Flight Testing of the Space Launch System (SLS) Adaptive Augmenting Control (AAC) Algorithm on an F/A-18

NASA Technical Reports Server (NTRS)

Dennehy, Cornelius J.; VanZwieten, Tannen S.; Hanson, Curtis E.; Wall, John H.; Miller, Chris J.; Gilligan, Eric T.; Orr, Jeb S.

2014-01-01

The Marshall Space Flight Center (MSFC) Flight Mechanics and Analysis Division developed an adaptive augmenting control (AAC) algorithm for launch vehicles that improves robustness and performance on an as-needed basis by adapting a classical control algorithm to unexpected environments or variations in vehicle dynamics. This was baselined as part of the Space Launch System (SLS) flight control system. The NASA Engineering and Safety Center (NESC) was asked to partner with the SLS Program and the Space Technology Mission Directorate (STMD) Game Changing Development Program (GCDP) to flight test the AAC algorithm on a manned aircraft that can achieve a high level of dynamic similarity to a launch vehicle and raise the technology readiness of the algorithm early in the program. This document reports the outcome of the NESC assessment.

Hybrid Differential Dynamic Programming with Stochastic Search

NASA Technical Reports Server (NTRS)

Aziz, Jonathan; Parker, Jeffrey; Englander, Jacob

2016-01-01

Differential dynamic programming (DDP) has been demonstrated as a viable approach to low-thrust trajectory optimization, namely with the recent success of NASAs Dawn mission. The Dawn trajectory was designed with the DDP-based Static Dynamic Optimal Control algorithm used in the Mystic software. Another recently developed method, Hybrid Differential Dynamic Programming (HDDP) is a variant of the standard DDP formulation that leverages both first-order and second-order state transition matrices in addition to nonlinear programming (NLP) techniques. Areas of improvement over standard DDP include constraint handling, convergence properties, continuous dynamics, and multi-phase capability. DDP is a gradient based method and will converge to a solution nearby an initial guess. In this study, monotonic basin hopping (MBH) is employed as a stochastic search method to overcome this limitation, by augmenting the HDDP algorithm for a wider search of the solution space.

An algorithm for the solution of dynamic linear programs

NASA Technical Reports Server (NTRS)

Psiaki, Mark L.

1989-01-01

The algorithm's objective is to efficiently solve Dynamic Linear Programs (DLP) by taking advantage of their special staircase structure. This algorithm constitutes a stepping stone to an improved algorithm for solving Dynamic Quadratic Programs, which, in turn, would make the nonlinear programming method of Successive Quadratic Programs more practical for solving trajectory optimization problems. The ultimate goal is to being trajectory optimization solution speeds into the realm of real-time control. The algorithm exploits the staircase nature of the large constraint matrix of the equality-constrained DLPs encountered when solving inequality-constrained DLPs by an active set approach. A numerically-stable, staircase QL factorization of the staircase constraint matrix is carried out starting from its last rows and columns. The resulting recursion is like the time-varying Riccati equation from multi-stage LQR theory. The resulting factorization increases the efficiency of all of the typical LP solution operations over that of a dense matrix LP code. At the same time numerical stability is ensured. The algorithm also takes advantage of dynamic programming ideas about the cost-to-go by relaxing active pseudo constraints in a backwards sweeping process. This further decreases the cost per update of the LP rank-1 updating procedure, although it may result in more changes of the active set that if pseudo constraints were relaxed in a non-stagewise fashion. The usual stability of closed-loop Linear/Quadratic optimally-controlled systems, if it carries over to strictly linear cost functions, implies that the saving due to reduced factor update effort may outweigh the cost of an increased number of updates. An aerospace example is presented in which a ground-to-ground rocket's distance is maximized. This example demonstrates the applicability of this class of algorithms to aerospace guidance. It also sheds light on the efficacy of the proposed pseudo constraint relaxation scheme.

A dynamic programming-based particle swarm optimization algorithm for an inventory management problem under uncertainty

NASA Astrophysics Data System (ADS)

Xu, Jiuping; Zeng, Ziqiang; Han, Bernard; Lei, Xiao

2013-07-01

This article presents a dynamic programming-based particle swarm optimization (DP-based PSO) algorithm for solving an inventory management problem for large-scale construction projects under a fuzzy random environment. By taking into account the purchasing behaviour and strategy under rules of international bidding, a multi-objective fuzzy random dynamic programming model is constructed. To deal with the uncertainties, a hybrid crisp approach is used to transform fuzzy random parameters into fuzzy variables that are subsequently defuzzified by using an expected value operator with optimistic-pessimistic index. The iterative nature of the authors' model motivates them to develop a DP-based PSO algorithm. More specifically, their approach treats the state variables as hidden parameters. This in turn eliminates many redundant feasibility checks during initialization and particle updates at each iteration. Results and sensitivity analysis are presented to highlight the performance of the authors' optimization method, which is very effective as compared to the standard PSO algorithm.

Hybrid Differential Dynamic Programming with Stochastic Search

NASA Technical Reports Server (NTRS)

Aziz, Jonathan; Parker, Jeffrey; Englander, Jacob A.

2016-01-01

Differential dynamic programming (DDP) has been demonstrated as a viable approach to low-thrust trajectory optimization, namely with the recent success of NASA's Dawn mission. The Dawn trajectory was designed with the DDP-based Static/Dynamic Optimal Control algorithm used in the Mystic software.1 Another recently developed method, Hybrid Differential Dynamic Programming (HDDP),2, 3 is a variant of the standard DDP formulation that leverages both first-order and second-order state transition matrices in addition to nonlinear programming (NLP) techniques. Areas of improvement over standard DDP include constraint handling, convergence properties, continuous dynamics, and multi-phase capability. DDP is a gradient based method and will converge to a solution nearby an initial guess. In this study, monotonic basin hopping (MBH) is employed as a stochastic search method to overcome this limitation, by augmenting the HDDP algorithm for a wider search of the solution space.

Joint Chance-Constrained Dynamic Programming

NASA Technical Reports Server (NTRS)

Ono, Masahiro; Kuwata, Yoshiaki; Balaram, J. Bob

2012-01-01

This paper presents a novel dynamic programming algorithm with a joint chance constraint, which explicitly bounds the risk of failure in order to maintain the state within a specified feasible region. A joint chance constraint cannot be handled by existing constrained dynamic programming approaches since their application is limited to constraints in the same form as the cost function, that is, an expectation over a sum of one-stage costs. We overcome this challenge by reformulating the joint chance constraint into a constraint on an expectation over a sum of indicator functions, which can be incorporated into the cost function by dualizing the optimization problem. As a result, the primal variables can be optimized by a standard dynamic programming, while the dual variable is optimized by a root-finding algorithm that converges exponentially. Error bounds on the primal and dual objective values are rigorously derived. We demonstrate the algorithm on a path planning problem, as well as an optimal control problem for Mars entry, descent and landing. The simulations are conducted using a real terrain data of Mars, with four million discrete states at each time step.

Optimal tracking control for a class of nonlinear discrete-time systems with time delays based on heuristic dynamic programming.

PubMed

Zhang, Huaguang; Song, Ruizhuo; Wei, Qinglai; Zhang, Tieyan

2011-12-01

In this paper, a novel heuristic dynamic programming (HDP) iteration algorithm is proposed to solve the optimal tracking control problem for a class of nonlinear discrete-time systems with time delays. The novel algorithm contains state updating, control policy iteration, and performance index iteration. To get the optimal states, the states are also updated. Furthermore, the "backward iteration" is applied to state updating. Two neural networks are used to approximate the performance index function and compute the optimal control policy for facilitating the implementation of HDP iteration algorithm. At last, we present two examples to demonstrate the effectiveness of the proposed HDP iteration algorithm.

Region of attraction analysis for nonlinear vehicle lateral dynamics using sum-of-squares programming

NASA Astrophysics Data System (ADS)

Imani Masouleh, Mehdi; Limebeer, David J. N.

2018-07-01

In this study we will estimate the region of attraction (RoA) of the lateral dynamics of a nonlinear single-track vehicle model. The tyre forces are approximated using rational functions that are shown to capture the nonlinearities of tyre curves significantly better than polynomial functions. An existing sum-of-squares (SOS) programming algorithm for estimating regions of attraction is extended to accommodate the use of rational vector fields. This algorithm is then used to find an estimate of the RoA of the vehicle lateral dynamics. The influence of vehicle parameters and driving conditions on the stability region are studied. It is shown that SOS programming techniques can be used to approximate the stability region without resorting to numerical integration. The RoA estimate from the SOS algorithm is compared to the existing results in the literature. The proposed method is shown to obtain significantly better RoA estimates.

MyDTW - Dynamic Time Warping program for stratigraphical time series

NASA Astrophysics Data System (ADS)

Kotov, Sergey; Paelike, Heiko

2017-04-01

One of the general tasks in many geological disciplines is matching of one time or space signal to another. It can be classical correlation between two cores or cross-sections in sedimentology or marine geology. For example, tuning a paleoclimatic signal to a target curve, driven by variations in the astronomical parameters, is a powerful technique to construct accurate time scales. However, these methods can be rather time-consuming and can take ours of routine work even with the help of special semi-automatic software. Therefore, different approaches to automate the processes have been developed during last decades. Some of them are based on classical statistical cross-correlations such as the 'Correlator' after Olea [1]. Another ones use modern ideas of dynamic programming. A good example is as an algorithm developed by Lisiecki and Lisiecki [2] or dynamic time warping based algorithm after Pälike [3]. We introduce here an algorithm and computer program, which are also stemmed from the Dynamic Time Warping algorithm class. Unlike the algorithm of Lisiecki and Lisiecki, MyDTW does not lean on a set of penalties to follow geological logics, but on a special internal structure and specific constrains. It differs also from [3] in basic ideas of implementation and constrains design. The algorithm is implemented as a computer program with a graphical user interface using Free Pascal and Lazarus IDE and available for Windows, Mac OS, and Linux. Examples with synthetic and real data are demonstrated. Program is available for free download at http://www.marum.de/Sergey_Kotov.html . References: 1. Olea, R.A. Expert systems for automated correlation and interpretation of wireline logs // Math Geol (1994) 26: 879. doi:10.1007/BF02083420 2. Lisiecki L. and Lisiecki P. Application of dynamic programming to the correlation of paleoclimate records // Paleoceanography (2002), Volume 17, Issue 4, pp. 1-1, CiteID 1049, doi: 10.1029/2001PA000733 3. Pälike, H. Extending the astronomical calibration of the Geological Time Scale PhD thesis, University of Cambridge, (2002)

Discrete-Time Local Value Iteration Adaptive Dynamic Programming: Admissibility and Termination Analysis.

PubMed

Wei, Qinglai; Liu, Derong; Lin, Qiao

In this paper, a novel local value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. The focuses of this paper are to study admissibility properties and the termination criteria of discrete-time local value iteration ADP algorithms. In the discrete-time local value iteration ADP algorithm, the iterative value functions and the iterative control laws are both updated in a given subset of the state space in each iteration, instead of the whole state space. For the first time, admissibility properties of iterative control laws are analyzed for the local value iteration ADP algorithm. New termination criteria are established, which terminate the iterative local ADP algorithm with an admissible approximate optimal control law. Finally, simulation results are given to illustrate the performance of the developed algorithm.In this paper, a novel local value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon optimal control problems for discrete-time nonlinear systems. The focuses of this paper are to study admissibility properties and the termination criteria of discrete-time local value iteration ADP algorithms. In the discrete-time local value iteration ADP algorithm, the iterative value functions and the iterative control laws are both updated in a given subset of the state space in each iteration, instead of the whole state space. For the first time, admissibility properties of iterative control laws are analyzed for the local value iteration ADP algorithm. New termination criteria are established, which terminate the iterative local ADP algorithm with an admissible approximate optimal control law. Finally, simulation results are given to illustrate the performance of the developed algorithm.

On program restructuring, scheduling, and communication for parallel processor systems

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

Polychronopoulos, Constantine D.

1986-08-01

This dissertation discusses several software and hardware aspects of program execution on large-scale, high-performance parallel processor systems. The issues covered are program restructuring, partitioning, scheduling and interprocessor communication, synchronization, and hardware design issues of specialized units. All this work was performed focusing on a single goal: to maximize program speedup, or equivalently, to minimize parallel execution time. Parafrase, a Fortran restructuring compiler was used to transform programs in a parallel form and conduct experiments. Two new program restructuring techniques are presented, loop coalescing and subscript blocking. Compile-time and run-time scheduling schemes are covered extensively. Depending on the program construct, thesemore » algorithms generate optimal or near-optimal schedules. For the case of arbitrarily nested hybrid loops, two optimal scheduling algorithms for dynamic and static scheduling are presented. Simulation results are given for a new dynamic scheduling algorithm. The performance of this algorithm is compared to that of self-scheduling. Techniques for program partitioning and minimization of interprocessor communication for idealized program models and for real Fortran programs are also discussed. The close relationship between scheduling, interprocessor communication, and synchronization becomes apparent at several points in this work. Finally, the impact of various types of overhead on program speedup and experimental results are presented.« less

Concurrent extensions to the FORTRAN language for parallel programming of computational fluid dynamics algorithms

NASA Technical Reports Server (NTRS)

Weeks, Cindy Lou

1986-01-01

Experiments were conducted at NASA Ames Research Center to define multi-tasking software requirements for multiple-instruction, multiple-data stream (MIMD) computer architectures. The focus was on specifying solutions for algorithms in the field of computational fluid dynamics (CFD). The program objectives were to allow researchers to produce usable parallel application software as soon as possible after acquiring MIMD computer equipment, to provide researchers with an easy-to-learn and easy-to-use parallel software language which could be implemented on several different MIMD machines, and to enable researchers to list preferred design specifications for future MIMD computer architectures. Analysis of CFD algorithms indicated that extensions of an existing programming language, adaptable to new computer architectures, provided the best solution to meeting program objectives. The CoFORTRAN Language was written in response to these objectives and to provide researchers a means to experiment with parallel software solutions to CFD algorithms on machines with parallel architectures.

DEGAS: Dynamic Exascale Global Address Space Programming Environments

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

Demmel, James

The Dynamic, Exascale Global Address Space programming environment (DEGAS) project will develop the next generation of programming models and runtime systems to meet the challenges of Exascale computing. The Berkeley part of the project concentrated on communication-optimal code generation to optimize speed and energy efficiency by reducing data movement. Our work developed communication lower bounds, and/or communication avoiding algorithms (that either meet the lower bound, or do much less communication than their conventional counterparts) for a variety of algorithms, including linear algebra, machine learning and genomics. The Berkeley part of the project concentrated on communication-optimal code generation to optimize speedmore » and energy efficiency by reducing data movement. Our work developed communication lower bounds, and/or communication avoiding algorithms (that either meet the lower bound, or do much less communication than their conventional counterparts) for a variety of algorithms, including linear algebra, machine learning and genomics.« less

Dynamic programming on a shared-memory multiprocessor

NASA Technical Reports Server (NTRS)

Edmonds, Phil; Chu, Eleanor; George, Alan

1993-01-01

Three new algorithms for solving dynamic programming problems on a shared-memory parallel computer are described. All three algorithms attempt to balance work load, while keeping synchronization cost low. In particular, for a multiprocessor having p processors, an analysis of the best algorithm shows that the arithmetic cost is O(n-cubed/6p) and that the synchronization cost is O(absolute value of log sub C n) if p much less than n, where C = (2p-1)/(2p + 1) and n is the size of the problem. The low synchronization cost is important for machines where synchronization is expensive. Analysis and experiments show that the best algorithm is effective in balancing the work load and producing high efficiency.

Computational Workbench for Multibody Dynamics

NASA Technical Reports Server (NTRS)

Edmonds, Karina

2007-01-01

PyCraft is a computer program that provides an interactive, workbenchlike computing environment for developing and testing algorithms for multibody dynamics. Examples of multibody dynamic systems amenable to analysis with the help of PyCraft include land vehicles, spacecraft, robots, and molecular models. PyCraft is based on the Spatial-Operator- Algebra (SOA) formulation for multibody dynamics. The SOA operators enable construction of simple and compact representations of complex multibody dynamical equations. Within the Py-Craft computational workbench, users can, essentially, use the high-level SOA operator notation to represent the variety of dynamical quantities and algorithms and to perform computations interactively. PyCraft provides a Python-language interface to underlying C++ code. Working with SOA concepts, a user can create and manipulate Python-level operator classes in order to implement and evaluate new dynamical quantities and algorithms. During use of PyCraft, virtually all SOA-based algorithms are available for computational experiments.

Space shuttle propulsion estimation development verification, volume 1

NASA Technical Reports Server (NTRS)

Rogers, Robert M.

1989-01-01

The results of the Propulsion Estimation Development Verification are summarized. A computer program developed under a previous contract (NAS8-35324) was modified to include improved models for the Solid Rocket Booster (SRB) internal ballistics, the Space Shuttle Main Engine (SSME) power coefficient model, the vehicle dynamics using quaternions, and an improved Kalman filter algorithm based on the U-D factorized algorithm. As additional output, the estimated propulsion performances, for each device are computed with the associated 1-sigma bounds. The outputs of the estimation program are provided in graphical plots. An additional effort was expended to examine the use of the estimation approach to evaluate single engine test data. In addition to the propulsion estimation program PFILTER, a program was developed to produce a best estimate of trajectory (BET). The program LFILTER, also uses the U-D factorized algorithm form of the Kalman filter as in the propulsion estimation program PFILTER. The necessary definitions and equations explaining the Kalman filtering approach for the PFILTER program, the models used for this application for dynamics and measurements, program description, and program operation are presented.

Optimization algorithms for large-scale multireservoir hydropower systems

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

Hiew, K.L.

Five optimization algorithms were vigorously evaluated based on applications on a hypothetical five-reservoir hydropower system. These algorithms are incremental dynamic programming (IDP), successive linear programing (SLP), feasible direction method (FDM), optimal control theory (OCT) and objective-space dynamic programming (OSDP). The performance of these algorithms were comparatively evaluated using unbiased, objective criteria which include accuracy of results, rate of convergence, smoothness of resulting storage and release trajectories, computer time and memory requirements, robustness and other pertinent secondary considerations. Results have shown that all the algorithms, with the exception of OSDP converge to optimum objective values within 1.0% difference from one another.more » The highest objective value is obtained by IDP, followed closely by OCT. Computer time required by these algorithms, however, differ by more than two orders of magnitude, ranging from 10 seconds in the case of OCT to a maximum of about 2000 seconds for IDP. With a well-designed penalty scheme to deal with state-space constraints, OCT proves to be the most-efficient algorithm based on its overall performance. SLP, FDM, and OCT were applied to the case study of Mahaweli project, a ten-powerplant system in Sri Lanka.« less

Fast Fourier Transform algorithm design and tradeoffs

NASA Technical Reports Server (NTRS)

Kamin, Ray A., III; Adams, George B., III

1988-01-01

The Fast Fourier Transform (FFT) is a mainstay of certain numerical techniques for solving fluid dynamics problems. The Connection Machine CM-2 is the target for an investigation into the design of multidimensional Single Instruction Stream/Multiple Data (SIMD) parallel FFT algorithms for high performance. Critical algorithm design issues are discussed, necessary machine performance measurements are identified and made, and the performance of the developed FFT programs are measured. Fast Fourier Transform programs are compared to the currently best Cray-2 FFT program.

Javascript Library for Developing Interactive Micro-Level Animations for Teaching and Learning Algorithms on One-Dimensional Arrays

ERIC Educational Resources Information Center

Végh, Ladislav

2016-01-01

The first data structure that first-year undergraduate students learn during the programming and algorithms courses is the one-dimensional array. For novice programmers, it might be hard to understand different algorithms on arrays (e.g. searching, mirroring, sorting algorithms), because the algorithms dynamically change the values of elements. In…

Dynamic programming algorithms for biological sequence comparison.

PubMed

Pearson, W R; Miller, W

1992-01-01

Efficient dynamic programming algorithms are available for a broad class of protein and DNA sequence comparison problems. These algorithms require computer time proportional to the product of the lengths of the two sequences being compared [O(N2)] but require memory space proportional only to the sum of these lengths [O(N)]. Although the requirement for O(N2) time limits use of the algorithms to the largest computers when searching protein and DNA sequence databases, many other applications of these algorithms, such as calculation of distances for evolutionary trees and comparison of a new sequence to a library of sequence profiles, are well within the capabilities of desktop computers. In particular, the results of library searches with rapid searching programs, such as FASTA or BLAST, should be confirmed by performing a rigorous optimal alignment. Whereas rapid methods do not overlook significant sequence similarities, FASTA limits the number of gaps that can be inserted into an alignment, so that a rigorous alignment may extend the alignment substantially in some cases. BLAST does not allow gaps in the local regions that it reports; a calculation that allows gaps is very likely to extend the alignment substantially. Although a Monte Carlo evaluation of the statistical significance of a similarity score with a rigorous algorithm is much slower than the heuristic approach used by the RDF2 program, the dynamic programming approach should take less than 1 hr on a 386-based PC or desktop Unix workstation. For descriptive purposes, we have limited our discussion to methods for calculating similarity scores and distances that use gap penalties of the form g = rk. Nevertheless, programs for the more general case (g = q+rk) are readily available. Versions of these programs that run either on Unix workstations, IBM-PC class computers, or the Macintosh can be obtained from either of the authors.

Policy Gradient Adaptive Dynamic Programming for Data-Based Optimal Control.

PubMed

Luo, Biao; Liu, Derong; Wu, Huai-Ning; Wang, Ding; Lewis, Frank L

2017-10-01

The model-free optimal control problem of general discrete-time nonlinear systems is considered in this paper, and a data-based policy gradient adaptive dynamic programming (PGADP) algorithm is developed to design an adaptive optimal controller method. By using offline and online data rather than the mathematical system model, the PGADP algorithm improves control policy with a gradient descent scheme. The convergence of the PGADP algorithm is proved by demonstrating that the constructed Q -function sequence converges to the optimal Q -function. Based on the PGADP algorithm, the adaptive control method is developed with an actor-critic structure and the method of weighted residuals. Its convergence properties are analyzed, where the approximate Q -function converges to its optimum. Computer simulation results demonstrate the effectiveness of the PGADP-based adaptive control method.

Error bounds of adaptive dynamic programming algorithms for solving undiscounted optimal control problems.

PubMed

Liu, Derong; Li, Hongliang; Wang, Ding

2015-06-01

In this paper, we establish error bounds of adaptive dynamic programming algorithms for solving undiscounted infinite-horizon optimal control problems of discrete-time deterministic nonlinear systems. We consider approximation errors in the update equations of both value function and control policy. We utilize a new assumption instead of the contraction assumption in discounted optimal control problems. We establish the error bounds for approximate value iteration based on a new error condition. Furthermore, we also establish the error bounds for approximate policy iteration and approximate optimistic policy iteration algorithms. It is shown that the iterative approximate value function can converge to a finite neighborhood of the optimal value function under some conditions. To implement the developed algorithms, critic and action neural networks are used to approximate the value function and control policy, respectively. Finally, a simulation example is given to demonstrate the effectiveness of the developed algorithms.

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.

Algorithms and software for nonlinear structural dynamics

NASA Technical Reports Server (NTRS)

Belytschko, Ted; Gilbertsen, Noreen D.; Neal, Mark O.

1989-01-01

The objective of this research is to develop efficient methods for explicit time integration in nonlinear structural dynamics for computers which utilize both concurrency and vectorization. As a framework for these studies, the program WHAMS, which is described in Explicit Algorithms for the Nonlinear Dynamics of Shells (T. Belytschko, J. I. Lin, and C.-S. Tsay, Computer Methods in Applied Mechanics and Engineering, Vol. 42, 1984, pp 225 to 251), is used. There are two factors which make the development of efficient concurrent explicit time integration programs a challenge in a structural dynamics program: (1) the need for a variety of element types, which complicates the scheduling-allocation problem; and (2) the need for different time steps in different parts of the mesh, which is here called mixed delta t integration, so that a few stiff elements do not reduce the time steps throughout the mesh.

Dynamic Programming and Transitive Closure on Linear Pipelines.

DTIC Science & Technology

1984-05-01

four partitions. 2.0 - 1.9 1.0t N. N 3N N -8 4 24 Figure41 An ideal solution to small problem sizes is to design an algorithm on an array where the...12 References [1] A.V. Aho, J. Hopcroft, and J.D. Ullman. The Design and Analysis of Computer Algorithms, Addison-Wesley, (1974). - " [2] R. Aubusson...K.E. Batcher, " Design of a Massively Parallel Processor," IEEE-TC, Vol. C-9, No. 9, (September, 1980), pp. 83-840. [4] K.Q. Brown, "Dynamic Programming

Ascent guidance algorithm using lidar wind measurements

NASA Technical Reports Server (NTRS)

Cramer, Evin J.; Bradt, Jerre E.; Hardtla, John W.

1990-01-01

The formulation of a general nonlinear programming guidance algorithm that incorporates wind measurements in the computation of ascent guidance steering commands is discussed. A nonlinear programming (NLP) algorithm that is designed to solve a very general problem has the potential to address the diversity demanded by future launch systems. Using B-splines for the command functional form allows the NLP algorithm to adjust the shape of the command profile to achieve optimal performance. The algorithm flexibility is demonstrated by simulation of ascent with dynamic loading constraints through a set of random wind profiles with and without wind sensing capability.

Phi-s correlation and dynamic time warping - Two methods for tracking ice floes in SAR images

NASA Technical Reports Server (NTRS)

Mcconnell, Ross; Kober, Wolfgang; Kwok, Ronald; Curlander, John C.; Pang, Shirley S.

1991-01-01

The authors present two algorithms for performing shape matching on ice floe boundaries in SAR (synthetic aperture radar) images. These algorithms quickly produce a set of ice motion and rotation vectors that can be used to guide a pixel value correlator. The algorithms match a shape descriptor known as the Phi-s curve. The first algorithm uses normalized correlation to match the Phi-s curves, while the second uses dynamic programming to compute an elastic match that better accommodates ice floe deformation. Some empirical data on the performance of the algorithms on Seasat SAR images are presented.

A dynamically adaptive multigrid algorithm for the incompressible Navier-Stokes equations: Validation and model problems

NASA Technical Reports Server (NTRS)

Thompson, C. P.; Leaf, G. K.; Vanrosendale, J.

1991-01-01

An algorithm is described for the solution of the laminar, incompressible Navier-Stokes equations. The basic algorithm is a multigrid based on a robust, box-based smoothing step. Its most important feature is the incorporation of automatic, dynamic mesh refinement. This algorithm supports generalized simple domains. The program is based on a standard staggered-grid formulation of the Navier-Stokes equations for robustness and efficiency. Special grid transfer operators were introduced at grid interfaces in the multigrid algorithm to ensure discrete mass conservation. Results are presented for three models: the driven-cavity, a backward-facing step, and a sudden expansion/contraction.

Space station dynamics, attitude control and momentum management

NASA Technical Reports Server (NTRS)

Sunkel, John W.; Singh, Ramen P.; Vengopal, Ravi

1989-01-01

The Space Station Attitude Control System software test-bed provides a rigorous environment for the design, development and functional verification of GN and C algorithms and software. The approach taken for the simulation of the vehicle dynamics and environmental models using a computationally efficient algorithm is discussed. The simulation includes capabilities for docking/berthing dynamics, prescribed motion dynamics associated with the Mobile Remote Manipulator System (MRMS) and microgravity disturbances. The vehicle dynamics module interfaces with the test-bed through the central Communicator facility which is in turn driven by the Station Control Simulator (SCS) Executive. The Communicator addresses issues such as the interface between the discrete flight software and the continuous vehicle dynamics, and multi-programming aspects such as the complex flow of control in real-time programs. Combined with the flight software and redundancy management modules, the facility provides a flexible, user-oriented simulation platform.

Spatial cluster detection using dynamic programming.

PubMed

Sverchkov, Yuriy; Jiang, Xia; Cooper, Gregory F

2012-03-25

The task of spatial cluster detection involves finding spatial regions where some property deviates from the norm or the expected value. In a probabilistic setting this task can be expressed as finding a region where some event is significantly more likely than usual. Spatial cluster detection is of interest in fields such as biosurveillance, mining of astronomical data, military surveillance, and analysis of fMRI images. In almost all such applications we are interested both in the question of whether a cluster exists in the data, and if it exists, we are interested in finding the most accurate characterization of the cluster. We present a general dynamic programming algorithm for grid-based spatial cluster detection. The algorithm can be used for both Bayesian maximum a-posteriori (MAP) estimation of the most likely spatial distribution of clusters and Bayesian model averaging over a large space of spatial cluster distributions to compute the posterior probability of an unusual spatial clustering. The algorithm is explained and evaluated in the context of a biosurveillance application, specifically the detection and identification of Influenza outbreaks based on emergency department visits. A relatively simple underlying model is constructed for the purpose of evaluating the algorithm, and the algorithm is evaluated using the model and semi-synthetic test data. When compared to baseline methods, tests indicate that the new algorithm can improve MAP estimates under certain conditions: the greedy algorithm we compared our method to was found to be more sensitive to smaller outbreaks, while as the size of the outbreaks increases, in terms of area affected and proportion of individuals affected, our method overtakes the greedy algorithm in spatial precision and recall. The new algorithm performs on-par with baseline methods in the task of Bayesian model averaging. We conclude that the dynamic programming algorithm performs on-par with other available methods for spatial cluster detection and point to its low computational cost and extendability as advantages in favor of further research and use of the algorithm.

Spatial cluster detection using dynamic programming

PubMed Central

2012-01-01

Background The task of spatial cluster detection involves finding spatial regions where some property deviates from the norm or the expected value. In a probabilistic setting this task can be expressed as finding a region where some event is significantly more likely than usual. Spatial cluster detection is of interest in fields such as biosurveillance, mining of astronomical data, military surveillance, and analysis of fMRI images. In almost all such applications we are interested both in the question of whether a cluster exists in the data, and if it exists, we are interested in finding the most accurate characterization of the cluster. Methods We present a general dynamic programming algorithm for grid-based spatial cluster detection. The algorithm can be used for both Bayesian maximum a-posteriori (MAP) estimation of the most likely spatial distribution of clusters and Bayesian model averaging over a large space of spatial cluster distributions to compute the posterior probability of an unusual spatial clustering. The algorithm is explained and evaluated in the context of a biosurveillance application, specifically the detection and identification of Influenza outbreaks based on emergency department visits. A relatively simple underlying model is constructed for the purpose of evaluating the algorithm, and the algorithm is evaluated using the model and semi-synthetic test data. Results When compared to baseline methods, tests indicate that the new algorithm can improve MAP estimates under certain conditions: the greedy algorithm we compared our method to was found to be more sensitive to smaller outbreaks, while as the size of the outbreaks increases, in terms of area affected and proportion of individuals affected, our method overtakes the greedy algorithm in spatial precision and recall. The new algorithm performs on-par with baseline methods in the task of Bayesian model averaging. Conclusions We conclude that the dynamic programming algorithm performs on-par with other available methods for spatial cluster detection and point to its low computational cost and extendability as advantages in favor of further research and use of the algorithm. PMID:22443103

Dynamic Optimization

NASA Technical Reports Server (NTRS)

Laird, Philip

1992-01-01

We distinguish static and dynamic optimization of programs: whereas static optimization modifies a program before runtime and is based only on its syntactical structure, dynamic optimization is based on the statistical properties of the input source and examples of program execution. Explanation-based generalization is a commonly used dynamic optimization method, but its effectiveness as a speedup-learning method is limited, in part because it fails to separate the learning process from the program transformation process. This paper describes a dynamic optimization technique called a learn-optimize cycle that first uses a learning element to uncover predictable patterns in the program execution and then uses an optimization algorithm to map these patterns into beneficial transformations. The technique has been used successfully for dynamic optimization of pure Prolog.

Semantic Edge Based Disparity Estimation Using Adaptive Dynamic Programming for Binocular Sensors

PubMed Central

Zhu, Dongchen; Li, Jiamao; Wang, Xianshun; Peng, Jingquan; Shi, Wenjun; Zhang, Xiaolin

2018-01-01

Disparity calculation is crucial for binocular sensor ranging. The disparity estimation based on edges is an important branch in the research of sparse stereo matching and plays an important role in visual navigation. In this paper, we propose a robust sparse stereo matching method based on the semantic edges. Some simple matching costs are used first, and then a novel adaptive dynamic programming algorithm is proposed to obtain optimal solutions. This algorithm makes use of the disparity or semantic consistency constraint between the stereo images to adaptively search parameters, which can improve the robustness of our method. The proposed method is compared quantitatively and qualitatively with the traditional dynamic programming method, some dense stereo matching methods, and the advanced edge-based method respectively. Experiments show that our method can provide superior performance on the above comparison. PMID:29614028

Semantic Edge Based Disparity Estimation Using Adaptive Dynamic Programming for Binocular Sensors.

PubMed

Zhu, Dongchen; Li, Jiamao; Wang, Xianshun; Peng, Jingquan; Shi, Wenjun; Zhang, Xiaolin

2018-04-03

Disparity calculation is crucial for binocular sensor ranging. The disparity estimation based on edges is an important branch in the research of sparse stereo matching and plays an important role in visual navigation. In this paper, we propose a robust sparse stereo matching method based on the semantic edges. Some simple matching costs are used first, and then a novel adaptive dynamic programming algorithm is proposed to obtain optimal solutions. This algorithm makes use of the disparity or semantic consistency constraint between the stereo images to adaptively search parameters, which can improve the robustness of our method. The proposed method is compared quantitatively and qualitatively with the traditional dynamic programming method, some dense stereo matching methods, and the advanced edge-based method respectively. Experiments show that our method can provide superior performance on the above comparison.

Stereo Image Dense Matching by Integrating Sift and Sgm Algorithm

NASA Astrophysics Data System (ADS)

Zhou, Y.; Song, Y.; Lu, J.

2018-05-01

Semi-global matching(SGM) performs the dynamic programming by treating the different path directions equally. It does not consider the impact of different path directions on cost aggregation, and with the expansion of the disparity search range, the accuracy and efficiency of the algorithm drastically decrease. This paper presents a dense matching algorithm by integrating SIFT and SGM. It takes the successful matching pairs matched by SIFT as control points to direct the path in dynamic programming with truncating error propagation. Besides, matching accuracy can be improved by using the gradient direction of the detected feature points to modify the weights of the paths in different directions. The experimental results based on Middlebury stereo data sets and CE-3 lunar data sets demonstrate that the proposed algorithm can effectively cut off the error propagation, reduce disparity search range and improve matching accuracy.

Penalty dynamic programming algorithm for dim targets detection in sensor systems.

PubMed

Huang, Dayu; Xue, Anke; Guo, Yunfei

2012-01-01

In order to detect and track multiple maneuvering dim targets in sensor systems, an improved dynamic programming track-before-detect algorithm (DP-TBD) called penalty DP-TBD (PDP-TBD) is proposed. The performances of tracking techniques are used as a feedback to the detection part. The feedback is constructed by a penalty term in the merit function, and the penalty term is a function of the possible target state estimation, which can be obtained by the tracking methods. With this feedback, the algorithm combines traditional tracking techniques with DP-TBD and it can be applied to simultaneously detect and track maneuvering dim targets. Meanwhile, a reasonable constraint that a sensor measurement can originate from one target or clutter is proposed to minimize track separation. Thus, the algorithm can be used in the multi-target situation with unknown target numbers. The efficiency and advantages of PDP-TBD compared with two existing methods are demonstrated by several simulations.

Adaptive dynamic programming for finite-horizon optimal control of discrete-time nonlinear systems with ε-error bound.

PubMed

Wang, Fei-Yue; Jin, Ning; Liu, Derong; Wei, Qinglai

2011-01-01

In this paper, we study the finite-horizon optimal control problem for discrete-time nonlinear systems using the adaptive dynamic programming (ADP) approach. The idea is to use an iterative ADP algorithm to obtain the optimal control law which makes the performance index function close to the greatest lower bound of all performance indices within an ε-error bound. The optimal number of control steps can also be obtained by the proposed ADP algorithms. A convergence analysis of the proposed ADP algorithms in terms of performance index function and control policy is made. In order to facilitate the implementation of the iterative ADP algorithms, neural networks are used for approximating the performance index function, computing the optimal control policy, and modeling the nonlinear system. Finally, two simulation examples are employed to illustrate the applicability of the proposed method.

Value Iteration Adaptive Dynamic Programming for Optimal Control of Discrete-Time Nonlinear Systems.

PubMed

Wei, Qinglai; Liu, Derong; Lin, Hanquan

2016-03-01

In this paper, a value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon undiscounted optimal control problems for discrete-time nonlinear systems. The present value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize the algorithm. A novel convergence analysis is developed to guarantee that the iterative value function converges to the optimal performance index function. Initialized by different initial functions, it is proven that the iterative value function will be monotonically nonincreasing, monotonically nondecreasing, or nonmonotonic and will converge to the optimum. In this paper, for the first time, the admissibility properties of the iterative control laws are developed for value iteration algorithms. It is emphasized that new termination criteria are established to guarantee the effectiveness of the iterative control laws. Neural networks are used to approximate the iterative value function and compute the iterative control law, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.

Dynamic programming and graph algorithms in computer vision.

PubMed

Felzenszwalb, Pedro F; Zabih, Ramin

2011-04-01

Optimization is a powerful paradigm for expressing and solving problems in a wide range of areas, and has been successfully applied to many vision problems. Discrete optimization techniques are especially interesting since, by carefully exploiting problem structure, they often provide nontrivial guarantees concerning solution quality. In this paper, we review dynamic programming and graph algorithms, and discuss representative examples of how these discrete optimization techniques have been applied to some classical vision problems. We focus on the low-level vision problem of stereo, the mid-level problem of interactive object segmentation, and the high-level problem of model-based recognition.

Parallel solution of sparse one-dimensional dynamic programming problems

NASA Technical Reports Server (NTRS)

Nicol, David M.

1989-01-01

Parallel computation offers the potential for quickly solving large computational problems. However, it is often a non-trivial task to effectively use parallel computers. Solution methods must sometimes be reformulated to exploit parallelism; the reformulations are often more complex than their slower serial counterparts. We illustrate these points by studying the parallelization of sparse one-dimensional dynamic programming problems, those which do not obviously admit substantial parallelization. We propose a new method for parallelizing such problems, develop analytic models which help us to identify problems which parallelize well, and compare the performance of our algorithm with existing algorithms on a multiprocessor.

BWM*: A Novel, Provable, Ensemble-based Dynamic Programming Algorithm for Sparse Approximations of Computational Protein Design.

PubMed

Jou, Jonathan D; Jain, Swati; Georgiev, Ivelin S; Donald, Bruce R

2016-06-01

Sparse energy functions that ignore long range interactions between residue pairs are frequently used by protein design algorithms to reduce computational cost. Current dynamic programming algorithms that fully exploit the optimal substructure produced by these energy functions only compute the GMEC. This disproportionately favors the sequence of a single, static conformation and overlooks better binding sequences with multiple low-energy conformations. Provable, ensemble-based algorithms such as A* avoid this problem, but A* cannot guarantee better performance than exhaustive enumeration. We propose a novel, provable, dynamic programming algorithm called Branch-Width Minimization* (BWM*) to enumerate a gap-free ensemble of conformations in order of increasing energy. Given a branch-decomposition of branch-width w for an n-residue protein design with at most q discrete side-chain conformations per residue, BWM* returns the sparse GMEC in O([Formula: see text]) time and enumerates each additional conformation in merely O([Formula: see text]) time. We define a new measure, Total Effective Search Space (TESS), which can be computed efficiently a priori before BWM* or A* is run. We ran BWM* on 67 protein design problems and found that TESS discriminated between BWM*-efficient and A*-efficient cases with 100% accuracy. As predicted by TESS and validated experimentally, BWM* outperforms A* in 73% of the cases and computes the full ensemble or a close approximation faster than A*, enumerating each additional conformation in milliseconds. Unlike A*, the performance of BWM* can be predicted in polynomial time before running the algorithm, which gives protein designers the power to choose the most efficient algorithm for their particular design problem.

A Polyhedral Outer-approximation, Dynamic-discretization optimization solver, 1.x

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

Bent, Rusell; Nagarajan, Harsha; Sundar, Kaarthik

2017-09-25

In this software, we implement an adaptive, multivariate partitioning algorithm for solving mixed-integer nonlinear programs (MINLP) to global optimality. The algorithm combines ideas that exploit the structure of convex relaxations to MINLPs and bound tightening procedures

Hamilton-Jacobi-Bellman equations and approximate dynamic programming on time scales.

PubMed

Seiffertt, John; Sanyal, Suman; Wunsch, Donald C

2008-08-01

The time scales calculus is a key emerging area of mathematics due to its potential use in a wide variety of multidisciplinary applications. We extend this calculus to approximate dynamic programming (ADP). The core backward induction algorithm of dynamic programming is extended from its traditional discrete case to all isolated time scales. Hamilton-Jacobi-Bellman equations, the solution of which is the fundamental problem in the field of dynamic programming, are motivated and proven on time scales. By drawing together the calculus of time scales and the applied area of stochastic control via ADP, we have connected two major fields of research.

Performance tradeoffs in static and dynamic load balancing strategies

NASA Technical Reports Server (NTRS)

Iqbal, M. A.; Saltz, J. H.; Bokhart, S. H.

1986-01-01

The problem of uniformly distributing the load of a parallel program over a multiprocessor system was considered. A program was analyzed whose structure permits the computation of the optimal static solution. Then four strategies for load balancing were described and their performance compared. The strategies are: (1) the optimal static assignment algorithm which is guaranteed to yield the best static solution, (2) the static binary dissection method which is very fast but sub-optimal, (3) the greedy algorithm, a static fully polynomial time approximation scheme, which estimates the optimal solution to arbitrary accuracy, and (4) the predictive dynamic load balancing heuristic which uses information on the precedence relationships within the program and outperforms any of the static methods. It is also shown that the overhead incurred by the dynamic heuristic is reduced considerably if it is started off with a static assignment provided by either of the other three strategies.

A boundedness result for the direct heuristic dynamic programming.

PubMed

Liu, Feng; Sun, Jian; Si, Jennie; Guo, Wentao; Mei, Shengwei

2012-08-01

Approximate/adaptive dynamic programming (ADP) has been studied extensively in recent years for its potential scalability to solve large state and control space problems, including those involving continuous states and continuous controls. The applicability of ADP algorithms, especially the adaptive critic designs has been demonstrated in several case studies. Direct heuristic dynamic programming (direct HDP) is one of the ADP algorithms inspired by the adaptive critic designs. It has been shown applicable to industrial scale, realistic and complex control problems. In this paper, we provide a uniformly ultimately boundedness (UUB) result for the direct HDP learning controller under mild and intuitive conditions. By using a Lyapunov approach we show that the estimation errors of the learning parameters or the weights in the action and critic networks remain UUB. This result provides a useful controller convergence guarantee for the first time for the direct HDP design. Copyright © 2012 Elsevier Ltd. All rights reserved.

Enhancements and Algorithms for Avionic Information Processing System Design Methodology.

DTIC Science & Technology

1982-06-16

programming algorithm is enhanced by incorporating task precedence constraints and hardware failures. Stochastic network methods are used to analyze...allocations in the presence of random fluctuations. Graph theoretic methods are used to analyze hardware designs, and new designs are constructed with...There, spatial dynamic programming (SDP) was used to solve a static, deterministic software allocation problem. Under the current contract the SDP

Development of program package for investigation and modeling of carbon nanostructures in diamond like carbon films with the help of Raman scattering and infrared absorption spectra line resolving

NASA Astrophysics Data System (ADS)

Hayrapetyan, David B.; Hovhannisyan, Levon; Mantashyan, Paytsar A.

2013-04-01

The analysis of complex spectra is an actual problem for modern science. The work is devoted to the creation of a software package, which analyzes spectrum in the different formats, possesses by dynamic knowledge database and self-study mechanism, performs automated analysis of the spectra compound based on knowledge database by application of certain algorithms. In the software package as searching systems, hyper-spherical random search algorithms, gradient algorithms and genetic searching algorithms were used. The analysis of Raman and IR spectrum of diamond-like carbon (DLC) samples were performed by elaborated program. After processing the data, the program immediately displays all the calculated parameters of DLC.

Solving mixed integer nonlinear programming problems using spiral dynamics optimization algorithm

NASA Astrophysics Data System (ADS)

Kania, Adhe; Sidarto, Kuntjoro Adji

2016-02-01

Many engineering and practical problem can be modeled by mixed integer nonlinear programming. This paper proposes to solve the problem with modified spiral dynamics inspired optimization method of Tamura and Yasuda. Four test cases have been examined, including problem in engineering and sport. This method succeeds in obtaining the optimal result in all test cases.

A Dynamic Scheduling Method of Earth-Observing Satellites by Employing Rolling Horizon Strategy

PubMed Central

Dishan, Qiu; Chuan, He; Jin, Liu; Manhao, Ma

2013-01-01

Focused on the dynamic scheduling problem for earth-observing satellites (EOS), an integer programming model is constructed after analyzing the main constraints. The rolling horizon (RH) strategy is proposed according to the independent arriving time and deadline of the imaging tasks. This strategy is designed with a mixed triggering mode composed of periodical triggering and event triggering, and the scheduling horizon is decomposed into a series of static scheduling intervals. By optimizing the scheduling schemes in each interval, the dynamic scheduling of EOS is realized. We also propose three dynamic scheduling algorithms by the combination of the RH strategy and various heuristic algorithms. Finally, the scheduling results of different algorithms are compared and the presented methods in this paper are demonstrated to be efficient by extensive experiments. PMID:23690742

A dynamic scheduling method of Earth-observing satellites by employing rolling horizon strategy.

PubMed

Dishan, Qiu; Chuan, He; Jin, Liu; Manhao, Ma

2013-01-01

Focused on the dynamic scheduling problem for earth-observing satellites (EOS), an integer programming model is constructed after analyzing the main constraints. The rolling horizon (RH) strategy is proposed according to the independent arriving time and deadline of the imaging tasks. This strategy is designed with a mixed triggering mode composed of periodical triggering and event triggering, and the scheduling horizon is decomposed into a series of static scheduling intervals. By optimizing the scheduling schemes in each interval, the dynamic scheduling of EOS is realized. We also propose three dynamic scheduling algorithms by the combination of the RH strategy and various heuristic algorithms. Finally, the scheduling results of different algorithms are compared and the presented methods in this paper are demonstrated to be efficient by extensive experiments.

Robust Adaptive Dynamic Programming of Two-Player Zero-Sum Games for Continuous-Time Linear Systems.

PubMed

Fu, Yue; Fu, Jun; Chai, Tianyou

2015-12-01

In this brief, an online robust adaptive dynamic programming algorithm is proposed for two-player zero-sum games of continuous-time unknown linear systems with matched uncertainties, which are functions of system outputs and states of a completely unknown exosystem. The online algorithm is developed using the policy iteration (PI) scheme with only one iteration loop. A new analytical method is proposed for convergence proof of the PI scheme. The sufficient conditions are given to guarantee globally asymptotic stability and suboptimal property of the closed-loop system. Simulation studies are conducted to illustrate the effectiveness of the proposed method.

Dynamic Programming and Graph Algorithms in Computer Vision*

PubMed Central

Felzenszwalb, Pedro F.; Zabih, Ramin

2013-01-01

Optimization is a powerful paradigm for expressing and solving problems in a wide range of areas, and has been successfully applied to many vision problems. Discrete optimization techniques are especially interesting, since by carefully exploiting problem structure they often provide non-trivial guarantees concerning solution quality. In this paper we briefly review dynamic programming and graph algorithms, and discuss representative examples of how these discrete optimization techniques have been applied to some classical vision problems. We focus on the low-level vision problem of stereo; the mid-level problem of interactive object segmentation; and the high-level problem of model-based recognition. PMID:20660950

Automated Flight Routing Using Stochastic Dynamic Programming

NASA Technical Reports Server (NTRS)

Ng, Hok K.; Morando, Alex; Grabbe, Shon

2010-01-01

Airspace capacity reduction due to convective weather impedes air traffic flows and causes traffic congestion. This study presents an algorithm that reroutes flights in the presence of winds, enroute convective weather, and congested airspace based on stochastic dynamic programming. A stochastic disturbance model incorporates into the reroute design process the capacity uncertainty. A trajectory-based airspace demand model is employed for calculating current and future airspace demand. The optimal routes minimize the total expected traveling time, weather incursion, and induced congestion costs. They are compared to weather-avoidance routes calculated using deterministic dynamic programming. The stochastic reroutes have smaller deviation probability than the deterministic counterpart when both reroutes have similar total flight distance. The stochastic rerouting algorithm takes into account all convective weather fields with all severity levels while the deterministic algorithm only accounts for convective weather systems exceeding a specified level of severity. When the stochastic reroutes are compared to the actual flight routes, they have similar total flight time, and both have about 1% of travel time crossing congested enroute sectors on average. The actual flight routes induce slightly less traffic congestion than the stochastic reroutes but intercept more severe convective weather.

A new algorithm for modeling friction in dynamic mechanical systems

NASA Technical Reports Server (NTRS)

Hill, R. E.

1988-01-01

A method of modeling friction forces that impede the motion of parts of dynamic mechanical systems is described. Conventional methods in which the friction effect is assumed a constant force, or torque, in a direction opposite to the relative motion, are applicable only to those cases where applied forces are large in comparison to the friction, and where there is little interest in system behavior close to the times of transitions through zero velocity. An algorithm is described that provides accurate determination of friction forces over a wide range of applied force and velocity conditions. The method avoids the simulation errors resulting from a finite integration interval used in connection with a conventional friction model, as is the case in many digital computer-based simulations. The algorithm incorporates a predictive calculation based on initial conditions of motion, externally applied forces, inertia, and integration step size. The predictive calculation in connection with an external integration process provides an accurate determination of both static and Coulomb friction forces and resulting motions in dynamic simulations. Accuracy of the results is improved over that obtained with conventional methods and a relatively large integration step size is permitted. A function block for incorporation in a specific simulation program is described. The general form of the algorithm facilitates implementation with various programming languages such as FORTRAN or C, as well as with other simulation programs.

Parallel processors and nonlinear structural dynamics algorithms and software

NASA Technical Reports Server (NTRS)

Belytschko, Ted; Gilbertsen, Noreen D.; Neal, Mark O.; Plaskacz, Edward J.

1989-01-01

The adaptation of a finite element program with explicit time integration to a massively parallel SIMD (single instruction multiple data) computer, the CONNECTION Machine is described. The adaptation required the development of a new algorithm, called the exchange algorithm, in which all nodal variables are allocated to the element with an exchange of nodal forces at each time step. The architectural and C* programming language features of the CONNECTION Machine are also summarized. Various alternate data structures and associated algorithms for nonlinear finite element analysis are discussed and compared. Results are presented which demonstrate that the CONNECTION Machine is capable of outperforming the CRAY XMP/14.

An energy management for series hybrid electric vehicle using improved dynamic programming

NASA Astrophysics Data System (ADS)

Peng, Hao; Yang, Yaoquan; Liu, Chunyu

2018-02-01

With the increasing numbers of hybrid electric vehicle (HEV), management for two energy sources, engine and battery, is more and more important to achieve the minimum fuel consumption. This paper introduces several working modes of series hybrid electric vehicle (SHEV) firstly and then describes the mathematical model of main relative components in SHEV. On the foundation of this model, dynamic programming is applied to distribute energy of engine and battery on the platform of matlab and acquires less fuel consumption compared with traditional control strategy. Besides, control rule recovering energy in brake profiles is added into dynamic programming, so shorter computing time is realized by improved dynamic programming and optimization on algorithm.

An historical survey of computational methods in optimal control.

NASA Technical Reports Server (NTRS)

Polak, E.

1973-01-01

Review of some of the salient theoretical developments in the specific area of optimal control algorithms. The first algorithms for optimal control were aimed at unconstrained problems and were derived by using first- and second-variation methods of the calculus of variations. These methods have subsequently been recognized as gradient, Newton-Raphson, or Gauss-Newton methods in function space. A much more recent addition to the arsenal of unconstrained optimal control algorithms are several variations of conjugate-gradient methods. At first, constrained optimal control problems could only be solved by exterior penalty function methods. Later algorithms specifically designed for constrained problems have appeared. Among these are methods for solving the unconstrained linear quadratic regulator problem, as well as certain constrained minimum-time and minimum-energy problems. Differential-dynamic programming was developed from dynamic programming considerations. The conditional-gradient method, the gradient-projection method, and a couple of feasible directions methods were obtained as extensions or adaptations of related algorithms for finite-dimensional problems. Finally, the so-called epsilon-methods combine the Ritz method with penalty function techniques.

Efficient dynamic optimization of logic programs

NASA Technical Reports Server (NTRS)

Laird, Phil

1992-01-01

A summary is given of the dynamic optimization approach to speed up learning for logic programs. The problem is to restructure a recursive program into an equivalent program whose expected performance is optimal for an unknown but fixed population of problem instances. We define the term 'optimal' relative to the source of input instances and sketch an algorithm that can come within a logarithmic factor of optimal with high probability. Finally, we show that finding high-utility unfolding operations (such as EBG) can be reduced to clause reordering.

A generalized global alignment algorithm.

PubMed

Huang, Xiaoqiu; Chao, Kun-Mao

2003-01-22

Homologous sequences are sometimes similar over some regions but different over other regions. Homologous sequences have a much lower global similarity if the different regions are much longer than the similar regions. We present a generalized global alignment algorithm for comparing sequences with intermittent similarities, an ordered list of similar regions separated by different regions. A generalized global alignment model is defined to handle sequences with intermittent similarities. A dynamic programming algorithm is designed to compute an optimal general alignment in time proportional to the product of sequence lengths and in space proportional to the sum of sequence lengths. The algorithm is implemented as a computer program named GAP3 (Global Alignment Program Version 3). The generalized global alignment model is validated by experimental results produced with GAP3 on both DNA and protein sequences. The GAP3 program extends the ability of standard global alignment programs to recognize homologous sequences of lower similarity. The GAP3 program is freely available for academic use at http://bioinformatics.iastate.edu/aat/align/align.html.

Array distribution in data-parallel programs

NASA Technical Reports Server (NTRS)

Chatterjee, Siddhartha; Gilbert, John R.; Schreiber, Robert; Sheffler, Thomas J.

1994-01-01

We consider distribution at compile time of the array data in a distributed-memory implementation of a data-parallel program written in a language like Fortran 90. We allow dynamic redistribution of data and define a heuristic algorithmic framework that chooses distribution parameters to minimize an estimate of program completion time. We represent the program as an alignment-distribution graph. We propose a divide-and-conquer algorithm for distribution that initially assigns a common distribution to each node of the graph and successively refines this assignment, taking computation, realignment, and redistribution costs into account. We explain how to estimate the effect of distribution on computation cost and how to choose a candidate set of distributions. We present the results of an implementation of our algorithms on several test problems.

Prediction of dynamical systems by symbolic regression

NASA Astrophysics Data System (ADS)

Quade, Markus; Abel, Markus; Shafi, Kamran; Niven, Robert K.; Noack, Bernd R.

2016-07-01

We study the modeling and prediction of dynamical systems based on conventional models derived from measurements. Such algorithms are highly desirable in situations where the underlying dynamics are hard to model from physical principles or simplified models need to be found. We focus on symbolic regression methods as a part of machine learning. These algorithms are capable of learning an analytically tractable model from data, a highly valuable property. Symbolic regression methods can be considered as generalized regression methods. We investigate two particular algorithms, the so-called fast function extraction which is a generalized linear regression algorithm, and genetic programming which is a very general method. Both are able to combine functions in a certain way such that a good model for the prediction of the temporal evolution of a dynamical system can be identified. We illustrate the algorithms by finding a prediction for the evolution of a harmonic oscillator based on measurements, by detecting an arriving front in an excitable system, and as a real-world application, the prediction of solar power production based on energy production observations at a given site together with the weather forecast.

Penalty Dynamic Programming Algorithm for Dim Targets Detection in Sensor Systems

PubMed Central

Huang, Dayu; Xue, Anke; Guo, Yunfei

2012-01-01

In order to detect and track multiple maneuvering dim targets in sensor systems, an improved dynamic programming track-before-detect algorithm (DP-TBD) called penalty DP-TBD (PDP-TBD) is proposed. The performances of tracking techniques are used as a feedback to the detection part. The feedback is constructed by a penalty term in the merit function, and the penalty term is a function of the possible target state estimation, which can be obtained by the tracking methods. With this feedback, the algorithm combines traditional tracking techniques with DP-TBD and it can be applied to simultaneously detect and track maneuvering dim targets. Meanwhile, a reasonable constraint that a sensor measurement can originate from one target or clutter is proposed to minimize track separation. Thus, the algorithm can be used in the multi-target situation with unknown target numbers. The efficiency and advantages of PDP-TBD compared with two existing methods are demonstrated by several simulations. PMID:22666074

Discrete sequence prediction and its applications

NASA Technical Reports Server (NTRS)

Laird, Philip

1992-01-01

Learning from experience to predict sequences of discrete symbols is a fundamental problem in machine learning with many applications. We apply sequence prediction using a simple and practical sequence-prediction algorithm, called TDAG. The TDAG algorithm is first tested by comparing its performance with some common data compression algorithms. Then it is adapted to the detailed requirements of dynamic program optimization, with excellent results.

SARDA Surface Schedulers

NASA Technical Reports Server (NTRS)

Malik, Waqar

2016-01-01

Provide an overview of algorithms used in SARDA (Spot and Runway Departure Advisor) HITL (Human-in-the-Loop) simulation for Dallas Fort-Worth International Airport and Charlotte Douglas International airport. Outline a multi-objective dynamic programming (DP) based algorithm that finds the exact solution to the single runway scheduling (SRS) problem, and discuss heuristics to restrict the search space for the DP based algorithm and provide improvements.

Agent-based traffic management and reinforcement learning in congested intersection network.

DOT National Transportation Integrated Search

2012-08-01

This study evaluates the performance of traffic control systems based on reinforcement learning (RL), also called approximate dynamic programming (ADP). Two algorithms have been selected for testing: 1) Q-learning and 2) approximate dynamic programmi...

User's guide to the Fault Inferring Nonlinear Detection System (FINDS) computer program

NASA Technical Reports Server (NTRS)

Caglayan, A. K.; Godiwala, P. M.; Satz, H. S.

1988-01-01

Described are the operation and internal structure of the computer program FINDS (Fault Inferring Nonlinear Detection System). The FINDS algorithm is designed to provide reliable estimates for aircraft position, velocity, attitude, and horizontal winds to be used for guidance and control laws in the presence of possible failures in the avionics sensors. The FINDS algorithm was developed with the use of a digital simulation of a commercial transport aircraft and tested with flight recorded data. The algorithm was then modified to meet the size constraints and real-time execution requirements on a flight computer. For the real-time operation, a multi-rate implementation of the FINDS algorithm has been partitioned to execute on a dual parallel processor configuration: one based on the translational dynamics and the other on the rotational kinematics. The report presents an overview of the FINDS algorithm, the implemented equations, the flow charts for the key subprograms, the input and output files, program variable indexing convention, subprogram descriptions, and the common block descriptions used in the program.

DynamO: a free O(N) general event-driven molecular dynamics simulator.

PubMed

Bannerman, M N; Sargant, R; Lue, L

2011-11-30

Molecular dynamics algorithms for systems of particles interacting through discrete or "hard" potentials are fundamentally different to the methods for continuous or "soft" potential systems. Although many software packages have been developed for continuous potential systems, software for discrete potential systems based on event-driven algorithms are relatively scarce and specialized. We present DynamO, a general event-driven simulation package, which displays the optimal O(N) asymptotic scaling of the computational cost with the number of particles N, rather than the O(N) scaling found in most standard algorithms. DynamO provides reference implementations of the best available event-driven algorithms. These techniques allow the rapid simulation of both complex and large (>10(6) particles) systems for long times. The performance of the program is benchmarked for elastic hard sphere systems, homogeneous cooling and sheared inelastic hard spheres, and equilibrium Lennard-Jones fluids. This software and its documentation are distributed under the GNU General Public license and can be freely downloaded from http://marcusbannerman.co.uk/dynamo. Copyright © 2011 Wiley Periodicals, Inc.

Iterative Adaptive Dynamic Programming for Solving Unknown Nonlinear Zero-Sum Game Based on Online Data.

PubMed

Zhu, Yuanheng; Zhao, Dongbin; Li, Xiangjun

2017-03-01

H ∞ control is a powerful method to solve the disturbance attenuation problems that occur in some control systems. The design of such controllers relies on solving the zero-sum game (ZSG). But in practical applications, the exact dynamics is mostly unknown. Identification of dynamics also produces errors that are detrimental to the control performance. To overcome this problem, an iterative adaptive dynamic programming algorithm is proposed in this paper to solve the continuous-time, unknown nonlinear ZSG with only online data. A model-free approach to the Hamilton-Jacobi-Isaacs equation is developed based on the policy iteration method. Control and disturbance policies and value are approximated by neural networks (NNs) under the critic-actor-disturber structure. The NN weights are solved by the least-squares method. According to the theoretical analysis, our algorithm is equivalent to a Gauss-Newton method solving an optimization problem, and it converges uniformly to the optimal solution. The online data can also be used repeatedly, which is highly efficient. Simulation results demonstrate its feasibility to solve the unknown nonlinear ZSG. When compared with other algorithms, it saves a significant amount of online measurement time.

Approximate Dynamic Programming Algorithms for United States Air Force Officer Sustainment

DTIC Science & Technology

2015-03-26

level of correction needed. While paying bonuses has an easily calculable cost, RIFs have more subtle costs. Mone (1994) discovered that in a steady...a regression is performed utilizing instrumental variables to minimize Bellman error. This algorithm uses a set of basis functions to approximate the...transitioned to an all-volunteer force. Charnes et al. (1972) utilize a goal programming model for General Schedule civilian manpower management in the

Clipping in neurocontrol by adaptive dynamic programming.

PubMed

Fairbank, Michael; Prokhorov, Danil; Alonso, Eduardo

2014-10-01

In adaptive dynamic programming, neurocontrol, and reinforcement learning, the objective is for an agent to learn to choose actions so as to minimize a total cost function. In this paper, we show that when discretized time is used to model the motion of the agent, it can be very important to do clipping on the motion of the agent in the final time step of the trajectory. By clipping, we mean that the final time step of the trajectory is to be truncated such that the agent stops exactly at the first terminal state reached, and no distance further. We demonstrate that when clipping is omitted, learning performance can fail to reach the optimum, and when clipping is done properly, learning performance can improve significantly. The clipping problem we describe affects algorithms that use explicit derivatives of the model functions of the environment to calculate a learning gradient. These include backpropagation through time for control and methods based on dual heuristic programming. However, the clipping problem does not significantly affect methods based on heuristic dynamic programming, temporal differences learning, or policy-gradient learning algorithms.

A variational dynamic programming approach to robot-path planning with a distance-safety criterion

NASA Technical Reports Server (NTRS)

Suh, Suk-Hwan; Shin, Kang G.

1988-01-01

An approach to robot-path planning is developed by considering both the traveling distance and the safety of the robot. A computationally-efficient algorithm is developed to find a near-optimal path with a weighted distance-safety criterion by using a variational calculus and dynamic programming (VCDP) method. The algorithm is readily applicable to any factory environment by representing the free workspace as channels. A method for deriving these channels is also proposed. Although it is developed mainly for two-dimensional problems, this method can be easily extended to a class of three-dimensional problems. Numerical examples are presented to demonstrate the utility and power of this method.

Analysis of the Multi Strategy Goal Programming for Micro-Grid Based on Dynamic ant Genetic Algorithm

NASA Astrophysics Data System (ADS)

Qiu, J. P.; Niu, D. X.

Micro-grid is one of the key technologies of the future energy supplies. Take economic planning. reliability, and environmental protection of micro grid as a basis for the analysis of multi-strategy objective programming problems for micro grid which contains wind power, solar power, and battery and micro gas turbine. Establish the mathematical model of each power generation characteristics and energy dissipation. and change micro grid planning multi-objective function under different operating strategies to a single objective model based on AHP method. Example analysis shows that in combination with dynamic ant mixed genetic algorithm can get the optimal power output of this model.

Developing and Implementing the Data Mining Algorithms in RAVEN

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

Sen, Ramazan Sonat; Maljovec, Daniel Patrick; Alfonsi, Andrea

The RAVEN code is becoming a comprehensive tool to perform probabilistic risk assessment, uncertainty quantification, and verification and validation. The RAVEN code is being developed to support many programs and to provide a set of methodologies and algorithms for advanced analysis. Scientific computer codes can generate enormous amounts of data. To post-process and analyze such data might, in some cases, take longer than the initial software runtime. Data mining algorithms/methods help in recognizing and understanding patterns in the data, and thus discover knowledge in databases. The methodologies used in the dynamic probabilistic risk assessment or in uncertainty and error quantificationmore » analysis couple system/physics codes with simulation controller codes, such as RAVEN. RAVEN introduces both deterministic and stochastic elements into the simulation while the system/physics code model the dynamics deterministically. A typical analysis is performed by sampling values of a set of parameter values. A major challenge in using dynamic probabilistic risk assessment or uncertainty and error quantification analysis for a complex system is to analyze the large number of scenarios generated. Data mining techniques are typically used to better organize and understand data, i.e. recognizing patterns in the data. This report focuses on development and implementation of Application Programming Interfaces (APIs) for different data mining algorithms, and the application of these algorithms to different databases.« less

A graph-based evolutionary algorithm: Genetic Network Programming (GNP) and its extension using reinforcement learning.

PubMed

Mabu, Shingo; Hirasawa, Kotaro; Hu, Jinglu

2007-01-01

This paper proposes a graph-based evolutionary algorithm called Genetic Network Programming (GNP). Our goal is to develop GNP, which can deal with dynamic environments efficiently and effectively, based on the distinguished expression ability of the graph (network) structure. The characteristics of GNP are as follows. 1) GNP programs are composed of a number of nodes which execute simple judgment/processing, and these nodes are connected by directed links to each other. 2) The graph structure enables GNP to re-use nodes, thus the structure can be very compact. 3) The node transition of GNP is executed according to its node connections without any terminal nodes, thus the past history of the node transition affects the current node to be used and this characteristic works as an implicit memory function. These structural characteristics are useful for dealing with dynamic environments. Furthermore, we propose an extended algorithm, "GNP with Reinforcement Learning (GNPRL)" which combines evolution and reinforcement learning in order to create effective graph structures and obtain better results in dynamic environments. In this paper, we applied GNP to the problem of determining agents' behavior to evaluate its effectiveness. Tileworld was used as the simulation environment. The results show some advantages for GNP over conventional methods.

Automated contour detection in X-ray left ventricular angiograms using multiview active appearance models and dynamic programming.

PubMed

Oost, Elco; Koning, Gerhard; Sonka, Milan; Oemrawsingh, Pranobe V; Reiber, Johan H C; Lelieveldt, Boudewijn P F

2006-09-01

This paper describes a new approach to the automated segmentation of X-ray left ventricular (LV) angiograms, based on active appearance models (AAMs) and dynamic programming. A coupling of shape and texture information between the end-diastolic (ED) and end-systolic (ES) frame was achieved by constructing a multiview AAM. Over-constraining of the model was compensated for by employing dynamic programming, integrating both intensity and motion features in the cost function. Two applications are compared: a semi-automatic method with manual model initialization, and a fully automatic algorithm. The first proved to be highly robust and accurate, demonstrating high clinical relevance. Based on experiments involving 70 patient data sets, the algorithm's success rate was 100% for ED and 99% for ES, with average unsigned border positioning errors of 0.68 mm for ED and 1.45 mm for ES. Calculated volumes were accurate and unbiased. The fully automatic algorithm, with intrinsically less user interaction was less robust, but showed a high potential, mostly due to a controlled gradient descent in updating the model parameters. The success rate of the fully automatic method was 91% for ED and 83% for ES, with average unsigned border positioning errors of 0.79 mm for ED and 1.55 mm for ES.

A Linked-Cell Domain Decomposition Method for Molecular Dynamics Simulation on a Scalable Multiprocessor

DOE PAGES

Yang, L. H.; Brooks III, E. D.; Belak, J.

1992-01-01

A molecular dynamics algorithm for performing large-scale simulations using the Parallel C Preprocessor (PCP) programming paradigm on the BBN TC2000, a massively parallel computer, is discussed. The algorithm uses a linked-cell data structure to obtain the near neighbors of each atom as time evoles. Each processor is assigned to a geometric domain containing many subcells and the storage for that domain is private to the processor. Within this scheme, the interdomain (i.e., interprocessor) communication is minimized.

Solution Methods for Stochastic Dynamic Linear Programs.

DTIC Science & Technology

1980-12-01

16, No. 11, pp. 652-675, July 1970. [28] Glassey, C.R., "Dynamic linear programs for production scheduling", OR 19, pp. 45-56. 1971 . 129 Glassey, C.R...Huang, C.C., I. Vertinsky, W.T. Ziemba, ’Sharp bounds on the value of perfect information", OR 25, pp. 128-139, 1977. [37 Kall , P., ’Computational... 1971 . [701 Ziemba, W.T., *Computational algorithms for convex stochastic programs with simple recourse", OR 8, pp. 414-431, 1970. 131 UNCLASSI FIED

Applying graph partitioning methods in measurement-based dynamic load balancing

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

Bhatele, Abhinav; Fourestier, Sebastien; Menon, Harshitha

Load imbalance leads to an increasing waste of resources as an application is scaled to more and more processors. Achieving the best parallel efficiency for a program requires optimal load balancing which is a NP-hard problem. However, finding near-optimal solutions to this problem for complex computational science and engineering applications is becoming increasingly important. Charm++, a migratable objects based programming model, provides a measurement-based dynamic load balancing framework. This framework instruments and then migrates over-decomposed objects to balance computational load and communication at runtime. This paper explores the use of graph partitioning algorithms, traditionally used for partitioning physical domains/meshes, formore » measurement-based dynamic load balancing of parallel applications. In particular, we present repartitioning methods developed in a graph partitioning toolbox called SCOTCH that consider the previous mapping to minimize migration costs. We also discuss a new imbalance reduction algorithm for graphs with irregular load distributions. We compare several load balancing algorithms using microbenchmarks on Intrepid and Ranger and evaluate the effect of communication, number of cores and number of objects on the benefit achieved from load balancing. New algorithms developed in SCOTCH lead to better performance compared to the METIS partitioners for several cases, both in terms of the application execution time and fewer number of objects migrated.« less

Semi-automatic 3D lung nodule segmentation in CT using dynamic programming

NASA Astrophysics Data System (ADS)

Sargent, Dustin; Park, Sun Young

2017-02-01

We present a method for semi-automatic segmentation of lung nodules in chest CT that can be extended to general lesion segmentation in multiple modalities. Most semi-automatic algorithms for lesion segmentation or similar tasks use region-growing or edge-based contour finding methods such as level-set. However, lung nodules and other lesions are often connected to surrounding tissues, which makes these algorithms prone to growing the nodule boundary into the surrounding tissue. To solve this problem, we apply a 3D extension of the 2D edge linking method with dynamic programming to find a closed surface in a spherical representation of the nodule ROI. The algorithm requires a user to draw a maximal diameter across the nodule in the slice in which the nodule cross section is the largest. We report the lesion volume estimation accuracy of our algorithm on the FDA lung phantom dataset, and the RECIST diameter estimation accuracy on the lung nodule dataset from the SPIE 2016 lung nodule classification challenge. The phantom results in particular demonstrate that our algorithm has the potential to mitigate the disparity in measurements performed by different radiologists on the same lesions, which could improve the accuracy of disease progression tracking.

Dynamic Distributed Cooperative Control of Multiple Heterogeneous Resources

DTIC Science & Technology

2012-10-01

of the UAVs to maximize the total sensor footprint over the region of interest. The algorithm utilized to solve this problem was based on sampling a...and moving obstacles. Obstacle positions were assumed known a priori. Kingston and Beard [22] presented an algorithm to keep moving UAVs equally spaced...Planning Algorithms , Cambridge University Press, 2006. 11. Ma, C. S. and Miller, R. H., “Mixed integer linear programming trajectory generation for

Enumerating Substituted Benzene Isomers of Tree-Like Chemical Graphs.

PubMed

Li, Jinghui; Nagamochi, Hiroshi; Akutsu, Tatsuya

2018-01-01

Enumeration of chemical structures is useful for drug design, which is one of the main targets of computational biology and bioinformatics. A chemical graph with no other cycles than benzene rings is called tree-like, and becomes a tree possibly with multiple edges if we contract each benzene ring into a single virtual atom of valence 6. All tree-like chemical graphs with a given tree representation are called the substituted benzene isomers of . When we replace each virtual atom in with a benzene ring to obtain a substituted benzene isomer, distinct isomers of are caused by the difference in arrangements of atom groups around a benzene ring. In this paper, we propose an efficient algorithm that enumerates all substituted benzene isomers of a given tree representation . Our algorithm first counts the number of all the isomers of the tree representation by a dynamic programming method. To enumerate all the isomers, for each , our algorithm then generates the th isomer by backtracking the counting phase of the dynamic programming. We also implemented our algorithm for computational experiments.

Memory-efficient dynamic programming backtrace and pairwise local sequence alignment.

PubMed

Newberg, Lee A

2008-08-15

A backtrace through a dynamic programming algorithm's intermediate results in search of an optimal path, or to sample paths according to an implied probability distribution, or as the second stage of a forward-backward algorithm, is a task of fundamental importance in computational biology. When there is insufficient space to store all intermediate results in high-speed memory (e.g. cache) existing approaches store selected stages of the computation, and recompute missing values from these checkpoints on an as-needed basis. Here we present an optimal checkpointing strategy, and demonstrate its utility with pairwise local sequence alignment of sequences of length 10,000. Sample C++-code for optimal backtrace is available in the Supplementary Materials. Supplementary data is available at Bioinformatics online.

Direct evaluation of fault trees using object-oriented programming techniques

NASA Technical Reports Server (NTRS)

Patterson-Hine, F. A.; Koen, B. V.

1989-01-01

Object-oriented programming techniques are used in an algorithm for the direct evaluation of fault trees. The algorithm combines a simple bottom-up procedure for trees without repeated events with a top-down recursive procedure for trees with repeated events. The object-oriented approach results in a dynamic modularization of the tree at each step in the reduction process. The algorithm reduces the number of recursive calls required to solve trees with repeated events and calculates intermediate results as well as the solution of the top event. The intermediate results can be reused if part of the tree is modified. An example is presented in which the results of the algorithm implemented with conventional techniques are compared to those of the object-oriented approach.

A New Powered Lower Limb Prosthesis Control Framework Based on Adaptive Dynamic Programming.

PubMed

Wen, Yue; Si, Jennie; Gao, Xiang; Huang, Stephanie; Huang, He Helen

2017-09-01

This brief presents a novel application of adaptive dynamic programming (ADP) for optimal adaptive control of powered lower limb prostheses, a type of wearable robots to assist the motor function of the limb amputees. Current control of these robotic devices typically relies on finite state impedance control (FS-IC), which lacks adaptability to the user's physical condition. As a result, joint impedance settings are often customized manually and heuristically in clinics, which greatly hinder the wide use of these advanced medical devices. This simulation study aimed at demonstrating the feasibility of ADP for automatic tuning of the twelve knee joint impedance parameters during a complete gait cycle to achieve balanced walking. Given that the accurate models of human walking dynamics are difficult to obtain, the model-free ADP control algorithms were considered. First, direct heuristic dynamic programming (dHDP) was applied to the control problem, and its performance was evaluated on OpenSim, an often-used dynamic walking simulator. For the comparison purposes, we selected another established ADP algorithm, the neural fitted Q with continuous action (NFQCA). In both cases, the ADP controllers learned to control the right knee joint and achieved balanced walking, but dHDP outperformed NFQCA in this application during a 200 gait cycle-based testing.

Novel Kalman Filter Algorithm for Statistical Monitoring of Extensive Landscapes with Synoptic Sensor Data

PubMed Central

Czaplewski, Raymond L.

2015-01-01

Wall-to-wall remotely sensed data are increasingly available to monitor landscape dynamics over large geographic areas. However, statistical monitoring programs that use post-stratification cannot fully utilize those sensor data. The Kalman filter (KF) is an alternative statistical estimator. I develop a new KF algorithm that is numerically robust with large numbers of study variables and auxiliary sensor variables. A National Forest Inventory (NFI) illustrates application within an official statistics program. Practical recommendations regarding remote sensing and statistical issues are offered. This algorithm has the potential to increase the value of synoptic sensor data for statistical monitoring of large geographic areas. PMID:26393588

Optimal Power Control in Wireless Powered Sensor Networks: A Dynamic Game-Based Approach

PubMed Central

Xu, Haitao; Guo, Chao; Zhang, Long

2017-01-01

In wireless powered sensor networks (WPSN), it is essential to research uplink transmit power control in order to achieve throughput performance balancing and energy scheduling. Each sensor should have an optimal transmit power level for revenue maximization. In this paper, we discuss a dynamic game-based algorithm for optimal power control in WPSN. The main idea is to use the non-cooperative differential game to control the uplink transmit power of wireless sensors in WPSN, to extend their working hours and to meet QoS (Quality of Services) requirements. Subsequently, the Nash equilibrium solutions are obtained through Bellman dynamic programming. At the same time, an uplink power control algorithm is proposed in a distributed manner. Through numerical simulations, we demonstrate that our algorithm can obtain optimal power control and reach convergence for an infinite horizon. PMID:28282945

Dynamic Programming for Structured Continuous Markov Decision Problems

NASA Technical Reports Server (NTRS)

Dearden, Richard; Meuleau, Nicholas; Washington, Richard; Feng, Zhengzhu

2004-01-01

We describe an approach for exploiting structure in Markov Decision Processes with continuous state variables. At each step of the dynamic programming, the state space is dynamically partitioned into regions where the value function is the same throughout the region. We first describe the algorithm for piecewise constant representations. We then extend it to piecewise linear representations, using techniques from POMDPs to represent and reason about linear surfaces efficiently. We show that for complex, structured problems, our approach exploits the natural structure so that optimal solutions can be computed efficiently.

Algorithmic analysis of relational learning processes in instructional technology: Some implications for basic, translational, and applied research.

PubMed

McIlvane, William J; Kledaras, Joanne B; Gerard, Christophe J; Wilde, Lorin; Smelson, David

2018-07-01

A few noteworthy exceptions notwithstanding, quantitative analyses of relational learning are most often simple descriptive measures of study outcomes. For example, studies of stimulus equivalence have made much progress using measures such as percentage consistent with equivalence relations, discrimination ratio, and response latency. Although procedures may have ad hoc variations, they remain fairly similar across studies. Comparison studies of training variables that lead to different outcomes are few. Yet to be developed are tools designed specifically for dynamic and/or parametric analyses of relational learning processes. This paper will focus on recent studies to develop (1) quality computer-based programmed instruction for supporting relational learning in children with autism spectrum disorders and intellectual disabilities and (2) formal algorithms that permit ongoing, dynamic assessment of learner performance and procedure changes to optimize instructional efficacy and efficiency. Because these algorithms have a strong basis in evidence and in theories of stimulus control, they may have utility also for basic and translational research. We present an overview of the research program, details of algorithm features, and summary results that illustrate their possible benefits. It also presents arguments that such algorithm development may encourage parametric research, help in integrating new research findings, and support in-depth quantitative analyses of stimulus control processes in relational learning. Such algorithms may also serve to model control of basic behavioral processes that is important to the design of effective programmed instruction for human learners with and without functional disabilities. Copyright © 2018 Elsevier B.V. All rights reserved.

Comparison of Dijkstra's algorithm and dynamic programming method in finding shortest path for order picker in a warehouse

NASA Astrophysics Data System (ADS)

Nordin, Noraimi Azlin Mohd; Omar, Mohd; Sharif, S. Sarifah Radiah

2017-04-01

Companies are looking forward to improve their productivity within their warehouse operations and distribution centres. In a typical warehouse operation, order picking contributes more than half percentage of the operating costs. Order picking is a benchmark in measuring the performance and productivity improvement of any warehouse management. Solving order picking problem is crucial in reducing response time and waiting time of a customer in receiving his demands. To reduce the response time, proper routing for picking orders is vital. Moreover, in production line, it is vital to always make sure the supplies arrive on time. Hence, a sample routing network will be applied on EP Manufacturing Berhad (EPMB) as a case study. The Dijkstra's algorithm and Dynamic Programming method are applied to find the shortest distance for an order picker in order picking. The results show that the Dynamic programming method is a simple yet competent approach in finding the shortest distance to pick an order that is applicable in a warehouse within a short time period.

A Branch-and-Bound Algorithm for Fitting Anti-Robinson Structures to Symmetric Dissimilarity Matrices.

ERIC Educational Resources Information Center

Brusco, Michael J.

2002-01-01

Developed a branch-and-bound algorithm that can be used to seriate a symmetric dissimilarity matrix by identifying a reordering of rows and columns of the matrix optimizing an anti-Robinson criterion. Computational results suggest that with respect to computational efficiency, the approach is generally competitive with dynamic programming. (SLD)

Adaptive Dynamic Programming for Discrete-Time Zero-Sum Games.

PubMed

Wei, Qinglai; Liu, Derong; Lin, Qiao; Song, Ruizhuo

2018-04-01

In this paper, a novel adaptive dynamic programming (ADP) algorithm, called "iterative zero-sum ADP algorithm," is developed to solve infinite-horizon discrete-time two-player zero-sum games of nonlinear systems. The present iterative zero-sum ADP algorithm permits arbitrary positive semidefinite functions to initialize the upper and lower iterations. A novel convergence analysis is developed to guarantee the upper and lower iterative value functions to converge to the upper and lower optimums, respectively. When the saddle-point equilibrium exists, it is emphasized that both the upper and lower iterative value functions are proved to converge to the optimal solution of the zero-sum game, where the existence criteria of the saddle-point equilibrium are not required. If the saddle-point equilibrium does not exist, the upper and lower optimal performance index functions are obtained, respectively, where the upper and lower performance index functions are proved to be not equivalent. Finally, simulation results and comparisons are shown to illustrate the performance of the present method.

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 several simulation timesteps. One MD application described here highlights the utility of including long range contributions to Lennard-Jones potential in constant pressure simulations. Another application shows the time dependence of long range forces in a multiple time step MD simulation.

Searching Dynamic Agents with a Team of Mobile Robots

PubMed Central

Juliá, Miguel; Gil, Arturo; Reinoso, Oscar

2012-01-01

This paper presents a new algorithm that allows a team of robots to cooperatively search for a set of moving targets. An estimation of the areas of the environment that are more likely to hold a target agent is obtained using a grid-based Bayesian filter. The robot sensor readings and the maximum speed of the moving targets are used in order to update the grid. This representation is used in a search algorithm that commands the robots to those areas that are more likely to present target agents. This algorithm splits the environment in a tree of connected regions using dynamic programming. This tree is used in order to decide the destination for each robot in a coordinated manner. The algorithm has been successfully tested in known and unknown environments showing the validity of the approach. PMID:23012519

Searching dynamic agents with a team of mobile robots.

PubMed

Juliá, Miguel; Gil, Arturo; Reinoso, Oscar

2012-01-01

This paper presents a new algorithm that allows a team of robots to cooperatively search for a set of moving targets. An estimation of the areas of the environment that are more likely to hold a target agent is obtained using a grid-based Bayesian filter. The robot sensor readings and the maximum speed of the moving targets are used in order to update the grid. This representation is used in a search algorithm that commands the robots to those areas that are more likely to present target agents. This algorithm splits the environment in a tree of connected regions using dynamic programming. This tree is used in order to decide the destination for each robot in a coordinated manner. The algorithm has been successfully tested in known and unknown environments showing the validity of the approach.

Probabilistic fusion of stereo with color and contrast for bilayer segmentation.

PubMed

Kolmogorov, Vladimir; Criminisi, Antonio; Blake, Andrew; Cross, Geoffrey; Rother, Carsten

2006-09-01

This paper describes models and algorithms for the real-time segmentation of foreground from background layers in stereo video sequences. Automatic separation of layers from color/contrast or from stereo alone is known to be error-prone. Here, color, contrast, and stereo matching information are fused to infer layers accurately and efficiently. The first algorithm, Layered Dynamic Programming (LDP), solves stereo in an extended six-state space that represents both foreground/background layers and occluded regions. The stereo-match likelihood is then fused with a contrast-sensitive color model that is learned on-the-fly and stereo disparities are obtained by dynamic programming. The second algorithm, Layered Graph Cut (LGC), does not directly solve stereo. Instead, the stereo match likelihood is marginalized over disparities to evaluate foreground and background hypotheses and then fused with a contrast-sensitive color model like the one used in LDP. Segmentation is solved efficiently by ternary graph cut. Both algorithms are evaluated with respect to ground truth data and found to have similar performance, substantially better than either stereo or color/ contrast alone. However, their characteristics with respect to computational efficiency are rather different. The algorithms are demonstrated in the application of background substitution and shown to give good quality composite video output.

An Interactive Multiobjective Programming Approach to Combinatorial Data Analysis.

ERIC Educational Resources Information Center

Brusco, Michael J.; Stahl, Stephanie

2001-01-01

Describes an interactive procedure for multiobjective asymmetric unidimensional seriation problems that uses a dynamic-programming algorithm to generate partially the efficient set of sequences for small to medium-sized problems and a multioperational heuristic to estimate the efficient set for larger problems. Applies the procedure to an…

Robust Control Algorithm for a Two Cart System and an Inverted Pendulum

NASA Technical Reports Server (NTRS)

Wilson, Chris L.; Capo-Lugo, Pedro

2011-01-01

The Rectilinear Control System can be used to simulate a launch vehicle during liftoff. Several control schemes have been developed that can control different dynamic models of the rectilinear plant. A robust control algorithm was developed that can control a pendulum to maintain an inverted position. A fluid slosh tank will be attached to the pendulum in order to test robustness in the presence of unknown slosh characteristics. The rectilinear plant consists of a DC motor and three carts mounted in series. Each cart s weight can be adjusted with brass masses and the carts can be coupled with springs. The pendulum is mounted on the first cart and an adjustable air damper can be attached to the third cart if desired. Each cart and the pendulum have a quadrature encoder to determine position. Full state feedback was implemented in order to develop the control algorithm along with a state estimator to determine the velocity states of the system. A MATLAB program was used to convert the state space matrices from continuous time to discrete time. This program also used a desired phase margin and damping ratio to determine the feedback gain matrix that would be used in the LabVIEW program. This experiment will allow engineers to gain a better understanding of liquid propellant slosh dynamics, therefore enabling them to develop more robust control algorithms for launch vehicle systems

CELFE: Coupled Eulerian-Lagrangian Finite Element program for high velocity impact. Part 1: Theory and formulation. [hydroelasto-viscoplastic model

NASA Technical Reports Server (NTRS)

Lee, C. H.

1978-01-01

A 3-D finite element program capable of simulating the dynamic behavior in the vicinity of the impact point, together with predicting the dynamic response in the remaining part of the structural component subjected to high velocity impact is discussed. The finite algorithm is formulated in a general moving coordinate system. In the vicinity of the impact point contained by a moving failure front, the relative velocity of the coordinate system will approach the material particle velocity. The dynamic behavior inside the region is described by Eulerian formulation based on a hydroelasto-viscoplastic model. The failure front which can be regarded as the boundary of the impact zone is described by a transition layer. The layer changes the representation from the Eulerian mode to the Lagrangian mode outside the failure front by varying the relative velocity of the coordinate system to zero. The dynamic response in the remaining part of the structure described by the Lagrangian formulation is treated using advanced structural analysis. An interfacing algorithm for coupling CELFE with NASTRAN is constructed to provide computational capabilities for large structures.

Novel Kalman filter algorithm for statistical monitoring of extensive landscapes with synoptic sensor data

Treesearch

Raymond L. Czaplewski

2015-01-01

Wall-to-wall remotely sensed data are increasingly available to monitor landscape dynamics over large geographic areas. However, statistical monitoring programs that use post-stratification cannot fully utilize those sensor data. The Kalman filter (KF) is an alternative statistical estimator. I develop a new KF algorithm that is numerically robust with large numbers of...

Lymph node segmentation by dynamic programming and active contours.

PubMed

Tan, Yongqiang; Lu, Lin; Bonde, Apurva; Wang, Deling; Qi, Jing; Schwartz, Lawrence H; Zhao, Binsheng

2018-03-03

Enlarged lymph nodes are indicators of cancer staging, and the change in their size is a reflection of treatment response. Automatic lymph node segmentation is challenging, as the boundary can be unclear and the surrounding structures complex. This work communicates a new three-dimensional algorithm for the segmentation of enlarged lymph nodes. The algorithm requires a user to draw a region of interest (ROI) enclosing the lymph node. Rays are cast from the center of the ROI, and the intersections of the rays and the boundary of the lymph node form a triangle mesh. The intersection points are determined by dynamic programming. The triangle mesh initializes an active contour which evolves to low-energy boundary. Three radiologists independently delineated the contours of 54 lesions from 48 patients. Dice coefficient was used to evaluate the algorithm's performance. The mean Dice coefficient between computer and the majority vote results was 83.2%. The mean Dice coefficients between the three radiologists' manual segmentations were 84.6%, 86.2%, and 88.3%. The performance of this segmentation algorithm suggests its potential clinical value for quantifying enlarged lymph nodes. © 2018 American Association of Physicists in Medicine.

Algorithm for predicting the evolution of series of dynamics of complex systems in solving information problems

NASA Astrophysics Data System (ADS)

Kasatkina, T. I.; Dushkin, A. V.; Pavlov, V. A.; Shatovkin, R. R.

2018-03-01

In the development of information, systems and programming to predict the series of dynamics, neural network methods have recently been applied. They are more flexible, in comparison with existing analogues and are capable of taking into account the nonlinearities of the series. In this paper, we propose a modified algorithm for predicting the series of dynamics, which includes a method for training neural networks, an approach to describing and presenting input data, based on the prediction by the multilayer perceptron method. To construct a neural network, the values of a series of dynamics at the extremum points and time values corresponding to them, formed based on the sliding window method, are used as input data. The proposed algorithm can act as an independent approach to predicting the series of dynamics, and be one of the parts of the forecasting system. The efficiency of predicting the evolution of the dynamics series for a short-term one-step and long-term multi-step forecast by the classical multilayer perceptron method and a modified algorithm using synthetic and real data is compared. The result of this modification was the minimization of the magnitude of the iterative error that arises from the previously predicted inputs to the inputs to the neural network, as well as the increase in the accuracy of the iterative prediction of the neural network.

Computationally efficient multibody simulations

NASA Technical Reports Server (NTRS)

Ramakrishnan, Jayant; Kumar, Manoj

1994-01-01

Computationally efficient approaches to the solution of the dynamics of multibody systems are presented in this work. The computational efficiency is derived from both the algorithmic and implementational standpoint. Order(n) approaches provide a new formulation of the equations of motion eliminating the assembly and numerical inversion of a system mass matrix as required by conventional algorithms. Computational efficiency is also gained in the implementation phase by the symbolic processing and parallel implementation of these equations. Comparison of this algorithm with existing multibody simulation programs illustrates the increased computational efficiency.

Multi-period response management to contaminated water distribution networks: dynamic programming versus genetic algorithms

NASA Astrophysics Data System (ADS)

Bashi-Azghadi, Seyyed Nasser; Afshar, Abbas; Afshar, Mohammad Hadi

2018-03-01

Previous studies on consequence management assume that the selected response action including valve closure and/or hydrant opening remains unchanged during the entire management period. This study presents a new embedded simulation-optimization methodology for deriving time-varying operational response actions in which the network topology may change from one stage to another. Dynamic programming (DP) and genetic algorithm (GA) are used in order to minimize selected objective functions. Two networks of small and large sizes are used in order to illustrate the performance of the proposed modelling schemes if a time-dependent consequence management strategy is to be implemented. The results show that for a small number of decision variables even in large-scale networks, DP is superior in terms of accuracy and computer runtime. However, as the number of potential actions grows, DP loses its merit over the GA approach. This study clearly proves the priority of the proposed dynamic operation strategy over the commonly used static strategy.

A summary report on system effectiveness and optimization study

NASA Technical Reports Server (NTRS)

Williamson, O. L.; Rydberg, A. J.; Dorris, G.

1973-01-01

Report treats optimization and effectiveness separately. Report illustrates example of dynamic programming solution to system optimization. Computer algorithm has been developed to solve effectiveness problem and is included in report.

Generation of high-dynamic range image from digital photo

NASA Astrophysics Data System (ADS)

Wang, Ying; Potemin, Igor S.; Zhdanov, Dmitry D.; Wang, Xu-yang; Cheng, Han

2016-10-01

A number of the modern applications such as medical imaging, remote sensing satellites imaging, virtual prototyping etc use the High Dynamic Range Image (HDRI). Generally to obtain HDRI from ordinary digital image the camera is calibrated. The article proposes the camera calibration method based on the clear sky as the standard light source and takes sky luminance from CIE sky model for the corresponding geographical coordinates and time. The article considers base algorithms for getting real luminance values from ordinary digital image and corresponding programmed implementation of the algorithms. Moreover, examples of HDRI reconstructed from ordinary images illustrate the article.

A novel approach to multiple sequence alignment using hadoop data grids.

PubMed

Sudha Sadasivam, G; Baktavatchalam, G

2010-01-01

Multiple alignment of protein sequences helps to determine evolutionary linkage and to predict molecular structures. The factors to be considered while aligning multiple sequences are speed and accuracy of alignment. Although dynamic programming algorithms produce accurate alignments, they are computation intensive. In this paper we propose a time efficient approach to sequence alignment that also produces quality alignment. The dynamic nature of the algorithm coupled with data and computational parallelism of hadoop data grids improves the accuracy and speed of sequence alignment. The principle of block splitting in hadoop coupled with its scalability facilitates alignment of very large sequences.

The MHOST finite element program: 3-D inelastic analysis methods for hot section components. Volume 1: Theoretical manual

NASA Technical Reports Server (NTRS)

Nakazawa, Shohei

1991-01-01

Formulations and algorithms implemented in the MHOST finite element program are discussed. The code uses a novel concept of the mixed iterative solution technique for the efficient 3-D computations of turbine engine hot section components. The general framework of variational formulation and solution algorithms are discussed which were derived from the mixed three field Hu-Washizu principle. This formulation enables the use of nodal interpolation for coordinates, displacements, strains, and stresses. Algorithmic description of the mixed iterative method includes variations for the quasi static, transient dynamic and buckling analyses. The global-local analysis procedure referred to as the subelement refinement is developed in the framework of the mixed iterative solution, of which the detail is presented. The numerically integrated isoparametric elements implemented in the framework is discussed. Methods to filter certain parts of strain and project the element discontinuous quantities to the nodes are developed for a family of linear elements. Integration algorithms are described for linear and nonlinear equations included in MHOST program.

Trajectory NG: portable, compressed, general molecular dynamics trajectories.

PubMed

Spångberg, Daniel; Larsson, Daniel S D; van der Spoel, David

2011-10-01

We present general algorithms for the compression of molecular dynamics trajectories. The standard ways to store MD trajectories as text or as raw binary floating point numbers result in very large files when efficient simulation programs are used on supercomputers. Our algorithms are based on the observation that differences in atomic coordinates/velocities, in either time or space, are generally smaller than the absolute values of the coordinates/velocities. Also, it is often possible to store values at a lower precision. We apply several compression schemes to compress the resulting differences further. The most efficient algorithms developed here use a block sorting algorithm in combination with Huffman coding. Depending on the frequency of storage of frames in the trajectory, either space, time, or combinations of space and time differences are usually the most efficient. We compare the efficiency of our algorithms with each other and with other algorithms present in the literature for various systems: liquid argon, water, a virus capsid solvated in 15 mM aqueous NaCl, and solid magnesium oxide. We perform tests to determine how much precision is necessary to obtain accurate structural and dynamic properties, as well as benchmark a parallelized implementation of the algorithms. We obtain compression ratios (compared to single precision floating point) of 1:3.3-1:35 depending on the frequency of storage of frames and the system studied.

Experimental evaluation of model predictive control and inverse dynamics control for spacecraft proximity and docking maneuvers

NASA Astrophysics Data System (ADS)

Virgili-Llop, Josep; Zagaris, Costantinos; Park, Hyeongjun; Zappulla, Richard; Romano, Marcello

2018-03-01

An experimental campaign has been conducted to evaluate the performance of two different guidance and control algorithms on a multi-constrained docking maneuver. The evaluated algorithms are model predictive control (MPC) and inverse dynamics in the virtual domain (IDVD). A linear-quadratic approach with a quadratic programming solver is used for the MPC approach. A nonconvex optimization problem results from the IDVD approach, and a nonlinear programming solver is used. The docking scenario is constrained by the presence of a keep-out zone, an entry cone, and by the chaser's maximum actuation level. The performance metrics for the experiments and numerical simulations include the required control effort and time to dock. The experiments have been conducted in a ground-based air-bearing test bed, using spacecraft simulators that float over a granite table.

Empirical valence bond models for reactive potential energy surfaces: a parallel multilevel genetic program approach.

PubMed

Bellucci, Michael A; Coker, David F

2011-07-28

We describe a new method for constructing empirical valence bond potential energy surfaces using a parallel multilevel genetic program (PMLGP). Genetic programs can be used to perform an efficient search through function space and parameter space to find the best functions and sets of parameters that fit energies obtained by ab initio electronic structure calculations. Building on the traditional genetic program approach, the PMLGP utilizes a hierarchy of genetic programming on two different levels. The lower level genetic programs are used to optimize coevolving populations in parallel while the higher level genetic program (HLGP) is used to optimize the genetic operator probabilities of the lower level genetic programs. The HLGP allows the algorithm to dynamically learn the mutation or combination of mutations that most effectively increase the fitness of the populations, causing a significant increase in the algorithm's accuracy and efficiency. The algorithm's accuracy and efficiency is tested against a standard parallel genetic program with a variety of one-dimensional test cases. Subsequently, the PMLGP is utilized to obtain an accurate empirical valence bond model for proton transfer in 3-hydroxy-gamma-pyrone in gas phase and protic solvent. © 2011 American Institute of Physics

The checkpoint ordering problem

PubMed Central

Hungerländer, P.

2017-01-01

Abstract We suggest a new variant of a row layout problem: Find an ordering of n departments with given lengths such that the total weighted sum of their distances to a given checkpoint is minimized. The Checkpoint Ordering Problem (COP) is both of theoretical and practical interest. It has several applications and is conceptually related to some well-studied combinatorial optimization problems, namely the Single-Row Facility Layout Problem, the Linear Ordering Problem and a variant of parallel machine scheduling. In this paper we study the complexity of the (COP) and its special cases. The general version of the (COP) with an arbitrary but fixed number of checkpoints is NP-hard in the weak sense. We propose both a dynamic programming algorithm and an integer linear programming approach for the (COP) . Our computational experiments indicate that the (COP) is hard to solve in practice. While the run time of the dynamic programming algorithm strongly depends on the length of the departments, the integer linear programming approach is able to solve instances with up to 25 departments to optimality. PMID:29170574

Algorithms and programs complex for solving inverse problems of artificial Earth satellite dynamics with using parallel computation. (Russian Title: Программно-математическое обеспечение для решения обратных задач динамики ИСЗ с использованием параллельных вычислений )

NASA Astrophysics Data System (ADS)

Chuvashov, I. N.

2011-07-01

In this paper complex of algorithms and programs for solving inverse problems of artificial earth satellite dynamics is described. Complex has been intended for satellite orbit improvement, calculation of motion model parameters and etc. Programs complex has been worked up for cluster "Skiff Cyberia". Results of numerical experiments obtained by using new complex in common the program "Numerical model of the system artificial satellites motion" is presented in this paper.

Runway Scheduling Using Generalized Dynamic Programming

NASA Technical Reports Server (NTRS)

Montoya, Justin; Wood, Zachary; Rathinam, Sivakumar

2011-01-01

A generalized dynamic programming method for finding a set of pareto optimal solutions for a runway scheduling problem is introduced. The algorithm generates a set of runway fight sequences that are optimal for both runway throughput and delay. Realistic time-based operational constraints are considered, including miles-in-trail separation, runway crossings, and wake vortex separation. The authors also model divergent runway takeoff operations to allow for reduced wake vortex separation. A modeled Dallas/Fort Worth International airport and three baseline heuristics are used to illustrate preliminary benefits of using the generalized dynamic programming method. Simulated traffic levels ranged from 10 aircraft to 30 aircraft with each test case spanning 15 minutes. The optimal solution shows a 40-70 percent decrease in the expected delay per aircraft over the baseline schedulers. Computational results suggest that the algorithm is promising for real-time application with an average computation time of 4.5 seconds. For even faster computation times, two heuristics are developed. As compared to the optimal, the heuristics are within 5% of the expected delay per aircraft and 1% of the expected number of runway operations per hour ad can be 100x faster.

Earth and ocean modeling

NASA Technical Reports Server (NTRS)

Knezovich, F. M.

1976-01-01

A modular structured system of computer programs is presented utilizing earth and ocean dynamical data keyed to finitely defined parameters. The model is an assemblage of mathematical algorithms with an inherent capability of maturation with progressive improvements in observational data frequencies, accuracies and scopes. The Eom in its present state is a first-order approach to a geophysical model of the earth's dynamics.

Iterative refinement of structure-based sequence alignments by Seed Extension

PubMed Central

Kim, Changhoon; Tai, Chin-Hsien; Lee, Byungkook

2009-01-01

Background Accurate sequence alignment is required in many bioinformatics applications but, when sequence similarity is low, it is difficult to obtain accurate alignments based on sequence similarity alone. The accuracy improves when the structures are available, but current structure-based sequence alignment procedures still mis-align substantial numbers of residues. In order to correct such errors, we previously explored the possibility of replacing the residue-based dynamic programming algorithm in structure alignment procedures with the Seed Extension algorithm, which does not use a gap penalty. Here, we describe a new procedure called RSE (Refinement with Seed Extension) that iteratively refines a structure-based sequence alignment. Results RSE uses SE (Seed Extension) in its core, which is an algorithm that we reported recently for obtaining a sequence alignment from two superimposed structures. The RSE procedure was evaluated by comparing the correctly aligned fractions of residues before and after the refinement of the structure-based sequence alignments produced by popular programs. CE, DaliLite, FAST, LOCK2, MATRAS, MATT, TM-align, SHEBA and VAST were included in this analysis and the NCBI's CDD root node set was used as the reference alignments. RSE improved the average accuracy of sequence alignments for all programs tested when no shift error was allowed. The amount of improvement varied depending on the program. The average improvements were small for DaliLite and MATRAS but about 5% for CE and VAST. More substantial improvements have been seen in many individual cases. The additional computation times required for the refinements were negligible compared to the times taken by the structure alignment programs. Conclusion RSE is a computationally inexpensive way of improving the accuracy of a structure-based sequence alignment. It can be used as a standalone procedure following a regular structure-based sequence alignment or to replace the traditional iterative refinement procedures based on residue-level dynamic programming algorithm in many structure alignment programs. PMID:19589133

ZettaBricks: A Language Compiler and Runtime System for Anyscale Computing

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

Amarasinghe, Saman

This grant supported the ZettaBricks and OpenTuner projects. ZettaBricks is a new implicitly parallel language and compiler where defining multiple implementations of multiple algorithms to solve a problem is the natural way of programming. ZettaBricks makes algorithmic choice a first class construct of the language. Choices are provided in a way that also allows our compiler to tune at a finer granularity. The ZettaBricks compiler autotunes programs by making both fine-grained as well as algorithmic choices. Choices also include different automatic parallelization techniques, data distributions, algorithmic parameters, transformations, and blocking. Additionally, ZettaBricks introduces novel techniques to autotune algorithms for differentmore » convergence criteria. When choosing between various direct and iterative methods, the ZettaBricks compiler is able to tune a program in such a way that delivers near-optimal efficiency for any desired level of accuracy. The compiler has the flexibility of utilizing different convergence criteria for the various components within a single algorithm, providing the user with accuracy choice alongside algorithmic choice. OpenTuner is a generalization of the experience gained in building an autotuner for ZettaBricks. OpenTuner is a new open source framework for building domain-specific multi-objective program autotuners. OpenTuner supports fully-customizable configuration representations, an extensible technique representation to allow for domain-specific techniques, and an easy to use interface for communicating with the program to be autotuned. A key capability inside OpenTuner is the use of ensembles of disparate search techniques simultaneously; techniques that perform well will dynamically be allocated a larger proportion of tests.« less

Introducing difference recurrence relations for faster semi-global alignment of long sequences.

PubMed

Suzuki, Hajime; Kasahara, Masahiro

2018-02-19

The read length of single-molecule DNA sequencers is reaching 1 Mb. Popular alignment software tools widely used for analyzing such long reads often take advantage of single-instruction multiple-data (SIMD) operations to accelerate calculation of dynamic programming (DP) matrices in the Smith-Waterman-Gotoh (SWG) algorithm with a fixed alignment start position at the origin. Nonetheless, 16-bit or 32-bit integers are necessary for storing the values in a DP matrix when sequences to be aligned are long; this situation hampers the use of the full SIMD width of modern processors. We proposed a faster semi-global alignment algorithm, "difference recurrence relations," that runs more rapidly than the state-of-the-art algorithm by a factor of 2.1. Instead of calculating and storing all the values in a DP matrix directly, our algorithm computes and stores mainly the differences between the values of adjacent cells in the matrix. Although the SWG algorithm and our algorithm can output exactly the same result, our algorithm mainly involves 8-bit integer operations, enabling us to exploit the full width of SIMD operations (e.g., 32) on modern processors. We also developed a library, libgaba, so that developers can easily integrate our algorithm into alignment programs. Our novel algorithm and optimized library implementation will facilitate accelerating nucleotide long-read analysis algorithms that use pairwise alignment stages. The library is implemented in the C programming language and available at https://github.com/ocxtal/libgaba .

Heuristic reusable dynamic programming: efficient updates of local sequence alignment.

PubMed

Hong, Changjin; Tewfik, Ahmed H

2009-01-01

Recomputation of the previously evaluated similarity results between biological sequences becomes inevitable when researchers realize errors in their sequenced data or when the researchers have to compare nearly similar sequences, e.g., in a family of proteins. We present an efficient scheme for updating local sequence alignments with an affine gap model. In principle, using the previous matching result between two amino acid sequences, we perform a forward-backward alignment to generate heuristic searching bands which are bounded by a set of suboptimal paths. Given a correctly updated sequence, we initially predict a new score of the alignment path for each contour to select the best candidates among them. Then, we run the Smith-Waterman algorithm in this confined space. Furthermore, our heuristic alignment for an updated sequence shows that it can be further accelerated by using reusable dynamic programming (rDP), our prior work. In this study, we successfully validate "relative node tolerance bound" (RNTB) in the pruned searching space. Furthermore, we improve the computational performance by quantifying the successful RNTB tolerance probability and switch to rDP on perturbation-resilient columns only. In our searching space derived by a threshold value of 90 percent of the optimal alignment score, we find that 98.3 percent of contours contain correctly updated paths. We also find that our method consumes only 25.36 percent of the runtime cost of sparse dynamic programming (sDP) method, and to only 2.55 percent of that of a normal dynamic programming with the Smith-Waterman algorithm.

Design and Evaluation of a Dynamic Programming Flight Routing Algorithm Using the Convective Weather Avoidance Model

NASA Technical Reports Server (NTRS)

Ng, Hok K.; Grabbe, Shon; Mukherjee, Avijit

2010-01-01

The optimization of traffic flows in congested airspace with varying convective weather is a challenging problem. One approach is to generate shortest routes between origins and destinations while meeting airspace capacity constraint in the presence of uncertainties, such as weather and airspace demand. This study focuses on development of an optimal flight path search algorithm that optimizes national airspace system throughput and efficiency in the presence of uncertainties. The algorithm is based on dynamic programming and utilizes the predicted probability that an aircraft will deviate around convective weather. It is shown that the running time of the algorithm increases linearly with the total number of links between all stages. The optimal routes minimize a combination of fuel cost and expected cost of route deviation due to convective weather. They are considered as alternatives to the set of coded departure routes which are predefined by FAA to reroute pre-departure flights around weather or air traffic constraints. A formula, which calculates predicted probability of deviation from a given flight path, is also derived. The predicted probability of deviation is calculated for all path candidates. Routes with the best probability are selected as optimal. The predicted probability of deviation serves as a computable measure of reliability in pre-departure rerouting. The algorithm can also be extended to automatically adjust its design parameters to satisfy the desired level of reliability.

Compensator improvement for multivariable control systems

NASA Technical Reports Server (NTRS)

Mitchell, J. R.; Mcdaniel, W. L., Jr.; Gresham, L. L.

1977-01-01

A theory and the associated numerical technique are developed for an iterative design improvement of the compensation for linear, time-invariant control systems with multiple inputs and multiple outputs. A strict constraint algorithm is used in obtaining a solution of the specified constraints of the control design. The result of the research effort is the multiple input, multiple output Compensator Improvement Program (CIP). The objective of the Compensator Improvement Program is to modify in an iterative manner the free parameters of the dynamic compensation matrix so that the system satisfies frequency domain specifications. In this exposition, the underlying principles of the multivariable CIP algorithm are presented and the practical utility of the program is illustrated with space vehicle related examples.

Adaptive dynamic programming for discrete-time linear quadratic regulation based on multirate generalised policy iteration

NASA Astrophysics Data System (ADS)

Chun, Tae Yoon; Lee, Jae Young; Park, Jin Bae; Choi, Yoon Ho

2018-06-01

In this paper, we propose two multirate generalised policy iteration (GPI) algorithms applied to discrete-time linear quadratic regulation problems. The proposed algorithms are extensions of the existing GPI algorithm that consists of the approximate policy evaluation and policy improvement steps. The two proposed schemes, named heuristic dynamic programming (HDP) and dual HDP (DHP), based on multirate GPI, use multi-step estimation (M-step Bellman equation) at the approximate policy evaluation step for estimating the value function and its gradient called costate, respectively. Then, we show that these two methods with the same update horizon can be considered equivalent in the iteration domain. Furthermore, monotonically increasing and decreasing convergences, so called value iteration (VI)-mode and policy iteration (PI)-mode convergences, are proved to hold for the proposed multirate GPIs. Further, general convergence properties in terms of eigenvalues are also studied. The data-driven online implementation methods for the proposed HDP and DHP are demonstrated and finally, we present the results of numerical simulations performed to verify the effectiveness of the proposed methods.

Implementation of the Hungarian Algorithm to Account for Ligand Symmetry and Similarity in Structure-Based Design

PubMed Central

2015-01-01

False negative docking outcomes for highly symmetric molecules are a barrier to the accurate evaluation of docking programs, scoring functions, and protocols. This work describes an implementation of a symmetry-corrected root-mean-square deviation (RMSD) method into the program DOCK based on the Hungarian algorithm for solving the minimum assignment problem, which dynamically assigns atom correspondence in molecules with symmetry. The algorithm adds only a trivial amount of computation time to the RMSD calculations and is shown to increase the reported overall docking success rate by approximately 5% when tested over 1043 receptor–ligand systems. For some families of protein systems the results are even more dramatic, with success rate increases up to 16.7%. Several additional applications of the method are also presented including as a pairwise similarity metric to compare molecules during de novo design, as a scoring function to rank-order virtual screening results, and for the analysis of trajectories from molecular dynamics simulation. The new method, including source code, is available to registered users of DOCK6 (http://dock.compbio.ucsf.edu). PMID:24410429

Sensitivity analysis of dynamic biological systems with time-delays.

PubMed

Wu, Wu Hsiung; Wang, Feng Sheng; Chang, Maw Shang

2010-10-15

Mathematical modeling has been applied to the study and analysis of complex biological systems for a long time. Some processes in biological systems, such as the gene expression and feedback control in signal transduction networks, involve a time delay. These systems are represented as delay differential equation (DDE) models. Numerical sensitivity analysis of a DDE model by the direct method requires the solutions of model and sensitivity equations with time-delays. The major effort is the computation of Jacobian matrix when computing the solution of sensitivity equations. The computation of partial derivatives of complex equations either by the analytic method or by symbolic manipulation is time consuming, inconvenient, and prone to introduce human errors. To address this problem, an automatic approach to obtain the derivatives of complex functions efficiently and accurately is necessary. We have proposed an efficient algorithm with an adaptive step size control to compute the solution and dynamic sensitivities of biological systems described by ordinal differential equations (ODEs). The adaptive direct-decoupled algorithm is extended to solve the solution and dynamic sensitivities of time-delay systems describing by DDEs. To save the human effort and avoid the human errors in the computation of partial derivatives, an automatic differentiation technique is embedded in the extended algorithm to evaluate the Jacobian matrix. The extended algorithm is implemented and applied to two realistic models with time-delays: the cardiovascular control system and the TNF-α signal transduction network. The results show that the extended algorithm is a good tool for dynamic sensitivity analysis on DDE models with less user intervention. By comparing with direct-coupled methods in theory, the extended algorithm is efficient, accurate, and easy to use for end users without programming background to do dynamic sensitivity analysis on complex biological systems with time-delays.

Genetic algorithms and genetic programming for multiscale modeling: Applications in materials science and chemistry and advances in scalability

NASA Astrophysics Data System (ADS)

Sastry, Kumara Narasimha

2007-03-01

Effective and efficient rnultiscale modeling is essential to advance both the science and synthesis in a, wide array of fields such as physics, chemistry, materials science; biology, biotechnology and pharmacology. This study investigates the efficacy and potential of rising genetic algorithms for rnultiscale materials modeling and addresses some of the challenges involved in designing competent algorithms that solve hard problems quickly, reliably and accurately. In particular, this thesis demonstrates the use of genetic algorithms (GAs) and genetic programming (GP) in multiscale modeling with the help of two non-trivial case studies in materials science and chemistry. The first case study explores the utility of genetic programming (GP) in multi-timescaling alloy kinetics simulations. In essence, GP is used to bridge molecular dynamics and kinetic Monte Carlo methods to span orders-of-magnitude in simulation time. Specifically, GP is used to regress symbolically an inline barrier function from a limited set of molecular dynamics simulations to enable kinetic Monte Carlo that simulate seconds of real time. Results on a non-trivial example of vacancy-assisted migration on a surface of a face-centered cubic (fcc) Copper-Cobalt (CuxCo 1-x) alloy show that GP predicts all barriers with 0.1% error from calculations for less than 3% of active configurations, independent of type of potentials used to obtain the learning set of barriers via molecular dynamics. The resulting method enables 2--9 orders-of-magnitude increase in real-time dynamics simulations taking 4--7 orders-of-magnitude less CPU time. The second case study presents the application of multiobjective genetic algorithms (MOGAs) in multiscaling quantum chemistry simulations. Specifically, MOGAs are used to bridge high-level quantum chemistry and semiempirical methods to provide accurate representation of complex molecular excited-state and ground-state behavior. Results on ethylene and benzene---two common building blocks in organic chemistry---indicate that MOGAs produce High-quality semiempirical methods that (1) are stable to small perturbations, (2) yield accurate configuration energies on untested and critical excited states, and (3) yield ab initio quality excited-state dynamics. The proposed method enables simulations of more complex systems to realistic, multi-picosecond timescales, well beyond previous attempts or expectation of human experts, and 2--3 orders-of-magnitude reduction in computational cost. While the two applications use simple evolutionary operators, in order to tackle more complex systems, their scalability and limitations have to be investigated. The second part of the thesis addresses some of the challenges involved with a successful design of genetic algorithms and genetic programming for multiscale modeling. The first issue addressed is the scalability of genetic programming, where facetwise models are built to assess the population size required by GP to ensure adequate supply of raw building blocks and also to ensure accurate decision-making between competing building blocks. This study also presents a design of competent genetic programming, where traditional fixed recombination operators are replaced by building and sampling probabilistic models of promising candidate programs. The proposed scalable GP, called extended compact GP (eCGP), combines the ideas from extended compact genetic algorithm (eCGA) and probabilistic incremental program evolution (PIPE) and adaptively identifies, propagates and exchanges important subsolutions of a search problem. Results show that eCGP scales cubically with problem size on both GP-easy and GP-hard problems. Finally, facetwise models are developed to explore limitations of scalability of MOGAs, where the scalability of multiobjective algorithms in reliably maintaining Pareto-optimal solutions is addressed. The results show that even when the building blocks are accurately identified, massive multimodality of the search problems can easily overwhelm the nicher (diversity preserving operator) and lead to exponential scale-up. Facetwise models are developed, which incorporate the combined effects of model accuracy, decision making, and sub-structure supply, as well as the effect of niching on the population sizing, to predict a limit on the growth rate of a maximum number of sub-structures that can compete in the two objectives to circumvent the failure of the niching method. The results show that if the number of competing building blocks between multiple objectives is less than the proposed limit, multiobjective GAs scale-up polynomially with the problem size on boundedly-difficult problems.

Interactive software tool to comprehend the calculation of optimal sequence alignments with dynamic programming.

PubMed

Ibarra, Ignacio L; Melo, Francisco

2010-07-01

Dynamic programming (DP) is a general optimization strategy that is successfully used across various disciplines of science. In bioinformatics, it is widely applied in calculating the optimal alignment between pairs of protein or DNA sequences. These alignments form the basis of new, verifiable biological hypothesis. Despite its importance, there are no interactive tools available for training and education on understanding the DP algorithm. Here, we introduce an interactive computer application with a graphical interface, for the purpose of educating students about DP. The program displays the DP scoring matrix and the resulting optimal alignment(s), while allowing the user to modify key parameters such as the values in the similarity matrix, the sequence alignment algorithm version and the gap opening/extension penalties. We hope that this software will be useful to teachers and students of bioinformatics courses, as well as researchers who implement the DP algorithm for diverse applications. The software is freely available at: http:/melolab.org/sat. The software is written in the Java computer language, thus it runs on all major platforms and operating systems including Windows, Mac OS X and LINUX. All inquiries or comments about this software should be directed to Francisco Melo at fmelo@bio.puc.cl.

Finding the Missing Physics: Simulating Polydisperse Polymer Melts

NASA Astrophysics Data System (ADS)

Rorrer, Nichoals; Dorgan, John

2014-03-01

A Monte Carlo algorithm has been developed to model polydisperse polymer melts. For the first time, this enables the specification of a predetermined molecular weight distribution for lattice based simulations. It is demonstrated how to map an arbitrary probability distributions onto a discrete number of chains residing on an fcc lattice. The resulting algorithm is able to simulate a wide variety of behaviors for polydisperse systems including confinement effects, shear flow, and parabolic flow. The dynamic version of the algorithm accurately captures Rouse dynamics for short polymer chains, and reptation-like dynamics for longer chain lengths.1 When polydispersity is introduced, smaller Rouse times and broadened the transition between different scaling regimes are observed. Rouse times also decrease under confinement for both polydisperse and monodisperse systems and chain length dependent migration effects are observed. The steady-state version of the algorithm enables the simulation of flow and when polydisperse systems are subject to parabolic (Poiseulle) flow, a migration phenomenon based on chain length is again present. These and other phenomena highlight the importance of including polydispersity in obtaining physically realistic simulations of polymeric melts. 1. Dorgan, J.R.; Rorrer, N.A.; Maupin, C.M., Macromolecules 2012, 45(21), 8833-8840. Work funded by the Fluid Dynamics program of the National Science Foundation under grant CBET-1067707.

Dynamic resource allocation in a hierarchical multiprocessor system: A preliminary study

NASA Technical Reports Server (NTRS)

Ngai, Tin-Fook

1986-01-01

An integrated system approach to dynamic resource allocation is proposed. Some of the problems in dynamic resource allocation and the relationship of these problems to system structures are examined. A general dynamic resource allocation scheme is presented. A hierarchial system architecture which dynamically maps between processor structure and programs at multiple levels of instantiations is described. Simulation experiments were conducted to study dynamic resource allocation on the proposed system. Preliminary evaluation based on simple dynamic resource allocation algorithms indicates that with the proposed system approach, the complexity of dynamic resource management could be significantly reduced while achieving reasonable effective dynamic resource allocation.

Heuristic algorithms for solving of the tool routing problem for CNC cutting machines

NASA Astrophysics Data System (ADS)

Chentsov, P. A.; Petunin, A. A.; Sesekin, A. N.; Shipacheva, E. N.; Sholohov, A. E.

2015-11-01

The article is devoted to the problem of minimizing the path of the cutting tool to shape cutting machines began. This problem can be interpreted as a generalized traveling salesman problem. Earlier version of the dynamic programming method to solve this problem was developed. Unfortunately, this method allows to process an amount not exceeding thirty circuits. In this regard, the task of constructing quasi-optimal route becomes relevant. In this paper we propose options for quasi-optimal greedy algorithms. Comparison of the results of exact and approximate algorithms is given.

Architecutres, Models, Algorithms, and Software Tools for Configurable Computing

DTIC Science & Technology

2000-03-06

and J.G. Nash. The gated interconnection network for dynamic programming. Plenum, 1988 . [18] Ju wook Jang, Heonchul Park, and Viktor K. Prasanna. A ...Sep. 1997. [2] C. Ebeling, D. C. Cronquist , P. Franklin and C. Fisher, "RaPiD - A configurable computing architecture for compute-intensive...ABSTRACT (Maximum 200 words) The Models, Algorithms, and Architectures for Reconfigurable Computing (MAARC) project developed a sound framework for

Simplified Calculation Of Solar Fluxes In Solar Receivers

NASA Technical Reports Server (NTRS)

Bhandari, Pradeep

1990-01-01

Simplified Calculation of Solar Flux Distribution on Side Wall of Cylindrical Cavity Solar Receivers computer program employs simple solar-flux-calculation algorithm for cylindrical-cavity-type solar receiver. Results compare favorably with those of more complicated programs. Applications include study of solar energy and transfer of heat, and space power/solar-dynamics engineering. Written in FORTRAN 77.

Forebody and base region real gas flow in severe planetary entry by a factored implicit numerical method. II - Equilibrium reactive gas

NASA Technical Reports Server (NTRS)

Davy, W. C.; Green, M. J.; Lombard, C. K.

1981-01-01

The factored-implicit, gas-dynamic algorithm has been adapted to the numerical simulation of equilibrium reactive flows. Changes required in the perfect gas version of the algorithm are developed, and the method of coupling gas-dynamic and chemistry variables is discussed. A flow-field solution that approximates a Jovian entry case was obtained by this method and compared with the same solution obtained by HYVIS, a computer program much used for the study of planetary entry. Comparison of surface pressure distribution and stagnation line shock-layer profiles indicates that the two solutions agree well.

Nonlinear dynamic macromodeling techniques for audio systems

NASA Astrophysics Data System (ADS)

Ogrodzki, Jan; Bieńkowski, Piotr

2015-09-01

This paper develops a modelling method and a models identification technique for the nonlinear dynamic audio systems. Identification is performed by means of a behavioral approach based on a polynomial approximation. This approach makes use of Discrete Fourier Transform and Harmonic Balance Method. A model of an audio system is first created and identified and then it is simulated in real time using an algorithm of low computational complexity. The algorithm consists in real time emulation of the system response rather than in simulation of the system itself. The proposed software is written in Python language using object oriented programming techniques. The code is optimized for a multithreads environment.

Distributed cooperative H∞ optimal tracking control of MIMO nonlinear multi-agent systems in strict-feedback form via adaptive dynamic programming

NASA Astrophysics Data System (ADS)

Luy, N. T.

2018-04-01

The design of distributed cooperative H∞ optimal controllers for multi-agent systems is a major challenge when the agents' models are uncertain multi-input and multi-output nonlinear systems in strict-feedback form in the presence of external disturbances. In this paper, first, the distributed cooperative H∞ optimal tracking problem is transformed into controlling the cooperative tracking error dynamics in affine form. Second, control schemes and online algorithms are proposed via adaptive dynamic programming (ADP) and the theory of zero-sum differential graphical games. The schemes use only one neural network (NN) for each agent instead of three from ADP to reduce computational complexity as well as avoid choosing initial NN weights for stabilising controllers. It is shown that despite not using knowledge of cooperative internal dynamics, the proposed algorithms not only approximate values to Nash equilibrium but also guarantee all signals, such as the NN weight approximation errors and the cooperative tracking errors in the closed-loop system, to be uniformly ultimately bounded. Finally, the effectiveness of the proposed method is shown by simulation results of an application to wheeled mobile multi-robot systems.

A Distributed Dynamic Programming-Based Solution for Load Management in Smart Grids

NASA Astrophysics Data System (ADS)

Zhang, Wei; Xu, Yinliang; Li, Sisi; Zhou, MengChu; Liu, Wenxin; Xu, Ying

2018-03-01

Load management is being recognized as an important option for active user participation in the energy market. Traditional load management methods usually require a centralized powerful control center and a two-way communication network between the system operators and energy end-users. The increasing user participation in smart grids may limit their applications. In this paper, a distributed solution for load management in emerging smart grids is proposed. The load management problem is formulated as a constrained optimization problem aiming at maximizing the overall utility of users while meeting the requirement for load reduction requested by the system operator, and is solved by using a distributed dynamic programming algorithm. The algorithm is implemented via a distributed framework and thus can deliver a highly desired distributed solution. It avoids the required use of a centralized coordinator or control center, and can achieve satisfactory outcomes for load management. Simulation results with various test systems demonstrate its effectiveness.

Model-Based Speech Signal Coding Using Optimized Temporal Decomposition for Storage and Broadcasting Applications

NASA Astrophysics Data System (ADS)

Athaudage, Chandranath R. N.; Bradley, Alan B.; Lech, Margaret

2003-12-01

A dynamic programming-based optimization strategy for a temporal decomposition (TD) model of speech and its application to low-rate speech coding in storage and broadcasting is presented. In previous work with the spectral stability-based event localizing (SBEL) TD algorithm, the event localization was performed based on a spectral stability criterion. Although this approach gave reasonably good results, there was no assurance on the optimality of the event locations. In the present work, we have optimized the event localizing task using a dynamic programming-based optimization strategy. Simulation results show that an improved TD model accuracy can be achieved. A methodology of incorporating the optimized TD algorithm within the standard MELP speech coder for the efficient compression of speech spectral information is also presented. The performance evaluation results revealed that the proposed speech coding scheme achieves 50%-60% compression of speech spectral information with negligible degradation in the decoded speech quality.

Pyrite: A blender plugin for visualizing molecular dynamics simulations using industry-standard rendering techniques.

PubMed

Rajendiran, Nivedita; Durrant, Jacob D

2018-05-05

Molecular dynamics (MD) simulations provide critical insights into many biological mechanisms. Programs such as VMD, Chimera, and PyMOL can produce impressive simulation visualizations, but they lack many advanced rendering algorithms common in the film and video-game industries. In contrast, the modeling program Blender includes such algorithms but cannot import MD-simulation data. MD trajectories often require many gigabytes of memory/disk space, complicating Blender import. We present Pyrite, a Blender plugin that overcomes these limitations. Pyrite allows researchers to visualize MD simulations within Blender, with full access to Blender's cutting-edge rendering techniques. We expect Pyrite-generated images to appeal to students and non-specialists alike. A copy of the plugin is available at http://durrantlab.com/pyrite/, released under the terms of the GNU General Public License Version 3. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

A computational algorithm for spacecraft control and momentum management

NASA Technical Reports Server (NTRS)

Dzielski, John; Bergmann, Edward; Paradiso, Joseph

1990-01-01

Developments in the area of nonlinear control theory have shown how coordinate changes in the state and input spaces of a dynamical system can be used to transform certain nonlinear differential equations into equivalent linear equations. These techniques are applied to the control of a spacecraft equipped with momentum exchange devices. An optimal control problem is formulated that incorporates a nonlinear spacecraft model. An algorithm is developed for solving the optimization problem using feedback linearization to transform to an equivalent problem involving a linear dynamical constraint and a functional approximation technique to solve for the linear dynamics in terms of the control. The original problem is transformed into an unconstrained nonlinear quadratic program that yields an approximate solution to the original problem. Two examples are presented to illustrate the results.

Adaptive critic designs for discrete-time zero-sum games with application to H(infinity) control.

PubMed

Al-Tamimi, Asma; Abu-Khalaf, Murad; Lewis, Frank L

2007-02-01

In this correspondence, adaptive critic approximate dynamic programming designs are derived to solve the discrete-time zero-sum game in which the state and action spaces are continuous. This results in a forward-in-time reinforcement learning algorithm that converges to the Nash equilibrium of the corresponding zero-sum game. The results in this correspondence can be thought of as a way to solve the Riccati equation of the well-known discrete-time H(infinity) optimal control problem forward in time. Two schemes are presented, namely: 1) a heuristic dynamic programming and 2) a dual-heuristic dynamic programming, to solve for the value function and the costate of the game, respectively. An H(infinity) autopilot design for an F-16 aircraft is presented to illustrate the results.

Generic Kalman Filter Software

NASA Technical Reports Server (NTRS)

Lisano, Michael E., II; Crues, Edwin Z.

2005-01-01

The Generic Kalman Filter (GKF) software provides a standard basis for the development of application-specific Kalman-filter programs. Historically, Kalman filters have been implemented by customized programs that must be written, coded, and debugged anew for each unique application, then tested and tuned with simulated or actual measurement data. Total development times for typical Kalman-filter application programs have ranged from months to weeks. The GKF software can simplify the development process and reduce the development time by eliminating the need to re-create the fundamental implementation of the Kalman filter for each new application. The GKF software is written in the ANSI C programming language. It contains a generic Kalman-filter-development directory that, in turn, contains a code for a generic Kalman filter function; more specifically, it contains a generically designed and generically coded implementation of linear, linearized, and extended Kalman filtering algorithms, including algorithms for state- and covariance-update and -propagation functions. The mathematical theory that underlies the algorithms is well known and has been reported extensively in the open technical literature. Also contained in the directory are a header file that defines generic Kalman-filter data structures and prototype functions and template versions of application-specific subfunction and calling navigation/estimation routine code and headers. Once the user has provided a calling routine and the required application-specific subfunctions, the application-specific Kalman-filter software can be compiled and executed immediately. During execution, the generic Kalman-filter function is called from a higher-level navigation or estimation routine that preprocesses measurement data and post-processes output data. The generic Kalman-filter function uses the aforementioned data structures and five implementation- specific subfunctions, which have been developed by the user on the basis of the aforementioned templates. The GKF software can be used to develop many different types of unfactorized Kalman filters. A developer can choose to implement either a linearized or an extended Kalman filter algorithm, without having to modify the GKF software. Control dynamics can be taken into account or neglected in the filter-dynamics model. Filter programs developed by use of the GKF software can be made to propagate equations of motion for linear or nonlinear dynamical systems that are deterministic or stochastic. In addition, filter programs can be made to operate in user-selectable "covariance analysis" and "propagation-only" modes that are useful in design and development stages.

Control algorithms for dynamic windows for residential buildings

DOE PAGES

Firlag, Szymon; Yazdanian, Mehrangiz; Curcija, Charlie; ...

2015-09-30

This study analyzes the influence of control algorithms for dynamic windows on energy consumption, number of hours of retracted shades during daylight and shade operations. Five different control algorithms - heating/cooling, simple rules, perfect citizen, heat flow and predictive weather were developed and compared. The performance of a typical residential building was modeled with EnergyPlus. The program Widow was used to generate a Bi-Directional Distribution Function (BSDF) for two window configurations. The BSDF was exported to EnergyPlus using the IDF file format. The EMS feature in EnergyPlus was used to develop custom control algorithms. The calculations were made for fourmore » locations with diverse climate. The results showed that: (a) use of automated shading with proposed control algorithms can reduce the site energy in the range of 11.6-13.0%; in regard to source (primary) energy in the range of 20.1-21.6%, (b) the differences between algorithms in regard to energy savings are not high, (c) the differences between algorithms in regard to number of hours of retracted shades are visible, (e) the control algorithms have a strong influence on shade operation and oscillation of shade can occur, (d) additional energy consumption caused by motor, sensors and a small microprocessor in the analyzed case is very small.« less

Reinforcement learning for partially observable dynamic processes: adaptive dynamic programming using measured output data.

PubMed

Lewis, F L; Vamvoudakis, Kyriakos G

2011-02-01

Approximate dynamic programming (ADP) is a class of reinforcement learning methods that have shown their importance in a variety of applications, including feedback control of dynamical systems. ADP generally requires full information about the system internal states, which is usually not available in practical situations. In this paper, we show how to implement ADP methods using only measured input/output data from the system. Linear dynamical systems with deterministic behavior are considered herein, which are systems of great interest in the control system community. In control system theory, these types of methods are referred to as output feedback (OPFB). The stochastic equivalent of the systems dealt with in this paper is a class of partially observable Markov decision processes. We develop both policy iteration and value iteration algorithms that converge to an optimal controller that requires only OPFB. It is shown that, similar to Q -learning, the new methods have the important advantage that knowledge of the system dynamics is not needed for the implementation of these learning algorithms or for the OPFB control. Only the order of the system, as well as an upper bound on its "observability index," must be known. The learned OPFB controller is in the form of a polynomial autoregressive moving-average controller that has equivalent performance with the optimal state variable feedback gain.

A new approach of optimal control for a class of continuous-time chaotic systems by an online ADP algorithm

NASA Astrophysics Data System (ADS)

Song, Rui-Zhuo; Xiao, Wen-Dong; Wei, Qing-Lai

2014-05-01

We develop an online adaptive dynamic programming (ADP) based optimal control scheme for continuous-time chaotic systems. The idea is to use the ADP algorithm to obtain the optimal control input that makes the performance index function reach an optimum. The expression of the performance index function for the chaotic system is first presented. The online ADP algorithm is presented to achieve optimal control. In the ADP structure, neural networks are used to construct a critic network and an action network, which can obtain an approximate performance index function and the control input, respectively. It is proven that the critic parameter error dynamics and the closed-loop chaotic systems are uniformly ultimately bounded exponentially. Our simulation results illustrate the performance of the established optimal control method.

CAMERRA: An analysis tool for the computation of conformational dynamics by evaluating residue-residue associations.

PubMed

Johnson, Quentin R; Lindsay, Richard J; Shen, Tongye

2018-02-21

A computational method which extracts the dominant motions from an ensemble of biomolecular conformations via a correlation analysis of residue-residue contacts is presented. The algorithm first renders the structural information into contact matrices, then constructs the collective modes based on the correlated dynamics of a selected set of dynamic contacts. Associated programs can bridge the results for further visualization using graphics software. The aim of this method is to provide an analysis of conformations of biopolymers from the contact viewpoint. It may assist a systematical uncovering of conformational switching mechanisms existing in proteins and biopolymer systems in general by statistical analysis of simulation snapshots. In contrast to conventional correlation analyses of Cartesian coordinates (such as distance covariance analysis and Cartesian principal component analysis), this program also provides an alternative way to locate essential collective motions in general. Herein, we detail the algorithm in a stepwise manner and comment on the importance of the method as applied to decoding allosteric mechanisms. © 2018 Wiley Periodicals, Inc. © 2018 Wiley Periodicals, Inc.

Benchmarking the D-Wave Two

NASA Astrophysics Data System (ADS)

Job, Joshua; Wang, Zhihui; Rønnow, Troels; Troyer, Matthias; Lidar, Daniel

2014-03-01

We report on experimental work benchmarking the performance of the D-Wave Two programmable annealer on its native Ising problem, and a comparison to available classical algorithms. In this talk we will focus on the comparison with an algorithm originally proposed and implemented by Alex Selby. This algorithm uses dynamic programming to repeatedly optimize over randomly selected maximal induced trees of the problem graph starting from a random initial state. If one is looking for a quantum advantage over classical algorithms, one should compare to classical algorithms which are designed and optimized to maximally take advantage of the structure of the type of problem one is using for the comparison. In that light, this classical algorithm should serve as a good gauge for any potential quantum speedup for the D-Wave Two.

Evaluation of a new parallel numerical parameter optimization algorithm for a dynamical system

NASA Astrophysics Data System (ADS)

Duran, Ahmet; Tuncel, Mehmet

2016-10-01

It is important to have a scalable parallel numerical parameter optimization algorithm for a dynamical system used in financial applications where time limitation is crucial. We use Message Passing Interface parallel programming and present such a new parallel algorithm for parameter estimation. For example, we apply the algorithm to the asset flow differential equations that have been developed and analyzed since 1989 (see [3-6] and references contained therein). We achieved speed-up for some time series to run up to 512 cores (see [10]). Unlike [10], we consider more extensive financial market situations, for example, in presence of low volatility, high volatility and stock market price at a discount/premium to its net asset value with varying magnitude, in this work. Moreover, we evaluated the convergence of the model parameter vector, the nonlinear least squares error and maximum improvement factor to quantify the success of the optimization process depending on the number of initial parameter vectors.

Integration of Libration Point Orbit Dynamics into a Universal 3-D Autonomous Formation Flying Algorithm

NASA Technical Reports Server (NTRS)

Folta, David; Bauer, Frank H. (Technical Monitor)

2001-01-01

The autonomous formation flying control algorithm developed by the Goddard Space Flight Center (GSFC) for the New Millennium Program (NMP) Earth Observing-1 (EO-1) mission is investigated for applicability to libration point orbit formations. In the EO-1 formation-flying algorithm, control is accomplished via linearization about a reference transfer orbit with a state transition matrix (STM) computed from state inputs. The effect of libration point orbit dynamics on this algorithm architecture is explored via computation of STMs using the flight proven code, a monodromy matrix developed from a N-body model of a libration orbit, and a standard STM developed from the gravitational and coriolis effects as measured at the libration point. A comparison of formation flying Delta-Vs calculated from these methods is made to a standard linear quadratic regulator (LQR) method. The universal 3-D approach is optimal in the sense that it can be accommodated as an open-loop or closed-loop control using only state information.

Review: Optimization methods for groundwater modeling and management

NASA Astrophysics Data System (ADS)

Yeh, William W.-G.

2015-09-01

Optimization methods have been used in groundwater modeling as well as for the planning and management of groundwater systems. This paper reviews and evaluates the various optimization methods that have been used for solving the inverse problem of parameter identification (estimation), experimental design, and groundwater planning and management. Various model selection criteria are discussed, as well as criteria used for model discrimination. The inverse problem of parameter identification concerns the optimal determination of model parameters using water-level observations. In general, the optimal experimental design seeks to find sampling strategies for the purpose of estimating the unknown model parameters. A typical objective of optimal conjunctive-use planning of surface water and groundwater is to minimize the operational costs of meeting water demand. The optimization methods include mathematical programming techniques such as linear programming, quadratic programming, dynamic programming, stochastic programming, nonlinear programming, and the global search algorithms such as genetic algorithms, simulated annealing, and tabu search. Emphasis is placed on groundwater flow problems as opposed to contaminant transport problems. A typical two-dimensional groundwater flow problem is used to explain the basic formulations and algorithms that have been used to solve the formulated optimization problems.

A biologically inspired neural network for dynamic programming.

PubMed

Francelin Romero, R A; Kacpryzk, J; Gomide, F

2001-12-01

An artificial neural network with a two-layer feedback topology and generalized recurrent neurons, for solving nonlinear discrete dynamic optimization problems, is developed. A direct method to assign the weights of neural networks is presented. The method is based on Bellmann's Optimality Principle and on the interchange of information which occurs during the synaptic chemical processing among neurons. The neural network based algorithm is an advantageous approach for dynamic programming due to the inherent parallelism of the neural networks; further it reduces the severity of computational problems that can occur in methods like conventional methods. Some illustrative application examples are presented to show how this approach works out including the shortest path and fuzzy decision making problems.

Transactions of the Army Conference on Applied Mathematics and Computing (1st) Held at Washington, DC on 9-11 May 1983

DTIC Science & Technology

1984-02-01

I . . . . . . An Introduction to Geometric Programming Patrick D. Allen and David W. Baker . . . . . . , . . . . . . . Space and Time...Zarwyn, US-Army Electronics R & D Comhiand GEOMETRIC PROGRAMING SPACE AND TIFFE ANALYSIS IN DYNAMIC PROGRAMING ALGORITHMS Renne..tf Stizti, AkeanXa...physical and parameter space can be connected by asymptotic matching. The purpose of the asymptotic analysis is to define the simplest problems

Thermodynamics of RNA structures by Wang–Landau sampling

PubMed Central

Lou, Feng; Clote, Peter

2010-01-01

Motivation: Thermodynamics-based dynamic programming RNA secondary structure algorithms have been of immense importance in molecular biology, where applications range from the detection of novel selenoproteins using expressed sequence tag (EST) data, to the determination of microRNA genes and their targets. Dynamic programming algorithms have been developed to compute the minimum free energy secondary structure and partition function of a given RNA sequence, the minimum free-energy and partition function for the hybridization of two RNA molecules, etc. However, the applicability of dynamic programming methods depends on disallowing certain types of interactions (pseudoknots, zig-zags, etc.), as their inclusion renders structure prediction an nondeterministic polynomial time (NP)-complete problem. Nevertheless, such interactions have been observed in X-ray structures. Results: A non-Boltzmannian Monte Carlo algorithm was designed by Wang and Landau to estimate the density of states for complex systems, such as the Ising model, that exhibit a phase transition. In this article, we apply the Wang-Landau (WL) method to compute the density of states for secondary structures of a given RNA sequence, and for hybridizations of two RNA sequences. Our method is shown to be much faster than existent software, such as RNAsubopt. From density of states, we compute the partition function over all secondary structures and over all pseudoknot-free hybridizations. The advantage of the WL method is that by adding a function to evaluate the free energy of arbitary pseudoknotted structures and of arbitrary hybridizations, we can estimate thermodynamic parameters for situations known to be NP-complete. This extension to pseudoknots will be made in the sequel to this article; in contrast, the current article describes the WL algorithm applied to pseudoknot-free secondary structures and hybridizations. Availability: The WL RNA hybridization web server is under construction at http://bioinformatics.bc.edu/clotelab/. Contact: clote@bc.edu PMID:20529917

The development of a dynamic software for the user interaction from the geographic information system environment with the database of the calibration site of the satellite remote electro-optic sensors

NASA Astrophysics Data System (ADS)

Zyelyk, Ya. I.; Semeniv, O. V.

2015-12-01

The state of the problem of the post-launch calibration of the satellite electro-optic remote sensors and its solutions in Ukraine is analyzed. The database is improved and dynamic services for user interaction with database from the environment of open geographical information system Quantum GIS for information support of calibration activities are created. A dynamic application under QGIS is developed, implementing these services in the direction of the possibility of data entering, editing and extraction from the database, using the technology of object-oriented programming and of modern complex program design patterns. The functional and algorithmic support of this dynamic software and its interface are developed.

Quantum-Inspired Maximizer

NASA Technical Reports Server (NTRS)

Zak, Michail

2008-01-01

A report discusses an algorithm for a new kind of dynamics based on a quantum- classical hybrid-quantum-inspired maximizer. The model is represented by a modified Madelung equation in which the quantum potential is replaced by different, specially chosen 'computational' potential. As a result, the dynamics attains both quantum and classical properties: it preserves superposition and entanglement of random solutions, while allowing one to measure its state variables, using classical methods. Such optimal combination of characteristics is a perfect match for quantum-inspired computing. As an application, an algorithm for global maximum of an arbitrary integrable function is proposed. The idea of the proposed algorithm is very simple: based upon the Quantum-inspired Maximizer (QIM), introduce a positive function to be maximized as the probability density to which the solution is attracted. Then the larger value of this function will have the higher probability to appear. Special attention is paid to simulation of integer programming and NP-complete problems. It is demonstrated that the problem of global maximum of an integrable function can be found in polynomial time by using the proposed quantum- classical hybrid. The result is extended to a constrained maximum with applications to integer programming and TSP (Traveling Salesman Problem).

Interactive outlining: an improved approach using active contours

NASA Astrophysics Data System (ADS)

Daneels, Dirk; van Campenhout, David; Niblack, Carlton W.; Equitz, Will; Barber, Ron; Fierens, Freddy

1993-04-01

The purpose of our work is to outline objects on images in an interactive environment. We use an improved method based on energy minimizing active contours or `snakes.' Kass et al., proposed a variational technique; Amini used dynamic programming; and Williams and Shah introduced a fast, greedy algorithm. We combine the advantages of the latter two methods in a two-stage algorithm. The first stage is a greedy procedure that provides fast initial convergence. It is enhanced with a cost term that extends over a large number of points to avoid oscillations. The second stage, when accuracy becomes important, uses dynamic programming. This step is accelerated by the use of alternating search neighborhoods and by dropping stable points from the iterations. We have also added several features for user interaction. First, the user can define points of high confidence. Mathematically, this results in an extra cost term and, in that way, the robustness in difficult areas (e.g., noisy edges, sharp corners) is improved. We also give the user the possibility of incremental contour tracking, thus providing feedback on the refinement process. The algorithm has been tested on numerous photographic clip art images and extensive tests on medical images are in progress.

Automated Dynamic Demand Response Implementation on a Micro-grid

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

Kuppannagari, Sanmukh R.; Kannan, Rajgopal; Chelmis, Charalampos

In this paper, we describe a system for real-time automated Dynamic and Sustainable Demand Response with sparse data consumption prediction implemented on the University of Southern California campus microgrid. Supply side approaches to resolving energy supply-load imbalance do not work at high levels of renewable energy penetration. Dynamic Demand Response (D 2R) is a widely used demand-side technique to dynamically adjust electricity consumption during peak load periods. Our D 2R system consists of accurate machine learning based energy consumption forecasting models that work with sparse data coupled with fast and sustainable load curtailment optimization algorithms that provide the ability tomore » dynamically adapt to changing supply-load imbalances in near real-time. Our Sustainable DR (SDR) algorithms attempt to distribute customer curtailment evenly across sub-intervals during a DR event and avoid expensive demand peaks during a few sub-intervals. It also ensures that each customer is penalized fairly in order to achieve the targeted curtailment. We develop near linear-time constant-factor approximation algorithms along with Polynomial Time Approximation Schemes (PTAS) for SDR curtailment that minimizes the curtailment error defined as the difference between the target and achieved curtailment values. Our SDR curtailment problem is formulated as an Integer Linear Program that optimally matches customers to curtailment strategies during a DR event while also explicitly accounting for customer strategy switching overhead as a constraint. We demonstrate the results of our D 2R system using real data from experiments performed on the USC smartgrid and show that 1) our prediction algorithms can very accurately predict energy consumption even with noisy or missing data and 2) our curtailment algorithms deliver DR with extremely low curtailment errors in the 0.01-0.05 kWh range.« less

Dynamic Modeling, Model-Based Control, and Optimization of Solid Oxide Fuel Cells

NASA Astrophysics Data System (ADS)

Spivey, Benjamin James

2011-07-01

Solid oxide fuel cells are a promising option for distributed stationary power generation that offers efficiencies ranging from 50% in stand-alone applications to greater than 80% in cogeneration. To advance SOFC technology for widespread market penetration, the SOFC should demonstrate improved cell lifetime and load-following capability. This work seeks to improve lifetime through dynamic analysis of critical lifetime variables and advanced control algorithms that permit load-following while remaining in a safe operating zone based on stress analysis. Control algorithms typically have addressed SOFC lifetime operability objectives using unconstrained, single-input-single-output control algorithms that minimize thermal transients. Existing SOFC controls research has not considered maximum radial thermal gradients or limits on absolute temperatures in the SOFC. In particular, as stress analysis demonstrates, the minimum cell temperature is the primary thermal stress driver in tubular SOFCs. This dissertation presents a dynamic, quasi-two-dimensional model for a high-temperature tubular SOFC combined with ejector and prereformer models. The model captures dynamics of critical thermal stress drivers and is used as the physical plant for closed-loop control simulations. A constrained, MIMO model predictive control algorithm is developed and applied to control the SOFC. Closed-loop control simulation results demonstrate effective load-following, constraint satisfaction for critical lifetime variables, and disturbance rejection. Nonlinear programming is applied to find the optimal SOFC size and steady-state operating conditions to minimize total system costs.

Approximate dynamic programming for optimal stationary control with control-dependent noise.

PubMed

Jiang, Yu; Jiang, Zhong-Ping

2011-12-01

This brief studies the stochastic optimal control problem via reinforcement learning and approximate/adaptive dynamic programming (ADP). A policy iteration algorithm is derived in the presence of both additive and multiplicative noise using Itô calculus. The expectation of the approximated cost matrix is guaranteed to converge to the solution of some algebraic Riccati equation that gives rise to the optimal cost value. Moreover, the covariance of the approximated cost matrix can be reduced by increasing the length of time interval between two consecutive iterations. Finally, a numerical example is given to illustrate the efficiency of the proposed ADP methodology.

Online Learning Flight Control for Intelligent Flight Control Systems (IFCS)

NASA Technical Reports Server (NTRS)

Niewoehner, Kevin R.; Carter, John (Technical Monitor)

2001-01-01

The research accomplishments for the cooperative agreement 'Online Learning Flight Control for Intelligent Flight Control Systems (IFCS)' include the following: (1) previous IFC program data collection and analysis; (2) IFC program support site (configured IFC systems support network, configured Tornado/VxWorks OS development system, made Configuration and Documentation Management Systems Internet accessible); (3) Airborne Research Test Systems (ARTS) II Hardware (developed hardware requirements specification, developing environmental testing requirements, hardware design, and hardware design development); (4) ARTS II software development laboratory unit (procurement of lab style hardware, configured lab style hardware, and designed interface module equivalent to ARTS II faceplate); (5) program support documentation (developed software development plan, configuration management plan, and software verification and validation plan); (6) LWR algorithm analysis (performed timing and profiling on algorithm); (7) pre-trained neural network analysis; (8) Dynamic Cell Structures (DCS) Neural Network Analysis (performing timing and profiling on algorithm); and (9) conducted technical interchange and quarterly meetings to define IFC research goals.

Managing Algorithmic Skeleton Nesting Requirements in Realistic Image Processing Applications: The Case of the SKiPPER-II Parallel Programming Environment's Operating Model

NASA Astrophysics Data System (ADS)

Coudarcher, Rémi; Duculty, Florent; Serot, Jocelyn; Jurie, Frédéric; Derutin, Jean-Pierre; Dhome, Michel

2005-12-01

SKiPPER is a SKeleton-based Parallel Programming EnviRonment being developed since 1996 and running at LASMEA Laboratory, the Blaise-Pascal University, France. The main goal of the project was to demonstrate the applicability of skeleton-based parallel programming techniques to the fast prototyping of reactive vision applications. This paper deals with the special features embedded in the latest version of the project: algorithmic skeleton nesting capabilities and a fully dynamic operating model. Throughout the case study of a complete and realistic image processing application, in which we have pointed out the requirement for skeleton nesting, we are presenting the operating model of this feature. The work described here is one of the few reported experiments showing the application of skeleton nesting facilities for the parallelisation of a realistic application, especially in the area of image processing. The image processing application we have chosen is a 3D face-tracking algorithm from appearance.

Tracking Subpixel Targets with Critically Sampled Optical Sensors

DTIC Science & Technology

2012-09-01

5 [32]. The Viterbi algorithm is a dynamic programming method for calculating the MAP in O(tn2) time . The most common use of this algorithm is in the... method to detect subpixel point targets using the sensor’s PSF as an identifying characteristic. Using matched filtering theory, a measure is defined to...ocean surface beneath the cloud will have a different distribution. While the basic methods will adapt to changes in cloud cover over time , it is also

A concept for a fuel efficient flight planning aid for general aviation

NASA Technical Reports Server (NTRS)

Collins, B. P.; Haines, A. L.; Wales, C. J.

1982-01-01

A core equation for estimation of fuel burn from path profile data was developed. This equation was used as a necessary ingredient in a dynamic program to define a fuel efficient flight path. The resultant algorithm is oriented toward use by general aviation. The pilot provides a description of the desired ground track, standard aircraft parameters, and weather at selected waypoints. The algorithm then derives the fuel efficient altitudes and velocities at the waypoints.

Probabilistic DHP adaptive critic for nonlinear stochastic control systems.

PubMed

Herzallah, Randa

2013-06-01

Following the recently developed algorithms for fully probabilistic control design for general dynamic stochastic systems (Herzallah & Káarnáy, 2011; Kárný, 1996), this paper presents the solution to the probabilistic dual heuristic programming (DHP) adaptive critic method (Herzallah & Káarnáy, 2011) and randomized control algorithm for stochastic nonlinear dynamical systems. The purpose of the randomized control input design is to make the joint probability density function of the closed loop system as close as possible to a predetermined ideal joint probability density function. This paper completes the previous work (Herzallah & Káarnáy, 2011; Kárný, 1996) by formulating and solving the fully probabilistic control design problem on the more general case of nonlinear stochastic discrete time systems. A simulated example is used to demonstrate the use of the algorithm and encouraging results have been obtained. Copyright © 2013 Elsevier Ltd. All rights reserved.

A new procedure for dynamic adaption of three-dimensional unstructured grids

NASA Technical Reports Server (NTRS)

Biswas, Rupak; Strawn, Roger

1993-01-01

A new procedure is presented for the simultaneous coarsening and refinement of three-dimensional unstructured tetrahedral meshes. This algorithm allows for localized grid adaption that is used to capture aerodynamic flow features such as vortices and shock waves in helicopter flowfield simulations. The mesh-adaption algorithm is implemented in the C programming language and uses a data structure consisting of a series of dynamically-allocated linked lists. These lists allow the mesh connectivity to be rapidly reconstructed when individual mesh points are added and/or deleted. The algorithm allows the mesh to change in an anisotropic manner in order to efficiently resolve directional flow features. The procedure has been successfully implemented on a single processor of a Cray Y-MP computer. Two sample cases are presented involving three-dimensional transonic flow. Computed results show good agreement with conventional structured-grid solutions for the Euler equations.

Synthesis of concentric circular antenna arrays using dragonfly algorithm

NASA Astrophysics Data System (ADS)

Babayigit, B.

2018-05-01

Due to the strong non-linear relationship between the array factor and the array elements, concentric circular antenna array (CCAA) synthesis problem is challenging. Nature-inspired optimisation techniques have been playing an important role in solving array synthesis problems. Dragonfly algorithm (DA) is a novel nature-inspired optimisation technique which is based on the static and dynamic swarming behaviours of dragonflies in nature. This paper presents the design of CCAAs to get low sidelobes using DA. The effectiveness of the proposed DA is investigated in two different (with and without centre element) cases of two three-ring (having 4-, 6-, 8-element or 8-, 10-, 12-element) CCAA design. The radiation pattern of each design cases is obtained by finding optimal excitation weights of the array elements using DA. Simulation results show that the proposed algorithm outperforms the other state-of-the-art techniques (symbiotic organisms search, biogeography-based optimisation, sequential quadratic programming, opposition-based gravitational search algorithm, cat swarm optimisation, firefly algorithm, evolutionary programming) for all design cases. DA can be a promising technique for electromagnetic problems.

Open-Universe Theory for Bayesian Inference, Decision, and Sensing (OUTBIDS)

DTIC Science & Technology

2014-01-01

using a novel dynamic programming algorithm [6]. The second allows for tensor data, in which observations at a given time step exhibit...unlimited. 5 We developed a dynamical tensor model that gives far better estimation and system- identification results than the standard vectorization...inference. Third, unlike prior work that learns different pieces of the model independently, use matching between 3D models and 2D views and/or voting

Network Speech Systems Technology Program.

DTIC Science & Technology

1980-09-30

ognized that the lumped-speaker approximation could be extended even more generally to include cases of combined circuit-switched speech and packet...based on these tables. The first function is an im- portant element of the more general task of system control for a switched network, which in...programs are in preparation, as described below, for both steady-state evaluation and dynamic performance simulation of the algorithm in general

Analysis of spacecraft data

NASA Technical Reports Server (NTRS)

1984-01-01

A software program for the production and analysis of data from the Dynamics Explorer-A (DE-A) satellite was maintained and modified and new software initiated. A capability was developed to process DE-A plasma-wave instrument mission analysis files on the Tektronic 4027 color CRT, for which two programs were written. The algorithm for the calibration lookup table for the plasma-wave instrument data was modified and verified, and a production program to generate color FR-80 spectrograms was written.

A versatile pitch tracking algorithm: from human speech to killer whale vocalizations.

PubMed

Shapiro, Ari Daniel; Wang, Chao

2009-07-01

In this article, a pitch tracking algorithm [named discrete logarithmic Fourier transformation-pitch detection algorithm (DLFT-PDA)], originally designed for human telephone speech, was modified for killer whale vocalizations. The multiple frequency components of some of these vocalizations demand a spectral (rather than temporal) approach to pitch tracking. The DLFT-PDA algorithm derives reliable estimations of pitch and the temporal change of pitch from the harmonic structure of the vocal signal. Scores from both estimations are combined in a dynamic programming search to find a smooth pitch track. The algorithm is capable of tracking killer whale calls that contain simultaneous low and high frequency components and compares favorably across most signal to noise ratio ranges to the peak-picking and sidewinder algorithms that have been used for tracking killer whale vocalizations previously.

Discrete-Time Stable Generalized Self-Learning Optimal Control With Approximation Errors.

PubMed

Wei, Qinglai; Li, Benkai; Song, Ruizhuo

2018-04-01

In this paper, a generalized policy iteration (GPI) algorithm with approximation errors is developed for solving infinite horizon optimal control problems for nonlinear systems. The developed stable GPI algorithm provides a general structure of discrete-time iterative adaptive dynamic programming algorithms, by which most of the discrete-time reinforcement learning algorithms can be described using the GPI structure. It is for the first time that approximation errors are explicitly considered in the GPI algorithm. The properties of the stable GPI algorithm with approximation errors are analyzed. The admissibility of the approximate iterative control law can be guaranteed if the approximation errors satisfy the admissibility criteria. The convergence of the developed algorithm is established, which shows that the iterative value function is convergent to a finite neighborhood of the optimal performance index function, if the approximate errors satisfy the convergence criterion. Finally, numerical examples and comparisons are presented.

Parallel Computing Strategies for Irregular Algorithms

NASA Technical Reports Server (NTRS)

Biswas, Rupak; Oliker, Leonid; Shan, Hongzhang; Biegel, Bryan (Technical Monitor)

2002-01-01

Parallel computing promises several orders of magnitude increase in our ability to solve realistic computationally-intensive problems, but relies on their efficient mapping and execution on large-scale multiprocessor architectures. Unfortunately, many important applications are irregular and dynamic in nature, making their effective parallel implementation a daunting task. Moreover, with the proliferation of parallel architectures and programming paradigms, the typical scientist is faced with a plethora of questions that must be answered in order to obtain an acceptable parallel implementation of the solution algorithm. In this paper, we consider three representative irregular applications: unstructured remeshing, sparse matrix computations, and N-body problems, and parallelize them using various popular programming paradigms on a wide spectrum of computer platforms ranging from state-of-the-art supercomputers to PC clusters. We present the underlying problems, the solution algorithms, and the parallel implementation strategies. Smart load-balancing, partitioning, and ordering techniques are used to enhance parallel performance. Overall results demonstrate the complexity of efficiently parallelizing irregular algorithms.

A Specification for a Godunov-type Eulerian 2-D Hydrocode, Revision 0

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

Nystrom, William D; Robey, Jonathan M

2012-05-01

The purpose of this code specification is to describe an algorithm for solving the Euler equations of hydrodynamics in a 2D rectangular region in sufficient detail to allow a software developer to produce an implementation on their target platform using their programming language of choice without requiring detailed knowledge and experience in the field of computational fluid dynamics. It should be possible for a software developer who is proficient in the programming language of choice and is knowledgable of the target hardware to produce an efficient implementation of this specification if they also possess a thorough working knowledge of parallelmore » programming and have some experience in scientific programming using fields and meshes. On modern architectures, it will be important to focus on issues related to the exploitation of the fine grain parallelism and data locality present in this algorithm. This specification aims to make that task easier by presenting the essential details of the algorithm in a systematic and language neutral manner while also avoiding the inclusion of implementation details that would likely be specific to a particular type of programming paradigm or platform architecture.« less

Segmentation of lung nodules in computed tomography images using dynamic programming and multidirection fusion techniques.

PubMed

Wang, Qian; Song, Enmin; Jin, Renchao; Han, Ping; Wang, Xiaotong; Zhou, Yanying; Zeng, Jianchao

2009-06-01

The aim of this study was to develop a novel algorithm for segmenting lung nodules on three-dimensional (3D) computed tomographic images to improve the performance of computer-aided diagnosis (CAD) systems. The database used in this study consists of two data sets obtained from the Lung Imaging Database Consortium. The first data set, containing 23 nodules (22% irregular nodules, 13% nonsolid nodules, 17% nodules attached to other structures), was used for training. The second data set, containing 64 nodules (37% irregular nodules, 40% nonsolid nodules, 62% nodules attached to other structures), was used for testing. Two key techniques were developed in the segmentation algorithm: (1) a 3D extended dynamic programming model, with a newly defined internal cost function based on the information between adjacent slices, allowing parameters to be adapted to each slice, and (2) a multidirection fusion technique, which makes use of the complementary relationships among different directions to improve the final segmentation accuracy. The performance of this approach was evaluated by the overlap criterion, complemented by the true-positive fraction and the false-positive fraction criteria. The mean values of the overlap, true-positive fraction, and false-positive fraction for the first data set achieved using the segmentation scheme were 66%, 75%, and 15%, respectively, and the corresponding values for the second data set were 58%, 71%, and 22%, respectively. The experimental results indicate that this segmentation scheme can achieve better performance for nodule segmentation than two existing algorithms reported in the literature. The proposed 3D extended dynamic programming model is an effective way to segment sequential images of lung nodules. The proposed multidirection fusion technique is capable of reducing segmentation errors especially for no-nodule and near-end slices, thus resulting in better overall performance.

Approximately adaptive neural cooperative control for nonlinear multiagent systems with performance guarantee

NASA Astrophysics Data System (ADS)

Wang, Jing; Yang, Tianyu; Staskevich, Gennady; Abbe, Brian

2017-04-01

This paper studies the cooperative control problem for a class of multiagent dynamical systems with partially unknown nonlinear system dynamics. In particular, the control objective is to solve the state consensus problem for multiagent systems based on the minimisation of certain cost functions for individual agents. Under the assumption that there exist admissible cooperative controls for such class of multiagent systems, the formulated problem is solved through finding the optimal cooperative control using the approximate dynamic programming and reinforcement learning approach. With the aid of neural network parameterisation and online adaptive learning, our method renders a practically implementable approximately adaptive neural cooperative control for multiagent systems. Specifically, based on the Bellman's principle of optimality, the Hamilton-Jacobi-Bellman (HJB) equation for multiagent systems is first derived. We then propose an approximately adaptive policy iteration algorithm for multiagent cooperative control based on neural network approximation of the value functions. The convergence of the proposed algorithm is rigorously proved using the contraction mapping method. The simulation results are included to validate the effectiveness of the proposed algorithm.

Approximate dynamic programming approaches for appointment scheduling with patient preferences.

PubMed

Li, Xin; Wang, Jin; Fung, Richard Y K

2018-04-01

During the appointment booking process in out-patient departments, the level of patient satisfaction can be affected by whether or not their preferences can be met, including the choice of physicians and preferred time slot. In addition, because the appointments are sequential, considering future possible requests is also necessary for a successful appointment system. This paper proposes a Markov decision process model for optimizing the scheduling of sequential appointments with patient preferences. In contrast to existing models, the evaluation of a booking decision in this model focuses on the extent to which preferences are satisfied. Characteristics of the model are analysed to develop a system for formulating booking policies. Based on these characteristics, two types of approximate dynamic programming algorithms are developed to avoid the curse of dimensionality. Experimental results suggest directions for further fine-tuning of the model, as well as improving the efficiency of the two proposed algorithms. Copyright © 2018 Elsevier B.V. All rights reserved.

Performance of a parallel code for the Euler equations on hypercube computers

NASA Technical Reports Server (NTRS)

Barszcz, Eric; Chan, Tony F.; Jesperson, Dennis C.; Tuminaro, Raymond S.

1990-01-01

The performance of hypercubes were evaluated on a computational fluid dynamics problem and the parallel environment issues were considered that must be addressed, such as algorithm changes, implementation choices, programming effort, and programming environment. The evaluation focuses on a widely used fluid dynamics code, FLO52, which solves the two dimensional steady Euler equations describing flow around the airfoil. The code development experience is described, including interacting with the operating system, utilizing the message-passing communication system, and code modifications necessary to increase parallel efficiency. Results from two hypercube parallel computers (a 16-node iPSC/2, and a 512-node NCUBE/ten) are discussed and compared. In addition, a mathematical model of the execution time was developed as a function of several machine and algorithm parameters. This model accurately predicts the actual run times obtained and is used to explore the performance of the code in interesting but yet physically realizable regions of the parameter space. Based on this model, predictions about future hypercubes are made.

Application of snakes and dynamic programming optimisation technique in modeling of buildings in informal settlement areas

NASA Astrophysics Data System (ADS)

Rüther, Heinz; Martine, Hagai M.; Mtalo, E. G.

This paper presents a novel approach to semiautomatic building extraction in informal settlement areas from aerial photographs. The proposed approach uses a strategy of delineating buildings by optimising their approximate building contour position. Approximate building contours are derived automatically by locating elevation blobs in digital surface models. Building extraction is then effected by means of the snakes algorithm and the dynamic programming optimisation technique. With dynamic programming, the building contour optimisation problem is realized through a discrete multistage process and solved by the "time-delayed" algorithm, as developed in this work. The proposed building extraction approach is a semiautomatic process, with user-controlled operations linking fully automated subprocesses. Inputs into the proposed building extraction system are ortho-images and digital surface models, the latter being generated through image matching techniques. Buildings are modeled as "lumps" or elevation blobs in digital surface models, which are derived by altimetric thresholding of digital surface models. Initial windows for building extraction are provided by projecting the elevation blobs centre points onto an ortho-image. In the next step, approximate building contours are extracted from the ortho-image by region growing constrained by edges. Approximate building contours thus derived are inputs into the dynamic programming optimisation process in which final building contours are established. The proposed system is tested on two study areas: Marconi Beam in Cape Town, South Africa, and Manzese in Dar es Salaam, Tanzania. Sixty percent of buildings in the study areas have been extracted and verified and it is concluded that the proposed approach contributes meaningfully to the extraction of buildings in moderately complex and crowded informal settlement areas.

Wheat forecast economics effect study. [value of improved information on crop inventories, production, imports and exports

NASA Technical Reports Server (NTRS)

Mehra, R. K.; Rouhani, R.; Jones, S.; Schick, I.

1980-01-01

A model to assess the value of improved information regarding the inventories, productions, exports, and imports of crop on a worldwide basis is discussed. A previously proposed model is interpreted in a stochastic control setting and the underlying assumptions of the model are revealed. In solving the stochastic optimization problem, the Markov programming approach is much more powerful and exact as compared to the dynamic programming-simulation approach of the original model. The convergence of a dual variable Markov programming algorithm is shown to be fast and efficient. A computer program for the general model of multicountry-multiperiod is developed. As an example, the case of one country-two periods is treated and the results are presented in detail. A comparison with the original model results reveals certain interesting aspects of the algorithms and the dependence of the value of information on the incremental cost function.

A Hybrid Genetic Programming Algorithm for Automated Design of Dispatching Rules.

PubMed

Nguyen, Su; Mei, Yi; Xue, Bing; Zhang, Mengjie

2018-06-04

Designing effective dispatching rules for production systems is a difficult and timeconsuming task if it is done manually. In the last decade, the growth of computing power, advanced machine learning, and optimisation techniques has made the automated design of dispatching rules possible and automatically discovered rules are competitive or outperform existing rules developed by researchers. Genetic programming is one of the most popular approaches to discovering dispatching rules in the literature, especially for complex production systems. However, the large heuristic search space may restrict genetic programming from finding near optimal dispatching rules. This paper develops a new hybrid genetic programming algorithm for dynamic job shop scheduling based on a new representation, a new local search heuristic, and efficient fitness evaluators. Experiments show that the new method is effective regarding the quality of evolved rules. Moreover, evolved rules are also significantly smaller and contain more relevant attributes.

CAST: a new program package for the accurate characterization of large and flexible molecular systems.

PubMed

Grebner, Christoph; Becker, Johannes; Weber, Daniel; Bellinger, Daniel; Tafipolski, Maxim; Brückner, Charlotte; Engels, Bernd

2014-09-15

The presented program package, Conformational Analysis and Search Tool (CAST) allows the accurate treatment of large and flexible (macro) molecular systems. For the determination of thermally accessible minima CAST offers the newly developed TabuSearch algorithm, but algorithms such as Monte Carlo (MC), MC with minimization, and molecular dynamics are implemented as well. For the determination of reaction paths, CAST provides the PathOpt, the Nudge Elastic band, and the umbrella sampling approach. Access to free energies is possible through the free energy perturbation approach. Along with a number of standard force fields, a newly developed symmetry-adapted perturbation theory-based force field is included. Semiempirical computations are possible through DFTB+ and MOPAC interfaces. For calculations based on density functional theory, a Message Passing Interface (MPI) interface to the Graphics Processing Unit (GPU)-accelerated TeraChem program is available. The program is available on request. Copyright © 2014 Wiley Periodicals, Inc.

Segmentation of ribs in digital chest radiographs

NASA Astrophysics Data System (ADS)

Cong, Lin; Guo, Wei; Li, Qiang

2016-03-01

Ribs and clavicles in posterior-anterior (PA) digital chest radiographs often overlap with lung abnormalities such as nodules, and cause missing of these abnormalities, it is therefore necessary to remove or reduce the ribs in chest radiographs. The purpose of this study was to develop a fully automated algorithm to segment ribs within lung area in digital radiography (DR) for removal of the ribs. The rib segmentation algorithm consists of three steps. Firstly, a radiograph was pre-processed for contrast adjustment and noise removal; second, generalized Hough transform was employed to localize the lower boundary of the ribs. In the third step, a novel bilateral dynamic programming algorithm was used to accurately segment the upper and lower boundaries of ribs simultaneously. The width of the ribs and the smoothness of the rib boundaries were incorporated in the cost function of the bilateral dynamic programming for obtaining consistent results for the upper and lower boundaries. Our database consisted of 93 DR images, including, respectively, 23 and 70 images acquired with a DR system from Shanghai United-Imaging Healthcare Co. and from GE Healthcare Co. The rib localization algorithm achieved a sensitivity of 98.2% with 0.1 false positives per image. The accuracy of the detected ribs was further evaluated subjectively in 3 levels: "1", good; "2", acceptable; "3", poor. The percentages of good, acceptable, and poor segmentation results were 91.1%, 7.2%, and 1.7%, respectively. Our algorithm can obtain good segmentation results for ribs in chest radiography and would be useful for rib reduction in our future study.

Torque-based optimal acceleration control for electric vehicle

NASA Astrophysics Data System (ADS)

Lu, Dongbin; Ouyang, Minggao

2014-03-01

The existing research of the acceleration control mainly focuses on an optimization of the velocity trajectory with respect to a criterion formulation that weights acceleration time and fuel consumption. The minimum-fuel acceleration problem in conventional vehicle has been solved by Pontryagin's maximum principle and dynamic programming algorithm, respectively. The acceleration control with minimum energy consumption for battery electric vehicle(EV) has not been reported. In this paper, the permanent magnet synchronous motor(PMSM) is controlled by the field oriented control(FOC) method and the electric drive system for the EV(including the PMSM, the inverter and the battery) is modeled to favor over a detailed consumption map. The analytical algorithm is proposed to analyze the optimal acceleration control and the optimal torque versus speed curve in the acceleration process is obtained. Considering the acceleration time, a penalty function is introduced to realize a fast vehicle speed tracking. The optimal acceleration control is also addressed with dynamic programming(DP). This method can solve the optimal acceleration problem with precise time constraint, but it consumes a large amount of computation time. The EV used in simulation and experiment is a four-wheel hub motor drive electric vehicle. The simulation and experimental results show that the required battery energy has little difference between the acceleration control solved by analytical algorithm and that solved by DP, and is greatly reduced comparing with the constant pedal opening acceleration. The proposed analytical and DP algorithms can minimize the energy consumption in EV's acceleration process and the analytical algorithm is easy to be implemented in real-time control.

Optimal Trajectories Generation in Robotic Fiber Placement Systems

NASA Astrophysics Data System (ADS)

Gao, Jiuchun; Pashkevich, Anatol; Caro, Stéphane

2017-06-01

The paper proposes a methodology for optimal trajectories generation in robotic fiber placement systems. A strategy to tune the parameters of the optimization algorithm at hand is also introduced. The presented technique transforms the original continuous problem into a discrete one where the time-optimal motions are generated by using dynamic programming. The developed strategy for the optimization algorithm tuning allows essentially reducing the computing time and obtaining trajectories satisfying industrial constraints. Feasibilities and advantages of the proposed methodology are confirmed by an application example.

Multi-Core Programming Design Patterns: Stream Processing Algorithms for Dynamic Scene Perceptions

DTIC Science & Technology

2014-05-01

processor developed by IBM and other companies , incorpo- rates the verb—POWER5— processor as the Power Processor Element (PPE), one of the early general...deliver an power efficient single-precision peak performance of more than 256 GFlops. Substantially more raw power became available later, when nVIDIA ...algorithms, including IBM’s Cell/B.E., GPUs from NVidia and AMD and many-core CPUs from Intel.27 The vast growth of digital video content has been a

Optimization of fuel-cell tram operation based on two dimension dynamic programming

NASA Astrophysics Data System (ADS)

Zhang, Wenbin; Lu, Xuecheng; Zhao, Jingsong; Li, Jianqiu

2018-02-01

This paper proposes an optimal control strategy based on the two-dimension dynamic programming (2DDP) algorithm targeting at minimizing operation energy consumption for a fuel-cell tram. The energy consumption model with the tram dynamics is firstly deduced. Optimal control problem are analyzed and the 2DDP strategy is applied to solve the problem. The optimal tram speed profiles are obtained for each interstation which consist of three stages: accelerate to the set speed with the maximum traction power, dynamically adjust to maintain a uniform speed and decelerate to zero speed with the maximum braking power at a suitable timing. The optimal control curves of all the interstations are connected with the parking time to form the optimal control method of the whole line. The optimized speed profiles are also simplified for drivers to follow.

Superelement model based parallel algorithm for vehicle dynamics

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

Agrawal, O.P.; Danhof, K.J.; Kumar, R.

1994-05-01

This paper presents a superelement model based parallel algorithm for a planar vehicle dynamics. The vehicle model is made up of a chassis and two suspension systems each of which consists of an axle-wheel assembly and two trailing arms. In this model, the chassis is treated as a Cartesian element and each suspension system is treated as a superelement. The parameters associated with the superelements are computed using an inverse dynamics technique. Suspension shock absorbers and the tires are modeled by nonlinear springs and dampers. The Euler-Lagrange approach is used to develop the system equations of motion. This leads tomore » a system of differential and algebraic equations in which the constraints internal to superelements appear only explicitly. The above formulation is implemented on a multiprocessor machine. The numerical flow chart is divided into modules and the computation of several modules is performed in parallel to gain computational efficiency. In this implementation, the master (parent processor) creates a pool of slaves (child processors) at the beginning of the program. The slaves remain in the pool until they are needed to perform certain tasks. Upon completion of a particular task, a slave returns to the pool. This improves the overall response time of the algorithm. The formulation presented is general which makes it attractive for a general purpose code development. Speedups obtained in the different modules of the dynamic analysis computation are also presented. Results show that the superelement model based parallel algorithm can significantly reduce the vehicle dynamics simulation time. 52 refs.« less

Dynamic modeling and optimal joint torque coordination of advanced robotic systems

NASA Astrophysics Data System (ADS)

Kang, Hee-Jun

The development is documented of an efficient dynamic modeling algorithm and the subsequent optimal joint input load coordination of advanced robotic systems for industrial application. A closed-form dynamic modeling algorithm for the general closed-chain robotic linkage systems is presented. The algorithm is based on the transfer of system dependence from a set of open chain Lagrangian coordinates to any desired system generalized coordinate set of the closed-chain. Three different techniques for evaluation of the kinematic closed chain constraints allow the representation of the dynamic modeling parameters in terms of system generalized coordinates and have no restriction with regard to kinematic redundancy. The total computational requirement of the closed-chain system model is largely dependent on the computation required for the dynamic model of an open kinematic chain. In order to improve computational efficiency, modification of an existing open-chain KIC based dynamic formulation is made by the introduction of the generalized augmented body concept. This algorithm allows a 44 pct. computational saving over the current optimized one (O(N4), 5995 when N = 6). As means of resolving redundancies in advanced robotic systems, local joint torque optimization is applied for effectively using actuator power while avoiding joint torque limits. The stability problem in local joint torque optimization schemes is eliminated by using fictitious dissipating forces which act in the necessary null space. The performance index representing the global torque norm is shown to be satisfactory. In addition, the resulting joint motion trajectory becomes conservative, after a transient stage, for repetitive cyclic end-effector trajectories. The effectiveness of the null space damping method is shown. The modular robot, which is built of well defined structural modules from a finite-size inventory and is controlled by one general computer system, is another class of evolving, highly versatile, advanced robotic systems. Therefore, finally, a module based dynamic modeling algorithm is presented for the dynamic coordination of such reconfigurable modular robotic systems. A user interactive module based manipulator analysis program (MBMAP) has been coded in C language running on 4D/70 Silicon Graphics.

An engineering approach to automatic programming

NASA Technical Reports Server (NTRS)

Rubin, Stuart H.

1990-01-01

An exploratory study of the automatic generation and optimization of symbolic programs using DECOM - a prototypical requirement specification model implemented in pure LISP was undertaken. It was concluded, on the basis of this study, that symbolic processing languages such as LISP can support a style of programming based upon formal transformation and dependent upon the expression of constraints in an object-oriented environment. Such languages can represent all aspects of the software generation process (including heuristic algorithms for effecting parallel search) as dynamic processes since data and program are represented in a uniform format.

Optimal Control of a Surge-Mode WEC in Random Waves

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

Chertok, Allan; Ceberio, Olivier; Staby, Bill

2016-08-30

The objective of this project was to develop one or more real-time feedback and feed-forward (MPC) control algorithms for an Oscillating Surge Wave Converter (OSWC) developed by RME called SurgeWEC™ that leverages recent innovations in wave energy converter (WEC) control theory to maximize power production in random wave environments. The control algorithms synthesized innovations in dynamic programming and nonlinear wave dynamics using anticipatory wave sensors and localized sensor measurements; e.g. position and velocity of the WEC Power Take Off (PTO), with predictive wave forecasting data. The result was an advanced control system that uses feedback or feed-forward data from anmore » array of sensor channels comprised of both localized and deployed sensors fused into a single decision process that optimally compensates for uncertainties in the system dynamics, wave forecasts, and sensor measurement errors.« less

Final Report on DOE Project entitled Dynamic Optimized Advanced Scheduling of Bandwidth Demands for Large-Scale Science Applications

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

Ramamurthy, Byravamurthy

2014-05-05

In this project, developed scheduling frameworks for dynamic bandwidth demands for large-scale science applications. In particular, we developed scheduling algorithms for dynamic bandwidth demands in this project. Apart from theoretical approaches such as Integer Linear Programming, Tabu Search and Genetic Algorithm heuristics, we have utilized practical data from ESnet OSCARS project (from our DOE lab partners) to conduct realistic simulations of our approaches. We have disseminated our work through conference paper presentations and journal papers and a book chapter. In this project we addressed the problem of scheduling of lightpaths over optical wavelength division multiplexed (WDM) networks. We published severalmore » conference papers and journal papers on this topic. We also addressed the problems of joint allocation of computing, storage and networking resources in Grid/Cloud networks and proposed energy-efficient mechanisms for operatin optical WDM networks.« less

Adaptive Augmenting Control Flight Characterization Experiment on an F/A-18

NASA Technical Reports Server (NTRS)

VanZwieten, Tannen S.; Orr, Jeb S.; Wall, John H.; Gilligan, Eric T.

2014-01-01

This paper summarizes the Adaptive Augmenting Control (AAC) flight characterization experiments performed using an F/A-18 (TN 853). AAC was designed and developed specifically for launch vehicles, and is currently part of the baseline autopilot design for NASA's Space Launch System (SLS). The scope covered here includes a brief overview of the algorithm (covered in more detail elsewhere), motivation and benefits of flight testing, top-level SLS flight test objectives, applicability of the F/A-18 as a platform for testing a launch vehicle control design, test cases designed to fully vet the AAC algorithm, flight test results, and conclusions regarding the functionality of AAC. The AAC algorithm developed at Marshall Space Flight Center is a forward loop gain multiplicative adaptive algorithm that modifies the total attitude control system gain in response to sensed model errors or undesirable parasitic mode resonances. The AAC algorithm provides the capability to improve or decrease performance by balancing attitude tracking with the mitigation of parasitic dynamics, such as control-structure interaction or servo-actuator limit cycles. In the case of the latter, if unmodeled or mismodeled parasitic dynamics are present that would otherwise result in a closed-loop instability or near instability, the adaptive controller decreases the total loop gain to reduce the interaction between these dynamics and the controller. This is in contrast to traditional adaptive control logic, which focuses on improving performance by increasing gain. The computationally simple AAC attitude control algorithm has stability properties that are reconcilable in the context of classical frequency-domain criteria (i.e., gain and phase margin). The algorithm assumes that the baseline attitude control design is well-tuned for a nominal trajectory and is designed to adapt only when necessary. Furthermore, the adaptation is attracted to the nominal design and adapts only on an as-needed basis (see Figure 1). The MSFC algorithm design was formulated during the Constellation Program and reached a high maturity level during SLS through simulation-based development and internal and external analytical review. The AAC algorithm design has three summary-level objectives: (1) "Do no harm;" return to baseline control design when not needed, (2) Increase performance; respond to error in ability of vehicle to track command, and (3) Regain stability; respond to undesirable control-structure interaction or other parasitic dynamics. AAC has been successfully implemented as part of the Space Launch System baseline design, including extensive testing in high-fidelity 6-DOF simulations the details of which are described in [1]. The Dryden Flight Research Center's F/A-18 Full-Scale Advanced Systems Testbed (FAST) platform is used to conduct an algorithm flight characterization experiment intended to fully vet the aforementioned design objectives. FAST was specifically designed with this type of test program in mind. The onboard flight control system has full-authority experiment control of ten aerodynamic effectors and two throttles. It has production and research sensor inputs and pilot engage/disengage and real-time configuration of up to eight different experiments on a single flight. It has failure detection and automatic reversion to fail-safe mode. The F/A-18 aircraft has an experiment envelope cleared for full-authority control and maneuvering and exhibits characteristics for robust recovery from unusual attitudes and configurations aided by the presence of a qualified test pilot. The F/A-18 aircraft has relatively high mass and inertia with exceptional performance; the F/A-18 also has a large thrust-to-weight ratio, owing to its military heritage. This enables the simulation of a portion of the ascent trajectory with a high degree of dynamic similarity to a launch vehicle, and the research flight control system can simulate unstable longitudinal dynamics. Parasitic dynamics such as slosh and bending modes, as well as atmospheric disturbances, are being produced by the airframe via modification of bending filters and the use of secondary control surfaces, including leading and trailing edge flaps, symmetric ailerons, and symmetric rudders. The platform also has the ability to inject signals in flight to simulate structural mode resonances or other challenging dynamics. This platform also offers more test maneuvers and longer maneuver times than a single rocket or missile test, which provides ample opportunity to fully and repeatedly exercise all aspects of the algorithm. Prior to testing on an F/A-18, AAC was the only component of the SLS autopilot design that had not been flight tested. The testing described in this paper raises the Technology Readiness Level (TRL) early in the SLS Program and is able to demonstrate its capabilities and robustness in a flight environment.

Logic integer programming models for signaling networks.

PubMed

Haus, Utz-Uwe; Niermann, Kathrin; Truemper, Klaus; Weismantel, Robert

2009-05-01

We propose a static and a dynamic approach to model biological signaling networks, and show how each can be used to answer relevant biological questions. For this, we use the two different mathematical tools of Propositional Logic and Integer Programming. The power of discrete mathematics for handling qualitative as well as quantitative data has so far not been exploited in molecular biology, which is mostly driven by experimental research, relying on first-order or statistical models. The arising logic statements and integer programs are analyzed and can be solved with standard software. For a restricted class of problems the logic models reduce to a polynomial-time solvable satisfiability algorithm. Additionally, a more dynamic model enables enumeration of possible time resolutions in poly-logarithmic time. Computational experiments are included.

Three pillars for achieving quantum mechanical molecular dynamics simulations of huge systems: Divide-and-conquer, density-functional tight-binding, and massively parallel computation.

PubMed

Nishizawa, Hiroaki; Nishimura, Yoshifumi; Kobayashi, Masato; Irle, Stephan; Nakai, Hiromi

2016-08-05

The linear-scaling divide-and-conquer (DC) quantum chemical methodology is applied to the density-functional tight-binding (DFTB) theory to develop a massively parallel program that achieves on-the-fly molecular reaction dynamics simulations of huge systems from scratch. The functions to perform large scale geometry optimization and molecular dynamics with DC-DFTB potential energy surface are implemented to the program called DC-DFTB-K. A novel interpolation-based algorithm is developed for parallelizing the determination of the Fermi level in the DC method. The performance of the DC-DFTB-K program is assessed using a laboratory computer and the K computer. Numerical tests show the high efficiency of the DC-DFTB-K program, a single-point energy gradient calculation of a one-million-atom system is completed within 60 s using 7290 nodes of the K computer. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

The dynamic programming high-order Dynamic Bayesian Networks learning for identifying effective connectivity in human brain from fMRI.

PubMed

Dang, Shilpa; Chaudhury, Santanu; Lall, Brejesh; Roy, Prasun Kumar

2017-06-15

Determination of effective connectivity (EC) among brain regions using fMRI is helpful in understanding the underlying neural mechanisms. Dynamic Bayesian Networks (DBNs) are an appropriate class of probabilistic graphical temporal-models that have been used in past to model EC from fMRI, specifically order-one. High-order DBNs (HO-DBNs) have still not been explored for fMRI data. A fundamental problem faced in the structure-learning of HO-DBN is high computational-burden and low accuracy by the existing heuristic search techniques used for EC detection from fMRI. In this paper, we propose using dynamic programming (DP) principle along with integration of properties of scoring-function in a way to reduce search space for structure-learning of HO-DBNs and finally, for identifying EC from fMRI which has not been done yet to the best of our knowledge. The proposed exact search-&-score learning approach HO-DBN-DP is an extension of the technique which was originally devised for learning a BN's structure from static data (Singh and Moore, 2005). The effectiveness in structure-learning is shown on synthetic fMRI dataset. The algorithm reaches globally-optimal solution in appreciably reduced time-complexity than the static counterpart due to integration of properties. The proof of optimality is provided. The results demonstrate that HO-DBN-DP is comparably more accurate and faster than currently used structure-learning algorithms used for identifying EC from fMRI. The real data EC from HO-DBN-DP shows consistency with previous literature than the classical Granger Causality method. Hence, the DP algorithm can be employed for reliable EC estimates from experimental fMRI data. Copyright © 2017 Elsevier B.V. All rights reserved.

Biological sequence compression algorithms.

PubMed

Matsumoto, T; Sadakane, K; Imai, H

2000-01-01

Today, more and more DNA sequences are becoming available. The information about DNA sequences are stored in molecular biology databases. The size and importance of these databases will be bigger and bigger in the future, therefore this information must be stored or communicated efficiently. Furthermore, sequence compression can be used to define similarities between biological sequences. The standard compression algorithms such as gzip or compress cannot compress DNA sequences, but only expand them in size. On the other hand, CTW (Context Tree Weighting Method) can compress DNA sequences less than two bits per symbol. These algorithms do not use special structures of biological sequences. Two characteristic structures of DNA sequences are known. One is called palindromes or reverse complements and the other structure is approximate repeats. Several specific algorithms for DNA sequences that use these structures can compress them less than two bits per symbol. In this paper, we improve the CTW so that characteristic structures of DNA sequences are available. Before encoding the next symbol, the algorithm searches an approximate repeat and palindrome using hash and dynamic programming. If there is a palindrome or an approximate repeat with enough length then our algorithm represents it with length and distance. By using this preprocessing, a new program achieves a little higher compression ratio than that of existing DNA-oriented compression algorithms. We also describe new compression algorithm for protein sequences.

Modeling human decision making behavior in supervisory control

NASA Technical Reports Server (NTRS)

Tulga, M. K.; Sheridan, T. B.

1977-01-01

An optimal decision control model was developed, which is based primarily on a dynamic programming algorithm which looks at all the available task possibilities, charts an optimal trajectory, and commits itself to do the first step (i.e., follow the optimal trajectory during the next time period), and then iterates the calculation. A Bayesian estimator was included which estimates the tasks which might occur in the immediate future and provides this information to the dynamic programming routine. Preliminary trials comparing the human subject's performance to that of the optimal model show a great similarity, but indicate that the human skips certain movements which require quick change in strategy.

An application of nonlinear programming to the design of regulators of a linear-quadratic formulation

NASA Technical Reports Server (NTRS)

Fleming, P.

1983-01-01

A design technique is proposed for linear regulators in which a feedback controller of fixed structure is chosen to minimize an integral quadratic objective function subject to the satisfaction of integral quadratic constraint functions. Application of a nonlinear programming algorithm to this mathematically tractable formulation results in an efficient and useful computer aided design tool. Particular attention is paid to computational efficiency and various recommendations are made. Two design examples illustrate the flexibility of the approach and highlight the special insight afforded to the designer. One concerns helicopter longitudinal dynamics and the other the flight dynamics of an aerodynamically unstable aircraft.

Jerk-level synchronous repetitive motion scheme with gradient-type and zeroing-type dynamics algorithms applied to dual-arm redundant robot system control

NASA Astrophysics Data System (ADS)

Chen, Dechao; Zhang, Yunong

2017-10-01

Dual-arm redundant robot systems are usually required to handle primary tasks, repetitively and synchronously in practical applications. In this paper, a jerk-level synchronous repetitive motion scheme is proposed to remedy the joint-angle drift phenomenon and achieve the synchronous control of a dual-arm redundant robot system. The proposed scheme is novelly resolved at jerk level, which makes the joint variables, i.e. joint angles, joint velocities and joint accelerations, smooth and bounded. In addition, two types of dynamics algorithms, i.e. gradient-type (G-type) and zeroing-type (Z-type) dynamics algorithms, for the design of repetitive motion variable vectors, are presented in detail with the corresponding circuit schematics. Subsequently, the proposed scheme is reformulated as two dynamical quadratic programs (DQPs) and further integrated into a unified DQP (UDQP) for the synchronous control of a dual-arm robot system. The optimal solution of the UDQP is found by the piecewise-linear projection equation neural network. Moreover, simulations and comparisons based on a six-degrees-of-freedom planar dual-arm redundant robot system substantiate the operation effectiveness and tracking accuracy of the robot system with the proposed scheme for repetitive motion and synchronous control.

Computer Program for Analysis, Design and Optimization of Propulsion, Dynamics, and Kinematics of Multistage Rockets

NASA Astrophysics Data System (ADS)

Lali, Mehdi

2009-03-01

A comprehensive computer program is designed in MATLAB to analyze, design and optimize the propulsion, dynamics, thermodynamics, and kinematics of any serial multi-staging rocket for a set of given data. The program is quite user-friendly. It comprises two main sections: "analysis and design" and "optimization." Each section has a GUI (Graphical User Interface) in which the rocket's data are entered by the user and by which the program is run. The first section analyzes the performance of the rocket that is previously devised by the user. Numerous plots and subplots are provided to display the performance of the rocket. The second section of the program finds the "optimum trajectory" via billions of iterations and computations which are done through sophisticated algorithms using numerical methods and incremental integrations. Innovative techniques are applied to calculate the optimal parameters for the engine and designing the "optimal pitch program." This computer program is stand-alone in such a way that it calculates almost every design parameter in regards to rocket propulsion and dynamics. It is meant to be used for actual launch operations as well as educational and research purposes.

An Introduction to Computational Physics

NASA Astrophysics Data System (ADS)

Pang, Tao

2010-07-01

Preface to first edition; Preface; Acknowledgements; 1. Introduction; 2. Approximation of a function; 3. Numerical calculus; 4. Ordinary differential equations; 5. Numerical methods for matrices; 6. Spectral analysis; 7. Partial differential equations; 8. Molecular dynamics simulations; 9. Modeling continuous systems; 10. Monte Carlo simulations; 11. Genetic algorithm and programming; 12. Numerical renormalization; References; Index.

Algorithmic and software for definition the chaotic parameter MEGNO in the problems of asteroids dynamics. (Russian Title: Алгоритмическое и программное обеспечение для определения параметра MEGNO в задачах динамики астероидов)

NASA Astrophysics Data System (ADS)

Razdymakhina, O. N.

2011-07-01

In the paper description of algorithm and program of definition of average MEGNO parameter for asteroids is presented. This program is developed in an environment of parallel programming on the cluster "SKIF Cyberia". The parameter was determined by combined integration of motion equations of the asteroid, equations of variation and two equations of MEGNO parameters. The choice of the algorithm is explained by the fact that method of definition of average MEGNO parameter allows us to specify the boundary of the transition from a regular regime of asteroid motion to chaotic one. A testing of the program was conducted at several objects with a different character of the motion.

Browndye: A software package for Brownian dynamics

NASA Astrophysics Data System (ADS)

Huber, Gary A.; McCammon, J. Andrew

2010-11-01

A new software package, Browndye, is presented for simulating the diffusional encounter of two large biological molecules. It can be used to estimate second-order rate constants and encounter probabilities, and to explore reaction trajectories. Browndye builds upon previous knowledge and algorithms from software packages such as UHBD, SDA, and Macrodox, while implementing algorithms that scale to larger systems. Program summaryProgram title: Browndye Catalogue identifier: AEGT_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEGT_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: MIT license, included in distribution No. of lines in distributed program, including test data, etc.: 143 618 No. of bytes in distributed program, including test data, etc.: 1 067 861 Distribution format: tar.gz Programming language: C++, OCaml ( http://caml.inria.fr/) Computer: PC, Workstation, Cluster Operating system: Linux Has the code been vectorised or parallelized?: Yes. Runs on multiple processors with shared memory using pthreads RAM: Depends linearly on size of physical system Classification: 3 External routines: uses the output of APBS [1] ( http://www.poissonboltzmann.org/apbs/) as input. APBS must be obtained and installed separately. Expat 2.0.1, CLAPACK, ocaml-expat, Mersenne Twister. These are included in the Browndye distribution. Nature of problem: Exploration and determination of rate constants of bimolecular interactions involving large biological molecules. Solution method: Brownian dynamics with electrostatic, excluded volume, van der Waals, and desolvation forces. Running time: Depends linearly on size of physical system and quadratically on precision of results. The included example executes in a few minutes.

Reducing False Positives in Runtime Analysis of Deadlocks

NASA Technical Reports Server (NTRS)

Bensalem, Saddek; Havelund, Klaus; Clancy, Daniel (Technical Monitor)

2002-01-01

This paper presents an improvement of a standard algorithm for detecting dead-lock potentials in multi-threaded programs, in that it reduces the number of false positives. The standard algorithm works as follows. The multi-threaded program under observation is executed, while lock and unlock events are observed. A graph of locks is built, with edges between locks symbolizing locking orders. Any cycle in the graph signifies a potential for a deadlock. The typical standard example is the group of dining philosophers sharing forks. The algorithm is interesting because it can catch deadlock potentials even though no deadlocks occur in the examined trace, and at the same time it scales very well in contrast t o more formal approaches to deadlock detection. The algorithm, however, can yield false positives (as well as false negatives). The extension of the algorithm described in this paper reduces the amount of false positives for three particular cases: when a gate lock protects a cycle, when a single thread introduces a cycle, and when the code segments in different threads that cause the cycle can actually not execute in parallel. The paper formalizes a theory for dynamic deadlock detection and compares it to model checking and static analysis techniques. It furthermore describes an implementation for analyzing Java programs and its application to two case studies: a planetary rover and a space craft altitude control system.

Field-Programmable Gate Array Computer in Structural Analysis: An Initial Exploration

NASA Technical Reports Server (NTRS)

Singleterry, Robert C., Jr.; Sobieszczanski-Sobieski, Jaroslaw; Brown, Samuel

2002-01-01

This paper reports on an initial assessment of using a Field-Programmable Gate Array (FPGA) computational device as a new tool for solving structural mechanics problems. A FPGA is an assemblage of binary gates arranged in logical blocks that are interconnected via software in a manner dependent on the algorithm being implemented and can be reprogrammed thousands of times per second. In effect, this creates a computer specialized for the problem that automatically exploits all the potential for parallel computing intrinsic in an algorithm. This inherent parallelism is the most important feature of the FPGA computational environment. It is therefore important that if a problem offers a choice of different solution algorithms, an algorithm of a higher degree of inherent parallelism should be selected. It is found that in structural analysis, an 'analog computer' style of programming, which solves problems by direct simulation of the terms in the governing differential equations, yields a more favorable solution algorithm than current solution methods. This style of programming is facilitated by a 'drag-and-drop' graphic programming language that is supplied with the particular type of FPGA computer reported in this paper. Simple examples in structural dynamics and statics illustrate the solution approach used. The FPGA system also allows linear scalability in computing capability. As the problem grows, the number of FPGA chips can be increased with no loss of computing efficiency due to data flow or algorithmic latency that occurs when a single problem is distributed among many conventional processors that operate in parallel. This initial assessment finds the FPGA hardware and software to be in their infancy in regard to the user conveniences; however, they have enormous potential for shrinking the elapsed time of structural analysis solutions if programmed with algorithms that exhibit inherent parallelism and linear scalability. This potential warrants further development of FPGA-tailored algorithms for structural analysis.

Loci-STREAM Version 0.9

NASA Technical Reports Server (NTRS)

Wright, Jeffrey; Thakur, Siddharth

2006-01-01

Loci-STREAM is an evolving computational fluid dynamics (CFD) software tool for simulating possibly chemically reacting, possibly unsteady flows in diverse settings, including rocket engines, turbomachines, oil refineries, etc. Loci-STREAM implements a pressure- based flow-solving algorithm that utilizes unstructured grids. (The benefit of low memory usage by pressure-based algorithms is well recognized by experts in the field.) The algorithm is robust for flows at all speeds from zero to hypersonic. The flexibility of arbitrary polyhedral grids enables accurate, efficient simulation of flows in complex geometries, including those of plume-impingement problems. The present version - Loci-STREAM version 0.9 - includes an interface with the Portable, Extensible Toolkit for Scientific Computation (PETSc) library for access to enhanced linear-equation-solving programs therein that accelerate convergence toward a solution. The name "Loci" reflects the creation of this software within the Loci computational framework, which was developed at Mississippi State University for the primary purpose of simplifying the writing of complex multidisciplinary application programs to run in distributed-memory computing environments including clusters of personal computers. Loci has been designed to relieve application programmers of the details of programming for distributed-memory computers.

Degree-Pruning Dynamic Programming Approaches to Central Time Series Minimizing Dynamic Time Warping Distance.

PubMed

Sun, Tao; Liu, Hongbo; Yu, Hong; Chen, C L Philip

2016-06-28

The central time series crystallizes the common patterns of the set it represents. In this paper, we propose a global constrained degree-pruning dynamic programming (g(dp)²) approach to obtain the central time series through minimizing dynamic time warping (DTW) distance between two time series. The DTW matching path theory with global constraints is proved theoretically for our degree-pruning strategy, which is helpful to reduce the time complexity and computational cost. Our approach can achieve the optimal solution between two time series. An approximate method to the central time series of multiple time series [called as m_g(dp)²] is presented based on DTW barycenter averaging and our g(dp)² approach by considering hierarchically merging strategy. As illustrated by the experimental results, our approaches provide better within-group sum of squares and robustness than other relevant algorithms.

Hypersonic research at Stanford University

NASA Technical Reports Server (NTRS)

Candler, Graham; Maccormack, Robert

1988-01-01

The status of the hypersonic research program at Stanford University is discussed and recent results are highlighted. The main areas of interest in the program are the numerical simulation of radiating, reacting and thermally excited flows, the investigation and numerical solution of hypersonic shock wave physics, the extension of the continuum fluid dynamic equations to the transition regime between continuum and free-molecule flow, and the development of novel numerical algorithms for efficient particulate simulations of flowfields.

MATLAB implementation of a dynamic clamp with bandwidth >125 KHz capable of generating INa at 37°C

PubMed Central

Clausen, Chris; Valiunas, Virginijus; Brink, Peter R.; Cohen, Ira S.

2012-01-01

We describe the construction of a dynamic clamp with bandwidth >125 KHz that utilizes a high performance, yet low cost, standard home/office PC interfaced with a high-speed (16 bit) data acquisition module. High bandwidth is achieved by exploiting recently available software advances (code-generation technology, optimized real-time kernel). Dynamic-clamp programs are constructed using Simulink, a visual programming language. Blocks for computation of membrane currents are written in the high-level matlab language; no programming in C is required. The instrument can be used in single- or dual-cell configurations, with the capability to modify programs while experiments are in progress. We describe an algorithm for computing the fast transient Na+ current (INa) in real time, and test its accuracy and stability using rate constants appropriate for 37°C. We then construct a program capable of supplying three currents to a cell preparation: INa, the hyperpolarizing-activated inward pacemaker current (If), and an inward-rectifier K+ current (IK1). The program corrects for the IR drop due to electrode current flow, and also records all voltages and currents. We tested this program on dual patch-clamped HEK293 cells where the dynamic clamp controls a current-clamp amplifier and a voltage-clamp amplifier controls membrane potential, and current-clamped HEK293 cells where the dynamic clamp produces spontaneous pacing behavior exhibiting Na+ spikes in otherwise passive cells. PMID:23224681

A portable approach for PIC on emerging architectures

NASA Astrophysics Data System (ADS)

Decyk, Viktor

2016-03-01

A portable approach for designing Particle-in-Cell (PIC) algorithms on emerging exascale computers, is based on the recognition that 3 distinct programming paradigms are needed. They are: low level vector (SIMD) processing, middle level shared memory parallel programing, and high level distributed memory programming. In addition, there is a memory hierarchy associated with each level. Such algorithms can be initially developed using vectorizing compilers, OpenMP, and MPI. This is the approach recommended by Intel for the Phi processor. These algorithms can then be translated and possibly specialized to other programming models and languages, as needed. For example, the vector processing and shared memory programming might be done with CUDA instead of vectorizing compilers and OpenMP, but generally the algorithm itself is not greatly changed. The UCLA PICKSC web site at http://www.idre.ucla.edu/ contains example open source skeleton codes (mini-apps) illustrating each of these three programming models, individually and in combination. Fortran2003 now supports abstract data types, and design patterns can be used to support a variety of implementations within the same code base. Fortran2003 also supports interoperability with C so that implementations in C languages are also easy to use. Finally, main codes can be translated into dynamic environments such as Python, while still taking advantage of high performing compiled languages. Parallel languages are still evolving with interesting developments in co-Array Fortran, UPC, and OpenACC, among others, and these can also be supported within the same software architecture. Work supported by NSF and DOE Grants.

A spatial operator algebra for manipulator modeling and control

NASA Technical Reports Server (NTRS)

Rodriguez, G.; Kreutz, Kenneth; Jain, Abhinandan

1989-01-01

A recently developed spatial operator algebra, useful for modeling, control, and trajectory design of manipulators is discussed. The elements of this algebra are linear operators whose domain and range spaces consist of forces, moments, velocities, and accelerations. The effect of these operators is equivalent to a spatial recursion along the span of a manipulator. Inversion of operators can be efficiently obtained via techniques of recursive filtering and smoothing. The operator algebra provides a high level framework for describing the dynamic and kinematic behavior of a manipulator and control and trajectory design algorithms. The interpretation of expressions within the algebraic framework leads to enhanced conceptual and physical understanding of manipulator dynamics and kinematics. Furthermore, implementable recursive algorithms can be immediately derived from the abstract operator expressions by inspection. Thus, the transition from an abstract problem formulation and solution to the detailed mechanizaton of specific algorithms is greatly simplified. The analytical formulation of the operator algebra, as well as its implementation in the Ada programming language are discussed.

Dynamic Appliances Scheduling in Collaborative MicroGrids System

PubMed Central

Bilil, Hasnae; Aniba, Ghassane; Gharavi, Hamid

2017-01-01

In this paper a new approach which is based on a collaborative system of MicroGrids (MG’s), is proposed to enable household appliance scheduling. To achieve this, appliances are categorized into flexible and non-flexible Deferrable Loads (DL’s), according to their electrical components. We propose a dynamic scheduling algorithm where users can systematically manage the operation of their electric appliances. The main challenge is to develop a flattening function calculus (reshaping) for both flexible and non-flexible DL’s. In addition, implementation of the proposed algorithm would require dynamically analyzing two successive multi-objective optimization (MOO) problems. The first targets the activation schedule of non-flexible DL’s and the second deals with the power profiles of flexible DL’s. The MOO problems are resolved by using a fast and elitist multi-objective genetic algorithm (NSGA-II). Finally, in order to show the efficiency of the proposed approach, a case study of a collaborative system that consists of 40 MG’s registered in the load curve for the flattening program has been developed. The results verify that the load curve can indeed become very flat by applying the proposed scheduling approach. PMID:28824226

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.

Physics Computing '92: Proceedings of the 4th International Conference

NASA Astrophysics Data System (ADS)

de Groot, Robert A.; Nadrchal, Jaroslav

1993-04-01

The Table of Contents for the book is as follows: * Preface * INVITED PAPERS * Ab Initio Theoretical Approaches to the Structural, Electronic and Vibrational Properties of Small Clusters and Fullerenes: The State of the Art * Neural Multigrid Methods for Gauge Theories and Other Disordered Systems * Multicanonical Monte Carlo Simulations * On the Use of the Symbolic Language Maple in Physics and Chemistry: Several Examples * Nonequilibrium Phase Transitions in Catalysis and Population Models * Computer Algebra, Symmetry Analysis and Integrability of Nonlinear Evolution Equations * The Path-Integral Quantum Simulation of Hydrogen in Metals * Digital Optical Computing: A New Approach of Systolic Arrays Based on Coherence Modulation of Light and Integrated Optics Technology * Molecular Dynamics Simulations of Granular Materials * Numerical Implementation of a K.A.M. Algorithm * Quasi-Monte Carlo, Quasi-Random Numbers and Quasi-Error Estimates * What Can We Learn from QMC Simulations * Physics of Fluctuating Membranes * Plato, Apollonius, and Klein: Playing with Spheres * Steady States in Nonequilibrium Lattice Systems * CONVODE: A REDUCE Package for Differential Equations * Chaos in Coupled Rotators * Symplectic Numerical Methods for Hamiltonian Problems * Computer Simulations of Surfactant Self Assembly * High-dimensional and Very Large Cellular Automata for Immunological Shape Space * A Review of the Lattice Boltzmann Method * Electronic Structure of Solids in the Self-interaction Corrected Local-spin-density Approximation * Dedicated Computers for Lattice Gauge Theory Simulations * Physics Education: A Survey of Problems and Possible Solutions * Parallel Computing and Electronic-Structure Theory * High Precision Simulation Techniques for Lattice Field Theory * CONTRIBUTED PAPERS * Case Study of Microscale Hydrodynamics Using Molecular Dynamics and Lattice Gas Methods * Computer Modelling of the Structural and Electronic Properties of the Supported Metal Catalysis * Ordered Particle Simulations for Serial and MIMD Parallel Computers * "NOLP" -- Program Package for Laser Plasma Nonlinear Optics * Algorithms to Solve Nonlinear Least Square Problems * Distribution of Hydrogen Atoms in Pd-H Computed by Molecular Dynamics * A Ray Tracing of Optical System for Protein Crystallography Beamline at Storage Ring-SIBERIA-2 * Vibrational Properties of a Pseudobinary Linear Chain with Correlated Substitutional Disorder * Application of the Software Package Mathematica in Generalized Master Equation Method * Linelist: An Interactive Program for Analysing Beam-foil Spectra * GROMACS: A Parallel Computer for Molecular Dynamics Simulations * GROMACS Method of Virial Calculation Using a Single Sum * The Interactive Program for the Solution of the Laplace Equation with the Elimination of Singularities for Boundary Functions * Random-Number Generators: Testing Procedures and Comparison of RNG Algorithms * Micro-TOPIC: A Tokamak Plasma Impurities Code * Rotational Molecular Scattering Calculations * Orthonormal Polynomial Method for Calibrating of Cryogenic Temperature Sensors * Frame-based System Representing Basis of Physics * The Role of Massively Data-parallel Computers in Large Scale Molecular Dynamics Simulations * Short-range Molecular Dynamics on a Network of Processors and Workstations * An Algorithm for Higher-order Perturbation Theory in Radiative Transfer Computations * Hydrostochastics: The Master Equation Formulation of Fluid Dynamics * HPP Lattice Gas on Transputers and Networked Workstations * Study on the Hysteresis Cycle Simulation Using Modeling with Different Functions on Intervals * Refined Pruning Techniques for Feed-forward Neural Networks * Random Walk Simulation of the Motion of Transient Charges in Photoconductors * The Optical Hysteresis in Hydrogenated Amorphous Silicon * Diffusion Monte Carlo Analysis of Modern Interatomic Potentials for He * A Parallel Strategy for Molecular Dynamics Simulations of Polar Liquids on Transputer Arrays * Distribution of Ions Reflected on Rough Surfaces * The Study of Step Density Distribution During Molecular Beam Epitaxy Growth: Monte Carlo Computer Simulation * Towards a Formal Approach to the Construction of Large-scale Scientific Applications Software * Correlated Random Walk and Discrete Modelling of Propagation through Inhomogeneous Media * Teaching Plasma Physics Simulation * A Theoretical Determination of the Au-Ni Phase Diagram * Boson and Fermion Kinetics in One-dimensional Lattices * Computational Physics Course on the Technical University * Symbolic Computations in Simulation Code Development and Femtosecond-pulse Laser-plasma Interaction Studies * Computer Algebra and Integrated Computing Systems in Education of Physical Sciences * Coordinated System of Programs for Undergraduate Physics Instruction * Program Package MIRIAM and Atomic Physics of Extreme Systems * High Energy Physics Simulation on the T_Node * The Chapman-Kolmogorov Equation as Representation of Huygens' Principle and the Monolithic Self-consistent Numerical Modelling of Lasers * Authoring System for Simulation Developments * Molecular Dynamics Study of Ion Charge Effects in the Structure of Ionic Crystals * A Computational Physics Introductory Course * Computer Calculation of Substrate Temperature Field in MBE System * Multimagnetical Simulation of the Ising Model in Two and Three Dimensions * Failure of the CTRW Treatment of the Quasicoherent Excitation Transfer * Implementation of a Parallel Conjugate Gradient Method for Simulation of Elastic Light Scattering * Algorithms for Study of Thin Film Growth * Algorithms and Programs for Physics Teaching in Romanian Technical Universities * Multicanonical Simulation of 1st order Transitions: Interface Tension of the 2D 7-State Potts Model * Two Numerical Methods for the Calculation of Periodic Orbits in Hamiltonian Systems * Chaotic Behavior in a Probabilistic Cellular Automata? * Wave Optics Computing by a Networked-based Vector Wave Automaton * Tensor Manipulation Package in REDUCE * Propagation of Electromagnetic Pulses in Stratified Media * The Simple Molecular Dynamics Model for the Study of Thermalization of the Hot Nucleon Gas * Electron Spin Polarization in PdCo Alloys Calculated by KKR-CPA-LSD Method * Simulation Studies of Microscopic Droplet Spreading * A Vectorizable Algorithm for the Multicolor Successive Overrelaxation Method * Tetragonality of the CuAu I Lattice and Its Relation to Electronic Specific Heat and Spin Susceptibility * Computer Simulation of the Formation of Metallic Aggregates Produced by Chemical Reactions in Aqueous Solution * Scaling in Growth Models with Diffusion: A Monte Carlo Study * The Nucleus as the Mesoscopic System * Neural Network Computation as Dynamic System Simulation * First-principles Theory of Surface Segregation in Binary Alloys * Data Smooth Approximation Algorithm for Estimating the Temperature Dependence of the Ice Nucleation Rate * Genetic Algorithms in Optical Design * Application of 2D-FFT in the Study of Molecular Exchange Processes by NMR * Advanced Mobility Model for Electron Transport in P-Si Inversion Layers * Computer Simulation for Film Surfaces and its Fractal Dimension * Parallel Computation Techniques and the Structure of Catalyst Surfaces * Educational SW to Teach Digital Electronics and the Corresponding Text Book * Primitive Trinomials (Mod 2) Whose Degree is a Mersenne Exponent * Stochastic Modelisation and Parallel Computing * Remarks on the Hybrid Monte Carlo Algorithm for the ∫4 Model * An Experimental Computer Assisted Workbench for Physics Teaching * A Fully Implicit Code to Model Tokamak Plasma Edge Transport * EXPFIT: An Interactive Program for Automatic Beam-foil Decay Curve Analysis * Mapping Technique for Solving General, 1-D Hamiltonian Systems * Freeway Traffic, Cellular Automata, and Some (Self-Organizing) Criticality * Photonuclear Yield Analysis by Dynamic Programming * Incremental Representation of the Simply Connected Planar Curves * Self-convergence in Monte Carlo Methods * Adaptive Mesh Technique for Shock Wave Propagation * Simulation of Supersonic Coronal Streams and Their Interaction with the Solar Wind * The Nature of Chaos in Two Systems of Ordinary Nonlinear Differential Equations * Considerations of a Window-shopper * Interpretation of Data Obtained by RTP 4-Channel Pulsed Radar Reflectometer Using a Multi Layer Perceptron * Statistics of Lattice Bosons for Finite Systems * Fractal Based Image Compression with Affine Transformations * Algorithmic Studies on Simulation Codes for Heavy-ion Reactions * An Energy-Wise Computer Simulation of DNA-Ion-Water Interactions Explains the Abnormal Structure of Poly[d(A)]:Poly[d(T)] * Computer Simulation Study of Kosterlitz-Thouless-Like Transitions * Problem-oriented Software Package GUN-EBT for Computer Simulation of Beam Formation and Transport in Technological Electron-Optical Systems * Parallelization of a Boundary Value Solver and its Application in Nonlinear Dynamics * The Symbolic Classification of Real Four-dimensional Lie Algebras * Short, Singular Pulses Generation by a Dye Laser at Two Wavelengths Simultaneously * Quantum Monte Carlo Simulations of the Apex-Oxygen-Model * Approximation Procedures for the Axial Symmetric Static Einstein-Maxwell-Higgs Theory * Crystallization on a Sphere: Parallel Simulation on a Transputer Network * FAMULUS: A Software Product (also) for Physics Education * MathCAD vs. FAMULUS -- A Brief Comparison * First-principles Dynamics Used to Study Dissociative Chemisorption * A Computer Controlled System for Crystal Growth from Melt * A Time Resolved Spectroscopic Method for Short Pulsed Particle Emission * Green's Function Computation in Radiative Transfer Theory * Random Search Optimization Technique for One-criteria and Multi-criteria Problems * Hartley Transform Applications to Thermal Drift Elimination in Scanning Tunneling Microscopy * Algorithms of Measuring, Processing and Interpretation of Experimental Data Obtained with Scanning Tunneling Microscope * Time-dependent Atom-surface Interactions * Local and Global Minima on Molecular Potential Energy Surfaces: An Example of N3 Radical * Computation of Bifurcation Surfaces * Symbolic Computations in Quantum Mechanics: Energies in Next-to-solvable Systems * A Tool for RTP Reactor and Lamp Field Design * Modelling of Particle Spectra for the Analysis of Solid State Surface * List of Participants

Cooperatively surrounding control for multiple Euler-Lagrange systems subjected to uncertain dynamics and input constraints

NASA Astrophysics Data System (ADS)

Chen, Liang-Ming; Lv, Yue-Yong; Li, Chuan-Jiang; Ma, Guang-Fu

2016-12-01

In this paper, we investigate cooperatively surrounding control (CSC) of multi-agent systems modeled by Euler-Lagrange (EL) equations under a directed graph. With the consideration of the uncertain dynamics in an EL system, a backstepping CSC algorithm combined with neural-networks is proposed first such that the agents can move cooperatively to surround the stationary target. Then, a command filtered backstepping CSC algorithm is further proposed to deal with the constraints on control input and the absence of neighbors’ velocity information. Numerical examples of eight satellites surrounding one space target illustrate the effectiveness of the theoretical results. Project supported by the National Basic Research Program of China (Grant No. 2012CB720000) and the National Natural Science Foundation of China (Grant Nos. 61304005 and 61403103).

Mobile robot dynamic path planning based on improved genetic algorithm

NASA Astrophysics Data System (ADS)

Wang, Yong; Zhou, Heng; Wang, Ying

2017-08-01

In dynamic unknown environment, the dynamic path planning of mobile robots is a difficult problem. In this paper, a dynamic path planning method based on genetic algorithm is proposed, and a reward value model is designed to estimate the probability of dynamic obstacles on the path, and the reward value function is applied to the genetic algorithm. Unique coding techniques reduce the computational complexity of the algorithm. The fitness function of the genetic algorithm fully considers three factors: the security of the path, the shortest distance of the path and the reward value of the path. The simulation results show that the proposed genetic algorithm is efficient in all kinds of complex dynamic environments.

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.

Approximation algorithms for scheduling unrelated parallel machines with release dates

NASA Astrophysics Data System (ADS)

Avdeenko, T. V.; Mesentsev, Y. A.; Estraykh, I. V.

2017-01-01

In this paper we propose approaches to optimal scheduling of unrelated parallel machines with release dates. One approach is based on the scheme of dynamic programming modified with adaptive narrowing of search domain ensuring its computational effectiveness. We discussed complexity of the exact schedules synthesis and compared it with approximate, close to optimal, solutions. Also we explain how the algorithm works for the example of two unrelated parallel machines and five jobs with release dates. Performance results that show the efficiency of the proposed approach have been given.

Modeling of biological intelligence for SCM system optimization.

PubMed

Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

2012-01-01

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms.

Modeling of Biological Intelligence for SCM System Optimization

PubMed Central

Chen, Shengyong; Zheng, Yujun; Cattani, Carlo; Wang, Wanliang

2012-01-01

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms. PMID:22162724

Regulation of Dynamical Systems to Optimal Solutions of Semidefinite Programs: Algorithms and Applications to AC Optimal Power Flow

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

Dall'Anese, Emiliano; Dhople, Sairaj V.; Giannakis, Georgios B.

2015-07-01

This paper considers a collection of networked nonlinear dynamical systems, and addresses the synthesis of feedback controllers that seek optimal operating points corresponding to the solution of pertinent network-wide optimization problems. Particular emphasis is placed on the solution of semidefinite programs (SDPs). The design of the feedback controller is grounded on a dual e-subgradient approach, with the dual iterates utilized to dynamically update the dynamical-system reference signals. Global convergence is guaranteed for diminishing stepsize rules, even when the reference inputs are updated at a faster rate than the dynamical-system settling time. The application of the proposed framework to the controlmore » of power-electronic inverters in AC distribution systems is discussed. The objective is to bridge the time-scale separation between real-time inverter control and network-wide optimization. Optimization objectives assume the form of SDP relaxations of prototypical AC optimal power flow problems.« less

Scheduling algorithms for rapid imaging using agile Cubesat constellations

NASA Astrophysics Data System (ADS)

Nag, Sreeja; Li, Alan S.; Merrick, James H.

2018-02-01

Distributed Space Missions such as formation flight and constellations, are being recognized as important Earth Observation solutions to increase measurement samples over space and time. Cubesats are increasing in size (27U, ∼40 kg in development) with increasing capabilities to host imager payloads. Given the precise attitude control systems emerging in the commercial market, Cubesats now have the ability to slew and capture images within short notice. We propose a modular framework that combines orbital mechanics, attitude control and scheduling optimization to plan the time-varying, full-body orientation of agile Cubesats in a constellation such that they maximize the number of observed images and observation time, within the constraints of Cubesat hardware specifications. The attitude control strategy combines bang-bang and PD control, with constraints such as power consumption, response time, and stability factored into the optimality computations and a possible extension to PID control to account for disturbances. Schedule optimization is performed using dynamic programming with two levels of heuristics, verified and improved upon using mixed integer linear programming. The automated scheduler is expected to run on ground station resources and the resultant schedules uplinked to the satellites for execution, however it can be adapted for onboard scheduling, contingent on Cubesat hardware and software upgrades. The framework is generalizable over small steerable spacecraft, sensor specifications, imaging objectives and regions of interest, and is demonstrated using multiple 20 kg satellites in Low Earth Orbit for two case studies - rapid imaging of Landsat's land and coastal images and extended imaging of global, warm water coral reefs. The proposed algorithm captures up to 161% more Landsat images than nadir-pointing sensors with the same field of view, on a 2-satellite constellation over a 12-h simulation. Integer programming was able to verify that optimality of the dynamic programming solution for single satellites was within 10%, and find up to 5% more optimal solutions. The optimality gap for constellations was found to be 22% at worst, but the dynamic programming schedules were found at nearly four orders of magnitude better computational speed than integer programming. The algorithm can include cloud cover predictions, ground downlink windows or any other spatial, temporal or angular constraints into the orbital module and be integrated into planning tools for agile constellations.

Towards robust algorithms for current deposition and dynamic load-balancing in a GPU particle in cell code

NASA Astrophysics Data System (ADS)

Rossi, Francesco; Londrillo, Pasquale; Sgattoni, Andrea; Sinigardi, Stefano; Turchetti, Giorgio

2012-12-01

We present `jasmine', an implementation of a fully relativistic, 3D, electromagnetic Particle-In-Cell (PIC) code, capable of running simulations in various laser plasma acceleration regimes on Graphics-Processing-Units (GPUs) HPC clusters. Standard energy/charge preserving FDTD-based algorithms have been implemented using double precision and quadratic (or arbitrary sized) shape functions for the particle weighting. When porting a PIC scheme to the GPU architecture (or, in general, a shared memory environment), the particle-to-grid operations (e.g. the evaluation of the current density) require special care to avoid memory inconsistencies and conflicts. Here we present a robust implementation of this operation that is efficient for any number of particles per cell and particle shape function order. Our algorithm exploits the exposed GPU memory hierarchy and avoids the use of atomic operations, which can hurt performance especially when many particles lay on the same cell. We show the code multi-GPU scalability results and present a dynamic load-balancing algorithm. The code is written using a python-based C++ meta-programming technique which translates in a high level of modularity and allows for easy performance tuning and simple extension of the core algorithms to various simulation schemes.

Optimisation algorithms for ECG data compression.

PubMed

Haugland, D; Heber, J G; Husøy, J H

1997-07-01

The use of exact optimisation algorithms for compressing digital electrocardiograms (ECGs) is demonstrated. As opposed to traditional time-domain methods, which use heuristics to select a small subset of representative signal samples, the problem of selecting the subset is formulated in rigorous mathematical terms. This approach makes it possible to derive algorithms guaranteeing the smallest possible reconstruction error when a bounded selection of signal samples is interpolated. The proposed model resembles well-known network models and is solved by a cubic dynamic programming algorithm. When applied to standard test problems, the algorithm produces a compressed representation for which the distortion is about one-half of that obtained by traditional time-domain compression techniques at reasonable compression ratios. This illustrates that, in terms of the accuracy of decoded signals, existing time-domain heuristics for ECG compression may be far from what is theoretically achievable. The paper is an attempt to bridge this gap.

Knapsack - TOPSIS Technique for Vertical Handover in Heterogeneous Wireless Network

PubMed Central

2015-01-01

In a heterogeneous wireless network, handover techniques are designed to facilitate anywhere/anytime service continuity for mobile users. Consistent best-possible access to a network with widely varying network characteristics requires seamless mobility management techniques. Hence, the vertical handover process imposes important technical challenges. Handover decisions are triggered for continuous connectivity of mobile terminals. However, bad network selection and overload conditions in the chosen network can cause fallout in the form of handover failure. In order to maintain the required Quality of Service during the handover process, decision algorithms should incorporate intelligent techniques. In this paper, a new and efficient vertical handover mechanism is implemented using a dynamic programming method from the operation research discipline. This dynamic programming approach, which is integrated with the Technique to Order Preference by Similarity to Ideal Solution (TOPSIS) method, provides the mobile user with the best handover decisions. Moreover, in this proposed handover algorithm a deterministic approach which divides the network into zones is incorporated into the network server in order to derive an optimal solution. The study revealed that this method is found to achieve better performance and QoS support to users and greatly reduce the handover failures when compared to the traditional TOPSIS method. The decision arrived at the zone gateway using this operational research analytical method (known as the dynamic programming knapsack approach together with Technique to Order Preference by Similarity to Ideal Solution) yields remarkably better results in terms of the network performance measures such as throughput and delay. PMID:26237221

Knapsack--TOPSIS Technique for Vertical Handover in Heterogeneous Wireless Network.

PubMed

Malathy, E M; Muthuswamy, Vijayalakshmi

2015-01-01

In a heterogeneous wireless network, handover techniques are designed to facilitate anywhere/anytime service continuity for mobile users. Consistent best-possible access to a network with widely varying network characteristics requires seamless mobility management techniques. Hence, the vertical handover process imposes important technical challenges. Handover decisions are triggered for continuous connectivity of mobile terminals. However, bad network selection and overload conditions in the chosen network can cause fallout in the form of handover failure. In order to maintain the required Quality of Service during the handover process, decision algorithms should incorporate intelligent techniques. In this paper, a new and efficient vertical handover mechanism is implemented using a dynamic programming method from the operation research discipline. This dynamic programming approach, which is integrated with the Technique to Order Preference by Similarity to Ideal Solution (TOPSIS) method, provides the mobile user with the best handover decisions. Moreover, in this proposed handover algorithm a deterministic approach which divides the network into zones is incorporated into the network server in order to derive an optimal solution. The study revealed that this method is found to achieve better performance and QoS support to users and greatly reduce the handover failures when compared to the traditional TOPSIS method. The decision arrived at the zone gateway using this operational research analytical method (known as the dynamic programming knapsack approach together with Technique to Order Preference by Similarity to Ideal Solution) yields remarkably better results in terms of the network performance measures such as throughput and delay.

Dynamical differences of hemoglobin and the ionotropic glutamate receptor in different states revealed by a new dynamics alignment method.

PubMed

Tobi, Dror

2017-08-01

A new algorithm for comparison of protein dynamics is presented. Compared protein structures are superposed and their modes of motions are calculated using the anisotropic network model. The obtained modes are aligned using the dynamic programming algorithm of Needleman and Wunsch, commonly used for sequence alignment. Dynamical comparison of hemoglobin in the T and R2 states reveals that the dynamics of the allosteric effector 2,3-bisphosphoglycerate binding site is different in the two states. These differences can contribute to the selectivity of the effector to the T state. Similar comparison of the ionotropic glutamate receptor in the kainate+(R,R)-2b and ZK bound states reveals that the kainate+(R,R)-2b bound states slow modes describe upward motions of ligand binding domain and the transmembrane domain regions. Such motions may lead to the opening of the receptor. The upper lobes of the LBDs of the ZK bound state have a smaller interface with the amino terminal domains above them and have a better ability to move together. The present study exemplifies the use of dynamics comparison as a tool to study protein function. Proteins 2017; 85:1507-1517. © 2014 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Spatial operator factorization and inversion of the manipulator mass matrix

NASA Technical Reports Server (NTRS)

Rodriguez, Guillermo; Kreutz-Delgado, Kenneth

1992-01-01

This paper advances two linear operator factorizations of the manipulator mass matrix. Embedded in the factorizations are many of the techniques that are regarded as very efficient computational solutions to inverse and forward dynamics problems. The operator factorizations provide a high-level architectural understanding of the mass matrix and its inverse, which is not visible in the detailed algorithms. They also lead to a new approach to the development of computer programs or organize complexity in robot dynamics.

An Introduction to Computational Physics - 2nd Edition

NASA Astrophysics Data System (ADS)

Pang, Tao

2006-01-01

Preface to first edition; Preface; Acknowledgements; 1. Introduction; 2. Approximation of a function; 3. Numerical calculus; 4. Ordinary differential equations; 5. Numerical methods for matrices; 6. Spectral analysis; 7. Partial differential equations; 8. Molecular dynamics simulations; 9. Modeling continuous systems; 10. Monte Carlo simulations; 11. Genetic algorithm and programming; 12. Numerical renormalization; References; Index.

MCFire model technical description

Treesearch

David R. Conklin; James M. Lenihan; Dominique Bachelet; Ronald P. Neilson; John B. Kim

2016-01-01

MCFire is a computer program that simulates the occurrence and effects of wildfire on natural vegetation, as a submodel within the MC1 dynamic global vegetation model. This report is a technical description of the algorithms and parameter values used in MCFire, intended to encapsulate its design and features a higher level that is more conceptual than the level...

Decision Support for Road Decommissioning and Restoration by Using Genetic Algorithms and Dynamic Programming

Treesearch

Elizabeth A. Eschenbach; Rebecca Teasley; Carlos Diaz; Mary Ann Madej

2007-01-01

Sediment contributions from unpaved forest roads have contributed to the degradation of anadromous fisheries streams in the Pacific Northwest.Efforts to reduce this degradation have included road decommissioning and road upgrading. These expensive activities have usually been implemented on a site specific basis without considering the sediment...

Large-Scale Dynamic Observation Planning for Unmanned Surface Vessels

DTIC Science & Technology

2007-06-01

programming language. In addition, the useful development software NetBeans IDE is free and makes the use of Java very user-friendly. 92...3. We implemented the greedy and 3PAA algorithms in Java using the NetBeans IDE version 5.5. 4. The test datasets were generated in MATLAB. 5

Accurate Detection of Dysmorphic Nuclei Using Dynamic Programming and Supervised Classification.

PubMed

Verschuuren, Marlies; De Vylder, Jonas; Catrysse, Hannes; Robijns, Joke; Philips, Wilfried; De Vos, Winnok H

2017-01-01

A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows.

Accurate Detection of Dysmorphic Nuclei Using Dynamic Programming and Supervised Classification

PubMed Central

Verschuuren, Marlies; De Vylder, Jonas; Catrysse, Hannes; Robijns, Joke; Philips, Wilfried

2017-01-01

A vast array of pathologies is typified by the presence of nuclei with an abnormal morphology. Dysmorphic nuclear phenotypes feature dramatic size changes or foldings, but also entail much subtler deviations such as nuclear protrusions called blebs. Due to their unpredictable size, shape and intensity, dysmorphic nuclei are often not accurately detected in standard image analysis routines. To enable accurate detection of dysmorphic nuclei in confocal and widefield fluorescence microscopy images, we have developed an automated segmentation algorithm, called Blebbed Nuclei Detector (BleND), which relies on two-pass thresholding for initial nuclear contour detection, and an optimal path finding algorithm, based on dynamic programming, for refining these contours. Using a robust error metric, we show that our method matches manual segmentation in terms of precision and outperforms state-of-the-art nuclear segmentation methods. Its high performance allowed for building and integrating a robust classifier that recognizes dysmorphic nuclei with an accuracy above 95%. The combined segmentation-classification routine is bound to facilitate nucleus-based diagnostics and enable real-time recognition of dysmorphic nuclei in intelligent microscopy workflows. PMID:28125723

A Dynamic Programming Approach for Base Station Sleeping in Cellular Networks

NASA Astrophysics Data System (ADS)

Gong, Jie; Zhou, Sheng; Niu, Zhisheng

The energy consumption of the information and communication technology (ICT) industry, which has become a serious problem, is mostly due to the network infrastructure rather than the mobile terminals. In this paper, we focus on reducing the energy consumption of base stations (BSs) by adjusting their working modes (active or sleep). Specifically, the objective is to minimize the energy consumption while satisfying quality of service (QoS, e.g., blocking probability) requirement and, at the same time, avoiding frequent mode switching to reduce signaling and delay overhead. The problem is modeled as a dynamic programming (DP) problem, which is NP-hard in general. Based on cooperation among neighboring BSs, a low-complexity algorithm is proposed to reduce the size of state space as well as that of action space. Simulations demonstrate that, with the proposed algorithm, the active BS pattern well meets the time variation and the non-uniform spatial distribution of system traffic. Moreover, the tradeoff between the energy saving from BS sleeping and the cost of switching is well balanced by the proposed scheme.

MONTE CARLO SIMULATIONS OF PERIODIC PULSED REACTOR WITH MOVING GEOMETRY PARTS

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

Cao, Yan; Gohar, Yousry

2015-11-01

In a periodic pulsed reactor, the reactor state varies periodically from slightly subcritical to slightly prompt supercritical for producing periodic power pulses. Such periodic state change is accomplished by a periodic movement of specific reactor parts, such as control rods or reflector sections. The analysis of such reactor is difficult to perform with the current reactor physics computer programs. Based on past experience, the utilization of the point kinetics approximations gives considerable errors in predicting the magnitude and the shape of the power pulse if the reactor has significantly different neutron life times in different zones. To accurately simulate themore » dynamics of this type of reactor, a Monte Carlo procedure using the transfer function TRCL/TR of the MCNP/MCNPX computer programs is utilized to model the movable reactor parts. In this paper, two algorithms simulating the geometry part movements during a neutron history tracking have been developed. Several test cases have been developed to evaluate these procedures. The numerical test cases have shown that the developed algorithms can be utilized to simulate the reactor dynamics with movable geometry parts.« less

A new 2D segmentation method based on dynamic programming applied to computer aided detection in mammography.

PubMed

Timp, Sheila; Karssemeijer, Nico

2004-05-01

Mass segmentation plays a crucial role in computer-aided diagnosis (CAD) systems for classification of suspicious regions as normal, benign, or malignant. In this article we present a robust and automated segmentation technique--based on dynamic programming--to segment mass lesions from surrounding tissue. In addition, we propose an efficient algorithm to guarantee resulting contours to be closed. The segmentation method based on dynamic programming was quantitatively compared with two other automated segmentation methods (region growing and the discrete contour model) on a dataset of 1210 masses. For each mass an overlap criterion was calculated to determine the similarity with manual segmentation. The mean overlap percentage for dynamic programming was 0.69, for the other two methods 0.60 and 0.59, respectively. The difference in overlap percentage was statistically significant. To study the influence of the segmentation method on the performance of a CAD system two additional experiments were carried out. The first experiment studied the detection performance of the CAD system for the different segmentation methods. Free-response receiver operating characteristics analysis showed that the detection performance was nearly identical for the three segmentation methods. In the second experiment the ability of the classifier to discriminate between malignant and benign lesions was studied. For region based evaluation the area Az under the receiver operating characteristics curve was 0.74 for dynamic programming, 0.72 for the discrete contour model, and 0.67 for region growing. The difference in Az values obtained by the dynamic programming method and region growing was statistically significant. The differences between other methods were not significant.

Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks

NASA Technical Reports Server (NTRS)

Cheung, Kar-Ming; Lee, Charles H.

2012-01-01

We developed framework and the mathematical formulation for optimizing communication network using mixed integer programming. The design yields a system that is much smaller, in search space size, when compared to the earlier approach. Our constrained network optimization takes into account the dynamics of link performance within the network along with mission and operation requirements. A unique penalty function is introduced to transform the mixed integer programming into the more manageable problem of searching in a continuous space. The constrained optimization problem was proposed to solve in two stages: first using the heuristic Particle Swarming Optimization algorithm to get a good initial starting point, and then feeding the result into the Sequential Quadratic Programming algorithm to achieve the final optimal schedule. We demonstrate the above planning and scheduling methodology with a scenario of 20 spacecraft and 3 ground stations of a Deep Space Network site. Our approach and framework have been simple and flexible so that problems with larger number of constraints and network can be easily adapted and solved.

Classification of motor activities through derivative dynamic time warping applied on accelerometer data.

PubMed

Muscillo, Rossana; Conforto, Silvia; Schmid, Maurizio; Caselli, Paolo; D'Alessio, Tommaso

2007-01-01

In the context of tele-monitoring, great interest is presently devoted to physical activity, mainly of elderly or people with disabilities. In this context, many researchers studied the recognition of activities of daily living by using accelerometers. The present work proposes a novel algorithm for activity recognition that considers the variability in movement speed, by using dynamic programming. This objective is realized by means of a matching and recognition technique that determines the distance between the signal input and a set of previously defined templates. Two different approaches are here presented, one based on Dynamic Time Warping (DTW) and the other based on the Derivative Dynamic Time Warping (DDTW). The algorithm was applied to the recognition of gait, climbing and descending stairs, using a biaxial accelerometer placed on the shin. The results on DDTW, obtained by using only one sensor channel on the shin showed an average recognition score of 95%, higher than the values obtained with DTW (around 85%). Both DTW and DDTW consistently show higher classification rate than classical Linear Time Warping (LTW).

Online adaptive optimal control for continuous-time nonlinear systems with completely unknown dynamics

NASA Astrophysics Data System (ADS)

Lv, Yongfeng; Na, Jing; Yang, Qinmin; Wu, Xing; Guo, Yu

2016-01-01

An online adaptive optimal control is proposed for continuous-time nonlinear systems with completely unknown dynamics, which is achieved by developing a novel identifier-critic-based approximate dynamic programming algorithm with a dual neural network (NN) approximation structure. First, an adaptive NN identifier is designed to obviate the requirement of complete knowledge of system dynamics, and a critic NN is employed to approximate the optimal value function. Then, the optimal control law is computed based on the information from the identifier NN and the critic NN, so that the actor NN is not needed. In particular, a novel adaptive law design method with the parameter estimation error is proposed to online update the weights of both identifier NN and critic NN simultaneously, which converge to small neighbourhoods around their ideal values. The closed-loop system stability and the convergence to small vicinity around the optimal solution are all proved by means of the Lyapunov theory. The proposed adaptation algorithm is also improved to achieve finite-time convergence of the NN weights. Finally, simulation results are provided to exemplify the efficacy of the proposed methods.

Dynamic Price Vector Formation Model-Based Automatic Demand Response Strategy for PV-Assisted EV Charging Stations

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

Chen, Qifang; Wang, Fei; Hodge, Bri-Mathias

A real-time price (RTP)-based automatic demand response (ADR) strategy for PV-assisted electric vehicle (EV) Charging Station (PVCS) without vehicle to grid is proposed. The charging process is modeled as a dynamic linear program instead of the normal day-ahead and real-time regulation strategy, to capture the advantages of both global and real-time optimization. Different from conventional price forecasting algorithms, a dynamic price vector formation model is proposed based on a clustering algorithm to form an RTP vector for a particular day. A dynamic feasible energy demand region (DFEDR) model considering grid voltage profiles is designed to calculate the lower and uppermore » bounds. A deduction method is proposed to deal with the unknown information of future intervals, such as the actual stochastic arrival and departure times of EVs, which make the DFEDR model suitable for global optimization. Finally, both the comparative cases articulate the advantages of the developed methods and the validity in reducing electricity costs, mitigating peak charging demand, and improving PV self-consumption of the proposed strategy are verified through simulation scenarios.« less

A Brownian dynamics program for the simulation of linear and circular DNA and other wormlike chain polyelectrolytes.

PubMed Central

Klenin, K; Merlitz, H; Langowski, J

1998-01-01

For the interpretation of solution structural and dynamic data of linear and circular DNA molecules in the kb range, and for the prediction of the effect of local structural changes on the global conformation of such DNAs, we have developed an efficient and easy way to set up a program based on a second-order explicit Brownian dynamics algorithm. The DNA is modeled by a chain of rigid segments interacting through harmonic spring potentials for bending, torsion, and stretching. The electrostatics are handled using precalculated energy tables for the interactions between DNA segments as a function of relative orientation and distance. Hydrodynamic interactions are treated using the Rotne-Prager tensor. While maintaining acceptable precision, the simulation can be accelerated by recalculating this tensor only once in a certain number of steps. PMID:9533691

Exact and Heuristic Algorithms for Runway Scheduling

NASA Technical Reports Server (NTRS)

Malik, Waqar A.; Jung, Yoon C.

2016-01-01

This paper explores the Single Runway Scheduling (SRS) problem with arrivals, departures, and crossing aircraft on the airport surface. Constraints for wake vortex separations, departure area navigation separations and departure time window restrictions are explicitly considered. The main objective of this research is to develop exact and heuristic based algorithms that can be used in real-time decision support tools for Air Traffic Control Tower (ATCT) controllers. The paper provides a multi-objective dynamic programming (DP) based algorithm that finds the exact solution to the SRS problem, but may prove unusable for application in real-time environment due to large computation times for moderate sized problems. We next propose a second algorithm that uses heuristics to restrict the search space for the DP based algorithm. A third algorithm based on a combination of insertion and local search (ILS) heuristics is then presented. Simulation conducted for the east side of Dallas/Fort Worth International Airport allows comparison of the three proposed algorithms and indicates that the ILS algorithm performs favorably in its ability to find efficient solutions and its computation times.

Quasi-Optimal Elimination Trees for 2D Grids with Singularities

DOE PAGES

Paszyńska, A.; Paszyński, M.; Jopek, K.; ...

2015-01-01

We consmore » truct quasi-optimal elimination trees for 2D finite element meshes with singularities. These trees minimize the complexity of the solution of the discrete system. The computational cost estimates of the elimination process model the execution of the multifrontal algorithms in serial and in parallel shared-memory executions. Since the meshes considered are a subspace of all possible mesh partitions, we call these minimizers quasi-optimal. We minimize the cost functionals using dynamic programming. Finding these minimizers is more computationally expensive than solving the original algebraic system. Nevertheless, from the insights provided by the analysis of the dynamic programming minima, we propose a heuristic construction of the elimination trees that has cost O N e log ⁡ N e , where N e is the number of elements in the mesh. We show that this heuristic ordering has similar computational cost to the quasi-optimal elimination trees found with dynamic programming and outperforms state-of-the-art alternatives in our numerical experiments.« less

Quasi-Optimal Elimination Trees for 2D Grids with Singularities

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

Paszyńska, A.; Paszyński, M.; Jopek, K.

We consmore » truct quasi-optimal elimination trees for 2D finite element meshes with singularities. These trees minimize the complexity of the solution of the discrete system. The computational cost estimates of the elimination process model the execution of the multifrontal algorithms in serial and in parallel shared-memory executions. Since the meshes considered are a subspace of all possible mesh partitions, we call these minimizers quasi-optimal. We minimize the cost functionals using dynamic programming. Finding these minimizers is more computationally expensive than solving the original algebraic system. Nevertheless, from the insights provided by the analysis of the dynamic programming minima, we propose a heuristic construction of the elimination trees that has cost O N e log ⁡ N e , where N e is the number of elements in the mesh. We show that this heuristic ordering has similar computational cost to the quasi-optimal elimination trees found with dynamic programming and outperforms state-of-the-art alternatives in our numerical experiments.« less

Polynomial algorithms for the Maximal Pairing Problem: efficient phylogenetic targeting on arbitrary trees

PubMed Central

2010-01-01

Background The Maximal Pairing Problem (MPP) is the prototype of a class of combinatorial optimization problems that are of considerable interest in bioinformatics: Given an arbitrary phylogenetic tree T and weights ωxy for the paths between any two pairs of leaves (x, y), what is the collection of edge-disjoint paths between pairs of leaves that maximizes the total weight? Special cases of the MPP for binary trees and equal weights have been described previously; algorithms to solve the general MPP are still missing, however. Results We describe a relatively simple dynamic programming algorithm for the special case of binary trees. We then show that the general case of multifurcating trees can be treated by interleaving solutions to certain auxiliary Maximum Weighted Matching problems with an extension of this dynamic programming approach, resulting in an overall polynomial-time solution of complexity (n4 log n) w.r.t. the number n of leaves. The source code of a C implementation can be obtained under the GNU Public License from http://www.bioinf.uni-leipzig.de/Software/Targeting. For binary trees, we furthermore discuss several constrained variants of the MPP as well as a partition function approach to the probabilistic version of the MPP. Conclusions The algorithms introduced here make it possible to solve the MPP also for large trees with high-degree vertices. This has practical relevance in the field of comparative phylogenetics and, for example, in the context of phylogenetic targeting, i.e., data collection with resource limitations. PMID:20525185

Dynamic Reconstruction Algorithm of Three-Dimensional Temperature Field Measurement by Acoustic Tomography

PubMed Central

Li, Yanqiu; Liu, Shi; Inaki, Schlaberg H.

2017-01-01

Accuracy and speed of algorithms play an important role in the reconstruction of temperature field measurements by acoustic tomography. Existing algorithms are based on static models which only consider the measurement information. A dynamic model of three-dimensional temperature reconstruction by acoustic tomography is established in this paper. A dynamic algorithm is proposed considering both acoustic measurement information and the dynamic evolution information of the temperature field. An objective function is built which fuses measurement information and the space constraint of the temperature field with its dynamic evolution information. Robust estimation is used to extend the objective function. The method combines a tunneling algorithm and a local minimization technique to solve the objective function. Numerical simulations show that the image quality and noise immunity of the dynamic reconstruction algorithm are better when compared with static algorithms such as least square method, algebraic reconstruction technique and standard Tikhonov regularization algorithms. An effective method is provided for temperature field reconstruction by acoustic tomography. PMID:28895930

Development of Quadratic Programming Algorithm Based on Interior Point Method with Estimation Mechanism of Active Constraints

NASA Astrophysics Data System (ADS)

Hashimoto, Hiroyuki; Takaguchi, Yusuke; Nakamura, Shizuka

Instability of calculation process and increase of calculation time caused by increasing size of continuous optimization problem remain the major issues to be solved to apply the technique to practical industrial systems. This paper proposes an enhanced quadratic programming algorithm based on interior point method mainly for improvement of calculation stability. The proposed method has dynamic estimation mechanism of active constraints on variables, which fixes the variables getting closer to the upper/lower limit on them and afterwards releases the fixed ones as needed during the optimization process. It is considered as algorithm-level integration of the solution strategy of active-set method into the interior point method framework. We describe some numerical results on commonly-used bench-mark problems called “CUTEr” to show the effectiveness of the proposed method. Furthermore, the test results on large-sized ELD problem (Economic Load Dispatching problems in electric power supply scheduling) are also described as a practical industrial application.

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.

The pEst version 2.1 user's manual

NASA Technical Reports Server (NTRS)

Murray, James E.; Maine, Richard E.

1987-01-01

This report is a user's manual for version 2.1 of pEst, a FORTRAN 77 computer program for interactive parameter estimation in nonlinear dynamic systems. The pEst program allows the user complete generality in definig the nonlinear equations of motion used in the analysis. The equations of motion are specified by a set of FORTRAN subroutines; a set of routines for a general aircraft model is supplied with the program and is described in the report. The report also briefly discusses the scope of the parameter estimation problem the program addresses. The report gives detailed explanations of the purpose and usage of all available program commands and a description of the computational algorithms used in the program.

Users manual for flight control design programs

NASA Technical Reports Server (NTRS)

Nalbandian, J. Y.

1975-01-01

Computer programs for the design of analog and digital flight control systems are documented. The program DIGADAPT uses linear-quadratic-gaussian synthesis algorithms in the design of command response controllers and state estimators, and it applies covariance propagation analysis to the selection of sampling intervals for digital systems. Program SCHED executes correlation and regression analyses for the development of gain and trim schedules to be used in open-loop explicit-adaptive control laws. A linear-time-varying simulation of aircraft motions is provided by the program TVHIS, which includes guidance and control logic, as well as models for control actuator dynamics. The programs are coded in FORTRAN and are compiled and executed on both IBM and CDC computers.

Application of the Hughes-LIU algorithm to the 2-dimensional heat equation

NASA Technical Reports Server (NTRS)

Malkus, D. S.; Reichmann, P. I.; Haftka, R. T.

1982-01-01

An implicit explicit algorithm for the solution of transient problems in structural dynamics is described. The method involved dividing the finite elements into implicit and explicit groups while automatically satisfying the conditions. This algorithm is applied to the solution of the linear, transient, two dimensional heat equation subject to an initial condition derived from the soluton of a steady state problem over an L-shaped region made up of a good conductor and an insulating material. Using the IIT/PRIME computer with virtual memory, a FORTRAN computer program code was developed to make accuracy, stability, and cost comparisons among the fully explicit Euler, the Hughes-Liu, and the fully implicit Crank-Nicholson algorithms. The Hughes-Liu claim that the explicit group governs the stability of the entire region while maintaining the unconditional stability of the implicit group is illustrated.

Recursive flexible multibody system dynamics using spatial operators

NASA Technical Reports Server (NTRS)

Jain, A.; Rodriguez, G.

1992-01-01

This paper uses spatial operators to develop new spatially recursive dynamics algorithms for flexible multibody systems. The operator description of the dynamics is identical to that for rigid multibody systems. Assumed-mode models are used for the deformation of each individual body. The algorithms are based on two spatial operator factorizations of the system mass matrix. The first (Newton-Euler) factorization of the mass matrix leads to recursive algorithms for the inverse dynamics, mass matrix evaluation, and composite-body forward dynamics for the systems. The second (innovations) factorization of the mass matrix, leads to an operator expression for the mass matrix inverse and to a recursive articulated-body forward dynamics algorithm. The primary focus is on serial chains, but extensions to general topologies are also described. A comparison of computational costs shows that the articulated-body, forward dynamics algorithm is much more efficient than the composite-body algorithm for most flexible multibody systems.

Cellular automata-based modelling and simulation of biofilm structure on multi-core computers.

PubMed

Skoneczny, Szymon

2015-01-01

The article presents a mathematical model of biofilm growth for aerobic biodegradation of a toxic carbonaceous substrate. Modelling of biofilm growth has fundamental significance in numerous processes of biotechnology and mathematical modelling of bioreactors. The process following double-substrate kinetics with substrate inhibition proceeding in a biofilm has not been modelled so far by means of cellular automata. Each process in the model proposed, i.e. diffusion of substrates, uptake of substrates, growth and decay of microorganisms and biofilm detachment, is simulated in a discrete manner. It was shown that for flat biofilm of constant thickness, the results of the presented model agree with those of a continuous model. The primary outcome of the study was to propose a mathematical model of biofilm growth; however a considerable amount of focus was also placed on the development of efficient algorithms for its solution. Two parallel algorithms were created, differing in the way computations are distributed. Computer programs were created using OpenMP Application Programming Interface for C++ programming language. Simulations of biofilm growth were performed on three high-performance computers. Speed-up coefficients of computer programs were compared. Both algorithms enabled a significant reduction of computation time. It is important, inter alia, in modelling and simulation of bioreactor dynamics.

Optimal reservoir operation policies using novel nested algorithms

NASA Astrophysics Data System (ADS)

Delipetrev, Blagoj; Jonoski, Andreja; Solomatine, Dimitri

2015-04-01

Historically, the two most widely practiced methods for optimal reservoir operation have been dynamic programming (DP) and stochastic dynamic programming (SDP). These two methods suffer from the so called "dual curse" which prevents them to be used in reasonably complex water systems. The first one is the "curse of dimensionality" that denotes an exponential growth of the computational complexity with the state - decision space dimension. The second one is the "curse of modelling" that requires an explicit model of each component of the water system to anticipate the effect of each system's transition. We address the problem of optimal reservoir operation concerning multiple objectives that are related to 1) reservoir releases to satisfy several downstream users competing for water with dynamically varying demands, 2) deviations from the target minimum and maximum reservoir water levels and 3) hydropower production that is a combination of the reservoir water level and the reservoir releases. Addressing such a problem with classical methods (DP and SDP) requires a reasonably high level of discretization of the reservoir storage volume, which in combination with the required releases discretization for meeting the demands of downstream users leads to computationally expensive formulations and causes the curse of dimensionality. We present a novel approach, named "nested" that is implemented in DP, SDP and reinforcement learning (RL) and correspondingly three new algorithms are developed named nested DP (nDP), nested SDP (nSDP) and nested RL (nRL). The nested algorithms are composed from two algorithms: 1) DP, SDP or RL and 2) nested optimization algorithm. Depending on the way we formulate the objective function related to deficits in the allocation problem in the nested optimization, two methods are implemented: 1) Simplex for linear allocation problems, and 2) quadratic Knapsack method in the case of nonlinear problems. The novel idea is to include the nested optimization algorithm into the state transition that lowers the starting problem dimension and alleviates the curse of dimensionality. The algorithms can solve multi-objective optimization problems, without significantly increasing the complexity and the computational expenses. The algorithms can handle dense and irregular variable discretization, and are coded in Java as prototype applications. The three algorithms were tested at the multipurpose reservoir Knezevo of the Zletovica hydro-system located in the Republic of Macedonia, with eight objectives, including urban water supply, agriculture, ensuring ecological flow, and generation of hydropower. Because the Zletovica hydro-system is relatively complex, the novel algorithms were pushed to their limits, demonstrating their capabilities and limitations. The nSDP and nRL derived/learned the optimal reservoir policy using 45 (1951-1995) years historical data. The nSDP and nRL optimal reservoir policy was tested on 10 (1995-2005) years historical data, and compared with nDP optimal reservoir operation in the same period. The nested algorithms and optimal reservoir operation results are analysed and explained.

A genetic algorithm solution to the unit commitment problem

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

Kazarlis, S.A.; Bakirtzis, A.G.; Petridis, V.

1996-02-01

This paper presents a Genetic Algorithm (GA) solution to the Unit Commitment problem. GAs are general purpose optimization techniques based on principles inspired from the biological evolution using metaphors of mechanisms such as natural selection, genetic recombination and survival of the fittest. A simple Ga algorithm implementation using the standard crossover and mutation operators could locate near optimal solutions but in most cases failed to converge to the optimal solution. However, using the Varying Quality Function technique and adding problem specific operators, satisfactory solutions to the Unit Commitment problem were obtained. Test results for systems of up to 100 unitsmore » and comparisons with results obtained using Lagrangian Relaxation and Dynamic Programming are also reported.« less

From Theory to Air Force Practice: Applications and Non-Binary Extensions of Probabilistic Model-Building Genetic Algorithms

DTIC Science & Technology

2006-05-31

dynamics (MD) and kinetic Monte Carlo ( KMC ) procedures. In 2D surface modeling our calculations project speedups of 9 orders of magnitude at 300 degrees...programming is used to perform customized statistical mechanics by bridging the different time scales of MD and KMC quickly and well. Speedups in

An adaptable, low cost test-bed for unmanned vehicle systems research

NASA Astrophysics Data System (ADS)

Goppert, James M.

2011-12-01

An unmanned vehicle systems test-bed has been developed. The test-bed has been designed to accommodate hardware changes and various vehicle types and algorithms. The creation of this test-bed allows research teams to focus on algorithm development and employ a common well-tested experimental framework. The ArduPilotOne autopilot was developed to provide the necessary level of abstraction for multiple vehicle types. The autopilot was also designed to be highly integrated with the Mavlink protocol for Micro Air Vehicle (MAV) communication. Mavlink is the native protocol for QGroundControl, a MAV ground control program. Features were added to QGroundControl to accommodate outdoor usage. Next, the Mavsim toolbox was developed for Scicoslab to allow hardware-in-the-loop testing, control design and analysis, and estimation algorithm testing and verification. In order to obtain linear models of aircraft dynamics, the JSBSim flight dynamics engine was extended to use a probabilistic Nelder-Mead simplex method. The JSBSim aircraft dynamics were compared with wind-tunnel data collected. Finally, a structured methodology for successive loop closure control design is proposed. This methodology is demonstrated along with the rest of the test-bed tools on a quadrotor, a fixed wing RC plane, and a ground vehicle. Test results for the ground vehicle are presented.

Dynamic graph cuts for efficient inference in Markov Random Fields.

PubMed

Kohli, Pushmeet; Torr, Philip H S

2007-12-01

Abstract-In this paper we present a fast new fully dynamic algorithm for the st-mincut/max-flow problem. We show how this algorithm can be used to efficiently compute MAP solutions for certain dynamically changing MRF models in computer vision such as image segmentation. Specifically, given the solution of the max-flow problem on a graph, the dynamic algorithm efficiently computes the maximum flow in a modified version of the graph. The time taken by it is roughly proportional to the total amount of change in the edge weights of the graph. Our experiments show that, when the number of changes in the graph is small, the dynamic algorithm is significantly faster than the best known static graph cut algorithm. We test the performance of our algorithm on one particular problem: the object-background segmentation problem for video. It should be noted that the application of our algorithm is not limited to the above problem, the algorithm is generic and can be used to yield similar improvements in many other cases that involve dynamic change.

Formulation and implementation of a practical algorithm for parameter estimation with process and measurement noise

NASA Technical Reports Server (NTRS)

Maine, R. E.; Iliff, K. W.

1980-01-01

A new formulation is proposed for the problem of parameter estimation of dynamic systems with both process and measurement noise. The formulation gives estimates that are maximum likelihood asymptotically in time. The means used to overcome the difficulties encountered by previous formulations are discussed. It is then shown how the proposed formulation can be efficiently implemented in a computer program. A computer program using the proposed formulation is available in a form suitable for routine application. Examples with simulated and real data are given to illustrate that the program works well.

The sequence relay selection strategy based on stochastic dynamic programming

NASA Astrophysics Data System (ADS)

Zhu, Rui; Chen, Xihao; Huang, Yangchao

2017-07-01

Relay-assisted (RA) network with relay node selection is a kind of effective method to improve the channel capacity and convergence performance. However, most of the existing researches about the relay selection did not consider the statically channel state information and the selection cost. This shortage limited the performance and application of RA network in practical scenarios. In order to overcome this drawback, a sequence relay selection strategy (SRSS) was proposed. And the performance upper bound of SRSS was also analyzed in this paper. Furthermore, in order to make SRSS more practical, a novel threshold determination algorithm based on the stochastic dynamic program (SDP) was given to work with SRSS. Numerical results are also presented to exhibit the performance of SRSS with SDP.

Optimal approach to quantum communication using dynamic programming.

PubMed

Jiang, Liang; Taylor, Jacob M; Khaneja, Navin; Lukin, Mikhail D

2007-10-30

Reliable preparation of entanglement between distant systems is an outstanding problem in quantum information science and quantum communication. In practice, this has to be accomplished by noisy channels (such as optical fibers) that generally result in exponential attenuation of quantum signals at large distances. A special class of quantum error correction protocols, quantum repeater protocols, can be used to overcome such losses. In this work, we introduce a method for systematically optimizing existing protocols and developing more efficient protocols. Our approach makes use of a dynamic programming-based searching algorithm, the complexity of which scales only polynomially with the communication distance, letting us efficiently determine near-optimal solutions. We find significant improvements in both the speed and the final-state fidelity for preparing long-distance entangled states.

Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

NASA Astrophysics Data System (ADS)

Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai

2013-09-01

In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

ARYANA: Aligning Reads by Yet Another Approach

PubMed Central

2014-01-01

Motivation Although there are many different algorithms and software tools for aligning sequencing reads, fast gapped sequence search is far from solved. Strong interest in fast alignment is best reflected in the $106 prize for the Innocentive competition on aligning a collection of reads to a given database of reference genomes. In addition, de novo assembly of next-generation sequencing long reads requires fast overlap-layout-concensus algorithms which depend on fast and accurate alignment. Contribution We introduce ARYANA, a fast gapped read aligner, developed on the base of BWA indexing infrastructure with a completely new alignment engine that makes it significantly faster than three other aligners: Bowtie2, BWA and SeqAlto, with comparable generality and accuracy. Instead of the time-consuming backtracking procedures for handling mismatches, ARYANA comes with the seed-and-extend algorithmic framework and a significantly improved efficiency by integrating novel algorithmic techniques including dynamic seed selection, bidirectional seed extension, reset-free hash tables, and gap-filling dynamic programming. As the read length increases ARYANA's superiority in terms of speed and alignment rate becomes more evident. This is in perfect harmony with the read length trend as the sequencing technologies evolve. The algorithmic platform of ARYANA makes it easy to develop mission-specific aligners for other applications using ARYANA engine. Availability ARYANA with complete source code can be obtained from http://github.com/aryana-aligner PMID:25252881

ARYANA: Aligning Reads by Yet Another Approach.

PubMed

Gholami, Milad; Arbabi, Aryan; Sharifi-Zarchi, Ali; Chitsaz, Hamidreza; Sadeghi, Mehdi

2014-01-01

Although there are many different algorithms and software tools for aligning sequencing reads, fast gapped sequence search is far from solved. Strong interest in fast alignment is best reflected in the $10(6) prize for the Innocentive competition on aligning a collection of reads to a given database of reference genomes. In addition, de novo assembly of next-generation sequencing long reads requires fast overlap-layout-concensus algorithms which depend on fast and accurate alignment. We introduce ARYANA, a fast gapped read aligner, developed on the base of BWA indexing infrastructure with a completely new alignment engine that makes it significantly faster than three other aligners: Bowtie2, BWA and SeqAlto, with comparable generality and accuracy. Instead of the time-consuming backtracking procedures for handling mismatches, ARYANA comes with the seed-and-extend algorithmic framework and a significantly improved efficiency by integrating novel algorithmic techniques including dynamic seed selection, bidirectional seed extension, reset-free hash tables, and gap-filling dynamic programming. As the read length increases ARYANA's superiority in terms of speed and alignment rate becomes more evident. This is in perfect harmony with the read length trend as the sequencing technologies evolve. The algorithmic platform of ARYANA makes it easy to develop mission-specific aligners for other applications using ARYANA engine. ARYANA with complete source code can be obtained from http://github.com/aryana-aligner.

Explicit symplectic algorithms based on generating functions for charged particle dynamics.

PubMed

Zhang, Ruili; Qin, Hong; Tang, Yifa; Liu, Jian; He, Yang; Xiao, Jianyuan

2016-07-01

Dynamics of a charged particle in the canonical coordinates is a Hamiltonian system, and the well-known symplectic algorithm has been regarded as the de facto method for numerical integration of Hamiltonian systems due to its long-term accuracy and fidelity. For long-term simulations with high efficiency, explicit symplectic algorithms are desirable. However, it is generally believed that explicit symplectic algorithms are only available for sum-separable Hamiltonians, and this restriction limits the application of explicit symplectic algorithms to charged particle dynamics. To overcome this difficulty, we combine the familiar sum-split method and a generating function method to construct second- and third-order explicit symplectic algorithms for dynamics of charged particle. The generating function method is designed to generate explicit symplectic algorithms for product-separable Hamiltonian with form of H(x,p)=p_{i}f(x) or H(x,p)=x_{i}g(p). Applied to the simulations of charged particle dynamics, the explicit symplectic algorithms based on generating functions demonstrate superiorities in conservation and efficiency.

Explicit symplectic algorithms based on generating functions for charged particle dynamics

NASA Astrophysics Data System (ADS)

Zhang, Ruili; Qin, Hong; Tang, Yifa; Liu, Jian; He, Yang; Xiao, Jianyuan

2016-07-01

Dynamics of a charged particle in the canonical coordinates is a Hamiltonian system, and the well-known symplectic algorithm has been regarded as the de facto method for numerical integration of Hamiltonian systems due to its long-term accuracy and fidelity. For long-term simulations with high efficiency, explicit symplectic algorithms are desirable. However, it is generally believed that explicit symplectic algorithms are only available for sum-separable Hamiltonians, and this restriction limits the application of explicit symplectic algorithms to charged particle dynamics. To overcome this difficulty, we combine the familiar sum-split method and a generating function method to construct second- and third-order explicit symplectic algorithms for dynamics of charged particle. The generating function method is designed to generate explicit symplectic algorithms for product-separable Hamiltonian with form of H (x ,p ) =pif (x ) or H (x ,p ) =xig (p ) . Applied to the simulations of charged particle dynamics, the explicit symplectic algorithms based on generating functions demonstrate superiorities in conservation and efficiency.

Numerical Modeling in Problems of Near-Earth Object Dynamics

NASA Astrophysics Data System (ADS)

Aleksandrova, A. G.; Bordovitsyna, T. V.; Chuvashov, I. N.

2017-05-01

A method of numerical modeling is used to solve three most interesting problems of artificial Earth satellite (AES) dynamics. Orbital evolution of an ensemble of near-Earth objects at altitudes in the range from 1 500 to 60 000 km is considered, chaoticity of motion of objects in the geosynchronous zone is studied by the MEGNOanalysis, the parameters of AES motion are determined, and the models of forces are considered from measurements for GLONASS satellites. The recent versions of algorithms and programs used to perform investigations are briefly described.

Nonlinear dynamics optimization with particle swarm and genetic algorithms for SPEAR3 emittance upgrade

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

Huang, Xiaobiao; Safranek, James

2014-09-01

Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.

Turbopump Performance Improved by Evolutionary Algorithms

NASA Technical Reports Server (NTRS)

Oyama, Akira; Liou, Meng-Sing

2002-01-01

The development of design optimization technology for turbomachinery has been initiated using the multiobjective evolutionary algorithm under NASA's Intelligent Synthesis Environment and Revolutionary Aeropropulsion Concepts programs. As an alternative to the traditional gradient-based methods, evolutionary algorithms (EA's) are emergent design-optimization algorithms modeled after the mechanisms found in natural evolution. EA's search from multiple points, instead of moving from a single point. In addition, they require no derivatives or gradients of the objective function, leading to robustness and simplicity in coupling any evaluation codes. Parallel efficiency also becomes very high by using a simple master-slave concept for function evaluations, since such evaluations often consume the most CPU time, such as computational fluid dynamics. Application of EA's to multiobjective design problems is also straightforward because EA's maintain a population of design candidates in parallel. Because of these advantages, EA's are a unique and attractive approach to real-world design optimization problems.

Launch flexibility using NLP guidance and remote wind sensing

NASA Technical Reports Server (NTRS)

Cramer, Evin J.; Bradt, Jerre E.; Hardtla, John W.

1990-01-01

This paper examines the use of lidar wind measurements in the implementation of a guidance strategy for a nonlinear programming (NLP) launch guidance algorithm. The NLP algorithm uses B-spline command function representation for flexibility in the design of the guidance steering commands. Using this algorithm, the guidance system solves a two-point boundary value problem at each guidance update. The specification of different boundary value problems at each guidance update provides flexibility that can be used in the design of the guidance strategy. The algorithm can use lidar wind measurements for on pad guidance retargeting and for load limiting guidance steering commands. Examples presented in the paper use simulated wind updates to correct wind induced final orbit errors and to adjust the guidance steering commands to limit the product of the dynamic pressure and angle-of-attack for launch vehicle load alleviation.

Manycast routing, modulation level and spectrum assignment over elastic optical networks

NASA Astrophysics Data System (ADS)

Luo, Xiao; Zhao, Yang; Chen, Xue; Wang, Lei; Zhang, Min; Zhang, Jie; Ji, Yuefeng; Wang, Huitao; Wang, Taili

2017-07-01

Manycast is a point to multi-point transmission framework that requires a subset of destination nodes successfully reached. It is particularly applicable for dealing with large amounts of data simultaneously in bandwidth-hungry, dynamic and cloud-based applications. As rapid increasing of traffics in these applications, the elastic optical networks (EONs) may be relied on to achieve high throughput manycast. In terms of finer spectrum granularity, the EONs could reach flexible accessing to network spectrum and efficient providing exact spectrum resource to demands. In this paper, we focus on the manycast routing, modulation level and spectrum assignment (MA-RMLSA) problem in EONs. Both EONs planning with static manycast traffic and EONs provisioning with dynamic manycast traffic are investigated. An integer linear programming (ILP) model is formulated to derive MA-RMLSA problem in static manycast scenario. Then corresponding heuristic algorithm called manycast routing, modulation level and spectrum assignment genetic algorithm (MA-RMLSA-GA) is proposed to adapt for both static and dynamic manycast scenarios. The MA-RMLSA-GA optimizes MA-RMLSA problem in destination nodes selection, routing light-tree constitution, modulation level allocation and spectrum resource assignment jointly, to achieve an effective improvement in network performance. Simulation results reveal that MA-RMLSA strategies offered by MA-RMLSA-GA have slightly disparity from the optimal solutions provided by ILP model in static scenario. Moreover, the results demonstrate that MA-RMLSA-GA realizes a highly efficient MA-RMLSA strategy with the lowest blocking probability in dynamic scenario compared with benchmark algorithms.

GPU accelerated dynamic functional connectivity analysis for functional MRI data.

PubMed

Akgün, Devrim; Sakoğlu, Ünal; Esquivel, Johnny; Adinoff, Bryon; Mete, Mutlu

2015-07-01

Recent advances in multi-core processors and graphics card based computational technologies have paved the way for an improved and dynamic utilization of parallel computing techniques. Numerous applications have been implemented for the acceleration of computationally-intensive problems in various computational science fields including bioinformatics, in which big data problems are prevalent. In neuroimaging, dynamic functional connectivity (DFC) analysis is a computationally demanding method used to investigate dynamic functional interactions among different brain regions or networks identified with functional magnetic resonance imaging (fMRI) data. In this study, we implemented and analyzed a parallel DFC algorithm based on thread-based and block-based approaches. The thread-based approach was designed to parallelize DFC computations and was implemented in both Open Multi-Processing (OpenMP) and Compute Unified Device Architecture (CUDA) programming platforms. Another approach developed in this study to better utilize CUDA architecture is the block-based approach, where parallelization involves smaller parts of fMRI time-courses obtained by sliding-windows. Experimental results showed that the proposed parallel design solutions enabled by the GPUs significantly reduce the computation time for DFC analysis. Multicore implementation using OpenMP on 8-core processor provides up to 7.7× speed-up. GPU implementation using CUDA yielded substantial accelerations ranging from 18.5× to 157× speed-up once thread-based and block-based approaches were combined in the analysis. Proposed parallel programming solutions showed that multi-core processor and CUDA-supported GPU implementations accelerated the DFC analyses significantly. Developed algorithms make the DFC analyses more practical for multi-subject studies with more dynamic analyses. Copyright © 2015 Elsevier Ltd. All rights reserved.

Summary of research in applied mathematics, numerical analysis, and computer sciences

NASA Technical Reports Server (NTRS)

1986-01-01

The major categories of current ICASE research programs addressed include: numerical methods, with particular emphasis on the development and analysis of basic numerical algorithms; control and parameter identification problems, with emphasis on effective numerical methods; computational problems in engineering and physical sciences, particularly fluid dynamics, acoustics, and structural analysis; and computer systems and software, especially vector and parallel computers.

Development of a 3D log sawing optimization system for small sawmills in central Appalachia, US

Treesearch

Wenshu Lin; Jingxin Wang; Edward Thomas

2011-01-01

A 3D log sawing optimization system was developed to perform log generation, opening face determination, sawing simulation, and lumber grading using 3D modeling techniques. Heuristic and dynamic programming algorithms were used to determine opening face and grade sawing optimization. Positions and shapes of internal log defects were predicted using a model developed by...

Modeling 2D and 3D diffusion.

PubMed

Saxton, Michael J

2007-01-01

Modeling obstructed diffusion is essential to the understanding of diffusion-mediated processes in the crowded cellular environment. Simple Monte Carlo techniques for modeling obstructed random walks are explained and related to Brownian dynamics and more complicated Monte Carlo methods. Random number generation is reviewed in the context of random walk simulations. Programming techniques and event-driven algorithms are discussed as ways to speed simulations.

Adaptive dynamic programming approach to experience-based systems identification and control.

PubMed

Lendaris, George G

2009-01-01

Humans have the ability to make use of experience while selecting their control actions for distinct and changing situations, and their process speeds up and have enhanced effectiveness as more experience is gained. In contrast, current technological implementations slow down as more knowledge is stored. A novel way of employing Approximate (or Adaptive) Dynamic Programming (ADP) is described that shifts the underlying Adaptive Critic type of Reinforcement Learning method "up a level", away from designing individual (optimal) controllers to that of developing on-line algorithms that efficiently and effectively select designs from a repository of existing controller solutions (perhaps previously developed via application of ADP methods). The resulting approach is called Higher-Level Learning Algorithm. The approach and its rationale are described and some examples of its application are given. The notions of context and context discernment are important to understanding the human abilities noted above. These are first defined, in a manner appropriate to controls and system-identification, and as a foundation relating to the application arena, a historical view of the various phases during development of the controls field is given, organized by how the notion 'context' was, or was not, involved in each phase.

User-Assisted Store Recycling for Dynamic Task Graph Schedulers

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

Kurt, Mehmet Can; Krishnamoorthy, Sriram; Agrawal, Gagan

The emergence of the multi-core era has led to increased interest in designing effective yet practical parallel programming models. Models based on task graphs that operate on single-assignment data are attractive in several ways: they can support dynamic applications and precisely represent the available concurrency. However, they also require nuanced algorithms for scheduling and memory management for efficient execution. In this paper, we consider memory-efficient dynamic scheduling of task graphs. Specifically, we present a novel approach for dynamically recycling the memory locations assigned to data items as they are produced by tasks. We develop algorithms to identify memory-efficient store recyclingmore » functions by systematically evaluating the validity of a set of (user-provided or automatically generated) alternatives. Because recycling function can be input data-dependent, we have also developed support for continued correct execution of a task graph in the presence of a potentially incorrect store recycling function. Experimental evaluation demonstrates that our approach to automatic store recycling incurs little to no overheads, achieves memory usage comparable to the best manually derived solutions, often produces recycling functions valid across problem sizes and input parameters, and efficiently recovers from an incorrect choice of store recycling functions.« less

The multi-disciplinary design study: A life cycle cost algorithm

NASA Technical Reports Server (NTRS)

Harding, R. R.; Pichi, F. J.

1988-01-01

The approach and results of a Life Cycle Cost (LCC) analysis of the Space Station Solar Dynamic Power Subsystem (SDPS) including gimbal pointing and power output performance are documented. The Multi-Discipline Design Tool (MDDT) computer program developed during the 1986 study has been modified to include the design, performance, and cost algorithms for the SDPS as described. As with the Space Station structural and control subsystems, the LCC of the SDPS can be computed within the MDDT program as a function of the engineering design variables. Two simple examples of MDDT's capability to evaluate cost sensitivity and design based on LCC are included. MDDT was designed to accept NASA's IMAT computer program data as input so that IMAT's detailed structural and controls design capability can be assessed with expected system LCC as computed by MDDT. No changes to IMAT were required. Detailed knowledge of IMAT is not required to perform the LCC analyses as the interface with IMAT is noninteractive.

Image processing meta-algorithm development via genetic manipulation of existing algorithm graphs

NASA Astrophysics Data System (ADS)

Schalkoff, Robert J.; Shaaban, Khaled M.

1999-07-01

Automatic algorithm generation for image processing applications is not a new idea, however previous work is either restricted to morphological operates or impractical. In this paper, we show recent research result in the development and use of meta-algorithms, i.e. algorithms which lead to new algorithms. Although the concept is generally applicable, the application domain in this work is restricted to image processing. The meta-algorithm concept described in this paper is based upon out work in dynamic algorithm. The paper first present the concept of dynamic algorithms which, on the basis of training and archived algorithmic experience embedded in an algorithm graph (AG), dynamically adjust the sequence of operations applied to the input image data. Each node in the tree-based representation of a dynamic algorithm with out degree greater than 2 is a decision node. At these nodes, the algorithm examines the input data and determines which path will most likely achieve the desired results. This is currently done using nearest-neighbor classification. The details of this implementation are shown. The constrained perturbation of existing algorithm graphs, coupled with a suitable search strategy, is one mechanism to achieve meta-algorithm an doffers rich potential for the discovery of new algorithms. In our work, a meta-algorithm autonomously generates new dynamic algorithm graphs via genetic recombination of existing algorithm graphs. The AG representation is well suited to this genetic-like perturbation, using a commonly- employed technique in artificial neural network synthesis, namely the blueprint representation of graphs. A number of exam. One of the principal limitations of our current approach is the need for significant human input in the learning phase. Efforts to overcome this limitation are discussed. Future research directions are indicated.

An Improved Co-evolutionary Particle Swarm Optimization for Wireless Sensor Networks with Dynamic Deployment

PubMed Central

Wang, Xue; Wang, Sheng; Ma, Jun-Jie

2007-01-01

The effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment, which is usually supported by the virtual force (VF) algorithm. However, in the VF algorithm, the virtual force exerted by stationary sensor nodes will hinder the movement of mobile sensor nodes. Particle swarm optimization (PSO) is introduced as another dynamic deployment algorithm, but in this case the computation time required is the big bottleneck. This paper proposes a dynamic deployment algorithm which is named “virtual force directed co-evolutionary particle swarm optimization” (VFCPSO), since this algorithm combines the co-evolutionary particle swarm optimization (CPSO) with the VF algorithm, whereby the CPSO uses multiple swarms to optimize different components of the solution vectors for dynamic deployment cooperatively and the velocity of each particle is updated according to not only the historical local and global optimal solutions, but also the virtual forces of sensor nodes. Simulation results demonstrate that the proposed VFCPSO is competent for dynamic deployment in WSNs and has better performance with respect to computation time and effectiveness than the VF, PSO and VFPSO algorithms.

Implementation of Finite Volume based Navier Stokes Algorithm Within General Purpose Flow Network Code

NASA Technical Reports Server (NTRS)

Schallhorn, Paul; Majumdar, Alok

2012-01-01

This paper describes a finite volume based numerical algorithm that allows multi-dimensional computation of fluid flow within a system level network flow analysis. There are several thermo-fluid engineering problems where higher fidelity solutions are needed that are not within the capacity of system level codes. The proposed algorithm will allow NASA's Generalized Fluid System Simulation Program (GFSSP) to perform multi-dimensional flow calculation within the framework of GFSSP s typical system level flow network consisting of fluid nodes and branches. The paper presents several classical two-dimensional fluid dynamics problems that have been solved by GFSSP's multi-dimensional flow solver. The numerical solutions are compared with the analytical and benchmark solution of Poiseulle, Couette and flow in a driven cavity.

A spatial operator algebra for manipulator modeling and control

NASA Technical Reports Server (NTRS)

Rodriguez, G.; Kreutz, K.; Jain, A.

1989-01-01

A spatial operator algebra for modeling the control and trajectory design of manipulation is discussed, with emphasis on its analytical formulation and implementation in the Ada programming language. The elements of this algebra are linear operators whose domain and range spaces consist of forces, moments, velocities, and accelerations. The effect of these operators is equivalent to a spatial recursion along the span of the manipulator. Inversion is obtained using techniques of recursive filtering and smoothing. The operator alegbra provides a high-level framework for describing the dynamic and kinematic behavior of a manipulator and control and trajectory design algorithms. Implementable recursive algorithms can be immediately derived from the abstract operator expressions by inspection, thus greatly simplifying the transition from an abstract problem formulation and solution to the detailed mechanization of a specific algorithm.

Implementation of a fully-balanced periodic tridiagonal solver on a parallel distributed memory architecture

NASA Technical Reports Server (NTRS)

Eidson, T. M.; Erlebacher, G.

1994-01-01

While parallel computers offer significant computational performance, it is generally necessary to evaluate several programming strategies. Two programming strategies for a fairly common problem - a periodic tridiagonal solver - are developed and evaluated. Simple model calculations as well as timing results are presented to evaluate the various strategies. The particular tridiagonal solver evaluated is used in many computational fluid dynamic simulation codes. The feature that makes this algorithm unique is that these simulation codes usually require simultaneous solutions for multiple right-hand-sides (RHS) of the system of equations. Each RHS solutions is independent and thus can be computed in parallel. Thus a Gaussian elimination type algorithm can be used in a parallel computation and the more complicated approaches such as cyclic reduction are not required. The two strategies are a transpose strategy and a distributed solver strategy. For the transpose strategy, the data is moved so that a subset of all the RHS problems is solved on each of the several processors. This usually requires significant data movement between processor memories across a network. The second strategy attempts to have the algorithm allow the data across processor boundaries in a chained manner. This usually requires significantly less data movement. An approach to accomplish this second strategy in a near-perfect load-balanced manner is developed. In addition, an algorithm will be shown to directly transform a sequential Gaussian elimination type algorithm into the parallel chained, load-balanced algorithm.

Load Balancing Scientific Applications

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

Pearce, Olga Tkachyshyn

2014-12-01

The largest supercomputers have millions of independent processors, and concurrency levels are rapidly increasing. For ideal efficiency, developers of the simulations that run on these machines must ensure that computational work is evenly balanced among processors. Assigning work evenly is challenging because many large modern parallel codes simulate behavior of physical systems that evolve over time, and their workloads change over time. Furthermore, the cost of imbalanced load increases with scale because most large-scale scientific simulations today use a Single Program Multiple Data (SPMD) parallel programming model, and an increasing number of processors will wait for the slowest one atmore » the synchronization points. To address load imbalance, many large-scale parallel applications use dynamic load balance algorithms to redistribute work evenly. The research objective of this dissertation is to develop methods to decide when and how to load balance the application, and to balance it effectively and affordably. We measure and evaluate the computational load of the application, and develop strategies to decide when and how to correct the imbalance. Depending on the simulation, a fast, local load balance algorithm may be suitable, or a more sophisticated and expensive algorithm may be required. We developed a model for comparison of load balance algorithms for a specific state of the simulation that enables the selection of a balancing algorithm that will minimize overall runtime.« less

Diffusion in liquid Germanium using ab initio molecular dynamics

NASA Astrophysics Data System (ADS)

Kulkarni, R. V.; Aulbur, W. G.; Stroud, D.

1996-03-01

We describe the results of calculations of the self-diffusion constant of liquid Ge over a range of temperatures. The calculations are carried out using an ab initio molecular dynamics scheme which combines an LDA model for the electronic structure with the Bachelet-Hamann-Schlüter norm-conserving pseudopotentials^1. The energies associated with electronic degrees of freedom are minimized using the Williams-Soler algorithm, and ionic moves are carried out using the Verlet algorithm. We use an energy cutoff of 10 Ry, which is sufficient to give results for the lattice constant and bulk modulus of crystalline Ge to within 1% and 12% of experiment. The program output includes not only the self-diffusion constant but also the structure factor, electronic density of states, and low-frequency electrical conductivity. We will compare our results with other ab initio and semi-empirical calculations, and discuss extension to impurity diffusion. ^1 We use the ab initio molecular dynamics code fhi94md, developed at 1cm the Fritz-Haber Institute, Berlin. ^2 Work supported by NASA, Grant NAG3-1437.

Power-Aware Intrusion Detection in Mobile Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Şen, Sevil; Clark, John A.; Tapiador, Juan E.

Mobile ad hoc networks (MANETs) are a highly promising new form of networking. However they are more vulnerable to attacks than wired networks. In addition, conventional intrusion detection systems (IDS) are ineffective and inefficient for highly dynamic and resource-constrained environments. Achieving an effective operational MANET requires tradeoffs to be made between functional and non-functional criteria. In this paper we show how Genetic Programming (GP) together with a Multi-Objective Evolutionary Algorithm (MOEA) can be used to synthesise intrusion detection programs that make optimal tradeoffs between security criteria and the power they consume.

Neural network error correction for solving coupled ordinary differential equations

NASA Technical Reports Server (NTRS)

Shelton, R. O.; Darsey, J. A.; Sumpter, B. G.; Noid, D. W.

1992-01-01

A neural network is presented to learn errors generated by a numerical algorithm for solving coupled nonlinear differential equations. The method is based on using a neural network to correctly learn the error generated by, for example, Runge-Kutta on a model molecular dynamics (MD) problem. The neural network programs used in this study were developed by NASA. Comparisons are made for training the neural network using backpropagation and a new method which was found to converge with fewer iterations. The neural net programs, the MD model and the calculations are discussed.

Noninvasive fetal QRS detection using an echo state network and dynamic programming.

PubMed

Lukoševičius, Mantas; Marozas, Vaidotas

2014-08-01

We address a classical fetal QRS detection problem from abdominal ECG recordings with a data-driven statistical machine learning approach. Our goal is to have a powerful, yet conceptually clean, solution. There are two novel key components at the heart of our approach: an echo state recurrent neural network that is trained to indicate fetal QRS complexes, and several increasingly sophisticated versions of statistics-based dynamic programming algorithms, which are derived from and rooted in probability theory. We also employ a standard technique for preprocessing and removing maternal ECG complexes from the signals, but do not take this as the main focus of this work. The proposed approach is quite generic and can be extended to other types of signals and annotations. Open-source code is provided.

Eigensystem realization algorithm user's guide forVAX/VMS computers: Version 931216

NASA Technical Reports Server (NTRS)

Pappa, Richard S.

1994-01-01

The eigensystem realization algorithm (ERA) is a multiple-input, multiple-output, time domain technique for structural modal identification and minimum-order system realization. Modal identification is the process of calculating structural eigenvalues and eigenvectors (natural vibration frequencies, damping, mode shapes, and modal masses) from experimental data. System realization is the process of constructing state-space dynamic models for modern control design. This user's guide documents VAX/VMS-based FORTRAN software developed by the author since 1984 in conjunction with many applications. It consists of a main ERA program and 66 pre- and post-processors. The software provides complete modal identification capabilities and most system realization capabilities.

An approximate dynamic programming approach to resource management in multi-cloud scenarios

NASA Astrophysics Data System (ADS)

Pietrabissa, Antonio; Priscoli, Francesco Delli; Di Giorgio, Alessandro; Giuseppi, Alessandro; Panfili, Martina; Suraci, Vincenzo

2017-03-01

The programmability and the virtualisation of network resources are crucial to deploy scalable Information and Communications Technology (ICT) services. The increasing demand of cloud services, mainly devoted to the storage and computing, requires a new functional element, the Cloud Management Broker (CMB), aimed at managing multiple cloud resources to meet the customers' requirements and, simultaneously, to optimise their usage. This paper proposes a multi-cloud resource allocation algorithm that manages the resource requests with the aim of maximising the CMB revenue over time. The algorithm is based on Markov decision process modelling and relies on reinforcement learning techniques to find online an approximate solution.

Forecast horizon of multi-item dynamic lot size model with perishable inventory.

PubMed

Jing, Fuying; Lan, Zirui

2017-01-01

This paper studies a multi-item dynamic lot size problem for perishable products where stock deterioration rates and inventory costs are age-dependent. We explore structural properties in an optimal solution under two cost structures and develop a dynamic programming algorithm to solve the problem in polynomial time when the number of products is fixed. We establish forecast horizon results that can help the operation manager to decide the precise forecast horizon in a rolling decision-making process. Finally, based on a detailed test bed of instance, we obtain useful managerial insights on the impact of deterioration rate and lifetime of products on the length of forecast horizon.

Forecast horizon of multi-item dynamic lot size model with perishable inventory

PubMed Central

Jing, Fuying

2017-01-01

This paper studies a multi-item dynamic lot size problem for perishable products where stock deterioration rates and inventory costs are age-dependent. We explore structural properties in an optimal solution under two cost structures and develop a dynamic programming algorithm to solve the problem in polynomial time when the number of products is fixed. We establish forecast horizon results that can help the operation manager to decide the precise forecast horizon in a rolling decision-making process. Finally, based on a detailed test bed of instance, we obtain useful managerial insights on the impact of deterioration rate and lifetime of products on the length of forecast horizon. PMID:29125856

Brownian dynamics simulation of rigid particles of arbitrary shape in external fields.

PubMed

Fernandes, Miguel X; de la Torre, José García

2002-12-01

We have developed a Brownian dynamics simulation algorithm to generate Brownian trajectories of an isolated, rigid particle of arbitrary shape in the presence of electric fields or any other external agents. Starting from the generalized diffusion tensor, which can be calculated with the existing HYDRO software, the new program BROWNRIG (including a case-specific subprogram for the external agent) carries out a simulation that is analyzed later to extract the observable dynamic properties. We provide a variety of examples of utilization of this method, which serve as tests of its performance, and also illustrate its applicability. Examples include free diffusion, transport in an electric field, and diffusion in a restricting environment.

Genetic algorithms with memory- and elitism-based immigrants in dynamic environments.

PubMed

Yang, Shengxiang

2008-01-01

In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.

Explicit symplectic algorithms based on generating functions for relativistic charged particle dynamics in time-dependent electromagnetic field

NASA Astrophysics Data System (ADS)

Zhang, Ruili; Wang, Yulei; He, Yang; Xiao, Jianyuan; Liu, Jian; Qin, Hong; Tang, Yifa

2018-02-01

Relativistic dynamics of a charged particle in time-dependent electromagnetic fields has theoretical significance and a wide range of applications. The numerical simulation of relativistic dynamics is often multi-scale and requires accurate long-term numerical simulations. Therefore, explicit symplectic algorithms are much more preferable than non-symplectic methods and implicit symplectic algorithms. In this paper, we employ the proper time and express the Hamiltonian as the sum of exactly solvable terms and product-separable terms in space-time coordinates. Then, we give the explicit symplectic algorithms based on the generating functions of orders 2 and 3 for relativistic dynamics of a charged particle. The methodology is not new, which has been applied to non-relativistic dynamics of charged particles, but the algorithm for relativistic dynamics has much significance in practical simulations, such as the secular simulation of runaway electrons in tokamaks.

Ndarts

NASA Technical Reports Server (NTRS)

Jain, Abhinandan

2011-01-01

Ndarts software provides algorithms for computing quantities associated with the dynamics of articulated, rigid-link, multibody systems. It is designed as a general-purpose dynamics library that can be used for the modeling of robotic platforms, space vehicles, molecular dynamics, and other such applications. The architecture and algorithms in Ndarts are based on the Spatial Operator Algebra (SOA) theory for computational multibody and robot dynamics developed at JPL. It uses minimal, internal coordinate models. The algorithms are low-order, recursive scatter/ gather algorithms. In comparison with the earlier Darts++ software, this version has a more general and cleaner design needed to support a larger class of computational dynamics needs. It includes a frames infrastructure, allows algorithms to operate on subgraphs of the system, and implements lazy and deferred computation for better efficiency. Dynamics modeling modules such as Ndarts are core building blocks of control and simulation software for space, robotic, mechanism, bio-molecular, and material systems modeling.

Sublattice parallel replica dynamics.

PubMed

Martínez, Enrique; Uberuaga, Blas P; Voter, Arthur F

2014-06-01

Exascale computing presents a challenge for the scientific community as new algorithms must be developed to take full advantage of the new computing paradigm. Atomistic simulation methods that offer full fidelity to the underlying potential, i.e., molecular dynamics (MD) and parallel replica dynamics, fail to use the whole machine speedup, leaving a region in time and sample size space that is unattainable with current algorithms. In this paper, we present an extension of the parallel replica dynamics algorithm [A. F. Voter, Phys. Rev. B 57, R13985 (1998)] by combining it with the synchronous sublattice approach of Shim and Amar [ and , Phys. Rev. B 71, 125432 (2005)], thereby exploiting event locality to improve the algorithm scalability. This algorithm is based on a domain decomposition in which events happen independently in different regions in the sample. We develop an analytical expression for the speedup given by this sublattice parallel replica dynamics algorithm and compare it with parallel MD and traditional parallel replica dynamics. We demonstrate how this algorithm, which introduces a slight additional approximation of event locality, enables the study of physical systems unreachable with traditional methodologies and promises to better utilize the resources of current high performance and future exascale computers.

Experiments in cooperative-arm object manipulation with a two-armed free-flying robot. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Koningstein, Ross

1990-01-01

Developing computed-torque controllers for complex manipulator systems using current techniques and tools is difficult because they address the issues pertinent to simulation, as opposed to control. A new formulation of computed-torque (CT) control that leads to an automated computer-torque robot controller program is presented. This automated tool is used for simulations and experimental demonstrations of endpoint and object control from a free-flying robot. A new computed-torque formulation states the multibody control problem in an elegant, homogeneous, and practical form. A recursive dynamics algorithm is presented that numerically evaluates kinematics and dynamics terms for multibody systems given a topological description. Manipulators may be free-flying, and may have closed-chain constraints. With the exception of object squeeze-force control, the algorithm does not deal with actuator redundancy. The algorithm is used to implement an automated 2D computed-torque dynamics and control package that allows joint, endpoint, orientation, momentum, and object squeeze-force control. This package obviates the need for hand-derivation of kinematics and dynamics, and is used for both simulation and experimental control. Endpoint control experiments are performed on a laboratory robot that has two arms to manipulate payloads, and uses an air bearing to achieve very-low drag characteristics. Simulations and experimental data for endpoint and object controllers are presented for the experimental robot - a complex dynamic system. There is a certain rather wide set of conditions under which CT endpoint controllers can neglect robot base accelerations (but not motions) and achieve comparable performance including base accelerations in the model. The regime over which this simplification holds is explored by simulation and experiment.

Reinforcement learning techniques for controlling resources in power networks

NASA Astrophysics Data System (ADS)

Kowli, Anupama Sunil

As power grids transition towards increased reliance on renewable generation, energy storage and demand response resources, an effective control architecture is required to harness the full functionalities of these resources. There is a critical need for control techniques that recognize the unique characteristics of the different resources and exploit the flexibility afforded by them to provide ancillary services to the grid. The work presented in this dissertation addresses these needs. Specifically, new algorithms are proposed, which allow control synthesis in settings wherein the precise distribution of the uncertainty and its temporal statistics are not known. These algorithms are based on recent developments in Markov decision theory, approximate dynamic programming and reinforcement learning. They impose minimal assumptions on the system model and allow the control to be "learned" based on the actual dynamics of the system. Furthermore, they can accommodate complex constraints such as capacity and ramping limits on generation resources, state-of-charge constraints on storage resources, comfort-related limitations on demand response resources and power flow limits on transmission lines. Numerical studies demonstrating applications of these algorithms to practical control problems in power systems are discussed. Results demonstrate how the proposed control algorithms can be used to improve the performance and reduce the computational complexity of the economic dispatch mechanism in a power network. We argue that the proposed algorithms are eminently suitable to develop operational decision-making tools for large power grids with many resources and many sources of uncertainty.

VB merch-lob: A growth-and-yield prediction system with a merchandising optimizer for planted loblolly pine in the west Gulf region

Treesearch

S.J. Chang; Rodney L. Busby; P.R. Pasala; Daniel J. Leduc

2005-01-01

A Visual Basic computer model that can be used to estimate the harvestvalue of loblolly pine plantations in the west gulf region is presented. Themodel uses a dynamic programming algorithm to convert stand tablespredicted by COMPUTE_P-LOB into a listing of seven products thatmaximizes the harvested value of the stand.

VB merch-slash: A growth-and-yield prediction system with a merchandising optimizer for planted slash pine in the west Gulf region

Treesearch

S.J. Chang; Rodney L. Busby; P.R. Pasala; Jeffrey C. Goelz

2005-01-01

A Visual Basic computer model that can be used to estimate the harvestvalue of slash pine plantations in the west gulf region is presented. Themodel uses a dynamic programming algorithm to convert stand tablespredicted by COMPUTE_P-SLASH into a listing of seven products thatmaximizes the harvested value of the stand.

Recursive dynamics for flexible multibody systems using spatial operators

NASA Technical Reports Server (NTRS)

Jain, A.; Rodriguez, G.

1990-01-01

Due to their structural flexibility, spacecraft and space manipulators are multibody systems with complex dynamics and possess a large number of degrees of freedom. Here the spatial operator algebra methodology is used to develop a new dynamics formulation and spatially recursive algorithms for such flexible multibody systems. A key feature of the formulation is that the operator description of the flexible system dynamics is identical in form to the corresponding operator description of the dynamics of rigid multibody systems. A significant advantage of this unifying approach is that it allows ideas and techniques for rigid multibody systems to be easily applied to flexible multibody systems. The algorithms use standard finite-element and assumed modes models for the individual body deformation. A Newton-Euler Operator Factorization of the mass matrix of the multibody system is first developed. It forms the basis for recursive algorithms such as for the inverse dynamics, the computation of the mass matrix, and the composite body forward dynamics for the system. Subsequently, an alternative Innovations Operator Factorization of the mass matrix, each of whose factors is invertible, is developed. It leads to an operator expression for the inverse of the mass matrix, and forms the basis for the recursive articulated body forward dynamics algorithm for the flexible multibody system. For simplicity, most of the development here focuses on serial chain multibody systems. However, extensions of the algorithms to general topology flexible multibody systems are described. While the computational cost of the algorithms depends on factors such as the topology and the amount of flexibility in the multibody system, in general, it appears that in contrast to the rigid multibody case, the articulated body forward dynamics algorithm is the more efficient algorithm for flexible multibody systems containing even a small number of flexible bodies. The variety of algorithms described here permits a user to choose the algorithm which is optimal for the multibody system at hand. The availability of a number of algorithms is even more important for real-time applications, where implementation on parallel processors or custom computing hardware is often necessary to maximize speed.

Optimal control of hydroelectric facilities

NASA Astrophysics Data System (ADS)

Zhao, Guangzhi

This thesis considers a simple yet realistic model of pump-assisted hydroelectric facilities operating in a market with time-varying but deterministic power prices. Both deterministic and stochastic water inflows are considered. The fluid mechanical and engineering details of the facility are described by a model containing several parameters. We present a dynamic programming algorithm for optimizing either the total energy produced or the total cash generated by these plants. The algorithm allows us to give the optimal control strategy as a function of time and to see how this strategy, and the associated plant value, varies with water inflow and electricity price. We investigate various cases. For a single pumped storage facility experiencing deterministic power prices and water inflows, we investigate the varying behaviour for an oversimplified constant turbine- and pump-efficiency model with simple reservoir geometries. We then generalize this simple model to include more realistic turbine efficiencies, situations with more complicated reservoir geometry, and the introduction of dissipative switching costs between various control states. We find many results which reinforce our physical intuition about this complicated system as well as results which initially challenge, though later deepen, this intuition. One major lesson of this work is that the optimal control strategy does not differ much between two differing objectives of maximizing energy production and maximizing its cash value. We then turn our attention to the case of stochastic water inflows. We present a stochastic dynamic programming algorithm which can find an on-average optimal control in the face of this randomness. As the operator of a facility must be more cautious when inflows are random, the randomness destroys facility value. Following this insight we quantify exactly how much a perfect hydrological inflow forecast would be worth to a dam operator. In our final chapter we discuss the challenging problem of optimizing a sequence of two hydro dams sharing the same river system. The complexity of this problem is magnified and we just scratch its surface here. The thesis concludes with suggestions for future work in this fertile area. Keywords: dynamic programming, hydroelectric facility, optimization, optimal control, switching cost, turbine efficiency.

Event-chain algorithm for the Heisenberg model: Evidence for z≃1 dynamic scaling.

PubMed

Nishikawa, Yoshihiko; Michel, Manon; Krauth, Werner; Hukushima, Koji

2015-12-01

We apply the event-chain Monte Carlo algorithm to the three-dimensional ferromagnetic Heisenberg model. The algorithm is rejection-free and also realizes an irreversible Markov chain that satisfies global balance. The autocorrelation functions of the magnetic susceptibility and the energy indicate a dynamical critical exponent z≈1 at the critical temperature, while that of the magnetization does not measure the performance of the algorithm. We show that the event-chain Monte Carlo algorithm substantially reduces the dynamical critical exponent from the conventional value of z≃2.

GENERAL: Application of Symplectic Algebraic Dynamics Algorithm to Circular Restricted Three-Body Problem

NASA Astrophysics Data System (ADS)

Lu, Wei-Tao; Zhang, Hua; Wang, Shun-Jin

2008-07-01

Symplectic algebraic dynamics algorithm (SADA) for ordinary differential equations is applied to solve numerically the circular restricted three-body problem (CR3BP) in dynamical astronomy for both stable motion and chaotic motion. The result is compared with those of Runge-Kutta algorithm and symplectic algorithm under the fourth order, which shows that SADA has higher accuracy than the others in the long-term calculations of the CR3BP.

Randomized Dynamic Mode Decomposition

NASA Astrophysics Data System (ADS)

Erichson, N. Benjamin; Brunton, Steven L.; Kutz, J. Nathan

2017-11-01

The dynamic mode decomposition (DMD) is an equation-free, data-driven matrix decomposition that is capable of providing accurate reconstructions of spatio-temporal coherent structures arising in dynamical systems. We present randomized algorithms to compute the near-optimal low-rank dynamic mode decomposition for massive datasets. Randomized algorithms are simple, accurate and able to ease the computational challenges arising with `big data'. Moreover, randomized algorithms are amenable to modern parallel and distributed computing. The idea is to derive a smaller matrix from the high-dimensional input data matrix using randomness as a computational strategy. Then, the dynamic modes and eigenvalues are accurately learned from this smaller representation of the data, whereby the approximation quality can be controlled via oversampling and power iterations. Here, we present randomized DMD algorithms that are categorized by how many passes the algorithm takes through the data. Specifically, the single-pass randomized DMD does not require data to be stored for subsequent passes. Thus, it is possible to approximately decompose massive fluid flows (stored out of core memory, or not stored at all) using single-pass algorithms, which is infeasible with traditional DMD algorithms.

Long-term power generation expansion planning with short-term demand response: Model, algorithms, implementation, and electricity policies

NASA Astrophysics Data System (ADS)

Lohmann, Timo

Electric sector models are powerful tools that guide policy makers and stakeholders. Long-term power generation expansion planning models are a prominent example and determine a capacity expansion for an existing power system over a long planning horizon. With the changes in the power industry away from monopolies and regulation, the focus of these models has shifted to competing electric companies maximizing their profit in a deregulated electricity market. In recent years, consumers have started to participate in demand response programs, actively influencing electricity load and price in the power system. We introduce a model that features investment and retirement decisions over a long planning horizon of more than 20 years, as well as an hourly representation of day-ahead electricity markets in which sellers of electricity face buyers. This combination makes our model both unique and challenging to solve. Decomposition algorithms, and especially Benders decomposition, can exploit the model structure. We present a novel method that can be seen as an alternative to generalized Benders decomposition and relies on dynamic linear overestimation. We prove its finite convergence and present computational results, demonstrating its superiority over traditional approaches. In certain special cases of our model, all necessary solution values in the decomposition algorithms can be directly calculated and solving mathematical programming problems becomes entirely obsolete. This leads to highly efficient algorithms that drastically outperform their programming problem-based counterparts. Furthermore, we discuss the implementation of all tailored algorithms and the challenges from a modeling software developer's standpoint, providing an insider's look into the modeling language GAMS. Finally, we apply our model to the Texas power system and design two electricity policies motivated by the U.S. Environment Protection Agency's recently proposed CO2 emissions targets for the power sector.

Wind noise in hearing aids: I. Effect of wide dynamic range compression and modulation-based noise reduction.

PubMed

Chung, King

2012-01-01

The objectives of this study were: (1) to examine the effect of wide dynamic range compression (WDRC) and modulation-based noise reduction (NR) algorithms on wind noise levels at the hearing aid output; and (2) to derive effective strategies for clinicians and engineers to reduce wind noise in hearing aids. Three digital hearing aids were fitted to KEMAR. The noise output was recorded at flow velocities of 0, 4.5, 9.0, and 13.5 m/s in a wind tunnel as the KEMAR head was turned from 0° to 360°. Flow noise levels were compared between the 1:1 linear and 3:1 WDRC conditions, and between NR-activated and NR-deactivated conditions when the hearing aid was programmed to the directional and omnidirectional modes. The results showed that: (1) WDRC increased low-level noise and reduced high-level noise; and (2) different noise reduction algorithms provided different amounts of wind noise reduction in different microphone modes, frequency regions, flow velocities, and head angles. Wind noise can be reduced by decreasing the gain for low-level inputs, increasing the compression ratio for high-level inputs, and activating modulation-based noise reduction algorithms.

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.

Variable-spot ion beam figuring

NASA Astrophysics Data System (ADS)

Wu, Lixiang; Qiu, Keqiang; Fu, Shaojun

2016-03-01

This paper introduces a new scheme of ion beam figuring (IBF), or rather variable-spot IBF, which is conducted at a constant scanning velocity with variable-spot ion beam collimated by a variable diaphragm. It aims at improving the reachability and adaptation of the figuring process within the limits of machine dynamics by varying the ion beam spot size instead of the scanning velocity. In contrast to the dwell time algorithm in the conventional IBF, the variable-spot IBF adopts a new algorithm, which consists of the scan path programming and the trajectory optimization using pattern search. In this algorithm, instead of the dwell time, a new concept, integral etching time, is proposed to interpret the process of variable-spot IBF. We conducted simulations to verify its feasibility and practicality. The simulation results indicate the variable-spot IBF is a promising alternative to the conventional approach.

Path planning on cellular nonlinear network using active wave computing technique

NASA Astrophysics Data System (ADS)

Yeniçeri, Ramazan; Yalçın, Müstak E.

2009-05-01

This paper introduces a simple algorithm to solve robot path finding problem using active wave computing techniques. A two-dimensional Cellular Neural/Nonlinear Network (CNN), consist of relaxation oscillators, has been used to generate active waves and to process the visual information. The network, which has been implemented on a Field Programmable Gate Array (FPGA) chip, has the feature of being programmed, controlled and observed by a host computer. The arena of the robot is modelled as the medium of the active waves on the network. Active waves are employed to cover the whole medium with their own dynamics, by starting from an initial point. The proposed algorithm is achieved by observing the motion of the wave-front of the active waves. Host program first loads the arena model onto the active wave generator network and command to start the generation. Then periodically pulls the network image from the generator hardware to analyze evolution of the active waves. When the algorithm is completed, vectorial data image is generated. The path from any of the pixel on this image to the active wave generating pixel is drawn by the vectors on this image. The robot arena may be a complicated labyrinth or may have a simple geometry. But, the arena surface always must be flat. Our Autowave Generator CNN implementation which is settled on the Xilinx University Program Virtex-II Pro Development System is operated by a MATLAB program running on the host computer. As the active wave generator hardware has 16, 384 neurons, an arena with 128 × 128 pixels can be modeled and solved by the algorithm. The system also has a monitor and network image is depicted on the monitor simultaneously.

Advisory Algorithm for Scheduling Open Sectors, Operating Positions, and Workstations

NASA Technical Reports Server (NTRS)

Bloem, Michael; Drew, Michael; Lai, Chok Fung; Bilimoria, Karl D.

2012-01-01

Air traffic controller supervisors configure available sector, operating position, and work-station resources to safely and efficiently control air traffic in a region of airspace. In this paper, an algorithm for assisting supervisors with this task is described and demonstrated on two sample problem instances. The algorithm produces configuration schedule advisories that minimize a cost. The cost is a weighted sum of two competing costs: one penalizing mismatches between configurations and predicted air traffic demand and another penalizing the effort associated with changing configurations. The problem considered by the algorithm is a shortest path problem that is solved with a dynamic programming value iteration algorithm. The cost function contains numerous parameters. Default values for most of these are suggested based on descriptions of air traffic control procedures and subject-matter expert feedback. The parameter determining the relative importance of the two competing costs is tuned by comparing historical configurations with corresponding algorithm advisories. Two sample problem instances for which appropriate configuration advisories are obvious were designed to illustrate characteristics of the algorithm. Results demonstrate how the algorithm suggests advisories that appropriately utilize changes in airspace configurations and changes in the number of operating positions allocated to each open sector. The results also demonstrate how the advisories suggest appropriate times for configuration changes.

Conjugate-Gradient Algorithms For Dynamics Of Manipulators

NASA Technical Reports Server (NTRS)

Fijany, Amir; Scheid, Robert E.

1993-01-01

Algorithms for serial and parallel computation of forward dynamics of multiple-link robotic manipulators by conjugate-gradient method developed. Parallel algorithms have potential for speedup of computations on multiple linked, specialized processors implemented in very-large-scale integrated circuits. Such processors used to stimulate dynamics, possibly faster than in real time, for purposes of planning and control.

Dynamic sensitivity analysis of biological systems

PubMed Central

Wu, Wu Hsiung; Wang, Feng Sheng; Chang, Maw Shang

2008-01-01

Background A mathematical model to understand, predict, control, or even design a real biological system is a central theme in systems biology. A dynamic biological system is always modeled as a nonlinear ordinary differential equation (ODE) system. How to simulate the dynamic behavior and dynamic parameter sensitivities of systems described by ODEs efficiently and accurately is a critical job. In many practical applications, e.g., the fed-batch fermentation systems, the system admissible input (corresponding to independent variables of the system) can be time-dependent. The main difficulty for investigating the dynamic log gains of these systems is the infinite dimension due to the time-dependent input. The classical dynamic sensitivity analysis does not take into account this case for the dynamic log gains. Results We present an algorithm with an adaptive step size control that can be used for computing the solution and dynamic sensitivities of an autonomous ODE system simultaneously. Although our algorithm is one of the decouple direct methods in computing dynamic sensitivities of an ODE system, the step size determined by model equations can be used on the computations of the time profile and dynamic sensitivities with moderate accuracy even when sensitivity equations are more stiff than model equations. To show this algorithm can perform the dynamic sensitivity analysis on very stiff ODE systems with moderate accuracy, it is implemented and applied to two sets of chemical reactions: pyrolysis of ethane and oxidation of formaldehyde. The accuracy of this algorithm is demonstrated by comparing the dynamic parameter sensitivities obtained from this new algorithm and from the direct method with Rosenbrock stiff integrator based on the indirect method. The same dynamic sensitivity analysis was performed on an ethanol fed-batch fermentation system with a time-varying feed rate to evaluate the applicability of the algorithm to realistic models with time-dependent admissible input. Conclusion By combining the accuracy we show with the efficiency of being a decouple direct method, our algorithm is an excellent method for computing dynamic parameter sensitivities in stiff problems. We extend the scope of classical dynamic sensitivity analysis to the investigation of dynamic log gains of models with time-dependent admissible input. PMID:19091016

CDRD and PNPR satellite passive microwave precipitation retrieval algorithms: EuroTRMM/EURAINSAT origins and H-SAF operations

NASA Astrophysics Data System (ADS)

Mugnai, A.; Smith, E. A.; Tripoli, G. J.; Bizzarri, B.; Casella, D.; Dietrich, S.; Di Paola, F.; Panegrossi, G.; Sanò, P.

2013-04-01

Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) is a EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) program, designed to deliver satellite products of hydrological interest (precipitation, soil moisture and snow parameters) over the European and Mediterranean region to research and operations users worldwide. Six satellite precipitation algorithms and concomitant precipitation products are the responsibility of various agencies in Italy. Two of these algorithms have been designed for maximum accuracy by restricting their inputs to measurements from conical and cross-track scanning passive microwave (PMW) radiometers mounted on various low Earth orbiting satellites. They have been developed at the Italian National Research Council/Institute of Atmospheric Sciences and Climate in Rome (CNR/ISAC-Rome), and are providing operational retrievals of surface rain rate and its phase properties. Each of these algorithms is physically based, however, the first of these, referred to as the Cloud Dynamics and Radiation Database (CDRD) algorithm, uses a Bayesian-based solution solver, while the second, referred to as the PMW Neural-net Precipitation Retrieval (PNPR) algorithm, uses a neural network-based solution solver. Herein we first provide an overview of the two initial EU research and applications programs that motivated their initial development, EuroTRMM and EURAINSAT (European Satellite Rainfall Analysis and Monitoring at the Geostationary Scale), and the current H-SAF program that provides the framework for their operational use and continued development. We stress the relevance of the CDRD and PNPR algorithms and their precipitation products in helping secure the goals of H-SAF's scientific and operations agenda, the former helpful as a secondary calibration reference to other algorithms in H-SAF's complete mix of algorithms. Descriptions of the algorithms' designs are provided including a few examples of their performance. This aspect of the development of the two algorithms is placed in the context of what we refer to as the TRMM era, which is the era denoting the active and ongoing period of the Tropical Rainfall Measuring Mission (TRMM) that helped inspire their original development. In 2015, the ISAC-Rome precipitation algorithms will undergo a transformation beginning with the upcoming Global Precipitation Measurement (GPM) mission, particularly the GPM Core Satellite technologies. A few years afterward, the first pair of imaging and sounding Meteosat Third Generation (MTG) satellites will be launched, providing additional technological advances. Various of the opportunities presented by the GPM Core and MTG satellites for improving the current CDRD and PNPR precipitation retrieval algorithms, as well as extending their product capability, are discussed.

Stochastic control approaches for sensor management in search and exploitation

NASA Astrophysics Data System (ADS)

Hitchings, Darin Chester

Recent improvements in the capabilities of autonomous vehicles have motivated their increased use in such applications as defense, homeland security, environmental monitoring, and surveillance. To enhance performance in these applications, new algorithms are required to control teams of robots autonomously and through limited interactions with human operators. In this dissertation we develop new algorithms for control of robots performing information-seeking missions in unknown environments. These missions require robots to control their sensors in order to discover the presence of objects, keep track of the objects, and learn what these objects are, given a fixed sensing budget. Initially, we investigate control of multiple sensors, with a finite set of sensing options and finite-valued measurements, to locate and classify objects given a limited resource budget. The control problem is formulated as a Partially Observed Markov Decision Problem (POMDP), but its exact solution requires excessive computation. Under the assumption that sensor error statistics are independent and time-invariant, we develop a class of algorithms using Lagrangian Relaxation techniques to obtain optimal mixed strategies using performance bounds developed in previous research. We investigate alternative Receding Horizon (RH) controllers to convert the mixed strategies to feasible adaptive-sensing strategies and evaluate the relative performance of these controllers in simulation. The resulting controllers provide superior performance to alternative algorithms proposed in the literature and obtain solutions to large-scale POMDP problems several orders of magnitude faster than optimal Dynamic Programming (DP) approaches with comparable performance quality. We extend our results for finite action, finite measurement sensor control to scenarios with moving objects. We use Hidden Markov Models (HMMs) for the evolution of objects, according to the dynamics of a birth-death process. We develop a new lower bound on the performance of adaptive controllers in these scenarios, develop algorithms for computing solutions to this lower bound, and use these algorithms as part of a RH controller for sensor allocation in the presence of moving objects We also consider an adaptive Search problem where sensing actions are continuous and the underlying measurement space is also continuous. We extend our previous hierarchical decomposition approach based on performance bounds to this problem and develop novel implementations of Stochastic Dynamic Programming (SDP) techniques to solve this problem. Our algorithms are nearly two orders of magnitude faster than previously proposed approaches and yield solutions of comparable quality. For supervisory control, we discuss how human operators can work with and augment robotic teams performing these tasks. Our focus is on how tasks are partitioned among teams of robots and how a human operator can make intelligent decisions for task partitioning. We explore these questions through the design of a game that involves robot automata controlled by our algorithms and a human supervisor that partitions tasks based on different levels of support information. This game can be used with human subject experiments to explore the effect of information on quality of supervisory control.

The performance analysis of three-dimensional track-before-detect algorithm based on Fisher-Tippett-Gnedenko theorem

NASA Astrophysics Data System (ADS)

Cho, Hoonkyung; Chun, Joohwan; Song, Sungchan

2016-09-01

The dim moving target tracking from the infrared image sequence in the presence of high clutter and noise has been recently under intensive investigation. The track-before-detect (TBD) algorithm processing the image sequence over a number of frames before decisions on the target track and existence is known to be especially attractive in very low SNR environments (⩽ 3 dB). In this paper, we shortly present a three-dimensional (3-D) TBD with dynamic programming (TBD-DP) algorithm using multiple IR image sensors. Since traditional two-dimensional TBD algorithm cannot track and detect the along the viewing direction, we use 3-D TBD with multiple sensors and also strictly analyze the detection performance (false alarm and detection probabilities) based on Fisher-Tippett-Gnedenko theorem. The 3-D TBD-DP algorithm which does not require a separate image registration step uses the pixel intensity values jointly read off from multiple image frames to compute the merit function required in the DP process. Therefore, we also establish the relationship between the pixel coordinates of image frame and the reference coordinates.

A dynamic scheduling algorithm for singe-arm two-cluster tools with flexible processing times

NASA Astrophysics Data System (ADS)

Li, Xin; Fung, Richard Y. K.

2018-02-01

This article presents a dynamic algorithm for job scheduling in two-cluster tools producing multi-type wafers with flexible processing times. Flexible processing times mean that the actual times for processing wafers should be within given time intervals. The objective of the work is to minimize the completion time of the newly inserted wafer. To deal with this issue, a two-cluster tool is decomposed into three reduced single-cluster tools (RCTs) in a series based on a decomposition approach proposed in this article. For each single-cluster tool, a dynamic scheduling algorithm based on temporal constraints is developed to schedule the newly inserted wafer. Three experiments have been carried out to test the dynamic scheduling algorithm proposed, comparing with the results the 'earliest starting time' heuristic (EST) adopted in previous literature. The results show that the dynamic algorithm proposed in this article is effective and practical.

Low-Complexity User Selection for Rate Maximization in MIMO Broadcast Channels with Downlink Beamforming

PubMed Central

Silva, Adão; Gameiro, Atílio

2014-01-01

We present in this work a low-complexity algorithm to solve the sum rate maximization problem in multiuser MIMO broadcast channels with downlink beamforming. Our approach decouples the user selection problem from the resource allocation problem and its main goal is to create a set of quasiorthogonal users. The proposed algorithm exploits physical metrics of the wireless channels that can be easily computed in such a way that a null space projection power can be approximated efficiently. Based on the derived metrics we present a mathematical model that describes the dynamics of the user selection process which renders the user selection problem into an integer linear program. Numerical results show that our approach is highly efficient to form groups of quasiorthogonal users when compared to previously proposed algorithms in the literature. Our user selection algorithm achieves a large portion of the optimum user selection sum rate (90%) for a moderate number of active users. PMID:24574928

Transcript mapping for handwritten English documents

NASA Astrophysics Data System (ADS)

Jose, Damien; Bharadwaj, Anurag; Govindaraju, Venu

2008-01-01

Transcript mapping or text alignment with handwritten documents is the automatic alignment of words in a text file with word images in a handwritten document. Such a mapping has several applications in fields ranging from machine learning where large quantities of truth data are required for evaluating handwriting recognition algorithms, to data mining where word image indexes are used in ranked retrieval of scanned documents in a digital library. The alignment also aids "writer identity" verification algorithms. Interfaces which display scanned handwritten documents may use this alignment to highlight manuscript tokens when a person examines the corresponding transcript word. We propose an adaptation of the True DTW dynamic programming algorithm for English handwritten documents. The integration of the dissimilarity scores from a word-model word recognizer and Levenshtein distance between the recognized word and lexicon word, as a cost metric in the DTW algorithm leading to a fast and accurate alignment, is our primary contribution. Results provided, confirm the effectiveness of our approach.

Simulating an underwater vehicle self-correcting guidance system with Simulink

NASA Astrophysics Data System (ADS)

Fan, Hui; Zhang, Yu-Wen; Li, Wen-Zhe

2008-09-01

Underwater vehicles have already adopted self-correcting directional guidance algorithms based on multi-beam self-guidance systems, not waiting for research to determine the most effective algorithms. The main challenges facing research on these guidance systems have been effective modeling of the guidance algorithm and a means to analyze the simulation results. A simulation structure based on Simulink that dealt with both issues was proposed. Initially, a mathematical model of relative motion between the vehicle and the target was developed, which was then encapsulated as a subsystem. Next, steps for constructing a model of the self-correcting guidance algorithm based on the Stateflow module were examined in detail. Finally, a 3-D model of the vehicle and target was created in VRML, and by processing mathematical results, the model was shown moving in a visual environment. This process gives more intuitive results for analyzing the simulation. The results showed that the simulation structure performs well. The simulation program heavily used modularization and encapsulation, so has broad applicability to simulations of other dynamic systems.

An Asymptotically-Optimal Sampling-Based Algorithm for Bi-directional Motion Planning

PubMed Central

Starek, Joseph A.; Gomez, Javier V.; Schmerling, Edward; Janson, Lucas; Moreno, Luis; Pavone, Marco

2015-01-01

Bi-directional search is a widely used strategy to increase the success and convergence rates of sampling-based motion planning algorithms. Yet, few results are available that merge both bi-directional search and asymptotic optimality into existing optimal planners, such as PRM*, RRT*, and FMT*. The objective of this paper is to fill this gap. Specifically, this paper presents a bi-directional, sampling-based, asymptotically-optimal algorithm named Bi-directional FMT* (BFMT*) that extends the Fast Marching Tree (FMT*) algorithm to bidirectional search while preserving its key properties, chiefly lazy search and asymptotic optimality through convergence in probability. BFMT* performs a two-source, lazy dynamic programming recursion over a set of randomly-drawn samples, correspondingly generating two search trees: one in cost-to-come space from the initial configuration and another in cost-to-go space from the goal configuration. Numerical experiments illustrate the advantages of BFMT* over its unidirectional counterpart, as well as a number of other state-of-the-art planners. PMID:27004130

Improved Seam-Line Searching Algorithm for UAV Image Mosaic with Optical Flow.

PubMed

Zhang, Weilong; Guo, Bingxuan; Li, Ming; Liao, Xuan; Li, Wenzhuo

2018-04-16

Ghosting and seams are two major challenges in creating unmanned aerial vehicle (UAV) image mosaic. In response to these problems, this paper proposes an improved method for UAV image seam-line searching. First, an image matching algorithm is used to extract and match the features of adjacent images, so that they can be transformed into the same coordinate system. Then, the gray scale difference, the gradient minimum, and the optical flow value of pixels in adjacent image overlapped area in a neighborhood are calculated, which can be applied to creating an energy function for seam-line searching. Based on that, an improved dynamic programming algorithm is proposed to search the optimal seam-lines to complete the UAV image mosaic. This algorithm adopts a more adaptive energy aggregation and traversal strategy, which can find a more ideal splicing path for adjacent UAV images and avoid the ground objects better. The experimental results show that the proposed method can effectively solve the problems of ghosting and seams in the panoramic UAV images.

A fuzzy reinforcement learning approach to power control in wireless transmitters.

PubMed

Vengerov, David; Bambos, Nicholas; Berenji, Hamid R

2005-08-01

We address the issue of power-controlled shared channel access in wireless networks supporting packetized data traffic. We formulate this problem using the dynamic programming framework and present a new distributed fuzzy reinforcement learning algorithm (ACFRL-2) capable of adequately solving a class of problems to which the power control problem belongs. Our experimental results show that the algorithm converges almost deterministically to a neighborhood of optimal parameter values, as opposed to a very noisy stochastic convergence of earlier algorithms. The main tradeoff facing a transmitter is to balance its current power level with future backlog in the presence of stochastically changing interference. Simulation experiments demonstrate that the ACFRL-2 algorithm achieves significant performance gains over the standard power control approach used in CDMA2000. Such a large improvement is explained by the fact that ACFRL-2 allows transmitters to learn implicit coordination policies, which back off under stressful channel conditions as opposed to engaging in escalating "power wars."

Optimizing Mars Airplane Trajectory with the Application Navigation System

NASA Technical Reports Server (NTRS)

Frumkin, Michael; Riley, Derek

2004-01-01

Planning complex missions requires a number of programs to be executed in concert. The Application Navigation System (ANS), developed in the NAS Division, can execute many interdependent programs in a distributed environment. We show that the ANS simplifies user effort and reduces time in optimization of the trajectory of a martian airplane. We use a software package, Cart3D, to evaluate trajectories and a shortest path algorithm to determine the optimal trajectory. ANS employs the GridScape to represent the dynamic state of the available computer resources. Then, ANS uses a scheduler to dynamically assign ready task to machine resources and the GridScape for tracking available resources and forecasting completion time of running tasks. We demonstrate system capability to schedule and run the trajectory optimization application with efficiency exceeding 60% on 64 processors.

Skin tumor area extraction using an improved dynamic programming approach.

PubMed

Abbas, Qaisar; Celebi, M E; Fondón García, Irene

2012-05-01

Border (B) description of melanoma and other pigmented skin lesions is one of the most important tasks for the clinical diagnosis of dermoscopy images using the ABCD rule. For an accurate description of the border, there must be an effective skin tumor area extraction (STAE) method. However, this task is complicated due to uneven illumination, artifacts present in the lesions and smooth areas or fuzzy borders of the desired regions. In this paper, a novel STAE algorithm based on improved dynamic programming (IDP) is presented. The STAE technique consists of the following four steps: color space transform, pre-processing, rough tumor area detection and refinement of the segmented area. The procedure is performed in the CIE L(*) a(*) b(*) color space, which is approximately uniform and is therefore related to dermatologist's perception. After pre-processing the skin lesions to reduce artifacts, the DP algorithm is improved by introducing a local cost function, which is based on color and texture weights. The STAE method is tested on a total of 100 dermoscopic images. In order to compare the performance of STAE with other state-of-the-art algorithms, various statistical measures based on dermatologist-drawn borders are utilized as a ground truth. The proposed method outperforms the others with a sensitivity of 96.64%, a specificity of 98.14% and an error probability of 5.23%. The results demonstrate that this STAE method by IDP is an effective solution when compared with other state-of-the-art segmentation techniques. The proposed method can accurately extract tumor borders in dermoscopy images. © 2011 John Wiley & Sons A/S.

Multiscale simulations of anisotropic particles combining molecular dynamics and Green's function reaction dynamics

NASA Astrophysics Data System (ADS)

Vijaykumar, Adithya; Ouldridge, Thomas E.; ten Wolde, Pieter Rein; Bolhuis, Peter G.

2017-03-01

The modeling of complex reaction-diffusion processes in, for instance, cellular biochemical networks or self-assembling soft matter can be tremendously sped up by employing a multiscale algorithm which combines the mesoscopic Green's Function Reaction Dynamics (GFRD) method with explicit stochastic Brownian, Langevin, or deterministic molecular dynamics to treat reactants at the microscopic scale [A. Vijaykumar, P. G. Bolhuis, and P. R. ten Wolde, J. Chem. Phys. 143, 214102 (2015)]. Here we extend this multiscale MD-GFRD approach to include the orientational dynamics that is crucial to describe the anisotropic interactions often prevalent in biomolecular systems. We present the novel algorithm focusing on Brownian dynamics only, although the methodology is generic. We illustrate the novel algorithm using a simple patchy particle model. After validation of the algorithm, we discuss its performance. The rotational Brownian dynamics MD-GFRD multiscale method will open up the possibility for large scale simulations of protein signalling networks.

Development of modular control software for construction 3D-printer

NASA Astrophysics Data System (ADS)

Bazhanov, A.; Yudin, D.; Porkhalo, V.

2018-03-01

This article discusses the approach to developing modular software for real-time control of an industrial construction 3D printer. The proposed structure of a two-level software solution is implemented for a robotic system that moves in a Cartesian coordinate system with multi-axis interpolation. An algorithm for the formation and analysis of a path is considered to enable the most effective control of printing through dynamic programming.

Multimodal Logistics Network Design over Planning Horizon through a Hybrid Meta-Heuristic Approach

NASA Astrophysics Data System (ADS)

Shimizu, Yoshiaki; Yamazaki, Yoshihiro; Wada, Takeshi

Logistics has been acknowledged increasingly as a key issue of supply chain management to improve business efficiency under global competition and diversified customer demands. This study aims at improving a quality of strategic decision making associated with dynamic natures in logistics network optimization. Especially, noticing an importance to concern with a multimodal logistics under multiterms, we have extended a previous approach termed hybrid tabu search (HybTS). The attempt intends to deploy a strategic planning more concretely so that the strategic plan can link to an operational decision making. The idea refers to a smart extension of the HybTS to solve a dynamic mixed integer programming problem. It is a two-level iterative method composed of a sophisticated tabu search for the location problem at the upper level and a graph algorithm for the route selection at the lower level. To keep efficiency while coping with the resulting extremely large-scale problem, we invented a systematic procedure to transform the original linear program at the lower-level into a minimum cost flow problem solvable by the graph algorithm. Through numerical experiments, we verified the proposed method outperformed the commercial software. The results indicate the proposed approach can make the conventional strategic decision much more practical and is promising for real world applications.

Solving Equations of Multibody Dynamics

NASA Technical Reports Server (NTRS)

Jain, Abhinandan; Lim, Christopher

2007-01-01

Darts++ is a computer program for solving the equations of motion of a multibody system or of a multibody model of a dynamic system. It is intended especially for use in dynamical simulations performed in designing and analyzing, and developing software for the control of, complex mechanical systems. Darts++ is based on the Spatial-Operator- Algebra formulation for multibody dynamics. This software reads a description of a multibody system from a model data file, then constructs and implements an efficient algorithm that solves the dynamical equations of the system. The efficiency and, hence, the computational speed is sufficient to make Darts++ suitable for use in realtime closed-loop simulations. Darts++ features an object-oriented software architecture that enables reconfiguration of system topology at run time; in contrast, in related prior software, system topology is fixed during initialization. Darts++ provides an interface to scripting languages, including Tcl and Python, that enable the user to configure and interact with simulation objects at run time.

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

A Grammatical Approach to RNA-RNA Interaction Prediction

NASA Astrophysics Data System (ADS)

Kato, Yuki; Akutsu, Tatsuya; Seki, Hiroyuki

2007-11-01

Much attention has been paid to two interacting RNA molecules involved in post-transcriptional control of gene expression. Although there have been a few studies on RNA-RNA interaction prediction based on dynamic programming algorithm, no grammar-based approach has been proposed. The purpose of this paper is to provide a new modeling for RNA-RNA interaction based on multiple context-free grammar (MCFG). We present a polynomial time parsing algorithm for finding the most likely derivation tree for the stochastic version of MCFG, which is applicable to RNA joint secondary structure prediction including kissing hairpin loops. Also, elementary tests on RNA-RNA interaction prediction have shown that the proposed method is comparable to Alkan et al.'s method.

Path Planning Algorithms for the Adaptive Sensor Fleet

NASA Technical Reports Server (NTRS)

Stoneking, Eric; Hosler, Jeff

2005-01-01

The Adaptive Sensor Fleet (ASF) is a general purpose fleet management and planning system being developed by NASA in coordination with NOAA. The current mission of ASF is to provide the capability for autonomous cooperative survey and sampling of dynamic oceanographic phenomena such as current systems and algae blooms. Each ASF vessel is a software model that represents a real world platform that carries a variety of sensors. The OASIS platform will provide the first physical vessel, outfitted with the systems and payloads necessary to execute the oceanographic observations described in this paper. The ASF architecture is being designed for extensibility to accommodate heterogenous fleet elements, and is not limited to using the OASIS platform to acquire data. This paper describes the path planning algorithms developed for the acquisition phase of a typical ASF task. Given a polygonal target region to be surveyed, the region is subdivided according to the number of vessels in the fleet. The subdivision algorithm seeks a solution in which all subregions have equal area and minimum mean radius. Once the subregions are defined, a dynamic programming method is used to find a minimum-time path for each vessel from its initial position to its assigned region. This path plan includes the effects of water currents as well as avoidance of known obstacles. A fleet-level planning algorithm then shuffles the individual vessel assignments to find the overall solution which puts all vessels in their assigned regions in the minimum time. This shuffle algorithm may be described as a process of elimination on the sorted list of permutations of a cost matrix. All these path planning algorithms are facilitated by discretizing the region of interest onto a hexagonal tiling.

MXLKID: a maximum likelihood parameter identifier. [In LRLTRAN for CDC 7600

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

Gavel, D.T.

MXLKID (MaXimum LiKelihood IDentifier) is a computer program designed to identify unknown parameters in a nonlinear dynamic system. Using noisy measurement data from the system, the maximum likelihood identifier computes a likelihood function (LF). Identification of system parameters is accomplished by maximizing the LF with respect to the parameters. The main body of this report briefly summarizes the maximum likelihood technique and gives instructions and examples for running the MXLKID program. MXLKID is implemented LRLTRAN on the CDC7600 computer at LLNL. A detailed mathematical description of the algorithm is given in the appendices. 24 figures, 6 tables.

Application of dynamic recurrent neural networks in nonlinear system identification

NASA Astrophysics Data System (ADS)

Du, Yun; Wu, Xueli; Sun, Huiqin; Zhang, Suying; Tian, Qiang

2006-11-01

An adaptive identification method of simple dynamic recurrent neural network (SRNN) for nonlinear dynamic systems is presented in this paper. This method based on the theory that by using the inner-states feed-back of dynamic network to describe the nonlinear kinetic characteristics of system can reflect the dynamic characteristics more directly, deduces the recursive prediction error (RPE) learning algorithm of SRNN, and improves the algorithm by studying topological structure on recursion layer without the weight values. The simulation results indicate that this kind of neural network can be used in real-time control, due to its less weight values, simpler learning algorithm, higher identification speed, and higher precision of model. It solves the problems of intricate in training algorithm and slow rate in convergence caused by the complicate topological structure in usual dynamic recurrent neural network.

Architectural Visualization of C/C++ Source Code for Program Comprehension

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

Panas, T; Epperly, T W; Quinlan, D

2006-09-01

Structural and behavioral visualization of large-scale legacy systems to aid program comprehension is still a major challenge. The challenge is even greater when applications are implemented in flexible and expressive languages such as C and C++. In this paper, we consider visualization of static and dynamic aspects of large-scale scientific C/C++ applications. For our investigation, we reuse and integrate specialized analysis and visualization tools. Furthermore, we present a novel layout algorithm that permits a compressive architectural view of a large-scale software system. Our layout is unique in that it allows traditional program visualizations, i.e., graph structures, to be seen inmore » relation to the application's file structure.« less

Control/structure interaction design methodology

NASA Technical Reports Server (NTRS)

Briggs, Hugh C.; Layman, William E.

1989-01-01

The Control Structure Interaction Program is a technology development program for spacecraft that exhibit interactions between the control system and structural dynamics. The program objectives include development and verification of new design concepts (such as active structure) and new tools (such as a combined structure and control optimization algorithm) and their verification in ground and possibly flight test. The new CSI design methodology is centered around interdisciplinary engineers using new tools that closely integrate structures and controls. Verification is an important CSI theme and analysts will be closely integrated to the CSI Test Bed laboratory. Components, concepts, tools and algorithms will be developed and tested in the lab and in future Shuttle-based flight experiments. The design methodology is summarized in block diagrams depicting the evolution of a spacecraft design and descriptions of analytical capabilities used in the process. The multiyear JPL CSI implementation plan is described along with the essentials of several new tools. A distributed network of computation servers and workstations was designed that will provide a state-of-the-art development base for the CSI technologies.

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.

An integrated and dynamic optimisation model for the multi-level emergency logistics network in anti-bioterrorism system

NASA Astrophysics Data System (ADS)

Liu, Ming; Zhao, Lindu

2012-08-01

Demand for emergency resources is usually uncertain and varies quickly in anti-bioterrorism system. Besides, emergency resources which had been allocated to the epidemic areas in the early rescue cycle will affect the demand later. In this article, an integrated and dynamic optimisation model with time-varying demand based on the epidemic diffusion rule is constructed. The heuristic algorithm coupled with the MATLAB mathematical programming solver is adopted to solve the optimisation model. In what follows, the application of the optimisation model as well as a short sensitivity analysis of the key parameters in the time-varying demand forecast model is presented. The results show that both the model and the solution algorithm are useful in practice, and both objectives of inventory level and emergency rescue cost can be controlled effectively. Thus, it can provide some guidelines for decision makers when coping with emergency rescue problem with uncertain demand, and offers an excellent reference when issues pertain to bioterrorism.

Policy oscillation is overshooting.

PubMed

Wagner, Paul

2014-04-01

A majority of approximate dynamic programming approaches to the reinforcement learning problem can be categorized into greedy value function methods and value-based policy gradient methods. The former approach, although fast, is well known to be susceptible to the policy oscillation phenomenon. We take a fresh view to this phenomenon by casting, within the context of non-optimistic policy iteration, a considerable subset of the former approach as a limiting special case of the latter. We explain the phenomenon in terms of this view and illustrate the underlying mechanism with artificial examples. We also use it to derive the constrained natural actor-critic algorithm that can interpolate between the aforementioned approaches. In addition, it has been suggested in the literature that the oscillation phenomenon might be subtly connected to the grossly suboptimal performance in the Tetris benchmark problem of all attempted approximate dynamic programming methods. Based on empirical findings, we offer a hypothesis that might explain the inferior performance levels and the associated policy degradation phenomenon, and which would partially support the suggested connection. Finally, we report scores in the Tetris problem that improve on existing dynamic programming based results by an order of magnitude. Copyright © 2014 Elsevier Ltd. All rights reserved.

Risk-Constrained Dynamic Programming for Optimal Mars Entry, Descent, and Landing

NASA Technical Reports Server (NTRS)

Ono, Masahiro; Kuwata, Yoshiaki

2013-01-01

A chance-constrained dynamic programming algorithm was developed that is capable of making optimal sequential decisions within a user-specified risk bound. This work handles stochastic uncertainties over multiple stages in the CEMAT (Combined EDL-Mobility Analyses Tool) framework. It was demonstrated by a simulation of Mars entry, descent, and landing (EDL) using real landscape data obtained from the Mars Reconnaissance Orbiter. Although standard dynamic programming (DP) provides a general framework for optimal sequential decisionmaking under uncertainty, it typically achieves risk aversion by imposing an arbitrary penalty on failure states. Such a penalty-based approach cannot explicitly bound the probability of mission failure. A key idea behind the new approach is called risk allocation, which decomposes a joint chance constraint into a set of individual chance constraints and distributes risk over them. The joint chance constraint was reformulated into a constraint on an expectation over a sum of an indicator function, which can be incorporated into the cost function by dualizing the optimization problem. As a result, the chance-constraint optimization problem can be turned into an unconstrained optimization over a Lagrangian, which can be solved efficiently using a standard DP approach.

Multithreaded Model for Dynamic Load Balancing Parallel Adaptive PDE Computations

NASA Technical Reports Server (NTRS)

Chrisochoides, Nikos

1995-01-01

We present a multithreaded model for the dynamic load-balancing of numerical, adaptive computations required for the solution of Partial Differential Equations (PDE's) on multiprocessors. Multithreading is used as a means of exploring concurrency in the processor level in order to tolerate synchronization costs inherent to traditional (non-threaded) parallel adaptive PDE solvers. Our preliminary analysis for parallel, adaptive PDE solvers indicates that multithreading can be used an a mechanism to mask overheads required for the dynamic balancing of processor workloads with computations required for the actual numerical solution of the PDE's. Also, multithreading can simplify the implementation of dynamic load-balancing algorithms, a task that is very difficult for traditional data parallel adaptive PDE computations. Unfortunately, multithreading does not always simplify program complexity, often makes code re-usability not an easy task, and increases software complexity.

A spatial operator algebra for manipulator modeling and control

NASA Technical Reports Server (NTRS)

Rodriguez, G.; Kreutz, K.; Milman, M.

1988-01-01

A powerful new spatial operator algebra for modeling, control, and trajectory design of manipulators is discussed along with its implementation in the Ada programming language. Applications of this algebra to robotics include an operator representation of the manipulator Jacobian matrix; the robot dynamical equations formulated in terms of the spatial algebra, showing the complete equivalence between the recursive Newton-Euler formulations to robot dynamics; the operator factorization and inversion of the manipulator mass matrix which immediately results in O(N) recursive forward dynamics algorithms; the joint accelerations of a manipulator due to a tip contact force; the recursive computation of the equivalent mass matrix as seen at the tip of a manipulator; and recursive forward dynamics of a closed chain system. Finally, additional applications and current research involving the use of the spatial operator algebra are discussed in general terms.

Comparison of Controller and Flight Deck Algorithm Performance During Interval Management with Dynamic Arrival Trees (STARS)

NASA Technical Reports Server (NTRS)

Battiste, Vernol; Lawton, George; Lachter, Joel; Brandt, Summer; Koteskey, Robert; Dao, Arik-Quang; Kraut, Josh; Ligda, Sarah; Johnson, Walter W.

2012-01-01

Managing the interval between arrival aircraft is a major part of the en route and TRACON controller s job. In an effort to reduce controller workload and low altitude vectoring, algorithms have been developed to allow pilots to take responsibility for, achieve and maintain proper spacing. Additionally, algorithms have been developed to create dynamic weather-free arrival routes in the presence of convective weather. In a recent study we examined an algorithm to handle dynamic re-routing in the presence of convective weather and two distinct spacing algorithms. The spacing algorithms originated from different core algorithms; both were enhanced with trajectory intent data for the study. These two algorithms were used simultaneously in a human-in-the-loop (HITL) simulation where pilots performed weather-impacted arrival operations into Louisville International Airport while also performing interval management (IM) on some trials. The controllers retained responsibility for separation and for managing the en route airspace and some trials managing IM. The goal was a stress test of dynamic arrival algorithms with ground and airborne spacing concepts. The flight deck spacing algorithms or controller managed spacing not only had to be robust to the dynamic nature of aircraft re-routing around weather but also had to be compatible with two alternative algorithms for achieving the spacing goal. Flight deck interval management spacing in this simulation provided a clear reduction in controller workload relative to when controllers were responsible for spacing the aircraft. At the same time, spacing was much less variable with the flight deck automated spacing. Even though the approaches taken by the two spacing algorithms to achieve the interval management goals were slightly different they seem to be simpatico in achieving the interval management goal of 130 sec by the TRACON boundary.

Evolutionary Dynamic Multiobjective Optimization Via Kalman Filter Prediction.

PubMed

Muruganantham, Arrchana; Tan, Kay Chen; Vadakkepat, Prahlad

2016-12-01

Evolutionary algorithms are effective in solving static multiobjective optimization problems resulting in the emergence of a number of state-of-the-art multiobjective evolutionary algorithms (MOEAs). Nevertheless, the interest in applying them to solve dynamic multiobjective optimization problems has only been tepid. Benchmark problems, appropriate performance metrics, as well as efficient algorithms are required to further the research in this field. One or more objectives may change with time in dynamic optimization problems. The optimization algorithm must be able to track the moving optima efficiently. A prediction model can learn the patterns from past experience and predict future changes. In this paper, a new dynamic MOEA using Kalman filter (KF) predictions in decision space is proposed to solve the aforementioned problems. The predictions help to guide the search toward the changed optima, thereby accelerating convergence. A scoring scheme is devised to hybridize the KF prediction with a random reinitialization method. Experimental results and performance comparisons with other state-of-the-art algorithms demonstrate that the proposed algorithm is capable of significantly improving the dynamic optimization performance.

Combinatorial-topological framework for the analysis of global dynamics.

PubMed

Bush, Justin; Gameiro, Marcio; Harker, Shaun; Kokubu, Hiroshi; Mischaikow, Konstantin; Obayashi, Ippei; Pilarczyk, Paweł

2012-12-01

We discuss an algorithmic framework based on efficient graph algorithms and algebraic-topological computational tools. The framework is aimed at automatic computation of a database of global dynamics of a given m-parameter semidynamical system with discrete time on a bounded subset of the n-dimensional phase space. We introduce the mathematical background, which is based upon Conley's topological approach to dynamics, describe the algorithms for the analysis of the dynamics using rectangular grids both in phase space and parameter space, and show two sample applications.

Combinatorial-topological framework for the analysis of global dynamics

NASA Astrophysics Data System (ADS)

Bush, Justin; Gameiro, Marcio; Harker, Shaun; Kokubu, Hiroshi; Mischaikow, Konstantin; Obayashi, Ippei; Pilarczyk, Paweł

2012-12-01

We discuss an algorithmic framework based on efficient graph algorithms and algebraic-topological computational tools. The framework is aimed at automatic computation of a database of global dynamics of a given m-parameter semidynamical system with discrete time on a bounded subset of the n-dimensional phase space. We introduce the mathematical background, which is based upon Conley's topological approach to dynamics, describe the algorithms for the analysis of the dynamics using rectangular grids both in phase space and parameter space, and show two sample applications.

Greedy algorithms in disordered systems

NASA Astrophysics Data System (ADS)

Duxbury, P. M.; Dobrin, R.

1999-08-01

We discuss search, minimal path and minimal spanning tree algorithms and their applications to disordered systems. Greedy algorithms solve these problems exactly, and are related to extremal dynamics in physics. Minimal cost path (Dijkstra) and minimal cost spanning tree (Prim) algorithms provide extremal dynamics for a polymer in a random medium (the KPZ universality class) and invasion percolation (without trapping) respectively.

Cache Locality Optimization for Recursive Programs

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

Lifflander, Jonathan; Krishnamoorthy, Sriram

We present an approach to optimize the cache locality for recursive programs by dynamically splicing--recursively interleaving--the execution of distinct function invocations. By utilizing data effect annotations, we identify concurrency and data reuse opportunities across function invocations and interleave them to reduce reuse distance. We present algorithms that efficiently track effects in recursive programs, detect interference and dependencies, and interleave execution of function invocations using user-level (non-kernel) lightweight threads. To enable multi-core execution, a program is parallelized using a nested fork/join programming model. Our cache optimization strategy is designed to work in the context of a random work stealing scheduler. Wemore » present an implementation using the MIT Cilk framework that demonstrates significant improvements in sequential and parallel performance, competitive with a state-of-the-art compile-time optimizer for loop programs and a domain- specific optimizer for stencil programs.« less

Ecotracer: analyzing concentration of contaminants and radioisotopes in an aquatic spatial-dynamic food web model.

PubMed

Walters, William J; Christensen, Villy

2018-01-01

Ecotracer is a tool in the Ecopath with Ecosim (EwE) software package used to simulate and analyze the transport of contaminants such as methylmercury or radiocesium through aquatic food webs. Ecotracer solves the contaminant dynamic equations simultaneously with the biomass dynamic equations in Ecosim/Ecospace. In this paper, we give a detailed description of the Ecotracer module and analyze the performance on two problems of differing complexity. Ecotracer was modified from previous versions to more accurately model contaminant excretion, and new numerical integration algorithms were implemented to increase accuracy and robustness. To test the mathematical robustness of the computational algorithm, Ecotracer was tested on a simple problem for which we know an analytical solution. These results demonstrated the effectiveness of the program numerics. A much more complex model, the release of the cesium radionuclide 137 Cs from the Fukushima Dai-ichi nuclear accident, was also modeled and analyzed. A comparison of the Ecotracer results to sampled 137 Cs measurements in the coastal ocean area around Fukushima show the promise of the tool but also highlight some important limitations. Copyright © 2017 Elsevier Ltd. All rights reserved.

Adaptive nearly optimal control for a class of continuous-time nonaffine nonlinear systems with inequality constraints.

PubMed

Fan, Quan-Yong; Yang, Guang-Hong

2017-01-01

The state inequality constraints have been hardly considered in the literature on solving the nonlinear optimal control problem based the adaptive dynamic programming (ADP) method. In this paper, an actor-critic (AC) algorithm is developed to solve the optimal control problem with a discounted cost function for a class of state-constrained nonaffine nonlinear systems. To overcome the difficulties resulting from the inequality constraints and the nonaffine nonlinearities of the controlled systems, a novel transformation technique with redesigned slack functions and a pre-compensator method are introduced to convert the constrained optimal control problem into an unconstrained one for affine nonlinear systems. Then, based on the policy iteration (PI) algorithm, an online AC scheme is proposed to learn the nearly optimal control policy for the obtained affine nonlinear dynamics. Using the information of the nonlinear model, novel adaptive update laws are designed to guarantee the convergence of the neural network (NN) weights and the stability of the affine nonlinear dynamics without the requirement for the probing signal. Finally, the effectiveness of the proposed method is validated by simulation studies. Copyright © 2016 ISA. Published by Elsevier Ltd. All rights reserved.

Dynamic measurement of fluorescent proteins spectral distribution on virus infected cells

NASA Astrophysics Data System (ADS)

Lee, Ja-Yun; Wu, Ming-Xiu; Kao, Chia-Yun; Wu, Tzong-Yuan; Hsu, I.-Jen

2006-09-01

We constructed a dynamic spectroscopy system that can simultaneously measure the intensity and spectral distributions of samples with multi-fluorophores in a single scan. The system was used to monitor the fluorescence distribution of cells infected by the virus, which is constructed by a recombinant baculoviruses, vAcD-Rhir-E, containing the red and green fluorescent protein gene that can simultaneously produce dual fluorescence in recombinant virus-infected Spodoptera frugiperda 21 cells (Sf21) under the control of a polyhedrin promoter. The system was composed of an excitation light source, a scanning system and a spectrometer. We also developed an algorithm and fitting process to analyze the pattern of fluorescence distribution of the dual fluorescence produced in the recombinant virus-infected cells. All the algorithm and calculation are automatically processed in a visualized scanning program and can monitor the specific region of sample by calculating its intensity distribution. The spectral measurement of each pixel was performed at millisecond range and the two dimensional distribution of full spectrum was recorded within several seconds. We have constructed a dynamic spectroscopy system to monitor the process of virus-infection of cells. The distributions of the dual fluorescence were simultaneously measured at micrometer resolution.

The spectral positioning algorithm of new spectrum vehicle based on convex programming in wireless sensor network

NASA Astrophysics Data System (ADS)

Zhang, Yongjun; Lu, Zhixin

2017-10-01

Spectrum resources are very precious, so it is increasingly important to locate interference signals rapidly. Convex programming algorithms in wireless sensor networks are often used as localization algorithms. But in view of the traditional convex programming algorithm is too much overlap of wireless sensor nodes that bring low positioning accuracy, the paper proposed a new algorithm. Which is mainly based on the traditional convex programming algorithm, the spectrum car sends unmanned aerial vehicles (uses) that can be used to record data periodically along different trajectories. According to the probability density distribution, the positioning area is segmented to further reduce the location area. Because the algorithm only increases the communication process of the power value of the unknown node and the sensor node, the advantages of the convex programming algorithm are basically preserved to realize the simple and real-time performance. The experimental results show that the improved algorithm has a better positioning accuracy than the original convex programming algorithm.

Extending molecular simulation time scales: Parallel in time integrations for high-level quantum chemistry and complex force representations

NASA Astrophysics Data System (ADS)

Bylaska, Eric J.; Weare, Jonathan Q.; Weare, John H.

2013-08-01

Parallel in time simulation algorithms are presented and applied to conventional molecular dynamics (MD) and ab initio molecular dynamics (AIMD) models of realistic complexity. Assuming that a forward time integrator, f (e.g., Verlet algorithm), is available to propagate the system from time ti (trajectory positions and velocities xi = (ri, vi)) to time ti + 1 (xi + 1) by xi + 1 = fi(xi), the dynamics problem spanning an interval from t0…tM can be transformed into a root finding problem, F(X) = [xi - f(x(i - 1)]i = 1, M = 0, for the trajectory variables. The root finding problem is solved using a variety of root finding techniques, including quasi-Newton and preconditioned quasi-Newton schemes that are all unconditionally convergent. The algorithms are parallelized by assigning a processor to each time-step entry in the columns of F(X). The relation of this approach to other recently proposed parallel in time methods is discussed, and the effectiveness of various approaches to solving the root finding problem is tested. We demonstrate that more efficient dynamical models based on simplified interactions or coarsening time-steps provide preconditioners for the root finding problem. However, for MD and AIMD simulations, such preconditioners are not required to obtain reasonable convergence and their cost must be considered in the performance of the algorithm. The parallel in time algorithms developed are tested by applying them to MD and AIMD simulations of size and complexity similar to those encountered in present day applications. These include a 1000 Si atom MD simulation using Stillinger-Weber potentials, and a HCl + 4H2O AIMD simulation at the MP2 level. The maximum speedup (serial execution time/parallel execution time) obtained by parallelizing the Stillinger-Weber MD simulation was nearly 3.0. For the AIMD MP2 simulations, the algorithms achieved speedups of up to 14.3. The parallel in time algorithms can be implemented in a distributed computing environment using very slow transmission control protocol/Internet protocol networks. Scripts written in Python that make calls to a precompiled quantum chemistry package (NWChem) are demonstrated to provide an actual speedup of 8.2 for a 2.5 ps AIMD simulation of HCl + 4H2O at the MP2/6-31G* level. Implemented in this way these algorithms can be used for long time high-level AIMD simulations at a modest cost using machines connected by very slow networks such as WiFi, or in different time zones connected by the Internet. The algorithms can also be used with programs that are already parallel. Using these algorithms, we are able to reduce the cost of a MP2/6-311++G(2d,2p) simulation that had reached its maximum possible speedup in the parallelization of the electronic structure calculation from 32 s/time step to 6.9 s/time step.

Extending molecular simulation time scales: Parallel in time integrations for high-level quantum chemistry and complex force representations

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

Bylaska, Eric J.; Weare, Jonathan Q.; Weare, John H.

2013-08-21

Parallel in time simulation algorithms are presented and applied to conventional molecular dynamics (MD) and ab initio molecular dynamics (AIMD) models of realistic complexity. Assuming that a forward time integrator, f , (e.g. Verlet algorithm) is available to propagate the system from time ti (trajectory positions and velocities xi = (ri; vi)) to time ti+1 (xi+1) by xi+1 = fi(xi), the dynamics problem spanning an interval from t0 : : : tM can be transformed into a root finding problem, F(X) = [xi - f (x(i-1)]i=1;M = 0, for the trajectory variables. The root finding problem is solved using amore » variety of optimization techniques, including quasi-Newton and preconditioned quasi-Newton optimization schemes that are all unconditionally convergent. The algorithms are parallelized by assigning a processor to each time-step entry in the columns of F(X). The relation of this approach to other recently proposed parallel in time methods is discussed and the effectiveness of various approaches to solving the root finding problem are tested. We demonstrate that more efficient dynamical models based on simplified interactions or coarsening time-steps provide preconditioners for the root finding problem. However, for MD and AIMD simulations such preconditioners are not required to obtain reasonable convergence and their cost must be considered in the performance of the algorithm. The parallel in time algorithms developed are tested by applying them to MD and AIMD simulations of size and complexity similar to those encountered in present day applications. These include a 1000 Si atom MD simulation using Stillinger-Weber potentials, and a HCl+4H2O AIMD simulation at the MP2 level. The maximum speedup obtained by parallelizing the Stillinger-Weber MD simulation was nearly 3.0. For the AIMD MP2 simulations the algorithms achieved speedups of up to 14.3. The parallel in time algorithms can be implemented in a distributed computing environment using very slow TCP/IP networks. Scripts written in Python that make calls to a precompiled quantum chemistry package (NWChem) are demonstrated to provide an actual speedup of 8.2 for a 2.5 ps AIMD simulation of HCl+4H2O at the MP2/6-31G* level. Implemented in this way these algorithms can be used for long time high-level AIMD simulations at a modest cost using machines connected by very slow networks such as WiFi, or in different time zones connected by the Internet. The algorithms can also be used with programs that are already parallel. By using these algorithms we are able to reduce the cost of a MP2/6-311++G(2d,2p) simulation that had reached its maximum possible speedup in the parallelization of the electronic structure calculation from 32 seconds per time step to 6.9 seconds per time step.« less

Extending molecular simulation time scales: Parallel in time integrations for high-level quantum chemistry and complex force representations.

PubMed

Bylaska, Eric J; Weare, Jonathan Q; Weare, John H

2013-08-21

Parallel in time simulation algorithms are presented and applied to conventional molecular dynamics (MD) and ab initio molecular dynamics (AIMD) models of realistic complexity. Assuming that a forward time integrator, f (e.g., Verlet algorithm), is available to propagate the system from time ti (trajectory positions and velocities xi = (ri, vi)) to time ti + 1 (xi + 1) by xi + 1 = fi(xi), the dynamics problem spanning an interval from t0[ellipsis (horizontal)]tM can be transformed into a root finding problem, F(X) = [xi - f(x(i - 1)]i = 1, M = 0, for the trajectory variables. The root finding problem is solved using a variety of root finding techniques, including quasi-Newton and preconditioned quasi-Newton schemes that are all unconditionally convergent. The algorithms are parallelized by assigning a processor to each time-step entry in the columns of F(X). The relation of this approach to other recently proposed parallel in time methods is discussed, and the effectiveness of various approaches to solving the root finding problem is tested. We demonstrate that more efficient dynamical models based on simplified interactions or coarsening time-steps provide preconditioners for the root finding problem. However, for MD and AIMD simulations, such preconditioners are not required to obtain reasonable convergence and their cost must be considered in the performance of the algorithm. The parallel in time algorithms developed are tested by applying them to MD and AIMD simulations of size and complexity similar to those encountered in present day applications. These include a 1000 Si atom MD simulation using Stillinger-Weber potentials, and a HCl + 4H2O AIMD simulation at the MP2 level. The maximum speedup (serial execution/timeparallel execution time) obtained by parallelizing the Stillinger-Weber MD simulation was nearly 3.0. For the AIMD MP2 simulations, the algorithms achieved speedups of up to 14.3. The parallel in time algorithms can be implemented in a distributed computing environment using very slow transmission control protocol/Internet protocol networks. Scripts written in Python that make calls to a precompiled quantum chemistry package (NWChem) are demonstrated to provide an actual speedup of 8.2 for a 2.5 ps AIMD simulation of HCl + 4H2O at the MP2/6-31G* level. Implemented in this way these algorithms can be used for long time high-level AIMD simulations at a modest cost using machines connected by very slow networks such as WiFi, or in different time zones connected by the Internet. The algorithms can also be used with programs that are already parallel. Using these algorithms, we are able to reduce the cost of a MP2/6-311++G(2d,2p) simulation that had reached its maximum possible speedup in the parallelization of the electronic structure calculation from 32 s/time step to 6.9 s/time step.

Bioinactivation: Software for modelling dynamic microbial inactivation.

PubMed

Garre, Alberto; Fernández, Pablo S; Lindqvist, Roland; Egea, Jose A

2017-03-01

This contribution presents the bioinactivation software, which implements functions for the modelling of isothermal and non-isothermal microbial inactivation. This software offers features such as user-friendliness, modelling of dynamic conditions, possibility to choose the fitting algorithm and generation of prediction intervals. The software is offered in two different formats: Bioinactivation core and Bioinactivation SE. Bioinactivation core is a package for the R programming language, which includes features for the generation of predictions and for the fitting of models to inactivation experiments using non-linear regression or a Markov Chain Monte Carlo algorithm (MCMC). The calculations are based on inactivation models common in academia and industry (Bigelow, Peleg, Mafart and Geeraerd). Bioinactivation SE supplies a user-friendly interface to selected functions of Bioinactivation core, namely the model fitting of non-isothermal experiments and the generation of prediction intervals. The capabilities of bioinactivation are presented in this paper through a case study, modelling the non-isothermal inactivation of Bacillus sporothermodurans. This study has provided a full characterization of the response of the bacteria to dynamic temperature conditions, including confidence intervals for the model parameters and a prediction interval of the survivor curve. We conclude that the MCMC algorithm produces a better characterization of the biological uncertainty and variability than non-linear regression. The bioinactivation software can be relevant to the food and pharmaceutical industry, as well as to regulatory agencies, as part of a (quantitative) microbial risk assessment. Copyright © 2017 Elsevier Ltd. All rights reserved.

Efficient Parallel Kernel Solvers for Computational Fluid Dynamics Applications

NASA Technical Reports Server (NTRS)

Sun, Xian-He

1997-01-01

Distributed-memory parallel computers dominate today's parallel computing arena. These machines, such as Intel Paragon, IBM SP2, and Cray Origin2OO, have successfully delivered high performance computing power for solving some of the so-called "grand-challenge" problems. Despite initial success, parallel machines have not been widely accepted in production engineering environments due to the complexity of parallel programming. On a parallel computing system, a task has to be partitioned and distributed appropriately among processors to reduce communication cost and to attain load balance. More importantly, even with careful partitioning and mapping, the performance of an algorithm may still be unsatisfactory, since conventional sequential algorithms may be serial in nature and may not be implemented efficiently on parallel machines. In many cases, new algorithms have to be introduced to increase parallel performance. In order to achieve optimal performance, in addition to partitioning and mapping, a careful performance study should be conducted for a given application to find a good algorithm-machine combination. This process, however, is usually painful and elusive. The goal of this project is to design and develop efficient parallel algorithms for highly accurate Computational Fluid Dynamics (CFD) simulations and other engineering applications. The work plan is 1) developing highly accurate parallel numerical algorithms, 2) conduct preliminary testing to verify the effectiveness and potential of these algorithms, 3) incorporate newly developed algorithms into actual simulation packages. The work plan has well achieved. Two highly accurate, efficient Poisson solvers have been developed and tested based on two different approaches: (1) Adopting a mathematical geometry which has a better capacity to describe the fluid, (2) Using compact scheme to gain high order accuracy in numerical discretization. The previously developed Parallel Diagonal Dominant (PDD) algorithm and Reduced Parallel Diagonal Dominant (RPDD) algorithm have been carefully studied on different parallel platforms for different applications, and a NASA simulation code developed by Man M. Rai and his colleagues has been parallelized and implemented based on data dependency analysis. These achievements are addressed in detail in the paper.

Dynamic and dual-site atrial pacing in the prevention of atrial fibrillation: The STimolazione Atrial DInamica Multisito (STADIM) Study.

PubMed

De Simone, Antonio; Senatore, Gaetano; Donnici, Giovanni; Turco, Pietro; Romano, Enrico; Gazzola, Carlo; Stabile, G

2007-01-01

The impact of new algorithms to consistently pace the atrium on the prevention of atrial fibrillation (AF) remains unclear. Our randomized, crossover study compared the efficacy of single- and dual-site atrial pacing, with versus without dynamic atrial overdrive pacing in preventing AF. We studied 72 patients (mean age = 69.6 +/- 6.5 years, 34 men) with sick sinus syndrome (SSS) and paroxysmal or persistent AF, who received dual-chamber pacemakers (PM) equipped with an AF prevention algorithm and two atrial leads placed in the right atrial appendage (RAA), by passive fixation, and in the coronary sinus ostium (CS), by active fixation, respectively. At implant, the patients were randomly assigned to unipolar CS versus RAA pacing. The PM was programmed in DDDR mode 1 month after implant. Each patient underwent four study phases of equal duration: (1) unipolar, single site (CS or RAA) pacing with the AF algorithm ON (atrial lower rate = 0 ppm); (2) unipolar, single site pacing with the AF algorithm OFF (atrial lower rate = 70 bpm); (3) bipolar, dual-site pacing with AF algorithm ON; (4) bipolar, dual-site pacing with the AF algorithm OFF. Among 40 patients (56%), who completed the follow-up (15 +/- 4 months) no difference was observed in the mean number of automatic mode switch (AMS) corrected for the duration of follow-up, in unipolar (5.6 +/- 22.8 vs 2.6 +/- 5.5) or bipolar mode (3.3 +/- 12.7 vs 2.1 +/- 4.9) with, respectively, the algorithm OFF or ON. With the AF prevention algorithm ON, the percentage of atrial pacing increased significantly from 78.7 +/- 22.1% to 92.4 +/- 4.9% (P < 0.001), while the average ventricular heart rate was significantly lower with the algorithm ON (62.4 +/- 17.5 vs 79.9 +/- 3 bpm (P < 0.001). The AF prevention algorithm increased the percentage of atrial pacing significantly, regardless of the atrial pulse configuration and pacing site, while maintaining a slower ventricular heart rate. It had no impact on the number of AMS in the unipolar and bipolar modes in patients with SSS.

A workstation-based evaluation of a far-field route planner for helicopters

NASA Technical Reports Server (NTRS)

Warner, David N., Jr.; Moran, Francis J.

1991-01-01

Helicopter flight missions at very low, nap of the Earth, altitudes place a heavy workload on the pilot. To aid in reducing this workload, Ames Research Center has been investigating various types of automated route planners. As part of an automated preflight mission planner, a route planner algorithm aids in selecting the overall (far-field) route to be flown. During the mission, the route planner can be used to replan a new route in case of unexpected threats or change in mission requirements. An evaluation of a candidate route planning algorithm, based on dynamic programming techniques is described. This algorithm meets most of the requirements for route planning, both preflight and during the mission. In general, the requirements are to minimize the distance and/or fuel and the deviation from a flight time schedule, and must be flyable within the constraints of available fuel and time.

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.

3D reconstruction from multi-view VHR-satellite images in MicMac

NASA Astrophysics Data System (ADS)

Rupnik, Ewelina; Pierrot-Deseilligny, Marc; Delorme, Arthur

2018-05-01

This work addresses the generation of high quality digital surface models by fusing multiple depths maps calculated with the dense image matching method. The algorithm is adapted to very high resolution multi-view satellite images, and the main contributions of this work are in the multi-view fusion. The algorithm is insensitive to outliers, takes into account the matching quality indicators, handles non-correlated zones (e.g. occlusions), and is solved with a multi-directional dynamic programming approach. No geometric constraints (e.g. surface planarity) or auxiliary data in form of ground control points are required for its operation. Prior to the fusion procedures, the RPC geolocation parameters of all images are improved in a bundle block adjustment routine. The performance of the algorithm is evaluated on two VHR (Very High Resolution)-satellite image datasets (Pléiades, WorldView-3) revealing its good performance in reconstructing non-textured areas, repetitive patterns, and surface discontinuities.

Handling Big Data in Medical Imaging: Iterative Reconstruction with Large-Scale Automated Parallel Computation

PubMed Central

Lee, Jae H.; Yao, Yushu; Shrestha, Uttam; Gullberg, Grant T.; Seo, Youngho

2014-01-01

The primary goal of this project is to implement the iterative statistical image reconstruction algorithm, in this case maximum likelihood expectation maximum (MLEM) used for dynamic cardiac single photon emission computed tomography, on Spark/GraphX. This involves porting the algorithm to run on large-scale parallel computing systems. Spark is an easy-to- program software platform that can handle large amounts of data in parallel. GraphX is a graph analytic system running on top of Spark to handle graph and sparse linear algebra operations in parallel. The main advantage of implementing MLEM algorithm in Spark/GraphX is that it allows users to parallelize such computation without any expertise in parallel computing or prior knowledge in computer science. In this paper we demonstrate a successful implementation of MLEM in Spark/GraphX and present the performance gains with the goal to eventually make it useable in clinical setting. PMID:27081299

Handling Big Data in Medical Imaging: Iterative Reconstruction with Large-Scale Automated Parallel Computation.

PubMed

Lee, Jae H; Yao, Yushu; Shrestha, Uttam; Gullberg, Grant T; Seo, Youngho

2014-11-01

The primary goal of this project is to implement the iterative statistical image reconstruction algorithm, in this case maximum likelihood expectation maximum (MLEM) used for dynamic cardiac single photon emission computed tomography, on Spark/GraphX. This involves porting the algorithm to run on large-scale parallel computing systems. Spark is an easy-to- program software platform that can handle large amounts of data in parallel. GraphX is a graph analytic system running on top of Spark to handle graph and sparse linear algebra operations in parallel. The main advantage of implementing MLEM algorithm in Spark/GraphX is that it allows users to parallelize such computation without any expertise in parallel computing or prior knowledge in computer science. In this paper we demonstrate a successful implementation of MLEM in Spark/GraphX and present the performance gains with the goal to eventually make it useable in clinical setting.

Statistical Inference in Hidden Markov Models Using k-Segment Constraints

PubMed Central

Titsias, Michalis K.; Holmes, Christopher C.; Yau, Christopher

2016-01-01

Hidden Markov models (HMMs) are one of the most widely used statistical methods for analyzing sequence data. However, the reporting of output from HMMs has largely been restricted to the presentation of the most-probable (MAP) hidden state sequence, found via the Viterbi algorithm, or the sequence of most probable marginals using the forward–backward algorithm. In this article, we expand the amount of information we could obtain from the posterior distribution of an HMM by introducing linear-time dynamic programming recursions that, conditional on a user-specified constraint in the number of segments, allow us to (i) find MAP sequences, (ii) compute posterior probabilities, and (iii) simulate sample paths. We collectively call these recursions k-segment algorithms and illustrate their utility using simulated and real examples. We also highlight the prospective and retrospective use of k-segment constraints for fitting HMMs or exploring existing model fits. Supplementary materials for this article are available online. PMID:27226674

One algorithm to rule them all? An evaluation and discussion of ten eye movement event-detection algorithms.

PubMed

Andersson, Richard; Larsson, Linnea; Holmqvist, Kenneth; Stridh, Martin; Nyström, Marcus

2017-04-01

Almost all eye-movement researchers use algorithms to parse raw data and detect distinct types of eye movement events, such as fixations, saccades, and pursuit, and then base their results on these. Surprisingly, these algorithms are rarely evaluated. We evaluated the classifications of ten eye-movement event detection algorithms, on data from an SMI HiSpeed 1250 system, and compared them to manual ratings of two human experts. The evaluation focused on fixations, saccades, and post-saccadic oscillations. The evaluation used both event duration parameters, and sample-by-sample comparisons to rank the algorithms. The resulting event durations varied substantially as a function of what algorithm was used. This evaluation differed from previous evaluations by considering a relatively large set of algorithms, multiple events, and data from both static and dynamic stimuli. The main conclusion is that current detectors of only fixations and saccades work reasonably well for static stimuli, but barely better than chance for dynamic stimuli. Differing results across evaluation methods make it difficult to select one winner for fixation detection. For saccade detection, however, the algorithm by Larsson, Nyström and Stridh (IEEE Transaction on Biomedical Engineering, 60(9):2484-2493,2013) outperforms all algorithms in data from both static and dynamic stimuli. The data also show how improperly selected algorithms applied to dynamic data misestimate fixation and saccade properties.

Hash Bit Selection for Nearest Neighbor Search.

PubMed

Xianglong Liu; Junfeng He; Shih-Fu Chang

2017-11-01

To overcome the barrier of storage and computation when dealing with gigantic-scale data sets, compact hashing has been studied extensively to approximate the nearest neighbor search. Despite the recent advances, critical design issues remain open in how to select the right features, hashing algorithms, and/or parameter settings. In this paper, we address these by posing an optimal hash bit selection problem, in which an optimal subset of hash bits are selected from a pool of candidate bits generated by different features, algorithms, or parameters. Inspired by the optimization criteria used in existing hashing algorithms, we adopt the bit reliability and their complementarity as the selection criteria that can be carefully tailored for hashing performance in different tasks. Then, the bit selection solution is discovered by finding the best tradeoff between search accuracy and time using a modified dynamic programming method. To further reduce the computational complexity, we employ the pairwise relationship among hash bits to approximate the high-order independence property, and formulate it as an efficient quadratic programming method that is theoretically equivalent to the normalized dominant set problem in a vertex- and edge-weighted graph. Extensive large-scale experiments have been conducted under several important application scenarios of hash techniques, where our bit selection framework can achieve superior performance over both the naive selection methods and the state-of-the-art hashing algorithms, with significant accuracy gains ranging from 10% to 50%, relatively.

NetFlow Dynamics

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

Corbet Jr., Thomas F; Beyeler, Walter E; Vanwestrienen, Dirk

NetFlow Dynamics is a web-accessible analysis environment for simulating dynamic flows of materials on model networks. Performing a simulation requires both the NetFlow Dynamics application and a network model which is a description of the structure of the nodes and edges of a network including the flow capacity of each edge and the storage capacity of each node, and the sources and sinks of the material flowing on the network. NetFlow Dynamics consists of databases for storing network models, algorithms to calculate flows on networks, and a GIS-based graphical interface for performing simulations and viewing simulation results. Simulated flows aremore » dynamic in the sense that flows on each edge of the network and inventories at each node change with time and can be out of equilibrium with boundary conditions. Any number of network models could be simulated using Net Flow Dynamics. To date, the models simulated have been models of petroleum infrastructure. The main model has been the National Transportation Fuels Model (NTFM), a network of U.S. oil fields, transmission pipelines, rail lines, refineries, tank farms, and distribution terminals. NetFlow Dynamics supports two different flow algorithms, the Gradient Flow algorithm and the Inventory Control algorithm, that were developed specifically for the NetFlow Dynamics application. The intent is to add additional algorithms in the future as needed. The ability to select from multiple algorithms is desirable because a single algorithm never covers all analysis needs. The current algorithms use a demand-driven capacity-constrained formulation which means that the algorithms strive to use all available capacity and stored inventory to meet desired flows to sinks, subject to the capacity constraints of each network component. The current flow algorithms are best suited for problems in which a material flows on a capacity-constrained network representing a supply chain in which the material supplied can be stored at each node of the network. In the petroleum models, the flowing materials are crude oil and refined products that can be stored at tank farms, refineries, or terminals (i.e. the nodes of the network). Examples of other network models that could be simulated are currency flowing in a financial network, agricultural products moving to market, or natural gas flowing on a pipeline network.« less

Semisupervised GDTW kernel-based fuzzy c-means algorithm for mapping vegetation dynamics in mining region using normalized difference vegetation index time series

NASA Astrophysics Data System (ADS)

Jia, Duo; Wang, Cangjiao; Lei, Shaogang

2018-01-01

Mapping vegetation dynamic types in mining areas is significant for revealing the mechanisms of environmental damage and for guiding ecological construction. Dynamic types of vegetation can be identified by applying interannual normalized difference vegetation index (NDVI) time series. However, phase differences and time shifts in interannual time series decrease mapping accuracy in mining regions. To overcome these problems and to increase the accuracy of mapping vegetation dynamics, an interannual Landsat time series for optimum vegetation growing status was constructed first by using the enhanced spatial and temporal adaptive reflectance fusion model algorithm. We then proposed a Markov random field optimized semisupervised Gaussian dynamic time warping kernel-based fuzzy c-means (FCM) cluster algorithm for interannual NDVI time series to map dynamic vegetation types in mining regions. The proposed algorithm has been tested in the Shengli mining region and Shendong mining region, which are typical representatives of China's open-pit and underground mining regions, respectively. Experiments show that the proposed algorithm can solve the problems of phase differences and time shifts to achieve better performance when mapping vegetation dynamic types. The overall accuracies for the Shengli and Shendong mining regions were 93.32% and 89.60%, respectively, with improvements of 7.32% and 25.84% when compared with the original semisupervised FCM algorithm.

Fuzzy Mixed Assembly Line Sequencing and Scheduling Optimization Model Using Multiobjective Dynamic Fuzzy GA

PubMed Central

Tahriri, Farzad; Dawal, Siti Zawiah Md; Taha, Zahari

2014-01-01

A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model. PMID:24982962

Semiannual report, 1 April - 30 September 1991

NASA Technical Reports Server (NTRS)

1991-01-01

The major categories of the current Institute for Computer Applications in Science and Engineering (ICASE) research program are: (1) numerical methods, with particular emphasis on the development and analysis of basic numerical algorithms; (2) control and parameter identification problems, with emphasis on effective numerical methods; (3) computational problems in engineering and the physical sciences, particularly fluid dynamics, acoustics, and structural analysis; and (4) computer systems and software for parallel computers. Research in these areas is discussed.

Detecting and Jamming Dynamic Communication Networks in Anti-Access Environments

DTIC Science & Technology

2011-03-01

P.M. Pardalos, Y. Ye. and C.W. Commander (eds) V. Boginski. Sensors: Theory , Algorithms, and Applications. Springer, to appear in 2009. [21] Joseph C...Sumeetpal S. Singh. Nikolaos Kantas , Ba-Ngu Vo, Arnaud Doucet, and Robin J. Evans. Simulation-based optimal sensor scheduling with application to...observer trajectory planning. Aotomatica, 43(5):817-830, 2007. [27] Anthony Man-Cho So and Yinyu Ye. Theory of semidefinite programming for sensor network

Linear programming model to develop geodiversity map using utility theory

NASA Astrophysics Data System (ADS)

Sepehr, Adel

2015-04-01

In this article, the classification and mapping of geodiversity based on a quantitative methodology was accomplished using linear programming, the central idea of which being that geosites and geomorphosites as main indicators of geodiversity can be evaluated by utility theory. A linear programming method was applied for geodiversity mapping over Khorasan-razavi province located in eastern north of Iran. In this route, the main criteria for distinguishing geodiversity potential in the studied area were considered regarding rocks type (lithology), faults position (tectonic process), karst area (dynamic process), Aeolian landforms frequency and surface river forms. These parameters were investigated by thematic maps including geology, topography and geomorphology at scales 1:100'000, 1:50'000 and 1:250'000 separately, imagery data involving SPOT, ETM+ (Landsat 7) and field operations directly. The geological thematic layer was simplified from the original map using a practical lithologic criterion based on a primary genetic rocks classification representing metamorphic, igneous and sedimentary rocks. The geomorphology map was provided using DEM at scale 30m extracted by ASTER data, geology and google earth images. The geology map shows tectonic status and geomorphology indicated dynamic processes and landform (karst, Aeolian and river). Then, according to the utility theory algorithms, we proposed a linear programming to classify geodiversity degree in the studied area based on geology/morphology parameters. The algorithm used in the methodology was consisted a linear function to be maximized geodiversity to certain constraints in the form of linear equations. The results of this research indicated three classes of geodiversity potential including low, medium and high status. The geodiversity potential shows satisfied conditions in the Karstic areas and Aeolian landscape. Also the utility theory used in the research has been decreased uncertainty of the evaluations.

Accurate 3D reconstruction by a new PDS-OSEM algorithm for HRRT

NASA Astrophysics Data System (ADS)

Chen, Tai-Been; Horng-Shing Lu, Henry; Kim, Hang-Keun; Son, Young-Don; Cho, Zang-Hee

2014-03-01

State-of-the-art high resolution research tomography (HRRT) provides high resolution PET images with full 3D human brain scanning. But, a short time frame in dynamic study causes many problems related to the low counts in the acquired data. The PDS-OSEM algorithm was proposed to reconstruct the HRRT image with a high signal-to-noise ratio that provides accurate information for dynamic data. The new algorithm was evaluated by simulated image, empirical phantoms, and real human brain data. Meanwhile, the time activity curve was adopted to validate a reconstructed performance of dynamic data between PDS-OSEM and OP-OSEM algorithms. According to simulated and empirical studies, the PDS-OSEM algorithm reconstructs images with higher quality, higher accuracy, less noise, and less average sum of square error than those of OP-OSEM. The presented algorithm is useful to provide quality images under the condition of low count rates in dynamic studies with a short scan time.

Fast and robust wavelet-based dynamic range compression and contrast enhancement model with color restoration

NASA Astrophysics Data System (ADS)

Unaldi, Numan; Asari, Vijayan K.; Rahman, Zia-ur

2009-05-01

Recently we proposed a wavelet-based dynamic range compression algorithm to improve the visual quality of digital images captured from high dynamic range scenes with non-uniform lighting conditions. The fast image enhancement algorithm that provides dynamic range compression, while preserving the local contrast and tonal rendition, is also a good candidate for real time video processing applications. Although the colors of the enhanced images produced by the proposed algorithm are consistent with the colors of the original image, the proposed algorithm fails to produce color constant results for some "pathological" scenes that have very strong spectral characteristics in a single band. The linear color restoration process is the main reason for this drawback. Hence, a different approach is required for the final color restoration process. In this paper the latest version of the proposed algorithm, which deals with this issue is presented. The results obtained by applying the algorithm to numerous natural images show strong robustness and high image quality.

Motion Cueing Algorithm Development: New Motion Cueing Program Implementation and Tuning

NASA Technical Reports Server (NTRS)

Houck, Jacob A. (Technical Monitor); Telban, Robert J.; Cardullo, Frank M.; Kelly, Lon C.

2005-01-01

A computer program has been developed for the purpose of driving the NASA Langley Research Center Visual Motion Simulator (VMS). This program includes two new motion cueing algorithms, the optimal algorithm and the nonlinear algorithm. A general description of the program is given along with a description and flowcharts for each cueing algorithm, and also descriptions and flowcharts for subroutines used with the algorithms. Common block variable listings and a program listing are also provided. The new cueing algorithms have a nonlinear gain algorithm implemented that scales each aircraft degree-of-freedom input with a third-order polynomial. A description of the nonlinear gain algorithm is given along with past tuning experience and procedures for tuning the gain coefficient sets for each degree-of-freedom to produce the desired piloted performance. This algorithm tuning will be needed when the nonlinear motion cueing algorithm is implemented on a new motion system in the Cockpit Motion Facility (CMF) at the NASA Langley Research Center.

Continuous-time quantum Monte Carlo impurity solvers

NASA Astrophysics Data System (ADS)

Gull, Emanuel; Werner, Philipp; Fuchs, Sebastian; Surer, Brigitte; Pruschke, Thomas; Troyer, Matthias

2011-04-01

Continuous-time quantum Monte Carlo impurity solvers are algorithms that sample the partition function of an impurity model using diagrammatic Monte Carlo techniques. The present paper describes codes that implement the interaction expansion algorithm originally developed by Rubtsov, Savkin, and Lichtenstein, as well as the hybridization expansion method developed by Werner, Millis, Troyer, et al. These impurity solvers are part of the ALPS-DMFT application package and are accompanied by an implementation of dynamical mean-field self-consistency equations for (single orbital single site) dynamical mean-field problems with arbitrary densities of states. Program summaryProgram title: dmft Catalogue identifier: AEIL_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEIL_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: ALPS LIBRARY LICENSE version 1.1 No. of lines in distributed program, including test data, etc.: 899 806 No. of bytes in distributed program, including test data, etc.: 32 153 916 Distribution format: tar.gz Programming language: C++ Operating system: The ALPS libraries have been tested on the following platforms and compilers: Linux with GNU Compiler Collection (g++ version 3.1 and higher), and Intel C++ Compiler (icc version 7.0 and higher) MacOS X with GNU Compiler (g++ Apple-version 3.1, 3.3 and 4.0) IBM AIX with Visual Age C++ (xlC version 6.0) and GNU (g++ version 3.1 and higher) compilers Compaq Tru64 UNIX with Compq C++ Compiler (cxx) SGI IRIX with MIPSpro C++ Compiler (CC) HP-UX with HP C++ Compiler (aCC) Windows with Cygwin or coLinux platforms and GNU Compiler Collection (g++ version 3.1 and higher) RAM: 10 MB-1 GB Classification: 7.3 External routines: ALPS [1], BLAS/LAPACK, HDF5 Nature of problem: (See [2].) Quantum impurity models describe an atom or molecule embedded in a host material with which it can exchange electrons. They are basic to nanoscience as representations of quantum dots and molecular conductors and play an increasingly important role in the theory of "correlated electron" materials as auxiliary problems whose solution gives the "dynamical mean field" approximation to the self-energy and local correlation functions. Solution method: Quantum impurity models require a method of solution which provides access to both high and low energy scales and is effective for wide classes of physically realistic models. The continuous-time quantum Monte Carlo algorithms for which we present implementations here meet this challenge. Continuous-time quantum impurity methods are based on partition function expansions of quantum impurity models that are stochastically sampled to all orders using diagrammatic quantum Monte Carlo techniques. For a review of quantum impurity models and their applications and of continuous-time quantum Monte Carlo methods for impurity models we refer the reader to [2]. Additional comments: Use of dmft requires citation of this paper. Use of any ALPS program requires citation of the ALPS [1] paper. Running time: 60 s-8 h per iteration.

A novel data hiding scheme for block truncation coding compressed images using dynamic programming strategy

NASA Astrophysics Data System (ADS)

Chang, Ching-Chun; Liu, Yanjun; Nguyen, Son T.

2015-03-01

Data hiding is a technique that embeds information into digital cover data. This technique has been concentrated on the spatial uncompressed domain, and it is considered more challenging to perform in the compressed domain, i.e., vector quantization, JPEG, and block truncation coding (BTC). In this paper, we propose a new data hiding scheme for BTC-compressed images. In the proposed scheme, a dynamic programming strategy was used to search for the optimal solution of the bijective mapping function for LSB substitution. Then, according to the optimal solution, each mean value embeds three secret bits to obtain high hiding capacity with low distortion. The experimental results indicated that the proposed scheme obtained both higher hiding capacity and hiding efficiency than the other four existing schemes, while ensuring good visual quality of the stego-image. In addition, the proposed scheme achieved a low bit rate as original BTC algorithm.

GATA: A graphic alignment tool for comparative sequenceanalysis

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

Nix, David A.; Eisen, Michael B.

2005-01-01

Several problems exist with current methods used to align DNA sequences for comparative sequence analysis. Most dynamic programming algorithms assume that conserved sequence elements are collinear. This assumption appears valid when comparing orthologous protein coding sequences. Functional constraints on proteins provide strong selective pressure against sequence inversions, and minimize sequence duplications and feature shuffling. For non-coding sequences this collinearity assumption is often invalid. For example, enhancers contain clusters of transcription factor binding sites that change in number, orientation, and spacing during evolution yet the enhancer retains its activity. Dotplot analysis is often used to estimate non-coding sequence relatedness. Yet dotmore » plots do not actually align sequences and thus cannot account well for base insertions or deletions. Moreover, they lack an adequate statistical framework for comparing sequence relatedness and are limited to pairwise comparisons. Lastly, dot plots and dynamic programming text outputs fail to provide an intuitive means for visualizing DNA alignments.« less

Dynamic optimization of metabolic networks coupled with gene expression.

PubMed

Waldherr, Steffen; Oyarzún, Diego A; Bockmayr, Alexander

2015-01-21

The regulation of metabolic activity by tuning enzyme expression levels is crucial to sustain cellular growth in changing environments. Metabolic networks are often studied at steady state using constraint-based models and optimization techniques. However, metabolic adaptations driven by changes in gene expression cannot be analyzed by steady state models, as these do not account for temporal changes in biomass composition. Here we present a dynamic optimization framework that integrates the metabolic network with the dynamics of biomass production and composition. An approximation by a timescale separation leads to a coupled model of quasi-steady state constraints on the metabolic reactions, and differential equations for the substrate concentrations and biomass composition. We propose a dynamic optimization approach to determine reaction fluxes for this model, explicitly taking into account enzyme production costs and enzymatic capacity. In contrast to the established dynamic flux balance analysis, our approach allows predicting dynamic changes in both the metabolic fluxes and the biomass composition during metabolic adaptations. Discretization of the optimization problems leads to a linear program that can be efficiently solved. We applied our algorithm in two case studies: a minimal nutrient uptake network, and an abstraction of core metabolic processes in bacteria. In the minimal model, we show that the optimized uptake rates reproduce the empirical Monod growth for bacterial cultures. For the network of core metabolic processes, the dynamic optimization algorithm predicted commonly observed metabolic adaptations, such as a diauxic switch with a preference ranking for different nutrients, re-utilization of waste products after depletion of the original substrate, and metabolic adaptation to an impending nutrient depletion. These examples illustrate how dynamic adaptations of enzyme expression can be predicted solely from an optimization principle. Copyright © 2014 Elsevier Ltd. All rights reserved.

Inversion for Refractivity Parameters Using a Dynamic Adaptive Cuckoo Search with Crossover Operator Algorithm

PubMed Central

Zhang, Zhihua; Sheng, Zheng; Shi, Hanqing; Fan, Zhiqiang

2016-01-01

Using the RFC technique to estimate refractivity parameters is a complex nonlinear optimization problem. In this paper, an improved cuckoo search (CS) algorithm is proposed to deal with this problem. To enhance the performance of the CS algorithm, a parameter dynamic adaptive operation and crossover operation were integrated into the standard CS (DACS-CO). Rechenberg's 1/5 criteria combined with learning factor were used to control the parameter dynamic adaptive adjusting process. The crossover operation of genetic algorithm was utilized to guarantee the population diversity. The new hybrid algorithm has better local search ability and contributes to superior performance. To verify the ability of the DACS-CO algorithm to estimate atmospheric refractivity parameters, the simulation data and real radar clutter data are both implemented. The numerical experiments demonstrate that the DACS-CO algorithm can provide an effective method for near-real-time estimation of the atmospheric refractivity profile from radar clutter. PMID:27212938

Adaptive control in the presence of unmodeled dynamics. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Rohrs, C. E.

1982-01-01

Stability and robustness properties of a wide class of adaptive control algorithms in the presence of unmodeled dynamics and output disturbances were investigated. The class of adaptive algorithms considered are those commonly referred to as model reference adaptive control algorithms, self-tuning controllers, and dead beat adaptive controllers, developed for both continuous-time systems and discrete-time systems. A unified analytical approach was developed to examine the class of existing adaptive algorithms. It was discovered that all existing algorithms contain an infinite gain operator in the dynamic system that defines command reference errors and parameter errors; it is argued that such an infinite gain operator appears to be generic to all adaptive algorithms, whether they exhibit explicit or implicit parameter identification. It is concluded that none of the adaptive algorithms considered can be used with confidence in a practical control system design, because instability will set in with a high probability.

TASK ALLOCATION IN GEO-DISTRIBUTED CYBER-PHYSICAL SYSTEMS

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

Aggarwal, Rachit; Smidts, Carol

This paper studies the task allocation algorithm for a distributed test facility (DTF), which aims to assemble geo-distributed cyber (software) and physical (hardware in the loop components into a prototype cyber-physical system (CPS). This allows low cost testing on an early conceptual prototype (ECP) of the ultimate CPS (UCPS) to be developed. The DTF provides an instrumentation interface for carrying out reliability experiments remotely such as fault propagation analysis and in-situ testing of hardware and software components in a simulated environment. Unfortunately, the geo-distribution introduces an overhead that is not inherent to the UCPS, i.e. a significant time delay inmore » communication that threatens the stability of the ECP and is not an appropriate representation of the behavior of the UCPS. This can be mitigated by implementing a task allocation algorithm to find a suitable configuration and assign the software components to appropriate computational locations, dynamically. This would allow the ECP to operate more efficiently with less probability of being unstable due to the delays introduced by geo-distribution. The task allocation algorithm proposed in this work uses a Monte Carlo approach along with Dynamic Programming to identify the optimal network configuration to keep the time delays to a minimum.« less

Computer Science Techniques Applied to Parallel Atomistic Simulation

NASA Astrophysics Data System (ADS)

Nakano, Aiichiro

1998-03-01

Recent developments in parallel processing technology and multiresolution numerical algorithms have established large-scale molecular dynamics (MD) simulations as a new research mode for studying materials phenomena such as fracture. However, this requires large system sizes and long simulated times. We have developed: i) Space-time multiresolution schemes; ii) fuzzy-clustering approach to hierarchical dynamics; iii) wavelet-based adaptive curvilinear-coordinate load balancing; iv) multilevel preconditioned conjugate gradient method; and v) spacefilling-curve-based data compression for parallel I/O. Using these techniques, million-atom parallel MD simulations are performed for the oxidation dynamics of nanocrystalline Al. The simulations take into account the effect of dynamic charge transfer between Al and O using the electronegativity equalization scheme. The resulting long-range Coulomb interaction is calculated efficiently with the fast multipole method. Results for temperature and charge distributions, residual stresses, bond lengths and bond angles, and diffusivities of Al and O will be presented. The oxidation of nanocrystalline Al is elucidated through immersive visualization in virtual environments. A unique dual-degree education program at Louisiana State University will also be discussed in which students can obtain a Ph.D. in Physics & Astronomy and a M.S. from the Department of Computer Science in five years. This program fosters interdisciplinary research activities for interfacing High Performance Computing and Communications with large-scale atomistic simulations of advanced materials. This work was supported by NSF (CAREER Program), ARO, PRF, and Louisiana LEQSF.

Variable threshold algorithm for division of labor analyzed as a dynamical system.

PubMed

Castillo-Cagigal, Manuel; Matallanas, Eduardo; Navarro, Iñaki; Caamaño-Martín, Estefanía; Monasterio-Huelin, Félix; Gutiérrez, Álvaro

2014-12-01

Division of labor is a widely studied aspect of colony behavior of social insects. Division of labor models indicate how individuals distribute themselves in order to perform different tasks simultaneously. However, models that study division of labor from a dynamical system point of view cannot be found in the literature. In this paper, we define a division of labor model as a discrete-time dynamical system, in order to study the equilibrium points and their properties related to convergence and stability. By making use of this analytical model, an adaptive algorithm based on division of labor can be designed to satisfy dynamic criteria. In this way, we have designed and tested an algorithm that varies the response thresholds in order to modify the dynamic behavior of the system. This behavior modification allows the system to adapt to specific environmental and collective situations, making the algorithm a good candidate for distributed control applications. The variable threshold algorithm is based on specialization mechanisms. It is able to achieve an asymptotically stable behavior of the system in different environments and independently of the number of individuals. The algorithm has been successfully tested under several initial conditions and number of individuals.

A rain pixel recovery algorithm for videos with highly dynamic scenes.

PubMed

Jie Chen; Lap-Pui Chau

2014-03-01

Rain removal is a very useful and important technique in applications such as security surveillance and movie editing. Several rain removal algorithms have been proposed these years, where photometric, chromatic, and probabilistic properties of the rain have been exploited to detect and remove the rainy effect. Current methods generally work well with light rain and relatively static scenes, when dealing with heavier rainfall in dynamic scenes, these methods give very poor visual results. The proposed algorithm is based on motion segmentation of dynamic scene. After applying photometric and chromatic constraints for rain detection, rain removal filters are applied on pixels such that their dynamic property as well as motion occlusion clue are considered; both spatial and temporal informations are then adaptively exploited during rain pixel recovery. Results show that the proposed algorithm has a much better performance for rainy scenes with large motion than existing algorithms.

Model of ballistic targets' dynamics used for trajectory tracking algorithms

NASA Astrophysics Data System (ADS)

Okoń-FÄ fara, Marta; Kawalec, Adam; Witczak, Andrzej

2017-04-01

There are known only few ballistic object tracking algorithms. To develop such algorithms and to its further testing, it is necessary to implement possibly simple and reliable objects' dynamics model. The article presents the dynamics' model of a tactical ballistic missile (TBM) including the three stages of flight: the boost stage and two passive stages - the ascending one and the descending one. Additionally, the procedure of transformation from the local coordinate system to the polar-radar oriented and the global is presented. The prepared theoretical data may be used to determine the tracking algorithm parameters and to its further verification.

Implementing a GPU-based numerical algorithm for modelling dynamics of a high-speed train

NASA Astrophysics Data System (ADS)

Sytov, E. S.; Bratus, A. S.; Yurchenko, D.

2018-04-01

This paper discusses the initiative of implementing a GPU-based numerical algorithm for studying various phenomena associated with dynamics of a high-speed railway transport. The proposed numerical algorithm for calculating a critical speed of the bogie is based on the first Lyapunov number. Numerical algorithm is validated by analytical results, derived for a simple model. A dynamic model of a carriage connected to a new dual-wheelset flexible bogie is studied for linear and dry friction damping. Numerical results obtained by CPU, MPU and GPU approaches are compared and appropriateness of these methods is discussed.

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.

Dynamic Harmony Search with Polynomial Mutation Algorithm for Valve-Point Economic Load Dispatch

PubMed Central

Karthikeyan, M.; Sree Ranga Raja, T.

2015-01-01

Economic load dispatch (ELD) problem is an important issue in the operation and control of modern control system. The ELD problem is complex and nonlinear with equality and inequality constraints which makes it hard to be efficiently solved. This paper presents a new modification of harmony search (HS) algorithm named as dynamic harmony search with polynomial mutation (DHSPM) algorithm to solve ORPD problem. In DHSPM algorithm the key parameters of HS algorithm like harmony memory considering rate (HMCR) and pitch adjusting rate (PAR) are changed dynamically and there is no need to predefine these parameters. Additionally polynomial mutation is inserted in the updating step of HS algorithm to favor exploration and exploitation of the search space. The DHSPM algorithm is tested with three power system cases consisting of 3, 13, and 40 thermal units. The computational results show that the DHSPM algorithm is more effective in finding better solutions than other computational intelligence based methods. PMID:26491710

Dynamic Harmony Search with Polynomial Mutation Algorithm for Valve-Point Economic Load Dispatch.

PubMed

Karthikeyan, M; Raja, T Sree Ranga

2015-01-01

Economic load dispatch (ELD) problem is an important issue in the operation and control of modern control system. The ELD problem is complex and nonlinear with equality and inequality constraints which makes it hard to be efficiently solved. This paper presents a new modification of harmony search (HS) algorithm named as dynamic harmony search with polynomial mutation (DHSPM) algorithm to solve ORPD problem. In DHSPM algorithm the key parameters of HS algorithm like harmony memory considering rate (HMCR) and pitch adjusting rate (PAR) are changed dynamically and there is no need to predefine these parameters. Additionally polynomial mutation is inserted in the updating step of HS algorithm to favor exploration and exploitation of the search space. The DHSPM algorithm is tested with three power system cases consisting of 3, 13, and 40 thermal units. The computational results show that the DHSPM algorithm is more effective in finding better solutions than other computational intelligence based methods.

Some Results of Weak Anticipative Concept Applied in Simulation Based Decision Support in Enterprise

NASA Astrophysics Data System (ADS)

Kljajić, Miroljub; Kofjač, Davorin; Kljajić Borštnar, Mirjana; Škraba, Andrej

2010-11-01

The simulation models are used as for decision support and learning in enterprises and in schools. Tree cases of successful applications demonstrate usefulness of weak anticipative information. Job shop scheduling production with makespan criterion presents a real case customized flexible furniture production optimization. The genetic algorithm for job shop scheduling optimization is presented. Simulation based inventory control for products with stochastic lead time and demand describes inventory optimization for products with stochastic lead time and demand. Dynamic programming and fuzzy control algorithms reduce the total cost without producing stock-outs in most cases. Values of decision making information based on simulation were discussed too. All two cases will be discussed from optimization, modeling and learning point of view.

Parallel programming of gradient-based iterative image reconstruction schemes for optical tomography.

PubMed

Hielscher, Andreas H; Bartel, Sebastian

2004-02-01

Optical tomography (OT) is a fast developing novel imaging modality that uses near-infrared (NIR) light to obtain cross-sectional views of optical properties inside the human body. A major challenge remains the time-consuming, computational-intensive image reconstruction problem that converts NIR transmission measurements into cross-sectional images. To increase the speed of iterative image reconstruction schemes that are commonly applied for OT, we have developed and implemented several parallel algorithms on a cluster of workstations. Static process distribution as well as dynamic load balancing schemes suitable for heterogeneous clusters and varying machine performances are introduced and tested. The resulting algorithms are shown to accelerate the reconstruction process to various degrees, substantially reducing the computation times for clinically relevant problems.

An Approach for Peptide Identification by De Novo Sequencing of Mixture Spectra.

PubMed

Liu, Yi; Ma, Bin; Zhang, Kaizhong; Lajoie, Gilles

2017-01-01

Mixture spectra occur quite frequently in a typical wet-lab mass spectrometry experiment, which result from the concurrent fragmentation of multiple precursors. The ability to efficiently and confidently identify mixture spectra is essential to alleviate the existent bottleneck of low mass spectra identification rate. However, most of the traditional computational methods are not suitable for interpreting mixture spectra, because they still take the assumption that the acquired spectra come from the fragmentation of a single precursor. In this manuscript, we formulate the mixture spectra de novo sequencing problem mathematically, and propose a dynamic programming algorithm for the problem. Additionally, we use both simulated and real mixture spectra data sets to verify the merits of the proposed algorithm.

Instrumentation, performance visualization, and debugging tools for multiprocessors

NASA Technical Reports Server (NTRS)

Yan, Jerry C.; Fineman, Charles E.; Hontalas, Philip J.

1991-01-01

The need for computing power has forced a migration from serial computation on a single processor to parallel processing on multiprocessor architectures. However, without effective means to monitor (and visualize) program execution, debugging, and tuning parallel programs becomes intractably difficult as program complexity increases with the number of processors. Research on performance evaluation tools for multiprocessors is being carried out at ARC. Besides investigating new techniques for instrumenting, monitoring, and presenting the state of parallel program execution in a coherent and user-friendly manner, prototypes of software tools are being incorporated into the run-time environments of various hardware testbeds to evaluate their impact on user productivity. Our current tool set, the Ames Instrumentation Systems (AIMS), incorporates features from various software systems developed in academia and industry. The execution of FORTRAN programs on the Intel iPSC/860 can be automatically instrumented and monitored. Performance data collected in this manner can be displayed graphically on workstations supporting X-Windows. We have successfully compared various parallel algorithms for computational fluid dynamics (CFD) applications in collaboration with scientists from the Numerical Aerodynamic Simulation Systems Division. By performing these comparisons, we show that performance monitors and debuggers such as AIMS are practical and can illuminate the complex dynamics that occur within parallel programs.

A self-learning rule base for command following in dynamical systems

NASA Technical Reports Server (NTRS)

Tsai, Wei K.; Lee, Hon-Mun; Parlos, Alexander

1992-01-01

In this paper, a self-learning Rule Base for command following in dynamical systems is presented. The learning is accomplished though reinforcement learning using an associative memory called SAM. The main advantage of SAM is that it is a function approximator with explicit storage of training samples. A learning algorithm patterned after the dynamic programming is proposed. Two artificially created, unstable dynamical systems are used for testing, and the Rule Base was used to generate a feedback control to improve the command following ability of the otherwise uncontrolled systems. The numerical results are very encouraging. The controlled systems exhibit a more stable behavior and a better capability to follow reference commands. The rules resulting from the reinforcement learning are explicitly stored and they can be modified or augmented by human experts. Due to overlapping storage scheme of SAM, the stored rules are similar to fuzzy rules.

The modern temperature-accelerated dynamics approach

DOE PAGES

Zamora, Richard J.; Uberuaga, Blas P.; Perez, Danny; ...

2016-06-01

Accelerated molecular dynamics (AMD) is a class of MD-based methods used to simulate atomistic systems in which the metastable state-to-state evolution is slow compared with thermal vibrations. Temperature-accelerated dynamics (TAD) is a particularly efficient AMD procedure in which the predicted evolution is hastened by elevating the temperature of the system and then recovering the correct state-to-state dynamics at the temperature of interest. TAD has been used to study various materials applications, often revealing surprising behavior beyond the reach of direct MD. This success has inspired several algorithmic performance enhancements, as well as the analysis of its mathematical framework. Recently, thesemore » enhancements have leveraged parallel programming techniques to enhance both the spatial and temporal scaling of the traditional approach. Here, we review the ongoing evolution of the modern TAD method and introduce the latest development: speculatively parallel TAD.« less

ISCFD Nagoya 1989 - International Symposium on Computational Fluid Dynamics, 3rd, Nagoya, Japan, Aug. 28-31, 1989, Technical Papers

NASA Astrophysics Data System (ADS)

Recent advances in computational fluid dynamics are discussed in reviews and reports. Topics addressed include large-scale LESs for turbulent pipe and channel flows, numerical solutions of the Euler and Navier-Stokes equations on parallel computers, multigrid methods for steady high-Reynolds-number flow past sudden expansions, finite-volume methods on unstructured grids, supersonic wake flow on a blunt body, a grid-characteristic method for multidimensional gas dynamics, and CIC numerical simulation of a wave boundary layer. Consideration is given to vortex simulations of confined two-dimensional jets, supersonic viscous shear layers, spectral methods for compressible flows, shock-wave refraction at air/water interfaces, oscillatory flow in a two-dimensional collapsible channel, the growth of randomness in a spatially developing wake, and an efficient simplex algorithm for the finite-difference and dynamic linear-programming method in optimal potential control.

DYNA3D: A computer code for crashworthiness engineering

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

Hallquist, J.O.; Benson, D.J.

1986-09-01

A finite element program with crashworthiness applications has been developed at LLNL. DYNA3D, an explicit, fully vectorized, finite deformation structural dynamics program, has four capabilities that are critical for the efficient and realistic modeling crash phenomena: (1) fully optimized nonlinear solid, shell, and beam elements for representing a structure; (2) a broad range of constitutive models for simulating material behavior; (3) sophisticated contact algorithms for impact interactions; (4) a rigid body capability to represent the bodies away from the impact region at a greatly reduced cost without sacrificing accuracy in the momentum calculations. Basic methodologies of the program are brieflymore » presented along with several crashworthiness calculations. Efficiencies of the Hughes-Liu and Belytschko-Tsay shell formulations are considered.« less

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.

Evaluation of dynamically dimensioned search algorithm for optimizing SWAT by altering sampling distributions and searching range

USDA-ARS?s Scientific Manuscript database

The primary advantage of Dynamically Dimensioned Search algorithm (DDS) is that it outperforms many other optimization techniques in both convergence speed and the ability in searching for parameter sets that satisfy statistical guidelines while requiring only one algorithm parameter (perturbation f...

A biologically inspired neural network model to transformation invariant object recognition

NASA Astrophysics Data System (ADS)

Iftekharuddin, Khan M.; Li, Yaqin; Siddiqui, Faraz

2007-09-01

Transformation invariant image recognition has been an active research area due to its widespread applications in a variety of fields such as military operations, robotics, medical practices, geographic scene analysis, and many others. The primary goal for this research is detection of objects in the presence of image transformations such as changes in resolution, rotation, translation, scale and occlusion. We investigate a biologically-inspired neural network (NN) model for such transformation-invariant object recognition. In a classical training-testing setup for NN, the performance is largely dependent on the range of transformation or orientation involved in training. However, an even more serious dilemma is that there may not be enough training data available for successful learning or even no training data at all. To alleviate this problem, a biologically inspired reinforcement learning (RL) approach is proposed. In this paper, the RL approach is explored for object recognition with different types of transformations such as changes in scale, size, resolution and rotation. The RL is implemented in an adaptive critic design (ACD) framework, which approximates the neuro-dynamic programming of an action network and a critic network, respectively. Two ACD algorithms such as Heuristic Dynamic Programming (HDP) and Dual Heuristic dynamic Programming (DHP) are investigated to obtain transformation invariant object recognition. The two learning algorithms are evaluated statistically using simulated transformations in images as well as with a large-scale UMIST face database with pose variations. In the face database authentication case, the 90° out-of-plane rotation of faces from 20 different subjects in the UMIST database is used. Our simulations show promising results for both designs for transformation-invariant object recognition and authentication of faces. Comparing the two algorithms, DHP outperforms HDP in learning capability, as DHP takes fewer steps to perform a successful recognition task in general. Further, the residual critic error in DHP is generally smaller than that of HDP, and DHP achieves a 100% success rate more frequently than HDP for individual objects/subjects. On the other hand, HDP is more robust than the DHP as far as success rate across the database is concerned when applied in a stochastic and uncertain environment, and the computational time involved in DHP is more.

Lifted worm algorithm for the Ising model

NASA Astrophysics Data System (ADS)

Elçi, Eren Metin; Grimm, Jens; Ding, Lijie; Nasrawi, Abrahim; Garoni, Timothy M.; Deng, Youjin

2018-04-01

We design an irreversible worm algorithm for the zero-field ferromagnetic Ising model by using the lifting technique. We study the dynamic critical behavior of an energylike observable on both the complete graph and toroidal grids, and compare our findings with reversible algorithms such as the Prokof'ev-Svistunov worm algorithm. Our results show that the lifted worm algorithm improves the dynamic exponent of the energylike observable on the complete graph and leads to a significant constant improvement on toroidal grids.

Computationally Efficient Multiconfigurational Reactive Molecular Dynamics

PubMed Central

Yamashita, Takefumi; Peng, Yuxing; Knight, Chris; Voth, Gregory A.

2012-01-01

It is a computationally demanding task to explicitly simulate the electronic degrees of freedom in a system to observe the chemical transformations of interest, while at the same time sampling the time and length scales required to converge statistical properties and thus reduce artifacts due to initial conditions, finite-size effects, and limited sampling. One solution that significantly reduces the computational expense consists of molecular models in which effective interactions between particles govern the dynamics of the system. If the interaction potentials in these models are developed to reproduce calculated properties from electronic structure calculations and/or ab initio molecular dynamics simulations, then one can calculate accurate properties at a fraction of the computational cost. Multiconfigurational algorithms model the system as a linear combination of several chemical bonding topologies to simulate chemical reactions, also sometimes referred to as “multistate”. These algorithms typically utilize energy and force calculations already found in popular molecular dynamics software packages, thus facilitating their implementation without significant changes to the structure of the code. However, the evaluation of energies and forces for several bonding topologies per simulation step can lead to poor computational efficiency if redundancy is not efficiently removed, particularly with respect to the calculation of long-ranged Coulombic interactions. This paper presents accurate approximations (effective long-range interaction and resulting hybrid methods) and multiple-program parallelization strategies for the efficient calculation of electrostatic interactions in reactive molecular simulations. PMID:25100924

Comparing, optimizing, and benchmarking quantum-control algorithms in a unifying programming framework

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

Machnes, S.; Institute for Theoretical Physics, University of Ulm, D-89069 Ulm; Sander, U.

2011-08-15

For paving the way to novel applications in quantum simulation, computation, and technology, increasingly large quantum systems have to be steered with high precision. It is a typical task amenable to numerical optimal control to turn the time course of pulses, i.e., piecewise constant control amplitudes, iteratively into an optimized shape. Here, we present a comparative study of optimal-control algorithms for a wide range of finite-dimensional applications. We focus on the most commonly used algorithms: GRAPE methods which update all controls concurrently, and Krotov-type methods which do so sequentially. Guidelines for their use are given and open research questions aremore » pointed out. Moreover, we introduce a unifying algorithmic framework, DYNAMO (dynamic optimization platform), designed to provide the quantum-technology community with a convenient matlab-based tool set for optimal control. In addition, it gives researchers in optimal-control techniques a framework for benchmarking and comparing newly proposed algorithms with the state of the art. It allows a mix-and-match approach with various types of gradients, update and step-size methods as well as subspace choices. Open-source code including examples is made available at http://qlib.info.« less

Time series modeling by a regression approach based on a latent process.

PubMed

Chamroukhi, Faicel; Samé, Allou; Govaert, Gérard; Aknin, Patrice

2009-01-01

Time series are used in many domains including finance, engineering, economics and bioinformatics generally to represent the change of a measurement over time. Modeling techniques may then be used to give a synthetic representation of such data. A new approach for time series modeling is proposed in this paper. It consists of a regression model incorporating a discrete hidden logistic process allowing for activating smoothly or abruptly different polynomial regression models. The model parameters are estimated by the maximum likelihood method performed by a dedicated Expectation Maximization (EM) algorithm. The M step of the EM algorithm uses a multi-class Iterative Reweighted Least-Squares (IRLS) algorithm to estimate the hidden process parameters. To evaluate the proposed approach, an experimental study on simulated data and real world data was performed using two alternative approaches: a heteroskedastic piecewise regression model using a global optimization algorithm based on dynamic programming, and a Hidden Markov Regression Model whose parameters are estimated by the Baum-Welch algorithm. Finally, in the context of the remote monitoring of components of the French railway infrastructure, and more particularly the switch mechanism, the proposed approach has been applied to modeling and classifying time series representing the condition measurements acquired during switch operations.

Brain-Inspired Constructive Learning Algorithms with Evolutionally Additive Nonlinear Neurons

NASA Astrophysics Data System (ADS)

Fang, Le-Heng; Lin, Wei; Luo, Qiang

In this article, inspired partially by the physiological evidence of brain’s growth and development, we developed a new type of constructive learning algorithm with evolutionally additive nonlinear neurons. The new algorithms have remarkable ability in effective regression and accurate classification. In particular, the algorithms are able to sustain a certain reduction of the loss function when the dynamics of the trained network are bogged down in the vicinity of the local minima. The algorithm augments the neural network by adding only a few connections as well as neurons whose activation functions are nonlinear, nonmonotonic, and self-adapted to the dynamics of the loss functions. Indeed, we analytically demonstrate the reduction dynamics of the algorithm for different problems, and further modify the algorithms so as to obtain an improved generalization capability for the augmented neural networks. Finally, through comparing with the classical algorithm and architecture for neural network construction, we show that our constructive learning algorithms as well as their modified versions have better performances, such as faster training speed and smaller network size, on several representative benchmark datasets including the MNIST dataset for handwriting digits.

Dynamic Transfers Of Tasks Among Computers

NASA Technical Reports Server (NTRS)

Liu, Howard T.; Silvester, John A.

1989-01-01

Allocation scheme gives jobs to idle computers. Ideal resource-sharing algorithm should have following characteristics: Dynamics, decentralized, and heterogeneous. Proposed enhanced receiver-initiated dynamic algorithm (ERIDA) for resource sharing fulfills all above criteria. Provides method balancing workload among hosts, resulting in improvement in response time and throughput performance of total system. Adjusts dynamically to traffic load of each station.

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.

Minimizing the average distance to a closest leaf in a phylogenetic tree.

PubMed

Matsen, Frederick A; Gallagher, Aaron; McCoy, Connor O

2013-11-01

When performing an analysis on a collection of molecular sequences, it can be convenient to reduce the number of sequences under consideration while maintaining some characteristic of a larger collection of sequences. For example, one may wish to select a subset of high-quality sequences that represent the diversity of a larger collection of sequences. One may also wish to specialize a large database of characterized "reference sequences" to a smaller subset that is as close as possible on average to a collection of "query sequences" of interest. Such a representative subset can be useful whenever one wishes to find a set of reference sequences that is appropriate to use for comparative analysis of environmentally derived sequences, such as for selecting "reference tree" sequences for phylogenetic placement of metagenomic reads. In this article, we formalize these problems in terms of the minimization of the Average Distance to the Closest Leaf (ADCL) and investigate algorithms to perform the relevant minimization. We show that the greedy algorithm is not effective, show that a variant of the Partitioning Around Medoids (PAM) heuristic gets stuck in local minima, and develop an exact dynamic programming approach. Using this exact program we note that the performance of PAM appears to be good for simulated trees, and is faster than the exact algorithm for small trees. On the other hand, the exact program gives solutions for all numbers of leaves less than or equal to the given desired number of leaves, whereas PAM only gives a solution for the prespecified number of leaves. Via application to real data, we show that the ADCL criterion chooses chimeric sequences less often than random subsets, whereas the maximization of phylogenetic diversity chooses them more often than random. These algorithms have been implemented in publicly available software.

Cognitive programs: software for attention's executive

PubMed Central

Tsotsos, John K.; Kruijne, Wouter

2014-01-01

What are the computational tasks that an executive controller for visual attention must solve? This question is posed in the context of the Selective Tuning model of attention. The range of required computations go beyond top-down bias signals or region-of-interest determinations, and must deal with overt and covert fixations, process timing and synchronization, information routing, memory, matching control to task, spatial localization, priming, and coordination of bottom-up with top-down information. During task execution, results must be monitored to ensure the expected results. This description includes the kinds of elements that are common in the control of any kind of complex machine or system. We seek a mechanistic integration of the above, in other words, algorithms that accomplish control. Such algorithms operate on representations, transforming a representation of one kind into another, which then forms the input to yet another algorithm. Cognitive Programs (CPs) are hypothesized to capture exactly such representational transformations via stepwise sequences of operations. CPs, an updated and modernized offspring of Ullman's Visual Routines, impose an algorithmic structure to the set of attentional functions and play a role in the overall shaping of attentional modulation of the visual system so that it provides its best performance. This requires that we consider the visual system as a dynamic, yet general-purpose processor tuned to the task and input of the moment. This differs dramatically from the almost universal cognitive and computational views, which regard vision as a passively observing module to which simple questions about percepts can be posed, regardless of task. Differing from Visual Routines, CPs explicitly involve the critical elements of Visual Task Executive (vTE), Visual Attention Executive (vAE), and Visual Working Memory (vWM). Cognitive Programs provide the software that directs the actions of the Selective Tuning model of visual attention. PMID:25505430

Analysis of shadowing effects on spacecraft power systems

NASA Technical Reports Server (NTRS)

Fincannon, H. J.

1995-01-01

This paper describes the Orbiting Spacecraft Shadowing Analysis (OSSA) computer program that was developed at NASA Lewis Research Center in order to assess the shadowing effects on various power systems. The algorithms, inputs and outputs are discussed. Examples of typical shadowing analyses that have been performed for the International Space Station Freedom, International Space Station Alpha and the joint United States/Russian Mir Solar Dynamic Flight Experiment Project are covered. Effects of shadowing on power systems are demonstrated.

Molecular Dynamics Simulations of Hugoniot Relations for Poly[methyl methacrylate

DTIC Science & Technology

2011-11-01

A Method for Atomistic Simulations of Shocked Materials. Physical Review E 2000, 63, 016121. 5. Plimpton , S . Fast Parallel Algorithms for Short...5c. PROGRAM ELEMENT NUMBER 6. AUTHOR( S ) Tanya L. Chantawansri, Edward F. C. Byrd, Betsy M. Rice, and Jan W. Andzelm 5d. PROJECT NUMBER 5e. TASK...NUMBER 5f. WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME( S ) AND ADDRESS(ES) U.S. Army Research Laboratory ATTN: RDRL-WMM-G Aberdeen

Kernel-Based Approximate Dynamic Programming Using Bellman Residual Elimination

DTIC Science & Technology

2010-02-01

framework is the ability to utilize stochastic system models, thereby allowing the system to make sound decisions even if there is randomness in the system ...approximate policy when a system model is unavailable. We present theoretical analysis of all BRE algorithms proving convergence to the optimal policy in...policies based on MDPs is that there may be parameters of the system model that are poorly known and/or vary with time as the system operates. System

Dynamic Grover search: applications in recommendation systems and optimization problems

NASA Astrophysics Data System (ADS)

Chakrabarty, Indranil; Khan, Shahzor; Singh, Vanshdeep

2017-06-01

In the recent years, we have seen that Grover search algorithm (Proceedings, 28th annual ACM symposium on the theory of computing, pp. 212-219, 1996) by using quantum parallelism has revolutionized the field of solving huge class of NP problems in comparisons to classical systems. In this work, we explore the idea of extending Grover search algorithm to approximate algorithms. Here we try to analyze the applicability of Grover search to process an unstructured database with a dynamic selection function in contrast to the static selection function used in the original work (Grover in Proceedings, 28th annual ACM symposium on the theory of computing, pp. 212-219, 1996). We show that this alteration facilitates us to extend the application of Grover search to the field of randomized search algorithms. Further, we use the dynamic Grover search algorithm to define the goals for a recommendation system based on which we propose a recommendation algorithm which uses binomial similarity distribution space giving us a quadratic speedup over traditional classical unstructured recommendation systems. Finally, we see how dynamic Grover search can be used to tackle a wide range of optimization problems where we improve complexity over existing optimization algorithms.

Segmentation of whole cells and cell nuclei from 3-D optical microscope images using dynamic programming.

PubMed

McCullough, D P; Gudla, P R; Harris, B S; Collins, J A; Meaburn, K J; Nakaya, M A; Yamaguchi, T P; Misteli, T; Lockett, S J

2008-05-01

Communications between cells in large part drive tissue development and function, as well as disease-related processes such as tumorigenesis. Understanding the mechanistic bases of these processes necessitates quantifying specific molecules in adjacent cells or cell nuclei of intact tissue. However, a major restriction on such analyses is the lack of an efficient method that correctly segments each object (cell or nucleus) from 3-D images of an intact tissue specimen. We report a highly reliable and accurate semi-automatic algorithmic method for segmenting fluorescence-labeled cells or nuclei from 3-D tissue images. Segmentation begins with semi-automatic, 2-D object delineation in a user-selected plane, using dynamic programming (DP) to locate the border with an accumulated intensity per unit length greater that any other possible border around the same object. Then the two surfaces of the object in planes above and below the selected plane are found using an algorithm that combines DP and combinatorial searching. Following segmentation, any perceived errors can be interactively corrected. Segmentation accuracy is not significantly affected by intermittent labeling of object surfaces, diffuse surfaces, or spurious signals away from surfaces. The unique strength of the segmentation method was demonstrated on a variety of biological tissue samples where all cells, including irregularly shaped cells, were accurately segmented based on visual inspection.

Slope Estimation in Noisy Piecewise Linear Functions✩

PubMed Central

Ingle, Atul; Bucklew, James; Sethares, William; Varghese, Tomy

2014-01-01

This paper discusses the development of a slope estimation algorithm called MAPSlope for piecewise linear data that is corrupted by Gaussian noise. The number and locations of slope change points (also known as breakpoints) are assumed to be unknown a priori though it is assumed that the possible range of slope values lies within known bounds. A stochastic hidden Markov model that is general enough to encompass real world sources of piecewise linear data is used to model the transitions between slope values and the problem of slope estimation is addressed using a Bayesian maximum a posteriori approach. The set of possible slope values is discretized, enabling the design of a dynamic programming algorithm for posterior density maximization. Numerical simulations are used to justify choice of a reasonable number of quantization levels and also to analyze mean squared error performance of the proposed algorithm. An alternating maximization algorithm is proposed for estimation of unknown model parameters and a convergence result for the method is provided. Finally, results using data from political science, finance and medical imaging applications are presented to demonstrate the practical utility of this procedure. PMID:25419020

Slope Estimation in Noisy Piecewise Linear Functions.

PubMed

Ingle, Atul; Bucklew, James; Sethares, William; Varghese, Tomy

2015-03-01

This paper discusses the development of a slope estimation algorithm called MAPSlope for piecewise linear data that is corrupted by Gaussian noise. The number and locations of slope change points (also known as breakpoints) are assumed to be unknown a priori though it is assumed that the possible range of slope values lies within known bounds. A stochastic hidden Markov model that is general enough to encompass real world sources of piecewise linear data is used to model the transitions between slope values and the problem of slope estimation is addressed using a Bayesian maximum a posteriori approach. The set of possible slope values is discretized, enabling the design of a dynamic programming algorithm for posterior density maximization. Numerical simulations are used to justify choice of a reasonable number of quantization levels and also to analyze mean squared error performance of the proposed algorithm. An alternating maximization algorithm is proposed for estimation of unknown model parameters and a convergence result for the method is provided. Finally, results using data from political science, finance and medical imaging applications are presented to demonstrate the practical utility of this procedure.

Improved Seam-Line Searching Algorithm for UAV Image Mosaic with Optical Flow

PubMed Central

Zhang, Weilong; Guo, Bingxuan; Liao, Xuan; Li, Wenzhuo

2018-01-01

Ghosting and seams are two major challenges in creating unmanned aerial vehicle (UAV) image mosaic. In response to these problems, this paper proposes an improved method for UAV image seam-line searching. First, an image matching algorithm is used to extract and match the features of adjacent images, so that they can be transformed into the same coordinate system. Then, the gray scale difference, the gradient minimum, and the optical flow value of pixels in adjacent image overlapped area in a neighborhood are calculated, which can be applied to creating an energy function for seam-line searching. Based on that, an improved dynamic programming algorithm is proposed to search the optimal seam-lines to complete the UAV image mosaic. This algorithm adopts a more adaptive energy aggregation and traversal strategy, which can find a more ideal splicing path for adjacent UAV images and avoid the ground objects better. The experimental results show that the proposed method can effectively solve the problems of ghosting and seams in the panoramic UAV images. PMID:29659526

Improved genetic algorithm for the protein folding problem by use of a Cartesian combination operator.

PubMed Central

Rabow, A. A.; Scheraga, H. A.

1996-01-01

We have devised a Cartesian combination operator and coding scheme for improving the performance of genetic algorithms applied to the protein folding problem. The genetic coding consists of the C alpha Cartesian coordinates of the protein chain. The recombination of the genes of the parents is accomplished by: (1) a rigid superposition of one parent chain on the other, to make the relation of Cartesian coordinates meaningful, then, (2) the chains of the children are formed through a linear combination of the coordinates of their parents. The children produced with this Cartesian combination operator scheme have similar topology and retain the long-range contacts of their parents. The new scheme is significantly more efficient than the standard genetic algorithm methods for locating low-energy conformations of proteins. The considerable superiority of genetic algorithms over Monte Carlo optimization methods is also demonstrated. We have also devised a new dynamic programming lattice fitting procedure for use with the Cartesian combination operator method. The procedure finds excellent fits of real-space chains to the lattice while satisfying bond-length, bond-angle, and overlap constraints. PMID:8880904

TH-EF-BRB-04: 4π Dynamic Conformal Arc Therapy Dynamic Conformal Arc Therapy (DCAT) for SBRT

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

Chiu, T; Long, T; Tian, Z.

2016-06-15

Purpose: To develop an efficient and effective trajectory optimization methodology for 4π dynamic conformal arc treatment (4π DCAT) with synchronized gantry and couch motion; and to investigate potential clinical benefits for stereotactic body radiation therapy (SBRT) to breast, lung, liver and spine tumors. Methods: The entire optimization framework for 4π DCAT inverse planning consists of two parts: 1) integer programming algorithm and 2) particle swarm optimization (PSO) algorithm. The integer programming is designed to find an optimal solution for arc delivery trajectory with both couch and gantry rotation, while PSO minimize a non-convex objective function based on the selected trajectorymore » and dose-volume constraints. In this study, control point interaction is explicitly taken into account. Beam trajectory was modeled as a series of control points connected together to form a deliverable path. With linear treatment planning objectives, a mixed-integer program (MIP) was formulated. Under mild assumptions, the MIP is tractable. Assigning monitor units to control points along the path can be integrated into the model and done by PSO. The developed 4π DCAT inverse planning strategy is evaluated on SBRT cases and compared to clinically treated plans. Results: The resultant dose distribution of this technique was evaluated between 3D conformal treatment plan generated by Pinnacle treatment planning system and 4π DCAT on a lung SBRT patient case. Both plans share the same scale of MU, 3038 and 2822 correspondingly to 3D conformal plan and 4π DCAT. The mean doses for most of OARs were greatly reduced at 32% (cord), 70% (esophagus), 2.8% (lung) and 42.4% (stomach). Conclusion: Initial results in this study show the proposed 4π DCAT treatment technique can achieve better OAR sparing and lower MUs, which indicates that the developed technique is promising for high dose SBRT to reduce the risk of secondary cancer.« less

Analyzing gene expression time-courses based on multi-resolution shape mixture model.

PubMed

Li, Ying; He, Ye; Zhang, Yu

2016-11-01

Biological processes actually are a dynamic molecular process over time. Time course gene expression experiments provide opportunities to explore patterns of gene expression change over a time and understand the dynamic behavior of gene expression, which is crucial for study on development and progression of biology and disease. Analysis of the gene expression time-course profiles has not been fully exploited so far. It is still a challenge problem. We propose a novel shape-based mixture model clustering method for gene expression time-course profiles to explore the significant gene groups. Based on multi-resolution fractal features and mixture clustering model, we proposed a multi-resolution shape mixture model algorithm. Multi-resolution fractal features is computed by wavelet decomposition, which explore patterns of change over time of gene expression at different resolution. Our proposed multi-resolution shape mixture model algorithm is a probabilistic framework which offers a more natural and robust way of clustering time-course gene expression. We assessed the performance of our proposed algorithm using yeast time-course gene expression profiles compared with several popular clustering methods for gene expression profiles. The grouped genes identified by different methods are evaluated by enrichment analysis of biological pathways and known protein-protein interactions from experiment evidence. The grouped genes identified by our proposed algorithm have more strong biological significance. A novel multi-resolution shape mixture model algorithm based on multi-resolution fractal features is proposed. Our proposed model provides a novel horizons and an alternative tool for visualization and analysis of time-course gene expression profiles. The R and Matlab program is available upon the request. Copyright © 2016 Elsevier Inc. All rights reserved.

Bellman Ford algorithm - in Routing Information Protocol (RIP)

NASA Astrophysics Data System (ADS)

Krianto Sulaiman, Oris; Mahmud Siregar, Amir; Nasution, Khairuddin; Haramaini, Tasliyah

2018-04-01

In a large scale network need a routing that can handle a lot number of users, one of the solutions to cope with large scale network is by using a routing protocol, There are 2 types of routing protocol that is static and dynamic, Static routing is manually route input based on network admin, while dynamic routing is automatically route input formed based on existing network. Dynamic routing is efficient used to network extensively because of the input of route automatic formed, Routing Information Protocol (RIP) is one of dynamic routing that uses the bellman-ford algorithm where this algorithm will search for the best path that traversed the network by leveraging the value of each link, so with the bellman-ford algorithm owned by RIP can optimize existing networks.

MaMiCo: Transient multi-instance molecular-continuum flow simulation on supercomputers

NASA Astrophysics Data System (ADS)

Neumann, Philipp; Bian, Xin

2017-11-01

We present extensions of the macro-micro-coupling tool MaMiCo, which was designed to couple continuum fluid dynamics solvers with discrete particle dynamics. To enable local extraction of smooth flow field quantities especially on rather short time scales, sampling over an ensemble of molecular dynamics simulations is introduced. We provide details on these extensions including the transient coupling algorithm, open boundary forcing, and multi-instance sampling. Furthermore, we validate the coupling in Couette flow using different particle simulation software packages and particle models, i.e. molecular dynamics and dissipative particle dynamics. Finally, we demonstrate the parallel scalability of the molecular-continuum simulations by using up to 65 536 compute cores of the supercomputer Shaheen II located at KAUST. Program Files doi:http://dx.doi.org/10.17632/w7rgdrhb85.1 Licensing provisions: BSD 3-clause Programming language: C, C++ External routines/libraries: For compiling: SCons, MPI (optional) Subprograms used: ESPResSo, LAMMPS, ls1 mardyn, waLBerla For installation procedures of the MaMiCo interfaces, see the README files in the respective code directories located in coupling/interface/impl. Journal reference of previous version: P. Neumann, H. Flohr, R. Arora, P. Jarmatz, N. Tchipev, H.-J. Bungartz. MaMiCo: Software design for parallel molecular-continuum flow simulations, Computer Physics Communications 200: 324-335, 2016 Does the new version supersede the previous version?: Yes. The functionality of the previous version is completely retained in the new version. Nature of problem: Coupled molecular-continuum simulation for multi-resolution fluid dynamics: parts of the domain are resolved by molecular dynamics or another particle-based solver whereas large parts are covered by a mesh-based CFD solver, e.g. a lattice Boltzmann automaton. Solution method: We couple existing MD and CFD solvers via MaMiCo (macro-micro coupling tool). Data exchange and coupling algorithmics are abstracted and incorporated in MaMiCo. Once an algorithm is set up in MaMiCo, it can be used and extended, even if other solvers are used (as soon as the respective interfaces are implemented/available). Reasons for the new version: We have incorporated a new algorithm to simulate transient molecular-continuum systems and to automatically sample data over multiple MD runs that can be executed simultaneously (on, e.g., a compute cluster). MaMiCo has further been extended by an interface to incorporate boundary forcing to account for open molecular dynamics boundaries. Besides support for coupling with various MD and CFD frameworks, the new version contains a test case that allows to run molecular-continuum Couette flow simulations out-of-the-box. No external tools or simulation codes are required anymore. However, the user is free to switch from the included MD simulation package to LAMMPS. For details on how to run the transient Couette problem, see the file README in the folder coupling/tests, Remark on MaMiCo V1.1. Summary of revisions: Open boundary forcing; Multi-instance MD sampling; support for transient molecular-continuum systems Restrictions: Currently, only single-centered systems are supported. For access to the LAMMPS-based implementation of DPD boundary forcing, please contact Xin Bian, xin.bian@tum.de. Additional comments: Please see file license_mamico.txt for further details regarding distribution and advertising of this software.

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.

Dynamic biometric identification from multiple views using the GLBP-TOP method.

PubMed

Wang, Yu; Shen, Xuanjing; Chen, Haipeng; Zhai, Yujie

2014-01-01

To realize effective and rapid dynamic biometric identification with low computational complexity, a video-based facial texture program that extracts local binary patterns from three orthogonal planes in the frequency domain of the Gabor transform (GLBP-TOP) was proposed. Firstly, each normalized face was transformed by Gabor wavelet to get the enhanced Gabor magnitude map, and then the LBP-TOP operator was applied to the maps to extract video texture. Finally, weighted Chi square statistics based on the Fisher Criterion were used to realize the identification. The proposed algorithm was proved effective through the biometric experiments using the Honda/UCSD database, and was robust against changes of illumination and expressions.

Object-oriented sequence analysis: SCL--a C++ class library.

PubMed

Vahrson, W; Hermann, K; Kleffe, J; Wittig, B

1996-04-01

SCL (Sequence Class Library) is a class library written in the C++ programming language. Designed using object-oriented programming principles, SCL consists of classes of objects performing tasks typically needed for analyzing DNA or protein sequences. Among them are very flexible sequence classes, classes accessing databases in various formats, classes managing collections of sequences, as well as classes performing higher-level tasks like calculating a pairwise sequence alignment. SCL also includes classes that provide general programming support, like a dynamically growing array, sets, matrices, strings, classes performing file input/output, and utilities for error handling. By providing these components, SCL fosters an explorative programming style: experimenting with algorithms and alternative implementations is encouraged rather than punished. A description of SCL's overall structure as well as an overview of its classes is given. Important aspects of the work with SCL are discussed in the context of a sample program.

Increasing the power of accelerated molecular dynamics methods and plans to exploit the coming exascale

NASA Astrophysics Data System (ADS)

Voter, Arthur

Many important materials processes take place on time scales that far exceed the roughly one microsecond accessible to molecular dynamics simulation. Typically, this long-time evolution is characterized by a succession of thermally activated infrequent events involving defects in the material. In the accelerated molecular dynamics (AMD) methodology, known characteristics of infrequent-event systems are exploited to make reactive events take place more frequently, in a dynamically correct way. For certain processes, this approach has been remarkably successful, offering a view of complex dynamical evolution on time scales of microseconds, milliseconds, and sometimes beyond. We have recently made advances in all three of the basic AMD methods (hyperdynamics, parallel replica dynamics, and temperature accelerated dynamics (TAD)), exploiting both algorithmic advances and novel parallelization approaches. I will describe these advances, present some examples of our latest results, and discuss what should be possible when exascale computing arrives in roughly five years. Funded by the U.S. Department of Energy, Office of Basic Energy Sciences, Materials Sciences and Engineering Division, and by the Los Alamos Laboratory Directed Research and Development program.

Extending molecular simulation time scales: Parallel in time integrations for high-level quantum chemistry and complex force representations

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

Bylaska, Eric J., E-mail: Eric.Bylaska@pnnl.gov; Weare, Jonathan Q., E-mail: weare@uchicago.edu; Weare, John H., E-mail: jweare@ucsd.edu

2013-08-21

Parallel in time simulation algorithms are presented and applied to conventional molecular dynamics (MD) and ab initio molecular dynamics (AIMD) models of realistic complexity. Assuming that a forward time integrator, f (e.g., Verlet algorithm), is available to propagate the system from time t{sub i} (trajectory positions and velocities x{sub i} = (r{sub i}, v{sub i})) to time t{sub i+1} (x{sub i+1}) by x{sub i+1} = f{sub i}(x{sub i}), the dynamics problem spanning an interval from t{sub 0}…t{sub M} can be transformed into a root finding problem, F(X) = [x{sub i} − f(x{sub (i−1})]{sub i} {sub =1,M} = 0, for themore » trajectory variables. The root finding problem is solved using a variety of root finding techniques, including quasi-Newton and preconditioned quasi-Newton schemes that are all unconditionally convergent. The algorithms are parallelized by assigning a processor to each time-step entry in the columns of F(X). The relation of this approach to other recently proposed parallel in time methods is discussed, and the effectiveness of various approaches to solving the root finding problem is tested. We demonstrate that more efficient dynamical models based on simplified interactions or coarsening time-steps provide preconditioners for the root finding problem. However, for MD and AIMD simulations, such preconditioners are not required to obtain reasonable convergence and their cost must be considered in the performance of the algorithm. The parallel in time algorithms developed are tested by applying them to MD and AIMD simulations of size and complexity similar to those encountered in present day applications. These include a 1000 Si atom MD simulation using Stillinger-Weber potentials, and a HCl + 4H{sub 2}O AIMD simulation at the MP2 level. The maximum speedup ((serial execution time)/(parallel execution time) ) obtained by parallelizing the Stillinger-Weber MD simulation was nearly 3.0. For the AIMD MP2 simulations, the algorithms achieved speedups of up to 14.3. The parallel in time algorithms can be implemented in a distributed computing environment using very slow transmission control protocol/Internet protocol networks. Scripts written in Python that make calls to a precompiled quantum chemistry package (NWChem) are demonstrated to provide an actual speedup of 8.2 for a 2.5 ps AIMD simulation of HCl + 4H{sub 2}O at the MP2/6-31G* level. Implemented in this way these algorithms can be used for long time high-level AIMD simulations at a modest cost using machines connected by very slow networks such as WiFi, or in different time zones connected by the Internet. The algorithms can also be used with programs that are already parallel. Using these algorithms, we are able to reduce the cost of a MP2/6-311++G(2d,2p) simulation that had reached its maximum possible speedup in the parallelization of the electronic structure calculation from 32 s/time step to 6.9 s/time step.« less

NEW IMPROVEMENTS TO MFIRE TO ENHANCE FIRE MODELING CAPABILITIES.

PubMed

Zhou, L; Smith, A C; Yuan, L

2016-06-01

NIOSH's mine fire simulation program, MFIRE, is widely accepted as a standard for assessing and predicting the impact of a fire on the mine ventilation system and the spread of fire contaminants in coal and metal/nonmetal mines, which has been used by U.S. and international companies to simulate fires for planning and response purposes. MFIRE is a dynamic, transient-state, mine ventilation network simulation program that performs normal planning calculations. It can also be used to analyze ventilation networks under thermal and mechanical influence such as changes in ventilation parameters, external influences such as changes in temperature, and internal influences such as a fire. The program output can be used to analyze the effects of these influences on the ventilation system. Since its original development by Michigan Technological University for the Bureau of Mines in the 1970s, several updates have been released over the years. In 2012, NIOSH completed a major redesign and restructuring of the program with the release of MFIRE 3.0. MFIRE's outdated FORTRAN programming language was replaced with an object-oriented C++ language and packaged into a dynamic link library (DLL). However, the MFIRE 3.0 release made no attempt to change or improve the fire modeling algorithms inherited from its previous version, MFIRE 2.20. This paper reports on improvements that have been made to the fire modeling capabilities of MFIRE 3.0 since its release. These improvements include the addition of fire source models of the t-squared fire and heat release rate curve data file, the addition of a moving fire source for conveyor belt fire simulations, improvement of the fire location algorithm, and the identification and prediction of smoke rollback phenomena. All the improvements discussed in this paper will be termed as MFIRE 3.1 and released by NIOSH in the near future.

Faster than Real-Time Dynamic Simulation for Large-Size Power System with Detailed Dynamic Models using High-Performance Computing Platform

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

Huang, Renke; Jin, Shuangshuang; Chen, Yousu

This paper presents a faster-than-real-time dynamic simulation software package that is designed for large-size power system dynamic simulation. It was developed on the GridPACKTM high-performance computing (HPC) framework. The key features of the developed software package include (1) faster-than-real-time dynamic simulation for a WECC system (17,000 buses) with different types of detailed generator, controller, and relay dynamic models, (2) a decoupled parallel dynamic simulation algorithm with optimized computation architecture to better leverage HPC resources and technologies, (3) options for HPC-based linear and iterative solvers, (4) hidden HPC details, such as data communication and distribution, to enable development centered on mathematicalmore » models and algorithms rather than on computational details for power system researchers, and (5) easy integration of new dynamic models and related algorithms into the software package.« less

Self-balancing dynamic scheduling of electrical energy for energy-intensive enterprises

NASA Astrophysics Data System (ADS)

Gao, Yunlong; Gao, Feng; Zhai, Qiaozhu; Guan, Xiaohong

2013-06-01

Balancing production and consumption with self-generation capacity in energy-intensive enterprises has huge economic and environmental benefits. However, balancing production and consumption with self-generation capacity is a challenging task since the energy production and consumption must be balanced in real time with the criteria specified by power grid. In this article, a mathematical model for minimising the production cost with exactly realisable energy delivery schedule is formulated. And a dynamic programming (DP)-based self-balancing dynamic scheduling algorithm is developed to obtain the complete solution set for such a multiple optimal solutions problem. For each stage, a set of conditions are established to determine whether a feasible control trajectory exists. The state space under these conditions is partitioned into subsets and each subset is viewed as an aggregate state, the cost-to-go function is then expressed as a function of initial and terminal generation levels of each stage and is proved to be a staircase function with finite steps. This avoids the calculation of the cost-to-go of every state to resolve the issue of dimensionality in DP algorithm. In the backward sweep process of the algorithm, an optimal policy is determined to maximise the realisability of energy delivery schedule across the entire time horizon. And then in the forward sweep process, the feasible region of the optimal policy with the initial and terminal state at each stage is identified. Different feasible control trajectories can be identified based on the region; therefore, optimising for the feasible control trajectory is performed based on the region with economic and reliability objectives taken into account.

Harnessing graphical structure in Markov chain Monte Carlo learning

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

Stolorz, P.E.; Chew P.C.

1996-12-31

The Monte Carlo method is recognized as a useful tool in learning and probabilistic inference methods common to many datamining problems. Generalized Hidden Markov Models and Bayes nets are especially popular applications. However, the presence of multiple modes in many relevant integrands and summands often renders the method slow and cumbersome. Recent mean field alternatives designed to speed things up have been inspired by experience gleaned from physics. The current work adopts an approach very similar to this in spirit, but focusses instead upon dynamic programming notions as a basis for producing systematic Monte Carlo improvements. The idea is tomore » approximate a given model by a dynamic programming-style decomposition, which then forms a scaffold upon which to build successively more accurate Monte Carlo approximations. Dynamic programming ideas alone fail to account for non-local structure, while standard Monte Carlo methods essentially ignore all structure. However, suitably-crafted hybrids can successfully exploit the strengths of each method, resulting in algorithms that combine speed with accuracy. The approach relies on the presence of significant {open_quotes}local{close_quotes} information in the problem at hand. This turns out to be a plausible assumption for many important applications. Example calculations are presented, and the overall strengths and weaknesses of the approach are discussed.« less

A Graphical User Interface for Software-assisted Tracking of Protein Concentration in Dynamic Cellular Protrusions.

PubMed

Saha, Tanumoy; Rathmann, Isabel; Galic, Milos

2017-07-11

Filopodia are dynamic, finger-like cellular protrusions associated with migration and cell-cell communication. In order to better understand the complex signaling mechanisms underlying filopodial initiation, elongation and subsequent stabilization or retraction, it is crucial to determine the spatio-temporal protein activity in these dynamic structures. To analyze protein function in filopodia, we recently developed a semi-automated tracking algorithm that adapts to filopodial shape-changes, thus allowing parallel analysis of protrusion dynamics and relative protein concentration along the whole filopodial length. Here, we present a detailed step-by-step protocol for optimized cell handling, image acquisition and software analysis. We further provide instructions for the use of optional features during image analysis and data representation, as well as troubleshooting guidelines for all critical steps along the way. Finally, we also include a comparison of the described image analysis software with other programs available for filopodia quantification. Together, the presented protocol provides a framework for accurate analysis of protein dynamics in filopodial protrusions using image analysis software.

Application of Dynamic Analysis in Semi-Analytical Finite Element Method.

PubMed

Liu, Pengfei; Xing, Qinyan; Wang, Dawei; Oeser, Markus

2017-08-30

Analyses of dynamic responses are significantly important for the design, maintenance and rehabilitation of asphalt pavement. In order to evaluate the dynamic responses of asphalt pavement under moving loads, a specific computational program, SAFEM, was developed based on a semi-analytical finite element method. This method is three-dimensional and only requires a two-dimensional FE discretization by incorporating Fourier series in the third dimension. In this paper, the algorithm to apply the dynamic analysis to SAFEM was introduced in detail. Asphalt pavement models under moving loads were built in the SAFEM and commercial finite element software ABAQUS to verify the accuracy and efficiency of the SAFEM. The verification shows that the computational accuracy of SAFEM is high enough and its computational time is much shorter than ABAQUS. Moreover, experimental verification was carried out and the prediction derived from SAFEM is consistent with the measurement. Therefore, the SAFEM is feasible to reliably predict the dynamic response of asphalt pavement under moving loads, thus proving beneficial to road administration in assessing the pavement's state.

Dynamic Flow Management Problems in Air Transportation

NASA Technical Reports Server (NTRS)

Patterson, Sarah Stock

1997-01-01

In 1995, over six hundred thousand licensed pilots flew nearly thirty-five million flights into over eighteen thousand U.S. airports, logging more than 519 billion passenger miles. Since demand for air travel has increased by more than 50% in the last decade while capacity has stagnated, congestion is a problem of undeniable practical significance. In this thesis, we will develop optimization techniques that reduce the impact of congestion on the national airspace. We start by determining the optimal release times for flights into the airspace and the optimal speed adjustment while airborne taking into account the capacitated airspace. This is called the Air Traffic Flow Management Problem (TFMP). We address the complexity, showing that it is NP-hard. We build an integer programming formulation that is quite strong as some of the proposed inequalities are facet defining for the convex hull of solutions. For practical problems, the solutions of the LP relaxation of the TFMP are very often integral. In essence, we reduce the problem to efficiently solving large scale linear programming problems. Thus, the computation times are reasonably small for large scale, practical problems involving thousands of flights. Next, we address the problem of determining how to reroute aircraft in the airspace system when faced with dynamically changing weather conditions. This is called the Air Traffic Flow Management Rerouting Problem (TFMRP) We present an integrated mathematical programming approach for the TFMRP, which utilizes several methodologies, in order to minimize delay costs. In order to address the high dimensionality, we present an aggregate model, in which we formulate the TFMRP as a multicommodity, integer, dynamic network flow problem with certain side constraints. Using Lagrangian relaxation, we generate aggregate flows that are decomposed into a collection of flight paths using a randomized rounding heuristic. This collection of paths is used in a packing integer programming formulation, the solution of which generates feasible and near-optimal routes for individual flights. The algorithm, termed the Lagrangian Generation Algorithm, is used to solve practical problems in the southwestern portion of United States in which the solutions are within 1% of the corresponding lower bounds.

Grid integration and smart grid implementation of emerging technologies in electric power systems through approximate dynamic programming

NASA Astrophysics Data System (ADS)

Xiao, Jingjie

A key hurdle for implementing real-time pricing of electricity is a lack of consumers' responses. Solutions to overcome the hurdle include the energy management system that automatically optimizes household appliance usage such as plug-in hybrid electric vehicle charging (and discharging with vehicle-to-grid) via a two-way communication with the grid. Real-time pricing, combined with household automation devices, has a potential to accommodate an increasing penetration of plug-in hybrid electric vehicles. In addition, the intelligent energy controller on the consumer-side can help increase the utilization rate of the intermittent renewable resource, as the demand can be managed to match the output profile of renewables, thus making the intermittent resource such as wind and solar more economically competitive in the long run. One of the main goals of this dissertation is to present how real-time retail pricing, aided by control automation devices, can be integrated into the wholesale electricity market under various uncertainties through approximate dynamic programming. What distinguishes this study from the existing work in the literature is that whole- sale electricity prices are endogenously determined as we solve a system operator's economic dispatch problem on an hourly basis over the entire optimization horizon. This modeling and algorithm framework will allow a feedback loop between electricity prices and electricity consumption to be fully captured. While we are interested in a near-optimal solution using approximate dynamic programming; deterministic linear programming benchmarks are use to demonstrate the quality of our solutions. The other goal of the dissertation is to use this framework to provide numerical evidence to the debate on whether real-time pricing is superior than the current flat rate structure in terms of both economic and environmental impacts. For this purpose, the modeling and algorithm framework is tested on a large-scale test case with hundreds of power plants based on data available for California, making our findings useful for policy makers, system operators and utility companies to gain a concrete understanding on the scale of the impact with real-time pricing.

MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions

NASA Astrophysics Data System (ADS)

Novosad, Philip; Reader, Andrew J.

2016-06-01

Recent advances in dynamic positron emission tomography (PET) reconstruction have demonstrated that it is possible to achieve markedly improved end-point kinetic parameter maps by incorporating a temporal model of the radiotracer directly into the reconstruction algorithm. In this work we have developed a highly constrained, fully dynamic PET reconstruction algorithm incorporating both spectral analysis temporal basis functions and spatial basis functions derived from the kernel method applied to a co-registered T1-weighted magnetic resonance (MR) image. The dynamic PET image is modelled as a linear combination of spatial and temporal basis functions, and a maximum likelihood estimate for the coefficients can be found using the expectation-maximization (EM) algorithm. Following reconstruction, kinetic fitting using any temporal model of interest can be applied. Based on a BrainWeb T1-weighted MR phantom, we performed a realistic dynamic [18F]FDG simulation study with two noise levels, and investigated the quantitative performance of the proposed reconstruction algorithm, comparing it with reconstructions incorporating either spectral analysis temporal basis functions alone or kernel spatial basis functions alone, as well as with conventional frame-independent reconstruction. Compared to the other reconstruction algorithms, the proposed algorithm achieved superior performance, offering a decrease in spatially averaged pixel-level root-mean-square-error on post-reconstruction kinetic parametric maps in the grey/white matter, as well as in the tumours when they were present on the co-registered MR image. When the tumours were not visible in the MR image, reconstruction with the proposed algorithm performed similarly to reconstruction with spectral temporal basis functions and was superior to both conventional frame-independent reconstruction and frame-independent reconstruction with kernel spatial basis functions. Furthermore, we demonstrate that a joint spectral/kernel model can also be used for effective post-reconstruction denoising, through the use of an EM-like image-space algorithm. Finally, we applied the proposed algorithm to reconstruction of real high-resolution dynamic [11C]SCH23390 data, showing promising results.

MR-guided dynamic PET reconstruction with the kernel method and spectral temporal basis functions.

PubMed

Novosad, Philip; Reader, Andrew J

2016-06-21

Recent advances in dynamic positron emission tomography (PET) reconstruction have demonstrated that it is possible to achieve markedly improved end-point kinetic parameter maps by incorporating a temporal model of the radiotracer directly into the reconstruction algorithm. In this work we have developed a highly constrained, fully dynamic PET reconstruction algorithm incorporating both spectral analysis temporal basis functions and spatial basis functions derived from the kernel method applied to a co-registered T1-weighted magnetic resonance (MR) image. The dynamic PET image is modelled as a linear combination of spatial and temporal basis functions, and a maximum likelihood estimate for the coefficients can be found using the expectation-maximization (EM) algorithm. Following reconstruction, kinetic fitting using any temporal model of interest can be applied. Based on a BrainWeb T1-weighted MR phantom, we performed a realistic dynamic [(18)F]FDG simulation study with two noise levels, and investigated the quantitative performance of the proposed reconstruction algorithm, comparing it with reconstructions incorporating either spectral analysis temporal basis functions alone or kernel spatial basis functions alone, as well as with conventional frame-independent reconstruction. Compared to the other reconstruction algorithms, the proposed algorithm achieved superior performance, offering a decrease in spatially averaged pixel-level root-mean-square-error on post-reconstruction kinetic parametric maps in the grey/white matter, as well as in the tumours when they were present on the co-registered MR image. When the tumours were not visible in the MR image, reconstruction with the proposed algorithm performed similarly to reconstruction with spectral temporal basis functions and was superior to both conventional frame-independent reconstruction and frame-independent reconstruction with kernel spatial basis functions. Furthermore, we demonstrate that a joint spectral/kernel model can also be used for effective post-reconstruction denoising, through the use of an EM-like image-space algorithm. Finally, we applied the proposed algorithm to reconstruction of real high-resolution dynamic [(11)C]SCH23390 data, showing promising results.

An Extended Deterministic Dendritic Cell Algorithm for Dynamic Job Shop Scheduling

NASA Astrophysics Data System (ADS)

Qiu, X. N.; Lau, H. Y. K.

The problem of job shop scheduling in a dynamic environment where random perturbation exists in the system is studied. In this paper, an extended deterministic Dendritic Cell Algorithm (dDCA) is proposed to solve such a dynamic Job Shop Scheduling Problem (JSSP) where unexpected events occurred randomly. This algorithm is designed based on dDCA and makes improvements by considering all types of signals and the magnitude of the output values. To evaluate this algorithm, ten benchmark problems are chosen and different kinds of disturbances are injected randomly. The results show that the algorithm performs competitively as it is capable of triggering the rescheduling process optimally with much less run time for deciding the rescheduling action. As such, the proposed algorithm is able to minimize the rescheduling times under the defined objective and to keep the scheduling process stable and efficient.

Efficient characterisation of large deviations using population dynamics

NASA Astrophysics Data System (ADS)

Brewer, Tobias; Clark, Stephen R.; Bradford, Russell; Jack, Robert L.

2018-05-01

We consider population dynamics as implemented by the cloning algorithm for analysis of large deviations of time-averaged quantities. We use the simple symmetric exclusion process with periodic boundary conditions as a prototypical example and investigate the convergence of the results with respect to the algorithmic parameters, focussing on the dynamical phase transition between homogeneous and inhomogeneous states, where convergence is relatively difficult to achieve. We discuss how the performance of the algorithm can be optimised, and how it can be efficiently exploited on parallel computing platforms.

Genetic algorithm dynamics on a rugged landscape

NASA Astrophysics Data System (ADS)

Bornholdt, Stefan

1998-04-01

The genetic algorithm is an optimization procedure motivated by biological evolution and is successfully applied to optimization problems in different areas. A statistical mechanics model for its dynamics is proposed based on the parent-child fitness correlation of the genetic operators, making it applicable to general fitness landscapes. It is compared to a recent model based on a maximum entropy ansatz. Finally it is applied to modeling the dynamics of a genetic algorithm on the rugged fitness landscape of the NK model.

3-D parallel program for numerical calculation of gas dynamics problems with heat conductivity on distributed memory computational systems (CS)

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

Sofronov, I.D.; Voronin, B.L.; Butnev, O.I.

1997-12-31

The aim of the work performed is to develop a 3D parallel program for numerical calculation of gas dynamics problem with heat conductivity on distributed memory computational systems (CS), satisfying the condition of numerical result independence from the number of processors involved. Two basically different approaches to the structure of massive parallel computations have been developed. The first approach uses the 3D data matrix decomposition reconstructed at temporal cycle and is a development of parallelization algorithms for multiprocessor CS with shareable memory. The second approach is based on using a 3D data matrix decomposition not reconstructed during a temporal cycle.more » The program was developed on 8-processor CS MP-3 made in VNIIEF and was adapted to a massive parallel CS Meiko-2 in LLNL by joint efforts of VNIIEF and LLNL staffs. A large number of numerical experiments has been carried out with different number of processors up to 256 and the efficiency of parallelization has been evaluated in dependence on processor number and their parameters.« less

Dynamic optical tags

NASA Astrophysics Data System (ADS)

Griggs, Steven P.; Mark, Martin B.; Feldman, Barry J.

2004-07-01

The goal of the DARPA Dynamic Optical Tags (DOTs) program is to develop a small, robust, persistent, 2-way tagging, tracking and locating device that also supports communications at data rates greater than 100 kbps and can be interrogated at significant range. These tags will allow for two-way data exchange and tagging operations in friendly and denied areas. The DOTs will be passive and non-RF. To accomplish this, the DOTs program will develop small, thin, retro-reflecting modulators. The tags will operate for long periods of time (greater than two months) in real-world environmental conditions (-40° to +70° C) and allow for a wide interrogation angle (+/-60°). The tags will be passive (in the sleep mode) for most of the time and only become active when interrogated by a laser with the correct code. Once correctly interrogated, the tags will begin to modulate and retro-reflect the incoming beam. The program will also develop two tag specific transceiver systems that are eye-safe, employ automated scanning algorithms, and are capable of short search and interrogate times.

Strategies for Energy Efficient Resource Management of Hybrid Programming Models

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

Li, Dong; Supinski, Bronis de; Schulz, Martin

2013-01-01

Many scientific applications are programmed using hybrid programming models that use both message-passing and shared-memory, due to the increasing prevalence of large-scale systems with multicore, multisocket nodes. Previous work has shown that energy efficiency can be improved using software-controlled execution schemes that consider both the programming model and the power-aware execution capabilities of the system. However, such approaches have focused on identifying optimal resource utilization for one programming model, either shared-memory or message-passing, in isolation. The potential solution space, thus the challenge, increases substantially when optimizing hybrid models since the possible resource configurations increase exponentially. Nonetheless, with the accelerating adoptionmore » of hybrid programming models, we increasingly need improved energy efficiency in hybrid parallel applications on large-scale systems. In this work, we present new software-controlled execution schemes that consider the effects of dynamic concurrency throttling (DCT) and dynamic voltage and frequency scaling (DVFS) in the context of hybrid programming models. Specifically, we present predictive models and novel algorithms based on statistical analysis that anticipate application power and time requirements under different concurrency and frequency configurations. We apply our models and methods to the NPB MZ benchmarks and selected applications from the ASC Sequoia codes. Overall, we achieve substantial energy savings (8.74% on average and up to 13.8%) with some performance gain (up to 7.5%) or negligible performance loss.« less

Developments in the CCP4 molecular-graphics project.

PubMed

Potterton, Liz; McNicholas, Stuart; Krissinel, Eugene; Gruber, Jan; Cowtan, Kevin; Emsley, Paul; Murshudov, Garib N; Cohen, Serge; Perrakis, Anastassis; Noble, Martin

2004-12-01

Progress towards structure determination that is both high-throughput and high-value is dependent on the development of integrated and automatic tools for electron-density map interpretation and for the analysis of the resulting atomic models. Advances in map-interpretation algorithms are extending the resolution regime in which fully automatic tools can work reliably, but at present human intervention is required to interpret poor regions of macromolecular electron density, particularly where crystallographic data is only available to modest resolution [for example, I/sigma(I) < 2.0 for minimum resolution 2.5 A]. In such cases, a set of manual and semi-manual model-building molecular-graphics tools is needed. At the same time, converting the knowledge encapsulated in a molecular structure into understanding is dependent upon visualization tools, which must be able to communicate that understanding to others by means of both static and dynamic representations. CCP4 mg is a program designed to meet these needs in a way that is closely integrated with the ongoing development of CCP4 as a program suite suitable for both low- and high-intervention computational structural biology. As well as providing a carefully designed user interface to advanced algorithms of model building and analysis, CCP4 mg is intended to present a graphical toolkit to developers of novel algorithms in these fields.

Graphical models for optimal power flow

DOE PAGES

Dvijotham, Krishnamurthy; Chertkov, Michael; Van Hentenryck, Pascal; ...

2016-09-13

Optimal power flow (OPF) is the central optimization problem in electric power grids. Although solved routinely in the course of power grid operations, it is known to be strongly NP-hard in general, and weakly NP-hard over tree networks. In this paper, we formulate the optimal power flow problem over tree networks as an inference problem over a tree-structured graphical model where the nodal variables are low-dimensional vectors. We adapt the standard dynamic programming algorithm for inference over a tree-structured graphical model to the OPF problem. Combining this with an interval discretization of the nodal variables, we develop an approximation algorithmmore » for the OPF problem. Further, we use techniques from constraint programming (CP) to perform interval computations and adaptive bound propagation to obtain practically efficient algorithms. Compared to previous algorithms that solve OPF with optimality guarantees using convex relaxations, our approach is able to work for arbitrary tree-structured distribution networks and handle mixed-integer optimization problems. Further, it can be implemented in a distributed message-passing fashion that is scalable and is suitable for “smart grid” applications like control of distributed energy resources. In conclusion, numerical evaluations on several benchmark networks show that practical OPF problems can be solved effectively using this approach.« less

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.

Inverse algorithms for 2D shallow water equations in presence of wet dry fronts: Application to flood plain dynamics

NASA Astrophysics Data System (ADS)

Monnier, J.; Couderc, F.; Dartus, D.; Larnier, K.; Madec, R.; Vila, J.-P.

2016-11-01

The 2D shallow water equations adequately model some geophysical flows with wet-dry fronts (e.g. flood plain or tidal flows); nevertheless deriving accurate, robust and conservative numerical schemes for dynamic wet-dry fronts over complex topographies remains a challenge. Furthermore for these flows, data are generally complex, multi-scale and uncertain. Robust variational inverse algorithms, providing sensitivity maps and data assimilation processes may contribute to breakthrough shallow wet-dry front dynamics modelling. The present study aims at deriving an accurate, positive and stable finite volume scheme in presence of dynamic wet-dry fronts, and some corresponding inverse computational algorithms (variational approach). The schemes and algorithms are assessed on classical and original benchmarks plus a real flood plain test case (Lèze river, France). Original sensitivity maps with respect to the (friction, topography) pair are performed and discussed. The identification of inflow discharges (time series) or friction coefficients (spatially distributed parameters) demonstrate the algorithms efficiency.

An Alternative Approach to the Operation of Multinational Reservoir Systems: Application to the Amistad & Falcon System (Lower Rio Grande/Rí-o Bravo)

NASA Astrophysics Data System (ADS)

Serrat-Capdevila, A.; Valdes, J. B.

2005-12-01

An optimization approach for the operation of international multi-reservoir systems is presented. The approach uses Stochastic Dynamic Programming (SDP) algorithms, both steady-state and real-time, to develop two models. In the first model, the reservoirs and flows of the system are aggregated to yield an equivalent reservoir, and the obtained operating policies are disaggregated using a non-linear optimization procedure for each reservoir and for each nation water balance. In the second model a multi-reservoir approach is applied, disaggregating the releases for each country water share in each reservoir. The non-linear disaggregation algorithm uses SDP-derived operating policies as boundary conditions for a local time-step optimization. Finally, the performance of the different approaches and methods is compared. These models are applied to the Amistad-Falcon International Reservoir System as part of a binational dynamic modeling effort to develop a decision support system tool for a better management of the water resources in the Lower Rio Grande Basin, currently enduring a severe drought.

A genetic algorithm for a bi-objective mathematical model for dynamic virtual cell formation problem

NASA Astrophysics Data System (ADS)

Moradgholi, Mostafa; Paydar, Mohammad Mahdi; Mahdavi, Iraj; Jouzdani, Javid

2016-09-01

Nowadays, with the increasing pressure of the competitive business environment and demand for diverse products, manufacturers are force to seek for solutions that reduce production costs and rise product quality. Cellular manufacturing system (CMS), as a means to this end, has been a point of attraction to both researchers and practitioners. Limitations of cell formation problem (CFP), as one of important topics in CMS, have led to the introduction of virtual CMS (VCMS). This research addresses a bi-objective dynamic virtual cell formation problem (DVCFP) with the objective of finding the optimal formation of cells, considering the material handling costs, fixed machine installation costs and variable production costs of machines and workforce. Furthermore, we consider different skills on different machines in workforce assignment in a multi-period planning horizon. The bi-objective model is transformed to a single-objective fuzzy goal programming model and to show its performance; numerical examples are solved using the LINGO software. In addition, genetic algorithm (GA) is customized to tackle large-scale instances of the problems to show the performance of the solution method.

Breast mass segmentation in mammography using plane fitting and dynamic programming.

PubMed

Song, Enmin; Jiang, Luan; Jin, Renchao; Zhang, Lin; Yuan, Yuan; Li, Qiang

2009-07-01

Segmentation is an important and challenging task in a computer-aided diagnosis (CAD) system. Accurate segmentation could improve the accuracy in lesion detection and characterization. The objective of this study is to develop and test a new segmentation method that aims at improving the performance level of breast mass segmentation in mammography, which could be used to provide accurate features for classification. This automated segmentation method consists of two main steps and combines the edge gradient, the pixel intensity, as well as the shape characteristics of the lesions to achieve good segmentation results. First, a plane fitting method was applied to a background-trend corrected region-of-interest (ROI) of a mass to obtain the edge candidate points. Second, dynamic programming technique was used to find the "optimal" contour of the mass from the edge candidate points. Area-based similarity measures based on the radiologist's manually marked annotation and the segmented region were employed as criteria to evaluate the performance level of the segmentation method. With the evaluation criteria, the new method was compared with 1) the dynamic programming method developed by Timp and Karssemeijer, and 2) the normalized cut segmentation method, based on 337 ROIs extracted from a publicly available image database. The experimental results indicate that our segmentation method can achieve a higher performance level than the other two methods, and the improvements in segmentation performance level were statistically significant. For instance, the mean overlap percentage for the new algorithm was 0.71, whereas those for Timp's dynamic programming method and the normalized cut segmentation method were 0.63 (P < .001) and 0.61 (P < .001), respectively. We developed a new segmentation method by use of plane fitting and dynamic programming, which achieved a relatively high performance level. The new segmentation method would be useful for improving the accuracy of computerized detection and classification of breast cancer in mammography.

Control of water distribution networks with dynamic DMA topology using strictly feasible sequential convex programming

NASA Astrophysics Data System (ADS)

Wright, Robert; Abraham, Edo; Parpas, Panos; Stoianov, Ivan

2015-12-01

The operation of water distribution networks (WDN) with a dynamic topology is a recently pioneered approach for the advanced management of District Metered Areas (DMAs) that integrates novel developments in hydraulic modeling, monitoring, optimization, and control. A common practice for leakage management is the sectorization of WDNs into small zones, called DMAs, by permanently closing isolation valves. This facilitates water companies to identify bursts and estimate leakage levels by measuring the inlet flow for each DMA. However, by permanently closing valves, a number of problems have been created including reduced resilience to failure and suboptimal pressure management. By introducing a dynamic topology to these zones, these disadvantages can be eliminated while still retaining the DMA structure for leakage monitoring. In this paper, a novel optimization method based on sequential convex programming (SCP) is outlined for the control of a dynamic topology with the objective of reducing average zone pressure (AZP). A key attribute for control optimization is reliable convergence. To achieve this, the SCP method we propose guarantees that each optimization step is strictly feasible, resulting in improved convergence properties. By using a null space algorithm for hydraulic analyses, the computations required are also significantly reduced. The optimized control is actuated on a real WDN operated with a dynamic topology. This unique experimental program incorporates a number of technologies set up with the objective of investigating pioneering developments in WDN management. Preliminary results indicate AZP reductions for a dynamic topology of up to 6.5% over optimally controlled fixed topology DMAs. This article was corrected on 12 JAN 2016. See the end of the full text for details.

A Scheduling Algorithm for Cloud Computing System Based on the Driver of Dynamic Essential Path.

PubMed

Xie, Zhiqiang; Shao, Xia; Xin, Yu

2016-01-01

To solve the problem of task scheduling in the cloud computing system, this paper proposes a scheduling algorithm for cloud computing based on the driver of dynamic essential path (DDEP). This algorithm applies a predecessor-task layer priority strategy to solve the problem of constraint relations among task nodes. The strategy assigns different priority values to every task node based on the scheduling order of task node as affected by the constraint relations among task nodes, and the task node list is generated by the different priority value. To address the scheduling order problem in which task nodes have the same priority value, the dynamic essential long path strategy is proposed. This strategy computes the dynamic essential path of the pre-scheduling task nodes based on the actual computation cost and communication cost of task node in the scheduling process. The task node that has the longest dynamic essential path is scheduled first as the completion time of task graph is indirectly influenced by the finishing time of task nodes in the longest dynamic essential path. Finally, we demonstrate the proposed algorithm via simulation experiments using Matlab tools. The experimental results indicate that the proposed algorithm can effectively reduce the task Makespan in most cases and meet a high quality performance objective.

A Scheduling Algorithm for Cloud Computing System Based on the Driver of Dynamic Essential Path

PubMed Central

Xie, Zhiqiang; Shao, Xia; Xin, Yu

2016-01-01

To solve the problem of task scheduling in the cloud computing system, this paper proposes a scheduling algorithm for cloud computing based on the driver of dynamic essential path (DDEP). This algorithm applies a predecessor-task layer priority strategy to solve the problem of constraint relations among task nodes. The strategy assigns different priority values to every task node based on the scheduling order of task node as affected by the constraint relations among task nodes, and the task node list is generated by the different priority value. To address the scheduling order problem in which task nodes have the same priority value, the dynamic essential long path strategy is proposed. This strategy computes the dynamic essential path of the pre-scheduling task nodes based on the actual computation cost and communication cost of task node in the scheduling process. The task node that has the longest dynamic essential path is scheduled first as the completion time of task graph is indirectly influenced by the finishing time of task nodes in the longest dynamic essential path. Finally, we demonstrate the proposed algorithm via simulation experiments using Matlab tools. The experimental results indicate that the proposed algorithm can effectively reduce the task Makespan in most cases and meet a high quality performance objective. PMID:27490901

Efficient conjugate gradient algorithms for computation of the manipulator forward dynamics

NASA Technical Reports Server (NTRS)

Fijany, Amir; Scheid, Robert E.

1989-01-01

The applicability of conjugate gradient algorithms for computation of the manipulator forward dynamics is investigated. The redundancies in the previously proposed conjugate gradient algorithm are analyzed. A new version is developed which, by avoiding these redundancies, achieves a significantly greater efficiency. A preconditioned conjugate gradient algorithm is also presented. A diagonal matrix whose elements are the diagonal elements of the inertia matrix is proposed as the preconditioner. In order to increase the computational efficiency, an algorithm is developed which exploits the synergism between the computation of the diagonal elements of the inertia matrix and that required by the conjugate gradient algorithm.

Multi-heuristic dynamic task allocation using genetic algorithms in a heterogeneous distributed system

PubMed Central

Page, Andrew J.; Keane, Thomas M.; Naughton, Thomas J.

2010-01-01

We present a multi-heuristic evolutionary task allocation algorithm to dynamically map tasks to processors in a heterogeneous distributed system. It utilizes a genetic algorithm, combined with eight common heuristics, in an effort to minimize the total execution time. It operates on batches of unmapped tasks and can preemptively remap tasks to processors. The algorithm has been implemented on a Java distributed system and evaluated with a set of six problems from the areas of bioinformatics, biomedical engineering, computer science and cryptography. Experiments using up to 150 heterogeneous processors show that the algorithm achieves better efficiency than other state-of-the-art heuristic algorithms. PMID:20862190

Dynamic traffic assignment : genetic algorithms approach

DOT National Transportation Integrated Search

1997-01-01

Real-time route guidance is a promising approach to alleviating congestion on the nations highways. A dynamic traffic assignment model is central to the development of guidance strategies. The artificial intelligence technique of genetic algorithm...

Efficient dynamic simulation for multiple chain robotic mechanisms

NASA Technical Reports Server (NTRS)

Lilly, Kathryn W.; Orin, David E.

1989-01-01

An efficient O(mN) algorithm for dynamic simulation of simple closed-chain robotic mechanisms is presented, where m is the number of chains, and N is the number of degrees of freedom for each chain. It is based on computation of the operational space inertia matrix (6 x 6) for each chain as seen by the body, load, or object. Also, computation of the chain dynamics, when opened at one end, is required, and the most efficient algorithm is used for this purpose. Parallel implementation of the dynamics for each chain results in an O(N) + O(log sub 2 m+1) algorithm.

Algorithms of GPU-enabled reactive force field (ReaxFF) molecular dynamics.

PubMed

Zheng, Mo; Li, Xiaoxia; Guo, Li

2013-04-01

Reactive force field (ReaxFF), a recent and novel bond order potential, allows for reactive molecular dynamics (ReaxFF MD) simulations for modeling larger and more complex molecular systems involving chemical reactions when compared with computation intensive quantum mechanical methods. However, ReaxFF MD can be approximately 10-50 times slower than classical MD due to its explicit modeling of bond forming and breaking, the dynamic charge equilibration at each time-step, and its one order smaller time-step than the classical MD, all of which pose significant computational challenges in simulation capability to reach spatio-temporal scales of nanometers and nanoseconds. The very recent advances of graphics processing unit (GPU) provide not only highly favorable performance for GPU enabled MD programs compared with CPU implementations but also an opportunity to manage with the computing power and memory demanding nature imposed on computer hardware by ReaxFF MD. In this paper, we present the algorithms of GMD-Reax, the first GPU enabled ReaxFF MD program with significantly improved performance surpassing CPU implementations on desktop workstations. The performance of GMD-Reax has been benchmarked on a PC equipped with a NVIDIA C2050 GPU for coal pyrolysis simulation systems with atoms ranging from 1378 to 27,283. GMD-Reax achieved speedups as high as 12 times faster than Duin et al.'s FORTRAN codes in Lammps on 8 CPU cores and 6 times faster than the Lammps' C codes based on PuReMD in terms of the simulation time per time-step averaged over 100 steps. GMD-Reax could be used as a new and efficient computational tool for exploiting very complex molecular reactions via ReaxFF MD simulation on desktop workstations. Copyright © 2013 Elsevier Inc. All rights reserved.

Dynamic Pricing Criteria in Linear Programming

DTIC Science & Technology

1988-07-01

DTICE’ECTE h QSEPO08 19880 Department of Operations Researchs Stanford University Stanford, CA 94305 Fl . dommd lum b dLvulbcjasa Im %ailmft@d.I &~ T...information about positive ones. 38 C- M .9 ~ ,~- - ~ fl .’ %’% ’ % % .,h.] However, this rule works extremely well on the PILOT set, achieving...34 .r " .:." ," "e .-r".’.€ .-,N., N REFERENCES [1] Adler, I., Resende, M.G. and Veiga , G. (1986). An implementation of Karmax- kar’s algorithm for

Template Based Low Data Rate Speech Encoder

DTIC Science & Technology

1993-09-30

Nasality Distinguishes In/ from d/ 95.6 96.9 1m/ from /b/, etc. Sustention Distinguishes /f/ from /p/, $7.5 88.3 ibi from N/, Al from /0 8. etc. Sibilation...processor performs mainly Processor Workstation input/output (I/O) operations. The dynamic random access memory (DRAM) has 16 million bytes of...storage capacity. To execute the 800-b/s voice algorithm, the following amount of memory is needed: 5 MB for tables, 1.5 MB for it "program, and 30 KB for

Quantum Algorithms for Computational Physics: Volume 3 of Lattice Gas Dynamics

DTIC Science & Technology

2007-01-03

time- dependent state |q(t)〉 of a two- energy level quantum mechanical system, which is a fermionic qubit and is governed by the Schroedinger wave...on-site ket of size 2B |Ψ〉 total system ket of size 2Q 2.2 The quantum state in the number representation From the previous section, a time- dependent ...duration depend on the particular experimental realization, so that the natural coupling along with the program of externally applied pulses together

Special Focus

PubMed Central

Nawrocki, Eric P.; Burge, Sarah W.

2013-01-01

The development of RNA bioinformatic tools began more than 30 y ago with the description of the Nussinov and Zuker dynamic programming algorithms for single sequence RNA secondary structure prediction. Since then, many tools have been developed for various RNA sequence analysis problems such as homology search, multiple sequence alignment, de novo RNA discovery, read-mapping, and many more. In this issue, we have collected a sampling of reviews and original research that demonstrate some of the many ways bioinformatics is integrated with current RNA biology research. PMID:23948768

Research in applied mathematics, numerical analysis, and computer science

NASA Technical Reports Server (NTRS)

1984-01-01

Research conducted at the Institute for Computer Applications in Science and Engineering (ICASE) in applied mathematics, numerical analysis, and computer science is summarized and abstracts of published reports are presented. The major categories of the ICASE research program are: (1) numerical methods, with particular emphasis on the development and analysis of basic numerical algorithms; (2) control and parameter identification; (3) computational problems in engineering and the physical sciences, particularly fluid dynamics, acoustics, and structural analysis; and (4) computer systems and software, especially vector and parallel computers.

An integrated environment for tactical guidance research and evaluation

NASA Technical Reports Server (NTRS)

Goodrich, Kenneth H.; Mcmanus, John W.

1990-01-01

NASA-Langley's Tactical Guidance Research and Evaluation System (TGRES) constitutes an integrated environment for the development of tactical guidance algorithms and evaluating the effects of novel technologies; the modularity of the system allows easy modification or replacement of system elements in order to conduct evaluations of alternative technologies. TGRES differs from existing systems in its capitalization on AI programming techniques for guidance-logic implementation. Its ability to encompass high-fidelity, six-DOF simulation models will facilitate the analysis of complete aircraft dynamics.

Nation-Building Modeling and Resource Allocation Via Dynamic Programming

DTIC Science & Technology

2014-09-01

Figure 2. RAND Study Models[59:98,115] (WMA) and used both the k-Nearest Neighbor ( KNN ) and Nearest Centroid (NC) algorithms to classify future features...The study found that KNN performed bet- ter than NC with 85% or greater accuracy in all test cases. The methodology was adopted for use under the...analysis feature of the model. 3.7.1 The No Surge Alternative. On the 10th of January 2007, President George W. Bush delivered a speech to the American

Study of Anticyclogenesis Affecting the Mediterranean

NASA Astrophysics Data System (ADS)

Hatzaki, M.; Flocas, H. A.; Simmonds, I.; Kouroutzoglou, J.; Garde, L.; Keay, K.; Bitsa, E.

2014-12-01

A comprehensive climatology of migratory anticyclones affecting the Mediterranean was generated by the University of Melbourne finding and tracking algorithm (MS algorithm), applied to 34 years (1979-2012) of ERA-Interim MSLP on a 1.5°x1.5° resolution. The algorithm was employed for the first time for anticyclones in this region, thus, its robustness and reliability in efficiently capturing the individual characteristics of the anticyclonic tracks in such a closed basin with complex topography were checked and verified. Then, the tracks and the statistical properties of the migratory systems were calculated and analyzed. Considering that cold-core anticyclones are shallow and weaken with height contrary to the warm-core that exhibit a vertically well-organized structure, the vertical thermal extend of the systems was studied with an algorithm developed as an extension module of the MS algorithm using ERA-Interim temperatures on several isobaric levels from 1000hPa to 100hPa on an 1.5°x1.5° resolution. The results verified that during both cold and warm period, cold-core anticyclones mainly affect the northern parts of the Mediterranean basin, with their behavior to be strongly regulated by cyclonic activity from the main storm track areas of the North Atlantic and Europe. On the other hand, warm-core anticyclones were found mainly in the southern Mediterranean and North African areas. Here, in order to get a perspective on the dynamic and thermodynamic processes in anticyclonic formation, a dynamical analysis at several vertical levels is performed. The study of mean fields of potential vorticity, temperature advection, vorticity advection at various levels can elucidate the role of upper and low levels during anticyclogenesis and system evolvement and help to further understand the dynamic mechanisms which are responsible for the anticyclogenesis over the Mediterranean region. Acknowledgement: This research project is implemented within the framework of the Action «Supporting Postdoctoral Researchers» of the Operational Program "Education and Lifelong Learning" (Action's Beneficiary: General Secretariat for Research and Technology) and is co-financed by the European Social Fund (ESF) and the Greek State. Some funding from the Australian Research Council is also acknowledged.

Dynamic Group Formation Based on a Natural Phenomenon

ERIC Educational Resources Information Center

Zedadra, Amina; Lafifi, Yacine; Zedadra, Ouarda

2016-01-01

This paper presents a new approach of learners grouping in collaborative learning systems. This grouping process is based on traces left by learners. The goal is the circular dynamic grouping to achieve collaborative projects. The proposed approach consists of two main algorithms: (1) the circular grouping algorithm and (2) the dynamic grouping…

Single step optimization of manipulator maneuvers with variable structure control

NASA Technical Reports Server (NTRS)

Chen, N.; Dwyer, T. A. W., III

1987-01-01

One step ahead optimization has been recently proposed for spacecraft attitude maneuvers as well as for robot manipulator maneuvers. Such a technique yields a discrete time control algorithm implementable as a sequence of state-dependent, quadratic programming problems for acceleration optimization. Its sensitivity to model accuracy, for the required inversion of the system dynamics, is shown in this paper to be alleviated by a fast variable structure control correction, acting between the sampling intervals of the slow one step ahead discrete time acceleration command generation algorithm. The slow and fast looping concept chosen follows that recently proposed for optimal aiming strategies with variable structure control. Accelerations required by the VSC correction are reserved during the slow one step ahead command generation so that the ability to overshoot the sliding surface is guaranteed.

Portfolio optimization by using linear programing models based on genetic algorithm

NASA Astrophysics Data System (ADS)

Sukono; Hidayat, Y.; Lesmana, E.; Putra, A. S.; Napitupulu, H.; Supian, S.

2018-01-01

In this paper, we discussed the investment portfolio optimization using linear programming model based on genetic algorithms. It is assumed that the portfolio risk is measured by absolute standard deviation, and each investor has a risk tolerance on the investment portfolio. To complete the investment portfolio optimization problem, the issue is arranged into a linear programming model. Furthermore, determination of the optimum solution for linear programming is done by using a genetic algorithm. As a numerical illustration, we analyze some of the stocks traded on the capital market in Indonesia. Based on the analysis, it is shown that the portfolio optimization performed by genetic algorithm approach produces more optimal efficient portfolio, compared to the portfolio optimization performed by a linear programming algorithm approach. Therefore, genetic algorithms can be considered as an alternative on determining the investment portfolio optimization, particularly using linear programming models.

MM Algorithms for Geometric and Signomial Programming

PubMed Central

Lange, Kenneth; Zhou, Hua

2013-01-01

This paper derives new algorithms for signomial programming, a generalization of geometric programming. The algorithms are based on a generic principle for optimization called the MM algorithm. In this setting, one can apply the geometric-arithmetic mean inequality and a supporting hyperplane inequality to create a surrogate function with parameters separated. Thus, unconstrained signomial programming reduces to a sequence of one-dimensional minimization problems. Simple examples demonstrate that the MM algorithm derived can converge to a boundary point or to one point of a continuum of minimum points. Conditions under which the minimum point is unique or occurs in the interior of parameter space are proved for geometric programming. Convergence to an interior point occurs at a linear rate. Finally, the MM framework easily accommodates equality and inequality constraints of signomial type. For the most important special case, constrained quadratic programming, the MM algorithm involves very simple updates. PMID:24634545

MM Algorithms for Geometric and Signomial Programming.

PubMed

Lange, Kenneth; Zhou, Hua

2014-02-01

This paper derives new algorithms for signomial programming, a generalization of geometric programming. The algorithms are based on a generic principle for optimization called the MM algorithm. In this setting, one can apply the geometric-arithmetic mean inequality and a supporting hyperplane inequality to create a surrogate function with parameters separated. Thus, unconstrained signomial programming reduces to a sequence of one-dimensional minimization problems. Simple examples demonstrate that the MM algorithm derived can converge to a boundary point or to one point of a continuum of minimum points. Conditions under which the minimum point is unique or occurs in the interior of parameter space are proved for geometric programming. Convergence to an interior point occurs at a linear rate. Finally, the MM framework easily accommodates equality and inequality constraints of signomial type. For the most important special case, constrained quadratic programming, the MM algorithm involves very simple updates.

A method for solution of the Euler-Bernoulli beam equation in flexible-link robotic systems

NASA Technical Reports Server (NTRS)

Tzes, Anthony P.; Yurkovich, Stephen; Langer, F. Dieter

1989-01-01

An efficient numerical method for solving the partial differential equation (PDE) governing the flexible manipulator control dynamics is presented. A finite-dimensional model of the equation is obtained through discretization in both time and space coordinates by using finite-difference approximations to the PDE. An expert program written in the Macsyma symbolic language is utilized in order to embed the boundary conditions into the program, accounting for a mass carried at the tip of the manipulator. The advantages of the proposed algorithm are many, including the ability to (1) include any distributed actuation term in the partial differential equation, (2) provide distributed sensing of the beam displacement, (3) easily modify the boundary conditions through an expert program, and (4) modify the structure for running under a multiprocessor environment.

Simulation evaluation of a low-altitude helicopter flight guidance system adapted for a helmet-mounted display

NASA Technical Reports Server (NTRS)

Swenson, Harry N.; Zelenka, Richard E.; Hardy, Gordon H.; Dearing, Munro G.

1992-01-01

A computer aiding concept for low-altitude helicopter flight was developed and evaluated in a real-time piloted simulation. The concept included an optimal control trajectory-generation algorithm based upon dynamic programming and a helmet-mounted display (HMD) presentation of a pathway-in-the-sky, a phantom aircraft, and flight-path vector/predictor guidance symbology. The trajectory-generation algorithm uses knowledge of the global mission requirements, a digital terrain map, aircraft performance capabilities, and advanced navigation information to determine a trajectory between mission way points that seeks valleys to minimize threat exposure. The pilot evaluation was conducted at NASA ARC moving base Vertical Motion Simulator (VMS) by pilots representing NASA, the U.S. Army, the Air Force, and the helicopter industry. The pilots manually tracked the trajectory generated by the algorithm utilizing the HMD symbology. The pilots were able to satisfactorily perform the tracking tasks while maintaining a high degree of awareness of the outside world.

Computer aiding for low-altitude helicopter flight

NASA Technical Reports Server (NTRS)

Swenson, Harry N.

1991-01-01

A computer-aiding concept for low-altitude helicopter flight was developed and evaluated in a real-time piloted simulation. The concept included an optimal control trajectory-generated algorithm based on dynamic programming, and a head-up display (HUD) presentation of a pathway-in-the-sky, a phantom aircraft, and flight-path vector/predictor symbol. The trajectory-generation algorithm uses knowledge of the global mission requirements, a digital terrain map, aircraft performance capabilities, and advanced navigation information to determine a trajectory between mission waypoints that minimizes threat exposure by seeking valleys. The pilot evaluation was conducted at NASA Ames Research Center's Sim Lab facility in both the fixed-base Interchangeable Cab (ICAB) simulator and the moving-base Vertical Motion Simulator (VMS) by pilots representing NASA, the U.S. Army, and the U.S. Air Force. The pilots manually tracked the trajectory generated by the algorithm utilizing the HUD symbology. They were able to satisfactorily perform the tracking tasks while maintaining a high degree of awareness of the outside world.

Self-Organizing Hidden Markov Model Map (SOHMMM).

PubMed

Ferles, Christos; Stafylopatis, Andreas

2013-12-01

A hybrid approach combining the Self-Organizing Map (SOM) and the Hidden Markov Model (HMM) is presented. The Self-Organizing Hidden Markov Model Map (SOHMMM) establishes a cross-section between the theoretic foundations and algorithmic realizations of its constituents. The respective architectures and learning methodologies are fused in an attempt to meet the increasing requirements imposed by the properties of deoxyribonucleic acid (DNA), ribonucleic acid (RNA), and protein chain molecules. The fusion and synergy of the SOM unsupervised training and the HMM dynamic programming algorithms bring forth a novel on-line gradient descent unsupervised learning algorithm, which is fully integrated into the SOHMMM. Since the SOHMMM carries out probabilistic sequence analysis with little or no prior knowledge, it can have a variety of applications in clustering, dimensionality reduction and visualization of large-scale sequence spaces, and also, in sequence discrimination, search and classification. Two series of experiments based on artificial sequence data and splice junction gene sequences demonstrate the SOHMMM's characteristics and capabilities. Copyright © 2013 Elsevier Ltd. All rights reserved.

Determination of the Earth's Plasmapause Location from the CE-3 EUVC Images

NASA Technical Reports Server (NTRS)

He, Fei; Zhang, Xiao-Xin; Chen, Bo; Fok, Mei-Ching; Nakano, Shinya

2016-01-01

The Moon-based Extreme Ultraviolet Camera (EUVC) aboard China's Chang'e-3 (CE-3) mission has successfully imaged the entire Earth's plasmasphere for the first time from the side views on lunar surface. An EUVC image on 21 April 2014 is used in this study to demonstrate the characteristics and configurations of the Moon-based EUV imaging and to illustrate the determination algorithm of the plasmapause locations on the magnetic equator. The plasmapause locations determined from all the available EUVC images with the Minimum L Algorithm are quantitatively compared with those extracted from insitu observations (Defense Meteorological Satellite Program, Time History of Events and Macroscale Interactions during Substorms, and Radiation Belt Storm Probes). Excellent agreement between the determined plasmapauses seen by EUVC and the extracted ones from other satellites indicates the reliability of the Moon-based EUVC images as well as the determination algorithm. This preliminary study provides an important basis for future investigation of the dynamics of the plasmasphere with the Moon-based EUVC imaging.

Handling Dynamic Weights in Weighted Frequent Pattern Mining

NASA Astrophysics Data System (ADS)

Ahmed, Chowdhury Farhan; Tanbeer, Syed Khairuzzaman; Jeong, Byeong-Soo; Lee, Young-Koo

Even though weighted frequent pattern (WFP) mining is more effective than traditional frequent pattern mining because it can consider different semantic significances (weights) of items, existing WFP algorithms assume that each item has a fixed weight. But in real world scenarios, the weight (price or significance) of an item can vary with time. Reflecting these changes in item weight is necessary in several mining applications, such as retail market data analysis and web click stream analysis. In this paper, we introduce the concept of a dynamic weight for each item, and propose an algorithm, DWFPM (dynamic weighted frequent pattern mining), that makes use of this concept. Our algorithm can address situations where the weight (price or significance) of an item varies dynamically. It exploits a pattern growth mining technique to avoid the level-wise candidate set generation-and-test methodology. Furthermore, it requires only one database scan, so it is eligible for use in stream data mining. An extensive performance analysis shows that our algorithm is efficient and scalable for WFP mining using dynamic weights.

Power Control and Optimization of Photovoltaic and Wind Energy Conversion Systems

NASA Astrophysics Data System (ADS)

Ghaffari, Azad

Power map and Maximum Power Point (MPP) of Photovoltaic (PV) and Wind Energy Conversion Systems (WECS) highly depend on system dynamics and environmental parameters, e.g., solar irradiance, temperature, and wind speed. Power optimization algorithms for PV systems and WECS are collectively known as Maximum Power Point Tracking (MPPT) algorithm. Gradient-based Extremum Seeking (ES), as a non-model-based MPPT algorithm, governs the system to its peak point on the steepest descent curve regardless of changes of the system dynamics and variations of the environmental parameters. Since the power map shape defines the gradient vector, then a close estimate of the power map shape is needed to create user assignable transients in the MPPT algorithm. The Hessian gives a precise estimate of the power map in a neighborhood around the MPP. The estimate of the inverse of the Hessian in combination with the estimate of the gradient vector are the key parts to implement the Newton-based ES algorithm. Hence, we generate an estimate of the Hessian using our proposed perturbation matrix. Also, we introduce a dynamic estimator to calculate the inverse of the Hessian which is an essential part of our algorithm. We present various simulations and experiments on the micro-converter PV systems to verify the validity of our proposed algorithm. The ES scheme can also be used in combination with other control algorithms to achieve desired closed-loop performance. The WECS dynamics is slow which causes even slower response time for the MPPT based on the ES. Hence, we present a control scheme, extended from Field-Oriented Control (FOC), in combination with feedback linearization to reduce the convergence time of the closed-loop system. Furthermore, the nonlinear control prevents magnetic saturation of the stator of the Induction Generator (IG). The proposed control algorithm in combination with the ES guarantees the closed-loop system robustness with respect to high level parameter uncertainty in the IG dynamics. The simulation results verify the effectiveness of the proposed algorithm.

A Sensor Dynamic Measurement Error Prediction Model Based on NAPSO-SVM.

PubMed

Jiang, Minlan; Jiang, Lan; Jiang, Dingde; Li, Fei; Song, Houbing

2018-01-15

Dynamic measurement error correction is an effective way to improve sensor precision. Dynamic measurement error prediction is an important part of error correction, and support vector machine (SVM) is often used for predicting the dynamic measurement errors of sensors. Traditionally, the SVM parameters were always set manually, which cannot ensure the model's performance. In this paper, a SVM method based on an improved particle swarm optimization (NAPSO) is proposed to predict the dynamic measurement errors of sensors. Natural selection and simulated annealing are added in the PSO to raise the ability to avoid local optima. To verify the performance of NAPSO-SVM, three types of algorithms are selected to optimize the SVM's parameters: the particle swarm optimization algorithm (PSO), the improved PSO optimization algorithm (NAPSO), and the glowworm swarm optimization (GSO). The dynamic measurement error data of two sensors are applied as the test data. The root mean squared error and mean absolute percentage error are employed to evaluate the prediction models' performances. The experimental results show that among the three tested algorithms the NAPSO-SVM method has a better prediction precision and a less prediction errors, and it is an effective method for predicting the dynamic measurement errors of sensors.

A novel comprehensive learning artificial bee colony optimizer for dynamic optimization biological problems.

PubMed

Su, Weixing; Chen, Hanning; Liu, Fang; Lin, Na; Jing, Shikai; Liang, Xiaodan; Liu, Wei

2017-03-01

There are many dynamic optimization problems in the real world, whose convergence and searching ability is cautiously desired, obviously different from static optimization cases. This requires an optimization algorithm adaptively seek the changing optima over dynamic environments, instead of only finding the global optimal solution in the static environment. This paper proposes a novel comprehensive learning artificial bee colony optimizer (CLABC) for optimization in dynamic environments problems, which employs a pool of optimal foraging strategies to balance the exploration and exploitation tradeoff. The main motive of CLABC is to enrich artificial bee foraging behaviors in the ABC model by combining Powell's pattern search method, life-cycle, and crossover-based social learning strategy. The proposed CLABC is a more bee-colony-realistic model that the bee can reproduce and die dynamically throughout the foraging process and population size varies as the algorithm runs. The experiments for evaluating CLABC are conducted on the dynamic moving peak benchmarks. Furthermore, the proposed algorithm is applied to a real-world application of dynamic RFID network optimization. Statistical analysis of all these cases highlights the significant performance improvement due to the beneficial combination and demonstrates the performance superiority of the proposed algorithm.