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1

Identification of regulatory structure and kinetic parameters of biochemical networks via mixed-integer dynamic optimization  

PubMed Central

Background Recovering the network topology and associated kinetic parameter values from time-series data are central topics in systems biology. Nevertheless, methods that simultaneously do both are few and lack generality. Results Here, we present a rigorous approach for simultaneously estimating the parameters and regulatory topology of biochemical networks from time-series data. The parameter estimation task is formulated as a mixed-integer dynamic optimization problem with: (i) binary variables, used to model the existence of regulatory interactions and kinetic effects of metabolites in the network processes; and (ii) continuous variables, denoting metabolites concentrations and kinetic parameters values. The approach simultaneously optimizes the Akaike criterion, which captures the trade-off between complexity (measured by the number of parameters), and accuracy of the fitting. This simultaneous optimization mitigates a possible overfitting that could result from addition of spurious regulatory interactions. Conclusion The capabilities of our approach were tested in one benchmark problem. Our algorithm is able to identify a set of plausible network topologies with their associated parameters.

2013-01-01

2

Mixed integer programming optimization models for brachytherapy treatment planning.  

PubMed Central

Mixed integer programming is proposed as an approach for generating treatment plans for brachytherapy. Two related but distinct, mixed integer programming models are tested on data from eight prostate cancer patients. The results demonstrate that in some cases, "good" treatment plans can be obtained in less than five CPU minutes.

Gallagher, R. J.; Lee, E. K.

1997-01-01

3

Toward the development of a Trust-Tech-based methodology for solving mixed integer nonlinear optimization  

NASA Astrophysics Data System (ADS)

Many applications of smart grid can be formulated as constrained optimization problems. Because of the discrete controls involved in power systems, these problems are essentially mixed-integer nonlinear programs. In this paper, we review the Trust-Tech-based methodology for solving mixed-integer nonlinear optimization. Specifically, we have developed a two-stage Trust-Tech-based methodology to systematically compute all the local optimal solutions for constrained mixed-integer nonlinear programming (MINLP) problems. In the first stage, for a given MINLP problem this methodology starts with the construction of a new, continuous, unconstrained problem through relaxation and the penalty function method. A corresponding dynamical system is then constructed to search for a set of local optimal solutions for the unconstrained problem. In the second stage, a reduced constrained NLP is defined for each local optimal solution by determining and fixing the values of integral variables of the MINLP problem. The Trust-Tech-based method is used to compute a set of local optimal solutions for these reduced NLP problems, from which the optimal solution of the original MINLP problem is determined. A numerical simulation of several testing problems is provided to illustrate the effectiveness of our proposed method.

Wang, Bin; Chiang, Hsiao-Dong

4

Approximate mixed-integer nonlinear programming methods for optimal aquifer remediation design  

Microsoft Academic Search

An optimal aquifer remediation design model employing a nonlinear programming algorithm was developed to find the minimum cost design of a pump-and-treat aquifer remediation system. The mixed-integer nonlinear programming model includes the discontinuous fixed costs of system construction and installation as well as operation and maintenance. The fixed cost terms in the objective function have been approximated by continuous functions

Daene C. McKinney; Min-Der Lin

1995-01-01

5

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

2011-01-01

6

A Mixed-Integer Optimization Framework for De Novo Peptide Identification  

PubMed Central

A novel methodology for the de novo identification of peptides by mixed-integer optimization and tandem mass spectrometry is presented in this article. The various features of the mathematical model are presented and examples are used to illustrate the key concepts of the proposed approach. Several problems are examined to illustrate the proposed method's ability to address (1) residue-dependent fragmentation properties and (2) the variability of resolution in different mass analyzers. A preprocessing algorithm is used to identify important m/z values in the tandem mass spectrum. Missing peaks, resulting from residue-dependent fragmentation characteristics, are dealt with using a two-stage algorithmic framework. A cross-correlation approach is used to resolve missing amino acid assignments and to identify the most probable peptide by comparing the theoretical spectra of the candidate sequences that were generated from the MILP sequencing stages with the experimental tandem mass spectrum.

DiMaggio, Peter A.

2009-01-01

7

An iterative sequential mixed-integer approach to automated prostate brachytherapy treatment plan optimization  

NASA Astrophysics Data System (ADS)

Conventional treatment planning for interstitial prostate brachytherapy is generally a `trial and error' process in which improved treatment plans are generated by iteratively changing, via expert judgement, the configuration of sources within the target volume in order to achieve a satisfactory dose distribution. We have utilized linear mixed-integer programming (MIP) and the branch-and-bound method, a deterministic search algorithm, to generate treatment plans. The rapidity of dose falloff from an interstitial radioactive source requires fine sampling of the space in which dose is calculated. This leads to a large and complex model that is difficult to solve as a single 3D problem. We have therefore implemented an iterative sequential approach that optimizes pseudo-independent 2D slices to achieve a fine-grid 3D solution. Using our approach, treatment plans can be generated in 20-45 min on a 200 MHz processor. A comparison of our approach with the manual `trial and error' approach shows that the optimized plans are generally superior. The dose to the urethra and rectum is usually maintained below harmful levels without sacrificing target coverage. In the event that the dose to the urethra is undesirably high, we present a refined optimization approach that lowers urethra dose without significant loss in target coverage. An analysis of the sensitivity of the optimized plans to seed misplacement during the implantation process is also presented that indicates remarkable stability of the dose distribution in comparison with manual treatment plans.

D'Souza, W. D.; Meyer, R. R.; Thomadsen, B. R.; Ferris, M. C.

2001-02-01

8

An iterative sequential mixed-integer approach to automated prostate brachytherapy treatment plan optimization.  

PubMed

Conventional treatment planning for interstitial prostate brachytherapy is generally a 'trial and error' process in which improved treatment plans are generated by iteratively changing, via expert judgement, the configuration of sources within the target volume in order to achieve a satisfactory dose distribution. We have utilized linear mixed-integer programming (MIP) and the branch-and-bound method, a deterministic search algorithm, to generate treatment plans. The rapidity of dose falloff from an interstitial radioactive source requires fine sampling of the space in which dose is calculated. This leads to a large and complex model that is difficult to solve as a single 3D problem. We have therefore implemented an iterative sequential approach that optimizes pseudo-independent 2D slices to achieve a fine-grid 3D solution. Using our approach, treatment plans can be generated in 20-45 min on a 200 MHz processor. A comparison of our approach with the manual 'trial and error' approach shows that the optimized plans are generally superior. The dose to the urethra and rectum is usually maintained below harmful levels without sacrificing target coverage. In the event that the dose to the urethra is undesirably high, we present a refined optimization approach that lowers urethra dose without significant loss in target coverage. An analysis of the sensitivity of the optimized plans to seed misplacement during the implantation process is also presented that indicates remarkable stability of the dose distribution in comparison with manual treatment plans. PMID:11229716

D'Souza, W D; Meyer, R R; Thomadsen, B R; Ferris, M C

2001-02-01

9

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.

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

2011-01-01

10

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

PubMed

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 the 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 s, which confirms earlier results. We propose an iterative procedure for QP that allows us to prescribe the target dose as an interval, while retaining independence between the solution time and the number of dose calculation points. This iterative procedure is comparable in speed to the LP model and produces better plans than the non-iterative QP. We formulate a new dose-volume-based model that maximizes V(100%) while satisfying pre-set DVH criteria. This model optimizes both catheter positions and dwell times within a few minutes depending on prostate volume and number of catheters, optimizes dwell times within 35 s and gives better DVH statistics than dose-based models. The solutions suggest that the correlation between the objective value and the clinical plan quality is weak in the existing dose-based models. PMID:23363622

Gorissen, Bram L; den Hertog, Dick; Hoffmann, Aswin L

2013-02-21

11

Mixed integer programming model for optimizing the layout of an ICU vehicle  

PubMed Central

Background This paper presents a Mixed Integer Programming (MIP) model for designing the layout of the Intensive Care Units' (ICUs) patient care space. In particular, this MIP model was developed for optimizing the layout for materials to be used in interventions. This work was developed within the framework of a joint project between the Madrid Technical Unverstity and the Medical Emergency Services of the Madrid Regional Government (SUMMA 112). Methods The first task was to identify the relevant information to define the characteristics of the new vehicles and, in particular, to obtain a satisfactory interior layout to locate all the necessary materials. This information was gathered from health workers related to ICUs. With that information an optimization model was developed in order to obtain a solution. From the MIP model, a first solution was obtained, consisting of a grid to locate the different materials needed for the ICUs. The outcome from the MIP model was discussed with health workers to tune the solution, and after slightly altering that solution to meet some requirements that had not been included in the mathematical model, the eventual solution was approved by the persons responsible for specifying the characteristics of the new vehicles. According to the opinion stated by the SUMMA 112's medical group responsible for improving the ambulances (the so-called "coaching group"), the outcome was highly satisfactory. Indeed, the final design served as a basis to draw up the requirements of a public tender. Results As a result from solving the Optimization model, a grid was obtained to locate the different necessary materials for the ICUs. This grid had to be slightly altered to meet some requirements that had not been included in the mathematical model. The results were discussed with the persons responsible for specifying the characteristics of the new vehicles. Conclusion The outcome was highly satisfactory. Indeed, the final design served as a basis to draw up the requirements of a public tender. The authors advocate this approach to address similar problems within the field of Health Services to improve the efficiency and the effectiveness of the processes involved. Problems such as those in operation rooms or emergency rooms, where the availability of a large amount of material is critical are eligible to be dealt with in a simmilar manner.

2009-01-01

12

An iterative sequential mixed-integer approach to automated prostate brachytherapy treatment plan optimization  

Microsoft Academic Search

Conventional treatment planning for interstitial prostate brachytherapy is generally a `trial and error' process in which improved treatment plans are generated by iteratively changing, via expert judgement, the configuration of sources within the target volume in order to achieve a satisfactory dose distribution. We have utilized linear mixed-integer programming (MIP) and the branch-and-bound method, a deterministic search algorithm, to generate

W. D. D'Souza; R. R. Meyer; B. R. Thomadsen; M. C. Ferris

2001-01-01

13

Mixed Integer Programming Model and Incremental Optimization for Delivery and Storage Planning Using Truck Terminals  

NASA Astrophysics Data System (ADS)

We discuss the planning of transportation by trucks over a multi-day period. Each truck collects loads from suppliers and delivers them to assembly plants or a truck terminal. By exploiting the truck terminal as a temporal storage, we aim to increase the load ratio of each truck and to minimize the lead time for transportation. In this paper, we show a mixed integer programming model which represents each product explicitly, and discuss the decomposition of the problem into a problem of delivery and storage, and a problem of vehicle routing. Based on this model, we propose a relax-and-fix type heuristic in which decision variables are fixed one by one by mathematical programming techniques such as branch-and-bound methods.

Sakakibara, Kazutoshi; Tian, Yajie; Nishikawa, Ikuko

14

A mixed-integer simulation-based optimization approach with surrogate functions in water resources management  

Microsoft Academic Search

Efficient and powerful methods are needed to overcome the inherent difficulties in the numerical solution of many simulation-based\\u000a engineering design problems. Typically, expensive simulation codes are included as black-box function generators; therefore,\\u000a gradient information that is required by mathematical optimization methods is entirely unavailable. Furthermore, the simulation\\u000a code may contain iterative or heuristic methods, low-order approximations of tabular data, or

Thomas Hemker; Kathleen R. Fowler; Matthew W. Farthing; Oskar von Stryk

2008-01-01

15

Traffic Network Control Based on Hybrid Dynamical System Modeling and Mixed Integer Nonlinear Programming With Convexity Analysis  

Microsoft Academic Search

This paper presents a new framework for traffic flow control based on an integrated model description by means of a hybrid dynamical system. The geometrical information on the traffic network is characterized by a hybrid Petri net (HPN). Then, the algebraic behavior of the traffic flow is transformed into a mixed logical dynamical system (MLDS) form to introduce an optimization

Youngwoo Kim; Tatsuya Kato; Shigeru Okuma; Tatsuo Narikiyo

2008-01-01

16

Mixed-Integer Conic Linear Programming: Challenges and Perspectives.  

National Technical Information Service (NTIS)

Fundamental Disjunctive Conic Cut (DCC) methodology for Mixed- Integer Conic Linear Optimization (MICO) was developed. To describe the convex hull of the intersection of a convex set E and a linear disjunction is the fundamental problem, and that served a...

T. Terlaky

2013-01-01

17

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.

2012-01-01

18

Optimal planning of co-firing alternative fuels with coal in a power plant by grey nonlinear mixed integer programming model.  

PubMed

Energy supply and use is of fundamental importance to society. Although the interactions between energy and environment were originally local in character, they have now widened to cover regional and global issues, such as acid rain and the greenhouse effect. It is for this reason that there is a need for covering the direct and indirect economic and environmental impacts of energy acquisition, transport, production and use. In this paper, particular attention is directed to ways of resolving conflict between economic and environmental goals by encouraging a power plant to consider co-firing biomass and refuse-derived fuel (RDF) with coal simultaneously. It aims at reducing the emission level of sulfur dioxide (SO(2)) in an uncertain environment, using the power plant in Michigan City, Indiana as an example. To assess the uncertainty by a comparative way both deterministic and grey nonlinear mixed integer programming (MIP) models were developed to minimize the net operating cost with respect to possible fuel combinations. It aims at generating the optimal portfolio of alternative fuels while maintaining the same electricity generation simultaneously. To ease the solution procedure stepwise relaxation algorithm was developed for solving the grey nonlinear MIP model. Breakeven alternative fuel value can be identified in the post-optimization stage for decision-making. Research findings show that the inclusion of RDF does not exhibit comparative advantage in terms of the net cost, albeit relatively lower air pollution impact. Yet it can be sustained by a charge system, subsidy program, or emission credit as the price of coal increases over time. PMID:17395362

Ko, Andi Setiady; Chang, Ni-Bin

2008-07-01

19

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

2013-01-01

20

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

2002-01-01

21

Mixed integer programming & heuristic scheduling for space communication networks  

Microsoft Academic Search

We introduce mixed integer programming and heuristic scheduling for space communication networks in this paper. The considered communication network consists of space and ground assets with the link dynamics between any two assets vary 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

Charles H. Lee; Kar-Ming Cheung

2012-01-01

22

Trajectory Planning for Multiple Autonomous Systems Using Mixed Integer Linear Programming  

Microsoft Academic Search

This paper addresses the problem of finding optimal trajectories for multiple autonomous systems. Mixed integer linear programming (MILP) is described for designing time and energy- or fuel-optimal maneuvers that account for the presence of other vehicles. The paper shows how integer constraints can be added to linear programming to account for obstacle avoidance and collision avoidance among the group of

Taoridi A Ademoye; Asad Davari

2006-01-01

23

Mixed Integer Programming Approaches to Treatment Planning for Brachytherapy - Application to Permanent Prostate Implants  

Microsoft Academic Search

Mixed integer programming models and computational strategies developed for treatment plan- ning optimization in brachytherapy are described. The problem involves the designation of optimal place- ment of radioactive sources (seeds) inside a tumor site. Two MIP models are described. The resulting MIP instances are difficult to solve, due in large part to dense constraint matrices with large disparities in the

Eva K. Lee; Marco Zaider

2003-01-01

24

Membrane system design for multicomponent gas mixtures via mixed-integer nonlinear programming  

Microsoft Academic Search

An optimal design strategy for membrane networks separating multicomponent gas mixtures based on an approximate permeator model and mixed-integer nonlinear programming (MINLP) is proposed. A permeator system superstructure is used to embed a very large number of possible network configurations and allows the permeator feed-side pressure to be fixed or a design variable. A MINLP design model is developed to

Runhong Qi; Michael A. Henson

2000-01-01

25

A Mixed Integer Formulation for Energy-efficient Multistage Adsorption Dryer Design  

Microsoft Academic Search

This work presents a mixed integer nonlinear programming (MINLP) formulation for the design of energy-efficient multistage adsorption dryers within constraints on product temperature and moisture content. Apart from optimizing temperatures and flows, the aim is to select the most efficient adsorbent per stage and product to air flow configuration. Superstructure models consisting of commonly used adsorbents such as zeolite, alumina,

J. C. Atuonwu; G. van Straten; H. C. van Deventer; A. J. B. van Boxtel

2012-01-01

26

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

2006-01-01

27

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

Microsoft Academic Search

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

Heeman Lee; J. M. Pinto; I. E. Grossmann

1996-01-01

28

Flexible interval mixed-integer bi-infinite programming for environmental systems management under uncertainty.  

PubMed

A number of inexact programming methods have been developed for municipal solid waste management under uncertainty. However, most of them do not allow the parameters in the objective and constraints of a programming problem to be functional intervals (i.e., the lower and upper bounds of the intervals are functions of impact factors). In this study, a flexible interval mixed-integer bi-infinite programming (FIMIBIP) method is developed in response to the above concern. A case study is also conducted; the solutions are then compared with those obtained from interval mixed-integer bi-infinite programming (IMIBIP) and fuzzy interval mixed-integer programming (FIMIP) methods. It is indicated that the solutions through FIMIBIP can provide decision support for cost-effectively diverting municipal solid waste, and for sizing, timing and siting the facilities' expansion during the entire planning horizon. These schemes are more flexible than those identified through IMIBIP since the tolerance intervals are introduced to measure the level of constraints satisfaction. The FIMIBIP schemes may also be robust since the solutions are "globally-optimal" under all scenarios caused by the fluctuation of gas/energy prices, while the conventional ones are merely "locally-optimal" under a certain scenario. PMID:19111962

He, L; Huang, G H; Lu, H W

2009-04-01

29

Integer and Mixed-Integer Programming Models: General Properties.  

National Technical Information Service (NTIS)

It is well-known that mixed-integer formulations can be used to model important classes of non-convex functions such as fixed-charge functions and linear economy-of-scale cost functions. The purpose of the paper is to formulate a rigorous definition of a ...

R. R. Meyer

1973-01-01

30

ALGORITHMS AND SOFTWARE FOR CONVEX MIXED INTEGER NONLINEAR PROGRAMS  

Microsoft Academic Search

This paper provides a survey of recent progress and software for solving mixed integer nonlinear programs (MINLP) wherein the objective and constraints are defined by convex functions and integrality restrictions are imposed on a subset of the decision variables. Convex MINLPs have received sustained attention in very years. By exploiting analogies to the case of well-known techniques for solving mixed

PIERRE BONAMI; MUSTAFA KILINC; JEFF LINDEROTH

31

Mixed integer nonlinear programming using interior-point methods  

Microsoft Academic Search

In this paper, we outline an algorithm for solving mixed integer nonlinear programming (MINLP) problems. The approach uses a branch-and-bound framework for handling the integer variables and an infeasible interior-point method for solving the resulting nonlinear subproblems. We report on the details of the implementation, including the efficient pruning of the branch-and-bound tree via equilibrium constraints, warmstart strategies for interior-point

Hande Y. Benson

2010-01-01

32

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

2001-01-01

33

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

2013-05-30

34

Dynamic Optimization  

NASA Technical Reports Server (NTRS)

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

Laird, Philip

1992-01-01

35

Learning oncogenetic networks by reducing to mixed integer linear programming.  

PubMed

Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog. PMID:23799047

Shahrabi Farahani, Hossein; Lagergren, Jens

2013-01-01

36

Learning Oncogenetic Networks by Reducing to Mixed Integer Linear Programming  

PubMed Central

Cancer can be a result of accumulation of different types of genetic mutations such as copy number aberrations. The data from tumors are cross-sectional and do not contain the temporal order of the genetic events. Finding the order in which the genetic events have occurred and progression pathways are of vital importance in understanding the disease. In order to model cancer progression, we propose Progression Networks, a special case of Bayesian networks, that are tailored to model disease progression. Progression networks have similarities with Conjunctive Bayesian Networks (CBNs) [1],a variation of Bayesian networks also proposed for modeling disease progression. We also describe a learning algorithm for learning Bayesian networks in general and progression networks in particular. We reduce the hard problem of learning the Bayesian and progression networks to Mixed Integer Linear Programming (MILP). MILP is a Non-deterministic Polynomial-time complete (NP-complete) problem for which very good heuristics exists. We tested our algorithm on synthetic and real cytogenetic data from renal cell carcinoma. We also compared our learned progression networks with the networks proposed in earlier publications. The software is available on the website https://bitbucket.org/farahani/diprog.

Shahrabi Farahani, Hossein; Lagergren, Jens

2013-01-01

37

Mixed Integer Programming for Multi-Vehicle Path Planning  

Microsoft Academic Search

Abstract: This paper presents a new approach to fuel-optimal path planningof multiple vehicles using a combination of linear and integerprogramming. The basic problem formulation is to havethe vehicles move from an initial dynamic state to a final statewithout colliding with each other, while at the same time avoidingother stationary and moving obstacles. It is shown that thisproblem can be rewritten

T. Schouwenaars; B. Demoor; E. Feron

2001-01-01

38

Automatic design of synthetic gene circuits through mixed integer non-linear programming.  

PubMed

Automatic design of synthetic gene circuits poses a significant challenge to synthetic biology, primarily due to the complexity of biological systems, and the lack of rigorous optimization methods that can cope with the combinatorial explosion as the number of biological parts increases. Current optimization methods for synthetic gene design rely on heuristic algorithms that are usually not deterministic, deliver sub-optimal solutions, and provide no guaranties on convergence or error bounds. Here, we introduce an optimization framework for the problem of part selection in synthetic gene circuits that is based on mixed integer non-linear programming (MINLP), which is a deterministic method that finds the globally optimal solution and guarantees convergence in finite time. Given a synthetic gene circuit, a library of characterized parts, and user-defined constraints, our method can find the optimal selection of parts that satisfy the constraints and best approximates the objective function given by the user. We evaluated the proposed method in the design of three synthetic circuits (a toggle switch, a transcriptional cascade, and a band detector), with both experimentally constructed and synthetic promoter libraries. Scalability and robustness analysis shows that the proposed framework scales well with the library size and the solution space. The work described here is a step towards a unifying, realistic framework for the automated design of biological circuits. PMID:22536398

Huynh, Linh; Kececioglu, John; Köppe, Matthias; Tagkopoulos, Ilias

2012-01-01

39

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

2009-01-01

40

A Mixed Integer Linear Program for Solving a Multiple Route Taxi Scheduling Problem  

NASA Technical Reports Server (NTRS)

Aircraft movements on taxiways at busy airports often create bottlenecks. This paper introduces a mixed integer linear program to solve a Multiple Route Aircraft Taxi Scheduling Problem. The outputs of the model are in the form of optimal taxi schedules, which include routing decisions for taxiing aircraft. The model extends an existing single route formulation to include routing decisions. An efficient comparison framework compares the multi-route formulation and the single route formulation. The multi-route model is exercised for east side airport surface traffic at Dallas/Fort Worth International Airport to determine if any arrival taxi time savings can be achieved by allowing arrivals to have two taxi routes: a route that crosses an active departure runway and a perimeter route that avoids the crossing. Results indicate that the multi-route formulation yields reduced arrival taxi times over the single route formulation only when a perimeter taxiway is used. In conditions where the departure aircraft are given an optimal and fixed takeoff sequence, accumulative arrival taxi time savings in the multi-route formulation can be as high as 3.6 hours more than the single route formulation. If the departure sequence is not optimal, the multi-route formulation results in less taxi time savings made over the single route formulation, but the average arrival taxi time is significantly decreased.

Montoya, Justin Vincent; Wood, Zachary Paul; Rathinam, Sivakumar; Malik, Waqar Ahmad

2010-01-01

41

On the solution of mixed-integer nonlinear programming models for computer aided molecular design.  

PubMed

This paper addresses the efficient solution of computer aided molecular design (CAMD) problems, which have been posed as mixed-integer nonlinear programming models. The models of interest are those in which the number of linear constraints far exceeds the number of nonlinear constraints, and with most variables participating in the nonconvex terms. As a result global optimization methods are needed. A branch-and-bound algorithm (BB) is proposed that is specifically tailored to solving such problems. In a conventional BB algorithm, branching is performed on all the search variables that appear in the nonlinear terms. This translates to a large number of node traversals. To overcome this problem, we have proposed a new strategy for branching on a set of linear branchingfunctions, which depend linearly on the search variables. This leads to a significant reduction in the dimensionality of the search space. The construction of linear underestimators for a class of functions is also presented. The CAMD problem that is considered is the design of optimal solvents to be used as cleaning agents in lithographic printing. PMID:12385479

Ostrovsky, Guennadi M; Achenie, Luke E K; Sinha, Manish

2002-11-01

42

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

2009-01-01

43

Interval-parameter semi-infinite fuzzy-stochastic mixed-integer programming approach for environmental management under multiple uncertainties.  

PubMed

In this study, an interval-parameter semi-infinite fuzzy-chance-constrained mixed-integer linear programming (ISIFCIP) approach is developed for supporting long-term planning of waste-management systems under multiple uncertainties in the City of Regina, Canada. The method improves upon the existing interval-parameter semi-infinite programming (ISIP) and fuzzy-chance-constrained programming (FCCP) by incorporating uncertainties expressed as dual uncertainties of functional intervals and multiple uncertainties of distributions with fuzzy-interval admissible probability of violating constraint within a general optimization framework. The binary-variable solutions represent the decisions of waste-management-facility expansion, and the continuous ones are related to decisions of waste-flow allocation. The interval solutions can help decision-makers to obtain multiple decision alternatives, as well as provide bases for further analyses of tradeoffs between waste-management cost and system-failure risk. In the application to the City of Regina, Canada, two scenarios are considered. In Scenario 1, the City's waste-management practices would be based on the existing policy over the next 25 years. The total diversion rate for the residential waste would be approximately 14%. Scenario 2 is associated with a policy for waste minimization and diversion, where 35% diversion of residential waste should be achieved within 15 years, and 50% diversion over 25 years. In this scenario, not only landfill would be expanded, but also CF and MRF would be expanded. Through the scenario analyses, useful decision support for the City's solid-waste managers and decision-makers has been generated. Three special characteristics of the proposed method make it unique compared with other optimization techniques that deal with uncertainties. Firstly, it is useful for tackling multiple uncertainties expressed as intervals, functional intervals, probability distributions, fuzzy sets, and their combinations; secondly, it has capability in addressing the temporal variations of the functional intervals; thirdly, it can facilitate dynamic analysis for decisions of facility-expansion planning and waste-flow allocation within a multi-facility, multi-period and multi-option context. PMID:19854040

Guo, P; Huang, G H

2010-03-01

44

FATCOP 2.0: Advanced Features in an Opportunistic Mixed Integer Programming Solver  

Microsoft Academic Search

We describe FATCOP 2.0, a new parallel mixed integer program solver that works in an opportunistic computing environment provided by the Condor resource management system. We outline changes to the search strategy of FATCOP 1.0 that are necessary to improve re- source utilization, together with new techniques to exploit heteroge- neous resources. We detail several advanced features in the code

Qun Chen; Michael C. Ferris; Jeff Linderoth

2001-01-01

45

Quadratic mixed integer programming and support vectors for deleting outliers in robust regression  

Microsoft Academic Search

We consider the problem of deleting bad influential observations (outliers) in linear regression models. The problem is formulated as a Quadratic Mixed Integer Programming (QMIP) problem, where penalty costs for discarding outliers are used into the objective function. The optimum solution defines a robust regression estimator called penalized trimmed squares (PTS). Due to the high computational complexity of the resulting

G. Zioutas; Leonidas S. Pitsoulis; Antonios Avramidis

2009-01-01

46

Mixed Integer Programming for Automated Testing and Automated Verification of System Specifications  

Microsoft Academic Search

In this paper, we formulate Timed Abstract State Ma- chine (TASM) constraints as a Mixed Integer Program- ming (MIP) problem. The translation from TASM to MIP is provided in order to reason about properties of TASM specifications such as completeness and con- sistency. By formulating TASM constraints as an MIP problem, a generally available Linear Programming (LP) solver can be

Martin Ouimet; Mathieu Quenot; Kristina Lundqvist

47

Large-Scale Bi-Level Strain Design Approaches and Mixed-Integer Programming Solution Techniques  

PubMed Central

The use of computational models in metabolic engineering has been increasing as more genome-scale metabolic models and computational approaches become available. Various computational approaches have been developed to predict how genetic perturbations affect metabolic behavior at a systems level, and have been successfully used to engineer microbial strains with improved primary or secondary metabolite production. However, identification of metabolic engineering strategies involving a large number of perturbations is currently limited by computational resources due to the size of genome-scale models and the combinatorial nature of the problem. In this study, we present (i) two new bi-level strain design approaches using mixed-integer programming (MIP), and (ii) general solution techniques that improve the performance of MIP-based bi-level approaches. The first approach (SimOptStrain) simultaneously considers gene deletion and non-native reaction addition, while the second approach (BiMOMA) uses minimization of metabolic adjustment to predict knockout behavior in a MIP-based bi-level problem for the first time. Our general MIP solution techniques significantly reduced the CPU times needed to find optimal strategies when applied to an existing strain design approach (OptORF) (e.g., from ?10 days to ?5 minutes for metabolic engineering strategies with 4 gene deletions), and identified strategies for producing compounds where previous studies could not (e.g., malate and serine). Additionally, we found novel strategies using SimOptStrain with higher predicted production levels (for succinate and glycerol) than could have been found using an existing approach that considers network additions and deletions in sequential steps rather than simultaneously. Finally, using BiMOMA we found novel strategies involving large numbers of modifications (for pyruvate and glutamate), which sequential search and genetic algorithms were unable to find. The approaches and solution techniques developed here will facilitate the strain design process and extend the scope of its application to metabolic engineering.

Kim, Joonhoon; Reed, Jennifer L.; Maravelias, Christos T.

2011-01-01

48

Progress in computational mixed integer programming - A look back from the other side of the tipping point  

Microsoft Academic Search

The last few years have been a thrilling time for the commercial application of mixed integer programming. The technology has gone through an inflection point. Just a few years ago, MIP was viewed as a temptingly powerful modeling paradigm that would consistently disappoint in practice. In constrast, in the last few years MIP has become a vital capability that powers

Robert Bixby; Edward Rothberg

2007-01-01

49

Optimal Cyclic Multi-Hoist Scheduling: A Mixed Integer Programming Approach  

Microsoft Academic Search

In the manufacture of circuit boards, panels are immersed sequentially in a series of tanks, with upper and lower bounds on the processing time within each tank. The panels are mounted on carriers that are lowered into and raised from the tanks, and transported from tank to tank by programmable hoists. The sequence of hoist moves does not have to

Janny M. Y. Leung; Guoqing Zhang; Xiaoguang Yang; Raymond Mak; Kokin Lam

2004-01-01

50

Greenhouse gas emissions control in integrated municipal solid waste management through mixed integer bilevel decision-making  

Microsoft Academic Search

Recent studies indicated that municipal solid waste (MSW) is a major contributor to global warming due to extensive emissions of greenhouse gases (GHGs). However, most of them focused on investigating impacts of MSW on GHG emission amounts. This study presents two mixed integer bilevel decision-making models for integrated municipal solid waste management and GHG emissions control: MGU-MCL and MCU-MGL. The

Li He; G. H. Huang; Hongwei Lu

2011-01-01

51

Price maker self-scheduling in a pool-based electricity market: a mixed-integer LP approach  

Microsoft Academic Search

This paper addresses the self-scheduling problem faced by a price-maker to achieve maximum profit in a pool-based electricity market. An exact and computationally efficient mixed-integer linear programming (MILP) formulation of this problem is presented. This formulation models precisely the price-maker capability of altering market-clearing prices to its own benefits, through price quota curves. No assumptions are made on the characteristics

S. de la Torre; J. M. Arroyo; A. J. Conejo; J. Contreras

2002-01-01

52

Mixed-integer nonlinear optimisation approach to coarse-graining biochemical networks  

PubMed Central

Quantitative modelling and analysis of biochemical networks is challenging because of the inherent complexities and nonlinearities of the system and the limited availability of parameter values. Even if a mathematical model of the network can be developed, the lack of large-scale good-quality data makes accurate estimation of a large number of parameters impossible. Hence, coarse-grained models (CGMs) consisting of essential biochemical mechanisms are more suitable for computational analysis and for studying important systemic functions. The central question in constructing a CGM is which mechanisms should be deemed ‘essential’ and which can be ignored. Also, how should parameter values be defined when data are sparse? A mixed-integer nonlinear-programming (MINLP) based optimisation approach to coarse-graining is presented. Starting with a detailed biochemical model with associated computational details (reaction network and mathematical description) and data on the biochemical system, the structure and the parameters of a CGM can be determined simultaneously. In this optimisation problem, the authors use a genetic algorithm to simultaneously identify parameter values and remove unimportant reactions. The methodology is exemplified by developing two CGMs for the GTPase-cycle module of M1 muscarinic acetylcholine receptor, Gq, and regulator of G protein signalling 4 [RGS4, a GTPase-activating protein (GAP)] starting from a detailed model of 48 reactions. Both the CGMs have only 17 reactions, fit experimental data well and predict, as does the detailed model, four limiting signalling regimes (LSRs) corresponding to the extremes of receptor and GAP concentration. The authors demonstrate that coarse-graining, in addition to resulting in a reduced-order model, also provides insights into the mechanisms in the network. The best CGM obtained for the GTPase cycle also contains an unconventional mechanism and its predictions explain an old problem in pharmacology, the biphasic (bell-shaped) response to certain drugs. The MINLP methodology is broadly applicable to larger and complex (dense) biochemical modules.

Maurya, M.R.; Bornheimer, S.J.; Venkatasubramanian, V.; Subramaniam, S.

2009-01-01

53

Dynamic Ramping in Unit Commitment  

Microsoft Academic Search

This paper provides the modeling of dynamic ramping in unit commitment. Ramping up\\/down limits could be a constant, stepwise, or piecewise linear function of generation dispatch. The dynamic ramping limit is modeled as mixed integer linear constraints, and unit commitment is solved by a mixed integer programming (MIP) solver. Test results show the impact of dynamic ramping on unit commitment

Tao Li; Mohammad Shahidehpour

2007-01-01

54

Canalizing structure of genetic network dynamics: modelling and identification via mixed-integer programming  

Microsoft Academic Search

We discuss the identification of genetic networks based on a class of boolean gene activation rules known as hierarchically canalizing functions. We introduce a class of kinetic models for the concentration of the proteins in the network built on a family of canalizing functions that has been shown to capture the vast majority of the known interaction networks. The simultaneous

Eugenio Cinquemani; Riccardo Porreca; John Lygeros; Giancarlo Ferrari-Trecate

2009-01-01

55

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.

56

Automatic Design of Synthetic Gene Circuits through Mixed Integer Non-linear Programming  

Microsoft Academic Search

Automatic design of synthetic gene circuits poses a significant challenge to synthetic biology, primarily due to the complexity of biological systems, and the lack of rigorous optimization methods that can cope with the combinatorial explosion as the number of biological parts increases. Current optimization methods for synthetic gene design rely on heuristic algorithms that are usually not deterministic, deliver sub-optimal

Linh Huynh; John Kececioglu; Matthias Köppe; Ilias Tagkopoulos

2012-01-01

57

A new mixed integer linear programming model for product development using quality function deployment  

Microsoft Academic Search

Quality function deployment (QFD) is a product development process performed to maximize customer satisfaction. In the QFD, the design requirements (DRs) affecting the product performance are primarily identified, and product performance is improved to optimize customer needs (CNs). For product development, determining the fulfillment levels of design requirements (DRs) is crucial during QFD optimization. However, in real world applications, the

Elif K?l?ç Delice; Zülal Güngör

2009-01-01

58

A hydro power system operation using Genetic Algorithms and mixed-integer nonlinear programming  

NASA Astrophysics Data System (ADS)

This paper proposes a new hybrid optimization method for solving a hydrothermal coordination problem. In general, the problem is decomposed into smaller hydro and thermal sub-problems which are solved separately. The hydro sub-problem is solved by the peak shaving method using the proposed hybrid optimization method. It combines genetic algorithms with the traditional numerical optimization method. The hybrid method has been applied to a real hydrothermal system, i.e., the Slovak power system. The results have proved the efficiency of the proposed method.

Šulek, P.

2012-03-01

59

Large-Scale Traffic Network Control Based on Mixed Integer Non-linear System Formulation  

Microsoft Academic Search

This paper presents a new framework for traffic flow control based on an integrated model description by means of a hybrid dynamical system (HDS). The geometrical information on the traffic network is characterized by a hybrid Petri net (HPN). Then, the algebraic behavior of traffic flow is transformed into a mixed logical dynamical system (MLDS) in order to introduce an

Tatsuya Kato; YoungWoo Kim; Shigeru Okuma; Tatsuo Narikiyo

2006-01-01

60

Optimization techniques in molecular structure and function elucidation  

PubMed Central

This paper discusses recent optimization approaches to the protein side-chain prediction problem, protein structural alignment, and molecular structure determination from X-ray diffraction measurements. The machinery employed to solve these problems has included algorithms from linear programming, dynamic programming, combinatorial optimization, and mixed-integer nonlinear programming. Many of these problems are purely continuous in nature. Yet, to this date, they have been approached mostly via combinatorial optimization algorithms that are applied to discrete approximations. The main purpose of the paper is to offer an introduction and motivate further systems approaches to these problems.

Sahinidis, Nikolaos V.

2009-01-01

61

Optimization of environmental management strategies through a dynamic stochastic possibilistic multiobjective program.  

PubMed

Greenhouse gas (GHG) emissions from municipal solid waste (MSW) management facilities have become a serious environmental issue. In MSW management, not only economic objectives but also environmental objectives should be considered simultaneously. In this study, a dynamic stochastic possibilistic multiobjective programming (DSPMP) model is developed for supporting MSW management and associated GHG emission control. The DSPMP model improves upon the existing waste management optimization methods through incorporation of fuzzy possibilistic programming and chance-constrained programming into a general mixed-integer multiobjective linear programming (MOP) framework where various uncertainties expressed as fuzzy possibility distributions and probability distributions can be effectively reflected. Two conflicting objectives are integrally considered, including minimization of total system cost and minimization of total GHG emissions from waste management facilities. Three planning scenarios are analyzed and compared, representing different preferences of the decision makers for economic development and environmental-impact (i.e. GHG-emission) issues in integrated MSW management. Optimal decision schemes under three scenarios and different p(i) levels (representing the probability that the constraints would be violated) are generated for planning waste flow allocation and facility capacity expansions as well as GHG emission control. The results indicate that economic and environmental tradeoffs can be effectively reflected through the proposed DSPMP model. The generated decision variables can help the decision makers justify and/or adjust their waste management strategies based on their implicit knowledge and preferences. PMID:23313898

Zhang, Xiaodong; Huang, Gordon

2013-02-15

62

Planning Electric Power Generation: A Nonlinear Mixed Integer Model Employing Benders Decomposition  

Microsoft Academic Search

This paper describes the development and application of an optimization program that is used to help electric utilities plan investments for power generation. For each year over a planning horizon the program determines what types and sizes of generating plants should be constructed, so as to minimize total discounted cost while meeting reliably the system's forecasted demands for electricity. The

F. Noonan; R. J. Giglio

1977-01-01

63

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

2011-01-01

64

Three-Dimensional Path Planning of a Climbing Robot Using Mixed Integer Linear Programming  

Microsoft Academic Search

The City-Climber robot is a novel wall-climbing robot developed at The City College of New York that has the capability to move on floors, climb walls, walk on ceilings and transit between them. In this paper, we first develop the dynamic model of the City-Climber robot when it travel on different surfaces, i.e., floors, walls and ceilings, respectively. Then, we

Ronggang Yue; Jizhong Xiao; Shaoping Wang; Samleo L. Joseph

2010-01-01

65

Optimization with Extremal Dynamics  

SciTech Connect

We explore a new general-purpose heuristic for finding high-quality solutions to hard discrete optimization problems. The method, called extremal optimization, is inspired by self-organized criticality, a concept introduced to describe emergent complexity in physical systems. Extremal optimization successively updates extremely undesirable variables of a single suboptimal solution, assigning them new, random values. Large fluctuations ensue, efficiently exploring many local optima. We use extremal optimization to elucidate the phase transition in the 3-coloring problem, and we provide independent confirmation of previously reported extrapolations for the ground-state energy of {+-}J spin glasses in d=3 and 4 .

Boettcher, Stefan; Percus, Allon G.

2001-06-04

66

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.

2013-01-01

67

Optimal dynamic detection of explosives  

NASA Astrophysics Data System (ADS)

We are utilizing control of molecular processes at the quantum level via the best capabilities of recent laser technology and recent discoveries in optimal shaping of laser pulses to significantly enhance the standoff detection of explosives. Optimal dynamic detection of explosives (ODD-Ex) is a methodology whereby laser pulses are optimally shaped to simultaneously enhance the sensitivity and selectivity of any of a wide variety of spectroscopic methods for explosives signatures while reducing the influence of noise and environmental perturbations. We discuss here recent results using complementary ODD-Ex methods.

Moore, D. S.; Rabitz, Herschel; McGrane, S. D.; Greenfield, M. T.; Scharff, R. J.; Chalmers, R. E.; Roslund, J.

2011-05-01

68

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

2009-01-01

69

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

2008-10-10

70

Time optimal trajectory planning in dynamic environments  

Microsoft Academic Search

This paper presents a method for motion planning in dynamic environments, subject to robot dynamics and actuator constraints. The time optimal trajectory is computed by first generating an initial guess using the concept of velocity obstacle. The initial guess, computed by a global search over a tree of avoidance maneuvers, is then optimized using a dynamic optimization. This method is

Paolo Fiorini; Zvi Shiller

1996-01-01

71

Multiobjective optimization using dynamic neighborhood particle swarm optimization  

Microsoft Academic Search

This paper presents a particle swarm optimization (PSO) algorithm for multiobjective optimization problems. PSO is modified by using a dynamic neighborhood strategy, new particle memory updating, and one-dimension optimization to deal with multiple objectives. Several benchmark cases were tested and showed that PSO could efficiently find multiple Pareto optimal solutions

Xiaohui Hu; Russell C. Eberhart

2002-01-01

72

Multiobjective optimization of dynamic aperture  

NASA Astrophysics Data System (ADS)

Dynamic aperture (DA) is one of the key nonlinear properties for a storage ring. Although there have been both analytical and numerical methods to find the aperture, the reverse problem of how to optimize it is still a challenging problem. A general and flexible way of optimizing the DA is highly demanded in accelerator design and operation. In this paper, we discuss the use of multiobjective optimization for DA. First we consider using objective functions based only on numerical tracking results. Data mining of these results demonstrated a correlation between DA and low-order nonlinear driving terms. Next we considered using objective functions which included both numerical tracking results and analytical estimates of low-order nonlinear driving terms. This resulted in faster convergence. The National Synchrotron Light Source II (NSLS-II) lattice was taken as an example to illustrate this method. This multiobjective approach is not limited by particular linear or nonlinear lattice settings, and can also be applied for optimizing other properties of a storage ring.

Yang, Lingyun; Li, Yongjun; Guo, Weiming; Krinsky, Samuel

2011-05-01

73

Multiobjective optimization of dynamic aperture  

SciTech Connect

Dynamic aperture (DA) is one of the key nonlinear properties for a storage ring. Although there have been both analytical and numerical methods to find the aperture, the reverse problem of how to optimize it is still a challenging problem. A general and flexible way of optimizing the DA is highly demanded in accelerator design and operation. In this paper, we discuss the use of multiobjective optimization for DA. First we consider using objective functions based only on numerical tracking results. Data mining of these results demonstrated a correlation between DA and low-order nonlinear driving terms. Next we considered using objective functions which included both numerical tracking results and analytical estimates of low-order nonlinear driving terms. This resulted in faster convergence. The National Synchrotron Light Source II (NSLS-II) lattice was taken as an example to illustrate this method. This multiobjective approach is not limited by particular linear or nonlinear lattice settings, and can also be applied for optimizing other properties of a storage ring.

Yang, L.; Li, Y.; Guo, W.; Krinsky, S.

2011-05-02

74

Optimizations and oracle parallelism with dynamic translation  

Microsoft Academic Search

We describe several optimizations which can be employed in a dynamic binary translation (DBT) system, where low compilation\\/translation overhead is essential. These optimizations achieve a high degree of ILP, sometimes even surpassing a static compiler employing more sophisticated, and more time-consuming algorithms [9]. We present results in which we employ these optimizations in a dynamic binary translation system capable of

Kemal Ebcio?lu; Erik R. Altman; Michael Gschwind; Sumedh Sathaye

1999-01-01

75

A bilevel dynamic signal timing optimization problem  

Microsoft Academic Search

This paper formulates the dynamic signal timing optimization (DSTO) problem as a bilevel model. In the upper level, total network travel time is minimized subject to some necessary signalisation constraints. In the lower level, the dynamic user-optimal route choice is formulated as a variational inequality model, which complies with the dynamic extension of Wardrop's first principle. The sensitivity analysis using

Huey-Kuo Chen; Cheng-Yi Chou; Chieh-Tsun Lai

2004-01-01

76

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.

77

Optimal Dynamic Taxation, Saving and Investment.  

National Technical Information Service (NTIS)

The paper develops a framework for determining optimal dynamic taxation based on maximizing welfare in a macro-economic market economy with value-maximizing firms, which face costs of adjustment for investment and utility optimizing consumers. It derives ...

R. Gradus

1989-01-01

78

Mathematical control model for beam dynamics optimization  

Microsoft Academic Search

A special class of the problems attracting attention of numerous researches is represented by the problems associated with the beam dynamics optimization in accelerators of charged particles. In this paper problem of beam dynamics optimization is considered as a control theory problem. Problem statement is considered on the pattern of linear accelerating on traveling wave.

Dmitri A. Ovsyannikov; Alexander D. Ovsyannikov

2003-01-01

79

Time Optimal Trajectory Planning in Dynamic Environments  

NASA Technical Reports Server (NTRS)

A method is presented for planning the motion of a robot in a dynamic environment by computing a trajectory that avoids all obstacles and that satisfies the robot dynamics and its actuator constraints. This method consists of two steps -- the computation of the trajectory and its refinement with a dynamic optimization.

Fiorini, P.; Shiller, Z.

1996-01-01

80

TRACKING CODE DEVELOPMENT FOR BEAM DYNAMICS OPTIMIZATION  

SciTech Connect

Dynamic aperture (DA) optimization with direct particle tracking is a straight forward approach when the computing power is permitted. It can have various realistic errors included and is more close than theoretical estimations. In this approach, a fast and parallel tracking code could be very helpful. In this presentation, we describe an implementation of storage ring particle tracking code TESLA for beam dynamics optimization. It supports MPI based parallel computing and is robust as DA calculation engine. This code has been used in the NSLS-II dynamics optimizations and obtained promising performance.

Yang, L.

2011-03-28

81

Simultaneous beam geometry and intensity map optimization in intensity-modulated radiation therapy  

Microsoft Academic Search

Purpose: In current intensity-modulated radiation therapy (IMRT) plan optimization, the focus is on either finding optimal beam angles (or other beam delivery parameters such as field segments, couch angles, gantry angles) or optimal beam intensities. In this article we offer a mixed integer programming (MIP) approach for simultaneously determining an optimal intensity map and optimal beam angles for IMRT delivery.

Eva K.. Lee; Tim Fox; Ian Crocker

2006-01-01

82

Dynamic Binary Translation and Optimization  

Microsoft Academic Search

We describe a VLIW architecture designed specifically as a target for dynamic compilation of an existing instruction set architecture. This design approach offers the simplicity and high performance of statically scheduled architectures, achieves compatibility with an established architecture, and makes use of dynamic adaptation. Thus, the original architecture is implemented using dynamic compilation, a process we refer to as DAISY

Kemal Ebcioglu; Erik R. Altman; Michael Gschwind; Sumedh W. Sathaye

2001-01-01

83

Optimal reactive power dispatch based on harmony search algorithm  

Microsoft Academic Search

This paper presents a harmony search algorithm for optimal reactive power dispatch (ORPD) problem. Optimal reactive power dispatch is a mixed integer, nonlinear optimization problem which includes both continuous and discrete control variables. The proposed algorithm is used to find the settings of control variables such as generator voltages, tap positions of tap changing transformers and the amount of reactive

A. H. Khazali; M. Kalantar

2011-01-01

84

Utilizing parallel optimization in computational fluid dynamics  

Microsoft Academic Search

General problems of interest in computational fluid dynamics are investigated by means of optimization. Specifically, in the first part of the dissertation, a method of optimal incremental function approximation is developed for the adaptive solution of differential equations. Various concepts and ideas utilized by numerical techniques employed in computational mechanics and artificial neural networks (e.g. function approximation and error minimization,

Michael Kokkolaras

1998-01-01

85

Optimal facility layout design  

Microsoft Academic Search

The facility layout problem (FLP) is a fundamental optimization problem encountered in many manufacturing and service organizations. Montreuil introduced a mixed integer programming (MIP) model for FLP that has been used as the basis for several rounding heuristics. However, no further attempt has been made to solve this MIP optimally. In fact, though this MIP only has 2n(n?1) 0–1 variables,

Russell D. Meller; Venkat Narayanan; Pamela H. Vance

1998-01-01

86

Dynamic optimization and adaptive controller design  

NASA Astrophysics Data System (ADS)

In this work I present a new type of controller which is an adaptive tracking controller which employs dynamic optimization for optimizing current value of controller action for the temperature control of nonisothermal continuously stirred tank reactor (CSTR). We begin with a two-state model of nonisothermal CSTR which are mass and heat balance equations and then add cooling system dynamics to eliminate input multiplicity. The initial design value is obtained using local stability of steady states where approach temperature for cooling action is specified as a steady state and a design specification. Later we make a correction in the dynamics where material balance is manipulated to use feed concentration as a system parameter as an adaptive control measure in order to avoid actuator saturation for the main control loop. The analysis leading to design of dynamic optimization based parameter adaptive controller is presented. The important component of this mathematical framework is reference trajectory generation to form an adaptive control measure.

Inamdar, S. R.

2010-10-01

87

Efficient dynamic optimization of logic programs  

NASA Technical Reports Server (NTRS)

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

Laird, Phil

1992-01-01

88

Hybrid Particle Swarm Optimization Approach for Solving the Discrete OPF Problem Considering the Valve Loading Effects  

Microsoft Academic Search

This paper presents a hybrid particle swarm optimization algorithm (HPSO) as a modern optimization tool to solve the discrete optimal power flow (OPF) problem that has both discrete and continuous optimization variables. The problem is classified as constrained mixed integer nonlinear programming with multimodal characteristics. The objective functions considered are the system real power losses, fuel cost, and the gaseous

M. R. AlRashidi; M. E. El-Hawary

2007-01-01

89

Optimization of dynamic neural fields  

Microsoft Academic Search

There is a growing interest in using dynamic neural fields for modeling biological and technical systems, but constructive ways to set up such models are still missing. We discuss gradient-based, evolutionary and hybrid algorithms for data-driven adaptation of neural field parameters. The proposed methods are evaluated using artificial and neuro-physiological data.

Christian Igel; Wolfram Erlhagen; Dirk Jancke

2001-01-01

90

Optimal dynamic mobility management for PCS networks  

Microsoft Academic Search

We study a dynamic mobility management scheme: the movement-based location update scheme. An analytical model is applied to formulate the costs of location update and paging in the movement-based location update scheme. The problem of min- imizing the total cost is formulated as an optimization problem that finds the optimal threshold in the movement-based location update scheme. We prove that

Jie Li; Hisao Kameda; Keqin Li

2000-01-01

91

Role of controllability in optimizing quantum dynamics  

SciTech Connect

This paper reveals an important role that controllability plays in the complexity of optimizing quantum control dynamics. We show that the loss of controllability generally leads to multiple locally suboptimal controls when gate fidelity in a quantum control system is maximized, which does not happen if the system is controllable. Such local suboptimal controls may attract an optimization algorithm into a local trap when a global optimal solution is sought, even if the target gate can be perfectly realized. This conclusion results from an analysis of the critical topology of the corresponding quantum control landscape, which refers to the gate fidelity objective as a functional of the control fields. For uncontrollable systems, due to SU(2) and SU(3) dynamical symmetries, the control landscape corresponding to an implementable target gate is proven to possess multiple locally optimal critical points, and its ruggedness can be further increased if the target gate is not realizable. These results imply that the optimization of quantum dynamics can be seriously impeded when operating with local search algorithms under these conditions, and thus full controllability is demanded.

Wu Rebing; Hsieh, Michael A.; Rabitz, Herschel [Department of Automation, Tsinghua University, Beijing, 100084, China and Center for Quantum Information Science and Technology, TNList, Beijing, 100084 (China); Department of Chemistry and Center for Quantum Information Science and Technology, University of Southern California, Los Angeles, California 90025 (United States); Department of Chemistry, Princeton University, Princeton, New Jersey 08544 (United States)

2011-06-15

92

Dynamic Sociometry in Particle Swarm Optimization  

Microsoft Academic Search

The performance of Particle Swarm Optimization is greatly affected by the size and sociometry of the swarm. This research proposes a dynamic sociometry, which is shown to be more effective on some problems than the standard star and ring sociometries. The performance of various combinations of swarm size and sociometry on six different test functions is qualitatively analyzed.

Mark Richards; Dan Ventura

2003-01-01

93

Adapting Particle Swarm Optimization to Dynamic Environments  

Microsoft Academic Search

In this paper the authors propose a method for adapting the particle swarm optimizer for dynamic environments. The process consists of causing each particle to reset its record of its best position as the environment changes, to avoid making direction and velocity decisions on the basis of outdated information. Two methods for initiating this process are examined: periodic resetting, based

Anthony Carlisle; Gerry Dozier

2000-01-01

94

Optimized dynamical decoupling via genetic algorithms  

NASA Astrophysics Data System (ADS)

We utilize genetic algorithms aided by simulated annealing to find optimal dynamical decoupling (DD) sequences for a single-qubit system subjected to a general decoherence model under a variety of control pulse conditions. We focus on the case of sequences with equal pulse intervals and perform the optimization with respect to pulse type and order. In this manner, we obtain robust DD sequences, first in the limit of ideal pulses, then when including pulse imperfections such as finite-pulse duration and qubit rotation (flip-angle) errors. Although our optimization is numerical, we identify a deterministic structure that underlies the top-performing sequences. We use this structure to devise DD sequences which outperform previously designed concatenated DD (CDD) and quadratic DD (QDD) sequences in the presence of pulse errors. We explain our findings using time-dependent perturbation theory and provide a detailed scaling analysis of the optimal sequences.

Quiroz, Gregory; Lidar, Daniel A.

2013-11-01

95

Optimizing Motion Planning for Hyper Dynamic Manipulator  

NASA Astrophysics Data System (ADS)

This paper investigates the optimal motion planning for an hyper dynamic manipulator. As case study, we consider a golf swing robot which is consisting with two actuated joint and a mechanical stoppers. Genetic Algorithm (GA) technique is proposed to solve the optimal golf swing motion which is generated by Fourier series approximation. The objective function for GA approach is to minimizing the intermediate and final state, minimizing the robot's energy consummation and maximizing the robot's speed. Obtained simulation results show the effectiveness of the proposed scheme.

Aboura, Souhila; Omari, Abdelhafid; Meguenni, Kadda Zemalache

2012-01-01

96

Dynamic weighting in Monte Carlo and optimization  

PubMed Central

Dynamic importance weighting is proposed as a Monte Carlo method that has the capability to sample relevant parts of the configuration space even in the presence of many steep energy minima. The method relies on an additional dynamic variable (the importance weight) to help the system overcome steep barriers. A non-Metropolis theory is developed for the construction of such weighted samplers. Algorithms based on this method are designed for simulation and global optimization tasks arising from multimodal sampling, neural network training, and the traveling salesman problem. Numerical tests on these problems confirm the effectiveness of the method.

Wong, Wing Hung; Liang, Faming

1997-01-01

97

Optimization of Distributed Parameter Structures Under Dynamic Loads.  

National Technical Information Service (NTIS)

In this report, optimal design of continuous structural systems under dynamic loads is considered. An optimization technique is developed based on the generalized steepest descent method and optimal control techniques Bryson and Ho. The method is then app...

T. T. Feng E. J. Haug J. S. Arora

1976-01-01

98

Direct Optimal Control of Duffing Dynamics  

NASA Technical Reports Server (NTRS)

The "direct control method" is a novel concept that is an attractive alternative and competitor to the differential-equation-based methods. The direct method is equally well applicable to nonlinear, linear, time-varying, and time-invariant systems. For all such systems, the method yields explicit closed-form control laws based on minimization of a quadratic control performance measure. We present an application of the direct method to the dynamics and optimal control of the Duffing system where the control performance measure is not restricted to a quadratic form and hence may include a quartic energy term. The results we present in this report also constitute further generalizations of our earlier work in "direct optimal control methodology." The approach is demonstrated for the optimal control of the Duffing equation with a softening nonlinear stiffness.

Oz, Hayrani; Ramsey, John K.

2002-01-01

99

Optimal power flow computations with constraints limiting the number of control actions  

Microsoft Academic Search

This paper focuses on optimal power flow (OPF) computations in which no more than a pre-specified number of controls are allowed to move. The benchmark formulation of this OPF problem constitutes a mixed integer nonlinear programming (MINLP) problem. To avoid the prohibitive computational time required by classical MINLP approaches to provide a (potentially sub-optimal) solution, we propose instead two alternative

Florin Capitanescu; William Rosehart; Louis Wehenkel

2009-01-01

100

Realizing smart grid benefits requires energy optimization algorithms at residential level  

Microsoft Academic Search

One of the objectives of smart grid is to enable participation by informed customers in order to realize money and energy savings, and environmental benefits. This paper discusses the critical role that energy optimization algorithms will play at residential level to effectively achieve these benefits. A formulation of the optimization problem based on mixed-integer linear programming theory is proposed, and

Tanguy Hubert; Santiago Grijalva

2011-01-01

101

Impact of Biomass Availability on Selection of Optimal Energy Systems and Cost of Energy  

Microsoft Academic Search

This paper assesses the impact of biomass availability on the selection of optimal energy systems, allocation of energy to various energy needs, and cost of energy to villages. Proposals are considered for the development of biomass resources and subsidization of biomass-based energy systems. The analysis applies the Mixed Integer Linear Programming (MILP) optimization model to four villages under existing conditions,

P. R. Shukla; T. K. Moulik

1986-01-01

102

A particle swarm optimization for reactive power and voltage control in electric power systems  

Microsoft Academic Search

This paper presents a particle swarm optimization (PSO) for reactive power and voltage control (RPVC) in electric power systems. RPVC can be formulated as a mixed-integer nonlinear optimization problem (MINLP). The proposed method expands the original PSO to handle a MINLP and determines an RPVC strategy with continuous and discrete control variables such as automatic voltage regulator (AVR) operating values

Yoshikazu Fukuyama; Hirotaka Yoshida

2001-01-01

103

A particle swarm optimization for reactive power and voltage control considering voltage security assessment  

Microsoft Academic Search

This paper presents a particle swarm optimization (PSO) for reactive power and voltage control (volt\\/VAr control: VVC) considering voltage security assessment (VSA). VVC can be formulated as a mixed-integer nonlinear optimization problem (MINLP). The proposed method expands the original PSO to handle a MINLP and determines an online VVC strategy with continuous and discrete control variables such as automatic voltage

Hirotaka Yoshida; Kenichi Kawata; Yoshikazu Fukuyama; Shinichi Takayama; Yosuke Nakanishi

2000-01-01

104

Optimal operational planning of cogeneration systems with thermal storage by the decomposition method  

Microsoft Academic Search

An optimal operational planning method is proposed for cogeneration systems with thermal storage. The daily operational strategy of constituent equipment is determined so as to minimize the daily operational cost subject to the energy demand requirement. This optimization problem is formulated as a large-scale mixed-integer linear programming one, and it is solved by means of the decomposition method. Effects of

R. Yokoyama; K. Ito

1995-01-01

105

Optimal cyclic scheduling for printed circuit board production lines with multiple hoists and general processing sequence  

Microsoft Academic Search

We introduce a mixed-integer programming formulation for finding optimal cyclic schedules for printed circuit board lines with multiple hoists on a shared track, where the processing sequence may be different than the location sequence of the tanks. Computational results on some benchmark problems indicate that optimal cyclic schedules for problems of realistic size can be found in a reasonable time.

Janny Leung; Guoqing Zhang

2003-01-01

106

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.

2010-07-19

107

Mobile Robotic Systems: Dynamics, Control, and Optimization  

NASA Astrophysics Data System (ADS)

Several classes of mobile robotic systems are discussed that are based on certain non-conventional principles of motion and can move along different surfaces and inside various media. Namely, we consider wall-climbing robots equipped with pneumatic grippers and able to move along vertical walls; snake-like multilink mechanisms with actuators installed at their joints; and vibro-robots moving in resistive media and containing movable masses. Kinematics and dynamics of these types of robots are discussed. Optimal geometrical and mechanical parameters as well as optimal periodic motions of robots are determined that correspond to the maximal average speed of locomotion. Results of experiments with prototypes of robots as well as results of computer simulation are presented. The locomotion principles analyzed are applicable to robots that can move in a complicated and hazardous environment, along different surfaces, and inside tubes.

Chernousko, F. L.

2009-08-01

108

Dynamic optimization theory with multiple objectives  

NASA Technical Reports Server (NTRS)

Let V(t) be a vector-valued function for t belonging to closed interval a,b open interval, a real interval. The main purpose of this paper is to establish the existence of a closed interval alpha,beta contained in closed interval a,b for which there exists a t(sub O) belonging to closed interval alpha,beta contained in closed interval a,b such that V(t(sub O)) = 0, the zero vector. Use of such information in the dynamic optimization theory with multiple objectives present is needed. Examples of such systems will be given.

Jones, John, Jr.

1990-01-01

109

Optimizing compressor operation with dynamic programming  

SciTech Connect

Fuel consumption in natural gas compressor stations can be minimized using dynamic programming. Appropriate for installations with either reciprocating or centrifugal compressors in parallel or tandem configurations, this approach yields minimum fuel usage consistent with constraints on total throughput and operating limits on such individual parameters as speed, torque, and surge flow. The flow shares indicated by the optimization procedure may be achieved by adjusting pressure, temperature, or flow setpoints, depending on pipeline and compressor station configuration. Control may be exerted by changing compressor speed; reciprocating units can also be controlled by opening or closing the pockets.

Baqui, A.

1982-09-01

110

A Dynamic Mechanism for Distributed Optimization of Overlay Multicast Tree  

Microsoft Academic Search

To enhance the performance of overlay multicast networks, the overlay multicast tree should be optimized. This optimization prob- lem is a minimum diameter, degree-limited spanning tree (MDDLST) problem which is known to be NP-Hard. We present a new scheme to optimize an overlay multicast tree dynamically. Our algorithm can adapt the tree structure to the dynamic membership and network situation.

Han Choe; Seongho Cho; Chongkwon Kim

2004-01-01

111

Algorithm optimization in molecular dynamics simulation  

NASA Astrophysics Data System (ADS)

Establishing the neighbor list to efficiently calculate the inter-atomic forces consumes the majority of computation time in molecular dynamics (MD) simulation. Several algorithms have been proposed to improve the computation efficiency for short-range interaction in recent years, although an optimized numerical algorithm has not been provided. Based on a rigorous definition of Verlet radius with respect to temperature and list-updating interval in MD simulation, this paper has successfully developed an estimation formula of the computation time for each MD algorithm calculation so as to find an optimized performance for each algorithm. With the formula proposed here, the best algorithm can be chosen based on different total number of atoms, system average density and system average temperature for the MD simulation. It has been shown that the Verlet Cell-linked List (VCL) algorithm is better than other algorithms for a system with a large number of atoms. Furthermore, a generalized VCL algorithm optimized with a list-updating interval and cell-dividing number is analyzed and has been verified to reduce the computation time by 30˜60% in a MD simulation for a two-dimensional lattice system. Due to similarity, the analysis in this study can be extended to other many-particle systems.

Wang, Di-Bao; Hsiao, Fei-Bin; Chuang, Cheng-Hsin; Lee, Yung-Chun

2007-10-01

112

Tracking and optimizing dynamic systems with particle swarms  

Microsoft Academic Search

Using particle swarms to track and optimize dynamic systems is described. Issues related to tracking and optimizing dynamic systems are briefly reviewed. Three kinds of dynamic systems are defined for the purposes of this paper. One of them is chosen for preliminary analysis using the particle swarm on the parabolic benchmark function. Successful tracking of a 10-dimensional parabolic function with

Russell C. Eberhart; Yuhui Shi

2001-01-01

113

Structural Optimization of Rotor Blades with Integrated Dynamics and Aerodynamics.  

National Technical Information Service (NTIS)

The problem of structural optimization of helicopter rotor blades with integrated dynamic and aerodynamic design considerations is addressed. Results of recent optimization work on rotor blades for minimum weight with constraints on multiple coupled natur...

A. Chattopadhyay J. L. Walsh

1989-01-01

114

Optimal Power Flow Computations With a Limited Number of Controls Allowed to Move  

Microsoft Academic Search

This letter focuses on optimal power flow (OPF) computations in which no more than a pre-specified number of controls are allowed to move. To determine an efficient subset of controls satisfying this constraint, we rely on the solution of a mixed integer linear programming (MILP) problem fed with sensitivity information of controls' impact on the objective and constraints. We illustrate

Florin Capitanescu; Louis Wehenkel

2010-01-01

115

Optimization Model for Integrated Logistics Network Design in Green Manufacturing System  

Microsoft Academic Search

Previous researchers have developed ways of managing forward-oriented logistics network. In this study, The design of integrated logistics network is analyzed for green manufacturing system. An optimization model is proposed for the design of integrated logistics network handling product returns on the base of 0-1 mixed integer linear programming approach. It considers storing, reprocessing, remanufacturing facilities and new module suppliers

Zhao Ya Peng; Ding Yi Zhong

2008-01-01

116

Optimal selling price and energy procurement strategies for a retailer in an electricity market  

Microsoft Academic Search

In an electricity market, the retailer sets up contracts with the wholesale side for purchasing electricity and with the customers for its selling. This paper proposes a mathematical method based on mixed-integer stochastic programming to determine the optimal sale price of electricity to customers and the electricity procurement policy of a retailer for a specified period. The retailer has multiple

A. R. Hatami; H. Seifi; M. K. Sheikh-El-Eslami

2009-01-01

117

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

2009-01-01

118

Optimal Planning and Scheduling of Offshore Oil Field Infrastructure Investment and Operations  

Microsoft Academic Search

A multiperiod mixed-integer linear programming (MILP) model formulation is presented for the planning and scheduling of investment and operation in offshore oil field facilities. The formulation employs a general objective function that optimizes a selected economic indicator (e.g., net present value). For a given planning horizon, the decision variables in the model are the choice of reservoirs to develop, selection

R. R. Iyer; I. E. Grossmann; S. Vasantharajan; A. S. Cullick

1998-01-01

119

Water Supply Planning with Inter-basin Water Transfer by an Optimization Model  

Microsoft Academic Search

We developed an optimization model to evaluate the water supply capability of a hydrologic basin in which inter-basin transfers are involved. It is a multi-period mixed integer network flow linear programming (MINFLP) model that can simulate the process of water allocation to different uses at various demand locations using priority factors as cost coefficients. It is specifically designed to reduce

Sheung-Kown Kim; JaeHee Kim; YoungJoon Park

120

Reliability Evaluation and Optimization of Redundant Dynamic Systems  

Microsoft Academic Search

This paper analyzes the reliability of a simple dynamic system with redundant element and demonstrates an optimization technique for system reliability. A computer program was implemented to calculate the true reliability and the near optimal redundancy allocation for a dynamic subsystem. The solutions for the examples were always truly Optimum which is illustrated by an example.

Mirko Vujosevic; Daniel Meade

1985-01-01

121

An optimal force cueing algorithm for dynamic seat  

Microsoft Academic Search

The force cueing algorithm is a primary source of flight simulation fidelity for dynamic seat. This paper presents a new optimal force cueing algorithm which incorporates a mathematical model of human body pressure system and otolith system. Linear perceptual models of the pilot in both dynamic seat and aircraft are built, then the cueing algorithm is derived by optimizing the

Hua Shao; Liwen Guan; Jinsong Wang; Liping Wang; Yang Fu

2009-01-01

122

Adaptation to the optimal learning rate in simple perceptron dynamics  

Microsoft Academic Search

A simple perceptron has an optimal learning rate for a given set of patterns. Beyond the optimal learning rate, the error dynamics oscillates and becomes divergent at a critical value, the edge of learning. We study systems with low-pass filtered feedback from the dynamics of the neurons to their learning rate. We find that these adapt to the edge of

Peter Fleck; Alfred Hubler

2004-01-01

123

Optimal Controller Synthesis Using Approximating-Graph Dynamic Programming  

Microsoft Academic Search

Dynamic programming is well known as a method of calculating optimal control but is not often used in practice because it is assumed to be computationally expensive. We introduce a new stageless version of dynamic programming that produces numerical approximations to optimal control laws for continuous systems. The method creates an approximating graph that models the possible state transitions in

Michiel van de Panne; Eugene Fiume; Zvonko Vranesic

1993-01-01

124

Using of Dynamic and Rollout Neuro - Dynamic Programming for Static and Dynamic Optimization of a Fed-batch Fermentation Process  

Microsoft Academic Search

A fed-batch fermentation process is examined in this paper for experimental and further dynamic optimization. The static optimization is developed for to be found out the optimal initial concentrations of the basic biochemical variables - biomass, substrate and substrate in the feeding solution. For the static optimization of the process the method of Dynamic programming is used. After that these

Tatiana Ilkova

2005-01-01

125

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.

2007-05-01

126

Pseudospectral Chebyshev Optimal Control of Constrained Nonlinear Dynamical Systems  

Microsoft Academic Search

A pseudospectral method for generating optimal trajectories of linear and nonlinear constrained dynamic systems is proposed. The method consists of representing the solution of the optimal control problem by an mth degree interpolating polynomial, using Chebyshev nodes, and then discretizing the problem using a cell-averaging technique. The optimal control problem is thereby transformed into an algebraic nonlinear programming problem. Due

Gamal N. Elnagar; Mohammad A. Kazemi

1998-01-01

127

Dynamic multiobective optimization of power plant using PSO techniques  

Microsoft Academic Search

The Coordinate Control Scheme (CCS) requires references to provide control inputs to a power plant. The references are obtained by mapping the unit load demand to pressure set-point. In order to achieve the optimal power plant operation, the mapping should be optimized under a dynamic environment by considering the multiobjectives of the power system. In this paper, the multiobjective optimal

J. S. Heo; K. Y. Lee; R. Garduno-Ramirez

2005-01-01

128

Adaptation to the optimal learning rate in simple perceptron dynamics  

NASA Astrophysics Data System (ADS)

A simple perceptron has an optimal learning rate for a given set of patterns. Beyond the optimal learning rate, the error dynamics oscillates and becomes divergent at a critical value, the edge of learning. We study systems with low-pass filtered feedback from the dynamics of the neurons to their learning rate. We find that these adapt to the edge of learning, whereas perceptrons with randomized low-pass-filtered feedback adapt to the optimal learning rate. We discuss potential implementations.

Fleck, Peter; Hubler, Alfred

2004-03-01

129

A hybrid simulated annealing for solving an extended model of dynamic cellular manufacturing system  

Microsoft Academic Search

This paper develops a mixed-integer programming model to design the cellular manufacturing systems (CMSs) under dynamic environment. In dynamic environment, the product mix and part demand change under a multi-period planning horizon. Thus, the best designed cells for one period may not be efficient for subsequent periods and reconfiguration of cells is required. Reconfiguration may involve adding, removing or relocating

Nima Safaei; Mohammad Saidi-mehrabad; M. S. Jabal-ameli

2008-01-01

130

An optimal design for robot dynamic control  

Microsoft Academic Search

In this paper we consider the problem of the optimal design in robotics systems. First we discuss the problems relating to the optimization of the mechanical structure in dimensions and performance. Then the control system and the controller optimal time-energy trajectory planning based on the complete model of the robot taking into account intrinsic and extrinsic constraints, the robot singularities

Amar Khoukhi

1999-01-01

131

Dynamic systems of regional economy management optimization  

NASA Astrophysics Data System (ADS)

One of the most actual problems of the Russian economic life is a regional economic systems formation. The hierarchy of economic and branch priorities should follow from the general idea of an industrial policy. The matter is that the concept of an industrial policy is defined by the system of priorities mainly incorporated in it. The problem of priorities is not solved yet neither on federal, nor at a regional level. It is necessary to recognize, that a substantiation of this or that variant of priorities - objectively a challenge. Such substantiation can be received with the help of dynamic structural modeling and management technology. At formation of the regional industrial policy program the special attention is given to creation of modern type commercial structures. In regions there are headquarters and branches of many largest corporations, holdings and banks. Besides it, many regional enterprises already became inter-regional or even the transnational companies. In this connection an assistance of transformation of the industrial enterprises and their groups in vertically integrated companies and modern type holdings can become a prominent aspect of an industrial policy. Regional economic structures should be reconstructed gradually on the general model of the world class competitive companies. Assistance to creation of new corporational control systems, the organization of headquarters and the central services work - all this can be included into the sphere of regional administration industrial policy. The special attention should be turned on necessity of development of own system of the corporate structures, capable to provide to the region an independent participation in use of the natural resources and industrial-technological potential, at the stage of a regional industrial policy program formation. Transformation of the industrial enterprises and their groups into modern type vertically-integrated companies and holdings can become one of the major directions of an industrial policy of region. The situational-analytical centers (SAC) of regional administration The major component of SAC is dynamic modeling, analysis, forecasting and optimization systems, based on modern intellectual information technologies. Spheres of SAC are not only financial streams management and investments optimization, but also strategic forecasting functions, which provide an optimum choice, "aiming", search of optimum ways of regional development and corresponding investments. It is expedient to consider an opportunity of formation of the uniform organizational-methodical center of an industrial policy of region. This organization can be directly connected to the scheduled-analytical services of the largest economic structures, local authorities, the ministries and departments. Such "direct communication" is capable to provide an effective regional development strategic management. Anyway, the output on foreign markets demands concentration of resources and support of authorities. Offered measures are capable to provide a necessary coordination of efforts of a various level economic structures. For maintenance of a regional industrial policy an attraction of all newest methods of strategic planning and management is necessary. Their activity should be constructed on the basis of modern approaches of economic systems management, cause the essence of an industrial policy is finally reduced to an effective regional and corporate economic activities control centers formation. Opportunities of optimum regional economy planning and management as uniform system Approaches to planning regional economic systems can be different. We will consider some most effective methods of planning and control over a regional facilities condition. All of them are compact and evident, that allows to put them into the group of average complexity technologies. At the decision of problems of a regional resource management is rather perspective the so-called "topographical" approach, which is used by intellectual information tec

Trofimov, S.; Kudzh, S.

132

Multi-Swarm Optimization for Dynamic Combinatorial Problems: A Case Study on Dynamic Vehicle Routing Problem  

Microsoft Academic Search

\\u000a Many combinatorial real-world problems are mostly dynamic. They are dynamic in the sense that the global optimum location\\u000a and its value change over the time, in contrast to static problems. The task of the optimization algorithm is to track this\\u000a shifting optimum. Particle Swarm Optimization (PSO) has been previously used to solve continuous dynamic optimization problems,\\u000a whereas only a few

Mostepha Redouane Khouadjia; Enrique Alba; Laetitia Jourdan; El-Ghazali Talbi

2010-01-01

133

Evolutionary Optimization of Dynamic Multi-objective Test Functions  

Microsoft Academic Search

Multi-objective as well as dynamic characteristics appear in many real-world problems. In order to use multi-objective evolutionary optimization algorithms (MOEA) efficiently, a systematic analysis of the algorithms' behavior in dynamic environments by means of dynamic test functions is necessary. These functions can be classified into problems with changing Pareto sets and\\/or Pareto fronts with different dynamic criteria. Thus, a test

Jorn Mehnen; Tobias Wagner; Gunter Rudolph

134

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.

1990-01-01

135

Molecular Dynamics Simulator for Optimal Control of Molecular Motion.  

National Technical Information Service (NTIS)

In recognition of recent interest in developing optimal control techniques for manipulating molecular motion, this paper introduces a computer-driven electro-mechanical analog of this process. The resultant Molecular Dynamic Simulator (MDS) is centered ar...

H. Rabitz

1990-01-01

136

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

2003-01-01

137

Optimal short-term operation and sizing of pumped-storage power plants in systems with high penetration of wind energy  

Microsoft Academic Search

In this paper the short-term optimal operation of an electric system comprising several thermal power plants and one pumped storage plant is studied in several scenarios of power demand and wind penetration in order to draw conclusions about the contribution of the pumped storage plant to system operation costs. A mixed integer linear programming model is used to obtain the

Juan I. Perez-Diaz; Alejandro Perea; Jose R. Wilhelmi

2010-01-01

138

Aerodynamic design optimization using sensitivity analysis and computational fluid dynamics  

Microsoft Academic Search

An efficient aerodynamic shape optimization method based on a computational fluid dynamics\\/sensitivity analysis algorithm has been developed which determines automatically the geometrical definition of an optimal surface starting from any initial arbitrary geometry. This method is not limited to any number of design variables or to any class of surfaces for shape definition.

Oktay Baysal; Mohamad E. Eleshaky

1992-01-01

139

Stability analysis of the particle dynamics in particle swarm optimizer  

Microsoft Academic Search

Previous stability analysis of the particle swarm optimizer was restricted to the assumption that all parameters are nonrandom, in effect a deterministic particle swarm optimizer. We analyze the stability of the particle dynamics without this restrictive assumption using Lyapunov stability analysis and the concept of passive systems. Sufficient conditions for stability are derived, and an illustrative example is given. Simulation

Visakan Kadirkamanathan; Kirusnapillai Selvarajah; Peter J. Fleming

2006-01-01

140

Simple Distributed Particle Swarm Optimization for Dynamic and Noisy Environments.  

National Technical Information Service (NTIS)

In this paper, we present a Simple Distributed Particle Swarm Optimization (SDPSO) algorithm that can be used to track the optimal solution in a dynamic and noisy environment. The classic PSO algorithm lacks the ability to track changing optimum in a dyna...

J. St. Charles T. E. Potok X. Cui

2009-01-01

141

MOPSO based day-ahead optimal self-scheduling of generators under electricity price forecast uncertainty  

Microsoft Academic Search

In competitive electricity markets, self-scheduling for power producer is a conflicting bi-objective mixed-integer nonlinear optimization problem, where a producer tries to maximize his profit and at the same time, minimizes the risk associated with price forecast uncertainty, while satisfying all the operational constraints. This paper proposes a multi-objective particle swarm optimization (MOPSO) based meta-heuristic technique to provide Pareto optimal solution

N. M. Pindoriya; S. N. Singh

2009-01-01

142

Nonlinear FOPDT model identification for the superheat dynamic in a refrigeration system  

Microsoft Academic Search

An on-line nonlinear FOPDT system identification method is proposed and applied to model the superheat dynamic in a supermarket refrigeration system. The considered nonlinear FOPDT model is an extension of the standard FOPDT model by means that its parameters are time dependent. After the considered system is discretized, the nonlinear FOPDT identification problem is formulated as a Mixed Integer Non-Linear

Zhenyu Yang; Zhen Sun; Casper Andersen

2011-01-01

143

Optimal analysis of a space solar dynamic power system  

Microsoft Academic Search

The major purpose of the present study is the theoretical modeling, numerical simulation and optimal analysis of a space solar dynamic power system. Using the method of system analysis, a mathematical and physical model is developed to describe the process of energy transfer and conversion in a space solar dynamic power system. As a new assessing criterion for total launch

Yu-Ting Wu; Jian-Xun Ren; Zeng-Yuan Guo; Xin-Gang Liang

2003-01-01

144

Bridging Developmental Systems Theory and Evolutionary Psychology Using Dynamic Optimization  

ERIC Educational Resources Information Center

Interactions between evolutionary psychologists and developmental systems theorists have been largely antagonistic. This is unfortunate because potential synergies between the two approaches remain unexplored. This article presents a method that may help to bridge the divide, and that has proven fruitful in biology: dynamic optimization. Dynamic

Frankenhuis, Willem E.; Panchanathan, Karthik; Clark Barrett, H.

2013-01-01

145

Computer aided analysis and optimization of mechanical system dynamics  

NASA Technical Reports Server (NTRS)

The purpose is to outline a computational approach to spatial dynamics of mechanical systems that substantially enlarges the scope of consideration to include flexible bodies, feedback control, hydraulics, and related interdisciplinary effects. Design sensitivity analysis and optimization is the ultimate goal. The approach to computer generation and solution of the system dynamic equations and graphical methods for creating animations as output is outlined.

Haug, E. J.

1984-01-01

146

Optimal Dynamic Spectrum Access via Periodic Channel Sensing  

Microsoft Academic Search

The problem of dynamically accessing a set of parallel channels occupied by primary users is considered. The secondary user is allowed to sense and to transmit in a single channel. By exploiting idle periods between bursty transmissions of primary users, and by using a periodic sensing strategy, optimal dynamic access is achieved by maximizing the throughput of the secondary user

Qianchuan Zhao; Stefan Geirhofer; Lang Tong; Brian M. Sadler

2007-01-01

147

Optimized dynamical control of state transfer through noisy spin chains  

NASA Astrophysics Data System (ADS)

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.

Zwick, Analia; Álvarez, Gonzalo A.; Bensky, Guy; Kurizki, Gershon

2014-06-01

148

Dynamic multi-swarm particle swarm optimizer with local search for Large Scale Global Optimization  

Microsoft Academic Search

In this paper, the performance of dynamic multi-swarm particle swarm optimizer (DMS-PSO) on the set of benchmark functions provided for the CEC2008 Special Session on Large Scale optimization is reported. Different from the existing multi-swarm PSOs and local versions of PSO, the sub-swarms are dynamic and the sub-swarmspsila size is very small. The whole population is divided into a large

Shizheng Zhao; Jing J. Liang; Ponnuthurai N. Suganthan; Mehmet Fatih Tasgetiren

2008-01-01

149

Optimal control of molecular motion expressed through quantum fluid dynamics  

NASA Astrophysics Data System (ADS)

A quantum fluid-dynamic (QFD) control formulation is presented for optimally manipulating atomic and molecular systems. In QFD the control quantum system is expressed in terms of the probability density ? and the quantum current j. This choice of variables is motivated by the generally expected slowly varying spatial-temporal dependence of the fluid-dynamical variables. The QFD approach is illustrated for manipulation of the ground electronic state dynamics of HCl induced by an external electric field.

Dey, Bijoy K.; Rabitz, Herschel; Askar, Attila

2000-04-01

150

Integrated aerodynamic/dynamic optimization of helicopter rotor blades  

NASA Technical Reports Server (NTRS)

An integrated aerodynamic/dynamic optimization procedure is used to minimize blade weight and 4 per rev vertical hub shear for a rotor blade in forward flight. The coupling of aerodynamics and dynamics is accomplished through the inclusion of airloads which vary with the design variables during the optimization process. Both single and multiple objective functions are used in the optimization formulation. The Global Criteria Approach is used to formulate the multiple objective optimization and results are compared with those obtained by using single objective function formulations. Constraints are imposed on natural frequencies, autorotational inertia, and centrifugal stress. The program CAMRAD is used for the blade aerodynamic and dynamic analyses, and the program CONMIN is used for the optimization. Since the spanwise and the azimuthal variations of loading are responsible for most rotor vibration and noise, the vertical airload distributions on the blade, before and after optimization, are compared. The total power required by the rotor to produce the same amount of thrust for a given area is also calculated before and after optimization. Results indicate that integrated optimization can significantly reduce the blade weight, the hub shear and the amplitude of the vertical airload distributions on the blade and the total power required by the rotor.

Chattopadhyay, Aditi; Walsh, Joanne L.; Riley, Michael F.

1989-01-01

151

Structural optimization of rotor blades with integrated dynamics and aerodynamics  

NASA Technical Reports Server (NTRS)

The problem of structural optimization of helicopter rotor blades with integrated dynamic and aerodynamic design considerations is addressed. Results of recent optimization work on rotor blades for minimum weight with constraints on multiple coupled natural flap-lag frequencies, blade autorotational inertia and centrifugal stress has been reviewed. A strategy has been defined for the ongoing activities in the integrated dynamic/aerodynamic optimization of rotor blades. As a first step, the integrated dynamic/airload optimization problem has been formulated. To calculate system sensitivity derivatives necessary for the optimization recently developed, Global Sensitivity Equations (GSE) are being investigated. A need for multiple objective functions for the integrated optimization problem has been demonstrated and various techniques for solving the multiple objective function optimization are being investigated. The method called the Global Criteria Approach has been applied to a test problem with the blade in vacuum and the blade weight and the centrifugal stress as the multiple objectives. The results indicate that the method is quite effective in solving optimization problems with conflicting objective functions.

Chattopadhyay, Aditi; Walsh, Joanne L.

1989-01-01

152

Structural optimization of rotor blades with integrated dynamics and aerodynamics  

NASA Technical Reports Server (NTRS)

The problem of structural optimization of helicopter rotor blades with integrated dynamic and aerodynamic design considerations is addressed. Results of recent optimization work on rotor blades for minimum weight with constraints on multiple coupled natural flap-lag frequencies, blade autorotational inertia and centrifugal stress has been reviewed. A strategy has been defined for the ongoing activities in the integrated dynamic/aerodynamic optimization of rotor blades. As a first step, the integrated dynamic/airload optimization problem has been formulated. To calculate system sensitivity derivatives necessary for the optimization recently developed, Global Sensitivity Equations (GSE) are being investigated. A need for multiple objective functions for the integrated optimization problem has been demonstrated and various techniques for solving the multiple objective function optimization are being investigated. The method called the Global Criteria Approach has been applied to a test problem with the blade in vacuum and the blade weight and the centrifugal stress as the multiple objectives. The results indicate that the method is quite effective in solving optimization problems with conflicting objective functions.

Chattopadhyay, Aditi; Walsh, Joanne L.

1988-01-01

153

Local Protein Threading by Mixed Integer Programming  

Microsoft Academic Search

Abstract: During the last decade, significant progresses have been made in solving the Protein Threading Problem (PTP). However, all previous ap- proaches to PTP only perform global sequence–structure alignment. This ob- vious limitation is in clear contrast with the ”world of sequences”, where local sequence-sequence alignments are widely used to find functionally important re- gions in families of proteins. This

Guillaume Collet; Rumen Andonov; Nicola Yanev; Jean-François Gibrat

2009-01-01

154

Powertrain Hybrid System Optimization Using Dynamic Programming  

Microsoft Academic Search

Automotive powertrain system consists of several interactive and coupled nonlinear systems. This research focuses on the coordination of Gasoline Direct Injection (GDI) engine, transmission and emission aftertreatment systems. The goal is to design an optimal control strategy on driving performance, emissions (HC, CO, NOX), fuel economy as well as the transition smoothness of engine mode switching and gear shifting, under

ZHENGMAO YE

155

Evolutionary structural optimization for dynamic problems  

Microsoft Academic Search

This paper presents a simple method for structural optimization with frequency constraints. The structure is modelled by a fine mesh of finite elements. At the end of each eigenvalue analysis, part of the material is removed from the structure so that the frequencies of the resulting structure will be shifted towards a desired direction. A sensitivity number indicating the optimum

Y. M. Xie; G. P. Steven

1996-01-01

156

Interactive Optimal Design of Dynamically Loaded Structures.  

National Technical Information Service (NTIS)

An interactive software system for optimal design of civil engineering structures is described. Operating on a DEC VAX 11/780 computer, the system is a virtual memory version of UNIX (a Bell Labs System). This report reviews previous efforts to improve op...

M. A. Bhatti K. S. Pister E. Polak

1980-01-01

157

Optimal entangling capacity of dynamical processes  

SciTech Connect

We investigate the entangling capacity of dynamical operations when provided with local ancilla. A comparison is made between the entangling capacity with and without the assistance of prior entanglement. An analytic solution is found for the log-negativity entangling capacity of two-qubit gates, which equals the entanglement of the Choi matrix isomorphic to the unitary operator. Surprisingly, the availability of prior entanglement does not affect this result, a property we call resource independence of the entangling capacity. We prove several useful upper bounds on the entangling capacity that hold for general qudit dynamical operations and for a whole family of entanglement monotones including log negativity and log robustness. The log-robustness entangling capacity is shown to be resource independent for general dynamics. We provide numerical results supporting a conjecture that the log-negativity entangling capacity is resource independent for all two-qudit unitary operators.

Campbell, Earl T. [Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT (United Kingdom)

2010-10-15

158

Optimal motor control may mask sensory dynamics  

Microsoft Academic Search

Properties of neural controllers for closed-loop sensorimotor behavior can be inferred with system identification. Under the\\u000a standard paradigm, the closed-loop system is perturbed (input), measurements are taken (output), and the relationship between\\u000a input and output reveals features of the system under study. Here we show that under common assumptions made about such systems\\u000a (e.g. the system implements optimal control with

Sean G. Carver; Tim Kiemel; Noah J. Cowan; John J. Jeka

2009-01-01

159

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.

1992-01-01

160

Fully integrated aerodynamic/dynamic optimization of helicopter rotor blades  

NASA Technical Reports Server (NTRS)

This paper describes a fully integrated aerodynamic/dynamic optimization procedure for helicopter rotor blades. The procedure combines performance and dynamics 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 maneuver; 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.

1992-01-01

161

Fast Route Optimization for Dynamic Nested NEMO  

Microsoft Academic Search

As wireless communication and terminal technologies develop, the mobility of users rapidly increases. The users in this dynamic state also want to connect their devices and to receive seamlessly services anywhere, anytime. Standard groups therefore progress the research and standardization for 'mobility support'. Network Mobility (NEMO) proposed by IETF focuses on the condition where the entire network moves. However, in

Jae-kwon Seo; Sung-hyun Nam; Kyung-geun Lee

2007-01-01

162

Optimized reduction of uncertainty in bursty human dynamics  

NASA Astrophysics Data System (ADS)

Human dynamics is known to be inhomogeneous and bursty but the detailed understanding of the role of human factors in bursty dynamics is still lacking. In order to investigate their role we devise an agent-based model, where an agent in an uncertain situation tries to reduce the uncertainty by communicating with information providers while having to wait time for responses. Here the waiting time can be considered as cost. We show that the optimal choice of the waiting time under uncertainty gives rise to the bursty dynamics, characterized by the heavy tailed distribution of optimal waiting time. We find that in all cases the efficiency for communication is relevant to the scaling behavior of the optimal waiting time distribution. On the other hand, the cost turns out in some cases to be irrelevant depending on the degree of uncertainty and efficiency.

Jo, Hang-Hyun; Moon, Eunyoung; Kaski, Kimmo

2012-01-01

163

Dynamic simulation and optimization for batch reactor control profiles  

NASA Astrophysics Data System (ADS)

Batch crystallization is one of the most important chemical separation unit operations. Due to the complex mechanism and dynamic nature of this process the mathematical model research is a challenging task. In this paper, the authors present research achievement on batch crystallization modeling, simulation, optimization and parameter estimation. Within the proposed control strategy, a dynamic optimization is first preformed with the objective to obtain the optimal cooling temperature policy of a batch crystallizer, maximizing the total volume of seeded crystals. Next, owing to the complex and highly nonlinear behavior of the batch crystallizer, the nonlinear control strategy based on a generic model control (GMC) algorithm is implemented to track the resulting optimal temperature profile.

Niu, Lin; Yang, Dongyue

2013-03-01

164

Optimal Reservoir Operation for Flood Control Using Folded Dynamic Programming  

Microsoft Academic Search

Folded Dynamic Programming (FDP) is adopted for developing optimal reservoir operation policies for flood control. It is applied\\u000a to a case study of Hirakud Reservoir in Mahanadi basin, India with the objective of deriving optimal policy for flood control.\\u000a The river flows down to Naraj, the head of delta where a major city is located and finally joins the Bay

D. Nagesh Kumar; Falguni Baliarsingh; K. Srinivasa Raju

2010-01-01

165

Template based black-box optimization of dynamic neural fields.  

PubMed

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

2013-10-01

166

Optimization of Catheter Position and Dwell Time in Prostate HDR Brachytherapy using HIPO and Linear Programming  

Microsoft Academic Search

In this work the problem of determination of the optimal catheter position and source loading in HDR prostate brachytherapy\\u000a is modeled as a Mixed Integer Linear Programming (MILP) problem and solved by ILOG CPLEX. The results are compared to those\\u000a of HIPO, a state of the art inverse treatment plan optimization algorithm. Given that MILP is guaranteed to find the

A. Karabis; P. Belotti; D. Baltas

167

Capacitor Placement in Balanced and Unbalanced Radial Distribution System by Discrete Particle Swarm Optimization  

Microsoft Academic Search

Capacitor placement problem is a non-linear and non- differentiable mixed integer optimization problem with a set of equality and inequality constraints. Most conventional optimization techniques are incapable to solve this hard combinatorial problem. The radial distribution systems are unbalanced because of single-phase, two-phase and three-phase loads. Thus, load flow solution for balance radial distribution networks will not be sufficient to

S. Sivanagaraju; B. J. Viswanatha Rao

168

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

169

A Population Prediction Strategy for Evolutionary Dynamic Multiobjective Optimization.  

PubMed

This paper investigates how to use prediction strategies to improve the performance of multiobjective evolutionary optimization algorithms in dealing with dynamic environments. Prediction-based methods have been applied to predict some isolated points in both dynamic single objective optimization and dynamic multiobjective optimization. We extend this idea to predict a whole population by considering the properties of continuous dynamic multiobjective optimization problems. In our approach, called population prediction strategy (PPS), a Pareto set is divided into two parts: a center point and a manifold. A sequence of center points is maintained to predict the next center, and the previous manifolds are used to estimate the next manifold. Thus, PPS could initialize a whole population by combining the predicted center and estimated manifold when a change is detected. We systematically compare PPS with a random initialization strategy and a hybrid initialization strategy on a variety of test instances with linear or nonlinear correlation between design variables. The statistical results show that PPS is promising for dealing with dynamic environments. PMID:23757532

Zhou, Aimin; Jin, Yaochu; Zhang, Qingfu

2013-02-26

170

Speeding up critical system dynamics through optimized evolution  

SciTech Connect

The number of defects which are generated upon crossing a quantum phase transition can be minimized by choosing properly designed time-dependent pulses. In this work we determine what are the ultimate limits of this optimization. We discuss under which conditions the production of defects across the phase transition is vanishing small. Furthermore we show that the minimum time required to enter this regime is T{approx}{pi}/{Delta}, where {Delta} is the minimum spectral gap, unveiling an intimate connection between an optimized unitary dynamics and the intrinsic measure of the Hilbert space for pure states. Surprisingly, the dynamics is nonadiabatic; this result can be understood by assuming a simple two-level dynamics for the many-body system. Finally we classify the possible dynamical regimes in terms of the action s=T{Delta}.

Caneva, Tommaso [International School for Advanced Studies (SISSA), Via Beirut 2-4, I-34014 Trieste (Italy); Institut fuer Quanteninformationsverarbeitung, Universitaet Ulm, D-89069 Ulm (Germany); Calarco, Tommaso; Montangero, Simone [Institut fuer Quanteninformationsverarbeitung, Universitaet Ulm, D-89069 Ulm (Germany); Fazio, Rosario [NEST, Scuola Normale Superiore and Istituto di Nanoscienze-CNR, Piazza dei Cavalieri 7, I-56126 Pisa (Italy); Santoro, Giuseppe E. [International School for Advanced Studies (SISSA), Via Beirut 2-4, I-34014 Trieste (Italy); CNR-INFM Democritos National Simulation Center, Via Beirut 2-4, I-34014 Trieste (Italy); International Centre for Theoretical Physics (ICTP), P.O. Box 586, I-34014 Trieste (Italy)

2011-07-15

171

MULTIOBJECTIVE DYNAMIC APERTURE OPTIMIZATION AT NSLS-II  

SciTech Connect

In this paper we present a multiobjective approach to the dynamic aperture (DA) optimization. Taking the NSLS-II lattice as an example, we have used both sextupoles and quadrupoles as tuning variables to optimize both on-momentum and off-momentum DA. The geometric and chromatic sextupoles are used for nonlinear properties while the tunes are independently varied by quadrupoles. The dispersion and emittance are fixed during tunes variation. The algorithms, procedures, performances and results of our optimization of DA will be discussed and they are found to be robust, general and easy to apply to similar problems.

Yang, L.; Li, Y.; Guo, W.; Krinsky, S.

2011-03-28

172

Optimal Trading Strategy and Supply\\/Demand Dynamics  

Microsoft Academic Search

The supply\\/demand of a security in the market is an intertemporal, not a static, object and its dynamics is crucial in determining market participants' trading behavior. Previous studies on the optimal trading strategy to execute a given order focuses mostly on the static properties of the supply\\/demand. In this paper, we show that the dynamics of the supply\\/demand is of

Anna Obizhaeva; Jiang Wang

2005-01-01

173

Optimization of natural-gas pipeline systems via dynamic programming  

Microsoft Academic Search

The complexity and expense of operating natural-gas pipeline systems have made optimum operation and planning of increased interest to the natural-gas pipeline industries. Since the operations of natural-gas pipeline sytems are characterized by inherent nonlinearities and numerous constraints, dynamic programming provides an extremely powerful method for optimizing such systems. This paper summarizes the application of dynamic programming techniques to solve

P. Wong; R. Larson

1968-01-01

174

Dynamic multi-swarm particle swarm optimizer with local search  

Microsoft Academic Search

In this paper, the performance of a modified dynamic multi-swarm particle swarm optimizer (DMS-PSO) on the set of benchmark functions provided by CEC2005 is reported. Different from the existing multi-swarm PSOs and local versions of PSO, the swarms are dynamic and the swarms' size is small. The whole population is divided into many small swarms, these swarms are regrouped frequently

Jing J. Liang; Ponnuthurai N. Suganthan

2005-01-01

175

An optimized replica exchange molecular dynamics method.  

PubMed

We introduce a new way to perform swaps between replicas in replica exchange molecular dynamics simulations. The method is based on a generalized canonical probability distribution function and flattens the potential of mean force along the temperature coordinate, such that a random walk in temperature space is achieved. Application to a Go model of protein A showed that the method is more efficient than conventional replica exchange. The method results in a constant probability distribution of the replicas over the thermostats, yields a minimum round-trip time between extremum temperatures, and leads to faster ergodic convergence. PMID:19239315

Kamberaj, Hiqmet; van der Vaart, Arjan

2009-02-21

176

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.

1995-01-01

177

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.

1995-01-01

178

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.

1995-01-01

179

Optimal motor control may mask sensory dynamics  

PubMed Central

Properties of neural controllers for closed-loop sensorimotor behavior can be inferred with system identification. Under the standard paradigm, the closed-loop system is perturbed (input), measurements are taken (output), and the relationship between input and output reveals features of the system under study. Here we show that under common assumptions made about such systems (e.g. the system implements optimal control with a penalty on mechanical, but not sensory, states) important aspects of the neural controller (its zeros mask the modes of the sensors) remain hidden from standard system identification techniques. Only by perturbing or measuring the closed-loop system “between” the sensor and the control can these features be exposed with closed-loop system identification methods; while uncommon, there exist noninvasive techniques such as galvanic vestibular stimulation that perturb between sensor and controller in this way.

Kiemel, Tim; Cowan, Noah J.; Jeka, John J.

2009-01-01

180

Dynamic Scheduling of a Multi-Class Queue II: Discount Optimal Dynamic Policies.  

National Technical Information Service (NTIS)

The author continues the study of the dynamic scheduling problem introduced and formulated in the paper's predecessor. The positive interest rate is considered fixed throughout. It is shown that there exists a static optimal policy, and the corresponding ...

J. M. Harrison

1972-01-01

181

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)

2006-11-27

182

Equilibrium selection under evolutionary game dynamics with optimizing behavior  

NASA Astrophysics Data System (ADS)

The purpose of this paper is to investigate equilibrium selection in a heterogeneous population composed of both optimizing and programmed agents using an evolutionary game-theoretic framework. Under the Smith dynamic, we are able to identify a class of games in which any programmed behavior will become extinct ultimately starting from any initial state, as well as a class of games in which all programmed agents can get wiped out eventually, as long as there are not enough initially. Besides, the long-run behavior is characterized under a variety of well-behaved dynamics such as the Brown-von Neumann-Nash dynamic.

Zhang, Yanfang; Mei, Shue; Zhong, Weijun

2012-09-01

183

Aerodynamic design optimization with sensitivity analysis and computational fluid dynamics  

NASA Technical Reports Server (NTRS)

An investigation was conducted from October 1, 1990 to May 31, 1994 on the development of methodologies to improve the designs (more specifically, the shape) of aerodynamic surfaces of coupling optimization algorithms (OA) with Computational Fluid Dynamics (CFD) algorithms via sensitivity analyses (SA). The study produced several promising methodologies and their proof-of-concept cases, which have been reported in the open literature.

Baysal, Oktay

1995-01-01

184

Optimal Dynamic Strategy of Building a Hydrogen Infrastructure in Beijing  

Microsoft Academic Search

Proceedings of the 4th Annual Asia Pacific Conference on Transportation and the Environment This paper describes the on-going Hydrogen Infrastructure Transition (HIT) modeling efforts with the Beijing case study. HIT uses dynamic programming to generate optimal decisions on when, where, at what sizes and by what technologies to build up a regional hydrogen infrastructure while minimizing the discounted value of

Zhenhong Lin; Joan M Ogden; Yueyue Fan; Dan Sperling

2005-01-01

185

OPTIMAL DESIGN AND DYNAMIC SIMULATION OF A HYBRID SOLAR VEHICLE  

Microsoft Academic Search

The paper deals with a detailed study on the optimal sizing of a solar hybrid car, based on a longitudinal vehicle dynamic model and considering energy flows, weight and costs. The model describes the effects of solar panels area and position, vehicle dimensions and propulsion system components on vehicle performance, weight, fuel savings and costs. It is shown that significant

Ivan Arsie; Gianfranco Rizzo; Marco Sorrentino

186

DESIGN SENSITIVITY ANALYSIS AND OPTIMIZATION OF NONLINEAR TRANSIENT DYNAMICS  

Microsoft Academic Search

A shape design sensitivity analysis (DSA) and optimization of structural transient dynamics are proposed for the finite deformation elastoplastic materials under impact with a rigid surface. A shape variation of the structure is considered using the material derivative approach in continuum mechanics. Hyperelasticitybased multiplicatively decomposed elastoplasticity is used for the constitutive model. The implicit Newmark time integration scheme is used

Nam H. Kim; Kyung K. Choi

2001-01-01

187

Optimal arrival tra c spacing via dynamic programming  

Microsoft Academic Search

We present the application of dynamic programming to a combinatorial optimization problem to achieve proper arrival runway spacing, which appears in the process of assign-ing speed during the transition to approach and approach phases of flight. We apply the algorithm to data from a fast-time simulation developed under NASA's Advanced Air Transportation Technologies Project for investigating new air tra c

Alexandre M. Bayen; Todd Callantine; Claire J. Tomlin; Yinyu Ye; Jiawei Zhang

188

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

2001-01-01

189

Preserving electron spin coherence in solids by optimal dynamical decoupling  

NASA Astrophysics Data System (ADS)

To exploit the quantum coherence of electron spins in solids in future technologies such as quantum computing, it is first vital to overcome the problem of spin decoherence due to their coupling to the noisy environment. Dynamical decoupling, which uses stroboscopic spin flips to give an average coupling to the environment that is effectively zero, is a particularly promising strategy for combating decoherence because it can be naturally integrated with other desired functionalities, such as quantum gates. Errors are inevitably introduced in each spin flip, so it is desirable to minimize the number of control pulses used to realize dynamical decoupling having a given level of precision. Such optimal dynamical decoupling sequences have recently been explored. The experimental realization of optimal dynamical decoupling in solid-state systems, however, remains elusive. Here we use pulsed electron paramagnetic resonance to demonstrate experimentally optimal dynamical decoupling for preserving electron spin coherence in irradiated malonic acid crystals at temperatures from 50K to room temperature [1]. Using a seven-pulse optimal dynamical decoupling sequence, we prolonged the spin coherence time to about 30 ms; it would otherwise be about 0.04 ms without control or 6.2 ms under one-pulse control. By comparing experiments with microscopic theories, we have identified the relevant electron spin decoherence mechanisms in the solid. Recently, we demonstrate experimentally that dynamical decoupling can preserve bipartite pseudo-entanglement in phosphorous donors in a silicon system [2]. In particular, the lifetime of pseudo entangled states is extended from 0.4 us in the absence of decoherence control to 30 us in the presence of a two-flip dynamical decoupling sequence. [4pt] [1]. Jiangfeng Du, Xing Rong, Nan Zhao, Ya Wang, Jiahui Yang and R. B. Liu, Preserving electron spin coherence in solids by optimal dynamical decoupling, Nature 461, 1265-1268 (2009). [0pt] [2] Ya Wang, Xing Rong, Pengbo Feng, Wanjie Xu, Bo Chong, Ji-Hu Su, Jiangbin Gong, and Jiangfeng Du, Preservation of bipartite pseudo-entanglement in solids using dynamical decoupling, submitted to Phys. Rev. Lett.

Du, Jiangfeng

2011-03-01

190

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.

2009-12-01

191

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.

2011-01-01

192

Optimization-based structure identification of dynamical networks  

NASA Astrophysics Data System (ADS)

The topological structure of a dynamical network plays a pivotal part in its properties, dynamics and control. Thus, understanding and modeling the structure of a network will lead to a better knowledge of its evolutionary mechanisms and to a better cottoning on its dynamical and functional behaviors. However, in many practical situations, the topological structure of a dynamical network is usually unknown or uncertain. Thus, exploring the underlying topological structure of a dynamical network is of great value. In recent years, there has been a growing interest in structure identification of dynamical networks. As a result, various methods for identifying the network structure have been proposed. However, in most of the previous work, few of them were discussed in the perspective of optimization. In this paper, an optimization algorithm based on the projected conjugate gradient method is proposed to identify a network structure. It is straightforward and applicable to networks with or without observation noise. Furthermore, the proposed algorithm is applicable to dynamical networks with partially observed component variables for each multidimensional node, as well as small-scale networks with time-varying structures. Numerical experiments are conducted to illustrate the good performance and universality of the new algorithm.

He, Tao; Lu, Xiliang; Wu, Xiaoqun; Lu, Jun-an; Zheng, Wei Xing

2013-02-01

193

Dynamics and linear quadratic optimal control of flexible multibody systems  

NASA Astrophysics Data System (ADS)

An efficient algorithm for the modeling, dynamic analysis, and optimal control of flexible multibody systems (FMBS) is presented. The cantilevered Bernoulli-Euler beam model and the assumed mode method are used to represent flexibility of elastic bodies in 3D vibration problems. Centrifugal stiffening effects are introduced to correctly represent the dynamic response. The governing equations of motion are based on Kane's equations, adopting a recursive formulation and strategic positioning of the generalized coordinates. The linear quadratic optimization scheme is employed to formulate the vibration control problem. The solutions to the Riccati equation and the use of Kalman gain as optimal control feedbacks to the control of flexibility are also introduced. Based on the optimal control theory and the property of the built-in redundancy for flexible multibody systems, the performance index measure in the optimization control of such systems can be classified into two manifolds: (1) using the extra degrees of freedom resulting from redundancy as control inputs and choosing an integral-type performance index which results in a global optimization scheme and (2) using the joint forces and torques as control inputs and allowing the system output state to keep close track to a reference state while the performance index is kept minimum. Several numerical examples are presented to demonstrate the effectiveness of the methodologies developed.

Tung, Chin-Wei

1994-12-01

194

Analysis and optimization of pulse dynamics for magnetic stimulation.  

PubMed

Magnetic stimulation is a standard tool in brain research and has found important clinical applications in neurology, psychiatry, and rehabilitation. Whereas coil designs and the spatial field properties have been intensively studied in the literature, the temporal dynamics of the field has received less attention. Typically, the magnetic field waveform is determined by available device circuit topologies rather than by consideration of what is optimal for neural stimulation. This paper analyzes and optimizes the waveform dynamics using a nonlinear model of a mammalian axon. The optimization objective was to minimize the pulse energy loss. The energy loss drives power consumption and heating, which are the dominating limitations of magnetic stimulation. The optimization approach is based on a hybrid global-local method. Different coordinate systems for describing the continuous waveforms in a limited parameter space are defined for numerical stability. The optimization results suggest that there are waveforms with substantially higher efficiency than that of traditional pulse shapes. One class of optimal pulses is analyzed further. Although the coil voltage profile of these waveforms is almost rectangular, the corresponding current shape presents distinctive characteristics, such as a slow low-amplitude first phase which precedes the main pulse and reduces the losses. Representatives of this class of waveforms corresponding to different maximum voltages are linked by a nonlinear transformation. The main phase, however, scales with time only. As with conventional magnetic stimulation pulses, briefer pulses result in lower energy loss but require higher coil voltage than longer pulses. PMID:23469168

Goetz, Stefan M; Truong, Cong Nam; Gerhofer, Manuel G; Peterchev, Angel V; Herzog, Hans-Georg; Weyh, Thomas

2013-01-01

195

Beam Dynamics Optimization for the Xfel Photo Injector  

NASA Astrophysics Data System (ADS)

The main challenge for the European XFEL photo injector is the production of 1 nC electron beams with a normalized transverse emittance of 0.9 mm mrad. The photo injector setup consists of a 1.5-cell L-band rf gun cavity supplied with solenoids for beam focusing and emittance compensation and the first accelerating section with 8 TESLA superconducting cavities. The first 4 cavities are used as a booster to provide by proper choice of its position, gradient and phase matching conditions for the emittance conservation. For optimization of the beam dynamics in the photo injector, a staged algorithm, based on ASTRA simulations, has been developed. The first stage considers the emission of electrons from a photo cathode. The cathode laser energy and its transverse parameters are adjusted to produce a bunch charge of 1 nC in presence of space charge forces (including image charge at the cathode) and Schottky-like effects. The second stage contains rf gun cavity and solenoid optimization. The booster position, gradient and initial phase are optimized at the third stage yielding the minimum emittance at the photo injector exit. Results of the XFEL photo injector optimization will be presented. Besides simulations experimental studies towards XFEL photo injector are carried out. The photo injector test facility at DESY in Zeuthen (PITZ) develops photo injectors for FELs, including FLASH and the European XFEL. A thorough comparison of measured data with results of beam dynamics simulations is one of the main PITZ goals. Detailed experimental studies on photo emission processes, thermal emittance, transverse and longitudinal phase space of the electron beam are being performed together with beam dynamics simulations. This aims to result in better understanding of beam dynamics in high brightness photo injectors. Experimentally obtained photo injector characteristics (like thermal emittance) have to be used in an additional optimization of the photo injector resulting in more realistic beam dynamics simulations. Results of these studies will be reported as well.

Krasilnikov, Mikhail

196

Optimal Control of HIV Dynamic Using Embedding Method  

PubMed Central

This present study proposes an optimal control problem, with the final goal of implementing an optimal treatment protocol which could maximize the survival time of patients and minimize the cost of drug utilizing a system of ordinary differential equations which describes the interaction of the immune system with the human immunodeficiency virus (HIV). Optimal control problem transfers into a modified problem in measure space using an embedding method in which the existence of optimal solution is guaranteed by compactness of the space. Then the metamorphosed problem is approximated by a linear programming (LP) problem, and by solving this LP problem a suboptimal piecewise constant control function, which is more practical from the clinical viewpoint, is achieved. The comparison between the immune system dynamics in treated and untreated patients is introduced. Finally, the relationships between the healthy cells and virus are shown.

Zarei, H.; Kamyad, A. V.; Farahi, M. H.

2011-01-01

197

Label free optimal dynamic discrimination of biological macromolecules  

NASA Astrophysics Data System (ADS)

The fast development of laser techniques, in particular, the generation of ultrashort femtosecond and even attosecond pulses opens new frontiers and various experimental tools for biomedical applications. The combination of pulse shaping and optimal control is a very promising tool based on coherent manipulation of wavepackets on an ultrafast time scale. It already has successfully been applied for optimal dynamic discrimination (ODD) experiments of biomolecules like free amino acids and flavins which are indistinguishable by spectroscopic means. This approach can be extended toward to label free cellular imaging and detection of chemical or biological substances.

Afonina, S.; Rondi, A.; Kiselev, D.; Bonacina, L.; Wolf, J. P.

2013-03-01

198

Optimization Research of Generation Investment Based on Linear Programming Model  

NASA Astrophysics Data System (ADS)

Linear programming is an important branch of operational research and it is a mathematical method to assist the people to carry out scientific management. GAMS is an advanced simulation and optimization modeling language and it will combine a large number of complex mathematical programming, such as linear programming LP, nonlinear programming NLP, MIP and other mixed-integer programming with the system simulation. In this paper, based on the linear programming model, the optimized investment decision-making of generation is simulated and analyzed. At last, the optimal installed capacity of power plants and the final total cost are got, which provides the rational decision-making basis for optimized investments.

Wu, Juan; Ge, Xueqian

199

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)

1996-02-01

200

Sequential activation of metabolic pathways: a dynamic optimization approach.  

PubMed

The regulation of cellular metabolism facilitates robust cellular operation in the face of changing external conditions. The cellular response to this varying environment may include the activation or inactivation of appropriate metabolic pathways. Experimental and numerical observations of sequential timing in pathway activation have been reported in the literature. It has been argued that such patterns can be rationalized by means of an underlying optimal metabolic design. In this paper we pose a dynamic optimization problem that accounts for time-resource minimization in pathway activation under constrained total enzyme abundance. The optimized variables are time-dependent enzyme concentrations that drive the pathway to a steady state characterized by a prescribed metabolic flux. The problem formulation addresses unbranched pathways with irreversible kinetics. Neither specific reaction kinetics nor fixed pathway length are assumed.In the optimal solution, each enzyme follows a switching profile between zero and maximum concentration, following a temporal sequence that matches the pathway topology. This result provides an analytic justification of the sequential activation previously described in the literature. In contrast with the existent numerical approaches, the activation sequence is proven to be optimal for a generic class of monomolecular kinetics. This class includes, but is not limited to, Mass Action, Michaelis-Menten, Hill, and some Power-law models. This suggests that sequential enzyme expression may be a common feature of metabolic regulation, as it is a robust property of optimal pathway activation. PMID:19412635

Oyarzún, Diego A; Ingalls, Brian P; Middleton, Richard H; Kalamatianos, Dimitrios

2009-11-01

201

Shape Optimization of Vehicle Radiator Using Computational Fluid Dynamics (cfd)  

NASA Astrophysics Data System (ADS)

Automotive manufacturers need to improve the efficiency and lifetime of all engine components. In the case of radiators, performance depends significantly on coolant flow homogeneity across the tubes and overall pressure drop between the inlet and outlet. Design improvements are especially needed in tube-flow uniformity to prevent premature fouling and failure of heat exchangers. Rather than relying on ad-hoc geometry changes, the current study combines Computational Fluid Dynamics with shape optimization methods to improve radiator performance. The goal is to develop an automated suite of virtual tools to assist in radiator design. Two objective functions are considered: a flow non-uniformity coefficient,Cf, and the overall pressure drop, dP*. The methodology used to automate the CFD and shape optimization procedures is discussed. In the first phase, single and multi-variable optimization methods, coupled with CFD, are applied to simplified 2-D radiator models to investigate effects of inlet and outlet positions on the above functions. The second phase concentrates on CFD simulations of a simplified 3-D radiator model. The results, which show possible improvements in both pressure and flow uniformity, validate the optimization criteria that were developed, as well as the potential of shape optimization methods with CFD to improve heat exchanger design. * Improving Radiator Design Through Shape Optimization, L. Guessous and S. Maddipatla, Paper # IMECE2002-33888, Proceedings of the 2002 ASME International Mechanical Engineering Congress and Exposition, November 2002

Maddipatla, Sridhar; Guessous, Laila

2002-11-01

202

Power Optimization for the MLCA Using Dynamic Voltage Scaling  

Microsoft Academic Search

Dynamic voltage scaling (DVS) is an eectiv e method for re- ducing processor power consumption. We present a compiler- based technique for DVS-based power optimizations of mul- timedia applications in the context of the Multi-Level Com- puting Architecture (MLCA)|a novel architecture for par- allel systems-on-a-chip. Our technique combines dependence analysis of long-running loops with proling information in order to identify

Ivan Matosevic; Tarek S. Abdelrahman; Faraydon Karim; Alain Mellan

2005-01-01

203

Near-optimal fully-dynamic graph connectivity  

Microsoft Academic Search

In this paper we present near-optimal bounds for fullydynamic graph connectivity which is the most basic nontrivial fully-dynamic graph problem. Connectivity queries are supported in O(log n\\/log log log n) time while the updates are supported in O(log n(log log n) 3) expected amortized time. The previous best update time was O((log n)2). Our new bound is only doubly-logarithmic factors

Mikkel Thorup

2000-01-01

204

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: hoemberg@wias-berlin.de; Krumbiegel, K., E-mail: krumbieg@wias-berlin.de [Weierstrass Institute for Applied Mathematics and Stochastics, Nonlinear Optimization and Inverse Problems (Germany); Rehberg, J., E-mail: rehberg@wias-berlin.de [Weierstrass Institute for Applied Mathematics and Stochastics, Partial Differential Equations (Germany)

2013-02-15

205

Complex mission optimization for Multiple-UAVs using Linear Temporal Logic  

Microsoft Academic Search

This paper discusses a class of mission planning problems in which mission objectives and relative timing constraints are specified using the linear temporal logic language LTL-x. Among all mission plans that satisfy the LTL-x specifications, it is desired to find those minimizing a given cost functional. We show that such an optimization problem can be formulated as a mixed-integer linear

Sertac Karaman; Emilio Frazzoli

2008-01-01

206

Algorithms for optimal sequencing of dynamic multileaf collimators  

NASA Astrophysics Data System (ADS)

Dynamic multileaf collimator (DMLC) intensity modulated radiation therapy (IMRT) is used to deliver intensity modulated beams using a multileaf collimator (MLC), with the leaves in motion. DMLC-IMRT requires the conversion of a radiation intensity map into a leaf sequence file that controls the movement of the MLC while the beam is on. It is imperative that the intensity map delivered using the leaf sequence file be as close as possible to the intensity map generated by the dose optimization algorithm, while satisfying hardware constraints of the delivery system. Optimization of the leaf-sequencing algorithm has been the subject of several recent investigations. In this work, we present a systematic study of the optimization of leaf-sequencing algorithms for dynamic multileaf collimator beam delivery and provide rigorous mathematical proofs of optimized leaf sequence settings in terms of monitor unit (MU) efficiency under the most common leaf movement constraints that include leaf interdigitation constraint. Our analytical analysis shows that leaf sequencing based on unidirectional movement of the MLC leaves is as MU efficient as bi-directional movement of the MLC leaves.

Kamath, Srijit; Sahni, Sartaj; Palta, Jatinder; Ranka, Sanjay

2004-01-01

207

Optimal forwarding ratio on dynamical networks with heterogeneous mobility  

NASA Astrophysics Data System (ADS)

Since the discovery of non-Poisson statistics of human mobility trajectories, more attention has been paid to understand the role of these patterns in different dynamics. In this study, we first introduce the heterogeneous mobility of mobile agents into dynamical networks, and then investigate packet forwarding strategy on the heterogeneous dynamical networks. We find that the faster speed and the higher proportion of high-speed agents can enhance the network throughput and reduce the mean traveling time in random forwarding. A hierarchical structure in the dependence of high-speed is observed: the network throughput remains unchanged at small and large high-speed value. It is also interesting to find that a slightly preferential forwarding to high-speed agents can maximize the network capacity. Through theoretical analysis and numerical simulations, we show that the optimal forwarding ratio stems from the local structural heterogeneity of low-speed agents.

Gan, Yu; Tang, Ming; Yang, Hanxin

2013-05-01

208

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.

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

2013-01-01

209

Human opinion dynamics: An inspiration to solve complex optimization problems  

NASA Astrophysics Data System (ADS)

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.

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

2013-10-01

210

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

1996-10-01

211

Optimal approach to quantum communication using dynamic programming  

PubMed Central

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

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

2007-01-01

212

Technique for the optimization of the powerhead configuration and performance of liquid rocket engines  

NASA Astrophysics Data System (ADS)

The development and optimization of liquid rocket engines is an integral part of space vehicle design, since most Earth-to-orbit launch vehicles to date have used liquid rockets as their main propulsion system. Rocket engine design tools range in fidelity from very simple conceptual level tools to full computational fluid dynamics (CFD) simulations. The level of fidelity of interest in this research is a design tool that determines engine thrust and specific impulse as well as models the powerhead of the engine. This is the highest level of fidelity applicable to a conceptual level design environment where faster running analyses are desired. The optimization of liquid rocket engines using a powerhead analysis tool is a difficult problem, because it involves both continuous and discrete inputs as well as a nonlinear design space. Example continuous inputs are the main combustion chamber pressure, nozzle area ratio, engine mixture ratio, and desired thrust. Example discrete variable inputs are the engine cycle (staged-combustion, gas generator, etc.), fuel/oxidizer combination, and engine material choices. Nonlinear optimization problems involving both continuous and discrete inputs are referred to as Mixed-Integer Nonlinear Programming (MINLP) problems. Many methods exist in literature for solving MINLP problems; however none are applicable for this research. All of the existing MINLP methods require the relaxation of the discrete variables as part of their analysis procedure. This means that the discrete choices must be evaluated at non-discrete values. This is not possible with an engine powerhead design code. Therefore, a new optimization method was developed that uses modified response surface equations to provide lower bounds of the continuous design space for each unique discrete variable combination. These lower bounds are then used to efficiently solve the optimization problem. The new optimization procedure was used to find optimal rocket engine designs subject to various weight, cost and performance constraints. The results show that the new method efficiently solved the mixed-input optimization problem without requiring discrete variable relaxation.

St. Germain, Brad David

213

Integrated aerodynamic/dynamic optimization of helicopter rotor blades  

NASA Technical Reports Server (NTRS)

An integrated aerodynamic/dynamic optimization procedure is used to minimize blade weight and 4/rev vertical shear of a rotor blade in forward flight. Both single and multiple objective functions are used, with constraints imposed on the first four coupled natural frequencies (elastic modes only), the blade aerorotational inertia, and the centrifugal stress. The global criteria approach is used for the multiple objective formulation, and the results are compared with those obtained from single objective function formulations. Optimum designs are compared against a reference blade, and it is shown that optimum results can be obtained in 7-10 cycles.

Chattopadhyay, Aditi; Walsh, Joanne L.; Riley, Michael F.

1989-01-01

214

SNS accumulator ring collimator optimization with beam dynamics  

NASA Astrophysics Data System (ADS)

It is required to have an uncontrolled beam loss less than 1 nA/m at 1 GeV energy for hands-on maintenance of the Spallation Neutron Source (SNS) Accumulator Ring. Collimators will be used for this end. Various beam dynamics questions related with the collimators have been studied. Various factors are optimized with the given ring lattice and error study is done. Numerical simulations also indicate that movable shielding is necessary for a few hot places downstream of the primary collimator. These simulations indicate that with properly designed collimators the uncontrolled beam-loss requirements of the SNS accumulator ring may be achievable.

Jeon, D.; Danilov, V. V.; Galambos, J. D.; Holmes, J. A.; Olsen, D. K.

1999-10-01

215

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)

2010-01-15

216

Set-valued dynamic treatment regimes for competing outcomes.  

PubMed

Dynamic treatment regimes (DTRs) operationalize the clinical decision process as a sequence of functions, one for each clinical decision, where each function maps up-to-date patient information to a single recommended treatment. Current methods for estimating optimal DTRs, for example Q-learning, require the specification of a single outcome by which the "goodness" of competing dynamic treatment regimes is measured. However, this is an over-simplification of the goal of clinical decision making, which aims to balance several potentially competing outcomes, for example, symptom relief and side-effect burden. When there are competing outcomes and patients do not know or cannot communicate their preferences, formation of a single composite outcome that correctly balances the competing outcomes is not possible. This problem also occurs when patient preferences evolve over time. We propose a method for constructing DTRs that accommodates competing outcomes by recommending sets of treatments at each decision point. Formally, we construct a sequence of set-valued functions that take as input up-to-date patient information and give as output a recommended subset of the possible treatments. For a given patient history, the recommended set of treatments contains all treatments that produce non-inferior outcome vectors. Constructing these set-valued functions requires solving a non-trivial enumeration problem. We offer an exact enumeration algorithm by recasting the problem as a linear mixed integer program. The proposed methods are illustrated using data from the CATIE schizophrenia study. PMID:24400912

Laber, Eric B; Lizotte, Daniel J; Ferguson, Bradley

2014-03-01

217

Stochastic Depletion Problems: Effective Myopic Policies for a Class of Dynamic Optimization Problems  

Microsoft Academic Search

This paper presents a general class of dynamic stochastic optimization problems we refer to as Stochastic Depletion Problems. A number of challenging dynamic optimization problems of practical interest are stochastic depletion problems. Optimal solutions for such problems are difficult to obtain, both from a pragmatic computational perspective as also from a theoretical perspective. As such, simple heuristics are highly desirable.

Carri W. Chan; Vivek F. Farias

2009-01-01

218

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.

2010-11-01

219

An optimal strategy for functional mapping of dynamic trait loci.  

PubMed

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

2010-02-01

220

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

1992-01-01

221

Nonlinear dynamic analysis of an optimal particle damper  

NASA Astrophysics Data System (ADS)

We study the dynamical behavior of a single degree of freedom mechanical system with a particle damper. The particle (granular) damping was optimized for the primary system operating condition by using an appropriate gap size for a prismatic enclosure. The particles absorb the kinetic energy of the vibrating structure and convert it into heat through the inelastic collisions and friction. This results in a highly nonlinear mechanical system. Considering linear signal analysis, state space reconstruction, Poincaré sections and the determination of maximal Lyapunov exponents, the motion of the granular system inside the enclosure is characterized for a wide frequency range. With the excitation frequency as control parameter, either regular or chaotic motion of the granular bed is found and the influence on the damping is analyzed.

Sánchez, Martín; Manuel Carlevaro, C.

2013-04-01

222

Improved self-protection using dynamically optimized expendable countermeasures  

NASA Astrophysics Data System (ADS)

The use of expendable countermeasures is still found to be a viable choice for self protection against Man Portable Air Defense Systems (MANPADS) due to their simplicity, low cost, flexibility, recent improvements in decoy technology, the ability to handle multiple threats simultaneously and the off-board nature of these countermeasures. In civil aviation, the risk of general hazards linked to the use of pyrotechnics is the main argument against expendable countermeasures, whereas for military platforms, the limitation in capacity due to a limited number of rounds is often used as an argument to replace expendable countermeasures by laser-based countermeasures. This latter argument is in general not substantiated by modelling or figures of merit, although it is often argued that a laser based system allows for more false alarms, hence enabling a more sensitive missile approach warning system. The author has developed a model that accounts for the statistical effects of running out of expendable countermeasures during a mission, in terms of the overall mission survival probability. The model includes key parameters of the missile approach warning system (MAWS), and can handle multiple missile types and missile attack configurations, as well as various statistical models of missile attacks. The model enables quantitative comparison between laser based and expendable countermeasures, but also a dynamic optimization of the countermeasures in terms of whether to use small or large countermeasure programs, as well as the dynamic tuning of MAWS key parameters to optimize the overall performance. The model is also well suited for determination of the contributions of the different components of the system in the overall survival probability.

Hovland, Harald

2007-04-01

223

Molecular dynamics simulator for optimal control of molecular motion  

NASA Astrophysics Data System (ADS)

In recognition of recent interest in developing optimal control techniques for manipulating molecular motion, this paper introduces a computer-driven electro-mechanical analog of this process. The resultant Molecular Dynamic Simulator (MDS) is centered around a linear air track for which the atoms of the controlled molecule are simulated as nearly frictionless carts on the track. Bonds in the simulated molecule are described by precision springs, and the interaction with an external optical field is simulated through a computer-based linear driver. When the MDS is operated in the harmonic regime, it can be used as an exact analog of molecular scale quantum systems through Ehrenfest's Theorem, or equivalently as a classical set of coupled oscillators. The tools of optimal control theory currently being applied at the molecular scale are used to design the forcing function for the MDS. Optical encoders are used to measure bond distances for graphic representation of the MDS behavior. Bond breaking can also be simulated by bond-length sensitive trigger-release mechanisms. The MDS is especially useful as a modelling tool to bridge theoretical studies and eventual laboratory experiments at the true molecular scale.

Rabitz, Herschel

1990-12-01

224

On performance metrics and particle swarm methods for dynamic multiobjective optimization problems  

Microsoft Academic Search

This paper describes two performance measures for measuring an EMO (evolutionary multiobjective optimization) algorithm's ability to track a time-varying Pareto-front in a dynamic environment. These measures are evaluated using a dynamic multiobjective test function and a dynamic multiobjective PSO, maximinPSOD, which is capable of handling dynamic multiobjective optimization problems. maximinPSOD is an extension from a previously proposed multiobjective PSO, maximinPSO.

Xiaodong Li; J. Branke; M. Kirley

2007-01-01

225

A Dual Ascent Procedure for Multiproduct Dynamic Demand Coordinated Replenishment with Backlogging  

Microsoft Academic Search

This paper describes a mixed-integer programming formulation and dual ascent based branch-and-bound algorithm for the multiproduct dynamic demand coordinated replenishment problem with backlogging. The single sourcing properties of the formulation and the hierarchical structure of the fixed-charge and continuous variables yield an extremely tight linear programming relaxation for the problem. A branch-and-bound algorithm based on Erlenkotter's dual ascent, dual adjustment,

E. Powell Robinson Jr.; Li-Lian Gao

1996-01-01

226

Indirect optimization of interplanetary trajectories including spiral dynamics  

NASA Astrophysics Data System (ADS)

In the future small, robotic probes and large, human-crewed spacecraft will utilize long-duration, finite-burning engines. These types of engines have already been proven in several missions and research is on-going for the design of newer engines of this class. The trajectories they generate are significantly different than those that use conventional high-thrust chemical propulsion. The indirect method was chosen for the optimization of the missions presented due to its mathematical elegance and other problem specific issues. The goal was to choose a difficult optimization problem for an engine of this class and determine techniques, trends, and formulations that help overcome the indirect method's traditional shortcoming: the numerical difficulty of generating an accurate first guess for complex missions. The problem chosen was the optimization of interplanetary trajectories, with particular results presented for the problem of transferring from Low Earth Orbit (LEO) to Low Mars Orbit (LMO). Most previous attempts at this problem use an array of simplifications to the engine system and/or problem dynamics to make the optimization feasible. These simplifications are systematically removed here. The complete trajectory was broken down into its component phases and careful study was paid to each. Analysis of the escape and capture spirals provided useful insight into the appropriate coordinate frames for spirals. This study yielded a technique that quickly and accurately estimates the unknown Lagrange multipliers. These results were applied to the full LEO to LMO mission as part of a sequential process for a two-dimensional solar system model and the equivalent three-dimensional model. This process includes many new derivations that facilitate the generation of an accurate first guess for these LEO to LMO missions. One new derivation in particular is vital where the co-states for a Mars capture spiral referenced to a Martian coordinate frame are transformed into their Earth based equivalents. This sets up a multiple shooting problem integrated in a single coordinate frame which is different than the single shooting method used in published benchmarks. The new approach generates more fuel efficient trajectories and significantly more complex numerically achievable capture sequence compared with such benchmarks.

Ranieri, Christopher Louis

227

A PSO-based optimal tuning strategy for constrained multivariable predictive controllers with model uncertainty.  

PubMed

This paper describes the development of a method to optimally tune constrained MPC algorithms with model uncertainty. The proposed method is formulated by using the worst-case control scenario, which is characterized by the Morari resiliency index and the condition number, and a given nonlinear multi-objective performance criterion. The resulting constrained mixed-integer nonlinear optimization problem is solved on the basis of a modified version of the particle swarm optimization technique, because of its effectiveness in dealing with this kind of problem. The performance of this PSO-based tuning method is evaluated through its application to the well-known Shell heavy oil fractionator process. PMID:24398055

Nery, Gesner A; Martins, Márcio A F; Kalid, Ricardo

2014-03-01

228

On the duality of quantum filtering and optimal feedback control in quantum open linear dynamical systems  

Microsoft Academic Search

The multi-dimensional quality open linear dynamical system with observation and feedback along a quantum linear transmission line is studied in discrete time. The linear least squares filtering and optimal control strategies are obtained as quantum analogies of the Kalman filter and Bellman dynamical programming. The duality of quantum filtering and optimal feedback control is observed for this particular case.

Simon C. Edwards; Viacheslav P. Belavkin

2003-01-01

229

Particle swarm optimization based neural-network model for hydro power plant dynamics  

Microsoft Academic Search

This paper addresses the modeling of hydro power plant dynamics using neural network approach. The cost function as root mean square error is optimized by particle swarm optimization technique. The identification performance is compared with fuzzy models based on GK clustering algorithm in application to study hydro power plant dynamics. It is found that the response obtained from the NN

Nand Kishor; Madhusudan Singh; A. S. Raghuvanshi

2007-01-01

230

Decision-making Model of Bank's Assets Portfolio based on Multi-period Dynamic Optimization  

Microsoft Academic Search

Based on the dynamic programming method, by the use of the constraints on VaR, laws, regulations, and operation, a multi-period dynamic portfolio optimal model for banks is successfully developed with the objective of maximizing the portfolio's yield. The characteristics and innovations of this paper are as follows. First, using the Backward Induction Method, the optimal portfolio in the current period

Guo-tai CHI; He-chao DONG; Xiu-yan SUN

2007-01-01

231

Dynamic multiobjective optimization problems: test cases, approximations, and applications  

Microsoft Academic Search

Abstract—After demonstrating adequately the usefulness of evolutionary multiobjective optimization (EMO) algorithms in finding multiple Pareto-optimal solutions for static multiobjective optimization problems, there is now a growing need for solving dy- namic multiobjective optimization problems in a similar manner. In this paper, we focus on addressing this issue by developing a number of test problems and by suggesting a baseline algorithm.

Marco Farina; Kalyanmoy Deb; Paolo Amato

2004-01-01

232

State evaluation strategy for exemplar-based policy optimization of dynamic decision problems  

Microsoft Academic Search

Direct policy search (DPS) that optimizes the parameters of a decision making model, combined with evolutionary algorithms which enable robust optimization, is a promising approach to dynamic decision problems. Exemplar- based policy (EBP) optimization is a novel framework for DPS in which the policy is composed of a set of exemplars and a case- based action selector, with the set

Kokolo Ikeda; Hajime Kita

2007-01-01

233

Optimal drift design model for multi-story buildings subjected to dynamic lateral forces  

Microsoft Academic Search

SUMMARY An optimal drift design model for a linear multi-story building structure under dynamic lateral forces is presented. The drift design model is formulated into a minimum weight design problem subjected to constraints on stresses, the displacement at the top of a building, and inter-story drift. The optimal drift design model consists of three main components: an optimizer, a response

Hyo Seon Park; Jun Hyeok Kwon

2003-01-01

234

Optimal control and optimal trajectories of regional macroeconomic dynamics based on the Pontryagin maximum principle  

NASA Astrophysics Data System (ADS)

The Pontryagin maximum principle is used to prove a theorem concerning optimal control in regional macroeconomics. A boundary value problem for optimal trajectories of the state and adjoint variables is formulated, and optimal curves are analyzed. An algorithm is proposed for solving the boundary value problem of optimal control. The performance of the algorithm is demonstrated by computing an optimal control and the corresponding optimal trajectories.

Bulgakov, V. K.; Strigunov, V. V.

2009-05-01

235

Optimization of conventional water treatment plant using dynamic programming.  

PubMed

In this research, the mathematical models, indicating the capability of various units, such as rapid mixing, coagulation and flocculation, sedimentation, and the rapid sand filtration are used. Moreover, cost functions were used for the formulation of conventional water and wastewater treatment plant by applying Clark's formula (Clark, 1982). Also, by applying dynamic programming algorithm, it is easy to design a conventional treatment system with minimal cost. The application of the model for a case reduced the annual cost. This reduction was approximately in the range of 4.5-9.5% considering variable limitations. Sensitivity analysis and prediction of system's feedbacks were performed for different alterations in proportion from parameters optimized amounts. The results indicated (1) that the objective function is more sensitive to design flow rate (Q), (2) the variations in the alum dosage (A), and (3) the sand filter head loss (H). Increasing the inflow by 20%, the total annual cost would increase to about 12.6%, while 20% reduction in inflow leads to 15.2% decrease in the total annual cost. Similarly, 20% increase in alum dosage causes 7.1% increase in the total annual cost, while 20% decrease results in 7.9% decrease in the total annual cost. Furthermore, the pressure decrease causes 2.95 and 3.39% increase and decrease in total annual cost of treatment plants. PMID:23625909

Mostafa, Khezri Seyed; Bahareh, Ghafari; Elahe, Dadvar; Pegah, Dadras

2013-04-26

236

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.

2012-12-01

237

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

2010-03-01

238

Function-valued adaptive dynamics and optimal control theory.  

PubMed

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

2013-09-01

239

A permutation-based dual genetic algorithm for dynamic optimization problems  

Microsoft Academic Search

Adaptation to dynamic optimization problems is currently receiving growing interest as one of the most important applications\\u000a of genetic algorithms. Inspired by dualism and dominance in nature, genetic algorithms with the dualism mechanism have been\\u000a applied for several dynamic problems with binary encoding. This paper investigates the idea of dualism for combinatorial optimization\\u000a problems in dynamic environments, which are also

Lili Liu; Dingwei Wang; W. H. Ip

2009-01-01

240

Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, Part II: proofs of results.  

PubMed

In this companion article to "Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part I: Main Content" [Orellana, Rotnitzky and Robins (2010), IJB, Vol. 6, Iss. 2, Art. 7] we present (i) proofs of the claims in that paper, (ii) a proposal for the computation of a confidence set for the optimal index when this lies in a finite set, and (iii) an example to aid the interpretation of the positivity assumption. PMID:20405047

Orellana, Liliana; Rotnitzky, Andrea; Robins, James M

2010-01-01

241

Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part II: Proofs of Results*  

PubMed Central

In this companion article to “Dynamic Regime Marginal Structural Mean Models for Estimation of Optimal Dynamic Treatment Regimes, Part I: Main Content” [Orellana, Rotnitzky and Robins (2010), IJB, Vol. 6, Iss. 2, Art. 7] we present (i) proofs of the claims in that paper, (ii) a proposal for the computation of a confidence set for the optimal index when this lies in a finite set, and (iii) an example to aid the interpretation of the positivity assumption.

Orellana, Liliana; Rotnitzky, Andrea; Robins, James M.

2010-01-01

242

An optimization model for energy generation and distribution in a dynamic facility  

NASA Technical Reports Server (NTRS)

An analytical model is described using linear programming for the optimum generation and distribution of energy demands among competing energy resources and different economic criteria. The model, which will be used as a general engineering tool in the analysis of the Deep Space Network ground facility, considers several essential decisions for better design and operation. The decisions sought for the particular energy application include: the optimum time to build an assembly of elements, inclusion of a storage medium of some type, and the size or capacity of the elements that will minimize the total life-cycle cost over a given number of years. The model, which is structured in multiple time divisions, employ the decomposition principle for large-size matrices, the branch-and-bound method in mixed-integer programming, and the revised simplex technique for efficient and economic computer use.

Lansing, F. L.

1981-01-01

243

Advanced solar dynamic space power systems. Part 1: Efficiency and surface optimization  

Microsoft Academic Search

The aim of this work is the proposal and the analysis of advanced solar dynamic space power systems for electrical space power generation. The detailed thermodynamic analysis of SDCC (Solar Dynamic Combined Cycle) and SDBC (Solar Dynamic Binary Cycle) systems is carried out. The analysis is completed with an optimization procedure that allows the maximum efficiency and minimum surface conditions

A. Agazzani; A. Massardo

1995-01-01

244

Advanced solar dynamic space power systems. Part 2: Detailed design and specific parameters optimization  

Microsoft Academic Search

The aim of this work is the proposal and the analysis of advanced solar dynamic space power systems for electrical space power generation. In the first part of this work (Agazzani and Massardo, 1995) a performance optimization procedure for a SDCC (Solar Dynamic Combined Cycle) and a SDBC (Solar Dynamic Binary Cycle) was presented. Results have pointed out improvements obtainable

A. Agazzani; A. Massardo

1995-01-01

245

Metamodeling and the Critic-based approach to multi-level optimization.  

PubMed

Large-scale networks with hundreds of thousands of variables and constraints are becoming more and more common in logistics, communications, and distribution domains. Traditionally, the utility functions defined on such networks are optimized using some variation of Linear Programming, such as Mixed Integer Programming (MIP). Despite enormous progress both in hardware (multiprocessor systems and specialized processors) and software (Gurobi) we are reaching the limits of what these tools can handle in real time. Modern logistic problems, for example, call for expanding the problem both vertically (from one day up to several days) and horizontally (combining separate solution stages into an integrated model). The complexity of such integrated models calls for alternative methods of solution, such as Approximate Dynamic Programming (ADP), which provide a further increase in the performance necessary for the daily operation. In this paper, we present the theoretical basis and related experiments for solving the multistage decision problems based on the results obtained for shorter periods, as building blocks for the models and the solution, via Critic-Model-Action cycles, where various types of neural networks are combined with traditional MIP models in a unified optimization system. In this system architecture, fast and simple feed-forward networks are trained to reasonably initialize more complicated recurrent networks, which serve as approximators of the value function (Critic). The combination of interrelated neural networks and optimization modules allows for multiple queries for the same system, providing flexibility and optimizing performance for large-scale real-life problems. A MATLAB implementation of our solution procedure for a realistic set of data and constraints shows promising results, compared to the iterative MIP approach. PMID:22386785

Werbos, Ludmilla; Kozma, Robert; Silva-Lugo, Rodrigo; Pazienza, Giovanni E; Werbos, Paul J

2012-08-01

246

An optimal operational advisory system for a brewery's energy supply plant  

SciTech Connect

An optimal operational advisory system is proposed to operate rationally a brewery's energy supply plant from the economical viewpoint. A mixed-integer linear programming problem is formulated so as to minimize the daily operational cost subject to constraints such as equipment performance characteristics, energy supply-demand relations, and some practical operational restrictions. This problem includes lots of unknown variables and a hierarchical approach is adopted to derive numerical solutions. The optimal solution obtained by this methods is indicated to the plant operators so as to support their decision making. Through the numerical study for a real brewery plant, the possibility of saving operational cost is ascertained.

Ito, K.; Shiba, T.; Yokoyama, R. (Univ. of Osaka Prefecture (Japan). Dept. of Energy Systems Engineering); Sakashita, S. (Mayekawa Manufacturing Co. Ltd., Tokyo (Japan). Mayekawa Energy Management Research Center)

1994-03-01

247

Optimal operational planning of cogeneration systems with thermal storage by the decomposition method  

SciTech Connect

An optimal operational planning method is proposed for cogeneration systems with thermal storage. The daily operational strategy of constituent equipment is determined so as to minimize the daily operational cost subject to the energy demand requirement. This optimization problem is formulated as a large-scale mixed-integer linear programming one, and it is solved by means of the decomposition method. Effects of thermal storage on the operation of cogeneration systems are examined through a numerical study on a gas engine-driven cogeneration system installed in a hotel. This method is a useful tool for evaluating the economic and energy-saving properties of cogeneration systems with thermal storage.

Yokoyama, R.; Ito, K. [Osaka Prefecture Univ., Sakai, Osaka (Japan). Dept. of Energy Systems Engineering

1995-12-01

248

Operational optimization of large-scale parallel-unit SWRO desalination plant using differential evolution algorithm.  

PubMed

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

2014-01-01

249

An archived multi-objective simulated annealing for a dynamic cellular manufacturing system  

NASA Astrophysics Data System (ADS)

To design a group layout of a cellular manufacturing system (CMS) in a dynamic environment, a multi-objective mixed-integer non-linear programming model is developed. The model integrates cell formation, group layout and production planning (PP) as three interrelated decisions involved in the design of a CMS. This paper provides an extensive coverage of important manufacturing features used in the design of CMSs and enhances the flexibility of an existing model in handling the fluctuations of part demands more economically by adding machine depot and PP decisions. Two conflicting objectives to be minimized are the total costs and the imbalance of workload among cells. As the considered objectives in this model are in conflict with each other, an archived multi-objective simulated annealing (AMOSA) algorithm is designed to find Pareto-optimal solutions. Matrix-based solution representation, a heuristic procedure generating an initial and feasible solution and efficient mutation operators are the advantages of the designed AMOSA. To demonstrate the efficiency of the proposed algorithm, the performance of AMOSA is compared with an exact algorithm (i.e., ?-constraint method) solved by the GAMS software and a well-known evolutionary algorithm, namely NSGA-II for some randomly generated problems based on some comparison metrics. The obtained results show that the designed AMOSA can obtain satisfactory solutions for the multi-objective model.

Shirazi, Hossein; Kia, Reza; Javadian, Nikbakhsh; Tavakkoli-Moghaddam, Reza

2014-05-01

250

OPTIMAL TASK ALLOCATION AND DYNAMIC TRAJECTORY PLANNING FOR MULTI-VEHICLE SYSTEMS USING NONLINEAR HYBRID OPTIMAL CONTROL  

Microsoft Academic Search

Based on a nonlinear hybrid dynamical systems model a new planning method for optimal coordination and control of multiple unmanned vehicles is investigated. The time dependent hybrid state of the overall system consists of discrete (roles, actions) and continuous (e.g. position, orientation, velocity) state v ariables of the vehicles involved. The evolution in time of the system's hybrid state is

Markus Glocker; Christian Reinl; Oskar von Stryk

251

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.

2013-08-01

252

An integrative and practical evolutionary optimization for a complex, dynamic model of biological networks.  

PubMed

Computer simulation is an important technique to capture the dynamics of biochemical networks. Numerical optimization is the key to estimate the values of kinetic parameters so that the dynamic model reproduces the behaviors of the existing experimental data. It is required to develop general strategies for the optimization of complex biochemical networks with a huge space of search parameters, under the condition that kinetic and quantitative data are hardly available. We propose an integrative and practical strategy for optimizing a complex dynamic model by using qualitative and incomplete experimental data. The key technologies are the divide and conquer method for reducing the search space, handling of multiple objective functions representing different types of biological behaviors, and design of rule-based objective functions that are suitable for qualitative and error-prone experimental data. This strategy is applied to optimizing a dynamic model of the yeast cell cycle to demonstrate the feasibility of it. PMID:21113727

Maeda, Kazuhiro; Fukano, Yuya; Yamamichi, Shunsuke; Nitta, Daichi; Kurata, Hiroyuki

2011-05-01

253

Optimal Piecewise State Feedback Control for Nonlinear Dynamical Systems  

Microsoft Academic Search

In this paper, we consider a class of optimal piecewise state feedback control problems. For this optimal control problem, the time horizon is divided into N subintervals with the end points of each subinterval being referred to as switching times. The control is given by a piecewise state feedback form, where different feedback gain matrices are allowed for different subintervals.

Rui Li; Kok Lay Teo; Guangren Duan

2006-01-01

254

Power flow and dynamic optimal power flow including wind farms  

Microsoft Academic Search

With the increasing levels of wind generator penetration in modern power systems, one of major challenges in the present and coming years is the optimization control, such as optimal power flow including wind farms. The power flow model for a fixed speed wind generator (FSWG) system and a variable speed wind generator (VSWG) system is discussed respectively. The expectation model

Gonggui Chen; Jinfu Chen; Xianzhong Duan

2009-01-01

255

Dynamic Sensor Planning and Control for Optimally Tracking Targets  

Microsoft Academic Search

In this paper, we present an approach to the problem of actively con- trolling the configuration of a team of mobile agents equipped with cameras so as to optimize the quality of the estimates derived from their measurements. The issue of optimizing the robots' configura- tion is particularly important in the context of teams equipped with vision sensors, since most

John R. Spletzer; Camillo J. Taylor

2003-01-01

256

A Dynamic Near-Optimal Algorithm for Online Linear Programming  

Microsoft Academic Search

A natural optimization model that formulates many online resource allocation and revenue management problems is the online linear program (LP) where the constraint matrix is revealed column by column along with the objective function. We provide a near-optimal algorithm for this surprisingly general class of online problems under the assumption of random order of arrival and some mild conditions on

Shipra Agrawal; Zizhuo Wang; Yinyu Ye

2009-01-01

257

Optimal Dynamic Advertising Strategy Under Age-Specific Market Segmentation  

NASA Astrophysics Data System (ADS)

We consider the model proposed by Faggian and Grosset for determining the advertising efforts and goodwill in the long run of a company under age segmentation of consumers. Reducing this model to optimal control sub problems we find the optimal advertising strategy and goodwill.

Krastev, Vladimir

2011-12-01

258

Optimal cleaning policies in heat exchanger networks under rapid fouling  

SciTech Connect

This paper addresses the problem of short-term cleaning scheduling in a special class of heat-exchanger networks (HENs). A salient characteristic of this problem is that the performance of each heat exchanger decreases with time and can then be restored to its initial state by performing cleaning operations. Because of its practical importance, a specific problem has been considered here involving decaying equipment performance due to milk fouling. A mixed-integer nonlinear-programming (MINLP) model is first presented incorporating general fouling profiles. This model is then linearized to a tight mixed-integer linear-programming (MILP) model which can be solved to global optimality. A detailed objective function is used to account for cleaning cost and energy requirements. The formulations can model serial and parallel HENs as well as network arrangements arising from the combination of these basic cases. The optimization algorithm determines simultaneously: (1) the number of cleaning operation tasks required along with their corresponding timings and (2) the optimal utility utilization profile over time. A number of complex heat-exchanger networks examples are presented to illustrate the applicability of the proposed models together with comparative performance results between the MINLP and MILP models.

Georgiadis, M.C.; Papageorgiou, L.G.; Macchietto, S.

2000-02-01

259

Modified descend curvature based fixed form fuzzy optimal control of nonlinear dynamical systems  

Microsoft Academic Search

In this study, a fuzzy rule-based optimal controller is designed for nonlinear dynamical systems. The direct second order method (or direct-descend-curvature algorithm) with a modification called “modified descend controller (MDC)” is used for calculating the parameters of the fuzzy feedback controller. The optimal control problem defined here has dynamic constraints of nonlinear system states and static constraint of a known

Yusuf Oysal; Yasar Becerikli; Ahmet Ferit Konar

2006-01-01

260

Dynamic simulation, optimal design and control of pin-fin heat sink processes  

Microsoft Academic Search

This paper considers the dynamic simulation, optimal design and direct adaptive control of cylindrical pin-fin heat sink processes. For dynamic heat-dispersion investigation of the pin-fin heat sinks, a 3D model that includes the heat transfer from the heat source to fins and the forced convective heat transfer by a horizontal air-cooling fan is proposed. To optimize the heat dispersion performance,

Chyi-Tsong Chen; Shi-Hung Jan

261

Numerical solution to the optimal birth feedback control of a population dynamics: viscosity solution approach  

Microsoft Academic Search

SUMMARY This paper is concerned with the optimal birth control of a McKendrick-type age-structured population dynamic system. We use the dynamic programming approach in our investigation. The Hamilton-Jacobi- Bellman equation satisfied by the value function is derived. It is shown that the value function is the viscosity solution of the Hamilton-Jacobi-Bellman equation. The optimal birth feedback control is found explicitly

Bao-Zhu Guo; Bing Sun

2005-01-01

262

Multi-objective Optimization For The Dynamic Multi-Pickup and Delivery Problem with Time Windows  

Microsoft Academic Search

The PDPTW is an optimization vehicles routing problem which must meet\\u000arequests for transport between suppliers and customers satisfying precedence,\\u000acapacity and time constraints. We present, in this paper, a genetic algorithm\\u000afor multi-objective optimization of a dynamic multi pickup and delivery problem\\u000awith time windows (Dynamic m-PDPTW). We propose a brief literature review of\\u000athe PDPTW, present our approach

Imen Harbaoui Dridi; Ryan Kammarti; Pierre Borne; Mekki Ksouri

2011-01-01

263

Two-level optimized tone mapping for high dynamic range images  

Microsoft Academic Search

In this paper, we propose a two-step tone-mapping algorithm to convert a high dynamic range (HDR) image to a low dynamic range (LDR) image. The first step S1 constructs a global tone mapping which optimizes between uniform quantization and histogram equalization. The second step S2 improves the visual quality by optimizing between global operation and local contrast maintenance. Both S1

Chun-Hung Liu; Oscar C. Au; Cheuk Hong Cheng; Ka Yue Yip

2010-01-01

264

An automatic design optimization tool and its application to computational fluid dynamics  

Microsoft Academic Search

In this paper we describe the Nimrod\\/O design optimization tool, and its application in computational fluid dynamics. Nimrod\\/O facilitates the use of an arbitrary computational model to drive an automatic optimization process. This means that the user can parameterise an arbitrary problem, and then ask the tool to compute the parameter values that minimize or maximise a design objective function.

David Abramson; Andrew Lewis; Tom Peachey; Clive Fletcher

2001-01-01

265

Optimization of biological nutrient removal in a SBR using simulation-based iterative dynamic programming  

Microsoft Academic Search

The purpose of this study was to simulate and optimize the nitrogen removal of a sequencing batch reactor (SBR) through the use of a simplified model derived from activated sludge model no. 1 (ASM1) and iterative dynamic programming (IDP), while meeting the treatment requirements. A new performance index for SBR optimization is proposed on the basis of minimum area criteria

Young-Hwang Kim; ChangKyoo Yoo; In-Beum Lee

2008-01-01

266

Turnpike and Optimal Trajectories in Integral Dynamic Models with Endogenous Delay  

Microsoft Academic Search

Nonlinear optimal control of dynamic systems with endogenous time delays is analyzed. Such systems have important applications and are described by Volterra integral equations with unknowns in the integration limits. The paper focuses on the structure and asymptotic behavior of solutions to several optimization problems with endogenous delay. It is shown that, in certain cases, a special delay trajectory exists

N. Hritonenko; Yu. Yatsenko

2005-01-01

267

Evolutionary Optimization of Dynamics Models in Sequential Monte Carlo Target Tracking  

Microsoft Academic Search

This article describes a new method for the online parameter optimization of various models used to represent the target dynamics in particle filters. The optimization is performed with an evolutionary strategy algorithm, by using the performance of the particle filter as a basis for the objective function. Two different approaches of forming the objective function are presented: the first assumes

Anders M. Johansson; Eric A. Lehmann

2009-01-01

268

Optimal harvesting control problem for linear periodic age-dependent population dynamics  

Microsoft Academic Search

In this paper, we investigate an optimal harvesting problem for linear periodic age-dependent population dynamics. Namely, we consider the Lotka–Mckendrick model with periodic vital rates and a periodic forcing term that sustains oscillations. By Mazur's Theorem, we demonstrate existence of solutions of the optimal control problem (OH) and by the conception of normal cone, we also obtain the first order

Zhixue Luo; Wan-Tong Li; Miansen Wang

2004-01-01

269

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

2005-01-01

270

Optimal Design of Dynamically Loaded Rigid-Plastic Structures. Application: Thick-Walled Concrete Tube  

Microsoft Academic Search

A general approximate method for the optimal design of perfectly plastic inhomogeneous structures exposed to dynamic pressure is presented. The solution is based on the kinematical approach and on simple rigid-plastic idealization and leads to mathematical programming. The application of the method is illustrated by the optimal design of thick-walled concrete tubes reinforced on their exterior surface by steel wires.

M. Heinloo; S. Kaliszky

1981-01-01

271

The Uncertainty Threshold Principle: Some Fundamental Limitations of Optimal Decision Making under Dynamic Uncertainty.  

National Technical Information Service (NTIS)

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 exi...

M. Athans R. Ku S. B. Gershwin

1976-01-01

272

Evolutionary Algorithms for Static and Dynamic Optimization of Fed-batch Fermentation Processes  

Microsoft Academic Search

In this work, Evolutionary Algorithms (EAs) are used to control a recombinant bacterial fed-batch fermentation process, that aims to produce a bio-pharmaceutical product. Initially, a novel EA was used to optimize the process, prior to its run, by simultaneously adjusting the feeding trajectory, the duration of the fermentation and the initial conditions of the process. Finally, dynamic optimization is proposed,

Miguel Rocha; Ana Veloso; Eugenio C. Ferreira; Isabel Rocha

273

Dynamic optimization of hybridoma growth in a fed-batch bioreactor.  

PubMed

This study addressed the problem of maximizing cell mass and monoclonal antibody production from a fed-batch hybridoma cell culture. We hypothesized that inaccuracies in the process model limited the mathematical optimization. On the basis of shaker flask data, we established a simple phenomenological model with cell mass and lactate production as the controlled variables. We then formulated an optimal control algorithm, which calculated the process-model mismatch at each sampling time, updated the model parameters, and re-optimized the substrate concentrations dynamically throughout the time course of the batch. Manipulated variables were feed rates of glucose and glutamine. Dynamic parameter adjustment was done using a fuzzy logic technique, while a heuristic random optimizer (HRO) optimized the feed rates. The parameters selected for updating were specific growth rate and the yield coefficient of lactate from glucose. These were chosen by a sensitivity analysis. The cell mass produced using dynamic optimization was compared to the cell mass produced for an unoptimized case, and for a one-time optimization at the beginning of the batch. Substantial improvements in reactor productivity resulted from dynamic re-optimization and parameter adjustment. We demonstrated first that a single offline optimization of substrate concentration at the start of the batch significantly increased the yield of cell mass by 27% over an unoptimized fermentation. Periodic optimization online increased yield of cell mass per batch by 44% over the single offline optimization. Concomitantly, the yield of monoclonal antibody increased by 31% over the off-line optimization case. For batch and fed-batch processes, this appears to be a suitable arrangement to account for inaccuracies in process models. This suggests that implementation of advanced yet inexpensive techniques can improve performance of fed-batch reactors employed in hybridoma cell culture. PMID:10592517

Dhir, S; Morrow, K J; Rhinehart, R R; Wiesner, T

2000-01-20

274

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

1992-01-01

275

MOTION AND ENERGY OPTIMIZATION OF VISION GUIDED MANIPULATOR FOR OPTIMAL DYNAMIC PERFORMANCE  

Microsoft Academic Search

This paper presents a step on how to optimize the energy and performance of an industrial robot. The project consist of three major phases; (1) theoretical, simulation and practical of forward and inverse kinematics for Fanuc LR Mate 200iB robot to determine their D-H parameters, (2) optimization of robot movements, and (3) implementation of practical tasks. The optimization process involves

M. J. Herman; S. Marizan

2008-01-01

276

Optimal dynamic control of resources in a distributed system  

NASA Technical Reports Server (NTRS)

The authors quantitatively formulate the problem of controlling resources in a distributed system so as to optimize a reward function and derive optimal control strategies using Markov decision theory. The control variables treated are quite general; they could be control decisions related to system configuration, repair, diagnostics, files, or data. Two algorithms for resource control in distributed systems are derived for time-invariant and periodic environments, respectively. A detailed example to demonstrate the power and usefulness of the approach is provided.

Shin, Kang G.; Krishna, C. M.; Lee, Yann-Hang

1989-01-01

277

Adaptive particle swarm optimization: detection and response to dynamic systems  

Microsoft Academic Search

This paper introduces an adaptive PSO, which automatically tracks various changes in a dynamic system. Different environment detection and response techniques are tested on the parabolic and Rosenbrock benchmark functions, and re-randomization is introduced to respond to the dynamic changes. Performance on the benchmark functions with various severities is analyzed

Xiaohui Hu I; Russell C. Eberhart

2002-01-01

278

Optimal Switched Dynamic Modulated Power Filter Compensator for Radial Distribution System  

Microsoft Academic Search

This paper presents a novel pulse width switched modulated power filter compensator (MPFC) for the voltage stability enhancement, energy utilization, loss reduction, and power factor correction in a radial distribution network using the Particle Swarm Optimization (PSO) technique. The MPFC is controlled by a novel dynamic tri-loop error driven controller. The dynamic controller is regulated to minimize the switching current

Adel M. Sharaf; Adel A. A. El-gammal

2009-01-01

279

A novel multi-objective particle swarm optimization based on dynamic crowding distance  

Microsoft Academic Search

In this article, a multi-objective particle swarm optimization algorithm based on dynamic crowding distance (DCD-MOPSO) was proposed, in which the definition of individual's DCD was based on the degree of difference between the crowding distances on different objectives. The proposed approach computed individual's DCD dynamically during the process of population maintenance to ensure sufficient diversity amongst the solutions of the

Liqin Liu; Xueliang Zhang; Liming Xie; Juan Du

2009-01-01

280

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

NASA Astrophysics Data System (ADS)

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

Huang, Xiaobiao; Safranek, James

2014-09-01

281

Tailoring of composite links for optimal damped elasto-dynamic performance  

NASA Technical Reports Server (NTRS)

A method is developed for the optimal design of composite links based on dynamic performance criteria directly related to structural modal damping and dynamic stiffness. An integrated mechanics theory correlates structural composite damping to the parameters of basic composite material systems, laminate parameters, link shape, and modal deformations. The inclusion of modal properties allows the selective minimization of vibrations associated with specific modes. Ply angles and fiber volumes are tailored to obtain optimal combinations of damping and stiffness. Applications to simple composite links indicate wide margins for trade-offs and illustrate the importance of various design variables to the optimal design.

Saravanos, D. A.; Chamis, C. C.

1989-01-01

282

Multi-strategy ensemble evolutionary algorithm for dynamic multi-objective optimization  

Microsoft Academic Search

Dynamic optimization and multi-objective optimization have separately gained increasing attention from the research community\\u000a during the last decade. However, few studies have been reported on dynamic multi-objective optimization (dMO) and scarce effective\\u000a dMO methods have been proposed. In this paper, we fulfill these gabs by developing new dMO test problems and new effective\\u000a dMO algorithm. In the newly designed dMO

Yu Wang; Bin Li

2010-01-01

283

A Simple Distributed Particle Swarm Optimization for Dynamic and Noisy Environments  

SciTech Connect

In this paper, we present a Simple Distributed Particle Swarm Optimization (SDPSO) algorithm that can be used to track the optimal solution in a dynamic and noisy environment. The classic PSO algorithm lacks the ability to track changing optimum in a dynamic environment. Several approaches have been investigated to enhance the PSO algorithm s ability in dynamic environments. However, in dealing with dynamic environments, these approaches have lost PSO s original strengths of decentralized control and ease of implementation. The SDPSO algorithm proposed in this paper maintains these classic PSO features as well as provides the optimum result tracking capability in dynamic environments. In this research, the DF1 multimodal dynamic environment generator proposed by Morrison and De Jong is used to evaluate the classic PSO, SDPSO and other two adaptive PSOs.

Cui, Xiaohui [ORNL; Potok, Thomas E [ORNL

2009-01-01

284

Optimization of ABS considering the static and dynamic characteristics for OFH  

NASA Astrophysics Data System (ADS)

The optical systems using the probe and solid immersion lens (SIL) have been researched as the technology to embody the near field recording (NFR) system. In NFR system, it is very important for the clearance between slider and disk surface to remain under 100nm in order to use effect on evanescent wave. The OFH should also have a small pitch to control the contamination effect. Because the larger the gap between leading edge of slider and disk is, the more particles come into SlL. As a result, they have a lot of bad influence on SIL. However, the flying stability turns bad as the pitch angle becomes small. Accordingly, it is necessary to consider dynamic characteristics in OFH ABS design. This research made two approximation models through the regression analysis and neural network, which are the parameter analysis based on optimization techniques. This study also used micro-genetic algorithm (GA) and design optimization tool (DOT). ABS shape of flying head was optimized with the object of remaining FH of several ten nano meter in static state. And the dynamic optimization was carried out toward decreasing FH modulation. As a result of static optimization, the flatness of flying height was restricted within 1% in inner diameter (ID) and outer diameter (OD). Besides, the flying height was remained about 50nm. In the dynamic optimization, the vertical sensitivity of dynamic optimal model was bigger than that of original model and the pitch sensitivities was also improved a bit.

Park, Kyoung-Su; Kim, Jiwon; Park, No-Chul; Lee, Jong Soo; Choi, Dong-Hoon; Park, Young-Pil

2005-09-01

285

Dynamic multi-project selection planning with inheritance-constraints and its immune genetic optimization  

Microsoft Academic Search

This work investigates a hybrid optimization model of resource-constrained dynamical multi-project selection planning and its immune genetic algorithm. The model is designed based oil the dynamical characteristic of multi-project selection planning and the limits of inheritance and resource. Further, a dynamical bi-level programming decision model is developed, relying upon term-subpopulations, topological sorting and the relations of inheritance and time precedence

Zhuhong Zhang; Hong Lei

2010-01-01

286

Integrated aerodynamic/dynamic/structural optimization of helicopter rotor blades using multilevel decomposition  

NASA Technical Reports Server (NTRS)

This paper describes an integrated aerodynamic/dynamic/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 global quantities (stiffness, 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 designs are performed at a global level and the structural design is carried out at a detailed level with considerable dialog and compromise among the aerodynamic, dynamic, and structural groups. The IADS procedure is demonstrated for several examples.

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

1995-01-01

287

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.

1994-01-01

288

Uncertainty Optimization for Robust Dynamic Optical Flow Estimation  

Microsoft Academic Search

Abstract We develop an optical flow estimation framework,that focuses on motion estimation over time formulated in a Dynamic,Bayesian Network. It realizes a spatiotemporal,integration of motion,information using a dynamic,and robust prior that incorporates spatial and temporal,coherence constraints on the flow field. The main contribution is the embedding,of these particular assumptions,on optical flow evolution into the Bayesian propagation,approach that leads to a

Volker Willert; Marc Toussaint; Julian Eggert; Edgar K Orner; Tu Berlin

289

Static and Dynamic Locality Optimizations Using Integer Linear Programming  

Microsoft Academic Search

Abstract?he delivered performance on modern processors that employ deep memory hierarchies is closely related to the performance of the memory subsystem. Compiler optimizations aimed at improving cache locality are critical in realizing the performance potential of powerful processors. For scientific applications, several loop transformations have been shown to be useful in improving both temporal and spatial locality. Recently, there has

Mahmut T. Kandemir; Prithviraj Banerjee; Alok N. Choudhary; J. Ramanujam; Eduard Ayguadé

2001-01-01

290

Optimal resource management with dynamic virtual paths in ATM networks  

Microsoft Academic Search

This paper presents a network model for virtual path (VP) management which considers both the topology and capacity allocation for a set of virtual path connections (VPCs). The objective of the model is to find an optimal VP pool which can be used for on-line network management and control. The solution of the model gives a set of VPCs with

Prasit Jiyapanichkul; Jim Lambert

1998-01-01

291

Dynamic probability route optimization for connection handoff in LEO networks  

Microsoft Academic Search

In LEO networks, the connections need to be rerouted after connection handoff as mobile users move among footprint of the satellites. The rerouting of the connections must be done quickly enough to produce the minimal disruption to traffic. In addition, the resulted routes must be optimal. A reasonable approach is to implement handoff in two phases. In the first phase

Wang Liang; Zhang NaiTong

2002-01-01

292

Dynamic Neighbor Cell List Management for Handover Optimization in LTE  

Microsoft Academic Search

Self-optimization of the neighbor cell list (NCL) is expected to improve handover performance and reduce the need for site surveys. 3GPP Long Term Evolution (LTE) has introduced automatic neighbor relation (ANR), which enables a base station to manage neighbor cells on the basis of measurements made by mobiles. Because the radio coverage changes during network operations, it is essential to

Yoshinori Watanabe; Yasuhiko Matsunaga; Kosei Kobayashi; Hiroto Sugahara; Kojiro Hamabe

2011-01-01

293

Dynamic hyperparameter optimization for bayesian topical trend analysis  

Microsoft Academic Search

This paper presents a new Bayesian topical trend analysis. We regard the parameters of topic Dirichlet priors in latent Dirichlet allocation as a function of document timestamps and optimize the parameters by a gradient-based algorithm. Since our method gives similar hyperparameters to the documents having similar timestamps, topic assignment in collapsed Gibbs sampling is affected by timestamp similarities. We compute

Tomonari Masada; Daiji Fukagawa; Atsuhiro Takasu; Tsuyoshi Hamada; Yuichiro Shibata; Kiyoshi Oguri

2009-01-01

294

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

2008-01-01

295

Optimization Treatment Planning for Interstitial Brachytherapy Using the Adjoint Transport Method  

SciTech Connect

Permanent radioactive seed implantation (interstitial brachytherapy) is becoming the preferred method of treating prostate cancers. The main goal of the treatment is to deliver a conformal dose to the tumor while simultaneously minimizing the dose to the normal tissue and sensitive tissue structures. The treatment plan determines the number of seeds, their embedded positions, and the dose delivered to the tissue by photons emitted from the seeds. This study adopts the adjoint method to calculate dose and mixed integer programming (MIP) to optimize the dose to regions of interest for the permanent implantation of {sup 125}I radioactive source seeds for prostate cancers.

Yoo, S.; Henderson, D.L.; Thomadsen, B.R.

2001-06-17

296

Dynamic programming algorithms for multi-stage safety stock optimization  

Microsoft Academic Search

The task of multi-stage safety stock optimization is very complex. Therefore, simplifying models with specific assumptions are considered. In this paper, the inventory system is controlled by a base-stock policy where each stockpoint of the inventory system follows a periodically reviewed order-up-to policy. End item demands are assumed to be normally distributed. To reduce the occurrences or size and duration

Stefan Minner

1997-01-01

297

Dynamic regime marginal structural mean models for estimation of optimal dynamic treatment regimes, Part I: main content.  

PubMed

Dynamic treatment regimes are set rules for sequential decision making based on patient covariate history. Observational studies are well suited for the investigation of the effects of dynamic treatment regimes because of the variability in treatment decisions found in them. This variability exists because different physicians make different decisions in the face of similar patient histories. In this article we describe an approach to estimate the optimal dynamic treatment regime among a set of enforceable regimes. This set is comprised by regimes defined by simple rules based on a subset of past information. The regimes in the set are indexed by a Euclidean vector. The optimal regime is the one that maximizes the expected counterfactual utility over all regimes in the set. We discuss assumptions under which it is possible to identify the optimal regime from observational longitudinal data. Murphy et al. (2001) developed efficient augmented inverse probability weighted estimators of the expected utility of one fixed regime. Our methods are based on an extension of the marginal structural mean model of Robins (1998, 1999) which incorporate the estimation ideas of Murphy et al. (2001). Our models, which we call dynamic regime marginal structural mean models, are specially suitable for estimating the optimal treatment regime in a moderately small class of enforceable regimes of interest. We consider both parametric and semiparametric dynamic regime marginal structural models. We discuss locally efficient, double-robust estimation of the model parameters and of the index of the optimal treatment regime in the set. In a companion paper in this issue of the journal we provide proofs of the main results. PMID:21969994

Orellana, Liliana; Rotnitzky, Andrea; Robins, James M

2010-01-01

298

Optimized expected information gain for nonlinear dynamical systems  

Microsoft Academic Search

This paper addresses the problem of active model selection for nonlinear dynamical systems. We propose a novel learning approach that selects the most informative subset of time-dependent variables for the purpose of Bayesian model inference. The model selection criterion maximizes the expected Kullback-Leibler divergence between the prior and the posterior probabilities over the models. The proposed strategy generalizes the standard

Alberto Giovanni Busetto; Cheng Soon Ong; Joachim M. Buhmann

2009-01-01

299

Optimal Control of Nonregular Dynamics in a Duffing Oscillator  

Microsoft Academic Search

A method for controlling nonlinear dynamics and chaos previouslydeveloped by the authors is applied to the classical Duffing oscillator.The method, which consists in choosing the best shape of externalperiodic excitations permitting to avoid the transverse intersection ofthe stable and unstable manifolds of the hilltop saddle, is firstillustrated and then applied by using the Melnikov method foranalytically detecting homoclinic bifurcations. Attention

Stefano Lenci; Giuseppe Rega

2003-01-01

300

Optimal workload sharing for mobile robotic networks in dynamic environments  

Microsoft Academic Search

I. INTRODUCTION Modern technological advances make the deployment of large groups of autonomous mobile agents with on-board computing and communication capabilities increasingly fea- sible and attractive. In the near future, large groups of such autonomous agents will be used to perform complex tasks in dynamic environments including transportation and distribu- tion, logistics, surveillance, search and rescue operations, hu- manitarian demining,

Marco Pavone; Ketan Savla; Emilio Frazzoli

301

Scaling and optimization of the radiation temperature in dynamic hohlraums  

Microsoft Academic Search

A quasianalytic model of the dynamic hohlraum is presented. Results of the model are compared to both experiments and full numerical simulations with good agreement. The computational simplicity of the model allows one to find the behavior of the hohlraum radiation temperature as a function of the various parameters of the system and thus find optimum parameters as a function

S. A. Slutz; M. R. Douglas; J. S. Lash; R. A. Vesey; G. A. Chandler; T. J. Nash; M. S. Derzon

2004-01-01

302

Optimal PMU Placement Evaluation for Power System Dynamic State Estimation  

SciTech Connect

Abstract - The synchronized phaor measurements unit (PMU), developed in the 1980s, is concidered to be one of the most important devices in the future of power systems. The recent development of PMU technology provides high-speed, precisely synchronized sensor data, which has been found to be usefule for dynamic, state estimation of power the power grid.

Zhang, Jinghe; Welch, Greg; Bishop, Gary; Huang, Zhenyu

2010-10-10

303

Using Harmonic Analysis and Optimization to Study Macromolecular Dynamics  

Microsoft Academic Search

Mechanical system dynamics plays an important role in the area of computational structural biology. Elastic network models (ENMs) for macromolecules (e.g., polymers, proteins, and nucleic acids such as DNA and RNA) have been developed to understand the relationship between their structure and biological function. For example, a protein, which is basically a folded polypeptide chain, can be simply modeled as

Moon K. Kim; Yunho Jang; Jay I. Jeong

2006-01-01

304

Uncertainty optimization for robust dynamic optical flow estimation  

Microsoft Academic Search

We develop an optical flow estimation framework that focuses on motion estimation over time formulated in a Dy- namic Bayesian Network. It realizes a spatiotemporal in- tegration of motion information using a dynamic and ro- bust prior that incorporates spatial and temporal coher- ence constraints on the flow field. The main contribution is the embedding of these particular assumptions on

Volker Willert; Marc Toussaint; Julian Eggert; Edgar Körner

2007-01-01

305

Optimized passive dynamics improve transparency of haptic devices  

Microsoft Academic Search

For haptic devices, compensation of the robot's gravity is a frequent strategy with the aim to reduce interaction forces between robot and human in zero-impedance control. However, a closer look at the composition of these interaction forces may reveal that the net effect of uncompensated gravitational components of the robot actually reduces interaction forces during dynamic movements, because inertial and

Heike Vallery; Alexander Duschau-wicke; Robert Riener

2009-01-01

306

Dynamic Optimal Random Access for Vehicle-to-Roadside Communications  

Microsoft Academic Search

In a drive-thru scenario where vehicles drive by a roadside access point (AP) to obtain temporary Internet access, it is important to design efficient resource allocation schemes to fully utilize the limited communication opportunities. In this paper, we study the random access problem in drive- thru communications in a dynamic environment, where both the channel contention level and channel capacity

Man Hon Cheung; Fen Hou; Vincent W. S. Wong; Jianwei Huang

2011-01-01

307

Control Design for Autonomous Vehicles: A Dynamic Optimization Perspective  

Microsoft Academic Search

Control design for autonomous vehicles involves a number of issues that are not satisfactorily addressed in classical control systems theory. There is typically the need for prescribing and commanding a collection of interacting dynamic control systems in order to meet the desired requirements for overall behavior, whereas conventional control design has only one system to govern. This context requires a

Fernando Lobo Pereira

2001-01-01

308

Application of a Dynamic Fuzzy Search Algorithm to Determine Optimal Wind Plant Sizes and Locations in Iowa.  

National Technical Information Service (NTIS)

This paper illustrates a method for choosing the optimal mix of wind capacity at several geographically dispersed locations. The method is based on a dynamic fuzzy search algorithm that can be applied to different optimization targets. We illustrate the m...

T. Factor

2001-01-01

309

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.

Xie, Zhaoxin; Chen, Chao; Sallam, Ahmed

2014-01-01

310

Dynamic multiobjective optimization algorithm based on average distance linear prediction model.  

PubMed

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

Li, Zhiyong; Chen, Hengyong; Xie, Zhaoxin; Chen, Chao; Sallam, Ahmed

2014-01-01

311

Optimization of incomplete dynamics for structural model refinement and damage assessment  

NASA Astrophysics Data System (ADS)

Model refinement and damage assessment of engineering structures can be achieved by estimating the physical design parameters from the measured dynamic characteristics. The process is often posed as an optimization problem based on either modal data matching (MDM) or dynamic residual optimization (DRO). The MDM methods attempt to minimize a nonlinear error function between the analytical and measured modal properties. Conversely, the DRO methods attempt to minimize the dynamic residual between the analytical model and the measured modal properties. This research explores new approaches to model refinement and damage assessment applications based on the MDM and DRO formulations under incomplete measurement. The initial effort of this research investigates the minimum rank perturbation theory (MRPT), which is a computationally attractive model update method that makes use of the dynamic residual. By introducing a new matrix property termed null symmetry, the MRPT is generalized to handle nonsymmetric system matrices in the equations of motion. A hybrid matrix update procedure that combines the MRPT and least squares estimation has also been extended in an iterative framework to deal with the incomplete measurement problem. The resulting algorithm minimizes the dynamic residual by implementing a form of repeated substitution. Then, the dynamic least squares method is developed to bypass the computation of the model matrix perturbation. The method solves a reduced linear least squares subproblem with quadratic inequality constraint in each major iteration. Next, the theory of reduced dynamic sensitivity is developed along with several of its applications. The theory formulates the first and second derivatives of both the modal error function and the dynamic residual function. It supports various applications including structural dynamic sensitivity analysis, optimal sensor placement, parameter selection, damage localization, model refinement, and damage assessment. These applications are studied and demonstrated using simulation and experimental data. The proposed optimal sensor placement methods provide new instrumentation tools that are consistent with the MDM and DRO formulations.

Yap, Keng C.

2000-11-01

312

A link-based variational inequality formulation of ideal dynamic user-optimal route choice problem  

Microsoft Academic Search

The ideal dynamic user-optimal (DUO) route choice problem is to determine vehicle flows on each link at each instant of time resulting from drivers using actual minimal-time routes. Actual route time is the travel time incurred while driving along the route. In a previous paper, we presented a route-based optimal control model for the ideal DUO route choice problem. However,

Bin Ran; David E. Boyce

1996-01-01

313

Construction of Fuzzy Models for Dynamic Systems Using Multi-population Cooperative Particle Swarm Optimizer  

Microsoft Academic Search

\\u000a A new fuzzy modeling method using Multi-population Cooperative Particle Swarm Optimizer (MCPSO) for identification and control\\u000a of nonlinear dynamic systems is presented in this paper. In MCPSO, the population consists of one master swarm and several\\u000a slave swarms. The slave swarms executeParticle Swarm Optimization (PSO) or its variants independently to maintain the diversity\\u000a of particles, while the particles in the

Ben Niu; Yunlong Zhu; Xiaoxian He

2005-01-01

314

A Software Tool for the Simulation and Optimization of Dynamic Metabolic Models  

Microsoft Academic Search

In Systems Biology, there is a growing need for simulation and optimization tools for the prediction of the phenotypical behavior\\u000a of microorganisms. In this paper, an open-source software platform is proposed to provide support for research in Metabolic\\u000a Engineering, by implementing tools that enable the simulation and optimization of dynamic metabolic models using ordinary\\u000a differential equations. Its main functionalities are

Pedro Evangelista; Isabel Rocha; Eugénio C. Ferreira; Miguel Rocha

2009-01-01

315

Reduction of dimension of optimal estimation problems for dynamical systems with singular perturbations  

NASA Astrophysics Data System (ADS)

The possibility of applying the method of integral manifolds to the reduction of optimal filtering problems for systems with low energy dissipation is explored. For such systems, it is shown that the slow subsystem of matrix Riccati differential equations turns out to have a higher dimension than expected, which leads to an increase in the dimension of the reduced problems. An optimal filter is constructed for the Langevin equation and for a dynamic model of a single-link flexible manipulator.

Osintsev, M. S.; Sobolev, V. A.

2014-01-01

316

Shaking the condensates: Optimal number squeezing in the dynamic splitting of a Bose–Einstein condensate  

Microsoft Academic Search

We apply optimal control theory to the dynamic splitting process of a Bose–Einstein condensate (BEC). Number squeezing of two spatially separated BECs is important for interferometry applications and inhibits phase diffusion due to the nonlinear atom–atom interactions. We show how optimal number squeezing can be obtained on time scales much shorter compared to adiabatic splitting. The non-adiabatic time evolution of

Julian Grond; Jörg Schmiedmayer; Ulrich Hohenester

2010-01-01

317

Role of optimization in the human dynamics of task execution  

NASA Astrophysics Data System (ADS)

In order to explain the empirical evidence that the dynamics of human activity may not be well modeled by Poisson processes, a model based on queuing processes was built in the literature [A. L. Barabasi, Nature (London) 435, 207 (2005)]. The main assumption behind that model is that people execute their tasks based on a protocol that first executes the high priority item. In this context, the purpose of this paper is to analyze the validity of that hypothesis assuming that people are rational agents that make their decisions in order to minimize the cost of keeping nonexecuted tasks on the list. Therefore, we build and analytically solve a dynamic programming model with two priority types of tasks and show that the validity of this hypothesis depends strongly on the structure of the instantaneous costs that a person has to face if a given task is kept on the list for more than one period. Moreover, one interesting finding is that in one of the situations the protocol used to execute the tasks generates complex one-dimensional dynamics.

Cajueiro, Daniel O.; Maldonado, Wilfredo L.

2008-03-01

318

Optimal Input Design for Aircraft Parameter Estimation using Dynamic Programming Principles  

NASA Technical Reports Server (NTRS)

A new technique was developed for designing optimal flight test inputs for aircraft parameter estimation experiments. The principles of dynamic programming were used for the design in the time domain. This approach made it possible to include realistic practical constraints on the input and output variables. A description of the new approach is presented, followed by an example for a multiple input linear model describing the lateral dynamics of a fighter aircraft. The optimal input designs produced by the new technique demonstrated improved quality and expanded capability relative to the conventional multiple input design method.

Morelli, Eugene A.; Klein, Vladislav

1990-01-01

319

Optimal input design for aircraft parameter estimation using dynamic programming principles  

NASA Technical Reports Server (NTRS)

A new technique was developed for designing optimal flight test inputs for aircraft parameter estimation experiments. The principles of dynamic programming were used for the design in the time domain. This approach made it possible to include realistic practical constraints on the input and output variables. A description of the new approach is presented, followed by an example for a multiple input linear model describing the lateral dynamics of a fighter aircraft. The optimal input designs produced by the new technique demonstrated improved quality and expanded capability relative to the conventional multiple input design method.

Klein, Vladislav; Morelli, Eugene A.

1990-01-01

320

Optimizing Dynamically-Dispatched Calls with Run-Time Type Feedback  

Microsoft Academic Search

Abstrach Object-oriented programs are difficult to optimize because they execute many dynamically-dispatched calls. These calls cannot easily be eliminated because the compiler does not know which callee will be invoked at runtime. We have developed a simple technique that feeds back type information from the runtime system to the compiler. With this type feedback, the compiler can inline any dynamically-dispatched

Urs Hölzle; David Ungar

1994-01-01

321

Dynamic post dispersion optimization at 40 Gb\\/s using a tunable fiber Bragg grating  

Microsoft Academic Search

A compact tunable fiber Bragg grating that uses distributed thin-film heaters on the surface of the fiber is used to dynamically optimize the post dispersion compensation of a multi-span 40-Gb\\/s nonreturn-to-zero (NRZ) transmission system. Dynamic post dispersion compensation with this device enables the system to operate over a much wider range of launch power than is otherwise possible with simple,

T. N. Nielsen; B. J. Eggleton; J. A. Rogers; P. S. Westbrook; P. B. Hansen; T. A. Strasser

2000-01-01

322

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

2009-01-01

323

An effective dynamic coarse model for optimization design of LTCC RF circuits with aggressive space mapping  

Microsoft Academic Search

A new concept called the dynamic coarse model is proposed and is applied to the optimization design of low-temperature co-fired ceramic (LTCC) multilayer RF circuits with the aggressive space mapping (ASM) technique. The dynamic coarse model is a combination of an evolutionary equivalent-circuit model and an efficient quasi-static numerical electromagnetic (EM) model-partial-element equivalent-circuit model. Namely, there are two forms of

Ke-Li Wu; Yong-Jiu Zhao; Jie Wang; Michael K. K. Cheng

2004-01-01

324

h 2 -norm optimal model reduction for large scale discrete dynamical MIMO systems  

Microsoft Academic Search

Modeling strategies often result in dynamical systems of very high dimension. It is then desirable to find systems of the same form but of lower complexity, whose input–output behavior approximates the behavior of the original system. Here we consider linear time-invariant discrete-time dynamical systems. The cornerstone of this paper is a relation between optimal model reduction in the h2-norm and

A. Bunse-Gerstner; D. Kubalinska; G. Vossen; D. Wilczek

2010-01-01

325

Scaling and optimization of the radiation temperature in dynamic hohlraums  

SciTech Connect

The authors have constructed a quasi-analytic model of the dynamic hohlraum. Solutions only require a numerical root solve, which can be done very quickly. Results of the model are compared to both experiments and full numerical simulations with good agreement. The computational simplicity of the model allows one to find the behavior of the hohlraum temperature as a function the various parameters of the system and thus find optimum parameters as a function of the driving current. The model is used to investigate the benefits of ablative standoff and axial convergence.

SLUTZ,STEPHEN A.; DOUGLAS,MELISSA R.; LASH,JOEL S.; VESEY,ROGER A.; CHANDLER,GORDON A.; NASH,THOMAS J.; DERZON,MARK S.

2000-04-13

326

A new method of optimal design for a two-dimensional diffuser by using dynamic programming  

NASA Technical Reports Server (NTRS)

A new method for predicting the optimal velocity distribution on the wall of a two dimensional diffuser is presented. The method uses dynamic programming to solve the optimal control problem with inequality constraints of state variables. The physical model of optimization is designed to prevent the separation of the boundary layer while approaching the maximum pressure ratio in a diffuser of a specified length. The computational results are in fair agreement with the experimental ones. Optimal velocity distribution on a diffuser wall is said to occur when the flow decelerates quickly at first and then smoothly, while the flow is near separation, but always protected from it. The optimal velocity distribution can be used to design the contour of the diffuser.

Gu, Chuangang; Zhang, Moujin; Chen, XI; Miao, Yongmiao

1991-01-01

327

Optimal design of a vehicle magnetorheological damper considering the damping force and dynamic range  

NASA Astrophysics Data System (ADS)

This paper presents an optimal design of a passenger vehicle magnetorheological (MR) damper based on finite element analysis. The MR damper is constrained in a specific volume and the optimization problem identifies the geometric dimensions of the damper that minimize an objective function. The objective function consists of the damping force, the dynamic range, and the inductive time constant of the damper. After describing the configuration of the MR damper, the damping force and dynamic range are obtained on the basis of the Bingham model of an MR fluid. Then, the control energy (power consumption of the damper coil) and the inductive time constant are derived. The objective function for the optimization problem is determined based on the solution of the magnetic circuit of the initial damper. Subsequently, the optimization procedure, using a golden-section algorithm and a local quadratic fitting technique, is constructed via commercial finite element method parametric design language. Using the developed optimization tool, optimal solutions of the MR damper, which are constrained in a specific cylindrical volume defined by its radius and height, are determined and a comparative work on damping force and inductive time constant between the initial and optimal design is undertaken.

Nguyen, Quoc-Hung; Choi, Seung-Bok

2009-01-01

328

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.

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

2014-01-01

329

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

PubMed

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

2014-01-01

330

Optimal mutation rates in dynamic environments: The Eigen model  

NASA Astrophysics Data System (ADS)

We consider the Eigen quasispecies model with a dynamic environment. For an environment with sharp-peak fitness in which the most-fit sequence moves by k spin-flips each period T we find an asymptotic stationary state in which the quasispecies population changes regularly according to the regular environmental change. From this stationary state we estimate the maximum and the minimum mutation rates for a quasispecies to survive under the changing environment and calculate the optimum mutation rate that maximizes the population growth. Interestingly we find that the optimum mutation rate in the Eigen model is lower than that in the Crow-Kimura model, and at their optimum mutation rates the corresponding mean fitness in the eigenmodel is lower than that in the Crow-Kimura model, suggesting that the mutation process which occurs in parallel to the replication process as in the Crow-Kimura model gives an adaptive advantage under changing environment.

Ancliff, Mark; Park, Jeong-Man

2010-08-01

331

Optimal mutation rates in dynamic environments: The eigen model  

NASA Astrophysics Data System (ADS)

We consider the Eigen quasispecies model with a dynamic environment. For an environment with sharp-peak fitness in which the most-fit sequence moves by k spin-flips each period T we find an asymptotic stationary state in which the quasispecies population changes regularly according to the regular environmental change. From this stationary state we estimate the maximum and the minimum mutation rates for a quasispecies to survive under the changing environment and calculate the optimum mutation rate that maximizes the population growth. Interestingly we find that the optimum mutation rate in the Eigen model is lower than that in the Crow-Kimura model, and at their optimum mutation rates the corresponding mean fitness in the Eigen model is lower than that in the Crow-Kimura model, suggesting that the mutation process which occurs in parallel to the replication process as in the Crow-Kimura model gives an adaptive advantage under changing environment.

Ancliff, Mark; Park, Jeong-Man

2011-03-01

332

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

2008-06-15

333

Synthesizing optimal flowsheets; Applications to IGCC system environmental control  

SciTech Connect

In this paper a new process synthesis capability implemented in the public version of the ASPEN chemical process simulator is demonstrated via an illustrative case study of a complex flowsheet. The objective of the case study is to minimize the cost of an advanced integrated gasification combined-cycle (IGCC) plant design featuring hot-gas cleanup, subject to environmental constraints. The problem is formulated as a mixed integer nonlinear programming (MINLP) optimization problem, involving the selection of both an optimal process configuration and optimal design parameters for that configuration. Performance and cost models of the IGCC system developed for the ASPEN simulator, along with the newly developed process synthesis capability, are used. As a first step, alternative in situ and external desulfurization are considered as process alternatives.

Diwekar, U.M.; Frey, A.C.; Rubin, E.S. (Carnegie-Mellon Univ., Pittsburgh, PA (United States). Dept. of Engineering and Public Policy)

1992-08-01

334

Gas dynamic optimization of the atomic nanocluster deposition system  

NASA Astrophysics Data System (ADS)

Numerical simulations of the gas flow and cluster velocities in a UHV compatible nanocluster deposition system were performed in order to understand the problems of optimization of the cluster deposition rate. The skimmer geometry was initially identified as the key factor in defining the cluster velocity: our modeling suggested that using skimmers of a greater internal angle should lead to higher cluster velocities. However, the experimental results revealed only a minor enhancement in the cluster velocity. The lack of an effect of the change in skimmer geometry was attributed to the decelerating effect of the background gas in the mass selection chamber. This assumption can be tested by using a pump with greater pumping speed or by reducing the size of the skimmer aperture (initial value was d = 3.25 mm). Indeed in experiments with skimmer diameter d = 1 mm, the scattering effect of background gas was decreased by an order of magnitude and the cluster velocity significantly increased, in accordance with model predictions.

Skovorodko, Petr A.; Brown, Simon A.; Beli?, Domagoj

2012-11-01

335

Optimal control evaluation of left ventricular systolic dynamics.  

PubMed

A model of the contracting left ventricle was developed, in which the left ventricle was represented as a time-varying compliance. The vascular load included the nonlinear (Bernoulli) resistance of the aortic valve, blood inertance, and a Windkessel model of the arterial tree. Owing to the obligatory aerobic nature of the heart, oxygen consumption can be used to characterize the energy utilized by the myocardium. An adaptive control law was developed for determining the systolic time course of ventricular pressure and volume that minimizes cardiac oxygen consumption. Three main determinants of myocardial oxygen consumption were included in the integral criterion function: developed wall tension, inotropic state, and external (mechanical) work. The optimal control problem was solved using the Pontryagin maximum principle. The model could predict, in good agreement with experimentally obtained data, systolic time course of ventricular pressure and volume, as well as directional changes in the duration of isovolumic contraction and ejection phase under various conditions of end-diastolic volume, mean aortic pressure, and inotropic state. PMID:7235053

Livnat, A; Yamashiro, S M

1981-05-01

336

Optimal control landscape for the generation of unitary transformations with constrained dynamics  

SciTech Connect

The reliable and precise generation of quantum unitary transformations is essential for the realization of a number of fundamental objectives, such as quantum control and quantum information processing. Prior work has explored the optimal control problem of generating such unitary transformations as a surface-optimization problem over the quantum control landscape, defined as a metric for realizing a desired unitary transformation as a function of the control variables. It was found that under the assumption of nondissipative and controllable dynamics, the landscape topology is trap free, which implies that any reasonable optimization heuristic should be able to identify globally optimal solutions. The present work is a control landscape analysis, which incorporates specific constraints in the Hamiltonian that correspond to certain dynamical symmetries in the underlying physical system. It is found that the presence of such symmetries does not destroy the trap-free topology. These findings expand the class of quantum dynamical systems on which control problems are intrinsically amenable to a solution by optimal control.

Hsieh, Michael [Center for Quantum Information Science and Technology, University of Southern California, Los Angeles, California 90089 (United States); Department of Chemistry, University of Southern California, Los Angeles, California 90089 (United States); Wu, Rebing [Department of Automation, Tsinghua University, Beijing, 100084 (China); Rabitz, Herschel [Department of Chemistry, Princeton University, Princeton, New Jersey 08544 (United States); Lidar, Daniel [Center for Quantum Information Science and Technology, University of Southern California, Los Angeles, California 90089 (United States); Department of Chemistry, University of Southern California, Los Angeles, California 90089 (United States); Department of Electrical Engineering, University of Southern California, Los Angeles, California 90089 (United States); Department of Physics, University of Southern California, Los Angeles, California 90089 (United States)

2010-06-15

337

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

338

Ant Colony Optimization Algorithms with Local Search for the Dynamic Vehicle Routing Problem  

Microsoft Academic Search

Abstract This report demonstrates the use of eective,local search to im- prove the performance of simple Ant Colony Optimization (ACO) algorithms as applied to an extension of the Vehicle Routing Problem (VRP) known as the Dynamic Vehicle Routing Problem (DVRP). The static VRP presents all orders a priori, however the DVRP requires scheduling to begin without a complete knowledge of

Andrew Runka

339

Newton-Type Algorithms for Dynamics-Based Robot Movement Optimization  

Microsoft Academic Search

This paper describes Newton and quasi-Newton optimization algorithms for dynamics-based robot movement generation. The robots that we consider are modeled as rigid multibody systems containing multiple closed loops, active and passive joints, and redundant actuators and sensors. While one can, in principle, always derive in analytic form the equations of motion for such systems, the ensuing complexity, both numeric and

Sung-Hee Lee; Junggon Kim; Frank Chongwoo Park; Munsang Kim; James E. Bobrow

2005-01-01

340

Optimal Dynamic Bit Assignment in Noise-free Quantized Linear Control Systems  

Microsoft Academic Search

This paper introduces a dynamic bit assignment policy (DBAP) for quantized feedback control systems without process or measurement noise. The proposed DBAP is a con- stant bit rate policy based on a similar policy analyzed in (1). We prove that the new policy is optimal for diagonalizable systems in the sense of minimizing the summed square quantization error subject to

Qiang Ling; Michael D. Lemmon

341

Dynamic shortest path in stochastic traffic networks based on fluid neural network and Particle Swarm Optimization  

Microsoft Academic Search

The shortest path algorithm is critical for dynamic traffic assignment and for the realization of route guidance in intelligent transportation systems (ITS). In this paper, a hybrid Particle Swarm Optimization (PSO) algorithm combined fluid neural network (FNN) to search for the shortest path in stochastic traffic networks is introduced. The algorithm overcomes the weight coefficient symmetry restrictions of the traditional

Yanfang Deng; Hengqing Tong; Xiedong Zhang

2010-01-01

342

A Dynamic Process Model for Optimizing the Hospital Environment Cash-Flow  

NASA Astrophysics Data System (ADS)

In this article is presented a new approach to some fundamental techniques of solving dynamic programming problems with the use of functional equations. We will analyze the problem of minimizing the cost of treatment in a hospital environment. Mathematical modeling of this process leads to an optimal control problem with a finite horizon.

Pater, Flavius; Rosu, Serban

2011-09-01

343

Reduction of large scale linear dynamic SISO and MIMO systems using differential evolution optimization algorithm  

Microsoft Academic Search

In this paper, a computationally simple approach is proposed for order reduction of large scale system linear dynamic SISO and MIMO system using differential evolutionary (DE) optimization technique. The method is based on minimizing the integral square error (ISE) between the transient responses of original and reduced order models pertaining to step input. The reduction procedure is simple, efficient and

G. Vasu; K. V. S. Santosh; G. Sandeep

2012-01-01

344

Dynamic optimization approach to modeling petroleum exploration with an application to the 1986 oil price decline  

Microsoft Academic Search

A long-standing problem with econometric oil and gas supply models is the lack of a dynamic optimization framework that incorporates expectations of future prices and costs as a basis for the econometric equations. This dissertation attempts to remedy this problem by using a rational-expectations model of the United States oil market in which a representative competitive firm chooses an exploratory

1988-01-01

345

Dynamic positioning of floating vessles based on Kalman filtering and optimal control  

Microsoft Academic Search

This paper describes computer-based, dynamic positioning system for floating vessels. The system is based on a detailed mathematical model of vessel motion in response to forces from thrusters, wind, waves and water current. The system uses a Kalman filter for optimal estimation of vessel motions and environmental forces from wind, waves and current. The control system is based on feedback

Jens G. Balchen; Nils A. Jenssen; Eldar Mathisen; Steinar Saelid

1980-01-01

346

Optimal synchronous pulsewidth modulation with a trajectory-tracking scheme for high-dynamic performance  

Microsoft Academic Search

Synchronous pulse-width modulation (PWM) based on precalculated and stored optimal pulse patterns could be a superior method for the control of high-power inverters operated at low switching frequency. The technique has rarely been applied in practice owing to its poor dynamic performance. A novel feedforward control technique eliminates this decisive drawback: the space vector of the machine currents is forced

Joachim Holtz; Bernd Beyer

1993-01-01

347

Perform - A performance optimizing computer program for dynamic systems subject to transient loadings  

NASA Technical Reports Server (NTRS)

A description and applications of a computer capability for determining the ultimate optimal behavior of a dynamically loaded structural-mechanical system are presented. This capability provides characteristics of the theoretically best, or limiting, design concept according to response criteria dictated by design requirements. Equations of motion of the system in first or second order form include incompletely specified elements whose characteristics are determined in the optimization of one or more performance indices subject to the response criteria in the form of constraints. The system is subject to deterministic transient inputs, and the computer capability is designed to operate with a large linear programming on-the-shelf software package which performs the desired optimization. The report contains user-oriented program documentation in engineering, problem-oriented form. Applications cover a wide variety of dynamics problems including those associated with such diverse configurations as a missile-silo system, impacting freight cars, and an aircraft ride control system.

Pilkey, W. D.; Wang, B. P.; Yoo, Y.; Clark, B.

1973-01-01

348

h2-norm optimal model reduction for large scale discrete dynamical MIMO systems  

NASA Astrophysics Data System (ADS)

Modeling strategies often result in dynamical systems of very high dimension. It is then desirable to find systems of the same form but of lower complexity, whose input-output behavior approximates the behavior of the original system. Here we consider linear time-invariant discrete-time dynamical systems. The cornerstone of this paper is a relation between optimal model reduction in the h2-norm and (tangential) rational Hermite interpolation. First order necessary conditions for h2-optimal model reduction are presented for discrete Multiple-Input-Multiple-Output (MIMO) systems. These conditions suggest a specific choice of interpolation data and a novel algorithm aiming for anh2-optimal model reduction for MIMO systems. It is also shown that the conditions are equivalent to two known gramian-based first order necessary conditions. Numerical experiments demonstrate the approximation quality of the method.

Bunse-Gerstner, A.; Kubalinska, D.; Vossen, G.; Wilczek, D.

2010-01-01

349

Dynamics of dissolved CO2 injection systems: Optimal design.  

NASA Astrophysics Data System (ADS)

Carbon dioxide (CO2) storage in deep saline aquifers represents a promising strategy for mitigating greenhouse gas emissions to the atmosphere. The standard approach to geologic storage is based on supercritical CO2 injection. The main risks associated with the standard approach of CO2 sequestration are that (i) injected CO2 may flow upward and leak to the ground because of gravity-driven flow; (ii) pressure build-up in the aquifer as a response to CO2 injection may promote reactivation of sealed fractures or the creation of new ones in the caprock, and (iii) injected CO2 may displace resident brine, causing it to contaminate freshwater bodies. An alternative storage approach that alleviates these concerns is to extract brine from the storage formation and then re-inject it together with the CO2 so that they mix in the well and this CO2-saturated brine flows into the storage formation. This strategy allows us to reduce the risk of buoyant escape of stored CO2 and to ensure the geomechanical stability and caprock integrity because pressure build-up is reduced due to pumping. In fact, CO2-saturated brine will sink to the aquifer bottom because it is denser than resident brine. The method is particularly favourable when the aquifer dips, because locating the extraction well upslope can ensure a very long operation without CO2 ever breaking through into the extraction well. The maximum efficiency of this alternative storage technique is achieved through proper design and optimization of CO2 injection rates, and the well locations. In order to define the proper design we perform numerical three-dimensional variable density flow simulations. Several sets of simulations were carried out to evaluate the parameters which play a major role in the degree of success and efficiency of this storage strategy. Keywords: CO2 sequestration, CO2-saturated brine, proper design, variable density flow simulations.

Pool, M.; Vilarrasa, V.; Carrera, J.

2012-04-01

350

Optimizing the temporal dynamics of light to human perception  

PubMed Central

No previous research has tuned the temporal characteristics of light-emitting devices to enhance brightness perception in human vision, despite the potential for significant power savings. The role of stimulus duration on perceived contrast is unclear, due to contradiction between the models proposed by Bloch and by Broca and Sulzer over 100 years ago. We propose that the discrepancy is accounted for by the observer’s “inherent expertise bias,” a type of experimental bias in which the observer’s life-long experience with interpreting the sensory world overcomes perceptual ambiguities and biases experimental outcomes. By controlling for this and all other known biases, we show that perceived contrast peaks at durations of 50–100 ms, and we conclude that the Broca–Sulzer effect best describes human temporal vision. We also show that the plateau in perceived brightness with stimulus duration, described by Bloch’s law, is a previously uncharacterized type of temporal brightness constancy that, like classical constancy effects, serves to enhance object recognition across varied lighting conditions in natural vision—although this is a constancy effect that normalizes perception across temporal modulation conditions. A practical outcome of this study is that tuning light-emitting devices to match the temporal dynamics of the human visual system’s temporal response function will result in significant power savings.

Rieiro, Hector; Martinez-Conde, Susana; Danielson, Andrew P.; Pardo-Vazquez, Jose L.; Srivastava, Nishit; Macknik, Stephen L.

2012-01-01

351

The optimal size of dynamic phosphorus models for coastal areas.  

PubMed

One method to assess environmental effects from industrial emissions to coastal and inland waters, e.g. from pulp and paper industries, is to quantify these emissions with mass balance models. In this study six different mass balance models for phosphorus with varying degrees of complexity have been tested in 11 Swedish coastal areas. The majority of these areas are recipients of pulp and paper industries. The accuracy of model predictions of phosphorus and chlorophyll is evaluated and compared between models. The results imply that for the included water bodies, models containing state variables for phosphorus in surface water and deep water are superior to models treating the water column as a completely mixed entity. The results do not justify the separation of phosphorus into dissolved and particulate fractions, but for chlorophyll predictions the results were significantly improved when phytoplankton was included as a state variable. Unless detailed descriptions or predictions of chlorophyll dynamics are required, modelling eutrophication in coastal areas may be considered as a matter of total phosphorus in two water compartments plus sediments. PMID:17486838

Malmaeus, J M; Karlsson, O M; Lindgren, D; Eklund, J

2007-01-01

352

Multidisciplinary Design Optimization Techniques: Implications and Opportunities for Fluid Dynamics Research  

NASA Technical Reports Server (NTRS)

A challenge for the fluid dynamics community is to adapt to and exploit the trend towards greater multidisciplinary focus in research and technology. The past decade has witnessed substantial growth in the research field of Multidisciplinary Design Optimization (MDO). MDO is a methodology for the design of complex engineering systems and subsystems that coherently exploits the synergism of mutually interacting phenomena. As evidenced by the papers, which appear in the biannual AIAA/USAF/NASA/ISSMO Symposia on Multidisciplinary Analysis and Optimization, the MDO technical community focuses on vehicle and system design issues. This paper provides an overview of the MDO technology field from a fluid dynamics perspective, giving emphasis to suggestions of specific applications of recent MDO technologies that can enhance fluid dynamics research itself across the spectrum, from basic flow physics to full configuration aerodynamics.

Zang, Thomas A.; Green, Lawrence L.

1999-01-01

353

Hybrid discrete dynamically dimensioned search (HD-DDS) algorithm for water distribution system design optimization  

NASA Astrophysics Data System (ADS)

The dynamically dimensioned search (DDS) continuous global optimization algorithm by Tolson and Shoemaker (2007) is modified to solve discrete, single-objective, constrained water distribution system (WDS) design problems. The new global optimization algorithm for WDS optimization is called hybrid discrete dynamically dimensioned search (HD-DDS) and combines two local search heuristics with a discrete DDS search strategy adapted from the continuous DDS algorithm. The main advantage of the HD-DDS algorithm compared with other heuristic global optimization algorithms, such as genetic and ant colony algorithms, is that its searching capability (i.e., the ability to find near globally optimal solutions) is as good, if not better, while being significantly more computationally efficient. The algorithm's computational efficiency is due to a number of factors, including the fact that it is not a population-based algorithm and only requires computationally expensive hydraulic simulations to be conducted for a fraction of the solutions evaluated. This paper introduces and evaluates the algorithm by comparing its performance with that of three other algorithms (specific versions of the genetic algorithm, ant colony optimization, and particle swarm optimization) on four WDS case studies (21- to 454-dimensional optimization problems) on which these algorithms have been found to perform well. The results obtained indicate that the HD-DDS algorithm outperforms the state-of-the-art existing algorithms in terms of searching ability and computational efficiency. In addition, the algorithm is easier to use, as it does not require any parameter tuning and automatically adjusts its search to find good solutions given the available computational budget.

Tolson, Bryan A.; Asadzadeh, Masoud; Maier, Holger R.; Zecchin, Aaron

2009-12-01

354

Extended Lagrangian Born-Oppenheimer molecular dynamics in the limit of vanishing self-consistent field optimization  

SciTech Connect

We present an efficient general approach to first principles molecular dynamics simulations based on 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 reduction of the optimization requirement reduces the computational cost to a minimum, but without causing any significant loss of accuracy or long-term energy drift. The optimization-free first principles molecular dynamics requires only one single diagonalization per time step, but is still able to provide trajectories at the same level of accuracy as “exact,” fully converged, Born-Oppenheimer molecular dynamics simulations. The optimization-free limit of extended Lagrangian Born-Oppenheimer molecular dynamics therefore represents an ideal starting point for robust and efficient first principles quantum mechanical molecular dynamics simulations.

Souvatzis, Petros, E-mail: petros.souvatsiz@fysik.uu.se [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: amn@lanl.gov [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)

2013-12-07

355

Computational fluid dynamics based bulbous bow optimization using a genetic algorithm  

NASA Astrophysics Data System (ADS)

Computational fluid dynamics (CFD) plays a major role in predicting the flow behavior of a ship. With the development of fast computers and robust CFD software, CFD has become an important tool for designers and engineers in the ship industry. In this paper, the hull form of a ship was optimized for total resistance using CFD as a calculation tool and a genetic algorithm as an optimization tool. CFD based optimization consists of major steps involving automatic generation of geometry based on design parameters, automatic generation of mesh, automatic analysis of fluid flow to calculate the required objective/cost function, and finally an optimization tool to evaluate the cost for optimization. In this paper, integration of a genetic algorithm program, written in MATLAB, was carried out with the geometry and meshing software GAMBIT and CFD analysis software FLUENT. Different geometries of additive bulbous bow were incorporated in the original hull based on design parameters. These design variables were optimized to achieve a minimum cost function of "total resistance". Integration of a genetic algorithm with CFD tools proves to be effective for hull form optimization.

Mahmood, Shahid; Huang, Debo

2012-09-01

356

Parametrized variational principles in dynamics applied to the optimization of dynamic models of plates  

Microsoft Academic Search

We investigate the use of Parametrized Variational Principles (PVP) in linear structural dynamics. Our main objective is\\u000a to assess whether the free parameters can be used to enhance the accuracy of dynamic models on a fixed mesh. Consistent, boundary-consistent\\u000a and lumped mass matrices are defined within the framework of the PVP. The accuracy provided by three different mass matrices\\u000a in

F. J. Brito Castro; C. Militello; C. A. Felippa

1997-01-01

357

Modeling a Dynamic Design System Using the Mahalanobis Taguchi System - Two-Step Optimal Algorithm  

Microsoft Academic Search

\\u000a This work presents a novel algorithm, the Mahalanobis Taguchi System- Two Step Optimal algorithm (MTS-TSO), which combines\\u000a the Mahalanobis Taguchi System (MTS) and Two-Step Optimal (TSO) algorithm for parameter selection of product design, and parameter\\u000a adjustment under the dynamic service industry environments.\\u000a \\u000a \\u000a From the results of the confirm experiment, a service industry company is adopted to applies in the methodology,

Tsung-Shin Hsu; Ching-Lien Huang

2010-01-01

358

Integration of Virtual Reality with Computational Fluid Dynamics for Process Optimization  

NASA Astrophysics Data System (ADS)

Computational Fluid Dynamics (CFD) has become a powerful simulation technology used in many industrial applications for process design and optimization to save energy, improve environment, and reduce costs. In order to better understand CFD results and more easily communicate with non-CFD experts, advanced virtual reality (VR) visualization is desired for CFD post-processing. Efforts have recently been made at Purdue University Calumet to integrate VR with CFD to visualize complex data in three dimensions in an interactive, virtual environment. The virtual engineering environment greatly enhances the value of CFD simulations and allows engineers to gain much needed process insights for the design and optimization of industrial processes.

Wu, B.; Chen, G. H.; Fu, D.; Moreland, John; Zhou, Chenn Q.

2010-03-01

359

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

PubMed

This brief studies the stochastic optimal control problem via reinforcement learning and approximate/adaptive dynamic programming (ADP). A policy iteration algorithm is derived in the presence of both additive and multiplicative noise using Itô calculus. The expectation of the approximated cost matrix is guaranteed to converge to the solution of some algebraic Riccati equation that gives rise to the optimal cost value. Moreover, the covariance of the approximated cost matrix can be reduced by increasing the length of time interval between two consecutive iterations. Finally, a numerical example is given to illustrate the efficiency of the proposed ADP methodology. PMID:21954203

Jiang, Yu; Jiang, Zhong-Ping

2011-12-01

360

Optimized stiffness for linear time-invariant dynamic system according to a new system design  

NASA Astrophysics Data System (ADS)

This paper deals with a linear time-invariant dynamic system such as spring-mass-damper system. General dynamic systems are quite commonly to be redesigned for another purpose of using. For example, if one automobile must be redesigned to have more weights, the existing suspension must be replaced due to that gained weight. Therefore the stiffness and damping coefficient must be recomputed in order to make the automobile become suitable for using as previous. Here the spring-mass-damper system is used as an example to demonstrate the technique through dynamic optimization where the problem is solved in two categories as minimum energy and maximum jerk. Once the state and control variables are provided from the problem of minimum energy and maximum jerk, respectively, these parameter will be substituted in dynamic equations and leave the stiffness and damping coefficient as the unknown parameters to be solved.

Veeraklaew, Tawiwat

2012-11-01

361

H? and H2 optimizations of a dynamic vibration absorber for suppressing vibrations in plates  

NASA Astrophysics Data System (ADS)

H? and H2 optimization problems with respect to a dynamic vibration absorber (DVA) in a single degree-of-freedom (sdof) system are classical optimization problems and solutions to them were found about half a century ago. Numerical solutions to the H? and H2 optimization problems with respect to DVA for a multi-degree-of-freedom (mdof) or continuous system can be found in the literature but their analytical solutions have not yet been found. In this article, we report the derivation of an analytical solution to the H? and H2 optimization problems of DVA applied to suppress random vibrations in plate structures. Analytical formulae are also proposed to express the optimal tuning frequency and damping ratios of the absorber. The established theory improves our understanding of the effects of different parameters including the mass, damping and tuning ratios and also the point of attachment of the absorber on the vibration absorption by the absorber. Numerical results show the usefulness of the optimization solutions in comparison to solutions suggested by other researchers based on other approaches to the problem.

Cheung, Y. L.; Wong, W. O.

2009-02-01

362

H2 optimization of three-element type dynamic vibration absorbers  

NASA Astrophysics Data System (ADS)

The dynamic vibration absorber (DVA) is a passive vibration control device which is attached to a vibrating body (called a primary system) subjected to exciting force or motion. In this paper, we will discuss an optimization problem of the three- element-type DVA on the basis of the H2 optimization criterion. The objective of the H2 optimization is to reduce the total vibration energy of the system for overall frequencies; the total area under the power spectrum response curve is minimized in this criterion. If the system is subjected to random excitation instead of sinusoidal excitation, then the H2 optimization is probably more desirable than the popular H(infinity ) optimization. In the past decade there has been increasing interest in the three-element type DVA. However, most previous studies on this type of DVA were based on the H(infinity ) optimization design, and no one has been able to find the algebraic solution as of yet. We found a closed-form exact solution for a special case where the primary system has no damping. Furthermore, the general case solution including the damped primary system is presented in the form of a numerical solution. The optimum parameters obtained here are compared to those of the conventional Voigt type DVA. They are also compared to other optimum parameters based on the H(infinity ) criterion.

Asami, Toshihiko; Nishihara, Osamu

2002-06-01

363

PSO-based multiobjective optimization with dynamic population size and adaptive local archives.  

PubMed

Recently, various multiobjective particle swarm optimization (MOPSO) algorithms have been developed to efficiently and effectively solve multiobjective optimization problems. However, the existing MOPSO designs generally adopt a notion to "estimate" a fixed population size sufficiently to explore the search space without incurring excessive computational complexity. To address the issue, this paper proposes the integration of a dynamic population strategy within the multiple-swarm MOPSO. The proposed algorithm is named dynamic population multiple-swarm MOPSO. An additional feature, adaptive local archives, is designed to improve the diversity within each swarm. Performance metrics and benchmark test functions are used to examine the performance of the proposed algorithm compared with that of five selected MOPSOs and two selected multiobjective evolutionary algorithms. In addition, the computational cost of the proposed algorithm is quantified and compared with that of the selected MOPSOs. The proposed algorithm shows competitive results with improved diversity and convergence and demands less computational cost. PMID:18784011

Leong, Wen-Fung; Yen, Gary G

2008-10-01

364

Human motion planning based on recursive dynamics and optimal control techniques  

NASA Technical Reports Server (NTRS)

This paper presents an efficient optimal control and recursive dynamics-based computer animation system for simulating and controlling the motion of articulated figures. A quasi-Newton nonlinear programming technique (super-linear convergence) is implemented to solve minimum torque-based human motion-planning problems. The explicit analytical gradients needed in the dynamics are derived using a matrix exponential formulation and Lie algebra. Cubic spline functions are used to make the search space for an optimal solution finite. Based on our formulations, our method is well conditioned and robust, in addition to being computationally efficient. To better illustrate the efficiency of our method, we present results of natural looking and physically correct human motions for a variety of human motion tasks involving open and closed loop kinematic chains.

Lo, Janzen; Huang, Gang; Metaxas, Dimitris

2002-01-01

365

Research on an optimization model for logistics nodes dynamic location and its solution algorithm  

Microsoft Academic Search

Based on the logistics nodes system consisting of first-degree logistics node (logistics park) secondary logistics nodes (including logistics center and distribution center), a dynamic logistics nodes location model of multi-period , multi-type cargo flow and multiple logistics nodes is given. The optimization model considers the factors including fixed cost for logistics opening, handling cost and economic of scale of different

Dezhi Zhang; Rune Xie; Ting Liu; Shuangyan Li

2007-01-01

366

Towards an Autonomous, Humanoid, and Dynamically Walking Robot: Modeling, Optimal Trajectory Planning, Hardware Architecture, and Experiments  

Microsoft Academic Search

The development process to achieve walking motion with a recently constructed humanoid robot is discussed. The desired motion is based on the solution of an optimal control problem whose constraints depend upon the high-dimensional nonlinear multibody system dynam- ics of the 17 DoF humanoid and physical contact constraints with the environment. On-line control strategies are developed to track the pre-

Martin Buss; Michael Hardt; Jutta Kiener; Marion Sobotka; Maximilian Stelzer; Oskar von Stryk; Dirk Wollherr

367

Flow analysis and nozzle-shape optimization for the cold-gas dynamic-spray process  

Microsoft Academic Search

An isentropic, one-dimensional model is used to analyse the dynamics of dilute two-phase (feed powder particles plus the carrier gas) flow during the cold-spray process. While the physical foundation of the model is quite straightforward, the solution for the model can be obtained only numerically. The results obtained show that there is a particle-velocity-dependent, carrier-gas- invariant optimal value of the

M Grujicic; W S DeRosset; D Helfritch

2003-01-01

368

Predictive Efficiency Optimization for DC–DC Converters With Highly Dynamic Digital Loads  

Microsoft Academic Search

This paper presents a novel technique and system for increasing the efficiency of dc-dc converters that supply dynamic electronic loads, such as modern audio and video equipment and other devices whose power consumption largely depends on the digital data they process. The optimization does not require a current-measurement circuit and is well-suited to portable applications. It is based on a

Olivier Trescases; Guowen Wei; Aleksandar Prodic ´; Wai Tung Ng

2008-01-01

369

Modeling no-notice mass evacuation using a dynamic traffic flow optimization model  

Microsoft Academic Search

This paper presents a network transformation and demand specification approach for no-notice evacuation modeling. The research is aimed at formulating the Joint Evacuation Destination–Route-Flow-Departure (JEDRFD) problem of a no-notice mass evacuation into a system optimal dynamic traffic assignment model. The proposed network transformation technique permits the conversion of a typical transportation planning network to an evacuation network configuration in which

Yi-Chang Chiu; Hong Zheng; Jorge Villalobos; Bikash Gautam

2007-01-01

370

A Dynamic Heart Rate Prediction Model for Training Optimization in Cycling (P83)  

Microsoft Academic Search

Heart rate can be considered as a reliable indicator of the physiological load both for immediate training monitoring and\\u000a for post-training analysis in cycling. The aim of this paper is to present a dynamic heart rate prediction model which will\\u000a be used by a model predictive controller to optimize the cycling training. This model predicts the heart rate of a

Ankang Le; Thomas Jaitner; Frank Tobias; Lothar Litz

371

Mathematical modeling of transmission dynamics and optimal control of vaccination and treatment for hepatitis B virus.  

PubMed

Hepatitis B virus (HBV) infection is a worldwide public health problem. In this paper, we study the dynamics of hepatitis B virus (HBV) infection which can be controlled by vaccination as well as treatment. Initially we consider constant controls for both vaccination and treatment. In the constant controls case, by determining the basic reproduction number, we study the existence and stability of the disease-free and endemic steady-state solutions of the model. Next, we take the controls as time and formulate the appropriate optimal control problem and obtain the optimal control strategy to minimize both the number of infectious humans and the associated costs. Finally at the end numerical simulation results show that optimal combination of vaccination and treatment is the most effective way to control hepatitis B virus infection. PMID:24812572

Kamyad, Ali Vahidian; Akbari, Reza; Heydari, Ali Akbar; Heydari, Aghileh

2014-01-01

372

A New Logistic Dynamic Particle Swarm Optimization Algorithm Based on Random Topology  

PubMed Central

Population topology of particle swarm optimization (PSO) will directly affect the dissemination of optimal information during the evolutionary process and will have a significant impact on the performance of PSO. Classic static population topologies are usually used in PSO, such as fully connected topology, ring topology, star topology, and square topology. In this paper, the performance of PSO with the proposed random topologies is analyzed, and the relationship between population topology and the performance of PSO is also explored from the perspective of graph theory characteristics in population topologies. Further, in a relatively new PSO variant which named logistic dynamic particle optimization, an extensive simulation study is presented to discuss the effectiveness of the random topology and the design strategies of population topology. Finally, the experimental data are analyzed and discussed. And about the design and use of population topology on PSO, some useful conclusions are proposed which can provide a basis for further discussion and research.

Deng, Jianming

2013-01-01

373

Dissipative particle dynamics simulations to optimize contact hole shrink process using graphoepitaxial directed self-assembly  

NASA Astrophysics Data System (ADS)

Dissipative particle dynamics (DPD) simulations are utilized to optimize contact hole shrink process using graphoepitaxial directed self-assembly (DSA). In this work, poly (styrene-block-methyl methacrylate) (PS-b-PMMA) was employed. In the contact hole shrink process, PS residual layer was formed on the bottom floor of the hole type prepattern. To realize reliable contact hole shrink process, minimization of the thickness of PS residual layer was one of the key issues. It was found that the minimization of the thickness of the PS residual layer and optimization of threedimensional configuration of the PMMA domain was trade-off relationship. By using DPD simulations, the parameters were successfully optimized to achieve residual layer free contact hole shrink of DSA lithography.

Sato, Hironobu; Yonemitsu, Hiroki; Seino, Yuriko; Kato, Hirokazu; Kanno, Masahiro; Kobayashi, Katsutoshi; Kawanishi, Ayako; Kodera, Katsuyoshi; Azuma, Tsukasa

2013-03-01

374

Mathematical Modeling of Transmission Dynamics and Optimal Control of Vaccination and Treatment for Hepatitis B Virus  

PubMed Central

Hepatitis B virus (HBV) infection is a worldwide public health problem. In this paper, we study the dynamics of hepatitis B virus (HBV) infection which can be controlled by vaccination as well as treatment. Initially we consider constant controls for both vaccination and treatment. In the constant controls case, by determining the basic reproduction number, we study the existence and stability of the disease-free and endemic steady-state solutions of the model. Next, we take the controls as time and formulate the appropriate optimal control problem and obtain the optimal control strategy to minimize both the number of infectious humans and the associated costs. Finally at the end numerical simulation results show that optimal combination of vaccination and treatment is the most effective way to control hepatitis B virus infection.

Kamyad, Ali Vahidian; Heydari, Ali Akbar; Heydari, Aghileh

2014-01-01

375

Optimal control study for the Space Station Solar Dynamic power module  

NASA Technical Reports Server (NTRS)

The authors present the design of an optimal control system for the Space Station Freedom's Solar Dynamic Fine Pointing and Tracking (SDFPT) module. A very large state model of six rigid body modes and 272 flexible modes is used in conjunction with classical LQG optimal control to produce a full-order controller which satisfies the requirements. The results obtained are compared with those of a classically designed PID (proportional plus integral plus derivative) controller that was implemented for a six-rigid-body-mode forty-flexible-mode model. A major difficulty with designing LQG controllers for large models is solving the Riccati equation that arises from the optimal formulation. A Riccati solver based on a Pade approximation to the matrix sign function is used. A symmetric version of this algorithm is derived for the special class of Hamiltonion matrices, thereby yielding, for large problems, a nearly twofold speed increase over a previous algorithm.

Papadopoulos, P. M.; Laub, A. J.; Kenney, C. S.; Pandey, P.; Ianculescu, G.; Ly, J.

1991-01-01

376

Chaotic Dynamics on a Simplex and Global Optimization Methods with Normalized Constraints  

NASA Astrophysics Data System (ADS)

The application of chaos to global optimization methods are, 1) maps with respect to inner variables derived by discretizing gradient method models with Euler’s method are unstabilized by setting their sampling time large, 2) chaotic trajectories of the optimizer’s variables confined in the bounded searching domain are generated by nonlinear transformations of the unstabilized inner variables, and 3) the chaotic annealing method is available which conversely stabilize their dynamics by gradually decreasing the sampling time of them. However, the efficiency of the above-mentioned method called the chaotic global optimization was only reported for optimization problems constrained by upper and lower bounds. In this paper, to the contrary, we attempt to apply the method to optimization problems with normalized equality and non-negativity constraints. First, based on the replicator model which is regarded as the gradient projection method with a variable metric, two types of chaotic maps on a simplex are presented. The one is a discretized steepest gradient model with respect to inner state variables to which the replicator model is equivalently transformed, and the other is a discretized replicator model for an unconstrained problem with respect to inner state variables obtained by variable transformation. Secondly, the bifurcation with respect to the sampling time of Euler’s method is shown and the feasibility of the trajectories on a simplex for normalized equality and non-negativity constraints is certified. Lastly, the chaotic global optimization methods with the annealing procedure are demonstrated in numerical simulations for a few constrained optimization problems.

Masuda, Kazuaki; Aiyoshi, Eitaro

377

Dynamic analysis of optimality in myocardial energy metabolism under normal and ischemic conditions  

PubMed Central

To better understand the dynamic regulation of optimality in metabolic networks under perturbed conditions, we reconstruct the energetic-metabolic network in mammalian myocardia using dynamic flux balance analysis (DFBA). Additionally, we modified the optimal objective from the maximization of ATP production to the minimal fluctuation of the profile of metabolite concentration under ischemic conditions, extending the hypothesis of original minimization of metabolic adjustment to create a composite modeling approach called M-DFBA. The simulation results are more consistent with experimental data than are those of the DFBA model, particularly the retentive predominant contribution of fatty acid to oxidative ATP synthesis, the exact mechanism of which has not been elucidated and seems to be unpredictable by the DFBA model. These results suggest that the systemic states of metabolic networks do not always remain optimal, but may become suboptimal when a transient perturbation occurs. This finding supports the relevance of our hypothesis and could contribute to the further exploration of the underlying mechanism of dynamic regulation in metabolic networks.

Luo, Ruo-Yu; Liao, Sha; Tao, Guan-Yang; Li, Yuan-Yuan; Zeng, Shaoqun; Li, Yi-Xue; Luo, Qingming

2006-01-01

378

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

2014-03-01

379

Use of a Batch Reactive Distillation with Dynamic Optimization Strategy to Achieve Industrial Grade Ethyl Acetate  

NASA Astrophysics Data System (ADS)

Industrial grade ethyl acetate is available with minimum purity of 85.0%. It is mostly produced by an ethanol esterification in a distillation process on both batch and continuous modes. However, researches on high purity production with short operating time are rarely achieved. Therefore, the objective in this work is to study an approach to produce ethyl acetate of 90.0% by 8 hours using a batch reactive distillation column. Based on open-loop simulations, the distillation with constant reflux ratio cannot achieve the product specification. Thus, the dynamic optimization strategy is proposed to handle this problem. For the process safety--preventing the dried column and fractured, a minimum reflux ratio must be determined in advance and then an optimal reflux profile is calculated to achieve optimal product yield. Simulation results show that the industrial grade ethyl acetate can be produced by the dynamic optimization programming with two or more time intervals. Besides, the increasing of time intervals can produce more distillate product.

Konakom, Kwantip; Saengchan, Aritsara; Kittisupakorn, Paisan; Mujtaba, Iqbal M.

2011-08-01

380

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

NASA Astrophysics Data System (ADS)

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

Lali, Mehdi

2009-03-01

381

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

382

A Lyapunov-Based Extension to Particle Swarm Dynamics for Continuous Function Optimization  

PubMed Central

The paper proposes three alternative extensions to the classical global-best particle swarm optimization dynamics, and compares their relative performance with the standard particle swarm algorithm. The first extension, which readily follows from the well-known Lyapunov's stability theorem, provides a mathematical basis of the particle dynamics with a guaranteed convergence at an optimum. The inclusion of local and global attractors to this dynamics leads to faster convergence speed and better accuracy than the classical one. The second extension augments the velocity adaptation equation by a negative randomly weighted positional term of individual particle, while the third extension considers the negative positional term in place of the inertial term. Computer simulations further reveal that the last two extensions outperform both the classical and the first extension in terms of convergence speed and accuracy.

Bhattacharya, Sayantani; Konar, Amit; Das, Swagatam; Han, Sang Yong

2009-01-01

383

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.

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

2014-01-01

384

Use of Ultrafast Molecular Dynamics and Optimal Control for Identifying Biomolecules  

NASA Astrophysics Data System (ADS)

With F.COURVOISIER,L.GUYON,V.BOUTOU, and M.ROTH,J. ROSLUND, H. RABITZ, Princeton University. The identification and discrimination of molecules that exhibit almost identical structures and spectra using fluorescence spectroscopy is considered quite difficult. In order to evaluate the capability of optimal control for discriminating between the optical emissions of nearly identical molecules, we developed a new approach called ``optimal dynamic discrimination (ODD). A proof of principle ODD experiment has been performed using Riboflavin (RBF) and Flavin Mononucleotide (FMN) as model system. We used a complex multipulse control field made of a pair of pulses (UV and IR). The UV part (400 nm) is optimally shaped using a control learning loop while the IR component (800 nm) is FT-limited (100 fs) and set at a definite time delay with respect to the UV pulse. Clear discrimination was observed for optimally shaped pulses, although the linear spectra from both molecules are virtually identical. A further experiment showed that, by using the optimal pulse shapes that maximize the fluorescence depletion in FMN and RBF in a differential manner, the concentration of both molecules could be retrieved while they were mixed in the same solution. The ODD demonstration sets out a promising path for future applications, as for example fluorescence microscopy where endogenous fluorescence spectra of many biomolecules overlap.

Wolf, Jean-Pierre

2008-03-01

385

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.

2012-01-01

386

Applying dynamic wake models to large swirl velocities for optimal propellers  

NASA Astrophysics Data System (ADS)

The dynamic wake model is applied to the optimal propeller systems originally studied by the classic aerodynamicists: Betz, Prandtl and Goldstein. Several modified forms of the model are theoretically developed to extend the applicable range to flight conditions with a large swirl velocity component. Dynamic wake model calculations accurately predict the inflow behavior for helicopter rotors, including axial flow for large tip-speed ratios, (OR/V infinity) ? 20. The swirl velocity is a prominent component for small tip-speed ratios (?5), typical of forward flight for tiltrotor craft such as the V-22 Osprey and the BA609. Dynamic wake calculation results are compared to the closed-form solutions by Prandtl and Goldstein. The exact and approximate solutions correlate strongly for infinite blade cases and finite blade cases with a large tip-speed ratio. The original form of the He-Peters and Morillo-Peters dynamic wake models converge poorly for small tip-speed ratios, due to neglect of the swirl velocity. Derivations are presented for several adaptations of the models to account for the large apparent mass at the inboard blade region. A best modified form is chosen and the associated empirical factor is optimized to correlate well with Prandtl's solution. Error norms for the original and modified forms of the dynamic wake model are presented for propellers of various number of blades and a range of tip-speed ratios. The Goldstein solution is also studied in depth and conclusions are drawn for improving the dynamic wake model.

Makinen, Stephen M.

387

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

388

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

2006-01-01

389

Optimal control design of NMR and dynamic nuclear polarization experiments using monotonically convergent algorithms.  

PubMed

Optimal control theory has recently been introduced to nuclear magnetic resonance (NMR) spectroscopy as a means to systematically design and optimize pulse sequences for liquid- and solid-state applications. This has so far primarily involved numerical optimization using gradient-based methods, which allow for the optimization of a large number of pulse sequence parameters in a concerted way to maximize the efficiency of transfer between given spin states or shape the nuclear spin Hamiltonian to a particular form, both within a given period of time. Using such tools, a variety of new pulse sequences with improved performance have been developed, and the NMR spin engineers have been challenged to consider alternative routes for analytical experiment design to meet similar performance. In addition, it has lead to increasing demands to the numerical procedures used in the optimization process in terms of computational speed and fast convergence. With the latter aspect in mind, here we introduce an alternative approach to numerical experiment design based on the Krotov formulation of optimal control theory. For practical reasons, the overall radio frequency power delivered to the sample should be minimized to facilitate experimental implementation and avoid excessive sample heating. The presented algorithm makes explicit use of this requirement and iteratively solves the stationary conditions making sure that the maximum of the objective is reached. It is shown that this method is faster per iteration and takes different paths within a control space than gradient-based methods. In the present work, the Krotov approach is demonstrated by the optimization of NMR and dynamic nuclear polarization experiments for various spin systems and using different constraints with respect to radio frequency and microwave power consumption. PMID:18532824

Maximov, Ivan I; Tosner, Zden?k; Nielsen, Niels Chr

2008-05-14

390

Optimal Projection Equations for Discrete-Time Fixed-Order Dynamic Compensation of Linear Systems with Multiplicative White Noise,  

National Technical Information Service (NTIS)

The optimal projection equations for discrete-time reduced-order dynamic compensation are generalized to include the effects of state, control-and measurement-dependent noise. In addition, the discrete-time static output feedback problem with multiplicati...

D. S. Bernstein W. M. Haddad

1987-01-01

391

Dynamic biclustering of microarray data by multi-objective immune optimization  

PubMed Central

Abstract Background Newly microarray technologies yield large-scale datasets. The microarray datasets are usually presented in 2D matrices, where rows represent genes and columns represent experimental conditions. Systematic analysis of those datasets provides the increasing amount of information, which is urgently needed in the post-genomic era. Biclustering, which is a technique developed to allow simultaneous clustering of rows and columns of a dataset, might be useful to extract more accurate information from those datasets. Biclustering requires the optimization of two conflicting objectives (residue and volume), and a multi-objective artificial immune system capable of performing a multi-population search. As a heuristic search technique, artificial immune systems (AISs) can be considered a new computational paradigm inspired by the immunological system of vertebrates and designed to solve a wide range of optimization problems. During biclustering several objectives in conflict with each other have to be optimized simultaneously, so multi-objective optimization model is suitable for solving biclustering problem. Results Based on dynamic population, this paper proposes a novel dynamic multi-objective immune optimization biclustering (DMOIOB) algorithm to mine coherent patterns from microarray data. Experimental results on two common and public datasets of gene expression profiles show that our approach can effectively find significant localized structures related to sets of genes that show consistent expression patterns across subsets of experimental conditions. The mined patterns present a significant biological relevance in terms of related biological processes, components and molecular functions in a species-independent manner. Conclusions The proposed DMOIOB algorithm is an efficient tool to analyze large microarray datasets. It achieves a good diversity and rapid convergence.

2011-01-01

392

An Optimization Principle for Deriving Nonequilibrium Statistical Models of Hamiltonian Dynamics  

NASA Astrophysics Data System (ADS)

A general method for deriving closed reduced models of Hamiltonian dynamical systems is developed using techniques from optimization and statistical estimation. Given a vector of resolved variables, selected to describe the macroscopic state of the system, a family of quasi-equilibrium probability densities on phase space corresponding to the resolved variables is employed as a statistical model, and the evolution of the mean resolved vector is estimated by optimizing over paths of these densities. Specifically, a cost function is constructed to quantify the lack-of-fit to the microscopic dynamics of any feasible path of densities from the statistical model; it is an ensemble-averaged, weighted, squared-norm of the residual that results from submitting the path of densities to the Liouville equation. The path that minimizes the time integral of the cost function determines the best-fit evolution of the mean resolved vector. The closed reduced equations satisfied by the optimal path are derived by Hamilton-Jacobi theory. When expressed in terms of the macroscopic variables, these equations have the generic structure of governing equations for nonequilibrium thermodynamics. In particular, the value function for the optimization principle coincides with the dissipation potential that defines the relation between thermodynamic forces and fluxes. The adjustable closure parameters in the best-fit reduced equations depend explicitly on the arbitrary weights that enter into the lack-of-fit cost function. Two particular model reductions are outlined to illustrate the general method. In each example the set of weights in the optimization principle contracts into a single effective closure parameter.

Turkington, Bruce

2013-08-01

393

Dynamic artificial bee colony algorithm for multi-parameters optimization of support vector machine-based soft-margin classifier  

NASA Astrophysics Data System (ADS)

This article proposes a `dynamic' artificial bee colony (D-ABC) algorithm for solving optimizing problems. It overcomes the poor performance of artificial bee colony (ABC) algorithm, when applied to multi-parameters optimization. A dynamic `activity' factor is introduced to D-ABC algorithm to speed up convergence and improve the quality of solution. This D-ABC algorithm is employed for multi-parameters optimization of support vector machine (SVM)-based soft-margin classifier. Parameter optimization is significant to improve classification performance of SVM-based classifier. Classification accuracy is defined as the objection function, and the many parameters, including `kernel parameter', `cost factor', etc., form a solution vector to be optimized. Experiments demonstrate that D-ABC algorithm has better performance than traditional methods for this optimizing problem, and better parameters of SVM are obtained which lead to higher classification accuracy.

Yan, Yiming; Zhang, Ye; Gao, Fengjiao

2012-12-01

394

Pharmacokinetic modeling of dynamic MR images using a simulated annealing-based optimization  

NASA Astrophysics Data System (ADS)

The aim of this work was to use dynamic contrast enhanced MR image (DEMRI) data to generate 'parameter images' which provide functional information about contrast agent access, in bone sarcoma. A simulated annealing based technique was applied to optimize the parameters of a pharmacokinetic model used to describe the kinetics of the tissue response during and after intravenous infusion of a paramagnetic contrast medium, Gd-DTPA. Optimization was performed on a pixel by pixel basis so as to minimize the sum of square deviations of the calculated values from the values obtained experimentally during dynamic contrast enhanced MR imaging. A cost function based on a priori information was introduced during the annealing procedure to ensure that the values obtained were within the expected ranges. The optimized parameters were used in the model to generate parameter images, which reveal functional information that is normally not visible in conventional Gd-DTPA enhanced MR images. This functional information, during and upon completion of pre-operative chemotherapy, is useful in predicting the probability of disease free survival.

Sawant, Amit R.; Reece, John H.; Reddick, Wilburn E.

2000-04-01

395

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

396

Design for geometric parameters of PEM fuel cell by integrating computational fluid dynamics code with optimization method  

Microsoft Academic Search

The present study is aimed at optimization of the geometric parameters of the proton exchange membrane (PEM) fuel cells through numerical simulation. The approach is developed by integrating a direct problem solver with an optimizer. A commercial computational fluid dynamics code is used as the direct problem solver, which is used to simulate the three-dimensional mass, momentum and species transport

Chin-Hsiang Cheng; Hung-Hsiang Lin; Guang-Jer Lai

2007-01-01

397

An integrated blade optimization approach based on parallel ANN and GA with hierarchical fair competition dynamic-niche  

Microsoft Academic Search

This work presented an available multi-point blade optimization procedure for better aerodynamic performances. Based on the\\u000a proposed Parallel ANN and GA with hierarchical fair competition dynamic-niche (GA-HFCDN), an integrated approach for the blade\\u000a optimization design was put forward by combining Bezier parameterization with FINE\\/TURBO solver. In the optimization design,\\u000a parallel ANN was employed to build a more proper approximate model.

Bin Zhang; Tong Wang; Chuan-gang Gu; Xin-Wei Shu

2011-01-01

398

Modeling of CMM dynamic error based on optimization of neural network using genetic algorithm  

NASA Astrophysics Data System (ADS)

By analyzing the dynamic error of CMM, a model is established using BP neural network for CMM .The most important 5 input parameters which affect the dynamic error of CMM are approximate rate, length of rod, diameter of probe, coordinate values of X and coordinate values of Y. But the training of BP neural network can be easily trapped in local minimums and its training speed is slow. In order to overcome these disadvantages, genetic algorithm (GA) is introduced for optimization. So the model of GA-BP network is built up. In order to verify the model, experiments are done on the CMM of type 9158. Experimental results indicate that the entire optimizing capability of genetic algorithm is perfect. Compared with traditional BP network, the GA-BP network has better accuracy and adaptability and the training time is halved using GA-BP network. The average dynamic error can be reduced from 3.5?m to 0.7?m. So the precision is improved by 76%.

Ying, Qu; Zai, Luo; Yi, Lu

2010-08-01

399

Inference for Optimal Dynamic Treatment Regimes using an Adaptive m-out-of-n Bootstrap Scheme  

PubMed Central

Summary A dynamic treatment regime consists of a set of decision rules that dictate how to individualize treatment to patients based on available treatment and covariate history. A common method for estimating an optimal dynamic treatment regime from data is Q-learning which involves nonsmooth operations of the data. This nonsmoothness causes standard asymptotic approaches for inference like the bootstrap or Taylor series arguments to breakdown if applied without correction. Here, we consider the m-out-of-n bootstrap for constructing confidence intervals for the parameters indexing the optimal dynamic regime. We propose an adaptive choice of m and show that it produces asymptotically correct confidence sets under fixed alternatives. Furthermore, the proposed method has the advantage of being conceptually and computationally much more simple than competing methods possessing this same theoretical property. We provide an extensive simulation study to compare the proposed method with currently available inference procedures. The results suggest that the proposed method delivers nominal coverage while being less conservative than alternatives. The proposed methods are implemented in the qLearn R-package and have been made available on the Comprehensive R-Archive Network (http://cran.r-project.org/). Analysis of the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) study is used as an illustrative example.

Chakraborty, Bibhas; Laber, Eric B.; Zhao, Yingqi

2013-01-01

400

On optimal dynamic sequential search for matching in real-time machine vision.  

PubMed

In the matching tasks of tracking and geometrical vision, there are usually priors available on the absolute and/or relative image locations of features of interest. In this paper, we use these priors dynamically to guide a feature by feature matching search that can achieve global matching with much fewer image processing operations and lower overall computational cost. First, the concept of dynamic sequential search (DSS) is presented. Then, the problem of determining an optimal search order for DSS is investigated, when the probabilistic distribution of the features can be described by a multivariate Gaussian model. Based on the general formulas for sequentially updating the predicted positions of the features as well as their innovation covariance, the theoretic lower bound for the sum of the areas of the features search-regions is derived, and the necessary and sufficient condition for the optimal search order to approach this lower bound is presented. After that, an algorithm for dynamically determining a suboptimal search order is presented, with a computational complexity of O(n3), which is two magnitudes lower than those of the state-of-the-art algorithms. The effectiveness of the proposed method is validated by both statistical simulation and real-world experiments with a monocular visual SLAM (simultaneous localization and mapping) system. The results verify that the performance of the proposed method is better than the state-of-the-art algorithms, with both fewer image processing operations and lower overall computational cost. PMID:20483685

Liu, Zhibin; Shi, Zongying; Xu, Wenli

2010-11-01

401

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

402

Cooperative quantum-behaved particle swarm optimization with dynamic varying search areas and Lévy flight disturbance.  

PubMed

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

2014-01-01

403

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

2010-01-01

404

Optimal dynamics for quantum-state and entanglement transfer through homogeneous quantum systems  

SciTech Connect

The capability of faithfully transmit quantum states and entanglement through quantum channels is one of the key requirements for the development of quantum devices. Different solutions have been proposed to accomplish such a challenging task, which, however, require either an ad hoc engineering of the internal interactions of the physical system acting as the channel or specific initialization procedures. Here we show that optimal dynamics for efficient quantum-state and entanglement transfer can be attained in generic quantum systems with homogeneous interactions by tuning the coupling between the system and the two attached qubits. We devise a general procedure to determine the optimal coupling, and we explicitly implement it in the case of a channel consisting of a spin-(1/2)XY chain. The quality of quantum-state and entanglement transfer is found to be very good and, remarkably, almost independent of the channel length.

Banchi, L. [Dipartimento di Fisica e Astronomia, Universita di Firenze, via G. Sansone 1, I-50019 Sesto Fiorentino (Italy); Apollaro, T. J. G. [Dipartimento di Fisica e Astronomia, Universita di Firenze, via G. Sansone 1, I-50019 Sesto Fiorentino (Italy); Istituto dei Sistemi Complessi, C.N.R., via Madonna del Piano 10, I-50019 Sesto Fiorentino (Italy); Cuccoli, A. [Dipartimento di Fisica e Astronomia, Universita di Firenze, via G. Sansone 1, I-50019 Sesto Fiorentino (Italy); INFN, Sezione di Firenze, via G. Sansone 1, I-50019 Sesto Fiorentino (Italy); Vaia, R. [Istituto dei Sistemi Complessi, C.N.R., via Madonna del Piano 10, I-50019 Sesto Fiorentino (Italy); Verrucchi, P. [Istituto dei Sistemi Complessi, C.N.R., via Madonna del Piano 10, I-50019 Sesto Fiorentino (Italy); Dipartimento di Fisica e Astronomia, Universita di Firenze, via G. Sansone 1, I-50019 Sesto Fiorentino (Italy); INFN, Sezione di Firenze, via G. Sansone 1, I-50019 Sesto Fiorentino (Italy)

2010-11-15

405

Biomechanical analysis and design of a dynamic spinal fixator using topology optimization: a finite element analysis.  

PubMed

Surgeons often use spinal fixators to manage spinal instability. Dynesys (DY) is a type of dynamic fixator that is designed to restore spinal stability and to provide flexibility. The aim of this study was to design a new spinal fixator using topology optimization [the topology design (TD) system]. Here, we constructed finite element (FE) models of degenerative disc disease, DY, and the TD system. A hybrid-controlled analysis was applied to each of the three FE models. The rod structure of the topology optimization was modelled at a 39 % reduced volume compared with the rigid rod. The TD system was similar to the DY system in terms of stiffness. In contrast, the TD system reduced the cranial adjacent disc stress and facet contact force at the adjacent level. The TD system also reduced pedicle screw stresses in flexion, extension, and lateral bending. PMID:24737048

Lin, Hung-Ming; Liu, Chien-Lin; Pan, Yung-Ning; Huang, Chang-Hung; Shih, Shih-Liang; Wei, Shun-Hwa; Chen, Chen-Sheng

2014-05-01

406

Model checking with residuals for g-estimation of optimal dynamic treatment regimes.  

PubMed

In this paper, we discuss model checking with residual diagnostic plots for g-estimation of optimal dynamic treatment regimes. The g-estimation method requires three different model specifications at each treatment interval under consideration: (1) the blip model; (2) the expected counterfactual model; and (3) the propensity model. Of these, the expected counterfactual model is especially difficult to specify correctly in practice and so far there has been little guidance as to how to check for model misspecification. Residual plots are a useful and standard tool for model diagnostics in the classical regression setting; we have adapted this approach for g-estimation. We demonstrate the usefulness of our approach in a simulation study, and apply it to real data in the context of estimating the optimal time to stop breastfeeding. PMID:21969996

Rich, Benjamin; Moodie, Erica E M; Stephens, David A; Platt, Robert W

2010-01-01

407

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.

1987-01-01

408

The salt marsh vegetation spread dynamics simulation and prediction based on conditions optimized CA  

NASA Astrophysics Data System (ADS)

The biodiversity conservation and management of the salt marsh vegetation relies on processing their spatial information. Nowadays, more attentions are focused on their classification surveying and describing qualitatively dynamics based on RS images interpreted, rather than on simulating and predicting their dynamics quantitatively, which is of greater importance for managing and planning the salt marsh vegetation. In this paper, our notion is to make a dynamic model on large-scale and to provide a virtual laboratory in which researchers can run it according requirements. Firstly, the characteristic of the cellular automata was analyzed and a conclusion indicated that it was necessary for a CA model to be extended geographically under varying conditions of space-time circumstance in order to make results matched the facts accurately. Based on the conventional cellular automata model, the author introduced several new conditions to optimize it for simulating the vegetation objectively, such as elevation, growth speed, invading ability, variation and inheriting and so on. Hence the CA cells and remote sensing image pixels, cell neighbors and pixel neighbors, cell rules and nature of the plants were unified respectively. Taking JiuDuanSha as the test site, where holds mainly Phragmites australis (P.australis) community, Scirpus mariqueter (S.mariqueter) community and Spartina alterniflora (S.alterniflora) community. The paper explored the process of making simulation and predictions about these salt marsh vegetable changing with the conditions optimized CA (COCA) model, and examined the links among data, statistical models, and ecological predictions. This study exploited the potential of applying Conditioned Optimized CA model technique to solve this problem.

Guan, Yujuan; Zhang, Liquan

2006-10-01

409

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

410

A dynamic human water and electrolyte balance model for verification and optimization of life support systems in space flight applications  

Microsoft Academic Search

In this paper we report on the development of a dynamic MATLAB SIMULINK® model for the water and electrolyte balance inside the human body. This model is part of an environmentally sensitive dynamic human model for the optimization and verification of environmental control and life support systems (ECLSS) in space flight applications.An ECLSS provides all vital supplies for supporting human

P. Hager; M. Czupalla; U. Walter

2010-01-01

411

Mixed heuristic and mathematical programming using reference points for dynamic data types optimization in multimedia embedded systems  

Microsoft Academic Search

New multimedia embedded applications are becoming in- creasingly dynamic. Thus, they cannot only rely on static data allocation, and must employ Dynamically-allocated Data Types (DDTs) to store their data and eciently use the limited physical resources of embedded devices. However, the optimization of the DDTs for each target embedded system is a very time-consuming process due to the large design

José Luis Risco-martín; José Ignacio Hidalgo; David Atienza; Juan Lanchares; Oscar Garnica

2009-01-01

412

Smart DRM (dynamic range management) for optimal IR seeker sensitivity and dynamic range control  

NASA Astrophysics Data System (ADS)

For an IR (infrared) sensor, the raw digital images coming out from the FPA (focal plane array) A/D converter contain strong non-uniformity/fixed pattern noise (FPN) as well as permanent and blinking dead pixels. Before performing the target detection and tracking functions, these raw images are processed by a CWF (chopper-wheel-free) MBPF NUC (Measurement-Based-Parametric-Fitting Non-Uniformity Correction) system to replace the dead pixels and to remove or reduce the FPN, as shown in Figure 1. The input to MBPF NUC is RIMi,j(the raw image), where 1<=i, j<=256, and the output is CIMi,j, the corrected image. It is important to note that as shown in Figure 1 the IT (integration time) for the FPA input capacitors is a critical parameter to control the sensor's sensitivity and temperature DR (dynamic range). From the results of our FPN measurement, the STD (standard deviation) of FPN from a raw uncorrected image can be as high as 300-400 counts. This high count FPN will severely reduce the sensor's sensitivity (we would like to detect a weak target as low as a couple of counts) and hamper the target tracking and/or ATR functions because of the high counts FPN artifacts. Therefore, the major purpose of the NUC system is to reduce FPN for early target detection, and the secondary purpose is to reduce FPN artifacts for reliable target tracking and ATR.

Chen, Hai-Wen; Olson, Teresa L. P.; Frey, Steven R., Jr.

2003-08-01

413

Integrated aerodynamic and dynamic optimization of tiltrotor wing and rotor systems  

NASA Astrophysics Data System (ADS)

Rotorcraft analysis and design must account for interdisciplinary interactions, especially between aerodynamics, structural, and dynamics responses. In this design domain, the work of disciplinary experts is segregated to a large extent. Furthermore, the design of subsystems is also segregated. In this environment it is difficult to account for interdisciplinary interactions and exploit the coupling between sub systems. This work examined the multidisciplinary nature of a tiltrotor aircraft focusing on the aerodynamic design of the rotor, and the structural and dynamic design of the rotor and wing systems. The design was considered to be in the preliminary design phase, after the basic configuration is set and before fine details of the design are defined. Attention was focused on developing an optimization problem formulation that was sufficiently complete so that optimization technologies and heuristic strategies could be developed and evaluated for tiltrotor design. Furthermore the level of detail in the analysis was consistent with the phase of design. Coupling between the aerodynamic, dynamic, and structural design was shown to be significant. Additionally, the coupling between the design of the wing and rotor systems was also significant, so that an integrated design was required. This work developed integrated design strategies for solving this design problem efficiently, while exploiting the couplings. Sum of system weights was the main objective along with vibratory rotor hub loads while hover performance, strength, frequency placement, and stability were constraints. Design variables included blade aerodynamic planform and twist as well as the details of composite D-spar blade sections, and details of composite torque-box wing sections. Cruise mode rotor speed and wing thickness were also included as design variables. A genetic algorithm based collaborative optimization was used as the solution framework for the global optimal search. The problem was decomposed between the wing and rotor systems. Response surface and neural network based approximations were used to represent the interactions between the temporarily decoupled systems. Surrogate models were also employed for the basic system responses. The approach was found to improve computational efficiency over the basic all-in-one optimization and the sequential design approach that is typical of industry practice.

Orr, Stanley

414

Optimizing conjunctive use of surface water and groundwater resources with stochastic dynamic programming  

NASA Astrophysics Data System (ADS)

Optimal management of conjunctive use of surface water and groundwater has been attempted with different algorithms in the literature. In this study, a hydro-economic modelling approach to optimize conjunctive use of scarce surface water and groundwater resources under uncertainty is presented. A stochastic dynamic programming (SDP) approach is used to minimize the basin-wide total costs arising from water allocations and water curtailments. Dynamic allocation problems with inclusion of groundwater resources proved to be more complex to solve with SDP than pure surface water allocation problems due to head-dependent pumping costs. These dynamic pumping costs strongly affect the total costs and can lead to non-convexity of the future cost function. The water user groups (agriculture, industry, domestic) are characterized by inelastic demands and fixed water allocation and water supply curtailment costs. As in traditional SDP approaches, one step-ahead sub-problems are solved to find the optimal management at any time knowing the inflow scenario and reservoir/aquifer storage levels. These non-linear sub-problems are solved using a genetic algorithm (GA) that minimizes the sum of the immediate and future costs for given surface water reservoir and groundwater aquifer end storages. The immediate cost is found by solving a simple linear allocation sub-problem, and the future costs are assessed by interpolation in the total cost matrix from the following time step. Total costs for all stages, reservoir states, and inflow scenarios are used as future costs to drive a forward moving simulation under uncertain water availability. The use of a GA to solve the sub-problems is computationally more costly than a traditional SDP approach with linearly interpolated future costs. However, in a two-reservoir system the future cost function would have to be represented by a set of planes, and strict convexity in both the surface water and groundwater dimension cannot be maintained. The optimization framework based on the GA is still computationally feasible and represents a clean and customizable method. The method has been applied to the Ziya River basin, China. The basin is located on the North China Plain and is subject to severe water scarcity, which includes surface water droughts and groundwater over-pumping. The head-dependent groundwater pumping costs will enable assessment of the long-term effects of increased electricity prices on the groundwater pumping. The coupled optimization framework is used to assess realistic alternative development scenarios for the basin. In particular the potential for using electricity pricing policies to reach sustainable groundwater pumping is investigated.

Davidsen, Claus; Liu, Suxia; Mo, Xingguo; Rosbjerg, Dan; Bauer-Gottwein, Peter

2014-05-01

415

High-Dynamic-Range Imaging of Nanoscale Magnetic Fields Using Optimal Control of a Single Qubit  

NASA Astrophysics Data System (ADS)

We present a novel spectroscopy protocol based on optimal control of a single quantum system. It enables measurements with quantum-limited sensitivity (???(1/T2*), T2* 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 nitrogen-vacancy 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.

Häberle, T.; Schmid-Lorch, D.; Karrai, K.; Reinhard, F.; Wrachtrup, J.

2013-10-01

416

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

PubMed

We present a novel spectroscopy protocol based on optimal control of a single quantum system. It enables measurements with quantum-limited sensitivity (?(?)[proportionality](1/?[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 nitrogen-vacancy 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. PMID:24206470

Häberle, T; Schmid-Lorch, D; Karrai, K; Reinhard, F; Wrachtrup, J

2013-10-25

417

A framework for optimal temporal reduced order modeling of nonlinear dynamical systems  

NASA Astrophysics Data System (ADS)

An optimal temporal reduced order modeling framework is proposed for nonlinear dynamical systems. The governing equations are modified for an under-resolved simulation with an arbitrary scheme and a coarse temporal grid. Subgrid-scale models are developed to account for the unresolved temporal structure via inclusion of statistical information on a multi-point temporal stencil. These models are based upon principles of mean-square error minimization, conditional expectations and stochastic estimation. In order to validate the proposed framework, we investigate time-periodic solutions for a canonical Duffing oscillator using a coarse harmonic balance scheme. In order to demonstrate application of the proposed framework to a high dimensional nonlinear dynamical system, we also investigate a simply supported, geometrically nonlinear beam under the influence of time-periodic external forcing. For both problems, the subgrid-scale models are shown to significantly improve the accuracy of coarse-grained solutions.

LaBryer, A.; Attar, P. J.; Vedula, P.

2013-02-01

418

Optimal control of transient dynamics in balanced networks supports generation of complex movements.  

PubMed

Populations of neurons in motor cortex engage in complex transient dynamics of large amplitude during the execution of limb movements. Traditional network models with stochastically assigned synapses cannot reproduce this behavior. Here we introduce a class of cortical architectures with strong and random excitatory recurrence that is stabilized by intricate, fine-tuned inhibition, optimized from a control theory perspective. Such networks transiently amplify specific activity states and can be used to reliably execute multidimensional movement patterns. Similar to the experimental observations, these transients must be preceded by a steady-state initialization phase from which the network relaxes back into the background state by way of complex internal dynamics. In our networks, excitation and inhibition are as tightly balanced as recently reported in experiments across several brain areas, suggesting inhibitory control of complex excitatory recurrence as a generic organizational principle in cortex. PMID:24945778

Hennequin, Guillaume; Vogels, Tim P; Gerstner, Wulfram

2014-06-18

419

An optimized leaf-setting algorithm for beam intensity modulation using dynamic multileaf collimators  

NASA Astrophysics Data System (ADS)

A leaf-setting algorithm is developed for generating arbitrary beam intensity profiles in discrete levels using dynamic multileaf collimators (DMLCs). The algorithm starts with the algebraic expression for the area under the beam profile. It is shown that the coefficients in this expression can be transformed into the specifications for the leaf-setting sequence. It is proven that the algorithm optimizes beam delivery time and total monitor units for the DMLC leaf setting for intensity modulated radiotherapy (IMRT). The algorithm is demonstrated to be applicable to both the `step-and-shoot' and `dynamic' type of beam delivery. The graphical interpretation and numerical implementation scheme of the algorithm is illustrated using a simplified example.

Ma, Lijun; Boyer, Arthur L.; Xing, L.; Ma, C.-M.

1998-06-01

420

Multi-objective evolutionary optimization of biological pest control with impulsive dynamics in soybean crops.  

PubMed

The biological pest control in agriculture, an environment-friendly practice, maintains the density of pests below an economic injury level by releasing a suitable quantity of their natural enemies. This work proposes a multi-objective numerical solution to biological pest control for soybean crops, considering both the cost of application of the control action and the cost of economic damages. The system model is nonlinear with impulsive control dynamics, in order to cope more effectively with the actual control action to be applied, which should be performed in a finite number of discrete time instants. The dynamic optimization problem is solved using the NSGA-II, a fast and trustworthy multi-objective genetic algorithm. The results suggest a dual pest control policy, in which the relative price of control action versus the associated additional harvest yield determines the usage of either a low control action strategy or a higher one. PMID:19267163

Cardoso, Rodrigo T N; da Cruz, André R; Wanner, Elizabeth F; Takahashi, Ricardo H C

2009-08-01

421

A synergic simulation-optimization approach for analyzing biomolecular dynamics in living organisms.  

PubMed

A synergic duo simulation-optimization approach was developed and implemented to study protein-substrate dynamics and binding kinetics in living organisms. The forward problem is a system of several coupled nonlinear partial differential equations which, with a given set of kinetics and diffusion parameters, can provide not only the commonly used bleached area-averaged time series in fluorescence microscopy experiments but more informative full biomolecular/drug space-time series and can be successfully used to study dynamics of both Dirac and Gaussian fluorescence-labeled biomacromolecules in vivo. The incomplete Cholesky preconditioner was coupled with the finite difference discretization scheme and an adaptive time-stepping strategy to solve the forward problem. The proposed approach was validated with analytical as well as reference solutions and used to simulate dynamics of GFP-tagged glucocorticoid receptor (GFP-GR) in mouse cancer cell during a fluorescence recovery after photobleaching experiment. Model analysis indicates that the commonly practiced bleach spot-averaged time series is not an efficient approach to extract physiological information from the fluorescence microscopy protocols. It was recommended that experimental biophysicists should use full space-time series, resulting from experimental protocols, to study dynamics of biomacromolecules and drugs in living organisms. It was also concluded that in parameterization of biological mass transfer processes, setting the norm of the gradient of the penalty function at the solution to zero is not an efficient stopping rule to end the inverse algorithm. Theoreticians should use multi-criteria stopping rules to quantify model parameters by optimization. PMID:21106190

Sadegh Zadeh, Kouroush

2011-01-01

422

Dynamic modelling and optimal time-energy off-line programming of mobile robots : A cybernetic problem  

Microsoft Academic Search

In this paper the problem of the dynamic optimal time-energy Off-Line programming of an autonomous mobile robot in a crowded environment is considered. First, kinematic model and planning are presented. Then a dynamic model based on Euler-Lagrange formalism is developed and a mobility estimation function of the robot is considered. This dynamic estimation of the robot mobility takes into account

Amar Khoukhi

2002-01-01

423

Optimal pultrusion process conditions for improving the dynamic properties of graphite-epoxy composite beams  

SciTech Connect

The manufacturing process variables significantly influence the mechanical properties of pultruded composites. In this study, a statistical central composite design (CCD) test pattern has been used to manufacture unidirectional graphite-epoxy pultruded composite beams under carefully controlled process conditions. The influences of significant pultrusion process variables and their effects/interactions on the dynamic mechanical properties were investigated. The pultruded specimens were subjected to free vibration decay tests to determine nondestructively the dynamic flexural modulus and loss factor (a measure of internal damping). Mathematical models were derived based on the observed values of the dynamic properties using regression analysis procedures. These models were used to determine the optimal pultrusion process conditions for improving the dynamic mechanical properties of the finished product. A theoretical model postulating varying distribution of fiber content through the thickness of the pultruded composite is also presented. Static flexural tests and microscopic evaluation were employed to validate the assumption that a thin distinct layer of matrix material is formed on the outer surface of these pultruded products.

Kowsika, M.V.S.L.N.; Mantena, P.R. [Univ. of Mississippi, University, MS (United States). Dept. of Mechanical Engineering

1996-03-01

424

An adaptive differential evolution algorithm for global optimization in dynamic environments.  

PubMed

This article proposes a multipopulation-based adaptive differential evolution (DE) algorithm to solve dynamic optimization problems (DOPs) in an efficient way. The algorithm uses Brownian and adaptive quantum individuals in conjunction with the DE individuals to maintain the diversity and exploration ability of the population. This algorithm, denoted as dynamic DE with Brownian and quantum individuals (DDEBQ), uses a neighborhood-driven double mutation strategy to control the perturbation and thereby prevents the algorithm from converging too quickly. In addition, an exclusion rule is used to spread the subpopulations over a larger portion of the search space as this enhances the optima tracking ability of the algorithm. Furthermore, an aging mechanism is incorporated to prevent the algorithm from stagnating at any local optimum. The performance of DDEBQ is compared with several state-of-the-art evolutionary algorithms using a suite of benchmarks from the generalized dynamic benchmark generator (GDBG) system used in the competition on evolutionary computation in dynamic and uncertain environments, held under the 2009 IEEE Congress on Evolutionary Computation (CEC). The simulation results indicate that DDEBQ outperforms other algorithms for most of the tested DOP instances in a statistically meaningful way. PMID:23996590

Das, Swagatam; Mandal, Ankush; Mukherjee, Rohan

2014-06-01

425

Optimal Reaction Coordinate as a Biomarker for the Dynamics of Recovery from Kidney Transplant  

PubMed Central

The evolution of disease or the progress of recovery of a patient is a complex process, which depends on many factors. A quantitative description of this process in real-time by a single, clinically measurable parameter (biomarker) would be helpful for early, informed and targeted treatment. Organ transplantation is an eminent case in which the evolution of the post-operative clinical condition is highly dependent on the individual case. The quality of management and monitoring of patients after kidney transplant often determines the long-term outcome of the graft. Using NMR spectra of blood samples, taken at different time points from just before to a week after surgery, we have shown that a biomarker can be found that quantitatively monitors the evolution of a clinical condition. We demonstrate that this is possible if the dynamics of the process is considered explicitly: the biomarker is defined and determined as an optimal reaction coordinate that provides a quantitatively accurate description of the stochastic recovery dynamics. The method, originally developed for the analysis of protein folding dynamics, is rigorous, robust and general, i.e., it can be applied in principle to analyze any type of biological dynamics. Such predictive biomarkers will promote improvement of long-term graft survival after renal transplantation, and have potentially unlimited applications as diagnostic tools.

Krivov, Sergei V.; Fenton, Hayley; Goldsmith, Paul J.; Prasad, Rajendra K.; Fisher, Julie; Paci, Emanuele

2014-01-01

426

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

2000-04-21

427

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

2005-11-01

428

Reduced-order model for dynamic optimization of pressure swing adsorption processes  

SciTech Connect

Over the past decades, pressure swing adsorption (PSA) processes have been widely used as energy-efficient gas and liquid separation techniques, especially for high purity hydrogen purification from refinery gases. The separation processes are based on solid-gas equilibrium and operate under periodic transient conditions. Models for PSA processes are therefore multiple instances of partial differential equations (PDEs) in time and space with periodic boundary conditions that link the processing steps together. The solution of this coupled stiff PDE system is governed by steep concentrations and temperature fronts moving with time. As a result, the optimization of such systems for either design or operation represents a significant computational challenge to current differential algebraic equation (DAE) optimization techniques and nonlinear programming algorithms. Model reduction is one approach to generate cost-efficient low-order models which can be used as surrogate models in the optimization problems. The study develops a reduced-order model (ROM) based on proper orthogonal decomposition (POD), which is a low-dimensional approximation to a dynamic PDE-based model. Initially, a representative ensemble of solutions of the dynamic PDE system is constructed by solving a higher-order discretization of the model using the method of lines, a two-stage approach that discretizes the PDEs in space and then integrates the resulting DAEs over time. Next, the ROM method applies the Karhunen-Loeve expansion to derive a small set of empirical eigenfunctions (POD modes) which are used as basis functions within a Galerkin's projection framework to derive a low-order DAE system that accurately describes the dominant dynamics of the PDE system. The proposed method leads to a DAE system of significantly lower order, thus replacing the one obtained from spatial discretization before and making optimization problem computationally-efficient. The method has been applied to the dynamic coupled PDE-based model of a two-bed four-step PSA process for separation of hydrogen from methane. Separate ROMs have been developed for each operating step with different POD modes for each of them. A significant reduction in the order of the number of states has been achieved. The gas-phase mole fraction, solid-state loading and temperature profiles from the low-order ROM and from the high-order simulations have been compared. Moreover, the profiles for a different set of inputs and parameter values fed to the same ROM were compared with the accurate profiles from the high-order simulations. Current results indicate the proposed ROM methodology as a promising surrogate modeling technique for cost-effective optimization purposes. Moreover, deviations from the ROM for different set of inputs and parameters suggest that a recalibration of the model is required for the optimization studies. Results for these will also be presented with the aforementioned results.

Agarwal, A.; Biegler, L.; Zitney, S.

2007-01-01

429

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

PubMed

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. PMID:21261372

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

2011-01-21

430

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

431

Optimal Control Strategies for Disinfection of Bacterial Populations with Persister and Susceptible Dynamics  

PubMed Central

It is increasingly clear that bacteria manage to evade killing by antibiotics and antimicrobials in a variety of ways, including mutation, phenotypic variations, and formation of biofilms. With recent advances in understanding the dynamics of the tolerance mechanisms, there have been subsequent advances in understanding how to manipulate the bacterial environments to eradicate the bacteria. This study focuses on using mathematical techniques to find the optimal disinfection strategy to eliminate the bacteria while managing the load of antibiotic that is applied. In this model, the bacterial population is separated into those that are tolerant to the antibiotic and those that are susceptible to disinfection. There are transitions between the two populations whose rates depend on the chemical environment. Our results extend previous mathematical studies to include more realistic methods of applying the disinfectant. The goal is to provide experimentally testable predictions that have been lacking in previous mathematical studies. In particular, we provide the optimal disinfection protocol under a variety of assumptions within the model that can be used to validate or invalidate our simplifying assumptions and the experimental hypotheses that we used to develop the model. We find that constant dosing is not the optimal method for disinfection. Rather, cycling between application and withdrawal of the antibiotic yields the fastest killing of the bacteria.

Brown, Jason; Darres, Kyle; Petty, Katherine

2012-01-01

432

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

2011-01-01

433

Stochastic optimal controller design for uncertain nonlinear networked control system via neuro dynamic programming.  

PubMed

The stochastic optimal controller design for the nonlinear networked control system (NNCS) with uncertain system dynamics is a challenging problem due to the presence of both system nonlinearities and communication network imperfections, such as random delays and packet losses, which are not unknown a priori. In the recent literature, neuro dynamic programming (NDP) techniques, based on value and policy iterations, have been widely reported to solve the optimal control of general affine nonlinear systems. However, for realtime control, value and policy iterations-based methodology are not suitable and time-based NDP techniques are preferred. In addition, output feedback-based controller designs are preferred for implementation. Therefore, in this paper, a novel NNCS representation incorporating the system uncertainties and network imperfections is introduced first by using input and output measurements for facilitating output feedback. Then, an online neural network (NN) identifier is introduced to estimate the control coefficient matrix, which is subsequently utilized for the controller design. Subsequently, the critic and action NNs are employed along with the NN identifier to determine the forward-in-time, time-based stochastic optimal control of NNCS without using value and policy iterations. Here, the value function and control inputs are updated once a sampling instant. By using novel NN weight update laws, Lyapunov theory is used to show that all the closed-loop signals and NN weights are uniformly ultimately bounded in the mean while the approximated control input converges close to its target value with time. Simulation results are included to show the effectiveness of the proposed scheme. PMID:24808319

Xu, Hao; Jagannathan, Sarangapani

2013-03-01

434

Identifying active faults in Switzerland using relocated earthquake catalogs and optimal anisotropic dynamic clustering  

NASA Astrophysics Data System (ADS)

Active fault zones are the causal locations of most earthquakes, which release tectonic stresses. Yet, identification and association of faults and earthquakes is not straightforward. On the one hand, many earthquakes occur on faults that are unknown. On the other hand, systematic biases and uncertainties in earthquake locations hamper the association of earthquakes and known faults. We tackle the problem of linking earthquakes to faults by relocating them in a non-linear probabilistic manner and by applying a three-dimensional optimal anisotropic dynamic clustering approach to the relocated events to map fault networks. Non-linear probabilistic earthquake location allows to compute probability density functions that provide the complete probabilistic solution to the earthquake hypocenter location problem, including improved information on location uncertainties. To improve absolute earthquake locations we use a newly developed combined controlled-source seismology and local earthquake tomography model, which allows the use of secondary phases, such as PmP. Dynamic clustering is a very general image processing technique that allows partitioning a set of data points. Our improved optimal anisotropic dynamic clustering technique accounts for uncertainties in earthquake locations by the use of probability density functions, as provided by non-linear probabilistic earthquake location. Hence, number and size of the reconstructed faults is controlled by earthquake location uncertainty. We apply our approach to seismicity in Switzerland to identify active faults in the region. Relocated earthquake catalogs and associated fault networks will be compared to already existing information on faults, such as geological and seismotectonic maps, to derive a more complete picture of active faulting in Switzerland.

Wagner, M.; Wang, Y.; Husen, S.; Woessner, J.; Kissling, E. H.; Ouillon, G.; Giardini, D.; Sornette, D.

2010-12-01

435

Incorporating Search History into the Dynamically Dimensioned Search (DDS) Optimization Algorithm  

NASA Astrophysics Data System (ADS)

The Dynamically Dimensioned Search (DDS) algorithm (Tolson and Shoemaker, 2007) was recently introduced as a parsimonious, efficient and robust optimization algorithm for automatic calibration of environmental models. DDS was designed to find practical or high quality solutions to a model calibration problem within a reasonable computational timeframe rather than the globally optimal solution. The simple structure of the original DDS algorithm only stores and utilizes the best current solution to guide the search. Population-based global optimization algorithms maintain a population of typically good quality solutions to influence the search. In this research, we examine how to utilize the search history of DDS to improve algorithm performance while maintaining the parsimonious and algorithmically simple nature of the original DDS algorithm. The modification to the original DDS algorithm involves storing a subset of relatively high quality solutions previously identified in the search and selecting one solution from which to make the next perturbation in order to sample a new candidate solution. Both the function value and their proximity to one another in multi-dimensional parameter space influences the likelihood of selecting a particular solution to perturb. This approach is motivated by initial results showing that for the same total computational budget, DDS with multiple restarts can sometimes be more effective than one longer DDS optimization trial. The history-based revisions discussed above allow the algorithm to search more of the parameter subspace, thus exploiting the strength of the less- refined restart approach, but with a higher likelihood of success. Results will be presented for a relatively simple problem as well as a more complex, high-dimensional automatic calibration problem. Results will also be assessed for various computational budgets.

Tolson, B. A.; Craig, J. R.; Esfahani, M. A.

2007-12-01

436

Development of a memetic algorithm for Dynamic Multi-Objective Optimization and its applications for online neural network modeling of UAVs  

Microsoft Academic Search

Dynamic multi-objective optimization (DMO) is one of the most challenging class of optimization problems where the objective functions change over time and the optimization algorithm is required to identify the corresponding Pareto optimal solutions with minimal time lag. DMO has received very little attention in the past and none of the existing multi-objective algorithms perform satisfactorily on test problems and

Amitay Isaacs; Vishwas R. Puttige; Tapabrata Ray; Warren Smith; Sreenatha G. Anavatti

2008-01-01

437

Optimization of a Brownian-dynamics algorithm for semidilute polymer solutions.  

PubMed

Simulating the static and dynamic properties of semidilute polymer solutions with Brownian dynamics (BD) requires the computation of a large system of polymer chains coupled to one another through excluded-volume and hydrodynamic interactions. In the presence of periodic boundary conditions, long-ranged hydrodynamic interactions are frequently summed with the Ewald summation technique. By performing detailed simulations that shed light on the influence of several tuning parameters involved both in the Ewald summation method, and in the efficient treatment of Brownian forces, we develop a BD algorithm in which the computational cost scales as O(N(1.8)), where N is the number of monomers in the simulation box. We show that Beenakker's original implementation of the Ewald sum, which is only valid for systems without bead overlap, can be modified so that ? solutions can be simulated by switching off excluded-volume interactions. A comparison of the predictions of the radius of gyration, the end-to-end vector, and the self-diffusion coefficient by BD, at a range of concentrations, with the hybrid lattice Boltzmann-molecular dynamics (LB-MD) method shows excellent agreement between the two methods. In contrast to the situation for dilute solutions, the LB-MD method is shown to be significantly more computationally efficient than the current implementation of BD for simulating semidilute solutions. We argue, however, that further optimizations should be possible. PMID:23005239

Jain, Aashish; Sunthar, P; Dünweg, B; Prakash, J Ravi

2012-06-01

438

Optimization of a Brownian-dynamics algorithm for semidilute polymer solutions  

NASA Astrophysics Data System (ADS)

Simulating the static and dynamic properties of semidilute polymer solutions with Brownian dynamics (BD) requires the computation of a large system of polymer chains coupled to one another through excluded-volume and hydrodynamic interactions. In the presence of periodic boundary conditions, long-ranged hydrodynamic interactions are frequently summed with the Ewald summation technique. By performing detailed simulations that shed light on the influence of several tuning parameters involved both in the Ewald summation method, and in the efficient treatment of Brownian forces, we develop a BD algorithm in which the computational cost scales as O(N1.8), where N is the number of monomers in the simulation box. We show that Beenakker's original implementation of the Ewald sum, which is only valid for systems without bead overlap, can be modified so that ? solutions can be simulated by switching off excluded-volume interactions. A comparison of the predictions of the radius of gyration, the end-to-end vector, and the self-diffusion coefficient by BD, at a range of concentrations, with the hybrid lattice Boltzmann-molecular dynamics (LB-MD) method shows excellent agreement between the two methods. In contrast to the situation for dilute solutions, the LB-MD method is shown to be significantly more computationally efficient than the current implementation of BD for simulating semidilute solutions. We argue, however, that further optimizations should be possible.

Jain, Aashish; Sunthar, P.; Dünweg, B.; Prakash, J. Ravi

2012-06-01

439

Pathway dynamics in the optimal quantum control of rubidium: Cooperation and competition  

NASA Astrophysics Data System (ADS)

The dynamics that take place in the optimal quantum control of atomic rubidium upon population transfer from state 5S1/2 to state 5D3/2 are investigated with Hamiltonian-encoding-observable-decoding (HE-OD). For modest laser powers two second-order pathways, 5S1/2?5P3/2?5D3/2 (pathway 1) and 5S1/2?5P1/2?5D3/2 (pathway 2), govern the population transfer process. Pathway 1 has larger transition dipoles than pathway 2. However, state 5P3/2 along pathway 1 may also be excited to an undesired state 5D5/2, which can result in population "leakage." Thus, the two pathways may either cooperate or compete with each other in various dynamical regimes. An important feature in the case of cooperation is that the ratio between the amplitudes of pathways 1 and 2 oscillates over time with a frequency equal to the detuning between transitions 5S1/2?5P3/2 and 5P3/2?5D3/2. We also study the regime in which pathway 2 dominates the dynamics when the larger transition dipoles of pathway 1 can no longer compensate for its population leakage. The overall analysis illustrates the utility of HE-OD as a tool to reveal the quantum control mechanism.

Gao, Fang; Rey-de-Castro, Roberto; Donovan, Ashley M.; Xu, Jian; Wang, Yaoxiong; Rabitz, Herschel; Shuang, Feng

2014-02-01

440

Dynamic simulation and optimal real-time operation of CHP systems for buildings  

NASA Astrophysics Data System (ADS)

Combined Cooling, Heating, and Power (CHP) systems have been widely recognized as a key alternative for electric and thermal energy generation because of their outstanding energy efficiency, reduced environmental emissions, and relative independence from centralized power grids. The systems provide simultaneous onsite or near-site electric and thermal energy generation in a single, integrated package. As CHP becomes increasingly popular worldwide and its total capacity increases rapidly, the research on the topics of CHP performance assessment, design, and operational strategy become increasingly important. Following this trend of research activities to improve energy efficiency, environmental emissions, and operational cost, this dissertation focuses on the following aspects: (a) performance evaluation of a CHP system using a transient simulation model; (b) development of a dynamic simulation model of a power generation unit that can be effectively used in transient simulations of CHP systems; (c) investigation of real-time operation of CHP systems based on optimization with respect to operational cost, primary energy consumption, and carbon dioxide emissions; and (d) development of optimal supervisory feed-forward control that can provide realistic real-time operation of CHP systems with electric and thermal energy storages using short-term weather forecasting. The results from a transient simulation of a CHP system show that technical and economical performance can be readily evaluated using the transient model and that the design, component selection, and control of a CHP system can be improved using this model. The results from the case studies using optimal real-time operation strategies demonstrate that CHP systems with an energy dispatch algorithm have the potential to yield savings in operational cost, primary energy consumption, and carbon dioxide emissions with respect to a conventional HVAC system. Finally, the results from the case study using a supervisory feed-forward control system illustrate that optimal realistic real-time operation of CHP systems with electric and thermal energy storages can be managed by this optimal control using weather forecasting information. Key words: CHP, transient simulation, power generation unit, optimization, real-time operation, feed-forward control, energy storage systems

Cho, Hee Jin

441

Optimality Based Dynamic Plant Allocation Model: Predicting Acclimation Response to Climate Change  

NASA Astrophysics Data System (ADS)

Allocation of assimilated carbon to different plant parts determines the future plant status and is important to predict long term (months to years) vegetated land surface fluxes. Plants have the ability to modify their allometry and exhibit plasticity by varying the relative proportions of the structural biomass contained in each of its tissue. The ability of plants to be plastic provides them with the potential to acclimate to changing environmental conditions in order to enhance their probability of survival. Allometry based allocation models and other empirical allocation models do not account for plant plasticity cause by acclimation due to environmental changes. In the absence of a detailed understanding of the various biophysical processes involved in plant growth and development an optimality approach is adopted here to predict carbon allocation in plants. Existing optimality based models of plant growth are either static or involve considerable empiricism. In this work, we adopt an optimality based approach (coupled with limitations on plant plasticity) to predict the dynamic allocation of assimilated carbon to different plant parts. We explore the applicability of this approach using several optimization variables such as net primary productivity, net transpiration, realized growth rate, total end of growing season reproductive biomass etc. We use this approach to predict the dynamic nature of plant acclimation in its allocation of carbon to different plant parts under current and future climate scenarios. This approach is designed as a growth sub-model in the multi-layer canopy plant model (MLCPM) and is used to obtain land surface fluxes and plant properties over the growing season. The framework of this model is such that it retains the generality and can be applied to different types of ecosystems. We test this approach using the data from free air carbon dioxide enrichment (FACE) experiments using soybean crop at the Soy-FACE research site. Our results show that there are significant changes in the allocation patterns of vegetation when subjected to elevated CO2 indicating that our model is able to account for plant plasticity arising from acclimation. Soybeans when grown under elevated CO2, increased their allocation to structural components such as leaves and decreased their allocation to reproductive biomass. This demonstrates that plant acclimation causes lower than expected crop yields when grown under elevated CO2. Our findings can have serious implications in estimating future crop yields under climate change scenarios where it is widely expected that rising CO2 will fully offset losses due to climate change.

Srinivasan, V.; Drewry, D.; Kumar, P.; Sivapalan, M.

2009-12-01

442

Robust estimation of optimal dynamic treatment regimes for sequential treatment decisions  

PubMed Central

Summary A dynamic treatment regime is a list of sequential decision rules for assigning treatment based on a patient’s history. Q- and A-learning are two main approaches for estimating the optimal regime, i.e., that yielding the most beneficial outcome in the patient population, using data from a clinical trial or observational study. Q-learning requires postulated regression models for the outcome, while A-learning involves models for that part of the outcome regression representing treatment contrasts and for treatment assignment. We propose an alternative to Q- and A-learning that maximizes a doubly robust augmented inverse probability weighted estimator for population mean outcome over a restricted class of regimes. Simulations demonstrate the method’s performance and robustness to model misspecification, which is a key concern.

Zhang, Baqun; Tsiatis, Anastasios A.; Laber, Eric B.; Davidian, Marie

2013-01-01

443

An Optimization Transfer Algorithm for Nonlinear Parametric Image Reconstruction from Dynamic PET Data  

PubMed Central

Direct reconstruction of kinetic parameters from raw projection data is a challenging task in molecular imaging using dynamic positron emission tomography (PET). This paper presents a new optimization transfer algorithm for penalized likelihood direct reconstruction of nonlinear parametric images that is easy to use and has a fast convergence rate. Each iteration of the proposed algorithm can be implemented in three simple steps: a frame-by-frame maximum likelihood EM-like image update, a frame-by-frame image smoothing, and a pixel-by-pixel time activity curve fitting. Computer simulation shows that the direct algorithm can achieve a better bias-variance performance than the indirect reconstruction algorithm. The convergence rate of the new algorithm is substantially faster than our previous algorithm that is based on a separable paraboloidal surrogate function. The proposed algorithm has been applied to real 4D PET data.

Wang, Guobao; Qi, Jinyi

2012-01-01

444

Sequentially Optimized Meshfree Approximation as a New Computational Fluid Dynamics Solver  

NASA Astrophysics Data System (ADS)

This thesis presents the Sequentially Optimized Meshfree Approximation (SOMA) method, a new and powerful Computational Fluid Dynamics (CFD) solver. While standard computational methods can be faster and cheaper that physical experimentation, both in cost and work time, these methods do have some time and user interaction overhead which SOMA eliminates. As a meshfree method which could use adaptive domain refinement methods, SOMA avoids the need for user generated and/or analyzed grids, volumes, and meshes. Incremental building of a feed-forward artificial neural network through machine learning to solve the flow problem significantly reduces user interaction and reduces computational cost. This is done by avoiding the creation and inversion of possibly dense block diagonal matrices and by focusing computational work on regions where the flow changes and ignoring regions where no changes occur.

Wilkinson, Matthew

445

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 iss