) data as inputs. Differences between experimental and model marker data and inconsistencies in the GRFs applied to these musculoskeletal models may not produce accurate simulations. Therefore, residual forces and moments are applied to these models in order to reduce these differences. Numerical optimization techniques can be used to determine optimal tracking weights of each degree of freedom of a musculoskeletal model in order to reduce differences between the experimental and model marker data as well as residual forces and moments. In this study, the particle swarm optimization (PSO) and simplex simulated annealing (SIMPSA) algorithms were used to determine optimal tracking weights for the simulation of a sidestep cut. The PSO and SIMPSA algorithms were able to produce model kinematics that were within 1.4° of experimental kinematics with residual forces and moments of less than 10 N and 18 Nm, respectively. The PSO algorithm was able to replicate the experimental kinematic data more closely and produce more dynamically consistent kinematic data for a sidestep cut compared to the SIMPSA algorithm. Future studies should use external optimization routines to determine dynamically consistent kinematic data and report the differences between experimental and model data for these musculoskeletal simulations.

  10. Parametric Quantum Search Algorithm as Quantum Walk: A Quantum Simulation

    NASA Astrophysics Data System (ADS)

    Ellinas, Demosthenes; Konstandakis, Christos

    2016-02-01

    Parametric quantum search algorithm (PQSA) is a form of quantum search that results by relaxing the unitarity of the original algorithm. PQSA can naturally be cast in the form of quantum walk, by means of the formalism of oracle algebra. This is due to the fact that the completely positive trace preserving search map used by PQSA, admits a unitarization (unitary dilation) a la quantum walk, at the expense of introducing auxiliary quantum coin-qubit space. The ensuing QW describes a process of spiral motion, chosen to be driven by two unitary Kraus generators, generating planar rotations of Bloch vector around an axis. The quadratic acceleration of quantum search translates into an equivalent quadratic saving of the number of coin qubits in the QW analogue. The associated to QW model Hamiltonian operator is obtained and is shown to represent a multi-particle long-range interacting quantum system that simulates parametric search. Finally, the relation of PQSA-QW simulator to the QW search algorithm is elucidated.

  11. Analysis of a simulation algorithm for direct brain drug delivery

    PubMed Central

    Rosenbluth, Kathryn Hammond; Eschermann, Jan Felix; Mittermeyer, Gabriele; Thomson, Rowena; Mittermeyer, Stephan; Bankiewicz, Krystof S.

    2011-01-01

    Convection enhanced delivery (CED) achieves targeted delivery of drugs with a pressure-driven infusion through a cannula placed stereotactically in the brain. This technique bypasses the blood brain barrier and gives precise distributions of drugs, minimizing off-target effects of compounds such as viral vectors for gene therapy or toxic chemotherapy agents. The exact distribution is affected by the cannula positioning, flow rate and underlying tissue structure. This study presents an analysis of a simulation algorithm for predicting the distribution using baseline MRI images acquired prior to inserting the cannula. The MRI images included diffusion tensor imaging (DTI) to estimate the tissue properties. The algorithm was adapted for the devices and protocols identified for upcoming trials and validated with direct MRI visualization of Gadolinium in 20 infusions in non-human primates. We found strong agreement between the size and location of the simulated and gadolinium volumes, demonstrating the clinical utility of this surgical planning algorithm. PMID:21945468

  12. OBJECT KINETIC MONTE CARLO SIMULATIONS OF CASCADE ANNEALING IN TUNGSTEN

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Nandipati, Giridhar; Setyawan, Wahyu; Heinisch, Howard L.

    2014-03-31

    The objective of this work is to study the annealing of primary cascade damage created by primary knock-on atoms (PKAs) of various energies, at various temperatures in bulk tungsten using the object kinetic Monte Carlo (OKMC) method.

  13. Relation of Parallel Discrete Event Simulation algorithms with physical models

    NASA Astrophysics Data System (ADS)

    Shchur, L. N.; Shchur, L. V.

    2015-09-01

    We extend concept of local simulation times in parallel discrete event simulation (PDES) in order to take into account architecture of the current hardware and software in high-performance computing. We shortly review previous research on the mapping of PDES on physical problems, and emphasise how physical results may help to predict parallel algorithms behaviour.

  14. Tree tensor network approach to simulating Shor's algorithm

    NASA Astrophysics Data System (ADS)

    Dumitrescu, Eugene

    2017-12-01

    Constructively simulating quantum systems furthers our understanding of qualitative and quantitative features which may be analytically intractable. In this paper, we directly simulate and explore the entanglement structure present in the paradigmatic example for exponential quantum speedups: Shor's algorithm. To perform our simulation, we construct a dynamic tree tensor network which manifestly captures two salient circuit features for modular exponentiation. These are the natural two-register bipartition and the invariance of entanglement with respect to permutations of the top-register qubits. Our construction help identify the entanglement entropy properties, which we summarize by a scaling relation. Further, the tree network is efficiently projected onto a matrix product state from which we efficiently execute the quantum Fourier transform. Future simulation of quantum information states with tensor networks exploiting circuit symmetries is discussed.

  15. Simulated Annealing Based Hybrid Forecast for Improving Daily Municipal Solid Waste Generation Prediction

    PubMed Central

    Song, Jingwei; He, Jiaying; Zhu, Menghua; Tan, Debao; Zhang, Yu; Ye, Song; Shen, Dingtao; Zou, Pengfei

    2014-01-01

    A simulated annealing (SA) based variable weighted forecast model is proposed to combine and weigh local chaotic model, artificial neural network (ANN), and partial least square support vector machine (PLS-SVM) to build a more accurate forecast model. The hybrid model was built and multistep ahead prediction ability was tested based on daily MSW generation data from Seattle, Washington, the United States. The hybrid forecast model was proved to produce more accurate and reliable results and to degrade less in longer predictions than three individual models. The average one-week step ahead prediction has been raised from 11.21% (chaotic model), 12.93% (ANN), and 12.94% (PLS-SVM) to 9.38%. Five-week average has been raised from 13.02% (chaotic model), 15.69% (ANN), and 15.92% (PLS-SVM) to 11.27%. PMID:25301508

  16. A hybrid algorithm for parallel molecular dynamics simulations

    NASA Astrophysics Data System (ADS)

    Mangiardi, Chris M.; Meyer, R.

    2017-10-01

    This article describes algorithms for the hybrid parallelization and SIMD vectorization of molecular dynamics simulations with short-range 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 Sandy Bridge and Haswell processors as well as systems with Xeon Phi many-core processors.

  17. Sensitivity of CO2 Simulation in a GCM to the Convective Transport Algorithms

    NASA Technical Reports Server (NTRS)

    Zhu, Z.; Pawson, S.; Collatz, G. J.; Gregg, W. W.; Kawa, S. R.; Baker, D.; Ott, L.

    2014-01-01

    Convection plays an important role in the transport of heat, moisture and trace gases. In this study, we simulated CO2 concentrations with an atmospheric general circulation model (GCM). Three different convective transport algorithms were used. One is a modified Arakawa-Shubert scheme that was native to the GCM; two others used in two off-line chemical transport models (CTMs) were added to the GCM here for comparison purposes. Advanced CO2 surfaced fluxes were used for the simulations. The results were compared to a large quantity of CO2 observation data. We find that the simulation results are sensitive to the convective transport algorithms. Overall, the three simulations are quite realistic and similar to each other in the remote marine regions, but are significantly different in some land regions with strong fluxes such as Amazon and Siberia during the convection seasons. Large biases against CO2 measurements are found in these regions in the control run, which uses the original GCM. The simulation with the simple diffusive algorithm is better. The difference of the two simulations is related to the very different convective transport speed.

  18. Integration of electromagnetic induction sensor data in soil sampling scheme optimization using simulated annealing.

    PubMed

    Barca, E; Castrignanò, A; Buttafuoco, G; De Benedetto, D; Passarella, G

    2015-07-01

    Soil survey is generally time-consuming, labor-intensive, and costly. Optimization of sampling scheme allows one to reduce the number of sampling points without decreasing or even increasing the accuracy of investigated attribute. Maps of bulk soil electrical conductivity (EC a ) recorded with electromagnetic induction (EMI) sensors could be effectively used to direct soil sampling design for assessing spatial variability of soil moisture. A protocol, using a field-scale bulk EC a survey, has been applied in an agricultural field in Apulia region (southeastern Italy). Spatial simulated annealing was used as a method to optimize spatial soil sampling scheme taking into account sampling constraints, field boundaries, and preliminary observations. Three optimization criteria were used. the first criterion (minimization of mean of the shortest distances, MMSD) optimizes the spreading of the point observations over the entire field by minimizing the expectation of the distance between an arbitrarily chosen point and its nearest observation; the second criterion (minimization of weighted mean of the shortest distances, MWMSD) is a weighted version of the MMSD, which uses the digital gradient of the grid EC a data as weighting function; and the third criterion (mean of average ordinary kriging variance, MAOKV) minimizes mean kriging estimation variance of the target variable. The last criterion utilizes the variogram model of soil water content estimated in a previous trial. The procedures, or a combination of them, were tested and compared in a real case. Simulated annealing was implemented by the software MSANOS able to define or redesign any sampling scheme by increasing or decreasing the original sampling locations. The output consists of the computed sampling scheme, the convergence time, and the cooling law, which can be an invaluable support to the process of sampling design. The proposed approach has found the optimal solution in a reasonable computation time. The

  19. Brain tissue segmentation in MR images based on a hybrid of MRF and social algorithms.

    PubMed

    Yousefi, Sahar; Azmi, Reza; Zahedi, Morteza

    2012-05-01

    Effective abnormality detection and diagnosis in Magnetic Resonance Images (MRIs) requires a robust segmentation strategy. Since manual segmentation is a time-consuming task which engages valuable human resources, automatic MRI segmentations received an enormous amount of attention. For this goal, various techniques have been applied. However, Markov Random Field (MRF) based algorithms have produced reasonable results in noisy images compared to other methods. MRF seeks a label field which minimizes an energy function. The traditional minimization method, simulated annealing (SA), uses Monte Carlo simulation to access the minimum solution with heavy computation burden. For this reason, MRFs are rarely used in real time processing environments. This paper proposed a novel method based on MRF and a hybrid of social algorithms that contain an ant colony optimization (ACO) and a Gossiping algorithm which can be used for segmenting single and multispectral MRIs in real time environments. Combining ACO with the Gossiping algorithm helps find the better path using neighborhood information. Therefore, this interaction causes the algorithm to converge to an optimum solution faster. Several experiments on phantom and real images were performed. Results indicate that the proposed algorithm outperforms the traditional MRF and hybrid of MRF-ACO in speed and accuracy. Copyright © 2012 Elsevier B.V. All rights reserved.

  20. Using experimental data to test an n -body dynamical model coupled with an energy-based clusterization algorithm at low incident energies

    NASA Astrophysics Data System (ADS)

    Kumar, Rohit; Puri, Rajeev K.

    2018-03-01

    Employing the quantum molecular dynamics (QMD) approach for nucleus-nucleus collisions, we test the predictive power of the energy-based clusterization algorithm, i.e., the simulating annealing clusterization algorithm (SACA), to describe the experimental data of charge distribution and various event-by-event correlations among fragments. The calculations are constrained into the Fermi-energy domain and/or mildly excited nuclear matter. Our detailed study spans over different system masses, and system-mass asymmetries of colliding partners show the importance of the energy-based clusterization algorithm for understanding multifragmentation. The present calculations are also compared with the other available calculations, which use one-body models, statistical models, and/or hybrid models.

  1. Application of integration algorithms in a parallel processing environment for the simulation of jet engines

    NASA Technical Reports Server (NTRS)

    Krosel, S. M.; Milner, E. J.

    1982-01-01

    The application of Predictor corrector integration algorithms developed for the digital parallel processing environment are investigated. The algorithms are implemented and evaluated through the use of a software simulator which provides an approximate representation of the parallel processing hardware. Test cases which focus on the use of the algorithms are presented and a specific application using a linear model of a turbofan engine is considered. Results are presented showing the effects of integration step size and the number of processors on simulation accuracy. Real time performance, interprocessor communication, and algorithm startup are also discussed.

  2. Algorithms and architecture for multiprocessor based circuit simulation

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Deutsch, J.T.

    Accurate electrical simulation is critical to the design of high performance integrated circuits. Logic simulators can verify function and give first-order timing information. Switch level simulators are more effective at dealing with charge sharing than standard logic simulators, but cannot provide accurate timing information or discover DC problems. Delay estimation techniques and cell level simulation can be used in constrained design methods, but must be tuned for each application, and circuit simulation must still be used to generate the cell models. None of these methods has the guaranteed accuracy that many circuit designers desire, and none can provide detailed waveformmore » information. Detailed electrical-level simulation can predict circuit performance if devices and parasitics are modeled accurately. However, the computational requirements of conventional circuit simulators make it impractical to simulate current large circuits. In this dissertation, the implementation of Iterated Timing Analysis (ITA), a relaxation-based technique for accurate circuit simulation, on a special-purpose multiprocessor is presented. The ITA method is an SOR-Newton, relaxation-based method which uses event-driven analysis and selective trace to exploit the temporal sparsity of the electrical network. Because event-driven selective trace techniques are employed, this algorithm lends itself to implementation on a data-driven computer.« less

  3. Recognition of partially occluded threat objects using the annealed Hopefield network

    NASA Technical Reports Server (NTRS)

    Kim, Jung H.; Yoon, Sung H.; Park, Eui H.; Ntuen, Celestine A.

    1992-01-01

    Recognition of partially occluded objects has been an important issue to airport security because occlusion causes significant problems in identifying and locating objects during baggage inspection. The neural network approach is suitable for the problems in the sense that the inherent parallelism of neural networks pursues many hypotheses in parallel resulting in high computation rates. Moreover, they provide a greater degree of robustness or fault tolerance than conventional computers. The annealed Hopfield network which is derived from the mean field annealing (MFA) has been developed to find global solutions of a nonlinear system. In the study, it has been proven that the system temperature of MFA is equivalent to the gain of the sigmoid function of a Hopfield network. In our early work, we developed the hybrid Hopfield network (HHN) for fast and reliable matching. However, HHN doesn't guarantee global solutions and yields false matching under heavily occluded conditions because HHN is dependent on initial states by its nature. In this paper, we present the annealed Hopfield network (AHN) for occluded object matching problems. In AHN, the mean field theory is applied to the hybird Hopfield network in order to improve computational complexity of the annealed Hopfield network and provide reliable matching under heavily occluded conditions. AHN is slower than HHN. However, AHN provides near global solutions without initial restrictions and provides less false matching than HHN. In conclusion, a new algorithm based upon a neural network approach was developed to demonstrate the feasibility of the automated inspection of threat objects from x-ray images. The robustness of the algorithm is proved by identifying occluded target objects with large tolerance of their features.

  4. A grid layout algorithm for automatic drawing of biochemical networks.

    PubMed

    Li, Weijiang; Kurata, Hiroyuki

    2005-05-01

    Visualization is indispensable in the research of complex biochemical networks. Available graph layout algorithms are not adequate for satisfactorily drawing such networks. New methods are required to visualize automatically the topological architectures and facilitate the understanding of the functions of the networks. We propose a novel layout algorithm to draw complex biochemical networks. A network is modeled as a system of interacting nodes on squared grids. A discrete cost function between each node pair is designed based on the topological relation and the geometric positions of the two nodes. The layouts are produced by minimizing the total cost. We design a fast algorithm to minimize the discrete cost function, by which candidate layouts can be produced efficiently. A simulated annealing procedure is used to choose better candidates. Our algorithm demonstrates its ability to exhibit cluster structures clearly in relatively compact layout areas without any prior knowledge. We developed Windows software to implement the algorithm for CADLIVE. All materials can be freely downloaded from http://kurata21.bio.kyutech.ac.jp/grid/grid_layout.htm; http://www.cadlive.jp/ http://kurata21.bio.kyutech.ac.jp/grid/grid_layout.htm; http://www.cadlive.jp/

  5. An Initial Examination for Verifying Separation Algorithms by Simulation

    NASA Technical Reports Server (NTRS)

    White, Allan L.; Neogi, Natasha; Herencia-Zapana, Heber

    2012-01-01

    An open question in algorithms for aircraft is what can be validated by simulation where the simulation shows that the probability of undesirable events is below some given level at some confidence level. The problem is including enough realism to be convincing while retaining enough efficiency to run the large number of trials needed for high confidence. The paper first proposes a goal based on the number of flights per year in several regions. The paper examines the probabilistic interpretation of this goal and computes the number of trials needed to establish it at an equivalent confidence level. Since any simulation is likely to consider the algorithms for only one type of event and there are several types of events, the paper examines under what conditions this separate consideration is valid. This paper is an initial effort, and as such, it considers separation maneuvers, which are elementary but include numerous aspects of aircraft behavior. The scenario includes decisions under uncertainty since the position of each aircraft is only known to the other by broadcasting where GPS believes each aircraft to be (ADS-B). Each aircraft operates under feedback control with perturbations. It is shown that a scenario three or four orders of magnitude more complex is feasible. The question of what can be validated by simulation remains open, but there is reason to be optimistic.

  6. A genetic algorithm-based approach to flexible flow-line scheduling with variable lot sizes.

    PubMed

    Lee, I; Sikora, R; Shaw, M J

    1997-01-01

    Genetic algorithms (GAs) have been used widely for such combinatorial optimization problems as the traveling salesman problem (TSP), the quadratic assignment problem (QAP), and job shop scheduling. In all of these problems there is usually a well defined representation which GA's use to solve the problem. We present a novel approach for solving two related problems-lot sizing and sequencing-concurrently using GAs. The essence of our approach lies in the concept of using a unified representation for the information about both the lot sizes and the sequence and enabling GAs to evolve the chromosome by replacing primitive genes with good building blocks. In addition, a simulated annealing procedure is incorporated to further improve the performance. We evaluate the performance of applying the above approach to flexible flow line scheduling with variable lot sizes for an actual manufacturing facility, comparing it to such alternative approaches as pair wise exchange improvement, tabu search, and simulated annealing procedures. The results show the efficacy of this approach for flexible flow line scheduling.

  7. Attenuation of harmonic noise in vibroseis data using Simulated Annealing

    NASA Astrophysics Data System (ADS)

    Sharma, S. P.; Tildy, Peter; Iranpour, Kambiz; Scholtz, Peter

    2009-04-01

    Processing of high productivity vibroseis seismic data (such as slip-sweep acquisition records) suffers from the well known disadvantage of harmonic distortion. Harmonic distortions are observed after cross-correlation of the recorded seismic signal with the pilot sweep and affect the signals in negative time (before the actual strong reflection event). Weak reflection events of the earlier sweeps falling in the negative time window of the cross-correlation sequence are being masked by harmonic distortions. Though the amplitude of the harmonic distortion is small (up to 10-20 %) compared to the fundamental amplitude of the reflection events, but it is significant enough to mask weak reflected signals. Elimination of harmonic noise due to source signal distortion from the cross-correlated seismic trace is a challenging task since the application of vibratory sources started and it still needs improvement. An approach has been worked out that minimizes the level of harmonic distortion by designing the signal similar to the harmonic distortion. An arbitrary length filter is optimized using the Simulated Annealing global optimization approach to design a harmonic signal. The approach deals with the convolution of a ratio trace (ratio of the harmonics with respect to the fundamental sweep) with the correlated "positive time" recorded signal and an arbitrary filter. Synthetic data study has revealed that this procedure of designing a signal similar to the desired harmonics using convolution of a suitable filter with theoretical ratio of harmonics with fundamental sweep helps in reducing the problem of harmonic distortion. Once we generate a similar signal for a vibroseis source using an optimized filter, then, this filter could be used to generate harmonics, which can be subtracted from the main cross-correlated trace to get the better, undistorted image of the subsurface. Designing the predicted harmonics to reduce the energy in the trace by considering weak reflection

  8. Algorithms for radiative transfer simulations for aerosol retrieval

    NASA Astrophysics Data System (ADS)

    Mukai, Sonoyo; Sano, Itaru; Nakata, Makiko

    2012-11-01

    Aerosol retrieval work from satellite data, i.e. aerosol remote sensing, is divided into three parts as: satellite data analysis, aerosol modeling and multiple light scattering calculation in the atmosphere model which is called radiative transfer simulation. The aerosol model is compiled from the accumulated measurements during more than ten years provided with the world wide aerosol monitoring network (AERONET). The radiative transfer simulations take Rayleigh scattering by molecules and Mie scattering by aerosols in the atmosphere, and reflection by the Earth surface into account. Thus the aerosol properties are estimated by comparing satellite measurements with the numerical values of radiation simulations in the Earth-atmosphere-surface model. It is reasonable to consider that the precise simulation of multiple light-scattering processes is necessary, and needs a long computational time especially in an optically thick atmosphere model. Therefore efficient algorithms for radiative transfer problems are indispensable to retrieve aerosols from space.

  9. New human-centered linear and nonlinear motion cueing algorithms for control of simulator motion systems

    NASA Astrophysics Data System (ADS)

    Telban, Robert J.

    While the performance of flight simulator motion system hardware has advanced substantially, the development of the motion cueing algorithm, the software that transforms simulated aircraft dynamics into realizable motion commands, has not kept pace. To address this, new human-centered motion cueing algorithms were developed. A revised "optimal algorithm" uses time-invariant filters developed by optimal control, incorporating human vestibular system models. The "nonlinear algorithm" is a novel approach that is also formulated by optimal control, but can also be updated in real time. It incorporates a new integrated visual-vestibular perception model that includes both visual and vestibular sensation and the interaction between the stimuli. A time-varying control law requires the matrix Riccati equation to be solved in real time by a neurocomputing approach. Preliminary pilot testing resulted in the optimal algorithm incorporating a new otolith model, producing improved motion cues. The nonlinear algorithm vertical mode produced a motion cue with a time-varying washout, sustaining small cues for longer durations and washing out large cues more quickly compared to the optimal algorithm. The inclusion of the integrated perception model improved the responses to longitudinal and lateral cues. False cues observed with the NASA adaptive algorithm were absent. As a result of unsatisfactory sensation, an augmented turbulence cue was added to the vertical mode for both the optimal and nonlinear algorithms. The relative effectiveness of the algorithms, in simulating aircraft maneuvers, was assessed with an eleven-subject piloted performance test conducted on the NASA Langley Visual Motion Simulator (VMS). Two methods, the quasi-objective NASA Task Load Index (TLX), and power spectral density analysis of pilot control, were used to assess pilot workload. TLX analysis reveals, in most cases, less workload and variation among pilots with the nonlinear algorithm. Control input

  10. Design of continuous surface-relief phase plates by surface-based simulated annealing to achieve control of focal-plane irradiance

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Lin, Y.; Kessler, T.J.; Lawrence, G.N.

    1996-10-01

    High-performance phase plates are of vital concern for controlling the far-field irradiance of laser-fusion systems. Several designs for solving this difficult problem have been reported in {ital Optics} {ital Letters} [e.g., S. N. Dixit {ital et} {ital al}., Opt. Lett. {bold 19}, 417 (1994)]. We report a surface-based form of simulated annealing that significantly improves the irradiance control while eliminating the high-scatter problems that have plagued other methods. {copyright} {ital 1996 Optical Society of America.}

  11. Routing design and fleet allocation optimization of freeway service patrol: Improved results using genetic algorithm

    NASA Astrophysics Data System (ADS)

    Sun, Xiuqiao; Wang, Jian

    2018-07-01

    Freeway service patrol (FSP), is considered to be an effective method for incident management and can help transportation agency decision-makers alter existing route coverage and fleet allocation. This paper investigates the FSP problem of patrol routing design and fleet allocation, with the objective of minimizing the overall average incident response time. While the simulated annealing (SA) algorithm and its improvements have been applied to solve this problem, they often become trapped in local optimal solution. Moreover, the issue of searching efficiency remains to be further addressed. In this paper, we employ the genetic algorithm (GA) and SA to solve the FSP problem. To maintain population diversity and avoid premature convergence, niche strategy is incorporated into the traditional genetic algorithm. We also employ elitist strategy to speed up the convergence. Numerical experiments have been conducted with the help of the Sioux Falls network. Results show that the GA slightly outperforms the dual-based greedy (DBG) algorithm, the very large-scale neighborhood searching (VLNS) algorithm, the SA algorithm and the scenario algorithm.

  12. A graph-based watershed merging using fuzzy C-means and simulated annealing for image segmentation

    NASA Astrophysics Data System (ADS)

    Vadiveloo, Mogana; Abdullah, Rosni; Rajeswari, Mandava

    2015-12-01

    In this paper, we have addressed the issue of over-segmented regions produced in watershed by merging the regions using global feature. The global feature information is obtained from clustering the image in its feature space using Fuzzy C-Means (FCM) clustering. The over-segmented regions produced by performing watershed on the gradient of the image are then mapped to this global information in the feature space. Further to this, the global feature information is optimized using Simulated Annealing (SA). The optimal global feature information is used to derive the similarity criterion to merge the over-segmented watershed regions which are represented by the region adjacency graph (RAG). The proposed method has been tested on digital brain phantom simulated dataset to segment white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) soft tissues regions. The experiments showed that the proposed method performs statistically better, with average of 95.242% regions are merged, than the immersion watershed and average accuracy improvement of 8.850% in comparison with RAG-based immersion watershed merging using global and local features.

  13. Modified artificial fish school algorithm for free space optical communication with sensor-less adaptive optics system

    NASA Astrophysics Data System (ADS)

    Cao, Jingtai; Zhao, Xiaohui; Li, Zhaokun; Liu, Wei; Gu, Haijun

    2017-11-01

    The performance of free space optical (FSO) communication system is limited by atmospheric turbulent extremely. Adaptive optics (AO) is the significant method to overcome the atmosphere disturbance. Especially, for the strong scintillation effect, the sensor-less AO system plays a major role for compensation. In this paper, a modified artificial fish school (MAFS) algorithm is proposed to compensate the aberrations in the sensor-less AO system. Both the static and dynamic aberrations compensations are analyzed and the performance of FSO communication before and after aberrations compensations is compared. In addition, MAFS algorithm is compared with artificial fish school (AFS) algorithm, stochastic parallel gradient descent (SPGD) algorithm and simulated annealing (SA) algorithm. It is shown that the MAFS algorithm has a higher convergence speed than SPGD algorithm and SA algorithm, and reaches the better convergence value than AFS algorithm, SPGD algorithm and SA algorithm. The sensor-less AO system with MAFS algorithm effectively increases the coupling efficiency at the receiving terminal with fewer numbers of iterations. In conclusion, the MAFS algorithm has great significance for sensor-less AO system to compensate atmospheric turbulence in FSO communication system.

  14. Design and Optimization of Low-thrust Orbit Transfers Using Q-law and Evolutionary Algorithms

    NASA Technical Reports Server (NTRS)

    Lee, Seungwon; vonAllmen, Paul; Fink, Wolfgang; Petropoulos, Anastassios; Terrile, Richard

    2005-01-01

    Future space missions will depend more on low-thrust propulsion (such as ion engines) thanks to its high specific impulse. Yet, the design of low-thrust trajectories is complex and challenging. Third-body perturbations often dominate the thrust, and a significant change to the orbit requires a long duration of thrust. In order to guide the early design phases, we have developed an efficient and efficacious method to obtain approximate propellant and flight-time requirements (i.e., the Pareto front) for orbit transfers. A search for the Pareto-optimal trajectories is done in two levels: optimal thrust angles and locations are determined by Q-law, while the Q-law is optimized with two evolutionary algorithms: a genetic algorithm and a simulated-annealing-related algorithm. The examples considered are several types of orbit transfers around the Earth and the asteroid Vesta.

  15. The atomic-scale nucleation mechanism of NiTi metallic glasses upon isothermal annealing studied via molecular dynamics simulations.

    PubMed

    Li, Yang; Li, JiaHao; Liu, BaiXin

    2015-10-28

    Nucleation is one of the most essential transformation paths in phase transition and exerts a significant influence on the crystallization process. Molecular dynamics simulations were performed to investigate the atomic-scale nucleation mechanisms of NiTi metallic glasses upon devitrification at various temperatures (700 K, 750 K, 800 K, and 850 K). Our simulations reveal that at 700 K and 750 K, nucleation is polynuclear with high nucleation density, while at 800 K it is mononuclear. The underlying nucleation mechanisms have been clarified, manifesting that nucleation can be induced either by the initial ordered clusters (IOCs) or by the other precursors of nuclei evolved directly from the supercooled liquid. IOCs and other precursors stem from the thermal fluctuations of bond orientational order in supercooled liquids during the quenching process and during the annealing process, respectively. The simulation results not only elucidate the underlying nucleation mechanisms varied with temperature, but also unveil the origin of nucleation. These discoveries offer new insights into the devitrification mechanism of metallic glasses.

  16. Parallel computing of physical maps--a comparative study in SIMD and MIMD parallelism.

    PubMed

    Bhandarkar, S M; Chirravuri, S; Arnold, J

    1996-01-01

    Ordering clones from a genomic library into physical maps of whole chromosomes presents a central computational problem in genetics. Chromosome reconstruction via clone ordering is usually isomorphic to the NP-complete Optimal Linear Arrangement problem. Parallel SIMD and MIMD algorithms for simulated annealing based on Markov chain distribution are proposed and applied to the problem of chromosome reconstruction via clone ordering. Perturbation methods and problem-specific annealing heuristics are proposed and described. The SIMD algorithms are implemented on a 2048 processor MasPar MP-2 system which is an SIMD 2-D toroidal mesh architecture whereas the MIMD algorithms are implemented on an 8 processor Intel iPSC/860 which is an MIMD hypercube architecture. A comparative analysis of the various SIMD and MIMD algorithms is presented in which the convergence, speedup, and scalability characteristics of the various algorithms are analyzed and discussed. On a fine-grained, massively parallel SIMD architecture with a low synchronization overhead such as the MasPar MP-2, a parallel simulated annealing algorithm based on multiple periodically interacting searches performs the best. For a coarse-grained MIMD architecture with high synchronization overhead such as the Intel iPSC/860, a parallel simulated annealing algorithm based on multiple independent searches yields the best results. In either case, distribution of clonal data across multiple processors is shown to exacerbate the tendency of the parallel simulated annealing algorithm to get trapped in a local optimum.

  17. Available Transfer Capability Determination Using Hybrid Evolutionary Algorithm

    NASA Astrophysics Data System (ADS)

    Jirapong, Peeraool; Ongsakul, Weerakorn

    2008-10-01

    This paper proposes a new hybrid evolutionary algorithm (HEA) based on evolutionary programming (EP), tabu search (TS), and simulated annealing (SA) to determine the available transfer capability (ATC) of power transactions between different control areas in deregulated power systems. The optimal power flow (OPF)-based ATC determination is used to evaluate the feasible maximum ATC value within real and reactive power generation limits, line thermal limits, voltage limits, and voltage and angle stability limits. The HEA approach simultaneously searches for real power generations except slack bus in a source area, real power loads in a sink area, and generation bus voltages to solve the OPF-based ATC problem. Test results on the modified IEEE 24-bus reliability test system (RTS) indicate that ATC determination by the HEA could enhance ATC far more than those from EP, TS, hybrid TS/SA, and improved EP (IEP) algorithms, leading to an efficient utilization of the existing transmission system.

  18. Particle Swarm Optimization algorithms for geophysical inversion, practical hints

    NASA Astrophysics Data System (ADS)

    Garcia Gonzalo, E.; Fernandez Martinez, J.; Fernandez Alvarez, J.; Kuzma, H.; Menendez Perez, C.

    2008-12-01

    PSO is a stochastic optimization technique that has been successfully used in many different engineering fields. PSO algorithm can be physically interpreted as a stochastic damped mass-spring system (Fernandez Martinez and Garcia Gonzalo 2008). Based on this analogy we present a whole family of PSO algorithms and their respective first order and second order stability regions. Their performance is also checked using synthetic functions (Rosenbrock and Griewank) showing a degree of ill-posedness similar to that found in many geophysical inverse problems. Finally, we present the application of these algorithms to the analysis of a Vertical Electrical Sounding inverse problem associated to a seawater intrusion in a coastal aquifer in South Spain. We analyze the role of PSO parameters (inertia, local and global accelerations and discretization step), both in convergence curves and in the a posteriori sampling of the depth of an intrusion. Comparison is made with binary genetic algorithms and simulated annealing. As result of this analysis, practical hints are given to select the correct algorithm and to tune the corresponding PSO parameters. Fernandez Martinez, J.L., Garcia Gonzalo, E., 2008a. The generalized PSO: a new door to PSO evolution. Journal of Artificial Evolution and Applications. DOI:10.1155/2008/861275.

  19. Wavelet Monte Carlo dynamics: A new algorithm for simulating the hydrodynamics of interacting Brownian particles

    NASA Astrophysics Data System (ADS)

    Dyer, Oliver T.; Ball, Robin C.

    2017-03-01

    We develop a new algorithm for the Brownian dynamics of soft matter systems that evolves time by spatially correlated Monte Carlo moves. The algorithm uses vector wavelets as its basic moves and produces hydrodynamics in the low Reynolds number regime propagated according to the Oseen tensor. When small moves are removed, the correlations closely approximate the Rotne-Prager tensor, itself widely used to correct for deficiencies in Oseen. We also include plane wave moves to provide the longest range correlations, which we detail for both infinite and periodic systems. The computational cost of the algorithm scales competitively with the number of particles simulated, N, scaling as N In N in homogeneous systems and as N in dilute systems. In comparisons to established lattice Boltzmann and Brownian dynamics algorithms, the wavelet method was found to be only a factor of order 1 times more expensive than the cheaper lattice Boltzmann algorithm in marginally semi-dilute simulations, while it is significantly faster than both algorithms at large N in dilute simulations. We also validate the algorithm by checking that it reproduces the correct dynamics and equilibrium properties of simple single polymer systems, as well as verifying the effect of periodicity on the mobility tensor.

  20. Effect of annealing on the laser induced damage of polished and CO2 laser-processed fused silica surfaces

    NASA Astrophysics Data System (ADS)

    Doualle, T.; Gallais, L.; Cormont, P.; Donval, T.; Lamaignère, L.; Rullier, J. L.

    2016-06-01

    We investigate the effect of different heat treatments on the laser-induced damage probabilities of fused silica samples. Isothermal annealing in a furnace is applied, with different temperatures in the range 700-1100 °C and 12 h annealing time, to super-polished fused silica samples. The surface flatness and laser damage probabilities at 3 ns, 351 nm are measured before and after the different annealing procedures. We have found a significant improvement of the initial laser damage probabilities of the silica surface after annealing at 1050 °C for 12 h. A similar study has been conducted on CO2 laser-processed sites on the surface of the samples. Before and after annealing, we have studied the morphology of the sites, the evolution of residual stress, and the laser-induced damage threshold measured at 351 nm, 3 ns. In this case, we observe that the laser damage resistance of the laser created craters can reach the damage level of the bare fused silica surface after the annealing process, with a complete stress relieve. The obtained results are then compared to the case of local annealing process by CO2 laser irradiation during 1 s, and we found similar improvements in both cases. The different results obtained in the study are compared to numerical simulations made with a thermo-mechanical model based on finite-element method that allows the simulation of the isothermal or the local annealing process, the evolution of stress and fictive temperature. The simulation results were found to be very consistent with experimental observations for the stresses evolution after annealing and estimation of the heat affected area during laser-processing based on the density dependence with fictive temperature. Following this work, the temperature for local annealing should reach 1330-1470 °C for an optimized reduction of damage probability and be below the threshold for material removal, whereas furnace annealing should be kept below the annealing point to avoid sample

  1. Prime factorization using quantum annealing and computational algebraic geometry

    NASA Astrophysics Data System (ADS)

    Dridi, Raouf; Alghassi, Hedayat

    2017-02-01

    We investigate prime factorization from two perspectives: quantum annealing and computational algebraic geometry, specifically Gröbner bases. We present a novel autonomous algorithm which combines the two approaches and leads to the factorization of all bi-primes up to just over 200000, the largest number factored to date using a quantum processor. We also explain how Gröbner bases can be used to reduce the degree of Hamiltonians.

  2. GOSA, a simulated annealing-based program for global optimization of nonlinear problems, also reveals transyears

    PubMed Central

    Czaplicki, Jerzy; Cornélissen, Germaine; Halberg, Franz

    2009-01-01

    Summary Transyears in biology have been documented thus far by the extended cosinor approach, including linear-nonlinear rhythmometry. We here confirm the existence of transyears by simulated annealing, a method originally developed for a much broader use, but described and introduced herein for validating its application to time series. The method is illustrated both on an artificial test case with known components and on biological data. We provide a table comparing results by the two methods and trust that the procedure will serve the budding sciences of chronobiology (the study of mechanisms underlying biological time structure), chronomics (the mapping of time structures in and around us), and chronobioethics, using the foregoing disciplines to add to concern for illnesses of individuals, and to budding focus on diseases of nations and civilizations. PMID:20414480

  3. Developing Subdomain Allocation Algorithms Based on Spatial and Communicational Constraints to Accelerate Dust Storm Simulation

    PubMed Central

    Gui, Zhipeng; Yu, Manzhu; Yang, Chaowei; Jiang, Yunfeng; Chen, Songqing; Xia, Jizhe; Huang, Qunying; Liu, Kai; Li, Zhenlong; Hassan, Mohammed Anowarul; Jin, Baoxuan

    2016-01-01

    Dust storm has serious disastrous impacts on environment, human health, and assets. The developments and applications of dust storm models have contributed significantly to better understand and predict the distribution, intensity and structure of dust storms. However, dust storm simulation is a data and computing intensive process. To improve the computing performance, high performance computing has been widely adopted by dividing the entire study area into multiple subdomains and allocating each subdomain on different computing nodes in a parallel fashion. Inappropriate allocation may introduce imbalanced task loads and unnecessary communications among computing nodes. Therefore, allocation is a key factor that may impact the efficiency of parallel process. An allocation algorithm is expected to consider the computing cost and communication cost for each computing node to minimize total execution time and reduce overall communication cost for the entire simulation. This research introduces three algorithms to optimize the allocation by considering the spatial and communicational constraints: 1) an Integer Linear Programming (ILP) based algorithm from combinational optimization perspective; 2) a K-Means and Kernighan-Lin combined heuristic algorithm (K&K) integrating geometric and coordinate-free methods by merging local and global partitioning; 3) an automatic seeded region growing based geometric and local partitioning algorithm (ASRG). The performance and effectiveness of the three algorithms are compared based on different factors. Further, we adopt the K&K algorithm as the demonstrated algorithm for the experiment of dust model simulation with the non-hydrostatic mesoscale model (NMM-dust) and compared the performance with the MPI default sequential allocation. The results demonstrate that K&K method significantly improves the simulation performance with better subdomain allocation. This method can also be adopted for other relevant atmospheric and numerical

  4. Simulating and Synthesizing Substructures Using Neural Network and Genetic Algorithms

    NASA Technical Reports Server (NTRS)

    Liu, Youhua; Kapania, Rakesh K.; VanLandingham, Hugh F.

    1997-01-01

    The feasibility of simulating and synthesizing substructures by computational neural network models is illustrated by investigating a statically indeterminate beam, using both a 1-D and a 2-D plane stress modelling. The beam can be decomposed into two cantilevers with free-end loads. By training neural networks to simulate the cantilever responses to different loads, the original beam problem can be solved as a match-up between two subsystems under compatible interface conditions. The genetic algorithms are successfully used to solve the match-up problem. Simulated results are found in good agreement with the analytical or FEM solutions.

  5. Enhanced Particle Swarm Optimization Algorithm: Efficient Training of ReaxFF Reactive Force Fields.

    PubMed

    Furman, David; Carmeli, Benny; Zeiri, Yehuda; Kosloff, Ronnie

    2018-06-12

    Particle swarm optimization (PSO) is a powerful metaheuristic population-based global optimization algorithm. However, when it is applied to nonseparable objective functions, its performance on multimodal landscapes is significantly degraded. Here we show that a significant improvement in the search quality and efficiency on multimodal functions can be achieved by enhancing the basic rotation-invariant PSO algorithm with isotropic Gaussian mutation operators. The new algorithm demonstrates superior performance across several nonlinear, multimodal benchmark functions compared with the rotation-invariant PSO algorithm and the well-established simulated annealing and sequential one-parameter parabolic interpolation methods. A search for the optimal set of parameters for the dispersion interaction model in the ReaxFF- lg reactive force field was carried out with respect to accurate DFT-TS calculations. The resulting optimized force field accurately describes the equations of state of several high-energy molecular crystals where such interactions are of crucial importance. The improved algorithm also presents better performance compared to a genetic algorithm optimization method in the optimization of the parameters of a ReaxFF- lg correction model. The computational framework is implemented in a stand-alone C++ code that allows the straightforward development of ReaxFF reactive force fields.

  6. Prime factorization using quantum annealing and computational algebraic geometry

    PubMed Central

    Dridi, Raouf; Alghassi, Hedayat

    2017-01-01

    We investigate prime factorization from two perspectives: quantum annealing and computational algebraic geometry, specifically Gröbner bases. We present a novel autonomous algorithm which combines the two approaches and leads to the factorization of all bi-primes up to just over 200000, the largest number factored to date using a quantum processor. We also explain how Gröbner bases can be used to reduce the degree of Hamiltonians. PMID:28220854

  7. Modeling Effects of Annealing on Coal Char Reactivity to O 2 and CO 2 , Based on Preparation Conditions

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Holland, Troy; Bhat, Sham; Marcy, Peter

    Oxy-fired coal combustion is a promising potential carbon capture technology. Predictive computational fluid dynamics (CFD) simulations are valuable tools in evaluating and deploying oxyfuel and other carbon capture technologies, either as retrofit technologies or for new construction. However, accurate predictive combustor simulations require physically realistic submodels with low computational requirements. A recent sensitivity analysis of a detailed char conversion model (Char Conversion Kinetics (CCK)) found thermal annealing to be an extremely sensitive submodel. In the present work, further analysis of the previous annealing model revealed significant disagreement with numerous datasets from experiments performed after that annealing model was developed. Themore » annealing model was accordingly extended to reflect experimentally observed reactivity loss, because of the thermal annealing of a variety of coals under diverse char preparation conditions. The model extension was informed by a Bayesian calibration analysis. In addition, since oxyfuel conditions include extraordinarily high levels of CO 2, the development of a first-ever CO 2 reactivity loss model due to annealing is presented.« less

  8. Modeling Effects of Annealing on Coal Char Reactivity to O 2 and CO 2 , Based on Preparation Conditions

    DOE PAGES

    Holland, Troy; Bhat, Sham; Marcy, Peter; ...

    2017-08-25

    Oxy-fired coal combustion is a promising potential carbon capture technology. Predictive computational fluid dynamics (CFD) simulations are valuable tools in evaluating and deploying oxyfuel and other carbon capture technologies, either as retrofit technologies or for new construction. However, accurate predictive combustor simulations require physically realistic submodels with low computational requirements. A recent sensitivity analysis of a detailed char conversion model (Char Conversion Kinetics (CCK)) found thermal annealing to be an extremely sensitive submodel. In the present work, further analysis of the previous annealing model revealed significant disagreement with numerous datasets from experiments performed after that annealing model was developed. Themore » annealing model was accordingly extended to reflect experimentally observed reactivity loss, because of the thermal annealing of a variety of coals under diverse char preparation conditions. The model extension was informed by a Bayesian calibration analysis. In addition, since oxyfuel conditions include extraordinarily high levels of CO 2, the development of a first-ever CO 2 reactivity loss model due to annealing is presented.« less

  9. A piloted simulator evaluation of a ground-based 4-D descent advisor algorithm

    NASA Technical Reports Server (NTRS)

    Davis, Thomas J.; Green, Steven M.; Erzberger, Heinz

    1990-01-01

    A ground-based, four dimensional (4D) descent-advisor algorithm is under development at NASA-Ames. The algorithm combines detailed aerodynamic, propulsive, and atmospheric models with an efficient numerical integration scheme to generate 4D descent advisories. The ability is investigated of the 4D descent advisor algorithm to provide adequate control of arrival time for aircraft not equipped with on-board 4D guidance systems. A piloted simulation was conducted to determine the precision with which the descent advisor could predict the 4D trajectories of typical straight-in descents flown by airline pilots under different wind conditions. The effects of errors in the estimation of wind and initial aircraft weight were also studied. A description of the descent advisor as well as the result of the simulation studies are presented.

  10. Molecular simulation workflows as parallel algorithms: the execution engine of Copernicus, a distributed high-performance computing platform.

    PubMed

    Pronk, Sander; Pouya, Iman; Lundborg, Magnus; Rotskoff, Grant; Wesén, Björn; Kasson, Peter M; Lindahl, Erik

    2015-06-09

    Computational chemistry and other simulation fields are critically dependent on computing resources, but few problems scale efficiently to the hundreds of thousands of processors available in current supercomputers-particularly for molecular dynamics. This has turned into a bottleneck as new hardware generations primarily provide more processing units rather than making individual units much faster, which simulation applications are addressing by increasingly focusing on sampling with algorithms such as free-energy perturbation, Markov state modeling, metadynamics, or milestoning. All these rely on combining results from multiple simulations into a single observation. They are potentially powerful approaches that aim to predict experimental observables directly, but this comes at the expense of added complexity in selecting sampling strategies and keeping track of dozens to thousands of simulations and their dependencies. Here, we describe how the distributed execution framework Copernicus allows the expression of such algorithms in generic workflows: dataflow programs. Because dataflow algorithms explicitly state dependencies of each constituent part, algorithms only need to be described on conceptual level, after which the execution is maximally parallel. The fully automated execution facilitates the optimization of these algorithms with adaptive sampling, where undersampled regions are automatically detected and targeted without user intervention. We show how several such algorithms can be formulated for computational chemistry problems, and how they are executed efficiently with many loosely coupled simulations using either distributed or parallel resources with Copernicus.

  11. Predicting patchy particle crystals: variable box shape simulations and evolutionary algorithms.

    PubMed

    Bianchi, Emanuela; Doppelbauer, Günther; Filion, Laura; Dijkstra, Marjolein; Kahl, Gerhard

    2012-06-07

    We consider several patchy particle models that have been proposed in literature and we investigate their candidate crystal structures in a systematic way. We compare two different algorithms for predicting crystal structures: (i) an approach based on Monte Carlo simulations in the isobaric-isothermal ensemble and (ii) an optimization technique based on ideas of evolutionary algorithms. We show that the two methods are equally successful and provide consistent results on crystalline phases of patchy particle systems.

  12. Annealing effect of the InAs dot-in-well structure grown by MBE

    NASA Astrophysics Data System (ADS)

    Zhao, Xuyi; Wang, Peng; Cao, Chunfang; Yan, Jinyi; Zha, Fangxing; Wang, Hailong; Gong, Qian

    2017-12-01

    We have demonstrated that in situ annealing effect has to be taken into account in order to realize the 1.31 μm InAs quantum dot (QD) lasers with the dot-in-well (DWELL) structure. The photoluminescence (PL) properties have been investigated for the InAs DWELL samples annealed at different temperatures in situ, simulating the annealing process during the growth of the top cladding AlGaAs layer in the laser structure. The QDs with large size in the DWELL structure are vulnerable to the annealing process at temperatures above 550 °C, revealed by the drastic change in the PL spectra. However, the DWELL structure is stable during the annealing process at 540 °C for three hours. The thermal stability of the QDs in the DWELL structure has to be considered in the growth of QD lasers for long wavelength operation.

  13. Real-time dynamics simulation of the Cassini spacecraft using DARTS. Part 1: Functional capabilities and the spatial algebra algorithm

    NASA Technical Reports Server (NTRS)

    Jain, A.; Man, G. K.

    1993-01-01

    This paper describes the Dynamics Algorithms for Real-Time Simulation (DARTS) real-time hardware-in-the-loop dynamics simulator for the National Aeronautics and Space Administration's Cassini spacecraft. The spacecraft model consists of a central flexible body with a number of articulated rigid-body appendages. The demanding performance requirements from the spacecraft control system require the use of a high fidelity simulator for control system design and testing. The DARTS algorithm provides a new algorithmic and hardware approach to the solution of this hardware-in-the-loop simulation problem. It is based upon the efficient spatial algebra dynamics for flexible multibody systems. A parallel and vectorized version of this algorithm is implemented on a low-cost, multiprocessor computer to meet the simulation timing requirements.

  14. A sonification algorithm for developing the off-roads models for driving simulators

    NASA Astrophysics Data System (ADS)

    Chiroiu, Veturia; Brişan, Cornel; Dumitriu, Dan; Munteanu, Ligia

    2018-01-01

    In this paper, a sonification algorithm for developing the off-road models for driving simulators, is proposed. The aim of this algorithm is to overcome difficulties of heuristics identification which are best suited to a particular off-road profile built by measurements. The sonification algorithm is based on the stochastic polynomial chaos analysis suitable in solving equations with random input data. The fluctuations are generated by incomplete measurements leading to inhomogeneities of the cross-sectional curves of off-roads before and after deformation, the unstable contact between the tire and the road and the unreal distribution of contact and friction forces in the unknown contact domains. The approach is exercised on two particular problems and results compare favorably to existing analytical and numerical solutions. The sonification technique represents a useful multiscale analysis able to build a low-cost virtual reality environment with increased degrees of realism for driving simulators and higher user flexibility.

  15. Object-Oriented/Data-Oriented Design of a Direct Simulation Monte Carlo Algorithm

    NASA Technical Reports Server (NTRS)

    Liechty, Derek S.

    2014-01-01

    Over the past decade, there has been much progress towards improved phenomenological modeling and algorithmic updates for the direct simulation Monte Carlo (DSMC) method, which provides a probabilistic physical simulation of gas Rows. These improvements have largely been based on the work of the originator of the DSMC method, Graeme Bird. Of primary importance are improved chemistry, internal energy, and physics modeling and a reduction in time to solution. These allow for an expanded range of possible solutions In altitude and velocity space. NASA's current production code, the DSMC Analysis Code (DAC), is well-established and based on Bird's 1994 algorithms written in Fortran 77 and has proven difficult to upgrade. A new DSMC code is being developed in the C++ programming language using object-oriented and data-oriented design paradigms to facilitate the inclusion of the recent improvements and future development activities. The development efforts on the new code, the Multiphysics Algorithm with Particles (MAP), are described, and performance comparisons are made with DAC.

  16. A physics-based algorithm for real-time simulation of electrosurgery procedures in minimally invasive surgery.

    PubMed

    Lu, Zhonghua; Arikatla, Venkata S; Han, Zhongqing; Allen, Brian F; De, Suvranu

    2014-12-01

    High-frequency electricity is used in the majority of surgical interventions. However, modern computer-based training and simulation systems rely on physically unrealistic models that fail to capture the interplay of the electrical, mechanical and thermal properties of biological tissue. We present a real-time and physically realistic simulation of electrosurgery by modelling the electrical, thermal and mechanical properties as three iteratively solved finite element models. To provide subfinite-element graphical rendering of vaporized tissue, a dual-mesh dynamic triangulation algorithm based on isotherms is proposed. The block compressed row storage (BCRS) structure is shown to be critical in allowing computationally efficient changes in the tissue topology due to vaporization. We have demonstrated our physics-based electrosurgery cutting algorithm through various examples. Our matrix manipulation algorithms designed for topology changes have shown low computational cost. Our simulator offers substantially greater physical fidelity compared to previous simulators that use simple geometry-based heat characterization. Copyright © 2013 John Wiley & Sons, Ltd.

  17. Finding a Hadamard matrix by simulated annealing of spin vectors

    NASA Astrophysics Data System (ADS)

    Bayu Suksmono, Andriyan

    2017-05-01

    Reformulation of a combinatorial problem into optimization of a statistical-mechanics system enables finding a better solution using heuristics derived from a physical process, such as by the simulated annealing (SA). In this paper, we present a Hadamard matrix (H-matrix) searching method based on the SA on an Ising model. By equivalence, an H-matrix can be converted into a seminormalized Hadamard (SH) matrix, whose first column is unit vector and the rest ones are vectors with equal number of -1 and +1 called SH-vectors. We define SH spin vectors as representation of the SH vectors, which play a similar role as the spins on Ising model. The topology of the lattice is generalized into a graph, whose edges represent orthogonality relationship among the SH spin vectors. Starting from a randomly generated quasi H-matrix Q, which is a matrix similar to the SH-matrix without imposing orthogonality, we perform the SA. The transitions of Q are conducted by random exchange of {+, -} spin-pair within the SH-spin vectors that follow the Metropolis update rule. Upon transition toward zeroth energy, the Q-matrix is evolved following a Markov chain toward an orthogonal matrix, at which the H-matrix is said to be found. We demonstrate the capability of the proposed method to find some low-order H-matrices, including the ones that cannot trivially be constructed by the Sylvester method.

  18. Inherent smoothness of intensity patterns for intensity modulated radiation therapy generated by simultaneous projection algorithms

    NASA Astrophysics Data System (ADS)

    Xiao, Ying; Michalski, Darek; Censor, Yair; Galvin, James M.

    2004-07-01

    The efficient delivery of intensity modulated radiation therapy (IMRT) depends on finding optimized beam intensity patterns that produce dose distributions, which meet given constraints for the tumour as well as any critical organs to be spared. Many optimization algorithms that are used for beamlet-based inverse planning are susceptible to large variations of neighbouring intensities. Accurately delivering an intensity pattern with a large number of extrema can prove impossible given the mechanical limitations of standard multileaf collimator (MLC) delivery systems. In this study, we apply Cimmino's simultaneous projection algorithm to the beamlet-based inverse planning problem, modelled mathematically as a system of linear inequalities. We show that using this method allows us to arrive at a smoother intensity pattern. Including nonlinear terms in the simultaneous projection algorithm to deal with dose-volume histogram (DVH) constraints does not compromise this property from our experimental observation. The smoothness properties are compared with those from other optimization algorithms which include simulated annealing and the gradient descent method. The simultaneous property of these algorithms is ideally suited to parallel computing technologies.

  19. A robust hybrid fuzzy-simulated annealing-intelligent water drops approach for tuning a distribution static compensator nonlinear controller in a distribution system

    NASA Astrophysics Data System (ADS)

    Bagheri Tolabi, Hajar; Hosseini, Rahil; Shakarami, Mahmoud Reza

    2016-06-01

    This article presents a novel hybrid optimization approach for a nonlinear controller of a distribution static compensator (DSTATCOM). The DSTATCOM is connected to a distribution system with the distributed generation units. The nonlinear control is based on partial feedback linearization. Two proportional-integral-derivative (PID) controllers regulate the voltage and track the output in this control system. In the conventional scheme, the trial-and-error method is used to determine the PID controller coefficients. This article uses a combination of a fuzzy system, simulated annealing (SA) and intelligent water drops (IWD) algorithms to optimize the parameters of the controllers. The obtained results reveal that the response of the optimized controlled system is effectively improved by finding a high-quality solution. The results confirm that using the tuning method based on the fuzzy-SA-IWD can significantly decrease the settling and rising times, the maximum overshoot and the steady-state error of the voltage step response of the DSTATCOM. The proposed hybrid tuning method for the partial feedback linearizing (PFL) controller achieved better regulation of the direct current voltage for the capacitor within the DSTATCOM. Furthermore, in the event of a fault the proposed controller tuned by the fuzzy-SA-IWD method showed better performance than the conventional controller or the PFL controller without optimization by the fuzzy-SA-IWD method with regard to both fault duration and clearing times.

  20. Annealing study of poly(etheretherketone)

    NASA Technical Reports Server (NTRS)

    Cebe, Peggy

    1988-01-01

    Annealing of PEEK has been studied for two materials cold-crystallized from the rubbery amorphous state. The first material is a low molecular weight PEEK; the second is commercially available neat resin. Differential scanning calorimetry was used to monitor the melting behavior of annealed samples. The effect of thermal history on melting behavior is very complex and depends upon annealing temperature, residence time at the annealing temperature, and subsequent scanning rate. Thermal stability of both materials is improved by annealing, and for an annealing temperature near the melting point, the polymer can be stabilized against reorganization during the scan. Variations of density, degree of crystallinity, and X-ray long period were studied as a function of annealing temperature for the commercial material.

  1. Simulation of biochemical reactions with time-dependent rates by the rejection-based algorithm

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Thanh, Vo Hong, E-mail: vo@cosbi.eu; Priami, Corrado, E-mail: priami@cosbi.eu; Department of Mathematics, University of Trento, Trento

    We address the problem of simulating biochemical reaction networks with time-dependent rates and propose a new algorithm based on our rejection-based stochastic simulation algorithm (RSSA) [Thanh et al., J. Chem. Phys. 141(13), 134116 (2014)]. The computation for selecting next reaction firings by our time-dependent RSSA (tRSSA) is computationally efficient. Furthermore, the generated trajectory is exact by exploiting the rejection-based mechanism. We benchmark tRSSA on different biological systems with varying forms of reaction rates to demonstrate its applicability and efficiency. We reveal that for nontrivial cases, the selection of reaction firings in existing algorithms introduces approximations because the integration of reactionmore » rates is very computationally demanding and simplifying assumptions are introduced. The selection of the next reaction firing by our approach is easier while preserving the exactness.« less

  2. Recent progress of quantum annealing

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Suzuki, Sei

    2015-03-10

    We review the recent progress of quantum annealing. Quantum annealing was proposed as a method to solve generic optimization problems. Recently a Canadian company has drawn a great deal of attention, as it has commercialized a quantum computer based on quantum annealing. Although the performance of quantum annealing is not sufficiently understood, it is likely that quantum annealing will be a practical method both on a conventional computer and on a quantum computer.

  3. Maximum-Entropy Inference with a Programmable Annealer

    PubMed Central

    Chancellor, Nicholas; Szoke, Szilard; Vinci, Walter; Aeppli, Gabriel; Warburton, Paul A.

    2016-01-01

    Optimisation problems typically involve finding the ground state (i.e. the minimum energy configuration) of a cost function with respect to many variables. If the variables are corrupted by noise then this maximises the likelihood that the solution is correct. The maximum entropy solution on the other hand takes the form of a Boltzmann distribution over the ground and excited states of the cost function to correct for noise. Here we use a programmable annealer for the information decoding problem which we simulate as a random Ising model in a field. We show experimentally that finite temperature maximum entropy decoding can give slightly better bit-error-rates than the maximum likelihood approach, confirming that useful information can be extracted from the excited states of the annealer. Furthermore we introduce a bit-by-bit analytical method which is agnostic to the specific application and use it to show that the annealer samples from a highly Boltzmann-like distribution. Machines of this kind are therefore candidates for use in a variety of machine learning applications which exploit maximum entropy inference, including language processing and image recognition. PMID:26936311

  4. Magnetic resonance electrical impedance tomography (MREIT): simulation study of J-substitution algorithm.

    PubMed

    Kwon, Ohin; Woo, Eung Je; Yoon, Jeong-Rock; Seo, Jin Keun

    2002-02-01

    We developed a new image reconstruction algorithm for magnetic resonance electrical impedance tomography (MREIT). MREIT is a new EIT imaging technique integrated into magnetic resonance imaging (MRI) system. Based on the assumption that internal current density distribution is obtained using magnetic resonance imaging (MRI) technique, the new image reconstruction algorithm called J-substitution algorithm produces cross-sectional static images of resistivity (or conductivity) distributions. Computer simulations show that the spatial resolution of resistivity image is comparable to that of MRI. MREIT provides accurate high-resolution cross-sectional resistivity images making resistivity values of various human tissues available for many biomedical applications.

  5. A fast sorting algorithm for a hypersonic rarefied flow particle simulation on the connection machine

    NASA Technical Reports Server (NTRS)

    Dagum, Leonardo

    1989-01-01

    The data parallel implementation of a particle simulation for hypersonic rarefied flow described by Dagum associates a single parallel data element with each particle in the simulation. The simulated space is divided into discrete regions called cells containing a variable and constantly changing number of particles. The implementation requires a global sort of the parallel data elements so as to arrange them in an order that allows immediate access to the information associated with cells in the simulation. Described here is a very fast algorithm for performing the necessary ranking of the parallel data elements. The performance of the new algorithm is compared with that of the microcoded instruction for ranking on the Connection Machine.

  6. Scalable effective-temperature reduction for quantum annealers via nested quantum annealing correction

    NASA Astrophysics Data System (ADS)

    Vinci, Walter; Lidar, Daniel A.

    2018-02-01

    Nested quantum annealing correction (NQAC) is an error-correcting scheme for quantum annealing that allows for the encoding of a logical qubit into an arbitrarily large number of physical qubits. The encoding replaces each logical qubit by a complete graph of degree C . The nesting level C represents the distance of the error-correcting code and controls the amount of protection against thermal and control errors. Theoretical mean-field analyses and empirical data obtained with a D-Wave Two quantum annealer (supporting up to 512 qubits) showed that NQAC has the potential to achieve a scalable effective-temperature reduction, Teff˜C-η , with 0 <η ≤2 . We confirm that this scaling is preserved when NQAC is tested on a D-Wave 2000Q device (supporting up to 2048 qubits). In addition, we show that NQAC can also be used in sampling problems to lower the effective-temperature of a quantum annealer. Such effective-temperature reduction is relevant for machine-learning applications. Since we demonstrate that NQAC achieves error correction via a reduction of the effective-temperature of the quantum annealing device, our results address the problem of the "temperature scaling law for quantum annealers," which requires the temperature of quantum annealers to be reduced as problems of larger sizes are attempted to be solved.

  7. Space-based Doppler lidar sampling strategies: Algorithm development and simulated observation experiments

    NASA Technical Reports Server (NTRS)

    Emmitt, G. D.; Wood, S. A.; Morris, M.

    1990-01-01

    Lidar Atmospheric Wind Sounder (LAWS) Simulation Models (LSM) were developed to evaluate the potential impact of global wind observations on the basic understanding of the Earth's atmosphere and on the predictive skills of current forecast models (GCM and regional scale). Fully integrated top to bottom LAWS Simulation Models for global and regional scale simulations were developed. The algorithm development incorporated the effects of aerosols, water vapor, clouds, terrain, and atmospheric turbulence into the models. Other additions include a new satellite orbiter, signal processor, line of sight uncertainty model, new Multi-Paired Algorithm and wind error analysis code. An atmospheric wind field library containing control fields, meteorological fields, phenomena fields, and new European Center for Medium Range Weather Forecasting (ECMWF) data was also added. The LSM was used to address some key LAWS issues and trades such as accuracy and interpretation of LAWS information, data density, signal strength, cloud obscuration, and temporal data resolution.

  8. Scheduling of hybrid types of machines with two-machine flowshop as the first type and a single machine as the second type

    NASA Astrophysics Data System (ADS)

    Hsiao, Ming-Chih; Su, Ling-Huey

    2018-02-01

    This research addresses the problem of scheduling hybrid machine types, in which one type is a two-machine flowshop and another type is a single machine. A job is either processed on the two-machine flowshop or on the single machine. The objective is to determine a production schedule for all jobs so as to minimize the makespan. The problem is NP-hard since the two parallel machines problem was proved to be NP-hard. Simulated annealing algorithms are developed to solve the problem optimally. A mixed integer programming (MIP) is developed and used to evaluate the performance for two SAs. Computational experiments demonstrate the efficiency of the simulated annealing algorithms, the quality of the simulated annealing algorithms will also be reported.

  9. An integrated algorithm for hypersonic fluid-thermal-structural numerical simulation

    NASA Astrophysics Data System (ADS)

    Li, Jia-Wei; Wang, Jiang-Feng

    2018-05-01

    In this paper, a fluid-structural-thermal integrated method is presented based on finite volume method. A unified integral equations system is developed as the control equations for physical process of aero-heating and structural heat transfer. The whole physical field is discretized by using an up-wind finite volume method. To demonstrate its capability, the numerical simulation of Mach 6.47 flow over stainless steel cylinder shows a good agreement with measured values, and this method dynamically simulates the objective physical processes. Thus, the integrated algorithm proves to be efficient and reliable.

  10. An order (n) algorithm for the dynamics simulation of robotic systems

    NASA Technical Reports Server (NTRS)

    Chun, H. M.; Turner, J. D.; Frisch, Harold P.

    1989-01-01

    The formulation of an Order (n) algorithm for DISCOS (Dynamics Interaction Simulation of Controls and Structures), which is an industry-standard software package for simulation and analysis of flexible multibody systems is presented. For systems involving many bodies, the new Order (n) version of DISCOS is much faster than the current version. Results of the experimental validation of the dynamics software are also presented. The experiment is carried out on a seven-joint robot arm at NASA's Goddard Space Flight Center. The algorithm used in the current version of DISCOS requires the inverse of a matrix whose dimension is equal to the number of constraints in the system. Generally, the number of constraints in a system is roughly proportional to the number of bodies in the system, and matrix inversion requires O(p exp 3) operations, where p is the dimension of the matrix. The current version of DISCOS is therefore considered an Order (n exp 3) algorithm. In contrast, the Order (n) algorithm requires inversion of matrices which are small, and the number of matrices to be inverted increases only linearly with the number of bodies. The newly-developed Order (n) DISCOS is currently capable of handling chain and tree topologies as well as multiple closed loops. Continuing development will extend the capability of the software to deal with typical robotics applications such as put-and-place, multi-arm hand-off and surface sliding.

  11. Development of a Tool for an Efficient Calibration of CORSIM Models

    DOT National Transportation Integrated Search

    2014-08-01

    This project proposes a Memetic Algorithm (MA) for the calibration of microscopic traffic flow simulation models. The proposed MA includes a combination of genetic and simulated annealing algorithms. The genetic algorithm performs the exploration of ...

  12. Shock, Post-Shock Annealing, and Post-Annealing Shock in Ureilites

    NASA Technical Reports Server (NTRS)

    Rubin, Alan E.

    2006-01-01

    The thermal and shock histories of ureilites can be divided into four periods: 1) formation, 2) initial shock, 3) post-shock annealing, and 4) post-annealing shock. Period 1 occurred approx.4.55 Ga ago when ureilites formed by melting chondritic material. Impact events during period 2 caused silicate darkening, undulose to mosaic extinction in olivines, and the formation of diamond, lonsdaleite, and chaoite from indigenous carbonaceous material. Alkali-rich fine-grained silicates may have been introduced by impact injection into ureilites during this period. About 57% of the ureilites were unchanged after period 2. During period 3 events, impact-induced annealing caused previously mosaicized olivine grains to become aggregates of small unstrained crystals. Some ureilites experienced reduction as FeO at the edges of olivine grains reacted with C from the matrix. Annealing may also be responsible for coarsening of graphite in a few ureilites, forming euhedral-appearing, idioblastic crystals. Orthopyroxene in Meteorite Hills (MET) 78008 may have formed from pigeonite by annealing during this period. The Rb-Sr internal isochron age of approx.4.0 Ga for MET 78008 probably dates the annealing event. At this late date, impacts are the only viable heat source. About 36% of ureilites experienced period 3 events, but remained unchanged afterwards. During period 4, approx.7% of the ureilites were shocked again, as is evident in the polymict breccia, Elephant Moraine (EET) 83309. This rock contains annealed mosaicized olivine aggregates composed of small individual olivine crystals that exhibit undulose extinction. Ureilites may have formed by impact-melting chondritic material on a primitive body with heterogeneous O isotopes. Plagioclase was preferentially lost from the system due to its low impedance to shock compression. Brief melting and rapid burial minimized the escape of planetary-type noble gases from the ureilitic melts. Incomplete separation of metal from silicates

  13. Simulation of a navigator algorithm for a low-cost GPS receiver

    NASA Technical Reports Server (NTRS)

    Hodge, W. F.

    1980-01-01

    The analytical structure of an existing navigator algorithm for a low cost global positioning system receiver is described in detail to facilitate its implementation on in-house digital computers and real-time simulators. The material presented includes a simulation of GPS pseudorange measurements, based on a two-body representation of the NAVSTAR spacecraft orbits, and a four component model of the receiver bias errors. A simpler test for loss of pseudorange measurements due to spacecraft shielding is also noted.

  14. Information theory-based algorithm for in silico prediction of PCR products with whole genomic sequences as templates.

    PubMed

    Cao, Youfang; Wang, Lianjie; Xu, Kexue; Kou, Chunhai; Zhang, Yulei; Wei, Guifang; He, Junjian; Wang, Yunfang; Zhao, Liping

    2005-07-26

    A new algorithm for assessing similarity between primer and template has been developed based on the hypothesis that annealing of primer to template is an information transfer process. Primer sequence is converted to a vector of the full potential hydrogen numbers (3 for G or C, 2 for A or T), while template sequence is converted to a vector of the actual hydrogen bond numbers formed after primer annealing. The former is considered as source information and the latter destination information. An information coefficient is calculated as a measure for fidelity of this information transfer process and thus a measure of similarity between primer and potential annealing site on template. Successful prediction of PCR products from whole genomic sequences with a computer program based on the algorithm demonstrated the potential of this new algorithm in areas like in silico PCR and gene finding.

  15. Confidence range estimate of extended source imagery acquisition algorithms via computer simulations. [in optical communication systems

    NASA Technical Reports Server (NTRS)

    Chen, CHIEN-C.; Hui, Elliot; Okamoto, Garret

    1992-01-01

    Spatial acquisition using the sun-lit Earth as a beacon source provides several advantages over active beacon-based systems for deep-space optical communication systems. However, since the angular extend of the Earth image is large compared to the laser beam divergence, the acquisition subsystem must be capable of resolving the image to derive the proper pointing orientation. The algorithms used must be capable of deducing the receiver location given the blurring introduced by the imaging optics and the large Earth albedo fluctuation. Furthermore, because of the complexity of modelling the Earth and the tracking algorithms, an accurate estimate of the algorithm accuracy can only be made via simulation using realistic Earth images. An image simulator was constructed for this purpose, and the results of the simulation runs are reported.

  16. Range Process Simulation Tool

    NASA Technical Reports Server (NTRS)

    Phillips, Dave; Haas, William; Barth, Tim; Benjamin, Perakath; Graul, Michael; Bagatourova, Olga

    2005-01-01

    Range Process Simulation Tool (RPST) is a computer program that assists managers in rapidly predicting and quantitatively assessing the operational effects of proposed technological additions to, and/or upgrades of, complex facilities and engineering systems such as the Eastern Test Range. Originally designed for application to space transportation systems, RPST is also suitable for assessing effects of proposed changes in industrial facilities and large organizations. RPST follows a model-based approach that includes finite-capacity schedule analysis and discrete-event process simulation. A component-based, scalable, open architecture makes RPST easily and rapidly tailorable for diverse applications. Specific RPST functions include: (1) definition of analysis objectives and performance metrics; (2) selection of process templates from a processtemplate library; (3) configuration of process models for detailed simulation and schedule analysis; (4) design of operations- analysis experiments; (5) schedule and simulation-based process analysis; and (6) optimization of performance by use of genetic algorithms and simulated annealing. The main benefits afforded by RPST are provision of information that can be used to reduce costs of operation and maintenance, and the capability for affordable, accurate, and reliable prediction and exploration of the consequences of many alternative proposed decisions.

  17. An End-to-End simulator for the development of atmospheric corrections and temperature - emissivity separation algorithms in the TIR spectral domain

    NASA Astrophysics Data System (ADS)

    Rock, Gilles; Fischer, Kim; Schlerf, Martin; Gerhards, Max; Udelhoven, Thomas

    2017-04-01

    The development and optimization of image processing algorithms requires the availability of datasets depicting every step from earth surface to the sensor's detector. The lack of ground truth data obliges to develop algorithms on simulated data. The simulation of hyperspectral remote sensing data is a useful tool for a variety of tasks such as the design of systems, the understanding of the image formation process, and the development and validation of data processing algorithms. An end-to-end simulator has been set up consisting of a forward simulator, a backward simulator and a validation module. The forward simulator derives radiance datasets based on laboratory sample spectra, applies atmospheric contributions using radiative transfer equations, and simulates the instrument response using configurable sensor models. This is followed by the backward simulation branch, consisting of an atmospheric correction (AC), a temperature and emissivity separation (TES) or a hybrid AC and TES algorithm. An independent validation module allows the comparison between input and output dataset and the benchmarking of different processing algorithms. In this study, hyperspectral thermal infrared scenes of a variety of surfaces have been simulated to analyze existing AC and TES algorithms. The ARTEMISS algorithm was optimized and benchmarked against the original implementations. The errors in TES were found to be related to incorrect water vapor retrieval. The atmospheric characterization could be optimized resulting in increasing accuracies in temperature and emissivity retrieval. Airborne datasets of different spectral resolutions were simulated from terrestrial HyperCam-LW measurements. The simulated airborne radiance spectra were subjected to atmospheric correction and TES and further used for a plant species classification study analyzing effects related to noise and mixed pixels.

  18. Exponentially-Biased Ground-State Sampling of Quantum Annealing Machines with Transverse-Field Driving Hamiltonians

    NASA Technical Reports Server (NTRS)

    Mandra, Salvatore

    2017-01-01

    We study the performance of the D-Wave 2X quantum annealing machine on systems with well-controlled ground-state degeneracy. While obtaining the ground state of a spin-glass benchmark instance represents a difficult task, the gold standard for any optimization algorithm or machine is to sample all solutions that minimize the Hamiltonian with more or less equal probability. Our results show that while naive transverse-field quantum annealing on the D-Wave 2X device can find the ground-state energy of the problems, it is not well suited in identifying all degenerate ground-state configurations associated to a particular instance. Even worse, some states are exponentially suppressed, in agreement with previous studies on toy model problems [New J. Phys. 11, 073021 (2009)]. These results suggest that more complex driving Hamiltonians are needed in future quantum annealing machines to ensure a fair sampling of the ground-state manifold.

  19. Novel models and algorithms of load balancing for variable-structured collaborative simulation under HLA/RTI

    NASA Astrophysics Data System (ADS)

    Yue, Yingchao; Fan, Wenhui; Xiao, Tianyuan; Ma, Cheng

    2013-07-01

    High level architecture(HLA) is the open standard in the collaborative simulation field. Scholars have been paying close attention to theoretical research on and engineering applications of collaborative simulation based on HLA/RTI, which extends HLA in various aspects like functionality and efficiency. However, related study on the load balancing problem of HLA collaborative simulation is insufficient. Without load balancing, collaborative simulation under HLA/RTI may encounter performance reduction or even fatal errors. In this paper, load balancing is further divided into static problems and dynamic problems. A multi-objective model is established and the randomness of model parameters is taken into consideration for static load balancing, which makes the model more credible. The Monte Carlo based optimization algorithm(MCOA) is excogitated to gain static load balance. For dynamic load balancing, a new type of dynamic load balancing problem is put forward with regards to the variable-structured collaborative simulation under HLA/RTI. In order to minimize the influence against the running collaborative simulation, the ordinal optimization based algorithm(OOA) is devised to shorten the optimization time. Furthermore, the two algorithms are adopted in simulation experiments of different scenarios, which demonstrate their effectiveness and efficiency. An engineering experiment about collaborative simulation under HLA/RTI of high speed electricity multiple units(EMU) is also conducted to indentify credibility of the proposed models and supportive utility of MCOA and OOA to practical engineering systems. The proposed research ensures compatibility of traditional HLA, enhances the ability for assigning simulation loads onto computing units both statically and dynamically, improves the performance of collaborative simulation system and makes full use of the hardware resources.

  20. Implementation of Tree and Butterfly Barriers with Optimistic Time Management Algorithms for Discrete Event Simulation

    NASA Astrophysics Data System (ADS)

    Rizvi, Syed S.; Shah, Dipali; Riasat, Aasia

    The Time Wrap algorithm [3] offers a run time recovery mechanism that deals with the causality errors. These run time recovery mechanisms consists of rollback, anti-message, and Global Virtual Time (GVT) techniques. For rollback, there is a need to compute GVT which is used in discrete-event simulation to reclaim the memory, commit the output, detect the termination, and handle the errors. However, the computation of GVT requires dealing with transient message problem and the simultaneous reporting problem. These problems can be dealt in an efficient manner by the Samadi's algorithm [8] which works fine in the presence of causality errors. However, the performance of both Time Wrap and Samadi's algorithms depends on the latency involve in GVT computation. Both algorithms give poor latency for large simulation systems especially in the presence of causality errors. To improve the latency and reduce the processor ideal time, we implement tree and butterflies barriers with the optimistic algorithm. Our analysis shows that the use of synchronous barriers such as tree and butterfly with the optimistic algorithm not only minimizes the GVT latency but also minimizes the processor idle time.

  1. A smooth particle-mesh Ewald algorithm for Stokes suspension simulations: The sedimentation of fibers

    NASA Astrophysics Data System (ADS)

    Saintillan, David; Darve, Eric; Shaqfeh, Eric S. G.

    2005-03-01

    Large-scale simulations of non-Brownian rigid fibers sedimenting under gravity at zero Reynolds number have been performed using a fast algorithm. The mathematical formulation follows the previous simulations by Butler and Shaqfeh ["Dynamic simulations of the inhomogeneous sedimentation of rigid fibres," J. Fluid Mech. 468, 205 (2002)]. The motion of the fibers is described using slender-body theory, and the line distribution of point forces along their lengths is approximated by a Legendre polynomial in which only the total force, torque, and particle stresslet are retained. Periodic boundary conditions are used to simulate an infinite suspension, and both far-field hydrodynamic interactions and short-range lubrication forces are considered in all simulations. The calculation of the hydrodynamic interactions, which is typically the bottleneck for large systems with periodic boundary conditions, is accelerated using a smooth particle-mesh Ewald (SPME) algorithm previously used in molecular dynamics simulations. In SPME the slowly decaying Green's function is split into two fast-converging sums: the first involves the distribution of point forces and accounts for the singular short-range part of the interactions, while the second is expressed in terms of the Fourier transform of the force distribution and accounts for the smooth and long-range part. Because of its smoothness, the second sum can be computed efficiently on an underlying grid using the fast Fourier transform algorithm, resulting in a significant speed-up of the calculations. Systems of up to 512 fibers were simulated on a single-processor workstation, providing a different insight into the formation, structure, and dynamics of the inhomogeneities that occur in sedimenting fiber suspensions.

  2. Athermal Annealing of Silicon

    NASA Astrophysics Data System (ADS)

    Fischer, R. P.; Grun, J.; Ting, A.; Felix, C.; Peckerar, M.; Fatemi, M.; Manka, C. K.

    1999-11-01

    Current semiconductor annealing methods are based on thermal processes which are accompanied by diffusion that degrades the definition of device features or causes other problems. This will be a serious obstacle for the production of next-generation ultra-high density, low power semiconductor devices. Experiments underway at NRL utilize a new annealing method which is much faster than thermal annealing and does not depend upon thermal energy (J. Grun, et al)., Phys. Rev. Letters 78, 1584 (1997).. A 10 J, 30 nsec, 1.053 nm wavelength laser pulse is focussed to approximately 1 mm diameter on a silicon sample. Acoustic and shock waves propagate from the impact region, which deposit mechanical energy into the material and anneal the silicon. Experimental results will be presented on annealing neutron-transmutation-doped (NTD) and ion implanted silicon samples with impurity concentrations from 1 × 10^15-3 × 10^20/cm^3.

  3. Effect of excimer laser annealing on a-InGaZnO thin-film transistors passivated by solution-processed hybrid passivation layers

    NASA Astrophysics Data System (ADS)

    Bermundo, Juan Paolo; Ishikawa, Yasuaki; Fujii, Mami N.; Nonaka, Toshiaki; Ishihara, Ryoichi; Ikenoue, Hiroshi; Uraoka, Yukiharu

    2016-01-01

    We demonstrate the use of excimer laser annealing (ELA) as a low temperature annealing alternative to anneal amorphous InGaZnO (a-IGZO) thin-film transistors (TFTs) passivated by a solution-processed hybrid passivation layer. Usually, a-IGZO is annealed using thermal annealing at high temperatures of up to 400 °C. As an alternative to high temperature thermal annealing, two types of ELA, XeCl (308 nm) and KrF (248 nm) ELA, are introduced. Both ELA types enhanced the electrical characteristics of a-IGZO TFTs leading to a mobility improvement of ~13 cm2 V-1 s-1 and small threshold voltage which varied from ~0-3 V. Furthermore, two-dimensional heat simulation using COMSOL Multiphysics was used to identify possible degradation sites, analyse laser heat localization, and confirm that the substrate temperature is below 50 °C. The two-dimensional heat simulation showed that the substrate temperature remained at very low temperatures, less than 30 °C, during ELA. This implies that any flexible material can be used as the substrate. These results demonstrate the large potential of ELA as a low temperature annealing alternative for already-passivated a-IGZO TFTs.

  4. Binomial tau-leap spatial stochastic simulation algorithm for applications in chemical kinetics.

    PubMed

    Marquez-Lago, Tatiana T; Burrage, Kevin

    2007-09-14

    In cell biology, cell signaling pathway problems are often tackled with deterministic temporal models, well mixed stochastic simulators, and/or hybrid methods. But, in fact, three dimensional stochastic spatial modeling of reactions happening inside the cell is needed in order to fully understand these cell signaling pathways. This is because noise effects, low molecular concentrations, and spatial heterogeneity can all affect the cellular dynamics. However, there are ways in which important effects can be accounted without going to the extent of using highly resolved spatial simulators (such as single-particle software), hence reducing the overall computation time significantly. We present a new coarse grained modified version of the next subvolume method that allows the user to consider both diffusion and reaction events in relatively long simulation time spans as compared with the original method and other commonly used fully stochastic computational methods. Benchmarking of the simulation algorithm was performed through comparison with the next subvolume method and well mixed models (MATLAB), as well as stochastic particle reaction and transport simulations (CHEMCELL, Sandia National Laboratories). Additionally, we construct a model based on a set of chemical reactions in the epidermal growth factor receptor pathway. For this particular application and a bistable chemical system example, we analyze and outline the advantages of our presented binomial tau-leap spatial stochastic simulation algorithm, in terms of efficiency and accuracy, in scenarios of both molecular homogeneity and heterogeneity.

  5. Stamping an AA5754 Train Window Panel with High Dent Resistance Using Locally Annealed Blanks

    NASA Astrophysics Data System (ADS)

    Piccininni, A.; Guglielmi, P.; Lo Franco, A.; Palumbo, G.

    2017-09-01

    The warm stamping of an AA5754-H32 window panel for railway vehicles applications has been proposed in the present work. The adoption of increased working temperatures can be surely considered the most effective solution for this alloy to overcome the limited material formability at room temperature [Palumbo et al. “Warm Forming of an AA5754 Component for Railway Vehicle Applications”, Procedia Engineering, Vol. 183, 2017, Pages 351-356] but, in order to improve the overall dent resistance of the component, the initial wrought conditions have been chosen in the present work. The manufacturing of the window panel was thus subdivided into a preliminary local heat treatment (assumed to be performed by laser) to anneal the material and a subsequent warm stamping step using heated tools. The best combination of temperature and holding time able to produce the annealing of the investigated alloy was determined using the physical simulator Gleeble 3180. On the contrary, the warm forming step was designed by means of thermo-mechanical simulations: in order to model the AA5754-H32 blank with annealed regions, an extensive experimental campaign (tensile and formability tests) was conducted using specimens in the annealed (H111) and in the wrought (H32) conditions. Through the numerical approach it was thus possible define: (i) the extent of the annealed regions; (ii) the punch speed to get a sound component.

  6. Accelerating the Gillespie Exact Stochastic Simulation Algorithm using hybrid parallel execution on graphics processing units.

    PubMed

    Komarov, Ivan; D'Souza, Roshan M

    2012-01-01

    The Gillespie Stochastic Simulation Algorithm (GSSA) and its variants are cornerstone techniques to simulate reaction kinetics in situations where the concentration of the reactant is too low to allow deterministic techniques such as differential equations. The inherent limitations of the GSSA include the time required for executing a single run and the need for multiple runs for parameter sweep exercises due to the stochastic nature of the simulation. Even very efficient variants of GSSA are prohibitively expensive to compute and perform parameter sweeps. Here we present a novel variant of the exact GSSA that is amenable to acceleration by using graphics processing units (GPUs). We parallelize the execution of a single realization across threads in a warp (fine-grained parallelism). A warp is a collection of threads that are executed synchronously on a single multi-processor. Warps executing in parallel on different multi-processors (coarse-grained parallelism) simultaneously generate multiple trajectories. Novel data-structures and algorithms reduce memory traffic, which is the bottleneck in computing the GSSA. Our benchmarks show an 8×-120× performance gain over various state-of-the-art serial algorithms when simulating different types of models.

  7. A three-dimensional spectral algorithm for simulations of transition and turbulence

    NASA Technical Reports Server (NTRS)

    Zang, T. A.; Hussaini, M. Y.

    1985-01-01

    A spectral algorithm for simulating three dimensional, incompressible, parallel shear flows is described. It applies to the channel, to the parallel boundary layer, and to other shear flows with one wall bounded and two periodic directions. Representative applications to the channel and to the heated boundary layer are presented.

  8. Advanced Dynamically Adaptive Algorithms for Stochastic Simulations on Extreme Scales

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Xiu, Dongbin

    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.

  9. Selecting Magnet Laminations Recipes Using the Meth-od of Sim-u-la-ted Annealing

    NASA Astrophysics Data System (ADS)

    Russell, A. D.; Baiod, R.; Brown, B. C.; Harding, D. J.; Martin, P. S.

    1997-05-01

    The Fermilab Main Injector project is building 344 dipoles using more than 7000 tons of steel. Budget and logistical constraints required that steel production, lamination stamping and magnet fabrication proceed in parallel. There were significant run-to-run variations in the magnetic properties of the steel (Martin, P.S., et al., Variations in the Steel Properties and the Excitation Characteristics of FMI Dipoles, this conference). The large lamination size (>0.5 m coil opening) resulted in variations of gap height due to differences in stress relief in the steel after stamping. To minimize magnet-to-magnet strength and field shape variations the laminations were shuffled based on the available magnetic and mechanical data and assigned to magnets using a computer program based on the method of simulated annealing. The lamination sets selected by the program have produced magnets which easily satisfy the design requirements. Variations of the average magnet gap are an order of magnitude smaller than the variations in lamination gaps. This paper discusses observed gap variations, the program structure and the strength uniformity results.

  10. A Comprehensive Study of Three Delay Compensation Algorithms for Flight Simulators

    NASA Technical Reports Server (NTRS)

    Guo, Liwen; Cardullo, Frank M.; Houck, Jacob A.; Kelly, Lon C.; Wolters, Thomas E.

    2005-01-01

    This paper summarizes a comprehensive study of three predictors used for compensating the transport delay in a flight simulator; The McFarland, Adaptive and State Space Predictors. The paper presents proof that the stochastic approximation algorithm can achieve the best compensation among all four adaptive predictors, and intensively investigates the relationship between the state space predictor s compensation quality and its reference model. Piloted simulation tests show that the adaptive predictor and state space predictor can achieve better compensation of transport delay than the McFarland predictor.

  11. SMMR Simulator radiative transfer calibration model. 2: Algorithm development

    NASA Technical Reports Server (NTRS)

    Link, S.; Calhoon, C.; Krupp, B.

    1980-01-01

    Passive microwave measurements performed from Earth orbit can be used to provide global data on a wide range of geophysical and meteorological phenomena. A Scanning Multichannel Microwave Radiometer (SMMR) is being flown on the Nimbus-G satellite. The SMMR Simulator duplicates the frequency bands utilized in the spacecraft instruments through an amalgamate of radiometer systems. The algorithm developed utilizes data from the fall 1978 NASA CV-990 Nimbus-G underflight test series and subsequent laboratory testing.

  12. Optimum gradient material for a functionally graded dental implant using metaheuristic algorithms.

    PubMed

    Sadollah, Ali; Bahreininejad, Ardeshir

    2011-10-01

    Despite dental implantation being a great success, one of the key issues facing it is a mismatch of mechanical properties between engineered and native biomaterials, which makes osseointegration and bone remodeling problematical. Functionally graded material (FGM) has been proposed as a potential upgrade to some conventional implant materials such as titanium for selection in prosthetic dentistry. The idea of an FGM dental implant is that the property would vary in a certain pattern to match the biomechanical characteristics required at different regions in the hosting bone. However, matching the properties does not necessarily guarantee the best osseointegration and bone remodeling. Little existing research has been reported on developing an optimal design of an FGM dental implant for promoting long-term success. Based upon remodeling results, metaheuristic algorithms such as the genetic algorithms (GAs) and simulated annealing (SA) have been adopted to develop a multi-objective optimal design for FGM implantation design. The results are compared with those in literature. Copyright © 2011 Elsevier Ltd. All rights reserved.

  13. Simulation-Based Evaluation of the Performances of an Algorithm for Detecting Abnormal Disease-Related Features in Cattle Mortality Records.

    PubMed

    Perrin, Jean-Baptiste; Durand, Benoît; Gay, Emilie; Ducrot, Christian; Hendrikx, Pascal; Calavas, Didier; Hénaux, Viviane

    2015-01-01

    We performed a simulation study to evaluate the performances of an anomaly detection algorithm considered in the frame of an automated surveillance system of cattle mortality. The method consisted in a combination of temporal regression and spatial cluster detection which allows identifying, for a given week, clusters of spatial units showing an excess of deaths in comparison with their own historical fluctuations. First, we simulated 1,000 outbreaks of a disease causing extra deaths in the French cattle population (about 200,000 herds and 20 million cattle) according to a model mimicking the spreading patterns of an infectious disease and injected these disease-related extra deaths in an authentic mortality dataset, spanning from January 2005 to January 2010. Second, we applied our algorithm on each of the 1,000 semi-synthetic datasets to identify clusters of spatial units showing an excess of deaths considering their own historical fluctuations. Third, we verified if the clusters identified by the algorithm did contain simulated extra deaths in order to evaluate the ability of the algorithm to identify unusual mortality clusters caused by an outbreak. Among the 1,000 simulations, the median duration of simulated outbreaks was 8 weeks, with a median number of 5,627 simulated deaths and 441 infected herds. Within the 12-week trial period, 73% of the simulated outbreaks were detected, with a median timeliness of 1 week, and a mean of 1.4 weeks. The proportion of outbreak weeks flagged by an alarm was 61% (i.e. sensitivity) whereas one in three alarms was a true alarm (i.e. positive predictive value). The performances of the detection algorithm were evaluated for alternative combination of epidemiologic parameters. The results of our study confirmed that in certain conditions automated algorithms could help identifying abnormal cattle mortality increases possibly related to unidentified health events.

  14. Simulation-Based Evaluation of the Performances of an Algorithm for Detecting Abnormal Disease-Related Features in Cattle Mortality Records

    PubMed Central

    Perrin, Jean-Baptiste; Durand, Benoît; Gay, Emilie; Ducrot, Christian; Hendrikx, Pascal; Calavas, Didier; Hénaux, Viviane

    2015-01-01

    We performed a simulation study to evaluate the performances of an anomaly detection algorithm considered in the frame of an automated surveillance system of cattle mortality. The method consisted in a combination of temporal regression and spatial cluster detection which allows identifying, for a given week, clusters of spatial units showing an excess of deaths in comparison with their own historical fluctuations. First, we simulated 1,000 outbreaks of a disease causing extra deaths in the French cattle population (about 200,000 herds and 20 million cattle) according to a model mimicking the spreading patterns of an infectious disease and injected these disease-related extra deaths in an authentic mortality dataset, spanning from January 2005 to January 2010. Second, we applied our algorithm on each of the 1,000 semi-synthetic datasets to identify clusters of spatial units showing an excess of deaths considering their own historical fluctuations. Third, we verified if the clusters identified by the algorithm did contain simulated extra deaths in order to evaluate the ability of the algorithm to identify unusual mortality clusters caused by an outbreak. Among the 1,000 simulations, the median duration of simulated outbreaks was 8 weeks, with a median number of 5,627 simulated deaths and 441 infected herds. Within the 12-week trial period, 73% of the simulated outbreaks were detected, with a median timeliness of 1 week, and a mean of 1.4 weeks. The proportion of outbreak weeks flagged by an alarm was 61% (i.e. sensitivity) whereas one in three alarms was a true alarm (i.e. positive predictive value). The performances of the detection algorithm were evaluated for alternative combination of epidemiologic parameters. The results of our study confirmed that in certain conditions automated algorithms could help identifying abnormal cattle mortality increases possibly related to unidentified health events. PMID:26536596

  15. Compiling Planning into Quantum Optimization Problems: A Comparative Study

    DTIC Science & Technology

    2015-06-07

    and Sipser, M. 2000. Quantum computation by adiabatic evolution. arXiv:quant- ph/0001106. Fikes, R. E., and Nilsson, N. J. 1972. STRIPS: A new...become available: quantum annealing. Quantum annealing is one of the most accessible quantum algorithms for a computer sci- ence audience not versed...in quantum computing because of its close ties to classical optimization algorithms such as simulated annealing. While large-scale universal quantum

  16. Improving search algorithms by using intelligent coordinates

    NASA Astrophysics Data System (ADS)

    Wolpert, David; Tumer, Kagan; Bandari, Esfandiar

    2004-01-01

    We consider algorithms that maximize a global function G in a distributed manner, using a different adaptive computational agent to set each variable of the underlying space. Each agent η is self-interested; it sets its variable to maximize its own function gη. Three factors govern such a distributed algorithm’s performance, related to exploration/exploitation, game theory, and machine learning. We demonstrate how to exploit all three factors by modifying a search algorithm’s exploration stage: rather than random exploration, each coordinate of the search space is now controlled by a separate machine-learning-based “player” engaged in a noncooperative game. Experiments demonstrate that this modification improves simulated annealing (SA) by up to an order of magnitude for bin packing and for a model of an economic process run over an underlying network. These experiments also reveal interesting small-world phenomena.

  17. Comparison of Reconstruction and Control algorithms on the ESO end-to-end simulator OCTOPUS

    NASA Astrophysics Data System (ADS)

    Montilla, I.; Béchet, C.; Lelouarn, M.; Correia, C.; Tallon, M.; Reyes, M.; Thiébaut, É.

    Extremely Large Telescopes are very challenging concerning their Adaptive Optics requirements. Their diameters, the specifications demanded by the science for which they are being designed for, and the planned use of Extreme Adaptive Optics systems, imply a huge increment in the number of degrees of freedom in the deformable mirrors. It is necessary to study new reconstruction algorithms to implement the real time control in Adaptive Optics at the required speed. We have studied the performance, applied to the case of the European ELT, of three different algorithms: the matrix-vector multiplication (MVM) algorithm, considered as a reference; the Fractal Iterative Method (FrIM); and the Fourier Transform Reconstructor (FTR). The algorithms have been tested on ESO's OCTOPUS software, which simulates the atmosphere, the deformable mirror, the sensor and the closed-loop control. The MVM is the default reconstruction and control method implemented in OCTOPUS, but it scales in O(N2) operations per loop so it is not considered as a fast algorithm for wave-front reconstruction and control on an Extremely Large Telescope. The two other methods are the fast algorithms studied in the E-ELT Design Study. The performance, as well as their response in the presence of noise and with various atmospheric conditions, has been compared using a Single Conjugate Adaptive Optics configuration for a 42 m diameter ELT, with a total amount of 5402 actuators. Those comparisons made on a common simulator allow to enhance the pros and cons of the various methods, and give us a better understanding of the type of reconstruction algorithm that an ELT demands.

  18. Baking a mass-spectrometry data PIE with McMC and simulated annealing: predicting protein post-translational modifications from integrated top-down and bottom-up data.

    PubMed

    Jefferys, Stuart R; Giddings, Morgan C

    2011-03-15

    Post-translational modifications are vital to the function of proteins, but are hard to study, especially since several modified isoforms of a protein may be present simultaneously. Mass spectrometers are a great tool for investigating modified proteins, but the data they provide is often incomplete, ambiguous and difficult to interpret. Combining data from multiple experimental techniques-especially bottom-up and top-down mass spectrometry-provides complementary information. When integrated with background knowledge this allows a human expert to interpret what modifications are present and where on a protein they are located. However, the process is arduous and for high-throughput applications needs to be automated. This article explores a data integration methodology based on Markov chain Monte Carlo and simulated annealing. Our software, the Protein Inference Engine (the PIE) applies these algorithms using a modular approach, allowing multiple types of data to be considered simultaneously and for new data types to be added as needed. Even for complicated data representing multiple modifications and several isoforms, the PIE generates accurate modification predictions, including location. When applied to experimental data collected on the L7/L12 ribosomal protein the PIE was able to make predictions consistent with manual interpretation for several different L7/L12 isoforms using a combination of bottom-up data with experimentally identified intact masses. Software, demo projects and source can be downloaded from http://pie.giddingslab.org/

  19. Validating module network learning algorithms using simulated data.

    PubMed

    Michoel, Tom; Maere, Steven; Bonnet, Eric; Joshi, Anagha; Saeys, Yvan; Van den Bulcke, Tim; Van Leemput, Koenraad; van Remortel, Piet; Kuiper, Martin; Marchal, Kathleen; Van de Peer, Yves

    2007-05-03

    In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Despite the demonstrated success of such algorithms in uncovering biologically relevant regulatory relations, further developments in the area are hampered by a lack of tools to compare the performance of alternative module network learning strategies. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module network learning algorithms. We introduce a software package for learning module networks, called LeMoNe, which incorporates a novel strategy for learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance. Overall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators. We show that data simulators such as SynTReN are very well suited for the purpose of developing, testing and improving module network

  20. Parallel 3D-TLM algorithm for simulation of the Earth-ionosphere cavity

    NASA Astrophysics Data System (ADS)

    Toledo-Redondo, Sergio; Salinas, Alfonso; Morente-Molinera, Juan Antonio; Méndez, Antonio; Fornieles, Jesús; Portí, Jorge; Morente, Juan Antonio

    2013-03-01

    A parallel 3D algorithm for solving time-domain electromagnetic problems with arbitrary geometries is presented. The technique employed is the Transmission Line Modeling (TLM) method implemented in Shared Memory (SM) environments. The benchmarking performed reveals that the maximum speedup depends on the memory size of the problem as well as multiple hardware factors, like the disposition of CPUs, cache, or memory. A maximum speedup of 15 has been measured for the largest problem. In certain circumstances of low memory requirements, superlinear speedup is achieved using our algorithm. The model is employed to model the Earth-ionosphere cavity, thus enabling a study of the natural electromagnetic phenomena that occur in it. The algorithm allows complete 3D simulations of the cavity with a resolution of 10 km, within a reasonable timescale.

  1. A Variational Formulation of Macro-Particle Algorithms for Kinetic Plasma Simulations

    NASA Astrophysics Data System (ADS)

    Shadwick, B. A.

    2013-10-01

    Macro-particle based simulations methods are in widespread use in plasma physics; their computational efficiency and intuitive nature are largely responsible for their longevity. In the main, these algorithms are formulated by approximating the continuous equations of motion. For systems governed by a variational principle (such as collisionless plasmas), approximations of the equations of motion is known to introduce anomalous behavior, especially in system invariants. We present a variational formulation of particle algorithms for plasma simulation based on a reduction of the distribution function onto a finite collection of macro-particles. As in the usual Particle-In-Cell (PIC) formulation, these macro-particles have a definite momentum and are spatially extended. The primary advantage of this approach is the preservation of the link between symmetries and conservation laws. For example, nothing in the reduction introduces explicit time dependence to the system and, therefore, the continuous-time equations of motion exactly conserve energy; thus, these models are free of grid-heating. In addition, the variational formulation allows for constructing models of arbitrary spatial and temporal order. In contrast, the overall accuracy of the usual PIC algorithm is at most second due to the nature of the force interpolation between the gridded field quantities and the (continuous) particle position. Again in contrast to the usual PIC algorithm, here the macro-particle shape is arbitrary; the spatial extent is completely decoupled from both the grid-size and the ``smoothness'' of the shape; smoother particle shapes are not necessarily larger. For simplicity, we restrict our discussion to one-dimensional, non-relativistic, un-magnetized, electrostatic plasmas. We comment on the extension to the electromagnetic case. Supported by the US DoE under contract numbers DE-FG02-08ER55000 and DE-SC0008382.

  2. Dissipative environment may improve the quantum annealing performances of the ferromagnetic p -spin model

    NASA Astrophysics Data System (ADS)

    Passarelli, G.; De Filippis, G.; Cataudella, V.; Lucignano, P.

    2018-02-01

    We investigate the quantum annealing of the ferromagnetic p -spin model in a dissipative environment (p =5 and p =7 ). This model, in the large-p limit, codifies Grover's algorithm for searching in an unsorted database [L. K. Grover, Proceedings of the 28th Annual ACM Symposium on Theory of Computing (ACM, New York, 1996), pp. 212-219]. The dissipative environment is described by a phonon bath in thermal equilibrium at finite temperature. The dynamics is studied in the framework of a Lindblad master equation for the reduced density matrix describing only the spins. Exploiting the symmetries of our model Hamiltonian, we can describe many spins and extrapolate expected trends for large N and p . While at weak system-bath coupling the dissipative environment has detrimental effects on the annealing results, we show that in the intermediate-coupling regime, the phonon bath seems to speed up the annealing at low temperatures. This improvement in the performance is likely not due to thermal fluctuation but rather arises from a correlated spin-bath state and persists even at zero temperature. This result may pave the way to a new scenario in which, by appropriately engineering the system-bath coupling, one may optimize quantum annealing performances below either the purely quantum or the classical limit.

  3. Practical algorithms for simulation and reconstruction of digital in-line holograms.

    PubMed

    Latychevskaia, Tatiana; Fink, Hans-Werner

    2015-03-20

    Here we present practical methods for simulation and reconstruction of in-line digital holograms recorded with plane and spherical waves. The algorithms described here are applicable to holographic imaging of an object exhibiting absorption as well as phase-shifting properties. Optimal parameters, related to distances, sampling rate, and other factors for successful simulation and reconstruction of holograms are evaluated and criteria for the achievable resolution are worked out. Moreover, we show that the numerical procedures for the reconstruction of holograms recorded with plane and spherical waves are identical under certain conditions. Experimental examples of holograms and their reconstructions are also discussed.

  4. A novel Monte Carlo algorithm for simulating crystals with McStas

    NASA Astrophysics Data System (ADS)

    Alianelli, L.; Sánchez del Río, M.; Felici, R.; Andersen, K. H.; Farhi, E.

    2004-07-01

    We developed an original Monte Carlo algorithm for the simulation of Bragg diffraction by mosaic, bent and gradient crystals. It has practical applications, as it can be used for simulating imperfect crystals (monochromators, analyzers and perhaps samples) in neutron ray-tracing packages, like McStas. The code we describe here provides a detailed description of the particle interaction with the microscopic homogeneous regions composing the crystal, therefore it can be used also for the calculation of quantities having a conceptual interest, as multiple scattering, or for the interpretation of experiments aiming at characterizing crystals, like diffraction topographs.

  5. Kinetics modeling of precipitation with characteristic shape during post-implantation annealing

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Li, Kun-Dar, E-mail: kundar@mail.nutn.edu.tw; Chen, Kwanyu

    2015-11-15

    In this study, we investigated the precipitation with characteristic shape in the microstructure during post-implantation annealing via a theoretical modeling approach. The processes of precipitates formation and evolution during phase separation were based on a nucleation and growth mechanism of atomic diffusion. Different stages of the precipitation, including the nucleation, growth and coalescence, were distinctly revealed in the numerical simulations. In addition, the influences of ion dose, temperature and crystallographic symmetry on the processes of faceted precipitation were also demonstrated. To comprehend the kinetic mechanism, the simulation results were further analyzed quantitatively by the Kolmogorov-Johnson-Mehl-Avrami (KJMA) equation. The Avrami exponentsmore » obtained from the regression curves varied from 1.47 to 0.52 for different conditions. With the increase of ion dose and temperature, the nucleation and growth of precipitations were expedited in accordance with the shortened incubation time and the raised coefficient of growth rate. A miscellaneous shape of precipitates in various crystallographic symmetry systems could be simulated through this anisotropic model. From the analyses of the kinetics, more fundamental information about the nucleation and growth mechanism of faceted precipitation during post-implantation annealing was acquired for future application.« less

  6. A gene network simulator to assess reverse engineering algorithms.

    PubMed

    Di Camillo, Barbara; Toffolo, Gianna; Cobelli, Claudio

    2009-03-01

    In the context of reverse engineering of biological networks, simulators are helpful to test and compare the accuracy of different reverse-engineering approaches in a variety of experimental conditions. A novel gene-network simulator is presented that resembles some of the main features of transcriptional regulatory networks related to topology, interaction among regulators of transcription, and expression dynamics. The simulator generates network topology according to the current knowledge of biological network organization, including scale-free distribution of the connectivity and clustering coefficient independent of the number of nodes in the network. It uses fuzzy logic to represent interactions among the regulators of each gene, integrated with differential equations to generate continuous data, comparable to real data for variety and dynamic complexity. Finally, the simulator accounts for saturation in the response to regulation and transcription activation thresholds and shows robustness to perturbations. It therefore provides a reliable and versatile test bed for reverse engineering algorithms applied to microarray data. Since the simulator describes regulatory interactions and expression dynamics as two distinct, although interconnected aspects of regulation, it can also be used to test reverse engineering approaches that use both microarray and protein-protein interaction data in the process of learning. A first software release is available at http://www.dei.unipd.it/~dicamill/software/netsim as an R programming language package.

  7. A simulation based method to assess inversion algorithms for transverse relaxation data

    NASA Astrophysics Data System (ADS)

    Ghosh, Supriyo; Keener, Kevin M.; Pan, Yong

    2008-04-01

    NMR relaxometry is a very useful tool for understanding various chemical and physical phenomena in complex multiphase systems. A Carr-Purcell-Meiboom-Gill (CPMG) [P.T. Callaghan, Principles of Nuclear Magnetic Resonance Microscopy, Clarendon Press, Oxford, 1991] experiment is an easy and quick way to obtain transverse relaxation constant (T2) in low field. Most of the samples usually have a distribution of T2 values. Extraction of this distribution of T2s from the noisy decay data is essentially an ill-posed inverse problem. Various inversion approaches have been used to solve this problem, to date. A major issue in using an inversion algorithm is determining how accurate the computed distribution is. A systematic analysis of an inversion algorithm, UPEN [G.C. Borgia, R.J.S. Brown, P. Fantazzini, Uniform-penalty inversion of multiexponential decay data, Journal of Magnetic Resonance 132 (1998) 65-77; G.C. Borgia, R.J.S. Brown, P. Fantazzini, Uniform-penalty inversion of multiexponential decay data II. Data spacing, T2 data, systematic data errors, and diagnostics, Journal of Magnetic Resonance 147 (2000) 273-285] was performed by means of simulated CPMG data generation. Through our simulation technique and statistical analyses, the effects of various experimental parameters on the computed distribution were evaluated. We converged to the true distribution by matching up the inversion results from a series of true decay data and a noisy simulated data. In addition to simulation studies, the same approach was also applied on real experimental data to support the simulation results.

  8. Mechanical annealing under low-amplitude cyclic loading in micropillars

    NASA Astrophysics Data System (ADS)

    Cui, Yi-nan; Liu, Zhan-li; Wang, Zhang-jie; Zhuang, Zhuo

    2016-04-01

    Mechanical annealing has been demonstrated to be an effective method for decreasing the overall dislocation density in submicron single crystal. However, simultaneously significant shape change always unexpectedly happens under extremely high monotonic loading to drive the pre-existing dislocations out of the free surfaces. In the present work, through in situ TEM experiments it is found that cyclic loading with low stress amplitude can drive most dislocations out of the submicron sample with virtually little change of the shape. The underlying dislocation mechanism is revealed by carrying out discrete dislocation dynamic (DDD) simulations. The simulation results indicate that the dislocation density decreases within cycles, while the accumulated plastic strain is small. By comparing the evolution of dislocation junction under monotonic, cyclic and relaxation deformation, the cumulative irreversible slip is found to be the key factor of promoting junction destruction and dislocation annihilation at free surface under low-amplitude cyclic loading condition. By introducing this mechanics into dislocation density evolution equations, the critical conditions for mechanical annealing under cyclic and monotonic loadings are discussed. Low-amplitude cyclic loading which strengthens the single crystal without seriously disturbing the structure has the potential applications in the manufacture of defect-free nano-devices.

  9. Airport Flight Departure Delay Model on Improved BN Structure Learning

    NASA Astrophysics Data System (ADS)

    Cao, Weidong; Fang, Xiangnong

    An high score prior genetic simulated annealing Bayesian network structure learning algorithm (HSPGSA) by combining genetic algorithm(GA) with simulated annealing algorithm(SAA) is developed. The new algorithm provides not only with strong global search capability of GA, but also with strong local hill climb search capability of SAA. The structure with the highest score is prior selected. In the mean time, structures with lower score are also could be choice. It can avoid efficiently prematurity problem by higher score individual wrong direct growing population. Algorithm is applied to flight departure delays analysis in a large hub airport. Based on the flight data a BN model is created. Experiments show that parameters learning can reflect departure delay.

  10. Simulation System of Car Crash Test in C-NCAP Analysis Based on an Improved Apriori Algorithm*

    NASA Astrophysics Data System (ADS)

    Xiang, LI

    In order to analysis car crash test in C-NCAP, an improved algorithm is given based on Apriori algorithm in this paper. The new algorithm is implemented with vertical data layout, breadth first searching, and intersecting. It takes advantage of the efficiency of vertical data layout and intersecting, and prunes candidate frequent item sets like Apriori. Finally, the new algorithm is applied in simulation of car crash test analysis system. The result shows that the relations will affect the C-NCAP test results, and it can provide a reference for the automotive design.

  11. An Algorithm for Interactive Modeling of Space-Transportation Engine Simulations: A Constraint Satisfaction Approach

    NASA Technical Reports Server (NTRS)

    Mitra, Debasis; Thomas, Ajai; Hemminger, Joseph; Sakowski, Barbara

    2001-01-01

    In this research we have developed an algorithm for the purpose of constraint processing by utilizing relational algebraic operators. Van Beek and others have investigated in the past this type of constraint processing from within a relational algebraic framework, producing some unique results. Apart from providing new theoretical angles, this approach also gives the opportunity to use the existing efficient implementations of relational database management systems as the underlying data structures for any relevant algorithm. Our algorithm here enhances that framework. The algorithm is quite general in its current form. Weak heuristics (like forward checking) developed within the Constraint-satisfaction problem (CSP) area could be also plugged easily within this algorithm for further enhancements of efficiency. The algorithm as developed here is targeted toward a component-oriented modeling problem that we are currently working on, namely, the problem of interactive modeling for batch-simulation of engineering systems (IMBSES). However, it could be adopted for many other CSP problems as well. The research addresses the algorithm and many aspects of the problem IMBSES that we are currently handling.

  12. DOE`s annealing prototype demonstration projects

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Warren, J.; Nakos, J.; Rochau, G.

    1997-02-01

    One of the challenges U.S. utilities face in addressing technical issues associated with the aging of nuclear power plants is the long-term effect of plant operation on reactor pressure vessels (RPVs). As a nuclear plant operates, its RPV is exposed to neutrons. For certain plants, this neutron exposure can cause embrittlement of some of the RPV welds which can shorten the useful life of the RPV. This RPV embrittlement issue has the potential to affect the continued operation of a number of operating U.S. pressurized water reactor (PWR) plants. However, RPV material properties affected by long-term irradiation are recoverable throughmore » a thermal annealing treatment of the RPV. Although a dozen Russian-designed RPVs and several U.S. military vessels have been successfully annealed, U.S. utilities have stated that a successful annealing demonstration of a U.S. RPV is a prerequisite for annealing a licensed U.S. nuclear power plant. In May 1995, the Department of Energy`s Sandia National Laboratories awarded two cost-shared contracts to evaluate the feasibility of annealing U.S. licensed plants by conducting an anneal of an installed RPV using two different heating technologies. The contracts were awarded to the American Society of Mechanical Engineers (ASME) Center for Research and Technology Development (CRTD) and MPR Associates (MPR). The ASME team completed its annealing prototype demonstration in July 1996, using an indirect gas furnace at the uncompleted Public Service of Indiana`s Marble Hill nuclear power plant. The MPR team`s annealing prototype demonstration was scheduled to be completed in early 1997, using a direct heat electrical furnace at the uncompleted Consumers Power Company`s nuclear power plant at Midland, Michigan. This paper describes the Department`s annealing prototype demonstration goals and objectives; the tasks, deliverables, and results to date for each annealing prototype demonstration; and the remaining annealing technology

  13. Applicability Analysis of Cloth Simulation Filtering Algorithm for Mobile LIDAR Point Cloud

    NASA Astrophysics Data System (ADS)

    Cai, S.; Zhang, W.; Qi, J.; Wan, P.; Shao, J.; Shen, A.

    2018-04-01

    Classifying the original point clouds into ground and non-ground points is a key step in LiDAR (light detection and ranging) data post-processing. Cloth simulation filtering (CSF) algorithm, which based on a physical process, has been validated to be an accurate, automatic and easy-to-use algorithm for airborne LiDAR point cloud. As a new technique of three-dimensional data collection, the mobile laser scanning (MLS) has been gradually applied in various fields, such as reconstruction of digital terrain models (DTM), 3D building modeling and forest inventory and management. Compared with airborne LiDAR point cloud, there are some different features (such as point density feature, distribution feature and complexity feature) for mobile LiDAR point cloud. Some filtering algorithms for airborne LiDAR data were directly used in mobile LiDAR point cloud, but it did not give satisfactory results. In this paper, we explore the ability of the CSF algorithm for mobile LiDAR point cloud. Three samples with different shape of the terrain are selected to test the performance of this algorithm, which respectively yields total errors of 0.44 %, 0.77 % and1.20 %. Additionally, large area dataset is also tested to further validate the effectiveness of this algorithm, and results show that it can quickly and accurately separate point clouds into ground and non-ground points. In summary, this algorithm is efficient and reliable for mobile LiDAR point cloud.

  14. MODA: a new algorithm to compute optical depths in multidimensional hydrodynamic simulations

    NASA Astrophysics Data System (ADS)

    Perego, Albino; Gafton, Emanuel; Cabezón, Rubén; Rosswog, Stephan; Liebendörfer, Matthias

    2014-08-01

    Aims: We introduce the multidimensional optical depth algorithm (MODA) for the calculation of optical depths in approximate multidimensional radiative transport schemes, equally applicable to neutrinos and photons. Motivated by (but not limited to) neutrino transport in three-dimensional simulations of core-collapse supernovae and neutron star mergers, our method makes no assumptions about the geometry of the matter distribution, apart from expecting optically transparent boundaries. Methods: Based on local information about opacities, the algorithm figures out an escape route that tends to minimize the optical depth without assuming any predefined paths for radiation. Its adaptivity makes it suitable for a variety of astrophysical settings with complicated geometry (e.g., core-collapse supernovae, compact binary mergers, tidal disruptions, star formation, etc.). We implement the MODA algorithm into both a Eulerian hydrodynamics code with a fixed, uniform grid and into an SPH code where we use a tree structure that is otherwise used for searching neighbors and calculating gravity. Results: In a series of numerical experiments, we compare the MODA results with analytically known solutions. We also use snapshots from actual 3D simulations and compare the results of MODA with those obtained with other methods, such as the global and local ray-by-ray method. It turns out that MODA achieves excellent accuracy at a moderate computational cost. In appendix we also discuss implementation details and parallelization strategies.

  15. A Multi-Scale Method for Dynamics Simulation in Continuum Solvent Models I: Finite-Difference Algorithm for Navier-Stokes Equation.

    PubMed

    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.

  16. A Multi-Scale Method for Dynamics Simulation in Continuum Solvent Models I: Finite-Difference Algorithm for Navier-Stokes Equation

    PubMed Central

    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 validation shows that the algorithm is effective and stable, with observed accuracy consistent with our design. PMID:25404761

  17. Accelerating simulation for the multiple-point statistics algorithm using vector quantization

    NASA Astrophysics Data System (ADS)

    Zuo, Chen; Pan, Zhibin; Liang, Hao

    2018-03-01

    Multiple-point statistics (MPS) is a prominent algorithm to simulate categorical variables based on a sequential simulation procedure. Assuming training images (TIs) as prior conceptual models, MPS extracts patterns from TIs using a template and records their occurrences in a database. However, complex patterns increase the size of the database and require considerable time to retrieve the desired elements. In order to speed up simulation and improve simulation quality over state-of-the-art MPS methods, we propose an accelerating simulation for MPS using vector quantization (VQ), called VQ-MPS. First, a variable representation is presented to make categorical variables applicable for vector quantization. Second, we adopt a tree-structured VQ to compress the database so that stationary simulations are realized. Finally, a transformed template and classified VQ are used to address nonstationarity. A two-dimensional (2D) stationary channelized reservoir image is used to validate the proposed VQ-MPS. In comparison with several existing MPS programs, our method exhibits significantly better performance in terms of computational time, pattern reproductions, and spatial uncertainty. Further demonstrations consist of a 2D four facies simulation, two 2D nonstationary channel simulations, and a three-dimensional (3D) rock simulation. The results reveal that our proposed method is also capable of solving multifacies, nonstationarity, and 3D simulations based on 2D TIs.

  18. Laboratory simulations of thermal annealing in proto-planetary discs - II. Crystallization of enstatite from amorphous thin films

    NASA Astrophysics Data System (ADS)

    Droeger, J.; Burchard, M.; Lattard, D.

    2011-12-01

    Amorphous silicates of olivine and pyroxene composition are thought to be common constituents of circumstellar, interstellar, and interplanetary dust. In proto-planetary discs amorphous dust crystallize essentially as a result of thermal annealing. The present project aims at deciphering the kinetics of crystallization pyroxene in proto-planetary dust on the basis of experiments on amorphous thin films. The thin films are deposited on Si-wafers (111) by pulsed laser deposition (PLD). The thin films are completely amorphous, chemically homogeneous (on the MgSiO3 composition) and with a continuous and flat surface. They are subsequently annealed for 1 to 216 h at 1073K and 1098K in a vertical quench furnace and drop-quenched on a copper block. To monitor the progress of crystallization, the samples are characterized by AFM and SEM imaging and IR spectroscopy. After short annealing durations (1 to 12 h) AFM and SE imaging reveal small shallow polygonal features (diameter 0.5-1 μm; height 2-3 nm) evenly distributed at the otherwise flat surface of the thin films. These shallow features are no longer visible after about 3 h at 1098 K, resp. >12 h at 1073 K. Meanwhile, two further types of features appear small protruding pyramids and slightly depressed spherolites. The orders of appearance of these features depend on temperature, but both persist and steadily grow with increasing annealing duration. Their sizes can reach about 12 μm. From TEM investigations on annealed thin films on the Mg2SiO4 composition we know that these features represent crystalline sites, which can be surrounded by a still amorphous matrix (Oehm et al. 2010). A quantitative evaluation of the size of the features will give insights on the progress of crystallization. IR spectra of the unprocessed thin films show only broad bands. In contrast, bands characteristic of crystalline enstatite are clearly recognizable in annealed samples, e.g. after 12 h at 1078 K. Small bands can also be assigned to

  19. State-dependent biasing method for importance sampling in the weighted stochastic simulation algorithm.

    PubMed

    Roh, Min K; Gillespie, Dan T; Petzold, Linda R

    2010-11-07

    The weighted stochastic simulation algorithm (wSSA) was developed by Kuwahara and Mura [J. Chem. Phys. 129, 165101 (2008)] to efficiently estimate the probabilities of rare events in discrete stochastic systems. The wSSA uses importance sampling to enhance the statistical accuracy in the estimation of the probability of the rare event. The original algorithm biases the reaction selection step with a fixed importance sampling parameter. In this paper, we introduce a novel method where the biasing parameter is state-dependent. The new method features improved accuracy, efficiency, and robustness.

  20. An Implicit Algorithm for the Numerical Simulation of Shape-Memory Alloys

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Becker, R; Stolken, J; Jannetti, C

    Shape-memory alloys (SMA) have the potential to be used in a variety of interesting applications due to their unique properties of pseudoelasticity and the shape-memory effect. However, in order to design SMA devices efficiently, a physics-based constitutive model is required to accurately simulate the behavior of shape-memory alloys. The scope of this work is to extend the numerical capabilities of the SMA constitutive model developed by Jannetti et. al. (2003), to handle large-scale polycrystalline simulations. The constitutive model is implemented within the finite-element software ABAQUS/Standard using a user defined material subroutine, or UMAT. To improve the efficiency of the numericalmore » simulations, so that polycrystalline specimens of shape-memory alloys can be modeled, a fully implicit algorithm has been implemented to integrate the constitutive equations. Using an implicit integration scheme increases the efficiency of the UMAT over the previously implemented explicit integration method by a factor of more than 100 for single crystal simulations.« less

  1. A Fourier analysis for a fast simulation algorithm. [for switching converters

    NASA Technical Reports Server (NTRS)

    King, Roger J.

    1988-01-01

    This paper presents a derivation of compact expressions for the Fourier series analysis of the steady-state solution of a typical switching converter. The modeling procedure for the simulation and the steady-state solution is described, and some desirable traits for its matrix exponential subroutine are discussed. The Fourier analysis algorithm was tested on a phase-controlled parallel-loaded resonant converter, providing an experimental confirmation.

  2. Manufacture of radio frequency micromachined switches with annealing.

    PubMed

    Lin, Cheng-Yang; Dai, Ching-Liang

    2014-01-17

    The fabrication and characterization of a radio frequency (RF) micromachined switch with annealing were presented. The structure of the RF switch consists of a membrane, coplanar waveguide (CPW) lines, and eight springs. The RF switch is manufactured using the complementary metal oxide semiconductor (CMOS) process. The switch requires a post-process to release the membrane and springs. The post-process uses a wet etching to remove the sacrificial silicon dioxide layer, and to obtain the suspended structures of the switch. In order to improve the residual stress of the switch, an annealing process is applied to the switch, and the membrane obtains an excellent flatness. The finite element method (FEM) software CoventorWare is utilized to simulate the stress and displacement of the RF switch. Experimental results show that the RF switch has an insertion loss of 0.9 dB at 35 GHz and an isolation of 21 dB at 39 GHz. The actuation voltage of the switch is 14 V.

  3. Manufacture of Radio Frequency Micromachined Switches with Annealing

    PubMed Central

    Lin, Cheng-Yang; Dai, Ching-Liang

    2014-01-01

    The fabrication and characterization of a radio frequency (RF) micromachined switch with annealing were presented. The structure of the RF switch consists of a membrane, coplanar waveguide (CPW) lines, and eight springs. The RF switch is manufactured using the complementary metal oxide semiconductor (CMOS) process. The switch requires a post-process to release the membrane and springs. The post-process uses a wet etching to remove the sacrificial silicon dioxide layer, and to obtain the suspended structures of the switch. In order to improve the residual stress of the switch, an annealing process is applied to the switch, and the membrane obtains an excellent flatness. The finite element method (FEM) software CoventorWare is utilized to simulate the stress and displacement of the RF switch. Experimental results show that the RF switch has an insertion loss of 0.9 dB at 35 GHz and an isolation of 21 dB at 39 GHz. The actuation voltage of the switch is 14 V. PMID:24445415

  4. Simulation and optimization of a pulsating heat pipe using artificial neural network and genetic algorithm

    NASA Astrophysics Data System (ADS)

    Jokar, Ali; Godarzi, Ali Abbasi; Saber, Mohammad; Shafii, Mohammad Behshad

    2016-11-01

    In this paper, a novel approach has been presented to simulate and optimize the pulsating heat pipes (PHPs). The used pulsating heat pipe setup was designed and constructed for this study. Due to the lack of a general mathematical model for exact analysis of the PHPs, a method has been applied for simulation and optimization using the natural algorithms. In this way, the simulator consists of a kind of multilayer perceptron neural network, which is trained by experimental results obtained from our PHP setup. The results show that the complex behavior of PHPs can be successfully described by the non-linear structure of this simulator. The input variables of the neural network are input heat flux to evaporator (q″), filling ratio (FR) and inclined angle (IA) and its output is thermal resistance of PHP. Finally, based upon the simulation results and considering the heat pipe's operating constraints, the optimum operating point of the system is obtained by using genetic algorithm (GA). The experimental results show that the optimum FR (38.25 %), input heat flux to evaporator (39.93 W) and IA (55°) that obtained from GA are acceptable.

  5. Stochastic resource allocation in emergency departments with a multi-objective simulation optimization algorithm.

    PubMed

    Feng, Yen-Yi; Wu, I-Chin; Chen, Tzu-Li

    2017-03-01

    The number of emergency cases or emergency room visits rapidly increases annually, thus leading to an imbalance in supply and demand and to the long-term overcrowding of hospital emergency departments (EDs). However, current solutions to increase medical resources and improve the handling of patient needs are either impractical or infeasible in the Taiwanese environment. Therefore, EDs must optimize resource allocation given limited medical resources to minimize the average length of stay of patients and medical resource waste costs. This study constructs a multi-objective mathematical model for medical resource allocation in EDs in accordance with emergency flow or procedure. The proposed mathematical model is complex and difficult to solve because its performance value is stochastic; furthermore, the model considers both objectives simultaneously. Thus, this study develops a multi-objective simulation optimization algorithm by integrating a non-dominated sorting genetic algorithm II (NSGA II) with multi-objective computing budget allocation (MOCBA) to address the challenges of multi-objective medical resource allocation. NSGA II is used to investigate plausible solutions for medical resource allocation, and MOCBA identifies effective sets of feasible Pareto (non-dominated) medical resource allocation solutions in addition to effectively allocating simulation or computation budgets. The discrete event simulation model of ED flow is inspired by a Taiwan hospital case and is constructed to estimate the expected performance values of each medical allocation solution as obtained through NSGA II. Finally, computational experiments are performed to verify the effectiveness and performance of the integrated NSGA II and MOCBA method, as well as to derive non-dominated medical resource allocation solutions from the algorithms.

  6. The diffusive finite state projection algorithm for efficient simulation of the stochastic reaction-diffusion master equation.

    PubMed

    Drawert, Brian; Lawson, Michael J; Petzold, Linda; Khammash, Mustafa

    2010-02-21

    We have developed a computational framework for accurate and efficient simulation of stochastic spatially inhomogeneous biochemical systems. The new computational method employs a fractional step hybrid strategy. A novel formulation of the finite state projection (FSP) method, called the diffusive FSP method, is introduced for the efficient and accurate simulation of diffusive transport. Reactions are handled by the stochastic simulation algorithm.

  7. Deformation and annealing response of TD-nickel chromium

    NASA Technical Reports Server (NTRS)

    Kane, R. D.; Ebert, L. J.

    1975-01-01

    The recrystallization and grain growth processes occurring in TD-NiCr were examined with respect to deformation severity, annealing time, and temperature. Results indicated that two different annealing responses of TD-NiCr are possible, depending on the initial state and processing history prior to annealing. As-received sheet showed a dramatic increase in grain size with decreasing annealing temperature, whereas sheet prior-annealed at 1316 C for 1 hr exhibited very little variation with subsequent annealing temperature.

  8. Ef: Software for Nonrelativistic Beam Simulation by Particle-in-Cell Algorithm

    NASA Astrophysics Data System (ADS)

    Boytsov, A. Yu.; Bulychev, A. A.

    2018-04-01

    Understanding of particle dynamics is crucial in construction of electron guns, ion sources and other types of nonrelativistic beam devices. Apart from external guiding and focusing systems, a prominent role in evolution of such low-energy beams is played by particle-particle interaction. Numerical simulations taking into account these effects are typically accomplished by a well-known particle-in-cell method. In practice, for convenient work a simulation program should not only implement this method, but also support parallelization, provide integration with CAD systems and allow access to details of the simulation algorithm. To address the formulated requirements, development of a new open source code - Ef - has been started. It's current features and main functionality are presented. Comparison with several analytical models demonstrates good agreement between the numerical results and the theory. Further development plans are discussed.

  9. An assessment of coupling algorithms for nuclear reactor core physics simulations

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Hamilton, Steven, E-mail: hamiltonsp@ornl.gov; Berrill, Mark, E-mail: berrillma@ornl.gov; Clarno, Kevin, E-mail: clarnokt@ornl.gov

    This paper evaluates the performance of multiphysics coupling algorithms applied to a light water nuclear reactor core simulation. The simulation couples the k-eigenvalue form of the neutron transport equation with heat conduction and subchannel flow equations. We compare Picard iteration (block Gauss–Seidel) to Anderson acceleration and multiple variants of preconditioned Jacobian-free Newton–Krylov (JFNK). The performance of the methods are evaluated over a range of energy group structures and core power levels. A novel physics-based approximation to a Jacobian-vector product has been developed to mitigate the impact of expensive on-line cross section processing steps. Numerical simulations demonstrating the efficiency of JFNKmore » and Anderson acceleration relative to standard Picard iteration are performed on a 3D model of a nuclear fuel assembly. Both criticality (k-eigenvalue) and critical boron search problems are considered.« less

  10. Depth profiles of oxygen precipitates in nitride-coated silicon wafers subjected to rapid thermal annealing

    NASA Astrophysics Data System (ADS)

    Voronkov, V. V.; Falster, R.; Kim, TaeHyeong; Park, SoonSung; Torack, T.

    2013-07-01

    Silicon wafers, coated with a silicon nitride layer and subjected to high temperature Rapid Thermal Annealing (RTA) in Ar, show—upon a subsequent two-step precipitation anneal cycle (such as 800 °C + 1000 °C)—peculiar depth profiles of oxygen precipitate densities. Some profiles are sharply peaked near the wafer surface, sometimes with a zero bulk density. Other profiles are uniform in depth. The maximum density is always the same. These profiles are well reproduced by simulations assuming that precipitation starts from a uniformly distributed small oxide plates originated from RTA step and composed of oxygen atoms and vacancies ("VO2 plates"). During the first step of the precipitation anneal, an oxide layer propagates around this core plate by a process of oxygen attachment, meaning that an oxygen-only ring-shaped plate emerges around the original plate. These rings, depending on their size, then either dissolve or grow during the second part of the anneal leading to a rich variety of density profiles.

  11. A Genetic Algorithm for the Generation of Packetization Masks for Robust Image Communication

    PubMed Central

    Zapata-Quiñones, Katherine; Duran-Faundez, Cristian; Gutiérrez, Gilberto; Lecuire, Vincent; Arredondo-Flores, Christopher; Jara-Lipán, Hugo

    2017-01-01

    Image interleaving has proven to be an effective solution to provide the robustness of image communication systems when resource limitations make reliable protocols unsuitable (e.g., in wireless camera sensor networks); however, the search for optimal interleaving patterns is scarcely tackled in the literature. In 2008, Rombaut et al. presented an interesting approach introducing a packetization mask generator based in Simulated Annealing (SA), including a cost function, which allows assessing the suitability of a packetization pattern, avoiding extensive simulations. In this work, we present a complementary study about the non-trivial problem of generating optimal packetization patterns. We propose a genetic algorithm, as an alternative to the cited work, adopting the mentioned cost function, then comparing it to the SA approach and a torus automorphism interleaver. In addition, we engage the validation of the cost function and provide results attempting to conclude about its implication in the quality of reconstructed images. Several scenarios based on visual sensor networks applications were tested in a computer application. Results in terms of the selected cost function and image quality metric PSNR show that our algorithm presents similar results to the other approaches. Finally, we discuss the obtained results and comment about open research challenges. PMID:28452934

  12. Annealing a magnetic cactus into phyllotaxis

    NASA Astrophysics Data System (ADS)

    Nisoli, Cristiano; Gabor, Nathaniel M.; Lammert, Paul E.; Maynard, J. D.; Crespi, Vincent H.

    2010-04-01

    The appearance of mathematical regularities in the disposition of leaves on a stem, scales on a pine-cone, and spines on a cactus has puzzled scholars for millennia; similar so-called phyllotactic patterns are seen in self-organized growth, polypeptides, convection, magnetic flux lattices and ion beams. Levitov showed that a cylindrical lattice of repulsive particles can reproduce phyllotaxis under the (unproved) assumption that minimum of energy would be achieved by two-dimensional Bravais lattices. Here we provide experimental and numerical evidence that the Phyllotactic lattice is actually a ground state. When mechanically annealed, our experimental “magnetic cactus” precisely reproduces botanical phyllotaxis, along with domain boundaries (called transitions in Botany) between different phyllotactic patterns. We employ a structural genetic algorithm to explore the more general axially unconstrained case, which reveals multijugate (multiple spirals) as well as monojugate (single-spiral) phyllotaxis.

  13. An accelerated algorithm for discrete stochastic simulation of reaction-diffusion systems using gradient-based diffusion and tau-leaping.

    PubMed

    Koh, Wonryull; Blackwell, Kim T

    2011-04-21

    Stochastic simulation of reaction-diffusion systems enables the investigation of stochastic events arising from the small numbers and heterogeneous distribution of molecular species in biological cells. Stochastic variations in intracellular microdomains and in diffusional gradients play a significant part in the spatiotemporal activity and behavior of cells. Although an exact stochastic simulation that simulates every individual reaction and diffusion event gives a most accurate trajectory of the system's state over time, it can be too slow for many practical applications. We present an accelerated algorithm for discrete stochastic simulation of reaction-diffusion systems designed to improve the speed of simulation by reducing the number of time-steps required to complete a simulation run. This method is unique in that it employs two strategies that have not been incorporated in existing spatial stochastic simulation algorithms. First, diffusive transfers between neighboring subvolumes are based on concentration gradients. This treatment necessitates sampling of only the net or observed diffusion events from higher to lower concentration gradients rather than sampling all diffusion events regardless of local concentration gradients. Second, we extend the non-negative Poisson tau-leaping method that was originally developed for speeding up nonspatial or homogeneous stochastic simulation algorithms. This method calculates each leap time in a unified step for both reaction and diffusion processes while satisfying the leap condition that the propensities do not change appreciably during the leap and ensuring that leaping does not cause molecular populations to become negative. Numerical results are presented that illustrate the improvement in simulation speed achieved by incorporating these two new strategies.

  14. Simulated Annealing in the Variable Landscape

    NASA Astrophysics Data System (ADS)

    Hasegawa, Manabu; Kim, Chang Ju

    An experimental analysis is conducted to test whether the appropriate introduction of the smoothness-temperature schedule enhances the optimizing ability of the MASSS method, the combination of the Metropolis algorithm (MA) and the search-space smoothing (SSS) method. The test is performed on two types of random traveling salesman problems. The results show that the optimization performance of the MA is substantially improved by a single smoothing alone and slightly more by a single smoothing with cooling and by a de-smoothing process with heating. The performance is compared to that of the parallel tempering method and a clear advantage of the idea of smoothing is observed depending on the problem.

  15. Performance evaluation of the Champagne source reconstruction algorithm on simulated and real M/EEG data.

    PubMed

    Owen, Julia P; Wipf, David P; Attias, Hagai T; Sekihara, Kensuke; Nagarajan, Srikantan S

    2012-03-01

    In this paper, we present an extensive performance evaluation of a novel source localization algorithm, Champagne. It is derived in an empirical Bayesian framework that yields sparse solutions to the inverse problem. It is robust to correlated sources and learns the statistics of non-stimulus-evoked activity to suppress the effect of noise and interfering brain activity. We tested Champagne on both simulated and real M/EEG data. The source locations used for the simulated data were chosen to test the performance on challenging source configurations. In simulations, we found that Champagne outperforms the benchmark algorithms in terms of both the accuracy of the source localizations and the correct estimation of source time courses. We also demonstrate that Champagne is more robust to correlated brain activity present in real MEG data and is able to resolve many distinct and functionally relevant brain areas with real MEG and EEG data. Copyright © 2011 Elsevier Inc. All rights reserved.

  16. The Separatrix Algorithm for Synthesis and Analysis of Stochastic Simulations with Applications in Disease Modeling

    PubMed Central

    Klein, Daniel J.; Baym, Michael; Eckhoff, Philip

    2014-01-01

    Decision makers in epidemiology and other disciplines are faced with the daunting challenge of designing interventions that will be successful with high probability and robust against a multitude of uncertainties. To facilitate the decision making process in the context of a goal-oriented objective (e.g., eradicate polio by ), stochastic models can be used to map the probability of achieving the goal as a function of parameters. Each run of a stochastic model can be viewed as a Bernoulli trial in which “success” is returned if and only if the goal is achieved in simulation. However, each run can take a significant amount of time to complete, and many replicates are required to characterize each point in parameter space, so specialized algorithms are required to locate desirable interventions. To address this need, we present the Separatrix Algorithm, which strategically locates parameter combinations that are expected to achieve the goal with a user-specified probability of success (e.g. 95%). Technically, the algorithm iteratively combines density-corrected binary kernel regression with a novel information-gathering experiment design to produce results that are asymptotically correct and work well in practice. The Separatrix Algorithm is demonstrated on several test problems, and on a detailed individual-based simulation of malaria. PMID:25078087

  17. An Integrated Qualitative and Quantitative Biochemical Model Learning Framework Using Evolutionary Strategy and Simulated Annealing.

    PubMed

    Wu, Zujian; Pang, Wei; Coghill, George M

    2015-01-01

    Both qualitative and quantitative model learning frameworks for biochemical systems have been studied in computational systems biology. In this research, after introducing two forms of pre-defined component patterns to represent biochemical models, we propose an integrative qualitative and quantitative modelling framework for inferring biochemical systems. In the proposed framework, interactions between reactants in the candidate models for a target biochemical system are evolved and eventually identified by the application of a qualitative model learning approach with an evolution strategy. Kinetic rates of the models generated from qualitative model learning are then further optimised by employing a quantitative approach with simulated annealing. Experimental results indicate that our proposed integrative framework is feasible to learn the relationships between biochemical reactants qualitatively and to make the model replicate the behaviours of the target system by optimising the kinetic rates quantitatively. Moreover, potential reactants of a target biochemical system can be discovered by hypothesising complex reactants in the synthetic models. Based on the biochemical models learned from the proposed framework, biologists can further perform experimental study in wet laboratory. In this way, natural biochemical systems can be better understood.

  18. High-temperature annealing of proton irradiated beryllium – A dilatometry-based study

    DOE PAGES

    Simos, Nikolaos; Elbakhshwan, Mohamed; Zhong, Zhong; ...

    2016-04-07

    S—200 F grade beryllium has been irradiated with 160 MeV protons up to 1.2 10 20 cm –2 peak fluence and irradiation temperatures in the range of 100–200 °C. To address the effect of proton irradiation on dimensional stability, an important parameter in its consideration in fusion reactor applications, and to simulate high temperature irradiation conditions, multi-stage annealing using high precision dilatometry to temperatures up to 740 °C were conducted in air. X-ray diffraction studies were also performed to compliment the macroscopic thermal study and offer a microscopic view of the irradiation effects on the crystal lattice. The primary objectivemore » was to qualify the competing dimensional change processes occurring at elevated temperatures namely manufacturing defect annealing, lattice parameter recovery, transmutation 4He and 3H diffusion and swelling and oxidation kinetics. Further, quantification of the effect of irradiation dose and annealing temperature and duration on dimensional changes is sought. Here, the study revealed the presence of manufacturing porosity in the beryllium grade, the oxidation acceleration effect of irradiation including the discontinuous character of oxidation advancement, the effect of annealing duration on the recovery of lattice parameters recovery and the triggering temperature for transmutation gas diffusion leading to swelling.« less

  19. An assessment of coupling algorithms for nuclear reactor core physics simulations

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Hamilton, Steven; Berrill, Mark; Clarno, Kevin

    This paper evaluates the performance of multiphysics coupling algorithms applied to a light water nuclear reactor core simulation. The simulation couples the k-eigenvalue form of the neutron transport equation with heat conduction and subchannel flow equations. We compare Picard iteration (block Gauss–Seidel) to Anderson acceleration and multiple variants of preconditioned Jacobian-free Newton–Krylov (JFNK). The performance of the methods are evaluated over a range of energy group structures and core power levels. A novel physics-based approximation to a Jacobian-vector product has been developed to mitigate the impact of expensive on-line cross section processing steps. Furthermore, numerical simulations demonstrating the efficiency ofmore » JFNK and Anderson acceleration relative to standard Picard iteration are performed on a 3D model of a nuclear fuel assembly. Both criticality (k-eigenvalue) and critical boron search problems are considered.« less

  20. An assessment of coupling algorithms for nuclear reactor core physics simulations

    DOE PAGES

    Hamilton, Steven; Berrill, Mark; Clarno, Kevin; ...

    2016-04-01

    This paper evaluates the performance of multiphysics coupling algorithms applied to a light water nuclear reactor core simulation. The simulation couples the k-eigenvalue form of the neutron transport equation with heat conduction and subchannel flow equations. We compare Picard iteration (block Gauss–Seidel) to Anderson acceleration and multiple variants of preconditioned Jacobian-free Newton–Krylov (JFNK). The performance of the methods are evaluated over a range of energy group structures and core power levels. A novel physics-based approximation to a Jacobian-vector product has been developed to mitigate the impact of expensive on-line cross section processing steps. Furthermore, numerical simulations demonstrating the efficiency ofmore » JFNK and Anderson acceleration relative to standard Picard iteration are performed on a 3D model of a nuclear fuel assembly. Both criticality (k-eigenvalue) and critical boron search problems are considered.« less

  1. 3D Radiative Hydrodynamics Simulations of Protoplanetary Disks: A Comparison Between Two Radiative Cooling Algorithms

    NASA Astrophysics Data System (ADS)

    Lord, Jesse W.; Boley, A. C.; Durisen, R. H.

    2006-12-01

    We present a comparison between two three-dimensional radiative hydrodynamics simulations of a gravitationally unstable 0.07 Msun protoplanetary disk around a 0.5 Msun star. The first simulation is the radiatively cooled disk described in Boley et al. (2006, ApJ, 651). This simulation employed an algorithm that uses 3D flux-limited diffusion wherever the vertical Rosseland optical depth is greater than 2/3, which defines the optically thick region. The optically thin atmosphere of the disk, which cools according to its emissivity, is coupled to the optically thick region through an Eddington-like boundary condition. The second simulation employed an algorithm that uses a combination of solving the radiative transfer equation along rays in the z direction and flux limited diffusion in the r and phi directions on a cylindrical grid. We compare the following characteristics of the disk simulations: the mass transport and torques induced by gravitational instabilities, the effective temperature profiles of the disks, the gravitational and Reynolds stresses measured in the disk and those expected in an alpha-disk, and the amplitudes of the Fourier modes. This work has been supported by the National Science Foundation through grant AST-0452975 (astronomy REU program to Indiana University).

  2. Polynomial-time quantum algorithm for the simulation of chemical dynamics

    PubMed Central

    Kassal, Ivan; Jordan, Stephen P.; Love, Peter J.; Mohseni, Masoud; Aspuru-Guzik, Alán

    2008-01-01

    The computational cost of exact methods for quantum simulation using classical computers grows exponentially with system size. As a consequence, these techniques can be applied only to small systems. By contrast, we demonstrate that quantum computers could exactly simulate chemical reactions in polynomial time. Our algorithm uses the split-operator approach and explicitly simulates all electron-nuclear and interelectronic interactions in quadratic time. Surprisingly, this treatment is not only more accurate than the Born–Oppenheimer approximation but faster and more efficient as well, for all reactions with more than about four atoms. This is the case even though the entire electronic wave function is propagated on a grid with appropriately short time steps. Although the preparation and measurement of arbitrary states on a quantum computer is inefficient, here we demonstrate how to prepare states of chemical interest efficiently. We also show how to efficiently obtain chemically relevant observables, such as state-to-state transition probabilities and thermal reaction rates. Quantum computers using these techniques could outperform current classical computers with 100 qubits. PMID:19033207

  3. Aeolus End-To-End Simulator and Wind Retrieval Algorithms up to Level 1B

    NASA Astrophysics Data System (ADS)

    Reitebuch, Oliver; Marksteiner, Uwe; Rompel, Marc; Meringer, Markus; Schmidt, Karsten; Huber, Dorit; Nikolaus, Ines; Dabas, Alain; Marshall, Jonathan; de Bruin, Frank; Kanitz, Thomas; Straume, Anne-Grete

    2018-04-01

    The first wind lidar in space ALADIN will be deployed on ESÁs Aeolus mission. In order to assess the performance of ALADIN and to optimize the wind retrieval and calibration algorithms an end-to-end simulator was developed. This allows realistic simulations of data downlinked by Aeolus. Together with operational processors this setup is used to assess random and systematic error sources and perform sensitivity studies about the influence of atmospheric and instrument parameters.

  4. SIMULATION OF AEROSOL DYNAMICS: A COMPARATIVE REVIEW OF ALGORITHMS USED IN AIR QUALITY MODELS

    EPA Science Inventory

    A comparative review of algorithms currently used in air quality models to simulate aerosol dynamics is presented. This review addresses coagulation, condensational growth, nucleation, and gas/particle mass transfer. Two major approaches are used in air quality models to repres...

  5. An algorithm to help design fire simulation and other data base work

    Treesearch

    Romain Mees

    1974-01-01

    The data necessary for fire simulation may be made available through an algorithm based on tracing of boundaries composed of straight-line segments. Useful assumptions are that if a closed boundary does not contain a given point, then any other closed boundary contained within the former one does not contain the location; and that a given location will be contained in...

  6. Improved Gravitation Field Algorithm and Its Application in Hierarchical Clustering

    PubMed Central

    Zheng, Ming; Sun, Ying; Liu, Gui-xia; Zhou, You; Zhou, Chun-guang

    2012-01-01

    Background Gravitation field algorithm (GFA) is a new optimization algorithm which is based on an imitation of natural phenomena. GFA can do well both for searching global minimum and multi-minima in computational biology. But GFA needs to be improved for increasing efficiency, and modified for applying to some discrete data problems in system biology. Method An improved GFA called IGFA was proposed in this paper. Two parts were improved in IGFA. The first one is the rule of random division, which is a reasonable strategy and makes running time shorter. The other one is rotation factor, which can improve the accuracy of IGFA. And to apply IGFA to the hierarchical clustering, the initial part and the movement operator were modified. Results Two kinds of experiments were used to test IGFA. And IGFA was applied to hierarchical clustering. The global minimum experiment was used with IGFA, GFA, GA (genetic algorithm) and SA (simulated annealing). Multi-minima experiment was used with IGFA and GFA. The two experiments results were compared with each other and proved the efficiency of IGFA. IGFA is better than GFA both in accuracy and running time. For the hierarchical clustering, IGFA is used to optimize the smallest distance of genes pairs, and the results were compared with GA and SA, singular-linkage clustering, UPGMA. The efficiency of IGFA is proved. PMID:23173043

  7. Modifications to Axially Symmetric Simulations Using New DSMC (2007) Algorithms

    NASA Technical Reports Server (NTRS)

    Liechty, Derek S.

    2008-01-01

    Several modifications aimed at improving physical accuracy are proposed for solving axially symmetric problems building on the DSMC (2007) algorithms introduced by Bird. Originally developed to solve nonequilibrium, rarefied flows, the DSMC method is now regularly used to solve complex problems over a wide range of Knudsen numbers. These new algorithms include features such as nearest neighbor collisions excluding the previous collision partners, separate collision and sampling cells, automatically adaptive variable time steps, a modified no-time counter procedure for collisions, and discontinuous and event-driven physical processes. Axially symmetric solutions require radial weighting for the simulated molecules since the molecules near the axis represent fewer real molecules than those farther away from the axis due to the difference in volume of the cells. In the present methodology, these radial weighting factors are continuous, linear functions that vary with the radial position of each simulated molecule. It is shown that how one defines the number of tentative collisions greatly influences the mean collision time near the axis. The method by which the grid is treated for axially symmetric problems also plays an important role near the axis, especially for scalar pressure. A new method to treat how the molecules are traced through the grid is proposed to alleviate the decrease in scalar pressure at the axis near the surface. Also, a modification to the duplication buffer is proposed to vary the duplicated molecular velocities while retaining the molecular kinetic energy and axially symmetric nature of the problem.

  8. Local Resistance Profiling of Ultra Shallow Junction Annealed with Combination of Spike Lamp and Laser Annealing Processes using Scanning Spreading Resistance Microscope

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Abo, Satoshi; Nishikawa, Kazuhisa; Ushigome, Naoya

    2011-01-07

    Local resistance profiles of ultra shallow boron and arsenic implanted into silicon with energies of 2.0 and 4.0 keV and doses of 2.0x10{sup 15} and 1.0x10{sup 15} ions/cm{sup 2} activated by a combination of conventional spike lamp and laser annealing processes were measured by scanning spreading resistance microscope (SSRM) with a depth resolution of less than 10 nm. The lowest local resistance at the low resistance region in 2.0 keV boron implanted silicon with 1050 deg. C spike lamp annealing followed by 0.35 kW/mm{sup 2} laser annealing was half of that without laser annealing. The lowest local resistance at themore » low resistance region in the arsenic implanted silicon activated by 1050 deg. C spike lamp annealing followed by 0.39 kW/mm{sup 2} laser annealing was 74% lower than that followed by 0.36 kW/mm{sup 2} laser annealing. The lowest local resistances at the low resistance regions in the arsenic implanted silicon with 0.36 and 0.39 kW/mm{sup 2} laser annealing followed by 1050 deg. C spike lamp annealing were 41 and 33% lower than those with spike lamp annealing followed by laser annealing. Laser annealing followed by spike lamp annealing could suppress the diffusion of the impurities and was suitable for making the ultra shallow and low resistance regions.« less

  9. Pulsed Laser Annealing of Carbon

    NASA Astrophysics Data System (ADS)

    Abrahamson, Joseph P.

    This dissertation investigates laser heating of carbon materials. The carbon industry has been annealing carbon via traditional furnace heating since at least 1800, when Sir Humphry Davy produced an electric arc with carbon electrodes made from carbonized wood. Much knowledge has been accumulated about carbon since then and carbon materials have become instrumental both scientifically and technologically. However, to this day the kinetics of annealing are not known due to the slow heating and cooling rates of furnaces. Additionally, consensus has yet to be reached on the cause of nongraphitizability. Annealing trajectories with respect to time at temperature are observed from a commercial carbon black (R250), model graphitizable carbon (anthracene coke) and a model nongraphitizable carbon (sucrose char) via rapid laser heating. Materials were heated with 1064 nm and 10.6 im laser radiation from a Q-switched Nd:YAG laser and a continuous wave CO2 laser, respectively. A pulse generator was used reduce the CO2 laser pulse width and provide high temporal control. Time-temperature-histories with nanosecond temporal resolution and temperature reproducibility within tens of degrees Celsius were determined by spectrally resolving the laser induced incandescence signal and applying multiwavelength pyrometry. The Nd:YAG laser fluences include: 25, 50, 100, 200, 300, and 550 mJ/cm2. The maximum observed temperature ranged from 2,400 °C to the C2 sublimation temperature of 4,180 °C. The CO2 laser was used to collect a series of isothermal (1,200 and 2,600 °C) heat treatments versus time (100 milliseconds to 30 seconds). Laser heated samples are compared to furnace annealing at 1,200 and 2,600 °C for 1 hour. The material transformation trajectory of Nd:YAG laser heated carbon is different than traditional furnace heating. The traditional furnace annealing pathway is followed for CO2 laser heating as based upon equivalent end structures. The nanostructure of sucrose char

  10. Algorithm Visualization System for Teaching Spatial Data Algorithms

    ERIC Educational Resources Information Center

    Nikander, Jussi; Helminen, Juha; Korhonen, Ari

    2010-01-01

    TRAKLA2 is a web-based learning environment for data structures and algorithms. The system delivers automatically assessed algorithm simulation exercises that are solved using a graphical user interface. In this work, we introduce a novel learning environment for spatial data algorithms, SDA-TRAKLA2, which has been implemented on top of the…

  11. Marcus canonical integral for non-Gaussian processes and its computation: pathwise simulation and tau-leaping algorithm.

    PubMed

    Li, Tiejun; Min, Bin; Wang, Zhiming

    2013-03-14

    The stochastic integral ensuring the Newton-Leibnitz chain rule is essential in stochastic energetics. Marcus canonical integral has this property and can be understood as the Wong-Zakai type smoothing limit when the driving process is non-Gaussian. However, this important concept seems not well-known for physicists. In this paper, we discuss Marcus integral for non-Gaussian processes and its computation in the context of stochastic energetics. We give a comprehensive introduction to Marcus integral and compare three equivalent definitions in the literature. We introduce the exact pathwise simulation algorithm and give the error analysis. We show how to compute the thermodynamic quantities based on the pathwise simulation algorithm. We highlight the information hidden in the Marcus mapping, which plays the key role in determining thermodynamic quantities. We further propose the tau-leaping algorithm, which advance the process with deterministic time steps when tau-leaping condition is satisfied. The numerical experiments and its efficiency analysis show that it is very promising.

  12. Pattern Laser Annealing by a Pulsed Laser

    NASA Astrophysics Data System (ADS)

    Komiya, Yoshio; Hoh, Koichiro; Murakami, Koichi; Takahashi, Tetsuo; Tarui, Yasuo

    1981-10-01

    Preliminary experiments with contact-type pattern laser annealing were made for local polycrystallization of a-Si, local evaporation of a-Si and local formation of Ni-Si alloy. These experiments showed that the mask patterns can be replicated as annealed regions with a resolution of a few microns on substrates. To overcome shortcomings due to the contact type pattern annealing, a projection type reduction pattern laser annealing system is proposed for resistless low temperature pattern forming processes.

  13. Automated medial axis seeding and guided evolutionary simulated annealing for optimization of gamma knife radiosurgery treatment plans

    NASA Astrophysics Data System (ADS)

    Zhang, Pengpeng

    The Leksell Gamma KnifeRTM (LGK) is a tool for providing accurate stereotactic radiosurgical treatment of brain lesions, especially tumors. Currently, the treatment planning team "forward" plans radiation treatment parameters while viewing a series of 2D MR scans. This primarily manual process is cumbersome and time consuming because the difficulty in visualizing the large search space for the radiation parameters (i.e., shot overlap, number, location, size, and weight). I hypothesize that a computer-aided "inverse" planning procedure that utilizes tumor geometry and treatment goals could significantly improve the planning process and therapeutic outcome of LGK radiosurgery. My basic observation is that the treatment team is best at identification of the location of the lesion and prescribing a lethal, yet safe, radiation dose. The treatment planning computer is best at determining both the 3D tumor geometry and optimal LGK shot parameters necessary to deliver a desirable dose pattern to the tumor while sparing adjacent normal tissue. My treatment planning procedure asks the neurosurgeon to identify the tumor and critical structures in MR images and the oncologist to prescribe a tumoricidal radiation dose. Computer-assistance begins with geometric modeling of the 3D tumor's medial axis properties. This begins with a new algorithm, a Gradient-Phase Plot (G-P Plot) decomposition of the tumor object's medial axis. I have found that medial axis seeding, while insufficient in most cases to produce an acceptable treatment plan, greatly reduces the solution space for Guided Evolutionary Simulated Annealing (GESA) treatment plan optimization by specifying an initial estimate for shot number, size, and location, but not weight. They are used to generate multiple initial plans which become initial seed plans for GESA. The shot location and weight parameters evolve and compete in the GESA procedure. The GESA objective function optimizes tumor irradiation (i.e., as close to

  14. Conformational space annealing scheme in the inverse design of functional materials

    NASA Astrophysics Data System (ADS)

    Kim, Sunghyun; Lee, In-Ho; Lee, Jooyoung; Oh, Young Jun; Chang, Kee Joo

    2015-03-01

    Recently, the so-called inverse method has drawn much attention, in which specific electronic properties are initially assigned and target materials are subsequently searched. In this work, we develop a new scheme for the inverse design of functional materials, in which the conformational space annealing (CSA) algorithm for global optimization is combined with first-principles density functional calculations. To implement the CSA, we need a series of ingredients, (i) an objective function to minimize, (ii) a 'distance' measure between two conformations, (iii) a local enthalpy minimizer of a given conformation, (iv) ways to combine two parent conformations to generate a daughter one, (v) a special conformation update scheme, and (vi) an annealing method in the 'distance' parameter axis. We show the results of applications for searching for Si crystals with direct band gaps and the lowest-enthalpy phase of boron at a finite pressure and discuss the efficiency of the present scheme. This work is supported by the National Research Foundation of Korea (NRF) under Grant No. NRF-2005-0093845 and by Samsung Science and Technology Foundation under Grant No. SSTFBA1401-08.

  15. Reduced annealing temperatures in silicon solar cells

    NASA Technical Reports Server (NTRS)

    Weinberg, I.; Swartz, C. K.

    1981-01-01

    Cells irradiated to a fluence of 5x10,000,000,000,000/square cm showed short circuit current on annealing at 200 C, with complete annealing occurring at 275 C. Cells irradiated to 100,000,000,000,000/square cm showed a reduction in annealing temperature from the usual 500 to 300 C. Annealing kinetic studies yield an activation energy of (1.5 + or - 2) eV for the low fluence, low temperature anneal. Comparison with activation energies previously obtained indicate that the presently obtained activation energy is consistent with the presence of either the divacancy or the carbon interstitial carbon substitutional pair, a result which agrees with the conclusion based on defect behavior in boron-doped silicon.

  16. Strain Evolution of Annealed Hydrogen-Implanted (0001) Sapphire

    NASA Astrophysics Data System (ADS)

    Wong, Christine Megan

    Exfoliation is a technique used to remove a thin, uniform layer of material from the bulk that involves the annealing of hydrogen ion-implanted materials in order to initiate defect nucleation and growth leading to guided crack propagation. This study presents an investigation into the annealing process required to initiate blistering (an essential precursor to exfoliation) in (0001) sapphire implanted at room temperature with hydrogen ions. Triple axis x-ray diffraction was used to characterize the evolution of the implanted layer for single crystal (0001) sapphire substrates implanted at room temperature at 360 keV with either a 5x1016 cm -2 or 8x1016 cm-2 dose of hydrogen ions. A simulation of the ion distribution in TRIM estimated that the projected range and thickness of the implanted layer for both doses was approximately 2.2 mum. Following implantation, the implanted sapphire was annealed using a two-step annealing procedure. The first step was performed at a lower temperature, ideally to nucleate and coarsen defects. Temperatures investigated ranged from 550 - 650 °C. The second step was performed at a higher temperature (800 °C) to induce further defect coarsening and surface blistering. After all annealing steps, triple axis o/2theta and o scans were taken to observe any changes in the diffraction profile - namely, any reduction in the amplitude and shift in the location of the fringes associated with strain in the crystal - which would correlate with defect growth and nucleation. It was found that significant strain fringe reduction first occurred after annealing at 650 °C for 8 hours for both doses; however, it was not clear whether or not this strain reduction was due primarily to hydrogen diffusion or to recovery of other defects induced during the ion implantation. The o/2theta curves were then fit using Bede RADS in order to quantify the strain within the crystal and confirm the reduction of the strained layer within the crystal. Finally

  17. Inferring genetic interactions via a nonlinear model and an optimization algorithm.

    PubMed

    Chen, Chung-Ming; Lee, Chih; Chuang, Cheng-Long; Wang, Chia-Chang; Shieh, Grace S

    2010-02-26

    Biochemical pathways are gradually becoming recognized as central to complex human diseases and recently genetic/transcriptional interactions have been shown to be able to predict partial pathways. With the abundant information made available by microarray gene expression data (MGED), nonlinear modeling of these interactions is now feasible. Two of the latest advances in nonlinear modeling used sigmoid models to depict transcriptional interaction of a transcription factor (TF) for a target gene, but do not model cooperative or competitive interactions of several TFs for a target. An S-shape model and an optimization algorithm (GASA) were developed to infer genetic interactions/transcriptional regulation of several genes simultaneously using MGED. GASA consists of a genetic algorithm (GA) and a simulated annealing (SA) algorithm, which is enhanced by a steepest gradient descent algorithm to avoid being trapped in local minimum. Using simulated data with various degrees of noise, we studied how GASA with two model selection criteria and two search spaces performed. Furthermore, GASA was shown to outperform network component analysis, the time series network inference algorithm (TSNI), GA with regular GA (GAGA) and GA with regular SA. Two applications are demonstrated. First, GASA is applied to infer a subnetwork of human T-cell apoptosis. Several of the predicted interactions are supported by the literature. Second, GASA was applied to infer the transcriptional factors of 34 cell cycle regulated targets in S. cerevisiae, and GASA performed better than one of the latest advances in nonlinear modeling, GAGA and TSNI. Moreover, GASA is able to predict multiple transcription factors for certain targets, and these results coincide with experiments confirmed data in YEASTRACT. GASA is shown to infer both genetic interactions and transcriptional regulatory interactions well. In particular, GASA seems able to characterize the nonlinear mechanism of transcriptional regulatory

  18. Rapid Annealing Of Amorphous Hydrogenated Carbon

    NASA Technical Reports Server (NTRS)

    Alterovitz, Samuel A.; Pouch, John J.; Warner, Joseph D.

    1989-01-01

    Report describes experiments to determine effects of rapid annealing on films of amorphous hydrogenated carbon. Study represents first efforts to provide information for applications of a-C:H films where rapid thermal processing required. Major finding, annealing causes abrupt increase in absorption and concomitant decrease in optical band gap. Most of change occurs during first 20 s, continues during longer annealing times. Extend of change increases with annealing temperature. Researchers hypothesize abrupt initial change caused by loss of hydrogen, while gradual subsequent change due to polymerization of remaining carbon into crystallites or sheets of graphite. Optical band gaps of unannealed specimens on silicon substrates lower than those of specimens on quartz substrates.

  19. Coherent Coupled Qubits for Quantum Annealing

    NASA Astrophysics Data System (ADS)

    Weber, Steven J.; Samach, Gabriel O.; Hover, David; Gustavsson, Simon; Kim, David K.; Melville, Alexander; Rosenberg, Danna; Sears, Adam P.; Yan, Fei; Yoder, Jonilyn L.; Oliver, William D.; Kerman, Andrew J.

    2017-07-01

    Quantum annealing is an optimization technique which potentially leverages quantum tunneling to enhance computational performance. Existing quantum annealers use superconducting flux qubits with short coherence times limited primarily by the use of large persistent currents Ip. Here, we examine an alternative approach using qubits with smaller Ip and longer coherence times. We demonstrate tunable coupling, a basic building block for quantum annealing, between two flux qubits with small (approximately 50-nA) persistent currents. Furthermore, we characterize qubit coherence as a function of coupler setting and investigate the effect of flux noise in the coupler loop on qubit coherence. Our results provide insight into the available design space for next-generation quantum annealers with improved coherence.

  20. Understanding disordered systems through numerical simulation and algorithm development

    NASA Astrophysics Data System (ADS)

    Sweeney, Sean Michael

    Disordered systems arise in many physical contexts. Not all matter is uniform, and impurities or heterogeneities can be modeled by fixed random disorder. Numerous complex networks also possess fixed disorder, leading to applications in transportation systems, telecommunications, social networks, and epidemic modeling, to name a few. Due to their random nature and power law critical behavior, disordered systems are difficult to study analytically. Numerical simulation can help overcome this hurdle by allowing for the rapid computation of system states. In order to get precise statistics and extrapolate to the thermodynamic limit, large systems must be studied over many realizations. Thus, innovative algorithm development is essential in order reduce memory or running time requirements of simulations. This thesis presents a review of disordered systems, as well as a thorough study of two particular systems through numerical simulation, algorithm development and optimization, and careful statistical analysis of scaling properties. Chapter 1 provides a thorough overview of disordered systems, the history of their study in the physics community, and the development of techniques used to study them. Topics of quenched disorder, phase transitions, the renormalization group, criticality, and scale invariance are discussed. Several prominent models of disordered systems are also explained. Lastly, analysis techniques used in studying disordered systems are covered. In Chapter 2, minimal spanning trees on critical percolation clusters are studied, motivated in part by an analytic perturbation expansion by Jackson and Read that I check against numerical calculations. This system has a direct mapping to the ground state of the strongly disordered spin glass. We compute the path length fractal dimension of these trees in dimensions d = {2, 3, 4, 5} and find our results to be compatible with the analytic results suggested by Jackson and Read. In Chapter 3, the random bond Ising

  1. An advanced retrieval algorithm for greenhouse gases using polarization information measured by GOSAT TANSO-FTS SWIR I: Simulation study

    NASA Astrophysics Data System (ADS)

    Kikuchi, N.; Yoshida, Y.; Uchino, O.; Morino, I.; Yokota, T.

    2016-11-01

    We present an algorithm for retrieving column-averaged dry air mole fraction of carbon dioxide (XCO2) and methane (XCH4) from reflected spectra in the shortwave infrared (SWIR) measured by the TANSO-FTS (Thermal And Near infrared Sensor for carbon Observation Fourier Transform Spectrometer) sensor on board the Greenhouse gases Observing SATellite (GOSAT). The algorithm uses the two linear polarizations observed by TANSO-FTS to improve corrections to the interference effects of atmospheric aerosols, which degrade the accuracy in the retrieved greenhouse gas concentrations. To account for polarization by the land surface reflection in the forward model, we introduced a bidirectional reflection matrix model that has two parameters to be retrieved simultaneously with other state parameters. The accuracy in XCO2 and XCH4 values retrieved with the algorithm was evaluated by using simulated retrievals over both land and ocean, focusing on the capability of the algorithm to correct imperfect prior knowledge of aerosols. To do this, we first generated simulated TANSO-FTS spectra using a global distribution of aerosols computed by the aerosol transport model SPRINTARS. Then the simulated spectra were submitted to the algorithms as measurements both with and without polarization information, adopting a priori profiles of aerosols that differ from the true profiles. We found that the accuracy of XCO2 and XCH4, as well as profiles of aerosols, retrieved with polarization information was considerably improved over values retrieved without polarization information, for simulated observations over land with aerosol optical thickness greater than 0.1 at 1.6 μm.

  2. GENESIS 1.1: A hybrid-parallel molecular dynamics simulator with enhanced sampling algorithms on multiple computational platforms.

    PubMed

    Kobayashi, Chigusa; Jung, Jaewoon; Matsunaga, Yasuhiro; Mori, Takaharu; Ando, Tadashi; Tamura, Koichi; Kamiya, Motoshi; Sugita, Yuji

    2017-09-30

    GENeralized-Ensemble SImulation System (GENESIS) is a software package for molecular dynamics (MD) simulation of biological systems. It is designed to extend limitations in system size and accessible time scale by adopting highly parallelized schemes and enhanced conformational sampling algorithms. In this new version, GENESIS 1.1, new functions and advanced algorithms have been added. The all-atom and coarse-grained potential energy functions used in AMBER and GROMACS packages now become available in addition to CHARMM energy functions. The performance of MD simulations has been greatly improved by further optimization, multiple time-step integration, and hybrid (CPU + GPU) computing. The string method and replica-exchange umbrella sampling with flexible collective variable choice are used for finding the minimum free-energy pathway and obtaining free-energy profiles for conformational changes of a macromolecule. These new features increase the usefulness and power of GENESIS for modeling and simulation in biological research. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

  3. A comparison of various algorithms to extract Magic Formula tyre model coefficients for vehicle dynamics simulations

    NASA Astrophysics Data System (ADS)

    Vijay Alagappan, A.; Narasimha Rao, K. V.; Krishna Kumar, R.

    2015-02-01

    Tyre models are a prerequisite for any vehicle dynamics simulation. Tyre models range from the simplest mathematical models that consider only the cornering stiffness to a complex set of formulae. Among all the steady-state tyre models that are in use today, the Magic Formula tyre model is unique and most popular. Though the Magic Formula tyre model is widely used, obtaining the model coefficients from either the experimental or the simulation data is not straightforward due to its nonlinear nature and the presence of a large number of coefficients. A common procedure used for this extraction is the least-squares minimisation that requires considerable experience for initial guesses. Various researchers have tried different algorithms, namely, gradient and Newton-based methods, differential evolution, artificial neural networks, etc. The issues involved in all these algorithms are setting bounds or constraints, sensitivity of the parameters, the features of the input data such as the number of points, noisy data, experimental procedure used such as slip angle sweep or tyre measurement (TIME) procedure, etc. The extracted Magic Formula coefficients are affected by these variants. This paper highlights the issues that are commonly encountered in obtaining these coefficients with different algorithms, namely, least-squares minimisation using trust region algorithms, Nelder-Mead simplex, pattern search, differential evolution, particle swarm optimisation, cuckoo search, etc. A key observation is that not all the algorithms give the same Magic Formula coefficients for a given data. The nature of the input data and the type of the algorithm decide the set of the Magic Formula tyre model coefficients.

  4. Simulation of radiation damage in minerals by sequential ion irradiations

    NASA Astrophysics Data System (ADS)

    Nakasuga, W. M.; Li, W.; Ewing, R. C.

    2015-12-01

    Radiation effects due to α-decay of U and Th and spontaneous fission of 238U control the production and recovery of the radiation-induced structure of minerals, as well as the diffusion of elements through the mineral host. However, details of how the damage microstructure is produced and annealed remain unknown. Our recent ion beam experiments demonstrate that ionizing radiation from the α-particle recovers the damage structure. Thus, the damage structure is not only the result of the thermal hisotry of the sample, but also of the complex interaction between ionizing and ballistic damage mechanisms. By combining ion irradiations with transmission electron microscopy (TEM), we have simulated the damage produced by α-decay and fission. The α-particle induced annealing has been simulated by in situ TEM observation of consecutive ion-irradiations: i.) 1 MeV Kr2+ (simulating 70 keV α-recoils induced damage), ii.) followed by 400 keV He+ (simulating 4.5 MeV α-particle induced annealing). Thus, in addition to the well-established effects of thermal annealing, the α-particle annealing effects, as evidenced by partical recrystallization of the originally, fully-amorphous apatite upon the α-particle irriadations, should also be considered when evaluating diffusion and release of elements, such as He. In addition, the fission track annealing has been simulated by a new sample preparation method that allows for direct observation of radiation damage recovery at each point along the length of latent tracks created by 80 MeV Xe ions (a typical fission fragment). The initial, rapid reduction in etched track length during isothermal annealing is explained by the rapid annealing of those sections of the track with smaller diameters, as observed directly by in situ TEM. In summary, the atomic-scale investigation of radiation damage in minerals is critical to understanding of the influence of raidation damage on diffusion and kinetics that are fundamental to geochronology.

  5. Regional Evaluation of the Severity-Based Stroke Triage Algorithm for Emergency Medical Services Using Discrete Event Simulation.

    PubMed

    Bogle, Brittany M; Asimos, Andrew W; Rosamond, Wayne D

    2017-10-01

    The Severity-Based Stroke Triage Algorithm for Emergency Medical Services endorses routing patients with suspected large vessel occlusion acute ischemic strokes directly to endovascular stroke centers (ESCs). We sought to evaluate different specifications of this algorithm within a region. We developed a discrete event simulation environment to model patients with suspected stroke transported according to algorithm specifications, which varied by stroke severity screen and permissible additional transport time for routing patients to ESCs. We simulated King County, Washington, and Mecklenburg County, North Carolina, distributing patients geographically into census tracts. Transport time to the nearest hospital and ESC was estimated using traffic-based travel times. We assessed undertriage, overtriage, transport time, and the number-needed-to-route, defined as the number of patients enduring additional transport to route one large vessel occlusion patient to an ESC. Undertriage was higher and overtriage was lower in King County compared with Mecklenburg County for each specification. Overtriage variation was primarily driven by screen (eg, 13%-55% in Mecklenburg County and 10%-40% in King County). Transportation time specifications beyond 20 minutes increased overtriage and decreased undertriage in King County but not Mecklenburg County. A low- versus high-specificity screen routed 3.7× more patients to ESCs. Emergency medical services spent nearly twice the time routing patients to ESCs in King County compared with Mecklenburg County. Our results demonstrate how discrete event simulation can facilitate informed decision making to optimize emergency medical services stroke severity-based triage algorithms. This is the first step toward developing a mature simulation to predict patient outcomes. © 2017 American Heart Association, Inc.

  6. Electron microscopy observations of radiation damage in irradiated and annealed tungsten

    NASA Astrophysics Data System (ADS)

    Grzonka, J.; Ciupiński, Ł.; Smalc-Koziorowska, J.; Ogorodnikova, O. V.; Mayer, M.; Kurzydłowski, K. J.

    2014-12-01

    In the present work tungsten samples were irradiated with W6+ ions with a kinetic energy of 20 MeV in order to simulate radiation damage by fast neutrons. Two samples with cumulative damage of 2.3 and 6.36 displacements per atom were produced. The scanning transmission electron microscopy investigations were carried out in order to determine structure changes resulting from the irradiation. The evolution of the damage with post implantation annealing in the temperature range 673-1100 K was also assessed. Damage profiles were studied at cross-sections. Scanning transmission electron microscopy studies of the lamellae after annealing revealed aggregation of defects and rearrangement as well as partial healing of dislocations at higher temperatures. The results confirm the higher density of radiation-induced dislocations in the near surface area of the sample (1.8 * 1014 m-2) in comparison with a deeper damage area (1.5 * 1014 m-2). Significant decrease of dislocation density was observed after annealing with a concurrent growth of dislocation loops. Transmission electron microscopy analyses show that the dislocation loops are perfect dislocations with the Burgers vectors of b = ½[ 1 1 1].

  7. Performance of Quantum Annealers on Hard Scheduling Problems

    NASA Astrophysics Data System (ADS)

    Pokharel, Bibek; Venturelli, Davide; Rieffel, Eleanor

    Quantum annealers have been employed to attack a variety of optimization problems. We compared the performance of the current D-Wave 2X quantum annealer to that of the previous generation D-Wave Two quantum annealer on scheduling-type planning problems. Further, we compared the effect of different anneal times, embeddings of the logical problem, and different settings of the ferromagnetic coupling JF across the logical vertex-model on the performance of the D-Wave 2X quantum annealer. Our results show that at the best settings, the scaling of expected anneal time to solution for D-WAVE 2X is better than that of the DWave Two, but still inferior to that of state of the art classical solvers on these problems. We discuss the implication of our results for the design and programming of future quantum annealers. Supported by NASA Ames Research Center.

  8. Motion Cueing Algorithm Development: Initial Investigation and Redesign of the Algorithms

    NASA Technical Reports Server (NTRS)

    Telban, Robert J.; Wu, Weimin; Cardullo, Frank M.; Houck, Jacob A. (Technical Monitor)

    2000-01-01

    In this project four motion cueing algorithms were initially investigated. The classical algorithm generated results with large distortion and delay and low magnitude. The NASA adaptive algorithm proved to be well tuned with satisfactory performance, while the UTIAS adaptive algorithm produced less desirable results. Modifications were made to the adaptive algorithms to reduce the magnitude of undesirable spikes. The optimal algorithm was found to have the potential for improved performance with further redesign. The center of simulator rotation was redefined. More terms were added to the cost function to enable more tuning flexibility. A new design approach using a Fortran/Matlab/Simulink setup was employed. A new semicircular canals model was incorporated in the algorithm. With these changes results show the optimal algorithm has some advantages over the NASA adaptive algorithm. Two general problems observed in the initial investigation required solutions. A nonlinear gain algorithm was developed that scales the aircraft inputs by a third-order polynomial, maximizing the motion cues while remaining within the operational limits of the motion system. A braking algorithm was developed to bring the simulator to a full stop at its motion limit and later release the brake to follow the cueing algorithm output.

  9. Interactive optimization approach for optimal impulsive rendezvous using primer vector and evolutionary algorithms

    NASA Astrophysics Data System (ADS)

    Luo, Ya-Zhong; Zhang, Jin; Li, Hai-yang; Tang, Guo-Jin

    2010-08-01

    In this paper, a new optimization approach combining primer vector theory and evolutionary algorithms for fuel-optimal non-linear impulsive rendezvous is proposed. The optimization approach is designed to seek the optimal number of impulses as well as the optimal impulse vectors. In this optimization approach, adding a midcourse impulse is determined by an interactive method, i.e. observing the primer-magnitude time history. An improved version of simulated annealing is employed to optimize the rendezvous trajectory with the fixed-number of impulses. This interactive approach is evaluated by three test cases: coplanar circle-to-circle rendezvous, same-circle rendezvous and non-coplanar rendezvous. The results show that the interactive approach is effective and efficient in fuel-optimal non-linear rendezvous design. It can guarantee solutions, which satisfy the Lawden's necessary optimality conditions.

  10. A Spectrally Selective Attenuation Mechanism-Based Kpar Algorithm for Biomass Heating Effect Simulation in the Open Ocean

    NASA Astrophysics Data System (ADS)

    Chen, Jun; Zhang, Xiangguang; Xing, Xiaogang; Ishizaka, Joji; Yu, Zhifeng

    2017-12-01

    Quantifying the diffuse attenuation coefficient of the photosynthetically available radiation (Kpar) can improve our knowledge of euphotic depth (Zeu) and biomass heating effects in the upper layers of oceans. An algorithm to semianalytically derive Kpar from remote sensing reflectance (Rrs) is developed for the global open oceans. This algorithm includes the following two portions: (1) a neural network model for deriving the diffuse attention coefficients (Kd) that considers the residual error in satellite Rrs, and (2) a three band depth-dependent Kpar algorithm (TDKA) for describing the spectrally selective attenuation mechanism of underwater solar radiation in the open oceans. This algorithm is evaluated with both in situ PAR profile data and satellite images, and the results show that it can produce acceptable PAR profile estimations while clearly removing the impacts of satellite residual errors on Kpar estimations. Furthermore, the performance of the TDKA algorithm is evaluated by its applicability in Zeu derivation and mean temperature within a mixed layer depth (TML) simulation, and the results show that it can significantly decrease the uncertainty in both compared with the classical chlorophyll-a concentration-based Kpar algorithm. Finally, the TDKA algorithm is applied in simulating biomass heating effects in the Sargasso Sea near Bermuda, with new Kpar data it is found that the biomass heating effects can lead to a 3.4°C maximum positive difference in temperature in the upper layers but could result in a 0.67°C maximum negative difference in temperature in the deep layers.

  11. Simulating Future GPS Clock Scenarios with Two Composite Clock Algorithms

    NASA Technical Reports Server (NTRS)

    Suess, Matthias; Matsakis, Demetrios; Greenhall, Charles A.

    2010-01-01

    Using the GPS Toolkit, the GPS constellation is simulated using 31 satellites (SV) and a ground network of 17 monitor stations (MS). At every 15-minutes measurement epoch, the monitor stations measure the time signals of all satellites above a parameterized elevation angle. Once a day, the satellite clock estimates the station and satellite clocks. The first composite clock (B) is based on the Brown algorithm, and is now used by GPS. The second one (G) is based on the Greenhall algorithm. The composite clock of G and B performance are investigated using three ground-clock models. Model C simulates the current GPS configuration, in which all stations are equipped with cesium clocks, except for masers at USNO and Alternate Master Clock (AMC) sites. Model M is an improved situation in which every station is equipped with active hydrogen masers. Finally, Models F and O are future scenarios in which the USNO and AMC stations are equipped with fountain clocks instead of masers. Model F is a rubidium fountain, while Model O is more precise but futuristic Optical Fountain. Each model is evaluated using three performance metrics. The timing-related user range error having all satellites available is the first performance index (PI1). The second performance index (PI2) relates to the stability of the broadcast GPS system time itself. The third performance index (PI3) evaluates the stability of the time scales computed by the two composite clocks. A distinction is made between the "Signal-in-Space" accuracy and that available through a GNSS receiver.

  12. Cell light scattering characteristic numerical simulation research based on FDTD algorithm

    NASA Astrophysics Data System (ADS)

    Lin, Xiaogang; Wan, Nan; Zhu, Hao; Weng, Lingdong

    2017-01-01

    In this study, finite-difference time-domain (FDTD) algorithm has been used to work out the cell light scattering problem. Before beginning to do the simulation contrast, finding out the changes or the differences between normal cells and abnormal cells which may be cancerous or maldevelopment is necessary. The preparation of simulation are building up the simple cell model of cell which consists of organelles, nucleus and cytoplasm and setting up the suitable precision of mesh. Meanwhile, setting up the total field scattering field source as the excitation source and far field projection analysis group is also important. Every step need to be explained by the principles of mathematic such as the numerical dispersion, perfect matched layer boundary condition and near-far field extrapolation. The consequences of simulation indicated that the position of nucleus changed will increase the back scattering intensity and the significant difference on the peak value of scattering intensity may result from the changes of the size of cytoplasm. The study may help us find out the regulations based on the simulation consequences and the regulations can be meaningful for early diagnosis of cancers.

  13. Plenoptic camera image simulation for reconstruction algorithm verification

    NASA Astrophysics Data System (ADS)

    Schwiegerling, Jim

    2014-09-01

    Plenoptic cameras have emerged in recent years as a technology for capturing light field data in a single snapshot. A conventional digital camera can be modified with the addition of a lenslet array to create a plenoptic camera. Two distinct camera forms have been proposed in the literature. The first has the camera image focused onto the lenslet array. The lenslet array is placed over the camera sensor such that each lenslet forms an image of the exit pupil onto the sensor. The second plenoptic form has the lenslet array relaying the image formed by the camera lens to the sensor. We have developed a raytracing package that can simulate images formed by a generalized version of the plenoptic camera. Several rays from each sensor pixel are traced backwards through the system to define a cone of rays emanating from the entrance pupil of the camera lens. Objects that lie within this cone are integrated to lead to a color and exposure level for that pixel. To speed processing three-dimensional objects are approximated as a series of planes at different depths. Repeating this process for each pixel in the sensor leads to a simulated plenoptic image on which different reconstruction algorithms can be tested.

  14. An evaluation of noise reduction algorithms for particle-based fluid simulations in multi-scale applications

    NASA Astrophysics Data System (ADS)

    Zimoń, M. J.; Prosser, R.; Emerson, D. R.; Borg, M. K.; Bray, D. J.; Grinberg, L.; Reese, J. M.

    2016-11-01

    Filtering of particle-based simulation data can lead to reduced computational costs and enable more efficient information transfer in multi-scale modelling. This paper compares the effectiveness of various signal processing methods to reduce numerical noise and capture the structures of nano-flow systems. In addition, a novel combination of these algorithms is introduced, showing the potential of hybrid strategies to improve further the de-noising performance for time-dependent measurements. The methods were tested on velocity and density fields, obtained from simulations performed with molecular dynamics and dissipative particle dynamics. Comparisons between the algorithms are given in terms of performance, quality of the results and sensitivity to the choice of input parameters. The results provide useful insights on strategies for the analysis of particle-based data and the reduction of computational costs in obtaining ensemble solutions.

  15. Algorithm for loading shot noise microbunching in multi-dimensional, free-electron laser simulation codes

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Fawley, William M.

    We discuss the underlying reasoning behind and the details of the numerical algorithm used in the GINGER free-electron laser(FEL) simulation code to load the initial shot noise microbunching on the electron beam. In particular, we point out that there are some additional subtleties which must be followed for multi-dimensional codes which are not necessary for one-dimensional formulations. Moreover, requiring that the higher harmonics of the microbunching also be properly initialized with the correct statistics leads to additional complexities. We present some numerical results including the predicted incoherent, spontaneous emission as tests of the shot noise algorithm's correctness.

  16. Boosting quantum annealer performance via sample persistence

    NASA Astrophysics Data System (ADS)

    Karimi, Hamed; Rosenberg, Gili

    2017-07-01

    We propose a novel method for reducing the number of variables in quadratic unconstrained binary optimization problems, using a quantum annealer (or any sampler) to fix the value of a large portion of the variables to values that have a high probability of being optimal. The resulting problems are usually much easier for the quantum annealer to solve, due to their being smaller and consisting of disconnected components. This approach significantly increases the success rate and number of observations of the best known energy value in samples obtained from the quantum annealer, when compared with calling the quantum annealer without using it, even when using fewer annealing cycles. Use of the method results in a considerable improvement in success metrics even for problems with high-precision couplers and biases, which are more challenging for the quantum annealer to solve. The results are further enhanced by applying the method iteratively and combining it with classical pre-processing. We present results for both Chimera graph-structured problems and embedded problems from a real-world application.

  17. Quantum Monte Carlo tunneling from quantum chemistry to quantum annealing

    NASA Astrophysics Data System (ADS)

    Mazzola, Guglielmo; Smelyanskiy, Vadim N.; Troyer, Matthias

    2017-10-01

    Quantum tunneling is ubiquitous across different fields, from quantum chemical reactions and magnetic materials to quantum simulators and quantum computers. While simulating the real-time quantum dynamics of tunneling is infeasible for high-dimensional systems, quantum tunneling also shows up in quantum Monte Carlo (QMC) simulations, which aim to simulate quantum statistics with resources growing only polynomially with the system size. Here we extend the recent results obtained for quantum spin models [Phys. Rev. Lett. 117, 180402 (2016), 10.1103/PhysRevLett.117.180402], and we study continuous-variable models for proton transfer reactions. We demonstrate that QMC simulations efficiently recover the scaling of ground-state tunneling rates due to the existence of an instanton path, which always connects the reactant state with the product. We discuss the implications of our results in the context of quantum chemical reactions and quantum annealing, where quantum tunneling is expected to be a valuable resource for solving combinatorial optimization problems.

  18. Advanced Models and Algorithms for Self-Similar IP Network Traffic Simulation and Performance Analysis

    NASA Astrophysics Data System (ADS)

    Radev, Dimitar; Lokshina, Izabella

    2010-11-01

    The paper examines self-similar (or fractal) properties of real communication network traffic data over a wide range of time scales. These self-similar properties are very different from the properties of traditional models based on Poisson and Markov-modulated Poisson processes. Advanced fractal models of sequentional generators and fixed-length sequence generators, and efficient algorithms that are used to simulate self-similar behavior of IP network traffic data are developed and applied. Numerical examples are provided; and simulation results are obtained and analyzed.

  19. Conjugate Gradient Algorithms For Manipulator Simulation

    NASA Technical Reports Server (NTRS)

    Fijany, Amir; Scheid, Robert E.

    1991-01-01

    Report discusses applicability of conjugate-gradient algorithms to computation of forward dynamics of robotic manipulators. Rapid computation of forward dynamics essential to teleoperation and other advanced robotic applications. Part of continuing effort to find algorithms meeting requirements for increased computational efficiency and speed. Method used for iterative solution of systems of linear equations.

  20. A novel algorithm for solving the true coincident counting issues in Monte Carlo simulations for radiation spectroscopy.

    PubMed

    Guan, Fada; Johns, Jesse M; Vasudevan, Latha; Zhang, Guoqing; Tang, Xiaobin; Poston, John W; Braby, Leslie A

    2015-06-01

    Coincident counts can be observed in experimental radiation spectroscopy. Accurate quantification of the radiation source requires the detection efficiency of the spectrometer, which is often experimentally determined. However, Monte Carlo analysis can be used to supplement experimental approaches to determine the detection efficiency a priori. The traditional Monte Carlo method overestimates the detection efficiency as a result of omitting coincident counts caused mainly by multiple cascade source particles. In this study, a novel "multi-primary coincident counting" algorithm was developed using the Geant4 Monte Carlo simulation toolkit. A high-purity Germanium detector for ⁶⁰Co gamma-ray spectroscopy problems was accurately modeled to validate the developed algorithm. The simulated pulse height spectrum agreed well qualitatively with the measured spectrum obtained using the high-purity Germanium detector. The developed algorithm can be extended to other applications, with a particular emphasis on challenging radiation fields, such as counting multiple types of coincident radiations released from nuclear fission or used nuclear fuel.

  1. Simulation of Long Lived Tracers Using an Improved Empirically Based Two-Dimensional Model Transport Algorithm

    NASA Technical Reports Server (NTRS)

    Fleming, E. L.; Jackman, C. H.; Stolarski, R. S.; Considine, D. B.

    1998-01-01

    We have developed a new empirically-based transport algorithm for use in our GSFC two-dimensional transport and chemistry model. The new algorithm contains planetary wave statistics, and parameterizations to account for the effects due to gravity waves and equatorial Kelvin waves. As such, this scheme utilizes significantly more information compared to our previous algorithm which was based only on zonal mean temperatures and heating rates. The new model transport captures much of the qualitative structure and seasonal variability observed in long lived tracers, such as: isolation of the tropics and the southern hemisphere winter polar vortex; the well mixed surf-zone region of the winter sub-tropics and mid-latitudes; the latitudinal and seasonal variations of total ozone; and the seasonal variations of mesospheric H2O. The model also indicates a double peaked structure in methane associated with the semiannual oscillation in the tropical upper stratosphere. This feature is similar in phase but is significantly weaker in amplitude compared to the observations. The model simulations of carbon-14 and strontium-90 are in good agreement with observations, both in simulating the peak in mixing ratio at 20-25 km, and the decrease with altitude in mixing ratio above 25 km. We also find mostly good agreement between modeled and observed age of air determined from SF6 outside of the northern hemisphere polar vortex. However, observations inside the vortex reveal significantly older air compared to the model. This is consistent with the model deficiencies in simulating CH4 in the northern hemisphere winter high latitudes and illustrates the limitations of the current climatological zonal mean model formulation. The propagation of seasonal signals in water vapor and CO2 in the lower stratosphere showed general agreement in phase, and the model qualitatively captured the observed amplitude decrease in CO2 from the tropics to midlatitudes. However, the simulated seasonal

  2. Simulation and experimental design of a new advanced variable step size Incremental Conductance MPPT algorithm for PV systems.

    PubMed

    Loukriz, Abdelhamid; Haddadi, Mourad; Messalti, Sabir

    2016-05-01

    Improvement of the efficiency of photovoltaic system based on new maximum power point tracking (MPPT) algorithms is the most promising solution due to its low cost and its easy implementation without equipment updating. Many MPPT methods with fixed step size have been developed. However, when atmospheric conditions change rapidly , the performance of conventional algorithms is reduced. In this paper, a new variable step size Incremental Conductance IC MPPT algorithm has been proposed. Modeling and simulation of different operational conditions of conventional Incremental Conductance IC and proposed methods are presented. The proposed method was developed and tested successfully on a photovoltaic system based on Flyback converter and control circuit using dsPIC30F4011. Both, simulation and experimental design are provided in several aspects. A comparative study between the proposed variable step size and fixed step size IC MPPT method under similar operating conditions is presented. The obtained results demonstrate the efficiency of the proposed MPPT algorithm in terms of speed in MPP tracking and accuracy. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

  3. A real-time simulation evaluation of an advanced detection. Isolation and accommodation algorithm for sensor failures in turbine engines

    NASA Technical Reports Server (NTRS)

    Merrill, W. C.; Delaat, J. C.

    1986-01-01

    An advanced sensor failure detection, isolation, and accommodation (ADIA) algorithm has been developed for use with an aircraft turbofan engine control system. In a previous paper the authors described the ADIA algorithm and its real-time implementation. Subsequent improvements made to the algorithm and implementation are discussed, and the results of an evaluation presented. The evaluation used a real-time, hybrid computer simulation of an F100 turbofan engine.

  4. Computational Multiqubit Tunnelling in Programmable Quantum Annealers

    DTIC Science & Technology

    2016-08-25

    ARTICLE Received 3 Jun 2015 | Accepted 26 Nov 2015 | Published 7 Jan 2016 Computational multiqubit tunnelling in programmable quantum annealers...state itself. Quantum tunnelling has been hypothesized as an advantageous physical resource for optimization in quantum annealing. However, computational ...qubit tunnelling plays a computational role in a currently available programmable quantum annealer. We devise a probe for tunnelling, a computational

  5. Thermal annealing response following irradiation of a CMOS imager for the JUICE JANUS instrument

    NASA Astrophysics Data System (ADS)

    Lofthouse-Smith, D.-D.; Soman, M. R.; Allanwood, E. A. H.; Stefanov, K. D.; Holland, A. D.; Leese, M.; Turne, P.

    2018-03-01

    ESA's JUICE (JUpiter ICy moon Explorer) spacecraft is an L-class mission destined for the Jovian system in 2030. Its primary goals are to investigate the conditions for planetary formation and the emergence of life, and how does the solar system work. The JANUS camera, an instrument on JUICE, uses a 4T back illuminated CMOS image sensor, the CIS115 designed by Teledyne e2v. JANUS imager test campaigns are studying the CIS115 following exposure to gammas, protons, electrons and heavy ions, simulating the harsh radiation environment present in the Jovian system. The degradation of 4T CMOS device performance following proton fluences is being studied, as well as the effectiveness of thermal annealing to reverse radiation damage. One key parameter for the JANUS mission is the Dark current of the CIS115, which has been shown to degrade in previous radiation campaigns. A thermal anneal of the CIS115 has been used to accelerate any annealing following the irradiation as well as to study the evolution of any performance characteristics. CIS115s have been irradiated to double the expected End of Life (EOL) levels for displacement damage radiation (2×1010 protons, 10 MeV equivalent). Following this, devices have undergone a thermal anneal cycle at 100oC for 168 hours to reveal the extent to which CIS115 recovers pre-irradiation performance. Dark current activation energy analysis following proton fluence gives information on trap species present in the device and how effective anneal is at removing these trap species. Thermal anneal shows no quantifiable change in the activation energy of the dark current following irradiation.

  6. An implementation of differential evolution algorithm for inversion of geoelectrical data

    NASA Astrophysics Data System (ADS)

    Balkaya, Çağlayan

    2013-11-01

    Differential evolution (DE), a population-based evolutionary algorithm (EA) has been implemented to invert self-potential (SP) and vertical electrical sounding (VES) data sets. The algorithm uses three operators including mutation, crossover and selection similar to genetic algorithm (GA). Mutation is the most important operator for the success of DE. Three commonly used mutation strategies including DE/best/1 (strategy 1), DE/rand/1 (strategy 2) and DE/rand-to-best/1 (strategy 3) were applied together with a binomial type crossover. Evolution cycle of DE was realized without boundary constraints. For the test studies performed with SP data, in addition to both noise-free and noisy synthetic data sets two field data sets observed over the sulfide ore body in the Malachite mine (Colorado) and over the ore bodies in the Neem-Ka Thana cooper belt (India) were considered. VES test studies were carried out using synthetically produced resistivity data representing a three-layered earth model and a field data set example from Gökçeada (Turkey), which displays a seawater infiltration problem. Mutation strategies mentioned above were also extensively tested on both synthetic and field data sets in consideration. Of these, strategy 1 was found to be the most effective strategy for the parameter estimation by providing less computational cost together with a good accuracy. The solutions obtained by DE for the synthetic cases of SP were quite consistent with particle swarm optimization (PSO) which is a more widely used population-based optimization algorithm than DE in geophysics. Estimated parameters of SP and VES data were also compared with those obtained from Metropolis-Hastings (M-H) sampling algorithm based on simulated annealing (SA) without cooling to clarify uncertainties in the solutions. Comparison to the M-H algorithm shows that DE performs a fast approximate posterior sampling for the case of low-dimensional inverse geophysical problems.

  7. Multidimensional generalized-ensemble algorithms for complex systems.

    PubMed

    Mitsutake, Ayori; Okamoto, Yuko

    2009-06-07

    We give general formulations of the multidimensional multicanonical algorithm, simulated tempering, and replica-exchange method. We generalize the original potential energy function E(0) by adding any physical quantity V of interest as a new energy term. These multidimensional generalized-ensemble algorithms then perform a random walk not only in E(0) space but also in V space. Among the three algorithms, the replica-exchange method is the easiest to perform because the weight factor is just a product of regular Boltzmann-like factors, while the weight factors for the multicanonical algorithm and simulated tempering are not a priori known. We give a simple procedure for obtaining the weight factors for these two latter algorithms, which uses a short replica-exchange simulation and the multiple-histogram reweighting techniques. As an example of applications of these algorithms, we have performed a two-dimensional replica-exchange simulation and a two-dimensional simulated-tempering simulation using an alpha-helical peptide system. From these simulations, we study the helix-coil transitions of the peptide in gas phase and in aqueous solution.

  8. Dosimetric evaluation of a commercial proton spot scanning Monte-Carlo dose algorithm: comparisons against measurements and simulations

    NASA Astrophysics Data System (ADS)

    Saini, Jatinder; Maes, Dominic; Egan, Alexander; Bowen, Stephen R.; St. James, Sara; Janson, Martin; Wong, Tony; Bloch, Charles

    2017-10-01

    RaySearch Americas Inc. (NY) has introduced a commercial Monte Carlo dose algorithm (RS-MC) for routine clinical use in proton spot scanning. In this report, we provide a validation of this algorithm against phantom measurements and simulations in the GATE software package. We also compared the performance of the RayStation analytical algorithm (RS-PBA) against the RS-MC algorithm. A beam model (G-MC) for a spot scanning gantry at our proton center was implemented in the GATE software package. The model was validated against measurements in a water phantom and was used for benchmarking the RS-MC. Validation of the RS-MC was performed in a water phantom by measuring depth doses and profiles for three spread-out Bragg peak (SOBP) beams with normal incidence, an SOBP with oblique incidence, and an SOBP with a range shifter and large air gap. The RS-MC was also validated against measurements and simulations in heterogeneous phantoms created by placing lung or bone slabs in a water phantom. Lateral dose profiles near the distal end of the beam were measured with a microDiamond detector and compared to the G-MC simulations, RS-MC and RS-PBA. Finally, the RS-MC and RS-PBA were validated against measured dose distributions in an Alderson-Rando (AR) phantom. Measurements were made using Gafchromic film in the AR phantom and compared to doses using the RS-PBA and RS-MC algorithms. For SOBP depth doses in a water phantom, all three algorithms matched the measurements to within  ±3% at all points and a range within 1 mm. The RS-PBA algorithm showed up to a 10% difference in dose at the entrance for the beam with a range shifter and  >30 cm air gap, while the RS-MC and G-MC were always within 3% of the measurement. For an oblique beam incident at 45°, the RS-PBA algorithm showed up to 6% local dose differences and broadening of distal fall-off by 5 mm. Both the RS-MC and G-MC accurately predicted the depth dose to within  ±3% and distal fall-off to within 2

  9. Dosimetric evaluation of a commercial proton spot scanning Monte-Carlo dose algorithm: comparisons against measurements and simulations.

    PubMed

    Saini, Jatinder; Maes, Dominic; Egan, Alexander; Bowen, Stephen R; St James, Sara; Janson, Martin; Wong, Tony; Bloch, Charles

    2017-09-12

    RaySearch Americas Inc. (NY) has introduced a commercial Monte Carlo dose algorithm (RS-MC) for routine clinical use in proton spot scanning. In this report, we provide a validation of this algorithm against phantom measurements and simulations in the GATE software package. We also compared the performance of the RayStation analytical algorithm (RS-PBA) against the RS-MC algorithm. A beam model (G-MC) for a spot scanning gantry at our proton center was implemented in the GATE software package. The model was validated against measurements in a water phantom and was used for benchmarking the RS-MC. Validation of the RS-MC was performed in a water phantom by measuring depth doses and profiles for three spread-out Bragg peak (SOBP) beams with normal incidence, an SOBP with oblique incidence, and an SOBP with a range shifter and large air gap. The RS-MC was also validated against measurements and simulations in heterogeneous phantoms created by placing lung or bone slabs in a water phantom. Lateral dose profiles near the distal end of the beam were measured with a microDiamond detector and compared to the G-MC simulations, RS-MC and RS-PBA. Finally, the RS-MC and RS-PBA were validated against measured dose distributions in an Alderson-Rando (AR) phantom. Measurements were made using Gafchromic film in the AR phantom and compared to doses using the RS-PBA and RS-MC algorithms. For SOBP depth doses in a water phantom, all three algorithms matched the measurements to within  ±3% at all points and a range within 1 mm. The RS-PBA algorithm showed up to a 10% difference in dose at the entrance for the beam with a range shifter and  >30 cm air gap, while the RS-MC and G-MC were always within 3% of the measurement. For an oblique beam incident at 45°, the RS-PBA algorithm showed up to 6% local dose differences and broadening of distal fall-off by 5 mm. Both the RS-MC and G-MC accurately predicted the depth dose to within  ±3% and distal fall-off to within 2

  10. Crystal growth and annealing method and apparatus

    DOEpatents

    Gianoulakis, Steven E.; Sparrow, Robert

    2001-01-01

    A method and apparatus for producing crystals that minimizes birefringence even at large crystal sizes, and is suitable for production of CaF.sub.2 crystals. The method of the present invention comprises annealing a crystal by maintaining a minimal temperature gradient in the crystal while slowly reducing the bulk temperature of the crystal. An apparatus according to the present invention includes a thermal control system added to a crystal growth and annealing apparatus, wherein the thermal control system allows a temperature gradient during crystal growth but minimizes the temperature gradient during crystal annealing. An embodiment of the present invention comprises a secondary heater incorporated into a conventional crystal growth and annealing apparatus. The secondary heater supplies heat to minimize the temperature gradients in the crystal during the annealing process. The secondary heater can mount near the bottom of the crucible to effectively maintain appropriate temperature gradients.

  11. Effective algorithm for ray-tracing simulations of lobster eye and similar reflective optical systems

    NASA Astrophysics Data System (ADS)

    Tichý, Vladimír; Hudec, René; Němcová, Šárka

    2016-06-01

    The algorithm presented is intended mainly for lobster eye optics. This type of optics (and some similar types) allows for a simplification of the classical ray-tracing procedure that requires great many rays to simulate. The method presented performs the simulation of a only few rays; therefore it is extremely effective. Moreover, to simplify the equations, a specific mathematical formalism is used. Only a few simple equations are used, therefore the program code can be simple as well. The paper also outlines how to apply the method to some other reflective optical systems.

  12. Quenched-in defects in flashlamp-annealed silicon

    NASA Technical Reports Server (NTRS)

    Borenstein, J. T.; Jones, J. T.; Corbett, J. W.; Oehrlein, G. S.; Kleinhenz, R. L.

    1986-01-01

    Deep levels introduced in boron-doped silicon by heat-pulse annealing with a tungsten-halogen flashlamp are investigated using deep-level transient spectroscopy. Two majority-carrier trapping levels in the band gap, at Ev + 0.32 eV and at Ev + 0.45 eV, are observed. These results are compared to those obtained by furnace-quenching and laser-annealing studies. Both the position in the gap and the annealing kinetics of the hole trap at Ev + 0.45 eV suggest that this center is due to an interstitial iron impurity in the lattice. The deep levels are not consistently observed in all flashlamp-annealed Si crystals utilized.

  13. Effect of stabilization annealing on SCC susceptibility of β-annealed Ti-6Al-4V alloy in 0.6 M NaCl solution

    NASA Astrophysics Data System (ADS)

    Jeong, Daeho; Park, Jiho; Ahn, Soojin; Sung, Hyokyung; Kwon, Yongnam; Kim, Sangshik

    2018-01-01

    The effect of stabilization annealing on the stress corrosion cracking (SCC) susceptibility of β-annealed Ti-6Al-4V (Ti64) alloy was examined in an aqueous 0.6 M NaCl solution under various applied potentials of +0.1, -0.05 and -0.1 V vs Ecorr, respectively, at a strain rate of 10 -6 s -1. The stabilization annealing substantially improved the resistance to SCC of β-annealed Ti64 alloy in 0.6 M NaCl solution under cathodic applied potentials, while the effect was marginal under an anodic applied potential. It was also noted that the areal fraction between ductile and brittle fracture of β-annealed Ti64 specimens, which were slow strain rate tested in 0.6 M NaCl solution, varied with stabilization annealing and applied potentials. The effect of stabilization annealing on the SCC behavior of β-annealed Ti64 alloy in SCC-causing environment was discussed based on the micrographic and fractographic observation.

  14. Regulation of multispanning membrane protein topology via post-translational annealing.

    PubMed

    Van Lehn, Reid C; Zhang, Bin; Miller, Thomas F

    2015-09-26

    The canonical mechanism for multispanning membrane protein topogenesis suggests that protein topology is established during cotranslational membrane integration. However, this mechanism is inconsistent with the behavior of EmrE, a dual-topology protein for which the mutation of positively charged loop residues, even close to the C-terminus, leads to dramatic shifts in its topology. We use coarse-grained simulations to investigate the Sec-facilitated membrane integration of EmrE and its mutants on realistic biological timescales. This work reveals a mechanism for regulating membrane-protein topogenesis, in which initially misintegrated configurations of the proteins undergo post-translational annealing to reach fully integrated multispanning topologies. The energetic barriers associated with this post-translational annealing process enforce kinetic pathways that dictate the topology of the fully integrated proteins. The proposed mechanism agrees well with the experimentally observed features of EmrE topogenesis and provides a range of experimentally testable predictions regarding the effect of translocon mutations on membrane protein topogenesis.

  15. Genetic Algorithms and Their Application to the Protein Folding Problem

    DTIC Science & Technology

    1993-12-01

    and symbolic methods, random methods such as Monte Carlo simulation and simulated annealing, distance geometry, and molecular dynamics. Many of these...calculated energies with those obtained using the molecular simulation software package called CHARMm. 10 9) Test both the simple and parallel simpie genetic...homology-based, and simplification techniques. 3.21 Molecular Dynamics. Perhaps the most natural approach is to actually simulate the folding process. This

  16. Prediction of the thermal annealing of thick oxide metal-oxide-semiconductor dosimeters irradiated in a harsh radiation environment

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Ravotti, F.; Glaser, M.; Saigne, F.

    Radiation-sensing metal-oxide-semiconductor field-effect transistors produced by the laboratory LAAS-CNRS were exposed to a harsh hadron field that represents the real radiation environment expected at the CERN Large Hadron Collider experiments. The long-term stability of the transistor's I{sub ds}-V{sub gs} characteristic was investigated using the isochronal annealing technique. In this work, devices exposed to high intensity hadron levels ({phi}{>=}10{sup 12} neutrons/cm{sup 2}) show evidences of displacement damages in the I{sub ds}-V{sub gs} annealing behavior. By comparing experimental and simulated results over 14 months, the isochronal annealing method, originally devoted to oxide trapped charge, is shown to enable prediction of the recoverymore » of silicon bulk defects.« less

  17. Maximizing the nurses' preferences in nurse scheduling problem: mathematical modeling and a meta-heuristic algorithm

    NASA Astrophysics Data System (ADS)

    Jafari, Hamed; Salmasi, Nasser

    2015-09-01

    The nurse scheduling problem (NSP) has received a great amount of attention in recent years. In the NSP, the goal is to assign shifts to the nurses in order to satisfy the hospital's demand during the planning horizon by considering different objective functions. In this research, we focus on maximizing the nurses' preferences for working shifts and weekends off by considering several important factors such as hospital's policies, labor laws, governmental regulations, and the status of nurses at the end of the previous planning horizon in one of the largest hospitals in Iran i.e., Milad Hospital. Due to the shortage of available nurses, at first, the minimum total number of required nurses is determined. Then, a mathematical programming model is proposed to solve the problem optimally. Since the proposed research problem is NP-hard, a meta-heuristic algorithm based on simulated annealing (SA) is applied to heuristically solve the problem in a reasonable time. An initial feasible solution generator and several novel neighborhood structures are applied to enhance performance of the SA algorithm. Inspired from our observations in Milad hospital, random test problems are generated to evaluate the performance of the SA algorithm. The results of computational experiments indicate that the applied SA algorithm provides solutions with average percentage gap of 5.49 % compared to the upper bounds obtained from the mathematical model. Moreover, the applied SA algorithm provides significantly better solutions in a reasonable time than the schedules provided by the head nurses.

  18. An Adaptive Immune Genetic Algorithm for Edge Detection

    NASA Astrophysics Data System (ADS)

    Li, Ying; Bai, Bendu; Zhang, Yanning

    An adaptive immune genetic algorithm (AIGA) based on cost minimization technique method for edge detection is proposed. The proposed AIGA recommends the use of adaptive probabilities of crossover, mutation and immune operation, and a geometric annealing schedule in immune operator to realize the twin goals of maintaining diversity in the population and sustaining the fast convergence rate in solving the complex problems such as edge detection. Furthermore, AIGA can effectively exploit some prior knowledge and information of the local edge structure in the edge image to make vaccines, which results in much better local search ability of AIGA than that of the canonical genetic algorithm. Experimental results on gray-scale images show the proposed algorithm perform well in terms of quality of the final edge image, rate of convergence and robustness to noise.

  19. An algorithm for emulsion stability simulations: account of flocculation, coalescence, surfactant adsorption and the process of Ostwald ripening.

    PubMed

    Urbina-Villalba, German

    2009-03-01

    The first algorithm for Emulsion Stability Simulations (ESS) was presented at the V Conferencia Iberoamericana sobre Equilibrio de Fases y Diseño de Procesos [Luis, J.; García-Sucre, M.; Urbina-Villalba, G. Brownian Dynamics Simulation of Emulsion Stability In: Equifase 99. Libro de Actas, 1(st) Ed., Tojo J., Arce, A., Eds.; Solucion's: Vigo, Spain, 1999; Volume 2, pp. 364-369]. The former version of the program consisted on a minor modification of the Brownian Dynamics algorithm to account for the coalescence of drops. The present version of the program contains elaborate routines for time-dependent surfactant adsorption, average diffusion constants, and Ostwald ripening.

  20. Selective molecular annealing: in situ small angle X-ray scattering study of microwave-assisted annealing of block copolymers.

    PubMed

    Toolan, Daniel T W; Adlington, Kevin; Isakova, Anna; Kalamiotis, Alexis; Mokarian-Tabari, Parvaneh; Dimitrakis, Georgios; Dodds, Christopher; Arnold, Thomas; Terrill, Nick J; Bras, Wim; Hermida Merino, Daniel; Topham, Paul D; Irvine, Derek J; Howse, Jonathan R

    2017-08-09

    Microwave annealing has emerged as an alternative to traditional thermal annealing approaches for optimising block copolymer self-assembly. A novel sample environment enabling small angle X-ray scattering to be performed in situ during microwave annealing is demonstrated, which has enabled, for the first time, the direct study of the effects of microwave annealing upon the self-assembly behavior of a model, commercial triblock copolymer system [polystyrene-block-poly(ethylene-co-butylene)-block-polystyrene]. Results show that the block copolymer is a poor microwave absorber, resulting in no change in the block copolymer morphology upon application of microwave energy. The block copolymer species may only indirectly interact with the microwave energy when a small molecule microwave-interactive species [diethylene glycol dibenzoate (DEGDB)] is incorporated directly into the polymer matrix. Then significant morphological development is observed at DEGDB loadings ≥6 wt%. Through spatial localisation of the microwave-interactive species, we demonstrate targeted annealing of specific regions of a multi-component system, opening routes for the development of "smart" manufacturing methodologies.

  1. Identification of Arbitrary Zonation in Groundwater Parameters using the Level Set Method and a Parallel Genetic Algorithm

    NASA Astrophysics Data System (ADS)

    Lei, H.; Lu, Z.; Vesselinov, V. V.; Ye, M.

    2017-12-01

    Simultaneous identification of both the zonation structure of aquifer heterogeneity and the hydrogeological parameters associated with these zones is challenging, especially for complex subsurface heterogeneity fields. In this study, a new approach, based on the combination of the level set method and a parallel genetic algorithm is proposed. Starting with an initial guess for the zonation field (including both zonation structure and the hydraulic properties of each zone), the level set method ensures that material interfaces are evolved through the inverse process such that the total residual between the simulated and observed state variables (hydraulic head) always decreases, which means that the inversion result depends on the initial guess field and the minimization process might fail if it encounters a local minimum. To find the global minimum, the genetic algorithm (GA) is utilized to explore the parameters that define initial guess fields, and the minimal total residual corresponding to each initial guess field is considered as the fitness function value in the GA. Due to the expensive evaluation of the fitness function, a parallel GA is adapted in combination with a simulated annealing algorithm. The new approach has been applied to several synthetic cases in both steady-state and transient flow fields, including a case with real flow conditions at the chromium contaminant site at the Los Alamos National Laboratory. The results show that this approach is capable of identifying the arbitrary zonation structures of aquifer heterogeneity and the hydrogeological parameters associated with these zones effectively.

  2. Annealing of Solar Cells and Other Thin Film Devices

    NASA Technical Reports Server (NTRS)

    Escobar, Hector; Kuhlman, Franz; Dils, D. W.; Lush, G. B.; Mackey, Willie R. (Technical Monitor)

    2001-01-01

    Annealing is a key step in most semiconductor fabrication processes, especially for thin films where annealing enhances performance by healing defects and increasing grain sizes. We have employed a new annealing oven for the annealing of CdTe-based solar cells and have been using this system in an attempt to grow US on top of CdTe by annealing in the presence of H2S gas. Preliminary results of this process on CdTe solar cells and other thin-film devices will be presented.

  3. Radiation damage annealing mechanisms and possible low temperature annealing in silicon solar cells

    NASA Technical Reports Server (NTRS)

    Weinberg, I.; Swartz, C. K.

    1980-01-01

    Deep level transient spectroscopy and the Shockley-Read-Hall recombination theory are used to identify the defect responsible for reverse annealing in 2 ohm-cm n+/p silicon solar cells. This defect, with energy level at Ev + 0.30 eV, has been tentatively identified as a boron-oxygen-vacancy complex. It has been also determined by calculation that the removal of this defect could result in significant annealing at temperatures as low as 200 C for 2 ohm-cm and lower resistivity cells.

  4. Correlation Equations for Condensing Heat Exchangers Based on an Algorithmic Performance-Data Classification

    NASA Astrophysics Data System (ADS)

    Pacheco-Vega, Arturo

    2016-09-01

    In this work a new set of correlation equations is developed and introduced to accurately describe the thermal performance of compact heat exchangers with possible condensation. The feasible operating conditions for the thermal system correspond to dry- surface, dropwise condensation, and film condensation. Using a prescribed form for each condition, a global regression analysis for the best-fit correlation to experimental data is carried out with a simulated annealing optimization technique. The experimental data were taken from the literature and algorithmically classified into three groups -related to the possible operating conditions- with a previously-introduced Gaussian-mixture-based methodology. Prior to their use in the analysis, the correct data classification was assessed and confirmed via artificial neural networks. Predictions from the correlations obtained for the different conditions are within the uncertainty of the experiments and substantially more accurate than those commonly used.

  5. Periodic annealing of radiation damage in GaAs solar cells

    NASA Technical Reports Server (NTRS)

    Loo, R. Y.; Knechtli, R. C.; Kamath, G. S.

    1980-01-01

    Continuous annealing of GaAs solar cells is compared with periodic annealing to determine their relative effectiveness in minimizing proton radiation damage. It is concluded that continuous annealing of the cells in space at 150 C can effectively reduce the proton radiation damage to the GaAs solar cells. Periodic annealing is most effective if it can be initiated at relatively low fluences (approximating continuous annealing), especially if low temperatures of less than 200 C are to be used. If annealing is started only after the fluence of the damaging protons has accumulated to a high value 10 to the 11th power sq/pcm), effective annealing is still possible at relatively high temperatures. Finally, since electron radiation damage anneals even more easily than proton radiation damage, substantial improvements in GaAs solar cell life can be achieved by incorporating the proper annealing capabilities in solar panels for practical space missions where both electron and proton radiation damage have to be minimized.

  6. Error suppression and correction for quantum annealing

    NASA Astrophysics Data System (ADS)

    Lidar, Daniel

    While adiabatic quantum computing and quantum annealing enjoy a certain degree of inherent robustness against excitations and control errors, there is no escaping the need for error correction or suppression. In this talk I will give an overview of our work on the development of such error correction and suppression methods. We have experimentally tested one such method combining encoding, energy penalties and decoding, on a D-Wave Two processor, with encouraging results. Mean field theory shows that this can be explained in terms of a softening of the closing of the gap due to the energy penalty, resulting in protection against excitations that occur near the quantum critical point. Decoding recovers population from excited states and enhances the success probability of quantum annealing. Moreover, we have demonstrated that using repetition codes with increasing code distance can lower the effective temperature of the annealer. References: K.L. Pudenz, T. Albash, D.A. Lidar, ``Error corrected quantum annealing with hundreds of qubits'', Nature Commun. 5, 3243 (2014). K.L. Pudenz, T. Albash, D.A. Lidar, ``Quantum annealing correction for random Ising problems'', Phys. Rev. A. 91, 042302 (2015). S. Matsuura, H. Nishimori, T. Albash, D.A. Lidar, ``Mean Field Analysis of Quantum Annealing Correction''. arXiv:1510.07709. W. Vinci et al., in preparation.

  7. Simulation of Long Lived Tracers Using an Improved Empirically-Based Two-Dimensional Model Transport Algorithm

    NASA Technical Reports Server (NTRS)

    Fleming, Eric L.; Jackman, Charles H.; Stolarski, Richard S.; Considine, David B.

    1998-01-01

    We have developed a new empirically-based transport algorithm for use in our GSFC two-dimensional transport and chemistry assessment model. The new algorithm contains planetary wave statistics, and parameterizations to account for the effects due to gravity waves and equatorial Kelvin waves. We will present an overview of the new algorithm, and show various model-data comparisons of long-lived tracers as part of the model validation. We will also show how the new algorithm gives substantially better agreement with observations compared to our previous model transport. The new model captures much of the qualitative structure and seasonal variability observed methane, water vapor, and total ozone. These include: isolation of the tropics and winter polar vortex, the well mixed surf-zone region of the winter sub-tropics and mid-latitudes, and the propagation of seasonal signals in the tropical lower stratosphere. Model simulations of carbon-14 and strontium-90 compare fairly well with observations in reproducing the peak in mixing ratio at 20-25 km, and the decrease with altitude in mixing ratio above 25 km. We also ran time dependent simulations of SF6 from which the model mean age of air values were derived. The oldest air (5.5 to 6 years) occurred in the high latitude upper stratosphere during fall and early winter of both hemispheres, and in the southern hemisphere lower stratosphere during late winter and early spring. The latitudinal gradient of the mean ages also compare well with ER-2 aircraft observations in the lower stratosphere.

  8. Platform for real-time simulation of dynamic systems and hardware-in-the-loop for control algorithms.

    PubMed

    de Souza, Isaac D T; Silva, Sergio N; Teles, Rafael M; Fernandes, Marcelo A C

    2014-10-15

    The development of new embedded algorithms for automation and control of industrial equipment usually requires the use of real-time testing. However, the equipment required is often expensive, which means that such tests are often not viable. The objective of this work was therefore to develop an embedded platform for the distributed real-time simulation of dynamic systems. This platform, called the Real-Time Simulator for Dynamic Systems (RTSDS), could be applied in both industrial and academic environments. In industrial applications, the RTSDS could be used to optimize embedded control algorithms. In the academic sphere, it could be used to support research into new embedded solutions for automation and control and could also be used as a tool to assist in undergraduate and postgraduate teaching related to the development of projects concerning on-board control systems.

  9. Platform for Real-Time Simulation of Dynamic Systems and Hardware-in-the-Loop for Control Algorithms

    PubMed Central

    de Souza, Isaac D. T.; Silva, Sergio N.; Teles, Rafael M.; Fernandes, Marcelo A. C.

    2014-01-01

    The development of new embedded algorithms for automation and control of industrial equipment usually requires the use of real-time testing. However, the equipment required is often expensive, which means that such tests are often not viable. The objective of this work was therefore to develop an embedded platform for the distributed real-time simulation of dynamic systems. This platform, called the Real-Time Simulator for Dynamic Systems (RTSDS), could be applied in both industrial and academic environments. In industrial applications, the RTSDS could be used to optimize embedded control algorithms. In the academic sphere, it could be used to support research into new embedded solutions for automation and control and could also be used as a tool to assist in undergraduate and postgraduate teaching related to the development of projects concerning on-board control systems. PMID:25320906

  10. [The application of non-annealing thermoluminescent dosimetry (TLD)].

    PubMed

    Wu, J M; Chen, C S; Lan, R H

    1993-06-01

    Conventional use of Thermoluminescence (TL) in radiation dosimetry is very time-consuming. It requires repeating the procedures of preheating and annealing. In an attempt to simplify these procedures, we conducted an experiment of non-annealing TL dosimetry. This article reports the experiment's results. We adopted Lithium Fluoride (LiF) chip (TLD-100) in polystyrene under the exposure of Co-60, and the result was taken by HAR-SHAW-4000 TL reading system. The TL response was analyzed, including linearity, reproducibility and fading test. Because non-annealing TL response was greatly influenced by residual electron, TLD calibration curves were separated into two parts: (1) high dose region (HDR, 50-1500 cGy); (2) low dose region (LDR, 0-50 cGy). When TL dosimeters were exposed to a single high does (about 500 cGy), the HDR could be reproduced within 3% and fit a good linearity. For LDR, we had to give up the tail of glow curve in the high temperature region. We could then get good linearity and reproducibility. Furthermore, fading of non-annealing was apparently larger than annealing. We could control the fading of non-annealing was apparently larger than annealing. We could control the fading influence within 1% by taking the TL reading one hour after exposure. On the other hand, a combination of photon and electron exposure was also performed by non-annealing TL dosimetry. The results were compatible with Co-60 exposure in the same system.

  11. Precision Laser Annealing of Focal Plane Arrays

    DOE Office of Scientific and Technical Information (OSTI.GOV)

    Bender, Daniel A.; DeRose, Christopher; Starbuck, Andrew Lea

    2015-09-01

    We present results from laser annealing experiments in Si using a passively Q-switched Nd:YAG microlaser. Exposure with laser at fluence values above the damage threshold of commercially available photodiodes results in electrical damage (as measured by an increase in photodiode dark current). We show that increasing the laser fluence to values in excess of the damage threshold can result in annealing of a damage site and a reduction in detector dark current by as much as 100x in some cases. A still further increase in fluence results in irreparable damage. Thus we demonstrate the presence of a laser annealing windowmore » over which performance of damaged detectors can be at least partially reconstituted. Moreover dark current reduction is observed over the entire operating range of the diode indicating that device performance has been improved for all values of reverse bias voltage. Additionally, we will present results of laser annealing in Si waveguides. By exposing a small (<10 um) length of a Si waveguide to an annealing laser pulse, the longitudinal phase of light acquired in propagating through the waveguide can be modified with high precision, <15 milliradian per laser pulse. Phase tuning by 180 degrees is exhibited with multiple exposures to one arm of a Mach-Zehnder interferometer at fluence values below the morphological damage threshold of an etched Si waveguide. No reduction in optical transmission at 1550 nm was found after 220 annealing laser shots. Modeling results for laser annealing in Si are also presented.« less

  12. Designing a practical system for spectral imaging of skylight.

    PubMed

    López-Alvarez, Miguel A; Hernández-Andrés, Javier; Romero, Javier; Lee, Raymond L

    2005-09-20

    In earlier work [J. Opt. Soc. Am. A 21, 13-23 (2004)], we showed that a combination of linear models and optimum Gaussian sensors obtained by an exhaustive search can recover daylight spectra reliably from broadband sensor data. Thus our algorithm and sensors could be used to design an accurate, relatively inexpensive system for spectral imaging of daylight. Here we improve our simulation of the multispectral system by (1) considering the different kinds of noise inherent in electronic devices such as change-coupled devices (CCDs) or complementary metal-oxide semiconductors (CMOS) and (2) extending our research to a different kind of natural illumination, skylight. Because exhaustive searches are expensive computationally, here we switch to a simulated annealing algorithm to define the optimum sensors for recovering skylight spectra. The annealing algorithm requires us to minimize a single cost function, and so we develop one that calculates both the spectral and colorimetric similarity of any pair of skylight spectra. We show that the simulated annealing algorithm yields results similar to the exhaustive search but with much less computational effort. Our technique lets us study the properties of optimum sensors in the presence of noise, one side effect of which is that adding more sensors may not improve the spectral recovery.

  13. Design of a genetic algorithm for the simulated evolution of a library of asymmetric transfer hydrogenation catalysts.

    PubMed

    Vriamont, Nicolas; Govaerts, Bernadette; Grenouillet, Pierre; de Bellefon, Claude; Riant, Olivier

    2009-06-15

    A library of catalysts was designed for asymmetric-hydrogen transfer to acetophenone. At first, the whole library was submitted to evaluation using high-throughput experiments (HTE). The catalysts were listed in ascending order, with respect to their performance, and best catalysts were identified. In the second step, various simulated evolution experiments, based on a genetic algorithm, were applied to this library. A small part of the library, called the mother generation (G0), thus evolved from generation to generation. The goal was to use our collection of HTE data to adjust the parameters of the genetic algorithm, in order to obtain a maximum of the best catalysts within a minimal number of generations. It was namely found that simulated evolution's results depended on the selection of G0 and that a random G0 should be preferred. We also demonstrated that it was possible to get 5 to 6 of the ten best catalysts while investigating only 10 % of the library. Moreover, we developed a double algorithm making this result still achievable if the evolution started with one of the worst G0.

  14. A modular tooling set-up for incremental sheet forming (ISF) with subsequent stress-relief annealing under partial constraints

    NASA Astrophysics Data System (ADS)

    Maqbool, Fawad; Bambach, Markus

    2017-10-01

    Incremental sheet forming (ISF) is a manufacturing process most suitable for small-batch production of sheet metal parts. In ISF, a CNC-controlled tool moves over the sheet metal, following a specified contour to form a part of the desired geometry. This study focuses on one of the dominant process limitations associated with the ISF, i.e., the limited geometrical accuracy. In this regard, a case study is performed which shows that increased geometrical accuracy of the formed part can be achieved by a using stress-relief annealing before unclamping. To keep the tooling costs low, a modular die design consisting of a stiff metal frame and inserts made from inexpensive plastics (Sika®) were devised. After forming, the plastics inserts are removed. The metal frame supports the part during stress-relief annealing. Finite Element (FE) simulations of the manufacturing process are performed. Due to the residual stresses induced during the forming, the geometry of the formed part, from FE simulation and the actual manufacturing process, shows severe distortion upon unclamping the part. Stress relief annealing of the formed part under partial constraints exerted by the tool frame shows that a part with high geometrical accuracy can be obtained.

  15. Optoelectronic analogs of self-programming neural nets - Architecture and methodologies for implementing fast stochastic learning by simulated annealing

    NASA Technical Reports Server (NTRS)

    Farhat, Nabil H.

    1987-01-01

    Self-organization and learning is a distinctive feature of neural nets and processors that sets them apart from conventional approaches to signal processing. It leads to self-programmability which alleviates the problem of programming complexity in artificial neural nets. In this paper architectures for partitioning an optoelectronic analog of a neural net into distinct layers with prescribed interconnectivity pattern to enable stochastic learning by simulated annealing in the context of a Boltzmann machine are presented. Stochastic learning is of interest because of its relevance to the role of noise in biological neural nets. Practical considerations and methodologies for appreciably accelerating stochastic learning in such a multilayered net are described. These include the use of parallel optical computing of the global energy of the net, the use of fast nonvolatile programmable spatial light modulators to realize fast plasticity, optical generation of random number arrays, and an adaptive noisy thresholding scheme that also makes stochastic learning more biologically plausible. The findings reported predict optoelectronic chips that can be used in the realization of optical learning machines.

  16. Solution of the hydrodynamic device model using high-order non-oscillatory shock capturing algorithms. [for junction diodes simulation

    NASA Technical Reports Server (NTRS)

    Fatemi, Emad; Osher, Stanley; Jerome, Joseph

    1991-01-01

    A micron n+ - n - n+ silicon diode is simulated via the hydrodynamic model for carrier transport. The numerical algorithms employed are for the non-steady case, and a limiting process is used to reach steady state. The simulation employs shock capturing algorithms, and indeed shocks, or very rapid transition regimes, are observed in the transient case for the coupled system, consisting of the potential equation and the conservation equations describing charge, momentum, and energy transfer for the electron carriers. These algorithms, termed essentially nonoscillatory, were successfully applied in other contexts to model the flow in gas dynamics, magnetohydrodynamics, and other physical situations involving the conservation laws in fluid mechanics. The method here is first order in time, but the use of small time steps allows for good accuracy. Runge-Kutta methods allow one to achieve higher accuracy in time if desired. The spatial accuracy is of high order in regions of smoothness.

  17. Solar Occultation Retrieval Algorithm Development

    NASA Technical Reports Server (NTRS)

    Lumpe, Jerry D.

    2004-01-01

    This effort addresses the comparison and validation of currently operational solar occultation retrieval algorithms, and the development of generalized algorithms for future application to multiple platforms. initial development of generalized forward model algorithms capable of simulating transmission data from of the POAM II/III and SAGE II/III instruments. Work in the 2" quarter will focus on: completion of forward model algorithms, including accurate spectral characteristics for all instruments, and comparison of simulated transmission data with actual level 1 instrument data for specific occultation events.

  18. Three-dimensional simulation and auto-stereoscopic 3D display of the battlefield environment based on the particle system algorithm

    NASA Astrophysics Data System (ADS)

    Ning, Jiwei; Sang, Xinzhu; Xing, Shujun; Cui, Huilong; Yan, Binbin; Yu, Chongxiu; Dou, Wenhua; Xiao, Liquan

    2016-10-01

    The army's combat training is very important now, and the simulation of the real battlefield environment is of great significance. Two-dimensional information has been unable to meet the demand at present. With the development of virtual reality technology, three-dimensional (3D) simulation of the battlefield environment is possible. In the simulation of 3D battlefield environment, in addition to the terrain, combat personnel and the combat tool ,the simulation of explosions, fire, smoke and other effects is also very important, since these effects can enhance senses of realism and immersion of the 3D scene. However, these special effects are irregular objects, which make it difficult to simulate with the general geometry. Therefore, the simulation of irregular objects is always a hot and difficult research topic in computer graphics. Here, the particle system algorithm is used for simulating irregular objects. We design the simulation of the explosion, fire, smoke based on the particle system and applied it to the battlefield 3D scene. Besides, the battlefield 3D scene simulation with the glasses-free 3D display is carried out with an algorithm based on GPU 4K super-multiview 3D video real-time transformation method. At the same time, with the human-computer interaction function, we ultimately realized glasses-free 3D display of the simulated more realistic and immersed 3D battlefield environment.

  19. Annealing Reduces Free Volumes In Thermoplastics

    NASA Technical Reports Server (NTRS)

    Singh, Jag J.; St. Clair, Terry L.

    1988-01-01

    Investigation conducted to determine free volumes and water-absorption characteristics of two types of thermoplastic polymide as functions of annealing histories. Reductions reach asymptotic values after several annealing cycles. High-temperature thermoplastics excellent candidates for use in aerospace applications. Graphite-fiber composites containing thermoplastic matrices have wide applicability.

  20. Quantum annealing correction with minor embedding

    NASA Astrophysics Data System (ADS)

    Vinci, Walter; Albash, Tameem; Paz-Silva, Gerardo; Hen, Itay; Lidar, Daniel A.

    2015-10-01

    Quantum annealing provides a promising route for the development of quantum optimization devices, but the usefulness of such devices will be limited in part by the range of implementable problems as dictated by hardware constraints. To overcome constraints imposed by restricted connectivity between qubits, a larger set of interactions can be approximated using minor embedding techniques whereby several physical qubits are used to represent a single logical qubit. However, minor embedding introduces new types of errors due to its approximate nature. We introduce and study quantum annealing correction schemes designed to improve the performance of quantum annealers in conjunction with minor embedding, thus leading to a hybrid scheme defined over an encoded graph. We argue that this scheme can be efficiently decoded using an energy minimization technique provided the density of errors does not exceed the per-site percolation threshold of the encoded graph. We test the hybrid scheme using a D-Wave Two processor on problems for which the encoded graph is a two-level grid and the Ising model is known to be NP-hard. The problems we consider are frustrated Ising model problem instances with "planted" (a priori known) solutions. Applied in conjunction with optimized energy penalties and decoding techniques, we find that this approach enables the quantum annealer to solve minor embedded instances with significantly higher success probability than it would without error correction. Our work demonstrates that quantum annealing correction can and should be used to improve the robustness of quantum annealing not only for natively embeddable problems but also when minor embedding is used to extend the connectivity of physical devices.