Optimal Shipboard Power System Management via Mixed Integer Dynamic Programming
Kwatny, Harry G.
, that converts any logical specification into a set of mixed- integer formulas (IP formulas). ThusOptimal Shipboard Power System Management via Mixed Integer Dynamic Programming Harry G. Kwatny) [8], [6] or a modified version that we call the 'mixed integer dynamic program' (MIDP). The action
2013-01-01
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. PMID:24176044
Adrián Ferrari; Soledad Gutierrez; Evaristo C. Biscaia
2009-01-01
Dynamic optimization in Sequential Batch Reactors for wastewater treatment represents an enormous challenge in order to improve the time and energy management in real industries. The non convex behaviour presented by these systems limits the application of deterministic techniques to optimize this kind of equipment. Although any real example has been found in the open literature, stochastic contributions to meet
Mitchell, John E.
Using MINTO on RCS MINTO stands for Mixed INTeger Optimizer. It is a package which solves mixed of being integrated into the RCS environment. This is an ongoing process but you can access MINTO right now. MINTO is available on fourteen selected RCS workstation by simply following these directions
Computational experiments for local search algorithms for binary and mixed integer optimization
Zhou, Jingting, S.M. Massachusetts Institute of Technology
2010-01-01
In this thesis, we implement and test two algorithms for binary optimization and mixed integer optimization, respectively. We fine tune the parameters of these two algorithms and achieve satisfactory performance. We also ...
Symbolic Construction of Dynamic Mixed Integer Programs for Power System Management
Harry G. Kwatny; Edoe F. Mensah; Dagmar Niebur; Carole Teolis
2006-01-01
In this presentation, we discuss a symbolic tool, implemented in Mathematica that converts a hybrid automaton model of a power system into either a mixed logical dynamic system (MLD) or a dynamic mixed integer program (DMIP). The tool, converts any logical specification into mixed-integer formulas (IP formulas). For example the transition specification for the automaton is converted into a set
Symbolic construction of dynamic mixed integer programs for power system management
Harry G. Kwatny; Edoe F. Mensah; Dagmar Niebur; Carole Teolis
2005-01-01
In this presentation, we discuss a symbolic tool, implemented in Mathematica©, that converts a hybrid automaton model of a power system into either a mixed logical dynamic system' (MLD) or a 'dynamic mixed integer program' (DMIP). The tool, converts any logical specification into mixed-integer formulas (IP formulas). For example the transition specification for the automaton is converted into a set
Grossmann, Ignacio E.
, operation, and control of chemical systems. #12;Mixed-integer programming methods for supply chain. Software Overview 6. Combinatorial Optimization 7. Chemical Production Scheduling 8. Integration of facilities and distribution options for the procurement of materials; transformation of materials
On the existence of optimal solutions to integer and mixed-integer programming problems
R. R. Meyer
1974-01-01
The purpose of this paper is to present sufficient conditions for the existence of optimal solutions to integer and mixed-integer programming problems in the absence of upper bounds on the integer variables. It is shown that (in addition to feasibility and boundedness of the objective function) (1) in the pure integer case a sufficient condition is that all of the
Multistage Robust Mixed Integer Optimization with Adaptive Partitions
2014-11-22
Nov 22, 2014 ... We use this information to construct partitions in the uncertainty set, leading ..... partition ? z? static. Proof. Consider the problem zhalfpart = min x,z z. (5) ...... optimization for the security constrained unit commitment problem.
LP formulations for mixed-integer polynomial optimization problems ...
2015-03-20
of the constraints has bounded tree-width our construction yields a class of ...... [9] D. Bienstock, Progress on solving power flow problems, Optima, 93 (2013), pp. .... Application of Semidefinite Optimization Techniques to Problems in Electric.
Optimizing a Medical Image Analysis System Using Mixed-Integer Evolution Strategies
Rui Li; Michael T. M. Emmerich; Jeroen Eggermont; Ernst G. P. Bovenkamp; Thomas Bäck; Jouke Dijkstra; Johan H. C. Reiber
We will discuss Mixed-Integer Evolution Strategies (MIES) and their application to the optimization of control parameters\\u000a of a semi-automatic image analysis system for Intravascular Ultrasound (IVUS) images. IVUS is a technique used to obtain real-time\\u000a high-resolution tomographic images from the inside of coronary vessels and other arteries. The IVUS image feature detectors\\u000a used in the analysis system are expert-designed and
Hugo Morais; Péter Kádár; Pedro Faria; Zita A. Vale; H. M. Khodr
2010-01-01
In the energy management of the isolated operation of small power system, the economic scheduling of the generation units is a crucial problem. Applying right timing can maximize the performance of the supply. The optimal operation of a wind turbine, a solar unit, a fuel cell and a storage battery is searched by a mixed-integer linear programming implemented in General
Gorissen, Bram L; den Hertog, Dick; Hoffmann, Aswin L
2013-02-21
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
NASA Astrophysics Data System (ADS)
Sakakibara, Kazutoshi; Tian, Yajie; Nishikawa, Ikuko
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.
Optimizing well-stimulation treatment size using mixed integer linear programming
Picon Aranguren, Oscar
2002-01-01
This thesis presents a model for optimum sizing of well stimulation treatments. The optimization scheme is based on a combination of a well stimulation type selection and treatment size, therefore determining the optimum type of stimulation...
Optimizing well-stimulation treatment size using mixed integer linear programming
Picon Aranguren, Oscar
2002-01-01
This thesis presents a model for optimum sizing of well stimulation treatments. The optimization scheme is based on a combination of a well stimulation type selection and treatment size, therefore determining the optimum type of stimulation...
Collision Avoidance in Air Traffic Management: A Mixed-Integer Linear Optimization Approach
Antonio Alonso-Ayuso; Laureano F. Escudero; Francisco Javier Martín-Campo
2011-01-01
This paper tackles the collision-avoidance problem in air traffic management. The problem consists of deciding the best strategy for new aircraft configurations (velocity and altitude changes) such that all conflicts in the airspace, i.e., the loss of the minimum safety distance that has to be kept between two aircraft, are avoided. A mixed 0-1 linear optimization model based on geometric
Mixed Integer Programming Computation
Andrea Lodi
\\u000a The first 50 years of Integer and Mixed-Integer Programming have taken us to a very stable paradigm for solving problems in\\u000a a reliable and effective way. We run over these 50 exciting years by showing some crucial milestones and we highlight the\\u000a building blocks that are making nowadays solvers effective from both a performance and an application viewpoint. Finally,\\u000a we
Overview on Mixed Integer Nonlinear Programming Problems
NASA Astrophysics Data System (ADS)
Fernandes, Florbela P.; Costa, M. Fernanda P.; Fernandes, Edite M. G. P.
2009-09-01
Many optimization problems involve integer and continuous variables that can be modeled as mixed integer nonlinear programming (MINLP) problems. This has led to a wide range of applications, in particular in some engineering areas. Here, we provide a brief overview on MINLP, and present a simple idea for a future nonconvex MINLP solution technique.
Online trajectory planning for UAVs using mixed integer linear programming
Culligan, Kieran Forbes
2006-01-01
This thesis presents a improved path planner using mixed-integer linear programming (MILP) to solve a receding horizon optimization problem for unmanned aerial vehicles (UAV's). Using MILP, hard constraints for obstacle ...
An Updated Mixed Integer Programming Library: MIPLIB 3.0
Cassandra M. Mczeal; Martin W. P. Savelsbergh; Robert E. Bixby; Sebastian Ceria
1996-01-01
IntroductionIn response to the needs of researchers for access to challenging mixed integer programs, Bixbyet al. [1] created MIPLIB, an electronically available library of both pure and mixed integerprograms, most of which arise from real-world applications.Since its introduction, MIPLIB has become a standard test set for comparing the performanceof mixed integer optimization codes. Its availability has provided an important stimulusfor
Kumar Abhishek; Sven Leyffer; Jeffrey T. Linderoth
2010-01-01
Optimal design applications are often modeled by using categorical variables to express discrete design decisions, such as\\u000a material types. A disadvantage of using categorical variables is the lack of continuous relaxations, which precludes the use\\u000a of modern integer programming techniques. We show how to express categorical variables with standard integer modeling techniques,\\u000a and we illustrate this approach on a load-bearing thermal
Mixed Integer Programming and Heuristic Scheduling for Space Communication
NASA Technical Reports Server (NTRS)
Lee, Charles H.; Cheung, Kar-Ming
2013-01-01
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.
Abhishek, K.; Leyffer, S.; Linderoth, J. T.; Mathematics and Computer Science; Lehigh Univ.
2010-06-01
Optimal design applications are often modeled by using categorical variables to express discrete design decisions, such as material types. A disadvantage of using categorical variables is the lack of continuous relaxations, which precludes the use of modern integer programming techniques. We show how to express categorical variables with standard integer modeling techniques, and we illustrate this approach on a load-bearing thermal insulation system. The system consists of a number of insulators of different materials and intercepts that minimize the heat flow from a hot surface to a cold surface. Our new model allows us to employ black-box modeling languages and solvers and illustrates the interplay between integer and nonlinear modeling techniques. We present numerical experience that illustrates the advantage of the standard integer model.
Aircraft trajectory planning with collision avoidance using mixed integer linear programming
Arthur Richards
2002-01-01
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
Semidefinite Relaxations for Non-Convex Quadratic Mixed-Integer ...
2011-11-08
Most algorithms and software tools for quadratic mixed-integer optimization ... closest vector problem (domains Z), and quadratic minimization with box con- .... The point to be separated is a pair (x0i,xii) from a positive semidefinite matrix X. By ...
Mixed Integer Linear Programming Formulation Techniques
Vielma, Juan Pablo
A wide range of problems can be modeled as Mixed Integer Linear Programming (MIP) problems using standard formulation techniques. However, in some cases the resulting MIP can be either too weak or too large to be effectively ...
The Group-Theoretic Approach in Mixed Integer Programming
Jean-Philippe P. Richard; Santanu S. Dey
\\u000a In this chapter, we provide an overview of the mathematical foundations and recent theoretical and computational advances\\u000a in the study of the grouptheoretic approach in mixed integer programming. We motivate the definition of group relaxation geometrically\\u000a and present methods to optimize linear functions over this set. We then discuss fundamental results about the structure of\\u000a group relaxations. We describe a
OPTIMAL TASK ALLOCATION AND DYNAMIC TRAJECTORY PLANNING FOR MULTI-VEHICLE
Stryk, Oskar von
model a new planning method for optimal coordination and control of multiple unmanned vehicles of optimal simultaneous waypoint or target sequencing and dynamic trajectory planning for a team of unmanned discrete dynamic system and solved using mixed integer linear programming methods. A standard task
Solid Waste Management System Analysis by Multiobjective Mixed Integer Programming Model
Ni-Bin Chang; S. F. Wang
1996-01-01
The conflict between economic optimization and environmental protection has received wide attention in recent research programs for solid waste management system planning. The purpose of this analysis is to apply multiobjective mixed integer programming techniques for reasoning the potential conflict between environmental and economic goals and for evaluating sustainable strategies for waste management in a metropolitan region. The information incorporated
CONCRETE STRUCTURE DESIGN USING MIXED-INTEGER NONLINEAR PROGRAMMING WITH COMPLEMENTARITY
CONCRETE STRUCTURE DESIGN USING MIXED-INTEGER NONLINEAR PROGRAMMING WITH COMPLEMENTARITY programming (MINLP) formulation to achieve mini- mum-cost designs for reinforced concrete (RC) structures-practice design solutions of 13 percent. Key words. complementarity problems, applications in optimization, mixed
An algorithmic framework for convex mixed integer nonlinear programs
Pierre Bonami; Lorenz T. Biegler; Andrew R. Conn; Gérard Cornuéjols; Ignacio E. Grossmann; Carl D. Laird; Jon Lee; Andrea Lodi; François Margot; Nicolas Sawaya; Andreas Wächter
2008-01-01
This paper is motivated by the fact that mixed integer nonlinear programming is an important and dicult area for which there is a need for developing new methods and soft- ware for solving large-scale problems. Moreover, both fundamental building blocks, namely mixed integer linear programming and nonlinear programming, have seen considerable and steady progress in recent years. Wishing to exploit
Mixed Integer Linear Programming Method for Absolute Value Equations
Longquan Yong
2009-01-01
We formulate the NP-hard absolute value equation as linear complementary problem when the singular values of A exceed one, and we proposed a mixed integer linear programming method to absolute value equation problem. The effectiveness of the method is demonstrated by its ability to solve random problems. Index Terms—absolute value equation; linear complementary problem; mixed integer linear programming. The basic
A Polyhedral Study of the Mixed Integer Cut
Steve Tyber; Ellis L. Johnson
2010-01-01
\\u000a General purpose cutting planes have played a central role in modern IP solvers. In practice, the Gomory mixed integer cut\\u000a has proven to be among the most useful general purpose cuts. One may obtain this inequality from the group relaxation of an\\u000a IP, which arises by relaxing non-negativity on the basic variables. We study the mixed integer cut as a
A mixed integer programming model for a closed-loop supply-chain network
Eren Özceylan; Turan Paksoy
2012-01-01
In this paper, a new mixed integer mathematical model for a closed-loop supply-chain network that includes both forward and reverse flows with multi-periods and multi-parts is proposed. The proposed model guarantees the optimal values of transportation amounts of manufactured and disassembled products in a closed-loop supply chain while determining the location of plants and retailers. Finally, computational results are presented
A Polyhedral Study of the Mixed Integer Cut
NASA Astrophysics Data System (ADS)
Tyber, Steve; Johnson, Ellis L.
General purpose cutting planes have played a central role in modern IP solvers. In practice, the Gomory mixed integer cut has proven to be among the most useful general purpose cuts. One may obtain this inequality from the group relaxation of an IP, which arises by relaxing non-negativity on the basic variables. We study the mixed integer cut as a facet of the master cyclic group polyhedron and characterize its extreme points and adjacent facets in this setting. Extensions are provided under automorphic and homomorphic mappings.
Radiation Treatment Planning: Mixed Integer Programming Formulations and Approaches
Ferris, Michael C.
diagnosed with cancer in the U.S will undergo treatment with radiation therapy. This form of therapy hasRadiation Treatment Planning: Mixed Integer Programming Formulations and Approaches Michael C-based radiation therapy treatment planning. In treatment planning problems, the objective is to deliver a homoge
Mixed Integer Programming model for pricing in telecommunication
Paris-Sud XI, Université de
of airline companies. However, so far, application to telecommunication industry have been scarce. Using conducted to apply YM to the telecommunication industry. Humair [2] propose to define basis and modelMixed Integer Programming model for pricing in telecommunication Mustapha Bouhtou and Jean
The Polar of a Simple Mixed-Integer Set
defarias
2005-09-13
Sep 13, 2005 ... M. Zhao and I.R. de Farias JR. ...... [5] G.B. Dantzig, D.R. Fulkerson, and S.M. Johnson, \\On a Linear-Programming, Combinatorial ... [12] A.J. Miller and L.A. Wolsey, \\Tight Formulations for some Simple Mixed Integer Programs.
A Unified Mixed-Integer Programming Model for Simultaneous ...
2014-11-23
In this paper, we propose and study a unified mixed-integer programming model that simul- taneously .... possible for minimal damage to healthy tissue, and iii. for the overall treatment time to be as short as ... for motion and setup. This area is ...
Application of mixed-integer programming in chemical engineering
Pogiatzis, Thomas
2013-06-11
Application of Mixed-Integer Programming in Chemical Engineering Thomas Pogiatzis Homerton College University of Cambridge This dissertation is submitted for the degree of Doctor of Philosophy November 2012 ^En tz?e˜inon t˜h tsili˜ac p?s> stä bol... Kazantzakis) Preface The work in this dissertation was undertaken at the Department of Chemical Engineering and Biotechnology, University of Cambridge, between October 2009 and November 2012. It is the original work of the author, except where specifically...
Mixed integer nonlinear programming using interior-point methods
Hande Y. Benson
2010-01-01
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
Diet planning for humans using mixed-integer linear programming.
Sklan, D; Dariel, I
1993-07-01
Human diet planning is generally carried out by selecting the food items or groups of food items to be used in the diet and then calculating the composition. If nutrient quantities do not reach the desired nutritional requirements, foods are exchanged or quantities altered and the composition recalculated. Iterations are repeated until a suitable diet is obtained. This procedure is cumbersome and slow and often leads to compromises in composition of the final diets. A computerized model, planning diets for humans at minimum cost while supplying all nutritional requirements, maintaining nutrient relationships and preserving eating practices is presented. This is based on a mixed-integer linear-programming algorithm. Linear equations were prepared for each nutritional requirement. To produce linear equations for relationships between nutrients, linear transformations were performed. Logical definitions for interactions such as the frequency of use of foods, relationships between exchange groups and the energy content of different meals were defined, and linear equations for these associations were written. Food items generally eaten in whole units were defined as integers. The use of this program is demonstrated for planning diets using a large selection of basic foods and for clinical situations where nutritional intervention is desirable. The system presented begins from a definition of the nutritional requirements and then plans the foods accordingly, and at minimum cost. This provides an accurate, efficient and versatile method of diet formulation. PMID:8399108
NASA Astrophysics Data System (ADS)
Onoyama, Takashi; Kubota, Sen; Maekawa, Takuya; Komoda, Norihisa
Adequate response performance is required for the planning of a cooperative logistic network covering multiple enterprises, because this process needs a human expert's evaluation from many aspects. To satisfy this requirement, we propose an accurate model based on mixed integer programming for optimizing cooperative logistics networks where “round transportation” exists together with “depot transportation” including lower limit constraints of loading ratio for round transportation vehicles. Furthermore, to achieve interactive response performance, a dummy load is introduced into the model instead of integer variables. The experimental result shows the proposed method obtains an accurate solution within interactive response time.
Planning Investments in Water Resources by Mixed-Integer Programming: The Vardar-Axios River Basin
Elliot, Dorothy P.
A mixed integer programming model for planning water resources investments is presented. The model is a sequencing model applied to the Vardar-Axios river basin in Yugoslavia and Greece. The structure of the model is ...
Beier, Eric
2012-02-14
The focus of this dissertation is solution strategies for stochastic mixed-integer programs with special structures. Motivation for the methods comes from the relatively sparse number of algorithms for solving stochastic ...
Solution of Mixed-Integer Programming Problems on the XT5
Hartman-Baker, Rebecca J [ORNL; Busch, Ingrid Karin [ORNL; Hilliard, Michael R [ORNL; Middleton, Richard S [ORNL; Schultze, Michael [ORNL
2009-01-01
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.
MISQP: A Fortran Subroutine of a Trust Region SQP Algorithm for Mixed-Integer Nonlinear Programming1
Schittkowski, Klaus
MISQP: A Fortran Subroutine of a Trust Region SQP Algorithm for Mixed-Integer Nonlinear Programming solves mixed-integer nonlinear programming problems by a modified sequential quadratic programming (SQP) method. Under the assumption that integer variables have a smooth influence on the model func- tions, i
A Lifted Linear Programming Branch-and-Bound Algorithm for Mixed Integer Conic Quadratic Programs
Ahmed, Shabbir
problems simply as conic programming (CP) and mixed integer conic programming (MICP) problems respectively. 1 #12;We are interested in solving MICP problems of the form zMICPP := max x,y cx + dy (1) s.t. Dx || · ||2 is the Euclidean norm. We denote the MICP problem given by (1)(5) as MICPP and its CP relaxation
Solving the Double Digestion Problem as a MixedInteger Linear Program \\Lambda
Zhang, Yin
Solving the Double Digestion Problem as a MixedInteger Linear Program \\Lambda Zhijun Wu y and Yin Zhang z August, 2001 Abstract. The double digestion problem for DNA restriction mapping is knownscale double digestion problems. Key Words. DNA sequencing, restriction mapping, double digestion, NP
An Analysis of Mixed Integer Linear Sets Based on Lattice Point Free Convex Sets
Kent Andersen; Quentin Louveaux; Robert Weismantel
2010-01-01
Split cuts are cutting planes for mixed integer programs whose validity is derived from maximal lattice point free polyhedra of the form S := {x : ! 0 ! ! Tx ! ! 0 +1 } called split sets. The set obtained by adding all split cuts is called the split closure, and the split closure is known to be
David L. Cooke; Thomas R. Rohleder; Edward A. Silver
2004-01-01
We extend past research on the economic lot scheduling problem to address some of the limitations of the earlier work. In particular we develop mixed integer programming formulations with the assumption of a production precedence sequence. When evaluated over a variety of problems from the literature, it is clear that finding a schedule that minimizes costs is not trivial. However,
Bootstrap method and its application to the hypothesis testing in GPS mixed integer linear model
J. Cai; E. Grafarend; C. Hu
2009-01-01
High accuracy GPS relative positioning is usually based on the double-differenced (DD) carrier phase observables. When considering short baseline (less than 20 km), the linear model for DD phase may be simplified to a mixed integer linear model, where the central problem of the determination of the integer phase ambiguities must be first solved. This topic has therefore been a
CORC Report TR-2005-07 Using mixed-integer programming to solve power grid blackout
Bienstock, Daniel
CORC Report TR-2005-07 Using mixed-integer programming to solve power grid blackout problems Daniel of three components: generation, transmission and distribution. At one end of the grid) The quantity pkq is called the active power flow, qkq is the reactive power flow. Both quantities have concrete
Monotonic bounds in multistage mixed-integer linear stochastic ...
2015-02-02
Mathematics Subject Classification (2000) 90C15 · 90C90 · 65K05 ·. 1 Introduction ..... by the primary Italian cement producer. ...... Harvard Business School, ... G.L. Nemhauser, and R. Weismantel, editors, Handbook of Discrete Optimization,.
Scheduling Smart Home Appliances Using Mixed Integer Linear Programming
Johansson, Karl Henrik
electricity generation, personal electrical vehicles (PEVs) and distributed electricity generation the wind power's dynamic contribution to electricity generation and the PEVs' random demand of electricity
Combinatorial therapy discovery using mixed integer linear programming
Pang, Kaifang; Wan, Ying-Wooi; Choi, William T.; Donehower, Lawrence A.; Sun, Jingchun; Pant, Dhruv; Liu, Zhandong
2014-01-01
Motivation: Combinatorial therapies play increasingly important roles in combating complex diseases. Owing to the huge cost associated with experimental methods in identifying optimal drug combinations, computational approaches can provide a guide to limit the search space and reduce cost. However, few computational approaches have been developed for this purpose, and thus there is a great need of new algorithms for drug combination prediction. Results: Here we proposed to formulate the optimal combinatorial therapy problem into two complementary mathematical algorithms, Balanced Target Set Cover (BTSC) and Minimum Off-Target Set Cover (MOTSC). Given a disease gene set, BTSC seeks a balanced solution that maximizes the coverage on the disease genes and minimizes the off-target hits at the same time. MOTSC seeks a full coverage on the disease gene set while minimizing the off-target set. Through simulation, both BTSC and MOTSC demonstrated a much faster running time over exhaustive search with the same accuracy. When applied to real disease gene sets, our algorithms not only identified known drug combinations, but also predicted novel drug combinations that are worth further testing. In addition, we developed a web-based tool to allow users to iteratively search for optimal drug combinations given a user-defined gene set. Availability: Our tool is freely available for noncommercial use at http://www.drug.liuzlab.org/. Contact: zhandong.liu@bcm.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24463180
An analysis of mixed integer linear sets based on lattice point free convex sets
Kent Andersen; Quentin Louveaux; Robert Weismantel
2009-01-01
Split cuts are cutting planes for mixed integer programs whose validity is derived from maximal lattice point free polyhedra of the form $S:=\\\\{x : \\\\pi_0 \\\\leq \\\\pi^T x \\\\leq \\\\pi_0+1 \\\\}$ called split sets. The set obtained by adding all split cuts is called the split closure, and the split closure is known to be a polyhedron. A split set
Thomas Tometzki; Sebastian Engell
2011-01-01
This paper introduces new initialization approaches for evolutionary algorithms that solve two-stage stochastic mixed- integer problems. The two-stage stochastic mixed-integer pro- grams are handled by a stage decomposition based hybrid algorithm where an evolutionary algorithm handles the first-stage decisions and mathematical programming handles the second- stage decisions. The population of the evolutionary algorithm is initialized by using solutions which are
DRIESSEN,BRIAN; SADEGH,NADER
2000-04-25
This work presents a method of finding near global optima to minimum-time trajectory generation problem for systems that would be linear if it were not for the presence of Coloumb friction. The required final state of the system is assumed to be maintainable by the system, and the input bounds are assumed to be large enough so that they can overcome the maximum static Coloumb friction force. Other than the previous work for generating minimum-time trajectories for non redundant robotic manipulators for which the path in joint space is already specified, this work represents, to the best of the authors' knowledge, the first approach for generating near global optima for minimum-time problems involving a nonlinear class of dynamic systems. The reason the optima generated are near global optima instead of exactly global optima is due to a discrete-time approximation of the system (which is usually used anyway to simulate such a system numerically). The method closely resembles previous methods for generating minimum-time trajectories for linear systems, where the core operation is the solution of a Phase I linear programming problem. For the nonlinear systems considered herein, the core operation is instead the solution of a mixed integer linear programming problem.
Pierre Martens; Erik Delarue; William D'haeseleer
2012-01-01
Carbon capture and storage (CCS) seems to be an indispensable technology to safeguard the future of fossil-fired generation in the context of global warming. The deployment of CCS has an impact on the functioning and balancing of the overall electricity generation system. In this paper a mixed integer linear programming (MILP) model is developed for an ultra super-critical pulverized coal
DOTcvpSB, a software toolbox for dynamic optimization in systems biology
Hirmajer, Tomáš; Balsa-Canto, Eva; Banga, Julio R
2009-01-01
Background Mathematical optimization aims to make a system or design as effective or functional as possible, computing the quality of the different alternatives using a mathematical model. Most models in systems biology have a dynamic nature, usually described by sets of differential equations. Dynamic optimization addresses this class of systems, seeking the computation of the optimal time-varying conditions (control variables) to minimize or maximize a certain performance index. Dynamic optimization can solve many important problems in systems biology, including optimal control for obtaining a desired biological performance, the analysis of network designs and computer aided design of biological units. Results Here, we present a software toolbox, DOTcvpSB, which uses a rich ensemble of state-of-the-art numerical methods for solving continuous and mixed-integer dynamic optimization (MIDO) problems. The toolbox has been written in MATLAB and provides an easy and user friendly environment, including a graphical user interface, while ensuring a good numerical performance. Problems are easily stated thanks to the compact input definition. The toolbox also offers the possibility of importing SBML models, thus enabling it as a powerful optimization companion to modelling packages in systems biology. It serves as a means of handling generic black-box models as well. Conclusion Here we illustrate the capabilities and performance of DOTcvpSB by solving several challenging optimization problems related with bioreactor optimization, optimal drug infusion to a patient and the minimization of intracellular oscillations. The results illustrate how the suite of solvers available allows the efficient solution of a wide class of dynamic optimization problems, including challenging multimodal ones. The toolbox is freely available for academic use. PMID:19558728
Aircraft deconfliction with speed regulation: new models from mixed-integer optimization
Boyer, Edmond
of automation on the ground. In the context of aircraft conflict detection and resolution, air traffic control is still widely performed manually on the ground by air traffic controllers watching the traffic movements of managing traffic in such a way as to increase the capacity of control in the air sectors. Aicraft potential
Optimal Dynamic XL Reinsurance
Christian HIPP; Michael VOGT
2003-01-01
We consider a risk process modelled as a compound Poisson process. We find the optimal dynamic unlimited excess of loss reinsurance strategy to minimize infinite time ruin probability, and prove the existence of a smooth solution of the corresponding Hamilton-Jacobi-Bellman equation as well as a verification theorem. Numerical examples with exponential, shifted exponential, and Pareto claims are given.
Optimization Online Digest -- March 2014
Choice Based Revenue Management for Parallel Flights ... An Improved Stochastic Optimization Model for Water Supply Pumping Systems in Urban Networks ... CBLIB 2014: A benchmark library for conic mixed-integer and continuous ...
Optimization Online Digest -- July 2012
Interior point methods for sufficient LCP in a wide neighborhood of the central path with ... A variable smoothing algorithm for solving convex optimization problems ... Mixed Integer Linear Programming Formulation Techniques ... POST
Wang, S; Huang, G H
2013-03-15
Flood disasters have been extremely severe in recent decades, and they account for about one third of all natural catastrophes throughout the world. In this study, a two-stage mixed-integer fuzzy programming with interval-valued membership functions (TMFP-IMF) approach is developed for flood-diversion planning under uncertainty. TMFP-IMF integrates the fuzzy flexible programming, two-stage stochastic programming, and integer programming within a general framework. A concept of interval-valued fuzzy membership function is introduced to address complexities of system uncertainties. TMFP-IMF can not only deal with uncertainties expressed as fuzzy sets and probability distributions, but also incorporate pre-regulated water-diversion policies directly into its optimization process. TMFP-IMF is applied to a hypothetical case study of flood-diversion planning for demonstrating its applicability. Results indicate that reasonable solutions can be generated for binary and continuous variables. A variety of flood-diversion and capacity-expansion schemes can be obtained under four scenarios, which enable decision makers (DMs) to identify the most desired one based on their perceptions and attitudes towards the objective-function value and constraints. PMID:23376303
Optimization techniques in molecular structure and function elucidation
Sahinidis, Nikolaos V.
2009-01-01
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. PMID:20160866
Zou, Meng; Zhang, Peng-Jun; Wen, Xin-Yu; Chen, Luonan; Tian, Ya-Ping; Wang, Yong
2015-07-15
Multi-biomarker panels can capture the nonlinear synergy among biomarkers and they are important to aid in the early diagnosis and ultimately battle complex diseases. However, identification of these multi-biomarker panels from case and control data is challenging. For example, the exhaustive search method is computationally infeasible when the data dimension is high. Here, we propose a novel method, MILP_k, to identify serum-based multi-biomarker panel to distinguish colorectal cancers (CRC) from benign colorectal tumors. Specifically, the multi-biomarker panel detection problem is modeled by a mixed integer programming to maximize the classification accuracy. Then we measured the serum profiling data for 101 CRC patients and 95 benign patients. The 61 biomarkers were analyzed individually and further their combinations by our method. We discovered 4 biomarkers as the optimal small multi-biomarker panel, including known CRC biomarkers CEA and IL-10 as well as novel biomarkers IMA and NSE. This multi-biomarker panel obtains leave-one-out cross-validation (LOOCV) accuracy to 0.7857 by nearest centroid classifier. An independent test of this panel by support vector machine (SVM) with threefold cross validation gets an AUC 0.8438. This greatly improves the predictive accuracy by 20% over the single best biomarker. Further extension of this 4-biomarker panel to a larger 13-biomarker panel improves the LOOCV to 0.8673 with independent AUC 0.8437. Comparison with the exhaustive search method shows that our method dramatically reduces the searching time by 1000-fold. Experiments on the early cancer stage samples reveal two panel of biomarkers and show promising accuracy. The proposed method allows us to select the subset of biomarkers with best accuracy to distinguish case and control samples given the number of selected biomarkers. Both receiver operating characteristic curve and precision-recall curve show our method's consistent performance gain in accuracy. Our method also shows its advantage in capturing synergy among selected biomarkers. The multi-biomarker panel far outperforms the simple combination of best single features. Close investigation of the multi-biomarker panel illustrates that our method possesses the ability to remove redundancy and reveals complementary biomarker combinations. In addition, our method is efficient and can select multi-biomarker panel with more than 5 biomarkers, for which the exhaustive methods fail. In conclusion, we propose a promising model to improve the clinical data interpretability and to serve as a useful tool for other complex disease studies. Our small multi-biomarker panel, CEA, IL-10, IMA, and NSE, may provide insights on the disease status of colorectal diseases. The implementation of our method in MATLAB is available via the website: http://doc.aporc.org/wiki/MILP_k. PMID:25980368
Zhang, Xiaodong; Huang, Gordon
2013-02-15
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
Optimization Online - Integer Programming Submissions - 2012
A conic representation of the convex hull of disjunctive sets and conic cuts for integer ... Two-stage Models and Algorithms for Optimizing Infrastructure Design and ... Solving mixed integer nonlinear programming problems for mine production ...
Optimization Online - All Areas Submissions - March 2014
Choice Based Revenue Management for Parallel Flights ... An Improved Stochastic Optimization Model for Water Supply Pumping Systems in Urban Networks ... CBLIB 2014: A benchmark library for conic mixed-integer and continuous ...
Review of Optimization Basics 1. Introduction
McCalley, James D.
markets: The real time or balancing market The day-ahead market These are two kinds of tools which. On the other hand, SCUC is most generally a mixed integer nonlinear optimization problem. Again, most
Namik Kemal Yilmaz; Constantinos Evangelinos; Pierre F. J. Lermusiaux; Nicholas M. Patrikalakis
2008-01-01
The goal of adaptive sampling in the ocean is to predict the types and locations of additional ocean measurements that would be most useful to collect. Quantitatively, what is most useful is defined by an objective function and the goal is then to optimize this objective under the constraints of the available observing network. Examples of objectives are better oceanic
Planning Electric Power Generation: A Nonlinear Mixed Integer Model Employing Benders Decomposition
F. Noonan; R. J. Giglio
1977-01-01
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
2012-01-01
Background The estimation of parameter values for mathematical models of biological systems is an optimization problem that is particularly challenging due to the nonlinearities involved. One major difficulty is the existence of multiple minima in which standard optimization methods may fall during the search. Deterministic global optimization methods overcome this limitation, ensuring convergence to the global optimum within a desired tolerance. Global optimization techniques are usually classified into stochastic and deterministic. The former typically lead to lower CPU times but offer no guarantee of convergence to the global minimum in a finite number of iterations. In contrast, deterministic methods provide solutions of a given quality (i.e., optimality gap), but tend to lead to large computational burdens. Results This work presents a deterministic outer approximation-based algorithm for the global optimization of dynamic problems arising in the parameter estimation of models of biological systems. Our approach, which offers a theoretical guarantee of convergence to global minimum, is based on reformulating the set of ordinary differential equations into an equivalent set of algebraic equations through the use of orthogonal collocation methods, giving rise to a nonconvex nonlinear programming (NLP) problem. This nonconvex NLP is decomposed into two hierarchical levels: a master mixed-integer linear programming problem (MILP) that provides a rigorous lower bound on the optimal solution, and a reduced-space slave NLP that yields an upper bound. The algorithm iterates between these two levels until a termination criterion is satisfied. Conclusion The capabilities of our approach were tested in two benchmark problems, in which the performance of our algorithm was compared with that of the commercial global optimization package BARON. The proposed strategy produced near optimal solutions (i.e., within a desired tolerance) in a fraction of the CPU time required by BARON. PMID:22574924
Compound Particle Swarm Optimization in Dynamic Environments
Yang, Shengxiang
applications of evolutionary algorithms. In this paper, a compound particle swarm optimization (CPSOCompound Particle Swarm Optimization in Dynamic Environments Lili Liu1 , Dingwei Wang1 been an increasing concern on investigating evolu- tionary algorithms (EAs) for dynamic optimization
Computational Study of a Family of Mixed-Integer Quadratic Programming Problems
Daniel Bienstock
1995-01-01
. We present computational experience with a branch-and-cutalgorithm to solve quadratic programming problems where there is an upperbound on the number of positive variables. Such problems arise in financialapplications. The algorithm solves the largest real-life problems in a few minutesof run-time.1 Introduction.We are interested in optimization problems QMIP of the form:min xTQx + cTxs.t.Ax b (1)jsupp(x)j K (2)0 x j
Optimized supply routing at Dell under non-stationary demand
Foreman, John William
2008-01-01
This thesis describes the design and implementation of an optimization model to manage inventory at Dell's American factories. Specifically, the model is a mixed integer program which makes routing decisions on incoming ...
Adaptive critics for dynamic optimization.
Kulkarni, Raghavendra V; Venayagamoorthy, Ganesh Kumar
2010-06-01
A novel action-dependent adaptive critic design (ACD) is developed for dynamic optimization. The proposed combination of a particle swarm optimization-based actor and a neural network critic is demonstrated through dynamic sleep scheduling of wireless sensor motes for wildlife monitoring. The objective of the sleep scheduler is to dynamically adapt the sleep duration to node's battery capacity and movement pattern of animals in its environment in order to obtain snapshots of the animal on its trajectory uniformly. Simulation results show that the sleep time of the node determined by the actor critic yields superior quality of sensory data acquisition and enhanced node longevity. PMID:20223635
Optimization in Dynamic Environments
Xiao, Jing
Scheduling Control problems Vehicle routing Portfolio optimization etc. Also: Co a cluster is detected in basis population Forking #12;Forking [Tsutsui et al. 1997] Basis population Scout population 1 Forking Scout population 2 Forking #12;How does it work, really? · When a cluster is detected
Optimal dynamic detection of explosives
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
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.
Optimal prediction in molecular dynamics
Benjamin Seibold
2008-08-22
Optimal prediction approximates the average solution of a large system of ordinary differential equations by a smaller system. We present how optimal prediction can be applied to a typical problem in the field of molecular dynamics, in order to reduce the number of particles to be tracked in the computations. We consider a model problem, which describes a surface coating process, and show how asymptotic methods can be employed to approximate the high dimensional conditional expectations, which arise in optimal prediction. The thus derived smaller system is compared to the original system in terms of statistical quantities, such as diffusion constants. The comparison is carried out by Monte-Carlo simulations, and it is shown under which conditions optimal prediction yields a valid approximation to the original system.
(Golden Optimal Value in Discrete-time Dynamic Optimization Processes)
Yasuda, Masami
University, Fukuoka 812-8581, Japan Email: $iwamotoQen.kyushu-u.ac.jp$ (Masami YASUDA) Chiba University(Golden Optimal Value in Discrete-time Dynamic Optimization Processes) (Seiichi IWAMOTO) Kyushu
Natural Dynamics for Combinatorial Optimization
Ovchinnikov, Igor V
2015-01-01
Stochastic and or natural dynamical systems (DSs) are dominated by sudden nonlinear processes such as neuroavalanches, gamma-ray bursts, solar flares, earthquakes etc. that exhibit scale-free statistics. These behaviors also occur in many nanosystems. On phase diagrams, these DSs belong to a finite-width phase that separates the phases of thermodynamic equilibrium and ordinary chaotic dynamics, and that is known under such names as intermittency, noise-induced chaos, and self-organized criticality. Within the recently formulated approximation-free cohomological theory of stochastic differential equations, the noise-induced chaos can be roughly interpreted as a noise-induced overlap between regular (integrable) and chaotic (non-integrable) deterministic dynamics so that DSs in this phase inherit the properties of the both. Here, we analyze this unique set of properties and conclude that such DSs must be the most efficient natural optimizers. Based on this understanding, we propose the method of the natural dyn...
Particle Swarms for Dynamic Optimization Problems
Li, Xiaodong
Particle Swarms for Dynamic Optimization Problems Tim Blackwell1 , J¨urgen Branke2 , and Xiaodong. An optimization algorithm, therefore, has to both find and subsequently track the changing optimum. Examples of particle swarm optimization. Particle swarm optimization (PSO) is a versatile population-based opti
A Hierarchical Particle Swarm Optimizer for Dynamic Optimization Problems
Stefan Janson; Martin Middendorf
2004-01-01
\\u000a Particle Swarm Optimization (PSO) methods for dynamic function optimization are studied in this paper. We compare dynamic\\u000a variants of standard PSO and Hierarchical PSO (H-PSO) on different dynamic benchmark functions. Moreover, a new type of hierarchical\\u000a PSO, called Partitioned H-PSO (PH-PSO), is proposed. In this algorithm the hierarchy is partitioned into several sub-swarms\\u000a for a limited number of generations after
Dynamic Pricing through Sampling Based Optimization
Lobel, Ruben
In this paper we develop an approach to dynamic pricing that combines ideas from data-driven and robust optimization to address the uncertain and dynamic aspects of the problem. In our setting, a firm off ers multiple ...
Optimizing molecular dynamics simulations with product lines
Rui C. Silva; João L. Sobral
2011-01-01
This paper presents a case study of using product-lines to address the variability of optimization methods and target platform mappings in high-performance molecular dynamics simulations. We use Feature Oriented Programming to incrementally extend the base algorithm by composing performance enhancement features with the core functionality. Developed features encapsulate common optimization methods in molecular dynamics simulations and target platform mappings. The
TRACKING CODE DEVELOPMENT FOR BEAM DYNAMICS OPTIMIZATION
Yang, L.
2011-03-28
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.
Optimization of Dynamical Decoupling Using Measurement Feedback
NASA Astrophysics Data System (ADS)
Uys, Hermann; Biercuk, Michael; Vandevender, Aaron; Shiga, Nobuyasu; Itano, Wayne; Bollinger, John
2009-05-01
We study the optimization of dynamical decoupling sequences using ^9Be^+ ions in a Penning ion trap. We artificially synthesize the noise environment the ions see to emulate a variety of physical systems. By incorporating measurement feedback with a Nelder-Mead search algorithm, our locally optimized dynamical decoupling sequences (LODD) attain an order of magnitude improved error suppression compared to known sequences in noise environments with power spectra that have sharp, high-frequency cutoffs. The technique requires no prior knowledge of the noise spectrum. This work shows that optimized dynamical decoupling will be a useful tool in suppressing qubit errors below the fault-tolerant threshold for quantum computation.
The Jalapeño dynamic optimizing compiler for Java
Michael G. Burke; Jong-Deok Choi; Stephen J. Fink; David Grove; Michael Hind; Vivek Sarkar; Mauricio J. Serrano; Vugranam C. Sreedhar; Harini Srinivasan; John Whaley
1999-01-01
The Jalapeiio Dynamic Optimizing Compiler is a key com- ponent of the Jalapeiio Virtual Machine, a new Java' Vir- tual Machine (JVM) designed to support efficient and scal- able execution of Java applications on SMP server machines. This paper describes the design of the Jalapefio Optimizing Compiler, and the implementation results that we have ob- tained thus far. To the
Utilizing parallel optimization in computational fluid dynamics
Michael Kokkolaras
1998-01-01
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,
Unified Particle Swarm Optimization in Dynamic Environments
Parsopoulos, Konstantinos
Introduction Particle Swarm Optimization (PSO) is a stochastic optimization algorithm that belongs to the category of swarm intelligence methods [1,2]. PSO has attained in- creasing popularity due to its ability a thorough investigation of PSO on a large number of dynamic test problems. Modifications of PSO that can
Optimizations for Dynamic Inverted Index Maintenance
Douglas R. Cutting; Jan O. Pedersen
1990-01-01
For free-text search over rapidly evolving corpora, dynamic update of inverted indices is a basic requirement. B-trees are an effective tool in implementing such indices. The Zipfian distribution of postings suggests space and time optimizations unique to this task. In particular, we present two novel optimizations, merge update, which performs better than straight forward block update, and pulsing which significantly
Dynamic Optimization in the Batch Chemical Industry
D. Bonvin; B. Srinivasan; D. Ruppen
Dynamic optimization of batch processes has attracted more attention in recent years since, in the face of growing competition, it is a natural choice for reducing production costs, improving product quality, and meeting safety requirements and environmental regulations. Since the models currently available in industry are poor and carry a large amount of uncertainty, standard model-based optimization techniques are by
Optimal ecosystem management with structural dynamics
Rui Pedro Mota; Tiago Domingos
2004-01-01
We address the problem of optimal management of a self organizing ecosystem along ecological succession. A dynamic carrying capacity is interpreted as depicting the dynamics of habitat creation and occupation along ecological succession. The ecosystem may have three growth modes: pure compensation (concave ecosystem regeneration function), depensation (convex-concave regeneration function) and critical depensation (additionally having negative growth rates for low
Fast Multi-swarm Optimization for Dynamic Optimization Problems Department of Computer Science
Yang, Shengxiang
algorithm based on fast parti- cle swarm optimization for dynamic optimization problems. The algorithmFast Multi-swarm Optimization for Dynamic Optimization Problems Changhe Li Department of Computer that the optimization algorithms need to not only find the global optimal solution but also track the trajectory
A PSO-based approach to optimal capacitor placement with harmonic distortion consideration
Xin-mei Yu; Xin-yin Xiong; Yao-wu Wu
2004-01-01
This paper presents a particle swarm optimization (PSO) based approach to achieve optimal capacitor placement in radial distribution systems. Harmonic distortion effects, discrete nature of capacitors, and different load levels are all taken into consideration in the problem formulation. Mathematically, the capacitor placement problem is a non-linear and non-differentiable mixed integer optimization problem with a set of equality and inequality
Role of controllability in optimizing quantum dynamics
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
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.
Dynamic Correlations and Optimal Hedge Ratios
Charles S. Bos; Phillip Gould
2007-01-01
The focus of this article is using dynamic correlation models for the calculation of minimum variance hedge ratios between pairs of assets. Finding an optimal hedge requires not only knowledge of the variability of both assets, but also of the co-movement between the two assets. For this purpose, use is made of industry standard methods, like the naive hedging or
Optimal Foraging and Predator-Prey Dynamics
Krivan
1996-06-01
A system consisting of a population of predators and two types of prey is considered. The dynamics of the system is described by differential equations with controls. The controls model how predators forage on each of the two types of prey. The choice of these controls is based on the standard assumption in the theory of optimal foraging which requires that each predator maximizes the net rate of energy intake during foraging. Since this choice depends on the densities of populations involved, this allows us to link the optimal behavior of an individual with the dynamics of the whole system. Simple qualitative analysis and some simulations show the qualitative behavior of such a system. The effect of the optimal diet choice on the stability of the system is discussed. PMID:8813025
T. HEMKER; K. R. FOWLER; O. VON STRYK
We consider a hydraulic capture application for water resources management that in- cludes a fixed installation cost in addition to operating costs. The result is a simulation- based, nonlinear, mixed-integer optimization problem. The motivation is that our prelim- inary studies have shown that convergence to an unsatisfactory, local minimum with many wells operating at low pumping rates is common when
Expressiveness and Optimization under Incentive Compatibility Constraints in Dynamic Auctions
Chen, Yiling
mechanism . . . . . . . . . . . . . . 22 2.2 Static characterizations of incentive compatibilityExpressiveness and Optimization under Incentive Compatibility Constraints in Dynamic Auctions and Optimization under Incentive Compatibility Constraints in Dynamic Auctions Abstract This thesis designs
A Probability Distribution Estimation Based Method for Dynamic Optimization
Huang, Yinlun
- heuristics-based approaches, such as evolutionary algorithm (EA) and particle swarm optimization (PSO), which in rigorous deterministic algorithms for dynamic optimization, meta-heuristic-based optimization may offer and Chachuat et al.1114 proposed deterministic global optimization algorithms for a type of dynamic systems
Dynamic Optimal Random Access for Vehicle-to-Roadside Communications
Huang, Jianwei
Dynamic Optimal Random Access for Vehicle-to-Roadside Communications Man Hon Cheung, Fen Hou the optimal transmission policy as a finite-horizon sequential decision problem. Then we solve the problem using dynamic programming, and design a dynamic optimal random access algorithm. Simulation results
Modeling the dynamics of ant colony optimization.
Merkle, Daniel; Middendorf, Martin
2002-01-01
The dynamics of Ant Colony Optimization (ACO) algorithms is studied using a deterministic model that assumes an average expected behavior of the algorithms. The ACO optimization metaheuristic is an iterative approach, where in every iteration, artificial ants construct solutions randomly but guided by pheromone information stemming from former ants that found good solutions. The behavior of ACO algorithms and the ACO model are analyzed for certain types of permutation problems. It is shown analytically that the decisions of an ant are influenced in an intriguing way by the use of the pheromone information and the properties of the pheromone matrix. This explains why ACO algorithms can show a complex dynamic behavior even when there is only one ant per iteration and no competition occurs. The ACO model is used to describe the algorithm behavior as a combination of situations with different degrees of competition between the ants. This helps to better understand the dynamics of the algorithm when there are several ants per iteration as is always the case when using ACO algorithms for optimization. Simulations are done to compare the behavior of the ACO model with the ACO algorithm. Results show that the deterministic model describes essential features of the dynamics of ACO algorithms quite accurately, while other aspects of the algorithms behavior cannot be found in the model. PMID:12227995
Optimization of fracture treatment designs
Rueda, Jose Ignacio
1992-01-01
integer-linear programming to adjust a treatment fluid selection, pump rate, proppant volume and fracture dimensions to arrive at an optimal design. Through mixed integer-linear programming, Thompson showed that it is possible to design a less expensive... design, the engineer must determine the fracturing fluid properties, injection rate, fracture dimensions and the fracture conductivity that maximize the profit of the well. Conventional optimization techniques have been used by industry for many years...
Pareto optimization in algebraic dynamic programming.
Saule, Cédric; Giegerich, Robert
2015-01-01
Pareto optimization combines independent objectives by computing the Pareto front of its search space, defined as the set of all solutions for which no other candidate solution scores better under all objectives. This gives, in a precise sense, better information than an artificial amalgamation of different scores into a single objective, but is more costly to compute. Pareto optimization naturally occurs with genetic algorithms, albeit in a heuristic fashion. Non-heuristic Pareto optimization so far has been used only with a few applications in bioinformatics. We study exact Pareto optimization for two objectives in a dynamic programming framework. We define a binary Pareto product operator [Formula: see text] on arbitrary scoring schemes. Independent of a particular algorithm, we prove that for two scoring schemes A and B used in dynamic programming, the scoring scheme [Formula: see text] correctly performs Pareto optimization over the same search space. We study different implementations of the Pareto operator with respect to their asymptotic and empirical efficiency. Without artificial amalgamation of objectives, and with no heuristics involved, Pareto optimization is faster than computing the same number of answers separately for each objective. For RNA structure prediction under the minimum free energy versus the maximum expected accuracy model, we show that the empirical size of the Pareto front remains within reasonable bounds. Pareto optimization lends itself to the comparative investigation of the behavior of two alternative scoring schemes for the same purpose. For the above scoring schemes, we observe that the Pareto front can be seen as a composition of a few macrostates, each consisting of several microstates that differ in the same limited way. We also study the relationship between abstract shape analysis and the Pareto front, and find that they extract information of a different nature from the folding space and can be meaningfully combined. PMID:26150892
Application of optimal prediction to molecular dynamics
Barber IV, John Letherman
2004-12-01
Optimal prediction is a general system reduction technique for large sets of differential equations. In this method, which was devised by Chorin, Hald, Kast, Kupferman, and Levy, a projection operator formalism is used to construct a smaller system of equations governing the dynamics of a subset of the original degrees of freedom. This reduced system consists of an effective Hamiltonian dynamics, augmented by an integral memory term and a random noise term. Molecular dynamics is a method for simulating large systems of interacting fluid particles. In this thesis, I construct a formalism for applying optimal prediction to molecular dynamics, producing reduced systems from which the properties of the original system can be recovered. These reduced systems require significantly less computational time than the original system. I initially consider first-order optimal prediction, in which the memory and noise terms are neglected. I construct a pair approximation to the renormalized potential, and ignore three-particle and higher interactions. This produces a reduced system that correctly reproduces static properties of the original system, such as energy and pressure, at low-to-moderate densities. However, it fails to capture dynamical quantities, such as autocorrelation functions. I next derive a short-memory approximation, in which the memory term is represented as a linear frictional force with configuration-dependent coefficients. This allows the use of a Fokker-Planck equation to show that, in this regime, the noise is {delta}-correlated in time. This linear friction model reproduces not only the static properties of the original system, but also the autocorrelation functions of dynamical variables.
A dynamic programming based Gas Pipeline Optimizer
Hemant S. Lall; Peter Percell
A dynamic programming based Gas Pipeline Optimizer (GPO) has been developed at Scientific Software-Intercomp for the HBJ gas\\u000a transmission pipeline system in India. Used as an operating and planning tool, the GPO will determine the discharge pressures\\u000a at the compressor stations and the number of compressor trains to operate at each compressor station so that fuel consumption\\u000a and start-up\\/shut-down costs
Dynamo: A Staged Compiler Architecture for Dynamic Program Optimization
Mark Leone; R. Kent Dybvig Indiana
1997-01-01
SyntaxPossibleDynamic InputValue-Specific OptimizationsRegister AllocationCoarse SchedulingCode GenerationAMMA GPossibleDynamic InputPeephole OptimizationCode LayoutBranch PredictionAssemblyELTA DPossibleDynamic InputPeephole OptimizationCode LayoutBranch PredictionAssemblyELTA DPossibleDynamic InputDynamic InputDynamically OptimizingNative CodePSILON ECreate Specialized Code GeneratorsJava VMCodeLPHA AHigh-Level IRMid-Level IRLow-Level IRNative Code...
On Optimization-Based Deadline Division for Workflow Scheduling
Yonglei Yao; Li Ma
2010-01-01
Complex applications are usually modeled as workflows, which are often represented by Directed Acyclic Graph (DAG) and require the power of Grid to run efficiently. Cost-optimized workflow scheduling under deadline constraints is a fundamental and intractable problem on Grids. In this paper, an effective and efficient heuristic for workflow scheduling is proposed. A mixed integer programming (MIP) approach is applied
A Taxonomy of Constraints in Simulation-Based Optimization
2015-05-28
field of derivative-free optimization (DFO), and more precisely black-box ... In addition, simulations may sometimes fail to return a value, even for points inside ...... an approach to stochastic programming of heating oil, Management Science, .... tion for mixed integer programming formulations, COAL Bulletin, 20 (1992), pp.
New approximate optimization method for distribution system planning
K. Aoki; K. Nara; T. Satoh; M. Kitagawa; K. Yamanaka
1990-01-01
An algorithm to obtain an approximate optimal solution to the problem of large-scale radial distribution system planning is proposed. The distribution planning problem is formulated as a MIP (mixed integer programming) problem. The set of constraints is reduced to a set of continuous variable linear equations by using the fact that the basis of the simplex tableau consists of the
Optimal Design of Electrical Machines: Mathematical Programming Formulations
Paris-Sud XI, Université de
model, formulation, modeling, local optimiza- tion, inverse problem, design, electrical machine design of electrical machines can be mathematically modeled as a (mixed-integer) nonlinear optimization.1108/03321641311305863 #12;1 Introduction The design of electromechanical actuators is known as an inverse problem, i.e. from
Graph-Optimization Techniques for IC Layout and Compaction
Gershon Kedem; Hiroyuki Watanabe
1984-01-01
This paper describes a new approach for IC layout and compaction. The compaction problem is translated into a mixed integer-linear programming problem of a very special form. A graph-based optimization algorithm is used to solve the resulting problem. An experimental program that uses the above techniques is described. The program could be used either as an aid to hand layout
Graph-optimization techniques for IC layout and compaction
Gershon Kedem; Hiroyuki Watanabe
1983-01-01
This paper describes a new approach for IC layout and compaction. The compaction problem is translated into a mixed integer-linear programming problem of a very special form. A graph based optimization algorithm is used to solve the resulting problem. An experimental program that uses the above techniques is described. The program could be used either as an aid to hand
A neural dynamics model for structural optimization—Theory
H. Adeli; Hyo Seon Park
1995-01-01
A neural dynamics model is presented for optimal design of structures. The Lyapunov function is used to develop the neural dynamics structural optimization model and prove its stability. An exterior penalty function method is adopted to formulate an objective function for the general constrained structural optimization problem in the form of the Lyapunov function. A learning rule is developed by
Direct Trajectory Optimization of Rigid Body Dynamical Systems Through Contact
Tedrake, Russ
Direct Trajectory Optimization of Rigid Body Dynamical Systems Through Contact Michael Posa and Russ Tedrake Abstract Direct methods for trajectory optimization are widely used for planning locally optimal trajectories of robotic systems. Most state-of-the-art techniques treat the discontinuous dynamics
Utilizing parallel optimization in computational fluid dynamics
NASA Astrophysics Data System (ADS)
Kokkolaras, Michael
1998-12-01
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, variational principles and weighted residuals, and adaptive grid optimization) are combined to formulate the proposed method. The basis functions and associated coefficients of a series expansion, representing the solution, are optimally selected by a parallel direct search technique at each step of the algorithm according to appropriate criteria; the solution is built sequentially. In this manner, the proposed method is adaptive in nature, although a grid is neither built nor adapted in the traditional sense using a-posteriori error estimates. Variational principles are utilized for the definition of the objective function to be extremized in the associated optimization problems, ensuring that the problem is well-posed. Complicated data structures and expensive remeshing algorithms and systems solvers are avoided. Computational efficiency is increased by using low-order basis functions and concurrent computing. Numerical results and convergence rates are reported for a range of steady-state problems, including linear and nonlinear differential equations associated with general boundary conditions, and illustrate the potential of the proposed method. Fluid dynamics applications are emphasized. Conclusions are drawn by discussing the method's limitations, advantages, and possible extensions. The second part of the dissertation is concerned with the optimization of the viscous-inviscid-interaction (VII) mechanism in an airfoil flow analysis code. The VII mechanism is based on the concept of a transpiration velocity boundary condition, whose convergence to steady state is accelerated. The number of variables in the associated optimization problem is reduced by means of function approximation concepts to ensure high number of parallel processors to number of necessary function evaluations ratio. Numerical results are presented for the NACA-0012 and the supercritical RAE-2822 airfoils subject to transonic flow conditions using a parallel direct search technique. They exhibit a satisfactory level of accuracy. Speed-up depends on the number of available computational units and increases for more challenging flow conditions and airfoil geometries. The enhanced code constitutes a useful tool for airfoil flow analysis and design and an acceptable alternative to computationally expensive high fidelity codes.
Integrated DFM framework for dynamic yield optimization
NASA Astrophysics Data System (ADS)
Pikus, Fedor G.
2006-10-01
We present a new methodology for a balanced yield-optimization and a new DFM 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 the expense of other factors with a comprehensive analysis of competing DFM techniques and trade-offs between them.
Baliban, Richard C.; DiMaggio, Peter A.; Plazas-Mayorca, Mariana D.; Garcia, Benjamin A.; Floudas, Christodoulos A.
2012-01-01
A novel protein identification framework, PILOT_PROTEIN, has been developed to construct a comprehensive list of all unmodified proteins that are present in a living sample. It uses the peptide identification results from the PILOT_SEQUEL algorithm to initially determine all unmodified proteins within the sample. Using a rigorous biclustering approach that groups incorrect peptide sequences with other homologous sequences, the number of false positives reported is minimized. A sequence tag procedure is then incorporated along with the untargeted PTM identification algorithm, PILOT_PTM, to determine a list of all modification types and sites for each protein. The unmodified protein identification algorithm, PILOT_PROTEIN, is compared to the methods SEQUEST, InsPecT, X!Tandem, VEMS, and ProteinProspector using both prepared protein samples and a more complex chromatin digest. The algorithm demonstrates superior protein identification accuracy with a lower false positive rate. All materials are freely available to the scientific community at http://pumpd.princeton.edu. PMID:22788846
Baliban, Richard C; Dimaggio, Peter A; Plazas-Mayorca, Mariana D; Garcia, Benjamin A; Floudas, Christodoulos A
2012-09-01
A novel protein identification framework, PILOT_PROTEIN, has been developed to construct a comprehensive list of all unmodified proteins that are present in a living sample. It uses the peptide identification results from the PILOT_SEQUEL algorithm to initially determine all unmodified proteins within the sample. Using a rigorous biclustering approach that groups incorrect peptide sequences with other homologous sequences, the number of false positives reported is minimized. A sequence tag procedure is then incorporated along with the untargeted PTM identification algorithm, PILOT_PTM, to determine a list of all modification types and sites for each protein. The unmodified protein identification algorithm, PILOT_PROTEIN, is compared to the methods SEQUEST, InsPecT, X!Tandem, VEMS, and ProteinProspector using both prepared protein samples and a more complex chromatin digest. The algorithm demonstrates superior protein identification accuracy with a lower false positive rate. All materials are freely available to the scientific community at http://pumpd.princeton.edu. PMID:22788846
Optimal bolt preload for dynamic loading
Duffey, T.A.
1992-01-01
A simple spring-mass model is developed for closure bolting systems, including the effects of bolt prestress. An analytical solution is developed for the case of an initially peaked, exponentially decaying internal pressure pulse acting on the closure. The dependence of peak bolt stresses and deflections on bolt prestress level is investigated and an optimal prestress that minimizes peak bolt stress is found in certain cases. Vulnerability curves are developed for bolted-closure systems to provide rapid evaluation of the dynamic capacity of designs for a range in bolt prestress.
Optimal bolt preload for dynamic loading
Duffey, T.A.
1992-08-01
A simple spring-mass model is developed for closure bolting systems, including the effects of bolt prestress. An analytical solution is developed for the case of an initially peaked, exponentially decaying internal pressure pulse acting on the closure. The dependence of peak bolt stresses and deflections on bolt prestress level is investigated and an optimal prestress that minimizes peak bolt stress is found in certain cases. Vulnerability curves are developed for bolted-closure systems to provide rapid evaluation of the dynamic capacity of designs for a range in bolt prestress.
Optimized Noise Filtration through Dynamical Decoupling
Hermann Uys; Michael J. Biercuk; John J. Bollinger
2009-06-24
One approach to maintaining phase coherence of qubits through dynamical decoupling consists of applying a sequence of Hahn spin-echo pulses. Recent studies have shown that, in certain noise environments, judicious choice of the delay times between these pulses can greatly improve the suppression of phase errors compared to traditional approaches. By enforcing a simple analytical condition, we obtain sets of dynamical decoupling sequences that are designed for optimized noise filtration and are spectrum-independent up to a single scaling factor set by the coherence time of the system. We demonstrate the efficacy of these sequences in suppressing phase errors through measurements on a model qubit system, $^{9}$Be$^{+}$ ions in a Penning trap. Our combined theoretical and experimental studies show that in high-frequency-dominated noise environments this approach may suppress phase errors orders of magnitude more efficiently than comparable techniques can.
Static and Dynamic Locality Optimizations Using Integer Linear Programming
Kuzmanovic, Aleksandar
, memory layouts, compiler optimizations, cache miss estimation, integer linear programming. æ 1Static and Dynamic Locality Optimizations Using Integer Linear Programming Mahmut Kandemir, Member programming (ILP) that attempts to derive the best combination of loop and data layout transformations. Prior
Distributed Adaptive Particle Swarm Optimizer in Dynamic Environment
Cui, Xiaohui [ORNL; Potok, Thomas E [ORNL
2007-01-01
In the real world, we have to frequently deal with searching and tracking an optimal solution in a dynamical and noisy environment. This demands that the algorithm not only find the optimal solution but also track the trajectory of the changing solution. Particle Swarm Optimization (PSO) is a population-based stochastic optimization technique, which can find an optimal, or near optimal, solution to a numerical and qualitative problem. In PSO algorithm, the problem solution emerges from the interactions between many simple individual agents called particles, which make PSO an inherently distributed algorithm. However, the traditional PSO algorithm lacks the ability to track the optimal solution in a dynamic and noisy environment. In this paper, we present a distributed adaptive PSO (DAPSO) algorithm that can be used for tracking a non-stationary optimal solution in a dynamically changing and noisy environment.
Optimal dynamic discrimination of similar quantum systems
NASA Astrophysics Data System (ADS)
Li, Baiqing
2005-07-01
The techniques for identifying and separating similar molecules have always been very important to chemistry and other branches of science and engineering. Similar quantum systems share comparable Hamiltonians, so their eigenenergy levels, transition dipole moments, and therefore their ordinary observable properties are alike. Traditional analytical methods have mostly been restricted by working with the subtle differences in the physical and chemical properties of the similar species. Optimal Dynamic Discrimination (ODD) aims at magnifying the dissimilarity of the agents by actively controlling their quantum evolution, drawing on the extremely rich information embedded in their dynamics. ODD is developed based on the tremendous flexibility of Optimal Control Theory (OCT) and on the practical implementation of closed-loop learning control, which has become a more and more indispensable tool for controlling quantum processes. The ODD experimental paradigm is designed to combat a number of factors that are detrimental to the discrimination of similar molecules: laser pulse noise, signal detection errors, finite time resolution in the signals, and environmental decoherence effects. It utilizes either static signals or time series signal, the latter capable of providing more information. Simulations are performed in this dissertation progressing from the wave function to the density matrix formulation, in order to study the decoherence effects. Analysis of the results reveals the roles of the adverse factors, unravels the underlying mechanisms of ODD, and provides insights on laboratory implementation. ODD emphasizes the incorporation of algorithmic development and laboratory design, and seeks to bridge the gap between theoretical/computational chemistry and experimental chemistry, with the help from applied mathematics and computer science.
Sreenath, Koushil
Optimal Trajectory Generation Under Homology Class Constraints Soonkyum Kim Koushil Sreenath are useful in trajectory generation for mobile robots. In this paper we present a method to generate the trajectory generation problem as a Mixed-Integer Quadratic Program (MIQP). We introduce binary variables
Pearl Q. Zheng; Benjamin F. Hobbs; Joseph F. Koonce
2009-01-01
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
Water Supply Planning with Inter-basin Water Transfer by an Optimization Model
Sheung-Kown Kim; JaeHee Kim; YoungJoon Park
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
Optimization Model for Integrated Logistics Network Design in Green Manufacturing System
Zhao Ya Peng; Ding Yi Zhong
2008-01-01
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
Weerakorn Ongsakul; Keerati Chayakulkheeree
2006-01-01
This paper proposes a coordinated fuzzy constrained optimal power dispatch (CFCOPD) algorithm for bilateral contract, balancing electricity, and ancillary services markets. The CFCOPD problem is decomposed into social welfare fuzzy maximization subproblem, which is solved by mixed-integer fuzzy linear programming (MIFLP), and combined reactive power cost and cost of real power loss fuzzy minimization subproblem, which is solved by fuzzy
Optimal control of dynamical systems and structures under stochastic uncertainty
K. Marti
Consider a dynamic mechanical control systems or structure under stochastic uncertainty, as e.g. the active control of a mechanical structure under stochastic applied dynamic loadings. Optimal controls, being most insensitive with respect to random parameter variations, are determined by finding stochastic optimal controls, i.e., controls minimizing the expected total costs composed of the costs arising along the trajectory, the costs
Decentralized optimal control of dynamical systems under uncertainty
NASA Astrophysics Data System (ADS)
Gabasov, R.; Dmitruk, N. M.; Kirillova, F. M.
2011-07-01
The problem of optimal control of a group of interconnected dynamical objects under uncertainty is considered. The cases are examined in which the centralized control of the group of objects is impossible due to delay in the channel for information exchange between the group members. Optimal self-control algorithms in real time for each dynamical object are proposed. Various types of a priori and current information about the behavior of the group members and about uncertainties in the system are examined. The proposed methods supplement the earlier developed optimal control methods for an individual dynamical system and the methods of decentralized optimal control of deterministic objects. The results are illustrated with examples.
Online optimization of storage ring nonlinear beam dynamics
Huang, Xiaobiao
2015-01-01
We propose to optimize the nonlinear beam dynamics of existing and future storage rings with direct online optimization techniques. This approach may have crucial importance for the implementation of diffraction limited storage rings. In this paper considerations and algorithms for the online optimization approach are discussed. We have applied this approach to experimentally improve the dynamic aperture of the SPEAR3 storage ring with the robust conjugate direction search method and the particle swarm optimization method. The dynamic aperture was improved by more than 5 mm within a short period of time. Experimental setup and results are presented.
Trajectory optimization for domains with contacts using inverse dynamics
Todorov, Emanuel
Trajectory optimization for domains with contacts using inverse dynamics Tom Erez and Emanuel of the arm and the opposite leg, eliminating undesired angular momentum; this is a key feature of bipedal the control that yields a trajectory of minimal total cost. Global methods of optimal control find an optimal
Chaotic Dynamics in Optimal Monetary Policy
Orlando Gomes; Vivaldo M. Mendes; Diana A. Mendes; J. Sousa Ramos
2006-12-11
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 (1997), Clarida et al. (1999), Svensson (1999) and Woodford (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.
Chaotic dynamics in optimal monetary policy
NASA Astrophysics Data System (ADS)
Gomes, O.; Mendes, V. M.; Mendes, D. A.; Sousa Ramos, J.
2007-05-01
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.
Dany Dionne; E. Pogossian; A. Grigoryan; Jean Couture; Elisa Shahbazian
2008-01-01
A sequential optimization technique is presented in application to the dynamic weapon-target allocation problem. This problem is in general NP-Complete. The purpose of the optimization technique is to decompose the original search space into a sequence of smaller subspaces. Optimization over these subspaces (still NP-Complete) is of reduced dimensionality. The sequential decomposition technique is proven to preserve optimality. This sequential
Unified approach for the optimization of nonlinear hydraulic systems
B. Ulanicki; C. H. Orr
1991-01-01
A theory for the optimization of nonlinear hydraulic systems is presented. The problem has been solved in spite of the nonlinear system model and the mixed-integer nature of the decision variables. The optimization problem is formulated in terms of the time-distribution-function concept. This leads to a numerically efficient two-level algorithm. No specific control model is needed: the algorithm employs a
Noise-optimal capture for high dynamic range photography
Hasinoff, Samuel William
Taking multiple exposures is a well-established approach both for capturing high dynamic range (HDR) scenes and for noise reduction. But what is the optimal set of photos to capture? The typical approach to HDR capture ...
PLASMA Approximate Dynamic Programming finally cracks the locomotive optimization problem
Powell, Warren B.
PLASMA Approximate Dynamic Programming finally cracks the locomotive optimization problem schedules and new operating policies. PLASMA is currently running at Norfolk Southern for strategic of PLASMA: Each locomotive is modeled individually, making it possible to capture both horsepower
An Optimization Framework for Dynamic, Distributed Real-Time Systems
NASA Technical Reports Server (NTRS)
Eckert, Klaus; Juedes, David; Welch, Lonnie; Chelberg, David; Bruggerman, Carl; Drews, Frank; Fleeman, David; Parrott, David; Pfarr, Barbara
2003-01-01
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.
Dynamic Optimal Control Models in Advertising: Recent Developments
Gustav Feichtinger; Richard F. Hartl; Suresh P. Sethi
1994-01-01
This paper presents a review of recent developments that have taken place in the area of dynamic optimal control models in advertising subsequent to the comprehensive survey of the literature by Sethi in 1977. The basic problem underlying these models is that of determining optimal advertising expenditures and possibly other variables of interest over time for a firm or a
Optimization Online - Second order forward-backward dynamical ...
Radu Ioan Bot
2015-03-16
Mar 16, 2015 ... ... Cauchy-Lipschitz-Picard Theorem and prove weak convergence for ... a convex closed set by means of second order dynamical systems. ... the function value along the ergodic trajectory to its minimum value. ... Category 1: Nonlinear Optimization (Systems governed by Differential Equations Optimization ).
Aerodynamic design optimization using sensitivity analysis and computational fluid dynamics
Oktay Baysal; Mohamad E. Eleshaky
1992-01-01
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.
Optimal Trajectory Generation for Nonholonomic Robots in Dynamic Environments
Guo, Yi
Optimal Trajectory Generation for Nonholonomic Robots in Dynamic Environments Yi Guo and Tang Tang Abstract-- We study optimal trajectory generation for non- holonomic mobile robots in the presence of moving obstacles. The trajectory is presented by a parameterized higher-order polynomial and is feasible
Dynamical Systems, Optimization, and Chaos John B. Moore
Moore, John Barratt
Dynamical Systems, Optimization, and Chaos John B. Moore Department of Systems Engineering. Usually, chaos is avoided in performing a system design or optimization. The challenge before en- gineers is to somehow exploit the fascinating properties of chaos to enhance their system designs, and to further
Optimization of Structural Dynamic Behaviour Based on Effective Modal Parameters
Paris-Sud XI, Université de
Optimization of Structural Dynamic Behaviour Based on Effective Modal Parameters S. Besset, L. J based on effective modal parameters. These parameters are close to the modal matrices used for the modal optimization criteria. First, we will explain the modal anal- ysis that we will use in this paper. A modal
Optimal Route Based on Dynamic Programming for Road Networks
Manoj Kanta Mainali; Kaoru Shimada; Shingo Mabu; Kotaro Hirasawa
2008-01-01
One of the main functions of the traffic nav- igation systems is to find the optimal route to the desti- nation. In this paper, we propose an iterative Q value updating algorithm, Q method, based on dynamic pro- gramming to search the optimal route and its opti- mal traveling time for a given Origin-Destination (OD) pair of road networks. The
A dynamic inertia weight particle swarm optimization algorithm
Bin Jiao; Zhigang Lian; Xingsheng Gu
2008-01-01
Particle swarm optimization (PSO) algorithm has been developing rapidly and has been applied widely since it was introduced, as it is easily understood and realized. This paper presents an improved particle swarm optimization algorithm (IPSO) to improve the performance of standard PSO, which uses the dynamic inertia weight that decreases according to iterative generation increasing. It is tested with a
First principles molecular dynamics without self-consistent field optimization
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)
2014-01-28
We present a first principles molecular dynamics approach that is based on time-reversible extended Lagrangian Born-Oppenheimer molecular dynamics [A. M. N. Niklasson, Phys. Rev. Lett. 100, 123004 (2008)] in the limit of vanishing self-consistent field optimization. The optimization-free dynamics keeps the computational cost to a minimum and typically provides molecular trajectories that closely follow the exact Born-Oppenheimer potential energy surface. Only one single diagonalization and Hamiltonian (or Fockian) construction are required in each integration time step. The proposed dynamics is derived for a general free-energy potential surface valid at finite electronic temperatures within hybrid density functional theory. Even in the event of irregular functional behavior that may cause a dynamical instability, the optimization-free limit represents a natural starting guess for force calculations that may require a more elaborate iterative electronic ground state optimization. Our optimization-free dynamics thus represents a flexible theoretical framework for a broad and general class of ab initio molecular dynamics simulations.
Optimization of naïve dynamic binary instrumentation Tools/
Kleckner, Reid (Reid N.)
2011-01-01
The proliferation of dynamic program analysis tools has done much to ease the burden of developing complex software. However, creating such tools remains a challenge. Dynamic binary instrumentation frameworks such as ...
An Optimization Framework for Dynamic, Distributed Real-Time Systems
Klaus H. Ecker; David W. Juedes; Lonnie R. Welch; David M. Chelberg; Carl Bruggeman; Frank Drews; David Fleeman; David Parrott; Barbara Pfarr
2003-01-01
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
Nonlinear Hybrid Dynamical Systems: Modeling, Optimal Control, and Applications
Stryk, Oskar von
hybrid modeling scheme well-suited to the study of hybrid dynamical systems has inspired many researchers structure, nonlinear differential equations have recently been published [15,19,28,41]. These efforts wereNonlinear Hybrid Dynamical Systems: Modeling, Optimal Control, and Applications Martin Buss1
STRUCTURAL ANALYSIS AND OPTIMAL DESIGN OF A DYNAMIC ABSORBING BEAM
Y.-H. Chen; C.-Y. Lin
1998-01-01
The structural analysis and optimal design of a dynamic absorbing beam which is attached to the main beam with a viscoelastic layer or other mechanism of similar effect is presented. The dynamic stiffness matrix of a composite beam composed of two parallel beams with viscoelastic layer between them has already been derived and can be employed for the structural analysis.
Application of dynamic merit function to nonimaging systems optimization
NASA Astrophysics Data System (ADS)
Fernández-Balbuena, Antonio Álvarez; Montes, Mario González; García-Botella, Angel; Vázquez-Moliní, Daniel
2015-02-01
Automatic optimization algorithms have been recently introduced as nonimaging optics design techniques. Unlike optimization of imaging systems, nonsequential ray tracing simulations and complex noncentered systems design must be considered, adding complexity to the problem. The merit function is a key element in the automatic optimization algorithm; nevertheless, the selection of each objective's weight, {wi}, inside the merit function needs a prior trial and error process for each optimization. The problem then is to determine appropriate weights' values for each objective. We propose a new dynamic merit function with variable weight factors {wi(n)}. The proposed algorithm automatically adapts weight factors during the evolution of the optimization process. This dynamic merit function avoids the previous trial and error procedure by selecting the right merit function and provides better results than conventional merit functions.
Observation-assisted optimal control of quantum dynamics
Feng Shuang; Alexander Pechen; Tak-San Ho; Herschel Rabitz
2007-05-31
This paper explores the utility of instantaneous and continuous observations in the optimal control of quantum dynamics. Simulations of the processes are performed on several multilevel quantum systems with the goal of population transfer. Optimal control fields are shown to be capable of cooperating or fighting with observations to achieve a good yield, and the nature of the observations may be optimized to more effectively control the quantum dynamics. Quantum observations also can break dynamical symmetries to increase the controllability of a quantum system. The quantum Zeno and anti-Zeno effects induced by observations are the key operating principles in these processes. The results indicate that quantum observations can be effective tools in the control of quantum dynamics.
Fault tolerant and dynamic evolutionary optimization engines
Morales Reyes, Alicia
2011-01-01
Mimicking natural evolution to solve hard optimization problems has played an important role in the artificial intelligence arena. Such techniques are broadly classified as Evolutionary Algorithms (EAs) and have been ...
Average case analysis of dynamic geometric optimization
Eppstein, David
for constructing geometric structures such as convex hulls and ar- rangements. Such algorithms can also be used structures: convex hulls, arrangements, and the like. However problems of geometric optimization have been ne spanning tree of a planar point set, as points are inserted or deleted, in O(log3 n) expected time per
Stochastic Optimization of Linear Dynamic Systems with Parametric Uncertainties
Yatsenko, Vadim
2009-01-01
This paper describes a new approach to solving some stochastic optimization problems for linear dynamic system with various parametric uncertainties. Proposed approach is based on application of tensor formalism for creation the mathematical model of parametric uncertainties. Within proposed approach following problems are considered: prediction, data processing and optimal control. Outcomes of carried out simulation are used as illustration of properties and effectiveness of proposed methods.
April 12, 2007 Dynamic Real-Time Optimization
Grossmann, Ignacio E.
functions considered to get parameters (p) Minp (x, y, p, w) s.t. c(x, u, p, w) = 0 x X, p P Plant DR-PE c-state On-line Optimization: Components Plant DR-PE c(x, u, p) = 0 RTO c(x, u, p) = 0 APC y p u w ·Data accurate model · Are we optimizing on the noise? Model mismatch? Dynamics? · Can lead to ping-ponging 7 #12
Recursive multibody dynamics and discrete-time optimal control
NASA Technical Reports Server (NTRS)
Deleuterio, G. M. T.; Damaren, C. J.
1989-01-01
A recursive algorithm is developed for the solution of the simulation dynamics problem for a chain of rigid bodies. Arbitrary joint constraints are permitted, that is, joints may allow translational and/or rotational degrees of freedom. The recursive procedure is shown to be identical to that encountered in a discrete-time optimal control problem. For each relevant quantity in the multibody dynamics problem, there exists an analog in the context of optimal control. The performance index that is minimized in the control problem is identified as Gibbs' function for the chain of bodies.
MULTIOBJECTIVE DYNAMIC APERTURE OPTIMIZATION AT NSLS-II
Yang, L.; Li, Y.; Guo, W.; Krinsky, S.
2011-03-28
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.
Optimizing the design of complex energy conversion systems by Branch and Cut
Turang Ahadi-Oskui; Stefan Vigerske; Ivo Nowak; George Tsatsaronis
2010-01-01
The paper examines the applicability of mathematical programming methods to the simultaneous optimization of the structure and the operational parameters of a combined-cycle-based cogeneration plant. Thus, the optimization problem is formulated as a highly non-convex mixed-integer nonlinear problem (MINLP) and solved by the MINLP solver LaGO. The algorithm generates a convex relaxation of the MINLP and applies a Branch and
Speeding up critical system dynamics through optimized evolution
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
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}.
Dynamic Optimization of Micro-Operations
Brian Slechta; David Crowe; Brian Fahs; Michael Fertig; Gregory A. Muthler; Justin Quek; Francesco Spadini; Sanjay J. Patel; Steven S. Lumetta
2003-01-01
Inherent within complex instruction set architectures such as x86 are inefficiencies that do not exist in a simpler ISAs. Modern x86 implementations decode instructions into one or more micro-operations in order to deal with the complexity of the ISA. Since these micro-operations are not visible to the compiler, the stream of micro-operations can contain redun- dancies even in statically optimized
Aerospace Applications of Integer and Combinatorial Optimization
NASA Technical Reports Server (NTRS)
Padula, S. L.; Kincaid, R. K.
1995-01-01
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.
Aerospace applications on integer and combinatorial optimization
NASA Technical Reports Server (NTRS)
Padula, S. L.; Kincaid, R. K.
1995-01-01
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.
Aerospace applications of integer and combinatorial optimization
NASA Technical Reports Server (NTRS)
Padula, S. L.; Kincaid, R. K.
1995-01-01
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.
Practicing JUDO: Java under dynamic optimizations
Michal Cierniak; Guei-Yuan Lueh; James M. Stichnoth
2000-01-01
ABSTRACT A high - performance implementation of a Java Virtual Machine (JVM) consists of efficient implementation of Just - In - Time (JIT) compilation, exception handling, synchronization mechanism, and garbage collection (GC) These components are tightly coupled to achieve high performance In this paper, we present some static and dynamic techniques implemented in the JIT compilation and exception handling of
Security Optimization of Dynamic Networks with Probabilistic Graph Modeling
Yao, Danfeng "Daphne'
1 Security Optimization of Dynamic Networks with Probabilistic Graph Modeling and Linear and Xinming Ou Abstract-- Securing the networks of large organizations is technically challenging due model and several algorithms for analyzing and improving the security of large networks. We demonstrate
Dynamic Optimization in Gas Pipeline Networks EWO MEETING, Spring 2012
Grossmann, Ignacio E.
Dynamic Optimization in Gas Pipeline Networks EWO MEETING, Spring 2012 Ajit Gopalakrishnan L. T Pipeline Networks First principles model developed in Baumrucker & Biegler (2010), moved into a GUI scenarios and verify results. #12;Gas Transmission Pipeline Networks Interconnected network of suppliers
Optimal Dynamic Regimes: Presenting a Case for Predictive Inference
Elja Arjas; Olli Saarela
2010-01-01
Dynamic treatment regime is a decision rule in which the choice of the treatment of an individual at any given time can depend on the known past history of that individual, including baseline covariates, earlier treatments, and their measured responses. In this paper we argue that finding an optimal regime can, at least in moderately simple cases, be accomplished by
Dynamic Particle Swarm Optimization Algorithm for Resolution of Overlapping Chromatograms
Yufeng Li
2009-01-01
Dynamic particle swarm optimization algorithm is proposed in this paper to resolve overlapping chromatographic peaks. To accelerate the convergence speed, clustering degree and evolution velocity are considered simultaneously to adjust inertia weight adaptively. The algorithm is tested on both simulated overlapping chromatographic peaks which are based on exponential modified Gaussian convolution model and experimental overlapping chromatographic peaks of multi-component which
Dynamic vs. Static Optimization Techniques for ObjectOriented Languages
Hölzle, Urs
oriented languages are type feedback (dynamic) and concrete type inference (static). We directly compare the two optimizations, can lead to poor runtime perfor mance. Thus, the key to efficient implementation of object previous executions of the program to determine the set possible receiver classes, and . Concrete type
Dynamic vs. Static Optimization Techniques for Object-Oriented Languages
Hölzle, Urs
-oriented languages are type feedback (dynamic) and concrete type inference (static). We directly compare the two optimizations, can lead to poor run-time perfor- mance. Thus, the key to efficient implementation of object previous executions of the program to determine the set possible receiver classes, and · Concrete type
Optimal Control of Dynamical Systems Governed by Partial Differential Equations
Dettweiler, Michael
mathematical models for complex dynam- ical systems, their analysis and numerical simulation are generally only in Appendix A. (2) Future concepts for intercontinental flights of passen- ger aircraft envisage aircraft of the aircraft is an issue which has to be taken into account. This multi-physics problem leads to an optimal
Dynamic Wavelength Routing in WDM Networks via Ant Colony Optimization
Barr, Richard
of all-optical networks is accomplished, in part, by send- ing multiple signals simultaneously through the same fiber-optic cable. This is achieved through wavelength-division multiplexing (WDM), whichDynamic Wavelength Routing in WDM Networks via Ant Colony Optimization Ryan M. Garlick1 and Richard
Improved dual decomposition based optimization for DSL dynamic spectrum management
Paschalis Tsiaflakis; Ion Necoara; Johan A. K. Suykens; Marc Moonen
2010-01-01
Dynamic spectrum management (DSM) has been recognized as a key technology to significantly improve the performance of digital subscriber line (DSL) broadband access networks. The basic concept of DSM is to coordinate transmission over multiple DSL lines so as to mitigate the impact of crosstalk interference amongst them. Many algorithms have been proposed to tackle the nonconvex optimization problems appearing
OPTIMIZED DYNAMIC TRANSLINEAR IMPLEMENTATION OF THE GAUSSIAN WAVELET TRANSFORM
Serdijn, Wouter A.
realization of the continuous wavelet transform enables its application in vivo, e.g. pacemakers and IECG 2.1. Choice of the mother wavelet The continuous time wavelet transform (WT) of a signal ( )xOPTIMIZED DYNAMIC TRANSLINEAR IMPLEMENTATION OF THE GAUSSIAN WAVELET TRANSFORM Sandro A. P. Haddad1
Adapting Evolutionary Dynamics of Variation for Multi-objective Optimization
Coello, Carlos A. Coello
Adapting Evolutionary Dynamics of Variation for Multi-objective Optimization E. J. Teoh, S. C problems are sensitive to the parameter setting of the operators. In an effort to adapt the evolutionary operator whose parameter value will be deterministically adapted during the algorithm run so as to maintain
OPTIMAL DESIGN AND DYNAMIC SIMULATION OF A HYBRID SOLAR VEHICLE
Ivan Arsie; Gianfranco Rizzo; Marco Sorrentino
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
Optimization of designed armadillo repeat proteins by molecular dynamics
Caflisch, Amedeo
Optimization of designed armadillo repeat proteins by molecular dynamics simulations and NMR the thermodynamic stability of armadillo repeat proteins (ArmRPs). ArmRPs can form the basis of modular peptide-naphthalene sulfonate; ArmRP, Armadillo Repeat Protein; CD, circular dichro- ism; GdnHCl, guanidine
UAV perimeter patrol operations optimization using efficient Dynamic Programming
K. Krishnamoorthy; M. Pachter; P. Chandler; D. Casbeer; S. Darbha
2011-01-01
A reduced order Dynamic Programming (DP) method that efficiently computes the optimal policy and value function for a class of controlled Markov chains is developed. We assume that the Markov chains exhibit the property that a subset of the states have a single (default) control action asso- ciated with them. Furthermore, we assume that the transition probabilities between the remaining
Optimal Dynamic Assignment for Low Earth Orbit Satellite Constellations
Alexander Melin; R. Scott Erwin; VijaySekhar Chellaboina
2006-01-01
In this paper we investigate the problem of autonomous task assignment for a class of uncertain systems described by dynamic weighted bipartite graphs. Specifically, we consider the optimal assignment problem for this class of system. First, we present necessary and sufficient conditions for the existence of a perfect matching in a given bipartite graph. Next, we present an algorithm to
HIV Dynamics: Modeling, Data Analysis, and Optimal Treatment Protocols
HIV Dynamics: Modeling, Data Analysis, and Optimal Treatment Protocols B. M. Adams 1 , H. T. Banks in model- ing HIV pathogenesis. After a brief discussion of motivation for and previous efforts in the development of mathematical models for progression of HIV infection and treatment, we discuss mathematical
PARKING GARAGES WITH OPTIMAL DYNAMICS MEITAL COHEN AND BARAK WEISS
Weiss, Barak
PARKING GARAGES WITH OPTIMAL DYNAMICS MEITAL COHEN AND BARAK WEISS Abstract. We construct generalized polygons (`parking garages') in which the billiard flow satisfies the Veech dichotomy, although and Statement of results A parking garage is an immersion h : N R2 , where N is a two dimensional compact
Dynamically Optimized Production Planning Ssing Cross-Layer SOA
Domnic Savio; Stamatis Karnouskos; Daniel Wuwer; Thomas Bangemann
2008-01-01
Responding to the dynamic requirements of the changing market and optimising costs are two challenges faced by corporate manufacturing plants globally. In order to be agile, modern manufacturing plants employ optimized supply chain mechanisms to reduce the response time of the market needs. However bringing changes to the shop floor after a production is planned is costly. In this paper
Dynamic optimization of district energy grid
NASA Astrophysics Data System (ADS)
Salsbery, Scott
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.
Analysis and Optimization of Pulse Dynamics for Magnetic Stimulation
Goetz, Stefan M.; Truong, Cong Nam; Gerhofer, Manuel G.; Peterchev, Angel V.; Herzog, Hans-Georg; Weyh, Thomas
2013-01-01
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
Optimal motor control may mask sensory dynamics
Kiemel, Tim; Cowan, Noah J.; Jeka, John J.
2009-01-01
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. PMID:19408009
Optimal Control of HIV Dynamic Using Embedding Method
Zarei, H.; Kamyad, A. V.; Farahi, M. H.
2011-01-01
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. PMID:21687584
Optimizing Laboratory Experiments for Dynamic Astrophysical Phenomena
Ryutov, D. D.; Remington, B. A. [Lawrence Livermore National Laboratory, Livermore, CA 94551 (United States)
2006-04-07
To make laboratory experiments an efficient tool for the studying the dynamical astrophysical phenomena, it is desirable to perform them in such a way as to satisfy the scaling invariance with respect to the astrophysical system under study. Several examples are presented of such scalings in the area of magnetohydrodynamic phenomena, where a number of scaled experiments have been performed. A difficult issue of the effect of fine-scale dissipative structures on the global scale dissipation-free flow is discussed. The second part of the paper is concerned with much less developed area of the scalings relevant to the interaction of an ultra-intense laser pulse with a pre-formed plasma. The use of the symmetry arguments in such experiments is also considered.
Optimizing Laboratory Experiments for Dynamic Astrophysical Phenomena
Ryutov, D; Remington, B
2005-09-13
To make a laboratory experiment an efficient tool for the studying the dynamical astrophysical phenomena, it is desirable to perform them in such a way as to observe the scaling invariance with respect to the astrophysical system under study. Several examples are presented of such scalings in the area of magnetohydrodynamic phenomena, where a number of scaled experiments have been performed. A difficult issue of the effect of fine-scale dissipative structures on the global scale dissipation-free flow is discussed. The second part of the paper is concerned with much less developed area of the scalings relevant to the interaction of an ultra-intense laser pulse with a pre-formed plasma. The use of the symmetry arguments in such experiments is also considered.
Dynamic Sociometry in Particle Swarm Optimization Mark Richards and Dan Ventura
Martinez, Tony R.
exploding [2]. Figure 1. The basic Particle Swarm Optimization algorithm. As with many other optimizationDynamic Sociometry in Particle Swarm Optimization Mark Richards and Dan Ventura Computer Science Optimization is greatly affected by the size and sociometry of the swarm. This research proposes a dynamic
Application of Genetic Algorithms To Identify Optimal Groundwater Monitoring Well Locations in 3D
NASA Astrophysics Data System (ADS)
Abdeh-Kolahchi, A.; Satish, M.; Datta, B.
2004-12-01
Monitoring groundwater aquifers for possible sources of contamination is an important aspect of water resources management. The design of monitoring networks has been one of the key concerns of researchers who deal with the management of groundwater quality. Optimal monitoring network design can be beneficial to both groundwater simulation as well as optimization modeling. This paper discusses the applications of various optimization techniques from traditional to global methods for the solution of groundwater monitoring network and groundwater quality management problems. In order to solve optimization-based groundwater management models, various mathematical programming techniques such as linear/nonlinear programming, mixed-integer programming, differential dynamic programming, stochastic programming, as well as global optimization methods such as Genetic Algorithms are used by researchers to obtain optimal solutions for groundwater management. The resent study will also discuss a state of the art method, which combines simulation of groundwater flow and transport with genetic algorithm optimization. In order to ensure that the optimal management strategy is physically acceptable, a simulation model is necessary to simulate the system behavior. The simulation model basically provides solutions that satisfy the equations governing the relevant processes in the system. Thus the simulation models can be used for checking the feasibility of a management strategy. Once the optimization model is formulated, a suitable mathematical programming technique such as genetic algorithm is applied to obtain the optimal solution. This approach not only accounts for the complex and non-linear behavior of the groundwater system, but also identifies the best monitoring strategy under a specific objective function with several constraints. The solution identifies the best location of monitoring wells.
Artificial bee colony algorithm for constrained possibilistic portfolio optimization problem
NASA Astrophysics Data System (ADS)
Chen, Wei
2015-07-01
In this paper, we discuss the portfolio optimization problem with real-world constraints under the assumption that the returns of risky assets are fuzzy numbers. A new possibilistic mean-semiabsolute deviation model is proposed, in which transaction costs, cardinality and quantity constraints are considered. Due to such constraints the proposed model becomes a mixed integer nonlinear programming problem and traditional optimization methods fail to find the optimal solution efficiently. Thus, a modified artificial bee colony (MABC) algorithm is developed to solve the corresponding optimization problem. Finally, a numerical example is given to illustrate the effectiveness of the proposed model and the corresponding algorithm.
SOLVING MIXED INTEGER BILINEAR PROBLEMS USING MILP ...
2013-01-29
†School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA. ...... credence to our primary motivation for this study: that on certain class of problems, ... [7] P. Belotti, Couenne: a user's manual, June 2012. .... disjunctions and bilinear covering sets, Mathematical Programming, 124 (2010), pp.
Exploring the capabilities of quantum optimal dynamic discrimination
NASA Astrophysics Data System (ADS)
Beltrani, Vincent; Ghosh, Pritha; Rabitz, Herschel
2009-04-01
Optimal dynamic discrimination (ODD) uses closed-loop learning control techniques to discriminate between similar quantum systems. ODD achieves discrimination by employing a shaped control (laser) pulse to simultaneously exploit the unique quantum dynamics particular to each system, even when they are quite similar. In this work, ODD is viewed in the context of multiobjective optimization, where the competing objectives are the degree of similarity of the quantum systems and the level of controlled discrimination that can be achieved. To facilitate this study, the D-MORPH gradient algorithm is extended to handle multiple quantum systems and multiple objectives. This work explores the trade-off between laser resources (e.g., the length of the pulse, fluence, etc.) and ODD's ability to discriminate between similar systems. A mechanism analysis is performed to identify the dominant pathways utilized to achieve discrimination between similar systems.
A Dynamically Optimized SSVEP Brain-Computer Interface (BCI) Speller.
Yin, Erwei; Zhou, Zongtan; Jiang, Jun; Yu, Yang; Hu, Dewen
2015-06-01
The aim of this study was to design a dynamically optimized steady-state visually evoked potential (SSVEP) brain-computer interface (BCI) system with enhanced performance relative to previous SSVEP BCIs in terms of the number of items selectable on the interface, accuracy, and speed. In this approach, the row/column (RC) paradigm was employed in a SSVEP speller to increase the number of items. The target is detected by subsequently determining the row and column coordinates. To improve spelling accuracy, we added a posterior processing after the canonical correlation analysis (CCA) approach to reduce the interfrequency variation between different subjects and named the new signal processing method CCA-RV, and designed a real-time biofeedback mechanism to increase attention on the visual stimuli. To achieve reasonable online spelling speed, both fixed and dynamic approaches for setting the optimal stimulus duration were implemented and compared. Experimental results for 11 subjects suggest that the CCA-RV method and the real-time biofeedback effectively increased accuracy compared with CCA and the absence of real-time feedback, respectively. In addition, both optimization approaches for setting stimulus duration achieved reasonable online spelling performance. However, the dynamic optimization approach yielded a higher practical information transfer rate (PITR) than the fixed optimization approach. The average online PITR achieved by the proposed adaptive SSVEP speller, including the time required for breaks between selections and error correction, was 41.08 bit/min. These results indicate that our BCI speller is promising for use in SSVEP-based BCI applications. PMID:24801483
Optimally combining dynamical decoupling and quantum error correction
Paz-Silva, Gerardo A.; Lidar, D. A.
2013-01-01
Quantum control and fault-tolerant quantum computing (FTQC) are two of the cornerstones on which the hope of realizing a large-scale quantum computer is pinned, yet only preliminary steps have been taken towards formalizing the interplay between them. Here we explore this interplay using the powerful strategy of dynamical decoupling (DD), and show how it can be seamlessly and optimally integrated with FTQC. To this end we show how to find the optimal decoupling generator set (DGS) for various subspaces relevant to FTQC, and how to simultaneously decouple them. We focus on stabilizer codes, which represent the largest contribution to the size of the DGS, showing that the intuitive choice comprising the stabilizers and logical operators of the code is in fact optimal, i.e., minimizes a natural cost function associated with the length of DD sequences. Our work brings hybrid DD-FTQC schemes, and their potentially considerable advantages, closer to realization. PMID:23559088
Optimal Control of a Parabolic Equation with Dynamic Boundary Condition
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
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.
An optimal tracking neuro-controller for nonlinear dynamic systems
Young-Moon Park; Myeon-Song Choi; Kwang Y. Lee
1996-01-01
Multilayer neural networks are used to design an optimal tracking neuro-controller (OTNC) for discrete-time nonlinear dynamic systems with quadratic cost function. The OTNC is made of two controllers: feedforward neuro-controller (FFNC) and feedback neuro-controller (FBNC). The FFNC controls the steady-state output of the plant, while the FBNC controls the transient-state output of the plant. The FFNC is designed using a
Critical-path-analysis-based dynamic component supplier optimization
X. G. Huang; Y. S. Wong; Z. J. Liu; Z. M. Qiu
2005-01-01
The global market today demands rapid introduction of products while maintaining high quality and minimal costs. To accomplish the goals in a timely and efficient manner, companies are considering the power of collaboration across the product lifecycle. This paper proposes a critical-path-analysis (CPA)-based method to optimize the component supplier selection in a dynamic collaborative environment. An objective function is advanced
Confronting dynamics and uncertainty in optimal decision making for conservation
NASA Astrophysics Data System (ADS)
Williams, Byron K.; Johnson, Fred A.
2013-06-01
The effectiveness of conservation efforts ultimately depends on the recognition that decision making, and the systems that it is designed to affect, are inherently dynamic and characterized by multiple sources of uncertainty. To cope with these challenges, conservation planners are increasingly turning to the tools of decision analysis, especially dynamic optimization methods. Here we provide a general framework for optimal, dynamic conservation and then explore its capacity for coping with various sources and degrees of uncertainty. In broadest terms, the dynamic optimization problem in conservation is choosing among a set of decision options at periodic intervals so as to maximize some conservation objective over the planning horizon. Planners must account for immediate objective returns, as well as the effect of current decisions on future resource conditions and, thus, on future decisions. Undermining the effectiveness of such a planning process are uncertainties concerning extant resource conditions (partial observability), the immediate consequences of decision choices (partial controllability), the outcomes of uncontrolled, environmental drivers (environmental variation), and the processes structuring resource dynamics (structural uncertainty). Where outcomes from these sources of uncertainty can be described in terms of probability distributions, a focus on maximizing the expected objective return, while taking state-specific actions, is an effective mechanism for coping with uncertainty. When such probability distributions are unavailable or deemed unreliable, a focus on maximizing robustness is likely to be the preferred approach. Here the idea is to choose an action (or state-dependent policy) that achieves at least some minimum level of performance regardless of the (uncertain) outcomes. We provide some examples of how the dynamic optimization problem can be framed for problems involving management of habitat for an imperiled species, conservation of a critically endangered population through captive breeding, control of invasive species, construction of biodiversity reserves, design of landscapes to increase habitat connectivity, and resource exploitation. Although these decision making problems and their solutions present significant challenges, we suggest that a systematic and effective approach to dynamic decision making in conservation need not be an onerous undertaking. The requirements are shared with any systematic approach to decision making—a careful consideration of values, actions, and outcomes.
Confronting dynamics and uncertainty in optimal decision making for conservation
Williams, Byron K.; Johnson, Fred A.
2013-01-01
The effectiveness of conservation efforts ultimately depends on the recognition that decision making, and the systems that it is designed to affect, are inherently dynamic and characterized by multiple sources of uncertainty. To cope with these challenges, conservation planners are increasingly turning to the tools of decision analysis, especially dynamic optimization methods. Here we provide a general framework for optimal, dynamic conservation and then explore its capacity for coping with various sources and degrees of uncertainty. In broadest terms, the dynamic optimization problem in conservation is choosing among a set of decision options at periodic intervals so as to maximize some conservation objective over the planning horizon. Planners must account for immediate objective returns, as well as the effect of current decisions on future resource conditions and, thus, on future decisions. Undermining the effectiveness of such a planning process are uncertainties concerning extant resource conditions (partial observability), the immediate consequences of decision choices (partial controllability), the outcomes of uncontrolled, environmental drivers (environmental variation), and the processes structuring resource dynamics (structural uncertainty). Where outcomes from these sources of uncertainty can be described in terms of probability distributions, a focus on maximizing the expected objective return, while taking state-specific actions, is an effective mechanism for coping with uncertainty. When such probability distributions are unavailable or deemed unreliable, a focus on maximizing robustness is likely to be the preferred approach. Here the idea is to choose an action (or state-dependent policy) that achieves at least some minimum level of performance regardless of the (uncertain) outcomes. We provide some examples of how the dynamic optimization problem can be framed for problems involving management of habitat for an imperiled species, conservation of a critically endangered population through captive breeding, control of invasive species, construction of biodiversity reserves, design of landscapes to increase habitat connectivity, and resource exploitation. Although these decision making problems and their solutions present significant challenges, we suggest that a systematic and effective approach to dynamic decision making in conservation need not be an onerous undertaking. The requirements are shared with any systematic approach to decision making--a careful consideration of values, actions, and outcomes.
Optimal yearly scheduling of generation and pumping for a price-maker hydro producer
Costas G. Baslis; Anastasios G. Bakirtzis
2010-01-01
The objective of this paper is to present a mixed-integer approach to the yearly self-scheduling problem of a price-maker hydro producer with pumped storage. The producer's bidding strategy is modeled through a residual demand curve, which provides market clearing price as a function of the producer's quota. The curve is properly modified, enabling the definition of optimal pumping bids. The
Optimization of Dynamic Aperture of PEP-X Baseline Design
Wang, Min-Huey; /SLAC; Cai, Yunhai; /SLAC; Nosochkov, Yuri; /SLAC; ,
2010-08-23
SLAC is developing a long-range plan to transfer the evolving scientific programs at SSRL from the SPEAR3 light source to a much higher performing photon source. Storage ring design is one of the possibilities that would be housed in the 2.2-km PEP-II tunnel. The design goal of PEPX storage ring is to approach an optimal light source design with horizontal emittance less than 100 pm and vertical emittance of 8 pm to reach the diffraction limit of 1-{angstrom} x-ray. The low emittance design requires a lattice with strong focusing leading to high natural chromaticity and therefore to strong sextupoles. The latter caused reduction of dynamic aperture. The dynamic aperture requirement for horizontal injection at injection point is about 10 mm. In order to achieve the desired dynamic aperture the transverse non-linearity of PEP-X is studied. The program LEGO is used to simulate the particle motion. The technique of frequency map is used to analyze the nonlinear behavior. The effect of the non-linearity is tried to minimize at the given constrains of limited space. The details and results of dynamic aperture optimization are discussed in this paper.
Human opinion dynamics: an inspiration to solve complex optimization problems.
Kaur, Rishemjit; Kumar, Ritesh; Bhondekar, Amol P; Kapur, Pawan
2013-01-01
Human interactions give rise to the formation of different kinds of opinions in a society. The study of formations and dynamics of opinions has been one of the most important areas in social physics. The opinion dynamics and associated social structure leads to decision making or so called opinion consensus. Opinion formation is a process of collective intelligence evolving from the integrative tendencies of social influence with the disintegrative effects of individualisation, and therefore could be exploited for developing search strategies. Here, we demonstrate that human opinion dynamics can be utilised to solve complex mathematical optimization problems. The results have been compared with a standard algorithm inspired from bird flocking behaviour and the comparison proves the efficacy of the proposed approach in general. Our investigation may open new avenues towards understanding the collective decision making. PMID:24141795
Human opinion dynamics: An inspiration to solve complex optimization problems
NASA Astrophysics Data System (ADS)
Kaur, Rishemjit; Kumar, Ritesh; Bhondekar, Amol P.; Kapur, Pawan
2013-10-01
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.
Khawaja, Sajid Gul; Mushtaq, Mian Hamza; Khan, Shoab A; Akram, M Usman; Jamal, Habib Ullah
2015-01-01
With the increase of transistors' density, popularity of System on Chip (SoC) has increased exponentially. As a communication module for SoC, Network on Chip (NoC) framework has been adapted as its backbone. In this paper, we propose a methodology for designing area-optimized application specific NoC while providing hard Quality of Service (QoS) guarantees for real time flows. The novelty of the proposed system lies in derivation of a Mixed Integer Linear Programming model which is then used to generate a resource optimal Network on Chip (NoC) topology and architecture while considering traffic and QoS requirements. We also present the micro-architectural design features used for enabling traffic and latency guarantees and discuss how the solution adapts for dynamic variations in the application traffic. The paper highlights the effectiveness of proposed method by generating resource efficient NoC solutions for both industrial and benchmark applications. The area-optimized results are generated in few seconds by proposed technique, without resorting to heuristics, even for an application with 48 traffic flows. PMID:25898016
Optimized Uncertainty Quantification Algorithm Within a Dynamic Event Tree Framework
J. W. Nielsen; Akira Tokuhiro; Robert Hiromoto
2014-06-01
Methods for developing Phenomenological Identification and Ranking Tables (PIRT) for nuclear power plants have been a useful tool in providing insight into modelling aspects that are important to safety. These methods have involved expert knowledge with regards to reactor plant transients and thermal-hydraulic codes to identify are of highest importance. Quantified PIRT provides for rigorous method for quantifying the phenomena that can have the greatest impact. The transients that are evaluated and the timing of those events are typically developed in collaboration with the Probabilistic Risk Analysis. Though quite effective in evaluating risk, traditional PRA methods lack the capability to evaluate complex dynamic systems where end states may vary as a function of transition time from physical state to physical state . Dynamic PRA (DPRA) methods provide a more rigorous analysis of complex dynamic systems. A limitation of DPRA is its potential for state or combinatorial explosion that grows as a function of the number of components; as well as, the sampling of transition times from state-to-state of the entire system. This paper presents a method for performing QPIRT within a dynamic event tree framework such that timing events which result in the highest probabilities of failure are captured and a QPIRT is performed simultaneously while performing a discrete dynamic event tree evaluation. The resulting simulation results in a formal QPIRT for each end state. The use of dynamic event trees results in state explosion as the number of possible component states increases. This paper utilizes a branch and bound algorithm to optimize the solution of the dynamic event trees. The paper summarizes the methods used to implement the branch-and-bound algorithm in solving the discrete dynamic event trees.
Real-time optimal trajectory generation for constrained dynamical systems
NASA Astrophysics Data System (ADS)
Milam, Mark Bradley
With the advent of powerful computing and efficient computational algorithms, real-time solutions to constrained optimal control problems are nearing a reality. In this thesis, we develop a computationally efficient Nonlinear Trajectory Generation (NTG) algorithm and describe its software implementation to solve, in real-time, nonlinear optimal trajectory generation problems for constrained systems. NTG is a nonlinear trajectory generation software package that combines nonlinear control theory, B-spline basis functions, and nonlinear programming. We compare NTG with other numerical optimal control problem solution techniques, such as direct collocation, shooting, adjoints, and differential inclusions. We demonstrate the performance of NTG on the Caltech Ducted Fan testbed. Aggressive, constrained optimal control problems are solved in real-time for hover-to-hover, forward flight, and terrain avoidance test cases. Real-time trajectory generation results are shown for both the two-degree of freedom and receding horizon control designs. Further experimental demonstration is provided with the station-keeping, reconfiguration, and deconfiguration of micro-satellite formation with complex nonlinear constraints. Successful application of NTG in these cases demonstrates reliable real-time trajectory generation, even for highly nonlinear and non-convex systems. The results are among the first to apply receding horizon control techniques for agile flight in an experimental setting, using representative dynamics and computation.
Modelling the Transmission Dynamics of Hepatitis B & Optimal control
Mehmood, Nayyar
2011-01-01
Hepatitis B is a potentially life-threatening viral infection that can cause illness and even death. The Hepatitis B virus is fifty to hundred times more infectious than HIV, and world wide an estimated two billion people have been affected (WHO2008). HBV is the most common serious viral infection and leading cause of deaths in main land in china South Asia and Africa and almost all parts of the world. Every year 300,000 people from HBV related diseases only in China. Hepatitis B is a common disease in Pakistan as every tenth person is carrier and every fifth person has been exposed to hepatitis B. We proposed a model to understand the transmission dynamics and prevalence of HBV. To control the prevalence of diseases, we use optimal control strategies, which reduce the latent, infected and carrier individuals and increase the total number of recovered and immune individuals. In order to do this, we show that an optimal control exists for this optimal control problem and then we use optimal control techniques ...
Exposure Time Optimization for Highly Dynamic Star Trackers
Wei, Xinguo; Tan, Wei; Li, Jian; Zhang, Guangjun
2014-01-01
Under highly dynamic conditions, the star-spots on the image sensor of a star tracker move across many pixels during the exposure time, which will reduce star detection sensitivity and increase star location errors. However, this kind of effect can be compensated well by setting an appropriate exposure time. This paper focuses on how exposure time affects the star tracker under highly dynamic conditions and how to determine the most appropriate exposure time for this case. Firstly, the effect of exposure time on star detection sensitivity is analyzed by establishing the dynamic star-spot imaging model. Then the star location error is deduced based on the error analysis of the sub-pixel centroiding algorithm. Combining these analyses, the effect of exposure time on attitude accuracy is finally determined. Some simulations are carried out to validate these effects, and the results show that there are different optimal exposure times for different angular velocities of a star tracker with a given configuration. In addition, the results of night sky experiments using a real star tracker agree with the simulation results. The summarized regularities in this paper should prove helpful in the system design and dynamic performance evaluation of the highly dynamic star trackers. PMID:24618776
Optimal approach to quantum communication using dynamic programming.
Jiang, Liang; Taylor, Jacob M; Khaneja, Navin; Lukin, Mikhail D
2007-10-30
Reliable preparation of entanglement between distant systems is an outstanding problem in quantum information science and quantum communication. In practice, this has to be accomplished by noisy channels (such as optical fibers) that generally result in exponential attenuation of quantum signals at large distances. A special class of quantum error correction protocols, quantum repeater protocols, can be used to overcome such losses. In this work, we introduce a method for systematically optimizing existing protocols and developing more efficient protocols. Our approach makes use of a dynamic programming-based searching algorithm, the complexity of which scales only polynomially with the communication distance, letting us efficiently determine near-optimal solutions. We find significant improvements in both the speed and the final-state fidelity for preparing long-distance entangled states. PMID:17959783
Stochastic resonance and energy optimization in spatially extended dynamical systems
NASA Astrophysics Data System (ADS)
Lai, Y.-C.; Park, K.; Rajagopalan, L.
2009-05-01
We investigate a class of nonlinear wave equations subject to periodic forcing and noise, and address the issue of energy optimization. Numerically, we use a pseudo-spectral method to solve the nonlinear stochastic partial differential equation and compute the energy of the system as a function of the driving amplitude in the presence of noise. In the fairly general setting where the system possesses two coexisting states, one with low and another with high energy, noise can induce intermittent switchings between the two states. A striking finding is that, for fixed noise, the system energy can be optimized by the driving in a form of resonance. The phenomenon can be explained by the Langevin dynamics of particle motion in a double-well potential system with symmetry breaking. The finding can have applications to small-size devices such as microelectromechanical resonators and to waves in fluid and plasma.
Optimal approach to quantum communication using dynamic programming
Jiang, Liang; Khaneja, Navin; Lukin, Mikhail D
2007-01-01
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 via 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 new, 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.
Optimal approach to quantum communication using dynamic programming
Liang Jiang; Jacob M. Taylor; Navin Khaneja; Mikhail D. Lukin
2007-10-31
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 via 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 new, 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.
Optimal dynamic regimes: presenting a case for predictive inference.
Arjas, Elja; Saarela, Olli
2010-01-01
Dynamic treatment regime is a decision rule in which the choice of the treatment of an individual at any given time can depend on the known past history of that individual, including baseline covariates, earlier treatments, and their measured responses. In this paper we argue that finding an optimal regime can, at least in moderately simple cases, be accomplished by a straightforward application of nonparametric Bayesian modeling and predictive inference. As an illustration we consider an inference problem in a subset of the Multicenter AIDS Cohort Study (MACS) data set, studying the effect of AZT initiation on future CD4-cell counts during a 12-month follow-up. PMID:20648215
Shahid Mahmood; De-bo Huang
2011-01-01
A study has been carried out for the optimization of a Trimaran hull form to minimize the total resistance of hull. Main hull of the Trimaran has been taken into consideration for optimization, while the geometry of outriggers is kept same. The optimization is based on the integration of genetic algorithm with computational fluid dynamics. The optimization process is kept
Dynamic Simulation and Optimization of Nuclear Hydrogen Production Systems
Paul I. Barton; Mujid S. Kaximi; Georgios Bollas; Patricio Ramirez Munoz
2009-07-31
This project is part of a research effort to design a hydrogen plant and its interface with a nuclear reactor. This project developed a dynamic modeling, simulation and optimization environment for nuclear hydrogen production systems. A hybrid discrete/continuous model captures both the continuous dynamics of the nuclear plant, the hydrogen plant, and their interface, along with discrete events such as major upsets. This hybrid model makes us of accurate thermodynamic sub-models for the description of phase and reaction equilibria in the thermochemical reactor. Use of the detailed thermodynamic models will allow researchers to examine the process in detail and have confidence in the accurary of the property package they use.
Optimized dynamical decoupling for power-law noise spectra
Pasini, S.; Uhrig, G. S. [Lehrstuhl fuer Theoretische Physik I, TU Dortmund, Otto-Hahn Strasse 4, D-44221 Dortmund (Germany)
2010-01-15
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.
Clustering Molecular Dynamics Trajectories for Optimizing Docking Experiments
De Paris, Renata; Quevedo, Christian V.; Ruiz, Duncan D.; Norberto de Souza, Osmar; Barros, Rodrigo C.
2015-01-01
Molecular dynamics simulations of protein receptors have become an attractive tool for rational drug discovery. However, the high computational cost of employing molecular dynamics trajectories in virtual screening of large repositories threats the feasibility of this task. Computational intelligence techniques have been applied in this context, with the ultimate goal of reducing the overall computational cost so the task can become feasible. Particularly, clustering algorithms have been widely used as a means to reduce the dimensionality of molecular dynamics trajectories. In this paper, we develop a novel methodology for clustering entire trajectories using structural features from the substrate-binding cavity of the receptor in order to optimize docking experiments on a cloud-based environment. The resulting partition was selected based on three clustering validity criteria, and it was further validated by analyzing the interactions between 20 ligands and a fully flexible receptor (FFR) model containing a 20?ns molecular dynamics simulation trajectory. Our proposed methodology shows that taking into account features of the substrate-binding cavity as input for the k-means algorithm is a promising technique for accurately selecting ensembles of representative structures tailored to a specific ligand. PMID:25873944
Yu Wang; Bin Li
2009-01-01
As the research of dynamic optimization arising, memory-based strategy has gained public attention recently. However, few studies on developing dynamic multi-objective optimization algorithms and even fewer studies on multi-objective memory-based strategy were reported previously. In this paper, we try to address such an issue by proposing several memory-based multi-objective evolutionary algorithms and experimentally investigating different multi-objective dynamic optimization schemes, which
Design optimization of blood shearing instrument by computational fluid dynamics.
Wu, Jingchun; Antaki, James F; Snyder, Trevor A; Wagner, William R; Borovetz, Harvey S; Paden, Bradley E
2005-06-01
Rational design of blood-wetted devices requires a careful consideration of shear-induced trauma and activation of blood elements. Critical levels of shear exposure may be established in vitro through the use of devices specifically designed to prescribe both the magnitude and duration of shear exposure. However, it is exceptionally difficult to create a homogeneous shear-exposure history by conventional means. This study was undertaken to develop a Blood Shearing Instrument (BSI) with an optimized flow path which localized shear exposure within a rotating outer ring and a stationary conical spindle. By adjustment of the rotational speed and the gap dimension, the BSI is designed to generate shear stress magnitudes up to 1500 Pa for exposure time between 0.0015 and 0.20 s with a pressure drop of 100 mm Hg. Computational fluid dynamics (CFD) revealed that a flow path designed by first-order analysis and intuition exhibited unfavorable pressure gradient, vortices, and undesirable regions of reverse flow. An optimized design was evolved utilizing a parameterized geometric model and automatic mesh generation to eliminate vortices and reversal flow and to avoid unfavorable pressure gradients. Analysis of the flow and shear fields for the extreme limits of the shear gap demonstrated an improvement in homogeneity due to shape optimization and the limitations of an annular shear device for achieving completely uniform shear exposure. PMID:15926986
Data-driven optimization of dynamic reconfigurable systems of systems.
Tucker, Conrad S.; Eddy, John P.
2010-11-01
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.
NASA Astrophysics Data System (ADS)
Hickey, Owen A.; Harden, James L.; Slater, Gary W.
2009-03-01
The suppression of electro-osmotic flow (EOF) through the use of a dynamically adsorbed polymer coating is a widely used technique in microfluidic devices. Recent experimental evidence has suggested that the most effective coatings are those which are only weakly adsorbed to the surface. We report molecular dynamics simulation results which show that the optimal adsorption strength for the suppression of EOF is around the transition for adsorption of the polymer. Our results should help to guide the design of novel polymer coatings for improved quenching of EOF.
A Formal Approach to Empirical Dynamic Model Optimization and Validation
NASA Technical Reports Server (NTRS)
Crespo, Luis G; Morelli, Eugene A.; Kenny, Sean P.; Giesy, Daniel P.
2014-01-01
A framework was developed for the optimization and validation of empirical dynamic models subject to an arbitrary set of validation criteria. The validation requirements imposed upon the model, which may involve several sets of input-output data and arbitrary specifications in time and frequency domains, are used to determine if model predictions are within admissible error limits. The parameters of the empirical model are estimated by finding the parameter realization for which the smallest of the margins of requirement compliance is as large as possible. The uncertainty in the value of this estimate is characterized by studying the set of model parameters yielding predictions that comply with all the requirements. Strategies are presented for bounding this set, studying its dependence on admissible prediction error set by the analyst, and evaluating the sensitivity of the model predictions to parameter variations. This information is instrumental in characterizing uncertainty models used for evaluating the dynamic model at operating conditions differing from those used for its identification and validation. A practical example based on the short period dynamics of the F-16 is used for illustration.
A Dynamic Near-Optimal Algorithm for Online Linear Programming
Agrawal, Shipra; Ye, Yinyu
2009-01-01
We consider the online linear programming problem where the constraint matrix is revealed column by column along with the objective function. We provide a 1-o(1) competitive algorithm for this surprisingly general class of online problems under the assumption of random order of arrival and some mild conditions on the right-hand-side input. Our learning-based algorithm works by dynamically updating a threshold price vector at geometric time intervals, the price learned from the previous steps is used to determine the decision for the current step. Our result provides a common near-optimal solution to a wide range of online problems including online routing and packing, online combinatorial auction, online adwords matching, many secretary problems, and various resource allocation and revenue management problems. Apart from online problems, the algorithm can also be applied for fast solution of large linear programs by sampling the columns of constraint matrix.
Optimal Control of Magnetization Dynamics in Ferromagnetic Materials using TDDFT
NASA Astrophysics Data System (ADS)
Elliott, Peter; Krieger, Kevin; Gross, E. K. U.
2015-03-01
Recently intense laser-field induced ultrafast demagnetization was observed in ab-initio simulations using Time-Dependent Density Functional Theory (TDDFT) for various ferromagnetic materials (Fe,Co,Ni). From a practical and technological viewpoint, it is useful if the induced dynamics (e.g. of the total magnetic moment) are controllable. In this talk we apply optimal control theory together with TDDFT calculations to tailor the intense laser pulses so as to achieve a particular outcome (e.g. maximize the total moment lost) while also including any required constraints (e.g pulse duration, pulse frequencies, maximum fluence, etc). Support from European Communities FP7, through the CRONOS project Grant No. 280879.
Geometry optimization for micro-pressure sensor considering dynamic interference
Yu, Zhongliang; Zhao, Yulong, E-mail: zhaoyulong@mail.xjtu.edu.cn; Li, Lili; Tian, Bian; Li, Cun [State Key Laboratory for Manufacturing Systems Engineering, Xi'an Jiaotong University, Xi'an, Shaanxi 710049 (China)
2014-09-15
Presented is the geometry optimization for piezoresistive absolute micro-pressure sensor. A figure of merit called the performance factor (PF) is defined as a quantitative index to describe the comprehensive performances of a sensor including sensitivity, resonant frequency, and acceleration interference. Three geometries are proposed through introducing islands and sensitive beams into typical flat diaphragm. The stress distributions of sensitive elements are analyzed by finite element method. Multivariate fittings based on ANSYS simulation results are performed to establish the equations about surface stress, deflection, and resonant frequency. Optimization by MATLAB is carried out to determine the dimensions of the geometries. Convex corner undercutting is evaluated. Each PF of the three geometries with the determined dimensions is calculated and compared. Silicon bulk micromachining is utilized to fabricate the prototypes of the sensors. The outputs of the sensors under both static and dynamic conditions are tested. Experimental results demonstrate the rationality of the defined performance factor and reveal that the geometry with quad islands presents the highest PF of 210.947 Hz{sup 1/4}. The favorable overall performances enable the sensor more suitable for altimetry.
Geometry optimization for micro-pressure sensor considering dynamic interference.
Yu, Zhongliang; Zhao, Yulong; Li, Lili; Tian, Bian; Li, Cun
2014-09-01
Presented is the geometry optimization for piezoresistive absolute micro-pressure sensor. A figure of merit called the performance factor (PF) is defined as a quantitative index to describe the comprehensive performances of a sensor including sensitivity, resonant frequency, and acceleration interference. Three geometries are proposed through introducing islands and sensitive beams into typical flat diaphragm. The stress distributions of sensitive elements are analyzed by finite element method. Multivariate fittings based on ANSYS simulation results are performed to establish the equations about surface stress, deflection, and resonant frequency. Optimization by MATLAB is carried out to determine the dimensions of the geometries. Convex corner undercutting is evaluated. Each PF of the three geometries with the determined dimensions is calculated and compared. Silicon bulk micromachining is utilized to fabricate the prototypes of the sensors. The outputs of the sensors under both static and dynamic conditions are tested. Experimental results demonstrate the rationality of the defined performance factor and reveal that the geometry with quad islands presents the highest PF of 210.947 Hz(1/4). The favorable overall performances enable the sensor more suitable for altimetry. PMID:25273764
Geometry optimization for micro-pressure sensor considering dynamic interference
NASA Astrophysics Data System (ADS)
Yu, Zhongliang; Zhao, Yulong; Li, Lili; Tian, Bian; Li, Cun
2014-09-01
Presented is the geometry optimization for piezoresistive absolute micro-pressure sensor. A figure of merit called the performance factor (PF) is defined as a quantitative index to describe the comprehensive performances of a sensor including sensitivity, resonant frequency, and acceleration interference. Three geometries are proposed through introducing islands and sensitive beams into typical flat diaphragm. The stress distributions of sensitive elements are analyzed by finite element method. Multivariate fittings based on ANSYS simulation results are performed to establish the equations about surface stress, deflection, and resonant frequency. Optimization by MATLAB is carried out to determine the dimensions of the geometries. Convex corner undercutting is evaluated. Each PF of the three geometries with the determined dimensions is calculated and compared. Silicon bulk micromachining is utilized to fabricate the prototypes of the sensors. The outputs of the sensors under both static and dynamic conditions are tested. Experimental results demonstrate the rationality of the defined performance factor and reveal that the geometry with quad islands presents the highest PF of 210.947 Hz1/4. The favorable overall performances enable the sensor more suitable for altimetry.
Optimization using surrogate models and partially converged computational fluid dynamics simulations
Alexander I. J. Forrester; Neil W. Bressloff; Andy J. Keane
2006-01-01
Efficient methods for global aerodynamic optimization using computational fluid dynamics simulations should aim to reduce both the time taken to evaluate design concepts and the number of evaluations needed for optimization. This paper investigates methods for improving such efficiency through the use of partially converged computational fluid dynamics results. These allow surrogate models to be built in a fraction of
Structural and Dynamic Requirements for Optimal Activity of the Essential Bacterial Enzyme
Hickman, Mark
Structural and Dynamic Requirements for Optimal Activity of the Essential Bacterial Enzyme) is an essential enzyme involved in the lysine biosynthesis pathway. DHDPS from E. coli is a homotetramer and Dynamic Requirements for Optimal Activity of the Essential Bacterial Enzyme Dihydrodipicolinate Synthase
Towards Dynamic Pricing-Based Collaborative Optimizations for Green Data Centers
Loo, Boon Thau
Towards Dynamic Pricing-Based Collaborative Optimizations for Green Data Centers Yang Li David Chiu pricing to resolve this issue by possibly shaping demand. Data centers, being significant consumers), are ideal candidates to participate in dynamic pricing markets. We propose a collaborative cost optimization
Thomas Butler; Nigel Goldenfeld; Damien Mathew; Zaida Luthey-Schulten
2009-01-01
A molecular dynamics calculation of the amino acid polar requirement is used to score the canonical genetic code. Monte Carlo simulation shows that this computational polar requirement has been optimized by the canonical genetic code, an order of magnitude more than any previously known measure, effectively ruling out a vertical evolution dynamics. The sensitivity of the optimization to the precise
Optimization-based dynamic scheduling and its testbed for IC sort and test
Jian Yang; Tsu-Shuan Chang; Han Chang; Jen Kao
1996-01-01
A dynamic scheduling system for an IC sort and test facility is developed by using a sort of feedback strategy. The scheduling system will take a snapshot to obtain the current floor information and use an optimizer to come up with a good pragmatic strategy, which is sub-globally optimal in terms of the current information. Dynamic rescheduling is then used
Dynamic Assortment Optimization with a Multinomial Logit Choice Model and Capacity Constraint
Rusmevichientong, Paat
in retail, online advertising, and revenue management. For instance, given a limited shelf capacityDynamic Assortment Optimization with a Multinomial Logit Choice Model and Capacity Constraint Paat by a multinomial logit choice model. We consider both the static and dynamic optimization problems. In the static
The method of Monotone Structural Evolution for dynamic optimization of switched systems
Maciej Szymkat; Adam Korytowski
2008-01-01
The paper presents application of the Monotone Structural Evolution (MSE) to dynamic optimization of switched systems. The MSE is a direct computational method of optimal control which automatically identifies the optimal structure. Its distinctive feature is that the decision space undergoes gradual evolution, driven by the discrepancy from the Maximum Principle conditions. The approach is illustrated with two examples: the
Optimal path for China's strategic petroleum reserve: A dynamic programming analysis
Y. Bai; D. Q. Zhou; P. Zhou; L. B. Zhang
2012-01-01
This paper proposes a dynamic programming model to explore the optimal stockpiling path for China's strategic petroleum reserve before 2020. The optimal oil acquisition sizes in 2008–2020 under different scenarios are estimated. The effects of oil price, risks and elasticity value on inventory size are further investigated. It is found that the optimal stockpile acquisition strategies are mainly determined by
One-Dimensional Infinite Horizon Nonconcave Optimal Control Problems Arising in Economic Dynamics
Zaslavski, Alexander J., E-mail: ajzasl@tx.technion.ac.il [Technion-Israel Institute of Technology, Department of Mathematics (Israel)
2011-12-15
We study the existence of optimal solutions for a class of infinite horizon nonconvex autonomous discrete-time optimal control problems. This class contains optimal control problems without discounting arising in economic dynamics which describe a model with a nonconcave utility function.
Felipe Antonio Chegury Viana; Giovanni Iamin Kotinda; Valder Steffen
The present contribution deals with the optimal tuning of a vibrating blade dynamic vibration absorber (VBDVA). To achieve\\u000a this aim, the natural optimization technique named Ant Colony Optimization (ACO) is applied to the finite element model of\\u000a the system. Dynamic vibration absorbers (DVAs) are systems constituted by mass, spring and damping elements (secondary structure),\\u000a which are coupled to a mechanical
Dai, C; Li, Y P; Huang, G H
2011-12-01
In this study, a two-stage support-vector-regression optimization model (TSOM) is developed for the planning of municipal solid waste (MSW) management in the urban districts of Beijing, China. It represents a new effort to enhance the analysis accuracy in optimizing the MSW management system through coupling the support-vector-regression (SVR) model with an interval-parameter mixed integer linear programming (IMILP). The developed TSOM can not only predict the city's future waste generation amount, but also reflect dynamic, interactive, and uncertain characteristics of the MSW management system. Four kernel functions such as linear kernel, polynomial kernel, radial basis function, and multi-layer perception kernel are chosen based on three quantitative simulation performance criteria [i.e. prediction accuracy (PA), fitting accuracy (FA) and over all accuracy (OA)]. The SVR with polynomial kernel has accurate prediction performance for MSW generation rate, with all of the three quantitative simulation performance criteria being over 96%. Two cases are considered based on different waste management policies. The results are valuable for supporting the adjustment of the existing waste-allocation patterns to raise the city's waste diversion rate, as well as the capacity planning of waste management system to satisfy the city's increasing waste treatment/disposal demands. PMID:21872384
On the Optimization of Riemann-Stieltjes-Control-Systems with Application in Vehicle Dynamics
Boyer, Edmond
On the Optimization of Riemann-Stieltjes-Control-Systems with Application in Vehicle Dynamics J of Riemann-Stieltjes-Control-Systems with Application in Vehicle Dynamics J. Michael Content Problem-Stieltjes-Control-Systems with Application in Vehicle Dynamics J. Michael ProactiveChassisControl Setting: Input: Vetical Road Model
Optimized dynamical decoupling in a model quantum memory.
Biercuk, Michael J; Uys, Hermann; VanDevender, Aaron P; Shiga, Nobuyasu; Itano, Wayne M; Bollinger, John J
2009-04-23
Any quantum system, such as those used in quantum information or magnetic resonance, is subject to random phase errors that can dramatically affect the fidelity of a desired quantum operation or measurement. In the context of quantum information, quantum error correction techniques have been developed to correct these errors, but resource requirements are extraordinary. The realization of a physically tractable quantum information system will therefore be facilitated if qubit (quantum bit) error rates are far below the so-called fault-tolerance error threshold, predicted to be of the order of 10(-3)-10(-6). The need to realize such low error rates motivates a search for alternative strategies to suppress dephasing in quantum systems. Here we experimentally demonstrate massive suppression of qubit error rates by the application of optimized dynamical decoupling pulse sequences, using a model quantum system capable of simulating a variety of qubit technologies. We demonstrate an analytically derived pulse sequence, UDD, and find novel sequences through active, real-time experimental feedback. The latter sequences are tailored to maximize error suppression without the need for a priori knowledge of the ambient noise environment, and are capable of suppressing errors by orders of magnitude compared to other existing sequences (including the benchmark multi-pulse spin echo). Our work includes the extension of a treatment to predict qubit decoherence under realistic conditions, yielding strong agreement between experimental data and theory for arbitrary pulse sequences incorporating nonidealized control pulses. These results demonstrate the robustness of qubit memory error suppression through dynamical decoupling techniques across a variety of qubit technologies. PMID:19396139
Metamodeling and the Critic-based approach to multi-level optimization.
Werbos, Ludmilla; Kozma, Robert; Silva-Lugo, Rodrigo; Pazienza, Giovanni E; Werbos, Paul J
2012-08-01
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
Function-valued adaptive dynamics and optimal control theory.
Parvinen, Kalle; Heino, Mikko; Dieckmann, Ulf
2013-09-01
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
Conceptualizing a Tool to Optimize Therapy Based on Dynamic Heterogeneity
Liao, David; Estévez-Salmerón, Luis; Tlsty, Thea D.
2012-01-01
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. PMID:23197078
Optimal spectral tracking--adapting to dynamic regime change.
Brittain, John-Stuart; Halliday, David M
2011-01-30
Real world data do not always obey the statistical restraints imposed upon them by sophisticated analysis techniques. In spectral analysis for instance, an ergodic process--the interchangeability of temporal for spatial averaging--is assumed for a repeat-trial design. Many evolutionary scenarios, such as learning and motor consolidation, do not conform to such linear behaviour and should be approached from a more flexible perspective. To this end we previously introduced the method of optimal spectral tracking (OST) in the study of trial-varying parameters. In this extension to our work we modify the OST routines to provide an adaptive implementation capable of reacting to dynamic transitions in the underlying system state. In so doing, we generalise our approach to characterise both slow-varying and rapid fluctuations in time-series, simultaneously providing a metric of system stability. The approach is first applied to a surrogate dataset and compared to both our original non-adaptive solution and spectrogram approaches. The adaptive OST is seen to display fast convergence and desirable statistical properties. All three approaches are then applied to a neurophysiological recording obtained during a study on anaesthetic monitoring. Local field potentials acquired from the posterior hypothalamic region of a deep brain stimulation patient undergoing anaesthesia were analysed. The characterisation of features such as response delay, time-to-peak and modulation brevity are considered. PMID:21115043
Conceptualizing a tool to optimize therapy based on dynamic heterogeneity
NASA Astrophysics Data System (ADS)
Liao, David; Estévez-Salmerón, Luis; Tlsty, Thea D.
2012-12-01
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.
Photocathode Optimization for a Dynamic Transmission Electron Microscope: Final Report
Ellis, P; Flom, Z; Heinselman, K; Nguyen, T; Tung, S; Haskell, R; Reed, B W; LaGrange, T
2011-08-04
The Dynamic Transmission Electron Microscope (DTEM) team at Harvey Mudd College has been sponsored by LLNL to design and build a test setup for optimizing the performance of the DTEM's electron source. Unlike a traditional TEM, the DTEM achieves much faster exposure times by using photoemission from a photocathode to produce electrons for imaging. The DTEM team's work is motivated by the need to improve the coherence and current density of the electron cloud produced by the electron gun in order to increase the image resolution and contrast achievable by DTEM. The photoemission test setup is nearly complete and the team will soon complete baseline tests of electron gun performance. The photoemission laser and high voltage power supply have been repaired; the optics path for relaying the laser to the photocathode has been finalized, assembled, and aligned; the internal setup of the vacuum chamber has been finalized and mostly implemented; and system control, synchronization, and data acquisition has been implemented in LabVIEW. Immediate future work includes determining a consistent alignment procedure to place the laser waist on the photocathode, and taking baseline performance measurements of the tantalum photocathode. Future research will examine the performance of the electron gun as a function of the photoemission laser profile, the photocathode material, and the geometry and voltages of the accelerating and focusing components in the electron gun. This report presents the team's progress and outlines the work that remains.
IMPACT OF DYNAMIC VOLTAGE SCALING (DVS) ON CIRCUIT OPTIMIZATION
Esquit Hernandez, Carlos A.
2010-01-16
during the optimization phase? 2) Does DVS impose any restrictions while performing design-time circuit optimizations?. This thesis is a case study of applying DVS to a circuit that has been optimized for speed and power, and aims at answering...
A Dynamic Theory of Optimal Capital Structure and Executive Compensation
Harold Cole; Andrew Atkeson
2004-01-01
In this paper, we put forward a theory of the optimal capital structure of the firm based on Jensen's (1986) hypothesis that a firm's choice of capital structure is determined by a trade-off between agency costs and monitoring costs. The problem of determining the optimal capital structure of the firm as well as the optimal compensation of the manager is
Optimal Dynamic Spectrum Management for DSL Interference/Broadcast Channel
) networks. Promising crosstalk mitigation and canceling techniques have been proposed in the last decade-Harashima pre- coder (THP), optimal linear pre-compensator (OLP), and THP with optimal transmit filters. The calculation of the optimal transmit filters (for both the linear pre-compensator and THP) is a non
Wang, Xiaolong; Jiang, Aipeng; Jiangzhou, Shu; Li, Ping
2014-01-01
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
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
Radoslaw Czarnecki; Stanislaw Deniziak; Krzysztof Sapiecha
2003-01-01
In this work, a HW\\/SW iterative improvement co-synthesis algorithm, which allows for optimization of heterogeneous system architecture with dynamically reconfigurable FPGAs is presented. The algorithm maximizes speed of the system taking into consideration cost constraints.
The Role of Ocean Dynamics in the Optimal Growth of Tropical SST Anomalies
Zanna, Laure
The role of ocean dynamics in optimally exciting interannual variability of tropical sea surface temperature (SST) anomalies is investigated using an idealized-geometry ocean general circulation model. Initial temperature ...
An optimization model for energy generation and distribution in a dynamic facility
NASA Technical Reports Server (NTRS)
Lansing, F. L.
1981-01-01
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.
Evacuation dynamic and exit optimization of a supermarket based on particle swarm optimization
NASA Astrophysics Data System (ADS)
Li, Lin; Yu, Zhonghai; Chen, Yang
2014-12-01
A modified particle swarm optimization algorithm is proposed in this paper to investigate the dynamic of pedestrian evacuation from a fire in a public building-a supermarket with multiple exits and configurations of counters. Two distinctive evacuation behaviours featured by the shortest-path strategy and the following-up strategy are simulated in the model, accounting for different categories of age and sex of the pedestrians along with the impact of the fire, including gases, heat and smoke. To examine the relationship among the progress of the overall evacuation and the layout and configuration of the site, a series of simulations are conducted in various settings: without a fire and with a fire at different locations. Those experiments reveal a general pattern of two-phase evacuation, i.e., a steep section and a flat section, in addition to the impact of the presence of multiple exits on the evacuation along with the geographic locations of the exits. For the study site, our simulations indicated the deficiency of the configuration and the current layout of this site in the process of evacuation and verified the availability of proposed solutions to resolve the deficiency. More specifically, for improvement of the effectiveness of the evacuation from the site, adding an exit between Exit 6 and Exit 7 and expanding the corridor at the right side of Exit 7 would significantly reduce the evacuation time.
Metaheuristic algorithms in structural dynamics: An application of tuned mass damper optimization
NASA Astrophysics Data System (ADS)
Bekda?, Gebrail; Nigdeli, Sinan Melih
2012-09-01
Metaheuristic algorithms imitate natural phenomena in order to solve optimization problems. These algorithms are effective on the optimization of structural dynamics problems including vibration control with tuned mass damper (TMD). In this paper, structural dynamics optimization problems were briefly reviewed. As an example, a TMD optimization problem was presented. Harmony Search (HS) algorithm was used to find optimum parameters of TMD mass, stiffness and damping coefficient. The optimization process was conducted to reduce structural displacements of a five story structure. The properties of the structure are the same for all stories except the third story mass. According to the analyses results, the TMD optimized with HS approach is effective to reduce all maximum story displacements.
An archived multi-objective simulated annealing for a dynamic cellular manufacturing system
NASA Astrophysics Data System (ADS)
Shirazi, Hossein; Kia, Reza; Javadian, Nikbakhsh; Tavakkoli-Moghaddam, Reza
2014-05-01
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.
David O. Olukanni; Joel J. Ducoste
2011-01-01
Waste stabilization ponds (WSPs) have been used extensively to provide wastewater treatment throughout the world. However, no rigorous assessment of WSPs that account for cost in addition to hydrodynamics and treatment efficiency has been performed. A study was conducted that utilized computational fluid dynamics (CFD) coupled with an optimization program to optimize the selection of the best WSP configuration based
An automatic design optimization tool and its application to computational fluid dynamics
David Abramson; Andrew Lewis; Tom Peachey; Clive Fletcher
2001-01-01
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.
Optimal intervention in the foreign exchange market when interventions affect market dynamics
Aluffi, Paolo
investments; and direct purchases or sales of foreign currency reserves in the foreign exchange marketOptimal intervention in the foreign exchange market when interventions affect market dynamics Alec Investment Board May 20, 2008 Abstract We address the problem of optimal Central Bank intervention
Optimal Acceleration-Bounded Trajectory Planning in Dynamic Environments Along a Specified Path
Indiana University
Optimal Acceleration-Bounded Trajectory Planning in Dynamic Environments Along a Specified Path, polynomial-time planner for optimal bounded-acceleration tra- jectories along a fixed, given path its acceleration and braking in order to negotiate safely between gaps in traffic. Such scenarios
Optimal dynamic regulation of the environmental impact of mining across diverse land types
Graeme J. Doole; Ben White
2012-01-01
Optimal dynamic regulation of mineral extraction and environmental rehabilitation across diverse land assets is studied using discrete-time, distributed optimal control. An extension of Hotelling's Rule is derived that indicates the need to manage both processes over space and time to maximise social welfare. Key empirical insights are drawn from a case study involving the Western Australian mineral sands industry. The
Optimal static-dynamic hedges for exotic options under convex risk measures
Aytaç ?lhan; Mattias Jonsson; Ronnie Sircar
2009-01-01
We study the problem of optimally hedging exotic derivatives positions using a combination of dynamic trading strategies in underlying stocks and static positions in vanilla options when the performance is quantified by a convex risk measure. We establish conditions for the existence of an optimal static position for general convex risk measures, and then analyze in detail the case of
Ying Wang; Jianzhong Zhou; Hui Qin; Youlin Lu
2010-01-01
Dynamic economic dispatch (DED) problem is one of the optimization issues in power system operation. In this paper, an improved chaotic particle swarm optimization (ICPSO) algorithm is proposed to solve DED with value-point effects. In proposed ICPSO, chaotic mutation is embedded to overcome the drawback of premature in PSO. What’s more, enhanced heuristic strategies are proposed to handling the various
Optimization of the Dynamic Aperture for SPEAR3 Low-Emittance Upgrade
Wang, Lanfa; Huang, Xiaobiao; Nosochkov, Yuri; Safranek, James A.; /SLAC; Borland, Michael; /Argonne
2012-05-30
A low emittance upgrade is planned for SPEAR3. As the first phase, the emittance is reduced from 10nm to 7nm without additional magnets. A further upgrade with even lower emittance will require a damping wiggler. There is a smaller dynamic aperture for the lower emittance optics due to a stronger nonlinearity. Elegant based Multi-Objective Genetic Algorithm (MOGA) is used to maximize the dynamic aperture. Both the dynamic aperture and beam lifetime are optimized simultaneously. Various configurations of the sextupole magnets have been studied in order to find the best configuration. The betatron tune also can be optimized to minimize resonance effects. The optimized dynamic aperture increases more than 15% from the nominal case and the lifetime increases from 14 hours to 17 hours. It is important that the increase of the dynamic aperture is mainly in the beam injection direction. Therefore the injection efficiency will benefit from this improvement.
Research on Particle Swarm Optimization with Dynamic Inertia Weight
Jin-zhu Hu; Jia Xu; Jin-qiao Wang; Ting Xu
2009-01-01
Particle swarm optimization (PSO) is a novel stochastic optimization algorithm based on the study of migration behaviors of bird flock in the process of searching food. Inertia weight, as an important parameter in PSO algorithm, plays a very important role in controlling the exploitation and exploration ability of algorithm. Recently, much more attention has been paid to the research of
IMPACT OF DYNAMIC VOLTAGE SCALING (DVS) ON CIRCUIT OPTIMIZATION
Esquit Hernandez, Carlos A.
2010-01-16
deck. A Perl script is used to read the information from the .mt0 file generated by HSPICE, and generates a comma-separated value (.csv) file ready to be imported by any standard spreadsheet tool. 4.4. SPEED OPTIMIZATION Speed optimizations...
Optimal Central Place Hierarchies: A Dynamic Programming Approach (Preliminary)
Wen-Tai Hsu
Central place theory is a key building block of economic geography and it describes how a hierarchical city system with difierent layers of cities serving difierent sized market areas forms from a uniformly populated space. This paper provides a formal economic theory of optimal central place hierarchy, and it also compares the optimal hierarchy with the equilibrium hierarchy in Hsu
Dynamic Sensor Planning and Control for Optimally Tracking Targets
John R. Spletzer; Camillo J. Taylor
2003-01-01
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
Power flow and dynamic optimal power flow including wind farms
Gonggui Chen; Jinfu Chen; Xianzhong Duan
2009-01-01
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
Multi-strategy ensemble evolutionary algorithm for dynamic multi-objective optimization
Yu Wang; Bin Li
2010-01-01
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
Tailoring of composite links for optimal damped elasto-dynamic performance
NASA Technical Reports Server (NTRS)
Saravanos, D. A.; Chamis, C. C.
1989-01-01
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.
Optimal Campaign Strategies in Fractional-Order Smoking Dynamics
NASA Astrophysics Data System (ADS)
Zeb, Anwar; Zaman, Gul; Jung, Il Hyo; Khan, Madad
2014-06-01
This paper deals with the optimal control problem in the giving up smoking model of fractional order. For the eradication of smoking in a community, we introduce three control variables in the form of education campaign, anti-smoking gum, and anti-nicotive drugs/medicine in the proposed fractional order model. We discuss the necessary conditions for the optimality of a general fractional optimal control problem whose fractional derivative is described in the Caputo sense. In order to do this, we minimize the number of potential and occasional smokers and maximize the number of ex-smokers. We use Pontryagin's maximum principle to characterize the optimal levels of the three controls. The resulting optimality system is solved numerically by MATLAB.
ITOMP: Incremental Trajectory Optimization for Real-time Replanning in Dynamic Environments
North Carolina at Chapel Hill, University of
Chonhyon Park and Jia Pan and Dinesh Manocha University of North Carolina at Chapel Hill http://gamma.cs.unc.edu/ITOMP/ Abstract We present a novel optimization-based algorithm for motion planning in dynamic environments. Our of dynamic obstacles. Rather, we com- pute a conservative local bound on the position or trajectory of each
Yiing-Yuh Lin; Gern-Liang Lin
1992-01-01
In this research, the dynamics and control of a rigid spacecraft with flexible structures were studied for the case of optimal simultaneous multiaxis reorientation. A model spacecraft consisting of a rigid hub in the middle and two solid bodies symmetrically connected to either side of the hub through uniformly distributed flexible beams is considered for the dynamic analysis and control
A dynamic optimization for operation of a compressed air energy storage system
Dan Weiner
1989-01-01
A mathematical model is derived, simulating the dynamic behavior of a cavern-type (constant volume) compressed air energy storage system (CAES). With the aid of the model, optimal control of the system decision variables such as charging and discharging timing and duration and the fuel injection policy are determined by periodic dynamic programming method. The performance criterion is maximizing the net
An Optimal Tone Reproduction Curve Operator for the Display of High Dynamic Range Images
Aickelin, Uwe
An Optimal Tone Reproduction Curve Operator for the Display of High Dynamic Range Images Guoping and digital imaging are to develop systems that match or maybe even exceed the capabilities of the human adaptation. Ordinary imaging sensors and reproduction media typically have dynamic ranges spanning a few
Shengxiang Yang; Xin Yao
2005-01-01
Evolutionary algorithms have been widely used for stationary optimization problems. However, the environments of real world problems are often dynamic. This seriously challenges traditional evolutionary algorithms. In this paper, the application of population-based incremental learning (PBIL) algorithms, a class of evolutionary algorithms, for dynamic problems is investigated. Inspired by the complementarity mechanism in nature a Dual PBIL is proposed, which
Morrow, Melissa M; Rankin, Jeffery W; Neptune, Richard R; Kaufman, Kenton R
2014-11-01
The primary purpose of this study was to compare static and dynamic optimization muscle force and work predictions during the push phase of wheelchair propulsion. A secondary purpose was to compare the differences in predicted shoulder and elbow kinetics and kinematics and handrim forces. The forward dynamics simulation minimized differences between simulated and experimental data (obtained from 10 manual wheelchair users) and muscle co-contraction. For direct comparison between models, the shoulder and elbow muscle moment arms and net joint moments from the dynamic optimization were used as inputs into the static optimization routine. RMS errors between model predictions were calculated to quantify model agreement. There was a wide range of individual muscle force agreement that spanned from poor (26.4% Fmax error in the middle deltoid) to good (6.4% Fmax error in the anterior deltoid) in the prime movers of the shoulder. The predicted muscle forces from the static optimization were sufficient to create the appropriate motion and joint moments at the shoulder for the push phase of wheelchair propulsion, but showed deviations in the elbow moment, pronation-supination motion and hand rim forces. These results suggest the static approach does not produce results similar enough to be a replacement for forward dynamics simulations, and care should be taken in choosing the appropriate method for a specific task and set of constraints. Dynamic optimization modeling approaches may be required for motions that are greatly influenced by muscle activation dynamics or that require significant co-contraction. PMID:25282075
NASA Technical Reports Server (NTRS)
Walsh, Joanne L.; Young, Katherine C.; Pritchard, Jocelyn I.; Adelman, Howard M.; Mantay, Wayne R.
1995-01-01
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.
Adaptive Robust Optimization with Dynamic Uncertainty Sets for ...
2014-09-29
Sep 9, 2014 ... constrained and a sampling based scenario approximation approach is ... current operational practice; robust optimization provides a data-driven way to ..... V-E also shows empirical evidence that this heuristic achieves good ...
NASA Astrophysics Data System (ADS)
Li, Y.; Kirchengast, G.; Scherllin-Pirscher, B.; Wu, S.; Schwaerz, M.; Fritzer, J.; Zhang, S.; Carter, B. A.; Zhang, K.
2013-12-01
Navigation Satellite System (GNSS)-based radio occultation (RO) is a satellite remote sensing technique providing accurate profiles of the Earth's atmosphere for weather and climate applications. Above about 30 km altitude, however, statistical optimization is a critical process for initializing the RO bending angles in order to optimize the climate monitoring utility of the retrieved atmospheric profiles. Here we introduce an advanced dynamic statistical optimization algorithm, which uses bending angles from multiple days of European Centre for Medium-range Weather Forecasts (ECMWF) short-range forecast and analysis fields, together with averaged-observed bending angles, to obtain background profiles and associated error covariance matrices with geographically varying background uncertainty estimates on a daily updated basis. The new algorithm is evaluated against the existing Wegener Center Occultation Processing System version 5.4 (OPSv5.4) algorithm, using several days of simulated MetOp and observed CHAMP and COSMIC data, for January and July conditions. We find the following for the new method's performance compared to OPSv5.4: 1.) it significantly reduces random errors (standard deviations), down to about half their size, and leaves less or about equal residual systematic errors (biases) in the optimized bending angles; 2.) the dynamic (daily) estimate of the background error correlation matrix alone already improves the optimized bending angles; 3.) the subsequently retrieved refractivity profiles and atmospheric (temperature) profiles benefit by improved error characteristics, especially above about 30 km. Based on these encouraging results, we work to employ similar dynamic error covariance estimation also for the observed bending angles and to apply the method to full months and subsequently to entire climate data records.
Optimization and Learning for Registration of Moving Dynamic Textures
Junzhou Huang; Xiaolei Huang; Dimitris N. Metaxas
2007-01-01
We address the problem of registering a sequence of im- ages in a moving dynamic texture video. This involves opti- mization with respect to camera motion, the average image, and the dynamic texture model. This problem is highly ill- posed and almost impossible to have good solutions without priors. In this paper, we introduce powerful priors for this problem, based
Dynamic Property Optimization of Suspension MBD Model based on Sensitivity Analysis
NASA Astrophysics Data System (ADS)
Ikezawa, Tomonori; Yoshimura, Takuya
In recent years, the needs to achieve the eigenvalue optimization considering the NVH performance increase in the initial design of suspension systems. This paper presents an application of MBD (Multi-Body Dynamics) model to dynamic property optimization. A vehicle suspension is modeled by MBD and the vibration properties are analyzed based on the linearization of the system equation. The model can solve the dynamic properties as modal parameters such as natural frequencies, modal dampings and mode shapes. The targets of this model are to calculate the eigenvalue sensitivity with respect to each design parameter (mass, stiffness, damping and geometry of the link) and to optimize the eigenvalue by combining the structural modifications of these high sensitivity elements. By the sensitivity analysis, we can make sure which elements contribute to the dynamic characteristics of the system. The feasibility of the dynamic property optimization is examined by applying the presented approach to the suspension system model. The structural modification is carried out based on the sensitivity analysis in order to attain the required natural frequency change. As a result it is shown that the natural frequency is optimized based on the presented sensitivity analysis.
A complex-valued neural dynamical optimization approach and its stability analysis.
Zhang, Songchuan; Xia, Youshen; Zheng, Weixing
2015-01-01
In this paper, we propose a complex-valued neural dynamical method for solving a complex-valued nonlinear convex programming problem. Theoretically, we prove that the proposed complex-valued neural dynamical approach is globally stable and convergent to the optimal solution. The proposed neural dynamical approach significantly generalizes the real-valued nonlinear Lagrange network completely in the complex domain. Compared with existing real-valued neural networks and numerical optimization methods for solving complex-valued quadratic convex programming problems, the proposed complex-valued neural dynamical approach can avoid redundant computation in a double real-valued space and thus has a low model complexity and storage capacity. Numerical simulations are presented to show the effectiveness of the proposed complex-valued neural dynamical approach. PMID:25462634
Performance evaluation of the inverse dynamics method for optimal spacecraft reorientation
NASA Astrophysics Data System (ADS)
Ventura, Jacopo; Romano, Marcello; Walter, Ulrich
2015-05-01
This paper investigates the application of the inverse dynamics in the virtual domain method to Euler angles, quaternions, and modified Rodrigues parameters for rapid optimal attitude trajectory generation for spacecraft reorientation maneuvers. The impact of the virtual domain and attitude representation is numerically investigated for both minimum time and minimum energy problems. Owing to the nature of the inverse dynamics method, it yields sub-optimal solutions for minimum time problems. Furthermore, the virtual domain improves the optimality of the solution, but at the cost of more computational time. The attitude representation also affects solution quality and computational speed. For minimum energy problems, the optimal solution can be obtained without the virtual domain with any considered attitude representation.
Solving Three-objective Optimization Problems Using Evolutionary Dynamic Weighted
Jin, Yaochu
() generates uniformly distributed random weights between 0 and , t is the generation number. This way, the weight space is divided uniformly into a certain number of cells and #12;an individual is generated-optimal surface. It is shown that such analyses are very helpful for recovering the true Pareto front. 1
Was Your Glass Left Half Full? Family Dynamics and Optimism
ERIC Educational Resources Information Center
Buri, John R.; Gunty, Amy
2008-01-01
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…
Statistically Optimal Dynamic Power Management for Streaming Data
Nathaniel Pettis; Le Cai; Yung-hsiang Lu
2006-01-01
This paper presents a method that uses data buffers to create long periods of idleness to exploit power management. This method considers the power consumed by the buffers and assigns an energy penalty for buffer underflow. Our approach provides analytic formulas for calculating the optimal buffer sizes without subjective or heuristic decisions. We simulate four different hardware configurations with MPEG-1,
Dynamic Contract Trading and Portfolio Optimization in Spectrum Markets
G. Kasbekar; P. Muthusamy; S. Sarkar; K. Kar; A. Gupta
We address the question of optimal trading of bandwidth (service) contracts in wireless spectrum markets, for the primary as well as the secondary spectrum providers in this context. We propose a structured spectrum market and consider two basic types of spectrum contracts that can help attain desired flexibilities and trade-offs in terms of service quality, spectrum usage efficiency and pricing
Integration of dynamic, aerodynamic, and structural optimization of helicopter rotor blades
NASA Technical Reports Server (NTRS)
Peters, David A.
1991-01-01
Summarized here is the first six years of research into the integration of structural, dynamic, and aerodynamic considerations in the design-optimization process for rotor blades. Specifically discussed here is the application of design optimization techniques for helicopter rotor blades. The reduction of vibratory shears and moments at the blade root, aeroelastic stability of the rotor, optimum airframe design, and an efficient procedure for calculating system sensitivities with respect to the design variables used are discussed.
Optimization of an ice-storage air conditioning system using dynamic programming method
Huei-Jiunn Chen; David W. P. Wang; Sih-Li Chen
2005-01-01
This paper explores the optimization of an ice-storage air conditioning system in consideration of both minimal life-cycle cost and efficiency of ice-storage tank. Such air-conditioning system consists primarily of ice-storage tank, screw-type chiller and auxiliary equipment. Optimization is carried out using dynamic programming algorithm, where the power consumption models of the chiller and its auxiliary equipment as well as the
An enhanced integrated aerodynamic load\\/dynamic optimization procedure for helicopter rotor blades
A. Chattopadhyay; Y. D. Chiu
1992-01-01
An enhanced integrated aerodynamic load\\/dynamic optimization procedure is developed for minimizing vibratory root shears and moments of a helicopter rotor blade. The optimization problem is formulated with 4\\/rev inplane shears at the blade root as objective functions. Constraints are imposed on 3\\/rev radial shear, 3\\/rev flapping and torsional moments, 4\\/rev lagging moment, blade natural frequencies, weight, autorotational inertia, centrifugal stress
Structure Design and Dynamic Analysis of Vehicle using Metamodeling and Optimization Techniques
José A. F. Borges; Marcus F. Leal; Rômulo R. P. Filho; Jean C. C. Rezende
The design and dynamic analysis of vehicles have been widely improved through numerical simulation techniques, mainly related\\u000a to the several analysis possibilities applied to complex and representative models. The automotive industry has already used\\u000a the numerical optimization techniques in product development. In this paper an optimization technique is applied to a full\\u000a vehicle model, using several modeling and simulation tools,
NASA Technical Reports Server (NTRS)
Price, D. B.; Gracey, C.
1983-01-01
This short paper will demonstrate that the separation of altitude and flight path angle dynamics using singular perturbation techniques for a transport fuel optimization problem results in an unacceptable oscillation in altitude. A technique for damping this oscillation by adding a penalty term to the cost function for the optimization problem will be discussed. This technique will be compared with a different approach that linearizes the altitude and flight path angle boundary layers.
Optimization of incomplete dynamics for structural model refinement and damage assessment
NASA Astrophysics Data System (ADS)
Yap, Keng C.
2000-11-01
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.
Dynamic modeling and optimization for space logistics using time-expanded networks
NASA Astrophysics Data System (ADS)
Ho, Koki; de Weck, Olivier L.; Hoffman, Jeffrey A.; Shishko, Robert
2014-12-01
This research develops a dynamic logistics network formulation for lifecycle optimization of mission sequences as a system-level integrated method to find an optimal combination of technologies to be used at each stage of the campaign. This formulation can find the optimal transportation architecture considering its technology trades over time. The proposed methodologies are inspired by the ground logistics analysis techniques based on linear programming network optimization. Particularly, the time-expanded network and its extension are developed for dynamic space logistics network optimization trading the quality of the solution with the computational load. In this paper, the methodologies are applied to a human Mars exploration architecture design problem. The results reveal multiple dynamic system-level trades over time and give recommendation of the optimal strategy for the human Mars exploration architecture. The considered trades include those between In-Situ Resource Utilization (ISRU) and propulsion technologies as well as the orbit and depot location selections over time. This research serves as a precursor for eventual permanent settlement and colonization of other planets by humans and us becoming a multi-planet species.
INDDGO: Integrated Network Decomposition & Dynamic programming for Graph Optimization
Groer, Christopher S [ORNL; Sullivan, Blair D [ORNL; Weerapurage, Dinesh P [ORNL
2012-10-01
It is well-known that dynamic programming algorithms can utilize tree decompositions to provide a way to solve some \\emph{NP}-hard problems on graphs where the complexity is polynomial in the number of nodes and edges in the graph, but exponential in the width of the underlying tree decomposition. However, there has been relatively little computational work done to determine the practical utility of such dynamic programming algorithms. We have developed software to construct tree decompositions using various heuristics and have created a fast, memory-efficient dynamic programming implementation for solving maximum weighted independent set. We describe our software and the algorithms we have implemented, focusing on memory saving techniques for the dynamic programming. We compare the running time and memory usage of our implementation with other techniques for solving maximum weighted independent set, including a commercial integer programming solver and a semi-definite programming solver. Our results indicate that it is possible to solve some instances where the underlying decomposition has width much larger than suggested by the literature. For certain types of problems, our dynamic programming code runs several times faster than these other methods.
A new method of optimal design for a two-dimensional diffuser by using dynamic programming
NASA Technical Reports Server (NTRS)
Gu, Chuangang; Zhang, Moujin; Chen, XI; Miao, Yongmiao
1991-01-01
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.
NASA Astrophysics Data System (ADS)
Kim, Sang-Myeong; Wang, Semyung; Brennan, Michael J.
2011-02-01
This paper is concerned with the dynamic analysis and parameter optimization of both passive and active piezo-electrical dynamic vibration absorbers that are strongly coupled with a single degree of freedom vibrating structure. The passive absorber is implemented by using an Rs? Ls parallel shunt circuit while the active absorber is implemented by feeding back the acceleration of the structure through a second-order lowpass filter. An impedance-mobility approach is used for the electromechanical coupling analysis of both types of absorbers coupled with the structure. Using this approach it is demonstrated that the passive and active absorbers can be made exactly equivalent. A maximally flat frequency response strategy is used to find the optimal damping ratio of the passive absorber while a robust, optimal control theory is used to find that for the active absorber. It is found that the passive optimization strategy corresponds to an optimal, robust feedback control of 2 dB spillover. Simulations and experiments are conducted to support the theoretical findings.
An optimized ultrasound digital beamformer with dynamic focusing implemented on FPGA.
Almekkawy, Mohamed; Xu, Jingwei; Chirala, Mohan
2014-01-01
We present a resource-optimized dynamic digital beamformer for an ultrasound system based on a field-programmable gate array (FPGA). A comprehensive 64-channel receive beamformer with full dynamic focusing is embedded in the Altera Arria V FPGA chip. To improve spatial and contrast resolution, full dynamic beamforming is implemented by a novel method with resource optimization. This was conceived using the implementation of the delay summation through a bulk (coarse) delay and fractional (fine) delay. The sampling frequency is 40 MHz and the beamformer includes a 240 MHz polyphase filter that enhances the temporal resolution of the system while relaxing the Analog-to-Digital converter (ADC) bandwidth requirement. The results indicate that our 64-channel dynamic beamformer architecture is amenable for a low power FPGA-based implementation in a portable ultrasound system. PMID:25570695
DYNAMIC APERTURE OPTIMIZATION FOR LOW EMITTANCE LIGHT SOURCES.
KRAMER, S.; BENGTSSON, J.
2005-05-15
State of the art low emittance light source lattices, require small bend angle dipole magnets and strong quadrupoles. This in turn creates large chromaticity and small value of dispersion in the lattice. To counter the high linear chromaticity, strong sextupoles are required which limit the dynamic aperture. Traditional methods for expanding the dynamic aperture use harmonic sextupoles to counter the tune shift with amplitude. This has been successful up to now, but is non-deterministic and limited as the sextupole strength increases, driving higher order nonlinearities. We have taken a different approach that makes use of the tune flexibility of a TBA lattice to minimize the lowest order nonlinearities, freeing the harmonic sextupoles to counter the higher order nonlinearities. This procedure is being used to improve the nonlinear dynamics of the NSLS-II lattice.
Optimizing Dynamically-Dispatched Calls with Run-Time Type Feedback
Urs Hölzle; David Ungar
1993-01-01
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 call.
Optimizing Dynamically-Dispatched Calls with Run-Time Type Feedback
Urs Hölzle; David Ungar
1994-01-01
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
Optimal dynamic dispatch owing to spinning-reserve and power-rate limits
Van den Bosh, P.P.J.
1985-12-01
This paper deals with the formulation and solution of the optimal dynamic dispatch problem owing to spinning-reserve and power-rate limits. The power production of a thermal unit is considered as a dynamic system, which limits the maximum increase and decrease of power. The solution is obtained with a special projection method having conjugate search directions that quickly and accurately solves the associated non-linear programming problem with up to 2400 variables and up to 9600 constraints.
Stochastic Optimization Algorithm Based Dynamic Resource Assignment for 3G Systems
Mustafa Karakoç; Adnan Kavak
2007-01-01
Orthogonal variable spreading factor (OVSF) codes are widely used to provide variable data rates for supporting different\\u000a bandwidth requirements in wideband code division multiple access (WCDMA) systems. Many works in the literature have intensively\\u000a investigated to find an optimal dynamic code assignment scheme for OVSF codes. Unlike earlier studies, which assign OVSF codes\\u000a using conventional (CCA) or dynamic (DCA) code
Effects of Optimal Antipredator Behavior of Prey on Predator–Prey Dynamics: The Role of Refuges
Vlastimil K?ivan
1998-01-01
The influence of optimal antipredator behavior of prey on predator–prey dynamics in a two-patch environment is studied. One patch represents an open habitat while the other is a refuge for prey. It is assumed that prey maximize their fitness measured by the instantaneous per capita growth rate. In each patch population dynamics is described by the Lotka–Volterra time continuous model.
W. F. Heard; P. K. Basu; T. Slawson; N. A. Nordendale
2011-01-01
The U.S. Army Engineer Research and Development Center has conducted multi-scale material research directed towards enhancing the response of a rapid-set, high-strength geopolymer cement under quasi-static and dynamic loads. Four unique tensile experiments were conducted to characterize and optimize material response of the fiber, matrix and interface. Single-fiber direct tension and single-fiber pull-out experiments were conducted with quasi-static and dynamic
An extended discrete particle swarm optimization algorithm for the dynamic facility layout problem
Hassan Rezazadeh; Mehdi Ghazanfari; Mohammad Saidi-Mehrabad; Seyed Jafar Sadjadi
2009-01-01
We extended an improved version of the discrete particle swarm optimization (DPSO) algorithm proposed by Liao et al. (2007) to solve the dynamic facility layout problem (DFLP). A computational study was performed with the existing heuristic\\u000a algorithms, including the dynamic programming (DP), genetic algorithm (GA), simulated annealing (SA), hybrid ant system (HAS),\\u000a hybrid simulated annealing (SA-EG), hybrid genetic algorithms (NLGA
On the optimal reconstruction and control of adaptive optical systems with mirror dynamics.
Correia, Carlos; Raynaud, Henri-François; Kulcsár, Caroline; Conan, Jean-Marc
2010-02-01
In adaptive optics (AO) the deformable mirror (DM) dynamics are usually neglected because, in general, the DM can be considered infinitely fast. Such assumption may no longer apply for the upcoming Extremely Large Telescopes (ELTs) with DM that are several meters in diameter with slow and/or resonant responses. For such systems an important challenge is to design an optimal regulator minimizing the variance of the residual phase. In this contribution, the general optimal minimum-variance (MV) solution to the full dynamical reconstruction and control problem of AO systems (AOSs) is established. It can be looked upon as the parent solution from which simpler (used hitherto) suboptimal solutions can be derived as special cases. These include either partial DM-dynamics-free solutions or solutions derived from the static minimum-variance reconstruction (where both atmospheric disturbance and DM dynamics are neglected altogether). Based on a continuous stochastic model of the disturbance, a state-space approach is developed that yields a fully optimal MV solution in the form of a discrete-time linear-quadratic-Gaussian (LQG) regulator design. From this LQG standpoint, the control-oriented state-space model allows one to (1) derive the optimal state-feedback linear regulator and (2) evaluate the performance of both the optimal and the sub-optimal solutions. Performance results are given for weakly damped second-order oscillatory DMs with large-amplitude resonant responses, in conditions representative of an ELT AO system. The highly energetic optical disturbance caused on the tip/tilt (TT) modes by the wind buffeting is considered. Results show that resonant responses are correctly handled with the MV regulator developed here. The use of sub-optimal regulators results in prohibitive performance losses in terms of residual variance; in addition, the closed-loop system may become unstable for resonant frequencies in the range of interest. PMID:20126246
Computational fluid dynamic analysis and design optimization of jet pumps
J. Fan; J. Eves; H. M. Thompson; V. V. Toropov; N. Kapur; D. Copley; A. Mincher
2011-01-01
Jet pumps have a wide variety of applications and are commonly used in thermal power plants and refrigeration systems. An initial jet-pump design was developed using an analytical approach and its efficiency was improved using an efficient and accurate computational fluid dynamics model of the compressible turbulent flow in the pump, whose predictions agreed well with corresponding experimental data. Parametric
Optimal automatic multi-pass shader partitioning by dynamic programming
Alan Heirich
2005-01-01
Complex shaders must be partitioned into multiple passes to execute on GPUs with limited hardware resources. Automatic partitioning gives rise to an NP-hard scheduling problem that can be solved by any number of established techniques. One such technique, Dynamic Programming (DP), is commonly used for instruction scheduling and register allocation in the code generation phase of compilers. Since automatic partitioning
Prediction-Based Throughput Optimization for Dynamic Spectrum Access
Sixing Yin; Dawei Chen; Qian Zhang; Shufang Li
2011-01-01
Cognitive radio (CR) for dynamic spectrum sensing and access has been a hot research topic in recent years. To avoid collision with the primary users, secondary users need to sense the channels before transmitting on them, which is referred to as sensing time overhead. Our previous work shows that the spectral correlations between the channels within the same service are
Dynamic Lexicographic Approach for Heuristic Multi-objective Optimization
Landa-Silva, Dario
, Dario Landa-Silva1 and Jos´e Moreno-P´erez2 1 ASAP Research Group, School of Computer Science, is changed in a dynamic fashion during the search. This approach eliminates the need for the decision-maker, the approach offers more flexibility to navigate constrained combinatorial search spaces than Pareto dominance
Optimal workload sharing for mobile robotic networks in dynamic environments
Marco Pavone; Ketan Savla; Emilio Frazzoli
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,
InformationBased Optimization Approaches to Dynamical System Safety Verification
Neller, Todd W.
parameters of a stepper motor (e.g. viscous fricÂ tion, inertial load), bounds on initial conditions (e, Stanford UniÂ versity, Stanford CA 94305Â9020. #12; 1 Introduction Given a simulated hybrid dynamical.g. angular displacement and velocity), and an openÂloop motor acceleration control, verify that no scenario
Optimization of the double-barrier Josephson junction switching dynamics
Serhii Shafranjuk; John B. Ketterson
2004-01-01
The switching dynamics of a double-barrier Josephson junction is analyzed as a function of the microscopic properties of its electrodes. In particular, it is found that the nonstationary behavior of the Josephson phase difference is very sensitive to dissipation mechanisms acting inside the intrinsic shunt. The leading factor that determines the dissipation is the local electron density of states N(E)
Optimized Dynamic Allocation Management for ERP Systems and Enterprise Services
Valentin Nicolescu; Martin Wimmer; Raphael Geissler; Daniel Gmach; Matthias Mohr; Alfons Kemper; Helmut Krcmar
2007-01-01
To ensure the operability and reliability of large scale Enterprise Resource Planning Systems (ERP) and enterprise services, a peak-load oriented hardware sizing is often used, which results in low average utilization. The evaluation of historical load data revealed that many applications show cyclical resource consumption. The identification of load patterns can be used for static as well as dynamic allocation
Non-Bayesian Optimal Search and Dynamic Implementation
Franz, Sven Oliver
, they also characterized the incentive-e¢ cient, second- best mechanism. In the present paper we study two been obtained for Bayesian learning in the same mechanism design environment. This highlights the role of the learning pro- cedure in dynamic mechanism design problems. We wish to thank Philippe Jehiel for helpful
Average Case Analysis of Dynamic Geometric Optimization David Eppstein
Eppstein, David
for constructing geometric structures such as convex hulls and arrangements. Such algorithms can also be used on problems of computing geometric structures: convex hulls, arrangements, and the like. However problems algorithms were considerably more complicated. Diameter. The dynamic planar diameter problem can be reduced
Tahriri, Farzad; Dawal, Siti Zawiah Md; Taha, Zahari
2014-01-01
A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model. PMID:24982962
Tahriri, Farzad; Dawal, Siti Zawiah Md; Taha, Zahari
2014-01-01
A new multiobjective dynamic fuzzy genetic algorithm is applied to solve a fuzzy mixed-model assembly line sequencing problem in which the primary goals are to minimize the total make-span and minimize the setup number simultaneously. Trapezoidal fuzzy numbers are implemented for variables such as operation and travelling time in order to generate results with higher accuracy and representative of real-case data. An improved genetic algorithm called fuzzy adaptive genetic algorithm (FAGA) is proposed in order to solve this optimization model. In establishing the FAGA, five dynamic fuzzy parameter controllers are devised in which fuzzy expert experience controller (FEEC) is integrated with automatic learning dynamic fuzzy controller (ALDFC) technique. The enhanced algorithm dynamically adjusts the population size, number of generations, tournament candidate, crossover rate, and mutation rate compared with using fixed control parameters. The main idea is to improve the performance and effectiveness of existing GAs by dynamic adjustment and control of the five parameters. Verification and validation of the dynamic fuzzy GA are carried out by developing test-beds and testing using a multiobjective fuzzy mixed production assembly line sequencing optimization problem. The simulation results highlight that the performance and efficacy of the proposed novel optimization algorithm are more efficient than the performance of the standard genetic algorithm in mixed assembly line sequencing model. PMID:24982962
Real-Time Optimized Trajectory Planning for a Fixed-Wing Vehicle Flying in a Dynamic Environment
Qu, Zhihua
Real-Time Optimized Trajectory Planning for a Fixed-Wing Vehicle Flying in a Dynamic Environment an optimal feasible trajectory, for a fixed wing flying vehicle moving in a dynamical three- dimensional, trajectories are described in terms of three param- eterized polynomials, and the family of feasible
Stefanopoulou, Anna
. The goal is to analyze the open loop dynamics and design a controller that achieves optimal steady stateDynamics, Optimization and Control of a Fuel Cell Based Combined Heat Power (CHP) System and the CB provides the energy for preheating the FPS inlet flows by burning any excess H2 from the FC
R. Azarafza; S. M. R. Khalili; A. A. Jafari; A. Davar
2009-01-01
Optimization is one of the important stages in the design process. In this paper the genetic algorithms method is applied for weight and transient dynamic response and two constraints including critical buckling loads and principle strains optimization of laminated composite cylindrical shells. The multi-objective function seeks the minimum structural weight and transient dynamic response. Nine design variables including material properties
Dynamic Planar Convex Hull with Optimal Query Time and O(log n log log n) Update Time
Riko Jacob
Dynamic Planar Convex Hull with Optimal Query Time and O(log n #1; log log n) Update Time Gerth St fgerth,rjacobg@brics.dk Abstract. The dynamic maintenance of the convex hull of a set of points(log n #1; log log n) time, and various queries about the convex hull in optimal O(log n) worst-case time
Improving the Dynamic Characteristics of Body-in-White Structure Using Structural Optimization
Yahaya Rashid, Aizzat S.; Mohamed Haris, Sallehuddin; Alias, Anuar
2014-01-01
The dynamic behavior of a body-in-white (BIW) structure has significant influence on the noise, vibration, and harshness (NVH) and crashworthiness of a car. Therefore, by improving the dynamic characteristics of BIW, problems and failures associated with resonance and fatigue can be prevented. The design objectives attempt to improve the existing torsion and bending modes by using structural optimization subjected to dynamic load without compromising other factors such as mass and stiffness of the structure. The natural frequency of the design was modified by identifying and reinforcing the structure at critical locations. These crucial points are first identified by topology optimization using mass and natural frequencies as the design variables. The individual components obtained from the analysis go through a size optimization step to find their target thickness of the structure. The thickness of affected regions of the components will be modified according to the analysis. The results of both optimization steps suggest several design modifications to achieve the target vibration specifications without compromising the stiffness of the structure. A method of combining both optimization approaches is proposed to improve the design modification process. PMID:25101312
An Implementation of Optimal Dynamic Load Balancing Based on Multipath IP Routing
Juan Pablo Saibene; Richard Lempert; Fernando Paganini
2010-01-01
We develop a protocol through which multipath enabled IP routers collectively engage in dynamic traffic engineering, to optimize performance in concert with legacy TCP congestion control. We build on recent theory which shows a globally optimum resource allocation across the TCP\\/IP layers can be achieved through control of rates and multipath routing fractions following a consistent congestion signal. In this
Willsky, Alan S.
for two reasons: firstly, measurements of target state are transmitted from sensors to the current leader to the algorithm presented here. 1. INTRODUCTION Energy is a limited resource in many sensor networks. It is oftenOPTIMIZATION APPROACHES TO DYNAMIC ROUTING OF MEASUREMENTS AND MODELS IN A SENSOR NETWORK OBJECT
Xiao-Bing Zhang; Ying Fan; Yi-Ming Wei
2009-01-01
China's Strategic Petroleum Reserve (SPR) is currently being prepared. But how large the optimal stockpile size for China should be, what the best acquisition strategies are, how to release the reserve if a disruption occurs, and other related issues still need to be studied in detail. In this paper, we develop a stochastic dynamic programming model based on a total
OPTIMAL INTERVENTION IN THE FOREIGN EXCHANGE MARKET WHEN INTERVENTIONS AFFECT MARKET DYNAMICS
: adjustment of domestic interest rate levels, which influences the attractiveness of foreign investments; and direct purchases or sales of foreign currency reserves in the foreign exchange market. The first formOPTIMAL INTERVENTION IN THE FOREIGN EXCHANGE MARKET WHEN INTERVENTIONS AFFECT MARKET DYNAMICS ALEC
Dynamic optimization of watering Satsuma mandarin using neural networks and genetic algorithms
T. Morimoto; Y. Ouchi; M. Shimizu; M. S. Baloch
2007-01-01
In this study, an optimal watering scheduling that improves the quality of Satsuma mandarins grown in the field, was investigated using neural networks and genetic algorithms. Fruit responses and climate factors were determined monthly from August to November, 1996–2004. Dynamic changes in the sugar and citric acid contents of the Satsuma mandarins, as affected by the rainfall and sunshine duration,
Optimal Control of Irrigation Canals using Recurrent Dynamic Neural Network (RDNN)
OMER FARUK DURDU; Ziraat Fakultesi
A recurrent dynamic neural network (RDNN) based on Hopfield model is applied to linear quadratic regulator controller of an irrigation canal. The Saint-Venant equations of open-channel flow are linearized using the Taylor series and a finite difference approximation of the original nonlinear, partial differential equations. Using the linear optimal control theory, a Linear Quadratic Regulator (LQR) controller is developed for
Optimal dynamic scheduling in a multiclass fluid model of Internet servers with transient overload
Junxia Chang; Hayriye Ayhan; Jim Dai; Zhen Liu; Mark S. Squillante; Cathy H. Xia
2003-01-01
We consider the optimal dynamic scheduling of different requests of service in a multiclass stochastic fluid model that is motivated by recent and emerging computing paradigms for Internet services and applications. Our primary focus is on environments with specific performance guarantees for each class under a profit model in which revenues are gained when performance guarantees are satisfied and penalties
A Dynamic Process Model for Optimizing the Hospital Environment Cash-Flow
NASA Astrophysics Data System (ADS)
Pater, Flavius; Rosu, Serban
2011-09-01
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.
Al Hanbali, Ahmad
:45PM, Citadel T300 Driven by public opinion, increased health expenditures, an ageing population and Operations Management to improve operational processes in health care. Transportation costs can be as muchOptimization in Health Care Restricted Dynamic Programming: A Flexible Framework for Solving
Renan U. Goetz
1997-01-01
A dynamic economic model of soil erosion is presented where the intensity of use of inputs and the choice of crops allow the farmer to control soil losses. The results show that it is predominately optimal to approach the singular-path\\/steady-state equilibrium most rapidly by the cultivation of a single crop. At the steady state, however, a mix of crops is
On Optimal Single Link Allocation of Spare Capacity for Dynamic Traffic Grooming1
Dutta, Rudra
signals simultaneously on the same optical fiber, on different wavelengths. These signals form wavelength a fraction of the band- width of a lightpath, at intermediate nodes the optical signals have to be terminatedOn Optimal Single Link Allocation of Spare Capacity for Dynamic Traffic Grooming1 Shu Huang
Troian, Sandra M.
Transient dynamics and structure of optimal excitations in thermocapillary spreading: Precursor operator is non-normal, which allows transient growth of perturbations. Our previous studies using a more.e., either boundary slip or van der Waals interactions have shown that the transient amplification
Mustafa
1989-01-01
This study presents a comprehensive physically based stochastic dynamic optimization model to assist planners in making decisions concerning mine soil depths and soil mixture ratios required to achieve successful revegetation of mine lands at different probability levels of success, subject to an uncertain weather regime. A perennial grass growth model was modified and validated for predicting vegetation growth in reclaimed
Dirk Helbing; Martin Schönhof; Daniel Kern
2002-01-01
The coordinated and efficient distribution of limited resources by individual decisions is a fundamental, unsolved problem. When individuals compete for road capacities, time, space, money, goods, etc, they normally make decisions based on aggregate rather than complete information, such as TV news or stock market indices. In related experiments, we have observed a volatile decision dynamics and far-from-optimal payoff distributions.
CFD Based Design Optimizations of the Diffuser of a Gas Dynamically Driven Laser Cavity
S. D. Ravi; M. A. Sriram; N. K. S. Rajan; P. S. Kulkarni
Based on the an earlier CFD analysis of the performance of the gas-dynamically controlled laser cavity (1) it was found that there is possibility of optimizing the geometry of the diffuser that can bring about reductions in both size and cost of the system by examining the critical dimensional requirements of the diffuser. Consequently, an extensive CFD analysis has been
Optimization of Fed-Batch Saccharomyces cereWisiae Fermentation Using Dynamic Flux Balance Models
Mountziaris, T. J.
ARTICLES Optimization of Fed-Batch Saccharomyces cereWisiae Fermentation Using Dynamic Flux Balance-batch Saccharomyces cereVisiae fermentation that couples a detailed steady-state description of primary carbon is that nutrient levels can be varied to achieve favorable growth conditions. Fed-batch yeast fermentation
Flow analysis and nozzle-shape optimization for the cold-gas dynamic-spray process
Grujicic, Mica
of deposited material. Cold spray as a coating technology was initially developed in the mid-1980sFlow analysis and nozzle-shape optimization for the cold-gas dynamic-spray process M Grujicic1*, W-phase (feed powder particles plus the carrier gas) flow during the cold-spray process. While the physical
Dynamic Resource Allocation for Optimizing Population Diffusion Shan Xue Alan Fern Daniel Sheldon
Dynamic Resource Allocation for Optimizing Population Diffusion Shan Xue Alan Fern Daniel Sheldon of EECS Oregon State University sheldon@cs.umass.edu University of Massachusetts Amherst Mount Holyoke problem. Sheldon et al. [2010] studies the non- adaptive, upfront planning problem, where it is as- sumed
Bacterial Temporal Dynamics Enable Optimal Design of Antibiotic Treatment
Meredith, Hannah R.; Lopatkin, Allison J.; Anderson, Deverick J.; You, Lingchong
2015-01-01
There is a critical need to better use existing antibiotics due to the urgent threat of antibiotic resistant bacteria coupled with the reduced effort in developing new antibiotics. ?-lactam antibiotics represent one of the most commonly used classes of antibiotics to treat a broad spectrum of Gram-positive and -negative bacterial pathogens. However, the rise of extended spectrum ?-lactamase (ESBL) producing bacteria has limited the use of ?-lactams. Due to the concern of complex drug responses, many ?-lactams are typically ruled out if ESBL-producing pathogens are detected, even if these pathogens test as susceptible to some ?-lactams. Using quantitative modeling, we show that ?-lactams could still effectively treat pathogens producing low or moderate levels of ESBLs when administered properly. We further develop a metric to guide the design of a dosing protocol to optimize treatment efficiency for any antibiotic-pathogen combination. Ultimately, optimized dosing protocols could allow reintroduction of a repertoire of first-line antibiotics with improved treatment outcomes and preserve last-resort antibiotics. PMID:25905796
Design of An Optimizing, Dynamically Retargetable Compiler for Common Lisp
Rodney A. Brooks; David B. Posner; James L. McDonald; Jon L. White; Eric Benson; Richard P. Gabriel
1986-01-01
We outline the components of a retargetable cross-compiler for the Common Lisp language. A de- scription is given of a method for modeling the various hardware features in the compiler's database, and a breakdown is shown of the compiler itself into various machine-independent and machine-dependent mod- ules. A novel feature of this development is the dynamic nature of the retargeting:
Dynamic programming algorithm optimization for spoken word recognition
HIROAKI SAKOE; SEIBI CHIBA
1978-01-01
This paper reports on an optimum dynamic progxamming (DP) based time-normalization algorithm for spoken word recognition. First, a general principle of time-normalization is given using time-warping function. Then, two time-normalized distance definitions, called symmetric and asymmetric forms, are derived from the principle. These two forms are compared with each other through theoretical discussions and experimental studies. The symmetric form algorithm
Role of optimization in the human dynamics of task execution.
Cajueiro, Daniel O; Maldonado, Wilfredo L
2008-03-01
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. PMID:18517447
Characterization of control noise effects in optimal quantum unitary dynamics
NASA Astrophysics Data System (ADS)
Hocker, David; Brif, Constantin; Grace, Matthew D.; Donovan, Ashley; Ho, Tak-San; Tibbetts, Katharine Moore; Wu, Rebing; Rabitz, Herschel
2014-12-01
This work develops measures for quantifying the effects of field noise upon targeted unitary transformations. Robustness to noise is assessed in the framework of the quantum control landscape, which is the mapping from the control to the unitary transformation performance measure (quantum gate fidelity). Within that framework, a geometric interpretation of stochastic noise effects naturally arises, where more robust optimal controls are associated with regions of small overlap between landscape curvature and the noise correlation function. Numerical simulations of this overlap in the context of quantum information processing reveal distinct noise spectral regimes that better support robust control solutions. This perspective shows the dual importance of both noise statistics and the control form for robustness, thereby opening up new avenues of investigation on how to mitigate noise effects in quantum systems.
Locusts use dynamic thermoregulatory behaviour to optimize nutritional outcomes
Coggan, Nicole; Clissold, Fiona J.; Simpson, Stephen J.
2011-01-01
Because key nutritional processes differ in their thermal optima, ectotherms may use temperature selection to optimize performance in changing nutritional environments. Such behaviour would be especially advantageous to small terrestrial animals, which have low thermal inertia and often have access to a wide range of environmental temperatures over small distances. Using the locust, Locusta migratoria, we have demonstrated a direct link between nutritional state and thermoregulatory behaviour. When faced with chronic restrictions to the supply of nutrients, locusts selected increasingly lower temperatures within a gradient, thereby maximizing nutrient use efficiency at the cost of slower growth. Over the shorter term, when locusts were unable to find a meal in the normal course of ad libitum feeding, they immediately adjusted their thermoregulatory behaviour, selecting a lower temperature at which assimilation efficiency was maximal. Thus, locusts use fine scale patterns of movement and temperature selection to adjust for reduced nutrient supply and thereby ameliorate associated life-history consequences. PMID:21288941
Optimizing bandwidth and dynamic range of lumped Josephson parametric amplifiers
NASA Astrophysics Data System (ADS)
Eddins, A.; Vijay, R.; Macklin, C.; Minev, Z.; Siddiqi, I.
2013-03-01
Superconducting parametric amplifiers have revolutionized the field of quantum measurement by providing high gain, ultra-low noise amplification. They have been used successfully for high-fidelity qubit state measurements, probing nano-mechanical resonators, quantum feedback, and for microwave quantum optics experiments. Though several designs exist, a simple and robust architecture is the Lumped Josephson Parametric Amplifier (LJPA). This device consists of a capacitively shunted SQUID directly coupled to a transmission line to form a low quality factor (Q) nonlinear resonator. We discuss amplifiers which can be tuned over the full 4-8 GHz band with 20-25 dB of gain and 10 - 50 MHz of signal bandwidth. However, similar to other parametric amplifiers employing a resonant circuit, the LJPA suffers from low dynamic range and has a -1 dB gain compression point of order -130 dBm. We explore new designs comprised of an array of SQUIDs to improve the dynamic range. We will present the results of numerical simulations and preliminary experiments. We will also briefly discuss improvements obtained from different biasing methods and packaging. Superconducting parametric amplifiers have revolutionized the field of quantum measurement by providing high gain, ultra-low noise amplification. They have been used successfully for high-fidelity qubit state measurements, probing nano-mechanical resonators, quantum feedback, and for microwave quantum optics experiments. Though several designs exist, a simple and robust architecture is the Lumped Josephson Parametric Amplifier (LJPA). This device consists of a capacitively shunted SQUID directly coupled to a transmission line to form a low quality factor (Q) nonlinear resonator. We discuss amplifiers which can be tuned over the full 4-8 GHz band with 20-25 dB of gain and 10 - 50 MHz of signal bandwidth. However, similar to other parametric amplifiers employing a resonant circuit, the LJPA suffers from low dynamic range and has a -1 dB gain compression point of order -130 dBm. We explore new designs comprised of an array of SQUIDs to improve the dynamic range. We will present the results of numerical simulations and preliminary experiments. We will also briefly discuss improvements obtained from different biasing methods and packaging. This research was supported by the Army Research Office under a QCT grant.
Multiple criteria dynamic spatial optimization to manage water quality on a watershed scale
Randhir, T.O.; Lee, J.G.; Engel, B.
2000-04-01
This article develops a dynamic spatial optimization algorithm for watershed modeling that reduces dimensionality and incorporates multiple objectives. Spatial optimization methods, which include spatially linear and nonlinear formulations, are applied to an experimental watershed and tested against a full enumeration frontier. The integrated algorithm includes biophysical simulation and economic decision-making within a geographic information system. It was observed that it is possible to achieve economic and water quality objectives in a watershed by spatially optimizing site-specific practices. It was observed that a spatially diversified watershed plan could achieve multiple goals in a watershed. The algorithm can be used to develop efficient policies towards environmental management of watersheds to address water quality issues by identifying optimal tradeoffs across objectives.
Synthesis of Optimal Nonlinear Feedback Laws for Dynamic Systems Using Neural Networks
NASA Technical Reports Server (NTRS)
Lee, Allan Y.; Smyth, Padhraic
1993-01-01
Open-loop solutions of dynamical optimization problems can be numerically computed usingexisting software packages. The computed time histories of the state and control variables, formultiple sets of end conditions can then be used to train a neural network to 'recognize' the optimal,nonlinear feedback relation between the states and controls of the system. The 'learned' network canthen be used to output an approximate optimal control given a full set (or a partial set) of measuredsystem states. With simple neural networks, we have successfully demonstrated the efficacy of theproposed approach using a minimum-time orbit injection problem. The usefulness and limitations ofthis novel approach on real-life optimal guidance and control problems, with many state and control variables as well as path inequality constraints, remain to be seen.
Optimizing the petroleum supply chain at petrobras
Mariza Aires; Abílio Lucena; Roger Rocha; Cláudio Santiago; Luidi Simonetti
2004-01-01
The main objective of this study is to describe how mathematical programming is being used to solve the Petroleum Allocation Problem at Petrobras. We propose a Mixed Integer Linear Programming formulation of the problem which relies on a time\\/space discretization network. The formulation involves some inequalities which are redundant to the mixed integer model but no necessarily so to the
OPTIMIZING THE DYNAMIC APERTURE FOR TRIPLE BEND ACHROMATIC LATTICES.
KRAMER, S.L.; BENGTSSON, J.
2006-06-26
The Triple Bend Achromatic (TBA) lattice has the potential for lower natural emittance per period than the Double Bend Achromatic (DBA) lattice for high brightness light sources. However, the DBA has been chosen for 3rd generation light sources more often due to the higher number of undulator straight section available for a comparable emittance. The TBA has considerable flexibility in linear optics tuning while maintaining this emittance advantage. We have used the tune and chromaticity flexibility of a TBA lattice to minimize the lowest order nonlinearities to implement a 3rd order achromatic tune, while maintaining a constant emittance. This frees the geometric sextupoles to counter the higher order nonlinearities. This procedure is being used to improve the nonlinear dynamics of the TBA as a proposed lattice for NSLS-II facility. The flexibility of the TBA lattice will also provide for future upgrade capabilities of the beam parameters.
Dynamical Arrest, Structural Disorder, and Optimization of Organic Photovoltaic Devices
Gould, Ian; Dmitry, Matyushov
2014-09-11
This project describes fundamental experimental and theoretical work that relates to charge separation and migration in the solid, heterogeneous or aggregated state. Marcus theory assumes a system in equilibrium with all possible solvent (dipolar) configurations, with rapid interconversion among these on the ET timescale. This project has addressed the more general situation where the medium is at least partially frozen on the ET timescale, i.e. under conditions of dynamical arrest. The approach combined theory and experiment and includes: (1) Computer simulations of model systems, (2) Development of analytical procedures consistent with computer experiment and (3) Experimental studies and testing of the formal theories on this data. Electron transfer processes are unique as a consequence of the close connection between kinetics, spectroscopy and theory, which is an essential component of this work.
Collision-free nonuniform dynamics within continuous optimal velocity models.
Tordeux, Antoine; Seyfried, Armin
2014-10-01
Optimal velocity (OV) car-following models give with few parameters stable stop-and -go waves propagating like in empirical data. Unfortunately, classical OV models locally oscillate with vehicles colliding and moving backward. In order to solve this problem, the models have to be completed with additional parameters. This leads to an increase of the complexity. In this paper, a new OV model with no additional parameters is defined. For any value of the inputs, the model is intrinsically asymmetric and collision-free. This is achieved by using a first-order ordinary model with two predecessors in interaction, instead of the usual inertial delayed first-order or second-order models coupled with the predecessor. The model has stable uniform solutions as well as various stable stop-and -go patterns with bimodal distribution of the speed. As observable in real data, the modal speed values in congested states are not restricted to the free flow speed and zero. They depend on the form of the OV function. Properties of linear, concave, convex, or sigmoid speed functions are explored with no limitation due to collisions. PMID:25375554
Arkun, Yaman; Erman, Burak
2010-01-01
An optimization model is introduced in which proteins try to evade high energy regions of the folding landscape, and prefer low entropy loss routes during folding. We make use of the framework of optimal control whose convenient solution provides practical and useful insight into the sequence of events during folding. We assume that the native state is available. As the protein folds, it makes different set of contacts at different folding steps. The dynamic contact map is constructed from these contacts. The topology of the dynamic contact map changes during the course of folding and this information is utilized in the dynamic optimization model. The solution is obtained using the optimal control theory. We show that the optimal solution can be cast into the form of a Gaussian Network that governs the optimal folding dynamics. Simulation results on three examples (CI2, Sso7d and Villin) show that folding starts by the formation of local clusters. Non-local clusters generally require the formation of several local clusters. Non-local clusters form cooperatively and not sequentially. We also observe that the optimal controller prefers “zipping” or small loop closure steps during folding. The folding routes predicted by the proposed method bear strong resemblance to the results in the literature. PMID:20967269
Linear dynamic model of production-inventory with debt repayment: optimal management strategies
Tuchnolobova, Ekaterina; Vasilieva, Olga
2012-01-01
In this paper, we present a simple microeconomic model with linear continuous-time dynamics that describes a production-inventory system with debt repayment. This model is formulated in terms of optimal control and its exact solutions are derived by prudent application of the maximum principle under different sets of initial conditions (scenarios). For a potentially profitable small firm, we also propose some alternative short-term control strategies resulting in a positive final profit and prove their optimality. Practical implementation of such strategies is also discussed.
Modeling a Dynamic Design System Using the Mahalanobis Taguchi System - Two-Step Optimal Algorithm
Tsung-Shin Hsu; Ching-Lien Huang
2010-01-01
\\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,
An Approach for Dynamic Optimization of Prevention Program Implementation in Stochastic Environments
NASA Astrophysics Data System (ADS)
Kang, Yuncheol; Prabhu, Vittal
The science of preventing youth problems has significantly advanced in developing evidence-based prevention program (EBP) by using randomized clinical trials. Effective EBP can reduce delinquency, aggression, violence, bullying and substance abuse among youth. Unfortunately the outcomes of EBP implemented in natural settings usually tend to be lower than in clinical trials, which has motivated the need to study EBP implementations. In this paper we propose to model EBP implementations in natural settings as stochastic dynamic processes. Specifically, we propose Markov Decision Process (MDP) for modeling and dynamic optimization of such EBP implementations. We illustrate these concepts using simple numerical examples and discuss potential challenges in using such approaches in practice.
A new mixed integer linear programming formulation for one ...
2014-05-31
formulation has small number of decision variables, so it is capable to find, by exact solver, upper and lower ... on the problem instances that were generated randomly by themselves, and ... This sentence is only partially true, about models
A mixed-integer mathematical modeling approach to exam timetabling
Salem M. Al-Yakoob; Hanif D. Sherali; Mona Al-Jazzaf
2010-01-01
This paper explores mathematical programming models for an exam timetabling problem related to Kuwait University (KU). In\\u000a particular, we consider two subproblems: (a) the ExamTimetabling Problem (ETP), which is concerned with assigning exams to\\u000a designated exam-periods and classrooms, and (b) the Proctor Assignment Problem (PAP), which deals with the assignment of proctors\\u000a to exams. While this exam timetabling problem is
Applications and algorithms for mixed integer nonlinear programming
Linderoth, Jeffrey T.
reactors [4], and minimization of the environmental impact of utility plants [5]. Unfortunately, current of electric power systems [2], the design of water distribution networks [3], operational reloading of nuclear
Binary Decision Rules for Multistage Adaptive Mixed-Integer ...
2014-08-20
Aug 20, 2014 ... Our numerical results demonstrate the effectiveness of the proposed binary ... point of view, with applications in many fields such as engineering [22, ... Nevertheless, their potential was not fully exploited until recently, when ...
Concrete Structure Design Using Mixed-Integer Nonlinear ...
2009-11-24
Nov 24, 2009 ... ‡Division of Economics and Business, Colorado School of Mines, Golden, CO 80401 ..... development length of reinforcement for all elements [m] ...... Complementarity Constraints,” Computer Methods in Applied Mechanics.
Solving Bilevel Mixed Integer Program by Reformulations and ...
2014-07-05
foundation for analysis and a simple algorithmic structure for implementation. In theoretical aspect .... and analysis of security and defense applications. When the ...... bound information that is parametric not only to x but also to z0. As shown in ...
A mixed integer programming approach to reduce fuel load ...
2015-02-13
normally based on the time to reach maturity of the sensitive species in the ..... burning on regional extent and incidence of wildfires-evidence from 50 years of ... sion support systems: reflections on past practices and emerging needs and ...
Solving Mixed-Integer Nonlinear Programs by QP-Diving
2012-03-26
Mar 26, 2012 ... transmission (Bacher, 1997; Momoh et al., 1997), including ... MINLPs also arise in the design of water distribution networks ( ...... User manual for filterSQP. .... ter line search algorithm for large-scale nonlinear programming.
Chance Constrained Mixed Integer Program: Bilinear and Linear ...
that aggregate Benders feasibility cuts are developed using the mixing set ..... cally introduced to model decisions with concrete definitions in practice, e.g., ...... J Luedtke, and S Küçükyavuz, Chance-constrained binary packing problems,.
Semi-Continuous Cuts for Mixed-Integer Programming
defarius
2003-12-07
Dec 7, 2003 ... I. R. de Farias JR. State University ...... [18] P.L. Hammer, E.L. Johnson, and U.N. Peled, \\Facets of Regular 0-1 Polytopes," Math- ... [32] L.A. Wolsey, \\Faces for a Linear Inequality in 0-1 Variables," Mathematical Program- ming ...
Convex Quadratic Relaxations for Mixed-Integer Nonlinear ...
H. Hijazi, C. Coffrin and P. Van Hentenryck
2014-06-03
respectively denoted p and q, but their size and design enforce upper and lower bounds (pl i, pu ...... system restoration. Proceedings of the 17th ... W?chter, A., Biegler, L.T.: On the implementation of a primal-dual interior point filter line search ...
A stochastic mixed integer programming approach to wildfire management systems
Lee, Won Ju
2009-06-02
Wildfires have become more destructive and are seriously threatening societies and our ecosystems throughout the world. Once a wildfire escapes from its initial suppression attack, it can easily develop into a destructive ...
Mixed-integer Quadratic Programming is in NP
2014-07-17
Jul 17, 2014 ... equations, Proceedings of the American Mathematical Society 55 (1976), ... problems, Lecture Notes in Economics and Mathematical Systems ... International Congress of Mathematicians Warsaw (1983). ... [9] C. H. Papadimitriou, On the complexity of integer programming, Journal of the Association.
Area aggregation in map generalisation by mixed-integer programming
Jan-Henrik Haunert; Alexander Wolff
2010-01-01
Topographic databases normally contain areas of different land cover classes, commonly defining a planar partition, that is, gaps and overlaps are not allowed. When reducing the scale of such a database, some areas become too small for representation and need to be aggregated. This unintentionally but unavoidably results in changes of classes. In this article we present an optimisation method
Mixed-Integer Rounding Enhanced Benders Decomposition for ...
2014-11-13
Nov 13, 2014 ... Therefore, we also introduce a general technique for ... and a distribution of the possible error from the point estimate. ...... the forecast distribution, and then sampling from the Poisson distribution with the given sampled.
Optimal control based dynamics exploration of a rigid car with load transfer
Rucco, Alessandro; Hauser, John
2011-01-01
In this paper we provide optimal control based strategies to explore the dynamic capabilities of a single-track rigid car which includes tire models and load transfer. Using an explicit formulation of the holonomic constraints imposed on the unconstrained rigid car, we design a car model which includes load transfer without adding suspension models. With this model in hand, we perform an analysis of the equilibrium manifold of the vehicle. That is, we design a continuation and predictor-corrector numerical strategy to compute cornering equilibria on the entire range of operation of the tires. Finally, as main contribution of the paper, we explore the system dynamics by use of novel nonlinear optimal control techniques. The proposed strategies allow to compute aggressive car trajectories and study how the vehicle behaves depending on its parameters. To show the effectiveness of the proposed strategies we compute aggressive maneuvers of the vehicle inspired to testing maneuvers from virtual and real prototyping...
Detectability thresholds and optimal algorithms for community structure in dynamic networks
Ghasemian, Amir; Clauset, Aaron; Moore, Cristopher; Peel, Leto
2015-01-01
We study the fundamental limits on learning latent community structure in dynamic networks. Specifically, we study dynamic stochastic block models where nodes change their community membership over time, but where edges are generated independently at each time step. In this setting (which is a special case of several existing models), we are able to derive the detectability threshold exactly, as a function of the rate of change and the strength of the communities. Below this threshold, we claim that no algorithm can identify the communities better than chance. We then give two algorithms that are optimal in the sense that they succeed all the way down to this limit. The first uses belief propagation (BP), which gives asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm, based on linearizing the BP equations. We verify our analytic and algorithmic results via numerical simulation, and close with a brief discussion of extensions and open questions.
Optimal control of the dynamics of a BEC: optimizing splitting and squeezing
NASA Astrophysics Data System (ADS)
Grond, J.; Hohenester, U.; von Winckel, G.; Scrinzi, A.; Schmiedmayer, J.
2010-03-01
Number squeezed states are useful for atom interferometers and reduce phase diffusion in split Bose-Einstein condensates. In this paper we show that counter-intuitive splitting protocols allow efficient number squeezing on much shorter time scales compared to quasi-adiabatic splitting ( J. Grond et al., Phys. Rev. A 80, 053625 (2009) ). This is achieved by controlling the interplay between tunneling and nonlinear interaction using optimal control theory (OCT) within the Multi-configurational time dependent Hartree equations for Bosons MCTDHB ( O. E. Alon, et al., Phys Rev. A 77, 033613 (2008) ) method. We are seeking for maximal squeezing, while the condensates should be at rest and decoupled at the end of the splitting. We proceed with MCTDHB simulations with more than two modes. Condensate excitations are shown to affect number squeezing in some cases, but are found to be of little importance for our OCT control fields. From these results we obtain insight about the limits of two-mode descriptions.
Evolutionary genetic optimization of the injector beam dynamics for the ERL test facility at IHEP
NASA Astrophysics Data System (ADS)
Jiao, Yi
2014-08-01
The energy recovery linac test facility (ERL-TF), a compact ERL-FEL (free electron laser) two-purpose machine, has been proposed at the Institute of High Energy Physics, Beijing. As one important component of the ERL-TF, the photo-injector was designed and preliminarily optimized. In this paper an evolutionary genetic method, non-dominated sorting genetic algorithm II, is applied to optimize the injector beam dynamics, especially in the high-charge operation mode. Study shows that using an incident laser with rms transverse size of 1-1.2 mm, the normalized emittance of the electron beam can be kept below 1 mm·mrad at the end of the injector. This work, together with the previous optimization of the low-charge operation mode by using the iterative scan method, provides guidance and confidence for future construction and commissioning of the ERL-TF injector.
Optimal control study for the Space Station Solar Dynamic power module
NASA Technical Reports Server (NTRS)
Papadopoulos, P. M.; Laub, A. J.; Kenney, C. S.; Pandey, P.; Ianculescu, G.; Ly, J.
1991-01-01
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.
NASA Astrophysics Data System (ADS)
Liu, Wenyuan; Wang, Chao; Li, Yanbin; Lao, Yuyang; Han, Yongjian; Guo, Guang-Can; Zhao, Yong-Hua; He, Lixin
2015-03-01
Tensor network states (TNS) methods combined with the Monte Carlo (MC) technique have been proven a powerful algorithm for simulating quantum many-body systems. However, because the ground state energy is a highly non-linear function of the tensors, it is easy to get stuck in local minima when optimizing the TNS of the simulated physical systems. To overcome this difficulty, we introduce a replica-exchange molecular dynamics optimization algorithm to obtain the TNS ground state, based on the MC sampling technique, by mapping the energy function of the TNS to that of a classical mechanical system. The method is expected to effectively avoid local minima. We make benchmark tests on a 1D Hubbard model based on matrix product states (MPS) and a Heisenberg J1–J2 model on square lattice based on string bond states (SBS). The results show that the optimization method is robust and efficient compared to the existing results.
Liu, Wenyuan; Wang, Chao; Li, Yanbin; Lao, Yuyang; Han, Yongjian; Guo, Guang-Can; Zhao, Yong-Hua; He, Lixin
2015-03-01
Tensor network states (TNS) methods combined with the Monte Carlo (MC) technique have been proven a powerful algorithm for simulating quantum many-body systems. However, because the ground state energy is a highly non-linear function of the tensors, it is easy to get stuck in local minima when optimizing the TNS of the simulated physical systems. To overcome this difficulty, we introduce a replica-exchange molecular dynamics optimization algorithm to obtain the TNS ground state, based on the MC sampling technique, by mapping the energy function of the TNS to that of a classical mechanical system. The method is expected to effectively avoid local minima. We make benchmark tests on a 1D Hubbard model based on matrix product states (MPS) and a Heisenberg J1-J2 model on square lattice based on string bond states (SBS). The results show that the optimization method is robust and efficient compared to the existing results. PMID:25654245
A new logistic dynamic particle swarm optimization algorithm based on random topology.
Ni, Qingjian; Deng, Jianming
2013-01-01
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. PMID:23818820
Research on an optimization model for logistics nodes dynamic location and its solution algorithm
Dezhi Zhang; Rune Xie; Ting Liu; Shuangyan Li
2007-01-01
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
Balasubramonian, Rajeev (Sandy, UT); Dwarkadas, Sandhya (Rochester, NY); Albonesi, David (Ithaca, NY)
2012-01-24
In a processor having multiple clusters which operate in parallel, the number of clusters in use can be varied dynamically. At the start of each program phase, the configuration option for an interval is run to determine the optimal configuration, which is used until the next phase change is detected. The optimum instruction interval is determined by starting with a minimum interval and doubling it until a low stability factor is reached.
Handling Dynamic Networks Using Ant Colony Optimization on a Distributed Architecture
Sorin Ilie; Costin Badica
2009-01-01
Nowadays organizations are willing to share and cooperate in building better services and products. A distributed framework\\u000a is needed to support these current trends. An ant colony metaphor is a great source of inspiration to build such a framework.\\u000a This paper proposes a study of Ant Colony Optimization on handling dynamic networks. The novelty of our work consists in using
Optimal solid shells for non-linear analyses of multilayer composites. II. Dynamics
L. Vuquoc; X. G. Tan
2003-01-01
We are presenting a simple low-order solid-shell element formulation––having only displacement degrees of freedom (dofs), i.e., without rotational dofs––that has an optimal number of parameters to pass the patch tests, and thus allows for efficient and accurate analyses of large deformable multilayer shell structures using elements at extremely high aspect ratio. With the dynamics referred to a fixed inertial frame,
The role of digital computers in the dynamic optimization of chemical reactions
R. E. Kalman; R. W. Koepcke
1959-01-01
Along with the increasing availability of high-speed, large-storage digital computers, there has been growing interest in their utilization for real-time control purposes. A typical problem in this connection and one of long-standing interest is the optimal static and dynamic operation of chemical reactors. To our knowledge, no digital computer is being used for this purpose, chiefly because of the many
2009-01-01
Abstract. The rapid increase in the performance of graphics hardware, coupled with re- cent improvements,in its programmability has lead to its adoption in many,non-graphics applications, including wide variety of scientific computing fields. At the same time, a number,of important dynamic,optimal policy problems in economics are athirst of computing,power to help overcome,dual curses of complexity and dimensionality. We investigate if computational
A Dynamic Heart Rate Prediction Model for Training Optimization in Cycling (P83)
Ankang Le; Thomas Jaitner; Frank Tobias; Lothar Litz
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
NASA Astrophysics Data System (ADS)
Grafton, R. Quentin; Chu, Hoang Long; Stewardson, Michael; Kompas, Tom
2011-12-01
A key challenge in managing semiarid basins, such as in the Murray-Darling in Australia, is to balance the trade-offs between the net benefits of allocating water for irrigated agriculture, and other uses, versus the costs of reduced surface flows for the environment. Typically, water planners do not have the tools to optimally and dynamically allocate water among competing uses. We address this problem by developing a general stochastic, dynamic programming model with four state variables (the drought status, the current weather, weather correlation, and current storage) and two controls (environmental release and irrigation allocation) to optimally allocate water between extractions and in situ uses. The model is calibrated to Australia's Murray River that generates: (1) a robust qualitative result that "pulse" or artificial flood events are an optimal way to deliver environmental flows over and above conveyance of base flows; (2) from 2001 to 2009 a water reallocation that would have given less to irrigated agriculture and more to environmental flows would have generated between half a billion and over 3 billion U.S. dollars in overall economic benefits; and (3) water markets increase optimal environmental releases by reducing the losses associated with reduced water diversions.
Optimized dynamic contrast-enhanced cone-beam CT for target visualization during liver SBRT
NASA Astrophysics Data System (ADS)
Jones, Bernard L.; Altunbas, Cem; Kavanagh, Brian; Schefter, Tracey; Miften, Moyed
2014-03-01
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.
Gene regulatory network modeling via global optimization of high-order dynamic Bayesian network
2012-01-01
Background Dynamic Bayesian network (DBN) is among the mainstream approaches for modeling various biological networks, including the gene regulatory network (GRN). Most current methods for learning DBN employ either local search such as hill-climbing, or a meta stochastic global optimization framework such as genetic algorithm or simulated annealing, which are only able to locate sub-optimal solutions. Further, current DBN applications have essentially been limited to small sized networks. Results To overcome the above difficulties, we introduce here a deterministic global optimization based DBN approach for reverse engineering genetic networks from time course gene expression data. For such DBN models that consist only of inter time slice arcs, we show that there exists a polynomial time algorithm for learning the globally optimal network structure. The proposed approach, named GlobalMIT+, employs the recently proposed information theoretic scoring metric named mutual information test (MIT). GlobalMIT+ is able to learn high-order time delayed genetic interactions, which are common to most biological systems. Evaluation of the approach using both synthetic and real data sets, including a 733 cyanobacterial gene expression data set, shows significantly improved performance over other techniques. Conclusions Our studies demonstrate that deterministic global optimization approaches can infer large scale genetic networks. PMID:22694481
Dynamic sensing optimization strategy for mobile nodes deployment in wireless sensor networks
NASA Astrophysics Data System (ADS)
Wang, Sheng; Wang, Xue; Ma, Junjie
2006-11-01
Sensor nodes deployment problem is one of the fundamental issues in wireless sensor networks (WSNs) which should consider a tradeoff among several metrics, such as coverage area, reliability, accuracy, lifetime etc. The mobile sensor nodes which can relocate themselves can be used to optimize the nodes deployment under various kinds of situations. Because coverage area is hard to be calculated by analytical method, an areas division method is introduced to evaluate the coverage area metric for simplifying calculation. Then we introduce a practically feasible combined metric which refers to coverage area, reliability, accuracy and lifetime, which uses areas division, detecting reliability, Mahalanobis distance and energy entropy as metric functions. Here, nodes deployment is considered as an optimization problem. Particle swarm optimization (PSO) algorithm, which has a series of advantages, such as, high-speed regional convergence, efficient global searching ability, and so on, is suitable for solving multi-dimension function optimization in continuous space. So we adopt PSO for nodes deployment optimization where the combined metric is considered as fitness function. Because the combined metric is multiform and changeable in PSO, we can adopt different combined metrics for different applications, while other strategies just consider the coverage area in nodes deployment. The experimental results verify that the PSO based mobile nodes deployment strategy has good performance in quickness, which can improve the capabilities of WSNs and dynamically adjust the deployment according to the changes of situation, especially when some areas need multiple-node-measurement.
NASA Technical Reports Server (NTRS)
Whiffen, Gregory J.
2006-01-01
Mystic software is designed to compute, analyze, and visualize optimal high-fidelity, low-thrust trajectories, The software can be used to analyze inter-planetary, planetocentric, and combination trajectories, Mystic also provides utilities to assist in the operation and navigation of low-thrust spacecraft. Mystic will be used to design and navigate the NASA's Dawn Discovery mission to orbit the two largest asteroids, The underlying optimization algorithm used in the Mystic software is called Static/Dynamic Optimal Control (SDC). SDC is a nonlinear optimal control method designed to optimize both 'static variables' (parameters) and dynamic variables (functions of time) simultaneously. SDC is a general nonlinear optimal control algorithm based on Bellman's principal.
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
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.
Shu-Kai S. Fan; Ju-Ming Chang
2010-01-01
This article presents a novel parallel multi-swarm optimization (PMSO) algorithm with the aim of enhancing the search ability of standard single-swarm PSOs for global optimization of very large-scale multimodal functions. Different from the existing multi-swarm structures, the multiple swarms work in parallel, and the search space is partitioned evenly and dynamically assigned in a weighted manner via the roulette wheel
Bin Zhang; Tong Wang; Chuan-gang Gu; Xin-Wei Shu
2011-01-01
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.
DYNAMIC RIDE-SHARING AND OPTIMAL FLEET SIZING FOR A SYSTEM OF1 SHARED AUTONOMOUS VEHICLES2
Kockelman, Kara M.
DYNAMIC RIDE-SHARING AND OPTIMAL FLEET SIZING FOR A SYSTEM OF1 SHARED AUTONOMOUS VEHICLES2 3 4 and for publication in Transportation21 22 23 ABSTRACT24 25 Shared autonomous (fully-automated) vehicles (SAVs, destinations and departure times in the same vehicle), optimizing fleet sizing, and32 anticipating
Chin Kuan Ho; Hong Tat Ewe
2009-01-01
We present the design of a novel hybrid genetic ant colony optimization (GACO) metaheuristic. Genetic ant colony optimization is designed to address the dynamic load-balanced clustering problem formulated from ad hoc networks. The main algorithm in GACO is ACO. Whenever an environment change occurs (i.e., nodes move), it makes use of a genetic algorithm to quickly achieve adaptation by refocusing
Ding Xiaoyan; Liu Lilan; Hua Zhengxiao; Yu Tao
2009-01-01
The error measurement and diagnosis process of roll grinder NC has dynamic complexity, non-linearity, and comprehensive characteristics. However, presently roll error measurement examination mostly uses the manual examination or single parameter optimization, and the efficiency of fault diagnosis is also inefficient. In this study, the multi-objective intelligence optimization model (MIOM) is applied to the roller error measurement and diagnosis. The
Dynamic biclustering of microarray data by multi-objective immune optimization
2011-01-01
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. PMID:21989068
Fast optimization of binary clusters using a novel dynamic lattice searching method
NASA Astrophysics Data System (ADS)
Wu, Xia; Cheng, Wen
2014-09-01
Global optimization of binary clusters has been a difficult task despite of much effort and many efficient methods. Directing toward two types of elements (i.e., homotop problem) in binary clusters, two classes of virtual dynamic lattices are constructed and a modified dynamic lattice searching (DLS) method, i.e., binary DLS (BDLS) method, is developed. However, it was found that the BDLS can only be utilized for the optimization of binary clusters with small sizes because homotop problem is hard to be solved without atomic exchange operation. Therefore, the iterated local search (ILS) method is adopted to solve homotop problem and an efficient method based on the BDLS method and ILS, named as BDLS-ILS, is presented for global optimization of binary clusters. In order to assess the efficiency of the proposed method, binary Lennard-Jones clusters with up to 100 atoms are investigated. Results show that the method is proved to be efficient. Furthermore, the BDLS-ILS method is also adopted to study the geometrical structures of (AuPd)79 clusters with DFT-fit parameters of Gupta potential.
Dynamics of hepatitis C under optimal therapy and sampling based analysis
NASA Astrophysics Data System (ADS)
Pachpute, Gaurav; Chakrabarty, Siddhartha P.
2013-08-01
We examine two models for hepatitis C viral (HCV) dynamics, one for monotherapy with interferon (IFN) and the other for combination therapy with IFN and ribavirin. Optimal therapy for both the models is determined using the steepest gradient method, by defining an objective functional which minimizes infected hepatocyte levels, virion population and side-effects of the drug(s). The optimal therapies for both the models show an initial period of high efficacy, followed by a gradual decline. The period of high efficacy coincides with a significant decrease in the viral load, whereas the efficacy drops after hepatocyte levels are restored. We use the Latin hypercube sampling technique to randomly generate a large number of patient scenarios and study the dynamics of each set under the optimal therapy already determined. Results show an increase in the percentage of responders (indicated by drop in viral load below detection levels) in case of combination therapy (72%) as compared to monotherapy (57%). Statistical tests performed to study correlations between sample parameters and time required for the viral load to fall below detection level, show a strong monotonic correlation with the death rate of infected hepatocytes, identifying it to be an important factor in deciding individual drug regimens.
ERIC Educational Resources Information Center
Brusco, Michael J.; Stahl, Stephanie
2005-01-01
There are two well-known methods for obtaining a guaranteed globally optimal solution to the problem of least-squares unidimensional scaling of a symmetric dissimilarity matrix: (a) dynamic programming, and (b) branch-and-bound. Dynamic programming is generally more efficient than branch-and-bound, but the former is limited to matrices with…
Empirical prediction of climate dynamics: optimal models, derived from time series
NASA Astrophysics Data System (ADS)
Mukhin, D.; Loskutov, E. M.; Gavrilov, A.; Feigin, A. M.
2013-12-01
The new empirical method for prediction of climate indices by the analysis of climatic fields' time series is suggested. The method is based on construction of prognostic models of evolution operator (EO) in low-dimensional subspaces of system's phase space. One of the main points of suggested analysis is reconstruction of appropriate basis of dynamical variables (predictors) from spatially distributed data: different ways of data decomposition are discussed in the report including EOFs, MSSA and other relevant data rotations. We consider the models of different complexity for EO reconstruction, from linear statistical models of particular indices to more complex artificial neural network (ANN) models of climatic patterns dynamics, which take the form of discrete random dynamical systems [1]. Very important problem, that always arises in empirical modeling approaches, is optimal model selection criterium: appropriate regularization procedure is needed to avoid overfitted model. Particulary, it includes finding the optimal structural parameters of the model such as dimension of variables vector, i.e. number of principal components used for modeling, number of states used for prediction, and number of parameters determining quality of approximation. In this report the minimal descriptive length (MDL) approach [2] is proposed for this purpose: the model providing most data compression is chosen. Results of application of suggested method to analysis of SST and SLP fields' time series [3] covering last 30-50 years are presented: predictions of different climate indices time series including NINO 3.4, MEI, PDO, NOA are shown. References: 1. Y. I. Molkov, E. M. Loskutov, D. N. Mukhin, and A. M. Feigin, Random dynamical models from time series, Phys. Rev. E 85, 036216, 2012 2. Molkov, Ya.I., D.N. Mukhin, E.M. Loskutov, A.M. Feigin, and G.A. Fidelin, Using the minimum description length principle for global reconstruction of dynamic systems from noisy time series. Phys. Rev. E, 80, 046207, 2009 3. IRI/LDEO (http://iridl.ldeo.columbia.edu/)
Hudecová, Jana; Hopmann, Kathrin H; Bou?, Petr
2012-01-12
Vibrational properties of solutions are frequently simulated with clusters of a solute and a few solvent molecules obtained during molecular dynamics (MD) simulations. The raw cluster geometries, however, often provide unrealistic vibrational band broadening, for both ab initio and empirical force fields. In this work, partial optimization in normal-mode coordinates is used on empirical basis to reduce the broadening. The origin of the error is discussed on a simplified two-dimensional system, which indicates that the problem is caused by the anharmonic MD potential, mode coupling, and neglect of quantum effects. Then the procedure of partial geometry optimization on Raman and Raman optical activity (ROA) spectra is applied and analyzed for the solvated lactamide molecule. Comparison to experiment demonstrates that the normal-mode partial optimization technique with a suitable frequency limit can significantly reduce the broadening error. For lactamide, experimental and simulated vibrational bandwidths are compared; the most realistic theoretical spectra are obtained for partially optimized clusters with the vibrational wavenumber cutoff of about 200 cm(-1). PMID:22132857
A model of bi-mode transmission dynamics of hepatitis C with optimal control.
Imran, Mudassar; Rafique, Hassan; Khan, Adnan; Malik, Tufail
2014-06-01
In this paper, we present a rigorous mathematical analysis of a deterministic model for the transmission dynamics of hepatitis C. The model is suitable for populations where two frequent modes of transmission of hepatitis C virus, namely unsafe blood transfusions and intravenous drug use, are dominant. The susceptible population is divided into two distinct compartments, the intravenous drug users and individuals undergoing unsafe blood transfusions. Individuals belonging to each compartment may develop acute and then possibly chronic infections. Chronically infected individuals may be quarantined. The analysis indicates that the eradication and persistence of the disease is completely determined by the magnitude of basic reproduction number R(c). It is shown that for the basic reproduction number R(c) < 1, the disease-free equilibrium is locally and globally asymptotically stable. For R(c) > 1, an endemic equilibrium exists and the disease is uniformly persistent. In addition, we present the uncertainty and sensitivity analyses to investigate the influence of different important model parameters on the disease prevalence. When the infected population persists, we have designed a time-dependent optimal quarantine strategy to minimize it. The Pontryagin's Maximum Principle is used to characterize the optimal control in terms of an optimality system which is solved numerically. Numerical results for the optimal control are compared against the constant controls and their efficiency is discussed. PMID:24374404
Eisa, Fabian; Brauweiler, Robert; Peetz, Alexander; Hupfer, Martin; Nowak, Tristan; Kalender, Willi A
2012-05-21
One of the biggest challenges in dynamic contrast-enhanced CT is the optimal synchronization of scan start and duration with contrast medium administration in order to optimize image contrast and to reduce the amount of contrast medium. We present a new optically based approach, which was developed to investigate and optimize bolus timing and shape. The time-concentration curve of an intravenously injected test bolus of a dye is measured in peripheral vessels with an optical sensor prior to the diagnostic CT scan. The curves can be used to assess bolus shapes as a function of injection protocols and to determine contrast medium arrival times. Preliminary results for phantom and animal experiments showed the expected linear behavior between dye concentration and absorption. The kinetics of the dye was compared to iodinated contrast medium and was found to be in good agreement. The contrast enhancement curves were reliably detected in three mice with individual bolus shapes and delay times of 2.1, 3.5 and 6.1 s, respectively. The optical sensor appears to be a promising approach to optimize injection protocols and contrast enhancement timing and is applicable to all modalities without implying any additional radiation dose. Clinical tests are still necessary. PMID:22517124
NASA Technical Reports Server (NTRS)
Welstead, Jason; Crouse, Gilbert L., Jr.
2014-01-01
Empirical sizing guidelines such as tail volume coefficients have long been used in the early aircraft design phases for sizing stabilizers, resulting in conservatively stable aircraft. While successful, this results in increased empty weight, reduced performance, and greater procurement and operational cost relative to an aircraft with optimally sized surfaces. Including flight dynamics in the conceptual design process allows the design to move away from empirical methods while implementing modern control techniques. A challenge of flight dynamics and control is the numerous design variables, which are changing fluidly throughout the conceptual design process, required to evaluate the system response to some disturbance. This research focuses on addressing that challenge not by implementing higher order tools, such as computational fluid dynamics, but instead by linking the lower order tools typically used within the conceptual design process so each discipline feeds into the other. In thisresearch, flight dynamics and control was incorporated into the conceptual design process along with the traditional disciplines of vehicle sizing, weight estimation, aerodynamics, and performance. For the controller, a linear quadratic regulator structure with constant gains has been specified to reduce the user input. Coupling all the disciplines in the conceptual design phase allows the aircraft designer to explore larger design spaces where stabilizers are sized according to dynamic response constraints rather than historical static margin and volume coefficient guidelines.
The Automatic Formulating Method of the Optimal Operating Planning Problem for Energy Supply Systems
NASA Astrophysics Data System (ADS)
Suzuki, Naohiko; Ueda, Takaharu; Sasakawa, Koichi
The problem of the optimal operating planning for energy supply system is formulated as mixed-integer linear programming (MILP), but, it is too complicated for most untrained operators with little experience to apply the method. This paper proposes an automatic evaluating method of the optimal operating planning for energy supply system in using simple data. The problem can be formulated only from characteristics of equipment, tariff of input energy, and energy demands. The connection of equipment is defined as a matrix, and generated from property data of equipment. The constraints and objective function of the problem are generated from relation-ship data in the matrix and characteristics of equipment. An optimization calculation for the problem is automatically carried out. It is confirmed that any operator can evaluate many alternative configurations of the energy supply systems.
Chang, C.S.; Liew, A.C.; Xu, J.X.; Wang, X.W.; Fan, B. [National Univ. of Singapore (Singapore). Dept. of Electrical Engineering] [National Univ. of Singapore (Singapore). Dept. of Electrical Engineering
1996-05-01
This paper is devoted to the dynamic performance assessment and model reduction for a longitudinally interconnected power system, with special emphasis on small-perturbation stability. A 2-machine equivalent is established for representing intersubsystem behavior of the test system, that is valid over a large range of interconnected system operation. Eigenvalue-sensitivity-based constraints are derived to represent variations of security levels with MW transfers between subsystems. These constraints can be stored in simple lookup tables and are used to extend the authors` work on a bicriterion optimization approach for determining the most appropriate generation dispatch solution taking into account the fuel and environmental costs, and to provide security constraints during optimization.
Yan, Xiaoxu; Xiao, Kang; Liang, Shuai; Lei, Ting; Liang, Peng; Xue, Tao; Yu, Kaichang; Guan, Jing; Huang, Xia
2014-11-01
Baffles are a key component of an airlift membrane bioreactor (MBR), which could enhance membrane surface shear for fouling control. In order to obtain an optimal hydraulic condition of the reactor, the effects of baffle location and size were systematically explored in this study. Computational fluid dynamics (CFD) was used to investigate the hydrodynamics in a bench-scale airlift flat sheet MBR with various baffle locations and sizes. Validated simulation results showed that side baffles were more effective in elevating membrane surface shear than front baffles. The maximum average shear stress was achieved by adjusting baffle size when both front and side baffles were installed. With the optimized baffle configuration, the shear stress was 10-30% higher than that without baffles at a same aeration intensity (specific air demand per membrane area in the range of 0-0.45m(3)m(-2)h(-1)). The effectiveness of baffles was particularly prominent at lower aeration intensities. PMID:25465790
Coherent control of plasma dynamics by feedback-optimized wavefront manipulationa)
NASA Astrophysics Data System (ADS)
He, Z.-H.; Hou, B.; Gao, G.; Lebailly, V.; Nees, J. A.; Clarke, R.; Krushelnick, K.; Thomas, A. G. R.
2015-05-01
Plasmas generated by an intense laser pulse can support coherent structures such as large amplitude wakefield that can affect the outcome of an experiment. We investigate the coherent control of plasma dynamics by feedback-optimized wavefront manipulation using a deformable mirror. The experimental outcome is directly used as feedback in an evolutionary algorithm for optimization of the phase front of the driving laser pulse. In this paper, we applied this method to two different experiments: (i) acceleration of electrons in laser driven plasma waves and (ii) self-compression of optical pulses induced by ionization nonlinearity. The manipulation of the laser wavefront leads to orders of magnitude improvement to electron beam properties such as the peak charge, beam divergence, and transverse emittance. The demonstration of coherent control for plasmas opens new possibilities for future laser-based accelerators and their applications.
Optimal dynamics for quantum-state and entanglement transfer through homogeneous quantum systems
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
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.
A robust optimization approach to experimental design for model discrimination of dynamical systems
Skanda, Dominik
2011-01-01
A high-ranking goal of interdisciplinary modeling approaches in the natural sciences are quantitative prediction of system dynamics and model based optimization. For this purpose, mathematical modeling, numerical simulation and scientific computing techniques are indispensable. Quantitative modeling closely combined with experimental investigations is required if the model is supposed to be used for sound mechanistic analysis and model predictions. Typically, before an appropriate model of a experimental system is found different hypothetical models might be reasonable and consistent with previous knowledge and available data. The parameters of the model up to an estimated confidence region are generally not known a priori. Therefore one has to incorporate possible parameter configurations of different models into a model discrimination algorithm. In this article we present a numerical algorithm which calculates a design of experiments which allows an optimal discrimination of different hypothetic candidate m...
Optimizing the dynamic range extension of a radiochromic film dosimetry system
Devic, Slobodan; Tomic, Nada; Soares, Christopher G.; Podgorsak, Ervin B. [Medical Physics Department, McGill University Health Centre, Montreal, Quebec H3G 1A4 (Canada); Department of Radiation Oncology, Jewish General Hospital, McGill University, Montreal, Quebec H3T 1E2 (Canada); Ionizing Radiation Division, National Institute of Standards and Technology, Gaithersburg, Maryland 20899 (United States); Medical Physics Department, McGill University Health Centre, Montreal, Quebec H3G 1A4 (Canada)
2009-02-15
The authors present a radiochromic film dosimetry protocol for a multicolor channel radiochromic film dosimetry system consisting of the external beam therapy (EBT) model GAFCHROMIC film and the Epson Expression 1680 flat-bed document scanner. Instead of extracting only the red color channel, the authors are using all three color channels in the absorption spectrum of the EBT film to extend the dynamic dose range of the radiochromic film dosimetry system. By optimizing the dose range for each color channel, they obtained a system that has both precision and accuracy below 1.5%, and the optimized ranges are 0-4 Gy for the red channel, 4-50 Gy for the green channel, and above 50 Gy for the blue channel.
Hurtado, F J; Kaiser, A S; Zamora, B
2015-03-15
Continuous stirred tank reactors (CSTR) are widely used in wastewater treatment plants to reduce the organic matter and microorganism present in sludge by anaerobic digestion. The present study carries out a numerical analysis of the fluid dynamic behaviour of a CSTR in order to optimize the process energetically. The characterization of the sludge flow inside the digester tank, the residence time distribution and the active volume of the reactor under different criteria are determined. The effects of design and power of the mixing system on the active volume of the CSTR are analyzed. The numerical model is solved under non-steady conditions by examining the evolution of the flow during the stop and restart of the mixing system. An intermittent regime of the mixing system, which kept the active volume between 94% and 99%, is achieved. The results obtained can lead to the eventual energy optimization of the mixing system of the CSTR. PMID:25635665
Optimization of Fluid Front Dynamics in Porous Media Using Rate Control: I. Equal Mobility Fluids
Sundaryanto, Bagus; Yortsos, Yanis C.
1999-10-18
In applications involving this injection of a fluid in a porous medium to displace another fluid, a main objective is the maximization of the displacement efficiency. For a fixed arrangement of injection and production points (sources and sinks), such optimization is possible by controlling the injection rate policy. Despite its practical relevance, however, this aspect has received scant attention in the literature. In this paper, a fundamental approach based on optimal control theory, for the case when the fluids are miscible, of equal viscosity and in the absence of dispersion and gravity effects. Both homogeneous and heterogeneous porous media are considered. From a fluid dynamics viewpoint, this is a problem in the deformation of material lines in porous media, as a function of time-varying injection rates.
Dynamic characteristics of mesh-based network control system under optimal resource circumstances
NASA Astrophysics Data System (ADS)
Huang, Dong; He, Bigui
2012-11-01
Network control system (NCS) has been presenting significance in industry and automation by supporting usability and convenience. However, for insufficiency for diversity of infrastructure and scalability, it has limitations in the field of flexibility for network architecture compared to the wireless mobile network. Hence, there shows the pressing need for tackling the existing network control system with control flow by wireless mesh network (WMN) with strong flexibility effectively. Consequently, for each control system, its stability is one of the prerequisites and criteria. In this paper, a novel optimization model was developed for planning the optimal resource utilization of wireless mesh network (WMN), and then the dynamic characteristics of mesh-based network control system are analyzed and discussed.
Li, Desheng
2014-01-01
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
Process simulation and dynamic control for marine oily wastewater treatment using UV irradiation.
Jing, Liang; Chen, Bing; Zhang, Baiyu; Li, Pu
2015-09-15
UV irradiation and advanced oxidation processes have been recently regarded as promising solutions in removing polycyclic aromatic hydrocarbons (PAHs) from marine oily wastewater. However, such treatment methods are generally not sufficiently understood in terms of reaction mechanisms, process simulation and process control. These deficiencies can drastically hinder their application in shipping and offshore petroleum industries which produce bilge/ballast water and produced water as the main streams of marine oily wastewater. In this study, the factorial design of experiment was carried out to investigate the degradation mechanism of a typical PAH, namely naphthalene, under UV irradiation in seawater. Based on the experimental results, a three-layer feed-forward artificial neural network simulation model was developed to simulate the treatment process and to forecast the removal performance. A simulation-based dynamic mixed integer nonlinear programming (SDMINP) approach was then proposed to intelligently control the treatment process by integrating the developed simulation model, genetic algorithm and multi-stage programming. The applicability and effectiveness of the developed approach were further tested though a case study. The experimental results showed that the influences of fluence rate and temperature on the removal of naphthalene were greater than those of salinity and initial concentration. The developed simulation model could well predict the UV-induced removal process under varying conditions. The case study suggested that the SDMINP approach, with the aid of the multi-stage control strategy, was able to significantly reduce treatment cost when comparing to the traditional single-stage process optimization. The developed approach and its concept/framework have high potential of applicability in other environmental fields where a treatment process is involved and experimentation and modeling are used for process simulation and control. PMID:26043376
Solving the equation for the Iberian upwelling biogeochemical dynamics: an optimization experience
NASA Astrophysics Data System (ADS)
Reboreda, R.; Santaren, D.; Castro, C. G.; Alvarez-Salgado, X. A.; Nolasco, R.; Queiroga, H.; Dubert, J.
2012-04-01
Trying to find a set of parameters to properly reproduce the biogeochemical dynamics of the region of study is a major concern in biogeochemical ocean modelling. Model parameters are constant values introduced in the equations that calculate the time and space evolution of the state variables of the biogeochemical model. A good set of parameters allows for a better representation of the biological and chemical processes in the system, and thus to model results more approximated to reality. However, it is not a straightforward task, because many parameters are not well constrained in the literature, or they may be unknown or vary considerably between different regions. Usually, the approach to find the appropriate values is running several simulations, after some sensitivity test to individual parameters, until a satisfactory result is obtained. This may be very time consuming and quite subjective. A more systematic way to find this set of parameters has arisen over the last years by using mathematical optimization techniques. The basic principle under optimization is to minimize the difference between an observed and a simulated data series by using a cost function. We have applied an optimization technique to find an appropriate set of parameters for modelling the biogeochemical dynamics of the western Iberian shelf, off the Atlantic coast of Portugal and Galicia (NW Spain), which is characterized by a conspicuous seasonal upwelling. The ocean model is a high resolution 3D regional configuration of ROMS coupled to a N2PZD2 biogeochemical model. Results using the a priori parameters and the optimized parameters are compared and discussed. The study is the result of a multidisciplinary collaborative effort between the University of Aveiro ocean modelling group (Portugal), the ETHZ (Switzerland) and the IIM-CSIC Vigo oceanography group (Spain).
Dynamic motion planning of 3D human locomotion using gradient-based optimization.
Kim, Hyung Joo; Wang, Qian; Rahmatalla, Salam; Swan, Colby C; Arora, Jasbir S; Abdel-Malek, Karim; Assouline, Jose G
2008-06-01
Since humans can walk with an infinite variety of postures and limb movements, there is no unique solution to the modeling problem to predict human gait motions. Accordingly, we test herein the hypothesis that the redundancy of human walking mechanisms makes solving for human joint profiles and force time histories an indeterminate problem best solved by inverse dynamics and optimization methods. A new optimization-based human-modeling framework is thus described for predicting three-dimensional human gait motions on level and inclined planes. The basic unknowns in the framework are the joint motion time histories of a 25-degree-of-freedom human model and its six global degrees of freedom. The joint motion histories are calculated by minimizing an objective function such as deviation of the trunk from upright posture that relates to the human model's performance. A variety of important constraints are imposed on the optimization problem, including (1) satisfaction of dynamic equilibrium equations by requiring the model's zero moment point (ZMP) to lie within the instantaneous geometrical base of support, (2) foot collision avoidance, (3) limits on ground-foot friction, and (4) vanishing yawing moment. Analytical forms of objective and constraint functions are presented and discussed for the proposed human-modeling framework in which the resulting optimization problems are solved using gradient-based mathematical programming techniques. When the framework is applied to the modeling of bipedal locomotion on level and inclined planes, acyclic human walking motions that are smooth and realistic as opposed to less natural robotic motions are obtained. The aspects of the modeling framework requiring further investigation and refinement, as well as potential applications of the framework in biomechanics, are discussed. PMID:18532851
The importance of functional form in optimal control solutions of problems in population dynamics
Runge, M.C.; Johnson, F.A.
2002-01-01
Optimal control theory is finding increased application in both theoretical and applied ecology, and it is a central element of adaptive resource management. One of the steps in an adaptive management process is to develop alternative models of system dynamics, models that are all reasonable in light of available data, but that differ substantially in their implications for optimal control of the resource. We explored how the form of the recruitment and survival functions in a general population model for ducks affected the patterns in the optimal harvest strategy, using a combination of analytical, numerical, and simulation techniques. We compared three relationships between recruitment and population density (linear, exponential, and hyperbolic) and three relationships between survival during the nonharvest season and population density (constant, logistic, and one related to the compensatory harvest mortality hypothesis). We found that the form of the component functions had a dramatic influence on the optimal harvest strategy and the ultimate equilibrium state of the system. For instance, while it is commonly assumed that a compensatory hypothesis leads to higher optimal harvest rates than an additive hypothesis, we found this to depend on the form of the recruitment function, in part because of differences in the optimal steady-state population density. This work has strong direct consequences for those developing alternative models to describe harvested systems, but it is relevant to a larger class of problems applying optimal control at the population level. Often, different functional forms will not be statistically distinguishable in the range of the data. Nevertheless, differences between the functions outside the range of the data can have an important impact on the optimal harvest strategy. Thus, development of alternative models by identifying a single functional form, then choosing different parameter combinations from extremes on the likelihood profile may end up producing alternatives that do not differ as importantly as if different functional forms had been used. We recommend that biological knowledge be used to bracket a range of possible functional forms, and robustness of conclusions be checked over this range.
High-dynamic-range imaging of nanoscale magnetic fields using optimal control of a single qubit.
Häberle, T; Schmid-Lorch, D; Karrai, K; Reinhard, F; Wrachtrup, J
2013-10-25
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
Integrating event detection system operation characteristics into sensor placement optimization.
Hart, William Eugene; McKenna, Sean Andrew; Phillips, Cynthia Ann; Murray, Regan Elizabeth (US Environmental Protection Agency, Cincinnati, OH); Hart, David Blaine
2010-05-01
We consider the problem of placing sensors in a municipal water network when we can choose both the location of sensors and the sensitivity and specificity of the contamination warning system. Sensor stations in a municipal water distribution network continuously send sensor output information to a centralized computing facility, and event detection systems at the control center determine when to signal an anomaly worthy of response. Although most sensor placement research has assumed perfect anomaly detection, signal analysis software has parameters that control the tradeoff between false alarms and false negatives. We describe a nonlinear sensor placement formulation, which we heuristically optimize with a linear approximation that can be solved as a mixed-integer linear program. We report the results of initial experiments on a real network and discuss tradeoffs between early detection of contamination incidents, and control of false alarms.
Optimization for Hub-and-Spoke Port Logistics Network of Dynamic Hinterland
NASA Astrophysics Data System (ADS)
Ming-Jun, Ji; Yan-Ling, Chu
The port logistics and its regional economic react on each other and develop in unison. This paper studies the Hub-and-Spoke port logistics network which is a transportation system between the sea routes and ports hinterland transport routes. An optimization model is proposed with the objective of the total transportation cost in the regional port group based on the conditions of dynamic hinterland. This paper not only ensures every port in the hub-and spoke port logistics network to achieve its maximum economic benefits, but also makes the entire system get the minimum total transportation cost in the view of quantitative point. In order to illustrate the validity of the model, the example was solved. The results show that the model is feasible. Furthermore, the competitiveness power of the port, the demarcation of hinterland and the traffic capacity are changed dynamically in the model, which is closer to the real system.
Riemannian geometric approach to human arm dynamics, movement optimization, and invariance
NASA Astrophysics Data System (ADS)
Biess, Armin; Flash, Tamar; Liebermann, Dario G.
2011-03-01
We present a generally covariant formulation of human arm dynamics and optimization principles in Riemannian configuration space. We extend the one-parameter family of mean-squared-derivative (MSD) cost functionals from Euclidean to Riemannian space, and we show that they are mathematically identical to the corresponding dynamic costs when formulated in a Riemannian space equipped with the kinetic energy metric. In particular, we derive the equivalence of the minimum-jerk and minimum-torque change models in this metric space. Solutions of the one-parameter family of MSD variational problems in Riemannian space are given by (reparametrized) geodesic paths, which correspond to movements with least muscular effort. Finally, movement invariants are derived from symmetries of the Riemannian manifold. We argue that the geometrical structure imposed on the arm’s configuration space may provide insights into the emerging properties of the movements generated by the motor system.
Dynamic optimization for commercialization of renewable energy: an example for solar photovoltaics
Richards, Kenneth, R.; Ashton, W. Bradley; McVeigh, James
2000-04-21
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.
Carver, Charles S.; Scheier, Michael F.; Segerstrom, Suzanne C.
2010-01-01
Optimism is an individual difference variable that reflects the extent to which people hold generalized favorable expectancies for their future. Higher levels of optimism have been related prospectively to better subjective well-being in times of adversity or difficulty (i.e., controlling for previous well-being). Consistent with such findings, optimism has been linked to higher levels of engagement coping and lower levels of avoidance, or disengagement, coping. There is evidence that optimism is associated with taking proactive steps to protect one's health, whereas pessimism is associated with health-damaging behaviors. Consistent with such findings, optimism is also related to indicators of better physical health. The energetic, task-focused approach that optimists take to goals also relates to benefits in the socioeconomic world. Some evidence suggests that optimism relates to more persistence in educational efforts and to higher later income. Optimists also appear to fare better than pessimists in relationships. Although there are instances in which optimism fails to convey an advantage, and instances in which it may convey a disadvantage, those instances are relatively rare. In sum, the behavioral patterns of optimists appear to provide models of living for others to learn from. PMID:20170998
A synergic simulation-optimization approach for analyzing biomolecular dynamics in living organisms.
Sadegh Zadeh, Kouroush
2011-01-01
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
Setrak J. Balian; Ren-Bao Liu; T. S. Monteiro
2015-04-22
There are two distinct techniques of proven effectiveness for extending the coherence lifetime of spin qubits in environments of other spins. One is dynamical decoupling, whereby the qubit is subjected to a carefully timed sequence of control pulses; the other is tuning the qubit towards 'optimal working points' (OWPs), which are sweet-spots for reduced decoherence in magnetic fields. By means of quantum many-body calculations, we investigate the effects of dynamical decoupling pulse sequences far from and near OWPs for a central donor qubit subject to decoherence from a nuclear spin bath. Key to understanding the behavior is to analyse the degree of suppression of the usually dominant contribution from independent pairs of flip-flopping spins within the many-body quantum bath. We find that to simulate recently measured Hahn echo decays at OWPs (lowest-order dynamical decoupling), one must consider clusters of three interacting spins, since independent pairs do not even give finite $T_2$ decay times. We show that while operating near OWPs, dynamical decoupling sequences require hundreds of pulses for a single order of magnitude enhancement of $T_2$, in contrast to regimes far from OWPs, where only about ten pulses are required.
NASA Astrophysics Data System (ADS)
Balian, S. J.; Liu, Ren-Bao; Monteiro, T. S.
2015-06-01
There are two distinct techniques of proven effectiveness for extending the coherence lifetime of spin qubits in environments of other spins. One is dynamical decoupling, whereby the qubit is subjected to a carefully timed sequence of control pulses; the other is tuning the qubit towards "optimal working points" (OWPs), which are sweet spots for reduced decoherence in magnetic fields. By means of quantum many-body calculations, we investigate the effects of dynamical decoupling pulse sequences far from and near OWPs for a central donor qubit subject to decoherence from a nuclear spin bath. Key to understanding the behavior is to analyze the degree of suppression of the usually dominant contribution from independent pairs of flip-flopping spins within the many-body quantum bath. We find that to simulate recently measured Hahn echo decays at OWPs (lowest-order dynamical decoupling), one must consider clusters of three interacting spins since independent pairs do not even give finite-T2 decay times. We show that while operating near OWPs, dynamical decoupling sequences require hundreds of pulses for a single order of magnitude enhancement of T2, in contrast to regimes far from OWPs, where only about 10 pulses are required.
Kumar, Navneet; Raj Chelliah, Thanga; Srivastava, S P
2015-07-01
Model Based Control (MBC) is one of the energy optimal controllers used in vector-controlled Induction Motor (IM) for controlling the excitation of motor in accordance with torque and speed. MBC offers energy conservation especially at part-load operation, but it creates ripples in torque and speed during load transition, leading to poor dynamic performance of the drive. This study investigates the opportunity for improving dynamic performance of a three-phase IM operating with MBC and proposes three control schemes: (i) MBC with a low pass filter (ii) torque producing current (iqs) injection in the output of speed controller (iii) Variable Structure Speed Controller (VSSC). The pre and post operation of MBC during load transition is also analyzed. The dynamic performance of a 1-hp, three-phase squirrel-cage IM with mine-hoist load diagram is tested. Test results are provided for the conventional field-oriented (constant flux) control and MBC (adjustable excitation) with proposed schemes. The effectiveness of proposed schemes is also illustrated for parametric variations. The test results and subsequent analysis confer that the motor dynamics improves significantly with all three proposed schemes in terms of overshoot/undershoot peak amplitude of torque and DC link power in addition to energy saving during load transitions. PMID:25820090
Fluid-dynamic optimal design of helical vascular graft for stenotic disturbed flow.
Ha, Hojin; Hwang, Dongha; Choi, Woo-Rak; Baek, Jehyun; Lee, Sang Joon
2014-01-01
Although a helical configuration of a prosthetic vascular graft appears to be clinically beneficial in suppressing thrombosis and intimal hyperplasia, an optimization of a helical design has yet to be achieved because of the lack of a detailed understanding on hemodynamic features in helical grafts and their fluid dynamic influences. In the present study, the swirling flow in a helical graft was hypothesized to have beneficial influences on a disturbed flow structure such as stenotic flow. The characteristics of swirling flows generated by helical tubes with various helical pitches and curvatures were investigated to prove the hypothesis. The fluid dynamic influences of these helical tubes on stenotic flow were quantitatively analysed by using a particle image velocimetry technique. Results showed that the swirling intensity and helicity of the swirling flow have a linear relation with a modified Germano number (Gn*) of the helical pipe. In addition, the swirling flow generated a beneficial flow structure at the stenosis by reducing the size of the recirculation flow under steady and pulsatile flow conditions. Therefore, the beneficial effects of a helical graft on the flow field can be estimated by using the magnitude of Gn*. Finally, an optimized helical design with a maximum Gn* was suggested for the future design of a vascular graft. PMID:25360705
Dynamic Response and Optimal Design of Curved Metallic Sandwich Panels under Blast Loading
Yang, Shu; Han, Shou-Hong; Lu, Zhen-Hua
2014-01-01
It is important to understand the effect of curvature on the blast response of curved structures so as to seek the optimal configurations of such structures with improved blast resistance. In this study, the dynamic response and protective performance of a type of curved metallic sandwich panel subjected to air blast loading were examined using LS-DYNA. The numerical methods were validated using experimental data in the literature. The curved panel consisted of an aluminum alloy outer face and a rolled homogeneous armour (RHA) steel inner face in addition to a closed-cell aluminum foam core. The results showed that the configuration of a “soft” outer face and a “hard” inner face worked well for the curved sandwich panel against air blast loading in terms of maximum deflection (MaxD) and energy absorption. The panel curvature was found to have a monotonic effect on the specific energy absorption (SEA) and a nonmonotonic effect on the MaxD of the panel. Based on artificial neural network (ANN) metamodels, multiobjective optimization designs of the panel were carried out. The optimization results revealed the trade-off relationships between the blast-resistant and the lightweight objectives and showed the great use of Pareto front in such design circumstances. PMID:25126606
Brown, Jason; Darres, Kyle; Petty, Katherine
2012-01-01
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. PMID:22751538
Integration of dynamic, aerodynamic and structural optimization of helicopter rotor blades
NASA Technical Reports Server (NTRS)
Peters, David A.
1987-01-01
The purpose of the research is to study the integration of structural, dynamic, and aerodynamic considerations in the design-optimization process for helicopter rotorblades. This is to be done in three phases. Task 1 is to bring on-line computer codes that could perform the finite-element frequency analyses of rotor blades. The major features of this program are summarized. The second task was to bring on-line an optimization code for the work. Several were tried and it was decided to use CONMIN. Explicit volume constraints on the thicknesses and lumped masses used in the optimization were added. The specific aeroelastic constraint that the center of mass must be forward of the quarter chord in order to prevent flutter was applied. The bending-torsion coupling due to cg-ea offset within the blade cross section was included. Also included were some very simple stress constraints. The first three constraints are completed, and the fourth constraint is being completed.
NASA Technical Reports Server (NTRS)
Nguyen, Nhan
2013-01-01
This paper presents the optimal control modification for linear uncertain plants. The Lyapunov analysis shows that the modification parameter has a limiting value depending on the nature of the uncertainty. The optimal control modification exhibits a linear asymptotic property that enables it to be analyzed in a linear time invariant framework for linear uncertain plants. The linear asymptotic property shows that the closed-loop plants in the limit possess a scaled input-output mapping. Using this property, we can derive an analytical closed-loop transfer function in the limit as the adaptive gain tends to infinity. The paper revisits the Rohrs counterexample problem that illustrates the nature of non-robustness of model-reference adaptive control in the presence of unmodeled dynamics. An analytical approach is developed to compute exactly the modification parameter for the optimal control modification that stabilizes the plant in the Rohrs counterexample. The linear asymptotic property is also used to address output feedback adaptive control for non-minimum phase plants with a relative degree 1.
NASA Astrophysics Data System (ADS)
Roslund, Jonathan; Roth, Matthias; Guyon, Laurent; Boutou, Véronique; Courvoisier, Francois; Wolf, Jean-Pierre; Rabitz, Herschel
2011-01-01
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.
Time-optimal path planning in dynamic flows using level set equations: theory and schemes
NASA Astrophysics Data System (ADS)
Lolla, Tapovan; Lermusiaux, Pierre F. J.; Ueckermann, Mattheus P.; Haley, Patrick J.
2014-09-01
We develop an accurate partial differential equation-based methodology that predicts the time-optimal paths of autonomous vehicles navigating in any continuous, strong, and dynamic ocean currents, obviating the need for heuristics. The goal is to predict a sequence of steering directions so that vehicles can best utilize or avoid currents to minimize their travel time. Inspired by the level set method, we derive and demonstrate that a modified level set equation governs the time-optimal path in any continuous flow. We show that our algorithm is computationally efficient and apply it to a number of experiments. First, we validate our approach through a simple benchmark application in a Rankine vortex flow for which an analytical solution is available. Next, we apply our methodology to more complex, simulated flow fields such as unsteady double-gyre flows driven by wind stress and flows behind a circular island. These examples show that time-optimal paths for multiple vehicles can be planned even in the presence of complex flows in domains with obstacles. Finally, we present and support through illustrations several remarks that describe specific features of our methodology.
Time-optimal path planning in dynamic flows using level set equations: theory and schemes
NASA Astrophysics Data System (ADS)
Lolla, Tapovan; Lermusiaux, Pierre F. J.; Ueckermann, Mattheus P.; Haley, Patrick J.
2014-10-01
We develop an accurate partial differential equation-based methodology that predicts the time-optimal paths of autonomous vehicles navigating in any continuous, strong, and dynamic ocean currents, obviating the need for heuristics. The goal is to predict a sequence of steering directions so that vehicles can best utilize or avoid currents to minimize their travel time. Inspired by the level set method, we derive and demonstrate that a modified level set equation governs the time-optimal path in any continuous flow. We show that our algorithm is computationally efficient and apply it to a number of experiments. First, we validate our approach through a simple benchmark application in a Rankine vortex flow for which an analytical solution is available. Next, we apply our methodology to more complex, simulated flow fields such as unsteady double-gyre flows driven by wind stress and flows behind a circular island. These examples show that time-optimal paths for multiple vehicles can be planned even in the presence of complex flows in domains with obstacles. Finally, we present and support through illustrations several remarks that describe specific features of our methodology.
Trajectory optimization for dynamic couch rotation during volumetric modulated arc radiotherapy.
Smyth, Gregory; Bamber, Jeffrey C; Evans, Philip M; Bedford, James L
2013-11-21
Non-coplanar radiation beams are often used in three-dimensional conformal and intensity modulated radiotherapy to reduce dose to organs at risk (OAR) by geometric avoidance. In volumetric modulated arc radiotherapy (VMAT) non-coplanar geometries are generally achieved by applying patient couch rotations to single or multiple full or partial arcs. This paper presents a trajectory optimization method for a non-coplanar technique, dynamic couch rotation during VMAT (DCR–VMAT), which combines ray tracing with a graph search algorithm. Four clinical test cases (partial breast, brain, prostate only, and prostate and pelvic nodes) were used to evaluate the potential OAR sparing for trajectory-optimized DCR–VMAT plans, compared with standard coplanar VMAT. In each case, ray tracing was performed and a cost map reflecting the number of OAR voxels intersected for each potential source position was generated. The least-cost path through the cost map, corresponding to an optimal DCR–VMAT trajectory, was determined using Dijkstra's algorithm. Results show that trajectory optimization can reduce dose to specified OARs for plans otherwise comparable to conventional coplanar VMAT techniques. For the partial breast case, the mean heart dose was reduced by 53%. In the brain case, the maximum lens doses were reduced by 61% (left) and 77% (right) and the globes by 37% (left) and 40% (right). Bowel mean dose was reduced by 15% in the prostate only case. For the prostate and pelvic nodes case, the bowel V50?Gy and V60?Gy were reduced by 9% and 45% respectively. Future work will involve further development of the algorithm and assessment of its performance over a larger number of cases in site-specific cohorts. PMID:24200876
Optimal variable flip angle schemes for dynamic acquisition of exchanging hyperpolarized substrates
NASA Astrophysics Data System (ADS)
Xing, Yan; Reed, Galen D.; Pauly, John M.; Kerr, Adam B.; Larson, Peder E. Z.
2013-09-01
In metabolic MRI with hyperpolarized contrast agents, the signal levels vary over time due to T1 decay, T2 decay following RF excitations, and metabolic conversion. Efficient usage of the nonrenewable hyperpolarized magnetization requires specialized RF pulse schemes. In this work, we introduce two novel variable flip angle schemes for dynamic hyperpolarized MRI in which the flip angle is varied between excitations and between metabolites. These were optimized to distribute the magnetization relatively evenly throughout the acquisition by accounting for T1 decay, prior RF excitations, and metabolic conversion. Simulation results are presented to confirm the flip angle designs and evaluate the variability of signal dynamics across typical ranges of T1 and metabolic conversion. They were implemented using multiband spectral-spatial RF pulses to independently modulate the flip angle at various chemical shift frequencies. With these schemes we observed increased SNR of [1-13C]lactate generated from [1-13C]pyruvate, particularly at later time points. This will allow for improved characterization of tissue perfusion and metabolic profiles in dynamic hyperpolarized MRI.
Bouncing between Model and Data: Stability, Passivity, and Optimality in Hybrid Dynamics
Ronsse, Renaud; Sternad, Dagmar
2012-01-01
Rhythmically bouncing a ball with a racket is a seemingly simple task, but it poses all the challenges critical for coordinative behavior: perceiving the ball’s trajectory to adapt position and velocity of the racket for the next ball contact. To gain insight into the underlying control strategies, the authors conducted a series of studies that tested models with experimental data, with an emphasis on deriving model-based hypotheses and trying to falsify them. Starting with a simple dynamical model of the racket and ball interactions, stability analyses showed that open-loop dynamics affords dynamical stability, such that small perturbations do not require corrections. To obtain this passive stability, the ball has to be impacted with negative acceleration—a strategy that subjects adopted in a variety of conditions at steady state. However, experimental tests that applied perturbations revealed that after perturbations, subjects applied active perceptually guided corrections to reestablish steady state faster than by relying on the passive model’s relaxation alone. Hence, the authors derived a model with active control based on optimality principles that considered each impact as a separate reaching-like movement. This model captured some additional features of the racket trajectory but failed to predict more fine-grained aspects of performance. The authors proceed to present a new model that accounts not only for fine-grained behavior but also reconciles passive and active control approaches with new predictions that will be put to test in the next set of experiments. PMID:21184357
It is a common practice to model general integer variables in a mixed-integer ... good progress towards optimal solution in the mixed-integer space may not be ...... Software and documentation distributed via the Internet ; accessible via the ...
Reboul, C. F.; Porebski, B. T.; Griffin, M. D. W.; Dobson, R. C. J.; Perugini, M. A.; Gerrard, J. A.; Buckle, A. M.
2012-01-01
Dihydrodipicolinate synthase (DHDPS) is an essential enzyme involved in the lysine biosynthesis pathway. DHDPS from E. coli is a homotetramer consisting of a ‘dimer of dimers’, with the catalytic residues found at the tight-dimer interface. Crystallographic and biophysical evidence suggest that the dimers associate to stabilise the active site configuration, and mutation of a central dimer-dimer interface residue destabilises the tetramer, thus increasing the flexibility and reducing catalytic efficiency and substrate specificity. This has led to the hypothesis that the tetramer evolved to optimise the dynamics within the tight-dimer. In order to gain insights into DHDPS flexibility and its relationship to quaternary structure and function, we performed comparative Molecular Dynamics simulation studies of native tetrameric and dimeric forms of DHDPS from E. coli and also the native dimeric form from methicillin-resistant Staphylococcus aureus (MRSA). These reveal a striking contrast between the dynamics of tetrameric and dimeric forms. Whereas the E. coli DHDPS tetramer is relatively rigid, both the E. coli and MRSA DHDPS dimers display high flexibility, resulting in monomer reorientation within the dimer and increased flexibility at the tight-dimer interface. The mutant E. coli DHDPS dimer exhibits disorder within its active site with deformation of critical catalytic residues and removal of key hydrogen bonds that render it inactive, whereas the similarly flexible MRSA DHDPS dimer maintains its catalytic geometry and is thus fully functional. Our data support the hypothesis that in both bacterial species optimal activity is achieved by fine tuning protein dynamics in different ways: E. coli DHDPS buttresses together two dimers, whereas MRSA dampens the motion using an extended tight-dimer interface. PMID:22685390
An 'optimal' spawning algorithm for adaptive basis set expansion in nonadiabatic dynamics
Yang, Sandy; Coe, Joshua D.; Kaduk, Benjamin; Martinez, Todd J. [Department of Chemistry and Beckman Institute, University of Illinois at Urbana-Champaign, 600 S. Mathews Ave., Urbana, Illinois 61801 (United States)
2009-04-07
The full multiple spawning (FMS) method has been developed to simulate quantum dynamics in the multistate electronic problem. In FMS, the nuclear wave function is represented in a basis of coupled, frozen Gaussians, and a 'spawning' procedure prescribes a means of adaptively increasing the size of this basis in order to capture population transfer between electronic states. Herein we detail a new algorithm for specifying the initial conditions of newly spawned basis functions that minimizes the number of spawned basis functions needed for convergence. 'Optimally' spawned basis functions are placed to maximize the coupling between parent and child trajectories at the point of spawning. The method is tested with a two-state, one-mode avoided crossing model and a two-state, two-mode conical intersection model.
Optimizing a Dynamical Decoupling Protocol for Solid-State Electronic Spin Ensembles in Diamond
Demitry Farfurnik; Andrey Jarmola; Linh M. Pham; Zhi-Hui Wang; Viatcheslav V. Dobrovitski; Ronald L. Walsworth; Dmitry Budker; Nir Bar-Gill
2015-07-14
We demonstrate significant improvements of the spin coherence time of a dense ensemble of nitrogen-vacancy (NV) centers in diamond through optimized dynamical decoupling (DD). Cooling the sample down to $77$ K suppresses longitudinal spin relaxation $T_1$ effects and DD microwave pulses are used to increase the transverse coherence time $T_2$ from $\\sim 0.7$ ms up to $\\sim 30$ ms. We extend previous work of single-axis (CPMG) DD towards the preservation of arbitrary spin states. Following a theoretical and experimental characterization of pulse and detuning errors, we compare the performance of various DD protocols. We identify that the optimal control scheme for preserving an arbitrary spin state is a recursive protocol, the concatenated version of the XY8 pulse sequence. The improved spin coherence might have an immediate impact on improvements of the sensitivities of AC magnetometry. Moreover, the protocol can be used on denser diamond samples to increase coherence times up to NV-NV interaction time scales, a major step towards the creation of quantum collective NV spin states.
NASA Astrophysics Data System (ADS)
Mousavi, Seyed Jamshid; Mahdizadeh, Kourosh; Afshar, Abbas
2004-08-01
Application of stochastic dynamic programming (SDP) models to reservoir optimization calls for state variables discretization. As an important variable discretization of reservoir storage volume has a pronounced effect on the computational efforts. The error caused by storage volume discretization is examined by considering it as a fuzzy state variable. In this approach, the point-to-point transitions between storage volumes at the beginning and end of each period are replaced by transitions between storage intervals. This is achieved by using fuzzy arithmetic operations with fuzzy numbers. In this approach, instead of aggregating single-valued crisp numbers, the membership functions of fuzzy numbers are combined. Running a simulated model with optimal release policies derived from fuzzy and non-fuzzy SDP models shows that a fuzzy SDP with a coarse discretization scheme performs as well as a classical SDP having much finer discretized space. It is believed that this advantage in the fuzzy SDP model is due to the smooth transitions between storage intervals which benefit from soft boundaries.
Patrick Asante; Glen W. Armstrong; Wiktor L. Adamowicz
2011-01-01
Carbon sequestration in forests is being considered as a mechanism to slow or reverse the trend of increasing concentrations of carbon dioxide in the atmosphere. We present results from a dynamic programming model used to determine the optimal harvest decision for a forest stand in the boreal forest of western Canada that provides both timber harvest volume and carbon sequestration
A parallel dynamic programming algorithm for multi-reservoir system optimization
NASA Astrophysics Data System (ADS)
Li, Xiang; Wei, Jiahua; Li, Tiejian; Wang, Guangqian; Yeh, William W.-G.
2014-05-01
This paper develops a parallel dynamic programming algorithm to optimize the joint operation of a multi-reservoir system. First, a multi-dimensional dynamic programming (DP) model is formulated for a multi-reservoir system. Second, the DP algorithm is parallelized using a peer-to-peer parallel paradigm. The parallelization is based on the distributed memory architecture and the message passing interface (MPI) protocol. We consider both the distributed computing and distributed computer memory in the parallelization. The parallel paradigm aims at reducing the computation time as well as alleviating the computer memory requirement associated with running a multi-dimensional DP model. Next, we test the parallel DP algorithm on the classic, benchmark four-reservoir problem on a high-performance computing (HPC) system with up to 350 cores. Results indicate that the parallel DP algorithm exhibits good performance in parallel efficiency; the parallel DP algorithm is scalable and will not be restricted by the number of cores. Finally, the parallel DP algorithm is applied to a real-world, five-reservoir system in China. The results demonstrate the parallel efficiency and practical utility of the proposed methodology.
Optimizing performance of hybrid FSO/RF networks in realistic dynamic scenarios
NASA Astrophysics Data System (ADS)
Llorca, Jaime; Desai, Aniket; Baskaran, Eswaran; Milner, Stuart; Davis, Christopher
2005-08-01
Hybrid Free Space Optical (FSO) and Radio Frequency (RF) networks promise highly available wireless broadband connectivity and quality of service (QoS), particularly suitable for emerging network applications involving extremely high data rate transmissions such as high quality video-on-demand and real-time surveillance. FSO links are prone to atmospheric obscuration (fog, clouds, snow, etc) and are difficult to align over long distances due the use of narrow laser beams and the effect of atmospheric turbulence. These problems can be mitigated by using adjunct directional RF links, which provide backup connectivity. In this paper, methodologies for modeling and simulation of hybrid FSO/RF networks are described. Individual link propagation models are derived using scattering theory, as well as experimental measurements. MATLAB is used to generate realistic atmospheric obscuration scenarios, including moving cloud layers at different altitudes. These scenarios are then imported into a network simulator (OPNET) to emulate mobile hybrid FSO/RF networks. This framework allows accurate analysis of the effects of node mobility, atmospheric obscuration and traffic demands on network performance, and precise evaluation of topology reconfiguration algorithms as they react to dynamic changes in the network. Results show how topology reconfiguration algorithms, together with enhancements to TCP/IP protocols which reduce the network response time, enable the network to rapidly detect and act upon link state changes in highly dynamic environments, ensuring optimized network performance and availability.
NASA Astrophysics Data System (ADS)
Ritto, T. G.; Soize, Christian; Sampaio, R.
2010-04-01
This work proposes a strategy for the robust optimization of the nonlinear dynamics of a drill-string, which is a structure that rotates and digs into the rock to search for oil. The nonparametric probabilistic approach is employed to model the uncertainties of the structure as well as the uncertainties of the bit-rock interaction model. This paper is particularly concerned with the robust optimization of the rate of penetration of the column, i.e., we aim to maximize the mathematical expectation of the mean rate of penetration, respecting the integrity of the system. The variables of the optimization problem are the rotational speed at the top and the initial reaction force at the bit; they are considered deterministic. The goal is to find the set of variables that maximizes the expected mean rate of penetration, respecting, vibration limits, stress limit and fatigue limit of the dynamical system.
Dynamic emulation modelling for the optimal operation of water systems: an overview
NASA Astrophysics Data System (ADS)
Castelletti, A.; Galelli, S.; Giuliani, M.
2014-12-01
Despite sustained increase in computing power over recent decades, computational limitations remain a major barrier to the effective and systematic use of large-scale, process-based simulation models in rational environmental decision-making. Whereas complex models may provide clear advantages when the goal of the modelling exercise is to enhance our understanding of the natural processes, they introduce problems of model identifiability caused by over-parameterization and suffer from high computational burden when used in management and planning problems. As a result, increasing attention is now being devoted to emulation modelling (or model reduction) as a way of overcoming these limitations. An emulation model, or emulator, is a low-order approximation of the process-based model that can be substituted for it in order to solve high resource-demanding problems. In this talk, an overview of emulation modelling within the context of the optimal operation of water systems will be provided. Particular emphasis will be given to Dynamic Emulation Modelling (DEMo), a special type of model complexity reduction in which the dynamic nature of the original process-based model is preserved, with consequent advantages in a wide range of problems, particularly feedback control problems. This will be contrasted with traditional non-dynamic emulators (e.g. response surface and surrogate models) that have been studied extensively in recent years and are mainly used for planning purposes. A number of real world numerical experiences will be used to support the discussion ranging from multi-outlet water quality control in water reservoir through erosion/sedimentation rebalancing in the operation of run-off-river power plants to salinity control in lake and reservoirs.
Niyogi, Ritwik K.; Wong-Lin, KongFatt
2013-01-01
Behavioural and neurophysiological studies in primates have increasingly shown the involvement of urgency signals during the temporal integration of sensory evidence in perceptual decision-making. Neuronal correlates of such signals have been found in the parietal cortex, and in separate studies, demonstrated attention-induced gain modulation of both excitatory and inhibitory neurons. Although previous computational models of decision-making have incorporated gain modulation, their abstract forms do not permit an understanding of the contribution of inhibitory gain modulation. Thus, the effects of co-modulating both excitatory and inhibitory neuronal gains on decision-making dynamics and behavioural performance remain unclear. In this work, we incorporate time-dependent co-modulation of the gains of both excitatory and inhibitory neurons into our previous biologically based decision circuit model. We base our computational study in the context of two classic motion-discrimination tasks performed in animals. Our model shows that by simultaneously increasing the gains of both excitatory and inhibitory neurons, a variety of the observed dynamic neuronal firing activities can be replicated. In particular, the model can exhibit winner-take-all decision-making behaviour with higher firing rates and within a significantly more robust model parameter range. It also exhibits short-tailed reaction time distributions even when operating near a dynamical bifurcation point. The model further shows that neuronal gain modulation can compensate for weaker recurrent excitation in a decision neural circuit, and support decision formation and storage. Higher neuronal gain is also suggested in the more cognitively demanding reaction time than in the fixed delay version of the task. Using the exact temporal delays from the animal experiments, fast recruitment of gain co-modulation is shown to maximize reward rate, with a timescale that is surprisingly near the experimentally fitted value. Our work provides insights into the simultaneous and rapid modulation of excitatory and inhibitory neuronal gains, which enables flexible, robust, and optimal decision-making. PMID:23825935
TAS: 89 0227: TAS Recovery Act - Optimization and Control of Electric Power Systems: ARRA
Chiang, Hsiao-Dong
2014-02-01
The name SuperOPF is used to refer several projects, problem formulations and soft-ware tools intended to extend, improve and re-define some of the standard methods of optimizing electric power systems. Our work included applying primal-dual interior point methods to standard AC optimal power flow problems of large size, as well as extensions of this problem to include co-optimization of multiple scenarios. The original SuperOPF problem formulation was based on co-optimizing a base scenario along with multiple post-contingency scenarios, where all AC power flow models and constraints are enforced for each, to find optimal energy contracts, endogenously determined locational reserves and appropriate nodal energy prices for a single period optimal power flow problem with uncertainty. This led to example non-linear programming problems on the order of 1 million constraints and half a million variables. The second generation SuperOPF formulation extends this by adding multiple periods and multiple base scenarios per period. It also incorporates additional variables and constraints to model load following reserves, ramping costs, and storage resources. A third generation of the multi-period SuperOPF, adds both integer variables and a receding horizon framework in which the problem type is more challenging (mixed integer), the size is even larger, and it must be solved more frequently, pushing the limits of currently available algorithms and solvers. The consideration of transient stability constraints in optimal power flow (OPF) problems has become increasingly important in modern power systems. Transient stability constrained OPF (TSCOPF) is a nonlinear optimization problem subject to a set of algebraic and differential equations. Solving a TSCOPF problem can be challenging due to (i) the differential-equation constraints in an optimization problem, (ii) the lack of a true analytical expression for transient stability in OPF. To handle the dynamics in TSCOPF, the set of differential equations can be approximated or converted into equivalent algebraic equations before they are included in an OPF formulation. In Chapter 4, a rigorous evaluation of using a predefined and fixed threshold for rotor angles as a mean to determine transient stability of the system is developed. TSCOPF can be modeled as a large-scale nonlinear programming problem including the constraints of differential-algebraic equations (DAE). Solving a TSCOPF problem can be challenging due to (i) the differential-equation constraints in an optimization problem, (ii) the lack of a true analytical expression for transient stability constraint in OPF. Unfortunately, even the current best TSCOPF solvers still suffer from the curse of dimensionality and unacceptable computational time, especially for large-scale power systems with multiple contingencies. In chapter 5, thse issues will be addressed and a new method to incorporate the transient stability constraints will be presented.
Fuzzy multiobjective models for optimal operation of a hydropower system
NASA Astrophysics Data System (ADS)
Teegavarapu, Ramesh S. V.; Ferreira, André R.; Simonovic, Slobodan P.
2013-06-01
Optimal operation models for a hydropower system using new fuzzy multiobjective mathematical programming models are developed and evaluated in this study. The models use (i) mixed integer nonlinear programming (MINLP) with binary variables and (ii) integrate a new turbine unit commitment formulation along with water quality constraints used for evaluation of reservoir downstream impairment. Reardon method used in solution of genetic algorithm optimization problems forms the basis for development of a new fuzzy multiobjective hydropower system optimization model with creation of Reardon type fuzzy membership functions. The models are applied to a real-life hydropower reservoir system in Brazil. Genetic Algorithms (GAs) are used to (i) solve the optimization formulations to avoid computational intractability and combinatorial problems associated with binary variables in unit commitment, (ii) efficiently address Reardon method formulations, and (iii) deal with local optimal solutions obtained from the use of traditional gradient-based solvers. Decision maker's preferences are incorporated within fuzzy mathematical programming formulations to obtain compromise operating rules for a multiobjective reservoir operation problem dominated by conflicting goals of energy production, water quality and conservation releases. Results provide insight into compromise operation rules obtained using the new Reardon fuzzy multiobjective optimization framework and confirm its applicability to a variety of multiobjective water resources problems.
Gabbouj, Moncef
Abstract-- Particle swarm optimization (PSO) was proposed as an optimization technique for static several major problems of PSO and exhibit a significant performance over multi-modal and non-stationary environments. In order to address the premature convergence problem and improve the rate of PSO's convergence
Hu, Dawei; Li, Ming; Zhou, Rui; Sun, Yi
2012-01-01
In this paper, a valid kinetic model of photo bioreactor (PBR) used for highly-effective cultivation of blue algae, Spirulina platensis, was developed for fully describing the dynamic characteristics of O(2) concentration, then a closed-loop PBR with Linear-Quadratic Gaussian (LQG) servo controller was established and optimized via digital simulation and dynamic response optimization, and the effectiveness of the closed-loop PBR was further tested and accredited by real-time simulation. The result showed that the closed-loop PBR could regulate and control the O(2) concentration in its gas phase according to the reference with desired dynamic response performance, hence microalgae with unique characteristic could be selected as a powerful tool for O(2) regulation and control whenever O(2) concentration in Bioregenerative Life Support System (BLSS) deviates from the nominal level in emergencies, and greatly enhance safety and reliability of BLSS on space and ground missions. PMID:22153599
NASA Astrophysics Data System (ADS)
Karakatsanis, Nicolas A.; Lodge, Martin A.; Tahari, Abdel K.; Zhou, Y.; Wahl, Richard L.; Rahmim, Arman
2013-10-01
Static whole-body PET/CT, employing the standardized uptake value (SUV), is considered the standard clinical approach to diagnosis and treatment response monitoring for a wide range of oncologic malignancies. Alternative PET protocols involving dynamic acquisition of temporal images have been implemented in the research setting, allowing quantification of tracer dynamics, an important capability for tumor characterization and treatment response monitoring. Nonetheless, dynamic protocols have been confined to single-bed-coverage limiting the axial field-of-view to ˜15-20 cm, and have not been translated to the routine clinical context of whole-body PET imaging for the inspection of disseminated disease. Here, we pursue a transition to dynamic whole-body PET parametric imaging, by presenting, within a unified framework, clinically feasible multi-bed dynamic PET acquisition protocols and parametric imaging methods. We investigate solutions to address the challenges of: (i) long acquisitions, (ii) small number of dynamic frames per bed, and (iii) non-invasive quantification of kinetics in the plasma. In the present study, a novel dynamic (4D) whole-body PET acquisition protocol of ˜45 min total length is presented, composed of (i) an initial 6 min dynamic PET scan (24 frames) over the heart, followed by (ii) a sequence of multi-pass multi-bed PET scans (six passes × seven bed positions, each scanned for 45 s). Standard Patlak linear graphical analysis modeling was employed, coupled with image-derived plasma input function measurements. Ordinary least squares Patlak estimation was used as the baseline regression method to quantify the physiological parameters of tracer uptake rate Ki and total blood distribution volume V on an individual voxel basis. Extensive Monte Carlo simulation studies, using a wide set of published kinetic FDG parameters and GATE and XCAT platforms, were conducted to optimize the acquisition protocol from a range of ten different clinically acceptable sampling schedules examined. The framework was also applied to six FDG PET patient studies, demonstrating clinical feasibility. Both simulated and clinical results indicated enhanced contrast-to-noise ratios (CNRs) for Ki images in tumor regions with notable background FDG concentration, such as the liver, where SUV performed relatively poorly. Overall, the proposed framework enables enhanced quantification of physiological parameters across the whole body. In addition, the total acquisition length can be reduced from 45 to ˜35 min and still achieve improved or equivalent CNR compared to SUV, provided the true Ki contrast is sufficiently high. In the follow-up companion paper, a set of advanced linear regression schemes is presented to particularly address the presence of noise, and attempt to achieve a better trade-off between the mean-squared error and the CNR metrics, resulting in enhanced task-based imaging.
NASA Astrophysics Data System (ADS)
Yang, Shichun; Li, Ming; Cui, Haigang; Cao, Yaoguang; Wang, Gang; Lei, Qiang
By using dynamic programming (DP) which is a kind of global optimization algorithm, an energy management control strategy for a parallel PHEV on different charging depleting range (CDR) had been studied. The results show that motor-dominant control strategy should be applied to the PHEV when CDR is less than 55km, and engine-dominant control strategy should be used when CDR is more than 55km. With optimal control strategies from DP, the best economic performance can be obtained as CDR is 55km; PHEV average equivalence fuel consumption can be reduced to 2.9L/100km which is 63% lower than that of prototype vehicle.
Using Plate Finite Elements for Modeling Fillets in Design, Optimization, and Dynamic Analysis
NASA Technical Reports Server (NTRS)
Brown, A. M.; Seugling, R. M.
2003-01-01
A methodology has been developed that allows the use of plate elements instead of numerically inefficient solid elements for modeling structures with 90 degree fillets. The technique uses plate bridges with pseudo Young's modulus (Eb) and thickness (tb) values placed between the tangent points of the fillets. These parameters are obtained by solving two nonlinear simultaneous equations in terms of the independent variables rlt and twallt. These equations are generated by equating the rotation at the tangent point of a bridge system with that of a fillet, where both rotations are derived using beam theory. Accurate surface fits of the solutions are also presented to provide the user with closed-form equations for the parameters. The methodology was verified on the subcomponent level and with a representative filleted structure, where the technique yielded a plate model exhibiting a level of accuracy better than or equal to a high-fidelity solid model and with a 90-percent reduction in the number of DOFs. The application of this method for parametric design studies, optimization, and dynamic analysis should prove extremely beneficial for the finite element practitioner. Although the method does not attempt to produce accurate stresses in the filleted region, it can also be used to obtain stresses elsewhere in the structure for preliminary analysis. A future avenue of study is to extend the theory developed here to other fillet geometries, including fillet angles other than 90 and multifaceted intersections.
Walls, M.A.
1988-01-01
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 drilling plan so as to maximize the expected discounted net present value of oil discoveries. The quadratic objective function and linear laws of motion for exogenous variables lead to a linear exploratory drilling decision rule. This decision rule is then estimated by full-information maximum likelihood using US monthly data for the 1973 through 1985 period. The 1986 oil price collapse is assumed to change the way that the representative firm forms price expectations. This change is the price law of motion is incorporated into the model and future domestic exploratory drilling levels and crude oil discoveries are then forecasted.
Bousige, Colin; Bo?an, Alexandru; Ulm, Franz-Josef; Pellenq, Roland J-M; Coasne, Benoît
2015-03-21
We report an efficient atom-scale reconstruction method that consists of combining the Hybrid Reverse Monte Carlo algorithm (HRMC) with Molecular Dynamics (MD) in the framework of a simulated annealing technique. In the spirit of the experimentally constrained molecular relaxation technique [Biswas et al., Phys. Rev. B 69, 195207 (2004)], this modified procedure offers a refined strategy in the field of reconstruction techniques, with special interest for heterogeneous and disordered solids such as amorphous porous materials. While the HRMC method generates physical structures, thanks to the use of energy penalties, the combination with MD makes the method at least one order of magnitude faster than HRMC simulations to obtain structures of similar quality. Furthermore, in order to ensure the transferability of this technique, we provide rational arguments to select the various input parameters such as the relative weight ? of the energy penalty with respect to the structure optimization. By applying the method to disordered porous carbons, we show that adsorption properties provide data to test the global texture of the reconstructed sample but are only weakly sensitive to the presence of defects. In contrast, the vibrational properties such as the phonon density of states are found to be very sensitive to the local structure of the sample. PMID:25796236
Optimal Intrinsic Dynamics for Bursting in a Three-Cell Network
NASA Astrophysics Data System (ADS)
Dunmyre, Justin R.; Rubin, Jonathan E.
2010-01-01
Previous numerical and analytical work has shown that synaptic coupling can allow a network of model neurons to synchronize despite heterogeneity in intrinsic parameter values. In particular, synchronous bursting oscillations can arise in a network with excitatory synaptic coupling, even in the absence of intrinsically bursting neurons. In this work, we explore how the intrinsic dynamics of neurons within a reduced three-cell network influence its ability to exhibit synchronous bursting and the frequency range over which such activity can occur. We establish necessary and sufficient conditions for the existence of synchronous bursting solutions and perform related numerical experiments in three-cell networks that include a quiescent cell, a tonically active cell, and a third added cell. Our results show that, in most cases, the addition of a quiescent cell is optimal for synchronous network bursting, in a variety of ways, and that intrinsically bursting cells can be detrimental to synchronous bursting, and we explain the mechanisms underlying these effects. These findings may help explain how robust synchronous oscillations arise in neuronal central pattern generators, such as the mammalian inspiratory network, despite the presence of significant cellular heterogeneity. They also support the idea that intrinsic burst capabilities of individual cells need not be central to these networks' rhythms.
Visuovestibular perception of self-motion modeled as a dynamic optimization process.
Reymond, Gilles; Droulez, Jacques; Kemeny, Andras
2002-10-01
This article describes a computational model for the sensory perception of self-motion, considered as a compromise between sensory information and physical coherence constraints. This compromise is realized by a dynamic optimization process minimizing a set of cost functions. Measure constraints are expressed as quadratic errors between motion estimates and corresponding sensory signals, using internal models of sensor transfer functions. Coherence constraints are expressed as quadratic errors between motion estimates, and their prediction is based on internal models of the physical laws governing the corresponding physical stimuli. This general scheme leads to a straightforward representation of fundamental sensory interactions (fusion of visual and canal rotational inputs, identification of the gravity component from the otolithic input, otolithic contribution to the perception of rotations, and influence of vection on the subjective vertical). The model is tuned and assessed using a range of well-known psychophysical results, including off-vertical axis rotations and centrifuge experiments. The ability of the model to predict and help analyze new situations is illustrated by a study of the vestibular contributions to self-motion perception during automobile driving and during acceleration cueing in driving simulators. The extendable structure of the model allows for further developments and applications, by using other cost functions representing additional sensory interactions. PMID:12386745
Qing, Zhang; Rongle, Xu; Xiang, Zheng; Yaobo, Fan
2014-01-01
The airlift external circulation membrane bioreactor (AEC-MBR) is a new MBR consisting of a separated aeration tank and membrane tank with circulating pipes fixed between the two tanks. The circulating pipe is called a H circulating pipe (HCP) because of its shape. With the complex configuration, it was difficult but necessary to master the AEC-MBR's hydraulic characteristics. In this paper, simulation and optimization of the AEC-MBR was performed using computational fluid dynamics. The distance from diffusers to membrane modules, i.e. the height of gas-liquid mixing zone (h(m)), and its effect on velocity distribution at membrane surfaces were studied. Additionally, the role of HCP and the effect of HCP's diameter on circulation were simulated and analyzed. The results showed that non-uniformity of cross-flow velocity existed in the flat-plate membrane modules, and the problem could be alleviated by increasing hm to an optimum range (h(m)/B ? 0.55; B is total static depth). Also, the low velocity in the boundary layer on the membrane surface was another reason for membrane fouling. The results also suggested that HCP was necessary and it had an optimum diameter to make circulation effective in the AEC-MBR. PMID:24804658
Optimization of a continuous hybrid impeller mixer via computational fluid dynamics.
Othman, N; Kamarudin, S K; Takriff, M S; Rosli, M I; Engku Chik, E M F; Meor Adnan, M A K
2014-01-01
This paper presents the preliminary steps required for conducting experiments to obtain the optimal operating conditions of a hybrid impeller mixer and to determine the residence time distribution (RTD) using computational fluid dynamics (CFD). In this paper, impeller speed and clearance parameters are examined. The hybrid impeller mixer consists of a single Rushton turbine mounted above a single pitched blade turbine (PBT). Four impeller speeds, 50, 100, 150, and 200 rpm, and four impeller clearances, 25, 50, 75, and 100 mm, were the operation variables used in this study. CFD was utilized to initially screen the parameter ranges to reduce the number of actual experiments needed. Afterward, the residence time distribution (RTD) was determined using the respective parameters. Finally, the Fluent-predicted RTD and the experimentally measured RTD were compared. The CFD investigations revealed that an impeller speed of 50 rpm and an impeller clearance of 25 mm were not viable for experimental investigations and were thus eliminated from further analyses. The determination of RTD using a k-? turbulence model was performed using CFD techniques. The multiple reference frame (MRF) was implemented and a steady state was initially achieved followed by a transient condition for RTD determination. PMID:25170524
Optimization of a Continuous Hybrid Impeller Mixer via Computational Fluid Dynamics
Othman, N.; Kamarudin, S. K.; Takriff, M. S.; Rosli, M. I.; Engku Chik, E. M. F.; Meor Adnan, M. A. K.
2014-01-01
This paper presents the preliminary steps required for conducting experiments to obtain the optimal operating conditions of a hybrid impeller mixer and to determine the residence time distribution (RTD) using computational fluid dynamics (CFD). In this paper, impeller speed and clearance parameters are examined. The hybrid impeller mixer consists of a single Rushton turbine mounted above a single pitched blade turbine (PBT). Four impeller speeds, 50, 100, 150, and 200 rpm, and four impeller clearances, 25, 50, 75, and 100 mm, were the operation variables used in this study. CFD was utilized to initially screen the parameter ranges to reduce the number of actual experiments needed. Afterward, the residence time distribution (RTD) was determined using the respective parameters. Finally, the Fluent-predicted RTD and the experimentally measured RTD were compared. The CFD investigations revealed that an impeller speed of 50 rpm and an impeller clearance of 25 mm were not viable for experimental investigations and were thus eliminated from further analyses. The determination of RTD using a k-? turbulence model was performed using CFD techniques. The multiple reference frame (MRF) was implemented and a steady state was initially achieved followed by a transient condition for RTD determination. PMID:25170524
PSO-Based Multiobjective Optimization With Dynamic Population Size and Adaptive Local Archives
Wen-fung Leong; Gary G. Yen
2008-01-01
Recently, various multiobjective particle swarm optimization (MOPSO) algorithms have been developed to efficiently and effectively solve multiobjective optimization problems. How ever, the existing MOPSO designs generally adopt a notion to \\
Optimal Regulation of Heating Systems with Metering Based on Dynamic Simulation
Zhao, H.; Wang, P.; Zeng, G.; Tian, Y.
2006-01-01
the radiator's parameters. The primary purpose of these works is the strategy of optimal regulation of heating system with metering. In a ramiform heating system with three heat users, an optimal scheme with certain combination of different object functions...
NASA Technical Reports Server (NTRS)
Majumdar, Alok; Schallhorn, Paul
1998-01-01
This paper describes a finite volume computational thermo-fluid dynamics method to solve for Navier-Stokes equations in conjunction with energy equation and thermodynamic equation of state in an unstructured coordinate system. The system of equations have been solved by a simultaneous Newton-Raphson method and compared with several benchmark solutions. Excellent agreements have been obtained in each case and the method has been found to be significantly faster than conventional Computational Fluid Dynamic(CFD) methods and therefore has the potential for implementation in Multi-Disciplinary analysis and design optimization in fluid and thermal systems. The paper also describes an algorithm of design optimization based on Newton-Raphson method which has been recently tested in a turbomachinery application.
Dynamic optimization of injection molding process variables by evolutionary programming methods
S. Jawaha; P. Ramamoorthy
2012-01-01
Injection molding is a nonlinear, multivariable process which involves several critical parameter settings for quality product. Determining optimal parameter settings is the challenging task due to the influence of environonmental conditions on the resin flow within the injection molding machine. This paper deals with various optimization techniques (differential evolution, DE, genetic algorithm, GA and Particle Swarm Optimization, PSO) which could
Optimality Certificate of Dynamic Spectrum Management in Multi-Carrier Interference Channels
Yi, Yung
. On the other hand, Optimal Spectrum Balancing (OSB) [2], Branch and Bound Optimal Spectrum Bal, spectrum balancing, or multi-carrier power control. Spectrum level coordination corresponds to a multi but no theoretical characterization of optimality condition. Indeed, on the one hand, polynomial-time algorithms like
Guy Jumarie
2007-01-01
By using the variational calculus of fractional order, one derives a Hamilton-Jacobi equation and a Lagrangian variational\\u000a approach to the optimal control of one-dimensional fractional dynamics with fractional cost function. It is shown that these\\u000a two methods are equivalent, as a result of the Lagrange’s characteristics method (a new approach) for solving nonlinear fractional\\u000a partial differential equations. The key of
Robert Miguel W. K. Kollman
1997-01-01
This paper studies dynamic-optimizing model of a semi-small open economy with sticky nominal prices and wages. The model exhibits exchange rate overshooting in response to money supply shocks. The predicted variability of nominal and real exchange rates is roughly consistent with that of G-7 effective exchange rates during the post-Bretton Woods era. The model predicts that a positive domestic money
Zhu, Z. W., E-mail: zhuzhiwen@tju.edu.cn [Department of Mechanics, Tianjin University, 300072, Tianjin (China); Tianjin Key Laboratory of Non-linear Dynamics and Chaos Control, 300072, Tianjin (China); Zhang, W. D., E-mail: zhangwenditju@126.com; Xu, J., E-mail: xujia-ld@163.com [Department of Mechanics, Tianjin University, 300072, Tianjin (China)
2014-03-15
The non-linear dynamic characteristics and optimal control of a giant magnetostrictive film (GMF) subjected to in-plane stochastic excitation were studied. Non-linear differential items were introduced to interpret the hysteretic phenomena of the GMF, and the non-linear dynamic model of the GMF subjected to in-plane stochastic excitation was developed. The stochastic stability was analysed, and the probability density function was obtained. The condition of stochastic Hopf bifurcation and noise-induced chaotic response were determined, and the fractal boundary of the system's safe basin was provided. The reliability function was solved from the backward Kolmogorov equation, and an optimal control strategy was proposed in the stochastic dynamic programming method. Numerical simulation shows that the system stability varies with the parameters, and stochastic Hopf bifurcation and chaos appear in the process; the area of the safe basin decreases when the noise intensifies, and the boundary of the safe basin becomes fractal; the system reliability improved through stochastic optimal control. Finally, the theoretical and numerical results were proved by experiments. The results are helpful in the engineering applications of GMF.
Optimal response to attacks on the open science grids.
Altunay, M.; Leyffer, S.; Linderoth, J. T.; Xie, Z. (Mathematics and Computer Science); (FNAL); (Univ. of Wisconsin at Madison)
2011-01-01
Cybersecurity is a growing concern, especially in open grids, where attack propagation is easy because of prevalent collaborations among thousands of users and hundreds of institutions. The collaboration rules that typically govern large science experiments as well as social networks of scientists span across the institutional security boundaries. A common concern is that the increased openness may allow malicious attackers to spread more readily around the grid. We consider how to optimally respond to attacks in open grid environments. To show how and why attacks spread more readily around the grid, we first discuss how collaborations manifest themselves in the grids and form the collaboration network graph, and how this collaboration network graph affects the security threat levels of grid participants. We present two mixed-integer program (MIP) models to find the optimal response to attacks in open grid environments, and also calculate the threat level associated with each grid participant. Given an attack scenario, our optimal response model aims to minimize the threat levels at unaffected participants while maximizing the uninterrupted scientific production (continuing collaborations). By adopting some of the collaboration rules (e.g., suspending a collaboration or shutting down a site), the model finds optimal response to subvert an attack scenario.
Alessandro B. Romeo
1998-04-27
Modelling gravity is a fundamental problem that must be tackled in N-body simulations of stellar systems, and satisfactory solutions require a deep understanding of the dynamical effects of softening. In a previous paper (Romeo 1997), we have devised a method for exploring such effects, and we have focused on two applications that reveal the dynamical differences between the most representative types of softened gravity. In the present paper we show that our method can be applied in another, more fruitful, way: for developing new ideas about softening. Indeed, it opens a direct route to the discovery of optimal types of softened gravity for given dynamical requirements, and thus to the accomplishment of a physically consistent modelling of disc galaxies, even in the presence of a cold interstellar gaseous component and in situations that demand anisotropic resolution.
Zhang, Yunong; Ge, Shuzhi Sam; Lee, Tong Heng
2004-10-01
In this paper, for joint torque optimization of redundant manipulators subject to physical constraints, we show that velocity-level and acceleration-level redundancy-resolution schemes both can be formulated as a quadratic programming (QP) problem subject to equality and inequality/bound constraints. To solve this QP problem online, a primal-dual dynamical system solver is further presented based on linear variational inequalities. Compared to previous researches, the presented QP-solver has simple piecewise-linear dynamics, does not entail real-time matrix inversion, and could also provide joint-acceleration information for manipulator torque control in the velocity-level redundancy-resolution schemes. The proposed QP-based dynamical system approach is simulated based on the PUMA560 robot arm with efficiency and effectiveness demonstrated. PMID:15503508
NASA Astrophysics Data System (ADS)
Peralta, Richard C.; Forghani, Ali; Fayad, Hala
2014-04-01
Many real water resources optimization problems involve conflicting objectives for which the main goal is to find a set of optimal solutions on, or near to the Pareto front. E-constraint and weighting multiobjective optimization techniques have shortcomings, especially as the number of objectives increases. Multiobjective Genetic Algorithms (MGA) have been previously proposed to overcome these difficulties. Here, an MGA derives a set of optimal solutions for multiobjective multiuser conjunctive use of reservoir, stream, and (un)confined groundwater resources. The proposed methodology is applied to a hydraulically and economically nonlinear system in which all significant flows, including stream-aquifer-reservoir-diversion-return flow interactions, are simulated and optimized simultaneously for multiple periods. Neural networks represent constrained state variables. The addressed objectives that can be optimized simultaneously in the coupled simulation-optimization model are: (1) maximizing water provided from sources, (2) maximizing hydropower production, and (3) minimizing operation costs of transporting water from sources to destinations. Results show the efficiency of multiobjective genetic algorithms for generating Pareto optimal sets for complex nonlinear multiobjective optimization problems.
Ahmed Yousuf Saber; Tomonobu Senjyu
2007-01-01
Swarm-inspired optimization has become very popular in recent years. Ant colony optimization (ACO) is successfully applied in the traveling salesman problem. Performance of the basic ACO for small problems with moderate dimensions and searching space is satisfactory. As the searching space grows exponentially in large-scale power systems generator planning, the basic ACO is not applicable for the vast size of
Optimization algorithms and weighting factors for analysis of dynamic PET studies
Maqsood Yaqub; Ronald Boellaard; Marc A. Kropholler; Adriaan A. Lammertsma
2006-01-01
Positron emission tomography (PET) pharmacokinetic analysis involves fitting of measured PET data to a PET pharmacokinetic model. The fitted parameters may, however, suffer from bias or be unrealistic, especially in the case of noisy data. There are many optimization algorithms, each having different characteristics. The purpose of the present study was to evaluate (1) the performance of different optimization algorithms
Dynamics and real-time optimal control of satellite attitude and satellite formation systems
Yan, Hui
2006-10-30
with a pseudospectral algorithm are examined. The time-optimal magnetic control is bang-bang and the optimal slew time is about 232.7 seconds. The start time occurs when the maneuver is symmetric about the maximum field strength. For real...