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Optimal Shipboard Power System Management via Mixed Integer Dynamic Programming  

E-print Network

is discussed. The use of the transformed model in a dynamic pro- gramming approach to the design of optimal various discrete controllers like tap-changing transformers, capacitor banks, load shedding devices programming. Consequently, the idea of reducing logical specifications into IP formulas has along history, see

Kwatny, Harry G.


Portfolio optimization and mixed integer quadratic programming  

SciTech Connect

We begin with the well-known Markowitz model of risk and return fora portfolio of stocks. This model is used to choose a minimum risk portfolio from a universe of stocks that achieves a specified level of expected return subject to a budget constraint and possibly other (linear) constraints. This model is a convex quadratic programming model over continuous variables. As such, many of the variables may have small and unrealistic values in an optimal solution. In addition, a minimum risk portfolio may contain far too many stocks for an investor to hold. Eliminating these unwanted characteristics of minimum risk solutions can be accomplished using binary decision variables. We discuss an implementation of branch-and-bound for convex quadratic programming using the Optimization Subroutine Library. We then discuss how the structure of these particular types of additional constraints can be used to obtain a more efficient implementation. We present some computational experience with instances from the finance industry.

Jensen, D.



Using MINTO on RCS MINTO stands for Mixed INTeger Optimizer. It is a package which solves mixed integer programmingproblems  

E-print Network

Using MINTO on RCS MINTO stands for Mixed INTeger Optimizer. It is a package which solves mixed of being integrated into the RCS environment. This is an ongoing process but you can access MINTO right now. MINTO is available on fourteen selected RCS workstation by simply following these directions

Mitchell, John E.


Designing Networks: A Mixed-Integer Linear Optimization Approach  

E-print Network

Designing networks with specified collective properties is useful in a variety of application areas, enabling the study of how given properties affect the behavior of network models, the downscaling of empirical networks to workable sizes, and the analysis of network evolution. Despite the importance of the task, there currently exists a gap in our ability to systematically generate networks that adhere to theoretical guarantees for the given property specifications. In this paper, we propose the use of Mixed-Integer Linear Optimization modeling and solution methodologies to address this Network Generation Problem. We present a number of useful modeling techniques and apply them to mathematically express and constrain network properties in the context of an optimization formulation. We then develop complete formulations for the generation of networks that attain specified levels of connectivity, spread, assortativity and robustness, and we illustrate these via a number of computational case studies.

Gounaris, Chrysanthos E; Kevrekidis, Ioannis G; Floudas, Christodoulos A



Computational experiments for local search algorithms for binary and mixed integer optimization  

E-print Network

In this thesis, we implement and test two algorithms for binary optimization and mixed integer optimization, respectively. We fine tune the parameters of these two algorithms and achieve satisfactory performance. We also ...

Zhou, Jingting, S.M. Massachusetts Institute of Technology



An Exact Penalty Global Optimization Approach for Mixed-Integer ...  

E-print Network

(34) or, equivalently, a point p ? Sz? such that x ? z?. ? = x ? p? + p ? z? ... [3] C. S. Adjiman, I. P. Androulakis and C. A. Floudas, A global optimization method, ?- ... [10] Jones, D.R., Perttunen, C.D., Stuckman, B.E.,Lipschitzian optimization ...



Enhanced index tracking modeling in portfolio optimization with mixed-integer programming z approach  

NASA Astrophysics Data System (ADS)

Enhanced index tracking is a popular form of portfolio management in stock market investment. Enhanced index tracking aims to construct an optimal portfolio to generate excess return over the return achieved by the stock market index without purchasing all of the stocks that make up the index. The objective of this paper is to construct an optimal portfolio using mixed-integer programming model which adopts regression approach in order to generate higher portfolio mean return than stock market index return. In this study, the data consists of 24 component stocks in Malaysia market index which is FTSE Bursa Malaysia Kuala Lumpur Composite Index from January 2010 until December 2012. The results of this study show that the optimal portfolio of mixed-integer programming model is able to generate higher mean return than FTSE Bursa Malaysia Kuala Lumpur Composite Index return with only selecting 30% out of the total stock market index components.

Siew, Lam Weng; Jaaman, Saiful Hafizah Hj.; Ismail, Hamizun bin



The Integer Approximation Error in Mixed-Integer Optimal Control  

E-print Network

In there, the most important part of the proof for the algorithm's termination in a ... A related result on error bounds has recently been obtained independently of this work by ...... A case study from automobile test-driving with gear shift. Optimal ...



Optimizing Constrained Mixed-Integer Nonlinear Programming Problems Using Nature Selection  

Microsoft Academic Search

Many practical engineering optimization problems involving real and integer\\/discrete design variables have been drawing much more attention from researchers. In this paper, an effective adaptive real-parameter simulated annealing genetic algorithm (ARSAGA) was proposed, applied to cope with constrained mixed-integer nonlinear programming problems. The performances of this proposed algorithm, including reliability and convergence speed are demonstrated by examples. It is noted

Rong-Song He



Integer tree-based search and mixed-integer optimal control of distribution chains  

Microsoft Academic Search

The use of integer tree-based search and mixed-integer programming is investigated for the purpose of control of multi-item multi-echelon distribution chains. A discrete-time model is considered to describe the dynamics of a generic distribution chain. The decisions on the amounts of goods to transfer are made by referring to a performance index that accounts for transportation, storage, and backlog costs

A. Alessandri; M. Gaggero; F. Tonelli



Mixed integer programming improves comprehensibility and plan quality in inverse optimization of prostate HDR-brachytherapy  

E-print Network

Current inverse treatment planning methods that optimize both catheter positions and dwell times in prostate HDR brachytherapy use surrogate linear or quadratic objective functions that have no direct interpretation in terms of dose-volume histogram (DVH) criteria, do not result in an optimum or have long solution times. We decrease the solution time of existing linear and quadratic dose-based programming models (LP and QP, respectively) to allow optimizing over potential catheter positions using mixed integer programming. An additional average speed-up of 75% can be obtained by stopping the solver at an early stage, without deterioration of the plan quality. For a fixed catheter configuration, the dwell time optimization model LP solves to optimality in less than 15 seconds, which confirms earlier results. We propose an iterative procedure for QP that allows to prescribe the target dose as an interval, while retaining independence between the solution time and the number of dose calculation points. This iter...

Gorissen, Bram L; Hoffmann, Aswin L



A Parallel Mixed Integer Programming-Finite Element Method Technique for Global Design Optimization of Power Transformers  

Microsoft Academic Search

Transformer design optimization is determined by minimizing the transformer cost taking into consideration constraints imposed both by international specifications and customer needs. The main purpose of this work is the development and validation of an optimization technique based on a parallel mixed integer nonlinear programming methodology in conjunction with the finite element method, in order to reach a global optimum

Eleftherios I. Amoiralis; Marina A. Tsili; Pavlos S. Georgilakis; Antonios G. Kladas; Athanassios T. Souflaris



Optimization of beam orientations and beam weights for conformal radiotherapy using mixed integer programming  

NASA Astrophysics Data System (ADS)

An algorithm for optimizing beam orientations and beam weights for conformal radiotherapy has been developed. The algorithm models the optimization of beam orientations and beam weights as a problem of mixed integer linear programming (MILP), and optimizes the beam orientations and beam weights simultaneously. The application process of the algorithm has four steps: (a) prepare a pool of beam orientation candidates with the consideration of avoiding any patient-gantry collision and avoiding direct irradiation of organs at risk with quite low tolerances (e.g., eyes). (b) Represent each beam orientation candidate with a binary variable, and each beam weight with a continuous variable. (c) Set up an optimization problem according to dose prescriptions and the maximum allowed number of beam orientations. (d) Solve the optimization problem with a ready-to-use MILP solver. After optimization, the candidates with unity binary variables remain in the final beam configuration. The performance of the algorithm was tested with clinical cases. Compared with standard treatment plans, the beam-orientation-optimized plans had better dose distributions in terms of target coverage and avoidance of critical structures. The optimization processes took less than 1 h on a PC with a Pentium IV 2.4 GHz processor.

Wang, Chuang; Dai, Jianrong; Hu, Yimin



A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: I. Robust Linear Optimization and Robust Mixed Integer Linear Optimization  

PubMed Central

Robust counterpart optimization techniques for linear optimization and mixed integer linear optimization problems are studied in this paper. Different uncertainty sets, including those studied in literature (i.e., interval set; combined interval and ellipsoidal set; combined interval and polyhedral set) and new ones (i.e., adjustable box; pure ellipsoidal; pure polyhedral; combined interval, ellipsoidal, and polyhedral set) are studied in this work and their geometric relationship is discussed. For uncertainty in the left hand side, right hand side, and objective function of the optimization problems, robust counterpart optimization formulations induced by those different uncertainty sets are derived. Numerical studies are performed to compare the solutions of the robust counterpart optimization models and applications in refinery production planning and batch process scheduling problem are presented. PMID:21935263

Li, Zukui; Ding, Ran; Floudas, Christodoulos A.



Optimization of the Thermosetting Pultrusion Process by Using Hybrid and Mixed Integer Genetic Algorithms  

NASA Astrophysics Data System (ADS)

In this paper thermo-chemical simulation of the pultrusion process of a composite rod is first used as a validation case to ensure that the utilized numerical scheme is stable and converges to results given in literature. Following this validation case, a cylindrical die block with heaters is added to the pultrusion domain of a composite part and thermal contact resistance (TCR) regions at the die-part interface are defined. Two optimization case studies are performed on this new configuration. In the first one, optimal die radius and TCR values are found by using a hybrid genetic algorithm based on a sequential combination of a genetic algorithm (GA) and a local search technique to fit the centerline temperature of the composite with the one calculated in the validation case. In the second optimization study, the productivity of the process is improved by using a mixed integer genetic algorithm (MIGA) such that the total number of heaters is minimized while satisfying the constraints for the maximum composite temperature, the mean of the cure degree at the die exit and the pulling speed.

Baran, Ismet; Tutum, Cem C.; Hattel, Jesper H.



Designing cost-effective biopharmaceutical facilities using mixed-integer optimization.  


Chromatography operations are identified as critical steps in a monoclonal antibody (mAb) purification process and can represent a significant proportion of the purification material costs. This becomes even more critical with increasing product titers that result in higher mass loads onto chromatography columns, potentially causing capacity bottlenecks. In this work, a mixed-integer nonlinear programming (MINLP) model was created and applied to an industrially relevant case study to optimize the design of a facility by determining the most cost-effective chromatography equipment sizing strategies for the production of mAbs. Furthermore, the model was extended to evaluate the ability of a fixed facility to cope with higher product titers up to 15 g/L. Examination of the characteristics of the optimal chromatography sizing strategies across different titer values enabled the identification of the maximum titer that the facility could handle using a sequence of single column chromatography steps as well as multi-column steps. The critical titer levels for different ratios of upstream to dowstream trains where multiple parallel columns per step resulted in the removal of facility bottlenecks were identified. Different facility configurations in terms of number of upstream trains were considered and the trade-off between their cost and ability to handle higher titers was analyzed. The case study insights demonstrate that the proposed modeling approach, combining MINLP models with visualization tools, is a valuable decision-support tool for the design of cost-effective facility configurations and to aid facility fit decisions. 2013. PMID:23956206

Liu, Songsong; Simaria, Ana S; Farid, Suzanne S; Papageorgiou, Lazaros G



On mixed integer reformulations of monotonic probabilistic ...  

E-print Network

May 16, 2010 ... Abstract. The paper studies large scale mixed integer reformulation approach to stochastic ...... optimization for US investors in emerging global, Asian and Latin American markets. Pacific-Basin Finance Journal, 12, 91–116.




Optimizing well-stimulation treatment size using mixed integer linear programming  

E-print Network

This thesis presents a model for optimum sizing of well stimulation treatments. The optimization scheme is based on a combination of a well stimulation type selection and treatment size, therefore determining the optimum type of stimulation...

Picon Aranguren, Oscar



Efficient upper and lower bounds for global mixed-integer optimal ...  

E-print Network

Sum Up Rounding strategy, and calculation of global lower bounds by means of the method of ... They have been attracting a lot of interest in the last three decades, accelerated ... dealing with electrical motors is the different time scale of the variations of the .... of a finite dimensional nonlinear optimization problem (




E-print Network

Abstract: The classical non-linear mixed integer formulation of the transmission ... disjunctive model, optimality can be proven for several hard problem instances. ..... manner by repeatedly randomly drawing promising candidates from a restricted ... performance problem which measures the total load shedding required for ...




Optimal fleetwide emissions reductions for passenger ferries: an application of a mixed-integer nonlinear programming model for the New York-New Jersey Harbor.  


Emissions from passenger ferries operating in urban harbors may contribute significantly to emissions inventories and commuter exposure to air pollution. In particular, ferries are problematic because of high emissions of oxides of nitrogen (NOx) and particulate matter (PM) from primarily unregulated diesel engines. This paper explores technical solutions to reduce pollution from passenger ferries operating in the New York-New Jersey Harbor. The paper discusses and demonstrates a mixed-integer, non-linear programming model used to identify optimal control strategies for meeting NOx and PM reduction targets for 45 privately owned commuter ferries in the harbor. Results from the model can be used by policy-makers to craft programs aimed at achieving least-cost reduction targets. PMID:15887889

Winebrake, James J; Corbett, James J; Wang, Chengfeng; Farrell, Alexander E; Woods, Pippa



Online trajectory planning for UAVs using mixed integer linear programming  

E-print Network

This thesis presents a improved path planner using mixed-integer linear programming (MILP) to solve a receding horizon optimization problem for unmanned aerial vehicles (UAV's). Using MILP, hard constraints for obstacle ...

Culligan, Kieran Forbes



Mixed Integer Programming and Heuristic Scheduling for Space Communication Networks  

NASA Technical Reports Server (NTRS)

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.

Cheung, Kar-Ming; Lee, Charles H.



Global optimization of multiscenario mixed integer nonlinear programming models arising in the synthesis of integrated water networks under uncertainty  

Microsoft Academic Search

The problem of optimal synthesis of an integrated water system is addressed in this work, where water using processes and water treatment operations are combined into a single network such that the total cost of designing the network and operating it optimally is globally minimized. The network design has to be feasible and optimal over a given set of scenarios

Ramkumar Karuppiah; Ignacio E. Grossmann



Mixed-integer quadratic programming  

Microsoft Academic Search

This paper considers mixed-integer quadratic programs in which the objective function is quadratic in the integer and in the continuous variables, and the constraints are linear in the variables of both types. The generalized Benders' decomposition is a suitable approach for solving such programs. However, the program does not become more tractable if this method is used, since Benders' cuts

Rafael Lazimy



Modeling without categorical variables : a mixed-integer nonlinear program for the optimization of thermal insulation systems.  

SciTech Connect

Optimal design applications are often modeled by using categorical variables to express discrete design decisions, such as material types. A disadvantage of using categorical variables is the lack of continuous relaxations, which precludes the use of modern integer programming techniques. We show how to express categorical variables with standard integer modeling techniques, and we illustrate this approach on a load-bearing thermal insulation system. The system consists of a number of insulators of different materials and intercepts that minimize the heat flow from a hot surface to a cold surface. Our new model allows us to employ black-box modeling languages and solvers and illustrates the interplay between integer and nonlinear modeling techniques. We present numerical experience that illustrates the advantage of the standard integer model.

Abhishek, K.; Leyffer, S.; Linderoth, J. T.; Mathematics and Computer Science; Lehigh Univ.



Mixed Integer Programming and Heuristic Scheduling for Space Communication  

NASA Technical Reports Server (NTRS)

Optimal planning and scheduling for a communication network was created where the nodes within the network are communicating at the highest possible rates while meeting the mission requirements and operational constraints. The planning and scheduling problem was formulated in the framework of Mixed Integer Programming (MIP) to introduce a special penalty function to convert the MIP problem into a continuous optimization problem, and to solve the constrained optimization problem using heuristic optimization. The communication network consists of space and ground assets with the link dynamics between any two assets varying with respect to time, distance, and telecom configurations. One asset could be communicating with another at very high data rates at one time, and at other times, communication is impossible, as the asset could be inaccessible from the network due to planetary occultation. Based on the network's geometric dynamics and link capabilities, the start time, end time, and link configuration of each view period are selected to maximize the communication efficiency within the network. Mathematical formulations for the constrained mixed integer optimization problem were derived, and efficient analytical and numerical techniques were developed to find the optimal solution. By setting up the problem using MIP, the search space for the optimization problem is reduced significantly, thereby speeding up the solution process. The ratio of the dimension of the traditional method over the proposed formulation is approximately an order N (single) to 2*N (arraying), where N is the number of receiving antennas of a node. By introducing a special penalty function, the MIP problem with non-differentiable cost function and nonlinear constraints can be converted into a continuous variable problem, whose solution is possible.

Lee, Charles H.; Cheung, Kar-Ming



560 IEEE TRANSACTIONS ON POWER SYSTEMS, VOL. 16, NO. 3, AUGUST 2001 A Mixed Integer Disjunctive Model for Transmission  

E-print Network

conditioning properties than the standard disjunctive model. The mixed integer program is solved and the mixed integer disjunctive model, optimality can be proven for several hard problem instances. Index. In many real world large-scale applications, the mathemat- ical model is a large-scale mixed integer

McCalley, James D.


Aircraft trajectory planning with collision avoidance using mixed integer linear programming  

Microsoft Academic Search

Describes a method for finding optimal trajectories for multiple aircraft avoiding collisions. Developments in spacecraft path-planning have shown that trajectory optimization including collision avoidance can be written as a linear program subject to mixed integer constraints, known as a mixed-integer linear program (MILP). This can be solved using commercial software written for the operations research community. In the paper, an

Arthur Richards



Mixed-integer nonlinear programming techniques for process systems engineering  

Microsoft Academic Search

This paper presents an overview of mixed-integer nonlinear programming techniques by first providing a unified treatment of the Branch and Bound, Outer-Approximation, Generalized Benders and Extended Cutting Plane methods as applied to nonlinear discrete optimization problems that are expressed in algebraic form. The extension of these methods is also considered for logic based representations. Finally, an overview of the applications

Ignacio E. Grossmann; Zdravko Kravanja



Mixed Integer Programming for Finding Tensegrity Structures  

E-print Network

Mixed Integer Programming for Finding Tensegrity Structures Shintaro Ehara Yoshihiro Kanno University of Tokyo (Japan) November 4, 2009 #12;tensegrity -- definition MIP for Finding Tensegrities CODE at most one strut s topology () #12;tensegrity -- definition MIP for Finding Tensegrities CODE 2009 ­ 2

Kanno, Yoshihiro


A Lagrangean based branch-and-cut algorithm for global optimization of nonconvex mixed-integer nonlinear programs with decomposable structures  

Microsoft Academic Search

In this work we present a global optimization algorithm for solving a class of large-scale nonconvex optimization models that\\u000a have a decomposable structure. Such models, which are very expensive to solve to global optimality, are frequently encountered\\u000a in two-stage stochastic programming problems, engineering design, and also in planning and scheduling. A generic formulation\\u000a and reformulation of the decomposable models is

Ramkumar Karuppiah; Ignacio E. Grossmann



Preprint of the paper which appeares in Proc. CEM 2006, Aachen, Germany, April 4-6, 2006 Mixed-integer simulation-based optimization for a  

E-print Network

of the coil (Fig. 1). Especially the position of the coil blocks and the number of turns in each coil block such as, e.g., a minimal distance between two adjacent coil blocks. Invoking a separate real-valued optimization for every possible distribution of the integer number of turns over the coil blocks

Stryk, Oskar von


A mixed integer program for flight-level assignment and speed control for conflict resolution  

Microsoft Academic Search

We consider the air traffic conflict resolution problem and develop an optimization model for generating speed trajectories that minimize the fuel expended to avoid conflicts. The problem is formulated by metering aircraft at potential conflict points. The developed model is a mixed integer linear program that can be solved in near real-time for large number of aircraft.

Adan Vela; Senay Solak; William Singhose; John-Paul Clarke



Mixed-Integer Nonlinear Programming Approach for Short-Term Hydro Scheduling  

Microsoft Academic Search

This paper is on the problem of short-term hydro scheduling, particularly concerning a head-sensitive hydro chain. A novel mixed-integer nonlinear programming approach is proposed for optimizing power generation efficiency. The proposed approach considers not only the nonlinear dependence between power generation, water discharge and head, but also start-up costs for the hydro units and discontinuous operating regions, in order to

J. P. S. Catalao; H. M. I. Pousinho; V. M. F. Mendes



A mixed-integer linear programming model for the continuous casting planning  

Microsoft Academic Search

The development of optimization models for planning and scheduling is one of the most useful tools for improving productivity of a large number of manufacturing companies. This paper presents a mixed-integer programming model for scheduling steelmaking-continuous casting production. We first review the recent works in continuous casting planning. We focus on a model inspired from an application of steelmaking-continuous casting

A. Bellabdaoui; J. Teghem



Orbital rendezvous mission planning using mixed integer nonlinear programming  

NASA Astrophysics Data System (ADS)

The rendezvous and docking mission is usually divided into several phases, and the mission planning is performed phase by phase. A new planning method using mixed integer nonlinear programming, which investigates single phase parameters and phase connecting parameters simultaneously, is proposed to improve the rendezvous mission's overall performance. The design variables are composed of integers and continuous-valued numbers. The integer part consists of the parameters for station-keeping and sensor-switching, the number of maneuvers in each rendezvous phase and the number of repeating periods to start the rendezvous mission. The continuous part consists of the orbital transfer time and the station-keeping duration. The objective function is a combination of the propellant consumed, the sun angle which represents the power available, and the terminal precision of each rendezvous phase. The operational requirements for the spacecraft-ground communication, sun illumination and the sensor transition are considered. The simple genetic algorithm, which is a combination of the integer-coded and real-coded genetic algorithm, is chosen to obtain the optimal solution. A practical rendezvous mission planning problem is solved by the proposed method. The results show that the method proposed can solve the integral rendezvous mission planning problem effectively, and the solution obtained can satisfy the operational constraints and has a good overall performance.

Zhang, Jin; Tang, Guo-jin; Luo, Ya-Zhong; Li, Hai-yang



Radiation Treatment Planning: Mixed Integer Programming Formulations and Approaches  

E-print Network

Radiation Treatment Planning: Mixed Integer Programming Formulations and Approaches Michael C. Ferris Robert R. Meyer Warren D'Souza October 2002 Abstract Radiation therapy is extensively used diagnosed with cancer in the U.S will undergo treatment with radiation therapy. This form of therapy has

Ferris, Michael C.


Mixed-integer Quadratic Programming is in NP  

E-print Network

Jul 17, 2014 ... showing that if the decision version of mixed-integer quadratic ... where the minimal binary encoding length of any feasible integral ..... for every S ? {1,...,n}; note that the complexity of the additional constraints is O(n) and.



A mixed integer programming approach to reduce fuel load ...  

E-print Network

In this paper a Mixed Integer Programming (MIP) model is formulated which ... structure of fuels through methods including prescribed burning and mechanical clearing (King et al.,. 2008). ... wide range of problems related to fire management, forestry management, and ... mathematical model used in this study is presented.



Selection of vendors — A mixed-integer programming approach  

Microsoft Academic Search

In this paper, we propose a mixed-integer programming model to select vendors and determine the order quantities. The model considers the stochastic nature of demand, the quality of supplied parts, the cost of purchasing and transportation, the fixed cost for establishing vendors, and the cost of receiving poor quality parts. The model also considers the lead time requirements for the

Raja G. Kasilingam; Chee P. Lee



Perspective Reformulations of Mixed Integer Nonlinear Programs ...  

E-print Network

lection of indicator variables where each indicator variable controls a subset of the decision variables. ...... that were solved within a time limit of 8 hours, the average number of ... Note that the speed-up is solely due to the reduced time to solve ..... the instances are reported at html.



Application of mixed-integer programming in chemical engineering  

E-print Network

) collocation over finite element . . . . . . . . 69 5.5 Units in serial configuration (solid line: cold stream; dashed line: hot stream) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 5.6 Units in parallel configuration (solid line: cold stream... Application of Mixed-Integer Programming in Chemical Engineering Thomas Pogiatzis Homerton College University of Cambridge This dissertation is submitted for the degree of Doctor of Philosophy November 2012 ^En tz?e˜inon t˜h tsili˜ac p?s> stä bol...

Pogiatzis, Thomas



Second order symmetric duality for nonlinear minimax mixed integer programs  

Microsoft Academic Search

Wolfe type second order minimax mixed integer dual programs are formulated and a symmetric duality theorem is e established under separability and bonvexity\\/boncavity of the kernel function K(x, y). Mond-Weir type symmetric duality is also discussed under weaker bonvexity assumptions. Moreover, self-duality theorems for these pairs are obtained assuming K(x, y) to be skew symmetric.

T. R. Gulati; Izhar Ahmad



A Harmony Search Algorithm Combined with Differential Operator Applied to Reliability-Redundancy Optimization  

Microsoft Academic Search

The reliability-redundancy allocation problem can be approached as a mixed-integer programming problem. It has been solved by using optimization techniques such as dynamic programming, integer programming, and mixed-integer nonlinear programming. On the other hand, a broad class of meta-heuristics has been developed for reliability-redundancy optimization. Recently, a new meta-heuristics called harmony search (HS) algorithm has emerged. HS was conceptualized using

Leandro dos Santos Coelho; Diego Luis de Andrade Bernert; Viviana Cocco Mariani



PySP : modeling and solving stochastic mixed-integer programs in Python.  

SciTech Connect

Although stochastic programming is a powerful tool for modeling decision-making under uncertainty, various impediments have historically prevented its widespread use. One key factor involves the ability of non-specialists to easily express stochastic programming problems as extensions of deterministic models, which are often formulated first. A second key factor relates to the difficulty of solving stochastic programming models, particularly the general mixed-integer, multi-stage case. Intricate, configurable, and parallel decomposition strategies are frequently required to achieve tractable run-times. We simultaneously address both of these factors in our PySP software package, which is part of the COIN-OR Coopr open-source Python project for optimization. To formulate a stochastic program in PySP, the user specifies both the deterministic base model and the scenario tree with associated uncertain parameters in the Pyomo open-source algebraic modeling language. Given these two models, PySP provides two paths for solution of the corresponding stochastic program. The first alternative involves writing the extensive form and invoking a standard deterministic (mixed-integer) solver. For more complex stochastic programs, we provide an implementation of Rockafellar and Wets Progressive Hedging algorithm. Our particular focus is on the use of Progressive Hedging as an effective heuristic for approximating general multi-stage, mixed-integer stochastic programs. By leveraging the combination of a high-level programming language (Python) and the embedding of the base deterministic model in that language (Pyomo), we are able to provide completely generic and highly configurable solver implementations. PySP has been used by a number of research groups, including our own, to rapidly prototype and solve difficult stochastic programming problems.

Woodruff, David L. (University of California, Davis); Watson, Jean-Paul



Final Report---Next-Generation Solvers for Mixed-Integer Nonlinear Programs: Structure, Search, and Implementation  

SciTech Connect

The mathematical modeling of systems often requires the use of both nonlinear and discrete components. Problems involving both discrete and nonlinear components are known as mixed-integer nonlinear programs (MINLPs) and are among the most challenging computational optimization problems. This research project added to the understanding of this area by making a number of fundamental advances. First, the work demonstrated many novel, strong, tractable relaxations designed to deal with non-convexities arising in mathematical formulation. Second, the research implemented the ideas in software that is available to the public. Finally, the work demonstrated the importance of these ideas on practical applications and disseminated the work through scholarly journals, survey publications, and conference presentations.

Linderoth, Jeff T. [University of Wisconsin-Madison] [University of Wisconsin-Madison; Luedtke, James R. [University of Wisconsin-Madison] [University of Wisconsin-Madison



Outer approximation algorithms for separable nonconvex mixed-integer nonlinear programs  

Microsoft Academic Search

A rigorous decomposition approach to solve separable mixed-integer nonlinear programs where the participating functions are nonconvex is presented. The proposed algorithms consist of solving an alternating sequence of Relaxed Master Problems (mixed-integer linear program) and two nonlinear programming problems (NLPs). A sequence of valid nondecreasing lower bounds and upper bounds is generated by the algorithms which converge in a finite

Padmanaban Kesavan; Russell J. Allgor; Edward P. Gatzke; Paul I. Barton



FATCOP: A Fault Tolerant Condor-PVM Mixed Integer Programming Solver  

Microsoft Academic Search

We describe FATCOP, a new parallel mixed integer program solver written in PVM. The implementation uses the Condor resource management system to provide a virtual machine composed of otherwise idle computers. The solver differs from previous parallel branch-and-bound codes by implementing a general purpose parallel mixed integer programming algorithm in an op- portunistic multiple processor environment, as opposed to a

Qun Chen; Michael C. Ferris



The use of mixed-integer programming for inverse treatment planning with pre-defined field segments  

NASA Astrophysics Data System (ADS)

Complex intensity patterns generated by traditional beamlet-based inverse treatment plans are often very difficult to deliver. In the approach presented in this work the intensity maps are controlled by pre-defining field segments to be used for dose optimization. A set of simple rules was used to define a pool of allowable delivery segments and the mixed-integer programming (MIP) method was used to optimize segment weights. The optimization problem was formulated by combining real variables describing segment weights with a set of binary variables, used to enumerate voxels in targets and critical structures. The MIP method was compared to the previously used Cimmino projection algorithm. The field segmentation approach was compared to an inverse planning system with a traditional beamlet-based beam intensity optimization. In four complex cases of oropharyngeal cancer the segmental inverse planning produced treatment plans, which competed with traditional beamlet-based IMRT plans. The mixed-integer programming provided mechanism for imposition of dose-volume constraints and allowed for identification of the optimal solution for feasible problems. Additional advantages of the segmental technique presented here are: simplified dosimetry, quality assurance and treatment delivery.

Bednarz, Greg; Michalski, Darek; Houser, Chris; Saiful Huq, M.; Xiao, Ying; Rani Anne, Pramila; Galvin, James M.



A Mixed Integer Linear Program for Airport Departure Scheduling  

NASA Technical Reports Server (NTRS)

Aircraft departing from an airport are subject to numerous constraints while scheduling departure times. These constraints include wake-separation constraints for successive departures, miles-in-trail separation for aircraft bound for the same departure fixes, and time-window or prioritization constraints for individual flights. Besides these, emissions as well as increased fuel consumption due to inefficient scheduling need to be included. Addressing all the above constraints in a single framework while allowing for resequencing of the aircraft using runway queues is critical to the implementation of the Next Generation Air Transport System (NextGen) concepts. Prior work on airport departure scheduling has addressed some of the above. However, existing methods use pre-determined runway queues, and schedule aircraft from these departure queues. The source of such pre-determined queues is not explicit, and could potentially be a subjective controller input. Determining runway queues and scheduling within the same framework would potentially result in better scheduling. This paper presents a mixed integer linear program (MILP) for the departure-scheduling problem. The program takes as input the incoming sequence of aircraft for departure from a runway, along with their earliest departure times and an optional prioritization scheme based on time-window of departure for each aircraft. The program then assigns these aircraft to the available departure queues and schedules departure times, explicitly considering wake separation and departure fix restrictions to minimize total delay for all aircraft. The approach is generalized and can be used in a variety of situations, and allows for aircraft prioritization based on operational as well as environmental considerations. We present the MILP in the paper, along with benefits over the first-come-first-serve (FCFS) scheme for numerous randomized problems based on real-world settings. The MILP results in substantially reduced delays as compared to FCFS, and the magnitude of the savings depends on the queue and departure fix structure. The MILP assumes deterministic aircraft arrival times at the runway queues. However, due to taxi time uncertainty, aircraft might arrive either earlier or later than these deterministic times. Thus, to incorporate this uncertainty, we present a method for using the MILP with "overlap discounted rolling planning horizon". The approach is based on valuing near-term decision results more than future ones. We develop a model of taxitime uncertainty based on real-world data, and then compare the baseline FCFS delays with delays using the above MILP in a simple rolling-horizon method and in the overlap discounted scheme.

Gupta, Gautam; Jung, Yoon Chul



Mixed-integer linear programming model for refinery short-term scheduling of crude oil unloading with inventory management  

SciTech Connect

This paper addresses the problem of inventory management of a refinery that imports several types of crude oil which are delivered by different vessels. This problem involves optimal operation of crude oil unloading, its transfer from storage tanks to charging tanks, and the charging schedule for each crude oil distillation unit. A mixed-integer optimization model is developed which relies on time discretization. The problem involves bilinear equations due to mixing operations. However, the linearity in the form of a mixed-integer linear program (MILP) is maintained by replacing bilinear terms with individual component flows. The LP-based branch and bound method is applied to solve the model, and several techniques, such as priority branching and bounding, and special ordered sets are implemented to reduce the computation time. This formulation and solution method was applied to an industrial-size problem involving 3 vessels, 6 storage tanks, 4 charging tanks, and 3 crude oil distillation units over 15 time intervals. The MILP model contained 105 binary variables, 991 continuous variables, and 2,154 constraints and was effectively solved with the proposed solution approach.

Lee, H.; Park, S. [KAIST, Taejon (Korea, Republic of). Dept. of Chemical Engineering] [KAIST, Taejon (Korea, Republic of). Dept. of Chemical Engineering; Pinto, J.M.; Grossmann, I.E. [Carnegie-Mellon Univ., Pittsburgh, PA (United States). Dept. of Chemical Engineering] [Carnegie-Mellon Univ., Pittsburgh, PA (United States). Dept. of Chemical Engineering



An inexact fuzzy-chance-constrained two-stage mixed-integer linear programming approach for flood diversion planning under multiple uncertainties  

NASA Astrophysics Data System (ADS)

In this study, an inexact fuzzy-chance-constrained two-stage mixed-integer linear programming (IFCTIP) approach is developed for flood diversion planning under multiple uncertainties. A concept of the distribution with fuzzy boundary interval probability is defined to address multiple uncertainties expressed as integration of intervals, fuzzy sets and probability distributions. IFCTIP integrates the inexact programming, two-stage stochastic programming, integer programming and fuzzy-stochastic programming within a general optimization framework. IFCTIP incorporates the pre-regulated water-diversion policies directly into its optimization process to analyze various policy scenarios; each scenario has different economic penalty when the promised targets are violated. More importantly, it can facilitate dynamic programming for decisions of capacity-expansion planning under fuzzy-stochastic conditions. IFCTIP is applied to a flood management system. Solutions from IFCTIP provide desired flood diversion plans with a minimized system cost and a maximized safety level. The results indicate that reasonable solutions are generated for objective function values and decision variables, thus a number of decision alternatives can be generated under different levels of flood flows.

Guo, P.; Huang, G. H.; Li, Y. P.



Solution of Mixed-Integer Programming Problems on the XT5  

SciTech Connect

In this paper, we describe our experience with solving difficult mixed-integer linear programming problems (MILPs) on the petaflop Cray XT5 system at the National Center for Computational Sciences at Oak Ridge National Laboratory. We describe the algorithmic, software, and hardware needs for solving MILPs and present the results of using PICO, an open-source, parallel, mixed-integer linear programming solver developed at Sandia National Laboratories, to solve canonical MILPs as well as problems of interest arising from the logistics and supply chain management field.

Hartman-Baker, Rebecca J [ORNL; Busch, Ingrid Karin [ORNL; Hilliard, Michael R [ORNL; Middleton, Richard S [ORNL; Schultze, Michael [ORNL



Concrete Structure Design Using Mixed-Integer Nonlinear ...  

E-print Network

Nov 24, 2009 ... function includes material and labor costs for concrete, steel reinforcing bars, ... The design of minimum cost RC structures introduces a new class of optimization ...... data demonstrate that rounding is not an effective means of ...



Non-Convex Mixed-Integer Nonlinear Programming: A Survey  

E-print Network

Feb 28, 2012 ... (obtained when n1 = 0). This generality enables one to model a very wide ...... optimization have played critical roles in the development of algorithms .... for sustainable bioethanol supply chain considering detailed plant per-.



The type E simple assembly line balancing problem: A mixed integer ...  

E-print Network

Jan 20, 2015 ... This paper presents a mixed integer linear programming ... results of computational study on the benchmark data set demonstrate the efficacy of the ..... minimizing the number of stations (minimizing the station related costs) can be considered as the ...... types. Frontiers of Mechanical Engineering, 9(2):95.



A Hierarchy of Bounds for Stochastic Mixed-Integer Programs  

E-print Network

i.e., d? ? IRn2 , h? ? IRm2 , T? ? IRm2×n1 (the technology matrix), and W? ? IRm2×n2 (the ...... choosing alternative scenarios as the reference scenario can potentially change EGSO(k) ... much better job than the EVRS bound in terms of estimating the optimal objective value. .... to the algorithmic development of SMIPs.



A Mixed Integer Nonlinear Programming Framework for Fixed Path ...  

E-print Network

cost in journey time (as measured by the arrival time of the last-arriving robot). Results also ... We assume that for each vehicle, there exists a perfect lower level control to achieve the planned position and ... To achieve this, the authors cast the vehicle dynamic equations ... These piecewise cubic splines have continuous



Minimum-time control of systems with Coloumb friction: Near global optima via mixed integer linear programming  

SciTech Connect

This work presents a method of finding near global optima to minimum-time trajectory generation problem for systems that would be linear if it were not for the presence of Coloumb friction. The required final state of the system is assumed to be maintainable by the system, and the input bounds are assumed to be large enough so that they can overcome the maximum static Coloumb friction force. Other than the previous work for generating minimum-time trajectories for non redundant robotic manipulators for which the path in joint space is already specified, this work represents, to the best of the authors' knowledge, the first approach for generating near global optima for minimum-time problems involving a nonlinear class of dynamic systems. The reason the optima generated are near global optima instead of exactly global optima is due to a discrete-time approximation of the system (which is usually used anyway to simulate such a system numerically). The method closely resembles previous methods for generating minimum-time trajectories for linear systems, where the core operation is the solution of a Phase I linear programming problem. For the nonlinear systems considered herein, the core operation is instead the solution of a mixed integer linear programming problem.




DOTcvpSB, a software toolbox for dynamic optimization in systems biology  

PubMed Central

Background Mathematical optimization aims to make a system or design as effective or functional as possible, computing the quality of the different alternatives using a mathematical model. Most models in systems biology have a dynamic nature, usually described by sets of differential equations. Dynamic optimization addresses this class of systems, seeking the computation of the optimal time-varying conditions (control variables) to minimize or maximize a certain performance index. Dynamic optimization can solve many important problems in systems biology, including optimal control for obtaining a desired biological performance, the analysis of network designs and computer aided design of biological units. Results Here, we present a software toolbox, DOTcvpSB, which uses a rich ensemble of state-of-the-art numerical methods for solving continuous and mixed-integer dynamic optimization (MIDO) problems. The toolbox has been written in MATLAB and provides an easy and user friendly environment, including a graphical user interface, while ensuring a good numerical performance. Problems are easily stated thanks to the compact input definition. The toolbox also offers the possibility of importing SBML models, thus enabling it as a powerful optimization companion to modelling packages in systems biology. It serves as a means of handling generic black-box models as well. Conclusion Here we illustrate the capabilities and performance of DOTcvpSB by solving several challenging optimization problems related with bioreactor optimization, optimal drug infusion to a patient and the minimization of intracellular oscillations. The results illustrate how the suite of solvers available allows the efficient solution of a wide class of dynamic optimization problems, including challenging multimodal ones. The toolbox is freely available for academic use. PMID:19558728

Hirmajer, Tomáš; Balsa-Canto, Eva; Banga, Julio R



Aircraft deconfliction with speed regulation: new models from mixed-integer optimization  

E-print Network

of automation on the ground. In the context of aircraft conflict detection and resolution, air traffic control is still widely performed manually on the ground by air traffic controllers watching the traffic movements of managing traffic in such a way as to increase the capacity of control in the air sectors. Aicraft potential

Boyer, Edmond


On Revenue-Optimal Dynamic Auctions for Bidders with Interdependent Values  

NASA Astrophysics Data System (ADS)

In a dynamic market, being able to update one's value based on information available to other bidders currently in the market can be critical to having profitable transactions. This is nicely captured by the model of interdependent values (IDV): a bidder's value can explicitly depend on the private information of other bidders. In this paper we present preliminary results about the revenue properties of dynamic auctions for IDV bidders. We adopt a computational approach to design single-item revenue-optimal dynamic auctions with known arrivals and departures but (private) signals that arrive online. In leveraging a characterization of truthful auctions, we present a mixed-integer programming formulation of the design problem. Although a discretization is imposed on bidder signals the solution is a mechanism applicable to continuous signals. The formulation size grows exponentially in the dependence of bidders' values on other bidders' signals. We highlight general properties of revenue-optimal dynamic auctions in a simple parametrized example and study the sensitivity of prices and revenue to model parameters.

Constantin, Florin; Parkes, David C.


Final Report---Optimization Under Nonconvexity and Uncertainty: Algorithms and Software  

SciTech Connect

the goal of this work was to develop new algorithmic techniques for solving large-scale numerical optimization problems, focusing on problems classes that have proven to be among the most challenging for practitioners: those involving uncertainty and those involving nonconvexity. This research advanced the state-of-the-art in solving mixed integer linear programs containing symmetry, mixed integer nonlinear programs, and stochastic optimization problems. The focus of the work done in the continuation was on Mixed Integer Nonlinear Programs (MINLP)s and Mixed Integer Linear Programs (MILP)s, especially those containing a great deal of symmetry.

Jeff Linderoth



Optimization by record dynamics  

NASA Astrophysics Data System (ADS)

Large dynamical changes in thermalizing glassy systems are triggered by trajectories crossing record sized barriers, a behavior revealing the presence of a hierarchical structure in configuration space. The observation is here turned into a novel local search optimization algorithm dubbed record dynamics optimization, or RDO. RDO uses the Metropolis rule to accept or reject candidate solutions depending on the value of a parameter akin to the temperature and minimizes the cost function of the problem at hand through cycles where its ‘temperature’ is raised and subsequently decreased in order to expediently generate record high (and low) values of the cost function. Below, RDO is introduced and then tested by searching for the ground state of the Edwards-Anderson spin-glass model, in two and three spatial dimensions. A popular and highly efficient optimization algorithm, parallel tempering (PT), is applied to the same problem as a benchmark. RDO and PT turn out to produce solutions of similar quality for similar numerical effort, but RDO is simpler to program and additionally yields geometrical information on the system’s configuration space which is of interest in many applications. In particular, the effectiveness of RDO strongly indicates the presence of the above mentioned hierarchically organized configuration space, with metastable regions indexed by the cost (or energy) of the transition states connecting them.

Barettin, Daniele; Sibani, Paolo



Global Optimization of MINLP Problems in Process Synthesis and Design  

Microsoft Academic Search

Two new methodologies for the global optimization of MINLP models, the Special structure Mixed Integer Nonlinear BB, SMIN- BB, and the General structure Mixed Integer Nonlinear BB, GMIN- BB, are presented. Their theoretical foundations provide guarantees that the global optimum solution of MINLPs involving twice-differentiable nonconvex functions in the continuous variables can be identified. The conditions imposed on the functionality

C. S. Adjiman; I. P. Androulakis; C. A. Floudas



A mixed integer genetic algorithm used in biological and chemical defense applications  

Microsoft Academic Search

There are many problems in security and defense that require a robust optimization technique, including those that involve\\u000a the release of a chemical or biological contaminant. Our problem, in particular, is computing the parameters to be used in\\u000a modeling atmospheric transport and dispersion given field sensor measurements of contaminant concentration. This paper discusses\\u000a using a genetic algorithm for addressing this

Sue Ellen Haupt; Randy L. Haupt; George S. Young



Optimal dynamic detection of explosives  

SciTech Connect

The detection of explosives is a notoriously difficult problem, especially at stand-off distances, due to their (generally) low vapor pressure, environmental and matrix interferences, and packaging. We are exploring optimal dynamic detection to exploit the best capabilities of recent advances in laser technology and recent discoveries in optimal shaping of laser pulses for control of molecular processes to significantly enhance the standoff detection of explosives. The core of the ODD-Ex technique is the introduction of optimally shaped laser pulses to simultaneously enhance sensitivity of explosives signatures while reducing the influence of noise and the signals from background interferents in the field (increase selectivity). These goals are being addressed by operating in an optimal nonlinear fashion, typically with a single shaped laser pulse inherently containing within it coherently locked control and probe sub-pulses. With sufficient bandwidth, the technique is capable of intrinsically providing orthogonal broad spectral information for data fusion, all from a single optimal pulse.

Moore, David Steven [Los Alamos National Laboratory; Mcgrane, Shawn D [Los Alamos National Laboratory; Greenfield, Margo T [Los Alamos National Laboratory; Scharff, R J [Los Alamos National Laboratory; Rabitz, Herschel A [PRINCETON UNIV; Roslund, J [PRINCETON UNIV



New numerical methods for open-loop and feedback solutions to dynamic optimization problems  

NASA Astrophysics Data System (ADS)

The topic of the first part of this research is trajectory optimization of dynamical systems via computational swarm intelligence. Particle swarm optimization is a nature-inspired heuristic search method that relies on a group of potential solutions to explore the fitness landscape. Conceptually, each particle in the swarm uses its own memory as well as the knowledge accumulated by the entire swarm to iteratively converge on an optimal or near-optimal solution. It is relatively straightforward to implement and unlike gradient-based solvers, does not require an initial guess or continuity in the problem definition. Although particle swarm optimization has been successfully employed in solving static optimization problems, its application in dynamic optimization, as posed in optimal control theory, is still relatively new. In the first half of this thesis particle swarm optimization is used to generate near-optimal solutions to several nontrivial trajectory optimization problems including thrust programming for minimum fuel, multi-burn spacecraft orbit transfer, and computing minimum-time rest-to-rest trajectories for a robotic manipulator. A distinct feature of the particle swarm optimization implementation in this work is the runtime selection of the optimal solution structure. Optimal trajectories are generated by solving instances of constrained nonlinear mixed-integer programming problems with the swarming technique. For each solved optimal programming problem, the particle swarm optimization result is compared with a nearly exact solution found via a direct method using nonlinear programming. Numerical experiments indicate that swarm search can locate solutions to very great accuracy. The second half of this research develops a new extremal-field approach for synthesizing nearly optimal feedback controllers for optimal control and two-player pursuit-evasion games described by general nonlinear differential equations. A notable revelation from this development is that the resulting control law has an algebraic closed-form structure. The proposed method uses an optimal spatial statistical predictor called universal kriging to construct the surrogate model of a feedback controller, which is capable of quickly predicting an optimal control estimate based on current state (and time) information. With universal kriging, an approximation to the optimal feedback map is computed by conceptualizing a set of state-control samples from pre-computed extremals to be a particular realization of a jointly Gaussian spatial process. Feedback policies are computed for a variety of example dynamic optimization problems in order to evaluate the effectiveness of this methodology. This feedback synthesis approach is found to combine good numerical accuracy with low computational overhead, making it a suitable candidate for real-time applications. Particle swarm and universal kriging are combined for a capstone example, a near optimal, near-admissible, full-state feedback control law is computed and tested for the heat-load-limited atmospheric-turn guidance of an aeroassisted transfer vehicle. The performance of this explicit guidance scheme is found to be very promising; initial errors in atmospheric entry due to simulated thruster misfirings are found to be accurately corrected while closely respecting the algebraic state-inequality constraint.

Ghosh, Pradipto


Optimal prediction in molecular dynamics  

E-print Network

Optimal prediction approximates the average solution of a large system of ordinary differential equations by a smaller system. We present how optimal prediction can be applied to a typical problem in the field of molecular dynamics, in order to reduce the number of particles to be tracked in the computations. We consider a model problem, which describes a surface coating process, and show how asymptotic methods can be employed to approximate the high dimensional conditional expectations, which arise in optimal prediction. The thus derived smaller system is compared to the original system in terms of statistical quantities, such as diffusion constants. The comparison is carried out by Monte-Carlo simulations, and it is shown under which conditions optimal prediction yields a valid approximation to the original system.

Benjamin Seibold



Classification of drug molecules considering their IC50 values using mixed-integer linear programming based hyper-boxes method  

PubMed Central

Background A priori analysis of the activity of drugs on the target protein by computational approaches can be useful in narrowing down drug candidates for further experimental tests. Currently, there are a large number of computational methods that predict the activity of drugs on proteins. In this study, we approach the activity prediction problem as a classification problem and, we aim to improve the classification accuracy by introducing an algorithm that combines partial least squares regression with mixed-integer programming based hyper-boxes classification method, where drug molecules are classified as low active or high active regarding their binding activity (IC50 values) on target proteins. We also aim to determine the most significant molecular descriptors for the drug molecules. Results We first apply our approach by analyzing the activities of widely known inhibitor datasets including Acetylcholinesterase (ACHE), Benzodiazepine Receptor (BZR), Dihydrofolate Reductase (DHFR), Cyclooxygenase-2 (COX-2) with known IC50 values. The results at this stage proved that our approach consistently gives better classification accuracies compared to 63 other reported classification methods such as SVM, Naïve Bayes, where we were able to predict the experimentally determined IC50 values with a worst case accuracy of 96%. To further test applicability of this approach we first created dataset for Cytochrome P450 C17 inhibitors and then predicted their activities with 100% accuracy. Conclusion Our results indicate that this approach can be utilized to predict the inhibitory effects of inhibitors based on their molecular descriptors. This approach will not only enhance drug discovery process, but also save time and resources committed. PMID:18834515

Armutlu, Pelin; Ozdemir, Muhittin E; Uney-Yuksektepe, Fadime; Kavakli, I Halil; Turkay, Metin



DOI 10.1007/s11081-013-9226-6 A mixed-integer nonlinear program for the optimal  

E-print Network

in alternative sources of energy for commercial building applications due to their potential to supply on generators or to take advantage of time periods in which utility prices are low. Our research considers


Evaluating shortfalls in mixed-integer programming approaches for the optimal design and dispatch of distributed generation systems  

E-print Network

associated with volatile utility pricing and potentially high system capital costs. Energy technology and boilers), and/or thermal energy storage (e.g., hot water). For some markets, volatile utility pricing heat and power Fuel cells Building energy a b s t r a c t The distributed generation (DG) of combined


A mixed integer linear programming model to reconstruct phylogenies from single nucleotide polymorphism haplotypes under the maximum parsimony criterion  

PubMed Central

Background Phylogeny estimation from aligned haplotype sequences has attracted more and more attention in the recent years due to its importance in analysis of many fine-scale genetic data. Its application fields range from medical research, to drug discovery, to epidemiology, to population dynamics. The literature on molecular phylogenetics proposes a number of criteria for selecting a phylogeny from among plausible alternatives. Usually, such criteria can be expressed by means of objective functions, and the phylogenies that optimize them are referred to as optimal. One of the most important estimation criteria is the parsimony which states that the optimal phylogeny T?for a set H?of n haplotype sequences over a common set of variable loci is the one that satisfies the following requirements: (i) it has the shortest length and (ii) it is such that, for each pair of distinct haplotypes hi,hj?H, the sum of the edge weights belonging to the path from hi to hj in T? is not smaller than the observed number of changes between hi and hj. Finding the most parsimonious phylogeny for H?involves solving an optimization problem, called the Most Parsimonious Phylogeny Estimation Problem (MPPEP), which is NP-hard in many of its versions. Results In this article we investigate a recent version of the MPPEP that arises when input data consist of single nucleotide polymorphism haplotypes extracted from a population of individuals on a common genomic region. Specifically, we explore the prospects for improving on the implicit enumeration strategy of implicit enumeration strategy used in previous work using a novel problem formulation and a series of strengthening valid inequalities and preliminary symmetry breaking constraints to more precisely bound the solution space and accelerate implicit enumeration of possible optimal phylogenies. We present the basic formulation and then introduce a series of provable valid constraints to reduce the solution space. We then prove that these constraints can often lead to significant reductions in the gap between the optimal solution and its non-integral linear programming bound relative to the prior art as well as often substantially faster processing of moderately hard problem instances. Conclusion We provide an indication of the conditions under which such an optimal enumeration approach is likely to be feasible, suggesting that these strategies are usable for relatively large numbers of taxa, although with stricter limits on numbers of variable sites. The work thus provides methodology suitable for provably optimal solution of some harder instances that resist all prior approaches. PMID:23343437



Final Report-Optimization Under Uncertainty and Nonconvexity: Algorithms and Software  

SciTech Connect

The goal of this research was to develop new algorithmic techniques for solving large-scale numerical optimization problems, focusing on problems classes that have proven to be among the most challenging for practitioners: those involving uncertainty and those involving nonconvexity. This research advanced the state-of-the-art in solving mixed integer linear programs containing symmetry, mixed integer nonlinear programs, and stochastic optimization problems.

Jeff Linderoth



Static and Dynamic Optimization Models in Agriculture  

NSDL National Science Digital Library

Charles Moss, Associate Professor of Food and Resource Economics at the University of Florida developed this web site for his course on optimization models. The course aims to introduce students to classical optimization models, particularly mathematical programming and using optimal control theory to solve dynamic optimization models. The site provides lecture notes, slides from the lectures, assignments and solutions, and computer programs.


Creative dynamics approach to optimization problems  

Microsoft Academic Search

A type of dynamical system for solving optimization problems is introduced. The approach exploits a novel paradigm in nonlinear dynamics that is based upon the concept of terminal attractors and repellers. A class of dynamical systems-the unpredictable systems-is introduced and analyzed. These systems are represented in the form of coupled activation and learning dynamical equations whose ability to be spontaneously

Michail Zak; Nikzad Toomarian; Jacob Barhen



Golden optimal path in discrete-time dynamic optimization processes  

E-print Network

is called Golden if any state moves to the next state repeating the same Golden section in each transition divisions are the Golden section. Definition 2.1 A sequence x : {0, 1, . . .} R1 is called GoldenGolden optimal path in discrete-time dynamic optimization processes Seiichi IWAMOTO and Masami

Yasuda, Masami


Optimal dynamic interval management in external memory  

Microsoft Academic Search

The authors present a space- and I\\/O-optimal external-memory data structure for answering stabbing queries on a set of dynamically maintained intervals. The data structure settles an open problem in databases and I\\/O algorithms by providing the first optimal external-memory solution to the dynamic interval management problem, which is a special case of 2-dimensional range searching and a central problem for

Lars Arge; J. S. Vitter



Real-time path-planning using mixed-integer linear programming and global cost-to-go maps  

E-print Network

With the advance in the fields of computer science, control and optimization, it is now possible to build aerial vehicles which do not need pilots. An important capability for such autonomous vehicles is to be able to ...

Toupet, Olivier



Dynamic Mueller-polarimeter parameter optimization  

Microsoft Academic Search

The full polarization elements parameter optimization is conducted to minimize the determination error of Mueller matrix elements and to accelerate the measurement. The optimal retarders rotating frequency ratio is found. Expressions for the Mueller matrix elements is given in terms of components of the signal Fourier decomposition for the dynamic Muller-polarimeter (which scheme was proposed by R. Azzam in 1978)

Sergey N. Savenkov; Konstantin E. Yushtin; Borys M. Kolisnychenko; Yuri A. Skoblya



The Jalapeño dynamic optimizing compiler for Java  

Microsoft Academic Search

The Jalapeiio Dynamic Optimizing Compiler is a key com- ponent of the Jalapeiio Virtual Machine, a new Java' Vir- tual Machine (JVM) designed to support efficient and scal- able execution of Java applications on SMP server machines. This paper describes the design of the Jalapefio Optimizing Compiler, and the implementation results that we have ob- tained thus far. To the

Michael G. Burke; Jong-Deok Choi; Stephen J. Fink; David Grove; Michael Hind; Vivek Sarkar; Mauricio J. Serrano; Vugranam C. Sreedhar; Harini Srinivasan; John Whaley



Optimal Gait Analysis of Snake Robot Dynamics  

Microsoft Academic Search

Though there have been a lot of research in the area of snake-robot kinematics and dynamics, a little attention has been given to flnd out an optimal gait for the robot. This optimal gait until now is being calculated using a graphical method. An attempt, here, is made to get these optimum gait parameters using evolutionary algorithms. We intend to

Vipul Mehta


Proportional Integral Distributed Optimization for Dynamic Network Topologies  

E-print Network

Proportional Integral Distributed Optimization for Dynamic Network Topologies Greg Droge, Magnus Egerstedt Abstract--This paper investigates proportional-integral distributed optimization when the underlying informa- tion exchange network is dynamic. Proportional-integral distributed optimization

Egerstedt, Magnus


Optimal control of measure dynamics  

NASA Astrophysics Data System (ADS)

Optimal control problem is considered in the space of measures. The general principles of solving are presented. On the basis of these principles feedback control has been suggested for satisfying the state constraint in the form of equality. The principles of solving are developed for specific problems with a state constraint. In particular, optimal control problems of heat conductivity with the state constraints as equality are considered. Feedback is constructed by using bilinear control and integral transformation. Numerical solution is proposed on the base of the method of moments.

Kuzenkov, Oleg A.; Novozhenin, Alexey V.



Heuristic Optimization and Dynamical System Safety Verification  

E-print Network

for dynamical systems into a global optimization problem. We compare un­ tuned performance of several Simulated into the global optimization (GO) problem: min i2I (f(i)) ? ? 0 GO methods may therefore terminate when i is found b N b cos(N`) \\Gamma D sin(4N`) \\Gamma F v ! \\Gamma F c sign(!) \\Gamma F g ) (J l + Jm ) â?? -- a = (V

Neller, Todd W.


Heuristic Optimization and Dynamical System Safety Verification  

E-print Network

for dynamical systems into a global optimization problem. We compare untuned performance of several Simulated safety problem can be transformed into the global optimization (GO) problem: min i2I (f(i)) ? ? 0 GO]: â?? ` = ! â?? ! = (\\Gammai a N b sin(N`) + i b N b cos(N`) \\Gamma D sin(4N`) \\Gamma F v ! \\Gamma F c sign(!) \\Gamma F g )=(J

Neller, Todd W.


Optimal Dynamical Decoherence Control of a Qubit  

NASA Astrophysics Data System (ADS)

We present a theory of dynamical control by modulation for optimal decoherence reduction. The theory is based on the non-Markovian Euler-Lagrange equation for the energy-constrained field that minimizes the average dephasing rate of a qubit for any given dephasing spectrum.

Gordon, Goren; Kurizki, Gershon; Lidar, Daniel A.



Modeling Languages and Condor: Metacomputing for Optimization  

Microsoft Academic Search

A generic framework for utilizing the computational resources provided by a metacomputer to concurrently solve several optimization problems generated by a modeling language is postulated. A mechanism using the Condor resource manager and the AMPL and GAMS languages is devel- oped and applied to a technique for solving a mixed integer programming formulation of the feature selection problem. Due to

Michael C. Ferris; Todd S. Munson



Modeling the dynamics of ant colony optimization.  


The dynamics of Ant Colony Optimization (ACO) algorithms is studied using a deterministic model that assumes an average expected behavior of the algorithms. The ACO optimization metaheuristic is an iterative approach, where in every iteration, artificial ants construct solutions randomly but guided by pheromone information stemming from former ants that found good solutions. The behavior of ACO algorithms and the ACO model are analyzed for certain types of permutation problems. It is shown analytically that the decisions of an ant are influenced in an intriguing way by the use of the pheromone information and the properties of the pheromone matrix. This explains why ACO algorithms can show a complex dynamic behavior even when there is only one ant per iteration and no competition occurs. The ACO model is used to describe the algorithm behavior as a combination of situations with different degrees of competition between the ants. This helps to better understand the dynamics of the algorithm when there are several ants per iteration as is always the case when using ACO algorithms for optimization. Simulations are done to compare the behavior of the ACO model with the ACO algorithm. Results show that the deterministic model describes essential features of the dynamics of ACO algorithms quite accurately, while other aspects of the algorithms behavior cannot be found in the model. PMID:12227995

Merkle, Daniel; Middendorf, Martin



Application of optimal prediction to molecular dynamics  

SciTech Connect

Optimal prediction is a general system reduction technique for large sets of differential equations. In this method, which was devised by Chorin, Hald, Kast, Kupferman, and Levy, a projection operator formalism is used to construct a smaller system of equations governing the dynamics of a subset of the original degrees of freedom. This reduced system consists of an effective Hamiltonian dynamics, augmented by an integral memory term and a random noise term. Molecular dynamics is a method for simulating large systems of interacting fluid particles. In this thesis, I construct a formalism for applying optimal prediction to molecular dynamics, producing reduced systems from which the properties of the original system can be recovered. These reduced systems require significantly less computational time than the original system. I initially consider first-order optimal prediction, in which the memory and noise terms are neglected. I construct a pair approximation to the renormalized potential, and ignore three-particle and higher interactions. This produces a reduced system that correctly reproduces static properties of the original system, such as energy and pressure, at low-to-moderate densities. However, it fails to capture dynamical quantities, such as autocorrelation functions. I next derive a short-memory approximation, in which the memory term is represented as a linear frictional force with configuration-dependent coefficients. This allows the use of a Fokker-Planck equation to show that, in this regime, the noise is {delta}-correlated in time. This linear friction model reproduces not only the static properties of the original system, but also the autocorrelation functions of dynamical variables.

Barber IV, John Letherman




E-print Network

Jul 22, 2014 ... multi-commodity network flow problem with piecewise linear costs. ...... Consider the MIP formulation with irrational data given by S = {x ? R2 ..... projects with labor constraints, Discrete Applied Mathematics, 112 (2001), pp.



Rigorous bounds for optimal dynamical decoupling  

SciTech Connect

We present rigorous performance bounds for the optimal dynamical decoupling pulse sequence protecting a quantum bit (qubit) against pure dephasing. Our bounds apply under the assumption of instantaneous pulses and of bounded perturbing environment and qubit-environment Hamiltonians such as those realized by baths of nuclear spins in quantum dots. We show that if the total sequence time is fixed the optimal sequence can be used to make the distance between the protected and unperturbed qubit states arbitrarily small in the number of applied pulses. If, on the other hand, the minimum pulse interval is fixed and the total sequence time is allowed to scale with the number of pulses, then longer sequences need not always be advantageous. The rigorous bound may serve as a testbed for approximate treatments of optimal decoupling in bounded models of decoherence.

Uhrig, Goetz S. [Lehrstuhl fuer Theoretische Physik I, Technische Universitaet Dortmund, Otto-Hahn Strasse 4, D-44221 Dortmund (Germany); Lidar, Daniel A. [Departments of Chemistry, Electrical Engineering, and Physics, Center for Quantum Information and Technology, University of Southern California, Los Angeles, California 90089 (United States)



Integrated intelligent optimized dynamic scheduling of semiconductor fabrication facilities  

Microsoft Academic Search

Semiconductor wafer fabrication facilities (fabs) cry for optimized dynamic scheduling approach due to its high uncertainty and complexity. A novel intelligent optimized dynamic scheduling method, integrating intelligent sequencing with dynamic dispatching, is proposed (abbreviated as ODC). ODC includes three sub-algorithms, i.e., a Partheno-Genetic Algorithm (PGA) to obtain optimized sequencing plan, a dispatching rule (DR) to achieve dynamic dispatching solution and

Li Li; Fei Qiao



Integrated DFM Framework for Dynamic Yield Optimization  

NSDL National Science Digital Library

This website includes an abstract of the following article. Users may request access to the full article via the website, and a direct link will be emailed to them. We present a new methodology for a balanced yield optimization and a new DFM (design for manufacturability) framework which implements it. Our approach allows designers to dynamically balance multiple factors contributing to yield loss and select optimal combination of DFM enhancements based on the current information about the IC layout, the manufacturing process, and known causes of failures. We bring together the information gained from layout analysis, layout aware circuit analysis, resolution enhancement and optical proximity correction tools, parasitics extraction, timing estimates, and other tools, to suggest the DFM solution which is optimized within the existing constraints on design time and available data. The framework allows us to integrate all available sources of yield information, characterize and compare proposed DFM solutions, quickly adjust them when new data or new analysis tools become available, fine tune DFM optimization for a particular design and process and provide the IC designer with a customized solution which characterizes the manufacturability of the design, identifies and classifies areas with the most opportunities for improvement, and suggests DFM improvements. The proposed methodology replaces the ad hoc approach to DFM which targets one yield loss cause at a time at the expense of other factors with a comprehensive analysis of competing DFM techniques and trade offs between them.


PILOT_PROTEIN: Identification of unmodified and modified proteins via high-resolution mass spectrometry and mixed-integer linear optimization  

PubMed Central

A novel protein identification framework, PILOT_PROTEIN, has been developed to construct a comprehensive list of all unmodified proteins that are present in a living sample. It uses the peptide identification results from the PILOT_SEQUEL algorithm to initially determine all unmodified proteins within the sample. Using a rigorous biclustering approach that groups incorrect peptide sequences with other homologous sequences, the number of false positives reported is minimized. A sequence tag procedure is then incorporated along with the untargeted PTM identification algorithm, PILOT_PTM, to determine a list of all modification types and sites for each protein. The unmodified protein identification algorithm, PILOT_PROTEIN, is compared to the methods SEQUEST, InsPecT, X!Tandem, VEMS, and ProteinProspector using both prepared protein samples and a more complex chromatin digest. The algorithm demonstrates superior protein identification accuracy with a lower false positive rate. All materials are freely available to the scientific community at PMID:22788846

Baliban, Richard C.; DiMaggio, Peter A.; Plazas-Mayorca, Mariana D.; Garcia, Benjamin A.; Floudas, Christodoulos A.



certificates of optimality and sensitivity analysis using generalized ...  

E-print Network

Dec 9, 2014 ... A Certificate of Optimality for an MILP is information that can be used to ..... Then by theorem of Minkowski and Weyl (see [17] page 88), we can write .... to Mixed Integer Programming, Thesis (Ph.D.)–Carleton University, 2014.



Using MILP for UAVs Trajectory Optimization under Radar Detection Risk  

Microsoft Academic Search

This paper presents an approach to trajectories optimization for Unmanned Aerial Vehicle (UAV) in presence of obstacles, waypoints, and threat zones such as radar detection regions, using Mixed Integer Linear Programming (MILP). The main result is the linear approximation of a nonlinear radar detection risk function with integer constraints and indicator 0-1 variables. Several results are presented to show that

José Jaime Ruz; Orlando Arevalo; Jesús Manuel De La Cruz; Gonzalo Pajares



Conceptual modeling to optimize the haul and transfer of municipal solid waste  

Microsoft Academic Search

Two conceptual mixed integer linear optimization models were developed to optimize the haul and transfer of municipal solid waste (MSW) prior to landfilling. One model is based on minimizing time (h\\/d), whilst the second model is based on minimizing total cost (€\\/d). Both models aim to calculate the optimum pathway to haul MSW from source nodes (waste production nodes, such

D. P. Komilis



Robust optimization with transiently chaotic dynamical systems  

NASA Astrophysics Data System (ADS)

Efficiently solving hard optimization problems has been a strong motivation for progress in analog computing. In a recent study we presented a continuous-time dynamical system for solving the NP-complete Boolean satisfiability (SAT) problem, with a one-to-one correspondence between its stable attractors and the SAT solutions. While physical implementations could offer great efficiency, the transiently chaotic dynamics raises the question of operability in the presence of noise, unavoidable on analog devices. Here we show that the probability of finding solutions is robust to noise intensities well above those present on real hardware. We also developed a cellular neural network model realizable with analog circuits, which tolerates even larger noise intensities. These methods represent an opportunity for robust and efficient physical implementations.

Sumi, R.; Molnár, B.; Ercsey-Ravasz, M.



An Optimal Dynamic Threat Evaluation and Weapon Scheduling Technique  

NASA Astrophysics Data System (ADS)

Real time scheduling problems demand high level of flexibility and robustness under complex dynamic scenarios. Threat Evaluation (TE) and Weapon Assignment (WA), together TEWA is one such complex dynamic system having optimal or near optimal utilization of scarce defensive resources of supreme priority. Several static solutions of TEWA have been proposed. This paper discusses an optimal dynamic multi-air threat evaluation and weapon allocation algorithm using a variant of Stable Marriage Algorithm (SMA). WA uses a new dynamic weapon scheduling algorithm, allowing multiple engagements using shoot-look-shoot strategy, to compute near-optimal solution. Testing part of this paper shows feasibility of this approach for a range of scenarios.

Naeem, H.; Masood, A.


Nonsmooth dynamic optimization of systems with varying structure  

E-print Network

In this thesis, an open-loop numerical dynamic optimization method for a class of dynamic systems is developed. The structure of the governing equations of the systems under consideration change depending on the values of ...

Yunt, Mehmet, 1975-



Static and dynamic optimization solutions for gait are practically equivalent.  


The proposition that dynamic optimization provides better estimates of muscle forces during gait than static optimization is examined by comparing a dynamic solution with two static solutions. A 23-degree-of-freedom musculoskeletal model actuated by 54 Hill-type musculotendon units was used to simulate one cycle of normal gait. The dynamic problem was to find the muscle excitations which minimized metabolic energy per unit distance traveled, and which produced a repeatable gait cycle. In the dynamic problem, activation dynamics was described by a first-order differential equation. The joint moments predicted by the dynamic solution were used as input to the static problems. In each static problem, the problem was to find the muscle activations which minimized the sum of muscle activations squared, and which generated the joint moments input from the dynamic solution. In the first static problem, muscles were treated as ideal force generators; in the second, they were constrained by their force-length-velocity properties; and in both, activation dynamics was neglected. In terms of predicted muscle forces and joint contact forces, the dynamic and static solutions were remarkably similar. Also, activation dynamics and the force-length-velocity properties of muscle had little influence on the static solutions. Thus, for normal gait, if one can accurately solve the inverse dynamics problem and if one seeks only to estimate muscle forces, the use of dynamic optimization rather than static optimization is currently not justified. Scenarios in which the use of dynamic optimization is justified are suggested. PMID:11165278

Anderson, F C; Pandy, M G



Optimal control of HIV/AIDS dynamic: Education and treatment  

NASA Astrophysics Data System (ADS)

A mathematical model which describes the transmission dynamics of HIV/AIDS is developed. The optimal control representing education and treatment for this model is explored. The existence of optimal Control is established analytically by the use of optimal control theory. Numerical simulations suggest that education and treatment for the infected has a positive impact on HIV/AIDS control.

Sule, Amiru; Abdullah, Farah Aini



Chaotic dynamics in optimal monetary policy  

NASA Astrophysics Data System (ADS)

There is by now a large consensus in modern monetary policy. This consensus has been built upon a dynamic general equilibrium model of optimal monetary policy as developed by, e.g., Goodfriend and King [ NBER Macroeconomics Annual 1997 edited by B. Bernanke and J. Rotemberg (Cambridge, Mass.: MIT Press, 1997), pp. 231 282], Clarida et al. [J. Econ. Lit. 37, 1661 (1999)], Svensson [J. Mon. Econ. 43, 607 (1999)] and Woodford [ Interest and Prices: Foundations of a Theory of Monetary Policy (Princeton, New Jersey, Princeton University Press, 2003)]. In this paper we extend the standard optimal monetary policy model by introducing nonlinearity into the Phillips curve. Under the specific form of nonlinearity proposed in our paper (which allows for convexity and concavity and secures closed form solutions), we show that the introduction of a nonlinear Phillips curve into the structure of the standard model in a discrete time and deterministic framework produces radical changes to the major conclusions regarding stability and the efficiency of monetary policy. We emphasize the following main results: (i) instead of a unique fixed point we end up with multiple equilibria; (ii) instead of saddle-path stability, for different sets of parameter values we may have saddle stability, totally unstable equilibria and chaotic attractors; (iii) for certain degrees of convexity and/or concavity of the Phillips curve, where endogenous fluctuations arise, one is able to encounter various results that seem intuitively correct. Firstly, when the Central Bank pays attention essentially to inflation targeting, the inflation rate has a lower mean and is less volatile; secondly, when the degree of price stickiness is high, the inflation rate displays a larger mean and higher volatility (but this is sensitive to the values given to the parameters of the model); and thirdly, the higher the target value of the output gap chosen by the Central Bank, the higher is the inflation rate and its volatility.

Gomes, O.; Mendes, V. M.; Mendes, D. A.; Sousa Ramos, J.



Optimizing multiple dam removals under multiple objectives: Linking tributary habitat and the Lake Erie ecosystem  

Microsoft Academic Search

A model is proposed for optimizing the net benefits of removing multiple dams in U.S. watersheds of Lake Erie by quantifying impacts upon social, ecological, and economic objectives of importance to managers and stakeholders. Explicit consideration is given to the linkages between newly accessible tributary habitat and the lake's ecosystem. The model is a mixed integer linear program (MILP) that

Pearl Q. Zheng; Benjamin F. Hobbs; Joseph F. Koonce



A raster-based C program for siting a landfill with optimal compactness  

Microsoft Academic Search

Landfill siting requires performing spatial analyses for various factors to evaluate site suitability. A geographical information system, although capable of effectively manipulating spatial data, lacks the capability to locate an optimal site when compactness and other factors are considered simultaneously. In our previous work, a mixed-integer compactness model was proposed to overcome this difficulty. However, computational time with a conventional

Jehng-Jung Kao



Optimality-based Bound Contraction with Multiparametric Disaggregation for the Global  

E-print Network

unit commitment problem [19], which is a quadratic function of power; (ii) the power output in hydro power system, we show that this can be an efficient approach depending on the problem size. The relaxed. The global optimization of mixed-integer nonlinear problem (P) is important in areas such as power systems

Grossmann, Ignacio E.


Optimization of caviar and meat production from white sturgeon ( Acipenser transmontanus)  

Microsoft Academic Search

A multi-period mixed integer linear programming model was developed to optimize caviar and meat production from a white sturgeon production facility over a 51 year time horizon. The model is only moderately complex but is large with 10 102 equations and 17 905 variables. The model size allows for system stabilization and a minimization of end of model effects without

E. M. Wade; J. G. Fadel



Optimal planning of renewable energy-integrated electricity generation schemes with CO 2 reduction target  

Microsoft Academic Search

This paper presents a Mixed Integer Linear Programming (MILP) model that was developed for the optimal planning of electricity generation schemes for a nation to meet a specified CO2 emission target. The model was developed and implemented in General Algebraic Modeling System (GAMS) for the fleet of electricity generation in Peninsular Malaysia. In order to reduce the CO2 emissions by

Z. A. Muis; H. Hashim; Z. A. Manan; F. M. Taha; P. L. Douglas



Optimal supply chain design and management over a multi-period horizon under demand uncertainty. Part I: MINLP and MILP models  

E-print Network

1 Optimal supply chain design and management over a multi-period horizon under demand uncertainty motors. Keywords: supply chain, demand uncertainty, inventory management, mixed integer non An optimization model is proposed to redesign the supply chain of spare part delivery under demand uncertainty

Grossmann, Ignacio E.


Optimal damper positioning in beams for minimum dynamic compliance  

Microsoft Academic Search

The purpose of this paper is to propose an efficient and systematic procedure for finding the optimal damper positioning to minimize the dynamic compliance of a cantilever beam. The dynamic compliance is expressed in terms of the amplitude of a transfer function of the tip deflection evaluated at one of the undamped natural frequencies. The dynamic compliance is minimized subject

Izuru Takewaki



Multi-objective optimization for deepwater dynamic umbilical installation analysis  

NASA Astrophysics Data System (ADS)

We suggest a method of multi-objective optimization based on approximation model for dynamic umbilical installation. The optimization aims to find out the most cost effective size, quantity and location of buoyancy modules for umbilical installation while maintaining structural safety. The approximation model is constructed by the design of experiment (DOE) sampling and is utilized to solve the problem of time-consuming analyses. The non-linear dynamic analyses considering environmental loadings are executed on these sample points from DOE. Non-dominated Sorting Genetic Algorithm (NSGA-II) is employed to obtain the Pareto solution set through an evolutionary optimization process. Intuitionist fuzzy set theory is applied for selecting the best compromise solution from Pareto set. The optimization results indicate this optimization strategy with approximation model and multiple attribute decision-making method is valid, and provide the optimal deployment method for deepwater dynamic umbilical buoyancy modules.

Yang, HeZhen; Wang, AiJun; Li, HuaJun



February 15, 2006 Dynamic Optimization for  

E-print Network

Predictive Control Regulatory Control Feasibility Performance Corporate Decision Pyramid for Process Predictive Control Regulatory Control Feasibility Performance Corporate Decision Pyramid for Process Optimization (power utility system optimization, $2.5M in first year 7 #12

Grossmann, Ignacio E.


Structural optimization with constraints from dynamics in Lagrange  

NASA Technical Reports Server (NTRS)

Structural optimization problems are mostly solved under constraints from statics, such as stresses, strains, or displacements under static loads. But in the design process, dynamic quantities like eigenfrequencies or accelerations under dynamic loads become more and more important. Therefore, it is obvious that constraints from dynamics must be considered in structural optimization packages. This paper addresses the dynamics branch in MBB-LAGRANGE. It will concentrate on two topics, namely on the different formulations for eigenfrequency constraints and on frequency response constraints. For the latter the necessity of a system reduction is emphasized. The methods implemented in LAGRANGE are presented and examples are given.

Pfeiffer, F.; Kneppe, G.; Ross, C.



A Parallel Quadratic Programming Method for Dynamic Optimization ...  

E-print Network

A Parallel Quadratic Programming Method for Dynamic. Optimization ..... finite number of disjoint active sets further induces a subdivision of the dual ? space: ..... Cholesky factorization, the latter only regularizes those diagonal elements for.



Optimal dynamic pricing for clearance sales on the spot market  

E-print Network

In this thesis, we develop an optimal dynamic pricing strategy for clearance sales of a textile manufacturing company which allows last-minute cancellations of orders without penalty. This company faces the difficult task ...

Ileri, F?rat



Method to describe stochastic dynamics using an optimal coordinate  

NASA Astrophysics Data System (ADS)

A general method to describe the stochastic dynamics of Markov processes is suggested. The method aims to solve three related problems: the determination of an optimal coordinate for the description of stochastic dynamics; the reconstruction of time from an ensemble of stochastic trajectories; and the decomposition of stationary stochastic dynamics into eigenmodes which do not decay exponentially with time. The problems are solved by introducing additive eigenvectors which are transformed by a stochastic matrix in a simple way - every component is translated by a constant distance. Such solutions have peculiar properties. For example, an optimal coordinate for stochastic dynamics with detailed balance is a multivalued function. An optimal coordinate for a random walk on a line corresponds to the conventional eigenvector of the one-dimensional Dirac equation. The equation for the optimal coordinate in a slowly varying potential reduces to the Hamilton-Jacobi equation for the action function.

Krivov, Sergei V.



Dynamic optimization of fractionation schedules in radiation therapy  

E-print Network

In this thesis, we investigate the improvement in treatment effectiveness when dynamically optimizing the fractionation scheme in radiation therapy. In the first part of the thesis, we consider delivering a different dose ...

Ramakrishnan, Jagdish



An Optimization Framework for Dynamic, Distributed Real-Time Systems  

NASA Technical Reports Server (NTRS)

Abstract. This paper presents a model that is useful for developing resource allocation algorithms for distributed real-time systems .that operate in dynamic environments. Interesting aspects of the model include dynamic environments, utility and service levels, which provide a means for graceful degradation in resource-constrained situations and support optimization of the allocation of resources. The paper also provides an allocation algorithm that illustrates how to use the model for producing feasible, optimal resource allocations.

Eckert, Klaus; Juedes, David; Welch, Lonnie; Chelberg, David; Bruggerman, Carl; Drews, Frank; Fleeman, David; Parrott, David; Pfarr, Barbara



Modeling languages and Condor: metacomputing for optimization  

Microsoft Academic Search

.   A generic framework is postulated for utilizing the computational resources provided by a metacomputer to concurrently solve\\u000a a large number of optimization problems generated by a modeling language. An example of the framework using the Condor resource\\u000a manager and the AMPL and GAMS modeling languages is provided. A mixed integer programming formulation of a feature selection\\u000a problem from machine

Michael C. Ferris; Todd S. Munson



Dynamic Visualization in Modelling and Optimization of Ill Defined Problems  

E-print Network

Dynamic Visualization in Modelling and Optimization of Ill Defined Problems Case Studies and Generalizations William F. Eddy and Audris Mockus Abstract We consider visualization as a decision optimization of prevention and control. The fourth problem may be referred to as visual indexing. We perform exploratory


Optimization and realization of a rotor dynamic balance measureing algorithm  

Microsoft Academic Search

Based on the research on the least square influential coefficient method of rotor dynamic balance, to deal with some problems like excessive residual vibration and unsatisfied balance effect, genetic algorithm was introduced to optimize and realize the least square influential coefficient method by using the characteristic of global optimization search. Experimental result shows that the balance algorithm based on genetic

Zi-qiang Zhang; Chuan-jiang Li; Li-li Wan



Review of dynamic optimization methods in renewable natural resource management  

USGS Publications Warehouse

In recent years, the applications of dynamic optimization procedures in natural resource management have proliferated. A systematic review of these applications is given in terms of a number of optimization methodologies and natural resource systems. The applicability of the methods to renewable natural resource systems are compared in terms of system complexity, system size, and precision of the optimal solutions. Recommendations are made concerning the appropriate methods for certain kinds of biological resource problems.

Williams, B.K.



Vehicle dynamics applications of optimal control theory  

Microsoft Academic Search

The aim of the paper is to survey the various forms of optimal-control theory which have been applied to automotive problems and to present illustrative examples of applications studies, with assessments of the state of the art and of the contributions made through the use of optimal-control ideas. After a short introduction to the topic mentioning several questions to which

R. S. Sharp; Huei Peng



First principles molecular dynamics without self-consistent field optimization  

SciTech Connect

We present a first principles molecular dynamics approach that is based on time-reversible extended Lagrangian Born-Oppenheimer molecular dynamics [A. M. N. Niklasson, Phys. Rev. Lett. 100, 123004 (2008)] in the limit of vanishing self-consistent field optimization. The optimization-free dynamics keeps the computational cost to a minimum and typically provides molecular trajectories that closely follow the exact Born-Oppenheimer potential energy surface. Only one single diagonalization and Hamiltonian (or Fockian) construction are required in each integration time step. The proposed dynamics is derived for a general free-energy potential surface valid at finite electronic temperatures within hybrid density functional theory. Even in the event of irregular functional behavior that may cause a dynamical instability, the optimization-free limit represents a natural starting guess for force calculations that may require a more elaborate iterative electronic ground state optimization. Our optimization-free dynamics thus represents a flexible theoretical framework for a broad and general class of ab initio molecular dynamics simulations.

Souvatzis, Petros, E-mail: [Department of Physics and Astronomy, Division of Materials Theory, Uppsala University, Box 516, SE-75120 Uppsala (Sweden)] [Department of Physics and Astronomy, Division of Materials Theory, Uppsala University, Box 516, SE-75120 Uppsala (Sweden); Niklasson, Anders M. N., E-mail: [Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545 (United States)] [Theoretical Division, Los Alamos National Laboratory, Los Alamos, New Mexico 87545 (United States)



Optimized dynamical control of state transfer through noisy spin chains  

E-print Network

We propose a method of optimally controlling the tradeoff of speed and fidelity of state transfer through a noisy quantum channel (spin-chain). This process is treated as qubit state-transfer through a fermionic bath. We show that dynamical modulation of the boundary-qubits levels can ensure state transfer with the best tradeoff of speed and fidelity. This is achievable by dynamically optimizing the transmission spectrum of the channel. The resulting optimal control is robust against both static and fluctuating noise in the channel's spin-spin couplings. It may also facilitate transfer in the presence of diagonal disorder (on site energy noise) in the channel.

Analia Zwick; Gonzalo A. Alvarez; Guy Bensky; Gershon Kurizki



Optimal Trajectory Generation for Nonholonomic Robots in Dynamic Environments  

E-print Network

. INTRODUCTION Motion planning for car-like robots in dynamic environ- ments is an inherently difficult problem into a global motion planning framework [9]. In our early paper [9], we presented a global trajectory generationOptimal Trajectory Generation for Nonholonomic Robots in Dynamic Environments Yi Guo and Tang Tang

Guo, Yi



E-print Network

with understanding extremes of system behavior. Many analysis ques- tions take the form of boundary value problems be expressed as boundary value prob- lems, and solved using shooting methods. It is shown that performance system dynamics, dynamic embedded optimization, boundary value problems, limit cycles, grazing phenomena



E-print Network

with understanding extremes of system behavior. Many analysis ques- tions take the form of boundary value problems be expressed as boundary value prob- lems, and solved using shooting methods. It is shown that performance: Power system dynamics, dynamic embedded optimization, boundary value problems, limit cycles, grazing

Hiskens, Ian A.


An Optimization Framework for Dynamic, Distributed Real-Time Systems  

Microsoft Academic Search

This paper presents a model that is useful for developing resource allocation algorithms for distributed real-time systems .that operate in dynamic environments. Interesting aspects of the model include dynamic environments, utility and service levels, which provide a means for graceful degradation in resource-constrained situations and support optimization of the allocation of resources. The paper also provides an allocation algorithm that

Klaus H. Ecker; David W. Juedes; Lonnie R. Welch; David M. Chelberg; Carl Bruggeman; Frank Drews; David Fleeman; David Parrott; Barbara Pfarr



Dynamic Optimization for Optimal Control of Water Distribution Systems  

E-print Network

substation. The design is fully adaptable to changing operating conditions and has applicability to a wide Columbus OH 43201 ABSTRACT In this paper we consider the design of intelligent control policies for water dynamic programming and rules as design constraints, to minimize average costs over a long time horizon

Ertin, Emre


Fault tolerant and dynamic evolutionary optimization engines   

E-print Network

Mimicking natural evolution to solve hard optimization problems has played an important role in the artificial intelligence arena. Such techniques are broadly classified as Evolutionary Algorithms (EAs) and have been ...

Morales Reyes, Alicia



Dynamic optimization of metabolic networks coupled with gene expression.  


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. PMID:25451533

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



Dynamic optimization of chemotherapy outpatient scheduling with uncertainty.  


Chemotherapy outpatient scheduling is a complex, dynamic, uncertain problem. Chemotherapy centres are facing increasing demands and they need to increase their efficiency; however there are very few studies looking at using optimization technology on the chemotherapy scheduling problem. We address dynamic uncertainty that arises from requests for appointments that arrive in real time and uncertainty due to last minute scheduling changes. We propose dynamic template scheduling, a novel technique that combines proactive and online optimization and we apply it to the chemotherapy outpatient scheduling problem. We create a proactive template of an expected day in the chemotherapy centre using a deterministic optimization model and a sample of appointments. As requests for appointments arrive, we use the template to schedule them. When a request arrives that does not fit the template, we update the template online using the optimization model and a revised set of appointments. To accommodate last minute additions and cancellations to the schedule, we propose a shuffling algorithm that moves appointment start times within a predefined time limit. We test the use of dynamic template scheduling against the optimal offline solution and the actual performance of the cancer centre. We find improvements in makespan of up to 20 % when using dynamic template scheduling compared to current practice. PMID:24477637

Hahn-Goldberg, Shoshana; Carter, Michael W; Beck, J Christopher; Trudeau, Maureen; Sousa, Philomena; Beattie, Kathy




E-print Network

Jun 5, 2014 ... ... fi are smooth), as a dynamical model of allocation of resources ... In game theory, economics, social science, management, the multiobjective steep- ...... 1 and 2), making the process realistic in engineering and human.



Fully integrated aerodynamic/dynamic optimization of helicopter rotor blades  

NASA Technical Reports Server (NTRS)

A fully integrated aerodynamic/dynamic optimization procedure is described for helicopter rotor blades. The procedure combines performance and dynamic analyses with a general purpose optimizer. The procedure minimizes a linear combination of power required (in hover, forward flight, and maneuver) and vibratory hub shear. The design variables include pretwist, taper initiation, taper ratio, root chord, blade stiffnesses, tuning masses, and tuning mass locations. Aerodynamic constraints consist of limits on power required in hover, forward flight and maneuvers; airfoil section stall; drag divergence Mach number; minimum tip chord; and trim. Dynamic constraints are on frequencies, minimum autorotational inertia, and maximum blade weight. The procedure is demonstrated for two cases. In the first case, the objective function involves power required (in hover, forward flight and maneuver) and dynamics. The second case involves only hover power and dynamics. The designs from the integrated procedure are compared with designs from a sequential optimization approach in which the blade is first optimized for performance and then for dynamics. In both cases, the integrated approach is superior.

Walsh, Joanne L.; Lamarsh, William J., II; Adelman, Howard M.




Microsoft Academic Search

The paper outlines a beam non linear formulation capable of describing the three dimensional behaviours of composite blades and usable in an optimization system. The response couplings are all taken into account by the method used to characterize the beam cross section. Such a linear constitutive law represents the starting point for an updated Lagrangian formulation of the beam elastic

G. L. Ghiringhelli; M. Lanz


Particle swarm optimization with recombination and dynamic linkage discovery.  


In this paper, we try to improve the performance of the particle swarm optimizer by incorporating the linkage concept, which is an essential mechanism in genetic algorithms, and design a new linkage identification technique called dynamic linkage discovery to address the linkage problem in real-parameter optimization problems. Dynamic linkage discovery is a costless and effective linkage recognition technique that adapts the linkage configuration by employing only the selection operator without extra judging criteria irrelevant to the objective function. Moreover, a recombination operator that utilizes the discovered linkage configuration to promote the cooperation of particle swarm optimizer and dynamic linkage discovery is accordingly developed. By integrating the particle swarm optimizer, dynamic linkage discovery, and recombination operator, we propose a new hybridization of optimization methodologies called particle swarm optimization with recombination and dynamic linkage discovery (PSO-RDL). In order to study the capability of PSO-RDL, numerical experiments were conducted on a set of benchmark functions as well as on an important real-world application. The benchmark functions used in this paper were proposed in the 2005 Institute of Electrical and Electronics Engineers Congress on Evolutionary Computation. The experimental results on the benchmark functions indicate that PSO-RDL can provide a level of performance comparable to that given by other advanced optimization techniques. In addition to the benchmark, PSO-RDL was also used to solve the economic dispatch (ED) problem for power systems, which is a real-world problem and highly constrained. The results indicate that PSO-RDL can successfully solve the ED problem for the three-unit power system and obtain the currently known best solution for the 40-unit system. PMID:18179066

Chen, Ying-Ping; Peng, Wen-Chih; Jian, Ming-Chung



Global dynamic optimization approach to predict activation in metabolic pathways  

PubMed Central

Background During the last decade, a number of authors have shown that the genetic regulation of metabolic networks may follow optimality principles. Optimal control theory has been succesfully used to compute optimal enzyme profiles considering simple metabolic pathways. However, applying this optimal control framework to more general networks (e.g. branched networks, or networks incorporating enzyme production dynamics) yields problems that are analytically intractable and/or numerically very challenging. Further, these previous studies have only considered a single-objective framework. Results In this work we consider a more general multi-objective formulation and we present solutions based on recent developments in global dynamic optimization techniques. We illustrate the performance and capabilities of these techniques considering two sets of problems. First, we consider a set of single-objective examples of increasing complexity taken from the recent literature. We analyze the multimodal character of the associated non linear optimization problems, and we also evaluate different global optimization approaches in terms of numerical robustness, efficiency and scalability. Second, we consider generalized multi-objective formulations for several examples, and we show how this framework results in more biologically meaningful results. Conclusions The proposed strategy was used to solve a set of single-objective case studies related to unbranched and branched metabolic networks of different levels of complexity. All problems were successfully solved in reasonable computation times with our global dynamic optimization approach, reaching solutions which were comparable or better than those reported in previous literature. Further, we considered, for the first time, multi-objective formulations, illustrating how activation in metabolic pathways can be explained in terms of the best trade-offs between conflicting objectives. This new methodology can be applied to metabolic networks with arbitrary topologies, non-linear dynamics and constraints. PMID:24393148



Two Characterizations of Optimality in Dynamic Programming  

E-print Network

, a1), (s2, a2), . . . (2.1) of states and actions, along with a total discounted reward n=1 n-1 r as the expected total discounted reward, which we write as R (s) := E,s n=1 n-1 r(sn, an) . The optimal reward distribution q(· |s, a) on S, the daily reward r(· , ·) is a nonnegative function defined on pairs (s, a

Karatzas, Ioannis


Template based black-box optimization of dynamic neural fields.  


Due to their strong non-linear behavior, optimizing the parameters of dynamic neural fields is particularly challenging and often relies on expert knowledge and trial and error. In this paper, we study the ability of particle swarm optimization (PSO) and covariance matrix adaptation (CMA-ES) to solve this problem when scenarios specifying the input feeding the field and desired output profiles are provided. A set of spatial lower and upper bounds, called templates are introduced to define a set of desired output profiles. The usefulness of the method is illustrated on three classical scenarios of dynamic neural fields: competition, working memory and tracking. PMID:23692972

Fix, Jérémy



Optimal stabilization policy in a model of elasticity dynamics  

SciTech Connect

This paper considers the role of optimal fiscal policy in the context of a macrodynamic model of the open economy. The dynamics of the model are governed by the transition of aggregate demand elasticities from their short-run values to long-run values. If there are no instrument costs attributed to government expenditure, then optimal fiscal policy can achieve perfect stabilization of price and exchange-rate levels. With instrument costs however, this is no longer possible and the optimal fiscal rule is of the linear feedback variety. The paper investigates the properties of this rule.

Bhandari, J.S.; Hanson, D.A.



Shape optimization for maximum stability and dynamic stiffness  

NASA Technical Reports Server (NTRS)

Any optimization of structures for maximum stability or for maximum dynamic stiffness deals with an eigenvalue problem. The goal of this optimization is to raise the lowest eigenvalue (or eigenvalues) of the problem to its highest (optimal) level at a constant volume of the structure. Likely the lowest eigenvalue may be either inherently multi-modal or it can become multi-modal as a result of the optimization process. The multimodeness introduces some ambiguity to the eigenvalue problem and make the optimization difficult to handle. Thus far, only the simplest cases of multi-modal structures have been effectively optimized using rather elaborate analytical methods. Numerous publications report design of a minimum volume structure with different eigenvalues constraints, in which, however, the modality of the problem is assumed a priori. The method presented here utilizes a multi-modal optimality criteria and allows for inclusion of an arbitrary number of buckling or vibrations modes which might influence the optimization process. The real multi-modality of the problem, that is the number of modes participating in the final optimal design is determined iteratively. Because of a natural use of the FEM technique the method is easy to program and might be helpful in design of large flexible space structures.

Szyszkowski, W.



Practicing JUDO: Java under dynamic optimizations  

Microsoft Academic Search

ABSTRACT A high - performance implementation of a Java Virtual Machine (JVM) consists of efficient implementation of Just - In - Time (JIT) compilation, exception handling, synchronization mechanism, and garbage collection (GC) These components are tightly coupled to achieve high performance In this paper, we present some static and dynamic techniques implemented in the JIT compilation and exception handling of

Michal Cierniak; Guei-Yuan Lueh; James M. Stichnoth



Dynamic optimization of district energy grid  

NASA Astrophysics Data System (ADS)

The University of Iowa Power Plant operates utility generation and distribution for campus facilities, including electricity, steam, and chilled water. It is desirable to evaluate the optimal load combination of boilers, engines and chillers to meet the demand at minimal cost, particularly for future demand scenarios. An algorithm has been developed which takes into account the performance of individual units as part of the mix which ultimately supplies the campus and determine the degree that each should be operating to most efficiently meet demand. The algorithm is part of an integrated simulation tool which is specifically designed to apply traditional optimization techniques for a given (both current and possible) circumstance. The second component is to couple the algorithm with accurate estimates and historical data through which expected demand could be predicted. The simulation tool can account for any theoretical circumstance, which will be highly beneficial for strategic planning. As part of the process it is also necessary to determine the unique operating characteristics of the system components. The algorithms rely upon performance curves of individual system components (boiler, chiller, etc.) and those must be developed and refined when possible from experimental testing and commissioning or manufacturer supplied data.

Salsbery, Scott


Developing learning algorithms via optimized discretization of continuous dynamical systems.  


Most of the existing numerical optimization methods are based upon a discretization of some ordinary differential equations. In order to solve some convex and smooth optimization problems coming from machine learning, in this paper, we develop efficient batch and online algorithms based on a new principle, i.e., the optimized discretization of continuous dynamical systems (ODCDSs). First, a batch learning projected gradient dynamical system with Lyapunov's stability and monotonic property is introduced, and its dynamical behavior guarantees the accuracy of discretization-based optimizer and applicability of line search strategy. Furthermore, under fair assumptions, a new online learning algorithm achieving regret O(?T) or O(logT) is obtained. By using the line search strategy, the proposed batch learning ODCDS exhibits insensitivity to the step sizes and faster decrease. With only a small number of line search steps, the proposed stochastic algorithm shows sufficient stability and approximate optimality. Experimental results demonstrate the correctness of our theoretical analysis and efficiency of our algorithms. PMID:21880573

Tao, Qing; Sun, Zhengya; Kong, Kang



Multiobjective Optimization of Low-Energy Trajectories Using Optimal Control on Dynamical Channels  

NASA Technical Reports Server (NTRS)

We introduce a computational method to design efficient low-energy trajectories by extracting initial solutions from dynamical channels formed by invariant manifolds, and improving these solutions through variational optimal control. We consider trajectories connecting two unstable periodic orbits in the circular restricted 3-body problem (CR3BP). Our method leverages dynamical channels to generate a range of solutions, and approximates the areto front for impulse and time of flight through a multiobjective optimization of these solutions based on primer vector theory. We demonstrate the application of our method to a libration orbit transfer in the Earth-Moon system.

Coffee, Thomas M.; Anderson, Rodney L.; Lo, Martin W.




E-print Network

unknown. What are the critical points of such dynam- ical systems? What is their asymptotic behavior? Are these systems optimizing any aggregate function? In what way do these local interactions give rise was supported in part by DARPA/AFOSR MURI Award F49620-02-1-0325 and ONR YIP Award N00014

Bullo, Francesco


Dynamic SLA Management with Forecasting using Multi-Objective Optimizations  

E-print Network

. Robinson, T. Braun Technischer Bericht IAM-12-002 vom 5. September 2012 Institut f¨ur Informatik und angewandte Mathematik, #12;#12;Dynamic SLA Management with Forecasting using Multi-Objective Optimizations Alexandru-Florian Antonescu, Philip Robinson, Torsten Braun Technischer Bericht IAM-12-002 vom 5

Braun, Torsten


Dynamic Optimization of Chemical Processes using Ant Colony Framework  

Microsoft Academic Search

Ant colony framework is illustrated by considering dynamic optimization of six important bench marking examples. This new computational tool is simple to implement and can tackle problems with state as well as terminal constraints in a straightforward fashion. It requires fewer grid points to reach the global optimum at relatively very low computational effort. The examples with varying degree of

J. Rajesh; Kapil Gupta; Hari Shankar Kusumakar; Vaidyanathan K. Jayaraman; Bhaskar D. Kulkarni



Dynamic Neural Field Optimization using the Unscented Kalman Filter  

E-print Network

Dynamic Neural Field Optimization using the Unscented Kalman Filter Jeremy Fix, Matthieu Geist Kalman filters, a derivative-free algorithm for parameter estimation, which reveals to efficiently function which may be difficult or at least costly to perform. Kalman filters are a popular collection

Paris-Sud XI, Université de


Optimal control allocation in vehicle dynamics control for rollover mitigation  

Microsoft Academic Search

Vehicle dynamics control systems, previously only intended for yaw stabilization, are now being extended to incorporate rollover mitigation via braking. Current systems typically use a heuristic approach to control allocation, often utilizing only a subset of the available actuators. In this article a computationally-efficient, optimization-based control allocation strategy is used to map controller commands to braking forces on all four

Brad Schofield; T. Hagglund



Optimal Dynamic VideoOnDemand using Adaptive Broadcasting  

E-print Network

Optimal Dynamic Video­On­Demand using Adaptive Broadcasting Therese Biedl 1 , Erik D. Demaine 2 by any online strategy. 1 Introduction Video­on­demand. A drawback of traditional TV broadcasting schemes broadcast, neither of which is practical. One pro­ posed solution is to implement a video­on­demand (Vo

Demaine, Erik


Optimal Dynamic Video-On-Demand using Adaptive Broadcasting  

E-print Network

Optimal Dynamic Video-On-Demand using Adaptive Broadcasting Therese Biedl1 , Erik D. Demaine2 online strategy. 1 Introduction Video-on-demand. A drawback of traditional TV broadcasting schemes broadcast, neither of which is practical. One pro- posed solution is to implement a video-on-demand (Vo

Demaine, Erik


Dynamic optimization of a plate reactor start-up  

E-print Network

of a plate reactor #12;Safety conditions for ignition and conversion Avoid regions in the state-space, whereDynamic optimization of a plate reactor start-up Staffan Haugwitz, Per Hagander and John Bagterp Jørgensen Lund-Lyngby-�lborg-dagen 061101 Staffan Haugwitz et al Control of a plate reactor #12;Process


Can Structural Optimization Explain Slow Dynamics of Rocks?  

NASA Astrophysics Data System (ADS)

Slow dynamics is a recovery process that describes the return to an equilibrium state after some external energy input is applied and then removed. Experimental studies on many rocks have shown that a modest acoustic energy input results in slow dynamics. The recovery process of the stiffness has consistently been found to be linear to log(time) for a wide range of geomaterials and the time constants appear to be unique to the material [TenCate JA, Shankland TJ (1996), Geophys Res Lett 23, 3019-3022]. Measurements of this nonequilibrium effect in rocks (e.g. sandstones and limestones) have been linked directly to the cement holding the individual grains together [Darling TW, TenCate JA, Brown DW, Clausen B, Vogel SC (2004), Geophys Res Lett 31, L16604], also suggesting a potential link to porosity and permeability. Noting that slow dynamics consistently returns the overall stiffness of rocks to its maximum (original) state, it is hypothesized that the original state represents the global minimum strain energy state. Consequently the slow dynamics process represents the global minimization or optimization process. Structural optimization, which has been developed for engineering design, minimises the total strain energy by rearranging the material distribution [Kim H, Querin OM, Steven GP, Xie YM (2002), Struct Multidiscip Optim 24, 441-448]. The optimization process effectively rearranges the way the material is cemented. One of the established global optimization methods is simulated annealing (SA). Derived from cooling of metal to a thermal equilibrium, SA finds an optimum solution by iteratively moving the system towards the minimum energy state with a probability of 'uphill' moves. It has been established that the global optimum can be guaranteed by applying a log(time) linear cooling schedule [Hajek B (1988, Math Ops Res, 15, 311-329]. This work presents the original study of applying SA to the maximum stiffness optimization problem. Preliminary results indicate that the maximum stiffness solutions are achieved when using log(time) linear cooling schedule. The optimization history reveals that the overall stiffness of the structure is increased linearly to log(time). The results closely resemble the slow dynamics stiffness recovery of geomaterials and support the hypothesis that the slow dynamics is an optimization process for strain energy. [Work supported by the Department of Energy through the LANL/LDRD Program].

Kim, H.; Vistisen, O.; Tencate, J. A.



Optimal control analysis of the dynamic growth behavior of microorganisms.  


Understanding the growth behavior of microorganisms using modeling and optimization techniques is an active area of research in the fields of biochemical engineering and systems biology. In this paper, we propose a general modeling framework, based on Monod model, to model the growth of microorganisms. Utilizing the general framework, we formulate an optimal control problem with the objective of maximizing a long-term cellular goal and solve it analytically under various constraints for the growth of microorganisms in a two substrate batch environment. We investigate the relation between long term and short term cellular goals and show that the objective of maximizing cellular concentration at a fixed final time is equivalent to maximization of instantaneous growth rate. We then establish the mathematical connection between the generalized framework and optimal and cybernetic modeling frameworks and derive generalized governing dynamic equations for optimal and cybernetic models. We finally illustrate the influence of various constraints in the cybernetic modeling framework on the optimal growth behavior of microorganisms by solving several dynamic optimization problems using genetic algorithms. PMID:25223235

Mandli, Aravinda R; Modak, Jayant M



A New Particle Swarm Optimization Algorithm for Dynamic Environments  

NASA Astrophysics Data System (ADS)

Many real world optimization problems are dynamic in which global optimum and local optima change over time. Particle swarm optimization has performed well to find and track optima in dynamic environments. In this paper, we propose a new particle swarm optimization algorithm for dynamic environments. The proposed algorithm utilizes a parent swarm to explore the search space and some child swarms to exploit promising areas found by the parent swarm. To improve the search performance, when the search areas of two child swarms overlap, the worse child swarms will be removed. Moreover, in order to quickly track the changes in the environment, all particles in a child swarm perform a random local search around the best position found by the child swarm after a change in the environment is detected. Experimental results on different dynamic environments modelled by moving peaks benchmark show that the proposed algorithm outperforms other PSO algorithms, including FMSO, a similar particle swarm algorithm for dynamic environments, for all tested environments.

Kamosi, Masoud; Hashemi, Ali B.; Meybodi, M. R.


Aerospace applications on integer and combinatorial optimization  

NASA Technical Reports Server (NTRS)

Research supported by NASA Langley Research Center includes many applications of aerospace design optimization and is conducted by teams of applied mathematicians and aerospace engineers. This paper investigates the benefits from this combined expertise in formulating and solving integer and combinatorial optimization problems. Applications range from the design of large space antennas to interior noise control. A typical problem. for example, seeks the optimal locations for vibration-damping devices on an orbiting platform and is expressed as a mixed/integer linear programming problem with more than 1500 design variables.

Padula, S. L.; Kincaid, R. K.



Aerospace applications of integer and combinatorial optimization  

NASA Technical Reports Server (NTRS)

Research supported by NASA Langley Research Center includes many applications of aerospace design optimization and is conducted by teams of applied mathematicians and aerospace engineers. This paper investigates the benefits from this combined expertise in solving combinatorial optimization problems. Applications range from the design of large space antennas to interior noise control. A typical problem, for example, seeks the optimal locations for vibration-damping devices on a large space structure and is expressed as a mixed/integer linear programming problem with more than 1500 design variables.

Padula, S. L.; Kincaid, R. K.



Aerospace Applications of Integer and Combinatorial Optimization  

NASA Technical Reports Server (NTRS)

Research supported by NASA Langley Research Center includes many applications of aerospace design optimization and is conducted by teams of applied mathematicians and aerospace engineers. This paper investigates the benefits from this combined expertise in formulating and solving integer and combinatorial optimization problems. Applications range from the design of large space antennas to interior noise control. A typical problem, for example, seeks the optimal locations for vibration-damping devices on an orbiting platform and is expressed as a mixed/integer linear programming problem with more than 1500 design variables.

Padula, S. L.; Kincaid, R. K.



Optimization of aerodynamic designs using computational fluid dynamics  

NASA Astrophysics Data System (ADS)

An aerodynamic design optimization technique which couples direct optimization algorithms with the analysis capability provided by appropriate computational fluid dynamics (CFD) programs is presented. This technique is intended to be an aid in designing the aerodynamic shapes and test conditions required for the successful simulation of aircraft engine inlet conditions in a ground test environment. However, the method is applicable to other aerodynamic design problems. The approach minimizes a nonlinear least-squares objective function which may be defined in a region remote to the geometric surface being optimized. In this study finite-difference Euler and Navier-Stokes codes were applied to obtain the objective function evaluations, although the optimization method could be coupled with any CFD analysis technique. Results are presented for a NACA0012 airfoil, convergent/divergent nozzles, and a planar, supersonic forebody simulator design.

Huddleston, D. H.; Mastin, C. W.



Experimental Testing of Dynamically Optimized Photoelectron Beams  

NASA Astrophysics Data System (ADS)

We discuss the design of and initial results from an experiment in space-charge dominated beam dynamics which explores a new regime of high-brightness electron beam generation at the SPARC photoinjector. The scheme under study employs the tendency of intense electron beams to rearrange to produce uniform density, giving a nearly ideal beam from the viewpoint of space charge-induced emittance. The experiments are aimed at testing the marriage of this idea with a related concept, emittance compensation. We show that this new regime of operating photoinjector may be the preferred method of obtaining highest brightness beams with lower energy spread. We discuss the design of the experiment, including developing of a novel time-dependent, aerogel-based imaging system. This system has been installed at SPARC, and first evidence for nearly uniformly filled ellipsoidal charge distributions recorded.

Rosenzweig, J. B.; Cook, A. M.; Dunning, M.; England, R. J.; Musumeci, P.; Bellaveglia, M.; Boscolo, M.; Catani, L.; Cianchi, A.; Di Pirro, G.; Ferrario, M.; Fillipetto, D.; Gatti, G.; Palumbo, L.; Serafini, L.; Vicario, C.; Jones, S.



Experimental Testing of Dynamically Optimized Photoelectron Beams  

SciTech Connect

We discuss the design of and initial results from an experiment in space-charge dominated beam dynamics which explores a new regime of high-brightness electron beam generation at the SPARC photoinjector. The scheme under study employs the tendency of intense electron beams to rearrange to produce uniform density, giving a nearly ideal beam from the viewpoint of space charge-induced emittance. The experiments are aimed at testing the marriage of this idea with a related concept, emittance compensation. We show that this new regime of operating photoinjector may be the preferred method of obtaining highest brightness beams with lower energy spread. We discuss the design of the experiment, including developing of a novel time-dependent, aerogel-based imaging system. This system has been installed at SPARC, and first evidence for nearly uniformly filled ellipsoidal charge distributions recorded.

Rosenzweig, J. B.; Cook, A. M.; Dunning, M.; England, R. J. [UCLA Dept. of Physics and Astronomy, 405 Hilgard Ave., Los Angeles, CA 90034 (United States); Musumeci, P. [Istituto Nazionale di Fisica Nucleare, Sezione Roma 1, Rome (RM) (Italy); Bellaveglia, M.; Boscolo, M.; Catani, L.; Cianchi, A.; Di Pirro, G.; Ferrario, M.; Fillipetto, D.; Gatti, G.; Palumbo, L.; Vicario, C. [Istituto Nazionale di Fisica Nucleare, Laboratori Nazionale di Frascati, Frascati (RM) (Italy); Serafini, L.; Jones, S. [Jet Propulsion Laboratory, California Institute of Technology, 4800 Oak Grove Drive, Pasadena, CA 91101 (United States)



A Robust Optimization Approach to Dynamic Pricing and Inventory Control with no Backorders  

E-print Network

A Robust Optimization Approach to Dynamic Pricing and Inventory Control with no Backorders Elodie optimization formulation for dealing with demand uncertainty in a dynamic pricing and inventory control problem optimization model and its application to dynamic pricing and inventory control. In [1] we study the problem

Adida, Elodie


Optimization of the dynamic inducer wind turbine system  

NASA Astrophysics Data System (ADS)

The dynamic inducer, essentially a horizontal axis wind turbine (HAWT) rotor with small vanes at the tips is a promising, advanced technology wind turbine concept. By adding small vanes to the tip of the conventional rotor, significant increases in power can be obtained with the dynamic inducer system. The development of the system is reviewed, including past theoretical and experimental programs. Recent tow tests and wind tunnel tests established the predicted augmentation power. A new optimization program is outlined, based on advanced theory back by extensive wind tunnel testing, aimed at developing an advanced dynamic inducer system for a state-of-the art high performance, two-bladed rotor system. It is estimated that the dynamic inducer rotor is about 20% more cost-effective than a conventional system.

Lissaman, P. B. S.; Zalay, A. D.; Hibbs, B.


Lookahead Branching for Mixed Integer Programming  

E-print Network

as possible. In fact, a long line of integer programming research in the 1970's was fo- ..... nodes to date, like the method of Achterberg, Koch, and Martin [1]. ..... Deutschen Mathematiker-Vereinigung, International Congress of Mathematicians




E-print Network

descriptions of the convex hulls of these reformulated single term bilinear sets and use them in a ...... Thus, our cuts seem to be doing their primary job of obtaining .... problem, Industrial and Engineering Chemistry Research, 38 (1999), pp.



Optimal Control of a Parabolic Equation with Dynamic Boundary Condition  

SciTech Connect

We investigate a control problem for the heat equation. The goal is to find an optimal heat transfer coefficient in the dynamic boundary condition such that a desired temperature distribution at the boundary is adhered. To this end we consider a function space setting in which the heat flux across the boundary is forced to be an L{sup p} function with respect to the surface measure, which in turn implies higher regularity for the time derivative of temperature. We show that the corresponding elliptic operator generates a strongly continuous semigroup of contractions and apply the concept of maximal parabolic regularity. This allows to show the existence of an optimal control and the derivation of necessary and sufficient optimality conditions.

Hoemberg, D., E-mail:; Krumbiegel, K., E-mail: [Weierstrass Institute for Applied Mathematics and Stochastics, Nonlinear Optimization and Inverse Problems (Germany); Rehberg, J., E-mail: [Weierstrass Institute for Applied Mathematics and Stochastics, Partial Differential Equations (Germany)



Optimally combining dynamical decoupling and quantum error correction  

PubMed Central

Quantum control and fault-tolerant quantum computing (FTQC) are two of the cornerstones on which the hope of realizing a large-scale quantum computer is pinned, yet only preliminary steps have been taken towards formalizing the interplay between them. Here we explore this interplay using the powerful strategy of dynamical decoupling (DD), and show how it can be seamlessly and optimally integrated with FTQC. To this end we show how to find the optimal decoupling generator set (DGS) for various subspaces relevant to FTQC, and how to simultaneously decouple them. We focus on stabilizer codes, which represent the largest contribution to the size of the DGS, showing that the intuitive choice comprising the stabilizers and logical operators of the code is in fact optimal, i.e., minimizes a natural cost function associated with the length of DD sequences. Our work brings hybrid DD-FTQC schemes, and their potentially considerable advantages, closer to realization. PMID:23559088

Paz-Silva, Gerardo A.; Lidar, D. A.



Dynamic Selection of Optimal Cryptographic Algorithms in a Runtime Environment  

Microsoft Academic Search

This paper presents the results of research conducted by the author in support of dynamic selection of optimal cryptographic algorithms in a runtime environment (DSOCARE), the author's doctoral dissertation. Based on DSOCARE framework, a first full-scale proof-of-concept prototype was developed by the author in Java and C#\\/VB. The prototype was used to perform collection, selection, and reporting functions on common

Jalal Raissi



Confronting dynamics and uncertainty in optimal decision making for conservation  

NASA Astrophysics Data System (ADS)

The effectiveness of conservation efforts ultimately depends on the recognition that decision making, and the systems that it is designed to affect, are inherently dynamic and characterized by multiple sources of uncertainty. To cope with these challenges, conservation planners are increasingly turning to the tools of decision analysis, especially dynamic optimization methods. Here we provide a general framework for optimal, dynamic conservation and then explore its capacity for coping with various sources and degrees of uncertainty. In broadest terms, the dynamic optimization problem in conservation is choosing among a set of decision options at periodic intervals so as to maximize some conservation objective over the planning horizon. Planners must account for immediate objective returns, as well as the effect of current decisions on future resource conditions and, thus, on future decisions. Undermining the effectiveness of such a planning process are uncertainties concerning extant resource conditions (partial observability), the immediate consequences of decision choices (partial controllability), the outcomes of uncontrolled, environmental drivers (environmental variation), and the processes structuring resource dynamics (structural uncertainty). Where outcomes from these sources of uncertainty can be described in terms of probability distributions, a focus on maximizing the expected objective return, while taking state-specific actions, is an effective mechanism for coping with uncertainty. When such probability distributions are unavailable or deemed unreliable, a focus on maximizing robustness is likely to be the preferred approach. Here the idea is to choose an action (or state-dependent policy) that achieves at least some minimum level of performance regardless of the (uncertain) outcomes. We provide some examples of how the dynamic optimization problem can be framed for problems involving management of habitat for an imperiled species, conservation of a critically endangered population through captive breeding, control of invasive species, construction of biodiversity reserves, design of landscapes to increase habitat connectivity, and resource exploitation. Although these decision making problems and their solutions present significant challenges, we suggest that a systematic and effective approach to dynamic decision making in conservation need not be an onerous undertaking. The requirements are shared with any systematic approach to decision making—a careful consideration of values, actions, and outcomes.

Williams, Byron K.; Johnson, Fred A.



Confronting dynamics and uncertainty in optimal decision making for conservation  

USGS Publications Warehouse

The effectiveness of conservation efforts ultimately depends on the recognition that decision making, and the systems that it is designed to affect, are inherently dynamic and characterized by multiple sources of uncertainty. To cope with these challenges, conservation planners are increasingly turning to the tools of decision analysis, especially dynamic optimization methods. Here we provide a general framework for optimal, dynamic conservation and then explore its capacity for coping with various sources and degrees of uncertainty. In broadest terms, the dynamic optimization problem in conservation is choosing among a set of decision options at periodic intervals so as to maximize some conservation objective over the planning horizon. Planners must account for immediate objective returns, as well as the effect of current decisions on future resource conditions and, thus, on future decisions. Undermining the effectiveness of such a planning process are uncertainties concerning extant resource conditions (partial observability), the immediate consequences of decision choices (partial controllability), the outcomes of uncontrolled, environmental drivers (environmental variation), and the processes structuring resource dynamics (structural uncertainty). Where outcomes from these sources of uncertainty can be described in terms of probability distributions, a focus on maximizing the expected objective return, while taking state-specific actions, is an effective mechanism for coping with uncertainty. When such probability distributions are unavailable or deemed unreliable, a focus on maximizing robustness is likely to be the preferred approach. Here the idea is to choose an action (or state-dependent policy) that achieves at least some minimum level of performance regardless of the (uncertain) outcomes. We provide some examples of how the dynamic optimization problem can be framed for problems involving management of habitat for an imperiled species, conservation of a critically endangered population through captive breeding, control of invasive species, construction of biodiversity reserves, design of landscapes to increase habitat connectivity, and resource exploitation. Although these decision making problems and their solutions present significant challenges, we suggest that a systematic and effective approach to dynamic decision making in conservation need not be an onerous undertaking. The requirements are shared with any systematic approach to decision making--a careful consideration of values, actions, and outcomes.

Williams, Byron K.; Johnson, Fred A.



Power distribution system planning with reliability modeling and optimization  

SciTech Connect

A new approach for the systemized optimization of power distribution systems is presented in this paper. Distribution system reliability is modeled in the optimization objective function via outage costs and costs of switching devices, along with the nonlinear costs of investment, maintenance and energy losses of both the substations and the feeders. The optimization model established is multi-stage, mixed-integer and nonlinear, which is solved by a network-flow programming algorithm. A multi-stage interlacing strategy and a nonlinearity iteration method are also designed. Supported by an extensive database, the planning software tool has been applied to optimize the power distribution system of a developing city.

Tang, Y. [Siemens Power Corp., Roswell, GA (United States)] [Siemens Power Corp., Roswell, GA (United States)



Human opinion dynamics: An inspiration to solve complex optimization problems  

PubMed Central

Human interactions give rise to the formation of different kinds of opinions in a society. The study of formations and dynamics of opinions has been one of the most important areas in social physics. The opinion dynamics and associated social structure leads to decision making or so called opinion consensus. Opinion formation is a process of collective intelligence evolving from the integrative tendencies of social influence with the disintegrative effects of individualisation, and therefore could be exploited for developing search strategies. Here, we demonstrate that human opinion dynamics can be utilised to solve complex mathematical optimization problems. The results have been compared with a standard algorithm inspired from bird flocking behaviour and the comparison proves the efficacy of the proposed approach in general. Our investigation may open new avenues towards understanding the collective decision making. PMID:24141795

Kaur, Rishemjit; Kumar, Ritesh; Bhondekar, Amol P.; Kapur, Pawan



Dynamic optimization of the Tennessee Eastman process using the OptControlCentre  

Microsoft Academic Search

This study focuses on the performance of large-scale nonlinear programming (NLP) solvers for the dynamic optimization in real-time of large processes. The matlab-based OptControlCentre (OCC) is coupled with large-scale optimization tools and developed for on-line, real-time dynamic optimization. To demonstrate these new developments, we consider the on-line, real-time dynamic optimization of the Tennessee Eastman (TE) challenge process in a nonlinear

Tobias Jockenhövel; Lorenz T. Biegler; Andreas Wächter



Optimized Uncertainty Quantification Algorithm Within a Dynamic Event Tree Framework  

SciTech Connect

Methods for developing Phenomenological Identification and Ranking Tables (PIRT) for nuclear power plants have been a useful tool in providing insight into modelling aspects that are important to safety. These methods have involved expert knowledge with regards to reactor plant transients and thermal-hydraulic codes to identify are of highest importance. Quantified PIRT provides for rigorous method for quantifying the phenomena that can have the greatest impact. The transients that are evaluated and the timing of those events are typically developed in collaboration with the Probabilistic Risk Analysis. Though quite effective in evaluating risk, traditional PRA methods lack the capability to evaluate complex dynamic systems where end states may vary as a function of transition time from physical state to physical state . Dynamic PRA (DPRA) methods provide a more rigorous analysis of complex dynamic systems. A limitation of DPRA is its potential for state or combinatorial explosion that grows as a function of the number of components; as well as, the sampling of transition times from state-to-state of the entire system. This paper presents a method for performing QPIRT within a dynamic event tree framework such that timing events which result in the highest probabilities of failure are captured and a QPIRT is performed simultaneously while performing a discrete dynamic event tree evaluation. The resulting simulation results in a formal QPIRT for each end state. The use of dynamic event trees results in state explosion as the number of possible component states increases. This paper utilizes a branch and bound algorithm to optimize the solution of the dynamic event trees. The paper summarizes the methods used to implement the branch-and-bound algorithm in solving the discrete dynamic event trees.

J. W. Nielsen; Akira Tokuhiro; Robert Hiromoto



Real-time optimal trajectory generation for constrained dynamical systems  

NASA Astrophysics Data System (ADS)

With the advent of powerful computing and efficient computational algorithms, real-time solutions to constrained optimal control problems are nearing a reality. In this thesis, we develop a computationally efficient Nonlinear Trajectory Generation (NTG) algorithm and describe its software implementation to solve, in real-time, nonlinear optimal trajectory generation problems for constrained systems. NTG is a nonlinear trajectory generation software package that combines nonlinear control theory, B-spline basis functions, and nonlinear programming. We compare NTG with other numerical optimal control problem solution techniques, such as direct collocation, shooting, adjoints, and differential inclusions. We demonstrate the performance of NTG on the Caltech Ducted Fan testbed. Aggressive, constrained optimal control problems are solved in real-time for hover-to-hover, forward flight, and terrain avoidance test cases. Real-time trajectory generation results are shown for both the two-degree of freedom and receding horizon control designs. Further experimental demonstration is provided with the station-keeping, reconfiguration, and deconfiguration of micro-satellite formation with complex nonlinear constraints. Successful application of NTG in these cases demonstrates reliable real-time trajectory generation, even for highly nonlinear and non-convex systems. The results are among the first to apply receding horizon control techniques for agile flight in an experimental setting, using representative dynamics and computation.

Milam, Mark Bradley


Modelling the Transmission Dynamics of Hepatitis B & Optimal control  

E-print Network

Hepatitis B is a potentially life-threatening viral infection that can cause illness and even death. The Hepatitis B virus is fifty to hundred times more infectious than HIV, and world wide an estimated two billion people have been affected (WHO2008). HBV is the most common serious viral infection and leading cause of deaths in main land in china South Asia and Africa and almost all parts of the world. Every year 300,000 people from HBV related diseases only in China. Hepatitis B is a common disease in Pakistan as every tenth person is carrier and every fifth person has been exposed to hepatitis B. We proposed a model to understand the transmission dynamics and prevalence of HBV. To control the prevalence of diseases, we use optimal control strategies, which reduce the latent, infected and carrier individuals and increase the total number of recovered and immune individuals. In order to do this, we show that an optimal control exists for this optimal control problem and then we use optimal control techniques ...

Mehmood, Nayyar




E-print Network

are satisfied. The optimal control function u \\Lambda (t) is assumed to be continuous. In many applicationsAN SQP METHOD FOR THE OPTIMAL CONTROL OF LARGE­SCALE DYNAMICAL SYSTEMS \\Lambda PHILIP E. GILL y a sequential quadratic programming (SQP) method for the optimal control of large­scale dynamical systems

Petzold, Linda R.


A two-stage support-vector-regression optimization model for municipal solid waste management – A case study of Beijing, China  

Microsoft Academic Search

In this study, a two-stage support-vector-regression optimization model (TSOM) is developed for the planning of municipal solid waste (MSW) management in the urban districts of Beijing, China. It represents a new effort to enhance the analysis accuracy in optimizing the MSW management system through coupling the support-vector-regression (SVR) model with an interval-parameter mixed integer linear programming (IMILP). The developed TSOM

C. Dai; Y. P. Li; G. H. Huang



Optimized dynamical decoupling for power-law noise spectra  

SciTech Connect

We analyze the suppression of decoherence by means of dynamical decoupling in the pure-dephasing spin-boson model for baths with power law spectra. The sequence of ideal pi pulses is optimized according to the power of the bath. We expand the decoherence function and separate the canceling divergences from the relevant terms. The proposed sequence is chosen to be the one minimizing the decoherence function. By construction, it provides the best performance. We analytically derive the conditions that must be satisfied. The resulting equations are solved numerically. The solutions are very close to the Carr-Purcell-Meiboom-Gill sequence for a soft cutoff of the bath while they approach the Uhrig dynamical-decoupling sequence as the cutoff becomes harder.

Pasini, S.; Uhrig, G. S. [Lehrstuhl fuer Theoretische Physik I, TU Dortmund, Otto-Hahn Strasse 4, D-44221 Dortmund (Germany)



Dynamic Simulation and Optimization of Nuclear Hydrogen Production Systems  

SciTech Connect

This project is part of a research effort to design a hydrogen plant and its interface with a nuclear reactor. This project developed a dynamic modeling, simulation and optimization environment for nuclear hydrogen production systems. A hybrid discrete/continuous model captures both the continuous dynamics of the nuclear plant, the hydrogen plant, and their interface, along with discrete events such as major upsets. This hybrid model makes us of accurate thermodynamic sub-models for the description of phase and reaction equilibria in the thermochemical reactor. Use of the detailed thermodynamic models will allow researchers to examine the process in detail and have confidence in the accurary of the property package they use.

Paul I. Barton; Mujid S. Kaximi; Georgios Bollas; Patricio Ramirez Munoz



A Nonlinear Continuous Time Optimal Control Model of Dynamic Pricing and Inventory Control with no  

E-print Network

A Nonlinear Continuous Time Optimal Control Model of Dynamic Pricing and Inventory Control time optimal control model for studying a dynamic pricing and inventory control problem for a make and inventory control, where the evolution of the system evolves dynamically and justify a continuous time

Adida, Elodie


Data-driven optimization of dynamic reconfigurable systems of systems.  

SciTech Connect

This report documents the results of a Strategic Partnership (aka University Collaboration) LDRD program between Sandia National Laboratories and the University of Illinois at Urbana-Champagne. The project is titled 'Data-Driven Optimization of Dynamic Reconfigurable Systems of Systems' and was conducted during FY 2009 and FY 2010. The purpose of this study was to determine and implement ways to incorporate real-time data mining and information discovery into existing Systems of Systems (SoS) modeling capabilities. Current SoS modeling is typically conducted in an iterative manner in which replications are carried out in order to quantify variation in the simulation results. The expense of many replications for large simulations, especially when considering the need for optimization, sensitivity analysis, and uncertainty quantification, can be prohibitive. In addition, extracting useful information from the resulting large datasets is a challenging task. This work demonstrates methods of identifying trends and other forms of information in datasets that can be used on a wide range of applications such as quantifying the strength of various inputs on outputs, identifying the sources of variation in the simulation, and potentially steering an optimization process for improved efficiency.

Tucker, Conrad S.; Eddy, John P.



Optimizing dynamical similarity index extraction window for seizure detection.  


This paper addresses an optimization problem in choosing optimum window length for feature extraction in automatic seizure detection. The processing window length plays an important role in reducing the false positive and false negative rates and decreasing required processing time for seizure detection. This study presents an approach for selecting the optimum window length toward the extraction of dynamical similarity index (DSI) feature. Then, the optimal window value in DSI extraction was used to detect seizure onset automatically. The algorithm was applied to electroencephalogram (EEG) signals from European Epilepsy Database. Although the main purpose of this study was not the seizure detection and mainly focuses on proposing an approach for finding an optimum window length for feature extraction towards the early seizure detection, the results showed that the proposed method achieves 83.99% of sensitivity in seizure detection. The low false positive rate per hour (FPR/h) was also significant due to continuous EEG analysis. The method showed fast computation speed which promises a potential for the real time applications. The proposed method for the window optimization in feature extraction of DSI can be implemented for other features to further improve the performance of seizure detection. PMID:25570429

Azinfar, Leila; Rabbi, Ahmed; Ravanfar, Mohammdreza; Noghanian, Sima; Fazel-Rezai, Reza



Optimal control and cold war dynamics between plant and herbivore.  


Herbivores eat the leaves that a plant needs for photosynthesis. However, the degree of antagonism between plant and herbivore may depend critically on the timing of their interactions and the intrinsic value of a leaf. We present a model that investigates whether and when the timing of plant defense and herbivore feeding activity can be optimized by evolution so that their interactions can move from antagonistic to neutral. We assume that temporal changes in environmental conditions will affect intrinsic leaf value, measured as potential carbon gain. Using optimal-control theory, we model herbivore evolution, first in response to fixed plant strategies and then under coevolutionary dynamics in which the plant also evolves in response to the herbivore. In the latter case, we solve for the evolutionarily stable strategies of plant defense induction and herbivore hatching rate under different ecological conditions. Our results suggest that the optimal strategies for both plant and herbivore are to avoid direct conflict. As long as the plant has the capability for moderately lethal defense, the herbivore will modify its hatching rate to avoid plant defenses, and the plant will never have to use them. Insights from this model offer a possible solution to the paradox of sublethal defenses and provide a mechanism for stable plant-herbivore interactions without the need for natural enemy control. PMID:23852361

Low, Candace; Ellner, Stephen P; Holden, Matthew H



Optimal spatiotemporal reduced order modeling for nonlinear dynamical systems  

NASA Astrophysics Data System (ADS)

Proposed in this dissertation is a novel reduced order modeling (ROM) framework called optimal spatiotemporal reduced order modeling (OPSTROM) for nonlinear dynamical systems. The OPSTROM approach is a data-driven methodology for the synthesis of multiscale reduced order models (ROMs) which can be used to enhance the efficiency and reliability of under-resolved simulations for nonlinear dynamical systems. In the context of nonlinear continuum dynamics, the OPSTROM approach relies on the concept of embedding subgrid-scale models into the governing equations in order to account for the effects due to unresolved spatial and temporal scales. Traditional ROMs neglect these effects, whereas most other multiscale ROMs account for these effects in ways that are inconsistent with the underlying spatiotemporal statistical structure of the nonlinear dynamical system. The OPSTROM framework presented in this dissertation begins with a general system of partial differential equations, which are modified for an under-resolved simulation in space and time with an arbitrary discretization scheme. Basic filtering concepts are used to demonstrate the manner in which residual terms, representing subgrid-scale dynamics, arise with a coarse computational grid. Models for these residual terms are then developed by accounting for the underlying spatiotemporal statistical structure in a consistent manner. These subgrid-scale models are designed to provide closure by accounting for the dynamic interactions between spatiotemporal macroscales and microscales which are otherwise neglected in a ROM. For a given resolution, the predictions obtained with the modified system of equations are optimal (in a mean-square sense) as the subgrid-scale models are based upon principles of mean-square error minimization, conditional expectations and stochastic estimation. Methods are suggested for efficient model construction, appraisal, error measure, and implementation with a couple of well-known time-discretization schemes. Four nonlinear dynamical systems serve as testbeds to demonstrate the technique. First we consider an autonomous van der Pol oscillator for which all trajectories evolve to self-sustained limit cycle oscillations. Next we investigate a forced Duffing oscillator for which the response may be regular or chaotic. In order to demonstrate application for a problem in nonlinear wave propagation, we consider the viscous Burgers equation with large-amplitude inflow disturbances. For the fourth and final system, we analyze the nonlinear structural dynamics of a geometrically nonlinear beam under the influence of time-dependent external forcing. The practical utility of the proposed subgrid-scale models is enhanced if it can be shown that certain statistical moments amongst the subgrid-scale dynamics display to some extent the following properties: spatiotemporal homogeneity, ergodicity, smooth scaling with respect to the system parameters, and universality. To this end, we characterize the subgrid-scale dynamics for each of the four problems. The results in this dissertation indicate that temporal homogeneity and ergodicity are excellent assumptions for both regular and chaotic response types. Spatial homogeneity is found to be a very good assumption for the nonlinear beam problem with models based upon single-point but not multi-point spatial stencils. The viscous Burgers flow, however, requires spatially heterogeneous models regardless of the stencil. For each of the four problems, the required statistical moments display a functional dependence which can easily be characterized with respect to the physical parameters and the computational grid. This observed property, in particular, greatly simplifies model construction by way of moment estimation. We investigate the performance of the subgrid-scale models with under-resolved simulations (in space and time) and various discretization schemes. For the canonical Duffing and van der Pol oscillators, the subgrid-scale models are found to improve the accuracy of under-resolved time-marching and time-s

LaBryer, Allen


An inverse dynamics approach to trajectory optimization and guidance for an aerospace plane  

NASA Technical Reports Server (NTRS)

The optimal ascent problem for an aerospace planes is formulated as an optimal inverse dynamic problem. Both minimum-fuel and minimax type of performance indices are considered. Some important features of the optimal trajectory and controls are used to construct a nonlinear feedback midcourse controller, which not only greatly simplifies the difficult constrained optimization problem and yields improved solutions, but is also suited for onboard implementation. Robust ascent guidance is obtained by using combination of feedback compensation and onboard generation of control through the inverse dynamics approach. Accurate orbital insertion can be achieved with near-optimal control of the rocket through inverse dynamics even in the presence of disturbances.

Lu, Ping



A raster-based C program for siting a landfill with optimal compactness  

NASA Astrophysics Data System (ADS)

Landfill siting requires performing spatial analyses for various factors to evaluate site suitability. A geographical information system, although capable of effectively manipulating spatial data, lacks the capability to locate an optimal site when compactness and other factors are considered simultaneously. In our previous work, a mixed-integer compactness model was proposed to overcome this difficulty. However, computational time with a conventional mixed-integer programming package for solving the model is time consuming and impractical. Therefore, in this work, a C program is developed, based on a proposed raster-based branch-and-bound algorithm. The program can implement multi-factor analyses for compactness and other siting factors with weights prespecified by the user. An example is provided to demonstrate the effectiveness of the program.

Kao, Jehng-Jung



Molecular Dynamics Simulations of Optimal Dynamic Uncharged Polymer Coatings for Quenching Electro-osmotic Flow  

NASA Astrophysics Data System (ADS)

The suppression of electro-osmotic flow (EOF) through the use of a dynamically adsorbed polymer coating is a widely used technique in microfluidic devices. Recent experimental evidence has suggested that the most effective coatings are those which are only weakly adsorbed to the surface. We report molecular dynamics simulation results which show that the optimal adsorption strength for the suppression of EOF is around the transition for adsorption of the polymer. Our results should help to guide the design of novel polymer coatings for improved quenching of EOF.

Hickey, Owen A.; Harden, James L.; Slater, Gary W.



An optimal strategy for functional mapping of dynamic trait loci.  


As an emerging powerful approach for mapping quantitative trait loci (QTLs) responsible for dynamic traits, functional mapping models the time-dependent mean vector with biologically meaningful equations and are likely to generate biologically relevant and interpretable results. Given the autocorrelation nature of a dynamic trait, functional mapping needs the implementation of the models for the structure of the covariance matrix. In this article, we have provided a comprehensive set of approaches for modelling the covariance structure and incorporated each of these approaches into the framework of functional mapping. The Bayesian information criterion (BIC) values are used as a model selection criterion to choose the optimal combination of the submodels for the mean vector and covariance structure. In an example for leaf age growth from a rice molecular genetic project, the best submodel combination was found between the Gaussian model for the correlation structure, power equation of order 1 for the variance and the power curve for the mean vector. Under this combination, several significant QTLs for leaf age growth trajectories were detected on different chromosomes. Our model can be well used to study the genetic architecture of dynamic traits of agricultural values. PMID:20196894

Jin, Tianbo; Li, Jiahan; Guo, Ying; Zhou, Xiaojing; Yang, Runqing; Wu, Rongling



A Formal Approach to Empirical Dynamic Model Optimization and Validation  

NASA Technical Reports Server (NTRS)

A framework was developed for the optimization and validation of empirical dynamic models subject to an arbitrary set of validation criteria. The validation requirements imposed upon the model, which may involve several sets of input-output data and arbitrary specifications in time and frequency domains, are used to determine if model predictions are within admissible error limits. The parameters of the empirical model are estimated by finding the parameter realization for which the smallest of the margins of requirement compliance is as large as possible. The uncertainty in the value of this estimate is characterized by studying the set of model parameters yielding predictions that comply with all the requirements. Strategies are presented for bounding this set, studying its dependence on admissible prediction error set by the analyst, and evaluating the sensitivity of the model predictions to parameter variations. This information is instrumental in characterizing uncertainty models used for evaluating the dynamic model at operating conditions differing from those used for its identification and validation. A practical example based on the short period dynamics of the F-16 is used for illustration.

Crespo, Luis G; Morelli, Eugene A.; Kenny, Sean P.; Giesy, Daniel P.



Performance Study and Dynamic Optimization Design for Thread Pool Systems  

SciTech Connect

Thread pools have been widely used by many multithreaded applications. However, the determination of the pool size according to the application behavior still remains problematic. To automate this process, in this thesis we have developed a set of performance metrics for quantitatively analyzing thread pool performance. For our experiments, we built a thread pool system which provides a general framework for thread pool research. Based on this simulation environment, we studied the performance impact brought by the thread pool on different multithreaded applications. Additionally, the correlations between internal characterizations of thread pools and their throughput were also examined. We then proposed and evaluated a heuristic algorithm to dynamically determine the optimal thread pool size. The simulation results show that this approach is effective in improving overall application performance.

Dongping Xu



Optimal reconstruction of dynamical systems: A noise amplification approach  

E-print Network

In this work we propose an objective function to guide the search for a state space reconstruction of a dynamical system from a time series of measurements. This statistics can be evaluated on any reconstructed attractor, thereby allowing a direct comparison among different approaches: (uniform or non-uniform) delay vectors, PCA, Legendre coordinates, etc. It can also be used to select the most appropriate parameters of a reconstruction strategy. In the case of delay coordinates this translates into finding the optimal delay time and embedding dimension from the absolute minimum of the advocated cost function. Its definition is based on theoretical arguments on noise amplification, the complexity of the reconstructed attractor and a direct measure of local stretch which constitutes a novel irrelevance measure. The proposed method is demonstrated on synthetic and experimental time series.

L. C. Uzal; G. L. Grinblat; P. F. Verdes



Dynamical resetting of the human brain at epileptic seizures: application of nonlinear dynamics and global optimization techniques  

Microsoft Academic Search

Epileptic seizures occur intermittently as a result of complex dynamical interactions among many regions of the brain. By applying signal processing techniques from the theory of nonlinear dynamics and global optimization to the analysis of long-term (3.6 to 12 days) continuous multichannel electroencephalographic recordings from four epileptic patients, we present evidence that epileptic seizures appear to serve as dynamical resetting

Leon D. Iasemidis; Deng-Shan Shiau; J. Chris Sackellares; Panos M. Pardalos; Awadhesh Prasad



Geometry optimization for micro-pressure sensor considering dynamic interference.  


Presented is the geometry optimization for piezoresistive absolute micro-pressure sensor. A figure of merit called the performance factor (PF) is defined as a quantitative index to describe the comprehensive performances of a sensor including sensitivity, resonant frequency, and acceleration interference. Three geometries are proposed through introducing islands and sensitive beams into typical flat diaphragm. The stress distributions of sensitive elements are analyzed by finite element method. Multivariate fittings based on ANSYS simulation results are performed to establish the equations about surface stress, deflection, and resonant frequency. Optimization by MATLAB is carried out to determine the dimensions of the geometries. Convex corner undercutting is evaluated. Each PF of the three geometries with the determined dimensions is calculated and compared. Silicon bulk micromachining is utilized to fabricate the prototypes of the sensors. The outputs of the sensors under both static and dynamic conditions are tested. Experimental results demonstrate the rationality of the defined performance factor and reveal that the geometry with quad islands presents the highest PF of 210.947 Hz(1/4). The favorable overall performances enable the sensor more suitable for altimetry. PMID:25273764

Yu, Zhongliang; Zhao, Yulong; Li, Lili; Tian, Bian; Li, Cun



Geometry optimization for micro-pressure sensor considering dynamic interference  

NASA Astrophysics Data System (ADS)

Presented is the geometry optimization for piezoresistive absolute micro-pressure sensor. A figure of merit called the performance factor (PF) is defined as a quantitative index to describe the comprehensive performances of a sensor including sensitivity, resonant frequency, and acceleration interference. Three geometries are proposed through introducing islands and sensitive beams into typical flat diaphragm. The stress distributions of sensitive elements are analyzed by finite element method. Multivariate fittings based on ANSYS simulation results are performed to establish the equations about surface stress, deflection, and resonant frequency. Optimization by MATLAB is carried out to determine the dimensions of the geometries. Convex corner undercutting is evaluated. Each PF of the three geometries with the determined dimensions is calculated and compared. Silicon bulk micromachining is utilized to fabricate the prototypes of the sensors. The outputs of the sensors under both static and dynamic conditions are tested. Experimental results demonstrate the rationality of the defined performance factor and reveal that the geometry with quad islands presents the highest PF of 210.947 Hz1/4. The favorable overall performances enable the sensor more suitable for altimetry.

Yu, Zhongliang; Zhao, Yulong; Li, Lili; Tian, Bian; Li, Cun



Identification of dynamic parameters of an industrial robot using a recursively-optimized trajectory  

Microsoft Academic Search

Dynamic parameters of a robot affect the performance of advanced control schemes significantly. In this study experiments to identify the dynamic parameters of AT2 robot are carried out. The excitation trajectory for identification is parameterized with Fourier series. The trajectory is optimized to minimize effects of uncertainty using condition number as the index. Recursive optimization is proposed so that the

Yunjin Gu; Hyuk Wang; Jang Ho Cho; Doo Yong Lee



Structural and Dynamic Requirements for Optimal Activity of the Essential Bacterial Enzyme  

E-print Network

Structural and Dynamic Requirements for Optimal Activity of the Essential Bacterial Enzyme) is an essential enzyme involved in the lysine biosynthesis pathway. DHDPS from E. coli is a homotetramer and Dynamic Requirements for Optimal Activity of the Essential Bacterial Enzyme Dihydrodipicolinate Synthase

Hickman, Mark


Dynamic online optimization of a house heating system in a fluctuating energy price  

E-print Network

Dynamic online optimization of a house heating system in a fluctuating energy price scenario in this problem is the time- varying nature of the main disturbances, which are the energy price and outdoor that there is a great economical gain in using dynamic optimization for the case of variable energy price. 1

Skogestad, Sigurd


Topological optimization of mechanical amplifiers for piezoelectric actuators under dynamic motion  

Microsoft Academic Search

Topological optimization is used to systematically design mechanical amplifiers that magnify the limited actuation stroke of a piezoelectric actuator. The design problem is posed as a material distribution problem using a variable thickness method. Two design goals are formulated for the design of the mechanical amplifier. They are the maximum dynamic stroke and the maximum dynamic magnification factor. The optimization

Hejun Du; Gih Keong Lau; Mong King Lim; Jinhao Qui



One-Dimensional Infinite Horizon Nonconcave Optimal Control Problems Arising in Economic Dynamics  

SciTech Connect

We study the existence of optimal solutions for a class of infinite horizon nonconvex autonomous discrete-time optimal control problems. This class contains optimal control problems without discounting arising in economic dynamics which describe a model with a nonconcave utility function.

Zaslavski, Alexander J., E-mail: [Technion-Israel Institute of Technology, Department of Mathematics (Israel)



Optimal path for China's strategic petroleum reserve: A dynamic programming analysis  

Microsoft Academic Search

This paper proposes a dynamic programming model to explore the optimal stockpiling path for China's strategic petroleum reserve before 2020. The optimal oil acquisition sizes in 2008–2020 under different scenarios are estimated. The effects of oil price, risks and elasticity value on inventory size are further investigated. It is found that the optimal stockpile acquisition strategies are mainly determined by

Y. Bai; D. Q. Zhou; P. Zhou; L. B. Zhang



An inverse dynamic-based dynamic programming method for optimal point-to-point trajectory planning of robotic manipulators  

Microsoft Academic Search

This paper introduces an inverse dynamic-based dynamic programming (IDBDP) method for solving optimal point-to-point robot trajectory planning problems. Compared with the conventional dynamic programming method, the proposed method offers several advantages. First, it eliminates the interpolation requirement. Second, the proposed method requires only inverse dynamic computations. The requirement to integrate equations of motion is thus avoided. As a result, the




Optimized dynamical decoupling in a model quantum memory.  


Any quantum system, such as those used in quantum information or magnetic resonance, is subject to random phase errors that can dramatically affect the fidelity of a desired quantum operation or measurement. In the context of quantum information, quantum error correction techniques have been developed to correct these errors, but resource requirements are extraordinary. The realization of a physically tractable quantum information system will therefore be facilitated if qubit (quantum bit) error rates are far below the so-called fault-tolerance error threshold, predicted to be of the order of 10(-3)-10(-6). The need to realize such low error rates motivates a search for alternative strategies to suppress dephasing in quantum systems. Here we experimentally demonstrate massive suppression of qubit error rates by the application of optimized dynamical decoupling pulse sequences, using a model quantum system capable of simulating a variety of qubit technologies. We demonstrate an analytically derived pulse sequence, UDD, and find novel sequences through active, real-time experimental feedback. The latter sequences are tailored to maximize error suppression without the need for a priori knowledge of the ambient noise environment, and are capable of suppressing errors by orders of magnitude compared to other existing sequences (including the benchmark multi-pulse spin echo). Our work includes the extension of a treatment to predict qubit decoherence under realistic conditions, yielding strong agreement between experimental data and theory for arbitrary pulse sequences incorporating nonidealized control pulses. These results demonstrate the robustness of qubit memory error suppression through dynamical decoupling techniques across a variety of qubit technologies. PMID:19396139

Biercuk, Michael J; Uys, Hermann; VanDevender, Aaron P; Shiga, Nobuyasu; Itano, Wayne M; Bollinger, John J



Application of numerical optimization techniques to control system design for nonlinear dynamic models of aircraft  

NASA Technical Reports Server (NTRS)

Control system design for general nonlinear flight dynamic models is considered through numerical simulation. The design is accomplished through a numerical optimizer coupled with analysis of flight dynamic equations. The general flight dynamic equations are numerically integrated and dynamic characteristics are then identified from the dynamic response. The design variables are determined iteratively by the optimizer to optimize a prescribed objective function which is related to desired dynamic characteristics. Generality of the method allows nonlinear effects to aerodynamics and dynamic coupling to be considered in the design process. To demonstrate the method, nonlinear simulation models for an F-5A and an F-16 configurations are used to design dampers to satisfy specifications on flying qualities and control systems to prevent departure. The results indicate that the present method is simple in formulation and effective in satisfying the design objectives.

Lan, C. Edward; Ge, Fuying



Photocathode Optimization for a Dynamic Transmission Electron Microscope: Final Report  

SciTech Connect

The Dynamic Transmission Electron Microscope (DTEM) team at Harvey Mudd College has been sponsored by LLNL to design and build a test setup for optimizing the performance of the DTEM's electron source. Unlike a traditional TEM, the DTEM achieves much faster exposure times by using photoemission from a photocathode to produce electrons for imaging. The DTEM team's work is motivated by the need to improve the coherence and current density of the electron cloud produced by the electron gun in order to increase the image resolution and contrast achievable by DTEM. The photoemission test setup is nearly complete and the team will soon complete baseline tests of electron gun performance. The photoemission laser and high voltage power supply have been repaired; the optics path for relaying the laser to the photocathode has been finalized, assembled, and aligned; the internal setup of the vacuum chamber has been finalized and mostly implemented; and system control, synchronization, and data acquisition has been implemented in LabVIEW. Immediate future work includes determining a consistent alignment procedure to place the laser waist on the photocathode, and taking baseline performance measurements of the tantalum photocathode. Future research will examine the performance of the electron gun as a function of the photoemission laser profile, the photocathode material, and the geometry and voltages of the accelerating and focusing components in the electron gun. This report presents the team's progress and outlines the work that remains.

Ellis, P; Flom, Z; Heinselman, K; Nguyen, T; Tung, S; Haskell, R; Reed, B W; LaGrange, T



Function-valued adaptive dynamics and optimal control theory.  


In this article we further develop the theory of adaptive dynamics of function-valued traits. Previous work has concentrated on models for which invasion fitness can be written as an integral in which the integrand for each argument value is a function of the strategy value at that argument value only. For this type of models of direct effect, singular strategies can be found using the calculus of variations, with singular strategies needing to satisfy Euler's equation with environmental feedback. In a broader, more mechanistically oriented class of models, the function-valued strategy affects a process described by differential equations, and fitness can be expressed as an integral in which the integrand for each argument value depends both on the strategy and on process variables at that argument value. In general, the calculus of variations cannot help analyzing this much broader class of models. Here we explain how to find singular strategies in this class of process-mediated models using optimal control theory. In particular, we show that singular strategies need to satisfy Pontryagin's maximum principle with environmental feedback. We demonstrate the utility of this approach by studying the evolution of strategies determining seasonal flowering schedules. PMID:22763388

Parvinen, Kalle; Heino, Mikko; Dieckmann, Ulf



Multi-pinhole dynamic SPECT imaging: simulation and system optimization  

NASA Astrophysics Data System (ADS)

This work optimized a multi-pinhole collimator for a stationary three-camera Single Photon Emission Computed Tomography (SPECT) system designed for rapid (one-second) dynamic imaging through simulations. Multi-pinhole collimator designs were investigated to increase efficiency and angular sampling while maintaining adequate spatial resolution for small-animal imaging. The study first analytically investigated the tradeoffs between efficiency and spatial resolution as a function of the number of pinholes. An oval arrangement of pinholes was proposed, and the benefits compared to a circular arrangement were quantified through simulations. Finally, collimators with six to nine pinholes were simulated, and the resulting data compared with respect to efficiency, signal-to-noise ratio (SNR), and the angular coverage in Radon space. All simulations used the GATE Monte Carlo toolkit. The results suggest that an oval arrangement of nine pinholes improved the efficiency and SNR by factors of 1.65 and 1.3, respectively, compared to a circular arrangement. A nine-pinhole collimator was found to provide the highest geometric efficiency (~6.35*104cps/mCi) and improved the SNR by a factor of ~1.3 and ~1.1 compared to the six- and eight-pinhole collimators, respectively. Overall, the simulated multi-pinhole system depicted cylindrical objects despite the limited angular sampling and scan time of the one-second, stationary three-camera acquisition.

Ma, Dan; Clough, Anne V.; Gilat Schmidt, Taly



Conceptualizing a tool to optimize therapy based on dynamic heterogeneity  

NASA Astrophysics Data System (ADS)

Complex biological systems often display a randomness paralleled in processes studied in fundamental physics. This simple stochasticity emerges owing to the complexity of the system and underlies a fundamental aspect of biology called phenotypic stochasticity. Ongoing stochastic fluctuations in phenotype at the single-unit level can contribute to two emergent population phenotypes. Phenotypic stochasticity not only generates heterogeneity within a cell population, but also allows reversible transitions back and forth between multiple states. This phenotypic interconversion tends to restore a population to a previous composition after that population has been depleted of specific members. We call this tendency homeostatic heterogeneity. These concepts of dynamic heterogeneity can be applied to populations composed of molecules, cells, individuals, etc. Here we discuss the concept that phenotypic stochasticity both underlies the generation of heterogeneity within a cell population and can be used to control population composition, contributing, in particular, to both the ongoing emergence of drug resistance and an opportunity for depleting drug-resistant cells. Using notions of both ‘large’ and ‘small’ numbers of biomolecular components, we rationalize our use of Markov processes to model the generation and eradication of drug-resistant cells. Using these insights, we have developed a graphical tool, called a metronomogram, that we propose will allow us to optimize dosing frequencies and total course durations for clinical benefit. The authors dedicate this paper to Dr Barton Kamen who inspired its initiation and enthusiastically supported its pursuit.

Liao, David; Estévez-Salmerón, Luis; Tlsty, Thea D.



Solving Three-objective Optimization Problems Using Evolutionary Dynamic Weighted  

E-print Network

the definition function in the pa- rameter space that defines a Pareto-optimal front or the boundary of a Pareto- tionary optimization, the weights drift randomly during optimization. A method that explicitly uses random weights during selection for genetic algorithms has been suggested in [2]: wi = randomi/(random1

Jin, Yaochu


Optimized dynamic framing for PET-based myocardial blood flow estimation  

NASA Astrophysics Data System (ADS)

An optimal experiment design methodology was developed to select the framing schedule to be used in dynamic positron emission tomography (PET) for estimation of myocardial blood flow using 82Rb. A compartment model and an arterial input function based on measured data were used to calculate a D-optimality criterion for a wide range of candidate framing schedules. To validate the optimality calculation, noisy time-activity curves were simulated, from which parameter values were estimated using an efficient and robust decomposition of the estimation problem. D-optimized schedules improved estimate precision compared to non-optimized schedules, including previously published schedules. To assess robustness, a range of physiologic conditions were simulated. Schedules that were optimal for one condition were nearly-optimal for others. The effect of infusion duration was investigated. Optimality was better for shorter than for longer tracer infusion durations, with the optimal schedule for the shortest infusion duration being nearly optimal for other durations. Together this suggests that a framing schedule optimized for one set of conditions will also work well for others and it is not necessary to use different schedules for different infusion durations or for rest and stress studies. The method for optimizing schedules is general and could be applied in other dynamic PET imaging studies.

Kolthammer, Jeffrey A.; Muzic, Raymond F.



Evacuation dynamic and exit optimization of a supermarket based on particle swarm optimization  

NASA Astrophysics Data System (ADS)

A modified particle swarm optimization algorithm is proposed in this paper to investigate the dynamic of pedestrian evacuation from a fire in a public building-a supermarket with multiple exits and configurations of counters. Two distinctive evacuation behaviours featured by the shortest-path strategy and the following-up strategy are simulated in the model, accounting for different categories of age and sex of the pedestrians along with the impact of the fire, including gases, heat and smoke. To examine the relationship among the progress of the overall evacuation and the layout and configuration of the site, a series of simulations are conducted in various settings: without a fire and with a fire at different locations. Those experiments reveal a general pattern of two-phase evacuation, i.e., a steep section and a flat section, in addition to the impact of the presence of multiple exits on the evacuation along with the geographic locations of the exits. For the study site, our simulations indicated the deficiency of the configuration and the current layout of this site in the process of evacuation and verified the availability of proposed solutions to resolve the deficiency. More specifically, for improvement of the effectiveness of the evacuation from the site, adding an exit between Exit 6 and Exit 7 and expanding the corridor at the right side of Exit 7 would significantly reduce the evacuation time.

Li, Lin; Yu, Zhonghai; Chen, Yang



Optimal Regulation of Heating Systems with Metering Based on Dynamic Simulation  

E-print Network

is applied, and the optimal supply water temperatures are worked out by the estimate method of linear summation with weights based on dynamic simulation. The simulative results reflect the relationship between energy loss of heating systems and users' comfort....

Zhao, H.; Wang, P.; Zeng, G.; Tian, Y.



Optimal foreign borrowing in a multisector dynamic equilibrium model for Brazil  

E-print Network

This paper shows how a dynamic multisector equilibrium model can be formulated to be able to analyze the optimal borrowing policy of a developing country. It also describes how a non-linear programming model with the ...

Tourinho, Octv?io A. F.



Optimal motion planning with the half-car dynamical model for autonomous high-speed driving  

E-print Network

We discuss an implementation of the RRT* optimal motion planning algorithm for the half-car dynamical model to enable autonomous high-speed driving. To develop fast solutions of the associated local steering problem, we ...

Jeon, Jeong hwan


Metaheuristic algorithms in structural dynamics: An application of tuned mass damper optimization  

NASA Astrophysics Data System (ADS)

Metaheuristic algorithms imitate natural phenomena in order to solve optimization problems. These algorithms are effective on the optimization of structural dynamics problems including vibration control with tuned mass damper (TMD). In this paper, structural dynamics optimization problems were briefly reviewed. As an example, a TMD optimization problem was presented. Harmony Search (HS) algorithm was used to find optimum parameters of TMD mass, stiffness and damping coefficient. The optimization process was conducted to reduce structural displacements of a five story structure. The properties of the structure are the same for all stories except the third story mass. According to the analyses results, the TMD optimized with HS approach is effective to reduce all maximum story displacements.

Bekda?, Gebrail; Nigdeli, Sinan Melih



Generating optimal trajectory of humanoid arm that minimizes torque variation using differential dynamic programming  

Microsoft Academic Search

This paper proposes an optimal control method to generate a minimum-torque change trajectory of humanoid arm by using a differential dynamic programming (DDP). Since DDP is a locally optimal feedback controller, the convergence is not guaranteed unless DDP starts with a good reference trajectory for high-dimensional nonlinear dynamical systems. The reference trajectory is generated by using the minimum-jerk trajectory method,

In-Won Park; Young-Dae Hong; Bum-Joo Lee; Jong-Hwan Kim



Towards Optimal Solar Tracking: A Dynamic Programming Approach Athanasios Aris Panagopoulos Georgios Chalkiadakis Nicholas R. Jennings  

E-print Network

Towards Optimal Solar Tracking: A Dynamic Programming Approach Athanasios Aris Panagopoulos solar tracking tech- niques. However, current techniques suffer from several drawbacks in their tracking-optimal trajectories for effective and efficient day-ahead solar tracking, based on weather forecasts coming from on


Fast and Dynamically Stable Optimization-based Planning for High-DOF Robots  

E-print Network

constraints. There is considerable work on motion planning for high- DOF robots. At a broad level, they canFast and Dynamically Stable Optimization-based Planning for High-DOF Robots Chonhyon Park and Dinesh Manocha Abstract-- We present a novel optimization-based motion planning algorithm for high degree

North Carolina at Chapel Hill, University of


Optimal control of coupled spin dynamics in the presence of relaxation  

Microsoft Academic Search

In this thesis, we study methods for optimal manipulation of coupled spin dynamics in the presence of relaxation. We use these methods to compute analytical upper bounds for the efficiency of coherence and polarization transfer between coupled nuclear spins in multidimensional nuclear magnetic resonance (NMR) experiments, under the presence of relaxation. We derive relaxation optimized pulse sequences which achieve or

Dionisis Stefanatos



The uncertainty threshold principle: Some fundamental limitations of optimal decision making under dynamic uncertainty  

Microsoft Academic Search

This note shows that the optimal control of dynamic systems with uncertain parameters has certain limitations. In particular, by means of a simple scalar linear-quadratic optimal control example, it is shown that the infinite horizon solution does not exist if the parameter uncertainty exceeds a certain quantifiable threshold; we call this the uncertainty threshold principle. The philosophical and design implications

M. Athans; R. Ku; S. Gershwin



Optimal Second Best Taxation of Addictive Goods in Dynamic General Equilibrium  

Microsoft Academic Search

Abstract In this paper we derive conditions under which optimal tax rates for addictive goods exceed tax rates for non-addictive consumption goods in a rational addiction framework where exogenous government spending cannot be financed with lump sum taxes. We reexamine classic results on optimal commodity,taxation and find a rich set of new findings. Our dynamic results imply tax rates on

Luca Bossi; Pedro Gomis-Porqueras



Interim monitoring of clinical trials: decision theory, dynamic programming and optimal stopping  

E-print Network

Interim monitoring of clinical trials: decision theory, dynamic programming and optimal stopping.S.A ABSTRACT It is standard practice to monitor clinical trials with a view to stopping early if results multipliers. Applications of these methods in clinical trial design include the derivation of optimal adaptive

Budd, Chris


The dynamic modeling of wind farms considering wake effects and its optimal distribution  

Microsoft Academic Search

Under the four-component wind speed model, the wind turbine output power was researched based on the Jensen wake model, which combined of the dynamic wind turbine model; anglicizing with the distribution of wind power from the theoretical, optimization method that named quadratic interpolation was used, and the optimization tool of MatlabSimulink verified that the correct conclusion has a certain rationality.

Youjie Ma; Haishan Yang; Xuesong Zhou; Ji Li; Hulong Wen



Dynamic Assortment Optimization with a Multinomial Logit Choice Model and Capacity Constraint  

E-print Network

and van Ryzin, 1998, 2001). The retailer might not know the demand a priori, and a customer's productDynamic Assortment Optimization with a Multinomial Logit Choice Model and Capacity Constraint Paat, 2009 Abstract We consider an assortment optimization problem where a retailer chooses an assortment

Rusmevichientong, Paat


Two adaptive mutation operators for optima tracking in dynamic optimization problems with evolution strategies  

Microsoft Academic Search

The dynamic optimization problem concerns finding an op- timum in a changing environment. In the tracking problem, the optimizer should be able to follow the optimum's changes over time. In this paper we present two adaptive muta- tion operators designed to improve the following of a time- changing optimum, under the assumption that the changes follow a non-random law. Such

Claudio Rossi; Antonio Barrientos; Jaime Del Cerro



Selection of prestress for optimal dynamic\\/control performance of tensegrity structures  

Microsoft Academic Search

This paper concerns prestress optimization of a tensegrity structure for its optimal LQR performance. A linearized dynamic model of the structure is derived in which the force-density variables that parameterize the prestress of the structure appear linearly. A feasible region for these parameters is defined in terms of the extreme directions of the prestress cone. A numerical method for computing

Milenko Masic; Robert E. Skelton



A Note on the Existence of Optimal Policies in Total Reward Dynamic Programs with  

E-print Network

A Note on the Existence of Optimal Policies in Total Reward Dynamic Programs-DE-OCA Abstract. This work deals with Markov Decision Processes (MDPs) with expected total rewards, discrete assumption under which the existence of total-reward optimal stationary policies can be guaranteed

Feinberg, Eugene A.


An inverse dynamics approach to trajectory optimization for an aerospace plane  

NASA Technical Reports Server (NTRS)

An inverse dynamics approach for trajectory optimization is proposed. This technique can be useful in many difficult trajectory optimization and control problems. The application of the approach is exemplified by ascent trajectory optimization for an aerospace plane. Both minimum-fuel and minimax types of performance indices are considered. When rocket augmentation is available for ascent, it is shown that accurate orbital insertion can be achieved through the inverse control of the rocket in the presence of disturbances.

Lu, Ping



Pole vault performance for anthropometric variability via a dynamical optimal control model  

Microsoft Academic Search

Optimal performance of a dynamical pole vault process was modeled as a constrained nonlinear optimization problem. That is, given a vaulter’s anthropomorphic data and approach speed, the vaulter chose a specific take-off angle, pole stiffness and gripping height in order to yield the greatest jumping height compromised by feasible bar-crossing velocities. The optimization problem was solved by nesting a technique

Guangyu Liu; Sing-Kiong Nguang; Yanxin Zhang



Optimization of the Dynamic Aperture for SPEAR3 Low-Emittance Upgrade  

SciTech Connect

A low emittance upgrade is planned for SPEAR3. As the first phase, the emittance is reduced from 10nm to 7nm without additional magnets. A further upgrade with even lower emittance will require a damping wiggler. There is a smaller dynamic aperture for the lower emittance optics due to a stronger nonlinearity. Elegant based Multi-Objective Genetic Algorithm (MOGA) is used to maximize the dynamic aperture. Both the dynamic aperture and beam lifetime are optimized simultaneously. Various configurations of the sextupole magnets have been studied in order to find the best configuration. The betatron tune also can be optimized to minimize resonance effects. The optimized dynamic aperture increases more than 15% from the nominal case and the lifetime increases from 14 hours to 17 hours. It is important that the increase of the dynamic aperture is mainly in the beam injection direction. Therefore the injection efficiency will benefit from this improvement.

Wang, Lanfa; Huang, Xiaobiao; Nosochkov, Yuri; Safranek, James A.; /SLAC; Borland, Michael; /Argonne



Optimal Perturbations for the Identification of Stochastic Reaction Dynamics  

E-print Network

on an information-theoretic setting we present novel Monte Carlo sampling techniques to determine optimal temporal. Keywords: Optimal experiment design; excitation; identifiability; parameter estimation; identification design for stochastic chemical kinetics. Experiment design has a long-standing tradition in chem- ical

Lygeros, John


Optimal Perturbations for the Identification of Stochastic Reaction Dynamics  

E-print Network

on an information-theoretic setting we provide novel Monte Carlo sampling techniques to determine optimal temporal. Keywords: Optimal experiment design; excitation; identifiability; parameter estimation; identification experiment design for stochastic chemical kinetics. Experiment design has a long-standing tradition in chem

Lygeros, John



E-print Network

............................................................. 8 3. PROBLEM FORMULATION............................................................................ 9 4. EXPERIMENTAL SETUP................................................................................. 13 4.1 From HDL to Layout... optimization algorithm (Dual-VT) HSPICE simulation for DVS Change circuit voltage Optimize for another voltage? Finished 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. YES NO Fast enough? NO YES 14 4.1. FROM HDL TO LAYOUT The starting...

Esquit Hernandez, Carlos A.



Real-Time Optimal Trajectory Generation for Constrained Dynamical Systems  

E-print Network

theory, B-spline basis functions, and nonlinear programming. We compare NTG with other numerical optimal met our goal of implementing receding horizon control on the experiment before I graduated. I would its software implementation to solve, in real-time, nonlinear optimal trajectory generation problems

Murray, Richard M.


Optimal GENCO bidding strategy  

NASA Astrophysics Data System (ADS)

Electricity industries worldwide are undergoing a period of profound upheaval. The conventional vertically integrated mechanism is being replaced by a competitive market environment. Generation companies have incentives to apply novel technologies to lower production costs, for example: Combined Cycle units. Economic dispatch with Combined Cycle units becomes a non-convex optimization problem, which is difficult if not impossible to solve by conventional methods. Several techniques are proposed here: Mixed Integer Linear Programming, a hybrid method, as well as Evolutionary Algorithms. Evolutionary Algorithms share a common mechanism, stochastic searching per generation. The stochastic property makes evolutionary algorithms robust and adaptive enough to solve a non-convex optimization problem. This research implements GA, EP, and PS algorithms for economic dispatch with Combined Cycle units, and makes a comparison with classical Mixed Integer Linear Programming. The electricity market equilibrium model not only helps Independent System Operator/Regulator analyze market performance and market power, but also provides Market Participants the ability to build optimal bidding strategies based on Microeconomics analysis. Supply Function Equilibrium (SFE) is attractive compared to traditional models. This research identifies a proper SFE model, which can be applied to a multiple period situation. The equilibrium condition using discrete time optimal control is then developed for fuel resource constraints. Finally, the research discusses the issues of multiple equilibria and mixed strategies, which are caused by the transmission network. Additionally, an advantage of the proposed model for merchant transmission planning is discussed. A market simulator is a valuable training and evaluation tool to assist sellers, buyers, and regulators to understand market performance and make better decisions. A traditional optimization model may not be enough to consider the distributed, large-scale, and complex energy market. This research compares the performance and searching paths of different artificial life techniques such as Genetic Algorithm (GA), Evolutionary Programming (EP), and Particle Swarm (PS), and look for a proper method to emulate Generation Companies' (GENCOs) bidding strategies. After deregulation, GENCOs face risk and uncertainty associated with the fast-changing market environment. A profit-based bidding decision support system is critical for GENCOs to keep a competitive position in the new environment. Most past research do not pay special attention to the piecewise staircase characteristic of generator offer curves. This research proposes an optimal bidding strategy based on Parametric Linear Programming. The proposed algorithm is able to handle actual piecewise staircase energy offer curves. The proposed method is then extended to incorporate incomplete information based on Decision Analysis. Finally, the author develops an optimal bidding tool (GenBidding) and applies it to the RTS96 test system.

Gao, Feng


Operational Optimization of Large-Scale Parallel-Unit SWRO Desalination Plant Using Differential Evolution Algorithm  

PubMed Central

A large-scale parallel-unit seawater reverse osmosis desalination plant contains many reverse osmosis (RO) units. If the operating conditions change, these RO units will not work at the optimal design points which are computed before the plant is built. The operational optimization problem (OOP) of the plant is to find out a scheduling of operation to minimize the total running cost when the change happens. In this paper, the OOP is modelled as a mixed-integer nonlinear programming problem. A two-stage differential evolution algorithm is proposed to solve this OOP. Experimental results show that the proposed method is satisfactory in solution quality. PMID:24701180

Wang, Xiaolong; Jiang, Aipeng; Jiangzhou, Shu; Li, Ping



Gain-scheduled `1 -optimal control for boiler-turbine dynamics  

E-print Network

Gain-scheduled `1 -optimal control for boiler-turbine dynamics with actuator saturation Pang; accepted 2 June 2003 Abstract This paper presents a gain-scheduled approach for boiler-turbine controller the magnitude and rate saturation constraints on actuators. The nonlinear boiler-turbine dynamics is brought

Shamma, Jeff S.


Sensitivity analysis and optimization of system dynamics models: Regression analysis and statistical design of experiments  

Microsoft Academic Search

This tutorial discusses what-if analysis and optimization of System Dynamics models. These problems are solved, using the statistical techniques of regression analysis and design of experiments (DOE). These issues are illustrated by applying the statistical techniques to a System Dynamics model for coal transportation, taken from Wolstenholme's book \\

Jack P. C. Kleijnen



A comparison of static and dynamic optimization muscle force predictions during wheelchair propulsion.  


The primary purpose of this study was to compare static and dynamic optimization muscle force and work predictions during the push phase of wheelchair propulsion. A secondary purpose was to compare the differences in predicted shoulder and elbow kinetics and kinematics and handrim forces. The forward dynamics simulation minimized differences between simulated and experimental data (obtained from 10 manual wheelchair users) and muscle co-contraction. For direct comparison between models, the shoulder and elbow muscle moment arms and net joint moments from the dynamic optimization were used as inputs into the static optimization routine. RMS errors between model predictions were calculated to quantify model agreement. There was a wide range of individual muscle force agreement that spanned from poor (26.4% Fmax error in the middle deltoid) to good (6.4% Fmax error in the anterior deltoid) in the prime movers of the shoulder. The predicted muscle forces from the static optimization were sufficient to create the appropriate motion and joint moments at the shoulder for the push phase of wheelchair propulsion, but showed deviations in the elbow moment, pronation-supination motion and hand rim forces. These results suggest the static approach does not produce results similar enough to be a replacement for forward dynamics simulations, and care should be taken in choosing the appropriate method for a specific task and set of constraints. Dynamic optimization modeling approaches may be required for motions that are greatly influenced by muscle activation dynamics or that require significant co-contraction. PMID:25282075

Morrow, Melissa M; Rankin, Jeffery W; Neptune, Richard R; Kaufman, Kenton R



Multilevel decomposition approach to integrated aerodynamic/dynamic/structural optimization of helicopter rotor blades  

NASA Technical Reports Server (NTRS)

This paper describes an integrated aerodynamic, dynamic, and structural (IADS) optimization procedure for helicopter rotor blades. The procedure combines performance, dynamics, and structural analyses with a general purpose optimizer using multilevel decomposition techniques. At the upper level, the structure is defined in terms of local quantities (stiffnesses, mass, and average strains). At the lower level, the structure is defined in terms of local quantities (detailed dimensions of the blade structure and stresses). The IADS procedure provides an optimization technique that is compatible with industrial design practices in which the aerodynamic and dynamic design is performed at a global level and the structural design is carried out at a detailed level with considerable dialogue and compromise among the aerodynamic, dynamic, and structural groups. The IADS procedure is demonstrated for several cases.

Walsh, Joanne L.; Young, Katherine C.; Pritchard, Jocelyn I.; Adelman, Howard M.; Mantay, Wayne R.



Integrated Dynamic Simulation for Process Optimization and Control  

E-print Network

Sensor Simulator Run-to-Run Control Real-Time Control Comparison for Control Response real data virtualSimulators ==> virtual sensor data lComparison of real and virtual sensor data ==> control #12;Dynamic Simulation Simulator Structure ­ Dynamic Characteristics · Simulator Validation ­ Sensor Signals, Reaction Kinetics

Rubloff, Gary W.


A new dynamic approach for statistical optimization of GNSS radio occultation bending angles for optimal climate monitoring utility  

NASA Astrophysics Data System (ADS)

Navigation Satellite System (GNSS)-based radio occultation (RO) is a satellite remote sensing technique providing accurate profiles of the Earth's atmosphere for weather and climate applications. Above about 30 km altitude, however, statistical optimization is a critical process for initializing the RO bending angles in order to optimize the climate monitoring utility of the retrieved atmospheric profiles. Here we introduce an advanced dynamic statistical optimization algorithm, which uses bending angles from multiple days of European Centre for Medium-range Weather Forecasts (ECMWF) short-range forecast and analysis fields, together with averaged-observed bending angles, to obtain background profiles and associated error covariance matrices with geographically varying background uncertainty estimates on a daily updated basis. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.4 (OPSv5.4) algorithm, using several days of simulated MetOp and observed CHAMP and COSMIC data, for January and July conditions. We find the following for the new method's performance compared to OPSv5.4: 1.) it significantly reduces random errors (standard deviations), down to about half their size, and leaves less or about equal residual systematic errors (biases) in the optimized bending angles; 2.) the dynamic (daily) estimate of the background error correlation matrix alone already improves the optimized bending angles; 3.) the subsequently retrieved refractivity profiles and atmospheric (temperature) profiles benefit by improved error characteristics, especially above about 30 km. Based on these encouraging results, we work to employ similar dynamic error covariance estimation also for the observed bending angles and to apply the method to full months and subsequently to entire climate data records.

Li, Y.; Kirchengast, G.; Scherllin-Pirscher, B.; Wu, S.; Schwaerz, M.; Fritzer, J.; Zhang, S.; Carter, B. A.; Zhang, K.



Performance optimization of web-based medical simulation.  


This paper presents a technique for performance optimization of multimodal interactive web-based medical simulation. A web-based simulation framework is promising for easy access and wide dissemination of medical simulation. However, the real-time performance of the simulation highly depends on hardware capability on the client side. Providing consistent simulation in different hardware is critical for reliable medical simulation. This paper proposes a non-linear mixed integer programming model to optimize the performance of visualization and physics computation while considering hardware capability and application specific constraints. The optimization model identifies and parameterizes the rendering and computing capabilities of the client hardware using an exploratory proxy code. The parameters are utilized to determine the optimized simulation conditions including texture sizes, mesh sizes and canvas resolution. The test results show that the optimization model not only achieves a desired frame per second but also resolves visual artifacts due to low performance hardware. PMID:23400151

Halic, Tansel; Ahn, Woojin; De, Suvranu



A complex-valued neural dynamical optimization approach and its stability analysis.  


In this paper, we propose a complex-valued neural dynamical method for solving a complex-valued nonlinear convex programming problem. Theoretically, we prove that the proposed complex-valued neural dynamical approach is globally stable and convergent to the optimal solution. The proposed neural dynamical approach significantly generalizes the real-valued nonlinear Lagrange network completely in the complex domain. Compared with existing real-valued neural networks and numerical optimization methods for solving complex-valued quadratic convex programming problems, the proposed complex-valued neural dynamical approach can avoid redundant computation in a double real-valued space and thus has a low model complexity and storage capacity. Numerical simulations are presented to show the effectiveness of the proposed complex-valued neural dynamical approach. PMID:25462634

Zhang, Songchuan; Xia, Youshen; Zheng, Weixing



Dynamic vs. Static Optimization of Crossdocking Operations1  

E-print Network

In section 1, we briefly survey the large body of cross-docking research and identify the literature relevant to ...... systems using dynamic and static are essentially the same. The only ..... Efficiently employing the human resources is important.




Dynamic Optimal Model of Vehicle Fleet Size and Exact Algorithm  

Microsoft Academic Search

Incorporating single-periodic and static vehicle routing problem, the article analyzes multi-periodic vehicle fleet size and routing problem, and model dynamic vehicle fleet size. Furthermore, the authors decompose the model with Dantzig-Wolf decomposition method, and derive an exact algorithm for the model based on simplex method, dynamic programming method and branch and bound method. Finally, the authors use numerical example to

Yuan-yuan ZHANG; Jian-bin LI



Was Your Glass Left Half Full? Family Dynamics and Optimism  

ERIC Educational Resources Information Center

Students' levels of a frequently studied adaptive schema (optimism) as a function of parenting variables (parental authority, family intrusiveness, parental overprotection, parentification, parental psychological control, and parental nurturance) were investigated. Results revealed that positive parenting styles were positively related to the…

Buri, John R.; Gunty, Amy



Decentralized Optimization of Dynamic Bluetooth Scatternets Sewook Jung  

E-print Network

,brunato Abstract Previous work analytically showed that communication path length reduction through Asyn- chronous Connectionless Links (ACL) using time slots of ¾ s. Data packets may use the current traffic flows with optimal paths. In [6] we analytically show that the number of hops along all

Brunato, Mauro


Dynamic Optimal Finite-Volume LES and its Application to a Temporally Evolving Plane Mixing Layer  

NASA Astrophysics Data System (ADS)

A simple dynamic optimal finite-volume LES model has been developed and applied to a temporally evolving free shear layer. Unlike previous optimal models, the model used here does not depend on DNS data. The necessary velocity correlations for the stochastic estimation procedure, which yields the optimal model, are obtained by assuming isotropy of the turbulence at the filter scale, allowing the use of Kolmogorov's expressions for the third-order, two-point velocity structure functions. The model reduces to a second-order dissipation term whose strength is determined from the consistent kinetic energy dissipation rate and the average kinetic energy dissipation (anti-dissipation) of the numerical treatment of the nonlinear terms. Modifications to the modeling procedure due to the existence of a mean velocity profile and a direction of inhomogeneity are discussed. Results for the dynamic optimal finite-models are compared to the DNS simulations of Rogers and Moser (1994).

Moser, Robert; Zandonade, Paulo



Dynamic Multiobjective Optimization Algorithm Based on Average Distance Linear Prediction Model  

PubMed Central

Many real-world optimization problems involve objectives, constraints, and parameters which constantly change with time. Optimization in a changing environment is a challenging task, especially when multiple objectives are required to be optimized simultaneously. Nowadays the common way to solve dynamic multiobjective optimization problems (DMOPs) is to utilize history information to guide future search, but there is no common successful method to solve different DMOPs. In this paper, we define a kind of dynamic multiobjectives problem with translational Paretooptimal set (DMOP-TPS) and propose a new prediction model named ADLM for solving DMOP-TPS. We have tested and compared the proposed prediction model (ADLM) with three traditional prediction models on several classic DMOP-TPS test problems. The simulation results show that our proposed prediction model outperforms other prediction models for DMOP-TPS. PMID:24616625

Xie, Zhaoxin; Chen, Chao; Sallam, Ahmed



INDDGO: Integrated Network Decomposition & Dynamic programming for Graph Optimization  

SciTech Connect

It is well-known that dynamic programming algorithms can utilize tree decompositions to provide a way to solve some \\emph{NP}-hard problems on graphs where the complexity is polynomial in the number of nodes and edges in the graph, but exponential in the width of the underlying tree decomposition. However, there has been relatively little computational work done to determine the practical utility of such dynamic programming algorithms. We have developed software to construct tree decompositions using various heuristics and have created a fast, memory-efficient dynamic programming implementation for solving maximum weighted independent set. We describe our software and the algorithms we have implemented, focusing on memory saving techniques for the dynamic programming. We compare the running time and memory usage of our implementation with other techniques for solving maximum weighted independent set, including a commercial integer programming solver and a semi-definite programming solver. Our results indicate that it is possible to solve some instances where the underlying decomposition has width much larger than suggested by the literature. For certain types of problems, our dynamic programming code runs several times faster than these other methods.

Groer, Christopher S [ORNL; Sullivan, Blair D [ORNL; Weerapurage, Dinesh P [ORNL



Integration of dynamic, aerodynamic, and structural optimization of helicopter rotor blades  

NASA Technical Reports Server (NTRS)

Summarized here is the first six years of research into the integration of structural, dynamic, and aerodynamic considerations in the design-optimization process for rotor blades. Specifically discussed here is the application of design optimization techniques for helicopter rotor blades. The reduction of vibratory shears and moments at the blade root, aeroelastic stability of the rotor, optimum airframe design, and an efficient procedure for calculating system sensitivities with respect to the design variables used are discussed.

Peters, David A.



Online optimal control of nonlinear discrete-time systems using approximate dynamic programming  

Microsoft Academic Search

In this paper, the optimal control of a class of general affine nonlinear discrete-time (DT) systems is undertaken by solving\\u000a the Hamilton Jacobi-Bellman (HJB) equation online and forward in time. The proposed approach, referred normally as adaptive\\u000a or approximate dynamic programming (ADP), uses online approximators (OLAs) to solve the infinite horizon optimal regulation\\u000a and tracking control problems for affine nonlinear

Travis Dierks; Sarangapani Jagannathan



The Uncertainty Threshold Principle: Some Fundamental Limitations of Optimal Decision Making Under Dynamic Uncertainity  

NASA Technical Reports Server (NTRS)

This note shows that the optimal control of dynamic systems with uncertain parameters has certain limitations. In particular, by means of a simple scalar linear-quadratic optimal control example, it is shown that the infinite horizon solution does not exist if the parameter uncertainty exceeds a certain quantifiable threshold; we call this the uncertainty threshold principle. The philosophical and design implications of this result are discussed.

Athans, M.; Ku, R.; Gershwin, S. B.



The uncertainty threshold principle - Some fundamental limitations of optimal decision making under dynamic uncertainty  

NASA Technical Reports Server (NTRS)

This note shows that the optimal control of dynamic systems with uncertain parameters has certain limitations. In particular, by means of a simple scalar linear-quadratic optimal control example, it is shown that the infinite horizon solution does not exist if the parameter uncertainty exceeds a certain quantifiable threshold; we call this the uncertainty threshold principle. The philosophical and design implications of this result are discussed.

Athans, M.; Ku, R.; Gershwin, S. B.



An enhanced integrated aerodynamic load\\/dynamic optimization procedure for helicopter rotor blades  

Microsoft Academic Search

An enhanced integrated aerodynamic load\\/dynamic optimization procedure is developed for minimizing vibratory root shears and moments of a helicopter rotor blade. The optimization problem is formulated with 4\\/rev inplane shears at the blade root as objective functions. Constraints are imposed on 3\\/rev radial shear, 3\\/rev flapping and torsional moments, 4\\/rev lagging moment, blade natural frequencies, weight, autorotational inertia, centrifugal stress

A. Chattopadhyay; Y. D. Chiu



Optimization of large amorphous silicon and silica structures for molecular dynamics simulations of energetic impacts  

NASA Astrophysics Data System (ADS)

A practical method to create optimized amorphous silicon and silica structures for molecular dynamics simulations is developed and tested. The method is based on the Wooten, Winer, and Weaire algorithm and combination of small optimized blocks to larger structures. The method makes possible to perform simulations of either very large cluster hypervelocity impacts on amorphous targets or small displacements induced by low energy ion impacts in silicon.

Samela, Juha; Norris, Scott A.; Nordlund, Kai; Aziz, Michael J.



Dynamic optimal control for groundwater remediation with flexible management periods  

Microsoft Academic Search

A successive approximation linear quadratic regulator (SALQR) method with management periods is combined with a finite element groundwater flow and transport simulation model to determine optimal time-varying groundwater pump-and-treat reclamation policies. Management periods are groups of simulation time steps during which the pumping policy remains constant. In an example problem, management periods reduced the total computational demand, as measured by

Teresa B. Culver; Christine A. Shoemaker



The uncertainty threshold principle - Fundamental limitations of optimal decision making under dynamic uncertainty  

NASA Technical Reports Server (NTRS)

The fundamental limitations of the optimal control of dynamic systems with random parameters are analyzed by studying a scalar linear-quadratic optimal control example. It is demonstrated that optimum long-range decision making is possible only if the dynamic uncertainty (quantified by the means and covariances of the random parameters) is below a certain threshold. If this threshold is exceeded, there do not exist optimum decision rules. This phenomenon is called the 'uncertainty threshold principle'. The implications of this phenomenon to the field of modelling, identification, and adaptive control are discussed.

Athans, M.; Ku, R.; Gershwin, S. B.



Logic optimization by output phase assignment in dynamic logic synthesis  

Microsoft Academic Search

Domino logic is one of the most popular dynamic circuit configurations for implementing high- performance logic designs. Since domino logic is inherently non-inverting, it presents a fundamental constraint of implementing logic functions without any intermediate inversions. Removal of intermediate inverters requires logic duplication for generating both the negative and positive signal phases, which results in significant area overhead. This area

Ruchir Puri; Andrew Bjorksten; Thomas E. Rosser



Optimal Measurement and Control in Quantum Dynamical Systems.  

E-print Network

', February 1979 Abstract A Markovian model for a quantum automata, i.e. an open quantum dynamical system containing quantum channels. Due to fundamental limitations of quantum-mechanical measurement a speci c prob, and the time development of the discrete models of quantum open systems for commu- nication and control became

Belavkin, Viacheslav P.


Optimal dynamical range of excitable networks at criticality  

E-print Network

dynamics. We propose that the main functional role of electrical coupling is to provide an enhancement. The mechanism could provide a microscopic neural basis for psychophysical laws. P sychophysics is probably and neural2­4 level, little work has been done regarding the mechanism that produces such psychophysical laws

Loss, Daniel


Optimal Aviation Security Screening Strategies With Dynamic Passenger Risk Updates  

Microsoft Academic Search

Passenger screening is a critical component of aviation security systems. This paper introduces the multistage sequential passenger screening problem (MSPSP), which models passenger and carry-on baggage screening operations in an aviation security system with the capability of dynamically updating the perceived risk of passengers. The passenger screening operation at an airport terminal is subdivided into multiple screening stages, with decisions

Alexander G. Nikolaev; Adrian J. Lee; Sheldon H. Jacobson



Dynamic analysis and optimal design of a passive and an active piezo-electrical dynamic vibration absorber  

NASA Astrophysics Data System (ADS)

This paper is concerned with the dynamic analysis and parameter optimization of both passive and active piezo-electrical dynamic vibration absorbers that are strongly coupled with a single degree of freedom vibrating structure. The passive absorber is implemented by using an Rs? Ls parallel shunt circuit while the active absorber is implemented by feeding back the acceleration of the structure through a second-order lowpass filter. An impedance-mobility approach is used for the electromechanical coupling analysis of both types of absorbers coupled with the structure. Using this approach it is demonstrated that the passive and active absorbers can be made exactly equivalent. A maximally flat frequency response strategy is used to find the optimal damping ratio of the passive absorber while a robust, optimal control theory is used to find that for the active absorber. It is found that the passive optimization strategy corresponds to an optimal, robust feedback control of 2 dB spillover. Simulations and experiments are conducted to support the theoretical findings.

Kim, Sang-Myeong; Wang, Semyung; Brennan, Michael J.



Dynamic simulation and optimal control strategy of a decentralized supply chain system  

Microsoft Academic Search

Efficient management of inventory in supply chains is critical to the profitable operation of modern enterprises. The supply\\/demand networks characteristic of discrete-parts industries represent highly stochastic, nonlinear, and constrained dynamical systems whose study merits a control-oriented approach. Minimum variance control (MVC) strategy is applied to solve the dynamic optimization problems of the inventory for a decentralized supply chain system. Transfer

Dong Hai; Li Yan-ping



On the optimal reconstruction and control of adaptive optical systems with mirror dynamics.  


In adaptive optics (AO) the deformable mirror (DM) dynamics are usually neglected because, in general, the DM can be considered infinitely fast. Such assumption may no longer apply for the upcoming Extremely Large Telescopes (ELTs) with DM that are several meters in diameter with slow and/or resonant responses. For such systems an important challenge is to design an optimal regulator minimizing the variance of the residual phase. In this contribution, the general optimal minimum-variance (MV) solution to the full dynamical reconstruction and control problem of AO systems (AOSs) is established. It can be looked upon as the parent solution from which simpler (used hitherto) suboptimal solutions can be derived as special cases. These include either partial DM-dynamics-free solutions or solutions derived from the static minimum-variance reconstruction (where both atmospheric disturbance and DM dynamics are neglected altogether). Based on a continuous stochastic model of the disturbance, a state-space approach is developed that yields a fully optimal MV solution in the form of a discrete-time linear-quadratic-Gaussian (LQG) regulator design. From this LQG standpoint, the control-oriented state-space model allows one to (1) derive the optimal state-feedback linear regulator and (2) evaluate the performance of both the optimal and the sub-optimal solutions. Performance results are given for weakly damped second-order oscillatory DMs with large-amplitude resonant responses, in conditions representative of an ELT AO system. The highly energetic optical disturbance caused on the tip/tilt (TT) modes by the wind buffeting is considered. Results show that resonant responses are correctly handled with the MV regulator developed here. The use of sub-optimal regulators results in prohibitive performance losses in terms of residual variance; in addition, the closed-loop system may become unstable for resonant frequencies in the range of interest. PMID:20126246

Correia, Carlos; Raynaud, Henri-François; Kulcsár, Caroline; Conan, Jean-Marc



Optimal Common Sub-Expression Elimination Algorithm of Multiple Constant Multiplications with a Logic Depth Constraint  

NASA Astrophysics Data System (ADS)

In the context of multiple constant multiplication (MCM) design, we propose a novel common sub-expression elimination (CSE) algorithm that models the optimal synthesis of coefficients into a 0-1 mixed-integer linear programming (MILP) problem with a user-defined generic logic depth constraint. We also propose an efficient solution space, which combines all minimal signed digit (MSD) representations and the shifted sum (difference) of coefficients. In the examples we demonstrate, the combination of the proposed algorithm and solution space gives a better solution comparing to existing algorithms.

Ho, Yuen-Hong Alvin; Lei, Chi-Un; Kwan, Hing-Kit; Wong, Ngai


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

PubMed Central

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

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



Dynamic programming algorithm optimization for spoken word recognition  

Microsoft Academic Search

This paper reports on an optimum dynamic progxamming (DP) based time-normalization algorithm for spoken word recognition. First, a general principle of time-normalization is given using time-warping function. Then, two time-normalized distance definitions, called symmetric and asymmetric forms, are derived from the principle. These two forms are compared with each other through theoretical discussions and experimental studies. The symmetric form algorithm




Dynamics of ultrathin laser targets with optimal parameters  

NASA Astrophysics Data System (ADS)

The set of equations describing the motion of a thin (compared to the wavelength) target in the field of a laser pulse that takes into consideration separate motion of the electron and ion layers is derived. In the case of strong Coulomb coupling between the layers, the set of equation of motions of the layers is reduced to the well-known light-sail equation containing a self-consistent coefficient of nonlinear reflection of laser radiation by a moving target. The optimal thickness of the laser target at which the target acquires maximum energy for given laser-pulse parameters is determined. It is shown that this thickness depends not only on laser intensity, but also on laser-pulse duration and the ratio of electron and ion masses. The growth rates of transverse instability of optimal targets under their intense acceleration are analyzed. It is demonstrated that instability does not develop in the currently experimentally accessible range of laser intensities and pulse durations between 100 and 200 fs.

Andreev, A. A.; Platonov, K. Yu.; Chestnov, V. I.; Petrov, A. E.



Improving the dynamic characteristics of body-in-white structure using structural optimization.  


The dynamic behavior of a body-in-white (BIW) structure has significant influence on the noise, vibration, and harshness (NVH) and crashworthiness of a car. Therefore, by improving the dynamic characteristics of BIW, problems and failures associated with resonance and fatigue can be prevented. The design objectives attempt to improve the existing torsion and bending modes by using structural optimization subjected to dynamic load without compromising other factors such as mass and stiffness of the structure. The natural frequency of the design was modified by identifying and reinforcing the structure at critical locations. These crucial points are first identified by topology optimization using mass and natural frequencies as the design variables. The individual components obtained from the analysis go through a size optimization step to find their target thickness of the structure. The thickness of affected regions of the components will be modified according to the analysis. The results of both optimization steps suggest several design modifications to achieve the target vibration specifications without compromising the stiffness of the structure. A method of combining both optimization approaches is proposed to improve the design modification process. PMID:25101312

Yahaya Rashid, Aizzat S; Ramli, Rahizar; Mohamed Haris, Sallehuddin; Alias, Anuar



Improving the Dynamic Characteristics of Body-in-White Structure Using Structural Optimization  

PubMed Central

The dynamic behavior of a body-in-white (BIW) structure has significant influence on the noise, vibration, and harshness (NVH) and crashworthiness of a car. Therefore, by improving the dynamic characteristics of BIW, problems and failures associated with resonance and fatigue can be prevented. The design objectives attempt to improve the existing torsion and bending modes by using structural optimization subjected to dynamic load without compromising other factors such as mass and stiffness of the structure. The natural frequency of the design was modified by identifying and reinforcing the structure at critical locations. These crucial points are first identified by topology optimization using mass and natural frequencies as the design variables. The individual components obtained from the analysis go through a size optimization step to find their target thickness of the structure. The thickness of affected regions of the components will be modified according to the analysis. The results of both optimization steps suggest several design modifications to achieve the target vibration specifications without compromising the stiffness of the structure. A method of combining both optimization approaches is proposed to improve the design modification process. PMID:25101312

Yahaya Rashid, Aizzat S.; Mohamed Haris, Sallehuddin; Alias, Anuar



Autonomous and Decentralized Optimization of Large-Scale Heterogeneous Wireless Networks by Neural Network Dynamics  

NASA Astrophysics Data System (ADS)

We propose a neurodynamical approach to a large-scale optimization problem in Cognitive Wireless Clouds, in which a huge number of mobile terminals with multiple different air interfaces autonomously utilize the most appropriate infrastructure wireless networks, by sensing available wireless networks, selecting the most appropriate one, and reconfiguring themselves with seamless handover to the target networks. To deal with such a cognitive radio network, game theory has been applied in order to analyze the stability of the dynamical systems consisting of the mobile terminals' distributed behaviors, but it is not a tool for globally optimizing the state of the network. As a natural optimization dynamical system model suitable for large-scale complex systems, we introduce the neural network dynamics which converges to an optimal state since its property is to continually decrease its energy function. In this paper, we apply such neurodynamics to the optimization problem of radio access technology selection. We compose a neural network that solves the problem, and we show that it is possible to improve total average throughput simply by using distributed and autonomous neuron updates on the terminal side.

Hasegawa, Mikio; Tran, Ha Nguyen; Miyamoto, Goh; Murata, Yoshitoshi; Harada, Hiroshi; Kato, Shuzo


External optimal control of self-organisation dynamics in a chemotaxis reaction diffusion system  

E-print Network

External optimal control of self-organisation dynamics in a chemotaxis reaction diffusion system D. Pattern formation and self-organisation processes both in single cells and in distributed cell populations a highly organised spatiotemporal system structure. In particular chemotaxis is crucial for various

Maurer, Helmut


Explicit Speciation with few a priori Parameters for Dynamic Optimization Problems  

E-print Network

Explicit Speciation with few a priori Parameters for Dynamic Optimization Problems Christopher speciation cri­ terion. In a performance test on multiple stochastically moving fitness peaks speciation is generally advantageous because it enables the use of arbitrary (here generational) evolu


Global Query Optimization Based on Multistate Cost Models for a Dynamic Multidatabase System  

Microsoft Academic Search

Global query optimization in a multidatabase system (MDBS) is a challenging issue since some local opti- mization information such as local cost models may not be available at the global level due to local autonomy. It becomes even more difcult when dynamic environmental factors are taken into consideration. In our previous work, a qualitative approach was suggested to build so-called

Qiang Zhu; Jaidev Haridas; Wen-chi Hou



Coordinated Control of Variable Speed Limits Based on Neural Dynamic Optimization  

Microsoft Academic Search

The paper is concerned with modeling the effect of variable speed limits and their coordination control in highway systems. A new model is introduced by considering the normal response of drivers to speed limits. For the coordination control of speed limits, we propose a neural controller based on the technique of neural dynamic optimization. Moreover, some practical constraints about the

Jing Xu; Fu-Ming Liang; Wen-Sheng Yu



A New Dynamic Multicast Routing Model and Its Immune Optimization Algorithm in Integrated Network  

Microsoft Academic Search

A new dynamic multicast routing model was proposed in this paper. Specifically, we firstly considered two possible changes in integrated network: node movements and the change of link delay. Next, a mechanism called local rearrangement is used to handle changes in integrated networks. We designed an artificial immune algorithm based on clone process for optimizing the multicast sub-tree within the

Jiang-qing Wang; Jun Qin; Lishan Kang



ITOMP: Incremental Trajectory Optimization for Real-time Replanning in Dynamic Environments  

E-print Network

Introduction Motion planning is an important problem in many robotics applications, including autonomous- terval. Such uncertainty about moving objects makes it hard to plan a safe trajectory for the robot over:// Abstract We present a novel optimization-based algorithm for motion planning in dynamic environments. Our

North Carolina at Chapel Hill, University of


A model based on stochastic dynamic programming for determining China's optimal strategic petroleum reserve policy  

Microsoft Academic Search

China's Strategic Petroleum Reserve (SPR) is currently being prepared. But how large the optimal stockpile size for China should be, what the best acquisition strategies are, how to release the reserve if a disruption occurs, and other related issues still need to be studied in detail. In this paper, we develop a stochastic dynamic programming model based on a total

Xiao-Bing Zhang; Ying Fan; Yi-Ming Wei



Optimal Strategies of New Product Development in a Dynamic Environment of Possible Appearance of Competitors' Products  

Microsoft Academic Search

New product development has always been an important issue for firms who want to achieve competitive advantages. Facing potential competition during the development period of a new product, a firm may need to modify her strategies based on the current environment. We model optimal strategies of new product development (NPD) using a dynamic programming approach. Under this framework, four possible

Li Chen



Dynamic online optimization of a house heating system in a fluctuating energy price  

E-print Network

Dynamic online optimization of a house heating system in a fluctuating energy price scenario in this problem is the time- varying nature of the main disturbances, which are the energy price and outdoor energy price. 1. INTRODUCTION Recently, great attention has been given to renewable gen- eration sources

Skogestad, Sigurd


Considerations in the application of dynamic programming to optimal aircraft trajectory generation  

Microsoft Academic Search

The application of dynamic programming to optimal trajectory generation is reported with reference to the problems that arise in the selection of the size of the solution space, including the size of the grid spacing. Recommendations are given on the choice of the grid size and the size of the solution space boundaries to obtain the global optima. The effects

M. C. Waller; J. G. Rigopoulos; D. R. Blackman; T. F. Berreen



Optimal dynamic path of effort on marriage: differences between arranged and love marriages  

Microsoft Academic Search

People benefit from good marriages. Thus, everyone is willing to put effort into marriage to improve the quality of marriage. However, effort is costly. Then everyone faces the same question: how much effort should I put into marriage? A dynamic optimal control model is used in trying to answer this question. The study shows that for arranged marriages that start

Xuemei Liu



Lattice Dynamical Wavelet Neural Networks Implemented Using Particle Swarm Optimization for Spatio-Temporal System Identification  

Microsoft Academic Search

In this brief, by combining an efficient wavelet representation with a coupled map lattice model, a new family of adaptive wavelet neural networks, called lattice dynamical wavelet neural networks (LDWNNs), is introduced for spatio-temporal system identification. A new orthogonal projection pursuit (OPP) method, coupled with a particle swarm optimization (PSO) algorithm, is proposed for augmenting the proposed network. A novel

Hua-liang Wei; Stephen A. Billings; Yifan Zhao; Lingzhong Guo



A Note on the Existence of Optimal Policies in Total Reward Dynamic Programs with  

E-print Network

A Note on the Existence of Optimal Policies in Total Reward Dynamic Programs with Compact Action with Markov Decision Processes (MDPs) with expected total rewards, discrete state spaces, and compact action-called Structural Continuity Condition is a natural suÃ?cient assumption under which the existence of total-reward

Feinberg, Eugene A.


Optimization of Fed-Batch Saccharomyces cereWisiae Fermentation Using Dynamic Flux Balance Models  

E-print Network

ARTICLES Optimization of Fed-Batch Saccharomyces cereWisiae Fermentation Using Dynamic Flux Balance-batch Saccharomyces cereVisiae fermentation that couples a detailed steady-state description of primary carbon is that nutrient levels can be varied to achieve favorable growth conditions. Fed-batch yeast fermentation

Mountziaris, T. J.


Dynamic optimization of on-chip polymerase chain reaction by monitoring intracycle fluorescence using fast synchronous detection  

NASA Astrophysics Data System (ADS)

The authors report on-chip dynamic optimization of polymerase chain reaction (PCR) based on a feedback technique utilizing synchronous detection of intracycle fluorescence every 500ms. From a direct measurement of polymerase activity, the authors determine the optimum extension temperature. The authors dynamically optimize PCR in an inductively heated microchip by sensing the saturation of extension in each cycle and applying the feedback. They demonstrate that, even with fast ramp rates, dynamic optimization leads to faster reactions compared to fixed-duration extension protocols for long DNA (>500bp). This optimization scheme uses a fairly universal dye Sybr Green I and can be applied to most PCRs.

Mondal, Sudip; Paul, Debjani; Venkataraman, V.



Characterization of control noise effects in optimal quantum unitary dynamics  

NASA Astrophysics Data System (ADS)

This work develops measures for quantifying the effects of field noise upon targeted unitary transformations. Robustness to noise is assessed in the framework of the quantum control landscape, which is the mapping from the control to the unitary transformation performance measure (quantum gate fidelity). Within that framework, a geometric interpretation of stochastic noise effects naturally arises, where more robust optimal controls are associated with regions of small overlap between landscape curvature and the noise correlation function. Numerical simulations of this overlap in the context of quantum information processing reveal distinct noise spectral regimes that better support robust control solutions. This perspective shows the dual importance of both noise statistics and the control form for robustness, thereby opening up new avenues of investigation on how to mitigate noise effects in quantum systems.

Hocker, David; Brif, Constantin; Grace, Matthew D.; Donovan, Ashley; Ho, Tak-San; Tibbetts, Katharine Moore; Wu, Rebing; Rabitz, Herschel



Locusts use dynamic thermoregulatory behaviour to optimize nutritional outcomes  

PubMed Central

Because key nutritional processes differ in their thermal optima, ectotherms may use temperature selection to optimize performance in changing nutritional environments. Such behaviour would be especially advantageous to small terrestrial animals, which have low thermal inertia and often have access to a wide range of environmental temperatures over small distances. Using the locust, Locusta migratoria, we have demonstrated a direct link between nutritional state and thermoregulatory behaviour. When faced with chronic restrictions to the supply of nutrients, locusts selected increasingly lower temperatures within a gradient, thereby maximizing nutrient use efficiency at the cost of slower growth. Over the shorter term, when locusts were unable to find a meal in the normal course of ad libitum feeding, they immediately adjusted their thermoregulatory behaviour, selecting a lower temperature at which assimilation efficiency was maximal. Thus, locusts use fine scale patterns of movement and temperature selection to adjust for reduced nutrient supply and thereby ameliorate associated life-history consequences. PMID:21288941

Coggan, Nicole; Clissold, Fiona J.; Simpson, Stephen J.



No-go theorems and optimization of dynamical decoupling against noise with soft cutoff  

NASA Astrophysics Data System (ADS)

We study the performance of dynamical decoupling in suppressing decoherence caused by soft-cutoff Gaussian noise, using short-time expansion of the noise correlations and numerical optimization. For the noise with soft cutoff at high frequencies, there exists no dynamical decoupling scheme to eliminate the decoherence to arbitrary orders of the short time, regardless of the timing or pulse shaping of the control under the population conserving condition. We formulate the equations for optimizing pulse sequences that minimize decoherence up to the highest possible order of the short time for the noise correlations with odd power terms in the short-time expansion. In particular, we show that the Carr-Purcell-Meiboom-Gill sequence is optimal in the short-time limit for the noise correlations with a linear order term in the time expansion.

Wang, Zhen-Yu; Liu, Ren-Bao



An optimality-based model of the dynamic feedbacks between natural vegetation and the water balance  

NASA Astrophysics Data System (ADS)

The hypothesis that vegetation adapts optimally to its environment gives rise to a novel framework for modeling the interactions between vegetation dynamics and the catchment water balance that does not rely on prior knowledge about the vegetation at a particular site. We present a new model based on this framework that includes a multilayered physically based catchment water balance model and an ecophysiological gas exchange and photosynthesis model. The model uses optimization algorithms to find those static and dynamic vegetation properties that would maximize the net carbon profit under given environmental conditions. The model was tested at a savanna site near Howard Springs (Northern Territory, Australia) by comparing the modeled fluxes and vegetation properties with long-term observations at the site. The results suggest that optimality may be a useful way of approaching the prediction and estimation of vegetation cover, rooting depth, and fluxes such as transpiration and CO2 assimilation in ungauged basins without model calibration.

Schymanski, S. J.; Sivapalan, M.; Roderick, M. L.; Hutley, L. B.; Beringer, J.



Adaptive Dynamic Programming for Finite-Horizon Optimal Control of Discrete-Time Nonlinear Systems With varepsilon-Error Bound  

Microsoft Academic Search

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 ?-error bound. The optimal number of

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



Dynamic Response Optimization of Complex Multibody Systems in a Penalty Formulation using Adjoint Sensitivity  

E-print Network

Multibody dynamics simulations are currently widely accepted as valuable means for dynamic performance analysis of mechanical systems. The evolution of theoretical and computational aspects of the multibody dynamics discipline make it conducive these days for other types of applications, in addition to pure simulations. One very important such application is design optimization. A very important first step towards design optimization is sensitivity analysis of multibody system dynamics. Dynamic sensitivities are often calculated by means of finite differences. Depending of the number of parameters involved, this procedure can be computationally expensive. Moreover, in many cases, the results suffer from low accuracy when real perturbations are used. The main contribution to the state-of-the-art brought by this study is the development of the adjoint sensitivity approach of multibody systems in the context of the penalty formulation. The theory developed is demonstrated on one academic case study, a five-bar mechanism, and on one real-life system, a 14-DOF vehicle model. The five-bar mechanism is used to illustrate the sensitivity approach derived in this paper. The full vehicle model is used to demonstrate the capability of the new approach developed to perform sensitivity analysis and gradient-based optimization for large and complex multibody systems with respect to multiple design parameters.

Yitao Zhu; Daniel Dopico; Corina Sandu; Adrian Sandu



Dynamical Arrest, Structural Disorder, and Optimization of Organic Photovoltaic Devices  

SciTech Connect

This project describes fundamental experimental and theoretical work that relates to charge separation and migration in the solid, heterogeneous or aggregated state. Marcus theory assumes a system in equilibrium with all possible solvent (dipolar) configurations, with rapid interconversion among these on the ET timescale. This project has addressed the more general situation where the medium is at least partially frozen on the ET timescale, i.e. under conditions of dynamical arrest. The approach combined theory and experiment and includes: (1) Computer simulations of model systems, (2) Development of analytical procedures consistent with computer experiment and (3) Experimental studies and testing of the formal theories on this data. Electron transfer processes are unique as a consequence of the close connection between kinetics, spectroscopy and theory, which is an essential component of this work.

Gould, Ian; Dmitry, Matyushov



Collision-free nonuniform dynamics within continuous optimal velocity models  

NASA Astrophysics Data System (ADS)

Optimal velocity (OV) car-following models give with few parameters stable stop-and -go waves propagating like in empirical data. Unfortunately, classical OV models locally oscillate with vehicles colliding and moving backward. In order to solve this problem, the models have to be completed with additional parameters. This leads to an increase of the complexity. In this paper, a new OV model with no additional parameters is defined. For any value of the inputs, the model is intrinsically asymmetric and collision-free. This is achieved by using a first-order ordinary model with two predecessors in interaction, instead of the usual inertial delayed first-order or second-order models coupled with the predecessor. The model has stable uniform solutions as well as various stable stop-and -go patterns with bimodal distribution of the speed. As observable in real data, the modal speed values in congested states are not restricted to the free flow speed and zero. They depend on the form of the OV function. Properties of linear, concave, convex, or sigmoid speed functions are explored with no limitation due to collisions.

Tordeux, Antoine; Seyfried, Armin



Optimal purchasing of raw materials: A data-driven approach  

SciTech Connect

An approach to the optimal purchasing of raw materials that will achieve a desired product quality at a minimum cost is presented. A PLS (Partial Least Squares) approach to formulation modeling is used to combine databases on raw material properties and on past process operations and to relate these to final product quality. These PLS latent variable models are then used in a sequential quadratic programming (SQP) or mixed integer nonlinear programming (MINLP) optimization to select those raw-materials, among all those available on the market, the ratios in which to combine them and the process conditions under which they should be processed. The approach is illustrated for the optimal purchasing of metallurgical coals for coke making in the steel industry.

Muteki, K.; MacGregor, J.F. [McMaster University, Hamilton, ON (Canada). Dept. of Chemical Engineering



Dynamic pathway modeling: feasibility analysis and optimal experimental design.  


A major challenge in systems biology is to evaluate the feasibility of a biological research project prior to its realization. Since experiments are animals-, cost- and time-consuming, approaches allowing researchers to discriminate alternative hypotheses with a minimal set of experiments are highly desirable. Given a null hypothesis and alternative model, as well as laboratory constraints like observable players, sample size, noise level, and stimulation options, we suggest a method to obtain a list of required experiments in order to significantly reject the null hypothesis model M0 if a specified alternative model MA is realized. For this purpose, we estimate the power to detect a violation of M0 by means of Monte Carlo simulations. Iteratively, the power is maximized over all feasible stimulations of the system using multi-experiment fitting, leading to an optimal combination of experimental settings to discriminate the null hypothesis and alternative model. We prove the importance of simultaneous modeling of combined experiments with quantitative, highly sampled in vivo measurements from the Jak/STAT5 signaling pathway in fibroblasts, stimulated with erythropoietin (Epo). Afterwards we apply the presented iterative experimental design approach to the Jak/STAT3 pathway of primary hepatocytes stimulated with IL-6. Our approach offers the possibility of deciding which scientific questions can be answered based on existing laboratory constraints. To be able to concentrate on feasible questions on account of inexpensive computational simulations yields not only enormous cost and time saving, but also helps to specify realizable, systematic research projects in advance. PMID:18033750

Maiwald, Thomas; Kreutz, Clemens; Pfeifer, Andrea C; Bohl, Sebastian; Klingmüller, Ursula; Timmer, Jens



Optimizing electromagnetic induction sensors for dynamic munitions classification surveys  

NASA Astrophysics Data System (ADS)

Standard protocol for detection and classification of Unexploded Ordnance (UXO) comprises a two-step process that includes an initial digital geophysical mapping (DGM) survey to detect magnetic field anomalies followed by a cued survey at each anomaly location that enables classification of these anomalies. The initial DGM survey is typically performed using a low resolution single axis electromagnetic induction (EMI) sensor while the follow-up cued survey requires revisiting each anomaly location with a multi-axis high resolution EMI sensor. The DGM survey comprises data collection in tightly spaced transects over the entire survey area. Once data collection in this area is complete, a threshold analysis is applied to the resulting magnetic field anomaly map to identify anomalies corresponding to potential targets of interest (TOI). The cued sensor is deployed in static mode where this higher resolution sensor is placed over the location of each anomaly to record a number of soundings that may be stacked and averaged to produce low noise data. These data are of sufficient quality to subsequently classify the object as either TOI or clutter. While this approach has demonstrated success in producing effective classification of UXO, conducting successive surveys is time consuming. Additionally, the low resolution of the initial DGM survey often produces errors in the target picking process that results in poor placement of the cued sensor and often requires several revisits to the anomaly location to ensure adequate characterization of the target space. We present data and test results from an advanced multi-axis EMI sensor optimized to provide both detection and classification from a single survey. We demonstrate how the large volume of data from this sensor may be used to produce effective detection and classification decisions while only requiring one survey of the munitions response area.

Miller, Jonathan S.; Keranen, Joe; Schultz, Gregory



Simulating the Dynamics of Scale-Free Networks via Optimization  

PubMed Central

We deal here with the issue of complex network evolution. The analysis of topological evolution of complex networks plays a crucial role in predicting their future. While an impressive amount of work has been done on the issue, very little attention has been so far devoted to the investigation of how information theory quantifiers can be applied to characterize networks evolution. With the objective of dynamically capture the topological changes of a network's evolution, we propose a model able to quantify and reproduce several characteristics of a given network, by using the square root of the Jensen-Shannon divergence in combination with the mean degree and the clustering coefficient. To support our hypothesis, we test the model by copying the evolution of well-known models and real systems. The results show that the methodology was able to mimic the test-networks. By using this copycat model, the user is able to analyze the networks behavior over time, and also to conjecture about the main drivers of its evolution, also providing a framework to predict its evolution. PMID:24353752

Schieber, Tiago Alves; Ravetti, Martín Gómez



Prediction of optimal folding routes of proteins that satisfy the principle of lowest entropy loss: dynamic contact maps and optimal control.  


An optimization model is introduced in which proteins try to evade high energy regions of the folding landscape, and prefer low entropy loss routes during folding. We make use of the framework of optimal control whose convenient solution provides practical and useful insight into the sequence of events during folding. We assume that the native state is available. As the protein folds, it makes different set of contacts at different folding steps. The dynamic contact map is constructed from these contacts. The topology of the dynamic contact map changes during the course of folding and this information is utilized in the dynamic optimization model. The solution is obtained using the optimal control theory. We show that the optimal solution can be cast into the form of a Gaussian Network that governs the optimal folding dynamics. Simulation results on three examples (CI2, Sso7d and Villin) show that folding starts by the formation of local clusters. Non-local clusters generally require the formation of several local clusters. Non-local clusters form cooperatively and not sequentially. We also observe that the optimal controller prefers "zipping" or small loop closure steps during folding. The folding routes predicted by the proposed method bear strong resemblance to the results in the literature. PMID:20967269

Arkun, Yaman; Erman, Burak



Linear dynamic model of production-inventory with debt repayment: optimal management strategies  

E-print Network

In this paper, we present a simple microeconomic model with linear continuous-time dynamics that describes a production-inventory system with debt repayment. This model is formulated in terms of optimal control and its exact solutions are derived by prudent application of the maximum principle under different sets of initial conditions (scenarios). For a potentially profitable small firm, we also propose some alternative short-term control strategies resulting in a positive final profit and prove their optimality. Practical implementation of such strategies is also discussed.

Tuchnolobova, Ekaterina; Vasilieva, Olga



Optimizing the petroleum supply chain at petrobras  

Microsoft Academic Search

The main objective of this study is to describe how mathematical programming is being used to solve the Petroleum Allocation Problem at Petrobras. We propose a Mixed Integer Linear Programming formulation of the problem which relies on a time\\/space discretization network. The formulation involves some inequalities which are redundant to the mixed integer model but no necessarily so to the

Mariza Aires; Abílio Lucena; Roger Rocha; Cláudio Santiago; Luidi Simonetti



Dynamic Imaging of Genomic Loci in Living Human Cells by an Optimized CRISPR/Cas System  

PubMed Central

SUMMARY The spatiotemporal organization and dynamics of chromatin play critical roles in regulating genome function. However, visualizing specific, endogenous genomic loci remains challenging in living cells. Here, we demonstrate such an imaging technique by repurposing the bacterial CRISPR/Cas system. Using an EGFP-tagged endonuclease-deficient Cas9 protein and a structurally optimized small guide (sg) RNA, we show robust imaging of repetitive elements in telomeres and coding genes in living cells. Furthermore, an array of sgRNAs tiling along the target locus enables the visualization of non-repetitive genomic sequences. Using this method, we have studied telomere dynamics during elongation or disruption, the subnuclear localization of the MUC4 loci, the cohesion of replicated MUC4 loci on sister chromatids, and their dynamic behaviors during mitosis. This CRISPR imaging tool has potential to significantly improve the capacity to study the conformation and dynamics of native chromosomes in living human cells. PMID:24360272

Chen, Baohui; Gilbert, Luke A.; Cimini, Beth A.; Schnitzbauer, Joerg; Zhang, Wei; Li, Gene-Wei; Park, Jason; Blackburn, Elizabeth H.; Weissman, Jonathan S.; Qi, Lei S.; Huang, Bo



An Approach for Dynamic Optimization of Prevention Program Implementation in Stochastic Environments  

NASA Astrophysics Data System (ADS)

The science of preventing youth problems has significantly advanced in developing evidence-based prevention program (EBP) by using randomized clinical trials. Effective EBP can reduce delinquency, aggression, violence, bullying and substance abuse among youth. Unfortunately the outcomes of EBP implemented in natural settings usually tend to be lower than in clinical trials, which has motivated the need to study EBP implementations. In this paper we propose to model EBP implementations in natural settings as stochastic dynamic processes. Specifically, we propose Markov Decision Process (MDP) for modeling and dynamic optimization of such EBP implementations. We illustrate these concepts using simple numerical examples and discuss potential challenges in using such approaches in practice.

Kang, Yuncheol; Prabhu, Vittal


Replica exchange molecular dynamics optimization of tensor network states for quantum many-body systems.  


Tensor network states (TNS) methods combined with the Monte Carlo (MC) technique have been proven a powerful algorithm for simulating quantum many-body systems. However, because the ground state energy is a highly non-linear function of the tensors, it is easy to get stuck in local minima when optimizing the TNS of the simulated physical systems. To overcome this difficulty, we introduce a replica-exchange molecular dynamics optimization algorithm to obtain the TNS ground state, based on the MC sampling technique, by mapping the energy function of the TNS to that of a classical mechanical system. The method is expected to effectively avoid local minima. We make benchmark tests on a 1D Hubbard model based on matrix product states (MPS) and a Heisenberg J1-J2 model on square lattice based on string bond states (SBS). The results show that the optimization method is robust and efficient compared to the existing results. PMID:25654245

Liu, Wenyuan; Wang, Chao; Li, Yanbin; Lao, Yuyang; Han, Yongjian; Guo, Guang-Can; Zhao, Yong-Hua; He, Lixin



Nonlinear Modeling, Dynamic Analysis, and Optimal Design and Operation of Electromechanical Valve Systems  

NASA Astrophysics Data System (ADS)

In this dissertation, the actuator-valve systems as a critical part of the automation system are analyzed. Using physics-based high fidelity modeling, this research provides a set of tools to help understand, predict, optimize, and control the real performance of these complex systems. The work carried out is expected to add to the suite of analytical and numerical tools that are essential for the development of highly automated ship systems. We present an accurate dynamic model, perform nonlinear analysis, and develop optimal design and operation for electromechanical valve systems. The mathematical model derived includes electromagnetics, fluid mechanics, and mechanical dynamics. Nondimensionalization has been carried out in order to reduce the large number of parameters to a few critical independent sets to help carry out a parametric analysis. The system stability analysis is then carried out with the aid of the tools from nonlinear dynamic analysis. This reveals that the system is unstable in a certain region of the parameter space. The system is also shown to exhibit crisis and transient chaotic responses. Smart valves are often operated under local power supply (for various mission-critical reasons) and need to consume as little energy as possible in order to ensure continued operability. The Simulated Annealing (SA) algorithm is utilized to optimize the actuation subsystem yielding the most efficient configuration from the point of view of energy consumption for two sets of design variables. The optimization is particularly important when the smart valves are used in a distributed network. Another aspect of optimality is more subtle and concerns optimal operation given a designed system. Optimal operation comes after the optimal design process to explore if there is any particular method of the valve operation that would yield the minimum possible energy used. The results of our model developed are also validated with the aid of an experimental setup including an electrically actuated butterfly valve. Several pressure sensors are employed to measure the pressure drop across the valve in addition to a torque sensor to determine the total torque acting on the valve motion.

Naseradinmousavi, Peiman


The role of digital computers in the dynamic optimization of chemical reactions  

Microsoft Academic Search

Along with the increasing availability of high-speed, large-storage digital computers, there has been growing interest in their utilization for real-time control purposes. A typical problem in this connection and one of long-standing interest is the optimal static and dynamic operation of chemical reactors. To our knowledge, no digital computer is being used for this purpose, chiefly because of the many

R. E. Kalman; R. W. Koepcke



Performance monitoring for new phase dynamic optimization of instruction dispatch cluster configuration  


In a processor having multiple clusters which operate in parallel, the number of clusters in use can be varied dynamically. At the start of each program phase, the configuration option for an interval is run to determine the optimal configuration, which is used until the next phase change is detected. The optimum instruction interval is determined by starting with a minimum interval and doubling it until a low stability factor is reached.

Balasubramonian, Rajeev (Sandy, UT); Dwarkadas, Sandhya (Rochester, NY); Albonesi, David (Ithaca, NY)



Optimization of steering linkage and double-wishbone suspension via RW multi-body dynamic analysis  

Microsoft Academic Search

By employing R-W (Roberson and Wittenburg) methodology of multi-body dynamic analysis, both the original and reduced digraph representation of a steering linkage and double-wishbone suspension system were made. The kinematical and constrain equations of this system was obtained after a deducing of its incident matrices and tach matrices. Finally, an optimization model of a steering and suspension mechanism aiming to

X. L. Bian; B. A. Song; R. Walter



Chance Constrained Mixed Integer Program: Bilinear and Linear ...  

E-print Network

restrict the associated risk on real system operations [32], and is related to ... Such applications include service system design and management. [11, 23] ...... lations can be solved by professional solver CPLEX in a short time (e.g., less than 60 ...



Binary Decision Rules for Multistage Adaptive Mixed-Integer ...  

E-print Network

Aug 20, 2014 ... tures tend to produce good quality solutions at the expense of ... ?Sloan School of Management and Operations Research Center, ... This approximation provides significant improvements in solution quality, while retaining.



Strong Branching Inequalities for Convex Mixed Integer Nonlinear ...  

E-print Network

Sep 3, 2011 ... icantly improved by leveraging the information generated as a byproduct of strong branching .... A complete proof of Lemma 1 can be found in the Ph.D. thesis of K?l?nç [24] ... one can define ˜xi = 1 ? xi, ˜?i = ??i, and write (8) as.



The Polar of a Simple Mixed-Integer Set  

E-print Network

Sep 13, 2005 ... The model studied here can be generalized in several ways, and it would be interesting to ... [4] W. Cook, R. Kannan, and A. Schrijver, \\Chv¶atal Closures for ... and S.M. Johnson, \\On a Linear-Programming, Combinatorial.





E-print Network

, Eric Feron ¡ , Jonathan How ¢¤£ ¥ Esat-Sista, Katholieke Universiteit Leuven, Belgium¦ Laboratory for Information and Decision Systems,§ Space Systems Laboratory, Massachusetts Institute of Technology, Cambridge p Frg Asp H p tRT (3) Proceedings of th

How, Jonathan P.


Subset Selection by Mallows' Cp: A Mixed Integer Programming ...  

E-print Network

seconds when the number of candidate explanatory variables is less than 30. ... 15, 23], and it has recently received considerable attention in data mining and ..... Next, we evaluated the usefulness of MIQP as a heuristic method with a time limit. .... A future direction of study will be to speed up the MIQP computation by ...



Mixed Integer Second-Order Cone Programming Formulations for ...  

E-print Network

Jun 23, 2013 ... (ii) identifying a model that captures the essence of a system, and (iii) pro- viding a ... (eliminating one redundant variable) until a stopping condition is satisfied. ... Multiple linear regression analysis and variable selection. Given n data points, ... ficient vector a such that the sum of squared residuals. ?n i=1.



Solving Mixed-Integer Nonlinear Programs by QP-Diving  

E-print Network

Mar 26, 2012 ... 1992). These algorithmic developments have resulted in a number of robust implementations ..... that some MILP-based cut-generation techniques, such as flow-cover inequalities (Gu et al., 1999) ..... Identification of severe.



A Unified Mixed-Integer Programming Model for Simultaneous ...  

E-print Network

high-energy radiation is used to shrink tumors and kill cancer cells, where ... can be eliminated, ii. for organs at risk (OARs) to be spared from radiation as much as ...... independent points, where ? > 0 is a sufficiently small number and ?¯k,k ...



Analysis of mixed integer programming formulations for single ...  

E-print Network

ence of sequence-dependent setup times with release dates, the problem ...... In: Systems, Man and Cybernetics, 2002 IEEE International Conference on, vol 5, ... Fox KR (1973) Production scheduling on parallel lines with dependencies.



Mixed-Integer Rounding Enhanced Benders Decomposition for ...  

E-print Network

Nov 13, 2014 ... minimum cost, where quality-of-service can be measured in many ways, such as average ...... We first give details about the data used in our experiments. ...... Labor staffing and scheduling models for controlling service levels.



Dippy – a simplified interface for advanced mixed-integer ...  

E-print Network

The purpose of the research presented here is to provide a tool, Dippy, that supports easy ... work by showing example code for a common problem. We conclude in ...... 2All tests were run using Python 2.7.1 on a Windows 7 machine with an Intel Core 2 Duo ... than just an experimental “toy” or educational tool. It enables ...



A mixed integer programming approach for asset protection during ...  

E-print Network

May 15, 2014 ... Incident Management Teams (IMTs) are responsible for managing the response to ..... the roof, setting up a sprinkler system and applying fire retardant ..... tional Wildland Fire Safety Summit, Missoula, MT, 26–28 April 2005.



Convex Quadratic Relaxations for Mixed-Integer Nonlinear ...  

E-print Network

providing an interesting alternative to state-of-the-art semi-definite program- ... sion switching and capacitor placement demonstrate the benefits of the new ...... tions, planning, and analysis tools for power systems research and education. IEEE.

H. Hijazi, C. Coffrin and P. Van Hentenryck



Numerical Study on Dynamic Programming Applied to Optimization of Running Profile of a Train  

NASA Astrophysics Data System (ADS)

An algorithm optimizing train running profile with Bellman's Dynamic Programming (DP) is investigated in this paper. Optimal running trajectory of a train which minimizes amount of total energy consumption has been produced under fixed origin and destination, stipulated running time and various track profile. Many previous works on this area adopt the numerical techniques of calculus of variations, Pontryagin's maximum principle, and so on. But these methods often meet some difficulties accounting for complicated actual train running preconditions, e.g. complicated functions which describe electrical motive/brake torque, local constraints of the state variable as speed limitations, non-linear running resistance and variable grade profiles. Basic numerical DP algorithm can cope with such comlicated conditions and give the globally optimal solution. But this method consumes too large computation time for practical uses. We have made the improvements for shorter calculation time of whole optimization process and reducing the numerical error. The confined state space and irregular lattice play most important role for them. Dynamic meshing and effective utilization of system memory also realize shorter computation time. The effectiveness of the proposed method is demonstrated using various complicated running conditions.

Ko, Hideyoshi; Koseki, Takafumi; Miyatake, Masafumi


Optimal dynamic water allocation: Irrigation extractions and environmental tradeoffs in the Murray River, Australia  

NASA Astrophysics Data System (ADS)

A key challenge in managing semiarid basins, such as in the Murray-Darling in Australia, is to balance the trade-offs between the net benefits of allocating water for irrigated agriculture, and other uses, versus the costs of reduced surface flows for the environment. Typically, water planners do not have the tools to optimally and dynamically allocate water among competing uses. We address this problem by developing a general stochastic, dynamic programming model with four state variables (the drought status, the current weather, weather correlation, and current storage) and two controls (environmental release and irrigation allocation) to optimally allocate water between extractions and in situ uses. The model is calibrated to Australia's Murray River that generates: (1) a robust qualitative result that "pulse" or artificial flood events are an optimal way to deliver environmental flows over and above conveyance of base flows; (2) from 2001 to 2009 a water reallocation that would have given less to irrigated agriculture and more to environmental flows would have generated between half a billion and over 3 billion U.S. dollars in overall economic benefits; and (3) water markets increase optimal environmental releases by reducing the losses associated with reduced water diversions.

Grafton, R. Quentin; Chu, Hoang Long; Stewardson, Michael; Kompas, Tom



Applicability and efficiency of near-optimal spatial encoding for dynamically adaptive MRI.  


Adaptive near-optimal MRI spatial encoding entails, for the acquisition of each image update in a dynamic series, the computation of encodes in the form of a linear algebra-derived orthogonal basis set determined from an image estimate. The origins of adaptive encoding relevant to MRI are reviewed. Sources of error of this approach are identified from the linear algebraic perspective where MRI data acquisition is viewed as the projection of information from the field-of-view onto the encoding basis set. The definitions of ideal and non-ideal encoding follow, with nonideal encoding characterized by the principal angles between two vector spaces. An analysis of the distribution of principal angles is introduced and applied in several example cases to quantitatively describe the suitability of a basis set derived from a specific image estimate for the spatial encoding of a given field-of-view. The robustness of adaptive near-optimal spatial encoding for dynamic MRI is favorably shown by results computed using singular value decomposition encoding that simulates specific instances of worst case data acquisition when all objects have changed or new objects have appeared in the field-of-view. The mathematical analysis and simulations presented clarify the applicability and efficiency of adaptively determined near-optimal spatial encoding throughout a range of circumstances as may typically occur during use of dynamic MRI. PMID:9469703

Zientara, G P; Panych, L P; Jolesz, F A



Optimization of matrix tablets controlled drug release using Elman dynamic neural networks and decision trees.  


The main objective of the study was to develop artificial intelligence methods for optimization of drug release from matrix tablets regardless of the matrix type. Static and dynamic artificial neural networks of the same topology were developed to model dissolution profiles of different matrix tablets types (hydrophilic/lipid) using formulation composition, compression force used for tableting and tablets porosity and tensile strength as input data. Potential application of decision trees in discovering knowledge from experimental data was also investigated. Polyethylene oxide polymer and glyceryl palmitostearate were used as matrix forming materials for hydrophilic and lipid matrix tablets, respectively whereas selected model drugs were diclofenac sodium and caffeine. Matrix tablets were prepared by direct compression method and tested for in vitro dissolution profiles. Optimization of static and dynamic neural networks used for modeling of drug release was performed using Monte Carlo simulations or genetic algorithms optimizer. Decision trees were constructed following discretization of data. Calculated difference (f(1)) and similarity (f(2)) factors for predicted and experimentally obtained dissolution profiles of test matrix tablets formulations indicate that Elman dynamic neural networks as well as decision trees are capable of accurate predictions of both hydrophilic and lipid matrix tablets dissolution profiles. Elman neural networks were compared to most frequently used static network, Multi-layered perceptron, and superiority of Elman networks have been demonstrated. Developed methods allow simple, yet very precise way of drug release predictions for both hydrophilic and lipid matrix tablets having controlled drug release. PMID:22402474

Petrovi?, Jelena; Ibri?, Svetlana; Betz, Gabriele; ?uri?, Zorica



Optimized dynamic contrast-enhanced cone-beam CT for target visualization during liver SBRT  

NASA Astrophysics Data System (ADS)

The pharmacokinetic behavior of iodine contrast agents makes it difficult to achieve significant enhancement during contrast-enhanced cone-beam CT (CE-CBCT). This study modeled this dynamic behavior to optimize CE-CBCT and improve the localization of liver lesions for SBRT. We developed a model that allows for controlled study of changing iodine concentrations using static phantoms. A projection database consisting of multiple phantom images of differing iodine/scan conditions was built. To reconstruct images of dynamic hepatic concentrations, hepatic contrast enhancement data from conventional CT scans were used to re-assemble the projections to match the expected amount of contrast. In this way the effect of various parameters on image quality was isolated, and using our dynamic model we found parameters for iodine injection, CBCT scanning, and injection/scanning timing which optimize contrast enhancement. Increasing the iodine dose, iodine injection rate, and imaging dose led to significant increases in signal-to-noise ratio (SNR). Reducing the CBCT imaging time also increased SNR, as the image can be completed before the iodine exits the liver. Proper timing of image acquisition played a significant role, as a 30 second error in start time resulted in a 40% SNR decrease. The effect of IV contrast is severely degraded in CBCT, but there is promise that, with optimization of the injection and scan parameters to account for iodine pharmacokinetics, CE-CBCT which models venous-phase blood flow kinetics will be feasible for accurate localization of liver lesions.

Jones, Bernard L.; Altunbas, Cem; Kavanagh, Brian; Schefter, Tracey; Miften, Moyed



Dynamic modeling and optimal joint torque coordination of advanced robotic systems  

NASA Astrophysics Data System (ADS)

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.

Kang, Hee-Jun


Modeling multiple experiments using regularized optimization: A case study on bacterial glucose utilization dynamics.  


The aim of inverse modeling is to capture the systems? dynamics through a set of parameterized Ordinary Differential Equations (ODEs). Parameters are often required to fit multiple repeated measurements or different experimental conditions. This typically leads to a multi-objective optimization problem that can be formulated as a non-convex optimization problem. Modeling of glucose utilization of Lactococcus lactis bacteria is considered using in vivo Nuclear Magnetic Resonance (NMR) measurements in perturbation experiments. We propose an ODE model based on a modified time-varying exponential decay that is flexible enough to model several different experimental conditions. The starting point is an over-parameterized non-linear model that will be further simplified through an optimization procedure with regularization penalties. For the parameter estimation, a stochastic global optimization method, particle swarm optimization (PSO) is used. A regularization is introduced to the identification, imposing that parameters should be the same across several experiments in order to identify a general model. On the remaining parameter that varies across the experiments a function is fit in order to be able to predict new experiments for any initial condition. The method is cross-validated by fitting the model to two experiments and validating the third one. Finally, the proposed model is integrated with existing models of glycolysis in order to reconstruct the remaining metabolites. The method was found useful as a general procedure to reduce the number of parameters of unidentifiable and over-parameterized models, thus supporting feature selection methods for parametric models. PMID:25248561

Hartmann, András; Lemos, João M; Vinga, Susana



Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network  

PubMed Central

Background Dynamic Bayesian network (DBN) is among the mainstream approaches for modeling various biological networks, including the gene regulatory network (GRN). Most current methods for learning DBN employ either local search such as hill-climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing, which are only able to locate sub-optimal solutions. Further, current DBN applications have essentially been limited to small sized networks. Results To overcome the above difficulties, we introduce here a deterministic global optimization based DBN approach for reverse engineering genetic networks from time course gene expression data. For such DBN models that consist only of inter time slice arcs, we show that there exists a polynomial time algorithm for learning the globally optimal network structure. The proposed approach, named GlobalMIT+, employs the recently proposed information theoretic scoring metric named mutual information test (MIT). GlobalMIT+ is able to learn high-order time delayed genetic interactions, which are common to most biological systems. Evaluation of the approach using both synthetic and real data sets, including a 733 cyanobacterial gene expression data set, shows significantly improved performance over other techniques. Conclusions Our studies demonstrate that deterministic global optimization approaches can infer large scale genetic networks. PMID:22694481



H? optimization of dynamic vibration absorber variant for vibration control of damped linear systems  

NASA Astrophysics Data System (ADS)

This study focuses on the H? optimal design of a dynamic vibration absorber (DVA) variant for suppressing high-amplitude vibrations of damped primary systems. Unlike traditional DVA configurations, the damping element in this type of DVA is connected directly to the ground instead of the primary mass. First, a thorough graphical analysis of the variations in the maximum amplitude magnification factor depending on two design parameters, natural frequency and absorber damping ratios, is performed. The results of this analysis clearly show that any fixed-points-theory-based conventional method could provide, at best, only locally but not globally optimal parameters. Second, for directly handling the H? optimization for its optimal design, a novel meta-heuristic search engine, called the diversity-guided cyclic-network-topology-based constrained particle swarm optimization (Div-CNT-CPSO), is developed. The variant DVA system developed using the proposed Div-CNT-CPSO scheme is compared with those reported in the literature. The results of this comparison verified that the proposed system is better than the existing methods for suppressing the steady-state vibration amplitude of a controlled primary system.

Chun, Semin; Lee, Youngil; Kim, Tae-Hyoung



Optimal control for nonlinear dynamical system of microbial fed-batch culture  

NASA Astrophysics Data System (ADS)

In fed-batch culture of glycerol bio-dissimilation to 1, 3-propanediol (1, 3-PD), the aim of adding glycerol is to obtain as much 1, 3-PD as possible. So a proper feeding rate is required during the process. Taking the concentration of 1, 3-PD at the terminal time as the performance index and the feeding rate of glycerol as the control function, we propose an optimal control model subject to a nonlinear dynamical system and constraints of continuous state and non-stationary control. A computational approach is constructed to seek the solution of the above model in two aspects. On the one hand we transcribe the optimal control model into an unconstrained one based on the penalty functions and an extension of the state space; on the other hand, by approximating the control function with simple functions, we transform the unconstrained optimal control problem into a sequence of nonlinear programming problems, which can be solved using gradient-based optimization techniques. The convergence analysis of this approximation is also investigated. Numerical results show that, by employing the optimal control policy, the concentration of 1, 3-PD at the terminal time can be increased considerably.

Liu, Chongyang



A fast procedure for optimizing dynamic force distribution in multifingered grasping.  


This correspondence deals with the dynamic force distribution (DFD) problem, i.e., computing the contact forces to equilibrate a dynamic external wrench on the grasped object. The sum of the normal force components is minimized for enhancing safety and saving energy. By this optimality criterion, the DFD problem can be transformed into a linear programming (LP) problem. Its objective function is the inner product of the dynamic external wrench and a vector, and the constraints on the vector, given by a set of linear inequalities, define a polytope. The solution to the LP problem can always be attained at the vertex of the polytope called the solution vertex. We notice that the polytope is determined by the grasp configuration. Along with the direction change of the dynamic external wrench, only the solution vertex moves to an adjacent vertex sequentially, whereas the polytope with all its vertices remains unchanged. Therefore, the polytope and the adjacencies of each vertex can be computed in the offline phase. Then, in the online phase, simply search the adjacencies of the old solution vertex for the new one. Without lost of optimality, such a DFD algorithm runs a thousandfold faster than solving the LP problem by the simplex method in real time. PMID:17186817

Zheng, Yu; Qian, Wen-Han



Real-time Optimization-based Planning in Dynamic Environments using Chonhyon Park and Jia Pan and Dinesh Manocha  

E-print Network

or their motion. We use a replanning framework that interleaves optimization-based planning with execution planning. Some of the applications include automated wheelchairs, manufacturing tasks with robots appropriate algorithms for planning and executing appropriate trajectories in such dynamic scenes

North Carolina at Chapel Hill, University of


Solving the discrete network design problem to optimality  

SciTech Connect

The network design problem has various applications, such as construction of new links in transportation networks, topological design of computer communication networks and planning of empty freight car transportation on railways. The problem is a multicommodity minimal cost network flow problem with fixed costs on the arcs, i.e. a structured linear mixed-integer programming problem. We discuss solution methods for finding the exact optimal solution of the problem. Encouraging computational results are given for a Lagrangean heuristic within a branch-and-bound framework for the uncapacitated network design problem with single origins and destinations for each commodity (the simplest problem in this class, but still NP-hard). The Lagrangean heuristic uses a Lagrangean relaxation as subproblem, solving the Lagrange dual with subgradient optimization, combined with a primal heuristic (here the Benders subproblem) yielding primal feasible solutions.

Holmberg, K.; Hellstrand, J.



Developing a computationally efficient dynamic multilevel hybrid optimization scheme using multifidelity model interactions.  

SciTech Connect

Many engineering application problems use optimization algorithms in conjunction with numerical simulators to search for solutions. The formulation of relevant objective functions and constraints dictate possible optimization algorithms. Often, a gradient based approach is not possible since objective functions and constraints can be nonlinear, nonconvex, non-differentiable, or even discontinuous and the simulations involved can be computationally expensive. Moreover, computational efficiency and accuracy are desirable and also influence the choice of solution method. With the advent and increasing availability of massively parallel computers, computational speed has increased tremendously. Unfortunately, the numerical and model complexities of many problems still demand significant computational resources. Moreover, in optimization, these expenses can be a limiting factor since obtaining solutions often requires the completion of numerous computationally intensive simulations. Therefore, we propose a multifidelity optimization algorithm (MFO) designed to improve the computational efficiency of an optimization method for a wide range of applications. In developing the MFO algorithm, we take advantage of the interactions between multi fidelity models to develop a dynamic and computational time saving optimization algorithm. First, a direct search method is applied to the high fidelity model over a reduced design space. In conjunction with this search, a specialized oracle is employed to map the design space of this high fidelity model to that of a computationally cheaper low fidelity model using space mapping techniques. Then, in the low fidelity space, an optimum is obtained using gradient or non-gradient based optimization, and it is mapped back to the high fidelity space. In this paper, we describe the theory and implementation details of our MFO algorithm. We also demonstrate our MFO method on some example problems and on two applications: earth penetrators and groundwater remediation.

Hough, Patricia Diane (Sandia National Laboratories, Livermore, CA); Gray, Genetha Anne (Sandia National Laboratories, Livermore, CA); Castro, Joseph Pete Jr. (; .); Giunta, Anthony Andrew



A multi-objective dynamic programming approach to constrained discrete-time optimal control  

SciTech Connect

This work presents a multi-objective differential dynamic programming approach to constrained discrete-time optimal control. In the backward sweep of the dynamic programming in the quadratic sub problem, the sub problem input at a stage or time step is solved for in terms of the sub problem state entering that stage so as to minimize the summed immediate and future cost subject to minimizing the summed immediate and future constraint violations, for all such entering states. The method differs from previous dynamic programming methods, which used penalty methods, in that the constraints of the sub problem, which may include terminal constraints and path constraints, are solved exactly if they are solvable; otherwise, their total violation is minimized. Again, the resulting solution of the sub problem is an input history that minimizes the quadratic cost function subject to being a minimizer of the total constraint violation. The expected quadratic convergence of the proposed algorithm is demonstrated on a numerical example.

Driessen, B.J.; Kwok, K.S.



Game Theory and Extremal Optimization for Community Detection in Complex Dynamic Networks  

PubMed Central

The detection of evolving communities in dynamic complex networks is a challenging problem that recently received attention from the research community. Dynamics clearly add another complexity dimension to the difficult task of community detection. Methods should be able to detect changes in the network structure and produce a set of community structures corresponding to different timestamps and reflecting the evolution in time of network data. We propose a novel approach based on game theory elements and extremal optimization to address dynamic communities detection. Thus, the problem is formulated as a mathematical game in which nodes take the role of players that seek to choose a community that maximizes their profit viewed as a fitness function. Numerical results obtained for both synthetic and real-world networks illustrate the competitive performance of this game theoretical approach. PMID:24586257

Lung, Rodica Ioana; Chira, Camelia; Andreica, Anca



Mystic: Implementation of the Static Dynamic Optimal Control Algorithm for High-Fidelity, Low-Thrust Trajectory Design  

NASA Technical Reports Server (NTRS)

Mystic software is designed to compute, analyze, and visualize optimal high-fidelity, low-thrust trajectories, The software can be used to analyze inter-planetary, planetocentric, and combination trajectories, Mystic also provides utilities to assist in the operation and navigation of low-thrust spacecraft. Mystic will be used to design and navigate the NASA's Dawn Discovery mission to orbit the two largest asteroids, The underlying optimization algorithm used in the Mystic software is called Static/Dynamic Optimal Control (SDC). SDC is a nonlinear optimal control method designed to optimize both 'static variables' (parameters) and dynamic variables (functions of time) simultaneously. SDC is a general nonlinear optimal control algorithm based on Bellman's principal.

Whiffen, Gregory J.



Computational fluid dynamics based aerodynamic optimization of the wind tunnel primary nozzle  

NASA Astrophysics Data System (ADS)

The aerodynamic shape optimization of the supersonic flat nozzle is the aim of proposed paper. The nozzle discussed, is applied as a primary nozzle of the inlet part of supersonic wind tunnel. Supersonic nozzles of the measure area inlet parts need to guarantee several requirements of flow properties and quality. Mach number and minimal differences between real and required velocity and turbulence profiles at the nozzle exit are the most important parameters to meet. The aerodynamic shape optimization of the flat 2D nozzle in Computational Fluid Dynamics (CFD) is employed to reach as uniform exit velocity profile as possible, with the mean Mach number 1.4. Optimization process does not use any of standard routines of global or local optimum searching. Instead, newly formed routine, which exploits shape-based oriented sequence of nozzles, is used to research within whole discretized parametric space. The movement within optimization process is not driven by gradient or evolutionary too, instead, the Path of Minimal Shape Deformation is followed. Dynamic mesh approach is used to deform the shape and mesh from the actual nozzle to the subsequent one. Dynamic deformation of mesh allows to speed up whole converging process as an initialization of flow at the newly formed mesh is based on afore-computed shape. Shape-based similarity query in field of supersonic nozzles is discussed and applied. Evolutionary technique with genetic algorithm is used to search for minimal deformational path. As a result, the best variant from the set of solved shapes is analyzed at the base of momentum coefficient and desired Mach number at the nozzle exit.

Jan, Kolá?; Václav, Dvo?ák



Dynamic statistical optimization of GNSS radio occultation bending angles: an advanced algorithm and its performance analysis  

NASA Astrophysics Data System (ADS)

We introduce a new dynamic statistical optimization algorithm to initialize ionosphere-corrected bending angles of Global Navigation Satellite System (GNSS) based radio occultation (RO) measurements. The new algorithm estimates background and observation error covariance matrices with geographically-varying uncertainty profiles and realistic global-mean correlation matrices. The error covariance matrices estimated by the new approach are more accurate and realistic than in simplified existing approaches and can therefore be used in statistical optimization to provide optimal bending angle profiles for high-altitude initialization of the subsequent Abel transform retrieval of refractivity. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.6 (OPSv5.6) algorithm, using simulated data on two test days from January and July 2008 and real observed CHAMP and COSMIC measurements from the complete months of January and July 2008. The following is achieved for the new method's performance compared to OPSv5.6: (1) significant reduction in random errors (standard deviations) of optimized bending angles down to about two-thirds of their size or more; (2) reduction of the systematic differences in optimized bending angles for simulated MetOp data; (3) improved retrieval of refractivity and temperature profiles; (4) produces realistically estimated global-mean correlation matrices and realistic uncertainty fields for the background and observations. Overall the results indicate high suitability for employing the new dynamic approach in the processing of long-term RO data into a reference climate record, leading to well characterized and high-quality atmospheric profiles over the entire stratosphere.

Li, Y.; Kirchengast, G.; Scherllin-Pirscher, B.; Norman, R.; Yuan, Y. B.; Fritzer, J.; Schwaerz, M.; Zhang, K.



Exploring stock market dynamism in multi-nations with genetic algorithm, support vector regression, and optimal technical analysis  

Microsoft Academic Search

In this research, an approach in combination with support vector regression (SVR), genetic algorithm (GA), and optimal technical analysis is proposed to explore stock dynamism of multi-nations under different economical environments. First, we apply full search algorithm to select the optimal number of trading days used to calculate the technical indicator values. Genetic algorithm is then used to search the

Deng-Yiv Chiu; Shin-Yi Chian



Topology Mining for Optimization of Framed Structures  

NASA Astrophysics Data System (ADS)

A new heuristic method called Topology Mining (TM) is proposed for topology optimization of framed structures, where the problem is formulated as 0-1 mixed-integer optimization problem. TM uses the apriori algorithm, developed in the field of data mining, to efficiently extract the bar sets that frequently appears among superior solutions, and proceeds so as to preserve the sets. Hence, the process of optimization can be investigated by tracing the frequent bar sets, accordingly, the parameters for optimization can easily be adjusted. It is pointed out that the ground structure method based on nonlinear programming is not effective for finding optimal placement of braces for a given frame under local buckling constraints. We propose an integrated approach to obtain an accurate solution of this problem, where optimal placement of braces is searched by TM, and the sizing optimization is performed by nonlinear programming. Three numerical examples are solved to demonstrate the performance of TM in comparison with another heuristic method called tabu search.

Hagishita, Takao; Ohsaki, Makoto


Integrated Scheduling and DynamicIntegrated Scheduling and Dynamic Optimization of Batch Processes  

E-print Network

the intermediate in the distillation column for 2 hours with reflux ratio 3 #12;Motivating Example Reactor 20 Gantt.1176 Time(hr) h f i d d l 6 6.5 7 Retemp 5 6 7 Reflux Column Filter Reactor 20 20 18.6602 Gantt chart of integrated model 4.5 5 5.5 R 3 4 5 Gantt charts 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 Time(hr) Optimal control

Grossmann, Ignacio E.


Fast optimization of binary clusters using a novel dynamic lattice searching method  

NASA Astrophysics Data System (ADS)

Global optimization of binary clusters has been a difficult task despite of much effort and many efficient methods. Directing toward two types of elements (i.e., homotop problem) in binary clusters, two classes of virtual dynamic lattices are constructed and a modified dynamic lattice searching (DLS) method, i.e., binary DLS (BDLS) method, is developed. However, it was found that the BDLS can only be utilized for the optimization of binary clusters with small sizes because homotop problem is hard to be solved without atomic exchange operation. Therefore, the iterated local search (ILS) method is adopted to solve homotop problem and an efficient method based on the BDLS method and ILS, named as BDLS-ILS, is presented for global optimization of binary clusters. In order to assess the efficiency of the proposed method, binary Lennard-Jones clusters with up to 100 atoms are investigated. Results show that the method is proved to be efficient. Furthermore, the BDLS-ILS method is also adopted to study the geometrical structures of (AuPd)79 clusters with DFT-fit parameters of Gupta potential.

Wu, Xia; Cheng, Wen



Spike-timing-dependent plasticity and reliability optimization: the role of neuron dynamics.  


Plastic changes in synaptic efficacy can depend on the time ordering of presynaptic and postsynaptic spikes. This phenomenon is called spike-timing-dependent plasticity (STDP). One of the most striking aspects of this plasticity mechanism is that the STDP windows display a great variety of forms in different parts of the nervous system. We explore this issue from a theoretical point of view. We choose as the optimization principle the minimization of conditional entropy or maximization of reliability in the transmission of information. We apply this principle to two types of postsynaptic dynamics, designated type I and type II. The first is characterized as being an integrator, while the second is a resonator. We find that, depending on the parameters of the models, the optimization principle can give rise to a wide variety of STDP windows, such as antisymmetric Hebbian, predominantly depressing or symmetric with one positive region and two lateral negative regions. We can relate each of these forms to the dynamical behavior of the different models. We also propose experimental tests to assess the validity of the optimization principle. PMID:21492013

Pool, R Rossi; Mato, G



Real-time dynamic trajectory optimization with application to free-flying space robots  

NASA Astrophysics Data System (ADS)

The capability of robots to complete tasks or entire missions autonomously relies heavily on their ability to plan. Good planners must not only be able to produce efficient plans but must also be able to modify those plans quickly in response to unpredicted events. Unfortunately these two goals are often conflicting ones, with only slow, complex planners able to produce efficient plans, and only quick, simple planners able to react to unpredicted events. The research presented in this dissertation focuses on the development of a real-time dynamic trajectory optimization system that provides both highly efficient motion planning capabilities and the ability to react to uncertainty in the environment. This system achieves these capabilities by utilizing simultaneous planning and execution to improve the robot's trajectory while the robot is in motion along the trajectory. Although other robotic systems have used simultaneous planning and execution, this dissertation applies the concept to dynamic trajectory optimization, a sophisticated technique for computing highly efficient trajectories that has previously been used only in off-line planning applications and in simulation. The resulting system uses a non-linear optimization algorithm to improve an initial trajectory, subject to the dynamics of the system and constraints on the robot's motion, in order to minimize a weighted sum of the fuel and time required to complete the trajectory. Using this system, several motion planning tasks are demonstrated experimentally on a thruster propelled free-flying robot. The most complex of these tasks requires the robot to travel around a pair of stationary obstacles and intercept a moving, maneuvering target vehicle in a highly efficient manner. The experimental results show that for the sample moving-target-intercept task, the real-time planner provides 2.42 times better performance than a reactive planner and an on-line planner is unable to complete the task at all. This experimental demonstration highlights the advantages of real-time dynamic trajectory optimization in providing a high performance motion planning capability, even when operating in a dynamic, uncertain environment.

Miles, David Wilson


Experimental/analytical approaches to modeling, calibrating and optimizing shaking table dynamics for structural dynamic applications  

NASA Astrophysics Data System (ADS)

This thesis presents an Experimental/Analytical approach to modeling and calibrating shaking tables for structural dynamic applications. This approach was successfully applied to the shaking table recently built in the structural laboratory of the Civil Engineering Department at Rice University. This shaking table is capable of reproducing model earthquake ground motions with a peak acceleration of 6 g's, a peak velocity of 40 inches per second, and a peak displacement of 3 inches, for a maximum payload of 1500 pounds. It has a frequency bandwidth of approximately 70 Hz and is designed to test structural specimens up to 1/5 scale. The rail/table system is mounted on a reaction mass of about 70,000 pounds consisting of three 12 ft x 12 ft x 1 ft reinforced concrete slabs, post-tensioned together and connected to the strong laboratory floor. The slip table is driven by a hydraulic actuator governed by a 407 MTS controller which employs a proportional-integral-derivative-feedforward-differential pressure algorithm to control the actuator displacement. Feedback signals are provided by two LVDT's (monitoring the slip table relative displacement and the servovalve main stage spool position) and by one differential pressure transducer (monitoring the actuator force). The dynamic actuator-foundation-specimen system is modeled and analyzed by combining linear control theory and linear structural dynamics. The analytical model developed accounts for the effects of actuator oil compressibility, oil leakage in the actuator, time delay in the response of the servovalve spool to a given electrical signal, foundation flexibility, and dynamic characteristics of multi-degree-of-freedom specimens. In order to study the actual dynamic behavior of the shaking table, the transfer function between target and actual table accelerations were identified using experimental results and spectral estimation techniques. The power spectral density of the system input and the cross power spectral density of the table input and output were estimated using the Bartlett's spectral estimation method. The experimentally-estimated table acceleration transfer functions obtained for different working conditions are correlated with their analytical counterparts. As a result of this comprehensive correlation study, a thorough understanding of the shaking table dynamics and its sensitivities to control and payload parameters is obtained. Moreover, the correlation study leads to a calibrated analytical model of the shaking table of high predictive ability. It is concluded that, in its present conditions, the Rice shaking table is able to reproduce, with a high degree of accuracy, model earthquake accelerations time histories in the frequency bandwidth from 0 to 75 Hz. Furthermore, the exhaustive analysis performed indicates that the table transfer function is not significantly affected by the presence of a large (in terms of weight) payload with a fundamental frequency up to 20 Hz. Payloads having a higher fundamental frequency do affect significantly the shaking table performance and require a modification of the table control gain setting that can be easily obtained using the predictive analytical model of the shaking table. The complete description of a structural dynamic experiment performed using the Rice shaking table facility is also reported herein. The object of this experimentation was twofold: (1) to verify the testing capability of the shaking table and, (2) to experimentally validate a simplified theory developed by the author, which predicts the maximum rotational response developed by seismic isolated building structures characterized by non-coincident centers of mass and rigidity, when subjected to strong earthquake ground motions.

Trombetti, Tomaso


Optimal Control Strategy Design Based on Dynamic Programming for a Dual-Motor Coupling-Propulsion System  

PubMed Central

A dual-motor coupling-propulsion electric bus (DMCPEB) is modeled, and its optimal control strategy is studied in this paper. The necessary dynamic features of energy loss for subsystems is modeled. Dynamic programming (DP) technique is applied to find the optimal control strategy including upshift threshold, downshift threshold, and power split ratio between the main motor and auxiliary motor. Improved control rules are extracted from the DP-based control solution, forming near-optimal control strategies. Simulation results demonstrate that a significant improvement in reducing energy loss due to the dual-motor coupling-propulsion system (DMCPS) running is realized without increasing the frequency of the mode switch.

Zhang, Shuo; Zhang, Chengning; Han, Guangwei; Wang, Qinghui



Design of Optimal Treatments for Neuromusculoskeletal Disorders using Patient-Specific Multibody Dynamic Models.  


Disorders of the human neuromusculoskeletal system such as osteoarthritis, stroke, cerebral palsy, and paraplegia significantly affect mobility and result in a decreased quality of life. Surgical and rehabilitation treatment planning for these disorders is based primarily on static anatomic measurements and dynamic functional measurements filtered through clinical experience. While this subjective treatment planning approach works well in many cases, it does not predict accurate functional outcome in many others. This paper presents a vision for how patient-specific multibody dynamic models can serve as the foundation for an objective treatment planning approach that identifies optimal treatments and treatment parameters on an individual patient basis. First, a computational paradigm is presented for constructing patient-specific multibody dynamic models. This paradigm involves a combination of patient-specific skeletal models, muscle-tendon models, neural control models, and articular contact models, with the complexity of the complete model being dictated by the requirements of the clinical problem being addressed. Next, three clinical applications are presented to illustrate how such models could be used in the treatment design process. One application involves the design of patient-specific gait modification strategies for knee osteoarthritis rehabilitation, a second involves the selection of optimal patient-specific surgical parameters for a particular knee osteoarthritis surgery, and the third involves the design of patient-specific muscle stimulation patterns for stroke rehabilitation. The paper concludes by discussing important challenges that need to be overcome to turn this vision into reality. PMID:21785529

Fregly, Benjamin J



Conceptual Design Optimization of an Augmented Stability Aircraft Incorporating Dynamic Response and Actuator Constraints  

NASA Technical Reports Server (NTRS)

Empirical sizing guidelines such as tail volume coefficients have long been used in the early aircraft design phases for sizing stabilizers, resulting in conservatively stable aircraft. While successful, this results in increased empty weight, reduced performance, and greater procurement and operational cost relative to an aircraft with optimally sized surfaces. Including flight dynamics in the conceptual design process allows the design to move away from empirical methods while implementing modern control techniques. A challenge of flight dynamics and control is the numerous design variables, which are changing fluidly throughout the conceptual design process, required to evaluate the system response to some disturbance. This research focuses on addressing that challenge not by implementing higher order tools, such as computational fluid dynamics, but instead by linking the lower order tools typically used within the conceptual design process so each discipline feeds into the other. In thisresearch, flight dynamics and control was incorporated into the conceptual design process along with the traditional disciplines of vehicle sizing, weight estimation, aerodynamics, and performance. For the controller, a linear quadratic regulator structure with constant gains has been specified to reduce the user input. Coupling all the disciplines in the conceptual design phase allows the aircraft designer to explore larger design spaces where stabilizers are sized according to dynamic response constraints rather than historical static margin and volume coefficient guidelines.

Welstead, Jason; Crouse, Gilbert L., Jr.



Optical tracking of contrast medium bolus to optimize bolus shape and timing in dynamic computed tomography  

NASA Astrophysics Data System (ADS)

One of the biggest challenges in dynamic contrast-enhanced CT is the optimal synchronization of scan start and duration with contrast medium administration in order to optimize image contrast and to reduce the amount of contrast medium. We present a new optically based approach, which was developed to investigate and optimize bolus timing and shape. The time-concentration curve of an intravenously injected test bolus of a dye is measured in peripheral vessels with an optical sensor prior to the diagnostic CT scan. The curves can be used to assess bolus shapes as a function of injection protocols and to determine contrast medium arrival times. Preliminary results for phantom and animal experiments showed the expected linear behavior between dye concentration and absorption. The kinetics of the dye was compared to iodinated contrast medium and was found to be in good agreement. The contrast enhancement curves were reliably detected in three mice with individual bolus shapes and delay times of 2.1, 3.5 and 6.1 s, respectively. The optical sensor appears to be a promising approach to optimize injection protocols and contrast enhancement timing and is applicable to all modalities without implying any additional radiation dose. Clinical tests are still necessary.

Eisa, Fabian; Brauweiler, Robert; Peetz, Alexander; Hupfer, Martin; Nowak, Tristan; Kalender, Willi A.



A model of bi-mode transmission dynamics of hepatitis C with optimal control.  


In this paper, we present a rigorous mathematical analysis of a deterministic model for the transmission dynamics of hepatitis C. The model is suitable for populations where two frequent modes of transmission of hepatitis C virus, namely unsafe blood transfusions and intravenous drug use, are dominant. The susceptible population is divided into two distinct compartments, the intravenous drug users and individuals undergoing unsafe blood transfusions. Individuals belonging to each compartment may develop acute and then possibly chronic infections. Chronically infected individuals may be quarantined. The analysis indicates that the eradication and persistence of the disease is completely determined by the magnitude of basic reproduction number R(c). It is shown that for the basic reproduction number R(c) < 1, the disease-free equilibrium is locally and globally asymptotically stable. For R(c) > 1, an endemic equilibrium exists and the disease is uniformly persistent. In addition, we present the uncertainty and sensitivity analyses to investigate the influence of different important model parameters on the disease prevalence. When the infected population persists, we have designed a time-dependent optimal quarantine strategy to minimize it. The Pontryagin's Maximum Principle is used to characterize the optimal control in terms of an optimality system which is solved numerically. Numerical results for the optimal control are compared against the constant controls and their efficiency is discussed. PMID:24374404

Imran, Mudassar; Rafique, Hassan; Khan, Adnan; Malik, Tufail



Self-consistently optimized energy functions for protein structure prediction by molecular dynamics  

PubMed Central

The protein energy landscape theory is used to obtain optimal energy functions for protein structure prediction via simulated annealing. The analysis here takes advantage of a more complete statistical characterization of the protein energy landscape and thereby improves on previous approximations. This schema partially takes into account correlations in the energy landscape. It also incorporates the relationships between folding dynamics and characteristic energy scales that control the collapse of the proteins and modulate rigidity of short-range interactions. Simulated annealing for the optimal energy functions, which are associative memory hamiltonians using a database of folding patterns, generally leads to quantitatively correct structures. In some cases the algorithm achieves “creativity,” i.e., structures result that are better than any homolog in the database. PMID:9501193

Koretke, Kristin K.; Luthey-Schulten, Zaida; Wolynes, Peter G.



Cooperative Quantum-Behaved Particle Swarm Optimization with Dynamic Varying Search Areas and Lévy Flight Disturbance  

PubMed Central

This paper proposes a novel variant of cooperative quantum-behaved particle swarm optimization (CQPSO) algorithm with two mechanisms to reduce the search space and avoid the stagnation, called CQPSO-DVSA-LFD. One mechanism is called Dynamic Varying Search Area (DVSA), which takes charge of limiting the ranges of particles' activity into a reduced area. On the other hand, in order to escape the local optima, Lévy flights are used to generate the stochastic disturbance in the movement of particles. To test the performance of CQPSO-DVSA-LFD, numerical experiments are conducted to compare the proposed algorithm with different variants of PSO. According to the experimental results, the proposed method performs better than other variants of PSO on both benchmark test functions and the combinatorial optimization issue, that is, the job-shop scheduling problem. PMID:24851085

Li, Desheng



Lipschitzian Regularity of Minimizers for Optimal Control Problems with Control-Affine Dynamics  

SciTech Connect

We study the Lagrange Problem of Optimal Control with a functional {integral}{sub a}{sup b}L(t,x(t),u(t)) dt and control-affine dynamics x-dot= f(t,x) + g(t,x)u and (a priori) unconstrained control u element of bf R{sup m}. We obtain conditions under which the minimizing controls of the problem are bounded-a fact which is crucial for the applicability of many necessary optimality conditions, like, for example, the Pontryagin Maximum Principle. As a corollary we obtain conditions for the Lipschitzian regularity of minimizers of the Basic Problem of the Calculus of Variations and of the Problem of the Calculus of Variations with higher-order derivatives.

Sarychev, A. V.; Torres, D. F. M. [Department of Mathematics, University of Aveiro, 3810 Aveiro (Portugal)], E-mail:;



Optimization of crude and product tanker fleets: a dynamic profit maximization model  

SciTech Connect

The petroleum tanker market crashed in the mid-seventies and has remained weak ever since. The cyclical pattern in tanker trade has yet to rebound from famine to feast. This research develops an original multi-stage optimization to improve the management of specific fleet operating and composition decisions. The model maximizes profits over the long term via a relaxation of the demand constraint, and a compact heuristic solving for the general integer problem within a quadratic formulations. Alternative formulations are tested and the original model is compared to an actual fleet history. In all cases, the Dynamic Fleet Optimization Model (DFOM) consistently generates the largest profits. The performance of DFOM in the historical validation supports the hypothesis that improved fleet management has the potential to improve tanker market efficiency. The analysis identifies a trading syndrome in tanker management leading to the chronic oversupply of tankers, and prescribes management guidelines along with model applications to avoid the mistakes of past tanker operators,

Selman, M.L.



Dynamic Optimization of Multi-Spacecraft Relative Navigation Configurations in the Earth-Moon System  

NASA Technical Reports Server (NTRS)

In this paper, the notion of relative navigation introduced by Hill, Lo and Born is analyzed for a large class of periodic orbits in the Earth-Moon three-body problem, due to its potential in supporting Moon exploration efforts. In particular, a navigation metric is introduced and used as a cost function to optimize over a class of periodic orbits. While the problem could be solve locally as an optimal control problem, a dynamical based approach that allows for a global/systematic view of the problem is proposed. First, the simpler problem of multiple spacecraft placement on a given periodic orbit is solved before the notion of continuation and bifurcation analysis is used to expand the range of solutions thus obtained.

Villac, Benjamin; Chow, Channing; Lo, Martin; Hintz, Gerald; Nazari, Zahra



Reconfiguration Process Optimization of Dynamically Coarse Grain Reconfigurable Architecture for Multimedia Applications  

NASA Astrophysics Data System (ADS)

This paper presents a novel architecture design to optimize the reconfiguration process of a coarse-grained reconfigurable architecture (CGRA) called Reconfigurable Multimedia System II (REMUS-II). In REMUS-II, the tasks in multi-media applications are divided into two parts: computing-intensive tasks and control-intensive tasks. Two Reconfigurable Processor Units (RPUs) for accelerating computing-intensive tasks and a Micro-Processor Unit (µPU) for accelerating control-intensive tasks are contained in REMUS-II. As a large-scale CGRA, REMUS-II can provide satisfying solutions in terms of both efficiency and flexibility. This feature makes REMUS-II well-suited for video processing, where higher flexibility requirements are posed and a lot of computation tasks are involved. To meet the high requirement of the dynamic reconfiguration performance for multimedia applications, the reconfiguration architecture of REMUS-II should be well designed. To optimize the reconfiguration architecture of REMUS-II, a hierarchical configuration storage structure and a 3-stage reconfiguration processing structure are proposed. Furthermore, several optimization methods for configuration reusing are also introduced, to further improve the performance of reconfiguration process. The optimization methods include two aspects: the multi-target reconfiguration method and the configuration caching strategies. Experimental results showed that, with the reconfiguration architecture proposed, the performance of reconfiguration process will be improved by 4 times. Based on RTL simulation, REMUS-II can support the 1080p@32fps of H.264 HiP@Level4 and 1080p@40fps High-level MPEG-2 stream decoding at the clock frequency of 200MHz. The proposed REMUS-II system has been implemented on a TSMC 65nm process. The die size is 23.7mm2 and the estimated on-chip dynamic power is 620mW.

Liu, Bo; Cao, Peng; Zhu, Min; Yang, Jun; Liu, Leibo; Wei, Shaojun; Shi, Longxing


Dynamic motion planning of 3D human locomotion using gradient-based optimization.  


Since humans can walk with an infinite variety of postures and limb movements, there is no unique solution to the modeling problem to predict human gait motions. Accordingly, we test herein the hypothesis that the redundancy of human walking mechanisms makes solving for human joint profiles and force time histories an indeterminate problem best solved by inverse dynamics and optimization methods. A new optimization-based human-modeling framework is thus described for predicting three-dimensional human gait motions on level and inclined planes. The basic unknowns in the framework are the joint motion time histories of a 25-degree-of-freedom human model and its six global degrees of freedom. The joint motion histories are calculated by minimizing an objective function such as deviation of the trunk from upright posture that relates to the human model's performance. A variety of important constraints are imposed on the optimization problem, including (1) satisfaction of dynamic equilibrium equations by requiring the model's zero moment point (ZMP) to lie within the instantaneous geometrical base of support, (2) foot collision avoidance, (3) limits on ground-foot friction, and (4) vanishing yawing moment. Analytical forms of objective and constraint functions are presented and discussed for the proposed human-modeling framework in which the resulting optimization problems are solved using gradient-based mathematical programming techniques. When the framework is applied to the modeling of bipedal locomotion on level and inclined planes, acyclic human walking motions that are smooth and realistic as opposed to less natural robotic motions are obtained. The aspects of the modeling framework requiring further investigation and refinement, as well as potential applications of the framework in biomechanics, are discussed. PMID:18532851

Kim, Hyung Joo; Wang, Qian; Rahmatalla, Salam; Swan, Colby C; Arora, Jasbir S; Abdel-Malek, Karim; Assouline, Jose G



The importance of functional form in optimal control solutions of problems in population dynamics  

USGS Publications Warehouse

Optimal control theory is finding increased application in both theoretical and applied ecology, and it is a central element of adaptive resource management. One of the steps in an adaptive management process is to develop alternative models of system dynamics, models that are all reasonable in light of available data, but that differ substantially in their implications for optimal control of the resource. We explored how the form of the recruitment and survival functions in a general population model for ducks affected the patterns in the optimal harvest strategy, using a combination of analytical, numerical, and simulation techniques. We compared three relationships between recruitment and population density (linear, exponential, and hyperbolic) and three relationships between survival during the nonharvest season and population density (constant, logistic, and one related to the compensatory harvest mortality hypothesis). We found that the form of the component functions had a dramatic influence on the optimal harvest strategy and the ultimate equilibrium state of the system. For instance, while it is commonly assumed that a compensatory hypothesis leads to higher optimal harvest rates than an additive hypothesis, we found this to depend on the form of the recruitment function, in part because of differences in the optimal steady-state population density. This work has strong direct consequences for those developing alternative models to describe harvested systems, but it is relevant to a larger class of problems applying optimal control at the population level. Often, different functional forms will not be statistically distinguishable in the range of the data. Nevertheless, differences between the functions outside the range of the data can have an important impact on the optimal harvest strategy. Thus, development of alternative models by identifying a single functional form, then choosing different parameter combinations from extremes on the likelihood profile may end up producing alternatives that do not differ as importantly as if different functional forms had been used. We recommend that biological knowledge be used to bracket a range of possible functional forms, and robustness of conclusions be checked over this range.

Runge, M.C.; Johnson, F.A.



Dynamical symmetry breaking with optimal control: reducing the number of pieces  

E-print Network

We analyse the production of defects during the dynamical crossing of a mean-field phase transition with a real order parameter. When the parameter that brings the system across the critical point changes in time according to a power-law schedule, we recover the predictions dictated by the well-known Kibble-Zurek theory. For a fixed duration of the evolution, we show that the average number of defects can be drastically reduced for a very large but finite system, by optimising the time dependence of the driving using optimal control techniques. Furthermore, the optimised protocol is robust against small fluctuations.

Matthew J. M. Power; Gabriele De Chiara



High-dynamic-range imaging of nanoscale magnetic fields using optimal control of a single qubit  

E-print Network

We present a novel spectroscopy protocol based on optimal control of a single quantum system. It enables measurements with quantum-limited sensitivity (\\eta_\\omega ~ 1/sqrt(T_2^*),T_2^* denoting the system's coherence time) but has an orders of magnitude larger dynamic range than pulsed spectroscopy methods previously employed for this task. We employ this protocol to image nanoscale magnetic fields with a single scanning NV center in diamond. Here, our scheme enables quantitative imaging of a strongly inhomogeneous field in a single scan without closed-loop control, which has previously been necessary to achieve this goal.

Thomas Häberle; Dominik Schmid-Lorch; Khaled Karrai; Friedemann Reinhard; Jörg Wrachtrup



Particle Swarm Optimization and Varying Chemotactic Step-Size Bacterial Foraging Optimization Algorithms Based Dynamic Economic Dispatch with Non-smooth Fuel Cost Functions  

NASA Astrophysics Data System (ADS)

The Dynamic economic dispatch (DED) problem is an optimization problem with an objective to determine the optimal combination of power outputs for all generating units over a certain period of time in order to minimize the total fuel cost while satisfying dynamic operational constraints and load demand in each interval. Recently social foraging behavior of Escherichia coli bacteria has been explored to develop a novel algorithm for distributed optimization and control. The Bacterial Foraging Optimization Algorithm (BFOA) is currently gaining popularity in the community of researchers, for its effectiveness in solving certain difficult real-world optimization problems. This article comes up with a hybrid approach involving Particle Swarm Optimization (PSO) and BFO algorithms with varying chemo tactic step size for solving the DED problem of generating units considering valve-point effects. The proposed hybrid algorithm has been extensively compared with those methods reported in the literature. The new method is shown to be statistically significantly better on two test systems consisting of five and ten generating units.

Praveena, P.; Vaisakh, K.; Rama Mohana Rao, S.


Dynamic optimization for commercialization of renewable energy: an example for solar photovoltaics  

SciTech Connect

There are several studies of optimal allocation of research and development resources over the time horizon of a project. The primary result of the basic noncompetitive models in this literature is that the optimal strategy is to choose a research intensity and ending date for the project such that the marginal costs of accelerating the project equals the marginal benefits of introducing the product sooner. This literature provides useful insights for the government planner who must allocate R&D resources for renewable energy development. However, several characteristics distinguish the process from the typical R&D planning problem. Specifically, with PV development, where the goal is to maximize the net present value of activities leading to cost reduction in commercial modules, there are (1) significant lag-times between investment in laboratory research and resulting effects in the marketplace, (2) a learning curve associated with the manufacturing process that also reduces the cost s of PV modules, (3) interim benefits from technical advances, (4) no clear end point to the R&D process, but rather a tapering off of the value of advances in technical efficiency, (5) significant uncertainty in the R&D process, (6) a family of products rather than an individual technology, (7) a co-mingling of government and private resources with implications for efficient management. A dynamic model is developed to characterize the optimal intensity and timing of government and private resource allocation for basic research in improving the technical efficiency of cells and subsidies to the manufacturing process to encourage progress on the learning curve. A series of propositions regarding optimal paths for each are examined. While the research is purely analytical, the results are useful for conceptualizing the R&D planning process. They also provide a basis for a numerical study that can address whether current levels and historic patterns of funding are optimal.

Richards, Kenneth, R.; Ashton, W. Bradley; McVeigh, James



Network dynamics for optimal compressive-sensing input-signal recovery.  


By using compressive sensing (CS) theory, a broad class of static signals can be reconstructed through a sequence of very few measurements in the framework of a linear system. For networks with nonlinear and time-evolving dynamics, is it similarly possible to recover an unknown input signal from only a small number of network output measurements? We address this question for pulse-coupled networks and investigate the network dynamics necessary for successful input signal recovery. Determining the specific network characteristics that correspond to a minimal input reconstruction error, we are able to achieve high-quality signal reconstructions with few measurements of network output. Using various measures to characterize dynamical properties of network output, we determine that networks with highly variable and aperiodic output can successfully encode network input information with high fidelity and achieve the most accurate CS input reconstructions. For time-varying inputs, we also find that high-quality reconstructions are achievable by measuring network output over a relatively short time window. Even when network inputs change with time, the same optimal choice of network characteristics and corresponding dynamics apply as in the case of static inputs. PMID:25375568

Barranca, Victor J; Kova?i?, Gregor; Zhou, Douglas; Cai, David



Comparative Studies of Particle Swarm Optimization Techniques for Reactive Power Allocation Planning in Power Systems  

NASA Astrophysics Data System (ADS)

This paper compares particle swarm optimization (PSO) techniques for a reactive power allocation planning problem in power systems. The problem can be formulated as a mixed-integer nonlinear optimization problem (MINLP). The PSO based methods determines a reactive power allocation strategy with continuous and discrete state variables such as automatic voltage regulator (AVR) operating values of electric power generators, tap positions of on-load tap changer (OLTC) of transformers, and the number of reactive power compensation equipment. Namely, this paper investigates applicability of PSO techniques to one of the practical MINLPs in power systems. Four variations of PSO: PSO with inertia weight approach (IWA), PSO with constriction factor approach (CFA), hybrid particle swarm optimization (HPSO) with IWA, and HPSO with CFA are compared. The four methods are applied to the standard IEEE14 bus system and a practical 112 bus system.

Fukuyama, Yoshikazu


Dynamic nuclear polarization and optimal control spatial-selective 13C MRI and MRS  

NASA Astrophysics Data System (ADS)

Aimed at 13C metabolic magnetic resonance imaging (MRI) and spectroscopy (MRS) applications, we demonstrate that dynamic nuclear polarization (DNP) may be combined with optimal control 2D spatial selection to simultaneously obtain high sensitivity and well-defined spatial restriction. This is achieved through the development of spatial-selective single-shot spiral-readout MRI and MRS experiments combined with dynamic nuclear polarization hyperpolarized [1-13C]pyruvate on a 4.7 T pre-clinical MR scanner. The method stands out from related techniques by facilitating anatomic shaped region-of-interest (ROI) single metabolite signals available for higher image resolution or single-peak spectra. The 2D spatial-selective rf pulses were designed using a novel Krotov-based optimal control approach capable of iteratively fast providing successful pulse sequences in the absence of qualified initial guesses. The technique may be important for early detection of abnormal metabolism, monitoring disease progression, and drug research.

Vinding, Mads S.; Laustsen, Christoffer; Maximov, Ivan I.; Søgaard, Lise Vejby; Ardenkjær-Larsen, Jan H.; Nielsen, Niels Chr.



Fluid-Dynamic Optimal Design of Helical Vascular Graft for Stenotic Disturbed Flow  

PubMed Central

Although a helical configuration of a prosthetic vascular graft appears to be clinically beneficial in suppressing thrombosis and intimal hyperplasia, an optimization of a helical design has yet to be achieved because of the lack of a detailed understanding on hemodynamic features in helical grafts and their fluid dynamic influences. In the present study, the swirling flow in a helical graft was hypothesized to have beneficial influences on a disturbed flow structure such as stenotic flow. The characteristics of swirling flows generated by helical tubes with various helical pitches and curvatures were investigated to prove the hypothesis. The fluid dynamic influences of these helical tubes on stenotic flow were quantitatively analysed by using a particle image velocimetry technique. Results showed that the swirling intensity and helicity of the swirling flow have a linear relation with a modified Germano number (Gn*) of the helical pipe. In addition, the swirling flow generated a beneficial flow structure at the stenosis by reducing the size of the recirculation flow under steady and pulsatile flow conditions. Therefore, the beneficial effects of a helical graft on the flow field can be estimated by using the magnitude of Gn*. Finally, an optimized helical design with a maximum Gn* was suggested for the future design of a vascular graft. PMID:25360705

Ha, Hojin; Hwang, Dongha; Choi, Woo-Rak; Baek, Jehyun; Lee, Sang Joon



Optimal dynamic voltage scaling for wireless sensor nodes with real-time constraints  

NASA Astrophysics Data System (ADS)

Sensors are increasingly embedded in manufacturing systems and wirelessly networked to monitor and manage operations ranging from process and inventory control to tracking equipment and even post-manufacturing product monitoring. In building such sensor networks, a critical issue is the limited and hard to replenish energy in the devices involved. Dynamic voltage scaling is a technique that controls the operating voltage of a processor to provide desired performance while conserving energy and prolonging the overall network's lifetime. We consider such power-limited devices processing time-critical tasks which are non-preemptive, aperiodic and have uncertain arrival times. We treat voltage scaling as a dynamic optimization problem whose objective is to minimize energy consumption subject to hard or soft real-time execution constraints. In the case of hard constraints, we build on prior work (which engages a voltage scaling controller at task completion times) by developing an intra-task controller that acts at all arrival times of incoming tasks. We show that this optimization problem can be decomposed into two simpler ones whose solution leads to an algorithm that does not actually require solving any nonlinear programming problems. In the case of soft constraints, this decomposition must be partly relaxed, but it still leads to a scalable (linear in the number of tasks) algorithm. Simulation results are provided to illustrate performance improvements in systems with intra-task controllers compared to uncontrolled systems or those using inter-task control.

Cassandras, Christos G.; Zhuang, Shixin



Resolution of strongly competitive product channels with optimal dynamic discrimination: Application to flavins  

NASA Astrophysics Data System (ADS)

Fundamental molecular selectivity limits are probed by exploiting laser-controlled quantum interferences for the creation of distinct spectral signatures in two flavin molecules, erstwhile nearly indistinguishable via steady-state methods. Optimal dynamic discrimination (ODD) uses optimally shaped laser fields to transiently amplify minute molecular variations that would otherwise go unnoticed with linear absorption and fluorescence techniques. ODD is experimentally demonstrated by combining an optimally shaped UV pump pulse with a time-delayed, fluorescence-depleting IR pulse for discrimination amongst riboflavin and flavin mononucleotide in aqueous solution, which are structurally and spectroscopically very similar. Closed-loop, adaptive pulse shaping discovers a set of UV pulses that induce disparate responses from the two flavins and allows for concomitant flavin discrimination of ˜16?. Additionally, attainment of ODD permits quantitative, analytical detection of the individual constituents in a flavin mixture. The successful implementation of ODD on quantum systems of such high complexity bodes well for the future development of the field and the use of ODD techniques in a variety of demanding practical applications.

Roslund, Jonathan; Roth, Matthias; Guyon, Laurent; Boutou, Véronique; Courvoisier, Francois; Wolf, Jean-Pierre; Rabitz, Herschel



Date Flow Optimization of Dynamically Coarse Grain Reconfigurable Architecture for Multimedia Applications  

NASA Astrophysics Data System (ADS)

This paper proposes a novel sub-architecture to optimize the data flow of REMUS-II (REconfigurable MUltimedia System 2), a dynamically coarse grain reconfigurable architecture. REMUS-II consists of a µPU (Micro-Processor Unit) and two RPUs (Reconfigurable Processor Unit), which are used to speeds up control-intensive tasks and data-intensive tasks respectively. The parallel computing capability and flexibility of REMUS-II makes itself an excellent candidate to process multimedia applications, which require a large amount of memory accesses. In this paper, we specifically optimize the data flow to deal with those performance-hazard and energy-hungry memory accessing in order to meet the bandwidth requirement of parallel computing. The RPU internal memory could work in multiple modes, like 2D-access mode and transformation mode, according to different multimedia access patterns. This novel design can improve the performance up to 26% compared to traditional on-chip memory. Meanwhile, the block buffer is implemented to optimize the off-chip data flow through reducing off-chip memory accesses, which reducing up to 43% compared to direct DDR access. Based on RTL simulation, REMUS-II can achieve 1080p@30fps of H.264 High Profile@ Level 4 and High Level MPEG2 at 200MHz clock frequency. The REMUS-II is implemented into 23.7mm2 silicon on TSMC 65nm logic process with a 400MHz maximum working frequency.

Liu, Xinning; Mei, Chen; Cao, Peng; Zhu, Min; Shi, Longxing


Dynamic Response and Optimal Design of Curved Metallic Sandwich Panels under Blast Loading  

PubMed Central

It is important to understand the effect of curvature on the blast response of curved structures so as to seek the optimal configurations of such structures with improved blast resistance. In this study, the dynamic response and protective performance of a type of curved metallic sandwich panel subjected to air blast loading were examined using LS-DYNA. The numerical methods were validated using experimental data in the literature. The curved panel consisted of an aluminum alloy outer face and a rolled homogeneous armour (RHA) steel inner face in addition to a closed-cell aluminum foam core. The results showed that the configuration of a “soft” outer face and a “hard” inner face worked well for the curved sandwich panel against air blast loading in terms of maximum deflection (MaxD) and energy absorption. The panel curvature was found to have a monotonic effect on the specific energy absorption (SEA) and a nonmonotonic effect on the MaxD of the panel. Based on artificial neural network (ANN) metamodels, multiobjective optimization designs of the panel were carried out. The optimization results revealed the trade-off relationships between the blast-resistant and the lightweight objectives and showed the great use of Pareto front in such design circumstances. PMID:25126606

Yang, Shu; Han, Shou-Hong; Lu, Zhen-Hua



Adaptive Control for Linear Uncertain Systems with Unmodeled Dynamics Revisited via Optimal Control Modification  

NASA Technical Reports Server (NTRS)

This paper presents the optimal control modification for linear uncertain plants. The Lyapunov analysis shows that the modification parameter has a limiting value depending on the nature of the uncertainty. The optimal control modification exhibits a linear asymptotic property that enables it to be analyzed in a linear time invariant framework for linear uncertain plants. The linear asymptotic property shows that the closed-loop plants in the limit possess a scaled input-output mapping. Using this property, we can derive an analytical closed-loop transfer function in the limit as the adaptive gain tends to infinity. The paper revisits the Rohrs counterexample problem that illustrates the nature of non-robustness of model-reference adaptive control in the presence of unmodeled dynamics. An analytical approach is developed to compute exactly the modification parameter for the optimal control modification that stabilizes the plant in the Rohrs counterexample. The linear asymptotic property is also used to address output feedback adaptive control for non-minimum phase plants with a relative degree 1.

Nguyen, Nhan



Time-optimal path planning in dynamic flows using level set equations: theory and schemes  

NASA Astrophysics Data System (ADS)

We develop an accurate partial differential equation-based methodology that predicts the time-optimal paths of autonomous vehicles navigating in any continuous, strong, and dynamic ocean currents, obviating the need for heuristics. The goal is to predict a sequence of steering directions so that vehicles can best utilize or avoid currents to minimize their travel time. Inspired by the level set method, we derive and demonstrate that a modified level set equation governs the time-optimal path in any continuous flow. We show that our algorithm is computationally efficient and apply it to a number of experiments. First, we validate our approach through a simple benchmark application in a Rankine vortex flow for which an analytical solution is available. Next, we apply our methodology to more complex, simulated flow fields such as unsteady double-gyre flows driven by wind stress and flows behind a circular island. These examples show that time-optimal paths for multiple vehicles can be planned even in the presence of complex flows in domains with obstacles. Finally, we present and support through illustrations several remarks that describe specific features of our methodology.

Lolla, Tapovan; Lermusiaux, Pierre F. J.; Ueckermann, Mattheus P.; Haley, Patrick J.



Combating Obesity through Healthy Eating Behavior: A Call for System Dynamics Optimization.  


Poor eating behavior has been identified as one of the core contributory factors of the childhood obesity epidemic. The consequences of obesity on numerous aspects of life are thoroughly explored in the existing literature. For instance, evidence shows that obesity is linked to incidences of diseases such as heart disease, type-2 diabetes, and some cancers, as well as psychosocial problems. To respond to the increasing trends in the UK, in 2008 the government set a target to reverse the prevalence of obesity (POB) back to 2000 levels by 2020. This paper will outline the application of system dynamics (SD) optimization to simulate the effect of changes in the eating behavior of British children (aged 2 to 15 years) on weight and obesity. This study also will identify how long it will take to achieve the government's target. This paper proposed a simulation model called Intervention Childhood Obesity Dynamics (ICOD) by focusing the interrelations between various strands of knowledge in one complex human weight regulation system. The model offers distinct insights into the dynamics by capturing the complex interdependencies from the causal loop and feedback structure, with the intention to better understand how eating behaviors influence children's weight, body mass index (BMI), and POB measurement. This study proposed a set of equations that are revised from the original (baseline) equations. The new functions are constructed using a RAMP function of linear decrement in portion size and number of meal variables from 2013 until 2020 in order to achieve the 2020 desired target. Findings from the optimization analysis revealed that the 2020 target won't be achieved until 2026 at the earliest, six years late. Thus, the model suggested that a longer period may be needed to significantly reduce obesity in this population. PMID:25502170

Zainal Abidin, Norhaslinda; Mamat, Mustafa; Dangerfield, Brian; Zulkepli, Jafri Haji; Baten, Md Azizul; Wibowo, Antoni



Combating Obesity through Healthy Eating Behavior: A Call for System Dynamics Optimization  

PubMed Central

Poor eating behavior has been identified as one of the core contributory factors of the childhood obesity epidemic. The consequences of obesity on numerous aspects of life are thoroughly explored in the existing literature. For instance, evidence shows that obesity is linked to incidences of diseases such as heart disease, type-2 diabetes, and some cancers, as well as psychosocial problems. To respond to the increasing trends in the UK, in 2008 the government set a target to reverse the prevalence of obesity (POB) back to 2000 levels by 2020. This paper will outline the application of system dynamics (SD) optimization to simulate the effect of changes in the eating behavior of British children (aged 2 to 15 years) on weight and obesity. This study also will identify how long it will take to achieve the government’s target. This paper proposed a simulation model called Intervention Childhood Obesity Dynamics (ICOD) by focusing the interrelations between various strands of knowledge in one complex human weight regulation system. The model offers distinct insights into the dynamics by capturing the complex interdependencies from the causal loop and feedback structure, with the intention to better understand how eating behaviors influence children’s weight, body mass index (BMI), and POB measurement. This study proposed a set of equations that are revised from the original (baseline) equations. The new functions are constructed using a RAMP function of linear decrement in portion size and number of meal variables from 2013 until 2020 in order to achieve the 2020 desired target. Findings from the optimization analysis revealed that the 2020 target won’t be achieved until 2026 at the earliest, six years late. Thus, the model suggested that a longer period may be needed to significantly reduce obesity in this population. PMID:25502170

Zainal Abidin, Norhaslinda; Mamat, Mustafa; Dangerfield, Brian; Zulkepli, Jafri Haji; Baten, Md. Azizul; Wibowo, Antoni



Trajectory optimization for dynamic couch rotation during volumetric modulated arc radiotherapy  

NASA Astrophysics Data System (ADS)

Non-coplanar radiation beams are often used in three-dimensional conformal and intensity modulated radiotherapy to reduce dose to organs at risk (OAR) by geometric avoidance. In volumetric modulated arc radiotherapy (VMAT) non-coplanar geometries are generally achieved by applying patient couch rotations to single or multiple full or partial arcs. This paper presents a trajectory optimization method for a non-coplanar technique, dynamic couch rotation during VMAT (DCR-VMAT), which combines ray tracing with a graph search algorithm. Four clinical test cases (partial breast, brain, prostate only, and prostate and pelvic nodes) were used to evaluate the potential OAR sparing for trajectory-optimized DCR-VMAT plans, compared with standard coplanar VMAT. In each case, ray tracing was performed and a cost map reflecting the number of OAR voxels intersected for each potential source position was generated. The least-cost path through the cost map, corresponding to an optimal DCR-VMAT trajectory, was determined using Dijkstra’s algorithm. Results show that trajectory optimization can reduce dose to specified OARs for plans otherwise comparable to conventional coplanar VMAT techniques. For the partial breast case, the mean heart dose was reduced by 53%. In the brain case, the maximum lens doses were reduced by 61% (left) and 77% (right) and the globes by 37% (left) and 40% (right). Bowel mean dose was reduced by 15% in the prostate only case. For the prostate and pelvic nodes case, the bowel V50?Gy and V60?Gy were reduced by 9% and 45% respectively. Future work will involve further development of the algorithm and assessment of its performance over a larger number of cases in site-specific cohorts.

Smyth, Gregory; Bamber, Jeffrey C.; Evans, Philip M.; Bedford, James L.



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

NASA Astrophysics Data System (ADS)

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.

Spivey, Benjamin James



Optimal Variable Flip Angle Schemes For Dynamic Acquisition Of Exchanging Hyperpolarized Substrates  

PubMed Central

In metabolic MRI with hyperpolarized contrast agents, the signal levels vary over time due to T1 decay, T2 decay following RF excitations, and metabolic conversion. Efficient usage of the nonrenewable hyperpolarized magnetization requires specialized RF pulse schemes. In this work, we introduce two novel variable flip angle schemes for dynamic hyperpolarized MRI in which the flip angle is varied between excitations and between metabolites. These were optimized to distribute the magnetization relatively evenly throughout the acquisition by accounting for T1 decay, prior RF excitations, and metabolic conversion. Simulation results are presented to confirm the flip angle designs and evaluate the variability of signal dynamics across typical ranges of T1 and metabolic conversion. They were implemented using multiband spectral-spatial RF pulses to independently modulate the flip angle at various chemical shift frequencies. With these schemes we observed increased SNR of [1-13C]lactate generated from [1-13C]pyruvate, particularly at later time points. This will allow for improved characterization of tissue perfusion and metabolic profiles in dynamic hyperpolarized MRI. PMID:23845910

Xing, Yan; Reed, Galen D.; Pauly, John M.; Kerr, Adam B.; Larson, Peder E. Z.



Bouncing between Model and Data: Stability, Passivity, and Optimality in Hybrid Dynamics  

PubMed Central

Rhythmically bouncing a ball with a racket is a seemingly simple task, but it poses all the challenges critical for coordinative behavior: perceiving the ball’s trajectory to adapt position and velocity of the racket for the next ball contact. To gain insight into the underlying control strategies, the authors conducted a series of studies that tested models with experimental data, with an emphasis on deriving model-based hypotheses and trying to falsify them. Starting with a simple dynamical model of the racket and ball interactions, stability analyses showed that open-loop dynamics affords dynamical stability, such that small perturbations do not require corrections. To obtain this passive stability, the ball has to be impacted with negative acceleration—a strategy that subjects adopted in a variety of conditions at steady state. However, experimental tests that applied perturbations revealed that after perturbations, subjects applied active perceptually guided corrections to reestablish steady state faster than by relying on the passive model’s relaxation alone. Hence, the authors derived a model with active control based on optimality principles that considered each impact as a separate reaching-like movement. This model captured some additional features of the racket trajectory but failed to predict more fine-grained aspects of performance. The authors proceed to present a new model that accounts not only for fine-grained behavior but also reconciles passive and active control approaches with new predictions that will be put to test in the next set of experiments. PMID:21184357

Ronsse, Renaud; Sternad, Dagmar




Microsoft Academic Search

This paper presents the optimal mechanism design and dynamic analy- sis of a prototype 3-leg 6-DOF (degree-of-freedom) parallel manipulator. In- verse kinematics, forward kinematics, inverse dynamics and working space characterizing the platform motion are derived. In the presented architecture, the base platform has three linear slideways individually actuated by a syn- chronous linear servo motor, and each extensible vertical link

Thong-Shing Hwang; Ming-Yang Liao



Dynamic model of Escherichia coli tryptophan operon shows an optimal structural design.  


A mathematical model has been developed to study the effect of external tryptophan on the trp operon. The model accounts for the effect of feedback repression by tryptophan through the Hill equation. We demonstrate that the trp operon maintains an intracellular steady-state concentration in a fivefold range irrespective of extracellular conditions. Dynamic behavior of the trp operon corresponding to varying levels of extracellular tryptophan illustrates the adaptive nature of regulation. Depending on the external tryptophan level in the medium, the transient response ranges from a rapid and underdamped to a sluggish and highly overdamped response. To test model fidelity, simulation results are compared with experimental data available in the literature. We further demonstrate the significance of the biological structure of the operon on the overall performance. Our analysis suggests that the tryptophan operon has evolved to a truly optimal design. PMID:12787031

Bhartiya, Sharad; Rawool, Subodh; Venkatesh, K V



Optimal performance of the tryptophan operon of E. coli: A stochastic, dynamical, mathematical-modeling approach.  


In this work, we develop a detailed, stochastic, dynamical model for the tryptophan operon of E. coli, and estimate all of the model parameters from reported experimental data. We further employ the model to study the system performance, considering the amount of biochemical noise in the trp level, the system rise time after a nutritional shift, and the amount of repressor molecules necessary to maintain an adequate level of repression, as indicators of the system performance regime. We demonstrate that the level of cooperativity between repressor molecules bound to the first two operators in the trp promoter affects all of the above enlisted performance characteristics. Moreover, the cooperativity level found in the wild-type bacterial strain optimizes a cost-benefit function involving low biochemical noise in the tryptophan level, short rise time after a nutritional shift, and low number of regulatory molecules. PMID:24307084

Salazar-Cavazos, Emanuel; Santillán, Moisés



Physics Analysis and Optimization Studies for a Fusion Neutron Source Based on a Gas Dynamic Trap  

NASA Astrophysics Data System (ADS)

To further investigate the fusion neutron source based on a gas dynamic trap (GDT), characteristics of the GDT were analyzed and physics analyses were made for a fusion neutron source based on the GDT concept. The prior design of a GDT-based fusion neutron source was optimized based on a refreshed understanding of GDT operation. A two-step progressive development route of a GDT-based fusion neutron source was suggested. Potential applications of GDT are discussed. Preliminary analyses show that a fusion neutron source based on the GDT concept is suitable for plasma-material interaction research, fusion material and subcomponent testing, and capable of driving a proof-of-principle fusion fission hybrid experimental facility.

Du, Hongfei; Chen, Dehong; Duan, Wenxue; Jiang, Jieqiong; Wu, Yican; FDS Team



Q- and A-learning Methods for Estimating Optimal Dynamic Treatment Regimes  

PubMed Central

In clinical practice, physicians make a series of treatment decisions over the course of a patient’s disease based on his/her baseline and evolving characteristics. A dynamic treatment regime is a set of sequential decision rules that operationalizes this process. Each rule corresponds to a decision point and dictates the next treatment action based on the accrued information. Using existing data, a key goal is estimating the optimal regime, that, if followed by the patient population, would yield the most favorable outcome on average. Q- and A-learning are two main approaches for this purpose. We provide a detailed account of these methods, study their performance, and illustrate them using data from a depression study. PMID:25620840

Schulte, Phillip J.; Tsiatis, Anastasios A.; Laber, Eric B.; Davidian, Marie



Transmission Expansion Planning - A Multiyear Dynamic Approach Using a Discrete Evolutionary Particle Swarm Optimization Algorithm  

NASA Astrophysics Data System (ADS)

The basic objective of Transmission Expansion Planning (TEP) is to schedule a number of transmission projects along an extended planning horizon minimizing the network construction and operational costs while satisfying the requirement of delivering power safely and reliably to load centres along the horizon. This principle is quite simple, but the complexity of the problem and the impact on society transforms TEP on a challenging issue. This paper describes a new approach to solve the dynamic TEP problem, based on an improved discrete integer version of the Evolutionary Particle Swarm Optimization (EPSO) meta-heuristic algorithm. The paper includes sections describing in detail the EPSO enhanced approach, the mathematical formulation of the TEP problem, including the objective function and the constraints, and a section devoted to the application of the developed approach to this problem. Finally, the use of the developed approach is illustrated using a case study based on the IEEE 24 bus 38 branch test system.

Rocha, M. C.; Saraiva, J. T.



Two dynamic reconfiguration approaches for optimizing the restoration path length in p-cycle protection network  

NASA Astrophysics Data System (ADS)

p-cycle is one of the most promising technique of span protection in optical transport networks with mesh-like efficiency and ring-like speed. Longer p-cycle provides better efficiency in term of spare capacity, but longer restored path increases end-to-end propagation delay, which reduces the reliability of the restored network. Hence, minimization of restoration path is a critical issue in p-cycle based protection network. In this paper, two new dynamic reconfiguration approaches namely inter-cycles switching (ICS) and local restoration paths (LRP) are discussed to reduce the length of restored paths in existing optimal spare capacity design of p-cycle. Both proposed approaches are meant to utilize the idle p-cycles thus significantly reducing the path length. This reduction in restored path length also releases the redundant spare capacity.

Yadav, Raghav; Yadav, Rama Shankar



An 'optimal' spawning algorithm for adaptive basis set expansion in nonadiabatic dynamics  

SciTech Connect

The full multiple spawning (FMS) method has been developed to simulate quantum dynamics in the multistate electronic problem. In FMS, the nuclear wave function is represented in a basis of coupled, frozen Gaussians, and a 'spawning' procedure prescribes a means of adaptively increasing the size of this basis in order to capture population transfer between electronic states. Herein we detail a new algorithm for specifying the initial conditions of newly spawned basis functions that minimizes the number of spawned basis functions needed for convergence. 'Optimally' spawned basis functions are placed to maximize the coupling between parent and child trajectories at the point of spawning. The method is tested with a two-state, one-mode avoided crossing model and a two-state, two-mode conical intersection model.

Yang, Sandy; Coe, Joshua D.; Kaduk, Benjamin; Martinez, Todd J. [Department of Chemistry and Beckman Institute, University of Illinois at Urbana-Champaign, 600 S. Mathews Ave., Urbana, Illinois 61801 (United States)



Combinatorial Optimization Algorithms for Dynamic Multiple Fault Diagnosis in Automotive and Aerospace Applications  

NASA Astrophysics Data System (ADS)

In this thesis, we develop dynamic multiple fault diagnosis (DMFD) algorithms to diagnose faults that are sporadic and coupled. Firstly, we formulate a coupled factorial hidden Markov model-based (CFHMM) framework to diagnose dependent faults occurring over time (dynamic case). Here, we implement a mixed memory Markov coupling model to determine the most likely sequence of (dependent) fault states, the one that best explains the observed test outcomes over time. An iterative Gauss-Seidel coordinate ascent optimization method is proposed for solving the problem. A soft Viterbi algorithm is also implemented within the framework for decoding dependent fault states over time. We demonstrate the algorithm on simulated and real-world systems with coupled faults; the results show that this approach improves the correct isolation rate as compared to the formulation where independent fault states are assumed. Secondly, we formulate a generalization of set-covering, termed dynamic set-covering (DSC), which involves a series of coupled set-covering problems over time. The objective of the DSC problem is to infer the most probable time sequence of a parsimonious set of failure sources that explains the observed test outcomes over time. The DSC problem is NP-hard and intractable due to the fault-test dependency matrix that couples the failed tests and faults via the constraint matrix, and the temporal dependence of failure sources over time. Here, the DSC problem is motivated from the viewpoint of a dynamic multiple fault diagnosis problem, but it has wide applications in operations research, for e.g., facility location problem. Thus, we also formulated the DSC problem in the context of a dynamically evolving facility location problem. Here, a facility can be opened, closed, or can be temporarily unavailable at any time for a given requirement of demand points. These activities are associated with costs or penalties, viz., phase-in or phase-out for the opening or closing of a facility, respectively. The set-covering matrix encapsulates the relationship among the rows (tests or demand points) and columns (faults or locations) of the system at each time. By relaxing the coupling constraints using Lagrange multipliers, the DSC problem can be decoupled into independent subproblems, one for each column. Each subproblem is solved using the Viterbi decoding algorithm, and a primal feasible solution is constructed by modifying the Viterbi solutions via a heuristic. The proposed Viterbi-Lagrangian relaxation algorithm (VLRA) provides a measure of suboptimality via an approximate duality gap. As a major practical extension of the above problem, we also consider the problem of diagnosing faults with delayed test outcomes, termed delay-dynamic set-covering (DDSC), and experiment with real-world problems that exhibit masking faults. Also, we present simulation results on OR-library datasets (set-covering formulations are predominantly validated on these matrices in the literature), posed as facility location problems. Finally, we implement these algorithms to solve problems in aerospace and automotive applications. Firstly, we address the diagnostic ambiguity problem in aerospace and automotive applications by developing a dynamic fusion framework that includes dynamic multiple fault diagnosis algorithms. This improves the correct fault isolation rate, while minimizing the false alarm rates, by considering multiple faults instead of the traditional data-driven techniques based on single fault (class)-single epoch (static) assumption. The dynamic fusion problem is formulated as a maximum a posteriori decision problem of inferring the fault sequence based on uncertain outcomes of multiple binary classifiers over time. The fusion process involves three steps: the first step transforms the multi-class problem into dichotomies using error correcting output codes (ECOC), thereby solving the concomitant binary classification problems; the second step fuses the outcomes of multiple binary classifiers over time using a sliding window or block dynamic fusi

Kodali, Anuradha


Optimal and robust modal control of a flexible structure using an active dynamic vibration absorber  

NASA Astrophysics Data System (ADS)

This paper is concerned with feedback vibration control of a lightly damped flexible structure that has a large number of well-separated modes. A single active electrical dynamic absorber is used to reduce a particular single vibration mode selectively or multiple modes simultaneously. The absorber is realized electrically by feeding back the structural acceleration at one position to a collocated piezoceramic patch actuator via a controller consisting of one or several second order lowpass filters. A simple analytical method is presented to design a modal control filter that is optimal in that it maximally flattens the mobility frequency response of the target mode, as well as robust in that it works within a prescribed maximum control spillover of 2 dB at all frequencies. Experiments are conducted with a free-free beam to demonstrate its ability to control any single mode optimally and robustly. It is also shown that an active absorber with multiple such filters can effectively control multiple modes simultaneously.

Kim, Sang-Myeong; Wang, Semyung; Brennan, Michael J.



Optimal Model on Canal Water Distribution Based on Dynamic Penalty Function and Genetic Algorithm  

NASA Astrophysics Data System (ADS)

The present optimal water delivery scheduling models are based on the assumed equal design discharges of lateral canals, which are not in accordance with practical water delivery scheduling demand in most irrigation systems. In order to solve this problem, a model of lateral canals with unequal discharges and a solution method were proposed; At present, traditional fixed penalty factor have some problem, such as it is difficulty to use unified dimension and to get a higher searching precision, besides, it prematurely converge to local optimal solution. Therefore, the thought of simulated annealing was referred to design a dynamic penalty function. In the progress of genetic operation, the SGA (Simple Genetic Algorithm) adopted adaptive crossover mutation method, and compared distinct solutions of model which based on the method in this paper, Adaptive genetic algorithm (AGA) and traditional methods used in irrigation district widely respectively. Comparing with water delivery plan compiled using traditional methods, the results illustrate that using this method can get much more reasonable lateral canals water delivery time and homogeneous discharges of upper canal. AGA can adjust the genetic controlling parameters automatically on the basis of values of individual fitness and degree of population dispersion, and get a high precision solution. So it has a higher practical value in irrigation system management.

Zhao, Wenju; Ma, Xiaoyi; Kang, Yinhong; Ren, Hongyi; Su, Baofeng


Comparison of asynchronous particle swarm optimization and dynamic differential evolution for partially immersed conductor  

NASA Astrophysics Data System (ADS)

The application of two techniques for the of shape reconstruction of a perfectly two-dimensional conducting cylinder from mimic measurement data is studied in the present paper. After an integral formulation, the microwave imaging is recast as a nonlinear optimization problem; a cost function is defined by the norm of a difference between the measured scattered electric fields and the calculated scattered fields for an estimated shape of a conductor. Thus, the shape of conductor can be obtained by minimizing the cost function. In order to solve this inverse scattering problem, transverse electric (TE) waves are incident upon the objects and two techniques are employed to solve these problems. The first is based on an asynchronous particle swarm optimization (APSO) and the second is a dynamic differential evolution (DDE). Both techniques have been tested in the case of simulated mimic measurement data contaminated by additive white Gaussian noise. Numerical results indicate that the DDE algorithm and the APSO have almost the same reconstructed accuracy.

Chiu, Chien-Ching; Hsiao, Wei-Chun



Applying Dynamical Systems Theory to Optimize Libration Point Orbit Stationkeeping Maneuvers for WIND  

NASA Technical Reports Server (NTRS)

NASA's WIND mission has been operating in a large amplitude Lissajous orbit in the vicinity of the interior libration point of the Sun-Earth/Moon system since 2004. Regular stationkeeping maneuvers are required to maintain the orbit due to the instability around the collinear libration points. Historically these stationkeeping maneuvers have been performed by applying an incremental change in velocity, or (delta)v along the spacecraft-Sun vector as projected into the ecliptic plane. Previous studies have shown that the magnitude of libration point stationkeeping maneuvers can be minimized by applying the (delta)v in the direction of the local stable manifold found using dynamical systems theory. This paper presents the analysis of this new maneuver strategy which shows that the magnitude of stationkeeping maneuvers can be decreased by 5 to 25 percent, depending on the location in the orbit where the maneuver is performed. The implementation of the optimized maneuver method into operations is discussed and results are presented for the first two optimized stationkeeping maneuvers executed by WIND.

Brown, Jonathan M.; Petersen, Jeremy D.



Classification of holter registers by dynamic clustering using multi-dimensional particle swarm optimization.  


In this paper, we address dynamic clustering in high dimensional data or feature spaces as an optimization problem where multi-dimensional particle swarm optimization (MD PSO) is used to find out the true number of clusters, while fractional global best formation (FGBF) is applied to avoid local optima. Based on these techniques we then present a novel and personalized long-term ECG classification system, which addresses the problem of labeling the beats within a long-term ECG signal, known as Holter register, recorded from an individual patient. Due to the massive amount of ECG beats in a Holter register, visual inspection is quite difficult and cumbersome, if not impossible. Therefore the proposed system helps professionals to quickly and accurately diagnose any latent heart disease by examining only the representative beats (the so called master key-beats) each of which is representing a cluster of homogeneous (similar) beats. We tested the system on a benchmark database where the beats of each Holter register have been manually labeled by cardiologists. The selection of the right master key-beats is the key factor for achieving a highly accurate classification and the proposed systematic approach produced results that were consistent with the manual labels with 99.5% average accuracy, which basically shows the efficiency of the system. PMID:21096010

Kiranyaz, Serkan; Ince, Turker; Pulkkinen, Jenni; Gabbouj, Moncef



Demonstrating the Applicability of PAINT to Computationally Expensive Real-life Multiobjective Optimization  

E-print Network

We demonstrate the applicability of a new PAINT method to speed up iterations of interactive methods in multiobjective optimization. As our test case, we solve a computationally expensive non-linear, five-objective problem of designing and operating a wastewater treatment plant. The PAINT method interpolates between a given set of Pareto optimal outcomes and constructs a computationally inexpensive mixed integer linear surrogate problem for the original problem. We develop an IND-NIMBUS(R) PAINT module to combine the interactive NIMBUS method and the PAINT method and to find a preferred solution to the original problem. With the PAINT method, the solution process with the NIMBUS method take a comparatively short time even though the original problem is computationally expensive.

Hartikainen, Markus



Optimal GPS/accelerometer integration algorithm for monitoring the vertical structural dynamics  

NASA Astrophysics Data System (ADS)

The vertical structural dynamics is a crucial factor for structural health monitoring (SHM) of civil structures such as high-rise buildings, suspension bridges and towers. This paper presents an optimal GPS/accelerometer integration algorithm for an automated multi-sensor monitoring system. The closed loop feedback algorithm for integrating the vertical GPS and accelerometer measurements is proposed based on a 5 state extended KALMAN filter (EKF) and then the narrow moving window Fast Fourier Transform (FFT) analysis is applied to extract structural dynamics. A civil structural vibration is simulated and the analysed result shows the proposed algorithm can effectively integrate the online vertical measurements produced by GPS and accelerometer. Furthermore, the accelerometer bias and scale factor can also be estimated which is impossible with traditional integration algorithms. Further analysis shows the vibration frequencies detected in GPS or accelerometer are all included in the integrated vertical defection time series and the accelerometer can effectively compensate the short-term GPS outages with high quality. Finally, the data set collected with a time synchronised and integrated GPS/accelerometer monitoring system installed on the Nottingham Wilford Bridge when excited by 15 people jumping together at its mid-span are utilised to verify the effectiveness of this proposed algorithm. Its implementations are satisfactory and the detected vibration frequencies are 1.720 Hz, 1.870 Hz, 2.104 Hz, 2.905 Hz and also 10.050 Hz, which is not found in GPS or accelerometer only measurements.

Meng, Xiaolin; Wang, Jian; Han, Houzeng



A parallel dynamic programming algorithm for multi-reservoir system optimization  

NASA Astrophysics Data System (ADS)

This paper develops a parallel dynamic programming algorithm to optimize the joint operation of a multi-reservoir system. First, a multi-dimensional dynamic programming (DP) model is formulated for a multi-reservoir system. Second, the DP algorithm is parallelized using a peer-to-peer parallel paradigm. The parallelization is based on the distributed memory architecture and the message passing interface (MPI) protocol. We consider both the distributed computing and distributed computer memory in the parallelization. The parallel paradigm aims at reducing the computation time as well as alleviating the computer memory requirement associated with running a multi-dimensional DP model. Next, we test the parallel DP algorithm on the classic, benchmark four-reservoir problem on a high-performance computing (HPC) system with up to 350 cores. Results indicate that the parallel DP algorithm exhibits good performance in parallel efficiency; the parallel DP algorithm is scalable and will not be restricted by the number of cores. Finally, the parallel DP algorithm is applied to a real-world, five-reservoir system in China. The results demonstrate the parallel efficiency and practical utility of the proposed methodology.

Li, Xiang; Wei, Jiahua; Li, Tiejian; Wang, Guangqian; Yeh, William W.-G.



On the dynamics and optimization of a non-smooth bistable oscillator - Application to energy harvesting  

NASA Astrophysics Data System (ADS)

Bistable nonlinear oscillators can transform slow sinusoidal excitations into higher frequency periodic or quasi-periodic oscillations. This behaviour can be exploited to efficiently convert mechanical oscillations into electrical power, but being nonlinear, their dynamical behaviour is relatively complicated. In order to better understand the dynamics of bistable oscillators, an approximate bilinear analytical model, which is valid for narrow potential barriers, is developed. This model is expanded to the case of wider potential with experimental verification. Indeed, the model is verified by numerical simulations and a suitable Poincaré section that the analytical model captures most of bifurcations for large amplitude vibrations and can be used to optimize the harvested power of such devices. The method of Shaw and Holmes [1] is enhanced by exploiting symmetry to obtain closed form expressions of the Poincaré section and mapping. The approximate non-smooth model proves useful in the study of orbital stability, large amplitude oscillations and in explaining most of the period doubling and symmetry breaking bifurcations arising when such an oscillator is subjected to sinusoidal excitation. The proposed model is successfully verified through analytical numerical analysis and some experimental results.

Cohen, Nadav; Bucher, Izhak



Study of the Bus Dynamic Coscheduling Optimization Method under Urban Rail Transit Line Emergency  

PubMed Central

As one of the most important urban commuter transportation modes, urban rail transit (URT) has been acting as a key solution for supporting mobility needs in high-density urban areas. However, in recent years, high frequency of unexpected events has caused serious service disruptions in URT system, greatly harming passenger safety and resulting in severe traffic delays. Therefore, there is an urgent need to study emergency evacuation problem in URT. In this paper, a method of bus dynamic coscheduling is proposed and two models are built based on different evacuation destinations including URT stations and surrounding bus parking spots. A dynamic coscheduling scheme for buses can be obtained by the models. In the model solution process, a new concept—the equivalent parking spot—is proposed to transform the nonlinear model into an integer linear programming (ILP) problem. A case study is conducted to verify the feasibility of models. Also, sensitivity analysis of two vital factors is carried out to analyze their effects on the total evacuation time. The results reveal that the designed capacity of buses has a negative influence on the total evacuation time, while an increase in the number of passengers has a positive effect. Finally, some significant optimizing strategies are proposed.

Yan, Xuedong; Wang, Jiaxi; Chen, Shasha



Minimum charging-cost tracking based optimization algorithm with dynamic programming technique for plug-in hybrid electric vehicles  

Microsoft Academic Search

By 2015, one million PHEVs are estimated to posses U.S. automotive market. Optimized management of PHEVs charging activities is necessary since growing penetration of PHEV fleet would place significant influences on grid, either by providing bulky energy storages or by requiring charging capacities. In this paper, dynamic programming (DP) technique is applied to seek minimum cost of PHEVs charging activities.

Zhihao Li; Alireza Khaligh; Navid Sabbaghi



Multibody simulation of machine tools as mechatronic systems for optimization of motion dynamics in the design process  

Microsoft Academic Search

Increasing demands on the productivity of machine tools and their growing technological complexity call for improved methods in future product development processes. The paper explains the application of integrated CAx tools for setting up a virtual prototype that will permit evaluation and optimization of the entire machine tool's motion dynamics in early phases of the development process. Based on the

Gunther Reinhart; Martin Weissenberger



Optimization of source-sink dynamics in plant growth for ideotype breeding: a case1 study on maize2  

E-print Network

of source-sink dynamics in plant growth for ideotype breeding: a case1 study on maize2 3 Rui QI ab* , Yuntao strategies3 with optimization guidance. As a test case, maize (Zea mays L., DEA cultivar), which4 is one-yield maize, especially in the current21 agricultural context and the increasing importance of co

Boyer, Edmond


The exchange rate in a dynamic-optimizing business cycle model with nominal rigidities: a quantitative investigation  

Microsoft Academic Search

This paper studies a quantitative dynamic-optimizing business cycle model of a small open economy with staggered price and wage setting. The model exhibits exchange rate overshooting in response to money supply shocks. The predicted variability of the nominal and, especially, of the real exchange rate is noticeably higher than in standard Real Business Cycle models with flexible prices and wages.

Robert Kollmann



A dynamic multiarmed bandit-gene expression programming hyper-heuristic for combinatorial optimization problems.  


Hyper-heuristics are search methodologies that aim to provide high-quality solutions across a wide variety of problem domains, rather than developing tailor-made methodologies for each problem instance/domain. A traditional hyper-heuristic framework has two levels, namely, the high level strategy (heuristic selection mechanism and the acceptance criterion) and low level heuristics (a set of problem specific heuristics). Due to the different landscape structures of different problem instances, the high level strategy plays an important role in the design of a hyper-heuristic framework. In this paper, we propose a new high level strategy for a hyper-heuristic framework. The proposed high-level strategy utilizes a dynamic multiarmed bandit-extreme value-based reward as an online heuristic selection mechanism to select the appropriate heuristic to be applied at each iteration. In addition, we propose a gene expression programming framework to automatically generate the acceptance criterion for each problem instance, instead of using human-designed criteria. Two well-known, and very different, combinatorial optimization problems, one static (exam timetabling) and one dynamic (dynamic vehicle routing) are used to demonstrate the generality of the proposed framework. Compared with state-of-the-art hyper-heuristics and other bespoke methods, empirical results demonstrate that the proposed framework is able to generalize well across both domains. We obtain competitive, if not better results, when compared to the best known results obtained from other methods that have been presented in the scientific literature. We also compare our approach against the recently released hyper-heuristic competition test suite. We again demonstrate the generality of our approach when we compare against other methods that have utilized the same six benchmark datasets from this test suite. PMID:24951713

Sabar, Nasser R; Ayob, Masri; Kendall, Graham; Qu, Rong



Optimization of a multiple reservoir system operation using a combination of genetic algorithm and discrete differential dynamic programming: a case study in Mae Klong system, Thailand  

Microsoft Academic Search

A combination of genetic algorithm and discrete differential dynamic programming approach (called GA-DDDP) is proposed and developed to optimize the operation of the multiple reservoir system. The demonstration is carried out through application to the Mae Klong system in Thailand. The objective of optimization is to obtain the optimal operating policies by minimizing the total irrigation deficits during a critical

Janejira Tospornsampan; Ichiro Kita; Masayuki Ishii; Yoshinobu Kitamura



Decomposition and coordination of large-scale operations optimization  

NASA Astrophysics Data System (ADS)

Nowadays, highly integrated manufacturing has resulted in more and more large-scale industrial operations. As one of the most effective strategies to ensure high-level operations in modern industry, large-scale engineering optimization has garnered a great amount of interest from academic scholars and industrial practitioners. Large-scale optimization problems frequently occur in industrial applications, and many of them naturally present special structure or can be transformed to taking special structure. Some decomposition and coordination methods have the potential to solve these problems at a reasonable speed. This thesis focuses on three classes of large-scale optimization problems: linear programming, quadratic programming, and mixed-integer programming problems. The main contributions include the design of structural complexity analysis for investigating scaling behavior and computational efficiency of decomposition strategies, novel coordination techniques and algorithms to improve the convergence behavior of decomposition and coordination methods, as well as the development of a decentralized optimization framework which embeds the decomposition strategies in a distributed computing environment. The complexity study can provide fundamental guidelines to practical applications of the decomposition and coordination methods. In this thesis, several case studies imply the viability of the proposed decentralized optimization techniques for real industrial applications. A pulp mill benchmark problem is used to investigate the applicability of the LP/QP decentralized optimization strategies, while a truck allocation problem in the decision support of mining operations is used to study the MILP decentralized optimization strategies.

Cheng, Ruoyu


DAKOTA : a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis. Version 5.0, user's reference manual.  

SciTech Connect

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a reference manual for the commands specification for the DAKOTA software, providing input overviews, option descriptions, and example specifications.

Eldred, Michael Scott; Dalbey, Keith R.; Bohnhoff, William J.; Adams, Brian M.; Swiler, Laura Painton; Hough, Patricia Diane (Sandia National Laboratories, Livermore, CA); Gay, David M.; Eddy, John P.; Haskell, Karen H.



DAKOTA, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis:version 4.0 reference manual  

SciTech Connect

The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a reference manual for the commands specification for the DAKOTA software, providing input overviews, option descriptions, and example specifications.

Griffin, Joshua D. (Sandai National Labs, Livermore, CA); Eldred, Michael Scott; Martinez-Canales, Monica L. (Sandai National Labs, Livermore, CA); Watson, Jean-Paul; Kolda, Tamara Gibson (Sandai National Labs, Livermore, CA); Adams, Brian M.; Swiler, Laura Painton; Williams, Pamela J. (Sandai National Labs, Livermore, CA); Hough, Patricia Diane (Sandai National Labs, Livermore, CA); Gay, David M.; Dunlavy, Daniel M.; Eddy, John P.; Hart, William Eugene; Guinta, Anthony A.; Brown, Shannon L.



Dynamic whole-body PET parametric imaging: I. Concept, acquisition protocol optimization and clinical application  

NASA Astrophysics Data System (ADS)

Static whole-body PET/CT, employing the standardized uptake value (SUV), is considered the standard clinical approach to diagnosis and treatment response monitoring for a wide range of oncologic malignancies. Alternative PET protocols involving dynamic acquisition of temporal images have been implemented in the research setting, allowing quantification of tracer dynamics, an important capability for tumor characterization and treatment response monitoring. Nonetheless, dynamic protocols have been confined to single-bed-coverage limiting the axial field-of-view to ˜15-20 cm, and have not been translated to the routine clinical context of whole-body PET imaging for the inspection of disseminated disease. Here, we pursue a transition to dynamic whole-body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. We investigate solutions to address the challenges of: (i) long acquisitions, (ii) small number of dynamic frames per bed, and (iii) non-invasive quantification of kinetics in the plasma. In the present study, a novel dynamic (4D) whole-body PET acquisition protocol of ˜45 min total length is presented, composed of (i) an initial 6 min dynamic PET scan (24 frames) over the heart, followed by (ii) a sequence of multi-pass multi-bed PET scans (six passes × seven bed positions, each scanned for 45 s). Standard Patlak linear graphical analysis modeling was employed, coupled with image-derived plasma input function measurements. Ordinary least squares Patlak estimation was used as the baseline regression method to quantify the physiological parameters of tracer uptake rate Ki and total blood distribution volume V on an individual voxel basis. Extensive Monte Carlo simulation studies, using a wide set of published kinetic FDG parameters and GATE and XCAT platforms, were conducted to optimize the acquisition protocol from a range of ten different clinically acceptable sampling schedules examined. The framework was also applied to six FDG PET patient studies, demonstrating clinical feasibility. Both simulated and clinical results indicated enhanced contrast-to-noise ratios (CNRs) for Ki images in tumor regions with notable background FDG concentration, such as the liver, where SUV performed relatively poorly. Overall, the proposed framework enables enhanced quantification of physiological parameters across the whole body. In addition, the total acquisition length can be reduced from 45 to ˜35 min and still achieve improved or equivalent CNR compared to SUV, provided the true Ki contrast is sufficiently high. In the follow-up companion paper, a set of advanced linear regression schemes is presented to particularly address the presence of noise, and attempt to achieve a better trade-off between the mean-squared error and the CNR metrics, resulting in enhanced task-based imaging.

Karakatsanis, Nicolas A.; Lodge, Martin A.; Tahari, Abdel K.; Zhou, Y.; Wahl, Richard L.; Rahmim, Arman



Dynamic whole body PET parametric imaging: I. Concept, acquisition protocol optimization and clinical application  

PubMed Central

Static whole body PET/CT, employing the standardized uptake value (SUV), is considered the standard clinical approach to diagnosis and treatment response monitoring for a wide range of oncologic malignancies. Alternative PET protocols involving dynamic acquisition of temporal images have been implemented in the research setting, allowing quantification of tracer dynamics, an important capability for tumor characterization and treatment response monitoring. Nonetheless, dynamic protocols have been confined to single bed-coverage limiting the axial field-of-view to ~15–20 cm, and have not been translated to the routine clinical context of whole-body PET imaging for the inspection of disseminated disease. Here, we pursue a transition to dynamic whole body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. We investigate solutions to address the challenges of: (i) long acquisitions, (ii) small number of dynamic frames per bed, and (iii) non-invasive quantification of kinetics in the plasma. In the present study, a novel dynamic (4D) whole body PET acquisition protocol of ~45min total length is presented, composed of (i) an initial 6-min dynamic PET scan (24 frames) over the heart, followed by (ii) a sequence of multi-pass multi-bed PET scans (6 passes x 7 bed positions, each scanned for 45sec). Standard Patlak linear graphical analysis modeling was employed, coupled with image-derived plasma input function measurements. Ordinary least squares (OLS) Patlak estimation was used as the baseline regression method to quantify the physiological parameters of tracer uptake rate Ki and total blood distribution volume V on an individual voxel basis. Extensive Monte Carlo simulation studies, using a wide set of published kinetic FDG parameters and GATE and XCAT platforms, were conducted to optimize the acquisition protocol from a range of 10 different clinically acceptable sampling schedules examined. The framework was also applied to six FDG PET patient studies, demonstrating clinical feasibility. Both simulated and clinical results indicated enhanced contrast-to-noise ratios (CNRs) for Ki images in tumor regions with notable background FDG concentration, such as the liver, where SUV performed relatively poorly. Overall, the proposed framework enables enhanced quantification of physiological parameters across the whole-body. In addition, the total acquisition length can be reduced from 45min to ~35min and still achieve improved or equivalent CNR compared to SUV, provided the true Ki contrast is sufficiently high. In the follow-up companion paper, a set of advanced linear regression schemes is presented to particularly address the presence of noise, and attempt to achieve a better trade-off between the mean-squared error (MSE) and the CNR metrics, resulting in enhanced task-based imaging. PMID:24080962

Karakatsanis, Nicolas A.; Lodge, Martin A.; Tahari, Abdel K.; Zhou, Y.; Wahl, Richard L.; Rahmim, Arman



Dynamic whole-body PET parametric imaging: I. Concept, acquisition protocol optimization and clinical application.  


Static whole-body PET/CT, employing the standardized uptake value (SUV), is considered the standard clinical approach to diagnosis and treatment response monitoring for a wide range of oncologic malignancies. Alternative PET protocols involving dynamic acquisition of temporal images have been implemented in the research setting, allowing quantification of tracer dynamics, an important capability for tumor characterization and treatment response monitoring. Nonetheless, dynamic protocols have been confined to single-bed-coverage limiting the axial field-of-view to ~15-20 cm, and have not been translated to the routine clinical context of whole-body PET imaging for the inspection of disseminated disease. Here, we pursue a transition to dynamic whole-body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. We investigate solutions to address the challenges of: (i) long acquisitions, (ii) small number of dynamic frames per bed, and (iii) non-invasive quantification of kinetics in the plasma. In the present study, a novel dynamic (4D) whole-body PET acquisition protocol of ~45 min total length is presented, composed of (i) an initial 6 min dynamic PET scan (24 frames) over the heart, followed by (ii) a sequence of multi-pass multi-bed PET scans (six passes × seven bed positions, each scanned for 45 s). Standard Patlak linear graphical analysis modeling was employed, coupled with image-derived plasma input function measurements. Ordinary least squares Patlak estimation was used as the baseline regression method to quantify the physiological parameters of tracer uptake rate Ki and total blood distribution volume V on an individual voxel basis. Extensive Monte Carlo simulation studies, using a wide set of published kinetic FDG parameters and GATE and XCAT platforms, were conducted to optimize the acquisition protocol from a range of ten different clinically acceptable sampling schedules examined. The framework was also applied to six FDG PET patient studies, demonstrating clinical feasibility. Both simulated and clinical results indicated enhanced contrast-to-noise ratios (CNRs) for Ki images in tumor regions with notable background FDG concentration, such as the liver, where SUV performed relatively poorly. Overall, the proposed framework enables enhanced quantification of physiological parameters across the whole body. In addition, the total acquisition length can be reduced from 45 to ~35 min and still achieve improved or equivalent CNR compared to SUV, provided the true Ki contrast is sufficiently high. In the follow-up companion paper, a set of advanced linear regression schemes is presented to particularly address the presence of noise, and attempt to achieve a better trade-off between the mean-squared error and the CNR metrics, resulting in enhanced task-based imaging. PMID:24080962

Karakatsanis, Nicolas A; Lodge, Martin A; Tahari, Abdel K; Zhou, Y; Wahl, Richard L; Rahmim, Arman



Dynamic state-dependent modelling predicts optimal usage patterns of responsive defences.  


Chemical defences against predation often involve responses to specific predation events where the prey expels fluids, such as haemolymph or gut contents, which are aversive to the predator. The common link is that each predation attempt that is averted results in an energetic cost and a reduction in the chemical defences of the prey, which might leave the prey vulnerable if the next predation attempt occurs soon afterwards. Since prey appear to be able to control the magnitude of their responses, we should expect them to trade-off the need to repel the current threat against the need to preserve defences against future threats and conserve energy for other essential activities. Here we use dynamic state-dependent models to predict optimal strategies of defence deployment in the juvenile stage of an animal that has to survive to maturation. We explore the importance of resource level, predator density, and the costs of making defences on the magnitude of the responses and optimal age and size at maturation. We predict the patterns of investment and the magnitude of the deployment of defences to potentially multiple attacks over the juvenile period, and show that responses should be smaller when the costs of defences and/or predation risk are higher. The model enables us to predict that animals in which defences benefit the adult stage will employ different strategies than those that do not use the same defences as adults, and thereby experience a smaller reduction in body size as a result of repeated attacks. We also explore the effect of the importance of adult size, and find that the sex and mating system of the prey should also affect defensive strategies. Our work provides the first predictive theory of the adaptive use of responsive defences across taxa. PMID:19252933

Higginson, A D; Ruxton, G D



Combined mid- and short-term optimization of multireservoir systems via dynamic programming with function approximators  

NASA Astrophysics Data System (ADS)

A main challenge for the planning and management of water resources is the development of strategies for regulation of multireservoir systems under a complex stochastic environment. The sequential decision problem involving the release of water from multiple reservoirs depends on the stochastic variability of the hydrologic inflows over a spectrum of time scales. An important distinction is made between short-term and mid-term planning: the first is associated with regulation on the hourly scale within the one-week time horizon, whilst the second is associated with the weekly scale within the one-year horizon. Although a variety of optimization methods have been suggested, the achievement of a global optimum in the operation of large-scale systems is hindered by their high dimensional state space and by the stochastic nature of the hydrologic inflows. In this work, operational plans for multireservoir systems are derived via an approximate dynamic programming approach using a policy iteration algorithm. The algorithm is based on an off-line learning process in which policies are evaluated for a number of stochastic inflow scenarios by constructing approximations of their value functions, and the resulting value functions are used iteratively to design new, improved policies. In the mid-term planning phase, inflow scenarios are generated with a periodic autoregressive model that is calibrated against historical inflow data, and the policy iteration algorithm leads to a cyclostationary operating policy. In the short-term planning phase, the mid-term value function is used to calculate the value of a policy at the end of the short-term operating horizon, and synthetic inflow scenarios are generated by perturbing streamflow forecasts with Gaussian noise, following Zhao et al. (Water Resour. Res., 48, W01540, 2012). The variance of the noise is assumed to increase linearly over time and converges to the local variance of the historical time series. A case study is presented of a multi-reservoir system in the river Dalälven, Sweden, where the impact of forecast uncertainty and the performance of the proposed stochastic optimization model is evaluated using observed time series and synthetic inflow forecasts. The resulting electricity production is compared with the optimal production in case of perfect a priori information and the expected production from the application of a myopic operating policy.

Bottacin-Busolin, Andrea; Wörman, Anders; Zmijewski, Nicholas



Fuzzy multiobjective models for optimal operation of a hydropower system  

NASA Astrophysics Data System (ADS)

Optimal operation models for a hydropower system using new fuzzy multiobjective mathematical programming models are developed and evaluated in this study. The models use (i) mixed integer nonlinear programming (MINLP) with binary variables and (ii) integrate a new turbine unit commitment formulation along with water quality constraints used for evaluation of reservoir downstream impairment. Reardon method used in solution of genetic algorithm optimization problems forms the basis for development of a new fuzzy multiobjective hydropower system optimization model with creation of Reardon type fuzzy membership functions. The models are applied to a real-life hydropower reservoir system in Brazil. Genetic Algorithms (GAs) are used to (i) solve the optimization formulations to avoid computational intractability and combinatorial problems associated with binary variables in unit commitment, (ii) efficiently address Reardon method formulations, and (iii) deal with local optimal solutions obtained from the use of traditional gradient-based solvers. Decision maker's preferences are incorporated within fuzzy mathematical programming formulations to obtain compromise operating rules for a multiobjective reservoir operation problem dominated by conflicting goals of energy production, water quality and conservation releases. Results provide insight into compromise operation rules obtained using the new Reardon fuzzy multiobjective optimization framework and confirm its applicability to a variety of multiobjective water resources problems.

Teegavarapu, Ramesh S. V.; Ferreira, André R.; Simonovic, Slobodan P.



TAS: 89 0227: TAS Recovery Act - Optimization and Control of Electric Power Systems: ARRA  

SciTech Connect

The name SuperOPF is used to refer several projects, problem formulations and soft-ware tools intended to extend, improve and re-define some of the standard methods of optimizing electric power systems. Our work included applying primal-dual interior point methods to standard AC optimal power flow problems of large size, as well as extensions of this problem to include co-optimization of multiple scenarios. The original SuperOPF problem formulation was based on co-optimizing a base scenario along with multiple post-contingency scenarios, where all AC power flow models and constraints are enforced for each, to find optimal energy contracts, endogenously determined locational reserves and appropriate nodal energy prices for a single period optimal power flow problem with uncertainty. This led to example non-linear programming problems on the order of 1 million constraints and half a million variables. The second generation SuperOPF formulation extends this by adding multiple periods and multiple base scenarios per period. It also incorporates additional variables and constraints to model load following reserves, ramping costs, and storage resources. A third generation of the multi-period SuperOPF, adds both integer variables and a receding horizon framework in which the problem type is more challenging (mixed integer), the size is even larger, and it must be solved more frequently, pushing the limits of currently available algorithms and solvers. The consideration of transient stability constraints in optimal power flow (OPF) problems has become increasingly important in modern power systems. Transient stability constrained OPF (TSCOPF) is a nonlinear optimization problem subject to a set of algebraic and differential equations. Solving a TSCOPF problem can be challenging due to (i) the differential-equation constraints in an optimization problem, (ii) the lack of a true analytical expression for transient stability in OPF. To handle the dynamics in TSCOPF, the set of differential equations can be approximated or converted into equivalent algebraic equations before they are included in an OPF formulation. In Chapter 4, a rigorous evaluation of using a predefined and fixed threshold for rotor angles as a mean to determine transient stability of the system is developed. TSCOPF can be modeled as a large-scale nonlinear programming problem including the constraints of differential-algebraic equations (DAE). Solving a TSCOPF problem can be challenging due to (i) the differential-equation constraints in an optimization problem, (ii) the lack of a true analytical expression for transient stability constraint in OPF. Unfortunately, even the current best TSCOPF solvers still suffer from the curse of dimensionality and unacceptable computational time, especially for large-scale power systems with multiple contingencies. In chapter 5, thse issues will be addressed and a new method to incorporate the transient stability constraints will be presented.

Chiang, Hsiao-Dong



HybridArc: A novel radiation therapy technique combining optimized dynamic arcs and intensity modulation  

SciTech Connect

This investigation focuses on possible dosimetric and efficiency advantages of HybridArc-a novel treatment planning approach combining optimized dynamic arcs with intensity-modulated radiation therapy (IMRT) beams. Application of this technique to two disparate sites, complex cranial tumors, and prostate was examined. HybridArc plans were compared with either dynamic conformal arc (DCA) or IMRT plans to determine whether HybridArc offers a synergy through combination of these 2 techniques. Plans were compared with regard to target volume dose conformity, target volume dose homogeneity, sparing of proximal organs at risk, normal tissue sparing, and monitor unit (MU) efficiency. For cranial cases, HybridArc produced significantly improved dose conformity compared with both DCA and IMRT but did not improve sparing of the brainstem or optic chiasm. For prostate cases, conformity was improved compared with DCA but not IMRT. Compared with IMRT, the dose homogeneity in the planning target volume was improved, and the maximum doses received by the bladder and rectum were reduced. Both arc-based techniques distribute peripheral dose over larger volumes of normal tissue compared with IMRT, whereas HybridArc involved slightly greater volumes of normal tissues compared with DCA. Compared with IMRT, cranial cases required 38% more MUs, whereas for prostate cases, MUs were reduced by 7%. For cranial cases, HybridArc improves dose conformity to the target. For prostate cases, dose conformity and homogeneity are improved compared with DCA and IMRT, respectively. Compared with IMRT, whether required MUs increase or decrease with HybridArc was site-dependent.

Robar, James L., E-mail: [Department of Radiation Oncology, Dalhousie University, Halifax (Canada); Department of Physics and Atmospheric Science, Dalhousie University, Halifax (Canada); Thomas, Christopher [Department of Radiation Oncology, Dalhousie University, Halifax (Canada)



Paramfit: Automated optimization of force field parameters for molecular dynamics simulations.  


The generation of bond, angle, and torsion parameters for classical molecular dynamics force fields typically requires fitting parameters such that classical properties such as energies and gradients match precalculated quantum data for structures that scan the value of interest. We present a program, Paramfit, distributed as part of the AmberTools software package that automates and extends this fitting process, allowing for simplified parameter generation for applications ranging from single molecules to entire force fields. Paramfit implements a novel combination of a genetic and simplex algorithm to find the optimal set of parameters that replicate either quantum energy or force data. The program allows for the derivation of multiple parameters simultaneously using significantly fewer quantum calculations than previous methods, and can also fit parameters across multiple molecules with applications to force field development. Paramfit has been applied successfully to systems with a sparse number of structures, and has already proven crucial in the development of the Assisted Model Building with Energy Refinement Lipid14 force field. © 2014 Wiley Periodicals, Inc. PMID:25413259

Betz, Robin M; Walker, Ross C



Simulation and optimization of airlift external circulation membrane bioreactor using computational fluid dynamics.  


The airlift external circulation membrane bioreactor (AEC-MBR) is a new MBR consisting of a separated aeration tank and membrane tank with circulating pipes fixed between the two tanks. The circulating pipe is called a H circulating pipe (HCP) because of its shape. With the complex configuration, it was difficult but necessary to master the AEC-MBR's hydraulic characteristics. In this paper, simulation and optimization of the AEC-MBR was performed using computational fluid dynamics. The distance from diffusers to membrane modules, i.e. the height of gas-liquid mixing zone (h(m)), and its effect on velocity distribution at membrane surfaces were studied. Additionally, the role of HCP and the effect of HCP's diameter on circulation were simulated and analyzed. The results showed that non-uniformity of cross-flow velocity existed in the flat-plate membrane modules, and the problem could be alleviated by increasing hm to an optimum range (h(m)/B ? 0.55; B is total static depth). Also, the low velocity in the boundary layer on the membrane surface was another reason for membrane fouling. The results also suggested that HCP was necessary and it had an optimum diameter to make circulation effective in the AEC-MBR. PMID:24804658

Qing, Zhang; Rongle, Xu; Xiang, Zheng; Yaobo, Fan



Polypeptide dynamics: Experimental tests of an optimized Rouse-Zimm type model  

NASA Astrophysics Data System (ADS)

A theory for long time random coil peptide dynamics is developed based on a generalization of the optimized Rouse-Zimm model of Perico et al. [J. Chem. Phys. 87, 3677 (1987)] and Perico [J. Chem. Phys. 88, 3996 (1988) and Biopolymers 28, 1527 (1989)]. The generalized model employs the rotational potential energy for specific amino acid residues and amino acid friction coefficients to compute all input parameters in the model. Calculations of the fluorescence depolarization correlation function P2(t ) and of the local persistence length are found to be sensitive to the amino acid sequence, the length of the polypeptide chain, and the location of the probe. Model computations of P2(t ) are compared with new experimentally determined rotational correlation times (of the order of nanoseconds) from fluorescence depolarization measurements of three different synthetic 17-residue peptides, each containing a single tryptophan (TRP) residue as a probe. In addition, the previous anisotropy measurements on ACTH, glucagon, and their fragments are discussed and compared with the model calculations. Our results indicate that the theory gives a reasonable prediction for the fluorescence depolarization correlation times of random coil polypeptides, but the calculated rotational correlation function predicts a much faster initial decay and a slower final decay than is observed. Possible theoretical improvements are discussed.

Hu, Yi; MacInnis, Jean M.; Cherayil, Binny J.; Fleming, Graham R.; Freed, Karl F.; Perico, Angelo



MonALISA: An agent based, dynamic service system to monitor, control and optimize distributed systems  

NASA Astrophysics Data System (ADS)

The MonALISA (Monitoring Agents in a Large Integrated Services Architecture) framework provides a set of distributed services for monitoring, control, management and global optimization for large scale distributed systems. It is based on an ensemble of autonomous, multi-threaded, agent-based subsystems which are registered as dynamic services. They can be automatically discovered and used by other services or clients. The distributed agents can collaborate and cooperate in performing a wide range of management, control and global optimization tasks using real time monitoring information. Program summaryProgram title: MonALISA Catalogue identifier: AEEZ_v1_0 Program summary URL: Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Caltech License - free for all non-commercial activities No. of lines in distributed program, including test data, etc.: 147 802 No. of bytes in distributed program, including test data, etc.: 2 5913 689 Distribution format: tar.gz Programming language: Java, additional APIs available in Java, C, C++, Perl and python Computer: Computing Clusters, Network Devices, Storage Systems, Large scale data intensive applications Operating system: The MonALISA service is mainly used in Linux, the MonALISA client runs on all major platforms (Windows, Linux, Solaris, MacOS). Has the code been vectorized or parallelized?: It is a multithreaded application. It will efficiently use all the available processors. RAM: for the MonALISA service the minimum required memory is 64 MB; if the JVM is started allocating more memory this will be used for internal caching. The MonALISA client requires typically 256-512 MB of memory. Classification: 6.5 External routines: Requires Java: JRE or JDK to run. These external packages are used (they are included in the distribution): JINI, JFreeChart, PostgreSQL (optional). Nature of problem: To monitor and control distributed computing clusters and grids, the network infrastructure, the storage systems, and the applications used on such facilities. The monitoring information gathered is used for developing the required higher level services, the components that provide decision support and some degree of automated decisions and for maintaining and optimizing workflow in large scale distributed systems. Solution method: The MonALISA framework is designed as an ensemble of autonomous self-describing agent-based subsystems which are registered as dynamic services. These services are able to collaborate and cooperate in performing a wide range of distributed information-gathering and processing tasks. Running time: MonALISA services are designed to run continuously to collect monitoring data and to trigger alarms or to take automatic actions in case it is necessary. References:

Legrand, I.; Newman, H.; Voicu, R.; Cirstoiu, C.; Grigoras, C.; Dobre, C.; Muraru, A.; Costan, A.; Dediu, M.; Stratan, C.



Towards AGIPD1.0: optimization of the dynamic range and investigation of a pixel input protection  

NASA Astrophysics Data System (ADS)

AGIPD is a charge integrating, hybrid pixel readout ASIC, which is under development for the European XFEL [1,2]. A dynamic gain switching logic at the output of the preamplifier (preamp) is used to provide single photon resolution as well as covering a dynamic range of at least 104·12.4 keV photons [3,4]. Moreover, at each point of the dynamic range the electronics noise should be lower than the Poisson fluctuations, which is especially challenging at the points of gain switching. This paper reports on the progress of the chip design on the way to the first full-scale chip AGIPD1.0, focusing on the optimization of the dynamic range and the implementation of protection circuits at the preamplifier input to avoid pixel destruction due to high intense spots.

Greiffenberg, D.; Becker, J.; Bianco, L.; Dinapoli, R.; Goettlicher, P.; Graafsma, H.; Hirsemann, H.; Jack, S.; Klanner, R.; Klyuev, A.; Krüger, H.; Lange, S.; Marras, A.; Mozzanica, A.; Rah, S.; Schmitt, B.; Schwandt, J.; Sheviakov, I.; Shi, X.; Trunk, U.; Zhang, J.; Zimmer, M.; Mezza, D.; Allahgholi, A.; Xia, Q.



Materials optimization and ghz spin dynamics of metallic ferromagnetic thin film heterostructures  

NASA Astrophysics Data System (ADS)

Metallic ferromagnetic (FM) thin film heterostructures play an important role in emerging magnetoelectronic devices, which introduce the spin degree of freedom of electrons into conventional charge-based electronic devices. As the majority of magnetoelectronic devices operate in the GHz frequency range, it is critical to understand the high-frequency magnetization dynamics in these structures. In this thesis, we start with the static magnetic properties of FM thin films and their optimization via the field-sputtering process incorporating a specially designed in-situ electromagnet. We focus on the origins of anisotropy and hysteresis/coercivity in soft magnetic thin films, which are most relevant to magentic susceptibility and power dissipation in applications in the sub-GHz frequency regime, such as magnetic-core integrated inductors. Next we explore GHz magnetization dynamics in thin-film heterostructures, both in semi-infinite samples and confined geometries. All investigations are rooted in the Landau-Lifshitz-Gilbert (LLG) equation, the equation of motion for magnetization. The phenomenological Gilbert damping parameter in the LLG equation has been interpreted, since the 1970's, in terms of the electrical resistivity. We present the first interpretation of the size effect in Gilbert damping in single metallic FM films based on this electron theory of damping. The LLG equation is intrinsically nonlinear, which provides possibilities for rf signal processing. We analyze the frequency doubling effect at small-angle magnetization precession from the first-order expansion of the LLG equation, and demonstrate second harmonic generation from Ni81 Fe19 (Permalloy) thin film under ferromagnetic resonance (FMR), three orders of magnitude more efficient than in ferrites traditionally used in rf devices. Though the efficiency is less than in semiconductor devices, we provide field- and frequency-selectivity in the second harmonic generation. To address further the relationship between the rf excitation and the magnetization dynamics in systems with higher complexity, such as multilayered thin films consisting of nonmagnetic (NM) and FM layers, we employ the powerful time-resolved x-ray magnetic circular dichroism (TR-XMCD) spectroscopy. Soft x-rays have element-specific absorption, leading to layer-specific magnetization detection provided the FM layers have distinctive compositions. We discovered that in contrast to what has been routinely assumed, for layer thicknesses well below the skin depth of the EM wave, a significant phase difference exists between the rf magnetic fields H rf in different FM layers separated by a Cu spacer layer. We propose an analysis based on the distribution of the EM waves in the film stack and substrate to interpret this striking observation. For confined geometries with lateral dimensions in the sub-micron regime, there has been a critical absence of experimental techniques which can image small-amplitude dynamics of these structures. We extend the TR-XMCD technique to scanning transmission x-ray microscopy (STXM), to observe directly the local magnetization dynamics in nanoscale FM thin-film elements, demonstrated at picosecond temporal, 40 nm spatial and < 6° angular resolution. The experimental data are compared with our micromagnetic simulations based on the finite element analysis of the time-dependent LLG equation. We resolve standing spin wave modes in nanoscale Ni81 Fe19 thin film ellipses (1000 nm x 500 nm x 20 nm) with clear phase information to distinguish between degenerate eigenmodes with different symmetries for the first time. With the element-specific imaging capability of soft x-rays, spatial resolution up to 15 nm with improved optics, we see great potential for this technique to investigate functional devices with multiple FM layers, and provide insight into the studies of spin injection, manipulation and detection.

Cheng, Cheng


REopt: A Platform for Energy System Integration and Optimization: Preprint  

SciTech Connect

REopt is NREL's energy planning platform offering concurrent, multi-technology integration and optimization capabilities to help clients meet their cost savings and energy performance goals. The REopt platform provides techno-economic decision-support analysis throughout the energy planning process, from agency-level screening and macro planning to project development to energy asset operation. REopt employs an integrated approach to optimizing a site?s energy costs by considering electricity and thermal consumption, resource availability, complex tariff structures including time-of-use, demand and sell-back rates, incentives, net-metering, and interconnection limits. Formulated as a mixed integer linear program, REopt recommends an optimally-sized mix of conventional and renewable energy, and energy storage technologies; estimates the net present value associated with implementing those technologies; and provides the cost-optimal dispatch strategy for operating them at maximum economic efficiency. The REopt platform can be customized to address a variety of energy optimization scenarios including policy, microgrid, and operational energy applications. This paper presents the REopt techno-economic model along with two examples of recently completed analysis projects.

Simpkins, T.; Cutler, D.; Anderson, K.; Olis, D.; Elgqvist, E.; Callahan, M.; Walker, A.



Global resonance optimization analysis of nonlinear mechanical systems: Application to the uncertainty quantification problems in rotor dynamics  

NASA Astrophysics Data System (ADS)

An efficient method to obtain the worst quasi-periodic vibration response of nonlinear dynamical systems with uncertainties is presented. Based on the multi-dimensional harmonic balance method, a constrained, nonlinear optimization problem with the nonlinear equality constraints is derived. The MultiStart optimization algorithm is then used to optimize the vibration response within the specified range of physical parameters. In order to illustrate the efficiency and ability of the proposed method, several numerical examples are illustrated. The proposed method is then applied to a rotor system with multiple frequency excitations (unbalance and support) under several physical parameters uncertainties. Numerical examples show that the proposed approach is valid and effective for analyzing strongly nonlinear vibration problems with different types of nonlinearities in the presence of uncertainties.

Liao, Haitao



Optimal Design of CSD Coefficient FIR Filters Subject to Number of Nonzero Digits  

NASA Astrophysics Data System (ADS)

In a hardware implementation of FIR(Finite Impulse Response) digital filters, it is desired to reduce a total number of nonzero digits used for a representation of filter coefficients. In general, a design problem of FIR filters with CSD(Canonic Signed Digit) representation, which is efficient one for the reduction of numbers of multiplier units, is often considered as one of the 0-1 combinational problems. In such the problem, some difficult constraints make us prevent to linearize the problem. Although many kinds of heuristic approaches have been applied to solve the problem, the solution obtained by such a manner could not guarantee its optimality. In this paper, we attempt to formulate the design problem as the 0-1 mixed integer linear programming problem and solve it by using the branch and bound technique, which is a powerful method for solving integer programming problem. Several design examples are shown to present an efficient performance of the proposed method.

Ozaki, Yuichi; Suyama, Kenji


Optimal economy-based battery degradation management dynamics for fuel-cell plug-in hybrid electric vehicles  

NASA Astrophysics Data System (ADS)

This work analyses the economical dynamics of an optimized battery degradation management strategy intended for plug-in hybrid electric vehicles (PHEVs) with consideration given to low-cost technologies, such as lead-acid batteries. The optimal management algorithm described herein is based on discrete dynamic programming theory (DDP) and was designed for the purpose of PHEV battery degradation management; its operation relies on simulation models using data obtained experimentally on a physical PHEV platform. These tools are first used to define an optimal management strategy according to the economical weights of PHEV battery degradation and the secondary energy carriers spent to manage its deleterious effects. We then conduct a sensitivity study of the proposed optimization process to the fluctuating economic parameters associated with the fuel and energy costs involved in the degradation management process. Results demonstrate the influence of each parameter on the process's response, including daily total operating costs and expected battery lifetime, as well as establish boundaries for useful application of the method; in addition, they provide a case for the relevance of inexpensive battery technologies, such as lead-acid batteries, for economy-centric PHEV applications where battery degradation is a major concern.

Martel, François; Kelouwani, Sousso; Dubé, Yves; Agbossou, Kodjo



A code for the optimization of RF voltage waveform and longitudinal beam dynamics simulation in an RCS  

NASA Astrophysics Data System (ADS)

In a Rapid Cycling Synchrotron (RCS), the dipole and quadrupole fields oscillate sinusoidally with a high repetition rate. High RF voltage is required to match the rapid change of the dipole field, and the choices of the RF voltage and synchronous phase waveform are important issues in the design of an RCS. The longitudinal beam dynamics simulation plays a key role in optimizing the RF voltage waveform. A code has been developed for both RF voltage waveform optimization and longitudinal beam dynamics simulation in an RCS. The code can be applied to both fundamental and dual harmonic RF systems, with dipole field oscillating as a sine wave or slightly deviated sine wave. In the simulation, the space charge effect is included, and the influence of the higher order modes of RF cavity on the beam can also be simulated. The code was applied to the RCS of China Spallation Neutron Source (CSNS), for optimizing the RF voltage waveform of dual harmonic RF system and beam dynamics simulation study.

Yuan, Yao-Shuo; Wang, Na; Xu, Shou-Yan; Wang, Sheng



Non-linear dynamic characteristics and optimal control of giant magnetostrictive film subjected to in-plane stochastic excitation  

SciTech Connect

The non-linear dynamic characteristics and optimal control of a giant magnetostrictive film (GMF) subjected to in-plane stochastic excitation were studied. Non-linear differential items were introduced to interpret the hysteretic phenomena of the GMF, and the non-linear dynamic model of the GMF subjected to in-plane stochastic excitation was developed. The stochastic stability was analysed, and the probability density function was obtained. The condition of stochastic Hopf bifurcation and noise-induced chaotic response were determined, and the fractal boundary of the system's safe basin was provided. The reliability function was solved from the backward Kolmogorov equation, and an optimal control strategy was proposed in the stochastic dynamic programming method. Numerical simulation shows that the system stability varies with the parameters, and stochastic Hopf bifurcation and chaos appear in the process; the area of the safe basin decreases when the noise intensifies, and the boundary of the safe basin becomes fractal; the system reliability improved through stochastic optimal control. Finally, the theoretical and numerical results were proved by experiments. The results are helpful in the engineering applications of GMF.

Zhu, Z. W., E-mail: [Department of Mechanics, Tianjin University, 300072, Tianjin (China); Tianjin Key Laboratory of Non-linear Dynamics and Chaos Control, 300072, Tianjin (China); Zhang, W. D., E-mail:; Xu, J., E-mail: [Department of Mechanics, Tianjin University, 300072, Tianjin (China)



March 19, 2008 1 Abstract--In this paper, we continue to analyze optimal  

E-print Network

--Mixed integer programming, Power generation dispatch, Power system economics, Power transmission control, Power, both formally and informally, that system operators can, and do, change the topology of systems and transmission topology to meet load as a mixed integer program (MIP) with binary variables representing

Oren, Shmuel S.


Heuristic optimization methods for run-time intensive models (Dynamically Dimensioned Search, Particle Swarm Optimization, GA) - a comparison of performance and parallel implementation using R  

NASA Astrophysics Data System (ADS)

Calibrating complex hydrological models faces two major challenges: firstly, extended models, especially when spatially distributed, encompass a large number of parameters with different (and possibly a-priori unknown) sensitivity. Due to the usually rough surface of the objective function, this aggravates the risk of an algorithm to converge in a local optimum. Thus, gradient-based optimization methods are often bound to fail without a very good prior estimate. Secondly, despite growing computational power, it is not uncommon that models of large extent in space or time take several minutes to run, which severely restricts the total number of model evaluations under given computational and time resources. While various heuristic methods successfully address the first challenge, they tend to conflict with the second challenge due to the increased number of evaluations necessary. In that context we analyzed three methods (Dynamically Dimensioned Search / DDS, Particle Swarm Optimization / PSO, Genetic Algorithms /GA). We performed tests with common "synthetic" objective functions and a calibration of the hydrological model WASA-SED with different number of parameters. When looking at the reduction of the objective function within few (i.e.< 1000) evaluations, the methods generally perform in the order (best to worst) DDS-PSO-GA. Only at a larger number, GA can excel. To speed up optimization, we executed DDS and PSO as parallel applications, i.e. using multiple CPUs and/or computers. The parallelisation has been implemented in the ppso-package for the free computation environment R. Special focus has been laid onto the options to resume interrupted optimization runs and visualize progress.

Francke, Till; Bronster, Axel; Shoemaker, Christine A.




Microsoft Academic Search

The Generalized Generation Gap (G3) algorithm is one of the most efficient and effective state-of-the-art real- coded genetic algorithms (RCGAs) for unconstrained global optimization. However, its performance on multimodal optimization problems is known to be poor compared to unimodal optimization problems. The G3 algorithm currently relies on crossover operations only. The objective of this paper is to augment the G3

Jason Teo



Development and validation of a variance model for dynamic PET: uses in fitting kinetic data and optimizing the injected activity  

NASA Astrophysics Data System (ADS)

The precision of biological parameter estimates derived from dynamic PET data can be limited by the number of acquired coincidence events (prompts and randoms). These numbers are affected by the injected activity (A0). The benefits of optimizing A0 were assessed using a new model of data variance which is formulated as a function of A0. Seven cancer patients underwent dynamic [15O]H2O PET scans (32 scans) using a Biograph PET-CT scanner (Siemens), with A0 varied (142-839 MBq). These data were combined with simulations to (1) determine the accuracy of the new variance model, (2) estimate the improvements in parameter estimate precision gained by optimizing A0, and (3) examine changes in precision for different size regions of interest (ROIs). The new variance model provided a good estimate of the relative variance in dynamic PET data across a wide range of A0s and time frames for FBP reconstruction. Patient data showed that relative changes in estimate precision with A0 were in reasonable agreement with the changes predicted by the model: Pearson's correlation coefficients were 0.73 and 0.62 for perfusion (F) and the volume of distribution (VT), respectively. The between-scan variability in the parameter estimates agreed with the estimated precision for small ROIs (<5 mL). An A0 of 500-700 MBq was near optimal for estimating F and VT from abdominal [15O]H2O scans on this scanner. This optimization improved the precision of parameter estimates for small ROIs (<5 mL), with an injection of 600 MBq reducing the standard error on F by a factor of 1.13 as compared to the injection of 250 MBq, but by the more modest factor of 1.03 as compared to A0 = 400 MBq.

Walker, M. D.; Matthews, J. C.; Asselin, M.-C.; Watson, C. C.; Saleem, A.; Dickinson, C.; Charnley, N.; Julyan, P. J.; Price, P. M.; Jones, T.



Optimal response to attacks on the open science grids.  

SciTech Connect

Cybersecurity is a growing concern, especially in open grids, where attack propagation is easy because of prevalent collaborations among thousands of users and hundreds of institutions. The collaboration rules that typically govern large science experiments as well as social networks of scientists span across the institutional security boundaries. A common concern is that the increased openness may allow malicious attackers to spread more readily around the grid. We consider how to optimally respond to attacks in open grid environments. To show how and why attacks spread more readily around the grid, we first discuss how collaborations manifest themselves in the grids and form the collaboration network graph, and how this collaboration network graph affects the security threat levels of grid participants. We present two mixed-integer program (MIP) models to find the optimal response to attacks in open grid environments, and also calculate the threat level associated with each grid participant. Given an attack scenario, our optimal response model aims to minimize the threat levels at unaffected participants while maximizing the uninterrupted scientific production (continuing collaborations). By adopting some of the collaboration rules (e.g., suspending a collaboration or shutting down a site), the model finds optimal response to subvert an attack scenario.

Altunay, M.; Leyffer, S.; Linderoth, J. T.; Xie, Z. (Mathematics and Computer Science); (FNAL); (Univ. of Wisconsin at Madison)



A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: II. Probabilistic Guarantees on Constraint Satisfaction  

PubMed Central

Probabilistic guarantees on constraint satisfaction for robust counterpart optimization are studied in this paper. The robust counterpart optimization formulations studied are derived from box, ellipsoidal, polyhedral, “interval+ellipsoidal” and “interval+polyhedral” uncertainty sets (Li, Z., Ding, R., and Floudas, C.A., A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: I. Robust Linear and Robust Mixed Integer Linear Optimization, Ind. Eng. Chem. Res, 2011, 50, 10567). For those robust counterpart optimization formulations, their corresponding probability bounds on constraint satisfaction are derived for different types of uncertainty characteristic (i.e., bounded or unbounded uncertainty, with or without detailed probability distribution information). The findings of this work extend the results in the literature and provide greater flexibility for robust optimization practitioners in choosing tighter probability bounds so as to find less conservative robust solutions. Extensive numerical studies are performed to compare the tightness of the different probability bounds and the conservatism of different robust counterpart optimization formulations. Guiding rules for the selection of robust counterpart optimization models and for the determination of the size of the uncertainty set are discussed. Applications in production planning and process scheduling problems are presented. PMID:23329868

Li, Zukui; Floudas, Christodoulos A.



Dynamic and static distributed algorithms for optimal multi-sensor integration  

Microsoft Academic Search

General integration algorithms for dynamic and static multisensor systems are developed. The static sensor systems are considered as special cases of dynamic ones, and the algorithm for the static systems is developed from the algorithm for the dynamic systems. The algorithms take the network uncertainties into account and minimize the effects of the network uncertainties in the local estimation and

Lang Hong



A Theoretical Analysis of How Segmentation of Dynamic Visualizations Optimizes Students' Learning  

ERIC Educational Resources Information Center

This article reviews studies investigating segmentation of dynamic visualizations (i.e., showing dynamic visualizations in pieces with pauses in between) and discusses two not mutually exclusive processes that might underlie the effectiveness of segmentation. First, cognitive activities needed for dealing with the transience of dynamic

Spanjers, Ingrid A. E.; van Gog, Tamara; van Merrienboer, Jeroen J. G.



Computational fluid dynamics and interactive multiobjective optimization in the development of low-emission industrial boilers  

NASA Astrophysics Data System (ADS)

A CFD-based model is applied to study emission formation in a bubbling fluidized bed boiler burning biomass. After the model is validated to a certain extent, it is used for optimization. There are nine design variables (nine distinct NH3 injections in the selective non-catalytic reduction process) and two objective functions (which minimize NO and NH3 emissions in flue gas). The multiobjective optimization problem is solved using the reference-point method involving an achievement scalarizing function. The interactive reference-point method is applied to generate Pareto optimal solutions. Two inherently different optimization algorithms, viz. a genetic algorithm and Powell's conjugate-direction method, are applied in the solution of the resulting optimization problem. It is shown that optimization connected with CFD is a promising design tool for combustion optimization. The strengths and weaknesses of the proposed approach and of the methods applied are discussed from the point of view of a complex real-world optimization problem.

Saario, A.; Oksanen, A.



Optimization of Polling Systems and Dynamic Vehicle Routing Problems on Networks  

E-print Network

We consider the problem of optimizing a polling system, i.e., of optimally sequencing a server in a multi-class queueing system with switch-over times in order to minimize a linear objective function of the waiting times. ...

Bertsimas, Dimitris J.


Academic Optimism and Collective Responsibility: An Organizational Model of the Dynamics of Student Achievement  

ERIC Educational Resources Information Center

This study was designed to examine the construct of academic optimism and its relationship with collective responsibility in a sample of Taiwan elementary schools. The construct of academic optimism was tested using confirmatory factor analysis, and the whole structural model was tested with a structural equation modeling analysis. The data were…

Wu, Jason H.



Optimal intervention in the foreign exchange market when interventions affect market dynamics  

E-print Network

N. Kercheval Department of Mathematics Florida State University Juan F. Moreno State of Wisconsin market reactions to Bank interventions. We obtain new ex- plicit optimal impulse control strategies that account for these market reactions. Keywords: optimal impulse control, quasi-variational inequalities

Aluffi, Paolo


Dynamic metabolic modeling of a microaerobic yeast co-culture: predicting and optimizing ethanol production from glucose/xylose mixtures  

PubMed Central

Background A key step in any process that converts lignocellulose to biofuels is the efficient fermentation of both hexose and pentose sugars. The co-culture of respiratory-deficient Saccharomyces cerevisiae and wild-type Scheffersomyces stipitis has been identified as a promising system for microaerobic ethanol production because S. cerevisiae only consumes glucose while S. stipitis efficiently converts xylose to ethanol. Results To better predict how these two yeasts behave in batch co-culture and to optimize system performance, a dynamic flux balance model describing co-culture metabolism was developed from genome-scale metabolic reconstructions of the individual organisms. First a dynamic model was developed for each organism by estimating substrate uptake kinetic parameters from batch pure culture data and evaluating model extensibility to different microaerobic growth conditions. The co-culture model was constructed by combining the two individual models assuming a cellular objective of total growth rate maximization. To obtain accurate predictions of batch co-culture data collected at different microaerobic conditions, the S. cerevisiae maximum glucose uptake rate was reduced from its pure culture value to account for more efficient S. stipitis glucose uptake in co-culture. The dynamic co-culture model was used to predict the inoculum concentration and aeration level that maximized batch ethanol productivity. The model predictions were validated with batch co-culture experiments performed at the optimal conditions. Furthermore, the dynamic model was used to predict how engineered improvements to the S. stipitis xylose transport system could improve co-culture ethanol production. Conclusions These results demonstrate the utility of the dynamic co-culture metabolic model for guiding process and metabolic engineering efforts aimed at increasing microaerobic ethanol production from glucose/xylose mixtures. PMID:23548183



Robust design optimization of the vibrating rotor-shaft system subjected to selected dynamic constraints  

NASA Astrophysics Data System (ADS)

The commonly observed nowadays tendency to weight minimization of rotor-shafts of the rotating machinery leads to a decrease of shaft bending rigidity making a risk of dangerous stress concentrations and rubbing effects more probable. Thus, a determination of the optimal balance between reducing the rotor-shaft weight and assuring its admissible bending flexibility is a major goal of this study. The random nature of residual unbalances of the rotor-shaft as well as randomness of journal-bearing stiffness have been taken into account in the framework of robust design optimization. Such a formulation of the optimization problem leads to the optimal design that combines an acceptable structural weight with the robustness with respect to uncertainties of residual unbalances - the main source of bending vibrations causing the rubbing effects. The applied robust optimization technique is based on using Latin hypercubes in scatter analysis of the vibration response. The so-called optimal Latin hypercubes are used as experimental plans for building kriging approximations of the objective and constraint functions. The proposed method has been applied for the optimization of the typical single-span rotor-shaft of the 8-stage centrifugal compressor.

Stocki, R.; Szolc, T.; Tauzowski, P.; Knabel, J.



Development of a draft-tube airlift bioreactor for Botryococcus braunii with an optimized inner structure using computational fluid dynamics.  


The key parameters of the inner structure of a cylindrical airlift bioreactor, including the ratio of the cross-section area of the downcomer to the cross-section area of the riser, clearance from the upper edge of the draft tube to the water level, and clearance from the low edge of the draft tube to the bottom of the reactor, significantly affected the biomass production of Botryococcus braunii. In order to achieve high algal cultivation performance, the optimal structural parameters of the bioreactor were determined using computational fluid dynamics (CFD) simulation. The simulated results were validated by experimental data collected from the microalgal cultures in both 2 and 40-L airlift bioreactors. The CFD model developed in this study provides a powerful means for optimizing bioreactor design and scale-up without the need to perform numerous time-consuming bioreactor experiments. PMID:22750496

Xu, Ling; Liu, Rui; Wang, Feng; Liu, Chun-Zhao



Locomotive Planning at Norfolk Southern: An OptimizingSimulator using Approximate Dynamic Programming  

E-print Network

Locomotive Planning at Norfolk Southern: An OptimizingSimulator using Approximate; Abstract For decades locomotive planning has been approached using the classical tools of mathematical; Page 1 1 Locomotive planning is one of the most complex operational problems in freight

Powell, Warren B.


Risk-Sinsitive Dynamic Portfolio Optimization with Partial Information on Infinite Time Horizon  

Microsoft Academic Search

We consider an optimal investment problem for a factor model treated\\u000aby Bielecki and Pliska (Appl. Math. Optim. 39 337–360) as\\u000aa risk-sensitive stochastic control problem, where the mean returns of\\u000aindividual securities are explicitly affected by economic factors defined as\\u000aGaussian processes. We relax the measurability condition assumed as Bielecki\\u000aand Pliska for the investment strategies to select. Our

Hideo Nagai; Shige Peng



Design and optimization of reaction chamber and detection system in dynamic labs-on-chip for proteins detection.  


In this paper, the lab-on-chip section for a protein assay is designed and optimized. To avoid severe reliability problems related to activated surface stability, a dynamic assay approach is adopted: protein-to-protein neutralization is performed while proteins diffuse freely in the reaction chamber. The related refraction index change is detected via an integrated interferometer. The structure is also design to provide a functional test of the reference protein solution, which is generally required for qualification for medical uses. PMID:23475335

Briani, Maya; Germani, Giacomo; Iannone, Eugenio; Moroni, Maurizio; Natalini, Roberto



Optimization-based power management of hybrid power systems with applications in advanced hybrid electric vehicles and wind farms with battery storage  

NASA Astrophysics Data System (ADS)

Modern hybrid electric vehicles and many stationary renewable power generation systems combine multiple power generating and energy storage devices to achieve an overall system-level efficiency and flexibility which is higher than their individual components. The power or energy management control, "brain" of these "hybrid" systems, determines adaptively and based on the power demand the power split between multiple subsystems and plays a critical role in overall system-level efficiency. This dissertation proposes that a receding horizon optimal control (aka Model Predictive Control) approach can be a natural and systematic framework for formulating this type of power management controls. More importantly the dissertation develops new results based on the classical theory of optimal control that allow solving the resulting optimal control problem in real-time, in spite of the complexities that arise due to several system nonlinearities and constraints. The dissertation focus is on two classes of hybrid systems: hybrid electric vehicles in the first part and wind farms with battery storage in the second part. The first part of the dissertation proposes and fully develops a real-time optimization-based power management strategy for hybrid electric vehicles. Current industry practice uses rule-based control techniques with "else-then-if" logic and look-up maps and tables in the power management of production hybrid vehicles. These algorithms are not guaranteed to result in the best possible fuel economy and there exists a gap between their performance and a minimum possible fuel economy benchmark. Furthermore, considerable time and effort are spent calibrating the control system in the vehicle development phase, and there is little flexibility in real-time handling of constraints and re-optimization of the system operation in the event of changing operating conditions and varying parameters. In addition, a proliferation of different powertrain configurations may result in the need for repeated control system redesign. To address these shortcomings, we formulate the power management problem as a nonlinear and constrained optimal control problem. Solution of this optimal control problem in real-time on chronometric- and memory-constrained automotive microcontrollers is quite challenging; this computational complexity is due to the highly nonlinear dynamics of the powertrain subsystems, mixed-integer switching modes of their operation, and time-varying and nonlinear hard constraints that system variables should satisfy. The main contribution of the first part of the dissertation is that it establishes methods for systematic and step-by step improvements in fuel economy while maintaining the algorithmic computational requirements in a real-time implementable framework. More specifically a linear time-varying model predictive control approach is employed first which uses sequential quadratic programming to find sub-optimal solutions to the power management problem. Next the objective function is further refined and broken into a short and a long horizon segments; the latter approximated as a function of the state using the connection between the Pontryagin minimum principle and Hamilton-Jacobi-Bellman equations. The power management problem is then solved using a nonlinear MPC framework with a dynamic programming solver and the fuel economy is further improved. Typical simplifying academic assumptions are minimal throughout this work, thanks to close collaboration with research scientists at Ford research labs and their stringent requirement that the proposed solutions be tested on high-fidelity production models. Simulation results on a high-fidelity model of a hybrid electric vehicle over multiple standard driving cycles reveal the potential for substantial fuel economy gains. To address the control calibration challenges, we also present a novel and fast calibration technique utilizing parallel computing techniques. ^ The second part of this dissertation presents an optimization-based control str

Borhan, Hoseinali