Ruszczynski, Andrzej
2011-01-01
Optimization is one of the most important areas of modern applied mathematics, with applications in fields from engineering and economics to finance, statistics, management science, and medicine. While many books have addressed its various aspects, Nonlinear Optimization is the first comprehensive treatment that will allow graduate students and researchers to understand its modern ideas, principles, and methods within a reasonable time, but without sacrificing mathematical precision. Andrzej Ruszczynski, a leading expert in the optimization of nonlinear stochastic systems, integrates the theory and the methods of nonlinear optimization in a unified, clear, and mathematically rigorous fashion, with detailed and easy-to-follow proofs illustrated by numerous examples and figures. The book covers convex analysis, the theory of optimality conditions, duality theory, and numerical methods for solving unconstrained and constrained optimization problems. It addresses not only classical material but also modern top...
Nonlinear optimal control theory
Berkovitz, Leonard David
2012-01-01
Nonlinear Optimal Control Theory presents a deep, wide-ranging introduction to the mathematical theory of the optimal control of processes governed by ordinary differential equations and certain types of differential equations with memory. Many examples illustrate the mathematical issues that need to be addressed when using optimal control techniques in diverse areas. Drawing on classroom-tested material from Purdue University and North Carolina State University, the book gives a unified account of bounded state problems governed by ordinary, integrodifferential, and delay systems. It also dis
Interactive Nonlinear Multiobjective Optimization Methods
Miettinen, Kaisa; Hakanen, Jussi; Podkopaev, Dmitry
2016-01-01
An overview of interactive methods for solving nonlinear multiobjective optimization problems is given. In interactive methods, the decision maker progressively provides preference information so that the most satisfactory Pareto optimal solution can be found for her or his. The basic features of several methods are introduced and some theoretical results are provided. In addition, references to modifications and applications as well as to other methods are indicated. As the...
Nonlinear canonical correlation analysis with k sets of variables
van der Burg, Eeke; de Leeuw, Jan
1987-01-01
The multivariate technique OVERALS is introduced as a non-linear generalization of canonical correlation analysis (CCA). First, two sets CCA is introduced. Two sets CCA is a technique that computes linear combinations of sets of variables that correlate in an optimal way. Two sets CCA is then
Nonlinear Optimization with Financial Applications
Bartholomew-Biggs, Michael
2005-01-01
The book introduces the key ideas behind practical nonlinear optimization. Computational finance - an increasingly popular area of mathematics degree programs - is combined here with the study of an important class of numerical techniques. The financial content of the book is designed to be relevant and interesting to specialists. However, this material - which occupies about one-third of the text - is also sufficiently accessible to allow the book to be used on optimization courses of a more general nature. The essentials of most currently popular algorithms are described, and their performan
Formal Proofs for Nonlinear Optimization
Directory of Open Access Journals (Sweden)
Victor Magron
2015-01-01
Full Text Available We present a formally verified global optimization framework. Given a semialgebraic or transcendental function f and a compact semialgebraic domain K, we use the nonlinear maxplus template approximation algorithm to provide a certified lower bound of f over K.This method allows to bound in a modular way some of the constituents of f by suprema of quadratic forms with a well chosen curvature. Thus, we reduce the initial goal to a hierarchy of semialgebraic optimization problems, solved by sums of squares relaxations. Our implementation tool interleaves semialgebraic approximations with sums of squares witnesses to form certificates. It is interfaced with Coq and thus benefits from the trusted arithmetic available inside the proof assistant. This feature is used to produce, from the certificates, both valid underestimators and lower bounds for each approximated constituent.The application range for such a tool is widespread; for instance Hales' proof of Kepler's conjecture yields thousands of multivariate transcendental inequalities. We illustrate the performance of our formal framework on some of these inequalities as well as on examples from the global optimization literature.
Introduction to Nonlinear and Global Optimization
Hendrix, E.M.T.; Tóth, B.
2010-01-01
This self-contained text provides a solid introduction to global and nonlinear optimization, providing students of mathematics and interdisciplinary sciences with a strong foundation in applied optimization techniques. The book offers a unique hands-on and critical approach to applied optimization
Optimization for nonlinear inverse problem
International Nuclear Information System (INIS)
Boyadzhiev, G.; Brandmayr, E.; Pinat, T.; Panza, G.F.
2007-06-01
The nonlinear inversion of geophysical data in general does not yield a unique solution, but a single model, representing the investigated field, is preferred for an easy geological interpretation of the observations. The analyzed region is constituted by a number of sub-regions where the multi-valued nonlinear inversion is applied, which leads to a multi-valued solution. Therefore, combining the values of the solution in each sub-region, many acceptable models are obtained for the entire region and this complicates the geological interpretation of geophysical investigations. In this paper are presented new methodologies, capable to select one model, among all acceptable ones, that satisfies different criteria of smoothness in the explored space of solutions. In this work we focus on the non-linear inversion of surface waves dispersion curves, which gives structural models of shear-wave velocity versus depth, but the basic concepts have a general validity. (author)
Structural optimization for nonlinear dynamic response
DEFF Research Database (Denmark)
Dou, Suguang; Strachan, B. Scott; Shaw, Steven W.
2015-01-01
by a single vibrating mode, or by a pair of internally resonant modes. The approach combines techniques from nonlinear dynamics, computational mechanics and optimization, and it allows one to relate the geometric and material properties of structural elements to terms in the normal form for a given resonance......Much is known about the nonlinear resonant response of mechanical systems, but methods for the systematic design of structures that optimize aspects of these responses have received little attention. Progress in this area is particularly important in the area of micro-systems, where nonlinear...... resonant behaviour is being used for a variety of applications in sensing and signal conditioning. In this work, we describe a computational method that provides a systematic means for manipulating and optimizing features of nonlinear resonant responses of mechanical structures that are described...
Galerkin approximations of nonlinear optimal control problems in Hilbert spaces
Directory of Open Access Journals (Sweden)
Mickael D. Chekroun
2017-07-01
Full Text Available Nonlinear optimal control problems in Hilbert spaces are considered for which we derive approximation theorems for Galerkin approximations. Approximation theorems are available in the literature. The originality of our approach relies on the identification of a set of natural assumptions that allows us to deal with a broad class of nonlinear evolution equations and cost functionals for which we derive convergence of the value functions associated with the optimal control problem of the Galerkin approximations. This convergence result holds for a broad class of nonlinear control strategies as well. In particular, we show that the framework applies to the optimal control of semilinear heat equations posed on a general compact manifold without boundary. The framework is then shown to apply to geoengineering and mitigation of greenhouse gas emissions formulated here in terms of optimal control of energy balance climate models posed on the sphere $\\mathbb{S}^2$.
Nonlinear analysis approximation theory, optimization and applications
2014-01-01
Many of our daily-life problems can be written in the form of an optimization problem. Therefore, solution methods are needed to solve such problems. Due to the complexity of the problems, it is not always easy to find the exact solution. However, approximate solutions can be found. The theory of the best approximation is applicable in a variety of problems arising in nonlinear functional analysis and optimization. This book highlights interesting aspects of nonlinear analysis and optimization together with many applications in the areas of physical and social sciences including engineering. It is immensely helpful for young graduates and researchers who are pursuing research in this field, as it provides abundant research resources for researchers and post-doctoral fellows. This will be a valuable addition to the library of anyone who works in the field of applied mathematics, economics and engineering.
Methods for Large-Scale Nonlinear Optimization.
1980-05-01
STANFORD, CALIFORNIA 94305 METHODS FOR LARGE-SCALE NONLINEAR OPTIMIZATION by Philip E. Gill, Waiter Murray, I Michael A. Saunden, and Masgaret H. Wright...typical iteration can be partitioned so that where B is an m X m basise matrix. This partition effectively divides the vari- ables into three classes... attention is given to the standard of the coding or the documentation. A much better way of obtaining mathematical software is from a software library
Optimal non-linear health insurance.
Blomqvist, A
1997-06-01
Most theoretical and empirical work on efficient health insurance has been based on models with linear insurance schedules (a constant co-insurance parameter). In this paper, dynamic optimization techniques are used to analyse the properties of optimal non-linear insurance schedules in a model similar to one originally considered by Spence and Zeckhauser (American Economic Review, 1971, 61, 380-387) and reminiscent of those that have been used in the literature on optimal income taxation. The results of a preliminary numerical example suggest that the welfare losses from the implicit subsidy to employer-financed health insurance under US tax law may be a good deal smaller than previously estimated using linear models.
Optimization of nonlinear wave function parameters
International Nuclear Information System (INIS)
Shepard, R.; Minkoff, M.; Chemistry
2006-01-01
An energy-based optimization method is presented for our recently developed nonlinear wave function expansion form for electronic wave functions. This expansion form is based on spin eigenfunctions, using the graphical unitary group approach (GUGA). The wave function is expanded in a basis of product functions, allowing application to closed-shell and open-shell systems and to ground and excited electronic states. Each product basis function is itself a multiconfigurational function that depends on a relatively small number of nonlinear parameters called arc factors. The energy-based optimization is formulated in terms of analytic arc factor gradients and orbital-level Hamiltonian matrices that correspond to a specific kind of uncontraction of each of the product basis functions. These orbital-level Hamiltonian matrices give an intuitive representation of the energy in terms of disjoint subsets of the arc factors, they provide for an efficient computation of gradients of the energy with respect to the arc factors, and they allow optimal arc factors to be determined in closed form for subspaces of the full variation problem. Timings for energy and arc factor gradient computations involving expansion spaces of > 10 24 configuration state functions are reported. Preliminary convergence studies and molecular dissociation curves are presented for some small molecules
Simulation-based optimal Bayesian experimental design for nonlinear systems
Huan, Xun
2013-01-01
The optimal selection of experimental conditions is essential to maximizing the value of data for inference and prediction, particularly in situations where experiments are time-consuming and expensive to conduct. We propose a general mathematical framework and an algorithmic approach for optimal experimental design with nonlinear simulation-based models; in particular, we focus on finding sets of experiments that provide the most information about targeted sets of parameters.Our framework employs a Bayesian statistical setting, which provides a foundation for inference from noisy, indirect, and incomplete data, and a natural mechanism for incorporating heterogeneous sources of information. An objective function is constructed from information theoretic measures, reflecting expected information gain from proposed combinations of experiments. Polynomial chaos approximations and a two-stage Monte Carlo sampling method are used to evaluate the expected information gain. Stochastic approximation algorithms are then used to make optimization feasible in computationally intensive and high-dimensional settings. These algorithms are demonstrated on model problems and on nonlinear parameter inference problems arising in detailed combustion kinetics. © 2012 Elsevier Inc.
Vibration behavior optimization of planetary gear sets
Directory of Open Access Journals (Sweden)
Farshad Shakeri Aski
2014-12-01
Full Text Available This paper presents a global optimization method focused on planetary gear vibration reduction by means of tip relief profile modifications. A nonlinear dynamic model is used to study the vibration behavior. In order to investigate the optimal radius and amplitude, Brute Force method optimization is used. One approach in optimization is straightforward and requires considerable computation power: brute force methods try to calculate all possible solutions and decide afterwards which one is the best. Results show the influence of optimal profile on planetary gear vibrations.
Vector optimization set-valued and variational analysis
Chen, Guang-ya; Yang, Xiaogi
2005-01-01
This book is devoted to vector or multiple criteria approaches in optimization. Topics covered include: vector optimization, vector variational inequalities, vector variational principles, vector minmax inequalities and vector equilibrium problems. In particular, problems with variable ordering relations and set-valued mappings are treated. The nonlinear scalarization method is extensively used throughout the book to deal with various vector-related problems. The results presented are original and should be interesting to researchers and graduates in applied mathematics and operations research
Gradient-based optimization in nonlinear structural dynamics
DEFF Research Database (Denmark)
Dou, Suguang
The intrinsic nonlinearity of mechanical structures can give rise to rich nonlinear dynamics. Recently, nonlinear dynamics of micro-mechanical structures have contributed to developing new Micro-Electro-Mechanical Systems (MEMS), for example, atomic force microscope, passive frequency divider......, frequency stabilization, and disk resonator gyroscope. For advanced design of these structures, it is of considerable value to extend current optimization in linear structural dynamics into nonlinear structural dynamics. In this thesis, we present a framework for modelling, analysis, characterization......, and optimization of nonlinear structural dynamics. In the modelling, nonlinear finite elements are used. In the analysis, nonlinear frequency response and nonlinear normal modes are calculated based on a harmonic balance method with higher-order harmonics. In the characterization, nonlinear modal coupling...
COMPARISON OF NONLINEAR DYNAMICS OPTIMIZATION METHODS FOR APS-U
Energy Technology Data Exchange (ETDEWEB)
Sun, Y.; Borland, Michael
2017-06-25
Many different objectives and genetic algorithms have been proposed for storage ring nonlinear dynamics performance optimization. These optimization objectives include nonlinear chromaticities and driving/detuning terms, on-momentum and off-momentum dynamic acceptance, chromatic detuning, local momentum acceptance, variation of transverse invariant, Touschek lifetime, etc. In this paper, the effectiveness of several different optimization methods and objectives are compared for the nonlinear beam dynamics optimization of the Advanced Photon Source upgrade (APS-U) lattice. The optimized solutions from these different methods are preliminarily compared in terms of the dynamic acceptance, local momentum acceptance, chromatic detuning, and other performance measures.
Nonlinearity Analysis and Parameters Optimization for an Inductive Angle Sensor
Directory of Open Access Journals (Sweden)
Lin Ye
2014-02-01
Full Text Available Using the finite element method (FEM and particle swarm optimization (PSO, a nonlinearity analysis based on parameter optimization is proposed to design an inductive angle sensor. Due to the structure complexity of the sensor, understanding the influences of structure parameters on the nonlinearity errors is a critical step in designing an effective sensor. Key parameters are selected for the design based on the parameters’ effects on the nonlinearity errors. The finite element method and particle swarm optimization are combined for the sensor design to get the minimal nonlinearity error. In the simulation, the nonlinearity error of the optimized sensor is 0.053% in the angle range from −60° to 60°. A prototype sensor is manufactured and measured experimentally, and the experimental nonlinearity error is 0.081% in the angle range from −60° to 60°.
Topology optimization of nonlinear optical devices
DEFF Research Database (Denmark)
Jensen, Jakob Søndergaard
2011-01-01
This paper considers the design of nonlinear photonic devices. The nonlinearity stems from a nonlinear material model with a permittivity that depends on the local time-averaged intensity of the electric field. A finite element model is developed for time-harmonic wave propagation and an incremen......This paper considers the design of nonlinear photonic devices. The nonlinearity stems from a nonlinear material model with a permittivity that depends on the local time-averaged intensity of the electric field. A finite element model is developed for time-harmonic wave propagation...... limiter. Here, air, a linear and a nonlinear material are distributed so that the wave transmission displays a strong sensitivity to the amplitude of the incoming wave....
An hp symplectic pseudospectral method for nonlinear optimal control
Peng, Haijun; Wang, Xinwei; Li, Mingwu; Chen, Biaosong
2017-01-01
An adaptive symplectic pseudospectral method based on the dual variational principle is proposed and is successfully applied to solving nonlinear optimal control problems in this paper. The proposed method satisfies the first order necessary conditions of continuous optimal control problems, also the symplectic property of the original continuous Hamiltonian system is preserved. The original optimal control problem is transferred into a set of nonlinear equations which can be solved easily by Newton-Raphson iterations, and the Jacobian matrix is found to be sparse and symmetric. The proposed method, on one hand, exhibits exponent convergence rates when the number of collocation points are increasing with the fixed number of sub-intervals; on the other hand, exhibits linear convergence rates when the number of sub-intervals is increasing with the fixed number of collocation points. Furthermore, combining with the hp method based on the residual error of dynamic constraints, the proposed method can achieve given precisions in a few iterations. Five examples highlight the high precision and high computational efficiency of the proposed method.
Optimal perturbations for nonlinear systems using graph-based optimal transport
Grover, Piyush; Elamvazhuthi, Karthik
2018-06-01
We formulate and solve a class of finite-time transport and mixing problems in the set-oriented framework. The aim is to obtain optimal discrete-time perturbations in nonlinear dynamical systems to transport a specified initial measure on the phase space to a final measure in finite time. The measure is propagated under system dynamics in between the perturbations via the associated transfer operator. Each perturbation is described by a deterministic map in the measure space that implements a version of Monge-Kantorovich optimal transport with quadratic cost. Hence, the optimal solution minimizes a sum of quadratic costs on phase space transport due to the perturbations applied at specified times. The action of the transport map is approximated by a continuous pseudo-time flow on a graph, resulting in a tractable convex optimization problem. This problem is solved via state-of-the-art solvers to global optimality. We apply this algorithm to a problem of transport between measures supported on two disjoint almost-invariant sets in a chaotic fluid system, and to a finite-time optimal mixing problem by choosing the final measure to be uniform. In both cases, the optimal perturbations are found to exploit the phase space structures, such as lobe dynamics, leading to efficient global transport. As the time-horizon of the problem is increased, the optimal perturbations become increasingly localized. Hence, by combining the transfer operator approach with ideas from the theory of optimal mass transportation, we obtain a discrete-time graph-based algorithm for optimal transport and mixing in nonlinear systems.
Continuous nonlinear optimization for engineering applications in GAMS technology
Andrei, Neculai
2017-01-01
This book presents the theoretical details and computational performances of algorithms used for solving continuous nonlinear optimization applications imbedded in GAMS. Aimed toward scientists and graduate students who utilize optimization methods to model and solve problems in mathematical programming, operations research, business, engineering, and industry, this book enables readers with a background in nonlinear optimization and linear algebra to use GAMS technology to understand and utilize its important capabilities to optimize algorithms for modeling and solving complex, large-scale, continuous nonlinear optimization problems or applications. Beginning with an overview of constrained nonlinear optimization methods, this book moves on to illustrate key aspects of mathematical modeling through modeling technologies based on algebraically oriented modeling languages. Next, the main feature of GAMS, an algebraically oriented language that allows for high-level algebraic representation of mathematical opti...
Topology optimization of hyperelastic structures using a level set method
Chen, Feifei; Wang, Yiqiang; Wang, Michael Yu; Zhang, Y. F.
2017-12-01
Soft rubberlike materials, due to their inherent compliance, are finding widespread implementation in a variety of applications ranging from assistive wearable technologies to soft material robots. Structural design of such soft and rubbery materials necessitates the consideration of large nonlinear deformations and hyperelastic material models to accurately predict their mechanical behaviour. In this paper, we present an effective level set-based topology optimization method for the design of hyperelastic structures that undergo large deformations. The method incorporates both geometric and material nonlinearities where the strain and stress measures are defined within the total Lagrange framework and the hyperelasticity is characterized by the widely-adopted Mooney-Rivlin material model. A shape sensitivity analysis is carried out, in the strict sense of the material derivative, where the high-order terms involving the displacement gradient are retained to ensure the descent direction. As the design velocity enters into the shape derivative in terms of its gradient and divergence terms, we develop a discrete velocity selection strategy. The whole optimization implementation undergoes a two-step process, where the linear optimization is first performed and its optimized solution serves as the initial design for the subsequent nonlinear optimization. It turns out that this operation could efficiently alleviate the numerical instability and facilitate the optimization process. To demonstrate the validity and effectiveness of the proposed method, three compliance minimization problems are studied and their optimized solutions present significant mechanical benefits of incorporating the nonlinearities, in terms of remarkable enhancement in not only the structural stiffness but also the critical buckling load.
On a Highly Nonlinear Self-Obstacle Optimal Control Problem
Energy Technology Data Exchange (ETDEWEB)
Di Donato, Daniela, E-mail: daniela.didonato@unitn.it [University of Trento, Department of Mathematics (Italy); Mugnai, Dimitri, E-mail: dimitri.mugnai@unipg.it [Università di Perugia, Dipartimento di Matematica e Informatica (Italy)
2015-10-15
We consider a non-quadratic optimal control problem associated to a nonlinear elliptic variational inequality, where the obstacle is the control itself. We show that, fixed a desired profile, there exists an optimal solution which is not far from it. Detailed characterizations of the optimal solution are given, also in terms of approximating problems.
Parallel Nonlinear Optimization for Astrodynamic Navigation, Phase I
National Aeronautics and Space Administration — CU Aerospace proposes the development of a new parallel nonlinear program (NLP) solver software package. NLPs allow the solution of complex optimization problems,...
A Nonlinear Fuel Optimal Reaction Jet Control Law
National Research Council Canada - National Science Library
Breitfeller, Eric
2002-01-01
We derive a nonlinear fuel optimal attitude control system (ACS) that drives the final state to the desired state according to a cost function that weights the final state angular error relative to the angular rate error...
Optimization of nonlinear controller with an enhanced biogeography approach
Directory of Open Access Journals (Sweden)
Mohammed Salem
2014-07-01
Full Text Available This paper is dedicated to the optimization of nonlinear controllers basing of an enhanced Biogeography Based Optimization (BBO approach. Indeed, The BBO is combined to a predator and prey model where several predators are used with introduction of a modified migration operator to increase the diversification along the optimization process so as to avoid local optima and reach the optimal solution quickly. The proposed approach is used in tuning the gains of PID controller for nonlinear systems. Simulations are carried out over a Mass spring damper and an inverted pendulum and has given remarkable results when compared to genetic algorithm and BBO.
Directory of Open Access Journals (Sweden)
Jinmyoung Seok
2015-07-01
Full Text Available In this article, we are interested in singularly perturbed nonlinear elliptic problems involving a fractional Laplacian. Under a class of nonlinearity which is believed to be almost optimal, we construct a positive solution which exhibits multiple spikes near any given local minimum components of an exterior potential of the problem.
Discrete-time inverse optimal control for nonlinear systems
Sanchez, Edgar N
2013-01-01
Discrete-Time Inverse Optimal Control for Nonlinear Systems proposes a novel inverse optimal control scheme for stabilization and trajectory tracking of discrete-time nonlinear systems. This avoids the need to solve the associated Hamilton-Jacobi-Bellman equation and minimizes a cost functional, resulting in a more efficient controller. Design More Efficient Controllers for Stabilization and Trajectory Tracking of Discrete-Time Nonlinear Systems The book presents two approaches for controller synthesis: the first based on passivity theory and the second on a control Lyapunov function (CLF). Th
Huang, Hui; Ning, Jixian
2017-01-01
Prederivatives play an important role in the research of set optimization problems. First, we establish several existence theorems of prederivatives for γ -paraconvex set-valued mappings in Banach spaces with [Formula: see text]. Then, in terms of prederivatives, we establish both necessary and sufficient conditions for the existence of Pareto minimal solution of set optimization problems.
ROTAX: a nonlinear optimization program by axes rotation method
International Nuclear Information System (INIS)
Suzuki, Tadakazu
1977-09-01
A nonlinear optimization program employing the axes rotation method has been developed for solving nonlinear problems subject to nonlinear inequality constraints and its stability and convergence efficiency were examined. The axes rotation method is a direct search of the optimum point by rotating the orthogonal coordinate system in a direction giving the minimum objective. The searching direction is rotated freely in multi-dimensional space, so the method is effective for the problems represented with the contours having deep curved valleys. In application of the axes rotation method to the optimization problems subject to nonlinear inequality constraints, an improved version of R.R. Allran and S.E.J. Johnsen's method is used, which deals with a new objective function composed of the original objective and a penalty term to consider the inequality constraints. The program is incorporated in optimization code system SCOOP. (auth.)
A Linearized Relaxing Algorithm for the Specific Nonlinear Optimization Problem
Directory of Open Access Journals (Sweden)
Mio Horai
2016-01-01
Full Text Available We propose a new method for the specific nonlinear and nonconvex global optimization problem by using a linear relaxation technique. To simplify the specific nonlinear and nonconvex optimization problem, we transform the problem to the lower linear relaxation form, and we solve the linear relaxation optimization problem by the Branch and Bound Algorithm. Under some reasonable assumptions, the global convergence of the algorithm is certified for the problem. Numerical results show that this method is more efficient than the previous methods.
Optimal Nonlinear Filter for INS Alignment
Institute of Scientific and Technical Information of China (English)
赵瑞; 顾启泰
2002-01-01
All the methods to handle the inertial navigation system (INS) alignment were sub-optimal in the past. In this paper, particle filtering (PF) as an optimal method is used for solving the problem of INS alignment. A sub-optimal two-step filtering algorithm is presented to improve the real-time performance of PF. The approach combines particle filtering with Kalman filtering (KF). Simulation results illustrate the superior performance of these approaches when compared with extended Kalman filtering (EKF).
Introduction to the theory of nonlinear optimization
Jahn, Johannes
2007-01-01
This book serves as an introductory text to optimization theory in normed spaces. The topics of this book are existence results, various differentiability notions together with optimality conditions, the contingent cone, a generalization of the Lagrange multiplier rule, duality theory, extended semidefinite optimization, and the investigation of linear quadratic and time minimal control problems. This textbook presents fundamentals with particular emphasis on the application to problems in the calculus of variations, approximation and optimal control theory. The reader is expected to have a ba
Optimization of lift gas allocation in a gas lifted oil field as non-linear optimization problem
Directory of Open Access Journals (Sweden)
Roshan Sharma
2012-01-01
Full Text Available Proper allocation and distribution of lift gas is necessary for maximizing total oil production from a field with gas lifted oil wells. When the supply of the lift gas is limited, the total available gas should be optimally distributed among the oil wells of the field such that the total production of oil from the field is maximized. This paper describes a non-linear optimization problem with constraints associated with the optimal distribution of the lift gas. A non-linear objective function is developed using a simple dynamic model of the oil field where the decision variables represent the lift gas flow rate set points of each oil well of the field. The lift gas optimization problem is solved using the emph'fmincon' solver found in MATLAB. As an alternative and for verification, hill climbing method is utilized for solving the optimization problem. Using both of these methods, it has been shown that after optimization, the total oil production is increased by about 4. For multiple oil wells sharing lift gas from a common source, a cascade control strategy along with a nonlinear steady state optimizer behaves as a self-optimizing control structure when the total supply of lift gas is assumed to be the only input disturbance present in the process. Simulation results show that repeated optimization performed after the first time optimization under the presence of the input disturbance has no effect in the total oil production.
A new optimization algotithm with application to nonlinear MPC
Directory of Open Access Journals (Sweden)
Frode Martinsen
2005-01-01
Full Text Available This paper investigates application of SQP optimization algorithm to nonlinear model predictive control. It considers feasible vs. infeasible path methods, sequential vs. simultaneous methods and reduced vs full space methods. A new optimization algorithm coined rFOPT which remains feasibile with respect to inequality constraints is introduced. The suitable choices between these various strategies are assessed informally through a small CSTR case study. The case study also considers the effect various discretization methods have on the optimization problem.
Optimal beamforming in MIMO systems with HPA nonlinearity
Qi, Jian
2010-09-01
In this paper, multiple-input multiple-output (MIMO) transmit beamforming (TB) systems under the consideration of nonlinear high-power amplifiers (HPAs) are investigated. The optimal beamforming scheme, with the optimal beamforming weight vector and combining vector, is proposed for MIMO systems with HPA nonlinearity. The performance of the proposed MIMO beamforming scheme in the presence of HPA nonlinearity is evaluated in terms of average symbol error probability (SEP), outage probability and system capacity, considering transmission over uncorrelated quasi-static frequency-flat Rayleigh fading channels. Numerical results are provided and show the effects of several system parameters, namely, parameters of nonlinear HPA, numbers of transmit and receive antennas, and modulation order of phase-shift keying (PSK), on performance. ©2010 IEEE.
Optimal beamforming in MIMO systems with HPA nonlinearity
Qi, Jian; Aissa, Sonia
2010-01-01
In this paper, multiple-input multiple-output (MIMO) transmit beamforming (TB) systems under the consideration of nonlinear high-power amplifiers (HPAs) are investigated. The optimal beamforming scheme, with the optimal beamforming weight vector and combining vector, is proposed for MIMO systems with HPA nonlinearity. The performance of the proposed MIMO beamforming scheme in the presence of HPA nonlinearity is evaluated in terms of average symbol error probability (SEP), outage probability and system capacity, considering transmission over uncorrelated quasi-static frequency-flat Rayleigh fading channels. Numerical results are provided and show the effects of several system parameters, namely, parameters of nonlinear HPA, numbers of transmit and receive antennas, and modulation order of phase-shift keying (PSK), on performance. ©2010 IEEE.
Non-linear programming method in optimization of fast reactors
International Nuclear Information System (INIS)
Pavelesku, M.; Dumitresku, Kh.; Adam, S.
1975-01-01
Application of the non-linear programming methods on optimization of nuclear materials distribution in fast reactor is discussed. The programming task composition is made on the basis of the reactor calculation dependent on the fuel distribution strategy. As an illustration of this method application the solution of simple example is given. Solution of the non-linear program is done on the basis of the numerical method SUMT. (I.T.)
Conference on High Performance Software for Nonlinear Optimization
Murli, Almerico; Pardalos, Panos; Toraldo, Gerardo
1998-01-01
This book contains a selection of papers presented at the conference on High Performance Software for Nonlinear Optimization (HPSN097) which was held in Ischia, Italy, in June 1997. The rapid progress of computer technologies, including new parallel architec tures, has stimulated a large amount of research devoted to building software environments and defining algorithms able to fully exploit this new computa tional power. In some sense, numerical analysis has to conform itself to the new tools. The impact of parallel computing in nonlinear optimization, which had a slow start at the beginning, seems now to increase at a fast rate, and it is reasonable to expect an even greater acceleration in the future. As with the first HPSNO conference, the goal of the HPSN097 conference was to supply a broad overview of the more recent developments and trends in nonlinear optimization, emphasizing the algorithmic and high performance software aspects. Bringing together new computational methodologies with theoretical...
Optimization under uncertainty of parallel nonlinear energy sinks
Boroson, Ethan; Missoum, Samy; Mattei, Pierre-Olivier; Vergez, Christophe
2017-04-01
Nonlinear Energy Sinks (NESs) are a promising technique for passively reducing the amplitude of vibrations. Through nonlinear stiffness properties, a NES is able to passively and irreversibly absorb energy. Unlike the traditional Tuned Mass Damper (TMD), NESs do not require a specific tuning and absorb energy over a wider range of frequencies. Nevertheless, they are still only efficient over a limited range of excitations. In order to mitigate this limitation and maximize the efficiency range, this work investigates the optimization of multiple NESs configured in parallel. It is well known that the efficiency of a NES is extremely sensitive to small perturbations in loading conditions or design parameters. In fact, the efficiency of a NES has been shown to be nearly discontinuous in the neighborhood of its activation threshold. For this reason, uncertainties must be taken into account in the design optimization of NESs. In addition, the discontinuities require a specific treatment during the optimization process. In this work, the objective of the optimization is to maximize the expected value of the efficiency of NESs in parallel. The optimization algorithm is able to tackle design variables with uncertainty (e.g., nonlinear stiffness coefficients) as well as aleatory variables such as the initial velocity of the main system. The optimal design of several parallel NES configurations for maximum mean efficiency is investigated. Specifically, NES nonlinear stiffness properties, considered random design variables, are optimized for cases with 1, 2, 3, 4, 5, and 10 NESs in parallel. The distributions of efficiency for the optimal parallel configurations are compared to distributions of efficiencies of non-optimized NESs. It is observed that the optimization enables a sharp increase in the mean value of efficiency while reducing the corresponding variance, thus leading to more robust NES designs.
Stupishin, L. U.; Nikitin, K. E.; Kolesnikov, A. G.
2018-02-01
The article is concerned with a methodology of optimal design of geometrically nonlinear (flexible) shells of revolution of minimum weight with strength, stability and strain constraints. The problem of optimal design with constraints is reduced to the problem of unconstrained minimization using the penalty functions method. Stress-strain state of shell is determined within the geometrically nonlinear deformation theory. A special feature of the methodology is the use of a mixed finite-element formulation based on the Galerkin method. Test problems for determining the optimal form and thickness distribution of a shell of minimum weight are considered. The validity of the results obtained using the developed methodology is analyzed, and the efficiency of various optimization algorithms is compared to solve the set problem. The developed methodology has demonstrated the possibility and accuracy of finding the optimal solution.
Approaches to the Optimal Nonlinear Analysis of Microcalorimeter Pulses
Fowler, J. W.; Pappas, C. G.; Alpert, B. K.; Doriese, W. B.; O'Neil, G. C.; Ullom, J. N.; Swetz, D. S.
2018-03-01
We consider how to analyze microcalorimeter pulses for quantities that are nonlinear in the data, while preserving the signal-to-noise advantages of linear optimal filtering. We successfully apply our chosen approach to compute the electrothermal feedback energy deficit (the "Joule energy") of a pulse, which has been proposed as a linear estimator of the deposited photon energy.
New preconditioned conjugate gradient algorithms for nonlinear unconstrained optimization problems
International Nuclear Information System (INIS)
Al-Bayati, A.; Al-Asadi, N.
1997-01-01
This paper presents two new predilection conjugate gradient algorithms for nonlinear unconstrained optimization problems and examines their computational performance. Computational experience shows that the new proposed algorithms generally imp lone the efficiency of Nazareth's [13] preconditioned conjugate gradient algorithm. (authors). 16 refs., 1 tab
Nonlinear Dynamic Analysis and Optimization of Closed-Form Planetary Gear System
Directory of Open Access Journals (Sweden)
Qilin Huang
2013-01-01
Full Text Available A nonlinear purely rotational dynamic model of a multistage closed-form planetary gear set formed by two simple planetary stages is proposed in this study. The model includes time-varying mesh stiffness, excitation fluctuation and gear backlash nonlinearities. The nonlinear differential equations of motion are solved numerically using variable step-size Runge-Kutta. In order to obtain function expression of optimization objective, the nonlinear differential equations of motion are solved analytically using harmonic balance method (HBM. Based on the analytical solution of dynamic equations, the optimization mathematical model which aims at minimizing the vibration displacement of the low-speed carrier and the total mass of the gear transmission system is established. The optimization toolbox in MATLAB program is adopted to obtain the optimal solution. A case is studied to demonstrate the effectiveness of the dynamic model and the optimization method. The results show that the dynamic properties of the closed-form planetary gear transmission system have been improved and the total mass of the gear set has been decreased significantly.
Directory of Open Access Journals (Sweden)
Dalei Song
2012-10-01
Full Text Available The adaptive extended set-membership filter (AESMF for nonlinear ellipsoidal estimation suffers a mismatch between real process noise and its set boundaries, which may result in unstable estimation. In this paper, a MIT method-based adaptive set-membership filter, for the optimization of the set boundaries of process noise, is developed and applied to the nonlinear joint estimation of both time-varying states and parameters. As a result of using the proposed MIT-AESMF, the estimation effectiveness and boundary accuracy of traditional AESMF are substantially improved. Simulation results have shown the efficiency and robustness of the proposed method.
Route Monopolie and Optimal Nonlinear Pricing
Tournut, Jacques
2003-01-01
To cope with air traffic growth and congested airports, two solutions are apparent on the supply side: 1) use larger aircraft in the hub and spoke system; or 2) develop new routes through secondary airports. An enlarged route system through secondary airports may increase the proportion of route monopolies in the air transport market.The monopoly optimal non linear pricing policy is well known in the case of one dimension (one instrument, one characteristic) but not in the case of several dimensions. This paper explores the robustness of the one dimensional screening model with respect to increasing the number of instruments and the number of characteristics. The objective of this paper is then to link and fill the gap in both literatures. One of the merits of the screening model has been to show that a great varieD" of economic questions (non linear pricing, product line choice, auction design, income taxation, regulation...) could be handled within the same framework.VCe study a case of non linear pricing (2 instruments (2 routes on which the airline pro_ddes customers with services), 2 characteristics (demand of services on these routes) and two values per characteristic (low and high demand of services on these routes)) and we show that none of the conclusions of the one dimensional analysis remain valid. In particular, upward incentive compatibility constraint may be binding at the optimum. As a consequence, they may be distortion at the top of the distribution. In addition to this, we show that the optimal solution often requires a kind of form of bundling, we explain explicitly distortions and show that it is sometimes optimal for the monopolist to only produce one good (instead of two) or to exclude some buyers from the market. Actually, this means that the monopolist cannot fully apply his monopoly power and is better off selling both goods independently.We then define all the possible solutions in the case of a quadratic cost function for a uniform
Nonlinear optimal perturbations in a curved pipe
Rinaldi, Enrico; Canton, Jacopo; Marin, Oana; Schanen, Michel; Schlatter, Philipp
2017-11-01
We investigate the effect of curvature on transition to turbulence in pipes by comparing optimal perturbations of finite amplitude that maximise their energy growth in a toroidal geometry to the ones calculated in the absence of curvature. Our interest is motivated by the fact that even small curvatures, of the order of d =Rpipe /Rtorus art numerical algorithms, capable of tackling the optimisation problem on large computational domains, coupled to a high-order spectral-element code, which is used to perform direct numerical simulations (DNS) of the full Navier-Stokes and their adjoint equations. Results are compared to the corresponding states in straight pipes and differences in their structure and evolution are discussed. Furthermore, the newly calculated initial conditions are used to identify coherent flow structures that are compared to the ones observed in recent DNS of weakly turbulent and relaminarising flows in the same toroidal geometry.
Nonlinear adaptive optimization of biomass productivity in continuous bioreactors
Energy Technology Data Exchange (ETDEWEB)
Sauvaire, P; Mellichamp, D A; Agrawal, P [California Univ., Santa Barbara, CA (United States). Dept. of Chemical and Nuclear Engineering
1991-11-01
A novel on-line adaptive optimization algorithm is developed and applied to continuous biological reactors. The algorithm makes use of a simple nonlinear estimation model that relates either the cell-mass productivity or the cell-mass concentration to the dilution rate. On-line estimation is used to recursively identify the parameters in the nonlinear process model and to periodically calculate and steer the bioreactor to the dilution rate that yields optimum cell-mass productivity. Thus, the algorithm does not require an accurate process model, locates the optimum dilution rate online, and maintains the bioreactors at this optimum condition at all times. The features of the proposed new algorithm are compared with those of other adaptive optimization techniques presented in the literature. A detailed simulation study using three different microbial system models was conducted to illustrate the performance of the optimization algorithms. (orig.).
Optimization Settings in the Fuzzy Combined Mamdani PID Controller
Kudinov, Y. I.; Pashchenko, F. F.; Pashchenko, A. F.; Kelina, A. Y.; Kolesnikov, V. A.
2017-11-01
In the present work the actual problem of determining the optimal settings of fuzzy parallel proportional-integral-derivative (PID) controller is considered to control nonlinear plants that is not always possible to perform with classical linear PID controllers. In contrast to the linear fuzzy PID controllers there are no analytical methods of settings calculation. In this paper, we develop a numerical optimization approach to determining the coefficients of a fuzzy PID controller. Decomposition method of optimization is proposed, the essence of which was as follows. All homogeneous coefficients were distributed to the relevant groups, for example, three error coefficients, the three coefficients of the changes of errors and the three coefficients of the outputs P, I and D components. Consistently in each of such groups the search algorithm was selected that has determined the coefficients under which we receive the schedule of the transition process satisfying all the applicable constraints. Thus, with the help of Matlab and Simulink in a reasonable time were found the factors of a fuzzy PID controller, which meet the accepted limitations on the transition process.
Optimal timing for intravenous administration set replacement.
Gillies, D; O'Riordan, L; Wallen, M; Morrison, A; Rankin, K; Nagy, S
2005-10-19
Administration of intravenous therapy is a common occurrence within the hospital setting. Routine replacement of administration sets has been advocated to reduce intravenous infusion contamination. If decreasing the frequency of changing intravenous administration sets does not increase infection rates, a change in practice could result in considerable cost savings. The objective of this review was to identify the optimal interval for the routine replacement of intravenous administration sets when infusate or parenteral nutrition (lipid and non-lipid) solutions are administered to people in hospital via central or peripheral venous catheters. We searched The Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, CINAHL, EMBASE: all from inception to February 2004; reference lists of identified trials, and bibliographies of published reviews. We also contacted researchers in the field. We did not have a language restriction. We included all randomized or quasi-randomized controlled trials addressing the frequency of replacing intravenous administration sets when parenteral nutrition (lipid and non-lipid containing solutions) or infusions (excluding blood) were administered to people in hospital via a central or peripheral catheter. Two authors assessed all potentially relevant studies. We resolved disagreements between the two authors by discussion with a third author. We collected data for the outcomes; infusate contamination; infusate-related bloodstream infection; catheter contamination; catheter-related bloodstream infection; all-cause bloodstream infection and all-cause mortality. We identified 23 references for review. We excluded eight of these studies; five because they did not fit the inclusion criteria and three because of inadequate data. We extracted data from the remaining 15 references (13 studies) with 4783 participants. We conclude that there is no evidence that changing intravenous administration sets more often than every 96 hours
Imitative and best response behaviors in a nonlinear Cournotian setting
Cerboni Baiardi, Lorenzo; Naimzada, Ahmad K.
2018-05-01
We consider the competition among quantity setting players in a deterministic nonlinear oligopoly framework characterized by an isoelastic demand curve. Players are characterized by having heterogeneous decisional mechanisms to set their outputs: some players are imitators, while the remaining others adopt a rational-like rule according to which their past decisions are adjusted towards their static expectation best response. The Cournot-Nash production level is a stationary state of our model together with a further production level that can be interpreted as the competitive outcome in case only imitators are present. We found that both the number of players and the relative fraction of imitators influence stability of the Cournot-Nash equilibrium with an ambiguous role, and double instability thresholds may be observed. Global analysis shows that a wide variety of complex dynamic scenarios emerge. Chaotic trajectories as well as multi-stabilities, where different attractors coexist, are robust phenomena that can be observed for a wide spectrum of parameter sets.
New Exact Penalty Functions for Nonlinear Constrained Optimization Problems
Directory of Open Access Journals (Sweden)
Bingzhuang Liu
2014-01-01
Full Text Available For two kinds of nonlinear constrained optimization problems, we propose two simple penalty functions, respectively, by augmenting the dimension of the primal problem with a variable that controls the weight of the penalty terms. Both of the penalty functions enjoy improved smoothness. Under mild conditions, it can be proved that our penalty functions are both exact in the sense that local minimizers of the associated penalty problem are precisely the local minimizers of the original constrained problem.
Hierarchical optimal control of large-scale nonlinear chemical processes.
Ramezani, Mohammad Hossein; Sadati, Nasser
2009-01-01
In this paper, a new approach is presented for optimal control of large-scale chemical processes. In this approach, the chemical process is decomposed into smaller sub-systems at the first level, and a coordinator at the second level, for which a two-level hierarchical control strategy is designed. For this purpose, each sub-system in the first level can be solved separately, by using any conventional optimization algorithm. In the second level, the solutions obtained from the first level are coordinated using a new gradient-type strategy, which is updated by the error of the coordination vector. The proposed algorithm is used to solve the optimal control problem of a complex nonlinear chemical stirred tank reactor (CSTR), where its solution is also compared with the ones obtained using the centralized approach. The simulation results show the efficiency and the capability of the proposed hierarchical approach, in finding the optimal solution, over the centralized method.
Optimal analytic method for the nonlinear Hasegawa-Mima equation
Baxter, Mathew; Van Gorder, Robert A.; Vajravelu, Kuppalapalle
2014-05-01
The Hasegawa-Mima equation is a nonlinear partial differential equation that describes the electric potential due to a drift wave in a plasma. In the present paper, we apply the method of homotopy analysis to a slightly more general Hasegawa-Mima equation, which accounts for hyper-viscous damping or viscous dissipation. First, we outline the method for the general initial/boundary value problem over a compact rectangular spatial domain. We use a two-stage method, where both the convergence control parameter and the auxiliary linear operator are optimally selected to minimize the residual error due to the approximation. To do the latter, we consider a family of operators parameterized by a constant which gives the decay rate of the solutions. After outlining the general method, we consider a number of concrete examples in order to demonstrate the utility of this approach. The results enable us to study properties of the initial/boundary value problem for the generalized Hasegawa-Mima equation. In several cases considered, we are able to obtain solutions with extremely small residual errors after relatively few iterations are computed (residual errors on the order of 10-15 are found in multiple cases after only three iterations). The results demonstrate that selecting a parameterized auxiliary linear operator can be extremely useful for minimizing residual errors when used concurrently with the optimal homotopy analysis method, suggesting that this approach can prove useful for a number of nonlinear partial differential equations arising in physics and nonlinear mechanics.
Spin glasses and nonlinear constraints in portfolio optimization
Energy Technology Data Exchange (ETDEWEB)
Andrecut, M., E-mail: mircea.andrecut@gmail.com
2014-01-17
We discuss the portfolio optimization problem with the obligatory deposits constraint. Recently it has been shown that as a consequence of this nonlinear constraint, the solution consists of an exponentially large number of optimal portfolios, completely different from each other, and extremely sensitive to any changes in the input parameters of the problem, making the concept of rational decision making questionable. Here we reformulate the problem using a quadratic obligatory deposits constraint, and we show that from the physics point of view, finding an optimal portfolio amounts to calculating the mean-field magnetizations of a random Ising model with the constraint of a constant magnetization norm. We show that the model reduces to an eigenproblem, with 2N solutions, where N is the number of assets defining the portfolio. Also, in order to illustrate our results, we present a detailed numerical example of a portfolio of several risky common stocks traded on the Nasdaq Market.
Spin glasses and nonlinear constraints in portfolio optimization
International Nuclear Information System (INIS)
Andrecut, M.
2014-01-01
We discuss the portfolio optimization problem with the obligatory deposits constraint. Recently it has been shown that as a consequence of this nonlinear constraint, the solution consists of an exponentially large number of optimal portfolios, completely different from each other, and extremely sensitive to any changes in the input parameters of the problem, making the concept of rational decision making questionable. Here we reformulate the problem using a quadratic obligatory deposits constraint, and we show that from the physics point of view, finding an optimal portfolio amounts to calculating the mean-field magnetizations of a random Ising model with the constraint of a constant magnetization norm. We show that the model reduces to an eigenproblem, with 2N solutions, where N is the number of assets defining the portfolio. Also, in order to illustrate our results, we present a detailed numerical example of a portfolio of several risky common stocks traded on the Nasdaq Market.
Stiffness design of geometrically nonlinear structures using topology optimization
DEFF Research Database (Denmark)
Buhl, Thomas; Pedersen, Claus B. Wittendorf; Sigmund, Ole
2000-01-01
of the objective functions are found with the adjoint method and the optimization problem is solved using the Method of Moving Asymptotes. A filtering scheme is used to obtain checkerboard-free and mesh-independent designs and a continuation approach improves convergence to efficient designs. Different objective......The paper deals with topology optimization of structures undergoing large deformations. The geometrically nonlinear behaviour of the structures are modelled using a total Lagrangian finite element formulation and the equilibrium is found using a Newton-Raphson iterative scheme. The sensitivities...... functions are tested. Minimizing compliance for a fixed load results in degenerated topologies which are very inefficient for smaller or larger loads. The problem of obtaining degenerated "optimal" topologies which only can support the design load is even more pronounced than for structures with linear...
Global Optimization of Nonlinear Blend-Scheduling Problems
Directory of Open Access Journals (Sweden)
Pedro A. Castillo Castillo
2017-04-01
Full Text Available The scheduling of gasoline-blending operations is an important problem in the oil refining industry. This problem not only exhibits the combinatorial nature that is intrinsic to scheduling problems, but also non-convex nonlinear behavior, due to the blending of various materials with different quality properties. In this work, a global optimization algorithm is proposed to solve a previously published continuous-time mixed-integer nonlinear scheduling model for gasoline blending. The model includes blend recipe optimization, the distribution problem, and several important operational features and constraints. The algorithm employs piecewise McCormick relaxation (PMCR and normalized multiparametric disaggregation technique (NMDT to compute estimates of the global optimum. These techniques partition the domain of one of the variables in a bilinear term and generate convex relaxations for each partition. By increasing the number of partitions and reducing the domain of the variables, the algorithm is able to refine the estimates of the global solution. The algorithm is compared to two commercial global solvers and two heuristic methods by solving four examples from the literature. Results show that the proposed global optimization algorithm performs on par with commercial solvers but is not as fast as heuristic approaches.
Non-linear theory of elasticity and optimal design
Ratner, LW
2003-01-01
In order to select an optimal structure among possible similar structures, one needs to compare the elastic behavior of the structures. A new criterion that describes elastic behavior is the rate of change of deformation. Using this criterion, the safe dimensions of a structure that are required by the stress distributed in a structure can be calculated. The new non-linear theory of elasticity allows one to determine the actual individual limit of elasticity/failure of a structure using a simple non-destructive method of measurement of deformation on the model of a structure while presently it
Non-linear and signal energy optimal asymptotic filter design
Directory of Open Access Journals (Sweden)
Josef Hrusak
2003-10-01
Full Text Available The paper studies some connections between the main results of the well known Wiener-Kalman-Bucy stochastic approach to filtering problems based mainly on the linear stochastic estimation theory and emphasizing the optimality aspects of the achieved results and the classical deterministic frequency domain linear filters such as Chebyshev, Butterworth, Bessel, etc. A new non-stochastic but not necessarily deterministic (possibly non-linear alternative approach called asymptotic filtering based mainly on the concepts of signal power, signal energy and a system equivalence relation plays an important role in the presentation. Filtering error invariance and convergence aspects are emphasized in the approach. It is shown that introducing the signal power as the quantitative measure of energy dissipation makes it possible to achieve reasonable results from the optimality point of view as well. The property of structural energy dissipativeness is one of the most important and fundamental features of resulting filters. Therefore, it is natural to call them asymptotic filters. The notion of the asymptotic filter is carried in the paper as a proper tool in order to unify stochastic and non-stochastic, linear and nonlinear approaches to signal filtering.
Optimal Nonlinear Pricing, Bundling Commodities and Contingent Services
International Nuclear Information System (INIS)
Podesta, Marion; Poudou, Jean-Christophe
2008-01-01
In this paper, we propose to analyze optimal nonlinear pricing when a firm offers in a bundle a commodity and a contingent service. The paper studies a mechanism design where all private information can be captured in a single scalar variable in a monopoly context. We show that to propose the package for commodity and service is less costly for the consumer, the firm has lower consumers' rent than the situation where it sells their good and contingent service under an independent pricing strategy. In fact, the possibility to use price discrimination via the supply of package is dominated by the fact that it is costly for the consumer to sign two contracts. Bundling energy and a contingent service is a profitable strategy for a energetician monopoly practising optimal nonlinear tariff. We show that the rates of the energy and the contingent service depend to the optional character of the contingent service and depend to the degree of complementarity between commodities and services. (authors)
Photon attenuation correction technique in SPECT based on nonlinear optimization
International Nuclear Information System (INIS)
Suzuki, Shigehito; Wakabayashi, Misato; Okuyama, Keiichi; Kuwamura, Susumu
1998-01-01
Photon attenuation correction in SPECT was made using a nonlinear optimization theory, in which an optimum image is searched so that the sum of square errors between observed and reprojected projection data is minimized. This correction technique consists of optimization and step-width algorithms, which determine at each iteration a pixel-by-pixel directional value of search and its step-width, respectively. We used the conjugate gradient and quasi-Newton methods as the optimization algorithm, and Curry rule and the quadratic function method as the step-width algorithm. Statistical fluctuations in the corrected image due to statistical noise in the emission projection data grew as the iteration increased, depending on the combination of optimization and step-width algorithms. To suppress them, smoothing for directional values was introduced. Computer experiments and clinical applications showed a pronounced reduction in statistical fluctuations of the corrected image for all combinations. Combinations using the conjugate gradient method were superior in noise characteristic and computation time. The use of that method with the quadratic function method was optimum if noise property was regarded as important. (author)
Robust and fast nonlinear optimization of diffusion MRI microstructure models.
Harms, R L; Fritz, F J; Tobisch, A; Goebel, R; Roebroeck, A
2017-07-15
Advances in biophysical multi-compartment modeling for diffusion MRI (dMRI) have gained popularity because of greater specificity than DTI in relating the dMRI signal to underlying cellular microstructure. A large range of these diffusion microstructure models have been developed and each of the popular models comes with its own, often different, optimization algorithm, noise model and initialization strategy to estimate its parameter maps. Since data fit, accuracy and precision is hard to verify, this creates additional challenges to comparability and generalization of results from diffusion microstructure models. In addition, non-linear optimization is computationally expensive leading to very long run times, which can be prohibitive in large group or population studies. In this technical note we investigate the performance of several optimization algorithms and initialization strategies over a few of the most popular diffusion microstructure models, including NODDI and CHARMED. We evaluate whether a single well performing optimization approach exists that could be applied to many models and would equate both run time and fit aspects. All models, algorithms and strategies were implemented on the Graphics Processing Unit (GPU) to remove run time constraints, with which we achieve whole brain dataset fits in seconds to minutes. We then evaluated fit, accuracy, precision and run time for different models of differing complexity against three common optimization algorithms and three parameter initialization strategies. Variability of the achieved quality of fit in actual data was evaluated on ten subjects of each of two population studies with a different acquisition protocol. We find that optimization algorithms and multi-step optimization approaches have a considerable influence on performance and stability over subjects and over acquisition protocols. The gradient-free Powell conjugate-direction algorithm was found to outperform other common algorithms in terms of
Sulcal set optimization for cortical surface registration.
Joshi, Anand A; Pantazis, Dimitrios; Li, Quanzheng; Damasio, Hanna; Shattuck, David W; Toga, Arthur W; Leahy, Richard M
2010-04-15
Flat mapping based cortical surface registration constrained by manually traced sulcal curves has been widely used for inter subject comparisons of neuroanatomical data. Even for an experienced neuroanatomist, manual sulcal tracing can be quite time consuming, with the cost increasing with the number of sulcal curves used for registration. We present a method for estimation of an optimal subset of size N(C) from N possible candidate sulcal curves that minimizes a mean squared error metric over all combinations of N(C) curves. The resulting procedure allows us to estimate a subset with a reduced number of curves to be traced as part of the registration procedure leading to optimal use of manual labeling effort for registration. To minimize the error metric we analyze the correlation structure of the errors in the sulcal curves by modeling them as a multivariate Gaussian distribution. For a given subset of sulci used as constraints in surface registration, the proposed model estimates registration error based on the correlation structure of the sulcal errors. The optimal subset of constraint curves consists of the N(C) sulci that jointly minimize the estimated error variance for the subset of unconstrained curves conditioned on the N(C) constraint curves. The optimal subsets of sulci are presented and the estimated and actual registration errors for these subsets are computed. Copyright 2009 Elsevier Inc. All rights reserved.
Set-valued optimization an introduction with applications
Khan, Akhtar A; Zalinescu, Constantin
2014-01-01
Set-valued optimization is a vibrant and expanding branch of mathematics that deals with optimization problems where the objective map and/or the constraints maps are set-valued maps acting between certain spaces. Since set-valued maps subsumes single valued maps, set-valued optimization provides an important extension and unification of the scalar as well as the vector optimization problems. Therefore this relatively new discipline has justifiably attracted a great deal of attention in recent years. This book presents, in a unified framework, basic properties on ordering relations, solution c
Nonlinear Burn Control and Operating Point Optimization in ITER
Boyer, Mark; Schuster, Eugenio
2013-10-01
Control of the fusion power through regulation of the plasma density and temperature will be essential for achieving and maintaining desired operating points in fusion reactors and burning plasma experiments like ITER. In this work, a volume averaged model for the evolution of the density of energy, deuterium and tritium fuel ions, alpha-particles, and impurity ions is used to synthesize a multi-input multi-output nonlinear feedback controller for stabilizing and modulating the burn condition. Adaptive control techniques are used to account for uncertainty in model parameters, including particle confinement times and recycling rates. The control approach makes use of the different possible methods for altering the fusion power, including adjusting the temperature through auxiliary heating, modulating the density and isotopic mix through fueling, and altering the impurity density through impurity injection. Furthermore, a model-based optimization scheme is proposed to drive the system as close as possible to desired fusion power and temperature references. Constraints are considered in the optimization scheme to ensure that, for example, density and beta limits are avoided, and that optimal operation is achieved even when actuators reach saturation. Supported by the NSF CAREER award program (ECCS-0645086).
ARSTEC, Nonlinear Optimization Program Using Random Search Method
International Nuclear Information System (INIS)
Rasmuson, D. M.; Marshall, N. H.
1979-01-01
1 - Description of problem or function: The ARSTEC program was written to solve nonlinear, mixed integer, optimization problems. An example of such a problem in the nuclear industry is the allocation of redundant parts in the design of a nuclear power plant to minimize plant unavailability. 2 - Method of solution: The technique used in ARSTEC is the adaptive random search method. The search is started from an arbitrary point in the search region and every time a point that improves the objective function is found, the search region is centered at that new point. 3 - Restrictions on the complexity of the problem: Presently, the maximum number of independent variables allowed is 10. This can be changed by increasing the dimension of the arrays
Robust C subroutines for non-linear optimization
DEFF Research Database (Denmark)
Brock, Pernille; Madsen, Kaj; Nielsen, Hans Bruun
2004-01-01
This report presents a package of robust and easy-to-use C subroutines for solving unconstrained and constrained non-linear optimization problems. The intention is that the routines should use the currently best algorithms available. All routines have standardized calls, and the user does not have...... by changing 1 to 0. The present report is a new and updated version of a previous report NI-91-03 with the same title, [16]. Both the previous and the present report describe a collection of subroutines, which have been translated from Fortran to C. The reason for writing the present report is that some...... of the C subroutines have been replaced by more effective and robust versions translated from the original Fortran subroutines to C by the Bandler Group, see [1]. Also the test examples have been modi ed to some extent. For a description of the original Fortran subroutines see the report [17]. The software...
Robust Homography Estimation Based on Nonlinear Least Squares Optimization
Directory of Open Access Journals (Sweden)
Wei Mou
2014-01-01
Full Text Available The homography between image pairs is normally estimated by minimizing a suitable cost function given 2D keypoint correspondences. The correspondences are typically established using descriptor distance of keypoints. However, the correspondences are often incorrect due to ambiguous descriptors which can introduce errors into following homography computing step. There have been numerous attempts to filter out these erroneous correspondences, but it is unlikely to always achieve perfect matching. To deal with this problem, we propose a nonlinear least squares optimization approach to compute homography such that false matches have no or little effect on computed homography. Unlike normal homography computation algorithms, our method formulates not only the keypoints’ geometric relationship but also their descriptor similarity into cost function. Moreover, the cost function is parametrized in such a way that incorrect correspondences can be simultaneously identified while the homography is computed. Experiments show that the proposed approach can perform well even with the presence of a large number of outliers.
Optimal timing for intravascular administration set replacement.
Ullman, Amanda J; Cooke, Marie L; Gillies, Donna; Marsh, Nicole M; Daud, Azlina; McGrail, Matthew R; O'Riordan, Elizabeth; Rickard, Claire M
2013-09-15
The tubing (administration set) attached to both venous and arterial catheters may contribute to bacteraemia and other infections. The rate of infection may be increased or decreased by routine replacement of administration sets. This review was originally published in 2005 and was updated in 2012. The objective of this review was to identify any relationship between the frequency with which administration sets are replaced and rates of microbial colonization, infection and death. We searched The Cochrane Central Register of Controlled Trials (CENTRAL) (The Cochrane Library 2012, Issue 6), MEDLINE (1950 to June 2012), CINAHL (1982 to June 2012), EMBASE (1980 to June 2012), reference lists of identified trials and bibliographies of published reviews. The original search was performed in February 2004. We also contacted researchers in the field. We applied no language restriction. We included all randomized or controlled clinical trials on the frequency of venous or arterial catheter administration set replacement in hospitalized participants. Two review authors assessed all potentially relevant studies. We resolved disagreements between the two review authors by discussion with a third review author. We collected data for seven outcomes: catheter-related infection; infusate-related infection; infusate microbial colonization; catheter microbial colonization; all-cause bloodstream infection; mortality; and cost. We pooled results from studies that compared different frequencies of administration set replacement, for instance, we pooled studies that compared replacement ≥ every 96 hours versus every 72 hours with studies that compared replacement ≥ every 48 hours versus every 24 hours. We identified 26 studies for this updated review, 10 of which we excluded; six did not fulfil the inclusion criteria and four did not report usable data. We extracted data from the remaining 18 references (16 studies) with 5001 participants: study designs included neonate and adult
Optimization of CW Fiber Lasers With Strong Nonlinear Cavity Dynamics
Shtyrina, O. V.; Efremov, S. A.; Yarutkina, I. A.; Skidin, A. S.; Fedoruk, M. P.
2018-04-01
In present work the equation for the saturated gain is derived from one-level gain equations describing the energy evolution inside the laser cavity. It is shown how to derive the parameters of the mathematical model from the experimental results. The numerically-estimated energy and spectrum of the signal are in good agreement with the experiment. Also, the optimization of the output energy is performed for a given set of model parameters.
Chen, Jie; Li, Jiahong; Yang, Shuanghua; Deng, Fang
2017-11-01
The identification of the nonlinearity and coupling is crucial in nonlinear target tracking problem in collaborative sensor networks. According to the adaptive Kalman filtering (KF) method, the nonlinearity and coupling can be regarded as the model noise covariance, and estimated by minimizing the innovation or residual errors of the states. However, the method requires large time window of data to achieve reliable covariance measurement, making it impractical for nonlinear systems which are rapidly changing. To deal with the problem, a weighted optimization-based distributed KF algorithm (WODKF) is proposed in this paper. The algorithm enlarges the data size of each sensor by the received measurements and state estimates from its connected sensors instead of the time window. A new cost function is set as the weighted sum of the bias and oscillation of the state to estimate the "best" estimate of the model noise covariance. The bias and oscillation of the state of each sensor are estimated by polynomial fitting a time window of state estimates and measurements of the sensor and its neighbors weighted by the measurement noise covariance. The best estimate of the model noise covariance is computed by minimizing the weighted cost function using the exhaustive method. The sensor selection method is in addition to the algorithm to decrease the computation load of the filter and increase the scalability of the sensor network. The existence, suboptimality and stability analysis of the algorithm are given. The local probability data association method is used in the proposed algorithm for the multitarget tracking case. The algorithm is demonstrated in simulations on tracking examples for a random signal, one nonlinear target, and four nonlinear targets. Results show the feasibility and superiority of WODKF against other filtering algorithms for a large class of systems.
Design and optimization of carbon-nanotube-material/dielectric hybrid nonlinear optical waveguides
International Nuclear Information System (INIS)
Zhao, Xin; Zheng, Zheng; Lu, Zhiting; Zhu, Jinsong; Zhou, Tao
2011-01-01
The nonlinear optical characteristics of highly nonlinear waveguides utilizing carbon nanotube composite materials are investigated theoretically. The extremely high nonlinearity and relatively high loss of the carbon nanotube materials are shown to greatly affect the performance of such waveguides for nonlinear optical applications, in contrast to waveguides using conventional nonlinear materials. Different configurations based on applying the carbon nanotube materials to the popular ridge and buried waveguides are thoroughly studied, and the optimal geometries are derived through simulations. It is shown that, though the nonlinear coefficient is often huge for these waveguides, the loss characteristics can significantly limit the maximum achievable accumulated nonlinearity, e.g. the maximum nonlinear phase shift. Our results suggest that SOI-based high-index-contrast, carbon nanotube cladding waveguides, rather than the currently demonstrated low-contrast waveguides, could hold the promise of achieving significantly higher accumulated nonlinearity
Collimator setting optimization in intensity modulated radiotherapy
International Nuclear Information System (INIS)
Williams, M.; Hoban, P.
2001-01-01
Full text: The aim of this study was to investigate the role of collimator angle and bixel size settings in IMRT when using the step and shoot method of delivery. Of particular interest is minimisation of the total monitor units delivered. Beam intensity maps with bixel size 10 x 10 mm were segmented into MLC leaf sequences and the collimator angle optimised to minimise the total number of MU's. The monitor units were estimated from the maximum sum of positive-gradient intensity changes along the direction of leaf motion. To investigate the use of low resolution maps at optimum collimator angles, several high resolution maps with bixel size 5 x 5 mm were generated. These were resampled into bixel sizes, 5 x 10 mm and 10 x 10 mm and the collimator angle optimised to minimise the RMS error between the original and resampled map. Finally, a clinical IMRT case was investigated with the collimator angle optimised. Both the dose distribution and dose-volume histograms were compared between the standard IMRT plan and the optimised plan. For the 10 x 10 mm bixel maps there was a variation of 5% - 40% in monitor units at the different collimator angles. The maps with a high degree of radial symmetry showed little variation. For the resampled 5 x 5 mm maps, a small RMS error was achievable with a 5 x 10 mm bixel size at particular collimator positions. This was most noticeable for maps with an elongated intensity distribution. A comparison between the 5 x 5 mm bixel plan and the 5 x 10 mm showed no significant difference in dose distribution. The monitor units required to deliver an intensity modulated field can be reduced by rotating the collimator and aligning the direction of leaf motion with the axis of the fluence map that has the least intensity. Copyright (2001) Australasian College of Physical Scientists and Engineers in Medicine
Directory of Open Access Journals (Sweden)
Carlos Pozo
Full Text Available Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study
Pozo, Carlos; Guillén-Gosálbez, Gonzalo; Sorribas, Albert; Jiménez, Laureano
2012-01-01
Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA) representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study that optimizes the
A Study on the Analysis and Optimal Control of Nonlinear Systems via Walsh Function
Energy Technology Data Exchange (ETDEWEB)
Kim, Jin Tae; Kim, Tai Hoon; Ahn, Doo Soo [Sungkyunkwan University (Korea); Lee, Myung Kyu [Kyungsung University (Korea)
2000-07-01
This paper presents the new adaptive optimal scheme for the nonlinear systems, which is based on the Picard's iterative approximation and fast Walsh transform. It is well known that the Walsh function approach method is very difficult to apply for the analysis and optimal control of nonlinear systems. However, these problems can be easily solved by the improvement of the previous adaptive optimal scheme. The proposes method is easily applicable to the analysis and optimal control of nonlinear systems. (author). 15 refs., 6 figs., 1 tab.
A nonlinear optimal control approach for chaotic finance dynamics
Rigatos, G.; Siano, P.; Loia, V.; Tommasetti, A.; Troisi, O.
2017-11-01
A new nonlinear optimal control approach is proposed for stabilization of the dynamics of a chaotic finance model. The dynamic model of the financial system, which expresses interaction between the interest rate, the investment demand, the price exponent and the profit margin, undergoes approximate linearization round local operating points. These local equilibria are defined at each iteration of the control algorithm and consist of the present value of the systems state vector and the last value of the control inputs vector that was exerted on it. The approximate linearization makes use of Taylor series expansion and of the computation of the associated Jacobian matrices. The truncation of higher order terms in the Taylor series expansion is considered to be a modelling error that is compensated by the robustness of the control loop. As the control algorithm runs, the temporary equilibrium is shifted towards the reference trajectory and finally converges to it. The control method needs to compute an H-infinity feedback control law at each iteration, and requires the repetitive solution of an algebraic Riccati equation. Through Lyapunov stability analysis it is shown that an H-infinity tracking performance criterion holds for the control loop. This implies elevated robustness against model approximations and external perturbations. Moreover, under moderate conditions the global asymptotic stability of the control loop is proven.
Elitism set based particle swarm optimization and its application
Directory of Open Access Journals (Sweden)
Yanxia Sun
2017-01-01
Full Text Available Topology plays an important role for Particle Swarm Optimization (PSO to achieve good optimization performance. It is difficult to find one topology structure for the particles to achieve better optimization performance than the others since the optimization performance not only depends on the searching abilities of the particles, also depends on the type of the optimization problems. Three elitist set based PSO algorithm without using explicit topology structure is proposed in this paper. An elitist set, which is based on the individual best experience, is used to communicate among the particles. Moreover, to avoid the premature of the particles, different statistical methods have been used in these three proposed methods. The performance of the proposed PSOs is compared with the results of the standard PSO 2011 and several PSO with different topologies, and the simulation results and comparisons demonstrate that the proposed PSO with adaptive probabilistic preference can achieve good optimization performance.
Directory of Open Access Journals (Sweden)
Yutong Liu
2012-01-01
Full Text Available Purpose. To develop a technique to automate landmark selection for point-based interpolating transformations for nonlinear medical image registration. Materials and Methods. Interpolating transformations were calculated from homologous point landmarks on the source (image to be transformed and target (reference image. Point landmarks are placed at regular intervals on contours of anatomical features, and their positions are optimized along the contour surface by a function composed of curvature similarity and displacements of the homologous landmarks. The method was evaluated in two cases (=5 each. In one, MRI was registered to histological sections; in the second, geometric distortions in EPI MRI were corrected. Normalized mutual information and target registration error were calculated to compare the registration accuracy of the automatically and manually generated landmarks. Results. Statistical analyses demonstrated significant improvement (<0.05 in registration accuracy by landmark optimization in most data sets and trends towards improvement (<0.1 in others as compared to manual landmark selection.
NonLinear Parallel OPtimization Tool, Phase I
National Aeronautics and Space Administration — CU Aerospace, in partnership with the University of Illinois propose the further development of a new sparse nonlinear programming architecture that exploits...
Pozo, Carlos; Marín-Sanguino, Alberto; Alves, Rui; Guillén-Gosálbez, Gonzalo; Jiménez, Laureano; Sorribas, Albert
2011-08-25
Design of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA) models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization. Based on the GMA canonical representation, we have developed in previous works a highly efficient optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC) models that extend the power-law formalism to deal with saturation and cooperativity. Our results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task.
Directory of Open Access Journals (Sweden)
Sorribas Albert
2011-08-01
Full Text Available Abstract Background Design of newly engineered microbial strains for biotechnological purposes would greatly benefit from the development of realistic mathematical models for the processes to be optimized. Such models can then be analyzed and, with the development and application of appropriate optimization techniques, one could identify the modifications that need to be made to the organism in order to achieve the desired biotechnological goal. As appropriate models to perform such an analysis are necessarily non-linear and typically non-convex, finding their global optimum is a challenging task. Canonical modeling techniques, such as Generalized Mass Action (GMA models based on the power-law formalism, offer a possible solution to this problem because they have a mathematical structure that enables the development of specific algorithms for global optimization. Results Based on the GMA canonical representation, we have developed in previous works a highly efficient optimization algorithm and a set of related strategies for understanding the evolution of adaptive responses in cellular metabolism. Here, we explore the possibility of recasting kinetic non-linear models into an equivalent GMA model, so that global optimization on the recast GMA model can be performed. With this technique, optimization is greatly facilitated and the results are transposable to the original non-linear problem. This procedure is straightforward for a particular class of non-linear models known as Saturable and Cooperative (SC models that extend the power-law formalism to deal with saturation and cooperativity. Conclusions Our results show that recasting non-linear kinetic models into GMA models is indeed an appropriate strategy that helps overcoming some of the numerical difficulties that arise during the global optimization task.
Optimal set of selected uranium enrichments that minimizes blending consequences
International Nuclear Information System (INIS)
Nachlas, J.A.; Kurstedt, H.A. Jr.; Lobber, J.S. Jr.
1977-01-01
Identities, quantities, and costs associated with producing a set of selected enrichments and blending them to provide fuel for existing reactors are investigated using an optimization model constructed with appropriate constraints. Selected enrichments are required for either nuclear reactor fuel standardization or potential uranium enrichment alternatives such as the gas centrifuge. Using a mixed-integer linear program, the model minimizes present worth costs for a 39-product-enrichment reference case. For four ingredients, the marginal blending cost is only 0.18% of the total direct production cost. Natural uranium is not an optimal blending ingredient. Optimal values reappear in most sets of ingredient enrichments
Observer-based design of set-point tracking adaptive controllers for nonlinear chaotic systems
International Nuclear Information System (INIS)
Khaki-Sedigh, A.; Yazdanpanah-Goharrizi, A.
2006-01-01
A gradient based approach for the design of set-point tracking adaptive controllers for nonlinear chaotic systems is presented. In this approach, Lyapunov exponents are used to select the controller gain. In the case of unknown or time varying chaotic plants, the Lyapunov exponents may vary during the plant operation. In this paper, an effective adaptive strategy is used for online identification of Lyapunov exponents and adaptive control of nonlinear chaotic plants. Also, a nonlinear observer for estimation of the states is proposed. Simulation results are provided to show the effectiveness of the proposed methodology
Observer-based design of set-point tracking adaptive controllers for nonlinear chaotic systems
Energy Technology Data Exchange (ETDEWEB)
Khaki-Sedigh, A. [Department of Electrical Engineering, K.N. Toosi University of Technology, Sayyed Khandan Bridge, Shariati Street, Tehran 16314 (Iran, Islamic Republic of)]. E-mail: sedigh@kntu.ac.ir; Yazdanpanah-Goharrizi, A. [Department of Electrical Engineering, K.N. Toosi University of Technology, Sayyed Khandan Bridge, Shariati Street, Tehran 16314 (Iran, Islamic Republic of)]. E-mail: yazdanpanah@ee.kntu.ac.ir
2006-09-15
A gradient based approach for the design of set-point tracking adaptive controllers for nonlinear chaotic systems is presented. In this approach, Lyapunov exponents are used to select the controller gain. In the case of unknown or time varying chaotic plants, the Lyapunov exponents may vary during the plant operation. In this paper, an effective adaptive strategy is used for online identification of Lyapunov exponents and adaptive control of nonlinear chaotic plants. Also, a nonlinear observer for estimation of the states is proposed. Simulation results are provided to show the effectiveness of the proposed methodology.
Optimal projection of observations in a Bayesian setting
Giraldi, Loic
2018-03-18
Optimal dimensionality reduction methods are proposed for the Bayesian inference of a Gaussian linear model with additive noise in presence of overabundant data. Three different optimal projections of the observations are proposed based on information theory: the projection that minimizes the Kullback–Leibler divergence between the posterior distributions of the original and the projected models, the one that minimizes the expected Kullback–Leibler divergence between the same distributions, and the one that maximizes the mutual information between the parameter of interest and the projected observations. The first two optimization problems are formulated as the determination of an optimal subspace and therefore the solution is computed using Riemannian optimization algorithms on the Grassmann manifold. Regarding the maximization of the mutual information, it is shown that there exists an optimal subspace that minimizes the entropy of the posterior distribution of the reduced model; a basis of the subspace can be computed as the solution to a generalized eigenvalue problem; an a priori error estimate on the mutual information is available for this particular solution; and that the dimensionality of the subspace to exactly conserve the mutual information between the input and the output of the models is less than the number of parameters to be inferred. Numerical applications to linear and nonlinear models are used to assess the efficiency of the proposed approaches, and to highlight their advantages compared to standard approaches based on the principal component analysis of the observations.
Optimal Set-Point Synthesis in HVAC Systems
DEFF Research Database (Denmark)
Komareji, Mohammad; Stoustrup, Jakob; Rasmussen, Henrik
2007-01-01
This paper presents optimal set-point synthesis for a heating, ventilating, and air-conditioning (HVAC) system. This HVAC system is made of two heat exchangers: an air-to-air heat exchanger and a water-to-air heat exchanger. The objective function is composed of the electrical power for different...... components, encompassing fans, primary/secondary pump, tertiary pump, and air-to-air heat exchanger wheel; and a fraction of thermal power used by the HVAC system. The goals that have to be achieved by the HVAC system appear as constraints in the optimization problem. To solve the optimization problem......, a steady state model of the HVAC system is derived while different supplying hydronic circuits are studied for the water-to-air heat exchanger. Finally, the optimal set-points and the optimal supplying hydronic circuit are resulted....
Pasekov, V P
2013-03-01
The paper considers the problems in the adaptive evolution of life-history traits for individuals in the nonlinear Leslie model of age-structured population. The possibility to predict adaptation results as the values of organism's traits (properties) that provide for the maximum of a certain function of traits (optimization criterion) is studied. An ideal criterion of this type is Darwinian fitness as a characteristic of success of an individual's life history. Criticism of the optimization approach is associated with the fact that it does not take into account the changes in the environmental conditions (in a broad sense) caused by evolution, thereby leading to losses in the adequacy of the criterion. In addition, the justification for this criterion under stationary conditions is not usually rigorous. It has been suggested to overcome these objections in terms of the adaptive dynamics theory using the concept of invasive fitness. The reasons are given that favor the application of the average number of offspring for an individual, R(L), as an optimization criterion in the nonlinear Leslie model. According to the theory of quantitative genetics, the selection for fertility (that is, for a set of correlated quantitative traits determined by both multiple loci and the environment) leads to an increase in R(L). In terms of adaptive dynamics, the maximum R(L) corresponds to the evolutionary stability and, in certain cases, convergent stability of the values for traits. The search for evolutionarily stable values on the background of limited resources for reproduction is a problem of linear programming.
Directory of Open Access Journals (Sweden)
Akemi Gálvez
2013-01-01
Full Text Available Fitting spline curves to data points is a very important issue in many applied fields. It is also challenging, because these curves typically depend on many continuous variables in a highly interrelated nonlinear way. In general, it is not possible to compute these parameters analytically, so the problem is formulated as a continuous nonlinear optimization problem, for which traditional optimization techniques usually fail. This paper presents a new bioinspired method to tackle this issue. In this method, optimization is performed through a combination of two techniques. Firstly, we apply the indirect approach to the knots, in which they are not initially the subject of optimization but precomputed with a coarse approximation scheme. Secondly, a powerful bioinspired metaheuristic technique, the firefly algorithm, is applied to optimization of data parameterization; then, the knot vector is refined by using De Boor’s method, thus yielding a better approximation to the optimal knot vector. This scheme converts the original nonlinear continuous optimization problem into a convex optimization problem, solved by singular value decomposition. Our method is applied to some illustrative real-world examples from the CAD/CAM field. Our experimental results show that the proposed scheme can solve the original continuous nonlinear optimization problem very efficiently.
Global Optimization for Transport Network Expansion and Signal Setting
Liu, Haoxiang; Wang, David Z. W.; Yue, Hao
2015-01-01
This paper proposes a model to address an urban transport planning problem involving combined network design and signal setting in a saturated network. Conventional transport planning models usually deal with the network design problem and signal setting problem separately. However, the fact that network capacity design and capacity allocation determined by network signal setting combine to govern the transport network performance requires the optimal transport planning to consider the two pr...
Global Optimization for Bus Line Timetable Setting Problem
Directory of Open Access Journals (Sweden)
Qun Chen
2014-01-01
Full Text Available This paper defines bus timetables setting problem during each time period divided in terms of passenger flow intensity; it is supposed that passengers evenly arrive and bus runs are set evenly; the problem is to determine bus runs assignment in each time period to minimize the total waiting time of passengers on platforms if the number of the total runs is known. For such a multistage decision problem, this paper designed a dynamic programming algorithm to solve it. Global optimization procedures using dynamic programming are developed. A numerical example about bus runs assignment optimization of a single line is given to demonstrate the efficiency of the proposed methodology, showing that optimizing buses’ departure time using dynamic programming can save computational time and find the global optimal solution.
Nonlinear dynamic simulation of optimal depletion of crude oil in the lower 48 United States
International Nuclear Information System (INIS)
Ruth, M.; Cleveland, C.J.
1993-01-01
This study combines the economic theory of optimal resource use with econometric estimates of demand and supply parameters to develop a nonlinear dynamic model of crude oil exploration, development, and production in the lower 48 United States. The model is simulated with the graphical programming language STELLA, for the years 1985 to 2020. The procedure encourages use of economic theory and econometrics in combination with nonlinear dynamic simulation to enhance our understanding of complex interactions present in models of optimal resource use. (author)
Training set optimization under population structure in genomic selection.
Isidro, Julio; Jannink, Jean-Luc; Akdemir, Deniz; Poland, Jesse; Heslot, Nicolas; Sorrells, Mark E
2015-01-01
Population structure must be evaluated before optimization of the training set population. Maximizing the phenotypic variance captured by the training set is important for optimal performance. The optimization of the training set (TRS) in genomic selection has received much interest in both animal and plant breeding, because it is critical to the accuracy of the prediction models. In this study, five different TRS sampling algorithms, stratified sampling, mean of the coefficient of determination (CDmean), mean of predictor error variance (PEVmean), stratified CDmean (StratCDmean) and random sampling, were evaluated for prediction accuracy in the presence of different levels of population structure. In the presence of population structure, the most phenotypic variation captured by a sampling method in the TRS is desirable. The wheat dataset showed mild population structure, and CDmean and stratified CDmean methods showed the highest accuracies for all the traits except for test weight and heading date. The rice dataset had strong population structure and the approach based on stratified sampling showed the highest accuracies for all traits. In general, CDmean minimized the relationship between genotypes in the TRS, maximizing the relationship between TRS and the test set. This makes it suitable as an optimization criterion for long-term selection. Our results indicated that the best selection criterion used to optimize the TRS seems to depend on the interaction of trait architecture and population structure.
Distributed Optimization for a Class of Nonlinear Multiagent Systems With Disturbance Rejection.
Wang, Xinghu; Hong, Yiguang; Ji, Haibo
2016-07-01
The paper studies the distributed optimization problem for a class of nonlinear multiagent systems in the presence of external disturbances. To solve the problem, we need to achieve the optimal multiagent consensus based on local cost function information and neighboring information and meanwhile to reject local disturbance signals modeled by an exogenous system. With convex analysis and the internal model approach, we propose a distributed optimization controller for heterogeneous and nonlinear agents in the form of continuous-time minimum-phase systems with unity relative degree. We prove that the proposed design can solve the exact optimization problem with rejecting disturbances.
Directory of Open Access Journals (Sweden)
Olga Kostyukova
2017-11-01
Full Text Available The paper is devoted to study of a special class of semi-infinite problems arising in nonlinear parametric Semi-infinite Programming, when the differential properties of the solutions are being studied. These problems are convex and possess noncompact index sets. In the paper, we present conditions guaranteeing the existence of optimal solutions, and prove new optimality criterion. An example illustrating the obtained results is presented.
Heyde, Frank; Löhne, Andreas; Rudloff, Birgit; Schrage, Carola
2015-01-01
This volume presents five surveys with extensive bibliographies and six original contributions on set optimization and its applications in mathematical finance and game theory. The topics range from more conventional approaches that look for minimal/maximal elements with respect to vector orders or set relations, to the new complete-lattice approach that comprises a coherent solution concept for set optimization problems, along with existence results, duality theorems, optimality conditions, variational inequalities and theoretical foundations for algorithms. Modern approaches to scalarization methods can be found as well as a fundamental contribution to conditional analysis. The theory is tailor-made for financial applications, in particular risk evaluation and [super-]hedging for market models with transaction costs, but it also provides a refreshing new perspective on vector optimization. There is no comparable volume on the market, making the book an invaluable resource for researchers working in vector o...
Liolios, A
2003-01-01
The paper presents a new numerical approach for a non-linear optimal control problem arising in earthquake civil engineering. This problem concerns the elastoplastic softening-fracturing unilateral contact between neighbouring buildings during earthquakes when Coulomb friction is taken into account under second-order instabilizing effects. So, the earthquake response of the adjacent structures can appear instabilities and chaotic behaviour. The problem formulation presented here leads to a set of equations and inequalities, which is equivalent to a dynamic hemivariational inequality in the way introduced by Panagiotopoulos [Hemivariational Inequalities. Applications in Mechanics and Engineering, Springer-Verlag, Berlin, 1993]. The numerical procedure is based on an incremental problem formulation and on a double discretization, in space by the finite element method and in time by the Wilson-theta method. The generally non-convex constitutive contact laws are piecewise linearized, and in each time-step a non-c...
Estimation of dynamic reactivity using an H∞ optimal filter with a nonlinear term
International Nuclear Information System (INIS)
Suzuki, Katsuo; Watanabe, Koiti
1996-01-01
A method of nonlinear filtering is applied to the problem of estimating the dynamic reactivity of a nonlinear reactor system. The nonlinear filtering algorithm developed is a simple modification of a linear H ∞ optimal filter with a nonlinear feedback loop added. The linear filter is designed on the basis of a linearized dynamical system model that consists of linearized point reactor kinetic equations and a reactivity state equation driven by a fictitious signal. The latter is artificially introduced to deal with the reactivity as a state variable. The results of the computer simulation show that the nonlinear filtering algorithm can be applied to estimate the dynamic reactivity of the nonlinear reactor system, even under relatively large reactivity disturbances
Optimal Control Problems for Nonlinear Variational Evolution Inequalities
Directory of Open Access Journals (Sweden)
Eun-Young Ju
2013-01-01
Full Text Available We deal with optimal control problems governed by semilinear parabolic type equations and in particular described by variational inequalities. We will also characterize the optimal controls by giving necessary conditions for optimality by proving the Gâteaux differentiability of solution mapping on control variables.
Optimal regional biases in ECB interest rate setting
Arnold, I.J.M.
2005-01-01
This paper uses a simple model of optimal monetary policy to consider whether the influence of national output and inflation rates on ECB interest rate setting should equal a country’s weight in the eurozone economy. The findings depend on assumptions regarding interest rate elasticities, exchange
Level-Set Topology Optimization with Aeroelastic Constraints
Dunning, Peter D.; Stanford, Bret K.; Kim, H. Alicia
2015-01-01
Level-set topology optimization is used to design a wing considering skin buckling under static aeroelastic trim loading, as well as dynamic aeroelastic stability (flutter). The level-set function is defined over the entire 3D volume of a transport aircraft wing box. Therefore, the approach is not limited by any predefined structure and can explore novel configurations. The Sequential Linear Programming (SLP) level-set method is used to solve the constrained optimization problems. The proposed method is demonstrated using three problems with mass, linear buckling and flutter objective and/or constraints. A constraint aggregation method is used to handle multiple buckling constraints in the wing skins. A continuous flutter constraint formulation is used to handle difficulties arising from discontinuities in the design space caused by a switching of the critical flutter mode.
φq-field theory for portfolio optimization: “fat tails” and nonlinear correlations
Sornette, D.; Simonetti, P.; Andersen, J. V.
2000-08-01
Physics and finance are both fundamentally based on the theory of random walks (and their generalizations to higher dimensions) and on the collective behavior of large numbers of correlated variables. The archetype examplifying this situation in finance is the portfolio optimization problem in which one desires to diversify on a set of possibly dependent assets to optimize the return and minimize the risks. The standard mean-variance solution introduced by Markovitz and its subsequent developments is basically a mean-field Gaussian solution. It has severe limitations for practical applications due to the strongly non-Gaussian structure of distributions and the nonlinear dependence between assets. Here, we present in details a general analytical characterization of the distribution of returns for a portfolio constituted of assets whose returns are described by an arbitrary joint multivariate distribution. In this goal, we introduce a non-linear transformation that maps the returns onto Gaussian variables whose covariance matrix provides a new measure of dependence between the non-normal returns, generalizing the covariance matrix into a nonlinear covariance matrix. This nonlinear covariance matrix is chiseled to the specific fat tail structure of the underlying marginal distributions, thus ensuring stability and good conditioning. The portfolio distribution is then obtained as the solution of a mapping to a so-called φq field theory in particle physics, of which we offer an extensive treatment using Feynman diagrammatic techniques and large deviation theory, that we illustrate in details for multivariate Weibull distributions. The interaction (non-mean field) structure in this field theory is a direct consequence of the non-Gaussian nature of the distribution of asset price returns. We find that minimizing the portfolio variance (i.e. the relatively “small” risks) may often increase the large risks, as measured by higher normalized cumulants. Extensive
Liu, Tao; Djordjevic, Ivan B
2014-12-29
In this paper, we first describe an optimal signal constellation design algorithm suitable for the coherent optical channels dominated by the linear phase noise. Then, we modify this algorithm to be suitable for the nonlinear phase noise dominated channels. In optimization procedure, the proposed algorithm uses the cumulative log-likelihood function instead of the Euclidian distance. Further, an LDPC coded modulation scheme is proposed to be used in combination with signal constellations obtained by proposed algorithm. Monte Carlo simulations indicate that the LDPC-coded modulation schemes employing the new constellation sets, obtained by our new signal constellation design algorithm, outperform corresponding QAM constellations significantly in terms of transmission distance and have better nonlinearity tolerance.
Constructing DNA Barcode Sets Based on Particle Swarm Optimization.
Wang, Bin; Zheng, Xuedong; Zhou, Shihua; Zhou, Changjun; Wei, Xiaopeng; Zhang, Qiang; Wei, Ziqi
2018-01-01
Following the completion of the human genome project, a large amount of high-throughput bio-data was generated. To analyze these data, massively parallel sequencing, namely next-generation sequencing, was rapidly developed. DNA barcodes are used to identify the ownership between sequences and samples when they are attached at the beginning or end of sequencing reads. Constructing DNA barcode sets provides the candidate DNA barcodes for this application. To increase the accuracy of DNA barcode sets, a particle swarm optimization (PSO) algorithm has been modified and used to construct the DNA barcode sets in this paper. Compared with the extant results, some lower bounds of DNA barcode sets are improved. The results show that the proposed algorithm is effective in constructing DNA barcode sets.
Miró, Anton; Pozo, Carlos; Guillén-Gosálbez, Gonzalo; Egea, Jose A; Jiménez, Laureano
2012-05-10
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. 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. 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.
NonLinear Parallel OPtimization Tool, Phase II
National Aeronautics and Space Administration — The technological advancement proposed is a novel large-scale Noninear Parallel OPtimization Tool (NLPAROPT). This software package will eliminate the computational...
Wavefront optimized nonlinear microscopy of ex vivo human retinas
Gualda, Emilio J.; Bueno, Juan M.; Artal, Pablo
2010-03-01
A multiphoton microscope incorporating a Hartmann-Shack (HS) wavefront sensor to control the ultrafast laser beam's wavefront aberrations has been developed. This instrument allowed us to investigate the impact of the laser beam aberrations on two-photon autofluorescence imaging of human retinal tissues. We demonstrated that nonlinear microscopy images are improved when laser beam aberrations are minimized by realigning the laser system cavity while wavefront controlling. Nonlinear signals from several human retinal anatomical features have been detected for the first time, without the need of fixation or staining procedures. Beyond the improved image quality, this approach reduces the required excitation power levels, minimizing the side effects of phototoxicity within the imaged sample. In particular, this may be important to study the physiology and function of the healthy and diseased retina.
Energy Technology Data Exchange (ETDEWEB)
Qiang Lu; Yusong Sun; Yuanzhang Sun [Tsinghua University, Beijing (China). Dept. of Electrical Engineering; Felix F Wu; Yixin Ni [University of Hong Kong (China). Dept. of Electrical and Electronic Engineering; Yokoyama, Akihiko [University of Tokyo (Japan). Dept. of Electrical Engineering; Goto, Masuo; Konishi, Hiroo [Hitachi Ltd., Tokyo (Japan). Power System Div.
2004-06-01
A novel nonlinear decentralized robust governor control for hydroturbine-generator sets in multi-machine power systems is suggested in this paper. The nonelastic water hammer effect and disturbances are considered in the modeling. The advanced differential geometry theory, nonlinear robust control theory and the dynamic feedback method are combined to solve the problem. The nonlinear decentralized robust control law for the speed governor of hydroturbine-generators has been derived. The input signals to the proposed controller are all local measurements and independent to the system parameters. The derived control law guarantees the integrated system stability with disturbance attenuation, which is significant to the real power system application. Computer tests on an 8-machine, 36-bus power system show clearly the effectiveness of the new control strategy in transient stability enhancement and disturbance attenuation. The computer test results based on the suggested controller are compared favorably with those based on the conventional linear governor control. (author)
Self-optimizing robust nonlinear model predictive control
Lazar, M.; Heemels, W.P.M.H.; Jokic, A.; Thoma, M.; Allgöwer, F.; Morari, M.
2009-01-01
This paper presents a novel method for designing robust MPC schemes that are self-optimizing in terms of disturbance attenuation. The method employs convex control Lyapunov functions and disturbance bounds to optimize robustness of the closed-loop system on-line, at each sampling instant - a unique
Zhu, Yuanheng; Zhao, Dongbin; Yang, Xiong; Zhang, Qichao
2018-02-01
Sum of squares (SOS) polynomials have provided a computationally tractable way to deal with inequality constraints appearing in many control problems. It can also act as an approximator in the framework of adaptive dynamic programming. In this paper, an approximate solution to the optimal control of polynomial nonlinear systems is proposed. Under a given attenuation coefficient, the Hamilton-Jacobi-Isaacs equation is relaxed to an optimization problem with a set of inequalities. After applying the policy iteration technique and constraining inequalities to SOS, the optimization problem is divided into a sequence of feasible semidefinite programming problems. With the converged solution, the attenuation coefficient is further minimized to a lower value. After iterations, approximate solutions to the smallest -gain and the associated optimal controller are obtained. Four examples are employed to verify the effectiveness of the proposed algorithm.
Non-Linear Transaction Costs Inclusion in Mean-Variance Optimization
Directory of Open Access Journals (Sweden)
Christian Johannes Zimmer
2005-12-01
Full Text Available In this article we propose a new way to include transaction costs into a mean-variance portfolio optimization. We consider brokerage fees, bid/ask spread and the market impact of the trade. A pragmatic algorithm is proposed, which approximates the optimal portfolio, and we can show that is converges in the absence of restrictions. Using Brazilian financial market data we compare our approximation algorithm with the results of a non-linear optimizer.
The nurse scheduling problem: a goal programming and nonlinear optimization approaches
Hakim, L.; Bakhtiar, T.; Jaharuddin
2017-01-01
Nurses scheduling is an activity of allocating nurses to conduct a set of tasks at certain room at a hospital or health centre within a certain period. One of obstacles in the nurse scheduling is the lack of resources in order to fulfil the needs of the hospital. Nurse scheduling which is undertaken manually will be at risk of not fulfilling some nursing rules set by the hospital. Therefore, this study aimed to perform scheduling models that satisfy all the specific rules set by the management of Bogor State Hospital. We have developed three models to overcome the scheduling needs. Model 1 is designed to schedule nurses who are solely assigned to a certain inpatient unit and Model 2 is constructed to manage nurses who are assigned to an inpatient room as well as at Polyclinic room as conjunct nurses. As the assignment of nurses on each shift is uneven, then we propose Model 3 to minimize the variance of the workload in order to achieve equitable assignment on every shift. The first two models are formulated in goal programming framework, while the last model is in nonlinear optimization form.
Optimization of hardening/softening behavior of plane frame structures using nonlinear normal modes
DEFF Research Database (Denmark)
Dou, Suguang; Jensen, Jakob Søndergaard
2016-01-01
Devices that exploit essential nonlinear behavior such as hardening/softening and inter-modal coupling effects are increasingly used in engineering and fundamental studies. Based on nonlinear normal modes, we present a gradient-based structural optimization method for tailoring the hardening...... involving plane frame structures where the hardening/softening behavior is qualitatively and quantitatively tuned by simple changes in the geometry of the structures....
A Nonlinear GMRES Optimization Algorithm for Canonical Tensor Decomposition
De Sterck, Hans
2011-01-01
A new algorithm is presented for computing a canonical rank-R tensor approximation that has minimal distance to a given tensor in the Frobenius norm, where the canonical rank-R tensor consists of the sum of R rank-one components. Each iteration of the method consists of three steps. In the first step, a tentative new iterate is generated by a stand-alone one-step process, for which we use alternating least squares (ALS). In the second step, an accelerated iterate is generated by a nonlinear g...
Simulation-based optimal Bayesian experimental design for nonlinear systems
Huan, Xun; Marzouk, Youssef M.
2013-01-01
The optimal selection of experimental conditions is essential to maximizing the value of data for inference and prediction, particularly in situations where experiments are time-consuming and expensive to conduct. We propose a general mathematical
Nonlinear Multidimensional Assignment Problems Efficient Conic Optimization Methods and Applications
2015-06-24
WORK UNIT NUMBER 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Arizona State University School of Mathematical & Statistical Sciences 901 S...SUPPLEMENTARY NOTES 14. ABSTRACT The major goals of this project were completed: the exact solution of previously unsolved challenging combinatorial optimization... combinatorial optimization problem, the Directional Sensor Problem, was solved in two ways. First, heuristically in an engineering fashion and second, exactly
Yang, Qin; Zou, Hong-Yan; Zhang, Yan; Tang, Li-Juan; Shen, Guo-Li; Jiang, Jian-Hui; Yu, Ru-Qin
2016-01-15
Most of the proteins locate more than one organelle in a cell. Unmixing the localization patterns of proteins is critical for understanding the protein functions and other vital cellular processes. Herein, non-linear machine learning technique is proposed for the first time upon protein pattern unmixing. Variable-weighted support vector machine (VW-SVM) is a demonstrated robust modeling technique with flexible and rational variable selection. As optimized by a global stochastic optimization technique, particle swarm optimization (PSO) algorithm, it makes VW-SVM to be an adaptive parameter-free method for automated unmixing of protein subcellular patterns. Results obtained by pattern unmixing of a set of fluorescence microscope images of cells indicate VW-SVM as optimized by PSO is able to extract useful pattern features by optimally rescaling each variable for non-linear SVM modeling, consequently leading to improved performances in multiplex protein pattern unmixing compared with conventional SVM and other exiting pattern unmixing methods. Copyright © 2015 Elsevier B.V. All rights reserved.
Directory of Open Access Journals (Sweden)
Shaolong Chen
2016-01-01
Full Text Available Parameter estimation is an important problem in nonlinear system modeling and control. Through constructing an appropriate fitness function, parameter estimation of system could be converted to a multidimensional parameter optimization problem. As a novel swarm intelligence algorithm, chicken swarm optimization (CSO has attracted much attention owing to its good global convergence and robustness. In this paper, a method based on improved boundary chicken swarm optimization (IBCSO is proposed for parameter estimation of nonlinear systems, demonstrated and tested by Lorenz system and a coupling motor system. Furthermore, we have analyzed the influence of time series on the estimation accuracy. Computer simulation results show it is feasible and with desirable performance for parameter estimation of nonlinear systems.
Optimization Formulations for the Maximum Nonlinear Buckling Load of Composite Structures
DEFF Research Database (Denmark)
Lindgaard, Esben; Lund, Erik
2011-01-01
This paper focuses on criterion functions for gradient based optimization of the buckling load of laminated composite structures considering different types of buckling behaviour. A local criterion is developed, and is, together with a range of local and global criterion functions from literature......, benchmarked on a number of numerical examples of laminated composite structures for the maximization of the buckling load considering fiber angle design variables. The optimization formulations are based on either linear or geometrically nonlinear analysis and formulated as mathematical programming problems...... solved using gradient based techniques. The developed local criterion is formulated such it captures nonlinear effects upon loading and proves useful for both analysis purposes and as a criterion for use in nonlinear buckling optimization. © 2010 Springer-Verlag....
Lan, C. Edward; Ge, Fuying
1989-01-01
Control system design for general nonlinear flight dynamic models is considered through numerical simulation. The design is accomplished through a numerical optimizer coupled with analysis of flight dynamic equations. The general flight dynamic equations are numerically integrated and dynamic characteristics are then identified from the dynamic response. The design variables are determined iteratively by the optimizer to optimize a prescribed objective function which is related to desired dynamic characteristics. Generality of the method allows nonlinear effects to aerodynamics and dynamic coupling to be considered in the design process. To demonstrate the method, nonlinear simulation models for an F-5A and an F-16 configurations are used to design dampers to satisfy specifications on flying qualities and control systems to prevent departure. The results indicate that the present method is simple in formulation and effective in satisfying the design objectives.
Systems of general nonlinear set-valued mixed variational inequalities problems in Hilbert spaces
Directory of Open Access Journals (Sweden)
Cho Yeol
2011-01-01
Full Text Available Abstract In this paper, the existing theorems and methods for finding solutions of systems of general nonlinear set-valued mixed variational inequalities problems in Hilbert spaces are studied. To overcome the difficulties, due to the presence of a proper convex lower semi-continuous function, φ and a mapping g, which appeared in the considered problem, we have used some applications of the resolvent operator technique. We would like to point out that although many authors have proved results for finding solutions of the systems of nonlinear set-valued (mixed variational inequalities problems, it is clear that it cannot be directly applied to the problems that we have considered in this paper because of φ and g. 2000 AMS Subject Classification: 47H05; 47H09; 47J25; 65J15.
El-Khoury, O.; Kim, C.; Shafieezadeh, A.; Hur, J. E.; Heo, G. H.
2015-06-01
This study performs a series of numerical simulations and shake-table experiments to design and assess the performance of a nonlinear clipped feedback control algorithm based on optimal polynomial control (OPC) to mitigate the response of a two-span bridge equipped with a magnetorheological (MR) damper. As an extended conventional linear quadratic regulator, OPC provides more flexibility in the control design and further enhances system performance. The challenges encountered in this case are (1) the linearization of the nonlinear behavior of various components and (2) the selection of the weighting matrices in the objective function of OPC. The first challenge is addressed by using stochastic linearization which replaces the nonlinear portion of the system behavior with an equivalent linear time-invariant model considering the stochasticity in the excitation. Furthermore, a genetic algorithm is employed to find optimal weighting matrices for the control design. The input current to the MR damper installed between adjacent spans is determined using a clipped stochastic optimal polynomial control algorithm. The performance of the controlled system is assessed through a set of shake-table experiments for far-field and near-field ground motions. The proposed method showed considerable improvements over passive cases especially for the far-field ground motion.
International Nuclear Information System (INIS)
El-Khoury, O; Shafieezadeh, A; Hur, J E; Kim, C; Heo, G H
2015-01-01
This study performs a series of numerical simulations and shake-table experiments to design and assess the performance of a nonlinear clipped feedback control algorithm based on optimal polynomial control (OPC) to mitigate the response of a two-span bridge equipped with a magnetorheological (MR) damper. As an extended conventional linear quadratic regulator, OPC provides more flexibility in the control design and further enhances system performance. The challenges encountered in this case are (1) the linearization of the nonlinear behavior of various components and (2) the selection of the weighting matrices in the objective function of OPC. The first challenge is addressed by using stochastic linearization which replaces the nonlinear portion of the system behavior with an equivalent linear time-invariant model considering the stochasticity in the excitation. Furthermore, a genetic algorithm is employed to find optimal weighting matrices for the control design. The input current to the MR damper installed between adjacent spans is determined using a clipped stochastic optimal polynomial control algorithm. The performance of the controlled system is assessed through a set of shake-table experiments for far-field and near-field ground motions. The proposed method showed considerable improvements over passive cases especially for the far-field ground motion. (paper)
Complex fluid network optimization and control integrative design based on nonlinear dynamic model
International Nuclear Information System (INIS)
Sui, Jinxue; Yang, Li; Hu, Yunan
2016-01-01
In view of distribution according to complex fluid network’s needs, this paper proposed one optimization computation method of the nonlinear programming mathematical model based on genetic algorithm. The simulation result shows that the overall energy consumption of the optimized fluid network has a decrease obviously. The control model of the fluid network is established based on nonlinear dynamics. We design the control law based on feedback linearization, take the optimal value by genetic algorithm as the simulation data, can also solve the branch resistance under the optimal value. These resistances can provide technical support and reference for fluid network design and construction, so can realize complex fluid network optimization and control integration design.
Cluster analysis by optimal decomposition of induced fuzzy sets
Energy Technology Data Exchange (ETDEWEB)
Backer, E
1978-01-01
Nonsupervised pattern recognition is addressed and the concept of fuzzy sets is explored in order to provide the investigator (data analyst) additional information supplied by the pattern class membership values apart from the classical pattern class assignments. The basic ideas behind the pattern recognition problem, the clustering problem, and the concept of fuzzy sets in cluster analysis are discussed, and a brief review of the literature of the fuzzy cluster analysis is given. Some mathematical aspects of fuzzy set theory are briefly discussed; in particular, a measure of fuzziness is suggested. The optimization-clustering problem is characterized. Then the fundamental idea behind affinity decomposition is considered. Next, further analysis takes place with respect to the partitioning-characterization functions. The iterative optimization procedure is then addressed. The reclassification function is investigated and convergence properties are examined. Finally, several experiments in support of the method suggested are described. Four object data sets serve as appropriate test cases. 120 references, 70 figures, 11 tables. (RWR)
Galerkin v. discrete-optimal projection in nonlinear model reduction
Energy Technology Data Exchange (ETDEWEB)
Carlberg, Kevin Thomas [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Barone, Matthew Franklin [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Antil, Harbir [George Mason Univ., Fairfax, VA (United States)
2015-04-01
Discrete-optimal model-reduction techniques such as the Gauss{Newton with Approximated Tensors (GNAT) method have shown promise, as they have generated stable, accurate solutions for large-scale turbulent, compressible ow problems where standard Galerkin techniques have failed. However, there has been limited comparative analysis of the two approaches. This is due in part to difficulties arising from the fact that Galerkin techniques perform projection at the time-continuous level, while discrete-optimal techniques do so at the time-discrete level. This work provides a detailed theoretical and experimental comparison of the two techniques for two common classes of time integrators: linear multistep schemes and Runge{Kutta schemes. We present a number of new ndings, including conditions under which the discrete-optimal ROM has a time-continuous representation, conditions under which the two techniques are equivalent, and time-discrete error bounds for the two approaches. Perhaps most surprisingly, we demonstrate both theoretically and experimentally that decreasing the time step does not necessarily decrease the error for the discrete-optimal ROM; instead, the time step should be `matched' to the spectral content of the reduced basis. In numerical experiments carried out on a turbulent compressible- ow problem with over one million unknowns, we show that increasing the time step to an intermediate value decreases both the error and the simulation time of the discrete-optimal reduced-order model by an order of magnitude.
Nonlinear Shaping Architecture Designed with Using Evolutionary Structural Optimization Tools
Januszkiewicz, Krystyna; Banachowicz, Marta
2017-10-01
The paper explores the possibilities of using Structural Optimization Tools (ESO) digital tools in an integrated structural and architectural design in response to the current needs geared towards sustainability, combining ecological and economic efficiency. The first part of the paper defines the Evolutionary Structural Optimization tools, which were developed specifically for engineering purposes using finite element analysis as a framework. The development of ESO has led to several incarnations, which are all briefly discussed (Additive ESO, Bi-directional ESO, Extended ESO). The second part presents result of using these tools in structural and architectural design. Actual building projects which involve optimization as a part of the original design process will be presented (Crematorium in Kakamigahara Gifu, Japan, 2006 SANAA“s Learning Centre, EPFL in Lausanne, Switzerland 2008 among others). The conclusion emphasizes that the structural engineering and architectural design mean directing attention to the solutions which are used by Nature, designing works optimally shaped and forming their own environments. Architectural forms never constitute the optimum shape derived through a form-finding process driven only by structural optimization, but rather embody and integrate a multitude of parameters. It might be assumed that there is a similarity between these processes in nature and the presented design methods. Contemporary digital methods make the simulation of such processes possible, and thus enable us to refer back to the empirical methods of previous generations.
Optimal control of nonlinear continuous-time systems in strict-feedback form.
Zargarzadeh, Hassan; Dierks, Travis; Jagannathan, Sarangapani
2015-10-01
This paper proposes a novel optimal tracking control scheme for nonlinear continuous-time systems in strict-feedback form with uncertain dynamics. The optimal tracking problem is transformed into an equivalent optimal regulation problem through a feedforward adaptive control input that is generated by modifying the standard backstepping technique. Subsequently, a neural network-based optimal control scheme is introduced to estimate the cost, or value function, over an infinite horizon for the resulting nonlinear continuous-time systems in affine form when the internal dynamics are unknown. The estimated cost function is then used to obtain the optimal feedback control input; therefore, the overall optimal control input for the nonlinear continuous-time system in strict-feedback form includes the feedforward plus the optimal feedback terms. It is shown that the estimated cost function minimizes the Hamilton-Jacobi-Bellman estimation error in a forward-in-time manner without using any value or policy iterations. Finally, optimal output feedback control is introduced through the design of a suitable observer. Lyapunov theory is utilized to show the overall stability of the proposed schemes without requiring an initial admissible controller. Simulation examples are provided to validate the theoretical results.
Nonlinear Non-convex Optimization of Hydraulic Networks
DEFF Research Database (Denmark)
Tahavori, Maryamsadat; Kallesøe, Carsten; Leth, John-Josef
2013-01-01
Pressure management in water supply systems is an effective way to reduce the leakage in a system. In this paper, the pressure management and the reduction of power consumption of a water supply system is formulated as an optimization problem. The problem is to minimize the power consumption in p....... They can be used for a general hydraulic networks to optimize the leakage and energy consumption and to satisfy the demands at the end-users. The results in this paper show that the power consumption of the pumps is reduced.......Pressure management in water supply systems is an effective way to reduce the leakage in a system. In this paper, the pressure management and the reduction of power consumption of a water supply system is formulated as an optimization problem. The problem is to minimize the power consumption...
Optimal Set Anode Potentials Vary in Bioelectrochemical Systems
Wagner, Rachel C.
2010-08-15
In bioelectrochemical systems (BESs), the anode potential can be set to a fixed voltage using a potentiostat, but there is no accepted method for defining an optimal potential. Microbes can theoretically gain more energy by reducing a terminal electron acceptor with a more positive potential, for example oxygen compared to nitrate. Therefore, more positive anode potentials should allow microbes to gain more energy per electron transferred than a lower potential, but this can only occur if the microbe has metabolic pathways capable of capturing the available energy. Our review of the literature shows that there is a general trend of improved performance using more positive potentials, but there are several notable cases where biofilm growth and current generation improved or only occurred at more negative potentials. This suggests that even with diverse microbial communities, it is primarily the potential of the terminal respiratory proteins used by certain exoelectrogenic bacteria, and to a lesser extent the anode potential, that determines the optimal growth conditions in the reactor. Our analysis suggests that additional bioelectrochemical investigations of both pure and mixed cultures, over a wide range of potentials, are needed to better understand how to set and evaluate optimal anode potentials for improving BES performance. © 2010 American Chemical Society.
Optimal Control Of Nonlinear Wave Energy Point Converters
DEFF Research Database (Denmark)
Nielsen, Søren R.K.; Zhou, Qiang; Kramer, Morten
2013-01-01
idea behind the control strategy is to enforce the stationary velocity response of the absorber into phase with the wave excitation force at any time. The controller is optimal under monochromatic wave excitation. It is demonstrated that the devised causal controller, in plane irregular sea states...
Optimal control of dissipative nonlinear dynamical systems with triggers of coupled singularities
International Nuclear Information System (INIS)
Hedrih, K
2008-01-01
This paper analyses the controllability of motion of nonconservative nonlinear dynamical systems in which triggers of coupled singularities exist or appear. It is shown that the phase plane method is useful for the analysis of nonlinear dynamics of nonconservative systems with one degree of freedom of control strategies and also shows the way it can be used for controlling the relative motion in rheonomic systems having equivalent scleronomic conservative or nonconservative system For the system with one generalized coordinate described by nonlinear differential equation of nonlinear dynamics with trigger of coupled singularities, the functions of system potential energy and conservative force must satisfy some conditions defined by a Theorem on the existence of a trigger of coupled singularities and the separatrix in the form of 'an open a spiral form' of number eight. Task of the defined dynamical nonconservative system optimal control is: by using controlling force acting to the system, transfer initial state of the nonlinear dynamics of the system into the final state of the nonlinear dynamics in the minimal time for that optimal control task
Optimal control of dissipative nonlinear dynamical systems with triggers of coupled singularities
Stevanović Hedrih, K.
2008-02-01
This paper analyses the controllability of motion of nonconservative nonlinear dynamical systems in which triggers of coupled singularities exist or appear. It is shown that the phase plane method is useful for the analysis of nonlinear dynamics of nonconservative systems with one degree of freedom of control strategies and also shows the way it can be used for controlling the relative motion in rheonomic systems having equivalent scleronomic conservative or nonconservative system For the system with one generalized coordinate described by nonlinear differential equation of nonlinear dynamics with trigger of coupled singularities, the functions of system potential energy and conservative force must satisfy some conditions defined by a Theorem on the existence of a trigger of coupled singularities and the separatrix in the form of "an open a spiral form" of number eight. Task of the defined dynamical nonconservative system optimal control is: by using controlling force acting to the system, transfer initial state of the nonlinear dynamics of the system into the final state of the nonlinear dynamics in the minimal time for that optimal control task
Nonlinear Chance Constrained Problems: Optimality Conditions, Regularization and Solvers
Czech Academy of Sciences Publication Activity Database
Adam, Lukáš; Branda, Martin
2016-01-01
Roč. 170, č. 2 (2016), s. 419-436 ISSN 0022-3239 R&D Projects: GA ČR GA15-00735S Institutional support: RVO:67985556 Keywords : Chance constrained programming * Optimality conditions * Regularization * Algorithms * Free MATLAB codes Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 1.289, year: 2016 http://library.utia.cas.cz/separaty/2016/MTR/adam-0460909.pdf
Kim, Seongho; Li, Lang
2014-02-01
The statistical identifiability of nonlinear pharmacokinetic (PK) models with the Michaelis-Menten (MM) kinetic equation is considered using a global optimization approach, which is particle swarm optimization (PSO). If a model is statistically non-identifiable, the conventional derivative-based estimation approach is often terminated earlier without converging, due to the singularity. To circumvent this difficulty, we develop a derivative-free global optimization algorithm by combining PSO with a derivative-free local optimization algorithm to improve the rate of convergence of PSO. We further propose an efficient approach to not only checking the convergence of estimation but also detecting the identifiability of nonlinear PK models. PK simulation studies demonstrate that the convergence and identifiability of the PK model can be detected efficiently through the proposed approach. The proposed approach is then applied to clinical PK data along with a two-compartmental model. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.
Learning-Based Adaptive Optimal Tracking Control of Strict-Feedback Nonlinear Systems.
Gao, Weinan; Jiang, Zhong-Ping; Weinan Gao; Zhong-Ping Jiang; Gao, Weinan; Jiang, Zhong-Ping
2018-06-01
This paper proposes a novel data-driven control approach to address the problem of adaptive optimal tracking for a class of nonlinear systems taking the strict-feedback form. Adaptive dynamic programming (ADP) and nonlinear output regulation theories are integrated for the first time to compute an adaptive near-optimal tracker without any a priori knowledge of the system dynamics. Fundamentally different from adaptive optimal stabilization problems, the solution to a Hamilton-Jacobi-Bellman (HJB) equation, not necessarily a positive definite function, cannot be approximated through the existing iterative methods. This paper proposes a novel policy iteration technique for solving positive semidefinite HJB equations with rigorous convergence analysis. A two-phase data-driven learning method is developed and implemented online by ADP. The efficacy of the proposed adaptive optimal tracking control methodology is demonstrated via a Van der Pol oscillator with time-varying exogenous signals.
RCLED Optimization and Nonlinearity Compensation in a Polymer Optical Fiber DMT System
Directory of Open Access Journals (Sweden)
Pu Miao
2016-09-01
Full Text Available In polymer optical fiber (POF systems, the nonlinear transfer function of the resonant cavity light emitting diode (RCLED drastically degrades the communication performance. After investigating the characteristics of the RCLED nonlinear behavior, an improved digital look-up-table (LUT pre-distorter, based on an adaptive iterative algorithm, is proposed. Additionally, the system parameters, including the bias current, the average electrical power, the LUT size and the step factor are also jointly optimized to achieve a trade-off between the system linearity, reliability and the computational complexity. With the proposed methodology, both the operating point and efficiency of RCLED are enhanced. Moreover, in the practical 50 m POF communication system with the discrete multi-tone (DMT modulation, the bit error rate performance is improved by over 12 dB when RCLED is operating in the nonlinear region. Therefore, the proposed pre-distorter can both resist the nonlinearity and improve the operating point of RCLED.
Directory of Open Access Journals (Sweden)
B. Shank
2014-11-01
Full Text Available We present a detailed thermal and electrical model of superconducting transition edge sensors (TESs connected to quasiparticle (qp traps, such as the W TESs connected to Al qp traps used for CDMS (Cryogenic Dark Matter Search Ge and Si detectors. We show that this improved model, together with a straightforward time-domain optimal filter, can be used to analyze pulses well into the nonlinear saturation region and reconstruct absorbed energies with optimal energy resolution.
Directory of Open Access Journals (Sweden)
Sie Long Kek
2015-01-01
Full Text Available A computational approach is proposed for solving the discrete time nonlinear stochastic optimal control problem. Our aim is to obtain the optimal output solution of the original optimal control problem through solving the simplified model-based optimal control problem iteratively. In our approach, the adjusted parameters are introduced into the model used such that the differences between the real system and the model used can be computed. Particularly, system optimization and parameter estimation are integrated interactively. On the other hand, the output is measured from the real plant and is fed back into the parameter estimation problem to establish a matching scheme. During the calculation procedure, the iterative solution is updated in order to approximate the true optimal solution of the original optimal control problem despite model-reality differences. For illustration, a wastewater treatment problem is studied and the results show the efficiency of the approach proposed.
Optimization of piezoelectric cantilever energy harvesters including non-linear effects
International Nuclear Information System (INIS)
Patel, R; McWilliam, S; Popov, A A
2014-01-01
This paper proposes a versatile non-linear model for predicting piezoelectric energy harvester performance. The presented model includes (i) material non-linearity, for both substrate and piezoelectric layers, and (ii) geometric non-linearity incorporated by assuming inextensibility and accurately representing beam curvature. The addition of a sub-model, which utilizes the transfer matrix method to predict eigenfrequencies and eigenvectors for segmented beams, allows for accurate optimization of piezoelectric layer coverage. A validation of the overall theoretical model is performed through experimental testing on both uniform and non-uniform samples manufactured in-house. For the harvester composition used in this work, the magnitude of material non-linearity exhibited by the piezoelectric layer is 35 times greater than that of the substrate layer. It is also observed that material non-linearity, responsible for reductions in resonant frequency with increases in base acceleration, is dominant over geometric non-linearity for standard piezoelectric harvesting devices. Finally, over the tested range, energy loss due to damping is found to increase in a quasi-linear fashion with base acceleration. During an optimization study on piezoelectric layer coverage, results from the developed model were compared with those from a linear model. Unbiased comparisons between harvesters were realized by using devices with identical natural frequencies—created by adjusting the device substrate thickness. Results from three studies, each with a different assumption on mechanical damping variations, are presented. Findings showed that, depending on damping variation, a non-linear model is essential for such optimization studies with each model predicting vastly differing optimum configurations. (paper)
Infinite sets of conservation laws for linear and nonlinear field equations
International Nuclear Information System (INIS)
Mickelsson, J.
1984-01-01
The relation between an infinite set of conservation laws of a linear field equation and the enveloping algebra of the space-time symmetry group is established. It is shown that each symmetric element of the enveloping algebra of the space-time symmetry group of a linear field equation generates a one-parameter group of symmetries of the field equation. The cases of the Maxwell and Dirac equations are studied in detail. Then it is shown that (at least in the sense of a power series in the 'coupling constant') the conservation laws of the linear case can be deformed to conservation laws of a nonlinear field equation which is obtained from the linear one by adding a nonlinear term invariant under the group of space-time symmetries. As an example, our method is applied to the Korteweg-de Vries equation and to the massless Thirring model. (orig.)
Optimized Wavelength-Tuned Nonlinear Frequency Conversion Using a Liquid Crystal Clad Waveguide
Stephen, Mark A. (Inventor)
2018-01-01
An optimized wavelength-tuned nonlinear frequency conversion process using a liquid crystal clad waveguide. The process includes implanting ions on a top surface of a lithium niobate crystal to form an ion implanted lithium niobate layer. The process also includes utilizing a tunable refractive index of a liquid crystal to rapidly change an effective index of the lithium niobate crystal.
International Nuclear Information System (INIS)
Gao Fei; Gao Hongrui; Li Zhuoqiu; Tong Hengqing; Lee, Ju-Jang
2009-01-01
It is well known that set of unstable periodic orbits (UPOs) can be thought of as the skeleton for the dynamics. However, detecting UPOs of nonlinear map is one of the most challenging problems of nonlinear science in both numerical computations and experimental measures. In this paper, a new method is proposed to detect the UPOs in a non-Lyapunov way. Firstly three special techniques are added to quantum-behaved particle swarm optimization (QPSO), a novel mbest particle, contracting the searching space self-adaptively and boundaries restriction (NCB), then the new method NCB-QPSO is proposed. It can maintain an effective search mechanism with fine equilibrium between exploitation and exploration. Secondly, the problems of detecting the UPOs are converted into a non-negative functions' minimization through a proper translation in a non-Lyapunov way. Thirdly the simulations to 6 benchmark optimization problems and different high order UPOs of 5 classic nonlinear maps are done by the proposed method. And the results show that NCB-QPSO is a successful method in detecting the UPOs, and it has the advantages of fast convergence, high precision and robustness.
Optimized nonlinear inversion of surface-wave dispersion data
International Nuclear Information System (INIS)
Raykova, Reneta B.
2014-01-01
A new code for inversion of surface wave dispersion data is developed to obtain Earth’s crustal and upper mantle velocity structure. The author developed Optimized Non–Linear Inversion ( ONLI ) software, based on Monte-Carlo search. The values of S–wave velocity VS and thickness h for a number of horizontal homogeneous layers are parameterized. Velocity of P–wave VP and density ρ of relevant layers are calculated by empirical or theoretical relations. ONLI explores parameters space in two modes, selective and full search, and the main innovation of software is evaluation of tested models. Theoretical dispersion curves are calculated if tested model satisfied specific conditions only, reducing considerably the computation time. A number of tests explored impact of parameterization and proved the ability of ONLI approach to deal successfully with non–uniqueness of inversion problem. Key words: Earth’s structure, surface–wave dispersion, non–linear inversion, software
Nonlinear Thermodynamic Analysis and Optimization of a Carnot Engine Cycle
Directory of Open Access Journals (Sweden)
Michel Feidt
2016-06-01
Full Text Available As part of the efforts to unify the various branches of Irreversible Thermodynamics, the proposed work reconsiders the approach of the Carnot engine taking into account the finite physical dimensions (heat transfer conductances and the finite speed of the piston. The models introduce the irreversibility of the engine by two methods involving different constraints. The first method introduces the irreversibility by a so-called irreversibility ratio in the entropy balance applied to the cycle, while in the second method it is emphasized by the entropy generation rate. Various forms of heat transfer laws are analyzed, but most of the results are given for the case of the linear law. Also, individual cases are studied and reported in order to provide a simple analytical form of the results. The engine model developed allowed a formal optimization using the calculus of variations.
Ma, Lifeng; Wang, Zidong; Lam, Hak-Keung; Kyriakoulis, Nikos
2017-11-01
In this paper, the distributed set-membership filtering problem is investigated for a class of discrete time-varying system with an event-based communication mechanism over sensor networks. The system under consideration is subject to sector-bounded nonlinearity, unknown but bounded noises and sensor saturations. Each intelligent sensing node transmits the data to its neighbors only when certain triggering condition is violated. By means of a set of recursive matrix inequalities, sufficient conditions are derived for the existence of the desired distributed event-based filter which is capable of confining the system state in certain ellipsoidal regions centered at the estimates. Within the established theoretical framework, two additional optimization problems are formulated: one is to seek the minimal ellipsoids (in the sense of matrix trace) for the best filtering performance, and the other is to maximize the triggering threshold so as to reduce the triggering frequency with satisfactory filtering performance. A numerically attractive chaos algorithm is employed to solve the optimization problems. Finally, an illustrative example is presented to demonstrate the effectiveness and applicability of the proposed algorithm.
International Nuclear Information System (INIS)
Liolios, A.A.; Boglou, A.K.
2003-01-01
The paper presents a new numerical approach for a non-linear optimal control problem arising in earthquake civil engineering. This problem concerns the elastoplastic softening-fracturing unilateral contact between neighbouring buildings during earthquakes when Coulomb friction is taken into account under second-order instabilizing effects. So, the earthquake response of the adjacent structures can appear instabilities and chaotic behaviour. The problem formulation presented here leads to a set of equations and inequalities, which is equivalent to a dynamic hemivariational inequality in the way introduced by Panagiotopoulos [Hemivariational Inequalities. Applications in Mechanics and Engineering, Springer-Verlag, Berlin, 1993]. The numerical procedure is based on an incremental problem formulation and on a double discretization, in space by the finite element method and in time by the Wilson-θ method. The generally non-convex constitutive contact laws are piecewise linearized, and in each time-step a non-convex linear complementarity problem is solved with a reduced number of unknowns
Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai
2013-09-01
In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.
Yang, Xiong; Liu, Derong; Wang, Ding
2014-03-01
In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities with unknown structures. Two different types of neural networks (NNs) are employed to approximate the Hamilton-Jacobi-Bellman equation. That is, an recurrent NN is constructed to identify the unknown dynamical system, and two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal cost, respectively. Based on this framework, the action NN and the critic NN are tuned simultaneously, without the requirement for the knowledge of system drift dynamics. Moreover, by using Lyapunov's direct method, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. To demonstrate the effectiveness of the present approach, simulation results are illustrated.
International Nuclear Information System (INIS)
Huang, Xiaobiao; Safranek, James
2014-01-01
Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications
Energy Technology Data Exchange (ETDEWEB)
Huang, Xiaobiao, E-mail: xiahuang@slac.stanford.edu; Safranek, James
2014-09-01
Nonlinear dynamics optimization is carried out for a low emittance upgrade lattice of SPEAR3 in order to improve its dynamic aperture and Touschek lifetime. Two multi-objective optimization algorithms, a genetic algorithm and a particle swarm algorithm, are used for this study. The performance of the two algorithms are compared. The result shows that the particle swarm algorithm converges significantly faster to similar or better solutions than the genetic algorithm and it does not require seeding of good solutions in the initial population. These advantages of the particle swarm algorithm may make it more suitable for many accelerator optimization applications.
Directory of Open Access Journals (Sweden)
Xiao-Fang Zhong
2017-12-01
Full Text Available The irregular wave disturbance attenuation problem for jacket-type offshore platforms involving the nonlinear characteristics is studied. The main contribution is that a digital-control-based approximation of optimal wave disturbances attenuation controller (AOWDAC is proposed based on iteration control theory, which consists of a feedback item of offshore state, a feedforward item of wave force and a nonlinear compensated component with iterative sequences. More specifically, by discussing the discrete model of nonlinear offshore platform subject to wave forces generated from the Joint North Sea Wave Project (JONSWAP wave spectrum and linearized wave theory, the original wave disturbances attenuation problem is formulated as the nonlinear two-point-boundary-value (TPBV problem. By introducing two vector sequences of system states and nonlinear compensated item, the solution of introduced nonlinear TPBV problem is obtained. Then, a numerical algorithm is designed to realize the feasibility of AOWDAC based on the deviation of performance index between the adjacent iteration processes. Finally, applied the proposed AOWDAC to a jacket-type offshore platform in Bohai Bay, the vibration amplitudes of the displacement and the velocity, and the required energy consumption can be reduced significantly.
International Nuclear Information System (INIS)
Frolov, A.M.
1986-01-01
The problem of exact variational calculations of few-particle systems in the exponential basis of the relative coordinates using nonlinear parameters is studied. The techniques of stepwise optimization and global chaos of nonlinear parameters are used to calculate the S and P states of homonuclear muonic molecules with an error of no more than +0.001 eV. The global-chaos technique also has proved to be successful in the case of the nuclear systems 3 H and 3 He
Narayanan, Vignesh; Jagannathan, Sarangapani
2017-06-08
This paper presents an approximate optimal distributed control scheme for a known interconnected system composed of input affine nonlinear subsystems using event-triggered state and output feedback via a novel hybrid learning scheme. First, the cost function for the overall system is redefined as the sum of cost functions of individual subsystems. A distributed optimal control policy for the interconnected system is developed using the optimal value function of each subsystem. To generate the optimal control policy, forward-in-time, neural networks are employed to reconstruct the unknown optimal value function at each subsystem online. In order to retain the advantages of event-triggered feedback for an adaptive optimal controller, a novel hybrid learning scheme is proposed to reduce the convergence time for the learning algorithm. The development is based on the observation that, in the event-triggered feedback, the sampling instants are dynamic and results in variable interevent time. To relax the requirement of entire state measurements, an extended nonlinear observer is designed at each subsystem to recover the system internal states from the measurable feedback. Using a Lyapunov-based analysis, it is demonstrated that the system states and the observer errors remain locally uniformly ultimately bounded and the control policy converges to a neighborhood of the optimal policy. Simulation results are presented to demonstrate the performance of the developed controller.
International Nuclear Information System (INIS)
Charlton, L.A.; Carreras, B.A.; Holmes, J.A.; Lynch, V.E.
1988-01-01
The linear stability and nonlinear evolution of the resistive m = 1 mode in tokamaks is studied using a full set of resistive magnetohydrodynamic (MHD) equations in toroidal geometry. The modification of the linear and nonlinear properties of the mode by a combination of strong toroidal effects and low resistivity is the focus of this work. Linearly there is a transition from resistive kink to resistive tearing behavior as the aspect ratio and resistivity are reduced, and there is a corresponding modification of the nonlinear behavior, including a slowing of the island growth and development of a Rutherford regime, as the tearing regime is approached. In order to study the sensitivity of the stability and evolution to assumptions concerning the equation of state, two sets of full nonlinear resistive MHD equations (a pressure convection set and an incompressible set) are used. Both sets give more stable nonlinear behavior as the aspect ratio is reduced. The pressure convection set shows a transition from a Kadomtsev reconnection at large aspect ratio to a saturation at small aspect ratio. The incompressible set yields Kadomtsev reconnection for all aspect ratios, but with a significant lengthening of the reconnection time and development of a Rutherford regime at an aspect ratio approaching the transition from a resistive kink mode to a tearing mode. The pressure convection set gives an incomplete reconnection similar to that sometimes seen experimentally. The pressure convection set is, however, strictly justified only at high beta
Nonlinear analysis of vehicle control actuations based on controlled invariant sets
Directory of Open Access Journals (Sweden)
Németh Balázs
2016-03-01
Full Text Available In the paper, an analysis method is applied to the lateral stabilization problem of vehicle systems. The aim is to find the largest state-space region in which the lateral stability of the vehicle can be guaranteed by the peak-bounded control input. In the analysis, the nonlinear polynomial sum-of-squares programming method is applied. A practical computation technique is developed to calculate the maximum controlled invariant set of the system. The method calculates the maximum controlled invariant sets of the steering and braking control systems at various velocities and road conditions. Illustration examples show that, depending on the environments, different vehicle dynamic regions can be reached and stabilized by these controllers. The results can be applied to the theoretical basis of their interventions into the vehicle control system.
Yang, Xiong; He, Haibo
2018-05-26
In this paper, we develop a novel optimal control strategy for a class of uncertain nonlinear systems with unmatched interconnections. To begin with, we present a stabilizing feedback controller for the interconnected nonlinear systems by modifying an array of optimal control laws of auxiliary subsystems. We also prove that this feedback controller ensures a specified cost function to achieve optimality. Then, under the framework of adaptive critic designs, we use critic networks to solve the Hamilton-Jacobi-Bellman equations associated with auxiliary subsystem optimal control laws. The critic network weights are tuned through the gradient descent method combined with an additional stabilizing term. By using the newly established weight tuning rules, we no longer need the initial admissible control condition. In addition, we demonstrate that all signals in the closed-loop auxiliary subsystems are stable in the sense of uniform ultimate boundedness by using classic Lyapunov techniques. Finally, we provide an interconnected nonlinear plant to validate the present control scheme. Copyright © 2018 Elsevier Ltd. All rights reserved.
International Nuclear Information System (INIS)
Huang, C.-H.; Li, J.-X.
2006-01-01
A non-linear optimal control algorithm is examined in this study for the diffusion process of semiconductor materials. The purpose of this algorithm is to estimate an optimal control function such that the homogeneity of the concentration can be controlled during the diffusion process and the diffusion-induced stresses for the semiconductor materials can thus be reduced. The validation of this optimal control analysis utilizing the conjugate gradient method of minimization is analysed by using numerical experiments. Three different diffusion processing times are given and the corresponding optimal control functions are to be determined. Results show that the diffusion time can be shortened significantly by applying the optimal control function at the boundary and the homogeneity of the concentration is also guaranteed. This control function can be obtained within a very short CPU time on a Pentium III 600 MHz PC
Optimization of Thermal Object Nonlinear Control Systems by Energy Efficiency Criterion.
Velichkin, Vladimir A.; Zavyalov, Vladimir A.
2018-03-01
This article presents the results of thermal object functioning control analysis (heat exchanger, dryer, heat treatment chamber, etc.). The results were used to determine a mathematical model of the generalized thermal control object. The appropriate optimality criterion was chosen to make the control more energy-efficient. The mathematical programming task was formulated based on the chosen optimality criterion, control object mathematical model and technological constraints. The “maximum energy efficiency” criterion helped avoid solving a system of nonlinear differential equations and solve the formulated problem of mathematical programming in an analytical way. It should be noted that in the case under review the search for optimal control and optimal trajectory reduces to solving an algebraic system of equations. In addition, it is shown that the optimal trajectory does not depend on the dynamic characteristics of the control object.
Simplex sliding mode control for nonlinear uncertain systems via chaos optimization
International Nuclear Information System (INIS)
Lu, Zhao; Shieh, Leang-San; Chen, Guanrong; Coleman, Norman P.
2005-01-01
As an emerging effective approach to nonlinear robust control, simplex sliding mode control demonstrates some attractive features not possessed by the conventional sliding mode control method, from both theoretical and practical points of view. However, no systematic approach is currently available for computing the simplex control vectors in nonlinear sliding mode control. In this paper, chaos-based optimization is exploited so as to develop a systematic approach to seeking the simplex control vectors; particularly, the flexibility of simplex control is enhanced by making the simplex control vectors dependent on the Euclidean norm of the sliding vector rather than being constant, which result in both reduction of the chattering and speedup of the convergence. Computer simulation on a nonlinear uncertain system is given to illustrate the effectiveness of the proposed control method
State and parameter estimation in nonlinear systems as an optimal tracking problem
International Nuclear Information System (INIS)
Creveling, Daniel R.; Gill, Philip E.; Abarbanel, Henry D.I.
2008-01-01
In verifying and validating models of nonlinear processes it is important to incorporate information from observations in an efficient manner. Using the idea of synchronization of nonlinear dynamical systems, we present a framework for connecting a data signal with a model in a way that minimizes the required coupling yet allows the estimation of unknown parameters in the model. The need to evaluate unknown parameters in models of nonlinear physical, biophysical, and engineering systems occurs throughout the development of phenomenological or reduced models of dynamics. Our approach builds on existing work that uses synchronization as a tool for parameter estimation. We address some of the critical issues in that work and provide a practical framework for finding an accurate solution. In particular, we show the equivalence of this problem to that of tracking within an optimal control framework. This equivalence allows the application of powerful numerical methods that provide robust practical tools for model development and validation
International Nuclear Information System (INIS)
Saviz, M R
2015-01-01
In this paper a nonlinear approach to studying the vibration characteristic of laminated composite plate with surface-bonded piezoelectric layer/patch is formulated, based on the Green Lagrange type of strain–displacements relations, by incorporating higher-order terms arising from nonlinear relations of kinematics into mathematical formulations. The equations of motion are obtained through the energy method, based on Lagrange equations and by using higher-order shear deformation theories with von Karman–type nonlinearities, so that transverse shear strains vanish at the top and bottom surfaces of the plate. An isoparametric finite element model is provided to model the nonlinear dynamics of the smart plate with piezoelectric layer/ patch. Different boundary conditions are investigated. Optimal locations of piezoelectric patches are found using a genetic algorithm to maximize spatial controllability/observability and considering the effect of residual modes to reduce spillover effect. Active attenuation of vibration of laminated composite plate is achieved through an optimal control law with inequality constraint, which is related to the maximum and minimum values of allowable voltage in the piezoelectric elements. To keep the voltages of actuator pairs in an allowable limit, the Pontryagin’s minimum principle is implemented in a system with multi-inequality constraint of control inputs. The results are compared with similar ones, proving the accuracy of the model especially for the structures undergoing large deformations. The convergence is studied and nonlinear frequencies are obtained for different thickness ratios. The structural coupling between plate and piezoelectric actuators is analyzed. Some examples with new features are presented, indicating that the piezo-patches significantly improve the damping characteristics of the plate for suppressing the geometrically nonlinear transient vibrations. (paper)
International Nuclear Information System (INIS)
Xu Ruirui; Chen Tianlun; Gao Chengfeng
2006-01-01
Nonlinear time series prediction is studied by using an improved least squares support vector machine (LS-SVM) regression based on chaotic mutation evolutionary programming (CMEP) approach for parameter optimization. We analyze how the prediction error varies with different parameters (σ, γ) in LS-SVM. In order to select appropriate parameters for the prediction model, we employ CMEP algorithm. Finally, Nasdaq stock data are predicted by using this LS-SVM regression based on CMEP, and satisfactory results are obtained.
Stabilization of Hypersonic Boundary Layers by Linear and Nonlinear Optimal Perturbations
Paredes, Pedro; Choudhari, Meelan M.; Li, Fei
2017-01-01
The effect of stationary, finite-amplitude, linear and nonlinear optimal perturbations on the modal disturbance growth in a Mach 6 axisymmetric flow over a 7 deg. half-angle cone with 0:126 mm nose radius and 0:305 m length is investigated. The freestream parameters (M = 6, Re(exp 1) = 18 x 10(exp. 6) /m) are selected to match the flow conditions of a previous experiment in the VKI H3 hypersonic tunnel. Plane-marching parabolized stability equations are used in conjunction with a partial-differential equation based planar eigenvalue analysis to characterize the boundary layer instability in the presence of azimuthally periodic streaks. The streaks are observed to stabilize nominally planar Mack mode instabilities, although oblique Mack mode and first-mode disturbances are destabilized. Experimentally measured transition onset in the absence of any streaks correlates with an amplification factor of N = 6 for the planar Mack modes. For high enough streak amplitudes, the transition threshold of N = 6 is not reached by the Mack mode instabilities within the length of the cone; however, subharmonic first-mode instabilities, which are destabilized by the presence of the streaks, do reach N = 6 near the end of the cone. The highest stabilization is observed at streak amplitudes of approximately 20 percent of the freestream velocity. Because the use of initial disturbance profiles based on linear optimal growth theory may yield suboptimal control in the context of nonlinear streaks, the computational predictions are extended to nonlinear optimal growth theory. Results show that by using nonlinearly optimal perturbation leads to slightly enhanced stabilization of plane Mack mode disturbances as well as reduced destabilization of subharmonic first-mode disturbances.
Study on Rail Profile Optimization Based on the Nonlinear Relationship between Profile and Wear Rate
Directory of Open Access Journals (Sweden)
Jianxi Wang
2017-01-01
Full Text Available This paper proposes a rail profile optimization method that takes account of wear rate within design cycle so as to minimize rail wear at the curve in heavy haul railway and extend the service life of rail. Taking rail wear rate as the object function, the vertical coordinate of rail profile at range optimization as independent variable, and the geometric characteristics and grinding depth of rail profile as constraint conditions, the support vector machine regression theory was used to fit the nonlinear relationship between rail profile and its wear rate. Then, the profile optimization model was built. Based on the optimization principle of genetic algorithm, the profile optimization model was solved to achieve the optimal rail profile. A multibody dynamics model was used to check the dynamic performance of carriage running on optimal rail profile. The result showed that the average relative error of support vector machine regression model remained less than 10% after a number of training processes. The dynamic performance of carriage running on optimized rail profile met the requirements on safety index and stability. The wear rate of optimized profile was lower than that of standard profile by 5.8%; the allowable carrying gross weight increased by 12.7%.
International Nuclear Information System (INIS)
Chang, Ying-Pin
2010-01-01
A particle-swarm optimization method with nonlinear time-varying evolution (PSO-NTVE) is employed in determining the tilt angle of photovoltaic (PV) modules in Taiwan. The objective is to maximize the output electrical energy of the modules. In this study, seven Taiwanese cities were selected for analysis. First, the sun's position at any time and location was predicted by the mathematical procedure of Julian dating, and then the solar irradiation was obtained at each site under a clear sky. By combining the temperature effect, the PSO-NTVE method is adopted to calculate the optimal tilt angles for fixed south-facing PV modules. In this method, the parameters are determined by using matrix experiments with an orthogonal array, in which a minimal number of experiments have an effect that approximates the full factorial experiments. Statistical error analysis was performed to compare the results between the four PSO methods and experimental results. Hengchun city in which the minimum total error value of 6.12% the reasons for the weather more stability and less building shade. A comparison of the measurement results in electrical energy between the four PSO methods and the PV modules set a six tilt angles. Obviously four types of PSO methods simulation of electrical energy value from 231.12 kWh/m 2 for Taipei to 233.81 kWh/m 2 for Hengchun greater than the measurement values from 224.71 kWh/m 2 for Taichung to 228.47 kWh/m 2 for Hengchun by PV module which is due to instability caused by climate change. Finally, the results show that the annual optimal angle for the Taipei area is 18.16 o ; for Taichung, 17.3 o ; for Tainan, 16.15 o ; for Kaosiung, 15.79 o ; for Hengchung, 15.17 o ; for Hualian, 17.16 o ; and for Taitung, 15.94 o . It is evident that the authorized Industrial Technology Research Institute (ITRI) recommends that tilt angle of 23.5 o was not an appropriate use of Taiwan's seven cities. PV modules with the installation of the tilt angle should be
Yang, Guo Sheng; Wang, Xiao Yang; Li, Xue Dong
2018-03-01
With the establishment of the integrated model of relay protection and the scale of the power system expanding, the global setting and optimization of relay protection is an extremely difficult task. This paper presents a kind of application in relay protection of global optimization improved particle swarm optimization algorithm and the inverse time current protection as an example, selecting reliability of the relay protection, selectivity, quick action and flexibility as the four requires to establish the optimization targets, and optimizing protection setting values of the whole system. Finally, in the case of actual power system, the optimized setting value results of the proposed method in this paper are compared with the particle swarm algorithm. The results show that the improved quantum particle swarm optimization algorithm has strong search ability, good robustness, and it is suitable for optimizing setting value in the relay protection of the whole power system.
Optimality Conditions in Differentiable Vector Optimization via Second-Order Tangent Sets
International Nuclear Information System (INIS)
Jimenez, Bienvenido; Novo, Vicente
2004-01-01
We provide second-order necessary and sufficient conditions for a point to be an efficient element of a set with respect to a cone in a normed space, so that there is only a small gap between necessary and sufficient conditions. To this aim, we use the common second-order tangent set and the asymptotic second-order cone utilized by Penot. As an application we establish second-order necessary conditions for a point to be a solution of a vector optimization problem with an arbitrary feasible set and a twice Frechet differentiable objective function between two normed spaces. We also establish second-order sufficient conditions when the initial space is finite-dimensional so that there is no gap with necessary conditions. Lagrange multiplier rules are also given
Application of the optimal homotopy asymptotic method to nonlinear Bingham fluid dampers
Directory of Open Access Journals (Sweden)
Marinca Vasile
2017-10-01
Full Text Available Dynamic response time is an important feature for determining the performance of magnetorheological (MR dampers in practical civil engineering applications. The objective of this paper is to show how to use the Optimal Homotopy Asymptotic Method (OHAM to give approximate analytical solutions of the nonlinear differential equation of a modified Bingham model with non-viscous exponential damping. Our procedure does not depend upon small parameters and provides us with a convenient way to optimally control the convergence of the approximate solutions. OHAM is very efficient in practice for ensuring very rapid convergence of the solution after only one iteration and with a small number of steps.
Application of the optimal homotopy asymptotic method to nonlinear Bingham fluid dampers
Marinca, Vasile; Ene, Remus-Daniel; Bereteu, Liviu
2017-10-01
Dynamic response time is an important feature for determining the performance of magnetorheological (MR) dampers in practical civil engineering applications. The objective of this paper is to show how to use the Optimal Homotopy Asymptotic Method (OHAM) to give approximate analytical solutions of the nonlinear differential equation of a modified Bingham model with non-viscous exponential damping. Our procedure does not depend upon small parameters and provides us with a convenient way to optimally control the convergence of the approximate solutions. OHAM is very efficient in practice for ensuring very rapid convergence of the solution after only one iteration and with a small number of steps.
The optimization of the nonlinear parameters in the transcorrelated method: the hydrogen molecule
International Nuclear Information System (INIS)
Huggett, J.P.; Armour, E.A.G.
1976-01-01
The nonlinear parameters in a transcorrelated calculation of the groundstate energy and wavefunction of the hydrogen molecule are optimized using the method of Boys and Handy (Proc. R. Soc. A.; 309:195 and 209, 310:43 and 63, 311:309 (1969)). The method gives quite accurate results in all cases and in some cases the results are highly accurate. This is the first time the method has been applied to the optimization of a term in the correlation function which depends linearly on the interelectronic distance. (author)
Optimal Control of Nonlinear Hydraulic Networks in the Presence of Disturbance
DEFF Research Database (Denmark)
Tahavori, Maryamsadat; Leth, John-Josef; Kallesøe, Carsten
2014-01-01
Water leakage is an important component of water loss. Many methods have emerged from urban water supply systems for leakage control, but it still remains a challenge in many countries. Pressure management is an effective way to reduce the leakage in a system. It can also reduce the power...... consumption. To this end, an optimal control strategy is proposed in this paper. In the water supply system model, the hydraulic resistance of the valve is estimated by the real data from a water supply system and it is considered to be a disturbance. The method which is used to solve the nonlinear optimal...
Optimal projection of observations in a Bayesian setting
Giraldi, Loic; Le Maî tre, Olivier P.; Hoteit, Ibrahim; Knio, Omar
2018-01-01
, and the one that maximizes the mutual information between the parameter of interest and the projected observations. The first two optimization problems are formulated as the determination of an optimal subspace and therefore the solution is computed using
Yang, Haijian
2016-07-26
Fully implicit methods are drawing more attention in scientific and engineering applications due to the allowance of large time steps in extreme-scale simulations. When using a fully implicit method to solve two-phase flow problems in porous media, one major challenge is the solution of the resultant nonlinear system at each time step. To solve such nonlinear systems, traditional nonlinear iterative methods, such as the class of the Newton methods, often fail to achieve the desired convergent rate due to the high nonlinearity of the system and/or the violation of the boundedness requirement of the saturation. In the paper, we reformulate the two-phase model as a variational inequality that naturally ensures the physical feasibility of the saturation variable. The variational inequality is then solved by an active-set reduced-space method with a nonlinear elimination preconditioner to remove the high nonlinear components that often causes the failure of the nonlinear iteration for convergence. To validate the effectiveness of the proposed method, we compare it with the classical implicit pressure-explicit saturation method for two-phase flow problems with strong heterogeneity. The numerical results show that our nonlinear solver overcomes the often severe limits on the time step associated with existing methods, results in superior convergence performance, and achieves reduction in the total computing time by more than one order of magnitude.
Yang, Haijian; Yang, Chao; Sun, Shuyu
2016-01-01
Fully implicit methods are drawing more attention in scientific and engineering applications due to the allowance of large time steps in extreme-scale simulations. When using a fully implicit method to solve two-phase flow problems in porous media, one major challenge is the solution of the resultant nonlinear system at each time step. To solve such nonlinear systems, traditional nonlinear iterative methods, such as the class of the Newton methods, often fail to achieve the desired convergent rate due to the high nonlinearity of the system and/or the violation of the boundedness requirement of the saturation. In the paper, we reformulate the two-phase model as a variational inequality that naturally ensures the physical feasibility of the saturation variable. The variational inequality is then solved by an active-set reduced-space method with a nonlinear elimination preconditioner to remove the high nonlinear components that often causes the failure of the nonlinear iteration for convergence. To validate the effectiveness of the proposed method, we compare it with the classical implicit pressure-explicit saturation method for two-phase flow problems with strong heterogeneity. The numerical results show that our nonlinear solver overcomes the often severe limits on the time step associated with existing methods, results in superior convergence performance, and achieves reduction in the total computing time by more than one order of magnitude.
Gottlieb, Sigal
2015-04-10
High order spatial discretizations with monotonicity properties are often desirable for the solution of hyperbolic PDEs. These methods can advantageously be coupled with high order strong stability preserving time discretizations. The search for high order strong stability time-stepping methods with large allowable strong stability coefficient has been an active area of research over the last two decades. This research has shown that explicit SSP Runge-Kutta methods exist only up to fourth order. However, if we restrict ourselves to solving only linear autonomous problems, the order conditions simplify and this order barrier is lifted: explicit SSP Runge-Kutta methods of any linear order exist. These methods reduce to second order when applied to nonlinear problems. In the current work we aim to find explicit SSP Runge-Kutta methods with large allowable time-step, that feature high linear order and simultaneously have the optimal fourth order nonlinear order. These methods have strong stability coefficients that approach those of the linear methods as the number of stages and the linear order is increased. This work shows that when a high linear order method is desired, it may still be worthwhile to use methods with higher nonlinear order.
Directory of Open Access Journals (Sweden)
Patrick Piprek
2018-02-01
Full Text Available This paper presents an approach to model a ski jumper as a multi-body system for an optimal control application. The modeling is based on the constrained Newton-Euler-Equations. Within this paper the complete multi-body modeling methodology as well as the musculoskeletal modeling is considered. For the musculoskeletal modeling and its incorporation in the optimization model, we choose a nonlinear dynamic inversion control approach. This approach uses the muscle models as nonlinear reference models and links them to the ski jumper movement by a control law. This strategy yields a linearized input-output behavior, which makes the optimal control problem easier to solve. The resulting model of the ski jumper can then be used for trajectory optimization whose results are compared to literature jumps. Ultimately, this enables the jumper to get a very detailed feedback of the flight. To achieve the maximal jump length, exact positioning of his body with respect to the air can be displayed.
Actor-critic-based optimal tracking for partially unknown nonlinear discrete-time systems.
Kiumarsi, Bahare; Lewis, Frank L
2015-01-01
This paper presents a partially model-free adaptive optimal control solution to the deterministic nonlinear discrete-time (DT) tracking control problem in the presence of input constraints. The tracking error dynamics and reference trajectory dynamics are first combined to form an augmented system. Then, a new discounted performance function based on the augmented system is presented for the optimal nonlinear tracking problem. In contrast to the standard solution, which finds the feedforward and feedback terms of the control input separately, the minimization of the proposed discounted performance function gives both feedback and feedforward parts of the control input simultaneously. This enables us to encode the input constraints into the optimization problem using a nonquadratic performance function. The DT tracking Bellman equation and tracking Hamilton-Jacobi-Bellman (HJB) are derived. An actor-critic-based reinforcement learning algorithm is used to learn the solution to the tracking HJB equation online without requiring knowledge of the system drift dynamics. That is, two neural networks (NNs), namely, actor NN and critic NN, are tuned online and simultaneously to generate the optimal bounded control policy. A simulation example is given to show the effectiveness of the proposed method.
Optimizing Distributed Machine Learning for Large Scale EEG Data Set
Directory of Open Access Journals (Sweden)
M Bilal Shaikh
2017-06-01
Full Text Available Distributed Machine Learning (DML has gained its importance more than ever in this era of Big Data. There are a lot of challenges to scale machine learning techniques on distributed platforms. When it comes to scalability, improving the processor technology for high level computation of data is at its limit, however increasing machine nodes and distributing data along with computation looks as a viable solution. Different frameworks and platforms are available to solve DML problems. These platforms provide automated random data distribution of datasets which miss the power of user defined intelligent data partitioning based on domain knowledge. We have conducted an empirical study which uses an EEG Data Set collected through P300 Speller component of an ERP (Event Related Potential which is widely used in BCI problems; it helps in translating the intention of subject w h i l e performing any cognitive task. EEG data contains noise due to waves generated by other activities in the brain which contaminates true P300Speller. Use of Machine Learning techniques could help in detecting errors made by P300 Speller. We are solving this classification problem by partitioning data into different chunks and preparing distributed models using Elastic CV Classifier. To present a case of optimizing distributed machine learning, we propose an intelligent user defined data partitioning approach that could impact on the accuracy of distributed machine learners on average. Our results show better average AUC as compared to average AUC obtained after applying random data partitioning which gives no control to user over data partitioning. It improves the average accuracy of distributed learner due to the domain specific intelligent partitioning by the user. Our customized approach achieves 0.66 AUC on individual sessions and 0.75 AUC on mixed sessions, whereas random / uncontrolled data distribution records 0.63 AUC.
International Nuclear Information System (INIS)
Maldonado, G.I.; Turinsky, P.J.
1995-01-01
The determination of the family of optimum core loading patterns for pressurized water reactors (PWRs) involves the assessment of the core attributes for thousands of candidate loading patterns. For this reason, the computational capability to efficiently and accurately evaluate a reactor core's eigenvalue and power distribution versus burnup using a nodal diffusion generalized perturbation theory (GPT) model is developed. The GPT model is derived from the forward nonlinear iterative nodal expansion method (NEM) to explicitly enable the preservation of the finite difference matrix structure. This key feature considerably simplifies the mathematical formulation of NEM GPT and results in reduced memory storage and CPU time requirements versus the traditional response-matrix approach to NEM. In addition, a treatment within NEM GPT can account for localized nonlinear feedbacks, such as that due to fission product buildup and thermal-hydraulic effects. When compared with a standard nonlinear iterative NEM forward flux solve with feedbacks, the NEM GPT model can execute between 8 and 12 times faster. These developments are implemented within the PWR in-core nuclear fuel management optimization code FORMOSA-P, combining the robustness of its adaptive simulated annealing stochastic optimization algorithm with an NEM GPT neutronics model that efficiently and accurately evaluates core attributes associated with objective functions and constraints of candidate loading patterns
Robust Optimization Using Supremum of the Objective Function for Nonlinear Programming Problems
International Nuclear Information System (INIS)
Lee, Se Jung; Park, Gyung Jin
2014-01-01
In the robust optimization field, the robustness of the objective function emphasizes an insensitive design. In general, the robustness of the objective function can be achieved by reducing the change of the objective function with respect to the variation of the design variables and parameters. However, in conventional methods, when an insensitive design is emphasized, the performance of the objective function can be deteriorated. Besides, if the numbers of the design variables are increased, the numerical cost is quite high in robust optimization for nonlinear programming problems. In this research, the robustness index for the objective function and a process of robust optimization are proposed. Moreover, a method using the supremum of linearized functions is also proposed to reduce the computational cost. Mathematical examples are solved for the verification of the proposed method and the results are compared with those from the conventional methods. The proposed approach improves the performance of the objective function and its efficiency
Topology optimization problems with design-dependent sets of constraints
DEFF Research Database (Denmark)
Schou, Marie-Louise Højlund
Topology optimization is a design tool which is used in numerous fields. It can be used whenever the design is driven by weight and strength considerations. The basic concept of topology optimization is the interpretation of partial differential equation coefficients as effective material...... properties and designing through changing these coefficients. For example, consider a continuous structure. Then the basic concept is to represent this structure by small pieces of material that are coinciding with the elements of a finite element model of the structure. This thesis treats stress constrained...... structural topology optimization problems. For such problems a stress constraint for an element should only be present in the optimization problem when the structural design variable corresponding to this element has a value greater than zero. We model the stress constrained topology optimization problem...
Controller Parameter Optimization for Nonlinear Systems Using Enhanced Bacteria Foraging Algorithm
Directory of Open Access Journals (Sweden)
V. Rajinikanth
2012-01-01
Full Text Available An enhanced bacteria foraging optimization (EBFO algorithm-based Proportional + integral + derivative (PID controller tuning is proposed for a class of nonlinear process models. The EBFO algorithm is a modified form of standard BFO algorithm. A multiobjective performance index is considered to guide the EBFO algorithm for discovering the best possible value of controller parameters. The efficiency of the proposed scheme has been validated through a comparative study with classical BFO, adaptive BFO, PSO, and GA based controller tuning methods proposed in the literature. The proposed algorithm is tested in real time on a nonlinear spherical tank system. The real-time results show that, EBFO tuned PID controller gives a smooth response for setpoint tracking performance.
International Nuclear Information System (INIS)
Tian Shunqiang; Liu Guimin; Hou Jie; Chen Guangling; Wan Chenglan; Li Haohu
2009-01-01
In this paper, we present a rule to improve the nonlinear solution with frequency map analysis (FMA), and without frequently revisiting the optimization algorithm. Two aspects of FMA are emphasized. The first one is the tune shift with amplitude, which can be used to improve the solution of harmonic sextupoles, and thus obtain a large dynamic aperture. The second one is the tune diffusion rate, which can be used to select a quiet tune. Application of these ideas is carried out in the storage ring of the Shanghai Synchrotron Radiation Facility (SSRF), and the detailed processes, as well as better solutions, are presented in this paper. Discussions about the nonlinear behaviors of off-momentum particles are also presented. (authors)
Esfandiari, Kasra; Abdollahi, Farzaneh; Talebi, Heidar Ali
2017-09-01
In this paper, an identifier-critic structure is introduced to find an online near-optimal controller for continuous-time nonaffine nonlinear systems having saturated control signal. By employing two Neural Networks (NNs), the solution of Hamilton-Jacobi-Bellman (HJB) equation associated with the cost function is derived without requiring a priori knowledge about system dynamics. Weights of the identifier and critic NNs are tuned online and simultaneously such that unknown terms are approximated accurately and the control signal is kept between the saturation bounds. The convergence of NNs' weights, identification error, and system states is guaranteed using Lyapunov's direct method. Finally, simulation results are performed on two nonlinear systems to confirm the effectiveness of the proposed control strategy. Copyright © 2017 Elsevier Ltd. All rights reserved.
Anacker, Tony; Hill, J Grant; Friedrich, Joachim
2016-04-21
Minimal basis sets, denoted DSBSenv, based on the segmented basis sets of Ahlrichs and co-workers have been developed for use as environmental basis sets for the domain-specific basis set (DSBS) incremental scheme with the aim of decreasing the CPU requirements of the incremental scheme. The use of these minimal basis sets within explicitly correlated (F12) methods has been enabled by the optimization of matching auxiliary basis sets for use in density fitting of two-electron integrals and resolution of the identity. The accuracy of these auxiliary sets has been validated by calculations on a test set containing small- to medium-sized molecules. The errors due to density fitting are about 2-4 orders of magnitude smaller than the basis set incompleteness error of the DSBSenv orbital basis sets. Additional reductions in computational cost have been tested with the reduced DSBSenv basis sets, in which the highest angular momentum functions of the DSBSenv auxiliary basis sets have been removed. The optimized and reduced basis sets are used in the framework of the domain-specific basis set of the incremental scheme to decrease the computation time without significant loss of accuracy. The computation times and accuracy of the previously used environmental basis and that optimized in this work have been validated with a test set of medium- to large-sized systems. The optimized and reduced DSBSenv basis sets decrease the CPU time by about 15.4% and 19.4% compared with the old environmental basis and retain the accuracy in the absolute energy with standard deviations of 0.99 and 1.06 kJ/mol, respectively.
Directory of Open Access Journals (Sweden)
Syam Syafiie
2014-06-01
Full Text Available A comparative study of Model Predictive Control (MPC using active-set method and interior point methods is proposed as a control technique for highly non-linear pH process. The process is a strong acid-strong base system. A strong acid of hydrochloric acid (HCl and a strong base of sodium hydroxide (NaOH with the presence of buffer solution sodium bicarbonate (NaHCO3 are used in a neutralization process flowing into reactor. The non-linear pH neutralization model governed in this process is presented by multi-linear models. Performance of both controllers is studied by evaluating its ability of set-point tracking and disturbance-rejection. Besides, the optimization time is compared between these two methods; both MPC shows the similar performance with no overshoot, offset, and oscillation. However, the conventional active-set method gives a shorter control action time for small scale optimization problem compared to MPC using IPM method for pH control.
Simulation-based robust optimization for signal timing and setting.
2009-12-30
The performance of signal timing plans obtained from traditional approaches for : pre-timed (fixed-time or actuated) control systems is often unstable under fluctuating traffic : conditions. This report develops a general approach for optimizing the ...
Setting of the Optimal Parameters of Melted Glass
Czech Academy of Sciences Publication Activity Database
Luptáková, Natália; Matejíčka, L.; Krečmer, N.
2015-01-01
Roč. 10, č. 1 (2015), s. 73-79 ISSN 1802-2308 Institutional support: RVO:68081723 Keywords : Striae * Glass * Glass melting * Regression * Optimal parameters Subject RIV: JH - Ceramics, Fire-Resistant Materials and Glass
An Optimal Homotopy Asymptotic Approach Applied to Nonlinear MHD Jeffery-Hamel Flow
Directory of Open Access Journals (Sweden)
Vasile Marinca
2011-01-01
Full Text Available A simple and effective procedure is employed to propose a new analytic approximate solution for nonlinear MHD Jeffery-Hamel flow. This technique called the Optimal Homotopy Asymptotic Method (OHAM does not depend upon any small/large parameters and provides us with a convenient way to control the convergence of the solution. The examples given in this paper lead to the conclusion that the accuracy of the obtained results is growing along with increasing the number of constants in the auxiliary function, which are determined using a computer technique. The results obtained through the proposed method are in very good agreement with the numerical results.
Global stability, periodic solutions, and optimal control in a nonlinear differential delay model
Directory of Open Access Journals (Sweden)
Anatoli F. Ivanov
2010-09-01
Full Text Available A nonlinear differential equation with delay serving as a mathematical model of several applied problems is considered. Sufficient conditions for the global asymptotic stability and for the existence of periodic solutions are given. Two particular applications are treated in detail. The first one is a blood cell production model by Mackey, for which new periodicity criteria are derived. The second application is a modified economic model with delay due to Ramsey. An optimization problem for a maximal consumption is stated and solved for the latter.
DEFF Research Database (Denmark)
Petersen, Lars Norbert; Poulsen, Niels Kjølstad; Niemann, Hans Henrik
2015-01-01
In this paper, we compare the performance of an economically optimizing Nonlinear Model Predictive Controller (E-NMPC) to a linear tracking Model Predictive Controller (MPC) for a spray drying plant. We find in this simulation study, that the economic performance of the two controllers are almost...... equal. We evaluate the economic performance with an industrially recorded disturbance scenario, where unmeasured disturbances and model mismatch are present. The state of the spray dryer, used in the E-NMPC and MPC, is estimated using Kalman Filters with noise covariances estimated by a maximum...
Afifi, Ahmed; Nakaguchi, Toshiya; Tsumura, Norimichi
2010-03-01
In many medical applications, the automatic segmentation of deformable organs from medical images is indispensable and its accuracy is of a special interest. However, the automatic segmentation of these organs is a challenging task according to its complex shape. Moreover, the medical images usually have noise, clutter, or occlusion and considering the image information only often leads to meager image segmentation. In this paper, we propose a fully automated technique for the segmentation of deformable organs from medical images. In this technique, the segmentation is performed by fitting a nonlinear shape model with pre-segmented images. The kernel principle component analysis (KPCA) is utilized to capture the complex organs deformation and to construct the nonlinear shape model. The presegmentation is carried out by labeling each pixel according to its high level texture features extracted using the overcomplete wavelet packet decomposition. Furthermore, to guarantee an accurate fitting between the nonlinear model and the pre-segmented images, the particle swarm optimization (PSO) algorithm is employed to adapt the model parameters for the novel images. In this paper, we demonstrate the competence of proposed technique by implementing it to the liver segmentation from computed tomography (CT) scans of different patients.
Linear and nonlinear market correlations: Characterizing financial crises and portfolio optimization
Haluszczynski, Alexander; Laut, Ingo; Modest, Heike; Räth, Christoph
2017-12-01
Pearson correlation and mutual information-based complex networks of the day-to-day returns of U.S. S&P500 stocks between 1985 and 2015 have been constructed to investigate the mutual dependencies of the stocks and their nature. We show that both networks detect qualitative differences especially during (recent) turbulent market periods, thus indicating strongly fluctuating interconnections between the stocks of different companies in changing economic environments. A measure for the strength of nonlinear dependencies is derived using surrogate data and leads to interesting observations during periods of financial market crises. In contrast to the expectation that dependencies reduce mainly to linear correlations during crises, we show that (at least in the 2008 crisis) nonlinear effects are significantly increasing. It turns out that the concept of centrality within a network could potentially be used as some kind of an early warning indicator for abnormal market behavior as we demonstrate with the example of the 2008 subprime mortgage crisis. Finally, we apply a Markowitz mean variance portfolio optimization and integrate the measure of nonlinear dependencies to scale the investment exposure. This leads to significant outperformance as compared to a fully invested portfolio.
Optimizing optical nonlinearities in GaInAs/AlInAs quantum cascade lasers
Directory of Open Access Journals (Sweden)
Gajić Aleksandra D.
2014-01-01
Full Text Available Regardless of the huge advances made in the design and fabrication of mid-infrared and terahertz quantum cascade lasers, success in accessing the ~3-4 mm region of the electromagnetic spectrum has remained limited. This fact has brought about the need to exploit resonant intersubband transitions as powerful nonlinear oscillators, consequently enabling the occurrence of large nonlinear optical susceptibilities as a means of reaching desired wavelengths. In this work, we present a computational model developed for the optimization of second-order optical nonlinearities in In0.53Ga0.47As/Al0.48In0.52As quantum cascade laser structures based on the implementation of the Genetic algorithm. The carrier transport and the power output of the structure were calculated by self-consistent solutions to the system of rate equations for carriers and photons. Both stimulated and simultaneous double-photon absorption processes occurring between the second harmonic generation-relevant levels are incorporated into rate equations and the material-dependent effective mass and band non-parabolicity are taken into account, as well. The developed method is quite general and can be applied to any higher order effect which requires the inclusion of the photon density equation. [Projekat Ministarstva nauke Republike Srbije, br. III 45010
Economic Optimization of Spray Dryer Operation using Nonlinear Model Predictive Control
DEFF Research Database (Denmark)
Petersen, Lars Norbert; Poulsen, Niels Kjølstad; Niemann, Hans Henrik
2014-01-01
In this paper we investigate an economically optimizing Nonlinear Model Predictive Control (E-NMPC) for a spray drying process. By simulation we evaluate the economic potential of this E-NMPC compared to a conventional PID based control strategy. Spray drying is the preferred process to reduce...... the water content for many liquid foodstuffs and produces a free flowing powder. The main challenge in controlling the spray drying process is to meet the residual moisture specifications and avoid that the powder sticks to the chamber walls of the spray dryer. We present a model for a spray dryer that has...... been validated on experimental data from a pilot plant. We use this model for simulation as well as for prediction in the E-NMPC. The E-NMPC is designed with hard input constraints and soft output constraints. The open-loop optimal control problem in the E-NMPC is solved using the single...
Directory of Open Access Journals (Sweden)
Bingyong Yan
2015-01-01
Full Text Available A robust fault detection scheme for a class of nonlinear systems with uncertainty is proposed. The proposed approach utilizes robust control theory and parameter optimization algorithm to design the gain matrix of fault tracking approximator (FTA for fault detection. The gain matrix of FTA is designed to minimize the effects of system uncertainty on residual signals while maximizing the effects of system faults on residual signals. The design of the gain matrix of FTA takes into account the robustness of residual signals to system uncertainty and sensitivity of residual signals to system faults simultaneously, which leads to a multiobjective optimization problem. Then, the detectability of system faults is rigorously analyzed by investigating the threshold of residual signals. Finally, simulation results are provided to show the validity and applicability of the proposed approach.
DEFF Research Database (Denmark)
Petersen, Lars Norbert; Jørgensen, John Bagterp; Rawlings, James B.
2015-01-01
In this paper, we develop an economically optimizing Nonlinear Model Predictive Controller (E-NMPC) for a complete spray drying plant with multiple stages. In the E-NMPC the initial state is estimated by an extended Kalman Filter (EKF) with noise covariances estimated by an autocovariance least...... squares method (ALS). We present a model for the spray drying plant and use this model for simulation as well as for prediction in the E-NMPC. The open-loop optimal control problem in the E-NMPC is solved using the single-shooting method combined with a quasi-Newton Sequential Quadratic programming (SQP......) algorithm and the adjoint method for computation of gradients. We evaluate the economic performance when unmeasured disturbances are present. By simulation, we demonstrate that the E-NMPC improves the profit of spray drying by 17% compared to conventional PI control....
Robust non-gradient C subroutines for non-linear optimization
DEFF Research Database (Denmark)
Brock, Pernille; Madsen, Kaj; Nielsen, Hans Bruun
2004-01-01
This report presents a package of robust and easy-to-use C subroutines for solving unconstrained and constrained non-linear optimization problems, where gradient information is not required. The intention is that the routines should use the currently best algorithms available. All routines have...... subroutines are obtained by changing 0 to 1. The present report is a new and updated version of a previous report NI-91-04 with the title Non-gradient c Subroutines for Non- Linear Optimization, [16]. Both the previous and the present report describe a collection of subroutines, which have been translated...... from Fortran to C. The reason for writing the present report is that some of the C subroutines have been replaced by more e ective and robust versions translated from the original Fortran subroutines to C by the Bandler Group, see [1]. Also the test examples have been modified to some extent...
International Nuclear Information System (INIS)
Chidume, C.E.
1989-06-01
The fixed points of set-valued operators satisfying a condition of monotonicity type in real Banach spaces with uniformly convex dual spaces are approximated by recursive averaging processes. Applications to important classes of linear and nonlinear operator equations are also presented. (author). 33 refs
Thrust generation by a heaving flexible foil: Resonance, nonlinearities, and optimality
Paraz, Florine; Schouveiler, Lionel; Eloy, Christophe
2016-01-01
Flexibility of marine animal fins has been thought to enhance swimming performance. However, despite numerous experimental and numerical studies on flapping flexible foils, there is still no clear understanding of the effect of flexibility and flapping amplitude on thrust generation and swimming efficiency. Here, to address this question, we combine experiments on a model system and a weakly nonlinear analysis. Experiments consist in immersing a flexible rectangular plate in a uniform flow and forcing this plate into a heaving motion at its leading edge. A complementary theoretical model is developed assuming a two-dimensional inviscid problem. In this model, nonlinear effects are taken into account by considering a transverse resistive drag. Under these hypotheses, a modal decomposition of the system motion allows us to predict the plate response amplitude and the generated thrust, as a function of the forcing amplitude and frequency. We show that this model can correctly predict the experimental data on plate kinematic response and thrust generation, as well as other data found in the literature. We also discuss the question of efficiency in the context of bio-inspired propulsion. Using the proposed model, we show that the optimal propeller for a given thrust and a given swimming speed is achieved when the actuating frequency is tuned to a resonance of the system, and when the optimal forcing amplitude scales as the square root of the required thrust.
Mathematical modeling of zika virus disease with nonlinear incidence and optimal control
Goswami, Naba Kumar; Srivastav, Akhil Kumar; Ghosh, Mini; Shanmukha, B.
2018-04-01
The Zika virus was first discovered in a rhesus monkey in the Zika Forest of Uganda in 1947, and it was isolated from humans in Nigeria in 1952. Zika virus disease is primarily a mosquito-borne disease, which is transmitted to human primarily through the bite of an infected Aedes species mosquito. However, there is documented evidence of sexual transmission of this disease too. In this paper, a nonlinear mathematical model for Zika virus by considering nonlinear incidence is formulated and analyzed. The equilibria and the basic reproduction number (R0) of the model are found. The stability of the different equilibria of the model is discussed in detail. When the basic reproduction number R0 1, we have endemic equilibrium which is locally stable under some restriction on parameters. Further this model is extended to optimal control model and is analyzed by using Pontryagin’s Maximum Principle. It has been observed that optimal control plays a significant role in reducing the number of zika infectives. Finally, numerical simulation is performed to illustrate the analytical findings.
Nonlinear Optimization-Based Device-Free Localization with Outlier Link Rejection
Directory of Open Access Journals (Sweden)
Wendong Xiao
2015-04-01
Full Text Available Device-free localization (DFL is an emerging wireless technique for estimating the location of target that does not have any attached electronic device. It has found extensive use in Smart City applications such as healthcare at home and hospitals, location-based services at smart spaces, city emergency response and infrastructure security. In DFL, wireless devices are used as sensors that can sense the target by transmitting and receiving wireless signals collaboratively. Many DFL systems are implemented based on received signal strength (RSS measurements and the location of the target is estimated by detecting the changes of the RSS measurements of the wireless links. Due to the uncertainty of the wireless channel, certain links may be seriously polluted and result in erroneous detection. In this paper, we propose a novel nonlinear optimization approach with outlier link rejection (NOOLR for RSS-based DFL. It consists of three key strategies, including: (1 affected link identification by differential RSS detection; (2 outlier link rejection via geometrical positional relationship among links; (3 target location estimation by formulating and solving a nonlinear optimization problem. Experimental results demonstrate that NOOLR is robust to the fluctuation of the wireless signals with superior localization accuracy compared with the existing Radio Tomographic Imaging (RTI approach.
Nonlinear Multiuser Receiver for Optimized Chaos-Based DS-CDMA Systems
Directory of Open Access Journals (Sweden)
S. Shaerbaf
2011-09-01
Full Text Available Chaos based communications have drawn increasing attention over the past years. Chaotic signals are derived from non-linear dynamic systems. They are aperiodic, broadband and deterministic signals that appear random in the time domain. Because of these properties, chaotic signals have been proposed to generate spreading sequences for wide-band secure communication recently. Like conventional DS-CDMA systems, chaos-based CDMA systems suffer from multi-user interference (MUI due to other users transmitting in the cell. In this paper, we propose a novel method based on radial basis function (RBF for both blind and non-blind multiuser detection in chaos-based DS-CDMA systems. We also propose a new method for optimizing generation of binary chaotic sequences using Genetic Algorithm. Simulation results show that our proposed nonlinear receiver with optimized chaotic sequences outperforms in comparison to other conventional detectors such as a single-user detector, decorrelating detector and minimum mean square error detector, particularly for under-loaded CDMA condition, which the number of active users is less than processing gain.
Pal, Partha S; Kar, R; Mandal, D; Ghoshal, S P
2015-11-01
This paper presents an efficient approach to identify different stable and practically useful Hammerstein models as well as unstable nonlinear process along with its stable closed loop counterpart with the help of an evolutionary algorithm as Colliding Bodies Optimization (CBO) optimization algorithm. The performance measures of the CBO based optimization approach such as precision, accuracy are justified with the minimum output mean square value (MSE) which signifies that the amount of bias and variance in the output domain are also the least. It is also observed that the optimization of output MSE in the presence of outliers has resulted in a very close estimation of the output parameters consistently, which also justifies the effective general applicability of the CBO algorithm towards the system identification problem and also establishes the practical usefulness of the applied approach. Optimum values of the MSEs, computational times and statistical information of the MSEs are all found to be the superior as compared with those of the other existing similar types of stochastic algorithms based approaches reported in different recent literature, which establish the robustness and efficiency of the applied CBO based identification scheme. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
Lv, Yongfeng; Na, Jing; Yang, Qinmin; Wu, Xing; Guo, Yu
2016-01-01
An online adaptive optimal control is proposed for continuous-time nonlinear systems with completely unknown dynamics, which is achieved by developing a novel identifier-critic-based approximate dynamic programming algorithm with a dual neural network (NN) approximation structure. First, an adaptive NN identifier is designed to obviate the requirement of complete knowledge of system dynamics, and a critic NN is employed to approximate the optimal value function. Then, the optimal control law is computed based on the information from the identifier NN and the critic NN, so that the actor NN is not needed. In particular, a novel adaptive law design method with the parameter estimation error is proposed to online update the weights of both identifier NN and critic NN simultaneously, which converge to small neighbourhoods around their ideal values. The closed-loop system stability and the convergence to small vicinity around the optimal solution are all proved by means of the Lyapunov theory. The proposed adaptation algorithm is also improved to achieve finite-time convergence of the NN weights. Finally, simulation results are provided to exemplify the efficacy of the proposed methods.
Directory of Open Access Journals (Sweden)
R. Kotteeswaran
2014-01-01
Full Text Available A Multiobjective Particle Swarm Optimization (MOPSO algorithm is proposed to fine-tune the baseline PI controller parameters of Alstom gasifier. The existing baseline PI controller is not able to meet the performance requirements of Alstom gasifier for sinusoidal pressure disturbance at 0% load. This is considered the major drawback of controller design. A best optimal solution for Alstom gasifier is obtained from a set of nondominated solutions using MOPSO algorithm. Performance of gasifier is investigated at all load conditions. The controller with optimized controller parameters meets all the performance requirements at 0%, 50%, and 100% load conditions. The investigations are also extended for variations in coal quality, which shows an improved stability of the gasifier over a wide range of coal quality variations.
Depression screening optimization in an academic rural setting.
Aleem, Sohaib; Torrey, William C; Duncan, Mathew S; Hort, Shoshana J; Mecchella, John N
2015-01-01
Primary care plays a critical role in screening and management of depression. The purpose of this paper is to focus on leveraging the electronic health record (EHR) as well as work flow redesign to improve the efficiency and reliability of the process of depression screening in two adult primary care clinics of a rural academic institution in USA. The authors utilized various process improvement tools from lean six sigma methodology including project charter, swim lane process maps, critical to quality tree, process control charts, fishbone diagrams, frequency impact matrix, mistake proofing and monitoring plan in Define-Measure-Analyze-Improve-Control format. Interventions included change in depression screening tool, optimization of data entry in EHR. EHR data entry optimization; follow up of positive screen, staff training and EHR redesign. Depression screening rate for office-based primary care visits improved from 17.0 percent at baseline to 75.9 percent in the post-intervention control phase (p<0.001). Follow up of positive depression screen with Patient History Questionnaire-9 data collection remained above 90 percent. Duplication of depression screening increased from 0.6 percent initially to 11.7 percent and then decreased to 4.7 percent after optimization of data entry by patients and flow staff. Impact of interventions on clinical outcomes could not be evaluated. Successful implementation, sustainability and revision of a process improvement initiative to facilitate screening, follow up and management of depression in primary care requires accounting for voice of the process (performance metrics), system limitations and voice of the customer (staff and patients) to overcome various system, customer and human resource constraints.
COMPROMISE, OPTIMAL AND TRACTIONAL ACCOUNTS ON PARETO SET
Directory of Open Access Journals (Sweden)
V. V. Lahuta
2010-11-01
Full Text Available The problem of optimum traction calculations is considered as a problem about optimum distribution of a resource. The dynamic programming solution is based on a step-by-step calculation of set of points of Pareto-optimum values of a criterion function (energy expenses and a resource (time.
Optimization models using fuzzy sets and possibility theory
Orlovski, S
1987-01-01
Optimization is of central concern to a number of discip lines. Operations Research and Decision Theory are often consi dered to be identical with optimizationo But also in other areas such as engineering design, regional policy, logistics and many others, the search for optimal solutions is one of the prime goals. The methods and models which have been used over the last decades in these areas have primarily been "hard" or "crisp", i. e. the solutions were considered to be either fea sible or unfeasible, either above a certain aspiration level or below. This dichotomous structure of methods very often forced the modeller to approximate real problem situations of the more-or-less type by yes-or-no-type models, the solutions of which might turn out not to be the solutions to the real prob lems. This is particularly true if the problem under considera tion includes vaguely defined relationships, human evaluations, uncertainty due to inconsistent or incomplete evidence, if na tural language has to be...
Approximating the Pareto set of multiobjective linear programs via robust optimization
Gorissen, B.L.; den Hertog, D.
2012-01-01
We consider problems with multiple linear objectives and linear constraints and use adjustable robust optimization and polynomial optimization as tools to approximate the Pareto set with polynomials of arbitrarily large degree. The main difference with existing techniques is that we optimize a
Optimizing distance-based methods for large data sets
Scholl, Tobias; Brenner, Thomas
2015-10-01
Distance-based methods for measuring spatial concentration of industries have received an increasing popularity in the spatial econometrics community. However, a limiting factor for using these methods is their computational complexity since both their memory requirements and running times are in {{O}}(n^2). In this paper, we present an algorithm with constant memory requirements and shorter running time, enabling distance-based methods to deal with large data sets. We discuss three recent distance-based methods in spatial econometrics: the D&O-Index by Duranton and Overman (Rev Econ Stud 72(4):1077-1106, 2005), the M-function by Marcon and Puech (J Econ Geogr 10(5):745-762, 2010) and the Cluster-Index by Scholl and Brenner (Reg Stud (ahead-of-print):1-15, 2014). Finally, we present an alternative calculation for the latter index that allows the use of data sets with millions of firms.
Taylor, Ellen Meredith
Weighted essentially non-oscillatory (WENO) methods have been developed to simultaneously provide robust shock-capturing in compressible fluid flow and avoid excessive damping of fine-scale flow features such as turbulence. This is accomplished by constructing multiple candidate numerical stencils that adaptively combine so as to provide high order of accuracy and high bandwidth-resolving efficiency in continuous flow regions while averting instability-provoking interpolation across discontinuities. Under certain conditions in compressible turbulence, however, numerical dissipation remains unacceptably high even after optimization of the linear optimal stencil combination that dominates in smooth regions. The remaining nonlinear error arises from two primary sources: (i) the smoothness measurement that governs the application of adaptation away from the optimal stencil and (ii) the numerical properties of individual candidate stencils that govern numerical accuracy when adaptation engages. In this work, both of these sources are investigated, and corrective modifications to the WENO methodology are proposed and evaluated. Excessive nonlinear error due to the first source is alleviated through two separately considered procedures appended to the standard smoothness measurement technique that are designated the "relative smoothness limiter" and the "relative total variation limiter." In theory, appropriate values of their associated parameters should be insensitive to flow configuration, thereby sidestepping the prospect of costly parameter tuning; and this expectation of broad effectiveness is assessed in direct numerical simulations (DNS) of one-dimensional inviscid test problems, three-dimensional compressible isotropic turbulence of varying Reynolds and turbulent Mach numbers, and shock/isotropic-turbulence interaction (SITI). In the process, tools for efficiently comparing WENO adaptation behavior in smooth versus shock-containing regions are developed. The
Energy Technology Data Exchange (ETDEWEB)
Carlberg, Kevin Thomas [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Quantitative Modeling and Analysis; Drohmann, Martin [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Quantitative Modeling and Analysis; Tuminaro, Raymond S. [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Computational Mathematics; Boggs, Paul T. [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Quantitative Modeling and Analysis; Ray, Jaideep [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Quantitative Modeling and Analysis; van Bloemen Waanders, Bart Gustaaf [Sandia National Lab. (SNL-CA), Livermore, CA (United States). Optimization and Uncertainty Estimation
2014-10-01
-model errors. This enables ROMs to be rigorously incorporated in uncertainty-quantification settings, as the error model can be treated as a source of epistemic uncertainty. This work was completed as part of a Truman Fellowship appointment. We note that much additional work was performed as part of the Fellowship. One salient project is the development of the Trilinos-based model-reduction software module Razor , which is currently bundled with the Albany PDE code and currently allows nonlinear reduced-order models to be constructed for any application supported in Albany. Other important projects include the following: 1. ROMES-equipped ROMs for Bayesian inference: K. Carlberg, M. Drohmann, F. Lu (Lawrence Berkeley National Laboratory), M. Morzfeld (Lawrence Berkeley National Laboratory). 2. ROM-enabled Krylov-subspace recycling: K. Carlberg, V. Forstall (University of Maryland), P. Tsuji, R. Tuminaro. 3. A pseudo balanced POD method using only dual snapshots: K. Carlberg, M. Sarovar. 4. An analysis of discrete v. continuous optimality in nonlinear model reduction: K. Carlberg, M. Barone, H. Antil (George Mason University). Journal articles for these projects are in progress at the time of this writing.
Perturbing engine performance measurements to determine optimal engine control settings
Jiang, Li; Lee, Donghoon; Yilmaz, Hakan; Stefanopoulou, Anna
2014-12-30
Methods and systems for optimizing a performance of a vehicle engine are provided. The method includes determining an initial value for a first engine control parameter based on one or more detected operating conditions of the vehicle engine, determining a value of an engine performance variable, and artificially perturbing the determined value of the engine performance variable. The initial value for the first engine control parameter is then adjusted based on the perturbed engine performance variable causing the engine performance variable to approach a target engine performance variable. Operation of the vehicle engine is controlled based on the adjusted initial value for the first engine control parameter. These acts are repeated until the engine performance variable approaches the target engine performance variable.
A set of rules for constructing an admissible set of D optimal exact ...
African Journals Online (AJOL)
In the search for a D-optimal exact design using the combinatorial iterative technique introduced by Onukogu and Iwundu, 2008, all the support points that make up the experimental region are grouped into H concentric balls according to their distances from the centre. Any selection of N support points from the balls defines ...
Directory of Open Access Journals (Sweden)
Zhen Chen
2016-01-01
Full Text Available Accelerated degradation test (ADT has been widely used to assess highly reliable products’ lifetime. To conduct an ADT, an appropriate degradation model and test plan should be determined in advance. Although many historical studies have proposed quite a few models, there is still room for improvement. Hence we propose a Nonlinear Generalized Wiener Process (NGWP model with consideration of the effects of stress level, product-to-product variability, and measurement errors for a higher estimation accuracy and a wider range of use. Then under the constraints of sample size, test duration, and test cost, the plans of constant-stress ADT (CSADT with multiple stress levels based on the NGWP are designed by minimizing the asymptotic variance of the reliability estimation of the products under normal operation conditions. An optimization algorithm is developed to determine the optimal stress levels, the number of units allocated to each level, inspection frequency, and measurement times simultaneously. In addition, a comparison based on degradation data of LEDs is made to show better goodness-of-fit of the NGWP than that of other models. Finally, optimal two-level and three-level CSADT plans under various constraints and a detailed sensitivity analysis are demonstrated through examples in this paper.
Distributed Optimal Consensus Control for Nonlinear Multiagent System With Unknown Dynamic.
Zhang, Jilie; Zhang, Huaguang; Feng, Tao
2017-08-01
This paper focuses on the distributed optimal cooperative control for continuous-time nonlinear multiagent systems (MASs) with completely unknown dynamics via adaptive dynamic programming (ADP) technology. By introducing predesigned extra compensators, the augmented neighborhood error systems are derived, which successfully circumvents the system knowledge requirement for ADP. It is revealed that the optimal consensus protocols actually work as the solutions of the MAS differential game. Policy iteration algorithm is adopted, and it is theoretically proved that the iterative value function sequence strictly converges to the solution of the coupled Hamilton-Jacobi-Bellman equation. Based on this point, a novel online iterative scheme is proposed, which runs based on the data sampled from the augmented system and the gradient of the value function. Neural networks are employed to implement the algorithm and the weights are updated, in the least-square sense, to the ideal value, which yields approximated optimal consensus protocols. Finally, a numerical example is given to illustrate the effectiveness of the proposed scheme.
Directory of Open Access Journals (Sweden)
Siwei Han
2018-03-01
Full Text Available An optimal load-tracking operation strategy for a grid-connected tubular solid oxide fuel cell (SOFC is studied based on the steady-state analysis of the system thermodynamics and electrochemistry. Control of the SOFC is achieved by a two-level hierarchical control system. In the upper level, optimal setpoints of output voltage and the current corresponding to unit load demand is obtained through a nonlinear optimization by minimizing the SOFC’s internal power waste. In the lower level, a combined L1-MPC control strategy is designed to achieve fast set point tracking under system nonlinearities, while maintaining a constant fuel utilization factor. To prevent fuel starvation during the transient state resulting from the output power surging, a fuel flow constraint is imposed on the MPC with direct electron balance calculation. The proposed control schemes are testified on the grid-connected SOFC model.
DEFF Research Database (Denmark)
Le, T.H.A.; Pham, D. T.; Canh, Nam Nguyen
2010-01-01
Both the efficient and weakly efficient sets of an affine fractional vector optimization problem, in general, are neither convex nor given explicitly. Optimization problems over one of these sets are thus nonconvex. We propose two methods for optimizing a real-valued function over the efficient...... and weakly efficient sets of an affine fractional vector optimization problem. The first method is a local one. By using a regularization function, we reformulate the problem into a standard smooth mathematical programming problem that allows applying available methods for smooth programming. In case...... the objective function is linear, we have investigated a global algorithm based upon a branch-and-bound procedure. The algorithm uses Lagrangian bound coupling with a simplicial bisection in the criteria space. Preliminary computational results show that the global algorithm is promising....
Stabilization of business cycles of finance agents using nonlinear optimal control
Rigatos, G.; Siano, P.; Ghosh, T.; Sarno, D.
2017-11-01
Stabilization of the business cycles of interconnected finance agents is performed with the use of a new nonlinear optimal control method. First, the dynamics of the interacting finance agents and of the associated business cycles is described by a modeled of coupled nonlinear oscillators. Next, this dynamic model undergoes approximate linearization round a temporary operating point which is defined by the present value of the system's state vector and the last value of the control inputs vector that was exerted on it. The linearization procedure is based on Taylor series expansion of the dynamic model and on the computation of Jacobian matrices. The modelling error, which is due to the truncation of higher-order terms in the Taylor series expansion is considered as a disturbance which is compensated by the robustness of the control loop. Next, for the linearized model of the interacting finance agents, an H-infinity feedback controller is designed. The computation of the feedback control gain requires the solution of an algebraic Riccati equation at each iteration of the control algorithm. Through Lyapunov stability analysis it is proven that the control scheme satisfies an H-infinity tracking performance criterion, which signifies elevated robustness against modelling uncertainty and external perturbations. Moreover, under moderate conditions the global asymptotic stability features of the control loop are proven.
Gunnels, John; Lee, Jon; Margulies, Susan
2010-01-01
We provide a first demonstration of the idea that matrix-based algorithms for nonlinear combinatorial optimization problems can be efficiently implemented. Such algorithms were mainly conceived by theoretical computer scientists for proving efficiency. We are able to demonstrate the practicality of our approach by developing an implementation on a massively parallel architecture, and exploiting scalable and efficient parallel implementations of algorithms for ultra high-precision linear algebra. Additionally, we have delineated and implemented the necessary algorithmic and coding changes required in order to address problems several orders of magnitude larger, dealing with the limits of scalability from memory footprint, computational efficiency, reliability, and interconnect perspectives. © Springer and Mathematical Programming Society 2010.
Gunnels, John
2010-06-01
We provide a first demonstration of the idea that matrix-based algorithms for nonlinear combinatorial optimization problems can be efficiently implemented. Such algorithms were mainly conceived by theoretical computer scientists for proving efficiency. We are able to demonstrate the practicality of our approach by developing an implementation on a massively parallel architecture, and exploiting scalable and efficient parallel implementations of algorithms for ultra high-precision linear algebra. Additionally, we have delineated and implemented the necessary algorithmic and coding changes required in order to address problems several orders of magnitude larger, dealing with the limits of scalability from memory footprint, computational efficiency, reliability, and interconnect perspectives. © Springer and Mathematical Programming Society 2010.
Dynamics and optimal control of a non-linear epidemic model with relapse and cure
Lahrouz, A.; El Mahjour, H.; Settati, A.; Bernoussi, A.
2018-04-01
In this work, we introduce the basic reproduction number R0 for a general epidemic model with graded cure, relapse and nonlinear incidence rate in a non-constant population size. We established that the disease free-equilibrium state Ef is globally asymptotically exponentially stable if R0 1, we proved that the system model has at least one endemic state Ee. Then, by means of an appropriate Lyapunov function, we showed that Ee is unique and globally asymptotically stable under some acceptable biological conditions. On the other hand, we use two types of control to reduce the number of infectious individuals. The optimality system is formulated and solved numerically using a Gauss-Seidel-like implicit finite-difference method.
Nonlinear optimization method of ship floating condition calculation in wave based on vector
Ding, Ning; Yu, Jian-xing
2014-08-01
Ship floating condition in regular waves is calculated. New equations controlling any ship's floating condition are proposed by use of the vector operation. This form is a nonlinear optimization problem which can be solved using the penalty function method with constant coefficients. And the solving process is accelerated by dichotomy. During the solving process, the ship's displacement and buoyant centre have been calculated by the integration of the ship surface according to the waterline. The ship surface is described using an accumulative chord length theory in order to determine the displacement, the buoyancy center and the waterline. The draught forming the waterline at each station can be found out by calculating the intersection of the ship surface and the wave surface. The results of an example indicate that this method is exact and efficient. It can calculate the ship floating condition in regular waves as well as simplify the calculation and improve the computational efficiency and the precision of results.
Optimized parallel convolutions for non-linear fluid models of tokamak ηi turbulence
International Nuclear Information System (INIS)
Milovich, J.L.; Tomaschke, G.; Kerbel, G.D.
1993-01-01
Non-linear computational fluid models of plasma turbulence based on spectral methods typically spend a large fraction of the total computing time evaluating convolutions. Usually these convolutions arise from an explicit or semi implicit treatment of the convective non-linearities in the problem. Often the principal convective velocity is perpendicular to magnetic field lines allowing a reduction of the convolution to two dimensions in an appropriate geometry, but beyond this, different models vary widely in the particulars of which mode amplitudes are selectively evolved to get the most efficient representation of the turbulence. As the number of modes in the problem, N, increases, the amount of computation required for this part of the evolution algorithm then scales as N 2 /timestep for a direct or analytic method and N ln N/timestep for a pseudospectral method. The constants of proportionality depend on the particulars of mode selection and determine the size problem for which the method will perform equally. For large enough N, the pseudospectral method performance is always superior, though some problems do not require correspondingly high resolution. Further, the Courant condition for numerical stability requires that the timestep size must decrease proportionately as N increases, thus accentuating the need to have fast methods for larger N problems. The authors have developed a package for the Cray system which performs these convolutions for a rather arbitrary mode selection scheme using either method. The package is highly optimized using a combination of macro and microtasking techniques, as well as vectorization and in some cases assembly coded routines. Parts of the package have also been developed and optimized for the CM200 and CM5 system. Performance comparisons with respect to problem size, parallelization, selection schemes and architecture are presented
International Nuclear Information System (INIS)
Fonville, Judith M.; Bylesjoe, Max; Coen, Muireann; Nicholson, Jeremy K.; Holmes, Elaine; Lindon, John C.; Rantalainen, Mattias
2011-01-01
Highlights: → Non-linear modeling of metabonomic data using K-OPLS. → automated optimization of the kernel parameter by simulated annealing. → K-OPLS provides improved prediction performance for exemplar spectral data sets. → software implementation available for R and Matlab under GPL v2 license. - Abstract: Linear multivariate projection methods are frequently applied for predictive modeling of spectroscopic data in metabonomic studies. The OPLS method is a commonly used computational procedure for characterizing spectral metabonomic data, largely due to its favorable model interpretation properties providing separate descriptions of predictive variation and response-orthogonal structured noise. However, when the relationship between descriptor variables and the response is non-linear, conventional linear models will perform sub-optimally. In this study we have evaluated to what extent a non-linear model, kernel-based orthogonal projections to latent structures (K-OPLS), can provide enhanced predictive performance compared to the linear OPLS model. Just like its linear counterpart, K-OPLS provides separate model components for predictive variation and response-orthogonal structured noise. The improved model interpretation by this separate modeling is a property unique to K-OPLS in comparison to other kernel-based models. Simulated annealing (SA) was used for effective and automated optimization of the kernel-function parameter in K-OPLS (SA-K-OPLS). Our results reveal that the non-linear K-OPLS model provides improved prediction performance in three separate metabonomic data sets compared to the linear OPLS model. We also demonstrate how response-orthogonal K-OPLS components provide valuable biological interpretation of model and data. The metabonomic data sets were acquired using proton Nuclear Magnetic Resonance (NMR) spectroscopy, and include a study of the liver toxin galactosamine, a study of the nephrotoxin mercuric chloride and a study of
Zhang, Liping; Zheng, Yanling; Wang, Kai; Zhang, Xueliang; Zheng, Yujian
2014-06-01
In this paper, by using a particle swarm optimization algorithm to solve the optimal parameter estimation problem, an improved Nash nonlinear grey Bernoulli model termed PSO-NNGBM(1,1) is proposed. To test the forecasting performance, the optimized model is applied for forecasting the incidence of hepatitis B in Xinjiang, China. Four models, traditional GM(1,1), grey Verhulst model (GVM), original nonlinear grey Bernoulli model (NGBM(1,1)) and Holt-Winters exponential smoothing method, are also established for comparison with the proposed model under the criteria of mean absolute percentage error and root mean square percent error. The prediction results show that the optimized NNGBM(1,1) model is more accurate and performs better than the traditional GM(1,1), GVM, NGBM(1,1) and Holt-Winters exponential smoothing method. Copyright © 2014. Published by Elsevier Ltd.
DEFF Research Database (Denmark)
Carli, Lorenzo; Genta, Gianfranco; Cantatore, Angela
2010-01-01
The work deals with an experimental investigation on the influence of three Scanning Electron Microscope (SEM) instrument settings, accelerating voltage, spot size and magnification, on the image formation process. Pixel size and nonlinearity were chosen as output parameters related to image...... quality and resolution. A silicon grating calibrated artifact was employed to investigate qualitatively and quantitatively, through a designed experiment approach, the parameters relevance. SEM magnification was found to account by far for the largest contribution on both parameters under consideration...
Cameras and settings for optimal image capture from UAVs
Smith, Mike; O'Connor, James; James, Mike R.
2017-04-01
Aerial image capture has become very common within the geosciences due to the increasing affordability of low payload (markets. Their application to surveying has led to many studies being undertaken using UAV imagery captured from consumer grade cameras as primary data sources. However, image quality and the principles of image capture are seldom given rigorous discussion which can lead to experiments being difficult to accurately reproduce. In this contribution we revisit the underpinning concepts behind image capture, from which the requirements for acquiring sharp, well exposed and suitable imagery are derived. This then leads to discussion of how to optimise the platform, camera, lens and imaging settings relevant to image quality planning, presenting some worked examples as a guide. Finally, we challenge the community to make their image data open for review in order to ensure confidence in the outputs/error estimates, allow reproducibility of the results and have these comparable with future studies. We recommend providing open access imagery where possible, a range of example images, and detailed metadata to rigorously describe the image capture process.
Efficient Discovery of Nonlinear Dependencies in a Combinatorial Catalyst Data Set
Czech Academy of Sciences Publication Activity Database
Cawse, J.N.; Baerns, M.; Holeňa, Martin
2004-01-01
Roč. 44, č. 3 (2004), s. 143-146 ISSN 0095-2338 Source of funding: V - iné verejné zdroje Keywords : combinatorial catalysis * genetic algorithms * nonlinear dependency * data analysis * high-order interactions Subject RIV: IN - Informatics, Computer Science Impact factor: 2.810, year: 2004
Interrelation of alternative sets of Lax-pairs for a generalized nonlinear Schroedinger equation
International Nuclear Information System (INIS)
Iino, Kazuhiro; Ichikawa, Yoshihiko; Wadati, Miki.
1982-05-01
Examination of the inverse scattering transformation schemes for a generalized nonlinear Schroedinger equation reveals the fact that the algorithm of Chen-Lee-Liu gives rise to the Lax-pairs for the squared eigenfunctions of the Wadati-Konno-Ichikawa scheme, which has been formulated as superposition of the Ablowitz-Kaup-Newell-Segur scheme and the Kaup-Newell scheme. (author)
Directory of Open Access Journals (Sweden)
Alrijadjis .
2014-12-01
Full Text Available The proportional integral derivative (PID controllers have been widely used in most process control systems for a long time. However, it is a very important problem how to choose PID parameters, because these parameters give a great influence on the control performance. Especially, it is difficult to tune these parameters for nonlinear systems. In this paper, a new modified particle swarm optimization (PSO is presented to search for optimal PID parameters for such system. The proposed algorithm is to modify constriction coefficient which is nonlinearly decreased time-varying for improving the final accuracy and the convergence speed of PSO. To validate the control performance of the proposed method, a typical nonlinear system control, a continuous stirred tank reactor (CSTR process, is illustrated. The results testify that a new modified PSO algorithm can perform well in the nonlinear PID control system design in term of lesser overshoot, rise-time, settling-time, IAE and ISE. Keywords: PID controller, Particle Swarm Optimization (PSO,constriction factor, nonlinear system.
Zhang, Ridong; Tao, Jili; Lu, Renquan; Jin, Qibing
2018-02-01
Modeling of distributed parameter systems is difficult because of their nonlinearity and infinite-dimensional characteristics. Based on principal component analysis (PCA), a hybrid modeling strategy that consists of a decoupled linear autoregressive exogenous (ARX) model and a nonlinear radial basis function (RBF) neural network model are proposed. The spatial-temporal output is first divided into a few dominant spatial basis functions and finite-dimensional temporal series by PCA. Then, a decoupled ARX model is designed to model the linear dynamics of the dominant modes of the time series. The nonlinear residual part is subsequently parameterized by RBFs, where genetic algorithm is utilized to optimize their hidden layer structure and the parameters. Finally, the nonlinear spatial-temporal dynamic system is obtained after the time/space reconstruction. Simulation results of a catalytic rod and a heat conduction equation demonstrate the effectiveness of the proposed strategy compared to several other methods.
Directory of Open Access Journals (Sweden)
Sukhinov Alexander
2017-01-01
Full Text Available One of the practically important tasks of hydrophysics for sea coastal systems is the problem of modeling and forecasting bottom sediment transportation. A number of problems connected to ship safety traffic, water medium condition near the coastal line etc. depends on forecasting bottom deposit transportation under natural and technogenic influences. Coastal systems are characterized by a complicated form of coastline - the presence of long, narrow and curvilinear peninsulas and bays. Water currents and waves near the beach are strongly depend on complicated coastal line and in turn, exert on the bottom sediment transportation near the shore. The use of rectangular grids in the construction of discrete models leads to significant errors in both the specification of boundary conditions and in the modeling of hydrophysical processes in the coastal zone. In this paper, we consider the construction of a finite-element approximation of the initial-boundary value problem for the spatially two-dimensional linearized equation of sediment transportation using optimal boundary-adaptive grid. First, the linearization of a spatially two-dimensional nonlinear parabolic equation on the time grid is performed-when the coefficients of the equation that are nonlinearly dependent on the bottom relief function are set on the previous time layer, and the corresponding initial conditions are used on the first time layer. The algorithm for constructing the grid is based on the procedure for minimizing the generalized Dirichlet functional. On the constructed grid, finite element approximation using bilinear basis functions is performed, which completes the construction of a discrete model for the given problem. The using of curvilinear boundary adaptive grids leads to decreasing of total grid number in 5-20 times and respectively the total modeling time and/or it allows to improve modeling accuracy.
Value Iteration Adaptive Dynamic Programming for Optimal Control of Discrete-Time Nonlinear Systems.
Wei, Qinglai; Liu, Derong; Lin, Hanquan
2016-03-01
In this paper, a value iteration adaptive dynamic programming (ADP) algorithm is developed to solve infinite horizon undiscounted optimal control problems for discrete-time nonlinear systems. The present value iteration ADP algorithm permits an arbitrary positive semi-definite function to initialize the algorithm. A novel convergence analysis is developed to guarantee that the iterative value function converges to the optimal performance index function. Initialized by different initial functions, it is proven that the iterative value function will be monotonically nonincreasing, monotonically nondecreasing, or nonmonotonic and will converge to the optimum. In this paper, for the first time, the admissibility properties of the iterative control laws are developed for value iteration algorithms. It is emphasized that new termination criteria are established to guarantee the effectiveness of the iterative control laws. Neural networks are used to approximate the iterative value function and compute the iterative control law, respectively, for facilitating the implementation of the iterative ADP algorithm. Finally, two simulation examples are given to illustrate the performance of the present method.
Calibration of Mine Ventilation Network Models Using the Non-Linear Optimization Algorithm
Directory of Open Access Journals (Sweden)
Guang Xu
2017-12-01
Full Text Available Effective ventilation planning is vital to underground mining. To ensure stable operation of the ventilation system and to avoid airflow disorder, mine ventilation network (MVN models have been widely used in simulating and optimizing the mine ventilation system. However, one of the challenges for MVN model simulation is that the simulated airflow distribution results do not match the measured data. To solve this problem, a simple and effective calibration method is proposed based on the non-linear optimization algorithm. The calibrated model not only makes simulated airflow distribution results in accordance with the on-site measured data, but also controls the errors of other parameters within a minimum range. The proposed method was then applied to calibrate an MVN model in a real case, which is built based on ventilation survey results and Ventsim software. Finally, airflow simulation experiments are carried out respectively using data before and after calibration, whose results were compared and analyzed. This showed that the simulated airflows in the calibrated model agreed much better to the ventilation survey data, which verifies the effectiveness of calibrating method.
An integer optimization algorithm for robust identification of non-linear gene regulatory networks
Directory of Open Access Journals (Sweden)
Chemmangattuvalappil Nishanth
2012-09-01
Full Text Available Abstract Background Reverse engineering gene networks and identifying regulatory interactions are integral to understanding cellular decision making processes. Advancement in high throughput experimental techniques has initiated innovative data driven analysis of gene regulatory networks. However, inherent noise associated with biological systems requires numerous experimental replicates for reliable conclusions. Furthermore, evidence of robust algorithms directly exploiting basic biological traits are few. Such algorithms are expected to be efficient in their performance and robust in their prediction. Results We have developed a network identification algorithm to accurately infer both the topology and strength of regulatory interactions from time series gene expression data in the presence of significant experimental noise and non-linear behavior. In this novel formulism, we have addressed data variability in biological systems by integrating network identification with the bootstrap resampling technique, hence predicting robust interactions from limited experimental replicates subjected to noise. Furthermore, we have incorporated non-linearity in gene dynamics using the S-system formulation. The basic network identification formulation exploits the trait of sparsity of biological interactions. Towards that, the identification algorithm is formulated as an integer-programming problem by introducing binary variables for each network component. The objective function is targeted to minimize the network connections subjected to the constraint of maximal agreement between the experimental and predicted gene dynamics. The developed algorithm is validated using both in silico and experimental data-sets. These studies show that the algorithm can accurately predict the topology and connection strength of the in silico networks, as quantified by high precision and recall, and small discrepancy between the actual and predicted kinetic parameters
Energy optimized Gaussian basis sets for the atoms T1 - Rn
International Nuclear Information System (INIS)
Faegri, K. Jr.
1987-01-01
Energy optimized Gaussian basis sets have been derived for the atoms Tl-Rn. Two sets are presented - a (20,16,10,6) set and a (22,17,13,8) set. The smallest sets yield atomic energies 107 to 123 mH above the numerical Hartree-Fock values, while the larger sets give energies 11 mH above the numerical results. Energy trends from the smaller sets indicate that reduced shielding by p-electrons may place a greater demand on the flexibility of d- and f-orbital description for the lighter elements of the series
Rosenberg, D. E.; Alafifi, A.
2016-12-01
Water resources systems analysis often focuses on finding optimal solutions. Yet an optimal solution is optimal only for the modelled issues and managers often seek near-optimal alternatives that address un-modelled objectives, preferences, limits, uncertainties, and other issues. Early on, Modelling to Generate Alternatives (MGA) formalized near-optimal as the region comprising the original problem constraints plus a new constraint that allowed performance within a specified tolerance of the optimal objective function value. MGA identified a few maximally-different alternatives from the near-optimal region. Subsequent work applied Markov Chain Monte Carlo (MCMC) sampling to generate a larger number of alternatives that span the near-optimal region of linear problems or select portions for non-linear problems. We extend the MCMC Hit-And-Run method to generate alternatives that span the full extent of the near-optimal region for non-linear, non-convex problems. First, start at a feasible hit point within the near-optimal region, then run a random distance in a random direction to a new hit point. Next, repeat until generating the desired number of alternatives. The key step at each iterate is to run a random distance along the line in the specified direction to a new hit point. If linear equity constraints exist, we construct an orthogonal basis and use a null space transformation to confine hits and runs to a lower-dimensional space. Linear inequity constraints define the convex bounds on the line that runs through the current hit point in the specified direction. We then use slice sampling to identify a new hit point along the line within bounds defined by the non-linear inequity constraints. This technique is computationally efficient compared to prior near-optimal alternative generation techniques such MGA, MCMC Metropolis-Hastings, evolutionary, or firefly algorithms because search at each iteration is confined to the hit line, the algorithm can move in one
Multi-linear model set design based on the nonlinearity measure and H-gap metric.
Shaghaghi, Davood; Fatehi, Alireza; Khaki-Sedigh, Ali
2017-05-01
This paper proposes a model bank selection method for a large class of nonlinear systems with wide operating ranges. In particular, nonlinearity measure and H-gap metric are used to provide an effective algorithm to design a model bank for the system. Then, the proposed model bank is accompanied with model predictive controllers to design a high performance advanced process controller. The advantage of this method is the reduction of excessive switch between models and also decrement of the computational complexity in the controller bank that can lead to performance improvement of the control system. The effectiveness of the method is verified by simulations as well as experimental studies on a pH neutralization laboratory apparatus which confirms the efficiency of the proposed algorithm. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.
A nonlinear optimal control approach to stabilization of a macroeconomic development model
Rigatos, G.; Siano, P.; Ghosh, T.; Sarno, D.
2017-11-01
A nonlinear optimal (H-infinity) control approach is proposed for the problem of stabilization of the dynamics of a macroeconomic development model that is known as the Grossman-Helpman model of endogenous product cycles. The dynamics of the macroeconomic development model is divided in two parts. The first one describes economic activities in a developed country and the second part describes variation of economic activities in a country under development which tries to modify its production so as to serve the needs of the developed country. The article shows that through control of the macroeconomic model of the developed country, one can finally control the dynamics of the economy in the country under development. The control method through which this is achieved is the nonlinear H-infinity control. The macroeconomic model for the country under development undergoes approximate linearization round a temporary operating point. This is defined at each time instant by the present value of the system's state vector and the last value of the control input vector that was exerted on it. The linearization is based on Taylor series expansion and the computation of the associated Jacobian matrices. For the linearized model an H-infinity feedback controller is computed. The controller's gain is calculated by solving an algebraic Riccati equation at each iteration of the control method. The asymptotic stability of the control approach is proven through Lyapunov analysis. This assures that the state variables of the macroeconomic model of the country under development will finally converge to the designated reference values.
A LEVEL SET BASED SHAPE OPTIMIZATION METHOD FOR AN ELLIPTIC OBSTACLE PROBLEM
Burger, Martin
2011-04-01
In this paper, we construct a level set method for an elliptic obstacle problem, which can be reformulated as a shape optimization problem. We provide a detailed shape sensitivity analysis for this reformulation and a stability result for the shape Hessian at the optimal shape. Using the shape sensitivities, we construct a geometric gradient flow, which can be realized in the context of level set methods. We prove the convergence of the gradient flow to an optimal shape and provide a complete analysis of the level set method in terms of viscosity solutions. To our knowledge this is the first complete analysis of a level set method for a nonlocal shape optimization problem. Finally, we discuss the implementation of the methods and illustrate its behavior through several computational experiments. © 2011 World Scientific Publishing Company.
Memetic Algorithms to Solve a Global Nonlinear Optimization Problem. A Review
Directory of Open Access Journals (Sweden)
M. K. Sakharov
2015-01-01
Full Text Available In recent decades, evolutionary algorithms have proven themselves as the powerful optimization techniques of search engine. Their popularity is due to the fact that they are easy to implement and can be used in all areas, since they are based on the idea of universal evolution. For example, in the problems of a large number of local optima, the traditional optimization methods, usually, fail in finding the global optimum. To solve such problems using a variety of stochastic methods, in particular, the so-called population-based algorithms, which are a kind of evolutionary methods. The main disadvantage of this class of methods is their slow convergence to the exact solution in the neighborhood of the global optimum, as these methods incapable to use the local information about the landscape of the function. This often limits their use in largescale real-world problems where the computation time is a critical factor.One of the promising directions in the field of modern evolutionary computation are memetic algorithms, which can be regarded as a combination of population search of the global optimum and local procedures for verifying solutions, which gives a synergistic effect. In the context of memetic algorithms, the meme is an implementation of the local optimization method to refine solution in the search.The concept of memetic algorithms provides ample opportunities for the development of various modifications of these algorithms, which can vary the frequency of the local search, the conditions of its end, and so on. The practically significant memetic algorithm modifications involve the simultaneous use of different memes. Such algorithms are called multi-memetic.The paper gives statement of the global problem of nonlinear unconstrained optimization, describes the most promising areas of AI modifications, including hybridization and metaoptimization. The main content of the work is the classification and review of existing varieties of
Pappas, Iosif
2016-01-01
PID controllers are extensively used in industry. Although many tuning methodologies exist, finding good controller settings is not an easy task and frequently optimization-based design is preferred to satisfy more complex criteria. In this thesis, the focus was to find which tuning approaches, if any, present close to optimal behavior. Pareto-optimal controllers were found for different first and second-order processes with time delay. Performance was quantified in terms of the integrat...
A Novel Rough Set Reduct Algorithm for Medical Domain Based on Bee Colony Optimization
Suguna, N.; Thanushkodi, K.
2010-01-01
Feature selection refers to the problem of selecting relevant features which produce the most predictive outcome. In particular, feature selection task is involved in datasets containing huge number of features. Rough set theory has been one of the most successful methods used for feature selection. However, this method is still not able to find optimal subsets. This paper proposes a new feature selection method based on Rough set theory hybrid with Bee Colony Optimization (BCO) in an attempt...
Luo, Biao; Liu, Derong; Wu, Huai-Ning
2018-06-01
Reinforcement learning has proved to be a powerful tool to solve optimal control problems over the past few years. However, the data-based constrained optimal control problem of nonaffine nonlinear discrete-time systems has rarely been studied yet. To solve this problem, an adaptive optimal control approach is developed by using the value iteration-based Q-learning (VIQL) with the critic-only structure. Most of the existing constrained control methods require the use of a certain performance index and only suit for linear or affine nonlinear systems, which is unreasonable in practice. To overcome this problem, the system transformation is first introduced with the general performance index. Then, the constrained optimal control problem is converted to an unconstrained optimal control problem. By introducing the action-state value function, i.e., Q-function, the VIQL algorithm is proposed to learn the optimal Q-function of the data-based unconstrained optimal control problem. The convergence results of the VIQL algorithm are established with an easy-to-realize initial condition . To implement the VIQL algorithm, the critic-only structure is developed, where only one neural network is required to approximate the Q-function. The converged Q-function obtained from the critic-only VIQL method is employed to design the adaptive constrained optimal controller based on the gradient descent scheme. Finally, the effectiveness of the developed adaptive control method is tested on three examples with computer simulation.
DEFF Research Database (Denmark)
Stolpe, Mathias; Bendsøe, Martin P.
2007-01-01
This paper present some initial results pertaining to a search for globally optimal solutions to a challenging benchmark example proposed by Zhou and Rozvany. This means that we are dealing with global optimization of the classical single load minimum compliance topology design problem with a fixed...... finite element discretization and with discrete design variables. Global optimality is achieved by the implementation of some specially constructed convergent nonlinear branch and cut methods, based on the use of natural relaxations and by applying strengthening constraints (linear valid inequalities...
DEFF Research Database (Denmark)
Stolpe, Mathias; Bendsøe, Martin P.
2007-01-01
This paper present some initial results pertaining to a search for globally optimal solutions to a challenging benchmark example proposed by Zhou and Rozvany. This means that we are dealing with global optimization of the classical single load minimum compliance topology design problem with a fixed...... finite element discretization and with discrete design variables. Global optimality is achieved by the implementation of some specially constructed convergent nonlinear branch and cut methods, based on the use of natural relaxations and by applying strengthening constraints (linear valid inequalities......) and cuts....
Setting the optimal type of equipment to be adopted and the optimal time to replace it
Albici, Mihaela
2009-01-01
The mathematical models of equipment’s wear and tear, and replacement theory aim at deciding on the purchase selection of a certain equipment type, the optimal exploitation time of the equipment, the time and ways to replace or repair it, or to ensure its spare parts, the equipment’s performance in the technical progress context, the opportunities to modernize it etc.
Directory of Open Access Journals (Sweden)
Y. W. Sun
2013-08-01
Full Text Available In this paper, we present an optimized analysis algorithm for non-dispersive infrared (NDIR to in situ monitor stack emissions. The proposed algorithm simultaneously compensates for nonlinear absorption and cross interference among different gases. We present a mathematical derivation for the measurement error caused by variations in interference coefficients when nonlinear absorption occurs. The proposed algorithm is derived from a classical one and uses interference functions to quantify cross interference. The interference functions vary proportionally with the nonlinear absorption. Thus, interference coefficients among different gases can be modeled by the interference functions whether gases are characterized by linear or nonlinear absorption. In this study, the simultaneous analysis of two components (CO2 and CO serves as an example for the validation of the proposed algorithm. The interference functions in this case can be obtained by least-squares fitting with third-order polynomials. Experiments show that the results of cross interference correction are improved significantly by utilizing the fitted interference functions when nonlinear absorptions occur. The dynamic measurement ranges of CO2 and CO are improved by about a factor of 1.8 and 3.5, respectively. A commercial analyzer with high accuracy was used to validate the CO and CO2 measurements derived from the NDIR analyzer prototype in which the new algorithm was embedded. The comparison of the two analyzers show that the prototype works well both within the linear and nonlinear ranges.
International Nuclear Information System (INIS)
Frolov, A.M.
1986-01-01
Exact variational calculations are treated for few-particle systems in the exponential basis of relative coordinates using nonlinear parameters. The methods of step-by-step optimization and global chaos of nonlinear parameters are applied to calculate the S and P states of ppμ, ddμ, ttμ homonuclear mesomolecules within the error ≤±0.001 eV. The global chaos method turned out to be well applicable to nuclear 3 H and 3 He systems
Energy Technology Data Exchange (ETDEWEB)
Frolov, A M
1986-09-01
Exact variational calculations are treated for few-particle systems in the exponential basis of relative coordinates using nonlinear parameters. The methods of step-by-step optimization and global chaos of nonlinear parameters are applied to calculate the S and P states of pp..mu.., dd..mu.., tt..mu.. homonuclear mesomolecules within the error less than or equal to+-0.001 eV. The global chaos method turned out to be well applicable to nuclear /sup 3/H and /sup 3/He systems.
DEFF Research Database (Denmark)
Otomori, Masaki; Yamada, Takayuki; Andkjær, Jacob Anders
2013-01-01
. A level set-based topology optimization method incorporating a fictitious interface energy is used to find optimized configurations of the ferrite material. The numerical results demonstrate that the optimization successfully found an appropriate ferrite configuration that functions as an electromagnetic......This paper presents a structural optimization method for the design of an electromagnetic cloak made of ferrite material. Ferrite materials exhibit a frequency-dependent degree of permeability, due to a magnetic resonance phenomenon that can be altered by changing the magnitude of an externally...
A multilevel, level-set method for optimizing eigenvalues in shape design problems
International Nuclear Information System (INIS)
Haber, E.
2004-01-01
In this paper, we consider optimal design problems that involve shape optimization. The goal is to determine the shape of a certain structure such that it is either as rigid or as soft as possible. To achieve this goal we combine two new ideas for an efficient solution of the problem. First, we replace the eigenvalue problem with an approximation by using inverse iteration. Second, we use a level set method but rather than propagating the front we use constrained optimization methods combined with multilevel continuation techniques. Combining these two ideas we obtain a robust and rapid method for the solution of the optimal design problem
Aerostructural Level Set Topology Optimization for a Common Research Model Wing
Dunning, Peter D.; Stanford, Bret K.; Kim, H. Alicia
2014-01-01
The purpose of this work is to use level set topology optimization to improve the design of a representative wing box structure for the NASA common research model. The objective is to minimize the total compliance of the structure under aerodynamic and body force loading, where the aerodynamic loading is coupled to the structural deformation. A taxi bump case was also considered, where only body force loads were applied. The trim condition that aerodynamic lift must balance the total weight of the aircraft is enforced by allowing the root angle of attack to change. The level set optimization method is implemented on an unstructured three-dimensional grid, so that the method can optimize a wing box with arbitrary geometry. Fast matching and upwind schemes are developed for an unstructured grid, which make the level set method robust and efficient. The adjoint method is used to obtain the coupled shape sensitivities required to perform aerostructural optimization of the wing box structure.
Optimization of ultrasonic arrays design and setting using a differential evolution
International Nuclear Information System (INIS)
Puel, B.; Chatillon, S.; Calmon, P.; Lesselier, D.
2011-01-01
Optimization of both design and setting of phased arrays could be not so easy when they are performed manually via parametric studies. An optimization method based on an Evolutionary Algorithm and numerical simulation is proposed and evaluated. The Randomized Adaptive Differential Evolution has been adapted to meet the specificities of the non-destructive testing applications. In particular, the solution of multi-objective problems is aimed at with the implementation of the concept of pareto-optimal sets of solutions. The algorithm has been implemented and connected to the ultrasonic simulation modules of the CIVA software used as forward model. The efficiency of the method is illustrated on two realistic cases of application: optimization of the position and delay laws of a flexible array inspecting a nozzle, considered as a mono-objective problem; and optimization of the design of a surrounded array and its delay laws, considered as a constrained bi-objective problem. (authors)
A LEVEL SET BASED SHAPE OPTIMIZATION METHOD FOR AN ELLIPTIC OBSTACLE PROBLEM
Burger, Martin; Matevosyan, Norayr; Wolfram, Marie-Therese
2011-01-01
analysis of the level set method in terms of viscosity solutions. To our knowledge this is the first complete analysis of a level set method for a nonlocal shape optimization problem. Finally, we discuss the implementation of the methods and illustrate its
CHESS-changing horizon efficient set search: A simple principle for multiobjective optimization
DEFF Research Database (Denmark)
Borges, Pedro Manuel F. C.
2000-01-01
This paper presents a new concept for generating approximations to the non-dominated set in multiobjective optimization problems. The approximation set A is constructed by solving several single-objective minimization problems in which a particular function D(A, z) is minimized. A new algorithm t...
Optimal Interest-Rate Setting in a Dynamic IS/AS Model
DEFF Research Database (Denmark)
Jensen, Henrik
2011-01-01
This note deals with interest-rate setting in a simple dynamic macroeconomic setting. The purpose is to present some basic and central properties of an optimal interest-rate rule. The model framework predates the New-Keynesian paradigm of the late 1990s and onwards (it is accordingly dubbed “Old...
van der Burg, Eeke; de Leeuw, Jan; Verdegaal, Renée
1988-01-01
Homogeneity analysis, or multiple correspondence analysis, is usually applied tok separate variables. In this paper we apply it to sets of variables by using sums within sets. The resulting technique is called OVERALS. It uses the notion of optimal scaling, with transformations that can be multiple
Tutcuoglu, A.; Majidi, C.
2014-12-01
Using principles of damped harmonic oscillation with continuous media, we examine electrostatic energy harvesting with a "soft-matter" array of dielectric elastomer (DE) transducers. The array is composed of infinitely thin and deformable electrodes separated by layers of insulating elastomer. During vibration, it deforms longitudinally, resulting in a change in the capacitance and electrical enthalpy of the charged electrodes. Depending on the phase of electrostatic loading, the DE array can function as either an actuator that amplifies small vibrations or a generator that converts these external excitations into electrical power. Both cases are addressed with a comprehensive theory that accounts for the influence of viscoelasticity, dielectric breakdown, and electromechanical coupling induced by Maxwell stress. In the case of a linearized Kelvin-Voigt model of the dielectric, we obtain a closed-form estimate for the electrical power output and a scaling law for DE generator design. For the complete nonlinear model, we obtain the optimal electrostatic voltage input for maximum electrical power output.
Kong, Liang; Gu, Zexu; Li, Tao; Wu, Junjie; Hu, Kaijin; Liu, Yanpu; Zhou, Hongzhi; Liu, Baolin
2009-01-01
A nonlinear finite element method was applied to examine the effects of implant diameter and length on the maximum von Mises stresses in the jaw, and to evaluate the maximum displacement of the implant-abutment complex in immediate-loading models. The implant diameter (D) ranged from 3.0 to 5.0 mm and implant length (L) ranged from 6.0 to 16.0 mm. The results showed that the maximum von Mises stress in cortical bone was decreased by 65.8% under a buccolingual load with an increase in D. In cancellous bone, it was decreased by 71.5% under an axial load with an increase in L. The maximum displacement in the implant-abutment complex decreased by 64.8% under a buccolingual load with an increase in D. The implant was found to be more sensitive to L than to D under axial loads, while D played a more important role in enhancing its stability under buccolingual loads. When D exceeded 4.0 mm and L exceeded 11.0 mm, both minimum stress and displacement were obtained. Therefore, these dimensions were the optimal biomechanical selections for immediate-loading implants in type B/2 bone.
Utilization of reduced fuelling ripple set in ROP detector layout optimization
International Nuclear Information System (INIS)
Kastanya, Doddy
2012-01-01
Highlights: ► ADORE is an ROP detect layout optimization algorithm in CANDU reactors. ► The effect of using reduced set of fuelling ripples in ADORE is assessed. ► Significant speedup can be realized by adopting this approach. ► The quality of the results is comparable to results from full set of ripples. - Abstract: The ADORE (Alternative Detector layout Optimization for REgional overpower protection system) algorithm for performing the optimization of regional overpower protection (ROP) for CANDU® reactors has been recently developed. This algorithm utilizes the simulated annealing (SA) stochastic optimization technique to come up with an optimized detector layout for the ROP systems. For each history in the SA iteration where a particular detector layout is evaluated, the goodness of this detector layout is measured in terms of its trip set point value which is obtained by performing a probabilistic trip set point calculation using the ROVER-F code. Since during each optimization execution thousands of candidate detector layouts are evaluated, the overall optimization process is time consuming. Since for each ROVER-F evaluation the number of fuelling ripples controls the execution time, reducing the number of fuelling ripples will reduce the overall execution time. This approach has been investigated and the results are presented in this paper. The challenge is to construct a set of representative fuelling ripples which will significantly speedup the optimization process while guaranteeing that the resulting detector layout has similar quality to the ones produced when the complete set of fuelling ripples is employed.
Evaluating optimal CNR as a preset criteria for nonlinear moidal blending of dual energy CT data
Holmes, D. R., III; Apel, A.; Fletcher, J. G.; Guimaraes, L. S.; Eusemann, C. E.; Robb, R. A.
2009-02-01
Nonlinear blending of dual-energy CT data is available on current scanners. Selection of the blending parameters can be time-consuming and challenging. The purpose of this study was to determine if the Contrast-To-Noise Ratio (CNR) may be used ti automatic select of blending parameters. A Bovine liver was built with six syringes filled with varying concentrations of CT contrast yielding six 140kV HU levels (15, 47, 64, 79, 116, and 145). The phantom was scanned using 95 mAs @ 140kV and 404mAs @ 80 kV. The 80 and 140 kV datasets were blended using a modified sigmoid (moidal) function which requires two parameters - level and width. Every combination of moidal level and width was applied to the data, and the CNR was calculated as (mean(syringe ROI) - mean(liver ROI)) / STD(water). The maximum CNR was determined for each of the 6 HU levels. Pairs of blended images were presented in a blind manner to observers. Nine comparisons for each of the 6 HU settings were made by a staff radiologist, a resident, and a physicist. For each comparison, the observer selected the more "visually appealing" image. Outcomes from the study were compared using the Fisher Sign Test statistic. Analysis by observer showed a statistical (penergy CT data may provide consistency across radiologists and facilitate the clinical review process.
International Nuclear Information System (INIS)
Kim, D.S.; Seong, P.H.
1994-01-01
This paper describes the optimal testing input sets required for the fault diagnosis of the nuclear power plant digital electronic circuits. With the complicated systems such as very large scale integration (VLSI), nuclear power plant (NPP), and aircraft, testing is the major factor of the maintenance of the system. Particularly, diagnosis time grows quickly with the complexity of the component. In this research, for reduce diagnosis time the authors derived the optimal testing sets that are the minimal testing sets required for detecting the failure and for locating of the failed component. For reduced diagnosis time, the technique presented by Hayes fits best for the approach to testing sets generation among many conventional methods. However, this method has the following disadvantages: (a) it considers only the simple network (b) it concerns only whether the system is in failed state or not and does not provide the way to locate the failed component. Therefore the authors have derived the optimal testing input sets that resolve these problems by Hayes while preserving its advantages. When they applied the optimal testing sets to the automatic fault diagnosis system (AFDS) which incorporates the advanced fault diagnosis method of artificial intelligence technique, they found that the fault diagnosis using the optimal testing sets makes testing the digital electronic circuits much faster than that using exhaustive testing input sets; when they applied them to test the Universal (UV) Card which is a nuclear power plant digital input/output solid state protection system card, they reduced the testing time up to about 100 times
International Nuclear Information System (INIS)
Khan, Mohd Shariq; Lee, Moonyong
2013-01-01
The particle swarm paradigm is employed to optimize single mixed refrigerant natural gas liquefaction process. Liquefaction design involves multivariable problem solving and non-optimal execution of these variables can waste energy and contribute to process irreversibilities. Design optimization requires these variables to be optimized simultaneously; minimizing the compression energy requirement is selected as the optimization objective. Liquefaction is modeled using Honeywell UniSim Design ™ and the resulting rigorous model is connected with the particle swarm paradigm coded in MATLAB. Design constraints are folded into the objective function using the penalty function method. Optimization successfully improved efficiency by reducing the compression energy requirement by ca. 10% compared with the base case. -- Highlights: ► The particle swarm paradigm (PSP) is employed for design optimization of SMR NG liquefaction process. ► Rigorous SMR process model based on UniSim is connected with PSP coded in MATLAB. ► Stochastic features of PSP give more confidence in the optimality of complex nonlinear problems. ► Optimization with PSP notably improves energy efficiency of the SMR process.
Optimization of Nonlinear Figure-of-Merits of Integrated Power MOSFETs in Partial SOI Process
DEFF Research Database (Denmark)
Fan, Lin; Jørgensen, Ivan Harald Holger; Knott, Arnold
2016-01-01
State-of-the-art power semiconductor industry uses figure-of-merits (FOMs) for technology-to-technology and/or device-to-device comparisons. However, the existing FOMs are fundamentally nonlinear due to the nonlinearities of the parameters such as the gate charge and the output charge versus...
Adaptive Optimizing Nonlinear Control Design for an Over-actuated Aircraft Model
Van Oort, E.R.; Sonneveldt, L.; Chu, Q.P.; Mulder, J.A.
2011-01-01
In this paper nonlinear adaptive flight control laws based on the backstepping approach are proposed which are applicable to over-actuated nonlinear systems. Instead of solving the control allocation exactly, update laws for the desired control effector signals are defined such that they converge to
OPTIMIZATION OF AGGREGATION AND SEQUENTIAL-PARALLEL EXECUTION MODES OF INTERSECTING OPERATION SETS
Directory of Open Access Journals (Sweden)
G. М. Levin
2016-01-01
Full Text Available A mathematical model and a method for the problem of optimization of aggregation and of sequential- parallel execution modes of intersecting operation sets are proposed. The proposed method is based on the two-level decomposition scheme. At the top level the variant of aggregation for groups of operations is selected, and at the lower level the execution modes of operations are optimized for a fixed version of aggregation.
Energy Technology Data Exchange (ETDEWEB)
Welsch, Dominic Markus
2010-03-10
The High-Energy Storage Ring (HESR) is part of the upcoming Facility for Antiproton and Ion Research (FAIR) which is planned as a major extension to the present facility of the Helmholtzzentrum fuer Schwerionenforschung (GSI) in Darmstadt. The HESR will provide antiprotons in the momentum range from 1.5 to 15 GeV/c for the internal target experiment PANDA. The demanding requirements of PANDA in terms of beam quality and luminosity together with a limited production rate of antiprotons call for a long beam life time and a minimum of beam loss. Therefore, an effective closed orbit correction and a sufficiently large dynamic aperture of the HESR are crucial. With this thesis I present my work on both of these topics. The expected misalignments of beam guiding magnets have been estimated and used to simulate the closed orbit in the HESR. A closed orbit correction scheme has been developed for different ion optical settings of the HESR and numerical simulations have been performed to validate the scheme. The proposed closed orbit correction method which uses the orbit response matrix has been benchmarked at the Cooler Synchrotron COSY of the Forschungszentrum Juelich. A chromaticity correction scheme for the HESR consisting of sextupole magnets has been developed to reduce tune spread and thus to minimize the emittance growth caused by betatron resonances. The chromaticity correction scheme has been optimized through dynamic aperture calculations. The estimated field errors of the HESR dipole and quadrupole magnets have been included in the non-linear beam dynamics studies. Investigations concerning their optimization have been carried out. The ion optical settings of the HESR have been improved using dynamic aperture calculations and the technique of frequency map analysis. The related diffusion coefficient was also used to predict long-term stability based on short-term particle tracking. With a reasonable reduction of the quadrupole magnets field errors and a
Directory of Open Access Journals (Sweden)
Jens G. Balchen
1984-10-01
Full Text Available The problem of systematic derivation of a quasi-dynamic optimal control strategy for a non-linear dynamic process based upon a non-quadratic objective function is investigated. The wellknown LQG-control algorithm does not lead to an optimal solution when the process disturbances have non-zero mean. The relationships between the proposed control algorithm and LQG-control are presented. The problem of how to constrain process variables by means of 'penalty' - terms in the objective function is dealt with separately.
Directory of Open Access Journals (Sweden)
Jing Lei
2013-01-01
Full Text Available The paper considers the problem of variable structure control for nonlinear systems with uncertainty and time delays under persistent disturbance by using the optimal sliding mode surface approach. Through functional transformation, the original time-delay system is transformed into a delay-free one. The approximating sequence method is applied to solve the nonlinear optimal sliding mode surface problem which is reduced to a linear two-point boundary value problem of approximating sequences. The optimal sliding mode surface is obtained from the convergent solutions by solving a Riccati equation, a Sylvester equation, and the state and adjoint vector differential equations of approximating sequences. Then, the variable structure disturbance rejection control is presented by adopting an exponential trending law, where the state and control memory terms are designed to compensate the state and control delays, a feedforward control term is designed to reject the disturbance, and an adjoint compensator is designed to compensate the effects generated by the nonlinearity and the uncertainty. Furthermore, an observer is constructed to make the feedforward term physically realizable, and thus the dynamical observer-based dynamical variable structure disturbance rejection control law is produced. Finally, simulations are demonstrated to verify the effectiveness of the presented controller and the simplicity of the proposed approach.
Ramezanpour, H R; Setayeshi, S; Akbari, M E
2011-01-01
Determining the optimal and effective scheme for administrating the chemotherapy agents in breast cancer is the main goal of this scientific research. The most important issue here is the amount of drug or radiation administrated in chemotherapy and radiotherapy for increasing patient's survival. This is because in these cases, the therapy not only kills the tumor cells, but also kills some of the healthy tissues and causes serious damages. In this paper we investigate optimal drug scheduling effect for breast cancer model which consist of nonlinear ordinary differential time-delay equations. In this paper, a mathematical model of breast cancer tumors is discussed and then optimal control theory is applied to find out the optimal drug adjustment as an input control of system. Finally we use Sensitivity Approach (SA) to solve the optimal control problem. The goal of this paper is to determine optimal and effective scheme for administering the chemotherapy agent, so that the tumor is eradicated, while the immune systems remains above a suitable level. Simulation results confirm the effectiveness of our proposed procedure. In this paper a new scheme is proposed to design a therapy protocol for chemotherapy in Breast Cancer. In contrast to traditional pulse drug delivery, a continuous process is offered and optimized, according to the optimal control theory for time-delay systems.
Performance of a Nonlinear Real-Time Optimal Control System for HEVs/PHEVs during Car Following
Directory of Open Access Journals (Sweden)
Kaijiang Yu
2014-01-01
Full Text Available This paper presents a real-time optimal control approach for the energy management problem of hybrid electric vehicles (HEVs and plug-in hybrid electric vehicles (PHEVs with slope information during car following. The new features of this study are as follows. First, the proposed method can optimize the engine operating points and the driving profile simultaneously. Second, the proposed method gives the freedom of vehicle spacing between the preceding vehicle and the host vehicle. Third, using the HEV/PHEV property, the desired battery state of charge is designed according to the road slopes for better recuperation of free braking energy. Fourth, all of the vehicle operating modes engine charge, electric vehicle, motor assist and electric continuously variable transmission, and regenerative braking, can be realized using the proposed real-time optimal control approach. Computer simulation results are shown among the nonlinear real-time optimal control approach and the ADVISOR rule-based approach. The conclusion is that the nonlinear real-time optimal control approach is effective for the energy management problem of the HEV/PHEV system during car following.
International Nuclear Information System (INIS)
Zhang, Jianyun; Liu, Pei; Zhou, Zhe; Ma, Linwei; Li, Zheng; Ni, Weidou
2014-01-01
Highlights: • Integration of heat streams with HRSG in a polygeneration system is studied. • A mixed-integer nonlinear programming model is proposed to optimize heat network. • Operating parameters and heat network configuration are optimized simultaneously. • The optimized heat network highly depends on the HRSG type and model specification. - Abstract: A large number of heat flows at various temperature and pressure levels exist in a polygeneration plant which co-produces electricity and chemical products. Integration of these external heat flows in a heat recovery steam generator (HRSG) has great potential to further enhance energy efficiency of such a plant; however, it is a challenging problem arising from the large design space of heat exchanger network. In this paper, a mixed-integer nonlinear programming model is developed for the design optimization of a HRSG with consideration of all alternative matches between the HRSG and external heat flows. This model is applied to four polygeneration cases with different HRSG types, and results indicate that the optimized heat network mainly depends on the HRSG type and the model specification
International Nuclear Information System (INIS)
Jaafar, Hazriq Izzuan; Ali, Nursabillilah Mohd; Selamat, Nur Asmiza; Kassim, Anuar Mohamed; Mohamed, Z; Abidin, Amar Faiz Zainal; Jamian, J J
2013-01-01
This paper presents development of an optimal PID and PD controllers for controlling the nonlinear gantry crane system. The proposed Binary Particle Swarm Optimization (BPSO) algorithm that uses Priority-based Fitness Scheme is adopted in obtaining five optimal controller gains. The optimal gains are tested on a control structure that combines PID and PD controllers to examine system responses including trolley displacement and payload oscillation. The dynamic model of gantry crane system is derived using Lagrange equation. Simulation is conducted within Matlab environment to verify the performance of system in terms of settling time (Ts), steady state error (SSE) and overshoot (OS). This proposed technique demonstrates that implementation of Priority-based Fitness Scheme in BPSO is effective and able to move the trolley as fast as possible to the various desired position
Directory of Open Access Journals (Sweden)
Ray Richard Paul
2015-09-01
Full Text Available Geotechnical and structural engineers are faced with a difficult task when their designs interact with each other. For complex projects, this is more the norm than the exception. In order to help bridge that gap, a method for modeling the behavior of a foundation using a simple elasto-plastic subgrade reaction was developed. The method uses an optimization technique to position 4-6 springs along a pile foundation to produce similar load deflection characteristics that were modeled by more sophisticated geotechnical finite element software. The methodology uses an Excel spreadsheet for accepting user input and delivering an optimized subgrade spring stiffness, yield, and position along the pile. In this way, the behavior developed from the geotechnical software can be transferred to the structural analysis software. The optimization is achieved through the solver add-in within Excel. Additionally, a beam on a nonlinear elastic foundation model is used to compute deflections of the optimized subgrade reaction configuration.
van der Laan, G.; Talman, A.J.J.; Yang, Z.F.
2007-01-01
Tucker's well-known combinatorial lemma states that for any given symmetric triangulation of the n-dimensional unit cube and for any integer labeling that assigns to each vertex of the triangulation a label from the set f§1;§2; ¢ ¢ ¢ ;§ng with the property that antipodal vertices on the boundary of
Ultrafuzziness Optimization Based on Type II Fuzzy Sets for Image Thresholding
Directory of Open Access Journals (Sweden)
Hudan Studiawan
2010-11-01
Full Text Available Image thresholding is one of the processing techniques to provide high quality preprocessed image. Image vagueness and bad illumination are common obstacles yielding in a poor image thresholding output. By assuming image as fuzzy sets, several different fuzzy thresholding techniques have been proposed to remove these obstacles during threshold selection. In this paper, we proposed an algorithm for thresholding image using ultrafuzziness optimization to decrease uncertainty in fuzzy system by common fuzzy sets like type II fuzzy sets. Optimization was conducted by involving ultrafuzziness measurement for background and object fuzzy sets separately. Experimental results demonstrated that the proposed image thresholding method had good performances for images with high vagueness, low level contrast, and grayscale ambiguity.
Zaghian, Maryam; Cao, Wenhua; Liu, Wei; Kardar, Laleh; Randeniya, Sharmalee; Mohan, Radhe; Lim, Gino
2017-03-01
Robust optimization of intensity-modulated proton therapy (IMPT) takes uncertainties into account during spot weight optimization and leads to dose distributions that are resilient to uncertainties. Previous studies demonstrated benefits of linear programming (LP) for IMPT in terms of delivery efficiency by considerably reducing the number of spots required for the same quality of plans. However, a reduction in the number of spots may lead to loss of robustness. The purpose of this study was to evaluate and compare the performance in terms of plan quality and robustness of two robust optimization approaches using LP and nonlinear programming (NLP) models. The so-called "worst case dose" and "minmax" robust optimization approaches and conventional planning target volume (PTV)-based optimization approach were applied to designing IMPT plans for five patients: two with prostate cancer, one with skull-based cancer, and two with head and neck cancer. For each approach, both LP and NLP models were used. Thus, for each case, six sets of IMPT plans were generated and assessed: LP-PTV-based, NLP-PTV-based, LP-worst case dose, NLP-worst case dose, LP-minmax, and NLP-minmax. The four robust optimization methods behaved differently from patient to patient, and no method emerged as superior to the others in terms of nominal plan quality and robustness against uncertainties. The plans generated using LP-based robust optimization were more robust regarding patient setup and range uncertainties than were those generated using NLP-based robust optimization for the prostate cancer patients. However, the robustness of plans generated using NLP-based methods was superior for the skull-based and head and neck cancer patients. Overall, LP-based methods were suitable for the less challenging cancer cases in which all uncertainty scenarios were able to satisfy tight dose constraints, while NLP performed better in more difficult cases in which most uncertainty scenarios were hard to meet
Infinite sets of conservation laws for linear and non-linear field equations
International Nuclear Information System (INIS)
Niederle, J.
1984-01-01
The work was motivated by a desire to understand group theoretically the existence of an infinite set of conservation laws for non-interacting fields and to carry over these conservation laws to the case of interacting fields. The relation between an infinite set of conservation laws of a linear field equation and the enveloping algebra of its space-time symmetry group was established. It is shown that in the case of the Korteweg-de Vries (KdV) equation to each symmetry of the corresponding linear equation delta sub(o)uxxx=u sub() determined by an element of the enveloping algebra of the space translation algebra, there corresponds a symmetry of the full KdV equation
Sensitivity of the optimal parameter settings for a LTE packet scheduler
Fernandez-Diaz, I.; Litjens, R.; van den Berg, C.A.; Dimitrova, D.C.; Spaey, K.
Advanced packet scheduling schemes in 3G/3G+ mobile networks provide one or more parameters to optimise the trade-off between QoS and resource efficiency. In this paper we study the sensitivity of the optimal parameter setting for packet scheduling in LTE radio networks with respect to various
A Decomposition Model for HPLC-DAD Data Set and Its Solution by Particle Swarm Optimization
Directory of Open Access Journals (Sweden)
Lizhi Cui
2014-01-01
Full Text Available This paper proposes a separation method, based on the model of Generalized Reference Curve Measurement and the algorithm of Particle Swarm Optimization (GRCM-PSO, for the High Performance Liquid Chromatography with Diode Array Detection (HPLC-DAD data set. Firstly, initial parameters are generated to construct reference curves for the chromatogram peaks of the compounds based on its physical principle. Then, a General Reference Curve Measurement (GRCM model is designed to transform these parameters to scalar values, which indicate the fitness for all parameters. Thirdly, rough solutions are found by searching individual target for every parameter, and reinitialization only around these rough solutions is executed. Then, the Particle Swarm Optimization (PSO algorithm is adopted to obtain the optimal parameters by minimizing the fitness of these new parameters given by the GRCM model. Finally, spectra for the compounds are estimated based on the optimal parameters and the HPLC-DAD data set. Through simulations and experiments, following conclusions are drawn: (1 the GRCM-PSO method can separate the chromatogram peaks and spectra from the HPLC-DAD data set without knowing the number of the compounds in advance even when severe overlap and white noise exist; (2 the GRCM-PSO method is able to handle the real HPLC-DAD data set.
Approximating the Pareto Set of Multiobjective Linear Programs via Robust Optimization
Gorissen, B.L.; den Hertog, D.
2012-01-01
Abstract: The Pareto set of a multiobjective optimization problem consists of the solutions for which one or more objectives can not be improved without deteriorating one or more other objectives. We consider problems with linear objectives and linear constraints and use Adjustable Robust
Blankman, P; Groot Jebbink, E; Preis, C; Bikker, I.; Gommers, D.
2012-01-01
INTRODUCTION. Electrical Impedance Tomography (EIT) is a non-invasive imaging technique, which can be used to visualize ventilation. Ventilation will be measured by impedance changes due to ventilation. OBJECTIVES. The aim of this study was to optimize PEEP settings based on the ventilation area of
Energy Technology Data Exchange (ETDEWEB)
Keller, J.; Blarigan, P. Van [Sandia National Labs., Livermore, CA (United States)
1998-08-01
In this manuscript the authors report on two projects each of which the goal is to produce cost effective hydrogen utilization technologies. These projects are: (1) the development of an electrical generation system using a conventional four-stroke spark-ignited internal combustion engine generator combination (SI-GenSet) optimized for maximum efficiency and minimum emissions, and (2) the development of a novel internal combustion engine concept. The SI-GenSet will be optimized to run on either hydrogen or hydrogen-blends. The novel concept seeks to develop an engine that optimizes the Otto cycle in a free piston configuration while minimizing all emissions. To this end the authors are developing a rapid combustion homogeneous charge compression ignition (HCCI) engine using a linear alternator for both power take-off and engine control. Targeted applications include stationary electrical power generation, stationary shaft power generation, hybrid vehicles, and nearly any other application now being accomplished with internal combustion engines.
Yu, Xue; Chen, Wei-Neng; Gu, Tianlong; Zhang, Huaxiang; Yuan, Huaqiang; Kwong, Sam; Zhang, Jun
2017-08-07
This paper studies a specific class of multiobjective combinatorial optimization problems (MOCOPs), namely the permutation-based MOCOPs. Many commonly seen MOCOPs, e.g., multiobjective traveling salesman problem (MOTSP), multiobjective project scheduling problem (MOPSP), belong to this problem class and they can be very different. However, as the permutation-based MOCOPs share the inherent similarity that the structure of their search space is usually in the shape of a permutation tree, this paper proposes a generic multiobjective set-based particle swarm optimization methodology based on decomposition, termed MS-PSO/D. In order to coordinate with the property of permutation-based MOCOPs, MS-PSO/D utilizes an element-based representation and a constructive approach. Through this, feasible solutions under constraints can be generated step by step following the permutation-tree-shaped structure. And problem-related heuristic information is introduced in the constructive approach for efficiency. In order to address the multiobjective optimization issues, the decomposition strategy is employed, in which the problem is converted into multiple single-objective subproblems according to a set of weight vectors. Besides, a flexible mechanism for diversity control is provided in MS-PSO/D. Extensive experiments have been conducted to study MS-PSO/D on two permutation-based MOCOPs, namely the MOTSP and the MOPSP. Experimental results validate that the proposed methodology is promising.
Hariharan, M; Sindhu, R; Vijean, Vikneswaran; Yazid, Haniza; Nadarajaw, Thiyagar; Yaacob, Sazali; Polat, Kemal
2018-03-01
Infant cry signal carries several levels of information about the reason for crying (hunger, pain, sleepiness and discomfort) or the pathological status (asphyxia, deaf, jaundice, premature condition and autism, etc.) of an infant and therefore suited for early diagnosis. In this work, combination of wavelet packet based features and Improved Binary Dragonfly Optimization based feature selection method was proposed to classify the different types of infant cry signals. Cry signals from 2 different databases were utilized. First database contains 507 cry samples of normal (N), 340 cry samples of asphyxia (A), 879 cry samples of deaf (D), 350 cry samples of hungry (H) and 192 cry samples of pain (P). Second database contains 513 cry samples of jaundice (J), 531 samples of premature (Prem) and 45 samples of normal (N). Wavelet packet transform based energy and non-linear entropies (496 features), Linear Predictive Coding (LPC) based cepstral features (56 features), Mel-frequency Cepstral Coefficients (MFCCs) were extracted (16 features). The combined feature set consists of 568 features. To overcome the curse of dimensionality issue, improved binary dragonfly optimization algorithm (IBDFO) was proposed to select the most salient attributes or features. Finally, Extreme Learning Machine (ELM) kernel classifier was used to classify the different types of infant cry signals using all the features and highly informative features as well. Several experiments of two-class and multi-class classification of cry signals were conducted. In binary or two-class experiments, maximum accuracy of 90.18% for H Vs P, 100% for A Vs N, 100% for D Vs N and 97.61% J Vs Prem was achieved using the features selected (only 204 features out of 568) by IBDFO. For the classification of multiple cry signals (multi-class problem), the selected features could differentiate between three classes (N, A & D) with the accuracy of 100% and seven classes with the accuracy of 97.62%. The experimental
Trace element analysis in an optimized set-up for total reflection PIXE (TPIXE)
International Nuclear Information System (INIS)
Van Kan, J.A.; Vis, R.D.
1996-01-01
A newly constructed chamber for measuring with MeV proton beams at small incidence angles (0 to 35 mrad) is used to analyse trace elements on flat surfaces such as Si wafers, quartz substrates and perspex. This set-up is constructed in such a way that the X-ray detector can reach very large solid angles, larger than 1 sr. Using these large solid angles in combination with the reduction of bremsstrahlungs background, lower limits of detection (LOD) using TPIXE can be obtained as compared with PIXE in the conventional geometry. Standard solutions are used to determine the LODs obtainable with TPIXE in the optimized set-up. These solutions contain traces of As and Sr with concentrations down to 20 ppb in an insulin solution. The limits of detection found are compared with earlier ones obtained with TPIXE in a non optimized set-up and with TXRF results. (author)
Energy Technology Data Exchange (ETDEWEB)
Urquhart, B.; Sengupta, M.; Keller, J.
2012-09-01
A multi-objective optimization was performed to allocate 2MW of PV among four candidate sites on the island of Lanai such that energy was maximized and variability in the form of ramp rates was minimized. This resulted in an optimal solution set which provides a range of geographic allotment alternatives for the fixed PV capacity. Within the optimal set, a tradeoff between energy produced and variability experienced was found, whereby a decrease in variability always necessitates a simultaneous decrease in energy. A design point within the optimal set was selected for study which decreased extreme ramp rates by over 50% while only decreasing annual energy generation by 3% over the maximum generation allocation. To quantify the allotment mix selected, a metric was developed, called the ramp ratio, which compares ramping magnitude when all capacity is allotted to a single location to the aggregate ramping magnitude in a distributed scenario. The ramp ratio quantifies simultaneously how much smoothing a distributed scenario would experience over single site allotment and how much a single site is being under-utilized for its ability to reduce aggregate variability. This paper creates a framework for use by cities and municipal utilities to reduce variability impacts while planning for high penetration of PV on the distribution grid.
Topology optimization in acoustics and elasto-acoustics via a level-set method
Desai, J.; Faure, A.; Michailidis, G.; Parry, G.; Estevez, R.
2018-04-01
Optimizing the shape and topology (S&T) of structures to improve their acoustic performance is quite challenging. The exact position of the structural boundary is usually of critical importance, which dictates the use of geometric methods for topology optimization instead of standard density approaches. The goal of the present work is to investigate different possibilities for handling topology optimization problems in acoustics and elasto-acoustics via a level-set method. From a theoretical point of view, we detail two equivalent ways to perform the derivation of surface-dependent terms and propose a smoothing technique for treating problems of boundary conditions optimization. In the numerical part, we examine the importance of the surface-dependent term in the shape derivative, neglected in previous studies found in the literature, on the optimal designs. Moreover, we test different mesh adaptation choices, as well as technical details related to the implicit surface definition in the level-set approach. We present results in two and three-space dimensions.
Security Optimization for Distributed Applications Oriented on Very Large Data Sets
Directory of Open Access Journals (Sweden)
Mihai DOINEA
2010-01-01
Full Text Available The paper presents the main characteristics of applications which are working with very large data sets and the issues related to security. First section addresses the optimization process and how it is approached when dealing with security. The second section describes the concept of very large datasets management while in the third section the risks related are identified and classified. Finally, a security optimization schema is presented with a cost-efficiency analysis upon its feasibility. Conclusions are drawn and future approaches are identified.
Nonlinear H∞ Optimal Control Scheme for an Underwater Vehicle with Regional Function Formulation
Directory of Open Access Journals (Sweden)
Zool H. Ismail
2013-01-01
Full Text Available A conventional region control technique cannot meet the demands for an accurate tracking performance in view of its inability to accommodate highly nonlinear system dynamics, imprecise hydrodynamic coefficients, and external disturbances. In this paper, a robust technique is presented for an Autonomous Underwater Vehicle (AUV with region tracking function. Within this control scheme, nonlinear H∞ and region based control schemes are used. A Lyapunov-like function is presented for stability analysis of the proposed control law. Numerical simulations are presented to demonstrate the performance of the proposed tracking control of the AUV. It is shown that the proposed control law is robust against parameter uncertainties, external disturbances, and nonlinearities and it leads to uniform ultimate boundedness of the region tracking error.
Scott, Robert C.; Perry, Boyd, III; Pototzky, Anthony S.
1991-01-01
This paper describes and illustrates two matched-filter-theory based schemes for obtaining maximized and time-correlated gust-loads for a nonlinear airplane. The first scheme is computationally fast because it uses a simple one-dimensional search procedure to obtain its answers. The second scheme is computationally slow because it uses a more complex multidimensional search procedure to obtain its answers, but it consistently provides slightly higher maximum loads than the first scheme. Both schemes are illustrated with numerical examples involving a nonlinear control system.
A Binary Cat Swarm Optimization Algorithm for the Non-Unicost Set Covering Problem
Directory of Open Access Journals (Sweden)
Broderick Crawford
2015-01-01
Full Text Available The Set Covering Problem consists in finding a subset of columns in a zero-one matrix such that they cover all the rows of the matrix at a minimum cost. To solve the Set Covering Problem we use a metaheuristic called Binary Cat Swarm Optimization. This metaheuristic is a recent swarm metaheuristic technique based on the cat behavior. Domestic cats show the ability to hunt and are curious about moving objects. Based on this, the cats have two modes of behavior: seeking mode and tracing mode. We are the first ones to use this metaheuristic to solve this problem; our algorithm solves a set of 65 Set Covering Problem instances from OR-Library.
Optimal wage setting for an export oriented firm under labor taxes and labor mobility
Directory of Open Access Journals (Sweden)
Raúl Ponce Rodríguez
2005-01-01
Full Text Available In this paper it is developed a theoretical model to study the incentives that a labor tax might induce in terms of the optimal wage setting for an export oriented firm. In particular, we analyze the interaction of a labor tax that tends to reduce the wage due the firm is induced to shift backwards the tax burden to its employees minimizing the possible increase in the payroll costs and a fall of profits. However a lower wage might not be an optimal response to the establishment of a labor tax because it increases the labor turnover and as a result the firm faces both: an outputs opportunity cost and a labors turnover cost. The firm thus optimally decides to respond to the qualification and labor taxes by increasing the after tax wage.
Non-Linear Multi-Physics Analysis and Multi-Objective Optimization in Electroheating Applications
Czech Academy of Sciences Publication Activity Database
di Barba, P.; Doležel, Ivo; Mognaschi, M. E.; Savini, A.; Karban, P.
2014-01-01
Roč. 50, č. 2 (2014), s. 7016604-7016604 ISSN 0018-9464 Institutional support: RVO:61388998 Keywords : coupled multi-physics problems * finite element method * non-linear equations Subject RIV: JA - Electronics ; Optoelectronics, Electrical Engineering Impact factor: 1.386, year: 2014
A jazz-based approach for optimal setting of pressure reducing valves in water distribution networks
De Paola, Francesco; Galdiero, Enzo; Giugni, Maurizio
2016-05-01
This study presents a model for valve setting in water distribution networks (WDNs), with the aim of reducing the level of leakage. The approach is based on the harmony search (HS) optimization algorithm. The HS mimics a jazz improvisation process able to find the best solutions, in this case corresponding to valve settings in a WDN. The model also interfaces with the improved version of a popular hydraulic simulator, EPANET 2.0, to check the hydraulic constraints and to evaluate the performances of the solutions. Penalties are introduced in the objective function in case of violation of the hydraulic constraints. The model is applied to two case studies, and the obtained results in terms of pressure reductions are comparable with those of competitive metaheuristic algorithms (e.g. genetic algorithms). The results demonstrate the suitability of the HS algorithm for water network management and optimization.
Zou, Rui; Riverson, John; Liu, Yong; Murphy, Ryan; Sim, Youn
2015-03-01
Integrated continuous simulation-optimization models can be effective predictors of a process-based responses for cost-benefit optimization of best management practices (BMPs) selection and placement. However, practical application of simulation-optimization model is computationally prohibitive for large-scale systems. This study proposes an enhanced Nonlinearity Interval Mapping Scheme (NIMS) to solve large-scale watershed simulation-optimization problems several orders of magnitude faster than other commonly used algorithms. An efficient interval response coefficient (IRC) derivation method was incorporated into the NIMS framework to overcome a computational bottleneck. The proposed algorithm was evaluated using a case study watershed in the Los Angeles County Flood Control District. Using a continuous simulation watershed/stream-transport model, Loading Simulation Program in C++ (LSPC), three nested in-stream compliance points (CP)—each with multiple Total Maximum Daily Loads (TMDL) targets—were selected to derive optimal treatment levels for each of the 28 subwatersheds, so that the TMDL targets at all the CP were met with the lowest possible BMP implementation cost. Genetic Algorithm (GA) and NIMS were both applied and compared. The results showed that the NIMS took 11 iterations (about 11 min) to complete with the resulting optimal solution having a total cost of 67.2 million, while each of the multiple GA executions took 21-38 days to reach near optimal solutions. The best solution obtained among all the GA executions compared had a minimized cost of 67.7 million—marginally higher, but approximately equal to that of the NIMS solution. The results highlight the utility for decision making in large-scale watershed simulation-optimization formulations.
Searching for optimal integer solutions to set partitioning problems using column generation
Bredström, David; Jörnsten, Kurt; Rönnqvist, Mikael
2007-01-01
We describe a new approach to produce integer feasible columns to a set partitioning problem directly in solving the linear programming (LP) relaxation using column generation. Traditionally, column generation is aimed to solve the LP relaxation as quick as possible without any concern of the integer properties of the columns formed. In our approach we aim to generate the columns forming the optimal integer solution while simultaneously solving the LP relaxation. By this we can re...
A Method of Forming the Optimal Set of Disjoint Path in Computer Networks
Directory of Open Access Journals (Sweden)
As'ad Mahmoud As'ad ALNASER
2017-04-01
Full Text Available This work provides a short analysis of algorithms of multipath routing. The modified algorithm of formation of the maximum set of not crossed paths taking into account their metrics is offered. Optimization of paths is carried out due to their reconfiguration with adjacent deadlock path. Reconfigurations are realized within the subgraphs including only peaks of the main and an adjacent deadlock path. It allows to reduce the field of formation of an optimum path and time complexity of its formation.
Klammer, Martin; Dybowski, J Nikolaj; Hoffmann, Daniel; Schaab, Christoph
2015-01-01
Multivariate biomarkers that can predict the effectiveness of targeted therapy in individual patients are highly desired. Previous biomarker discovery studies have largely focused on the identification of single biomarker signatures, aimed at maximizing prediction accuracy. Here, we present a different approach that identifies multiple biomarkers by simultaneously optimizing their predictive power, number of features, and proximity to the drug target in a protein-protein interaction network. To this end, we incorporated NSGA-II, a fast and elitist multi-objective optimization algorithm that is based on the principle of Pareto optimality, into the biomarker discovery workflow. The method was applied to quantitative phosphoproteome data of 19 non-small cell lung cancer (NSCLC) cell lines from a previous biomarker study. The algorithm successfully identified a total of 77 candidate biomarker signatures predicting response to treatment with dasatinib. Through filtering and similarity clustering, this set was trimmed to four final biomarker signatures, which then were validated on an independent set of breast cancer cell lines. All four candidates reached the same good prediction accuracy (83%) as the originally published biomarker. Although the newly discovered signatures were diverse in their composition and in their size, the central protein of the originally published signature - integrin β4 (ITGB4) - was also present in all four Pareto signatures, confirming its pivotal role in predicting dasatinib response in NSCLC cell lines. In summary, the method presented here allows for a robust and simultaneous identification of multiple multivariate biomarkers that are optimized for prediction performance, size, and relevance.
Sambo, Francesco; de Oca, Marco A Montes; Di Camillo, Barbara; Toffolo, Gianna; Stützle, Thomas
2012-01-01
Reverse engineering is the problem of inferring the structure of a network of interactions between biological variables from a set of observations. In this paper, we propose an optimization algorithm, called MORE, for the reverse engineering of biological networks from time series data. The model inferred by MORE is a sparse system of nonlinear differential equations, complex enough to realistically describe the dynamics of a biological system. MORE tackles separately the discrete component of the problem, the determination of the biological network topology, and the continuous component of the problem, the strength of the interactions. This approach allows us both to enforce system sparsity, by globally constraining the number of edges, and to integrate a priori information about the structure of the underlying interaction network. Experimental results on simulated and real-world networks show that the mixed discrete/continuous optimization approach of MORE significantly outperforms standard continuous optimization and that MORE is competitive with the state of the art in terms of accuracy of the inferred networks.
Optimization of the primary collimator settings for fractionated IMRT stereotactic radiotherapy
International Nuclear Information System (INIS)
Tobler, Matt; Leavitt, Dennis D.; Watson, Gordon
2004-01-01
Advances in field-shaping techniques for stereotactic radiosurgery/radiotherapy have allowed dynamic adjustment of field shape with gantry rotation (dynamic conformal arc) in an effort to minimize dose to critical structures. Recent work evaluated the potential for increased sparing of dose to normal tissues when the primary collimator setting is optimized to only the size necessary to cover the largest shape of the dynamic micro multi leaf field. Intensity-modulated radiotherapy (IMRT) is now a treatment option for patients receiving stereotactic radiotherapy treatments. This multisegmentation of the dose delivered through multiple fixed treatment fields provides for delivery of uniform dose to the tumor volume while allowing sparing of critical structures, particularly for patients whose tumor volumes are less suited for rotational treatment. For these segmented fields, the total number of monitor units (MUs) delivered may be much greater than the number of MUs required if dose delivery occurred through an unmodulated treatment field. As a result, undesired dose delivered, as leakage through the leaves to tissues outside the area of interest, will be proportionally increased. This work will evaluate the role of optimization of the primary collimator setting for these IMRT treatment fields, and compare these results to treatment fields where the primary collimator settings have not been optimized
Application of HGSO to security based optimal placement and parameter setting of UPFC
International Nuclear Information System (INIS)
Tarafdar Hagh, Mehrdad; Alipour, Manijeh; Teimourzadeh, Saeed
2014-01-01
Highlights: • A new method for solving the security based UPFC placement and parameter setting problem is proposed. • The proposed method is a global method for all mixed-integer problems. • The proposed method has the ability of the parallel search in binary and continues space. • By using the proposed method, most of the problems due to line contingencies are solved. • Comparison studies are done to compare the performance of the proposed method. - Abstract: This paper presents a novel method to solve security based optimal placement and parameter setting of unified power flow controller (UPFC) problem based on hybrid group search optimization (HGSO) technique. Firstly, HGSO is introduced in order to solve mix-integer type problems. Afterwards, the proposed method is applied to the security based optimal placement and parameter setting of UPFC problem. The focus of the paper is to enhance the power system security through eliminating or minimizing the over loaded lines and the bus voltage limit violations under single line contingencies. Simulation studies are carried out on the IEEE 6-bus, IEEE 14-bus and IEEE 30-bus systems in order to verify the accuracy and robustness of the proposed method. The results indicate that by using the proposed method, the power system remains secure under single line contingencies
DEFF Research Database (Denmark)
Ghoreishi, Newsha; Sørensen, Jan Corfixen; Jørgensen, Bo Nørregaard
2015-01-01
Non-trivial real world decision-making processes usually involve multiple parties having potentially conflicting interests over a set of issues. State-of-the-art multi-objective evolutionary algorithms (MOEA) are well known to solve this class of complex real-world problems. In this paper, we...... compare the performance of state-of-the-art multi-objective evolutionary algorithms to solve a non-linear multi-objective multi-issue optimisation problem found in Greenhouse climate control. The chosen algorithms in the study includes NSGAII, eNSGAII, eMOEA, PAES, PESAII and SPEAII. The performance...... of all aforementioned algorithms is assessed and compared using performance indicators to evaluate proximity, diversity and consistency. Our insights to this comparative study enhanced our understanding of MOEAs performance in order to solve a non-linear complex climate control problem. The empirical...
International Nuclear Information System (INIS)
Lee, Chieh-Hsiu Jason; Aleman, Dionne M; Sharpe, Michael B
2011-01-01
The beam orientation optimization (BOO) problem in intensity modulated radiation therapy (IMRT) treatment planning is a nonlinear problem, and existing methods to obtain solutions to the BOO problem are time consuming due to the complex nature of the objective function and size of the solution space. These issues become even more difficult in total marrow irradiation (TMI), where many more beams must be used to cover a vastly larger treatment area than typical site-specific treatments (e.g., head-and-neck, prostate, etc). These complications result in excessively long computation times to develop IMRT treatment plans for TMI, so we attempt to develop methods that drastically reduce treatment planning time. We transform the BOO problem into the classical set cover problem (SCP) and use existing methods to solve SCP to obtain beam solutions. Although SCP is NP-Hard, our methods obtain beam solutions that result in quality treatments in minutes. We compare our approach to an integer programming solver for the SCP to illustrate the speed advantage of our approach.
Energy Technology Data Exchange (ETDEWEB)
Lee, Chieh-Hsiu Jason; Aleman, Dionne M [Department of Mechanical and Industrial Engineering, University of Toronto, 5 King' s College Road, Toronto, ON M5S 3G8 (Canada); Sharpe, Michael B, E-mail: chjlee@mie.utoronto.ca, E-mail: aleman@mie.utoronto.ca, E-mail: michael.sharpe@rmp.uhn.on.ca [Princess Margaret Hospital, Department of Radiation Oncology, University of Toronto, 610 University Avenue, Toronto, ON M5G 2M9 (Canada)
2011-09-07
The beam orientation optimization (BOO) problem in intensity modulated radiation therapy (IMRT) treatment planning is a nonlinear problem, and existing methods to obtain solutions to the BOO problem are time consuming due to the complex nature of the objective function and size of the solution space. These issues become even more difficult in total marrow irradiation (TMI), where many more beams must be used to cover a vastly larger treatment area than typical site-specific treatments (e.g., head-and-neck, prostate, etc). These complications result in excessively long computation times to develop IMRT treatment plans for TMI, so we attempt to develop methods that drastically reduce treatment planning time. We transform the BOO problem into the classical set cover problem (SCP) and use existing methods to solve SCP to obtain beam solutions. Although SCP is NP-Hard, our methods obtain beam solutions that result in quality treatments in minutes. We compare our approach to an integer programming solver for the SCP to illustrate the speed advantage of our approach.
International Nuclear Information System (INIS)
Maldonado, G.I.; Turinsky, P.J.; Kropaczek, D.J.
1993-01-01
The computational capability of efficiently and accurately evaluate reactor core attributes (i.e., k eff and power distributions as a function of cycle burnup) utilizing a second-order accurate advanced nodal Generalized Perturbation Theory (GPT) model has been developed. The GPT model is derived from the forward non-linear iterative Nodal Expansion Method (NEM) strategy, thereby extending its inherent savings in memory storage and high computational efficiency to also encompass GPT via the preservation of the finite-difference matrix structure. The above development was easily implemented into the existing coarse-mesh finite-difference GPT-based in-core fuel management optimization code FORMOSA-P, thus combining the proven robustness of its adaptive Simulated Annealing (SA) multiple-objective optimization algorithm with a high-fidelity NEM GPT neutronics model to produce a powerful computational tool used to generate families of near-optimum loading patterns for PWRs. (orig.)
Directory of Open Access Journals (Sweden)
Irwin Yousept
2010-07-01
Full Text Available An optimal control problem arising in the context of 3D electromagnetic induction heating is investigated. The state equation is given by a quasilinear stationary heat equation coupled with a semilinear time harmonic eddy current equation. The temperature-dependent electrical conductivity and the presence of pointwise inequality state-constraints represent the main challenge of the paper. In the first part of the paper, the existence and regularity of the state are addressed. The second part of the paper deals with the analysis of the corresponding linearized equation. Some suffcient conditions are presented which guarantee thesolvability of the linearized system. The final part of the paper is concerned with the optimal control. The aim of the optimization is to find the optimal voltage such that a desired temperature can be achieved optimally. The corresponding first-order necessary optimality condition is presented.
Directory of Open Access Journals (Sweden)
Yu Zhou
2017-01-01
Full Text Available The train-set circulation plan problem (TCPP belongs to the rolling stock scheduling (RSS problem and is similar to the aircraft routing problem (ARP in airline operations and the vehicle routing problem (VRP in the logistics field. However, TCPP involves additional complexity due to the maintenance constraint of train-sets: train-sets must conduct maintenance tasks after running for a certain time and distance. The TCPP is nondeterministic polynomial hard (NP-hard. There is no available algorithm that can obtain the optimal global solution, and many factors such as the utilization mode and the maintenance mode impact the solution of the TCPP. This paper proposes a train-set circulation optimization model to minimize the total connection time and maintenance costs and describes the design of an efficient multiple-population genetic algorithm (MPGA to solve this model. A realistic high-speed railway (HSR case is selected to verify our model and algorithm, and, then, a comparison of different algorithms is carried out. Furthermore, a new maintenance mode is proposed, and related implementation requirements are discussed.
Directory of Open Access Journals (Sweden)
Soumya Banerjee
2011-03-01
Full Text Available Congested roads, high traffic, and parking problems are major concerns for any modern city planning. Congestion of on-street spaces in official neighborhoods may give rise to inappropriate parking areas in office and shopping mall complex during the peak time of official transactions. This paper proposes an intelligent and optimized scheme to solve parking space problem for a small city (e.g., Mauritius using a reactive search technique (named as Tabu Search assisted by rough set. Rough set is being used for the extraction of uncertain rules that exist in the databases of parking situations. The inclusion of rough set theory depicts the accuracy and roughness, which are used to characterize uncertainty of the parking lot. Approximation accuracy is employed to depict accuracy of a rough classification [1] according to different dynamic parking scenarios. And as such, the hybrid metaphor proposed comprising of Tabu Search and rough set could provide substantial research directions for other similar hard optimization problems.
Mixed-integer nonlinear approach for the optimal scheduling of a head-dependent hydro chain
Energy Technology Data Exchange (ETDEWEB)
Catalao, J.P.S.; Pousinho, H.M.I. [Department of Electromechanical Engineering, University of Beira Interior, R. Fonte do Lameiro, 6201-001 Covilha (Portugal); Mendes, V.M.F. [Department of Electrical Engineering and Automation, Instituto Superior de Engenharia de Lisboa, R. Conselheiro Emidio Navarro, 1950-062 Lisbon (Portugal)
2010-08-15
This paper is on the problem of short-term hydro scheduling (STHS), particularly concerning a head-dependent hydro chain. We propose a novel mixed-integer nonlinear programming (MINLP) approach, considering hydroelectric power generation as a nonlinear function of water discharge and of the head. As a new contribution to earlier studies, we model the on-off behavior of the hydro plants using integer variables, in order to avoid water discharges at forbidden areas. Thus, an enhanced STHS is provided due to the more realistic modeling presented in this paper. Our approach has been applied successfully to solve a test case based on one of the Portuguese cascaded hydro systems with a negligible computational time requirement. (author)
Interior-Point Method for Non-Linear Non-Convex Optimization
Czech Academy of Sciences Publication Activity Database
Lukšan, Ladislav; Matonoha, Ctirad; Vlček, Jan
2004-01-01
Roč. 11, č. 5-6 (2004), s. 431-453 ISSN 1070-5325 R&D Projects: GA AV ČR IAA1030103 Institutional research plan: CEZ:AV0Z1030915 Keywords : non-linear programming * interior point methods * indefinite systems * indefinite preconditioners * preconditioned conjugate gradient method * merit functions * algorithms * computational experiments Subject RIV: BA - General Mathematics Impact factor: 0.727, year: 2004
A parametric level-set approach for topology optimization of flow domains
DEFF Research Database (Denmark)
Pingen, Georg; Waidmann, Matthias; Evgrafov, Anton
2010-01-01
of the design variables in the traditional approaches is seen as a possible cause for the slow convergence. Non-smooth material distributions are suspected to trigger premature onset of instationary flows which cannot be treated by steady-state flow models. In the present work, we study whether the convergence...... and the versatility of topology optimization methods for fluidic systems can be improved by employing a parametric level-set description. In general, level-set methods allow controlling the smoothness of boundaries, yield a non-local influence of design variables, and decouple the material description from the flow...... field discretization. The parametric level-set method used in this study utilizes a material distribution approach to represent flow boundaries, resulting in a non-trivial mapping between design variables and local material properties. Using a hydrodynamic lattice Boltzmann method, we study...
International Nuclear Information System (INIS)
Kari, R.E.; Mezey, P.G.; Csizmadia, I.G.
1975-01-01
Expressions are given for calculating the energy gradient vector in the exponent space of Gaussian basis sets and a technique to optimize orbital exponents using the method of conjugate gradients is described. The method is tested on the (9/sups/5/supp/) Gaussian basis space and optimum exponents are determined for the carbon atom. The analysis of the results shows that the calculated one-electron properties converge more slowly to their optimum values than the total energy converges to its optimum value. In addition, basis sets approximating the optimum total energy very well can still be markedly improved for the prediction of one-electron properties. For smaller basis sets, this improvement does not warrant the necessary expense
Non-linear optimization of track layouts in loop-sorting-systems
DEFF Research Database (Denmark)
Sørensen, Søren Emil; Hansen, Michael R.; Ebbesen, Morten K.
2013-01-01
Optimization used for enhancing geometric structures iswell known. Applying obstacles to the shape optimization problemis on the other hand not very common. It requires a fast contact search algorithmand an exact continuous formulation to solve the problem robustly. This paper focuses on combining...
Directory of Open Access Journals (Sweden)
M. Pattnaik
2013-08-01
Full Text Available In this paper the concept of fuzzy Non-Linear Programming Technique is applied to solve an economic order quantity (EOQ model under restricted space. Since various types of uncertainties and imprecision are inherent in real inventory problems they are classically modeled using the approaches from the probability theory. However, there are uncertainties that cannot be appropriately treated by usual probabilistic models. The questions how to define inventory optimization tasks in such environment how to interpret optimal solutions arise. This paper allows the modification of the Single item EOQ model in presence of fuzzy decision making process where demand is related to the unit price and the setup cost varies with the quantity produced/Purchased. This paper considers the modification of objective function and storage area in the presence of imprecisely estimated parameters. The model is developed for the problem by employing different modeling approaches over an infinite planning horizon. It incorporates all concepts of a fuzzy arithmetic approach, the quantity ordered and the demand per unit compares both fuzzy non linear and other models. Investigation of the properties of an optimal solution allows developing an algorithm whose validity is illustrated through an example problem and ugh MATLAB (R2009a version software, the two and three dimensional diagrams are represented to the application. Sensitivity analysis of the optimal solution is also studied with respect to changes in different parameter values and to draw managerial insights of the decision problem.
Seldner, K.
1977-01-01
An algorithm was developed to optimally control the traffic signals at each intersection using a discrete time traffic model applicable to heavy or peak traffic. Off line optimization procedures were applied to compute the cycle splits required to minimize the lengths of the vehicle queues and delay at each intersection. The method was applied to an extensive traffic network in Toledo, Ohio. Results obtained with the derived optimal settings are compared with the control settings presently in use.
Aschepkov, Leonid T; Kim, Taekyun; Agarwal, Ravi P
2016-01-01
This book is based on lectures from a one-year course at the Far Eastern Federal University (Vladivostok, Russia) as well as on workshops on optimal control offered to students at various mathematical departments at the university level. The main themes of the theory of linear and nonlinear systems are considered, including the basic problem of establishing the necessary and sufficient conditions of optimal processes. In the first part of the course, the theory of linear control systems is constructed on the basis of the separation theorem and the concept of a reachability set. The authors prove the closure of a reachability set in the class of piecewise continuous controls, and the problems of controllability, observability, identification, performance and terminal control are also considered. The second part of the course is devoted to nonlinear control systems. Using the method of variations and the Lagrange multipliers rule of nonlinear problems, the authors prove the Pontryagin maximum principle for prob...
Geminal embedding scheme for optimal atomic basis set construction in correlated calculations
Energy Technology Data Exchange (ETDEWEB)
Sorella, S., E-mail: sorella@sissa.it [International School for Advanced Studies (SISSA), Via Beirut 2-4, 34014 Trieste, Italy and INFM Democritos National Simulation Center, Trieste (Italy); Devaux, N.; Dagrada, M., E-mail: mario.dagrada@impmc.upmc.fr [Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, Université Pierre et Marie Curie, Case 115, 4 Place Jussieu, 75252 Paris Cedex 05 (France); Mazzola, G., E-mail: gmazzola@phys.ethz.ch [Theoretische Physik, ETH Zurich, 8093 Zurich (Switzerland); Casula, M., E-mail: michele.casula@impmc.upmc.fr [CNRS and Institut de Minéralogie, de Physique des Matériaux et de Cosmochimie, Université Pierre et Marie Curie, Case 115, 4 Place Jussieu, 75252 Paris Cedex 05 (France)
2015-12-28
We introduce an efficient method to construct optimal and system adaptive basis sets for use in electronic structure and quantum Monte Carlo calculations. The method is based on an embedding scheme in which a reference atom is singled out from its environment, while the entire system (atom and environment) is described by a Slater determinant or its antisymmetrized geminal power (AGP) extension. The embedding procedure described here allows for the systematic and consistent contraction of the primitive basis set into geminal embedded orbitals (GEOs), with a dramatic reduction of the number of variational parameters necessary to represent the many-body wave function, for a chosen target accuracy. Within the variational Monte Carlo method, the Slater or AGP part is determined by a variational minimization of the energy of the whole system in presence of a flexible and accurate Jastrow factor, representing most of the dynamical electronic correlation. The resulting GEO basis set opens the way for a fully controlled optimization of many-body wave functions in electronic structure calculation of bulk materials, namely, containing a large number of electrons and atoms. We present applications on the water molecule, the volume collapse transition in cerium, and the high-pressure liquid hydrogen.
Directory of Open Access Journals (Sweden)
Qingyan Wang
2015-01-01
Full Text Available Thrust bearing is one part with the highest failure rate in hydroturbine generator set, which is primarily due to heavy axial load. Such heavy load often makes oil film destruction, bearing friction, and even burning. It is necessary to study the load and the reduction method. The dynamic thrust is an important factor to influence the axial load and reduction design of electromagnetic device. Therefore, in the paper, combined with the structure features of vertical turbine, the hydraulic thrust is analyzed accurately. Then, take the turbine model HL-220-LT-550, for instance; the electromagnetic levitation load reduction device is designed, and its mathematical model is built, whose purpose is to minimize excitation loss and total quality under the constraints of installation space, connection layout, and heat dissipation. Particle swarm optimization (PSO is employed to search for the optimum solution; finally, the result is verified by finite element method (FEM, which demonstrates that the optimized structure is more effective.
Level set method for optimal shape design of MRAM core. Micromagnetic approach
International Nuclear Information System (INIS)
Melicher, Valdemar; Cimrak, Ivan; Keer, Roger van
2008-01-01
We aim at optimizing the shape of the magnetic core in MRAM memories. The evolution of the magnetization during the writing process is described by the Landau-Lifshitz equation (LLE). The actual shape of the core in one cell is characterized by the coefficient γ. Cost functional f=f(γ) expresses the quality of the writing process having in mind the competition between the full-select and the half-select element. We derive an explicit form of the derivative F=∂f/∂γ which allows for the use of gradient-type methods for the actual computation of the optimized shape (e.g., steepest descend method). The level set method (LSM) is employed for the representation of the piecewise constant coefficient γ
Optimal Operation of Industrial Batch Crystallizers : A Nonlinear Model-based Control Approach
Mesbah, A.
2010-01-01
Batch crystallization is extensively employed in the chemical, pharmaceutical, and food industries to separate and purify high value-added chemical substances. Despite their widespread application, optimal operation of batch crystallizers is particularly challenging. The difficulties primarily
On the optimal identification of tag sets in time-constrained RFID configurations.
Vales-Alonso, Javier; Bueno-Delgado, María Victoria; Egea-López, Esteban; Alcaraz, Juan José; Pérez-Mañogil, Juan Manuel
2011-01-01
In Radio Frequency Identification facilities the identification delay of a set of tags is mainly caused by the random access nature of the reading protocol, yielding a random identification time of the set of tags. In this paper, the cumulative distribution function of the identification time is evaluated using a discrete time Markov chain for single-set time-constrained passive RFID systems, namely those ones where a single group of tags is assumed to be in the reading area and only for a bounded time (sojourn time) before leaving. In these scenarios some tags in a set may leave the reader coverage area unidentified. The probability of this event is obtained from the cumulative distribution function of the identification time as a function of the sojourn time. This result provides a suitable criterion to minimize the probability of losing tags. Besides, an identification strategy based on splitting the set of tags in smaller subsets is also considered. Results demonstrate that there are optimal splitting configurations that reduce the overall identification time while keeping the same probability of losing tags.
DEFF Research Database (Denmark)
Yoon, Gil Ho; Joung, Young Soo; Kim, Yoon Young
2005-01-01
The topology design optimization of “three-dimensional geometrically-nonlinear” continuum structures is still a difficult problem not only because of its problem size but also the occurrence of unstable continuum finite elements during the design optimization. To overcome this difficulty, the ele......) stiffness matrix of continuum finite elements. Therefore, any finite element code, including commercial codes, can be readily used for the ECP implementation. The key ideas and characteristics of these methods will be presented in this paper....
Optimizing BAO measurements with non-linear transformations of the Lyman-α forest
Energy Technology Data Exchange (ETDEWEB)
Wang, Xinkang; Font-Ribera, Andreu; Seljak, Uroš, E-mail: xinkang.wang@berkeley.edu, E-mail: afont@lbl.gov, E-mail: useljak@berkeley.edu [Department of Physics, University of California, South Hall Rd, Berkeley (United States)
2015-04-01
We explore the effect of applying a non-linear transformation to the Lyman-α forest transmitted flux F=e{sup −τ} and the ability of analytic models to predict the resulting clustering amplitude. Both the large-scale bias of the transformed field (signal) and the amplitude of small scale fluctuations (noise) can be arbitrarily modified, but we were unable to find a transformation that increases significantly the signal-to-noise ratio on large scales using Taylor expansion up to the third order. In particular, however, we achieve a 33% improvement in signal to noise for Gaussianized field in transverse direction. On the other hand, we explore an analytic model for the large-scale biasing of the Lyα forest, and present an extension of this model to describe the biasing of the transformed fields. Using hydrodynamic simulations we show that the model works best to describe the biasing with respect to velocity gradients, but is less successful in predicting the biasing with respect to large-scale density fluctuations, especially for very nonlinear transformations.
Patnaik, Surya N.; Pai, Shantaram S.; Coroneos, Rula M.
2010-01-01
Structural design generated by traditional method, optimization method and the stochastic design concept are compared. In the traditional method, the constraints are manipulated to obtain the design and weight is back calculated. In design optimization, the weight of a structure becomes the merit function with constraints imposed on failure modes and an optimization algorithm is used to generate the solution. Stochastic design concept accounts for uncertainties in loads, material properties, and other parameters and solution is obtained by solving a design optimization problem for a specified reliability. Acceptable solutions were produced by all the three methods. The variation in the weight calculated by the methods was modest. Some variation was noticed in designs calculated by the methods. The variation may be attributed to structural indeterminacy. It is prudent to develop design by all three methods prior to its fabrication. The traditional design method can be improved when the simplified sensitivities of the behavior constraint is used. Such sensitivity can reduce design calculations and may have a potential to unify the traditional and optimization methods. Weight versus reliabilitytraced out an inverted-S-shaped graph. The center of the graph corresponded to mean valued design. A heavy design with weight approaching infinity could be produced for a near-zero rate of failure. Weight can be reduced to a small value for a most failure-prone design. Probabilistic modeling of load and material properties remained a challenge.
Wigdahl, J.; Agurto, C.; Murray, V.; Barriga, S.; Soliz, P.
2013-03-01
Diabetic retinopathy (DR) affects more than 4.4 million Americans age 40 and over. Automatic screening for DR has shown to be an efficient and cost-effective way to lower the burden on the healthcare system, by triaging diabetic patients and ensuring timely care for those presenting with DR. Several supervised algorithms have been developed to detect pathologies related to DR, but little work has been done in determining the size of the training set that optimizes an algorithm's performance. In this paper we analyze the effect of the training sample size on the performance of a top-down DR screening algorithm for different types of statistical classifiers. Results are based on partial least squares (PLS), support vector machines (SVM), k-nearest neighbor (kNN), and Naïve Bayes classifiers. Our dataset consisted of digital retinal images collected from a total of 745 cases (595 controls, 150 with DR). We varied the number of normal controls in the training set, while keeping the number of DR samples constant, and repeated the procedure 10 times using randomized training sets to avoid bias. Results show increasing performance in terms of area under the ROC curve (AUC) when the number of DR subjects in the training set increased, with similar trends for each of the classifiers. Of these, PLS and k-NN had the highest average AUC. Lower standard deviation and a flattening of the AUC curve gives evidence that there is a limit to the learning ability of the classifiers and an optimal number of cases to train on.
Optimal structural inference of signaling pathways from unordered and overlapping gene sets.
Acharya, Lipi R; Judeh, Thair; Wang, Guangdi; Zhu, Dongxiao
2012-02-15
A plethora of bioinformatics analysis has led to the discovery of numerous gene sets, which can be interpreted as discrete measurements emitted from latent signaling pathways. Their potential to infer signaling pathway structures, however, has not been sufficiently exploited. Existing methods accommodating discrete data do not explicitly consider signal cascading mechanisms that characterize a signaling pathway. Novel computational methods are thus needed to fully utilize gene sets and broaden the scope from focusing only on pairwise interactions to the more general cascading events in the inference of signaling pathway structures. We propose a gene set based simulated annealing (SA) algorithm for the reconstruction of signaling pathway structures. A signaling pathway structure is a directed graph containing up to a few hundred nodes and many overlapping signal cascades, where each cascade represents a chain of molecular interactions from the cell surface to the nucleus. Gene sets in our context refer to discrete sets of genes participating in signal cascades, the basic building blocks of a signaling pathway, with no prior information about gene orderings in the cascades. From a compendium of gene sets related to a pathway, SA aims to search for signal cascades that characterize the optimal signaling pathway structure. In the search process, the extent of overlap among signal cascades is used to measure the optimality of a structure. Throughout, we treat gene sets as random samples from a first-order Markov chain model. We evaluated the performance of SA in three case studies. In the first study conducted on 83 KEGG pathways, SA demonstrated a significantly better performance than Bayesian network methods. Since both SA and Bayesian network methods accommodate discrete data, use a 'search and score' network learning strategy and output a directed network, they can be compared in terms of performance and computational time. In the second study, we compared SA and
Padhi, Radhakant; Unnikrishnan, Nishant; Wang, Xiaohua; Balakrishnan, S N
2006-12-01
Even though dynamic programming offers an optimal control solution in a state feedback form, the method is overwhelmed by computational and storage requirements. Approximate dynamic programming implemented with an Adaptive Critic (AC) neural network structure has evolved as a powerful alternative technique that obviates the need for excessive computations and storage requirements in solving optimal control problems. In this paper, an improvement to the AC architecture, called the "Single Network Adaptive Critic (SNAC)" is presented. This approach is applicable to a wide class of nonlinear systems where the optimal control (stationary) equation can be explicitly expressed in terms of the state and costate variables. The selection of this terminology is guided by the fact that it eliminates the use of one neural network (namely the action network) that is part of a typical dual network AC setup. As a consequence, the SNAC architecture offers three potential advantages: a simpler architecture, lesser computational load and elimination of the approximation error associated with the eliminated network. In order to demonstrate these benefits and the control synthesis technique using SNAC, two problems have been solved with the AC and SNAC approaches and their computational performances are compared. One of these problems is a real-life Micro-Electro-Mechanical-system (MEMS) problem, which demonstrates that the SNAC technique is applicable to complex engineering systems.
DEFF Research Database (Denmark)
Otomori, Masaki; Yamada, Takayuki; Izui, Kazuhiro
2012-01-01
This paper presents a level set-based topology optimization method for the design of negative permeability dielectric metamaterials. Metamaterials are artificial materials that display extraordinary physical properties that are unavailable with natural materials. The aim of the formulated...... optimization problem is to find optimized layouts of a dielectric material that achieve negative permeability. The presence of grayscale areas in the optimized configurations critically affects the performance of metamaterials, positively as well as negatively, but configurations that contain grayscale areas...... are highly impractical from an engineering and manufacturing point of view. Therefore, a topology optimization method that can obtain clear optimized configurations is desirable. Here, a level set-based topology optimization method incorporating a fictitious interface energy is applied to a negative...
COMPARING INTRA- AND INTERENVIRONMENTAL PARAMETERS OF OPTIMAL SETTING IN BREEDING EXPERIMENTS
Directory of Open Access Journals (Sweden)
Domagoj Šimić
2004-06-01
Full Text Available A series of biometrical and quantitative-genetic parameters, not well known in Croatia, are being used for the most important agronomic traits to determine optimal genotype setting within a location as well as among locations. Objectives of the study are to estimate and to compare 1 parameters of intra-environment setting (effective mean square error EMSE, in lattice design, relative efficiency RE, of lattice design LD, compared to randomized complete block design RCBD, and repeatability Rep, of a plot value, and 2 operative heritability h2, as a parameter of inter-environment setting in an experiment with 72 maize hybrids. Trials were set up at four environments (two locations in two years evaluating grain yield and stalk rot. EMSE values corresponded across environments for both traits, while the estimations for RE of LD varied inconsistently over environments and traits. Rep estimates were more different over environments than traits. Rep values did not correspond with h2 estimates: Rep estimates for stalk rot were higher than those for grain yield, while h2 for grain yield was higher than for stalk rot in all instances. Our results suggest that due to importance of genotype × environment interaction, there is a need for multienvironment trials for both traits. If the experiment framework should be reduced due to economic or other reasons, decreasing number of locations in a year rather than decreasing number of years of investigation is recommended.
Xenopoulos, M. A.; Vogt, R. J.
2014-12-01
There is now increasing evidence that non-linearity is a common response in ecological systems to pressures caused by human activities. There is also increasing evidence that exogenous environmental drivers, such as climate, induce spatial and temporal synchrony in a wide range of ecological variables. Using Moran's I and Pearson's correlation, we quantified the synchrony of dissolved organic carbon concentration (DOC) and quality (DOM; e.g., specific UV absorbance, Fluorescence Index, PARAFAC), nutrients, discharge and temperature in 40 streams that span an agriculture gradient (0 to >70% cropland), over 10 years. We then used breakpoint regression, 2D-Kolmogorov-Smirnov test and significant zero crossings (SiZer) analyses to quantify the prevalence of nonlinearity and ecological thresholds (breakpoints) where applicable. There was a high degree of synchrony in DOM quality (r > 0.7) but not DOC (r < 0.4). The degree of synchrony was driven in part by the catchment's land use. With respect to the nonlinear analyses we found non-linearity in ~50% of bivariate datasets analyzed. Non-linearity was also driven in part by the catchment's land use. Breakpoints defined different DOM properties. Nonlinearity and synchronous behaviour in DOM are intimately linked to land use.
Computational Modelling and Optimal Control of Ebola Virus Disease with non-Linear Incidence Rate
Takaidza, I.; Makinde, O. D.; Okosun, O. K.
2017-03-01
The 2014 Ebola outbreak in West Africa has exposed the need to connect modellers and those with relevant data as pivotal to better understanding of how the disease spreads and quantifying the effects of possible interventions. In this paper, we model and analyse the Ebola virus disease with non-linear incidence rate. The epidemic model created is used to describe how the Ebola virus could potentially evolve in a population. We perform an uncertainty analysis of the basic reproductive number R 0 to quantify its sensitivity to other disease-related parameters. We also analyse the sensitivity of the final epidemic size to the time control interventions (education, vaccination, quarantine and safe handling) and provide the cost effective combination of the interventions.
Selecting Optimal Feature Set in High-Dimensional Data by Swarm Search
Directory of Open Access Journals (Sweden)
Simon Fong
2013-01-01
Full Text Available Selecting the right set of features from data of high dimensionality for inducing an accurate classification model is a tough computational challenge. It is almost a NP-hard problem as the combinations of features escalate exponentially as the number of features increases. Unfortunately in data mining, as well as other engineering applications and bioinformatics, some data are described by a long array of features. Many feature subset selection algorithms have been proposed in the past, but not all of them are effective. Since it takes seemingly forever to use brute force in exhaustively trying every possible combination of features, stochastic optimization may be a solution. In this paper, we propose a new feature selection scheme called Swarm Search to find an optimal feature set by using metaheuristics. The advantage of Swarm Search is its flexibility in integrating any classifier into its fitness function and plugging in any metaheuristic algorithm to facilitate heuristic search. Simulation experiments are carried out by testing the Swarm Search over some high-dimensional datasets, with different classification algorithms and various metaheuristic algorithms. The comparative experiment results show that Swarm Search is able to attain relatively low error rates in classification without shrinking the size of the feature subset to its minimum.
Application of Fuzzy Sets for the Improvement of Routing Optimization Heuristic Algorithms
Directory of Open Access Journals (Sweden)
Mattas Konstantinos
2016-12-01
Full Text Available The determination of the optimal circular path has become widely known for its difficulty in producing a solution and for the numerous applications in the scope of organization and management of passenger and freight transport. It is a mathematical combinatorial optimization problem for which several deterministic and heuristic models have been developed in recent years, applicable to route organization issues, passenger and freight transport, storage and distribution of goods, waste collection, supply and control of terminals, as well as human resource management. Scope of the present paper is the development, with the use of fuzzy sets, of a practical, comprehensible and speedy heuristic algorithm for the improvement of the ability of the classical deterministic algorithms to identify optimum, symmetrical or non-symmetrical, circular route. The proposed fuzzy heuristic algorithm is compared to the corresponding deterministic ones, with regard to the deviation of the proposed solution from the best known solution and the complexity of the calculations needed to obtain this solution. It is shown that the use of fuzzy sets reduced up to 35% the deviation of the solution identified by the classical deterministic algorithms from the best known solution.
International Nuclear Information System (INIS)
Kojima, Akihiro; Watanabe, Hiroyuki; Arao, Yuichi; Kawasaki, Masaaki; Takaki, Akihiro; Matsumoto, Masanori
2007-01-01
In this study, we examined whether the optimal energy window (EW) setting depending on an energy resolution of a gamma camera, which we previously proposed, is valid on planar scintigraphic imaging using Tl-201, Ga-67, Tc-99m, and I-123. Image acquisitions for line sources and paper sheet phantoms containing each radionuclide were performed in air and with scattering materials. For the six photopeaks excluding the Hg-201 characteristic x-rays' one, the conventional 20%-width energy window (EW20%) setting and the optimal energy window (optimal EW) setting (15%-width below 100 keV and 13%-width above 100 keV) were compared. For the Hg-201 characteristic x-rays' photopeak, the conventional on-peak EW20% setting was compared with the off-peak EW setting (73 keV-25%) and the wider off-peak EW setting (77 keV-29%). Image-count ratio (defined as the ratio of the image counts obtained with an EW and the total image counts obtained with the EW covered the whole photopeak for a line source in air), image quality, spatial resolutions (full width half maximum (FWHM) and full width tenth maximum (FWTM) values), count-profile curves, and defect-contrast values were compared between the conventional EW setting and the optimal EW setting. Except for the Hg-201 characteristic x-rays, the image-count ratios were 94-99% for the EW20% setting, but 78-89% for the optimal EW setting. However, the optimal EW setting reduced scatter fraction (defined as the scattered-to-primary counts ratio) effectively, as compared with the EW20% setting. Consequently, all the images with the optimal EW setting gave better image quality than ones with the EW20% setting. For the Hg-201 characteristic x-rays, the off-peak EW setting showed great improvement in image quality in comparison with the EW20% setting and the wider off-peak EW setting gave the best results. In conclusion, from our planar imaging study it was shown that although the optimal EW setting proposed by us gives less image-count ratio by
Flexible aluminum tubes and a least square multi-objective non-linear optimization scheme
International Nuclear Information System (INIS)
Endelt, Benny; Nielsen, Karl Brian; Olsen, Soeren
2004-01-01
The automotive industry currently uses rubber hoses as the media carrier between e.g. the radiator and the engine, and the basic idea is to replace the rubber hoses with flexible aluminum tubes.A good quality is defined through several quality measurements, i.e. in the current case the key objective is to produce a flexible convolution through optimization of the tool geometry, but the process should also be stable, and the process stability is evaluated through Forming Limit Diagrams. Typically the defined objectives are conflicting, i.e. the optimized configuration represents therefore a trade-off between the individual objectives, in this case flexibility versus process stability.The optimization problem is solved through iteratively minimizing the object function. A second-order least square scheme is used for the approximation of the quadratic model, and the change in the design parameters is evaluated through the trust region scheme and box constraints are introduced within the trust region framework. Furthermore, the object function is minimized by applying the non-monotone scheme, and the trust region subproblem is solved by applying the Cholesky factorization scheme.An optimal bell shaped geometry is identified and the design is verified experimentally
Wang, Danshi; Zhang, Min; Cai, Zhongle; Cui, Yue; Li, Ze; Han, Huanhuan; Fu, Meixia; Luo, Bin
2016-06-01
An effective machine learning algorithm, the support vector machine (SVM), is presented in the context of a coherent optical transmission system. As a classifier, the SVM can create nonlinear decision boundaries to mitigate the distortions caused by nonlinear phase noise (NLPN). Without any prior information or heuristic assumptions, the SVM can learn and capture the link properties from only a few training data. Compared with the maximum likelihood estimation (MLE) algorithm, a lower bit-error rate (BER) is achieved by the SVM for a given launch power; moreover, the launch power dynamic range (LPDR) is increased by 3.3 dBm for 8 phase-shift keying (8 PSK), 1.2 dBm for QPSK, and 0.3 dBm for BPSK. The maximum transmission distance corresponding to a BER of 1 ×10-3 is increased by 480 km for the case of 8 PSK. The larger launch power range and longer transmission distance improve the tolerance to amplitude and phase noise, which demonstrates the feasibility of the SVM in digital signal processing for M-PSK formats. Meanwhile, in order to apply the SVM method to 16 quadratic amplitude modulation (16 QAM) detection, we propose a parameter optimization scheme. By utilizing a cross-validation and grid-search techniques, the optimal parameters of SVM can be selected, thus leading to the LPDR improvement by 2.8 dBm. Additionally, we demonstrate that the SVM is also effective in combating the laser phase noise combined with the inphase and quadrature (I/Q) modulator imperfections, but the improvement is insignificant for the linear noise and separate I/Q imbalance. The computational complexity of SVM is also discussed. The relatively low complexity makes it possible for SVM to implement the real-time processing.
Assessing the optimality of ASHRAE climate zones using high resolution meteorological data sets
Fils, P. D.; Kumar, J.; Collier, N.; Hoffman, F. M.; Xu, M.; Forbes, W.
2017-12-01
Energy consumed by built infrastructure constitutes a significant fraction of the nation's energy budget. According to 2015 US Energy Information Agency report, 41% of the energy used in the US was going to residential and commercial buildings. Additional research has shown that 32% of commercial building energy goes into heating and cooling the building. The American National Standards Institute and the American Society of Heating Refrigerating and Air-Conditioning Engineers Standard 90.1 provides climate zones for current state-of-practice since heating and cooling demands are strongly influenced by spatio-temporal weather variations. For this reason, we have been assessing the optimality of the climate zones using high resolution daily climate data from NASA's DAYMET database. We analyzed time series of meteorological data sets for all ASHRAE climate zones between 1980-2016 inclusively. We computed the mean, standard deviation, and other statistics for a set of meteorological variables (solar radiation, maximum and minimum temperature)within each zone. By plotting all the zonal statistics, we analyzed patterns and trends in those data over the past 36 years. We compared the means of each zone to its standard deviation to determine the range of spatial variability that exist within each zone. If the band around the mean is too large, it indicates that regions in the zone experience a wide range of weather conditions and perhaps a common set of building design guidelines would lead to a non-optimal energy consumption scenario. In this study we have observed a strong variation in the different climate zones. Some have shown consistent patterns in the past 36 years, indicating that the zone was well constructed, while others have greatly deviated from their mean indicating that the zone needs to be reconstructed. We also looked at redesigning the climate zones based on high resolution climate data. We are using building simulations models like EnergyPlus to develop
Optimal Geometrical Set for Automated Marker Placement to Virtualized Real-Time Facial Emotions.
Directory of Open Access Journals (Sweden)
Vasanthan Maruthapillai
Full Text Available In recent years, real-time face recognition has been a major topic of interest in developing intelligent human-machine interaction systems. Over the past several decades, researchers have proposed different algorithms for facial expression recognition, but there has been little focus on detection in real-time scenarios. The present work proposes a new algorithmic method of automated marker placement used to classify six facial expressions: happiness, sadness, anger, fear, disgust, and surprise. Emotional facial expressions were captured using a webcam, while the proposed algorithm placed a set of eight virtual markers on each subject's face. Facial feature extraction methods, including marker distance (distance between each marker to the center of the face and change in marker distance (change in distance between the original and new marker positions, were used to extract three statistical features (mean, variance, and root mean square from the real-time video sequence. The initial position of each marker was subjected to the optical flow algorithm for marker tracking with each emotional facial expression. Finally, the extracted statistical features were mapped into corresponding emotional facial expressions using two simple non-linear classifiers, K-nearest neighbor and probabilistic neural network. The results indicate that the proposed automated marker placement algorithm effectively placed eight virtual markers on each subject's face and gave a maximum mean emotion classification rate of 96.94% using the probabilistic neural network.
Optimal Geometrical Set for Automated Marker Placement to Virtualized Real-Time Facial Emotions.
Maruthapillai, Vasanthan; Murugappan, Murugappan
2016-01-01
In recent years, real-time face recognition has been a major topic of interest in developing intelligent human-machine interaction systems. Over the past several decades, researchers have proposed different algorithms for facial expression recognition, but there has been little focus on detection in real-time scenarios. The present work proposes a new algorithmic method of automated marker placement used to classify six facial expressions: happiness, sadness, anger, fear, disgust, and surprise. Emotional facial expressions were captured using a webcam, while the proposed algorithm placed a set of eight virtual markers on each subject's face. Facial feature extraction methods, including marker distance (distance between each marker to the center of the face) and change in marker distance (change in distance between the original and new marker positions), were used to extract three statistical features (mean, variance, and root mean square) from the real-time video sequence. The initial position of each marker was subjected to the optical flow algorithm for marker tracking with each emotional facial expression. Finally, the extracted statistical features were mapped into corresponding emotional facial expressions using two simple non-linear classifiers, K-nearest neighbor and probabilistic neural network. The results indicate that the proposed automated marker placement algorithm effectively placed eight virtual markers on each subject's face and gave a maximum mean emotion classification rate of 96.94% using the probabilistic neural network.
International Nuclear Information System (INIS)
Procaccia, H.; Cordier, R.; Muller, S.
1994-07-01
Statistical decision theory could be a alternative for the optimization of preventive maintenance periodicity. In effect, this theory concerns the situation in which a decision maker has to make a choice between a set of reasonable decisions, and where the loss associated to a given decision depends on a probabilistic risk, called state of nature. In the case of maintenance optimization, the decisions to be analyzed are different periodicities proposed by the experts, given the observed feedback experience, the states of nature are the associated failure probabilities, and the losses are the expectations of the induced cost of maintenance and of consequences of the failures. As failure probabilities concern rare events, at the ultimate state of RCM analysis (failure of sub-component), and as expected foreseeable behaviour of equipment has to be evaluated by experts, Bayesian approach is successfully used to compute states of nature. In Bayesian decision theory, a prior distribution for failure probabilities is modeled from expert knowledge, and is combined with few stochastic information provided by feedback experience, giving a posterior distribution of failure probabilities. The optimized decision is the decision that minimizes the expected loss over the posterior distribution. This methodology has been applied to inspection and maintenance optimization of cylinders of diesel generator engines of 900 MW nuclear plants. In these plants, auxiliary electric power is supplied by 2 redundant diesel generators which are tested every 2 weeks during about 1 hour. Until now, during yearly refueling of each plant, one endoscopic inspection of diesel cylinders is performed, and every 5 operating years, all cylinders are replaced. RCM has shown that cylinder failures could be critical. So Bayesian decision theory has been applied, taking into account expert opinions, and possibility of aging when maintenance periodicity is extended. (authors). 8 refs., 5 figs., 1 tab
Pseudolinear functions and optimization
Mishra, Shashi Kant
2015-01-01
Pseudolinear Functions and Optimization is the first book to focus exclusively on pseudolinear functions, a class of generalized convex functions. It discusses the properties, characterizations, and applications of pseudolinear functions in nonlinear optimization problems.The book describes the characterizations of solution sets of various optimization problems. It examines multiobjective pseudolinear, multiobjective fractional pseudolinear, static minmax pseudolinear, and static minmax fractional pseudolinear optimization problems and their results. The authors extend these results to locally
International Nuclear Information System (INIS)
Ryjov, A.; Loginov, D.
1994-01-01
The problem of choosing an optimal set of significances of qualitative attributes for information retrieval in databases is addressed. Given a particular database, a set of significances is called optimal if it results in the minimization of losses of information and information noise for information retrieval in the data base. Obviously, such a set of significances depends on the statistical parameters of the data base. The software, which enables to calculate on the basis of the statistical parameters of the given data base, the losses of information and the information noise for arbitrary sets of significances of qualitative attributes, is described. The software also permits to compare various sets of significances of qualitative attributes and to choose the optimal set of significances
Luu, Keurfon; Noble, Mark; Gesret, Alexandrine; Belayouni, Nidhal; Roux, Pierre-François
2018-04-01
Seismic traveltime tomography is an optimization problem that requires large computational efforts. Therefore, linearized techniques are commonly used for their low computational cost. These local optimization methods are likely to get trapped in a local minimum as they critically depend on the initial model. On the other hand, global optimization methods based on MCMC are insensitive to the initial model but turn out to be computationally expensive. Particle Swarm Optimization (PSO) is a rather new global optimization approach with few tuning parameters that has shown excellent convergence rates and is straightforwardly parallelizable, allowing a good distribution of the workload. However, while it can traverse several local minima of the evaluated misfit function, classical implementation of PSO can get trapped in local minima at later iterations as particles inertia dim. We propose a Competitive PSO (CPSO) to help particles to escape from local minima with a simple implementation that improves swarm's diversity. The model space can be sampled by running the optimizer multiple times and by keeping all the models explored by the swarms in the different runs. A traveltime tomography algorithm based on CPSO is successfully applied on a real 3D data set in the context of induced seismicity.
Optimal nonlinear information processing capacity in delay-based reservoir computers
Grigoryeva, Lyudmila; Henriques, Julie; Larger, Laurent; Ortega, Juan-Pablo
2015-09-01
Reservoir computing is a recently introduced brain-inspired machine learning paradigm capable of excellent performances in the processing of empirical data. We focus in a particular kind of time-delay based reservoir computers that have been physically implemented using optical and electronic systems and have shown unprecedented data processing rates. Reservoir computing is well-known for the ease of the associated training scheme but also for the problematic sensitivity of its performance to architecture parameters. This article addresses the reservoir design problem, which remains the biggest challenge in the applicability of this information processing scheme. More specifically, we use the information available regarding the optimal reservoir working regimes to construct a functional link between the reservoir parameters and its performance. This function is used to explore various properties of the device and to choose the optimal reservoir architecture, thus replacing the tedious and time consuming parameter scannings used so far in the literature.
Directory of Open Access Journals (Sweden)
Quan Zheng
2015-12-01
Full Text Available In this paper, a family of Steffensen-type methods of optimal order of convergence with two parameters is constructed by direct Newtonian interpolation. It satisfies the conjecture proposed by Kung and Traub (J. Assoc. Comput. Math. 1974, 21, 634–651 that an iterative method based on m evaluations per iteration without memory would arrive at the optimal convergence of order 2m-1 . Furthermore, the family of Steffensen-type methods of super convergence is suggested by using arithmetic expressions for the parameters with memory but no additional new evaluation of the function. Their error equations, asymptotic convergence constants and convergence orders are obtained. Finally, they are compared with related root-finding methods in the numerical examples.
Optimal bounds and extremal trajectories for time averages in nonlinear dynamical systems
Tobasco, Ian; Goluskin, David; Doering, Charles R.
2018-02-01
For any quantity of interest in a system governed by ordinary differential equations, it is natural to seek the largest (or smallest) long-time average among solution trajectories, as well as the extremal trajectories themselves. Upper bounds on time averages can be proved a priori using auxiliary functions, the optimal choice of which is a convex optimization problem. We prove that the problems of finding maximal trajectories and minimal auxiliary functions are strongly dual. Thus, auxiliary functions provide arbitrarily sharp upper bounds on time averages. Moreover, any nearly minimal auxiliary function provides phase space volumes in which all nearly maximal trajectories are guaranteed to lie. For polynomial equations, auxiliary functions can be constructed by semidefinite programming, which we illustrate using the Lorenz system.
Nonlinear Dynamic in an Ecological System with Impulsive Effect and Optimal Foraging
Directory of Open Access Journals (Sweden)
Min Zhao
2014-01-01
Full Text Available The population dynamics of a three-species ecological system with impulsive effect are investigated. Using the theories of impulsive equations and small-amplitude perturbation scales, the conditions for the system to be permanent when the number of predators released is less than some critical value can be obtained. Furthermore, because the predator in the system follows the predictions of optimal foraging theory, it follows that optimal foraging promotes species coexistence. In particular, the less beneficial prey can support the predator alone when the more beneficial prey goes extinct. Moreover, the influences of the impulsive effect and optimal foraging on inherent oscillations are studied using simulation, which reveals rich dynamic behaviors such as period-halving bifurcations, a chaotic band, a periodic window, and chaotic crises. In addition, the largest Lyapunov exponent and the power spectra of the strange attractor, which can help analyze the chaotic dynamic behavior of the model, are investigated. This information will be useful for studying the dynamic complexity of ecosystems.
Zia, Haider
2017-06-01
This paper describes an updated exponential Fourier based split-step method that can be applied to a greater class of partial differential equations than previous methods would allow. These equations arise in physics and engineering, a notable example being the generalized derivative non-linear Schrödinger equation that arises in non-linear optics with self-steepening terms. These differential equations feature terms that were previously inaccessible to model accurately with low computational resources. The new method maintains a 3rd order error even with these additional terms and models the equation in all three spatial dimensions and time. The class of non-linear differential equations that this method applies to is shown. The method is fully derived and implementation of the method in the split-step architecture is shown. This paper lays the mathematical ground work for an upcoming paper employing this method in white-light generation simulations in bulk material.
Directory of Open Access Journals (Sweden)
Zhi-Ren Tsai
2013-01-01
Full Text Available A tracking problem, time-delay, uncertainty and stability analysis of a predictive control system are considered. The predictive control design is based on the input and output of neural plant model (NPM, and a recursive fuzzy predictive tracker has scaling factors which limit the value zone of measured data and cause the tuned parameters to converge to obtain a robust control performance. To improve the further control performance, the proposed random-local-optimization design (RLO for a model/controller uses offline initialization to obtain a near global optimal model/controller. Other issues are the considerations of modeling error, input-delay, sampling distortion, cost, greater flexibility, and highly reliable digital products of the model-based controller for the continuous-time (CT nonlinear system. They are solved by a recommended two-stage control design with the first-stage (offline RLO and second-stage (online adaptive steps. A theorizing method is then put forward to replace the sensitivity calculation, which reduces the calculation of Jacobin matrices of the back-propagation (BP method. Finally, the feedforward input of reference signals helps the digital fuzzy controller improve the control performance, and the technique works to control the CT systems precisely.
Xu, Y; Li, N
2014-09-01
Biological species have produced many simple but efficient rules in their complex and critical survival activities such as hunting and mating. A common feature observed in several biological motion strategies is that the predator only moves along paths in a carefully selected or iteratively refined subspace (or manifold), which might be able to explain why these motion strategies are effective. In this paper, a unified linear algebraic formulation representing such a predator-prey relationship is developed to simplify the construction and refinement process of the subspace (or manifold). Specifically, the following three motion strategies are studied and modified: motion camouflage, constant absolute target direction and local pursuit. The framework constructed based on this varying subspace concept could significantly reduce the computational cost in solving a class of nonlinear constrained optimal trajectory planning problems, particularly for the case with severe constraints. Two non-trivial examples, a ground robot and a hypersonic aircraft trajectory optimization problem, are used to show the capabilities of the algorithms in this new computational framework.
International Nuclear Information System (INIS)
Xu, Y; Li, N
2014-01-01
Biological species have produced many simple but efficient rules in their complex and critical survival activities such as hunting and mating. A common feature observed in several biological motion strategies is that the predator only moves along paths in a carefully selected or iteratively refined subspace (or manifold), which might be able to explain why these motion strategies are effective. In this paper, a unified linear algebraic formulation representing such a predator–prey relationship is developed to simplify the construction and refinement process of the subspace (or manifold). Specifically, the following three motion strategies are studied and modified: motion camouflage, constant absolute target direction and local pursuit. The framework constructed based on this varying subspace concept could significantly reduce the computational cost in solving a class of nonlinear constrained optimal trajectory planning problems, particularly for the case with severe constraints. Two non-trivial examples, a ground robot and a hypersonic aircraft trajectory optimization problem, are used to show the capabilities of the algorithms in this new computational framework. (paper)
Directory of Open Access Journals (Sweden)
Farong Kou
2018-01-01
Full Text Available In order to coordinate the damping performance and energy regenerative performance of energy regenerative suspension, this paper proposes a structure of a vehicle semi-active energy regenerative suspension with an electro-hydraulic actuator (EHA. In light of the proposed concept, a specific energy regenerative scheme is designed and a mechanical properties test is carried out. Based on the test results, the parameter identification for the system model is conducted using a recursive least squares algorithm. On the basis of the system principle, the nonlinear model of the semi-active energy regenerative suspension with an EHA is built. Meanwhile, linear-quadratic-Gaussian control strategy of the system is designed. Then, the influence of the main parameters of the EHA on the damping performance and energy regenerative performance of the suspension is analyzed. Finally, the main parameters of the EHA are optimized via the genetic algorithm. The test results show that when a sinusoidal is input at the frequency of 2 Hz and the amplitude of 30 mm, the spring mass acceleration root meam square value of the optimized EHA semi-active energy regenerative suspension is reduced by 22.23% and the energy regenerative power RMS value is increased by 40.51%, which means that while meeting the requirements of vehicle ride comfort and driving safety, the energy regenerative performance is improved significantly.
Adaptive Conflict-Free Optimization of Rule Sets for Network Security Packet Filtering Devices
Directory of Open Access Journals (Sweden)
Andrea Baiocchi
2015-01-01
Full Text Available Packet filtering and processing rules management in firewalls and security gateways has become commonplace in increasingly complex networks. On one side there is a need to maintain the logic of high level policies, which requires administrators to implement and update a large amount of filtering rules while keeping them conflict-free, that is, avoiding security inconsistencies. On the other side, traffic adaptive optimization of large rule lists is useful for general purpose computers used as filtering devices, without specific designed hardware, to face growing link speeds and to harden filtering devices against DoS and DDoS attacks. Our work joins the two issues in an innovative way and defines a traffic adaptive algorithm to find conflict-free optimized rule sets, by relying on information gathered with traffic logs. The proposed approach suits current technology architectures and exploits available features, like traffic log databases, to minimize the impact of ACO development on the packet filtering devices. We demonstrate the benefit entailed by the proposed algorithm through measurements on a test bed made up of real-life, commercial packet filtering devices.
International Nuclear Information System (INIS)
Ferreira, Jose C.; Gaspar-Cunha, Antonio; Fonseca, Carlos M.
2007-01-01
Most of the real world optimization problems involve multiple, usually conflicting, optimization criteria. Generating Pareto optimal solutions plays an important role in multi-objective optimization, and the problem is considered to be solved when the Pareto optimal set is found, i.e., the set of non-dominated solutions. Multi-Objective Evolutionary Algorithms based on the principle of Pareto optimality are designed to produce the complete set of non-dominated solutions. However, this is not allays enough since the aim is not only to know the Pareto set but, also, to obtain one solution from this Pareto set. Thus, the definition of a methodology able to select a single solution from the set of non-dominated solutions (or a region of the Pareto frontier), and taking into account the preferences of a Decision Maker (DM), is necessary. A different method, based on a weighted stress function, is proposed. It is able to integrate the user's preferences in order to find the best region of the Pareto frontier accordingly with these preferences. This method was tested on some benchmark test problems, with two and three criteria, and on a polymer extrusion problem. This methodology is able to select efficiently the best Pareto-frontier region for the specified relative importance of the criteria
Energy Technology Data Exchange (ETDEWEB)
Kudryashov, Nikolay A.; Shilnikov, Kirill E. [National Research Nuclear University MEPhI, Department of Applied Mathematics, Moscow (Russian Federation)
2016-06-08
Numerical computation of the three dimensional problem of the freezing interface propagation during the cryosurgery coupled with the multi-objective optimization methods is used in order to improve the efficiency and safety of the cryosurgery operations performing. Prostate cancer treatment and cutaneous cryosurgery are considered. The heat transfer in soft tissue during the thermal exposure to low temperature is described by the Pennes bioheat model and is coupled with an enthalpy method for blurred phase change computations. The finite volume method combined with the control volume approximation of the heat fluxes is applied for the cryosurgery numerical modeling on the tumor tissue of a quite arbitrary shape. The flux relaxation approach is used for the stability improvement of the explicit finite difference schemes. The method of the additional heating elements mounting is studied as an approach to control the cellular necrosis front propagation. Whereas the undestucted tumor tissue and destucted healthy tissue volumes are considered as objective functions, the locations of additional heating elements in cutaneous cryosurgery and cryotips in prostate cancer cryotreatment are considered as objective variables in multi-objective problem. The quasi-gradient method is proposed for the searching of the Pareto front segments as the multi-objective optimization problem solutions.
Optimal allocation of the limited oral cholera vaccine supply between endemic and epidemic settings.
Moore, Sean M; Lessler, Justin
2015-10-06
The World Health Organization (WHO) recently established a global stockpile of oral cholera vaccine (OCV) to be preferentially used in epidemic response (reactive campaigns) with any vaccine remaining after 1 year allocated to endemic settings. Hence, the number of cholera cases or deaths prevented in an endemic setting represents the minimum utility of these doses, and the optimal risk-averse response to any reactive vaccination request (i.e. the minimax strategy) is one that allocates the remaining doses between the requested epidemic response and endemic use in order to ensure that at least this minimum utility is achieved. Using mathematical models, we find that the best minimax strategy is to allocate the majority of doses to reactive campaigns, unless the request came late in the targeted epidemic. As vaccine supplies dwindle, the case for reactive use of the remaining doses grows stronger. Our analysis provides a lower bound for the amount of OCV to keep in reserve when responding to any request. These results provide a strategic context for the fulfilment of requests to the stockpile, and define allocation strategies that minimize the number of OCV doses that are allocated to suboptimal situations. © 2015 The Authors.
Optimal Inversion Parameters for Full Waveform Inversion using OBS Data Set
Kim, S.; Chung, W.; Shin, S.; Kim, D.; Lee, D.
2017-12-01
In recent years, full Waveform Inversion (FWI) has been the most researched technique in seismic data processing. It uses the residuals between observed and modeled data as an objective function; thereafter, the final subsurface velocity model is generated through a series of iterations meant to minimize the residuals.Research on FWI has expanded from acoustic media to elastic media. In acoustic media, the subsurface property is defined by P-velocity; however, in elastic media, properties are defined by multiple parameters, such as P-velocity, S-velocity, and density. Further, the elastic media can also be defined by Lamé constants, density or impedance PI, SI; consequently, research is being carried out to ascertain the optimal parameters.From results of advanced exploration equipment and Ocean Bottom Seismic (OBS) survey, it is now possible to obtain multi-component seismic data. However, to perform FWI on these data and generate an accurate subsurface model, it is important to determine optimal inversion parameters among (Vp, Vs, ρ), (λ, μ, ρ), and (PI, SI) in elastic media. In this study, staggered grid finite difference method was applied to simulate OBS survey. As in inversion, l2-norm was set as objective function. Further, the accurate computation of gradient direction was performed using the back-propagation technique and its scaling was done using the Pseudo-hessian matrix.In acoustic media, only Vp is used as the inversion parameter. In contrast, various sets of parameters, such as (Vp, Vs, ρ) and (λ, μ, ρ) can be used to define inversion in elastic media. Therefore, it is important to ascertain the parameter that gives the most accurate result for inversion with OBS data set.In this study, we generated Vp and Vs subsurface models by using (λ, μ, ρ) and (Vp, Vs, ρ) as inversion parameters in every iteration, and compared the final two FWI results.This research was supported by the Basic Research Project(17-3312) of the Korea Institute of
Wei, Qinglai; Song, Ruizhuo; Yan, Pengfei
2016-02-01
This paper is concerned with a new data-driven zero-sum neuro-optimal control problem for continuous-time unknown nonlinear systems with disturbance. According to the input-output data of the nonlinear system, an effective recurrent neural network is introduced to reconstruct the dynamics of the nonlinear system. Considering the system disturbance as a control input, a two-player zero-sum optimal control problem is established. Adaptive dynamic programming (ADP) is developed to obtain the optimal control under the worst case of the disturbance. Three single-layer neural networks, including one critic and two action networks, are employed to approximate the performance index function, the optimal control law, and the disturbance, respectively, for facilitating the implementation of the ADP method. Convergence properties of the ADP method are developed to show that the system state will converge to a finite neighborhood of the equilibrium. The weight matrices of the critic and the two action networks are also convergent to finite neighborhoods of their optimal ones. Finally, the simulation results will show the effectiveness of the developed data-driven ADP methods.
Suzuki, Taiji; Aihara, Kazuyuki
2013-09-01
These days prostate cancer is one of the most common types of malignant neoplasm in men. Androgen ablation therapy (hormone therapy) has been shown to be effective for advanced prostate cancer. However, continuous hormone therapy often causes recurrence. This results from the progression of androgen-dependent cancer cells to androgen-independent cancer cells during the continuous hormone therapy. One possible method to prevent the progression to the androgen-independent state is intermittent androgen suppression (IAS) therapy, which ceases dosing intermittently. In this paper, we propose two methods to estimate the dynamics of prostate cancer, and investigate the IAS therapy from the viewpoint of optimality. The two methods that we propose for dynamics estimation are a variational Bayesian method for a piecewise affine (PWA) system and a Gaussian process regression method. We apply the proposed methods to real clinical data and compare their predictive performances. Then, using the estimated dynamics of prostate cancer, we observe how prostate cancer behaves for various dosing schedules. It can be seen that the conventional IAS therapy is a way of imposing high cost for dosing while keeping the prostate cancer in a safe state. We would like to dedicate this paper to the memory of Professor Luigi M. Ricciardi. Copyright © 2013 The Authors. Published by Elsevier Inc. All rights reserved.
Corbetta, Matteo; Sbarufatti, Claudio; Giglio, Marco; Todd, Michael D.
2018-05-01
The present work critically analyzes the probabilistic definition of dynamic state-space models subject to Bayesian filters used for monitoring and predicting monotonic degradation processes. The study focuses on the selection of the random process, often called process noise, which is a key perturbation source in the evolution equation of particle filtering. Despite the large number of applications of particle filtering predicting structural degradation, the adequacy of the picked process noise has not been investigated. This paper reviews existing process noise models that are typically embedded in particle filters dedicated to monitoring and predicting structural damage caused by fatigue, which is monotonic in nature. The analysis emphasizes that existing formulations of the process noise can jeopardize the performance of the filter in terms of state estimation and remaining life prediction (i.e., damage prognosis). This paper subsequently proposes an optimal and unbiased process noise model and a list of requirements that the stochastic model must satisfy to guarantee high prognostic performance. These requirements are useful for future and further implementations of particle filtering for monotonic system dynamics. The validity of the new process noise formulation is assessed against experimental fatigue crack growth data from a full-scale aeronautical structure using dedicated performance metrics.
Design of a nonlinear torsional vibration absorber
Tahir, Ammaar Bin
Tuned mass dampers (TMD) utilizing linear spring mechanisms to mitigate destructive vibrations are commonly used in practice. A TMD is usually tuned for a specific resonant frequency or an operating frequency of a system. Recently, nonlinear vibration absorbers attracted attention of researchers due to some potential advantages they possess over the TMDs. The nonlinear vibration absorber, or the nonlinear energy sink (NES), has an advantage of being effective over a broad range of excitation frequencies, which makes it more suitable for systems with several resonant frequencies, or for a system with varying excitation frequency. Vibration dissipation mechanism in an NES is passive and ensures that there is no energy backflow to the primary system. In this study, an experimental setup of a rotational system has been designed for validation of the concept of nonlinear torsional vibration absorber with geometrically induced cubic stiffness nonlinearity. Dimensions of the primary system have been optimized so as to get the first natural frequency of the system to be fairly low. This was done in order to excite the dynamic system for torsional vibration response by the available motor. Experiments have been performed to obtain the modal parameters of the system. Based on the obtained modal parameters, the design optimization of the nonlinear torsional vibration absorber was carried out using an equivalent 2-DOF modal model. The optimality criterion was chosen to be maximization of energy dissipation in the nonlinear absorber attached to the equivalent 2-DOF system. The optimized design parameters of the nonlinear absorber were tested on the original 5-DOF system numerically. A comparison was made between the performance of linear and nonlinear absorbers using the numerical models. The comparison showed the superiority of the nonlinear absorber over its linear counterpart for the given set of primary system parameters as the vibration energy dissipation in the former is
Spectral theory and nonlinear functional analysis
Lopez-Gomez, Julian
2001-01-01
This Research Note addresses several pivotal problems in spectral theory and nonlinear functional analysis in connection with the analysis of the structure of the set of zeroes of a general class of nonlinear operators. It features the construction of an optimal algebraic/analytic invariant for calculating the Leray-Schauder degree, new methods for solving nonlinear equations in Banach spaces, and general properties of components of solutions sets presented with minimal use of topological tools. The author also gives several applications of the abstract theory to reaction diffusion equations and systems.The results presented cover a thirty-year period and include recent, unpublished findings of the author and his coworkers. Appealing to a broad audience, Spectral Theory and Nonlinear Functional Analysis contains many important contributions to linear algebra, linear and nonlinear functional analysis, and topology and opens the door for further advances.
Renormalization group invariance and optimal QCD renormalization scale-setting: a key issues review
Wu, Xing-Gang; Ma, Yang; Wang, Sheng-Quan; Fu, Hai-Bing; Ma, Hong-Hao; Brodsky, Stanley J.; Mojaza, Matin
2015-12-01
A valid prediction for a physical observable from quantum field theory should be independent of the choice of renormalization scheme—this is the primary requirement of renormalization group invariance (RGI). Satisfying scheme invariance is a challenging problem for perturbative QCD (pQCD), since a truncated perturbation series does not automatically satisfy the requirements of the renormalization group. In a previous review, we provided a general introduction to the various scale setting approaches suggested in the literature. As a step forward, in the present review, we present a discussion in depth of two well-established scale-setting methods based on RGI. One is the ‘principle of maximum conformality’ (PMC) in which the terms associated with the β-function are absorbed into the scale of the running coupling at each perturbative order; its predictions are scheme and scale independent at every finite order. The other approach is the ‘principle of minimum sensitivity’ (PMS), which is based on local RGI; the PMS approach determines the optimal renormalization scale by requiring the slope of the approximant of an observable to vanish. In this paper, we present a detailed comparison of the PMC and PMS procedures by analyzing two physical observables R e+e- and Γ(H\\to b\\bar{b}) up to four-loop order in pQCD. At the four-loop level, the PMC and PMS predictions for both observables agree within small errors with those of conventional scale setting assuming a physically-motivated scale, and each prediction shows small scale dependences. However, the convergence of the pQCD series at high orders, behaves quite differently: the PMC displays the best pQCD convergence since it eliminates divergent renormalon terms; in contrast, the convergence of the PMS prediction is questionable, often even worse than the conventional prediction based on an arbitrary guess for the renormalization scale. PMC predictions also have the property that any residual dependence on
On marker-based parentage verification via non-linear optimization.
Boerner, Vinzent
2017-06-15
Parentage verification by molecular markers is mainly based on short tandem repeat markers. Single nucleotide polymorphisms (SNPs) as bi-allelic markers have become the markers of choice for genotyping projects. Thus, the subsequent step is to use SNP genotypes for parentage verification as well. Recent developments of algorithms such as evaluating opposing homozygous SNP genotypes have drawbacks, for example the inability of rejecting all animals of a sample of potential parents. This paper describes an algorithm for parentage verification by constrained regression which overcomes the latter limitation and proves to be very fast and accurate even when the number of SNPs is as low as 50. The algorithm was tested on a sample of 14,816 animals with 50, 100 and 500 SNP genotypes randomly selected from 40k genotypes. The samples of putative parents of these animals contained either five random animals, or four random animals and the true sire. Parentage assignment was performed by ranking of regression coefficients, or by setting a minimum threshold for regression coefficients. The assignment quality was evaluated by the power of assignment (P[Formula: see text]) and the power of exclusion (P[Formula: see text]). If the sample of putative parents contained the true sire and parentage was assigned by coefficient ranking, P[Formula: see text] and P[Formula: see text] were both higher than 0.99 for the 500 and 100 SNP genotypes, and higher than 0.98 for the 50 SNP genotypes. When parentage was assigned by a coefficient threshold, P[Formula: see text] was higher than 0.99 regardless of the number of SNPs, but P[Formula: see text] decreased from 0.99 (500 SNPs) to 0.97 (100 SNPs) and 0.92 (50 SNPs). If the sample of putative parents did not contain the true sire and parentage was rejected using a coefficient threshold, the algorithm achieved a P[Formula: see text] of 1 (500 SNPs), 0.99 (100 SNPs) and 0.97 (50 SNPs). The algorithm described here is easy to implement
Optimization of GEANT4 settings for Proton Pencil Beam Scanning simulations using GATE
Energy Technology Data Exchange (ETDEWEB)
Grevillot, Loic, E-mail: loic.grevillot@gmail.co [Universite de Lyon, F-69622 Lyon (France); Creatis, CNRS UMR 5220, F-69622 Villeurbanne (France); Centre de Lutte Contre le Cancer Leon Berard, F-69373 Lyon (France); IBA, B-1348 Louvain-la-Neuve (Belgium); Frisson, Thibault [Universite de Lyon, F-69622 Lyon (France); Creatis, CNRS UMR 5220, F-69622 Villeurbanne (France); Centre de Lutte Contre le Cancer Leon Berard, F-69373 Lyon (France); Zahra, Nabil [Universite de Lyon, F-69622 Lyon (France); IPNL, CNRS UMR 5822, F-69622 Villeurbanne (France); Centre de Lutte Contre le Cancer Leon Berard, F-69373 Lyon (France); Bertrand, Damien; Stichelbaut, Frederic [IBA, B-1348 Louvain-la-Neuve (Belgium); Freud, Nicolas [Universite de Lyon, F-69622 Lyon (France); CNDRI, INSA-Lyon, F-69621 Villeurbanne Cedex (France); Sarrut, David [Universite de Lyon, F-69622 Lyon (France); Creatis, CNRS UMR 5220, F-69622 Villeurbanne (France); Centre de Lutte Contre le Cancer Leon Berard, F-69373 Lyon (France)
2010-10-15
This study reports the investigation of different GEANT4 settings for proton therapy applications in the context of Treatment Planning System comparisons. The GEANT4.9.2 release was used through the GATE platform. We focused on the Pencil Beam Scanning delivery technique, which allows for intensity modulated proton therapy applications. The most relevant options and parameters (range cut, step size, database binning) for the simulation that influence the dose deposition were investigated, in order to determine a robust, accurate and efficient simulation environment. In this perspective, simulations of depth-dose profiles and transverse profiles at different depths and energies between 100 and 230 MeV have been assessed against reference measurements in water and PMMA. These measurements were performed in Essen, Germany, with the IBA dedicated Pencil Beam Scanning system, using Bragg-peak chambers and radiochromic films. GEANT4 simulations were also compared to the PHITS.2.14 and MCNPX.2.5.0 Monte Carlo codes. Depth-dose simulations reached 0.3 mm range accuracy compared to NIST CSDA ranges, with a dose agreement of about 1% over a set of five different energies. The transverse profiles simulated using the different Monte Carlo codes showed discrepancies, with up to 15% difference in beam widening between GEANT4 and MCNPX in water. A 8% difference between the GEANT4 multiple scattering and single scattering algorithms was observed. The simulations showed the inability of reproducing the measured transverse dose spreading with depth in PMMA, corroborating the fact that GEANT4 underestimates the lateral dose spreading. GATE was found to be a very convenient simulation environment to perform this study. A reference physics-list and an optimized parameters-list have been proposed. Satisfactory agreement against depth-dose profiles measurements was obtained. The simulation of transverse profiles using different Monte Carlo codes showed significant deviations. This point
Optimization of super-resolution processing using incomplete image sets in PET imaging.
Chang, Guoping; Pan, Tinsu; Clark, John W; Mawlawi, Osama R
2008-12-01
Super-resolution (SR) techniques are used in PET imaging to generate a high-resolution image by combining multiple low-resolution images that have been acquired from different points of view (POVs). The number of low-resolution images used defines the processing time and memory storage necessary to generate the SR image. In this paper, the authors propose two optimized SR implementations (ISR-1 and ISR-2) that require only a subset of the low-resolution images (two sides and diagonal of the image matrix, respectively), thereby reducing the overall processing time and memory storage. In an N x N matrix of low-resolution images, ISR-1 would be generated using images from the two sides of the N x N matrix, while ISR-2 would be generated from images across the diagonal of the image matrix. The objective of this paper is to investigate whether the two proposed SR methods can achieve similar performance in contrast and signal-to-noise ratio (SNR) as the SR image generated from a complete set of low-resolution images (CSR) using simulation and experimental studies. A simulation, a point source, and a NEMA/IEC phantom study were conducted for this investigation. In each study, 4 (2 x 2) or 16 (4 x 4) low-resolution images were reconstructed from the same acquired data set while shifting the reconstruction grid to generate images from different POVs. SR processing was then applied in each study to combine all as well as two different subsets of the low-resolution images to generate the CSR, ISR-1, and ISR-2 images, respectively. For reference purpose, a native reconstruction (NR) image using the same matrix size as the three SR images was also generated. The resultant images (CSR, ISR-1, ISR-2, and NR) were then analyzed using visual inspection, line profiles, SNR plots, and background noise spectra. The simulation study showed that the contrast and the SNR difference between the two ISR images and the CSR image were on average 0.4% and 0.3%, respectively. Line profiles of
Oh, Jihoon; Chae, Jeong-Ho
2018-04-01
Although heart rate variability (HRV) may be a crucial marker of mental health, how it is related to positive psychological factors (i.e. attitude to life and positive thinking) is largely unknown. Here we investigated the correlation of HRV linear and nonlinear dynamics with psychological scales that measured degree of optimism and happiness in patients with anxiety disorders. Results showed that low- to high-frequency HRV ratio (LF/HF) was increased and the HRV HF parameter was decreased in subjects who were more optimistic and who felt happier in daily living. Nonlinear analysis also showed that HRV dispersion and regulation were significantly correlated with the subjects' optimism and purpose in life. Our findings showed that HRV properties might be related to degree of optimistic perspectives on life and suggests that HRV markers of autonomic nervous system function could reflect positive human mind states.
Optimally setting up directed searches for continuous gravitational waves in Advanced LIGO O1 data
Ming, Jing; Papa, Maria Alessandra; Krishnan, Badri; Prix, Reinhard; Beer, Christian; Zhu, Sylvia J.; Eggenstein, Heinz-Bernd; Bock, Oliver; Machenschalk, Bernd
2018-02-01
In this paper we design a search for continuous gravitational waves from three supernova remnants: Vela Jr., Cassiopeia A (Cas A) and G347.3. These systems might harbor rapidly rotating neutron stars emitting quasiperiodic gravitational radiation detectable by the advanced LIGO detectors. Our search is designed to use the volunteer computing project Einstein@Home for a few months and assumes the sensitivity and duty cycles of the advanced LIGO detectors during their first science run. For all three supernova remnants, the sky positions of their central compact objects are well known but the frequency and spin-down rates of the neutron stars are unknown which makes the searches computationally limited. In a previous paper we have proposed a general framework for deciding on what target we should spend computational resources and in what proportion, what frequency and spin-down ranges we should search for every target, and with what search setup. Here we further expand this framework and apply it to design a search directed at detecting continuous gravitational wave signals from the most promising three supernova remnants identified as such in the previous work. Our optimization procedure yields broad frequency and spin-down searches for all three objects, at an unprecedented level of sensitivity: The smallest detectable gravitational wave strain h0 for Cas A is expected to be 2 times smaller than the most sensitive upper limits published to date, and our proposed search, which was set up and ran on the volunteer computing project Einstein@Home, covers a much larger frequency range.
Alghamdi, Amal Mohammed
2012-04-01
Clawpack, a conservation laws package implemented in Fortran, and its Python-based version, PyClaw, are existing tools providing nonlinear wave propagation solvers that use state of the art finite volume methods. Simulations using those tools can have extensive computational requirements to provide accurate results. Therefore, a number of tools, such as BearClaw and MPIClaw, have been developed based on Clawpack to achieve significant speedup by exploiting parallel architectures. However, none of them has been shown to scale on a large number of cores. Furthermore, these tools, implemented in Fortran, achieve parallelization by inserting parallelization logic and MPI standard routines throughout the serial code in a non modular manner. Our contribution in this thesis research is three-fold. First, we demonstrate an advantageous use case of Python in implementing easy-to-use modular extensible scalable scientific software tools by developing an implementation of a parallelization framework, PetClaw, for PyClaw using the well-known Portable Extensible Toolkit for Scientific Computation, PETSc, through its Python wrapper petsc4py. Second, we demonstrate the possibility of getting acceptable Python code performance when compared to Fortran performance after introducing a number of serial optimizations to the Python code including integrating Clawpack Fortran kernels into PyClaw for low-level computationally intensive parts of the code. As a result of those optimizations, the Python overhead in PetClaw for a shallow water application is only 12 percent when compared to the corresponding Fortran Clawpack application. Third, we provide a demonstration of PetClaw scalability on up to the entirety of Shaheen; a 16-rack Blue Gene/P IBM supercomputer that comprises 65,536 cores and located at King Abdullah University of Science and Technology (KAUST). The PetClaw solver achieved above 0.98 weak scaling efficiency for an Euler application on the whole machine excluding the
A two-level strategy to realize life-cycle production optimization in an operational setting
Essen, van G.M.; Hof, Van den P.M.J.; Jansen, J.D.
2012-01-01
We present a two-level strategy to improve robustness against uncertainty and model errors in life-cycle flooding optimization. At the upper level, a physics-based large-scale reservoir model is used to determine optimal life-cycle injection and production profiles. At the lower level these profiles
A two-level strategy to realize life-cycle production optimization in an operational setting
Essen, van G.M.; Hof, Van den P.M.J.; Jansen, J.D.
2013-01-01
We present a two-level strategy to improve robustness against uncertainty and model errors in life-cycle flooding optimization. At the upper level, a physics-based large-scale reservoir model is used to determine optimal life-cycle injection and production profiles. At the lower level these profiles
Simulation of neuro-fuzzy model for optimization of combine header setting
Directory of Open Access Journals (Sweden)
S Zareei
2016-09-01
of reel tine bar from cutter bar and vertical distance of reel tine bar from cutter bar could be recommended according to minimize header loss. Conclusions In the final step, the designed controller was simulated in SIMULINK. The Controller can change setting of header components in order to their impaction gathering loss and in each step, compare gathering loss with optimal value and If it was more than optimum then change the settings again. The simulation results were evaluated satisfactory.
International Nuclear Information System (INIS)
Ma, Huan; Si, Fengqi; Kong, Yu; Zhu, Kangping; Yan, Wensheng
2017-01-01
Highlights: • Aerodynamic field around dry cooling tower is presented with numerical model. • Performances of cooling deltas are figured out by air inflow velocity analysis. • Setting angles of wind-break walls are optimized to improve cooling performance. • Optimized walls can reduce the interference on air inflow at low wind speeds. • Optimized walls create stronger outside secondary flow at high wind speeds. - Abstract: To get larger cooling performance enhancement for natural draft dry cooling tower with vertical cooling deltas under crosswind, setting angles of wind-break walls were optimized. Considering specific structure of each cooling delta, an efficient numerical model was established and validated by some published results. Aerodynamic fields around cooling deltas under various crosswind speeds were presented, and outlet water temperatures of the two columns of cooling delta were exported as well. It was found that for each cooling delta, there was a difference in cooling performance between the two columns, which is closely related to the characteristic of main airflow outside the tower. Using the present model, air inflow deviation angles at cooling deltas’ inlet were calculated, and the effects of air inflow deviation on outlet water temperatures of the two columns for corresponding cooling delta were explained in detail. Subsequently, at cooling deltas’ inlet along radial direction of the tower, setting angles of wind-break walls were optimized equal to air inflow deviation angles when no airflow separation appeared outside the tower, while equal to zero when outside airflow separation occurred. In addition, wind-break walls with optimized setting angles were verified to be extremely effective, compared to the previous radial walls.
Chapman, Larry S; Pelletier, Kenneth R
2004-01-01
This paper provides an (OHE) overview of a population health management (PHM) approach to the creation of optimal healing environments (OHEs) in worksite and corporate settings. It presents a framework for consideration as the context for potential research projects to examine the health, well-being, and economic effects of a set of newer "virtual" prevention interventions operating in an integrated manner in worksite settings. The main topics discussed are the fundamentals of PHM with basic terminology and core principles, a description of PHM core technology and implications of a PHM approach to creating OHEs.
Li, Zhongwei; Xin, Yuezhen; Wang, Xun; Sun, Beibei; Xia, Shengyu; Li, Hui
2016-01-01
Phellinus is a kind of fungus and is known as one of the elemental components in drugs to avoid cancers. With the purpose of finding optimized culture conditions for Phellinus production in the laboratory, plenty of experiments focusing on single factor were operated and large scale of experimental data were generated. In this work, we use the data collected from experiments for regression analysis, and then a mathematical model of predicting Phellinus production is achieved. Subsequently, a gene-set based genetic algorithm is developed to optimize the values of parameters involved in culture conditions, including inoculum size, PH value, initial liquid volume, temperature, seed age, fermentation time, and rotation speed. These optimized values of the parameters have accordance with biological experimental results, which indicate that our method has a good predictability for culture conditions optimization. PMID:27610365
Bizon, Nicu; Mahdavi Tabatabaei, Naser
2014-01-01
This book explains and analyzes the dynamic performance of linear and nonlinear systems, particularly for Power Systems including Hybrid Power Sources. Offers a detailed description of system stability using state space energy conservation principle, and more.
1992-06-01
Anal. Appl. 102 (1984), 399-414. 43 On B-subgradients and applications Alejandro Jofre Departamento de Ingenieria Matemrtica, Universidad de Chile...Universitd de Provence51)2S Catania Italic 3, place Victor Hugo 13331 Marseille Cedex Steve Robinson Michel Th~raDepartment of Industrial Engineering
Energy Technology Data Exchange (ETDEWEB)
Barus, R. P. P., E-mail: rismawan.ppb@gmail.com [Engineering Physics, Faculty of Industrial Technology, Institut Teknologi Bandung, Jalan Ganesa 10 Bandung and Centre for Material and Technical Product, Jalan Sangkuriang No. 14 Bandung (Indonesia); Tjokronegoro, H. A.; Leksono, E. [Engineering Physics, Faculty of Industrial Technology, Institut Teknologi Bandung, Jalan Ganesa 10 Bandung (Indonesia); Ismunandar [Chemistry Study, Faculty of Mathematics and Science, Institut Teknologi Bandung, Jalan Ganesa 10 Bandung (Indonesia)
2014-09-25
Fuel cells are promising new energy conversion devices that are friendly to the environment. A set of control systems are required in order to operate a fuel cell based power plant system optimally. For the purpose of control system design, an accurate fuel cell stack model in describing the dynamics of the real system is needed. Currently, linear model are widely used for fuel cell stack control purposes, but it has limitations in narrow operation range. While nonlinear models lead to nonlinear control implemnetation whos more complex and hard computing. In this research, nonlinear cancellation technique will be used to transform a nonlinear model into a linear form while maintaining the nonlinear characteristics. The transformation is done by replacing the input of the original model by a certain virtual input that has nonlinear relationship with the original input. Then the equality of the two models is tested by running a series of simulation. Input variation of H2, O2 and H2O as well as disturbance input I (current load) are studied by simulation. The error of comparison between the proposed model and the original nonlinear model are less than 1 %. Thus we can conclude that nonlinear cancellation technique can be used to represent fuel cell nonlinear model in a simple linear form while maintaining the nonlinear characteristics and therefore retain the wide operation range.
International Nuclear Information System (INIS)
Barus, R. P. P.; Tjokronegoro, H. A.; Leksono, E.; Ismunandar
2014-01-01
Fuel cells are promising new energy conversion devices that are friendly to the environment. A set of control systems are required in order to operate a fuel cell based power plant system optimally. For the purpose of control system design, an accurate fuel cell stack model in describing the dynamics of the real system is needed. Currently, linear model are widely used for fuel cell stack control purposes, but it has limitations in narrow operation range. While nonlinear models lead to nonlinear control implemnetation whos more complex and hard computing. In this research, nonlinear cancellation technique will be used to transform a nonlinear model into a linear form while maintaining the nonlinear characteristics. The transformation is done by replacing the input of the original model by a certain virtual input that has nonlinear relationship with the original input. Then the equality of the two models is tested by running a series of simulation. Input variation of H2, O2 and H2O as well as disturbance input I (current load) are studied by simulation. The error of comparison between the proposed model and the original nonlinear model are less than 1 %. Thus we can conclude that nonlinear cancellation technique can be used to represent fuel cell nonlinear model in a simple linear form while maintaining the nonlinear characteristics and therefore retain the wide operation range
The Bayesian statistical decision theory applied to the optimization of generating set maintenance
International Nuclear Information System (INIS)
Procaccia, H.; Cordier, R.; Muller, S.
1994-11-01
The difficulty in RCM methodology is the allocation of a new periodicity of preventive maintenance on one equipment when a critical failure has been identified: until now this new allocation has been based on the engineer's judgment, and one must wait for a full cycle of feedback experience before to validate it. Statistical decision theory could be a more rational alternative for the optimization of preventive maintenance periodicity. This methodology has been applied to inspection and maintenance optimization of cylinders of diesel generator engines of 900 MW nuclear plants, and has shown that previous preventive maintenance periodicity can be extended. (authors). 8 refs., 5 figs
Social welfare and the Affordable Care Act: is it ever optimal to set aside comparative cost?
Mortimer, Duncan; Peacock, Stuart
2012-10-01
The creation of the Patient-Centered Outcomes Research Institute (PCORI) under the Affordable Care Act has set comparative effectiveness research (CER) at centre stage of US health care reform. Comparative cost analysis has remained marginalised and it now appears unlikely that the PCORI will require comparative cost data to be collected as an essential component of CER. In this paper, we review the literature to identify ethical and distributional objectives that might motivate calls to set priorities without regard to comparative cost. We then present argument and evidence to consider whether there is any plausible set of objectives and constraints against which priorities can be set without reference to comparative cost. We conclude that - to set aside comparative cost even after accounting for ethical and distributional constraints - would be truly to act as if money is no object. Copyright © 2012 Elsevier Ltd. All rights reserved.
Directory of Open Access Journals (Sweden)
Toly Chen
2012-01-01
Full Text Available A nonlinear programming and artificial neural network approach is presented in this study to optimize the performance of a job dispatching rule in a wafer fabrication factory. The proposed methodology fuses two existing rules and constructs a nonlinear programming model to choose the best values of parameters in the two rules by dynamically maximizing the standard deviation of the slack, which has been shown to benefit scheduling performance by several studies. In addition, a more effective approach is also applied to estimate the remaining cycle time of a job, which is empirically shown to be conducive to the scheduling performance. The efficacy of the proposed methodology was validated with a simulated case; evidence was found to support its effectiveness. We also suggested several directions in which it can be exploited in the future.
Tauber, Sean; Navarro, Daniel J; Perfors, Amy; Steyvers, Mark
2017-07-01
Recent debates in the psychological literature have raised questions about the assumptions that underpin Bayesian models of cognition and what inferences they license about human cognition. In this paper we revisit this topic, arguing that there are 2 qualitatively different ways in which a Bayesian model could be constructed. The most common approach uses a Bayesian model as a normative standard upon which to license a claim about optimality. In the alternative approach, a descriptive Bayesian model need not correspond to any claim that the underlying cognition is optimal or rational, and is used solely as a tool for instantiating a substantive psychological theory. We present 3 case studies in which these 2 perspectives lead to different computational models and license different conclusions about human cognition. We demonstrate how the descriptive Bayesian approach can be used to answer different sorts of questions than the optimal approach, especially when combined with principled tools for model evaluation and model selection. More generally we argue for the importance of making a clear distinction between the 2 perspectives. Considerable confusion results when descriptive models and optimal models are conflated, and if Bayesians are to avoid contributing to this confusion it is important to avoid making normative claims when none are intended. (PsycINFO Database Record (c) 2017 APA, all rights reserved).
2016-10-13
and addressedwhen the qubit is usedwithin a fault-tolerant quantum computation scheme. 1. Introduction One of themain challenges in the physical...supplied in the supplementarymaterial. Additionally, we have supplied the datafiles constructed from the experiments, alongwith the Python notebook used to...New J. Phys. 18 (2016) 103018 doi:10.1088/1367-2630/18/10/103018 PAPER Optimization of a solid-state electron spin qubit using gate set tomography
Optimization of the size and shape of the set-in nozzle for a PWR reactor pressure vessel
Energy Technology Data Exchange (ETDEWEB)
Murtaza, Usman Tariq, E-mail: maniiut@yahoo.com; Javed Hyder, M., E-mail: hyder@pieas.edu.pk
2015-04-01
Highlights: • The size and shape of the set-in nozzle of the RPV have been optimized. • The optimized nozzle ensure the reduction of the mass around 198 kg per nozzle. • The mass of the RPV should be minimized for better fracture toughness. - Abstract: The objective of this research work is to optimize the size and shape of the set-in nozzle for a typical reactor pressure vessel (RPV) of a 300 MW pressurized water reactor. The analysis was performed by optimizing the four design variables which control the size and shape of the nozzle. These variables are inner radius of the nozzle, thickness of the nozzle, taper angle at the nozzle-cylinder intersection, and the point where taper of the nozzle starts from. It is concluded that the optimum design of the nozzle is the one that minimizes the two conflicting state variables, i.e., the stress intensity (Tresca yield criterion) and the mass of the RPV.
Directory of Open Access Journals (Sweden)
A. P. Karpenko
2016-01-01
Full Text Available We consider a class of algorithms for multi-objective optimization - Pareto-approximation algorithms, which suppose a preliminary building of finite-dimensional approximation of a Pareto set, thereby also a Pareto front of the problem. The article gives an overview of population and non-population algorithms of the Pareto-approximation, identifies their strengths and weaknesses, and presents a canonical algorithm "predator-prey", showing its shortcomings. We offer a number of modifications of the canonical algorithm "predator-prey" with the aim to overcome the drawbacks of this algorithm, present the results of a broad study of the efficiency of these modifications of the algorithm. The peculiarity of the study is the use of the quality indicators of the Pareto-approximation, which previous publications have not used. In addition, we present the results of the meta-optimization of the modified algorithm, i.e. determining the optimal values of some free parameters of the algorithm. The study of efficiency of the modified algorithm "predator-prey" has shown that the proposed modifications allow us to improve the following indicators of the basic algorithm: cardinality of a set of the archive solutions, uniformity of archive solutions, and computation time. By and large, the research results have shown that the modified and meta-optimized algorithm enables achieving exactly the same approximation as the basic algorithm, but with the number of preys being one order less. Computational costs are proportionally reduced.
Optimal Switching Control of Burner Setting for a Compact Marine Boiler Design
DEFF Research Database (Denmark)
Solberg, Brian; Andersen, Palle; Maciejowski, Jan M.
2010-01-01
This paper discusses optimal control strategies for switching between different burner modes in a novel compact marine boiler design. The ideal behaviour is defined in a performance index the minimisation of which defines an ideal trade-off between deviations in boiler pressure and water level...... approach is based on a generalisation of hysteresis control. The strategies are verified on a simulation model of the compact marine boiler for control of low/high burner load switches. ...
The FERMI (at) Elettra Technical Optimization Study: Preliminary Parameter Set and Initial Studies
International Nuclear Information System (INIS)
Byrd, John; Corlett, John; Doolittle, Larry; Fawley, William; Lidia, Steven; Penn, Gregory; Ratti, Alex; Staples, John; Wilcox, Russell; Wurtele, Jonathan; Zholents, Alexander
2005-01-01
The goal of the FERMI (at) Elettra Technical Optimization Study is to produce a machine design and layout consistent with user needs for radiation in the approximate ranges 100 nm to 40 nm, and 40 nm to 10 nm, using seeded FEL's. The Study will involve collaboration between Italian and US physicists and engineers, and will form the basis for the engineering design and the cost estimation
SETTING OF TASK OF OPTIMIZATION OF THE ACTIVITY OF A MACHINE-BUILDING CLUSTER COMPANY
Directory of Open Access Journals (Sweden)
A. V. Romanenko
2014-01-01
Full Text Available The work is dedicated to the development of methodological approaches to the management of machine-building enterprise on the basis of cost reduction, optimization of the portfolio of orders and capacity utilization in the process of operational management. Evaluation of economic efficiency of such economic entities of the real sector of the economy is determined, including the timing of orders, which depend on the issues of building a production facility, maintenance of fixed assets and maintain them at a given level. Formulated key components of economic-mathematical model of industrial activity and is defined as the optimization criterion. As proposed formula accumulating profits due to production capacity and technology to produce products current direct variable costs, the amount of property tax and expenses appearing as a result of manifestations of variance when performing replacement of production tasks for a single period of time. The main component of the optimization of the production activity of the enterprise on the basis of this criterion is the vector of direct variable costs. It depends on the number of types of products in the current portfolio of orders, production schedules production, the normative time for the release of a particular product available Fund time efficient production positions, the current valuation for certain groups of technological operations and the current priority of operations for the degree of readiness performed internal orders. Modeling of industrial activity based on the proposed provisions would allow the enterprises of machine-building cluster, active innovation, improve the efficient use of available production resources by optimizing current operations at the high uncertainty of the magnitude of the demand planning and carrying out maintenance and routine repairs.
Fast-Solving Quasi-Optimal LS-S3VM Based on an Extended Candidate Set.
Ma, Yuefeng; Liang, Xun; Kwok, James T; Li, Jianping; Zhou, Xiaoping; Zhang, Haiyan
2018-04-01
The semisupervised least squares support vector machine (LS-S 3 VM) is an important enhancement of least squares support vector machines in semisupervised learning. Given that most data collected from the real world are without labels, semisupervised approaches are more applicable than standard supervised approaches. Although a few training methods for LS-S 3 VM exist, the problem of deriving the optimal decision hyperplane efficiently and effectually has not been solved. In this paper, a fully weighted model of LS-S 3 VM is proposed, and a simple integer programming (IP) model is introduced through an equivalent transformation to solve the model. Based on the distances between the unlabeled data and the decision hyperplane, a new indicator is designed to represent the possibility that the label of an unlabeled datum should be reversed in each iteration during training. Using the indicator, we construct an extended candidate set consisting of the indices of unlabeled data with high possibilities, which integrates more information from unlabeled data. Our algorithm is degenerated into a special scenario of the previous algorithm when the extended candidate set is reduced into a set with only one element. Two strategies are utilized to determine the descent directions based on the extended candidate set. Furthermore, we developed a novel method for locating a good starting point based on the properties of the equivalent IP model. Combined with the extended candidate set and the carefully computed starting point, a fast algorithm to solve LS-S 3 VM quasi-optimally is proposed. The choice of quasi-optimal solutions results in low computational cost and avoidance of overfitting. Experiments show that our algorithm equipped with the two designed strategies is more effective than other algorithms in at least one of the following three aspects: 1) computational complexity; 2) generalization ability; and 3) flexibility. However, our algorithm and other algorithms have
International Nuclear Information System (INIS)
Won, Byung Hee; Kim, Kyung O; Kim, Jong Kyung; Kim, Soon Young
2012-01-01
The Core Protection Calculator System (CPCS) is an automated device which is adopted to inspect the safety parameters such as Departure from Nuclear Boiling Ratio (DNBR) and Local Power Density (LPD) during normal operation. One function of the CPCS is to predict the axial power distributions using function sets in cubic spline method. Another function of that is to impose penalty when the estimated distribution by the spline method disagrees with embedded data in CPCS (i.e., over 8%). In conventional CPCS, restricted function sets are used to synthesize axial power shape, whereby it occasionally can draw a disagreement between synthesized data and the embedded data. For this reason, the study on improvement for power distributions synthesis in CPCS has been conducted in many countries. In this study, many function sets (more than 18,000 types) differing from the conventional ones were evaluated in each power shape. Matlab code was used for calculating/arranging the numerous cases of function sets. Their synthesis performance was also evaluated through error between conventional data and consequences calculated by new function sets
Alcaraz, Kassandra I.; Kreuter, Matthew W.; Bryan, Rebecca P.
2009-01-01
Objective Rarely have Geographic Information Systems (GIS) been used to inform community-based outreach and intervention planning. This study sought to identify community settings most likely to reach individuals from geographically localized areas. Method An observational study conducted in an urban city in Missouri during 2003–2007 placed computerized breast cancer education kiosks in seven types of community settings: beauty salons, churches, health fairs, neighborhood health centers, Laundromats, public libraries and social service agencies. We used GIS to measure distance between kiosk users’ (n=7,297) home ZIP codes and the location where they used the kiosk. Mean distances were compared across settings. Results Mean distance between individuals’ home ZIP codes and the location where they used the kiosk varied significantly (pLaundromats (2.3 miles) and public libraries (2.8 miles) and greatest among kiosk users at health fairs (7.6 miles). Conclusion Some community settings are more likely than others to reach highly localized populations. A better understanding of how and where to reach specific populations can complement the progress already being made in identifying populations at increased disease risk. PMID:19422844
Training a whole-book LSTM-based recognizer with an optimal training set
Soheili, Mohammad Reza; Yousefi, Mohammad Reza; Kabir, Ehsanollah; Stricker, Didier
2018-04-01
Despite the recent progress in OCR technologies, whole-book recognition, is still a challenging task, in particular in case of old and historical books, that the unknown font faces or low quality of paper and print contributes to the challenge. Therefore, pre-trained recognizers and generic methods do not usually perform up to required standards, and usually the performance degrades for larger scale recognition tasks, such as of a book. Such reportedly low error-rate methods turn out to require a great deal of manual correction. Generally, such methodologies do not make effective use of concepts such redundancy in whole-book recognition. In this work, we propose to train Long Short Term Memory (LSTM) networks on a minimal training set obtained from the book to be recognized. We show that clustering all the sub-words in the book, and using the sub-word cluster centers as the training set for the LSTM network, we can train models that outperform any identical network that is trained with randomly selected pages of the book. In our experiments, we also show that although the sub-word cluster centers are equivalent to about 8 pages of text for a 101- page book, a LSTM network trained on such a set performs competitively compared to an identical network that is trained on a set of 60 randomly selected pages of the book.
Setting Optimal Bounds on Risk in Asset Allocation - a Convex Program
Directory of Open Access Journals (Sweden)
James E. Falk
2002-10-01
Full Text Available The 'Portfolio Selection Problem' is traditionally viewed as selecting a mix of investment opportunities that maximizes the expected return subject to a bound on risk. However, in reality, portfolios are made up of a few 'asset classes' that consist of similar opportunities. The asset classes are managed by individual `sub-managers', under guidelines set by an overall portfolio manager. Once a benchmark (the `strategic' allocation has been set, an overall manager may choose to allow the sub-managers some latitude in which opportunities make up the classes. He may choose some overall bound on risk (as measured by the variance and wish to set bounds that constrain the submanagers. Mathematically we show that the problem is equivalent to finding a hyper-rectangle of maximal volume within an ellipsoid. It is a convex program, albeit with potentially a large number of constraints. We suggest a cutting plane algorithm to solve the problem and include computational results on a set of randomly generated problems as well as a real-world problem taken from the literature.
An Optimized, Grid Independent, Narrow Band Data Structure for High Resolution Level Sets
DEFF Research Database (Denmark)
Nielsen, Michael Bang; Museth, Ken
2004-01-01
enforced by the convex boundaries of an underlying cartesian computational grid. Here we present a novel very memory efficient narrow band data structure, dubbed the Sparse Grid, that enables the representation of grid independent high resolution level sets. The key features our new data structure are...
International Nuclear Information System (INIS)
Jiang, He; Dong, Yao
2016-01-01
Highlights: • Eclat data mining algorithm is used to determine the possible predictors. • Support vector machine is converted into a ridge regularization problem. • Hard penalty selects the number of radial basis functions to simply the structure. • Glowworm swarm optimization is utilized to determine the optimal parameters. - Abstract: For a portion of the power which is generated by grid connected photovoltaic installations, an effective solar irradiation forecasting approach must be crucial to ensure the quality and the security of power grid. This paper develops and investigates a novel model to forecast 30 daily global solar radiation at four given locations of the United States. Eclat data mining algorithm is first presented to discover association rules between solar radiation and several meteorological factors laying a theoretical foundation for these correlative factors as input vectors. An effective and innovative intelligent optimization model based on nonlinear support vector machine and hard penalty function is proposed to forecast solar radiation by converting support vector machine into a regularization problem with ridge penalty, adding a hard penalty function to select the number of radial basis functions, and using glowworm swarm optimization algorithm to determine the optimal parameters of the model. In order to illustrate our validity of the proposed method, the datasets at four sites of the United States are split to into training data and test data, separately. The experiment results reveal that the proposed model delivers the best forecasting performances comparing with other competitors.
Role of pharmacists in optimizing the use of anticancer drugs in the clinical setting
Directory of Open Access Journals (Sweden)
Ma CSJ
2014-02-01
Full Text Available Carolyn SJ Ma Department of Pharmacy Practice, Daniel K. Inouye College of Pharmacy, University of Hawaii at Hilo, Honolulu, HI, USA Abstract: Oncology pharmacists, also known as oncology pharmacy specialists (OPSs have specialized knowledge of anticancer medications and their role in cancer. As essential member of the interdisciplinary team, OPSs optimize the benefits of drug therapy, help to minimize toxicities and work with patients on supportive care issues. The OPSs expanded role as experts in drug therapy extends to seven major key elements of medication management that include: selection, procurement, storage, preparation/dispensing, prescribing/dosing/transcribing, administration and monitoring/evaluation/education. As front line caregivers in hospital, ambulatory care, long-term care facilities, and community specialty pharmacies, the OPS also helps patients in areas of supportive care including nausea and vomiting, hematologic support, nutrition and infection control. This role helps the patient in the recovery phase between treatment cycles and adherence to chemotherapy treatment schedules essential for optimal treatment and outcome. Keywords: oncology pharmacist, oncology pharmacy specialist, medication management, chemotherapy
Optimizing the Nutritional Support of Adult Patients in the Setting of Cirrhosis.
Perumpail, Brandon J; Li, Andrew A; Cholankeril, George; Kumari, Radhika; Ahmed, Aijaz
2017-10-13
The aim of this work is to develop a pragmatic approach in the assessment and management strategies of patients with cirrhosis in order to optimize the outcomes in this patient population. A systematic review of literature was conducted through 8 July 2017 on the PubMed Database looking for key terms, such as malnutrition, nutrition, assessment, treatment, and cirrhosis. Articles and studies looking at associations between nutrition and cirrhosis were reviewed. An assessment of malnutrition should be conducted in two stages: the first, to identify patients at risk for malnutrition based on the severity of liver disease, and the second, to perform a complete multidisciplinary nutritional evaluation of these patients. Optimal management of malnutrition should focus on meeting recommended daily goals for caloric intake and inclusion of various nutrients in the diet. The nutritional goals should be pursued by encouraging and increasing oral intake or using other measures, such as oral supplementation, enteral nutrition, or parenteral nutrition. Although these strategies to improve nutritional support have been well established, current literature on the topic is limited in scope. Further research should be implemented to test if this enhanced approach is effective.
Optimizing the Nutritional Support of Adult Patients in the Setting of Cirrhosis
Directory of Open Access Journals (Sweden)
Brandon J. Perumpail
2017-10-01
Full Text Available Aim: The aim of this work is to develop a pragmatic approach in the assessment and management strategies of patients with cirrhosis in order to optimize the outcomes in this patient population. Method: A systematic review of literature was conducted through 8 July 2017 on the PubMed Database looking for key terms, such as malnutrition, nutrition, assessment, treatment, and cirrhosis. Articles and studies looking at associations between nutrition and cirrhosis were reviewed. Results: An assessment of malnutrition should be conducted in two stages: the first, to identify patients at risk for malnutrition based on the severity of liver disease, and the second, to perform a complete multidisciplinary nutritional evaluation of these patients. Optimal management of malnutrition should focus on meeting recommended daily goals for caloric intake and inclusion of various nutrients in the diet. The nutritional goals should be pursued by encouraging and increasing oral intake or using other measures, such as oral supplementation, enteral nutrition, or parenteral nutrition. Conclusions: Although these strategies to improve nutritional support have been well established, current literature on the topic is limited in scope. Further research should be implemented to test if this enhanced approach is effective.
Nonlinear Approaches in Engineering Applications
Jazar, Reza
2012-01-01
Nonlinear Approaches in Engineering Applications focuses on nonlinear phenomena that are common in the engineering field. The nonlinear approaches described in this book provide a sound theoretical base and practical tools to design and analyze engineering systems with high efficiency and accuracy and with less energy and downtime. Presented here are nonlinear approaches in areas such as dynamic systems, optimal control and approaches in nonlinear dynamics and acoustics. Coverage encompasses a wide range of applications and fields including mathematical modeling and nonlinear behavior as applied to microresonators, nanotechnologies, nonlinear behavior in soil erosion,nonlinear population dynamics, and optimization in reducing vibration and noise as well as vibration in triple-walled carbon nanotubes. This book also: Provides a complete introduction to nonlinear behavior of systems and the advantages of nonlinearity as a tool for solving engineering problems Includes applications and examples drawn from the el...
Directory of Open Access Journals (Sweden)
Amir Hossein Karimi
2017-08-01
Full Text Available In this article, the main purpose is nonlinear analysis of the cyclic behavior of the masonry shear walls including brickwork setting using finite element method. Three different brickwork-settings including running bond style, herringbone style and Zarbi style (herreh style were investigated. To this end, the walls (in dimension of 195×1500×1720 mm were tested in the laboratory and then were simulated using macro modeling method by Abaqus software, and their hysteretic curves was drawn. The concrete damaged plasticity criteria in the Abaqus software is a model used in this research.In this method, the main failure mechanisms of fracture are cracking in tension and crushing in compression. The macro modeling method was used for numerical assessment of the masonry walls. After numerical modeling and drawing hysteretic curves and contrasting them with laboratory results, it was proven that the concrete damaged plasticity model, which is behavioral model for simulating concrete material, can be used for modeling masonry materials under seismic loading. However, this model cannot be used to simulate pinching effect in hysteretic curve drawn from seismic loading. The envelope curve resulted from the numerical analysis of all three brickwork layouts had a good agreement with the results of the laboratory tests, but in Hysteretic curve of Herringbone style and Zarbi style the pinching effect did not match experimental results
The Role of eHealth in Optimizing Preventive Care in the Primary Care Setting.
Carey, Mariko; Noble, Natasha; Mansfield, Elise; Waller, Amy; Henskens, Frans; Sanson-Fisher, Rob
2015-05-22
Modifiable health risk behaviors such as smoking, overweight and obesity, risky alcohol consumption, physical inactivity, and poor nutrition contribute to a substantial proportion of the world's morbidity and mortality burden. General practitioners (GPs) play a key role in identifying and managing modifiable health risk behaviors. However, these are often underdetected and undermanaged in the primary care setting. We describe the potential of eHealth to help patients and GPs to overcome some of the barriers to managing health risk behaviors. In particular, we discuss (1) the role of eHealth in facilitating routine collection of patient-reported data on lifestyle risk factors, and (2) the role of eHealth in improving clinical management of identified risk factors through provision of tailored feedback, point-of-care reminders, tailored educational materials, and referral to online self-management programs. Strategies to harness the capacity of the eHealth medium, including the use of dynamic features and tailoring to help end users engage with, understand, and apply information need to be considered and maximized. Finally, the potential challenges in implementing eHealth solutions in the primary care setting are discussed. In conclusion, there is significant potential for innovative eHealth solutions to make a contribution to improving preventive care in the primary care setting. However, attention to issues such as data security and designing eHealth interfaces that maximize engagement from end users will be important to moving this field forward.
Using a Robust Design Approach to Optimize Chair Set-up in Wheelchair Sport
Directory of Open Access Journals (Sweden)
David S. Haydon
2018-02-01
Full Text Available Optimisation of wheelchairs for court sports is currently a difficult and time-consuming process due to the broad range of impairments across athletes, difficulties in monitoring on-court performance, and the trade-off set-up that parameters have on key performance variables. A robust design approach to this problem can potentially reduce the amount of testing required, and therefore allow for individual on-court assessments. This study used orthogonal design with four set-up factors (seat height, depth, and angle, as well as tyre pressure at three levels (current, decreased, and increased for three elite wheelchair rugby players. Each player performed two maximal effort sprints from a stationary position in nine different set-ups, with this allowing for detailed analysis of each factor and level. Whilst statistical significance is difficult to obtain due to the small sample size, meaningful difference results aligning with previous research findings were identified and provide support for the use of this approach.
Wright, Dannen D; Wright, Alex J; Boulter, Tyler D; Bernhisel, Ashlie A; Stagg, Brian C; Zaugg, Brian; Pettey, Jeff H; Ha, Larry; Ta, Brian T; Olson, Randall J
2017-09-01
To determine the optimum bottle height, vacuum, aspiration rate, and power settings in the peristaltic mode of the Whitestar Signature Pro machine with Ellips FX tip action (transversal). John A. Moran Eye Center Laboratories, University of Utah, Salt Lake City, Utah, USA. Experimental study. Porcine lens nuclei were hardened with formalin and cut into 2.0 mm cubes. Lens cubes were emulsified using transversal and fragment removal time (efficiency), and fragment bounces off the tip (chatter) were measured to determine optimum aspiration rate, bottle height, vacuum, and power settings in the peristaltic mode. Efficiency increased in a linear fashion with increasing bottle height and vacuum. The most efficient aspiration rate was 50 mL/min, with 60 mL/min statistically similar. Increasing power increased efficiency up to 90% with increased chatter at 100%. The most efficient values for the settings tested were bottle height at 100 cm, vacuum at 600 mm Hg, aspiration rate of 50 or 60 mL/min, and power at 90%. Copyright © 2017 ASCRS and ESCRS. Published by Elsevier Inc. All rights reserved.
A fast nonlinear conjugate gradient based method for 3D frictional contact problems
Zhao, J.; Vollebregt, E.A.H.; Oosterlee, C.W.
2014-01-01
This paper presents a fast numerical solver for a nonlinear constrained optimization problem, arising from a 3D frictional contact problem. It incorporates an active set strategy with a nonlinear conjugate gradient method. One novelty is to consider the tractions of each slip element in a polar
A fast nonlinear conjugate gradient based method for 3D concentrated frictional contact problems
J. Zhao (Jing); E.A.H. Vollebregt (Edwin); C.W. Oosterlee (Cornelis)
2015-01-01
htmlabstractThis paper presents a fast numerical solver for a nonlinear constrained optimization problem, arising from 3D concentrated frictional shift and rolling contact problems with dry Coulomb friction. The solver combines an active set strategy with a nonlinear conjugate gradient method. One
Xiao, Xun; Geyer, Veikko F; Bowne-Anderson, Hugo; Howard, Jonathon; Sbalzarini, Ivo F
2016-08-01
Biological filaments, such as actin filaments, microtubules, and cilia, are often imaged using different light-microscopy techniques. Reconstructing the filament curve from the acquired images constitutes the filament segmentation problem. Since filaments have lower dimensionality than the image itself, there is an inherent trade-off between tracing the filament with sub-pixel accuracy and avoiding noise artifacts. Here, we present a globally optimal filament segmentation method based on B-spline vector level-sets and a generalized linear model for the pixel intensity statistics. We show that the resulting optimization problem is convex and can hence be solved with global optimality. We introduce a simple and efficient algorithm to compute such optimal filament segmentations, and provide an open-source implementation as an ImageJ/Fiji plugin. We further derive an information-theoretic lower bound on the filament segmentation error, quantifying how well an algorithm could possibly do given the information in the image. We show that our algorithm asymptotically reaches this bound in the spline coefficients. We validate our method in comprehensive benchmarks, compare with other methods, and show applications from fluorescence, phase-contrast, and dark-field microscopy. Copyright © 2016 The Authors. Published by Elsevier B.V. All rights reserved.
Directory of Open Access Journals (Sweden)
Qian Xie
2016-07-01
Full Text Available This paper pays attention to magnetic flux linkage optimization of a direct-driven surface-mounted permanent magnet synchronous generator (D-SPMSG. A new compact representation of the D-SPMSG nonlinear dynamic differential equations to reduce system parameters is established. Furthermore, the nonlinear dynamic characteristics of new D-SPMSG equations in the process of varying magnetic flux linkage are considered, which are illustrated by Lyapunov exponent spectrums, phase orbits, Poincaré maps, time waveforms and bifurcation diagrams, and the magnetic flux linkage stable region of D-SPMSG is acquired concurrently. Based on the above modeling and analyses, a novel method of magnetic flux linkage optimization is presented. In addition, a 2 MW D-SPMSG 2D/3D model is designed by ANSYS software according to the practical design requirements. Finally, five cases of D-SPMSG models with different magnetic flux linkages are simulated by using the finite element analysis (FEA method. The nephograms of magnetic flux density are agreement with theoretical analysis, which both confirm the correctness and effectiveness of the proposed approach.
International Nuclear Information System (INIS)
Huang, Yanjun; Khajepour, Amir; Ding, Haitao; Bagheri, Farshid; Bahrami, Majid
2017-01-01
Highlights: • A novel two-layer energy-saving controller for automotive A/C-R system is developed. • A set-point optimizer at the outer loop is designed based on the steady state model. • A sliding mode controller in the inner loop is built. • Extensively experiments studies show that about 9% energy can be saving by this controller. - Abstract: This paper presents an energy-saving controller for automotive air-conditioning/refrigeration (A/C-R) systems. With their extensive application in homes, industry, and vehicles, A/C-R systems are consuming considerable amounts of energy. The proposed controller consists of two different time-scale layers. The outer or the slow time-scale layer called a set-point optimizer is used to find the set points related to energy efficiency by using the steady state model; whereas, the inner or the fast time-scale layer is used to track the obtained set points. In the inner loop, thanks to its robustness, a sliding mode controller (SMC) is utilized to track the set point of the cargo temperature. The currently used on/off controller is presented and employed as a basis for comparison to the proposed controller. More importantly, the real experimental results under several disturbed scenarios are analysed to demonstrate how the proposed controller can improve performance while reducing the energy consumption by 9% comparing with the on/off controller. The controller is suitable for any type of A/C-R system even though it is applied to an automotive A/C-R system in this paper.
Community-based interventions to optimize early childhood development in low resource settings.
Maulik, P K; Darmstadt, G L
2009-08-01
Interventions targeting the early childhood period (0 to 3 years) help to improve neuro-cognitive functioning throughout life. Some of the more low cost, low resource-intensive community practices for this age-group are play, reading, music and tactile stimulation. This research was conducted to summarize the evidence regarding the effectiveness of such strategies on child development, with particular focus on techniques that may be transferable to developing countries and to children at risk of developing secondary impairments. PubMed, PsycInfo, Embase, ERIC, CINAHL and Cochrane were searched for studies involving the above strategies for early intervention. Reference lists of these studies were scanned and other studies were incorporated based on snow-balling. Overall, 76 articles corresponding to 53 studies, 24 of which were randomized controlled trials, were identified. Sixteen of those studies were from low- and middle-income countries. Play and reading were the two commonest interventions and showed positive impact on intellectual development of the child. Music was evaluated primarily in intensive care settings. Kangaroo Mother Care, and to a lesser extent massage, also showed beneficial effects. Improvement in parent-child interaction was common to all the interventions. Play and reading were effective interventions for early childhood interventions in low- and middle-income countries. More research is needed to judge the effectiveness of music. Kangaroo Mother Care is effective for low birth weight babies in resource poor settings, but further research is needed in community settings. Massage is useful, but needs more rigorous research prior to being advocated for community-level interventions.
Foadi, James; Aller, Pierre; Alguel, Yilmaz; Cameron, Alex; Axford, Danny; Owen, Robin L.; Armour, Wes; Waterman, David G.; Iwata, So; Evans, Gwyndaf
2013-01-01
The availability of intense microbeam macromolecular crystallography beamlines at third-generation synchrotron sources has enabled data collection and structure solution from microcrystals of sets from many crystals of the same protein structure. The associated analysis and merging of multi-crystal data is currently a manual and time-consuming step. Here, a computer program, BLEND, that has been written to assist with and automate many of the steps in this process is described. It is demonstrated how BLEND has successfully been used in the solution of a novel membrane protein. PMID:23897484
Deptuła, A.; Partyka, M. A.
2014-08-01
The method of minimization of complex partial multi-valued logical functions determines the degree of importance of construction and exploitation parameters playing the role of logical decision variables. Logical functions are taken into consideration in the issues of modelling machine sets. In multi-valued logical functions with weighting products, it is possible to use a modified Quine - McCluskey algorithm of multi-valued functions minimization. Taking into account weighting coefficients in the logical tree minimization reflects a physical model of the object being analysed much better
Crop Evaluation System Optimization: Attribute Weights Determination Based on Rough Sets Theory
Directory of Open Access Journals (Sweden)
Ruihong Wang
2017-01-01
Full Text Available The present study is mainly a continuation of our previous study, which is about a crop evaluation system development that is based on grey relational analysis. In that system, the attribute weight determination affects the evaluation result directly. Attribute weight is usually ascertained by decision-makers experience knowledge. In this paper, we utilize rough sets theory to calculate attribute significance and then combine it with weight given by decision-maker. This method is a comprehensive consideration of subjective experience knowledge and objective situation; thus it can acquire much more ideal results. Finally, based on this method, we improve the system based on ASP.NET technology.
OPTIMIZATION-BASED APPROACH TO TILING OF FINITE AREAS WITH ARBITRARY SETS OF WANG TILES
Directory of Open Access Journals (Sweden)
Marek Tyburec
2017-11-01
Full Text Available Wang tiles proved to be a convenient tool for the design of aperiodic tilings in computer graphics and in materials engineering. While there are several algorithms for generation of finite-sized tilings, they exploit the specific structure of individual tile sets, which prevents their general usage. In this contribution, we reformulate the NP-complete tiling generation problem as a binary linear program, together with its linear and semidefinite relaxations suitable for the branch and bound method. Finally, we assess the performance of the established formulations on generations of several aperiodic tilings reported in the literature, and conclude that the linear relaxation is better suited for the problem.
Directory of Open Access Journals (Sweden)
V. A. Baturin
2017-03-01
Full Text Available An optimal control problem for discrete systems is considered. A method of successive improvements along with its modernization based on the expansion of the main structures of the core algorithm about the parameter is suggested. The idea of the method is based on local approximation of attainability set, which is described by the zeros of the Bellman function in the special problem of optimal control. The essence of the problem is as follows: from the end point of the phase is required to find a path that minimizes functional deviations of the norm from the initial state. If the initial point belongs to the attainability set of the original controlled system, the value of the Bellman function equal to zero, otherwise the value of the Bellman function is greater than zero. For this special task Bellman equation is considered. The support approximation and Bellman equation are selected. The Bellman function is approximated by quadratic terms. Along the allowable trajectory, this approximation gives nothing, because Bellman function and its expansion coefficients are zero. We used a special trick: an additional variable is introduced, which characterizes the degree of deviation of the system from the initial state, thus it is obtained expanded original chain. For the new variable initial nonzero conditions is selected, thus obtained trajectory is lying outside attainability set and relevant Bellman function is greater than zero, which allows it to hold a non-trivial approximation. As a result of these procedures algorithms of successive improvements is designed. Conditions for relaxation algorithms and conditions for the necessary conditions of optimality are also obtained.
Radenkovic, Dina; Kobayashi, Hisataka; Remsey-Semmelweis, Ernö; Seifalian, Alexander M
2016-08-01
Breast cancer is the most common cancer in the world. Sentinel lymph node (SLN) biopsy is used for staging of axillary lymph nodes. Organic dyes and radiocolloid are currently used for SLN mapping, but expose patients to ionizing radiation, are unstable during surgery and cause local tissue damage. Quantum dots (QD) could be used for SLN mapping without the need for biopsy. Surgical resection of the primary tumor is the optimal treatment for early-diagnosed breast cancer, but due to difficulties in defining tumor margins, cancer cells often remain leading to reoccurrences. Functionalized QD could be used for image-guided tumor resection to allow visualization of cancer cells. Near Infrared QD are photostable and have improved deep tissue penetration. Slow elimination of QD raises concerns of potential accumulation. Nevertheless, promising findings with cadmium-free QD in recent in vivo studies and first in-human trial suggest huge potential for cancer diagnostic and therapy. Copyright © 2016 Elsevier Inc. All rights reserved.
Directory of Open Access Journals (Sweden)
Rica Gonen
2013-11-01
Full Text Available We analyze the space of deterministic, dominant-strategy incentive compatible, individually rational and Pareto optimal combinatorial auctions. We examine a model with multidimensional types, nonidentical items, private values and quasilinear preferences for the players with one relaxation; the players are subject to publicly-known budget constraints. We show that the space includes dictatorial mechanisms and that if dictatorial mechanisms are ruled out by a natural anonymity property, then an impossibility of design is revealed. The same impossibility naturally extends to other abstract mechanisms with an arbitrary outcome set if one maintains the original assumptions of players with quasilinear utilities, public budgets and nonnegative prices.
Optimal set of agri-environmental indicators for the agricultural sector of Czech Republic
Directory of Open Access Journals (Sweden)
Jiří Hřebíček
2013-01-01
Full Text Available Current trends of agri-environmental indicators evaluation (i.e., the measurement of environmental performance and farm reporting are discussed in the paper focusing on the agriculture sector. From the perspective of agricultural policy, there are two broad decisions to make: which indicators to recommend and promote to farmers, and which indicators to collect to assist in agriculture policy-making. We introduce several general approaches for indicators to collect to assist in policy-making (European Union, Organization for Economic Cooperation and Development and Food and Agriculture Organization of the United Nations in the first part of our paper and given the differences in decision-making problems faced by these sets of decision makers. We continue in the second part of the paper with a proposal of indicators to recommend and promote to farmers in the Czech Republic.
International Nuclear Information System (INIS)
Foadi, James; Aller, Pierre; Alguel, Yilmaz; Cameron, Alex; Axford, Danny; Owen, Robin L.; Armour, Wes; Waterman, David G.; Iwata, So; Evans, Gwyndaf
2013-01-01
A systematic approach to the scaling and merging of data from multiple crystals in macromolecular crystallography is introduced and explained. The availability of intense microbeam macromolecular crystallography beamlines at third-generation synchrotron sources has enabled data collection and structure solution from microcrystals of <10 µm in size. The increased likelihood of severe radiation damage where microcrystals or particularly sensitive crystals are used forces crystallographers to acquire large numbers of data sets from many crystals of the same protein structure. The associated analysis and merging of multi-crystal data is currently a manual and time-consuming step. Here, a computer program, BLEND, that has been written to assist with and automate many of the steps in this process is described. It is demonstrated how BLEND has successfully been used in the solution of a novel membrane protein
Energy Technology Data Exchange (ETDEWEB)
Foadi, James [Diamond Light Source, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0DE (United Kingdom); Imperial College, London SW7 2AZ (United Kingdom); Aller, Pierre [Diamond Light Source, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0DE (United Kingdom); Alguel, Yilmaz; Cameron, Alex [Imperial College, London SW7 2AZ (United Kingdom); Axford, Danny; Owen, Robin L. [Diamond Light Source, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0DE (United Kingdom); Armour, Wes [Oxford e-Research Centre (OeRC), Keble Road, Oxford OX1 3QG (United Kingdom); Waterman, David G. [Research Complex at Harwell (RCaH), Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0FA (United Kingdom); Iwata, So [Diamond Light Source, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0DE (United Kingdom); Imperial College, London SW7 2AZ (United Kingdom); Evans, Gwyndaf, E-mail: gwyndaf.evans@diamond.ac.uk [Diamond Light Source, Harwell Science and Innovation Campus, Didcot, Oxfordshire OX11 0DE (United Kingdom)
2013-08-01
A systematic approach to the scaling and merging of data from multiple crystals in macromolecular crystallography is introduced and explained. The availability of intense microbeam macromolecular crystallography beamlines at third-generation synchrotron sources has enabled data collection and structure solution from microcrystals of <10 µm in size. The increased likelihood of severe radiation damage where microcrystals or particularly sensitive crystals are used forces crystallographers to acquire large numbers of data sets from many crystals of the same protein structure. The associated analysis and merging of multi-crystal data is currently a manual and time-consuming step. Here, a computer program, BLEND, that has been written to assist with and automate many of the steps in this process is described. It is demonstrated how BLEND has successfully been used in the solution of a novel membrane protein.
Inference of a Nonlinear Stochastic Model of the Cardiorespiratory Interaction
Smelyanskiy, V. N.; Luchinsky, D. G.; Stefanovska, A.; McClintock, P. V.
2005-03-01
We reconstruct a nonlinear stochastic model of the cardiorespiratory interaction in terms of a set of polynomial basis functions representing the nonlinear force governing system oscillations. The strength and direction of coupling and noise intensity are simultaneously inferred from a univariate blood pressure signal. Our new inference technique does not require extensive global optimization, and it is applicable to a wide range of complex dynamical systems subject to noise.
Hopkins, James; Gaudette, Jamie; Mehta, Priyanth
2013-10-01
With the advent of digital signal processing (DSP) in optical transmitters and receivers, the ability to finely tune the ratio of pre and post dispersion compensation can be exploited to best mitigate the nonlinear penalties caused by the Kerr effect. A portion of the nonlinear penalty in optical communication channels has been explained by an increase in peak to average power ratio (PAPR) inherent in highly dispersed signals. The standard approach for minimizing these impairments applies 50% pre dispersion compensation and 50% post dispersion compensation, thereby decreasing average PAPR along the length of the cable, as compared with either 100% pre or post dispersion compensation. In this paper we demonstrate that simply considering the net accumulated dispersion, and applying 50/50 pre/post dispersion is not necessarily the best way to minimize PAPR and subsequent Kerr nonlinearities. Instead, we consider the cumulative dispersion along the entire length of the cable, and, taking into account this additional information, derive an analytic formula for the minimization of PAPR. Alignment with simulation and experimental measurements is presented using a commercially available 100Gb/s dual-polarization binary phase-shift-keying (DP-BPSK) coherent modem, with transmitter and receiver DSP. Measurements are provided from two different 5000km dispersion managed Submarine test-beds, as well as a 3800km terrestrial test-bed with a mixture of SMF-28 and TWRS optical fiber. This method is shown to deviate significantly from the conventional 50/50 method described above, in dispersion managed communications systems, and more closely aligns with results obtained from simulation and data collected from laboratory test-beds.
Myosin-II sets the optimal response time scale of chemotactic amoeba
Hsu, Hsin-Fang; Westendorf, Christian; Tarantola, Marco; Bodenschatz, Eberhard; Beta, Carsten
2014-03-01
The response dynamics of the actin cytoskeleton to external chemical stimuli plays a fundamental role in numerous cellular functions. One of the key players that governs the dynamics of the actin network is the motor protein myosin-II. Here we investigate the role of myosin-II in the response of the actin system to external stimuli. We used a microfluidic device in combination with a photoactivatable chemoattractant to apply stimuli to individual cells with high temporal resolution. We directly compare the actin dynamics in Dictyostelium discodelium wild type (WT) cells to a knockout mutant that is deficient in myosin-II (MNL). Similar to the WT a small population of MNL cells showed self-sustained oscillations even in absence of external stimuli. The actin response of MNL cells to a short pulse of chemoattractant resembles WT during the first 15 sec but is significantly delayed afterward. The amplitude of the dominant peak in the power spectrum from the response time series of MNL cells to periodic stimuli with varying period showed a clear resonance peak at a forcing period of 36 sec, which is significantly delayed as compared to the resonance at 20 sec found for the WT. This shift indicates an important role of myosin-II in setting the response time scale of motile amoeba. Institute of Physics und Astronomy, University of Potsdam, Karl-Liebknecht-Str. 24/25, 14476 Potsdam, Germany.
International Nuclear Information System (INIS)
Lazariev, A; Graveron-Demilly, D; Allouche, A-R; Aubert-Frécon, M; Fauvelle, F; Piotto, M; Elbayed, K; Namer, I-J; Van Ormondt, D
2011-01-01
High-resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) is playing an increasingly important role for diagnosis. This technique enables setting up metabolite profiles of ex vivo pathological and healthy tissue. The need to monitor diseases and pharmaceutical follow-up requires an automatic quantitation of HRMAS 1 H signals. However, for several metabolites, the values of chemical shifts of proton groups may slightly differ according to the micro-environment in the tissue or cells, in particular to its pH. This hampers the accurate estimation of the metabolite concentrations mainly when using quantitation algorithms based on a metabolite basis set: the metabolite fingerprints are not correct anymore. In this work, we propose an accurate method coupling quantum mechanical simulations and quantitation algorithms to handle basis-set changes. The proposed algorithm automatically corrects mismatches between the signals of the simulated basis set and the signal under analysis by maximizing the normalized cross-correlation between the mentioned signals. Optimized chemical shift values of the metabolites are obtained. This method, QM-QUEST, provides more robust fitting while limiting user involvement and respects the correct fingerprints of metabolites. Its efficiency is demonstrated by accurately quantitating 33 signals from tissue samples of human brains with oligodendroglioma, obtained at 11.7 tesla. The corresponding chemical shift changes of several metabolites within the series are also analyzed
Lazariev, A.; Allouche, A.-R.; Aubert-Frécon, M.; Fauvelle, F.; Piotto, M.; Elbayed, K.; Namer, I.-J.; van Ormondt, D.; Graveron-Demilly, D.
2011-11-01
High-resolution magic angle spinning (HRMAS) nuclear magnetic resonance (NMR) is playing an increasingly important role for diagnosis. This technique enables setting up metabolite profiles of ex vivo pathological and healthy tissue. The need to monitor diseases and pharmaceutical follow-up requires an automatic quantitation of HRMAS 1H signals. However, for several metabolites, the values of chemical shifts of proton groups may slightly differ according to the micro-environment in the tissue or cells, in particular to its pH. This hampers the accurate estimation of the metabolite concentrations mainly when using quantitation algorithms based on a metabolite basis set: the metabolite fingerprints are not correct anymore. In this work, we propose an accurate method coupling quantum mechanical simulations and quantitation algorithms to handle basis-set changes. The proposed algorithm automatically corrects mismatches between the signals of the simulated basis set and the signal under analysis by maximizing the normalized cross-correlation between the mentioned signals. Optimized chemical shift values of the metabolites are obtained. This method, QM-QUEST, provides more robust fitting while limiting user involvement and respects the correct fingerprints of metabolites. Its efficiency is demonstrated by accurately quantitating 33 signals from tissue samples of human brains with oligodendroglioma, obtained at 11.7 tesla. The corresponding chemical shift changes of several metabolites within the series are also analyzed.
DEFF Research Database (Denmark)
Da Ros, Francesco; Rottwitt, Karsten; Peucheret, Christophe
2012-01-01
Combining Al-doped and Ge-doped HNLFs as gain media in FOPAs is proposed and optimized, resulting in efficient SBS mitigation while circumventing the additional loss of the high SBS threshold Al-doped fiber.......Combining Al-doped and Ge-doped HNLFs as gain media in FOPAs is proposed and optimized, resulting in efficient SBS mitigation while circumventing the additional loss of the high SBS threshold Al-doped fiber....
Belmiloudi, A.; Mahé, F.
2014-01-01
International audience; The paper investigates boundary optimal controls and parameter estimates to the well-posedness nonlinear model of dehydration of thermic problems. We summarize the general formulations for the boundary control for initial-boundary value problem for nonlinear partial differential equations modeling the heat transfer and derive necessary optimality conditions, including the adjoint equation, for the optimal set of parameters minimizing objective functions J. Numerical si...
Optimization of auxiliary basis sets for the LEDO expansion and a projection technique for LEDO-DFT.
Götz, Andreas W; Kollmar, Christian; Hess, Bernd A
2005-09-01
We present a systematic procedure for the optimization of the expansion basis for the limited expansion of diatomic overlap density functional theory (LEDO-DFT) and report on optimized auxiliary orbitals for the Ahlrichs split valence plus polarization basis set (SVP) for the elements H, Li--F, and Na--Cl. A new method to deal with near-linear dependences in the LEDO expansion basis is introduced, which greatly reduces the computational effort of LEDO-DFT calculations. Numerical results for a test set of small molecules demonstrate the accuracy of electronic energies, structural parameters, dipole moments, and harmonic frequencies. For larger molecular systems the numerical errors introduced by the LEDO approximation can lead to an uncontrollable behavior of the self-consistent field (SCF) process. A projection technique suggested by Löwdin is presented in the framework of LEDO-DFT, which guarantees for SCF convergence. Numerical results on some critical test molecules suggest the general applicability of the auxiliary orbitals presented in combination with this projection technique. Timing results indicate that LEDO-DFT is competitive with conventional density fitting methods. (c) 2005 Wiley Periodicals, Inc.
Directory of Open Access Journals (Sweden)
Dongliang Liu
2017-03-01
Full Text Available In order to optimize the ventilation effect of ammonia leakage in the refrigeration machinery room, a food processing enterprise is selected as the subject investigated. The velocity and concentration field distribution during the process of ammonia leakage are discussed through simulation of refrigeration machinery room using CFD software. The ventilation system of the room is optimized in three aspects which are named air distribution, ventilation volume and discharge outlet. The influence of the ammonia alarm system through ventilation is also analyzed. The results show that it will be better to set the discharge outlet at the top of the plant than at the side of the wall, and the smaller of the distance between the air outlet and the ammonia gathering area, the better of the effect of ventilation will be. The air flow can be improved and the vortex flow can be reduced if the ventilation volume, the number of air vents and the exhaust velocity are reasonably arranged. Not only the function of the alarm could be ensured, but also the scope of the detection area could be enlarged if the detectors are set on the ceiling of the refrigeration units or the ammonia storage vessel.
Szwedowski, T D; Fialkov, J; Pakdel, A; Whyne, C M
2013-01-01
Accurate representation of skeletal structures is essential for quantifying structural integrity, for developing accurate models, for improving patient-specific implant design and in image-guided surgery applications. The complex morphology of thin cortical structures of the craniofacial skeleton (CFS) represents a significant challenge with respect to accurate bony segmentation. This technical study presents optimized processing steps to segment the three-dimensional (3D) geometry of thin cortical bone structures from CT images. In this procedure, anoisotropic filtering and a connected components scheme were utilized to isolate and enhance the internal boundaries between craniofacial cortical and trabecular bone. Subsequently, the shell-like nature of cortical bone was exploited using boundary-tracking level-set methods with optimized parameters determined from large-scale sensitivity analysis. The process was applied to clinical CT images acquired from two cadaveric CFSs. The accuracy of the automated segmentations was determined based on their volumetric concurrencies with visually optimized manual segmentations, without statistical appraisal. The full CFSs demonstrated volumetric concurrencies of 0.904 and 0.719; accuracy increased to concurrencies of 0.936 and 0.846 when considering only the maxillary region. The highly automated approach presented here is able to segment the cortical shell and trabecular boundaries of the CFS in clinical CT images. The results indicate that initial scan resolution and cortical-trabecular bone contrast may impact performance. Future application of these steps to larger data sets will enable the determination of the method's sensitivity to differences in image quality and CFS morphology.
International Nuclear Information System (INIS)
Merton, S. R.; Smedley-Stevenson, R. P.; Pain, C. C.; Buchan, A. G.; Eaton, M. D.
2009-01-01
This paper describes a new Non-Linear Discontinuous Petrov-Galerkin (NDPG) method and application to the one-speed Boltzmann Transport Equation (BTE) for space-time problems. The purpose of the method is to remove unwanted oscillations in the transport solution which occur in the vicinity of sharp flux gradients, while improving computational efficiency and numerical accuracy. This is achieved by applying artificial dissipation in the solution gradient direction, internal to an element using a novel finite element (FE) Riemann approach. The amount of dissipation added acts internal to each element. This is done using a gradient-informed scaling of the advection velocities in the stabilisation term. This makes the method in its most general form non-linear. The method is designed to be independent of angular expansion framework. This is demonstrated for the both discrete ordinates (S N ) and spherical harmonics (P N ) descriptions of the angular variable. Results show the scheme performs consistently well in demanding time dependent and multi-dimensional radiation transport problems. (authors)
International Nuclear Information System (INIS)
Coz Diaz, J.J. del; Garcia Nieto, P.J.; Suarez Sierra, J.L.; Penuelas Sanchez, I.
2008-01-01
The aim of this work was carried out the optimization and numerical study by the finite element method of internal hollow bricks walls in order to determine the best candidate brick from the thermal point of view. With respect to the energy saving for housing and industrial structures, there is also a great interest in light building materials with good physical and thermal behaviors, which fulfills all thermal requirements of the new CTE Spanish rule. The conduction, convection and radiation phenomena are taking into account in this study for six different types of bricks varying the material conductivity obtained from five experimental tests. Mathematically, the non-linearity is due to the radiation boundary condition inside the inner recesses of the bricks. Optimization of the walls is carried out from the finite element analysis of the new hollow brick geometries by means of the average mass overall thermal efficiency and the equivalent thermal conductivity. Based on the previous thermal analysis and the optimization procedure described in this paper, the best candidate was chosen and then a full 1.22 x 0.23 x 1.05 m wall made of these bricks was simulated for fifteen different compositions. The main variables influencing the thermal conductivity of these walls are illustrated for different concrete and mortar properties and the temperature distribution is shown for some typical configurations. Finally, in order to select the appropriate wall satisfying the CTE requirements, detailed instructions are given and conclusions of this work are exposed
Energy Technology Data Exchange (ETDEWEB)
Del Coz Diaz, J.J.; Suarez Sierra, J.L.; Penuelas Sanchez, I. [Edificio Departamental Viesques, No. 7-33204 Gijon, Asturias (Spain); Garcia Nieto, P.J. [Departamento de Matematicas, Facultad de Ciencias, C/Calvo Sotelo s/n, 33007 Oviedo, Asturias (Spain)
2008-06-15
The aim of this work was carried out the optimization and numerical study by the finite element method of internal hollow bricks walls in order to determine the best candidate brick from the thermal point of view. With respect to the energy saving for housing and industrial structures, there is also a great interest in light building materials with good physical and thermal behaviors, which fulfills all thermal requirements of the new CTE Spanish rule. The conduction, convection and radiation phenomena are taking into account in this study for six different types of bricks varying the material conductivity obtained from five experimental tests. Mathematically, the non-linearity is due to the radiation boundary condition inside the inner recesses of the bricks. Optimization of the walls is carried out from the finite element analysis of the new hollow brick geometries by means of the average mass overall thermal efficiency and the equivalent thermal conductivity. Based on the previous thermal analysis and the optimization procedure described in this paper, the best candidate was chosen and then a full 1.22 x 0.23 x 1.05 m wall made of these bricks was simulated for fifteen different compositions. The main variables influencing the thermal conductivity of these walls are illustrated for different concrete and mortar properties and the temperature distribution is shown for some typical configurations. Finally, in order to select the appropriate wall satisfying the CTE requirements, detailed instructions are given and conclusions of this work are exposed. (author)
Energy Technology Data Exchange (ETDEWEB)
Coz Diaz, J.J. del [Edificio Departamental Viesques, No. 7-33204 Gijon, Asturias (Spain)], E-mail: juanjo@constru.uniovi.es; Garcia Nieto, P.J. [Departamento de Matematicas, Facultad de Ciencias, C/Calvo Sotelo s/n, 33007 Oviedo, Asturias (Spain); Suarez Sierra, J.L.; Penuelas Sanchez, I. [Edificio Departamental Viesques, No. 7-33204 Gijon, Asturias (Spain)
2008-06-15
The aim of this work was carried out the optimization and numerical study by the finite element method of internal hollow bricks walls in order to determine the best candidate brick from the thermal point of view. With respect to the energy saving for housing and industrial structures, there is also a great interest in light building materials with good physical and thermal behaviors, which fulfills all thermal requirements of the new CTE Spanish rule. The conduction, convection and radiation phenomena are taking into account in this study for six different types of bricks varying the material conductivity obtained from five experimental tests. Mathematically, the non-linearity is due to the radiation boundary condition inside the inner recesses of the bricks. Optimization of the walls is carried out from the finite element analysis of the new hollow brick geometries by means of the average mass overall thermal efficiency and the equivalent thermal conductivity. Based on the previous thermal analysis and the optimization procedure described in this paper, the best candidate was chosen and then a full 1.22 x 0.23 x 1.05 m wall made of these bricks was simulated for fifteen different compositions. The main variables influencing the thermal conductivity of these walls are illustrated for different concrete and mortar properties and the temperature distribution is shown for some typical configurations. Finally, in order to select the appropriate wall satisfying the CTE requirements, detailed instructions are given and conclusions of this work are exposed.
International Nuclear Information System (INIS)
Guerra, Fabio A.; Coelho, Leandro dos S.
2008-01-01
An important problem in engineering is the identification of nonlinear systems, among them radial basis function neural networks (RBF-NN) using Gaussian activation functions models, which have received particular attention due to their potential to approximate nonlinear behavior. Several design methods have been proposed for choosing the centers and spread of Gaussian functions and training the RBF-NN. The selection of RBF-NN parameters such as centers, spreads, and weights can be understood as a system identification problem. This paper presents a hybrid training approach based on clustering methods (k-means and c-means) to tune the centers of Gaussian functions used in the hidden layer of RBF-NNs. This design also uses particle swarm optimization (PSO) for centers (local clustering search method) and spread tuning, and the Penrose-Moore pseudoinverse for the adjustment of RBF-NN weight outputs. Simulations involving this RBF-NN design to identify Lorenz's chaotic system indicate that the performance of the proposed method is superior to that of the conventional RBF-NN trained for k-means and the Penrose-Moore pseudoinverse for multi-step ahead forecasting
Energy Technology Data Exchange (ETDEWEB)
Serkez, Svitozar; Kocharyan, Vitali; Saldin, Evgeni; Zagorodnov, Igor [Deutsches Elektronen-Synchrotron (DESY), Hamburg (Germany); Geloni, Gianluca [European XFEL GmbH, Hamburg (Germany)
2013-09-15
We demonstrate that the output radiation characteristics of the European XFEL sources at nominal operation point can be easily made significantly better than what is currently reported in the TDRs of scientific instruments and X-ray optics. In fact, the output SASE characteristics of the baseline European XFEL have been previously optimized assuming uniform undulators at a nominal operating point of 5 kA peak current, without considering the potential of undulator tapering in the SASE regime. In order to illustrate this point, we analyze the case of an electron bunch with nominal parameters. Based on start-to-end simulations, we demonstrate that nonlinear undulator tapering allows one to achieve up to a tenfold increase in peak power and photon spectral density in the conventional SASE regime, without modification to the baseline design. The FEL code Genesis has been extensively used for these studies. In order to increase our confidence in simulation results, we cross-checked outcomes by reproducing simulations in the deep nonlinear SASE regime with tapered undulator using the code ALICE.
Directory of Open Access Journals (Sweden)
Tamer Ömer
2016-03-01
Full Text Available The molecular modeling of p-nitroanilinium perchlorate molecule was carried out by using B3LYP and HSEH1PBE levels of density functional theory (DFT. The IR and Raman spectra were simulated and the assignments of vibrational modes were performed on the basis of relative contribution of various internal co-ordinates. NBO analysis was performed to demonstrate charge transfer, conjugative interactions and the formation of intramolecular hydrogen bonding interactions within PNAPC. Obtained large dipole moment values showed that PNAPC is a highly polarizable complex, and the charge transfer occurs within PNAPC. Hydrogen bonding and charge transfer interactions were also displayed by small HOMO-LUMO gap and molecular electrostatic potential (MEP surface. The strong evidences that the material can be used as an efficient nonlinear optical (NLO material of PNAPC were demonstrated by considerable polarizability and hyperpolarizability values obtained at DFT levels.
Energy Technology Data Exchange (ETDEWEB)
Delbos, F.
2004-11-01
Reflexion tomography allows the determination of a subsurface velocity model from the travel times of seismic waves. The introduction of a priori information in this inverse problem can lead to the resolution of a constrained non-linear least-squares problem. The goal of the thesis is to improve the resolution techniques of this optimization problem, whose main difficulties are its ill-conditioning, its large scale and an expensive cost function in terms of CPU time. Thanks to a detailed study of the problem and to numerous numerical experiments, we justify the use of a sequential quadratic programming method, in which the tangential quadratic programs are solved by an original augmented Lagrangian method. We show the global linear convergence of the latter. The efficiency and robustness of the approach are demonstrated on several synthetic examples and on two real data cases. (author)
Energy Technology Data Exchange (ETDEWEB)
Delbos, F
2004-11-01
Reflexion tomography allows the determination of a subsurface velocity model from the travel times of seismic waves. The introduction of a priori information in this inverse problem can lead to the resolution of a constrained non-linear least-squares problem. The goal of the thesis is to improve the resolution techniques of this optimization problem, whose main difficulties are its ill-conditioning, its large scale and an expensive cost function in terms of CPU time. Thanks to a detailed study of the problem and to numerous numerical experiments, we justify the use of a sequential quadratic programming method, in which the tangential quadratic programs are solved by an original augmented Lagrangian method. We show the global linear convergence of the latter. The efficiency and robustness of the approach are demonstrated on several synthetic examples and on two real data cases. (author)
Maher, G.D.; Hulshoff, S.J.
2014-01-01
The Variational Germano Identity [1, 2] is used to optimize the coefficients of residual-based subgrid-scale models that arise from the application of a Variational Multiscale Method [3, 4]. It is demonstrated that numerical iterative methods can be used to solve the Germano relations to obtain
Tiwari, Shivendra N.; Padhi, Radhakant
2018-01-01
Following the philosophy of adaptive optimal control, a neural network-based state feedback optimal control synthesis approach is presented in this paper. First, accounting for a nominal system model, a single network adaptive critic (SNAC) based multi-layered neural network (called as NN1) is synthesised offline. However, another linear-in-weight neural network (called as NN2) is trained online and augmented to NN1 in such a manner that their combined output represent the desired optimal costate for the actual plant. To do this, the nominal model needs to be updated online to adapt to the actual plant, which is done by synthesising yet another linear-in-weight neural network (called as NN3) online. Training of NN3 is done by utilising the error information between the nominal and actual states and carrying out the necessary Lyapunov stability analysis using a Sobolev norm based Lyapunov function. This helps in training NN2 successfully to capture the required optimal relationship. The overall architecture is named as 'Dynamically Re-optimised single network adaptive critic (DR-SNAC)'. Numerical results for two motivating illustrative problems are presented, including comparison studies with closed form solution for one problem, which clearly demonstrate the effectiveness and benefit of the proposed approach.
Energy Technology Data Exchange (ETDEWEB)
Koa, A.S.; Chang, N.B. [University of Central Florida, Orlando, FL (United States). Dept. for Civil & Environmental Engineering
2008-07-15
Energy supply and use is of fundamental importance to society. Although the interactions between energy and environment were originally local in character, they have now widened to cover regional and global issues, such as acid rain and the greenhouse effect. It is for this reason that there is a need for covering the direct and indirect economic and environmental impacts of energy acquisition, transport, production and use. In this paper, particular attention is directed to ways of resolving conflict between economic and environmental goals by encouraging a power plant to consider co-firing biomass and refuse-derived fuel (RDF) with coal simultaneously. It aims at reducing the emission level of sulfur dioxide (SO{sub 2}) in an uncertain environment, using the power plant in Michigan City, Indiana as an example. To assess the uncertainty by a comparative way both deterministic and grey nonlinear mixed integer programming (MIP) models were developed to minimize the net operating cost with respect to possible fuel combinations. It aims at generating the optimal portfolio of alternative fuels while maintaining the same electricity generation simultaneously. To case the solution procedure stepwise relaxation algorithm was developed for solving the grey nonlinear MIP model. Breakeven alternative fuel value can be identified in the post-optimization stage for decision-making. Research findings show that the inclusion of RDF does not exhibit comparative advantage in terms of the net cost, albeit relatively lower air pollution impact. Yet it can be sustained by a charge system, subsidy program, or emission credit as the price of coal increases over time.
Ko, Andi Setiady; Chang, Ni-Bin
2008-07-01
Energy supply and use is of fundamental importance to society. Although the interactions between energy and environment were originally local in character, they have now widened to cover regional and global issues, such as acid rain and the greenhouse effect. It is for this reason that there is a need for covering the direct and indirect economic and environmental impacts of energy acquisition, transport, production and use. In this paper, particular attention is directed to ways of resolving conflict between economic and environmental goals by encouraging a power plant to consider co-firing biomass and refuse-derived fuel (RDF) with coal simultaneously. It aims at reducing the emission level of sulfur dioxide (SO(2)) in an uncertain environment, using the power plant in Michigan City, Indiana as an example. To assess the uncertainty by a comparative way both deterministic and grey nonlinear mixed integer programming (MIP) models were developed to minimize the net operating cost with respect to possible fuel combinations. It aims at generating the optimal portfolio of alternative fuels while maintaining the same electricity generation simultaneously. To ease the solution procedure stepwise relaxation algorithm was developed for solving the grey nonlinear MIP model. Breakeven alternative fuel value can be identified in the post-optimization stage for decision-making. Research findings show that the inclusion of RDF does not exhibit comparative advantage in terms of the net cost, albeit relatively lower air pollution impact. Yet it can be sustained by a charge system, subsidy program, or emission credit as the price of coal increases over time.
Perkins, Rosie; Reid, Helen; Araújo, Liliana S; Clark, Terry; Williamon, Aaron
2017-01-01
Student health and wellbeing within higher education has been documented as poor in relation to the general population. This is a particular problem among students at music conservatoires, who are studying within a unique educational context that is known to generate both physical and psychological challenges. This article examines how conservatoire students experience health and wellbeing within their institutional context, using a framework from health promotion to focus attention on perceived enablers and barriers to optimal health in relation to three levels: lifestyle, support services, and conservatoire environment. In order to respond to the individuality of students' experiences, a qualitative approach was taken based on semi-structured interviews with 20 current or recent conservatoire students in the United Kingdom. Thematic analysis revealed a complex set of enablers and barriers: (i) lifestyle enablers included value placed on the importance of optimal health and wellbeing for musicians and daily practices to enable this; lifestyle barriers included struggling to maintain healthy lifestyles within the context of musical practice and learning; (ii) support enablers included accessible support sources within and beyond the conservatoire; support barriers included a perceived lack of availability or awareness of appropriate support; (iii) environmental enablers included positive and enjoyable experiences of performance as well as strong relationships and communities; environmental barriers included experiences of comparison and competition, pressure and stress, challenges with negative performance feedback, psychological distress, and perceived overwork. The findings reveal a need for health promotion to focus not only on individuals but also on the daily practices and routines of conservatoires. Additionally, they suggest that continued work is required to embed health and wellbeing support as an integral component of conservatoire education, raising
Directory of Open Access Journals (Sweden)
Rosie Perkins
2017-06-01
Full Text Available Student health and wellbeing within higher education has been documented as poor in relation to the general population. This is a particular problem among students at music conservatoires, who are studying within a unique educational context that is known to generate both physical and psychological challenges. This article examines how conservatoire students experience health and wellbeing within their institutional context, using a framework from health promotion to focus attention on perceived enablers and barriers to optimal health in relation to three levels: lifestyle, support services, and conservatoire environment. In order to respond to the individuality of students’ experiences, a qualitative approach was taken based on semi-structured interviews with 20 current or recent conservatoire students in the United Kingdom. Thematic analysis revealed a complex set of enablers and barriers: (i lifestyle enablers included value placed on the importance of optimal health and wellbeing for musicians and daily practices to enable this; lifestyle barriers included struggling to maintain healthy lifestyles within the context of musical practice and learning; (ii support enablers included accessible support sources within and beyond the conservatoire; support barriers included a perceived lack of availability or awareness of appropriate support; (iii environmental enablers included positive and enjoyable experiences of performance as well as strong relationships and communities; environmental barriers included experiences of comparison and competition, pressure and stress, challenges with negative performance feedback, psychological distress, and perceived overwork. The findings reveal a need for health promotion to focus not only on individuals but also on the daily practices and routines of conservatoires. Additionally, they suggest that continued work is required to embed health and wellbeing support as an integral component of conservatoire
Hamdi, Basma; Mabrouk, Mohamed Tahar; Kairouani, Lakdar; Kheiri, Abdelhamid
2017-06-01
Different configurations of organic Rankine cycle (ORC) systems are potential thermodynamic concepts for power generation from low grade heat. The aim of this work is to investigate and optimize the performances of the three main ORC systems configurations: basic ORC, ORC with internal heat exchange (IHE) and regenerative ORC. The evaluation for those configurations was performed using seven working fluids with typical different thermodynamic behaviours (R245fa, R601a, R600a, R227ea, R134a, R1234ze and R1234yf). The optimization has been performed using a genetic algorithm under a comprehensive set of operative parameters such as the fluid evaporating temperature, the fraction of flow rate or the pressure at the steam extracting point in the turbine. Results show that there is no general best ORC configuration for all those fluids. However, there is a suitable configuration for each fluid. Contribution to the topical issue "Materials for Energy harvesting, conversion and storage II (ICOME 2016)", edited by Jean-Michel Nunzi, Rachid Bennacer and Mohammed El Ganaoui
Stationary nonlinear Airy beams
International Nuclear Information System (INIS)
Lotti, A.; Faccio, D.; Couairon, A.; Papazoglou, D. G.; Panagiotopoulos, P.; Tzortzakis, S.; Abdollahpour, D.
2011-01-01
We demonstrate the existence of an additional class of stationary accelerating Airy wave forms that exist in the presence of third-order (Kerr) nonlinearity and nonlinear losses. Numerical simulations and experiments, in agreement with the analytical model, highlight how these stationary solutions sustain the nonlinear evolution of Airy beams. The generic nature of the Airy solution allows extension of these results to other settings, and a variety of applications are suggested.
Nazemizadeh, M.; Rahimi, H. N.; Amini Khoiy, K.
2012-03-01
This paper presents an optimal control strategy for optimal trajectory planning of mobile robots by considering nonlinear dynamic model and nonholonomic constraints of the system. The nonholonomic constraints of the system are introduced by a nonintegrable set of differential equations which represent kinematic restriction on the motion. The Lagrange's principle is employed to derive the nonlinear equations of the system. Then, the optimal path planning of the mobile robot is formulated as an optimal control problem. To set up the problem, the nonlinear equations of the system are assumed as constraints, and a minimum energy objective function is defined. To solve the problem, an indirect solution of the optimal control method is employed, and conditions of the optimality derived as a set of coupled nonlinear differential equations. The optimality equations are solved numerically, and various simulations are performed for a nonholonomic mobile robot to illustrate effectiveness of the proposed method.
International Nuclear Information System (INIS)
Banerjee, Amit; Abu-Mahfouz, Issam
2014-01-01
The use of evolutionary algorithms has been popular in recent years for solving the inverse problem of identifying system parameters given the chaotic response of a dynamical system. The inverse problem is reformulated as a minimization problem and population-based optimizers such as evolutionary algorithms have been shown to be efficient solvers of the minimization problem. However, to the best of our knowledge, there has been no published work that evaluates the efficacy of using the two most popular evolutionary techniques – particle swarm optimization and differential evolution algorithm, on a wide range of parameter estimation problems. In this paper, the two methods along with their variants (for a total of seven algorithms) are applied to fifteen different parameter estimation problems of varying degrees of complexity. Estimation results are analyzed using nonparametric statistical methods to identify if an algorithm is statistically superior to others over the class of problems analyzed. Results based on parameter estimation quality suggest that there are significant differences between the algorithms with the newer, more sophisticated algorithms performing better than their canonical versions. More importantly, significant differences were also found among variants of the particle swarm optimizer and the best performing differential evolution algorithm
Theory and design of nonlinear metamaterials
Rose, Alec Daniel
and oscillators. By applying this set of tools and knowledge to microwave metamaterials, I experimentally confirm several novel nonlinear phenomena. Most notably, I construct a backward wave nonlinear medium from varactor-loaded split ring resonators loaded in a rectangular waveguide, capable of generating second-harmonic opposite to conventional nonlinear materials with a conversion efficiency as high as 1.5%. In addition, I confirm nonlinear magnetoelectric coupling in two dual gap varactor-loaded split ring resonator metamaterials through measurement of the amplitude and phase of the second-harmonic generated in the forward and backward directions from a thin slab. I then use the presence of simultaneous nonlinearities in such metamaterials to observe nonlinear interference, manifest as unidirectional difference frequency generation with contrasts of 6 and 12 dB in the forward and backward directions, respectively. Finally, I apply these principles and intuition to several plasmonic platforms with the goal of achieving similar enhancements and configurations at optical frequencies. Using the example of fluorescence enhancement in optical patch antennas, I develop a semi-classical numerical model for the calculation of field-induced enhancements to both excitation and spontaneous emission rates of an embedded fluorophore, showing qualitative agreement with experimental results, with enhancement factors of more than 30,000. Throughout these series of works, I emphasize the indispensability of effective design and retrieval tools in understanding and optimizing both metamaterials and plasmonic systems. Ultimately, when weighed against the disadvantages in fabrication and optical losses, the results presented here provide a context for the application of nonlinear metamaterials within three distinct areas where a competitive advantage over conventional materials might be obtained: fundamental science demonstrations, linear and nonlinear anisotropy engineering, and
Model reduction of nonlinear systems subject to input disturbances
Ndoye, Ibrahima
2017-07-10
The method of convex optimization is used as a tool for model reduction of a class of nonlinear systems in the presence of disturbances. It is shown that under some conditions the nonlinear disturbed system can be approximated by a reduced order nonlinear system with similar disturbance-output properties to the original plant. The proposed model reduction strategy preserves the nonlinearity and the input disturbance nature of the model. It guarantees a sufficiently small error between the outputs of the original and the reduced-order systems, and also maintains the properties of input-to-state stability. The matrices of the reduced order system are given in terms of a set of linear matrix inequalities (LMIs). The paper concludes with a demonstration of the proposed approach on model reduction of a nonlinear electronic circuit with additive disturbances.
Luy, N. T.
2018-04-01
The design of distributed cooperative H∞ optimal controllers for multi-agent systems is a major challenge when the agents' models are uncertain multi-input and multi-output nonlinear systems in strict-feedback form in the presence of external disturbances. In this paper, first, the distributed cooperative H∞ optimal tracking problem is transformed into controlling the cooperative tracking error dynamics in affine form. Second, control schemes and online algorithms are proposed via adaptive dynamic programming (ADP) and the theory of zero-sum differential graphical games. The schemes use only one neural network (NN) for each agent instead of three from ADP to reduce computational complexity as well as avoid choosing initial NN weights for stabilising controllers. It is shown that despite not using knowledge of cooperative internal dynamics, the proposed algorithms not only approximate values to Nash equilibrium but also guarantee all signals, such as the NN weight approximation errors and the cooperative tracking errors in the closed-loop system, to be uniformly ultimately bounded. Finally, the effectiveness of the proposed method is shown by simulation results of an application to wheeled mobile multi-robot systems.
Nemirsky, Kristofer Kevin
In this thesis, the history and evolution of rotor aircraft with simulated annealing-based PID application were reviewed and quadcopter dynamics are presented. The dynamics of a quadcopter were then modeled, analyzed, and linearized. A cascaded loop architecture with PID controllers was used to stabilize the plant dynamics, which was improved upon through the application of simulated annealing (SA). A Simulink model was developed to test the controllers and verify the functionality of the proposed control system design. In addition, the data that the Simulink model provided were compared with flight data to present the validity of derived dynamics as a proper mathematical model representing the true dynamics of the quadcopter system. Then, the SA-based global optimization procedure was applied to obtain optimized PID parameters. It was observed that the tuned gains through the SA algorithm produced a better performing PID controller than the original manually tuned one. Next, we investigated the uncertain dynamics of the quadcopter setup. After adding uncertainty to the gyroscopic effects associated with pitch-and-roll rate dynamics, the controllers were shown to be robust against the added uncertainty. A discussion follows to summarize SA-based algorithm PID controller design and performance outcomes. Lastly, future work on SA application on multi-input-multi-output (MIMO) systems is briefly discussed.
Kleidon, Axel; Renner, Maik
2016-04-01
, which then links this thermodynamic approach to optimality in vegetation. We also contrast this approach to common, semi-empirical approaches of surface-atmosphere exchange and discuss how thermodynamics may set a broader range of transport limitations and optimality in the soil-plant-atmosphere system.
Connolly, Joseph W.; Csank, Jeffrey Thomas; Chicatelli, Amy; Kilver, Jacob
2013-01-01
This paper covers the development of a model-based engine control (MBEC) methodology featuring a self tuning on-board model applied to an aircraft turbofan engine simulation. Here, the Commercial Modular Aero-Propulsion System Simulation 40,000 (CMAPSS40k) serves as the MBEC application engine. CMAPSS40k is capable of modeling realistic engine performance, allowing for a verification of the MBEC over a wide range of operating points. The on-board model is a piece-wise linear model derived from CMAPSS40k and updated using an optimal tuner Kalman Filter (OTKF) estimation routine, which enables the on-board model to self-tune to account for engine performance variations. The focus here is on developing a methodology for MBEC with direct control of estimated parameters of interest such as thrust and stall margins. Investigations using the MBEC to provide a stall margin limit for the controller protection logic are presented that could provide benefits over a simple acceleration schedule that is currently used in traditional engine control architectures.
Ha, Linh Khanh; Krüger, Jens; Dihl Comba, João Luiz; Silva, Cláudio T; Joshi, Sarang
2012-06-01
Image population analysis is the class of statistical methods that plays a central role in understanding the development, evolution, and disease of a population. However, these techniques often require excessive computational power and memory that are compounded with a large number of volumetric inputs. Restricted access to supercomputing power limits its influence in general research and practical applications. In this paper we introduce ISP, an Image-Set Processing streaming framework that harnesses the processing power of commodity heterogeneous CPU/GPU systems and attempts to solve this computational problem. In ISP, we introduce specially designed streaming algorithms and data structures that provide an optimal solution for out-of-core multiimage processing problems both in terms of memory usage and computational efficiency. ISP makes use of the asynchronous execution mechanism supported by parallel heterogeneous systems to efficiently hide the inherent latency of the processing pipeline of out-of-core approaches. Consequently, with computationally intensive problems, the ISP out-of-core solution can achieve the same performance as the in-core solution. We demonstrate the efficiency of the ISP framework on synthetic and real datasets.
On Nonlinear Prices in Timed Automata
Directory of Open Access Journals (Sweden)
Devendra Bhave
2016-12-01
Full Text Available Priced timed automata provide a natural model for quantitative analysis of real-time systems and have been successfully applied in various scheduling and planning problems. The optimal reachability problem for linearly-priced timed automata is known to be PSPACE-complete. In this paper we investigate priced timed automata with more general prices and show that in the most general setting the optimal reachability problem is undecidable. We adapt and implement the construction of Audemard, Cimatti, Kornilowicz, and Sebastiani for non-linear priced timed automata using state-of-the-art theorem prover Z3 and present some preliminary results.
Rough set classification based on quantum logic
Hassan, Yasser F.
2017-11-01
By combining the advantages of quantum computing and soft computing, the paper shows that rough sets can be used with quantum logic for classification and recognition systems. We suggest the new definition of rough set theory as quantum logic theory. Rough approximations are essential elements in rough set theory, the quantum rough set model for set-valued data directly construct set approximation based on a kind of quantum similarity relation which is presented here. Theoretical analyses demonstrate that the new model for quantum rough sets has new type of decision rule with less redundancy which can be used to give accurate classification using principles of quantum superposition and non-linear quantum relations. To our knowledge, this is the first attempt aiming to define rough sets in representation of a quantum rather than logic or sets. The experiments on data-sets have demonstrated that the proposed model is more accuracy than the traditional rough sets in terms of finding optimal classifications.
Existence theory in optimal control
International Nuclear Information System (INIS)
Olech, C.
1976-01-01
This paper treats the existence problem in two main cases. One case is that of linear systems when existence is based on closedness or compactness of the reachable set and the other, non-linear case refers to a situation where for the existence of optimal solutions closedness of the set of admissible solutions is needed. Some results from convex analysis are included in the paper. (author)
Energy Technology Data Exchange (ETDEWEB)
Gómez, F.J., E-mail: javier.gomez@amsimulation.com [Advanced Material Simulation, AMS, Bilbao (Spain); Martin Rengel, M.A., E-mail: mamartin.rengel@upm.es [E.T.S.I. Caminos, Canales y Puertos, Universidad Politécnica de Madrid, C/Professor Aranguren SN, E-28040 Madrid (Spain); Ruiz-Hervias, J.; Puerta, M.A. [E.T.S.I. Caminos, Canales y Puertos, Universidad Politécnica de Madrid, C/Professor Aranguren SN, E-28040 Madrid (Spain)
2017-06-15
In this work, the hoop fracture toughness of ZIRLO{sup ®} fuel cladding is calculated as a function of three parameters: hydrogen concentration, temperature and displacement rate. To this end, pre-hydrided samples with nominal hydrogen concentrations of 0 (as-received), 150, 250, 500, 1200 and 2000 ppm were prepared. Hydrogen was precipitated as zirconium hydrides in the shape of platelets oriented along the hoop direction. Ring Compression Tests (RCTs) were conducted at three temperatures (20, 135 and 300 °C) and two displacement rates (0.5 and 100 mm/min). A new method has been proposed in this paper which allows the determination of fracture toughness from ring compression tests. The proposed method combines the experimental results, the cohesive crack model, finite elements simulations, numerical calculations and non-linear optimization techniques. The parameters of the cohesive crack model were calculated by minimizing the difference between the experimental data and the numerical results. An almost perfect fitting of the experimental results is achieved by this method. In addition, an estimation of the error in the calculated fracture toughness is also provided.
Nonlinear Multiantenna Detection Methods
Directory of Open Access Journals (Sweden)
Chen Sheng
2004-01-01
Full Text Available A nonlinear detection technique designed for multiple-antenna assisted receivers employed in space-division multiple-access systems is investigated. We derive the optimal solution of the nonlinear spatial-processing assisted receiver for binary phase shift keying signalling, which we refer to as the Bayesian detector. It is shown that this optimal Bayesian receiver significantly outperforms the standard linear beamforming assisted receiver in terms of a reduced bit error rate, at the expense of an increased complexity, while the achievable system capacity is substantially enhanced with the advent of employing nonlinear detection. Specifically, when the spatial separation expressed in terms of the angle of arrival between the desired and interfering signals is below a certain threshold, a linear beamformer would fail to separate them, while a nonlinear detection assisted receiver is still capable of performing adequately. The adaptive implementation of the optimal Bayesian detector can be realized using a radial basis function network. Two techniques are presented for constructing block-data-based adaptive nonlinear multiple-antenna assisted receivers. One of them is based on the relevance vector machine invoked for classification, while the other on the orthogonal forward selection procedure combined with the Fisher ratio class-separability measure. A recursive sample-by-sample adaptation procedure is also proposed for training nonlinear detectors based on an amalgam of enhanced -means clustering techniques and the recursive least squares algorithm.
Directory of Open Access Journals (Sweden)
Arkoprovo Biswas
2011-07-01
Full Text Available In the presence of conducting inhomogeneities in near-surface structures, apparent resistivity data in magnetotelluric sounding can be severely distorted. This is due to electric fields generated from boundary charges on surficial inhomogeneities. Such distortion persists throughout the entire recording range and is known as static shift in magnetotellurics. Frequency-independent static shifts manifest as vertical, parallel shifts that occur in plots of the dual logarithmic scale of apparent resistivity versus time period. The phase of magnetotelluric sounding data remains unaffected by the static shift and can be used to remove the static shift to some extent. However, individual inversion of phase data yields highly nonunique results, and alone it will not work to correctly remove the static shift. Inversions of uncorrected magnetotelluric data yield erroneous and unreliable estimations, while static-shift-corrected magnetotelluric data provide better and reliable estimations of the resistivities and thicknesses of subsurface structures. In the present study, static shift (a frequency-independent real constant is also considered as one of the model parameters and is optimized together with other model parameters (resistivity and thickness using the very fast simulated annealing global inversion technique. This implies that model parameters are determined simultaneously with the estimate of the static shift in the data. Synthetic and noisy data generated for a number of models are interpreted, to demonstrate the efficacy of the approach to yield reliable estimates of subsurface structures when the apparent resistivity data are affected by static shift. Individual inversions of static-shift-affected apparent resistivity data and phase data yield unreliable estimations of the model parameters. Furthermore, the estimated model parameters after individual data inversions do not show any systematic correlations with the amount of static shift in the
Fu, Y. B.; Ogden, R. W.
2001-05-01
This collection of papers by leading researchers in the field of finite, nonlinear elasticity concerns itself with the behavior of objects that deform when external forces or temperature gradients are applied. This process is extremely important in many industrial settings, such as aerospace and rubber industries. This book covers the various aspects of the subject comprehensively with careful explanations of the basic theories and individual chapters each covering a different research direction. The authors discuss the use of symbolic manipulation software as well as computer algorithm issues. The emphasis is placed firmly on covering modern, recent developments, rather than the very theoretical approach often found. The book will be an excellent reference for both beginners and specialists in engineering, applied mathematics and physics.
A Recurrent Neural Network for Nonlinear Fractional Programming
Directory of Open Access Journals (Sweden)
Quan-Ju Zhang
2012-01-01
Full Text Available This paper presents a novel recurrent time continuous neural network model which performs nonlinear fractional optimization subject to interval constraints on each of the optimization variables. The network is proved to be complete in the sense that the set of optima of the objective function to be minimized with interval constraints coincides with the set of equilibria of the neural network. It is also shown that the network is primal and globally convergent in the sense that its trajectory cannot escape from the feasible region and will converge to an exact optimal solution for any initial point being chosen in the feasible interval region. Simulation results are given to demonstrate further the global convergence and good performance of the proposing neural network for nonlinear fractional programming problems with interval constraints.
International Nuclear Information System (INIS)
Boyd, R.W.
1992-01-01
Nonlinear optics is the study of the interaction of intense laser light with matter. This book is a textbook on nonlinear optics at the level of a beginning graduate student. The intent of the book is to provide an introduction to the field of nonlinear optics that stresses fundamental concepts and that enables the student to go on to perform independent research in this field. This book covers the areas of nonlinear optics, quantum optics, quantum electronics, laser physics, electrooptics, and modern optics
Hou, Zeyu; Lu, Wenxi; Xue, Haibo; Lin, Jin
2017-08-01
Surrogate-based simulation-optimization technique is an effective approach for optimizing the surfactant enhanced aquifer remediation (SEAR) strategy for clearing DNAPLs. The performance of the surrogate model, which is used to replace the simulation model for the aim of reducing computation burden, is the key of corresponding researches. However, previous researches are generally based on a stand-alone surrogate model, and rarely make efforts to improve the approximation accuracy of the surrogate model to the simulation model sufficiently by combining various methods. In this regard, we present set pair analysis (SPA) as a new method to build ensemble surrogate (ES) model, and conducted a comparative research to select a better ES modeling pattern for the SEAR strategy optimization problems. Surrogate models were developed using radial basis function artificial neural network (RBFANN), support vector regression (SVR), and Kriging. One ES model is assembling RBFANN model, SVR model, and Kriging model using set pair weights according their performance, and the other is assembling several Kriging (the best surrogate modeling method of three) models built with different training sample datasets. Finally, an optimization model, in which the ES model was embedded, was established to obtain the optimal remediation strategy. The results showed the residuals of the outputs between the best ES model and simulation model for 100 testing samples were lower than 1.5%. Using an ES model instead of the simulation model was critical for considerably reducing the computation time of simulation-optimization process and maintaining high computation accuracy simultaneously.
Foreest, N. D. van; Wijngaard, J.
2010-01-01
The single-product, stationary inventory problem with set-up cost is one of the classical problems in stochastic operations research. Theories have been developed to cope with finite production capacity in periodic review systems, and it has been proved that optimal policies for these cases are not
Palacios, S.G.
2015-01-01
In health facilities in resource-constrained settings, a lack of access to sustainable and reliable electricity can result on a sub-optimal delivery of healthcare services, as they do not have lighting for medical procedures and power to run essential equipment and devices to treat their patients.
Directory of Open Access Journals (Sweden)
Majid Mohammadhosseini
2014-05-01
Full Text Available A reliable quantitative structure retention relationship (QSRR study has been evaluated to predict the retention indices (RIs of a broad spectrum of compounds, namely 118 non-linear, cyclic and heterocyclic terpenoids (both saturated and unsaturated, on an HP-5MS fused silica column. A principal component analysis showed that seven compounds lay outside of the main cluster. After elimination of the outliers, the data set was divided into training and test sets involving 80 and 28 compounds. The method was tested by application of the particle swarm optimization (PSO method to find the most effective molecular descriptors, followed by multiple linear regressions (MLR. The PSO-MLR model was further confirmed through “leave one out cross validation” (LOO-CV and “leave group out cross validation” (LGO-CV, as well as external validations. The promising statistical figures of merit associated with the proposed model (R2train=0.936, Q2LOO=0.928, Q2LGO=0.921, F=376.4 confirm its high ability to predict RIs with negligible relative errors of predictions (REP train=4.8%, REP test=6.0%.
Fuzzy optimization of primal-dual pair using piecewise linear membership functions
Directory of Open Access Journals (Sweden)
Pandey D.
2012-01-01
Full Text Available Present paper improves the model of Bector and Chandra [Fuzzy Sets and Systems, 125 (2002 317-325] on duality in fuzzy linear programming by using non-linear membership functions. Numerical problem discussed by these authors has also been worked out through our non-linear model to demonstrate improved optimality of the results.
Hallowell, Nina; Snowdon, Claire; Morrow, Susan; Norman, Jane E; Denison, Fiona C; Lawton, Julia
2016-06-01
Hope has therapeutic value because it enables people to cope with uncertainty about their future health. Indeed, hope, or therapeutic optimism (TO), is seen as an essential aspect of the provision and experience of medical care. The role of TO in clinical research has been briefly discussed, but the concept, and whether it can be transferred from care to research and from patients to clinicians, has not been fully investigated. The role played by TO in research emerged during interviews with staff involved in a peripartum trial. This paper unpacks the concept of TO in this setting and considers the role it may play in the wider delivery of clinical trials. The Got-it trial is a UK-based, randomised placebo-controlled trial that investigates the use of sublingual glyceryl trinitrate (GTN) spray to treat retained placenta. Qualitative data were collected in open-ended interviews with obstetricians, research and clinical midwives (n =27) involved in trial recruitment. Data were analysed using the method of constant comparison. TO influenced staff engagement with Got-it at different points in the trial and in different ways. Prior knowledge of, and familiarity with, GTN meant that from the outset staff perceived the trial as low risk. TO facilitated staff involvement in the trial; staff who already understood GTN's effects were optimistic that it would work, and staff collaborated because they hoped that the trial would address what they identified as an important clinical need. TO could fluctuate over the course of the trial, and was sustained or undermined by unofficial observation of clinical outcomes and speculations about treatment allocation. Thus, TO appeared to be influenced by key situational factors: prior knowledge and experience, clinical need and observed participant outcomes. Situational TO plays a role in facilitating staff engagement with clinical research. TO may affect trial recruitment by enabling staff to sustain the levels of uncertainty, or
International Nuclear Information System (INIS)
Osgouee, Ahmad
2010-01-01
many advanced control methods proposed for the control of nuclear SG water level, operators are still experiencing difficulties especially at low powers. Therefore, it seems that a suitable controller to replace the manual operations is still needed. In this paper optimization of SGL set-points and designing a robust control for SGL control system using will be discussed
Nonlinear robust hierarchical control for nonlinear uncertain systems
Directory of Open Access Journals (Sweden)
Leonessa Alexander
1999-01-01
Full Text Available A nonlinear robust control-system design framework predicated on a hierarchical switching controller architecture parameterized over a set of moving nominal system equilibria is developed. Specifically, using equilibria-dependent Lyapunov functions, a hierarchical nonlinear robust control strategy is developed that robustly stabilizes a given nonlinear system over a prescribed range of system uncertainty by robustly stabilizing a collection of nonlinear controlled uncertain subsystems. The robust switching nonlinear controller architecture is designed based on a generalized (lower semicontinuous Lyapunov function obtained by minimizing a potential function over a given switching set induced by the parameterized nominal system equilibria. The proposed framework robustly stabilizes a compact positively invariant set of a given nonlinear uncertain dynamical system with structured parametric uncertainty. Finally, the efficacy of the proposed approach is demonstrated on a jet engine propulsion control problem with uncertain pressure-flow map data.
Bloembergen, Nicolaas
1996-01-01
Nicolaas Bloembergen, recipient of the Nobel Prize for Physics (1981), wrote Nonlinear Optics in 1964, when the field of nonlinear optics was only three years old. The available literature has since grown by at least three orders of magnitude.The vitality of Nonlinear Optics is evident from the still-growing number of scientists and engineers engaged in the study of new nonlinear phenomena and in the development of new nonlinear devices in the field of opto-electronics. This monograph should be helpful in providing a historical introduction and a general background of basic ideas both for expe
Nonlinear programming analysis and methods
Avriel, Mordecai
2012-01-01
This text provides an excellent bridge between principal theories and concepts and their practical implementation. Topics include convex programming, duality, generalized convexity, analysis of selected nonlinear programs, techniques for numerical solutions, and unconstrained optimization methods.
Directory of Open Access Journals (Sweden)
Jinshui Zhang
2017-04-01
Full Text Available This paper developed an approach, the window-based validation set for support vector data description (WVS-SVDD, to determine optimal parameters for support vector data description (SVDD model to map specific land cover by integrating training and window-based validation sets. Compared to the conventional approach where the validation set included target and outlier pixels selected visually and randomly, the validation set derived from WVS-SVDD constructed a tightened hypersphere because of the compact constraint by the outlier pixels which were located neighboring to the target class in the spectral feature space. The overall accuracies for wheat and bare land achieved were as high as 89.25% and 83.65%, respectively. However, target class was underestimated because the validation set covers only a small fraction of the heterogeneous spectra of the target class. The different window sizes were then tested to acquire more wheat pixels for validation set. The results showed that classification accuracy increased with the increasing window size and the overall accuracies were higher than 88% at all window size scales. Moreover, WVS-SVDD showed much less sensitivity to the untrained classes than the multi-class support vector machine (SVM method. Therefore, the developed method showed its merits using the optimal parameters, tradeoff coefficient (C and kernel width (s, in mapping homogeneous specific land cover.
Ma, Y T; Wubs, A M; Mathieu, A; Heuvelink, E; Zhu, J Y; Hu, B G; Cournède, P H; de Reffye, P
2011-04-01
Many indeterminate plants can have wide fluctuations in the pattern of fruit-set and harvest. Fruit-set in these types of plants depends largely on the balance between source (assimilate supply) and sink strength (assimilate demand) within the plant. This study aims to evaluate the ability of functional-structural plant models to simulate different fruit-set patterns among Capsicum cultivars through source-sink relationships. A greenhouse experiment of six Capsicum cultivars characterized with different fruit weight and fruit-set was conducted. Fruit-set patterns and potential fruit sink strength were determined through measurement. Source and sink strength of other organs were determined via the GREENLAB model, with a description of plant organ weight and dimensions according to plant topological structure established from the measured data as inputs. Parameter optimization was determined using a generalized least squares method for the entire growth cycle. Fruit sink strength differed among cultivars. Vegetative sink strength was generally lower for large-fruited cultivars than for small-fruited ones. The larger the size of the fruit, the larger variation there was in fruit-set and fruit yield. Large-fruited cultivars need a higher source-sink ratio for fruit-set, which means higher demand for assimilates. Temporal heterogeneity of fruit-set affected both number and yield of fruit. The simulation study showed that reducing heterogeneity of fruit-set was obtained by different approaches: for example, increasing source strength; decreasing vegetative sink strength, source-sink ratio for fruit-set and flower appearance rate; and harvesting individual fruits earlier before full ripeness. Simulation results showed that, when we increased source strength or decreased vegetative sink strength, fruit-set and fruit weight increased. However, no significant differences were found between large-fruited and small-fruited groups of cultivars regarding the effects of source
Jacob, S A; Ng, W L; Do, V
2015-02-01
There is wide variation in the proportion of newly diagnosed cancer patients who receive chemotherapy, indicating the need for a benchmark rate of chemotherapy utilisation. This study describes an evidence-based model that estimates the proportion of new cancer patients in whom chemotherapy is indicated at least once (defined as the optimal chemotherapy utilisation rate). The optimal chemotherapy utilisation rate can act as a benchmark for measuring and improving the quality of care. Models of optimal chemotherapy utilisation were constructed for each cancer site based on indications for chemotherapy identified from evidence-based treatment guidelines. Data on the proportion of patient- and tumour-related attributes for which chemotherapy was indicated were obtained, using population-based data where possible. Treatment indications and epidemiological data were merged to calculate the optimal chemotherapy utilisation rate. Monte Carlo simulations and sensitivity analyses were used to assess the effect of controversial chemotherapy indications and variations in epidemiological data on our model. Chemotherapy is indicated at least once in 49.1% (95% confidence interval 48.8-49.6%) of all new cancer patients in Australia. The optimal chemotherapy utilisation rates for individual tumour sites ranged from a low of 13% in thyroid cancers to a high of 94% in myeloma. The optimal chemotherapy utilisation rate can serve as a benchmark for planning chemotherapy services on a population basis. The model can be used to evaluate service delivery by comparing the benchmark rate with patterns of care data. The overall estimate for other countries can be obtained by substituting the relevant distribution of cancer types. It can also be used to predict future chemotherapy workload and can be easily modified to take into account future changes in cancer incidence, presentation stage or chemotherapy indications. Copyright © 2014 The Royal College of Radiologists. Published by
Directory of Open Access Journals (Sweden)
Akanksha Mishra
2017-05-01
Full Text Available In a deregulated electricity market it may at times become difficult to dispatch all the required power that is scheduled to flow due to congestion in transmission lines. An Interline Power Flow Controller (IPFC can be used to reduce the system loss and power flow in the heavily loaded line, improve stability and loadability of the system. This paper proposes a Disparity Line Utilization Factor for the optimal placement and Gravitational Search algorithm based optimal tuning of IPFC to control the congestion in transmission lines. DLUF ranks the transmission lines in terms of relative line congestion. The IPFC is accordingly placed in the most congested and the least congested line connected to the same bus. Optimal sizing of IPFC is carried using Gravitational Search algorithm. A multi-objective function has been chosen for tuning the parameters of the IPFC. The proposed method is implemented on an IEEE-30 bus test system. Graphical representations have been included in the paper showing reduction in LUF of the transmission lines after the placement of an IPFC. A reduction in active power and reactive power loss of the system by about 6% is observed after an optimally tuned IPFC has been included in the power system. The effectiveness of the proposed tuning method has also been shown in the paper through the reduction in the values of the objective functions.
Walsh, David; McCartney, Gerry; McCullough, Sarah; van der Pol, Marjon; Buchanan, Duncan; Jones, Russell
2015-09-01
Many theories have been proposed to explain the high levels of 'excess' mortality (i.e. higher mortality over and above that explained by differences in socio-economic circumstances) shown in Scotland-and, especially, in its largest city, Glasgow-compared with elsewhere in the UK. One such proposal relates to differences in optimism, given previously reported evidence of the health benefits of an optimistic outlook. A representative survey of Glasgow, Liverpool and Manchester was undertaken in 2011. Optimism was measured by the Life Orientation Test (Revised) (LOT-R), and compared between the cities by means of multiple linear regression models, adjusting for any differences in sample characteristics. Unadjusted analyses showed LOT-R scores to be similar in Glasgow and Liverpool (mean score (SD): 14.7 (4.0) for both), but lower in Manchester (13.9 (3.8)). This was consistent in analyses by age, gender and social class. Multiple regression confirmed the city results: compared with Glasgow, optimism was either similar (Liverpool: adjusted difference in mean score: -0.16 (95% CI -0.45 to 0.13)) or lower (Manchester: -0.85 (-1.14 to -0.56)). The reasons for high levels of Scottish 'excess' mortality remain unclear. However, differences in psychological outlook such as optimism appear to be an unlikely explanation. © The Author 2015. Published by Oxford University Press on behalf of Faculty of Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
MacBain, Keith M
2009-01-01
Intends to supplement the engineer's box of analysis and design tools making optimization as commonplace as the finite element method in the engineering workplace. This title introduces structural optimization and the methods of nonlinear programming such as Lagrange multipliers, Kuhn-Tucker conditions, and calculus of variations.
Yoshida, Zensho
2010-01-01
This book gives a general, basic understanding of the mathematical structure "nonlinearity" that lies in the depths of complex systems. Analyzing the heterogeneity that the prefix "non" represents with respect to notions such as the linear space, integrability and scale hierarchy, "nonlinear science" is explained as a challenge of deconstruction of the modern sciences. This book is not a technical guide to teach mathematical tools of nonlinear analysis, nor a zoology of so-called nonlinear phenomena. By critically analyzing the structure of linear theories, and cl
Nayfeh, Ali Hasan
1995-01-01
Nonlinear Oscillations is a self-contained and thorough treatment of the vigorous research that has occurred in nonlinear mechanics since 1970. The book begins with fundamental concepts and techniques of analysis and progresses through recent developments and provides an overview that abstracts and introduces main nonlinear phenomena. It treats systems having a single degree of freedom, introducing basic concepts and analytical methods, and extends concepts and methods to systems having degrees of freedom. Most of this material cannot be found in any other text. Nonlinear Oscillations uses sim
Directory of Open Access Journals (Sweden)
Zhila Esna Ashari
Full Text Available Type IV secretion systems (T4SS are multi-protein complexes in a number of bacterial pathogens that can translocate proteins and DNA to the host. Most T4SSs function in conjugation and translocate DNA; however, approximately 13% function to secrete proteins, delivering effector proteins into the cytosol of eukaryotic host cells. Upon entry, these effectors manipulate the host cell's machinery for their own benefit, which can result in serious illness or death of the host. For this reason recognition of T4SS effectors has become an important subject. Much previous work has focused on verifying effectors experimentally, a costly endeavor in terms of money, time, and effort. Having good predictions for effectors will help to focus experimental validations and decrease testing costs. In recent years, several scoring and machine learning-based methods have been suggested for the purpose of predicting T4SS effector proteins. These methods have used different sets of features for prediction, and their predictions have been inconsistent. In this paper, an optimal set of features is presented for predicting T4SS effector proteins using a statistical approach. A thorough literature search was performed to find features that have been proposed. Feature values were calculated for datasets of known effectors and non-effectors for T4SS-containing pathogens for four genera with a sufficient number of known effectors, Legionella pneumophila, Coxiella burnetii, Brucella spp, and Bartonella spp. The features were ranked, and less important features were filtered out. Correlations between remaining features were removed, and dimensional reduction was accomplished using principal component analysis and factor analysis. Finally, the optimal features for each pathogen were chosen by building logistic regression models and evaluating each model. The results based on evaluation of our logistic regression models confirm the effectiveness of our four optimal sets of
Metzger-Filho, Otto; Azambuja, Evandro de; Bradbury, Ian; Saini, Kamal S.; Bines, Jose; Simon, Sergio D. [UNIFESP; Van Dooren, Veerle; Aktan, Gursel; Pritchard, Kathleen I.; Wolff, Antonio C.; Smith, Ian; Jackisch, Christian; Lang, Istvan; Untch, Michael; Boyle, Frances
2013-01-01
Purpose. This study measured the time taken for setting up the different facets of Adjuvant Lapatinib and/or Trastuzumab Treatment Optimization (ALTTO), an international phase III study being conducted in 44 participating countries.Methods. Time to regulatory authority (RA) approval, time to ethics committee/institutional review board (EC/IRB) approval, time from study approval by EC/IRB to first randomized patient, and time from first to last randomized patient were prospectively collected i...
Energy Technology Data Exchange (ETDEWEB)
Evarestov, R A; Panin, A I; Bandura, A V; Losev, M V [Department of Quantum Chemistry, St. Petersburg State University, University Prospect 26, Stary Peterghof, St. Petersburg, 198504 (Russian Federation)], E-mail: re1973@re1973.spb.edu
2008-06-01
The results of LCAO DFT calculations of lattice parameters, cohesive energy and bulk modulus of the crystalline uranium nitrides UN, U{sub 2}N{sub 3} and UN{sub 2} are presented and discussed. The LCAO computer codes Gaussian03 and Crystal06 are applied. The calculations are made with the uranium atom relativistic effective small core potential by Stuttgart-Cologne group (60 electrons in the core). The calculations include the U atom basis set optimization. Powell, Hooke-Jeeves, conjugated gradient and Box methods are implemented in the author's optimization package, being external to the codes for molecular and periodic calculations. The basis set optimization in LCAO calculations improves the agreement of the lattice parameter and bulk modulus of UN crystal with the experimental data, the change of the cohesive energy due to the optimization is small. The mixed metallic-covalent chemical bonding is found both in LCAO calculations of UN and U{sub 2}N{sub 3} crystals; UN{sub 2} crystal has the semiconducting nature.
International Nuclear Information System (INIS)
Evarestov, R A; Panin, A I; Bandura, A V; Losev, M V
2008-01-01
The results of LCAO DFT calculations of lattice parameters, cohesive energy and bulk modulus of the crystalline uranium nitrides UN, U 2 N 3 and UN 2 are presented and discussed. The LCAO computer codes Gaussian03 and Crystal06 are applied. The calculations are made with the uranium atom relativistic effective small core potential by Stuttgart-Cologne group (60 electrons in the core). The calculations include the U atom basis set optimization. Powell, Hooke-Jeeves, conjugated gradient and Box methods are implemented in the author's optimization package, being external to the codes for molecular and periodic calculations. The basis set optimization in LCAO calculations improves the agreement of the lattice parameter and bulk modulus of UN crystal with the experimental data, the change of the cohesive energy due to the optimization is small. The mixed metallic-covalent chemical bonding is found both in LCAO calculations of UN and U 2 N 3 crystals; UN 2 crystal has the semiconducting nature
Nonlinear surface Alfven waves
International Nuclear Information System (INIS)
Cramer, N.F.
1991-01-01
The problem of nonlinear surface Alfven waves propagating on an interface between a plasma and a vacuum is discussed, with dispersion provided by the finite-frequency effect, i.e. the finite ratio of the frequency to the ion-cyclotron frequency. A set of simplified nonlinear wave equations is derived using the method of stretched co-ordinates, and another approach uses the generation of a second-harmonic wave and its interaction with the first harmonic to obtain a nonlinear dispersion relation. A nonlinear Schroedinger equation is then derived, and soliton solutions found that propagate as solitary pulses in directions close to parallel and antiparallel to the background magnetic field. (author)
Research on optimizing pass schedule of tandem cold mill
International Nuclear Information System (INIS)
Lu, C.; Wang, J.S.; Zhao, Q.L.; Liu, X.H.; Wang, G.D.
2000-01-01
In this paper, research on pass schedule of tandem cold mill (TCM) is carried out. According to load (reduction, rolling force, motor power) balance, non-linear equations set with variables of inter-stand thickness is constructed. The pass schedule optimization is carried out by solving the non-linear equations set. As the traditional method, the Newton-Raphson method is used for solving the non-linear equations set. In this paper a new simple method is brought up. On basis of the monotone relations between thickness and load, the inter-stands thickness is adjusted dynamically. The solution of non-linear equations set can be converged by iterative calculation. This method can avoid the derivative calculation used by traditional method. So, this method is simple and calculation speed is high. It is suitable for on-line control. (author)
DEFF Research Database (Denmark)
Sessarego, Matias; Shen, Wen Zhong
2018-01-01
Modern wind turbine aero-structural blade design codes generally use a smaller fraction of the full design load base (DLB) or neglect turbulent inflow as defined by the International Electrotechnical Commission standards. The current article describes an automated blade design optimization method...... based on surrogate modeling that includes a very large number of design load cases (DLCs) including turbulence. In the present work, 325 DLCs representative of the full DLB are selected based on the message-passing-interface (MPI) limitations in Matlab. Other methods are currently being investigated, e.......g. a Python MPI implementation, to overcome the limitations in Matlab MPI and ultimately achieve a full DLB optimization framework. The reduced DLB and the annual energy production are computed using the state-of-the-art aero-servo-elastic tool HAWC2. Furthermore, some of the interior dimensions of the blade...
[Nonlinear magnetohydrodynamics
International Nuclear Information System (INIS)
1994-01-01
Resistive MHD equilibrium, even for small resistivity, differs greatly from ideal equilibrium, as do the dynamical consequences of its instabilities. The requirement, imposed by Faraday's law, that time independent magnetic fields imply curl-free electric fields, greatly restricts the electric fields allowed inside a finite-resistivity plasma. If there is no flow and the implications of the Ohm's law are taken into account (and they need not be, for ideal equilibria), the electric field must equal the resistivity times the current density. The vanishing of the divergence of the current density then provides a partial differential equation which, together with boundary conditions, uniquely determines the scalar potential, the electric field, and the current density, for any given resistivity profile. The situation parallels closely that of driven shear flows in hydrodynamics, in that while dissipative steady states are somewhat more complex than ideal ones, there are vastly fewer of them to consider. Seen in this light, the vast majority of ideal MHD equilibria are just irrelevant, incapable of being set up in the first place. The steady state whose stability thresholds and nonlinear behavior needs to be investigated ceases to be an arbitrary ad hoc exercise dependent upon the whim of the investigator, but is determined by boundary conditions and choice of resistivity profile