Homotopy optimization methods for global optimization.
Dunlavy, Daniel M.; O' Leary, Dianne P. (University of Maryland, College Park, MD)
2005-12-01
We define a new method for global optimization, the Homotopy Optimization Method (HOM). This method differs from previous homotopy and continuation methods in that its aim is to find a minimizer for each of a set of values of the homotopy parameter, rather than to follow a path of minimizers. We define a second method, called HOPE, by allowing HOM to follow an ensemble of points obtained by perturbation of previous ones. We relate this new method to standard methods such as simulated annealing and show under what circumstances it is superior. We present results of extensive numerical experiments demonstrating performance of HOM and HOPE.
Stochastic optimization methods
Marti, Kurt
2005-01-01
Optimization problems arising in practice involve random parameters. For the computation of robust optimal solutions, i.e., optimal solutions being insensitive with respect to random parameter variations, deterministic substitute problems are needed. Based on the distribution of the random data, and using decision theoretical concepts, optimization problems under stochastic uncertainty are converted into deterministic substitute problems. Due to the occurring probabilities and expectations, approximative solution techniques must be applied. Deterministic and stochastic approximation methods and their analytical properties are provided: Taylor expansion, regression and response surface methods, probability inequalities, First Order Reliability Methods, convex approximation/deterministic descent directions/efficient points, stochastic approximation methods, differentiation of probability and mean value functions. Convergence results of the resulting iterative solution procedures are given.
Practical methods of optimization
Fletcher, R
2013-01-01
Fully describes optimization methods that are currently most valuable in solving real-life problems. Since optimization has applications in almost every branch of science and technology, the text emphasizes their practical aspects in conjunction with the heuristics useful in making them perform more reliably and efficiently. To this end, it presents comparative numerical studies to give readers a feel for possibile applications and to illustrate the problems in assessing evidence. Also provides theoretical background which provides insights into how methods are derived. This edition offers rev
Stochastic optimization methods
Marti, Kurt
2008-01-01
Optimization problems arising in practice involve random model parameters. This book features many illustrations, several examples, and applications to concrete problems from engineering and operations research.
Analytical methods of optimization
Lawden, D F
2006-01-01
Suitable for advanced undergraduates and graduate students, this text surveys the classical theory of the calculus of variations. It takes the approach most appropriate for applications to problems of optimizing the behavior of engineering systems. Two of these problem areas have strongly influenced this presentation: the design of the control systems and the choice of rocket trajectories to be followed by terrestrial and extraterrestrial vehicles.Topics include static systems, control systems, additional constraints, the Hamilton-Jacobi equation, and the accessory optimization problem. Prereq
Optimizing Methods in Simulation
1981-08-01
exploited by Kiefer and Wolfowitz -; (1959). Wald (1943) used the criterion of D-optimality - in some other context and was so named by Kiefer and...of discrepency between the observed and expected value A is obtained in terms of mean squared errors ( MSE ). i Consider the model, E(Ylx) = a + ex and...V(YIX) = 0 2 Let L < x < U, be the interval of possible x values. The MSE (x) is the mean squared error of x as obtained from y. Let w(x) be a weight
Optimization methods for logical inference
Chandru, Vijay
2011-01-01
Merging logic and mathematics in deductive inference-an innovative, cutting-edge approach. Optimization methods for logical inference? Absolutely, say Vijay Chandru and John Hooker, two major contributors to this rapidly expanding field. And even though ""solving logical inference problems with optimization methods may seem a bit like eating sauerkraut with chopsticks. . . it is the mathematical structure of a problem that determines whether an optimization model can help solve it, not the context in which the problem occurs."" Presenting powerful, proven optimization techniques for logic in
Blindman-Walking Optimization Method
Chunming Li
2010-12-01
Full Text Available Optimization methods are all implemented with the hypothesis of unknowing the mathematic express of objective objection. Using the human analogy innovative method, the one-dimension blind-walking optimal method is proposed in this paper. The theory and the algorithm of this method includes halving, doubling, reversing probing step and verifying the applicability condition. Double-step is available to make current point moving to the extremum point. Half-step is available to accelerate convergence. In order to improve the optimization, the applicability condition decides whether update current point or not. The operation process, algorithmic flow chart and characteristic analysis of the method were given. Two optimization problems with unimodal or multimodal objective function were solved by the proposed method respectively. The simulation result shows that the proposed method is better than the ordinary method. The proposed method has the merit of rapid convergence, little calculation capacity, wide applicable range, etc. Taking the method as innovative kernel, the random research method, feasible direction method and complex shape method were improved. Taking the innovative content of this paper as innovative kernel, a monograph was published. The other innovations of the monograph are listed, such as applied algorithm of Karush-Kuhn-Tucker (KKT qualifications on judging the restriction extremum point, the design step of computing software, the complementarity and derivation of Powell criterion, the method of keeping the complex shape not to deduce dimension and the analysis of gradual optimization characteristic, the reinforced wall of inner point punish function method, the analysis of problem with constrained monstrosity extremum point, the improvement of Newton method and the validation of optimization idea of blind walking repeatedly, the explanation of later-day optimization method, the conformity of seeking algorithm needing the
Method of constrained global optimization
Altschuler, E.L.; Williams, T.J.; Ratner, E.R.; Dowla, F.; Wooten, F. (Lawrence Livermore National Laboratory, P.O. Box 808, Livermore, California 94551 (United States) Department of Applied Physics, Stanford University, Stanford, California 94305 (United States) Department of Applied Science, University of California, Davis/Livermore, P.O. Box 808, Livermore, California 94551 (United States))
1994-04-25
We present a new method for optimization: constrained global optimization (CGO). CGO iteratively uses a Glauber spin flip probability and the Metropolis algorithm. The spin flip probability allows changing only the values of variables contributing excessively to the function to be minimized. We illustrate CGO with two problems---Thomson's problem of finding the minimum-energy configuration of unit charges on a spherical surface, and a problem of assigning offices---for which CGO finds better minima than other methods. We think CGO will apply to a wide class of optimization problems.
Optimization methods in structural design
Rothwell, Alan
2017-01-01
This book offers an introduction to numerical optimization methods in structural design. Employing a readily accessible and compact format, the book presents an overview of optimization methods, and equips readers to properly set up optimization problems and interpret the results. A ‘how-to-do-it’ approach is followed throughout, with less emphasis at this stage on mathematical derivations. The book features spreadsheet programs provided in Microsoft Excel, which allow readers to experience optimization ‘hands-on.’ Examples covered include truss structures, columns, beams, reinforced shell structures, stiffened panels and composite laminates. For the last three, a review of relevant analysis methods is included. Exercises, with solutions where appropriate, are also included with each chapter. The book offers a valuable resource for engineering students at the upper undergraduate and postgraduate level, as well as others in the industry and elsewhere who are new to these highly practical techniques.Whi...
Optimization of Medical Teaching Methods
Wang Fei
2015-12-01
Full Text Available In order to achieve the goal of medical education, medicine and adapt to changes in the way doctors work, with the rapid medical teaching methods of modern science and technology must be reformed. Based on the current status of teaching in medical colleges method to analyze the formation and development of medical teaching methods, characteristics, about how to achieve optimal medical teaching methods for medical education teachers and management workers comprehensive and thorough change teaching ideas and teaching concepts provide a theoretical basis.
Optimal control linear quadratic methods
Anderson, Brian D O
2007-01-01
This augmented edition of a respected text teaches the reader how to use linear quadratic Gaussian methods effectively for the design of control systems. It explores linear optimal control theory from an engineering viewpoint, with step-by-step explanations that show clearly how to make practical use of the material.The three-part treatment begins with the basic theory of the linear regulator/tracker for time-invariant and time-varying systems. The Hamilton-Jacobi equation is introduced using the Principle of Optimality, and the infinite-time problem is considered. The second part outlines the
Roshni D. Tale
2014-04-01
Full Text Available Deception detection has important legal and medical applications, but the reliability of methods for the differentiation between truthful and deceptive responses is still limited. Deception detection can be more accurately achieved by measuring the brain correlates of lying in an individual. For the evaluation of the method, several participants were gone through the designed concealed information test paradigm and their respective brain signals were recorded. The electroencephalogram (EEG signals were recorded and separated into many single trials. To enhance signal noise ratio (SNR of P3 components, the independent component analysis (ICA method was adopted to separate non-P3 (i.e. artifacts and P3 components from every single trial. Then the P3 waveforms with high SNR were reconstructed. And then group of features based on time, frequency, and amplitude were extracted from the reconstructed P3 waveforms. Finally, two different class of feature samples were used to train a support vector machine (SVM classifier because it has higher performance compared with several other classifiers. The method presented in this paper improves the efficiency of CIT and deception detection in comparison with previous reported methods.
QUADRATIC OPTIMIZATION METHOD AND ITS APPLICATION ON OPTIMIZING MECHANISM PARAMETER
ZHAO Yun; CHEN Jianneng; YU Yaxin; YU Gaohong; ZHU Jianping
2006-01-01
In order that the mechanism designed meets the requirements of kinematics with optimal dynamics behaviors, a quadratic optimization method is proposed based on the different characteristics of kinematic and dynamic optimization. This method includes two steps of optimization, that is, kinematic and dynamic optimization. Meanwhile, it uses the results of the kinematic optimization as the constraint equations of dynamic optimization. This method is used in the parameters optimization of transplanting mechanism with elliptic planetary gears of high-speed rice seedling transplanter with remarkable significance. The parameters spectrum, which meets to the kinematic requirements, is obtained through visualized human-computer interactions in the kinematics optimization, and the optimal parameters are obtained based on improved genetic algorithm in dynamic optimization. In the dynamic optimization, the objective function is chosen as the optimal dynamic behavior and the constraint equations are from the results of the kinematic optimization. This method is suitable for multi-objective optimization when both the kinematic and dynamic performances act as objective functions.
Electromagnetics and antenna optimization using Taguchi's method
Weng, Wei-Chung
2007-01-01
This book presents a new global optimization technique using Taguchi's method and its applications in electromagnetics and antenna engineering. Compared with traditional optimization techniques, Taguchi's optimization method is easy to implement and very efficient in reaching optimum solutions.Taguchi's optimization method is developed based on the orthogonal array (OA) concept, which offers a systematic and efficient way to select design parameters. The book illustrates the basic implementation procedure of Taguchi's optimization method and discusses various advanced techniques for performanc
Adiabatic optimization versus diffusion Monte Carlo methods
Jarret, Michael; Jordan, Stephen P.; Lackey, Brad
2016-10-01
Most experimental and theoretical studies of adiabatic optimization use stoquastic Hamiltonians, whose ground states are expressible using only real nonnegative amplitudes. This raises a question as to whether classical Monte Carlo methods can simulate stoquastic adiabatic algorithms with polynomial overhead. Here we analyze diffusion Monte Carlo algorithms. We argue that, based on differences between L1 and L2 normalized states, these algorithms suffer from certain obstructions preventing them from efficiently simulating stoquastic adiabatic evolution in generality. In practice however, we obtain good performance by introducing a method that we call Substochastic Monte Carlo. In fact, our simulations are good classical optimization algorithms in their own right, competitive with the best previously known heuristic solvers for MAX-k -SAT at k =2 ,3 ,4 .
Adaptive scalarization methods in multiobjective optimization
Eichfelder, Gabriele
2008-01-01
This book presents adaptive solution methods for multiobjective optimization problems based on parameter dependent scalarization approaches. Readers will benefit from the new adaptive methods and ideas for solving multiobjective optimization.
Tensor methods for large, sparse unconstrained optimization
Bouaricha, A.
1996-11-01
Tensor methods for unconstrained optimization were first introduced by Schnabel and Chow [SIAM J. Optimization, 1 (1991), pp. 293-315], who describe these methods for small to moderate size problems. This paper extends these methods to large, sparse unconstrained optimization problems. This requires an entirely new way of solving the tensor model that makes the methods suitable for solving large, sparse optimization problems efficiently. We present test results for sets of problems where the Hessian at the minimizer is nonsingular and where it is singular. These results show that tensor methods are significantly more efficient and more reliable than standard methods based on Newton`s method.
Numerical methods for stellarator optimization
Morris, R.N.; Hedrick, C.L.; Hirshman, S.P.; Lyon, J.F.; Rome, J.A.
1989-01-01
A numerical optimization procedure utilizing an inverse 3-D equilibrium solver, a Mercier stability assessment, a deeply-trapped-particle loss assessment, and a nonlinear optimization package has been used to produce low aspect ratio (A = 4) stellarator designs. These designs combine good stability and improved transport with a compact configuration. 7 refs., 2 figs., 1 tab.
Global Optimization methods for Gravitational Lens Systems with Regularized Sources
Rogers, Adam
2012-01-01
Several approaches exist to model gravitational lens systems. In this study, we apply global optimization methods to find the optimal set of lens parameters using a genetic algorithm. We treat the full optimization procedure as a two-step process: an analytical description of the source plane intensity distribution is used to find an initial approximation to the optimal lens parameters. The second stage of the optimization uses a pixelated source plane with the semilinear method to determine an optimal source. Regularization is handled by means of an iterative method and the generalized cross validation (GCV) and unbiased predictive risk estimator (UPRE) functions that are commonly used in standard image deconvolution problems. This approach simultaneously estimates the optimal regularization parameter and the number of degrees of freedom in the source. Using the GCV and UPRE functions we are able to justify an estimation of the number of source degrees of freedom found in previous work. We test our approach ...
OPTIMIZATION METHODS AND SEO TOOLS
Maria Cristina ENACHE
2014-06-01
Full Text Available SEO is the activity of optimizing Web pages or whole sites in order to make them more search engine friendly, thus getting higher positions in search results. Search engine optimization (SEO involves designing, writing, and coding a website in a way that helps to improve the volume and quality of traffic to your website from people using search engines. While Search Engine Optimization is the focus of this booklet, keep in mind that it is one of many marketing techniques. A brief overview of other marketing techniques is provided at the end of this booklet.
Reliability-based concurrent subspace optimization method
FAN Hui; LI Wei-ji
2008-01-01
To avoid the high computational cost and much modification in the process of applying traditional re-liability-based design optimization method, a new reliability-based concurrent subspace optimization approach is proposed based on the comparison and analysis of the existing muhidisciplinary optimization techniques and reli-ability assessment methods. It is shown through a canard configuration optimization for a three-surface transport that the proposed method is computationally efficient and practical with the least modification to the current de-terministic optimization process.
Methods of term labour induction for women with a previous caesarean section.
West, Helen M; Jozwiak, Marta; Dodd, Jodie M
2017-06-09
Women with a prior caesarean delivery have an increased risk of uterine rupture and for women subsequently requiring induction of labour it is unclear which method is preferable to avoid adverse outcomes. This is an update of a review that was published in 2013. To assess the benefits and harms associated with different methods used to induce labour in women who have had a previous caesarean birth. We searched Cochrane Pregnancy and Childbirth's Trials Register (31 August 2016) and reference lists of retrieved studies. Randomised controlled trials (RCTs) comparing any method of third trimester cervical ripening or labour induction, with placebo/no treatment or other methods in women with prior caesarean section requiring labour induction in a subsequent pregnancy. Two review authors independently assessed studies for inclusion and trial quality, extracted data, and checked them for accuracy. Eight studies (data from 707 women and babies) are included in this updated review. Meta-analysis was not possible because studies compared different methods of labour induction. All included studies had at least one design limitation (i.e. lack of blinding, sample attrition, other bias, or reporting bias). One study stopped prematurely due to safety concerns. Vaginal PGE2 versus intravenous oxytocin (one trial, 42 women): no clear differences for caesarean section (risk ratio (RR) 0.67, 95% confidence interval (CI) 0.22 to 2.03, evidence graded low), serious neonatal morbidity or perinatal death (RR 3.00, 95% CI 0.13 to 69.70, evidence graded low), serious maternal morbidity or death (RR 3.00, 95% CI 0.13 to 69.70, evidence graded low). Also no clear differences between groups for the reported secondary outcomes. The GRADE outcomes vaginal delivery not achieved within 24 hours, and uterine hyperstimulation with fetal heart rate changes were not reported. Vaginal misoprostol versus intravenous oxytocin (one trial, 38 women): this trial stopped early because one woman who
Overview of multi-objective optimization methods
雷秀娟; 史忠科
2004-01-01
To assist readers to have a comprehensive understanding, the classical and intelligent methods roundly based on precursory research achievements are summarized in this paper. First, basic conception and description about multi-objective (MO) optimization are introduced. Then some definitions and related terminologies are given. Furthermore several MO optimization methods including classical and current intelligent methods are discussed one by one succinctly. Finally evaluations on advantages and disadvantages about these methods are made at the end of the paper.
Experimental validation of structural optimization methods
Adelman, Howard M.
1992-01-01
The topic of validating structural optimization methods by use of experimental results is addressed. The need for validating the methods as a way of effecting a greater and an accelerated acceptance of formal optimization methods by practicing engineering designers is described. The range of validation strategies is defined which includes comparison of optimization results with more traditional design approaches, establishing the accuracy of analyses used, and finally experimental validation of the optimization results. Examples of the use of experimental results to validate optimization techniques are described. The examples include experimental validation of the following: optimum design of a trussed beam; combined control-structure design of a cable-supported beam simulating an actively controlled space structure; minimum weight design of a beam with frequency constraints; minimization of the vibration response of helicopter rotor blade; minimum weight design of a turbine blade disk; aeroelastic optimization of an aircraft vertical fin; airfoil shape optimization for drag minimization; optimization of the shape of a hole in a plate for stress minimization; optimization to minimize beam dynamic response; and structural optimization of a low vibration helicopter rotor.
DESIGN OPTIMIZATION METHOD USED IN MECHANICAL ENGINEERING
SCURTU Iacob Liviu
2016-11-01
Full Text Available This paper presents an optimization study in mechanical engineering. First part of the research describe the structural optimization method used, followed by the presentation of several optimization studies conducted in recent years. The second part of the paper presents the CAD modelling of an agricultural plough component. The beam of the plough is analysed using finite element method. The plough component is meshed in solid elements, and the load case which mimics the working conditions of agricultural equipment of this are created. The model is prepared to find the optimal structural design, after the FEA study of the model is done. The mass reduction of part is the criterion applied for this optimization study. The end of this research presents the final results and the model optimized shape.
A Novel Multiobjective Optimization Method Based on Sensitivity Analysis
Tiane Li
2016-01-01
Full Text Available For multiobjective optimization problems, different optimization variables have different influences on objectives, which implies that attention should be paid to the variables according to their sensitivity. However, previous optimization studies have not considered the variables sensitivity or conducted sensitivity analysis independent of optimization. In this paper, an integrated algorithm is proposed, which combines the optimization method SPEA (Strength Pareto Evolutionary Algorithm with the sensitivity analysis method SRCC (Spearman Rank Correlation Coefficient. In the proposed algorithm, the optimization variables are worked as samples of sensitivity analysis, and the consequent sensitivity result is used to guide the optimization process by changing the evolutionary parameters. Three cases including a mathematical problem, an airship envelope optimization, and a truss topology optimization are used to demonstrate the computational efficiency of the integrated algorithm. The results showed that this algorithm is able to simultaneously achieve parameter sensitivity and a well-distributed Pareto optimal set, without increasing the computational time greatly in comparison with the SPEA method.
Multidisciplinary Optimization Methods for Aircraft Preliminary Design
Kroo, Ilan; Altus, Steve; Braun, Robert; Gage, Peter; Sobieski, Ian
1994-01-01
This paper describes a research program aimed at improved methods for multidisciplinary design and optimization of large-scale aeronautical systems. The research involves new approaches to system decomposition, interdisciplinary communication, and methods of exploiting coarse-grained parallelism for analysis and optimization. A new architecture, that involves a tight coupling between optimization and analysis, is intended to improve efficiency while simplifying the structure of multidisciplinary, computation-intensive design problems involving many analysis disciplines and perhaps hundreds of design variables. Work in two areas is described here: system decomposition using compatibility constraints to simplify the analysis structure and take advantage of coarse-grained parallelism; and collaborative optimization, a decomposition of the optimization process to permit parallel design and to simplify interdisciplinary communication requirements.
Zhang, Rui; Xie, Wen-Ming; Yu, Han-Qing; Li, Wen-Wei
2014-04-01
An improved multi-objective optimization (MOO) model was established and used for simultaneously optimizing the treatment cost and multiple effluent quality indexes (including effluent COD, NH4(+)-N, NO3(-)-N) of a municipal wastewater treatment plant (WWTP). Compared with previous models that were mainly based on the use of fixed decision factors and did not taken into account the treatment cost, this model introduces a relationship model based on back propagation algorithm to determine the set of decision factors according to the expected optimization targets. Thus, a more flexible and precise optimization of the treatment process was allowed. Moreover, a MOO of conflicting objectives (i.e., treatment cost and effluent quality) was achieved. Applying this method, an optimal balance between operating cost and effluent quality of a WWTP can be found. This model may offer a useful tool for optimized design and control of practical WWTPs.
Kucmin, Tomasz; Płowaś-Goral, Małgorzata; Nogalski, Adam
2015-02-01
Cardiopulmonary resuscitation (CPR) is relatively novel branch of medical science, however first descriptions of mouth-to-mouth ventilation are to be found in the Bible and literature is full of descriptions of different resuscitation methods - from flagellation and ventilation with bellows through hanging the victims upside down and compressing the chest in order to stimulate ventilation to rectal fumigation with tobacco smoke. The modern history of CPR starts with Kouwenhoven et al. who in 1960 published a paper regarding heart massage through chest compressions. Shortly after that in 1961Peter Safar presented a paradigm promoting opening the airway, performing rescue breaths and chest compressions. First CPR guidelines were published in 1966. Since that time guidelines were modified and improved numerously by two leading world expert organizations ERC (European Resuscitation Council) and AHA (American Heart Association) and published in a new version every 5 years. Currently 2010 guidelines should be obliged. In this paper authors made an attempt to present history of development of resuscitation techniques and methods and assess the influence of previous lifesaving methods on nowadays technologies, equipment and guidelines which allow to help those women and men whose life is in danger due to sudden cardiac arrest.
Topology optimization using the finite volume method
Gersborg-Hansen, Allan; Bendsøe, Martin P.; Sigmund, Ole
Computational procedures for topology optimization of continuum problems using a material distribution method are typically based on the application of the finite element method (FEM) (see, e.g. [1]). In the present work we study a computational framework based on the finite volume method (FVM, see......, e.g. [2]) in order to develop methods for topology design for applications where conservation laws are critical such that element--wise conservation in the discretized models has a high priority. This encompasses problems involving for example mass and heat transport. The work described...... in this presentation is focused on a prototype model for topology optimization of steady heat diffusion. This allows for a study of the basic ingredients in working with FVM methods when dealing with topology optimization problems. The FVM and FEM based formulations differ both in how one computes the design...
A topological derivative method for topology optimization
Norato, J.; Bendsøe, Martin P.; Haber, RB;
2007-01-01
resource constraint. A smooth and consistent projection of the region bounded by the level set onto the fictitious analysis domain simplifies the response analysis and enhances the convergence of the optimization algorithm. Moreover, the projection supports the reintroduction of solid material in void......We propose a fictitious domain method for topology optimization in which a level set of the topological derivative field for the cost function identifies the boundary of the optimal design. We describe a fixed-point iteration scheme that implements this optimality criterion subject to a volumetric...... regions, a critical requirement for robust topology optimization. We present several numerical examples that demonstrate compliance minimization of fixed-volume, linearly elastic structures....
Boolean methods of optimization over independence systems
Hulme, B.L.
1983-01-01
This paper presents both a direct and an iterative method of solving the combinatorial optimization problem associated with any independence system. The methods use Boolean algebraic computations to produce solutions. In addition, the iterative method employs a version of the greedy algorithm both to compute upper bounds on the optimum value and to produce the additional circuits needed at every stage. The methods are extensions of those used to solve a problem of fire protection at nuclear reactor power plants.
The optimal homotopy asymptotic method engineering applications
Marinca, Vasile
2015-01-01
This book emphasizes in detail the applicability of the Optimal Homotopy Asymptotic Method to various engineering problems. It is a continuation of the book “Nonlinear Dynamical Systems in Engineering: Some Approximate Approaches”, published at Springer in 2011, and it contains a great amount of practical models from various fields of engineering such as classical and fluid mechanics, thermodynamics, nonlinear oscillations, electrical machines, and so on. The main structure of the book consists of 5 chapters. The first chapter is introductory while the second chapter is devoted to a short history of the development of homotopy methods, including the basic ideas of the Optimal Homotopy Asymptotic Method. The last three chapters, from Chapter 3 to Chapter 5, are introducing three distinct alternatives of the Optimal Homotopy Asymptotic Method with illustrative applications to nonlinear dynamical systems. The third chapter deals with the first alternative of our approach with two iterations. Five application...
Topology optimization using the finite volume method
Computational procedures for topology optimization of continuum problems using a material distribution method are typically based on the application of the finite element method (FEM) (see, e.g. [1]). In the present work we study a computational framework based on the finite volume method (FVM, see...... in this presentation is focused on a prototype model for topology optimization of steady heat diffusion. This allows for a study of the basic ingredients in working with FVM methods when dealing with topology optimization problems. The FVM and FEM based formulations differ both in how one computes the design...... derivative of the system matrix K and in how one computes the discretized version of certain objective functions. Thus for a cost function for minimum dissipated energy (like minimum compliance for an elastic structure) one obtains an expression c = u^\\T \\tilde{K}u $, where \\tilde{K} is different from K...
Topology optimization using the finite volume method
Gersborg-Hansen, Allan; Bendsøe, Martin P.; Sigmund, Ole
Computational procedures for topology optimization of continuum problems using a material distribution method are typically based on the application of the finite element method (FEM) (see, e.g. [1]). In the present work we study a computational framework based on the finite volume method (FVM, see...... in this presentation is focused on a prototype model for topology optimization of steady heat diffusion. This allows for a study of the basic ingredients in working with FVM methods when dealing with topology optimization problems. The FVM and FEM based formulations differ both in how one computes the design...... $, where $\\tilde{\\mathbf K}$ is different from $\\mathbf K $; in a FEM scheme these matrices are equal following the principle of virtual work. Using a staggered mesh and averaging procedures consistent with the FVM the checkerboard problem is eliminated. Two averages are compared to FE solutions, being...
Topology optimization using the finite volume method
Computational procedures for topology optimization of continuum problems using a material distribution method are typically based on the application of the finite element method (FEM) (see, e.g. [1]). In the present work we study a computational framework based on the finite volume method (FVM, see...... the well known Reuss lower bound. [1] Bendsøe, M.P.; Sigmund, O. 2004: Topology Optimization - Theory, Methods, and Applications. Berlin Heidelberg: Springer Verlag [2] Versteeg, H. K.; W. Malalasekera 1995: An introduction to Computational Fluid Dynamics: the Finite Volume Method. London: Longman......, e.g. [2]) in order to develop methods for topology design for applications where conservation laws are critical such that element--wise conservation in the discretized models has a high priority. This encompasses problems involving for example mass and heat transport. The work described...
Optimal boarding method for airline passengers
Steffen, Jason H.; /Fermilab
2008-02-01
Using a Markov Chain Monte Carlo optimization algorithm and a computer simulation, I find the passenger ordering which minimizes the time required to board the passengers onto an airplane. The model that I employ assumes that the time that a passenger requires to load his or her luggage is the dominant contribution to the time needed to completely fill the aircraft. The optimal boarding strategy may reduce the time required to board and airplane by over a factor of four and possibly more depending upon the dimensions of the aircraft. I explore some features of the optimal boarding method and discuss practical modifications to the optimal. Finally, I mention some of the benefits that could come from implementing an improved passenger boarding scheme.
State space Newton's method for topology optimization
Evgrafov, Anton
2014-01-01
We introduce a new algorithm for solving certain classes of topology optimization problems, which enjoys fast local convergence normally achieved by the full space methods while working in a smaller reduced space. The computational complexity of Newton’s direction finding subproblem in the algori......We introduce a new algorithm for solving certain classes of topology optimization problems, which enjoys fast local convergence normally achieved by the full space methods while working in a smaller reduced space. The computational complexity of Newton’s direction finding subproblem...
An introduction to harmony search optimization method
Wang, Xiaolei; Zenger, Kai
2014-01-01
This brief provides a detailed introduction, discussion and bibliographic review of the nature1-inspired optimization algorithm called Harmony Search. It uses a large number of simulation results to demonstrate the advantages of Harmony Search and its variants and also their drawbacks. The authors show how weaknesses can be amended by hybridization with other optimization methods. The Harmony Search Method with Applications will be of value to researchers in computational intelligence in demonstrating the state of the art of research on an algorithm of current interest. It also helps researche
Optimization methods applied to hybrid vehicle design
Donoghue, J. F.; Burghart, J. H.
1983-01-01
The use of optimization methods as an effective design tool in the design of hybrid vehicle propulsion systems is demonstrated. Optimization techniques were used to select values for three design parameters (battery weight, heat engine power rating and power split between the two on-board energy sources) such that various measures of vehicle performance (acquisition cost, life cycle cost and petroleum consumption) were optimized. The apporach produced designs which were often significant improvements over hybrid designs already reported on in the literature. The principal conclusions are as follows. First, it was found that the strategy used to split the required power between the two on-board energy sources can have a significant effect on life cycle cost and petroleum consumption. Second, the optimization program should be constructed so that performance measures and design variables can be easily changed. Third, the vehicle simulation program has a significant effect on the computer run time of the overall optimization program; run time can be significantly reduced by proper design of the types of trips the vehicle takes in a one year period. Fourth, care must be taken in designing the cost and constraint expressions which are used in the optimization so that they are relatively smooth functions of the design variables. Fifth, proper handling of constraints on battery weight and heat engine rating, variables which must be large enough to meet power demands, is particularly important for the success of an optimization study. Finally, the principal conclusion is that optimization methods provide a practical tool for carrying out the design of a hybrid vehicle propulsion system.
Optimization Methods in Emotion Recognition System
L. Povoda
2016-09-01
Full Text Available Emotions play big role in our everyday communication and contain important information. This work describes a novel method of automatic emotion recognition from textual data. The method is based on well-known data mining techniques, novel approach based on parallel run of SVM (Support Vector Machine classifiers, text preprocessing and 3 optimization methods: sequential elimination of attributes, parameter optimization based on token groups, and method of extending train data sets during practical testing and production release final tuning. We outperformed current state of the art methods and the results were validated on bigger data sets (3346 manually labelled samples which is less prone to overfitting when compared to related works. The accuracy achieved in this work is 86.89% for recognition of 5 emotional classes. The experiments were performed in the real world helpdesk environment, was processing Czech language but the proposed methodology is general and can be applied to many different languages.
Adaptive finite element method for shape optimization
Morin, Pedro
2012-01-16
We examine shape optimization problems in the context of inexact sequential quadratic programming. Inexactness is a consequence of using adaptive finite element methods (AFEM) to approximate the state and adjoint equations (via the dual weighted residual method), update the boundary, and compute the geometric functional. We present a novel algorithm that equidistributes the errors due to shape optimization and discretization, thereby leading to coarse resolution in the early stages and fine resolution upon convergence, and thus optimizing the computational effort. We discuss the ability of the algorithm to detect whether or not geometric singularities such as corners are genuine to the problem or simply due to lack of resolution - a new paradigm in adaptivity. © EDP Sciences, SMAI, 2012.
Method for optimizing harvesting of crops
2008-01-01
In order e.g. to optimize harvesting crops of the kind which may be self dried on a field prior to a harvesting step (116, 118), there is disclosed a method of providing a mobile unit (102) for working (114, 116, 118) the field with crops, equipping the mobile unit (102) with crop biomass...
Non-probabilistic Robust Optimal Design Method
SUN Wei; XU Huanwei; ZHANG Xu
2009-01-01
For the purpose of dealing with uncertainty factors in engineering optimization problems, this paper presents a new non-probabilistic robust optimal design method based on maximum variation estimation. The method analyzes the effect of uncertain factors to objective and constraints functions, and then the maximal variations to a solution are calculated. In order to guarantee robust feasibility the maximal variations of constraints are added to original constraints as penalty term; the maximal variation of objective function is taken as a robust index to a solution; linear physical programming is used to adjust the values of quality characteristic and quality variation, and then a bi-level mathematical robust optimal model is coustructed. The method does not require presumed probability distribution of uncertain factors or continuous and differentiable of objective and constraints functions. To demonstrate the proposed method, the design of the two-bar structure acted by concentrated load is presented. In the example the robustness of the normal stress, feasibility of the total volume and the buckling stress are studied. The robust optimal design results show that in the condition of maintaining feasibility robustness, the proposed approach can obtain a robust solution which the designer is satisfied with the value of objective function and its variation.
Models and Methods for Free Material Optimization
Weldeyesus, Alemseged Gebrehiwot
FMO problem formulations with stress constraints. These problems are highly nonlinear and lead to the so-called singularity phenomenon. The method described in the thesis has successfully solved these problems. In the numerical experiments the stress constraints have been satisfied with high...... conditions for physical attainability, in the context that, it has to be symmetric and positive semidefinite. FMO problems have been studied for the last two decades in many articles that led to the development of a wide range of models, methods, and theories. As the design variables in FMO are the local....... These problems are more difficult to solve and demand higher computational efforts than the standard optimization problems. The focus of today’s development of solution methods for FMO problems is based on first-order methods that require a large number of iterations to obtain optimal solutions. The scope...
Optimization methods and silicon solar cell numerical models
Girardini, K.
1986-01-01
The goal of this project is the development of an optimization algorithm for use with a solar cell model. It is possible to simultaneously vary design variables such as impurity concentrations, front junction depth, back junctions depth, and cell thickness to maximize the predicted cell efficiency. An optimization algorithm has been developed and interfaced with the Solar Cell Analysis Program in 1 Dimension (SCAPID). SCAPID uses finite difference methods to solve the differential equations which, along with several relations from the physics of semiconductors, describe mathematically the operation of a solar cell. A major obstacle is that the numerical methods used in SCAPID require a significant amount of computer time, and during an optimization the model is called iteratively until the design variables converge to the value associated with the maximum efficiency. This problem has been alleviated by designing an optimization code specifically for use with numerically intensive simulations, to reduce the number of times the efficiency has to be calculated to achieve convergence to the optimal solution. Adapting SCAPID so that it could be called iteratively by the optimization code provided another means of reducing the cpu time required to complete an optimization. Instead of calculating the entire I-V curve, as is usually done in SCAPID, only the efficiency is calculated (maximum power voltage and current) and the solution from previous calculations is used to initiate the next solution.
Dead time optimization method for power converter
Deselaers, C.; Bergmann, U.; Gronwald, F.
2013-07-01
This paper introduces a method for dead time optimization in variable speed motor drive systems. The aim of this method is to reduce the conduction time of the freewheeling diode to a minimum without generation of cross conduction. This results in lower losses, improved EMC, and less overshooting of the phase voltage. The principle of the method is to detect beginning cross currents without adding additional components in the half bridge like resistors or inductances. Only the wave shape of the phase voltage needs to be monitored during switching. This is illustrated by an application of the method to a real power converter.
PART BUILDING ORIENTATION OPTIMIZATION METHOD IN STEREOLITHOGRAPHY
无
2006-01-01
Aiming at the part quality and building time problems in stereolithography (SL) caused by unreasonable building orientation, a part building orientation decision method in SL rapid prototyping (RP) is carried out. Bringing into full consideration of the deformation, stair-stepping effect, overcure effect and building time related to the part fabrication orientation, and using evaluation function method, a multi-objective optimization model for the building orientation is defined. According to the difference in the angles between normal vectors of triangular facets in standard triangulation language (STL) model and z axis, the expressions of deformation area, stair-stepping area, overcure area are established. According to the characteristics in SL process, part building time is divided into four sections, that is, hatching scanning time, outline scanning time, support building time and layer waiting time. Expressions of each building time section are given. Considering the features of this optimization model, genetic algorithm (GA) is used to derive the optimization objective, related software is developed and optimization results are tested through experiments. Application shows that this method can effectively solve the quality and efficiency troubles caused by unreasonable part building orientation, an automatic orientation-determining program is developed and verified through test.
On the wavelet optimized finite difference method
Jameson, Leland
1994-01-01
When one considers the effect in the physical space, Daubechies-based wavelet methods are equivalent to finite difference methods with grid refinement in regions of the domain where small scale structure exists. Adding a wavelet basis function at a given scale and location where one has a correspondingly large wavelet coefficient is, essentially, equivalent to adding a grid point, or two, at the same location and at a grid density which corresponds to the wavelet scale. This paper introduces a wavelet optimized finite difference method which is equivalent to a wavelet method in its multiresolution approach but which does not suffer from difficulties with nonlinear terms and boundary conditions, since all calculations are done in the physical space. With this method one can obtain an arbitrarily good approximation to a conservative difference method for solving nonlinear conservation laws.
Optimal design method for fast carry-skip adders
Lee, Songjun; Swartzlander, Earl E., Jr.
2001-11-01
A carry-skip adder is faster than a ripple carry adder and it has a simple structure. To maximize the speed it is necessary to optimize the width of the blocks that comprise the carry skip adder. This paper presents a simple algorithm to select the size of each block. Assuming that each logic gate has a unit delay, the algorithm achieves slightly faster designs for 64 and 128 bit adders than previous methods developed by Guyot, et al. and Kantabutra.
Layout optimization with algebraic multigrid methods
Regler, Hans; Ruede, Ulrich
1993-01-01
Finding the optimal position for the individual cells (also called functional modules) on the chip surface is an important and difficult step in the design of integrated circuits. This paper deals with the problem of relative placement, that is the minimization of a quadratic functional with a large, sparse, positive definite system matrix. The basic optimization problem must be augmented by constraints to inhibit solutions where cells overlap. Besides classical iterative methods, based on conjugate gradients (CG), we show that algebraic multigrid methods (AMG) provide an interesting alternative. For moderately sized examples with about 10000 cells, AMG is already competitive with CG and is expected to be superior for larger problems. Besides the classical 'multiplicative' AMG algorithm where the levels are visited sequentially, we propose an 'additive' variant of AMG where levels may be treated in parallel and that is suitable as a preconditioner in the CG algorithm.
Reduced basis method for source mask optimization
Pomplun, J; Burger, S; Schmidt, F; Tyminski, J; Flagello, D; Toshiharu, N; 10.1117/12.866101
2010-01-01
Image modeling and simulation are critical to extending the limits of leading edge lithography technologies used for IC making. Simultaneous source mask optimization (SMO) has become an important objective in the field of computational lithography. SMO is considered essential to extending immersion lithography beyond the 45nm node. However, SMO is computationally extremely challenging and time-consuming. The key challenges are due to run time vs. accuracy tradeoffs of the imaging models used for the computational lithography. We present a new technique to be incorporated in the SMO flow. This new approach is based on the reduced basis method (RBM) applied to the simulation of light transmission through the lithography masks. It provides a rigorous approximation to the exact lithographical problem, based on fully vectorial Maxwell's equations. Using the reduced basis method, the optimization process is divided into an offline and an online steps. In the offline step, a RBM model with variable geometrical param...
Decision-Theoretic Methods in Simulation Optimization
2014-09-24
Materiel Command REPORT DOCUMENTATION PAGE Form Approved OMB No. 0704-0188 Public reporting burden for this collection of information is...Alamos National Lab: Frazier visited LANL , hosted by Frank Alexander, in January 2013, where he discussed the use of simulation optimization methods for...Alexander, Turab Lookman, and others from LANL , at the Materials Informatics Workshop at the Sante Fe Institute in April 2013. In February 2014, Frazier
Lifecycle-Based Swarm Optimization Method for Numerical Optimization
Hai Shen
2014-01-01
Full Text Available Bioinspired optimization algorithms have been widely used to solve various scientific and engineering problems. Inspired by biological lifecycle, this paper presents a novel optimization algorithm called lifecycle-based swarm optimization (LSO. Biological lifecycle includes four stages: birth, growth, reproduction, and death. With this process, even though individual organism died, the species will not perish. Furthermore, species will have stronger ability of adaptation to the environment and achieve perfect evolution. LSO simulates Biological lifecycle process through six optimization operators: chemotactic, assimilation, transposition, crossover, selection, and mutation. In addition, the spatial distribution of initialization population meets clumped distribution. Experiments were conducted on unconstrained benchmark optimization problems and mechanical design optimization problems. Unconstrained benchmark problems include both unimodal and multimodal cases the demonstration of the optimal performance and stability, and the mechanical design problem was tested for algorithm practicability. The results demonstrate remarkable performance of the LSO algorithm on all chosen benchmark functions when compared to several successful optimization techniques.
portfolio optimization based on nonparametric estimation methods
mahsa ghandehari
2017-03-01
Full Text Available One of the major issues investors are facing with in capital markets is decision making about select an appropriate stock exchange for investing and selecting an optimal portfolio. This process is done through the risk and expected return assessment. On the other hand in portfolio selection problem if the assets expected returns are normally distributed, variance and standard deviation are used as a risk measure. But, the expected returns on assets are not necessarily normal and sometimes have dramatic differences from normal distribution. This paper with the introduction of conditional value at risk ( CVaR, as a measure of risk in a nonparametric framework, for a given expected return, offers the optimal portfolio and this method is compared with the linear programming method. The data used in this study consists of monthly returns of 15 companies selected from the top 50 companies in Tehran Stock Exchange during the winter of 1392 which is considered from April of 1388 to June of 1393. The results of this study show the superiority of nonparametric method over the linear programming method and the nonparametric method is much faster than the linear programming method.
Optimized Vertex Method and Hybrid Reliability
Smith, Steven A.; Krishnamurthy, T.; Mason, B. H.
2002-01-01
A method of calculating the fuzzy response of a system is presented. This method, called the Optimized Vertex Method (OVM), is based upon the vertex method but requires considerably fewer function evaluations. The method is demonstrated by calculating the response membership function of strain-energy release rate for a bonded joint with a crack. The possibility of failure of the bonded joint was determined over a range of loads. After completing the possibilistic analysis, the possibilistic (fuzzy) membership functions were transformed to probability density functions and the probability of failure of the bonded joint was calculated. This approach is called a possibility-based hybrid reliability assessment. The possibility and probability of failure are presented and compared to a Monte Carlo Simulation (MCS) of the bonded joint.
Swarm Optimization Methods in Microwave Imaging
Andrea Randazzo
2012-01-01
Full Text Available Swarm intelligence denotes a class of new stochastic algorithms inspired by the collective social behavior of natural entities (e.g., birds, ants, etc.. Such approaches have been proven to be quite effective in several applicative fields, ranging from intelligent routing to image processing. In the last years, they have also been successfully applied in electromagnetics, especially for antenna synthesis, component design, and microwave imaging. In this paper, the application of swarm optimization methods to microwave imaging is discussed, and some recent imaging approaches based on such methods are critically reviewed.
Binary discrete method of topology optimization
MEI Yu-lin; WANG Xiao-ming; CHENG Geng-dong
2007-01-01
The numerical non-stability of a discrete algorithm of topology optimization can result from the inaccurate evaluation of element sensitivities. Especially, when material is added to elements, the estimation of element sensitivities is very inaccurate,even their signs are also estimated wrong. In order to overcome the problem, a new incremental sensitivity analysis formula is constructed based on the perturbation analysis of the elastic equilibrium increment equation, which can provide us a good estimate of the change of the objective function whether material is removed from or added to elements,meanwhile it can also be considered as the conventional sensitivity formula modified by a non-local element stiffness matrix. As a consequence, a binary discrete method of topology optimization is established, in which each element is assigned either a stiffness value of solid material or a small value indicating no material, and the optimization process can remove material from elements or add material to elements so as to make the objective function decrease. And a main advantage of the method is simple and no need of much mathematics, particularly interesting in engineering application.
METHODS OF INTEGRATED OPTIMIZATION MAGLEV TRANSPORT SYSTEMS
A. Lasher
2013-09-01
example, this research proved the sustainability of the proposed integrated optimization parameters of transport systems. This approach could be applied not only for MTS, but also for other transport systems. Originality. The bases of the complex optimization of transport presented are the new system of universal scientific methods and approaches that ensure high accuracy and authenticity of calculations with the simulation of transport systems and transport networks taking into account the dynamics of their development. Practical value. The development of the theoretical and technological bases of conducting the complex optimization of transport makes it possible to create the scientific tool, which ensures the fulfillment of the automated simulation and calculating of technical and economic structure and technology of the work of different objects of transport, including its infrastructure.
Methods for Distributed Optimal Energy Management
Brehm, Robert
The presented research deals with the fundamental underlying methods and concepts of how the growing number of distributed generation units based on renewable energy resources and distributed storage devices can be most efficiently integrated into the existing utility grid. In contrast to convent......The presented research deals with the fundamental underlying methods and concepts of how the growing number of distributed generation units based on renewable energy resources and distributed storage devices can be most efficiently integrated into the existing utility grid. In contrast...... to conventional centralised optimal energy flow management systems, here-in, focus is set on how optimal energy management can be achieved in a decentralised distributed architecture such as a multi-agent system. Distributed optimisation methods are introduced, targeting optimisation of energy flow in virtual...... micro-grids by prevention of meteorologic power flows into high voltage grids. A method, based on mathematical optimisation and a consensus algorithm is introduced and evaluated to coordinate charge/discharge scheduling for batteries between a number of buildings in order to improve self...
PRODUCT OPTIMIZATION METHOD BASED ON ANALYSIS OF OPTIMAL VALUES OF THEIR CHARACTERISTICS
Constantin D. STANESCU
2016-05-01
Full Text Available The paper presents an original method of optimizing products based on the analysis of optimal values of their characteristics . Optimization method comprises statistical model and analytical model . With this original method can easily and quickly obtain optimal product or material .
Optimal Variational Method for Truly Nonlinear Oscillators
Vasile Marinca
2013-01-01
Full Text Available The Optimal Variational Method (OVM is introduced and applied for calculating approximate periodic solutions of “truly nonlinear oscillators”. The main advantage of this procedure consists in that it provides a convenient way to control the convergence of approximate solutions in a very rigorous way and allows adjustment of convergence regions where necessary. This approach does not depend upon any small or large parameters. A very good agreement was found between approximate and numerical solution, which proves that OVM is very efficient and accurate.
Method for optimizing harvesting of crops
2010-01-01
In order e.g. to optimize harvesting crops of the kind which may be self dried on a field prior to a harvesting step (116, 118), there is disclosed a method of providing a mobile unit (102) for working (114, 116, 118) the field with crops, equipping the mobile unit (102) with crop biomass measuring...... moving the mobile unit on the field and the moisture content (109a, 109b), and determining an optimised drying time (104a, 104b) prior to the following harvesting step (116, 118) in response to the spatial crop biomass and crop moisture content characteristics map and in response to a weather forecast...
Hybrid intelligent optimization methods for engineering problems
Pehlivanoglu, Yasin Volkan
The purpose of optimization is to obtain the best solution under certain conditions. There are numerous optimization methods because different problems need different solution methodologies; therefore, it is difficult to construct patterns. Also mathematical modeling of a natural phenomenon is almost based on differentials. Differential equations are constructed with relative increments among the factors related to yield. Therefore, the gradients of these increments are essential to search the yield space. However, the landscape of yield is not a simple one and mostly multi-modal. Another issue is differentiability. Engineering design problems are usually nonlinear and they sometimes exhibit discontinuous derivatives for the objective and constraint functions. Due to these difficulties, non-gradient-based algorithms have become more popular in recent decades. Genetic algorithms (GA) and particle swarm optimization (PSO) algorithms are popular, non-gradient based algorithms. Both are population-based search algorithms and have multiple points for initiation. A significant difference from a gradient-based method is the nature of the search methodologies. For example, randomness is essential for the search in GA or PSO. Hence, they are also called stochastic optimization methods. These algorithms are simple, robust, and have high fidelity. However, they suffer from similar defects, such as, premature convergence, less accuracy, or large computational time. The premature convergence is sometimes inevitable due to the lack of diversity. As the generations of particles or individuals in the population evolve, they may lose their diversity and become similar to each other. To overcome this issue, we studied the diversity concept in GA and PSO algorithms. Diversity is essential for a healthy search, and mutations are the basic operators to provide the necessary variety within a population. After having a close scrutiny of the diversity concept based on qualification and
Circular SAR Optimization Imaging Method of Buildings
Wang Jian-feng
2015-12-01
Full Text Available The Circular Synthetic Aperture Radar (CSAR can obtain the entire scattering properties of targets because of its great ability of 360° observation. In this study, an optimal orientation of the CSAR imaging algorithm of buildings is proposed by applying a combination of coherent and incoherent processing techniques. FEKO software is used to construct the electromagnetic scattering modes and simulate the radar echo. The FEKO imaging results are compared with the isotropic scattering results. On comparison, the optimal azimuth coherent accumulation angle of CSAR imaging of buildings is obtained. Practically, the scattering directions of buildings are unknown; therefore, we divide the 360° echo of CSAR into many overlapped and few angle echoes corresponding to the sub-aperture and then perform an imaging procedure on each sub-aperture. Sub-aperture imaging results are applied to obtain the all-around image using incoherent fusion techniques. The polarimetry decomposition method is used to decompose the all-around image and further retrieve the edge information of buildings successfully. The proposed method is validated with P-band airborne CSAR data from Sichuan, China.
Optimization methods for activities selection problems
Mahad, Nor Faradilah; Alias, Suriana; Yaakop, Siti Zulaika; Arshad, Norul Amanina Mohd; Mazni, Elis Sofia
2017-08-01
Co-curriculum activities must be joined by every student in Malaysia and these activities bring a lot of benefits to the students. By joining these activities, the students can learn about the time management and they can developing many useful skills. This project focuses on the selection of co-curriculum activities in secondary school using the optimization methods which are the Analytic Hierarchy Process (AHP) and Zero-One Goal Programming (ZOGP). A secondary school in Negeri Sembilan, Malaysia was chosen as a case study. A set of questionnaires were distributed randomly to calculate the weighted for each activity based on the 3 chosen criteria which are soft skills, interesting activities and performances. The weighted was calculated by using AHP and the results showed that the most important criteria is soft skills. Then, the ZOGP model will be analyzed by using LINGO Software version 15.0. There are two priorities to be considered. The first priority which is to minimize the budget for the activities is achieved since the total budget can be reduced by RM233.00. Therefore, the total budget to implement the selected activities is RM11,195.00. The second priority which is to select the co-curriculum activities is also achieved. The results showed that 9 out of 15 activities were selected. Thus, it can concluded that AHP and ZOGP approach can be used as the optimization methods for activities selection problem.
Computational methods applied to wind tunnel optimization
Lindsay, David
This report describes computational methods developed for optimizing the nozzle of a three-dimensional subsonic wind tunnel. This requires determination of a shape that delivers flow to the test section, typically with a speed increase of 7 or more and a velocity uniformity of .25% or better, in a compact length without introducing boundary layer separation. The need for high precision, smooth solutions, and three-dimensional modeling required the development of special computational techniques. These include: (1) alternative formulations to Neumann and Dirichlet boundary conditions, to deal with overspecified, ill-posed, or cyclic problems, and to reduce the discrepancy between numerical solutions and boundary conditions; (2) modification of the Finite Element Method to obtain solutions with numerically exact conservation properties; (3) a Matlab implementation of general degree Finite Element solvers for various element designs in two and three dimensions, exploiting vector indexing to obtain optimal efficiency; (4) derivation of optimal quadrature formulas for integration over simplexes in two and three dimensions, and development of a program for semi-automated generation of formulas for any degree and dimension; (5) a modification of a two-dimensional boundary layer formulation to provide accurate flow conservation in three dimensions, and modification of the algorithm to improve stability; (6) development of multi-dimensional spline functions to achieve smoother solutions in three dimensions by post-processing, new three-dimensional elements for C1 basis functions, and a program to assist in the design of elements with higher continuity; and (7) a development of ellipsoidal harmonics and Lame's equation, with generalization to any dimension and a demonstration that Cartesian, cylindrical, spherical, spheroidal, and sphero-conical harmonics are all limiting cases. The report includes a description of the Finite Difference, Finite Volume, and domain remapping
Constantin Bota
2014-01-01
Full Text Available The paper presents the optimal homotopy perturbation method, which is a new method to find approximate analytical solutions for nonlinear partial differential equations. Based on the well-known homotopy perturbation method, the optimal homotopy perturbation method presents an accelerated convergence compared to the regular homotopy perturbation method. The applications presented emphasize the high accuracy of the method by means of a comparison with previous results.
Optimization of methods for detecting norovirus on various fruit.
Kim, Hee-Yeon; Kwak, In-Shin; Hwang, In-Gyun; Ko, GwangPyo
2008-11-01
Methods for detecting norovirus (NoV) in food are crucial for investigation and prevention of outbreaks caused by NoV-contaminated food. However, current NoV detection methods have not been well examined or optimized. In this study, the effectiveness of various methods for eluting NoV from various fruit, concentrating the virus using polyethylene glycol (PEG), and extracting the viral RNA for subsequent assay by RT-PCR was optimized. First, six different buffers previously described for eluting NoV from fruit surfaces were evaluated. A known amount of NoV was spiked onto the surface of grapes, strawberries, and raspberries, and the virus was recovered with distilled water, 0.05 M glycine-0.14 M NaCl (pH 7.5), 2.9% tryptose phosphate broth-6% glycine, 100 mM Tris-HCl (pH 9.5), 50 mM glycine-50 mM MgCl(2) (pH 9.5), or 3% beef extract. Quantitation of the recovered virus using RT-PCR revealed that the most effective elution buffer was 3% beef extract. Secondly, to optimize a method for concentrating the recovered NoV, the key parameters of PEG precipitation, a typical method for concentrating enteric virus, were investigated. The influence of PEG molecular weight and the duration and temperature of the precipitation procedure were examined. NoV was concentrated most efficiently by precipitation when PEG (10,000) was used for 4h at room temperature. Finally, five different methods for nucleic acid extraction were evaluated. Among RNA extraction methods examined, QIAamp Viral RNA Mini kit showed the best recovery efficiency. Using the optimized method, approximately 6-80% of the seeded NoV was recovered from the various fruit.
A Study of Electrical Motors Controlling Optimization Methods
Saeid Fatemi
2013-11-01
Full Text Available In order to design an efficient motor cooling system, it is important to accurately predict the power optimization which is normally dissipated in form of heat. This study presents an analytical method for estimating bearing frictional optimization and numerical method for estimating electromagnetic optimization for an electric vehicle electrical motor. The power optimization obtained use heat sources when evaluating the thermal performance of the motor. The results showed that electromagnetic optimization are dominant and contributed over 80% of all optimization, while bearing optimization contributes about 2% of the total electric motor. The results also showed that bearing optimization increase significantly with increasing speed or load.
On Best Practice Optimization Methods in R
John C. Nash
2014-09-01
Full Text Available R (R Core Team 2014 provides a powerful and flexible system for statistical computations. It has a default-install set of functionality that can be expanded by the use of several thousand add-in packages as well as user-written scripts. While R is itself a programming language, it has proven relatively easy to incorporate programs in other languages, particularly Fortran and C. Success, however, can lead to its own costs: • Users face a confusion of choice when trying to select packages in approaching a problem. • A need to maintain workable examples using early methods may mean some tools offered as a default may be dated. • In an open-source project like R, how to decide what tools offer "best practice" choices, and how to implement such a policy, present a serious challenge. We discuss these issues with reference to the tools in R for nonlinear parameter estimation (NLPE and optimization, though for the present article `optimization` will be limited to function minimization of essentially smooth functions with at most bounds constraints on the parameters. We will abbreviate this class of problems as NLPE. We believe that the concepts proposed are transferable to other classes of problems seen by R users.
Optimization of Binder Jetting Using Taguchi Method
Shrestha, Sanjay; Manogharan, Guha
2017-03-01
Among several additive manufacturing (AM) methods, binder-jetting has undergone a recent advancement in its ability to process metal powders through selective deposition of binders on a powder bed followed by curing, sintering, and infiltration. This study analyzes the impact of various process parameters in binder jetting on mechanical properties of sintered AM metal parts. The Taguchi optimization method has been employed to determine the optimum AM parameters to improve transverse rupture strength (TRS), specifically: binder saturation, layer thickness, roll speed, and feed-to-powder ratio. The effects of the selected process parameters on the TRS performance of sintered SS 316L samples are studied with the American Society of Testing Materials (ASTM) standard test method. It was found that binder saturation and feed-to-powder ratio were the most critical parameters, which reflects the strong influence of binder powder interaction and density of powder bed on resulting mechanical properties. This article serves as an aid in understanding the optimum process parameters for binder jetting of SS 316L.
Optimization of Binder Jetting Using Taguchi Method
Shrestha, Sanjay; Manogharan, Guha
2017-01-01
Among several additive manufacturing (AM) methods, binder-jetting has undergone a recent advancement in its ability to process metal powders through selective deposition of binders on a powder bed followed by curing, sintering, and infiltration. This study analyzes the impact of various process parameters in binder jetting on mechanical properties of sintered AM metal parts. The Taguchi optimization method has been employed to determine the optimum AM parameters to improve transverse rupture strength (TRS), specifically: binder saturation, layer thickness, roll speed, and feed-to-powder ratio. The effects of the selected process parameters on the TRS performance of sintered SS 316L samples are studied with the American Society of Testing Materials (ASTM) standard test method. It was found that binder saturation and feed-to-powder ratio were the most critical parameters, which reflects the strong influence of binder powder interaction and density of powder bed on resulting mechanical properties. This article serves as an aid in understanding the optimum process parameters for binder jetting of SS 316L.
An Efficient Primal-Dual Prox Method for Non-Smooth Optimization
Yang, Tianbao; Mehdavi, Mehrdad; Zhu, Shenghuo
2012-01-01
We consider the non-smooth optimization problems in machine learning, where both the loss function and the regularizer are non-smooth functions. Previous studies on efficient empirical loss minimization assume either a smooth loss function or a strongly convex regularizer, making them unsuitable for non-smooth optimization. We develop an efficient method for a family of non-smooth optimization where the dual form of the loss function is bilinear in primal and dual variables. We cast a non-smooth optimization problem into a minimax optimization problem, and develop a primal dual prox method that solves the minimax optimization problem at a rate of $O(1/T)$, significantly faster than a standard gradient descent method ($O(1/\\sqrt{T})$). Our empirical study verifies the efficiency of the proposed method for non-smooth optimization by comparing it to the state-of-the-art first order methods.
DETERMINATION METHOD OF OPTIMAL SUPPORTING TIME IN HEADING FACE
杜长龙; 曹红波; 王燕宁; 张艳
1997-01-01
This paper has put forward a concept of optimal supporting time through analysing the influence of the supporting time in the heading face on the supporting result of surrounding rock. The method of the optimal supporting time determined by graphical method is discussed, and the calculating formula for determining the optimal supporting time through the analysis method is derived.
A LEVEL-VALUE ESTIMATION METHOD FOR SOLVING GLOBAL OPTIMIZATION
WU Dong-hua; YU Wu-yang; TIAN Wei-wen; ZHANG Lian-sheng
2006-01-01
A level-value estimation method was illustrated for solving the constrained global optimization problem. The equivalence between the root of a modified variance equation and the optimal value of the original optimization problem is shown. An alternate algorithm based on the Newton's method is presented and the convergence of its implementable approach is proved. Preliminary numerical results indicate that the method is effective.
Design optimization method for Francis turbine
Kawajiri, H.; Enomoto, Y.; Kurosawa, S.
2014-03-01
This paper presents a design optimization system coupled CFD. Optimization algorithm of the system employs particle swarm optimization (PSO). Blade shape design is carried out in one kind of NURBS curve defined by a series of control points. The system was applied for designing the stationary vanes and the runner of higher specific speed francis turbine. As the first step, single objective optimization was performed on stay vane profile, and second step was multi-objective optimization for runner in wide operating range. As a result, it was confirmed that the design system is useful for developing of hydro turbine.
Multimineral optimization processing method based on elemental capture spectroscopy logging
Feng Zhou; Li Xin-Tong; Wu Hong-Liang; Xia Shou-Ji; Liu Ying-Ming
2014-01-01
Calculating the mineral composition is a critical task in log interpretation. Elemental capture spectroscopy (ECS) log provides the weight percentages of twelve common elements, which lays the foundation for the accurate calculation of mineral compositions. Previous processing methods calculated the formation composition via the conversion relation between the formation chemistry and minerals. Thus, their applicability is limited and the method precision is relatively low. In this study, we present a multimineral optimization processing method based on the ECS log. We derived the ECS response equations for calculating the formation composition, then, determined the logging response values for the elements of common minerals using core data and theoretical calculations. Finally, a software module was developed. The results of the new method are consistent with core data and the mean absolute error is less than 10%.
Baeza, A; Salas, A; Guillén, J; Muñoz-Serrano, A
2014-02-01
The raw water used in Drinking Water Treatment Plants (DWTPs) can present high values of naturally occurring radionuclides. In order to reduce this content, the routine working conditions of DWTPs were successfully modified. This meant that those radionuclides were accumulated in the sludges generated, whose radioactive content was frequently above the exemption levels. It therefore becomes necessary to assess the association of naturally occurring radionuclides in the sludges for their potential use as agricultural fertilizers. Two approaches were studied: (a) the effect of different sequential extraction methods applied to a selected sludge; and (b) the effect of the different contents of inorganic complexes dissolved in the input water on the composition of the sludges generated by two DWTPs with different origins of their input water. Uranium and radium were mainly associated with the carbonated and reducible fractions, while (210)Po and (228)Th were associated with the residual fraction. There were differences between the two speciation methods, but the order of bioavailable radionuclides was roughly the same: (226)Ra≈(234,238)U>(228)Th>(210)Po. The major inorganic complexes content, mainly carbonate, in the raw water affected the radionuclide association. The greater the carbonate content in the raw water, the greater was the association of uranium and radium with the carbonated and easily reducible fractions.
UNDERSTANDING OF FUZZY OPTIMIZATION:THEORIES AND METHODS
TANG Jiafu; WANG Dingwei; Richard Y K FUNG; Kai-Leung Yung
2004-01-01
A brief summary on and comprehensive understanding of fuzzy optimizationis presentedThis summary is made on aspects of fuzzy modelling and fuzzy optimization,classification and formulation for the fuzzy optimization problems, models and methods.The importance of interpretation of the problem and formulation of the optimal solutionin fuzzy sense are emphasized in the summary of the fuzzy optimization.
Numerical methods and optimization a consumer guide
Walter, Éric
2014-01-01
Initial training in pure and applied sciences tends to present problem-solving as the process of elaborating explicit closed-form solutions from basic principles, and then using these solutions in numerical applications. This approach is only applicable to very limited classes of problems that are simple enough for such closed-form solutions to exist. Unfortunately, most real-life problems are too complex to be amenable to this type of treatment. Numerical Methods and Optimization – A Consumer Guide presents methods for dealing with them. Shifting the paradigm from formal calculus to numerical computation, the text makes it possible for the reader to · discover how to escape the dictatorship of those particular cases that are simple enough to receive a closed-form solution, and thus gain the ability to solve complex, real-life problems; · understand the principles behind recognized algorithms used in state-of-the-art numerical software; · learn the advantag...
On stochastic and discontinuous optimization methods
Ermoliev, Y.
1994-12-31
The talk is based on a joint article by Y. Ermoliev, V. Norkin and R. Wets. A new notion of subgradient is introduced which allows to develop easily implementable procedures of discontinuous optimization, in particular, finite-difference approximation schemes. The approach relies on the notion of differentiability in the sense of distributions converting a discontinuous optimization problem into a problem of the stochastic optimization. Applications involving risks and abrupt transitions are discussed.
Optimization: NURBS and the quasi-Newton method
Coburn, Todd Dale
Optimization is important in both engineering and mathematics. The Quasi-Newton Method is widely used for optimization due to its speed and efficiency. NonUniform Rational B-Splines (NURBS) are piecewise parametric approximations to curves and surfaces. NURBS have great curve-fitting properties that can be applied to improve optimization performance. This dissertation investigated the use of NURBS in optimization, focusing primarily on the coupling of NURBS with the Quasi-Newton Method. A hybrid optimization procedure dubbed the NURBS-Quasi-Newton (NQN) Method was developed and utilized that can virtually assure that the global minimum will be found. A Method was also developed to implement Pure NURBS Optimization (PNO), which can be used to optimize non-continuous and singular functions as well as functions of point cloud data. It was concluded that NURBS offer significant benefits for optimization, both individually and coupled with Quasi-Newton Methods.
Mathematical programming methods for large-scale topology optimization problems
Rojas Labanda, Susana
, and at the same time, reduce the number of function evaluations. Nonlinear optimization methods, such as sequential quadratic programming and interior point solvers, have almost not been embraced by the topology optimization community. Thus, this work is focused on the introduction of this kind of second......This thesis investigates new optimization methods for structural topology optimization problems. The aim of topology optimization is finding the optimal design of a structure. The physical problem is modelled as a nonlinear optimization problem. This powerful tool was initially developed...... for the classical minimum compliance problem. Two of the state-of-the-art optimization algorithms are investigated and implemented for this structural topology optimization problem. A Sequential Quadratic Programming (TopSQP) and an interior point method (TopIP) are developed exploiting the specific mathematical...
Adjoint Optimization of a Wing Using the CSRT Method
Straathof, M.H.; Van Tooren, M.J.L.
2011-01-01
This paper will demonstrate the potential of the Class-Shape-Refinement-Transformation (CSRT) method for aerodynamically optimizing three-dimensional surfaces. The CSRT method was coupled to an in-house Euler solver and this combination was used in an optimization framework to optimize the ONERA M6
Robust method optimization strategy-a useful tool for method transfer: the case of SFC.
Dispas, Amandine; Lebrun, Pierre; Andri, Bertyl; Rozet, Eric; Hubert, Philippe
2014-01-01
The concept of Quality by Design (QbD) is now well established in pharmaceutical industry and should be applied to the development of any analytical methods. In this context, the key concept of Design Space (DS) was introduced in the field of analytical method optimization. In chromatographic words, the DS is the space of chromatographic conditions that will ensure the quality of peaks separation, thus DS is a zone of robustness. In the present study, the interest of robust method optimization strategy was investigated in the context of direct method transfer from sending to receiving laboratory. The benefit of this approach is to speed up the method life cycle by performing only one quantitative validation step in the final environment of method use. A Supercritical Fluid Chromatography (SFC) method previously developed was used as a case study in this work. Moreover, the interest of geometric transfer was investigated simultaneously in order to stress a little bit more the transfer exercise and, by the way, emphasize the additional benefit of DS strategy in this particular context. Three successful transfers were performed on two column geometries. In order to compare original and transferred methods, the observed relative retention times (RT) were modelled as a function of the predicted relative RT and of the method type (original or transferred). The observed relative RT of the original and transferred methods are not statistically different and thus the method transfer is successfully achieved thanks to the robust optimization strategy. Furthermore, the analytical method was improved considering analysis time (reduced five times) and peak capacity (increased three times). To conclude, the advantage of using a DS strategy implemented for the optimization and transfer of SFC method was successfully demonstrated in this work.
A Review of Deterministic Optimization Methods in Engineering and Management
Ming-Hua Lin
2012-01-01
Full Text Available With the increasing reliance on modeling optimization problems in practical applications, a number of theoretical and algorithmic contributions of optimization have been proposed. The approaches developed for treating optimization problems can be classified into deterministic and heuristic. This paper aims to introduce recent advances in deterministic methods for solving signomial programming problems and mixed-integer nonlinear programming problems. A number of important applications in engineering and management are also reviewed to reveal the usefulness of the optimization methods.
A Study of Electrical Motors Controlling Optimization Methods
Saeid Fatemi
2013-01-01
In order to design an efficient motor cooling system, it is important to accurately predict the power optimization which is normally dissipated in form of heat. This study presents an analytical method for estimating bearing frictional optimization and numerical method for estimating electromagnetic optimization for an electric vehicle electrical motor. The power optimization obtained use heat sources when evaluating the thermal performance of the motor. The results showed that electromagneti...
Computational Methods for Design, Control and Optimization
2007-10-01
34scenario" that applies to channel flows ( Poiseuille flows , Couette flow ) and pipe flows . Over the past 75 years many complex "transition theories" have...other areas of flow control, optimization and aerodynamic design. approximate sensitivity calculations and optimization codes. The effort was built on a...for fluid flow problems. The improved robustness and computational efficiency of this approach makes it practical for a wide class of problems. The
A simple method to optimize HMC performance
Bussone, Andrea; Drach, Vincent; Hansen, Martin; Hietanen, Ari; Rantaharju, Jarno; Pica, Claudio
2016-01-01
We present a practical strategy to optimize a set of Hybrid Monte Carlo parameters in simulations of QCD and QCD-like theories. We specialize to the case of mass-preconditioning, with multiple time-step Omelyan integrators. Starting from properties of the shadow Hamiltonian we show how the optimal setup for the integrator can be chosen once the forces and their variances are measured, assuming that those only depend on the mass-preconditioning parameter.
An Efficient Method for Reliability-based Multidisciplinary Design Optimization
Fan Hui; Li Weiji
2008-01-01
Design for modem engineering system is becoming multidisciplinary and incorporates practical uncertainties; therefore, it is necessary to synthesize reliability analysis and the multidiscipLinary design optimization (MDO) techniques for the design of complex engineering system. An advanced first order second moment method-based concurrent subspace optimization approach is proposed based on the comparison and analysis of the existing multidisciplinary optimization techniques and the reliability analysis methods. It is seen through a canard configuration optimization for a three-surface transport that the proposed method is computationally efficient and practical with the least modification to the current deterministic optimization process.
An optimization method for metamorphic mechanisms based on multidisciplinary design optimization
Zhang Wuxiang
2014-12-01
Full Text Available The optimization of metamorphic mechanisms is different from that of the conventional mechanisms for its characteristics of multi-configuration. There exist complex coupled design variables and constraints in its multiple different configuration optimization models. To achieve the compatible optimized results of these coupled design variables, an optimization method for metamorphic mechanisms is developed in the paper based on the principle of multidisciplinary design optimization (MDO. Firstly, the optimization characteristics of the metamorphic mechanism are summarized distinctly by proposing the classification of design variables and constraints as well as coupling interactions among its different configuration optimization models. Further, collaborative optimization technique which is used in MDO is adopted for achieving the overall optimization performance. The whole optimization process is then proposed by constructing a two-level hierarchical scheme with global optimizer and configuration optimizer loops. The method is demonstrated by optimizing a planar five-bar metamorphic mechanism which has two configurations, and results show that it can achieve coordinated optimization results for the same parameters in different configuration optimization models.
An optimization method for metamorphic mechanisms based on multidisciplinary design optimization
Zhang Wuxiang; Wu Teng; Ding Xilun
2014-01-01
The optimization of metamorphic mechanisms is different from that of the conventional mechanisms for its characteristics of multi-configuration. There exist complex coupled design vari-ables and constraints in its multiple different configuration optimization models. To achieve the compatible optimized results of these coupled design variables, an optimization method for meta-morphic mechanisms is developed in the paper based on the principle of multidisciplinary design optimization (MDO). Firstly, the optimization characteristics of the metamorphic mechanism are summarized distinctly by proposing the classification of design variables and constraints as well as coupling interactions among its different configuration optimization models. Further, collabora-tive optimization technique which is used in MDO is adopted for achieving the overall optimization performance. The whole optimization process is then proposed by constructing a two-level hierar-chical scheme with global optimizer and configuration optimizer loops. The method is demon-strated by optimizing a planar five-bar metamorphic mechanism which has two configurations, and results show that it can achieve coordinated optimization results for the same parameters in different configuration optimization models.
Design Methods and Optimization for Morphing Aircraft
Crossley, William A.
2005-01-01
This report provides a summary of accomplishments made during this research effort. The major accomplishments are in three areas. The first is the use of a multiobjective optimization strategy to help identify potential morphing features that uses an existing aircraft sizing code to predict the weight, size and performance of several fixed-geometry aircraft that are Pareto-optimal based upon on two competing aircraft performance objectives. The second area has been titled morphing as an independent variable and formulates the sizing of a morphing aircraft as an optimization problem in which the amount of geometric morphing for various aircraft parameters are included as design variables. This second effort consumed most of the overall effort on the project. The third area involved a more detailed sizing study of a commercial transport aircraft that would incorporate a morphing wing to possibly enable transatlantic point-to-point passenger service.
Multimodel methods for optimal control of aeroacoustics.
Chen, Guoquan (Rice University, Houston, TX); Collis, Samuel Scott
2005-01-01
A new multidomain/multiphysics computational framework for optimal control of aeroacoustic noise has been developed based on a near-field compressible Navier-Stokes solver coupled with a far-field linearized Euler solver both based on a discontinuous Galerkin formulation. In this approach, the coupling of near- and far-field domains is achieved by weakly enforcing continuity of normal fluxes across a coupling surface that encloses all nonlinearities and noise sources. For optimal control, gradient information is obtained by the solution of an appropriate adjoint problem that involves the propagation of adjoint information from the far-field to the near-field. This computational framework has been successfully applied to study optimal boundary-control of blade-vortex interaction, which is a significant noise source for helicopters on approach to landing. In the model-problem presented here, the noise propagated toward the ground is reduced by 12dB.
A Method for Determining Optimal Residential Energy Efficiency Packages
Polly, B. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Gestwick, M. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Bianchi, M. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Anderson, R. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Horowitz, S. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Christensen, C. [National Renewable Energy Lab. (NREL), Golden, CO (United States); Judkoff, R. [National Renewable Energy Lab. (NREL), Golden, CO (United States)
2011-04-01
This report describes an analysis method for determining optimal residential energy efficiency retrofit packages and, as an illustrative example, applies the analysis method to a 1960s-era home in eight U.S. cities covering a range of International Energy Conservation Code (IECC) climate regions. The method uses an optimization scheme that considers average energy use (determined from building energy simulations) and equivalent annual cost to recommend optimal retrofit packages specific to the building, occupants, and location.
Advanced Topology Optimization Methods for Conceptual Architectural Design
Aage, Niels; Amir, Oded; Clausen, Anders
2014-01-01
This paper presents a series of new, advanced topology optimization methods, developed specifically for conceptual architectural design of structures. The proposed computational procedures are implemented as components in the framework of a Grasshopper plugin, providing novel capacities...... in topological optimization: Interactive control and continuous visualization; embedding flexible voids within the design space; consideration of distinct tension / compression properties; and optimization of dual material systems. In extension, optimization procedures for skeletal structures such as trusses...... and frames are implemented. The developed procedures allow for the exploration of new territories in optimization of architectural structures, and offer new methodological strategies for bridging conceptual gaps between optimization and architectural practice....
COMPARISON OF NONLINEAR DYNAMICS OPTIMIZATION METHODS FOR APS-U
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.
METHOD FOR OPTIMIZING THE ENERGY OF PUMPS
Skovmose Kallesøe, Carsten; De Persis, Claudio
2013-01-01
The device for energy-optimization on operation of several centrifugal pumps controlled in rotational speed, in a hydraulic installation, begins firstly with determining which pumps as pilot pumps are assigned directly to a consumer and which pumps are hydraulically connected in series upstream of t
Logic-based methods for optimization combining optimization and constraint satisfaction
Hooker, John
2011-01-01
A pioneering look at the fundamental role of logic in optimization and constraint satisfaction While recent efforts to combine optimization and constraint satisfaction have received considerable attention, little has been said about using logic in optimization as the key to unifying the two fields. Logic-Based Methods for Optimization develops for the first time a comprehensive conceptual framework for integrating optimization and constraint satisfaction, then goes a step further and shows how extending logical inference to optimization allows for more powerful as well as flexible
Enhanced Multi-Objective Energy Optimization by a Signaling Method
João Soares
2016-10-01
Full Text Available In this paper three metaheuristics are used to solve a smart grid multi-objective energy management problem with conflictive design: how to maximize profits and minimize carbon dioxide (CO2 emissions, and the results compared. The metaheuristics implemented are: weighted particle swarm optimization (W-PSO, multi-objective particle swarm optimization (MOPSO and non-dominated sorting genetic algorithm II (NSGA-II. The performance of these methods with the use of multi-dimensional signaling is also compared with this technique, which has previously been shown to boost metaheuristics performance for single-objective problems. Hence, multi-dimensional signaling is adapted and implemented here for the proposed multi-objective problem. In addition, parallel computing is used to mitigate the methods’ computational execution time. To validate the proposed techniques, a realistic case study for a chosen area of the northern region of Portugal is considered, namely part of Vila Real distribution grid (233-bus. It is assumed that this grid is managed by an energy aggregator entity, with reasonable amount of electric vehicles (EVs, several distributed generation (DG, customers with demand response (DR contracts and energy storage systems (ESS. The considered case study characteristics took into account several reported research works with projections for 2020 and 2050. The findings strongly suggest that the signaling method clearly improves the results and the Pareto front region quality.
Optimization of process condition of nanosilica production by hydrothermal method
Qisti, N.; Indrasti, N. S.; Suprihatin
2016-11-01
Bagasse ashes have high silica content thus it can be used in nanosilicaproduction to increase its benefit value. This study aimed to get the best time for synthesis and to determine the optimum synthesis time and temperature. This study used the hydrothermal method, a simple method with relatively low reaction temperature and provide a good chemical homogeneity. Time varieties in synthesizing silica were 8,10 and 12 hours, at the temperature of 150 °C. But the results were not as expected. Moreover, optimization of synthesis temperature and time used 4 hours at the temperature of 150 °C based on previous studies. Optimization was conducted using the Response Surface Methodology (RSM). Later, a testusing PSA (Particle Size Analyzer) was performed to obtain particle sizes and PDI values (Polydispersity Index). The results showed that the prediction model of temperature synthesis was 152.67 °C synthesis time of 6 hours, particle size of 276.288 nm and PDI value of 0.189642. The tests showed that the size of particle obtained was 330.39 nm and PDI value at 0.3580. Actual results and predicted results were not significant different.
Topology optimization: a systematic method to improve the performance of photonic crystal structures
Jensen, Jakob Søndergaard; Sigmund, Ole
2004-01-01
The method of topology optimization has previously been used to design exotic materials, MEMS and thermo-elastic mechanisms, as well as several other devices in mechanics and multi-physics applications [1]. Recently, the method was applied to design photonic and phononic crystals with maximum siz...... band gaps [2,3]. In [3] the optimization of phononic crystal structures was considered also, with examples demonstrating the possibility for designing wave-reflecting and wave-guiding structures with optimized performance.......The method of topology optimization has previously been used to design exotic materials, MEMS and thermo-elastic mechanisms, as well as several other devices in mechanics and multi-physics applications [1]. Recently, the method was applied to design photonic and phononic crystals with maximum size...
Numerical methods of mathematical optimization with Algol and Fortran programs
Künzi, Hans P; Zehnder, C A; Rheinboldt, Werner
1971-01-01
Numerical Methods of Mathematical Optimization: With ALGOL and FORTRAN Programs reviews the theory and the practical application of the numerical methods of mathematical optimization. An ALGOL and a FORTRAN program was developed for each one of the algorithms described in the theoretical section. This should result in easy access to the application of the different optimization methods.Comprised of four chapters, this volume begins with a discussion on the theory of linear and nonlinear optimization, with the main stress on an easily understood, mathematically precise presentation. In addition
Review of dynamic optimization methods in renewable natural resource management
Williams, B.K.
1989-01-01
In recent years, the applications of dynamic optimization procedures in natural resource management have proliferated. A systematic review of these applications is given in terms of a number of optimization methodologies and natural resource systems. The applicability of the methods to renewable natural resource systems are compared in terms of system complexity, system size, and precision of the optimal solutions. Recommendations are made concerning the appropriate methods for certain kinds of biological resource problems.
Study on optimization control method based on artificial neural network
FU Hua; SUN Shao-guang; XU Zhen-Iiang
2005-01-01
In the goal optimization and control optimization process the problems with common artificial neural network algorithm are unsure convergence, insufficient post-training network precision, and slow training speed, in which partial minimum value question tends to occur. This paper conducted an in-depth study on the causes of the limitations of the algorithm, presented a rapid artificial neural network algorithm, which is characterized by integrating multiple algorithms and by using their complementary advantages. The salient feature of the method is self-organization, which can effectively prevent the optimized results from tending to be partial minimum values. Overall optimization can be achieved with this method, goal function can be searched for in overall scope. With optimization control of coal mine ventilator as a practical application, the paper proves that by integrating multiple artificial neural network algorithms, best control optimization and goal optimized can be achieved.
Optimization and control methods in industrial engineering and construction
Wang, Xiangyu
2014-01-01
This book presents recent advances in optimization and control methods with applications to industrial engineering and construction management. It consists of 15 chapters authored by recognized experts in a variety of fields including control and operation research, industrial engineering, and project management. Topics include numerical methods in unconstrained optimization, robust optimal control problems, set splitting problems, optimum confidence interval analysis, a monitoring networks optimization survey, distributed fault detection, nonferrous industrial optimization approaches, neural networks in traffic flows, economic scheduling of CCHP systems, a project scheduling optimization survey, lean and agile construction project management, practical construction projects in Hong Kong, dynamic project management, production control in PC4P, and target contracts optimization. The book offers a valuable reference work for scientists, engineers, researchers and practitioners in industrial engineering and c...
DYNAMIC OPTIMIZATION FOR UNCERTAIN STRUCTURES USING INTERVAL METHOD
ChertSub-A-; WuJie; LiuChun
2003-01-01
An interval optimization method for the dynamic response of structures with interval parameters is presented. The matrices of structures with interval parameters are given. Combining the interval extension with the perturbation, the method for interval dynamic response analysis is derived. The interval optimization problem is transformed into a corresponding deterministic one. Because the mean values and the uncertainties of the interval parameters can be elected design variables, more information of the optimization results can be obtained by the present method than that obtained by the deterministic one. The present method is implemented for a truss structure. The numerical results show that the method is effective.
An uncertain multidisciplinary design optimization method using interval convex models
Li, Fangyi; Luo, Zhen; Sun, Guangyong; Zhang, Nong
2013-06-01
This article proposes an uncertain multi-objective multidisciplinary design optimization methodology, which employs the interval model to represent the uncertainties of uncertain-but-bounded parameters. The interval number programming method is applied to transform each uncertain objective function into two deterministic objective functions, and a satisfaction degree of intervals is used to convert both the uncertain inequality and equality constraints to deterministic inequality constraints. In doing so, an unconstrained deterministic optimization problem will be constructed in association with the penalty function method. The design will be finally formulated as a nested three-loop optimization, a class of highly challenging problems in the area of engineering design optimization. An advanced hierarchical optimization scheme is developed to solve the proposed optimization problem based on the multidisciplinary feasible strategy, which is a well-studied method able to reduce the dimensions of multidisciplinary design optimization problems by using the design variables as independent optimization variables. In the hierarchical optimization system, the non-dominated sorting genetic algorithm II, sequential quadratic programming method and Gauss-Seidel iterative approach are applied to the outer, middle and inner loops of the optimization problem, respectively. Typical numerical examples are used to demonstrate the effectiveness of the proposed methodology.
Zimmerman, Paul M
2013-05-14
The growing string method (GSM) has proven especially useful for locating chemical reaction paths at low computational cost. While many string methods use Cartesian coordinates, these methods can be substantially improved by changes in the coordinate system used for interpolation and optimization steps. The quality of the interpolation scheme is especially important because it determines how close the initial path is to the optimized reaction path, and this strongly affects the rate of convergence. In this article, a detailed description of the generation of internal coordinates (ICs) suitable for use in GSM as reactive tangents and in string optimization is given. Convergence of reaction paths is smooth because the IC tangent and orthogonal directions are better representations of chemical bonding compared to Cartesian coordinates. This is not only important quantitatively for reducing computational cost but also allows reaction paths to be described with smoothly varying chemically relevant coordinates. Benchmark computations with challenging reactions are compared to previous versions of GSM and show significant speedups. Finally, a climbing image scheme is included to improve the quality of the transition state approximation, ensuring high reliability of the method.
Gradient-based methods for production optimization of oil reservoirs
Suwartadi, Eka
2012-07-01
Production optimization for water flooding in the secondary phase of oil recovery is the main topic in this thesis. The emphasis has been on numerical optimization algorithms, tested on case examples using simple hypothetical oil reservoirs. Gradientbased optimization, which utilizes adjoint-based gradient computation, is used to solve the optimization problems. The first contribution of this thesis is to address output constraint problems. These kinds of constraints are natural in production optimization. Limiting total water production and water cut at producer wells are examples of such constraints. To maintain the feasibility of an optimization solution, a Lagrangian barrier method is proposed to handle the output constraints. This method incorporates the output constraints into the objective function, thus avoiding additional computations for the constraints gradient (Jacobian) which may be detrimental to the efficiency of the adjoint method. The second contribution is the study of the use of second-order adjoint-gradient information for production optimization. In order to speedup convergence rate in the optimization, one usually uses quasi-Newton approaches such as BFGS and SR1 methods. These methods compute an approximation of the inverse of the Hessian matrix given the first-order gradient from the adjoint method. The methods may not give significant speedup if the Hessian is ill-conditioned. We have developed and implemented the Hessian matrix computation using the adjoint method. Due to high computational cost of the Newton method itself, we instead compute the Hessian-timesvector product which is used in a conjugate gradient algorithm. Finally, the last contribution of this thesis is on surrogate optimization for water flooding in the presence of the output constraints. Two kinds of model order reduction techniques are applied to build surrogate models. These are proper orthogonal decomposition (POD) and the discrete empirical interpolation method (DEIM
Villanueva Perez, Carlos Hernan
Computational design optimization provides designers with automated techniques to develop novel and non-intuitive optimal designs. Topology optimization is a design optimization technique that allows for the evolution of a broad variety of geometries in the optimization process. Traditional density-based topology optimization methods often lack a sufficient resolution of the geometry and physical response, which prevents direct use of the optimized design in manufacturing and the accurate modeling of the physical response of boundary conditions. The goal of this thesis is to introduce a unified topology optimization framework that uses the Level Set Method (LSM) to describe the design geometry and the eXtended Finite Element Method (XFEM) to solve the governing equations and measure the performance of the design. The methodology is presented as an alternative to density-based optimization approaches, and is able to accommodate a broad range of engineering design problems. The framework presents state-of-the-art methods for immersed boundary techniques to stabilize the systems of equations and enforce the boundary conditions, and is studied with applications in 2D and 3D linear elastic structures, incompressible flow, and energy and species transport problems to test the robustness and the characteristics of the method. A comparison of the framework against density-based topology optimization approaches is studied with regards to convergence, performance, and the capability to manufacture the designs. Furthermore, the ability to control the shape of the design to operate within manufacturing constraints is developed and studied. The analysis capability of the framework is validated quantitatively through comparison against previous benchmark studies, and qualitatively through its application to topology optimization problems. The design optimization problems converge to intuitive designs and resembled well the results from previous 2D or density-based studies.
Mixed methods for viscoelastodynamics and topology optimization
Giacomo Maurelli
2014-07-01
Full Text Available A truly-mixed approach for the analysis of viscoelastic structures and continua is presented. An additive decomposition of the stress state into a viscoelastic part and a purely elastic one is introduced along with an Hellinger-Reissner variational principle wherein the stress represents the main variable of the formulation whereas the kinematic descriptor (that in the case at hand is the velocity field acts as Lagrange multiplier. The resulting problem is a Differential Algebraic Equation (DAE because of the need to introduce static Lagrange multipliers to comply with the Cauchy boundary condition on the stress. The associated eigenvalue problem is known in the literature as constrained eigenvalue problem and poses several difficulties for its solution that are addressed in the paper. The second part of the paper proposes a topology optimization approach for the rationale design of viscoelastic structures and continua. Details concerning density interpolation, compliance problems and eigenvalue-based objectives are given. Worked numerical examples are presented concerning both the dynamic analysis of viscoelastic structures and their topology optimization.
Amstel, van T.; Groen, M.; Post, J.; Huetink, J.
2005-01-01
This article describes an inverse optimization method for the Sandvik Nanoflex steel in cold forming processes. The optimization revolves around measured samples and calculations using the Finite Element Method. Sandvik Nanoflex is part of the group of meta-stable stainless steels. These materials a
Present-day Problems and Methods of Optimization in Mechatronics
Tarnowski Wojciech
2017-06-01
Full Text Available It is justified that design is an inverse problem, and the optimization is a paradigm. Classes of design problems are proposed and typical obstacles are recognized. Peculiarities of the mechatronic designing are specified as a proof of a particle importance of optimization in the mechatronic design. Two main obstacles of optimization are discussed: a complexity of mathematical models and an uncertainty of the value system, in concrete case. Then a set of non-standard approaches and methods are presented and discussed, illustrated by examples: a fuzzy description, a constraint-based iterative optimization, AHP ranking method and a few MADM functions in Matlab.
Computation of Optimal Monotonicity Preserving General Linear Methods
Ketcheson, David I.
2009-07-01
Monotonicity preserving numerical methods for ordinary differential equations prevent the growth of propagated errors and preserve convex boundedness properties of the solution. We formulate the problem of finding optimal monotonicity preserving general linear methods for linear autonomous equations, and propose an efficient algorithm for its solution. This algorithm reliably finds optimal methods even among classes involving very high order accuracy and that use many steps and/or stages. The optimality of some recently proposed methods is verified, and many more efficient methods are found. We use similar algorithms to find optimal strong stability preserving linear multistep methods of both explicit and implicit type, including methods for hyperbolic PDEs that use downwind-biased operators.
Tabu search method with random moves for globally optimal design
Hu, Nanfang
1992-09-01
Optimum engineering design problems are usually formulated as non-convex optimization problems of continuous variables. Because of the absence of convexity structure, they can have multiple minima, and global optimization becomes difficult. Traditional methods of optimization, such as penalty methods, can often be trapped at a local optimum. The tabu search method with random moves to solve approximately these problems is introduced. Its reliability and efficiency are examined with the help of standard test functions. By the analysis of the implementations, it is seen that this method is easy to use, and no derivative information is necessary. It outperforms the random search method and composite genetic algorithm. In particular, it is applied to minimum weight design examples of a three-bar truss, coil springs, a Z-section and a channel section. For the channel section, the optimal design using the tabu search method with random moves saved 26.14 percent over the weight of the SUMT method.
Fast sequential Monte Carlo methods for counting and optimization
Rubinstein, Reuven Y; Vaisman, Radislav
2013-01-01
A comprehensive account of the theory and application of Monte Carlo methods Based on years of research in efficient Monte Carlo methods for estimation of rare-event probabilities, counting problems, and combinatorial optimization, Fast Sequential Monte Carlo Methods for Counting and Optimization is a complete illustration of fast sequential Monte Carlo techniques. The book provides an accessible overview of current work in the field of Monte Carlo methods, specifically sequential Monte Carlo techniques, for solving abstract counting and optimization problems. Written by authorities in the
J. Lang; J.G. Verwer (Jan)
2013-01-01
htmlabstractThis paper addresses consistency and stability of W-methods up to order three for nonlinear ODE-constrained control problems with possible restrictions on the control. The analysis is based on the transformed adjoint system and the control uniqueness property. These methods can also be
A method optimization study for atomic absorption ...
Sadia Ata
2014-04-24
Apr 24, 2014 ... spectrophotometric determination of total zinc in insulin using .... A linear regression by the least squares method is then applied. The value of the determination coefficient (R2 =0.99842) showed excellent linearity of the calibration curve for the ... method is applied repeatedly to multiple sampling of homol-.
Control Methods Utilizing Energy Optimizing Schemes in Refrigeration Systems
Larsen, L.S; Thybo, C.; Stoustrup, Jakob
2003-01-01
The potential energy savings in refrigeration systems using energy optimal control has been proved to be substantial. This however requires an intelligent control that drives the refrigeration systems towards the energy optimal state. This paper proposes an approach for a control, which drives th...... the condenser pressure towards an optimal state. The objective of this is to present a feasible method that can be used for energy optimizing control. A simulation model of a simple refrigeration system will be used as basis for testing the control method.......The potential energy savings in refrigeration systems using energy optimal control has been proved to be substantial. This however requires an intelligent control that drives the refrigeration systems towards the energy optimal state. This paper proposes an approach for a control, which drives...
Flexible and generalized uncertainty optimization theory and methods
Lodwick, Weldon A
2017-01-01
This book presents the theory and methods of flexible and generalized uncertainty optimization. Particularly, it describes the theory of generalized uncertainty in the context of optimization modeling. The book starts with an overview of flexible and generalized uncertainty optimization. It covers uncertainties that are both associated with lack of information and that more general than stochastic theory, where well-defined distributions are assumed. Starting from families of distributions that are enclosed by upper and lower functions, the book presents construction methods for obtaining flexible and generalized uncertainty input data that can be used in a flexible and generalized uncertainty optimization model. It then describes the development of such a model in detail. All in all, the book provides the readers with the necessary background to understand flexible and generalized uncertainty optimization and develop their own optimization model. .
Extremal Optimization: Methods Derived from Co-Evolution
Boettcher, S.; Percus, A.G.
1999-07-13
We describe a general-purpose method for finding high-quality solutions to hard optimization problems, inspired by self-organized critical models of co-evolution such as the Bak-Sneppen model. The method, called Extremal Optimization, successively eliminates extremely undesirable components of sub-optimal solutions, rather than ''breeding'' better components. In contrast to Genetic Algorithms which operate on an entire ''gene-pool'' of possible solutions, Extremal Optimization improves on a single candidate solution by treating each of its components as species co-evolving according to Darwinian principles. Unlike Simulated Annealing, its non-equilibrium approach effects an algorithm requiring few parameters to tune. With only one adjustable parameter, its performance proves competitive with, and often superior to, more elaborate stochastic optimization procedures. We demonstrate it here on two classic hard optimization problems: graph partitioning and the traveling salesman problem.
Microstructure optimization design methods of the forging process and applications
WANG Guangchun; ZHAO Guoqun; GUAN Jing
2007-01-01
A microstructure optimization design method of the forging process is proposed. The optimization goal is the fine grain size and homogeneous grain distribution. The optimization object is the forging process parameters and the shape of the preform die. The grain size sub-objective function, the forgings shape sub-objective function and the whole objective function including the shape and the grain size are established, espectively. The detailed optimization steps are given. The microstructure optimization program is developed using the micro-genetic algorithm and the finite element method. Then, the upsetting process of the cylindrical billet is analyzed using a self-developed program. The forging parameters and the shape of preform die of the upsetting process are optimized respectively. The fine size and homogenous distribution of the grain can be achieved by controlling the shape of the preform die and improving the friction condition.
Numerical methods for control optimization in linear systems
Tyatyushkin, A. I.
2015-05-01
Numerical methods are considered for solving optimal control problems in linear systems, namely, terminal control problems with control and phase constraints and time-optimal control problems. Several algorithms with various computer storage requirements are proposed for solving these problems. The algorithms are intended for finding an optimal control in linear systems having certain features, for example, when the reachable set of a system has flat faces.
Method of product portfolio analysis based on optimization models
V.M. Lozyuk
2011-12-01
Full Text Available The research is devoted to optimization of the structure of product portfolio of trading company with using the principles of the investment modeling. We further developed the models of investment portfolio optimization, using the known Markowitz and Sharp methods to determine the optimal portfolio of trade company. Adapted to the goods market the models in this study could be applied to the business of trade companies.
Software for optimization using a sequential simplex method
Evandro Bona
2000-05-01
Full Text Available A computer program for process optimization influenced by continuous and qualitative variables was developed from the simplex method. Software was validated through case studies found in literature by predictive models with two distinct processes. The obtained results showed great concordance with values supplied by literature. The developed program is portable and friendly, and may be used in several optimization systems. Software complementation with other subroutines, as combined response optimization, may make its application more comprehensive.
Variable Neighborhood Simplex Search Methods for Global Optimization Models
Pongchanun Luangpaiboon
2012-01-01
Full Text Available Problem statement: Many optimization problems of practical interest are encountered in various fields of chemical, engineering and management sciences. They are computationally intractable. Therefore, a practical algorithm for solving such problems is to employ approximation algorithms that can find nearly optimums within a reasonable amount of computational time. Approach: In this study the hybrid methods combining the Variable Neighborhood Search (VNS and simplexs family methods are proposed to deal with the global optimization problems of noisy continuous functions including constrained models. Basically, the simplex methods offer a search scheme without the gradient information whereas the VNS has the better searching ability with a systematic change of neighborhood of the current solution within a local search. Results: The VNS modified simplex method has a better searching ability for optimization problems with noise. The VNS modified simplex method also outperforms in average on the characteristics of intensity and diversity during the evolution of design point moving stage for the constrained optimization. Conclusion: The adaptive hybrid versions have proved to obtain significantly better results than the conventional methods. The amount of computation effort required for successful optimization is very sensitive to the rate of noise decrease of the process yields. Under circumstances of constrained optimization and gradually increasing the noise during an optimization the most preferred approach is the VNS modified simplex method.
Genetic algorithm and particle swarm optimization combined with Powell method
Bento, David; Pinho, Diana; Pereira, Ana I.; Lima, Rui
2013-10-01
In recent years, the population algorithms are becoming increasingly robust and easy to use, based on Darwin's Theory of Evolution, perform a search for the best solution around a population that will progress according to several generations. This paper present variants of hybrid genetic algorithm - Genetic Algorithm and a bio-inspired hybrid algorithm - Particle Swarm Optimization, both combined with the local method - Powell Method. The developed methods were tested with twelve test functions from unconstrained optimization context.
Reliability-based design optimization with Cross-Entropy method
Ghidey, Hiruy
2015-01-01
Implementation of the Cross-entropy (CE) method to solve reliability-based design optimization (RBDO) problems was investigated. The emphasis of this implementation method was to solve independently both the reliability and optimization sub-problems within the RBDO problem; therefore, the main aim of this study was to evaluate the performance of the Cross-entropy method in terms of efficiency and accuracy to solve RBDO problems. A numerical approach was followed in which the implementatio...
A Decentralized Variable Ordering Method for Distributed Constraint Optimization
2005-05-01
Either aproach can be used depending on how densely the nodes are connected inside blocks. If inside-block connec- tivity is sparse, the latter method ...A Decentralized Variable Ordering Method for Distributed Constraint Optimization Anton Chechetka Katia Sycara CMU-RI-TR-05-18 May 2005 Robotics...00-00-2005 4. TITLE AND SUBTITLE A Decentralized Variable Ordering Method for Distributed Constraint Optimization 5a. CONTRACT NUMBER 5b. GRANT
Optimal design method for magnetization directions of a permanent magnet array
Choi, Jae Seok; Yoo, Jeonghoon
2010-08-01
In many magnetic systems, the permanent magnet (PM) pattern has a great influence on their performance. This study proposes a systematic optimization method for designing discrete magnetization directions. While previous works have been mostly dependent on researchers' intuition, the developed method is systematic and can be applied to a two-dimensional PM-type eddy current brake model. The effectiveness of the method is confirmed, where the design's aim is to maximize the braking force on a moving conductor. The sensitivity analysis is accomplished by the adjoint variable method and the sequential linear programming is used as an optimizer. Several optimization results for various conditions through the proposed design method are compared to each other and the optimal magnet configuration for an eddy current brake is suggested.
Enhancement of the downhill simplex method of optimization
Koshel, R. John
2002-12-01
The downhill simplex method of optimization is a "geometric" method to achieve function minimization. The standard algorithm uses arbitrary values for the deterministic factors that describe the "movement" of the simplex in the merit space. While it is a robust method of optimization, it is relatively slow to converge to local minima. However, its stability and the lack of use of derivatives make it useful for optical design optimization, especially for the field of illumination. This paper describes preliminary efforts of optimizing the performance of the simplex optimizer. This enhancement is accomplished by optimizing the various control factors: alpha (reflection), beta (contraction), and gamma (expansion). This effort is accomplished by investigating the "end game" of optimal design, i.e., the shape of the figure of merit space is parabolic in N-dimensions near local minima. The figure of merit for the control factor optimization is the number of iterations to achieve a solution in comparison to the same case using the standard control factors. This optimization is done for parabolic wells of order N equals 2 to 15. In this study it is shown that with the correct choice of the control factors, one can achieve up to a 35% improvement in convergence. Techniques using gradient weighting and the inclusion of additional control factors are proposed.
Co-iterative augmented Hessian method for orbital optimization
Sun, Qiming
2016-01-01
Orbital optimization procedure is widely called in electronic structure simulation. To efficiently find the orbital optimization solution, we developed a new second order orbital optimization algorithm, co-iteration augmented Hessian (CIAH) method. In this method, the orbital optimization is embedded in the diagonalization procedure for augmented Hessian (AH) eigenvalue equation. Hessian approximations can be easily employed in this method to improve the computational costs. We numerically performed the CIAH algorithm with SCF convergence of 20 challenging systems and Boys localization of C60 molecule. We found that CIAH algorithm has better SCF convergence and less computational costs than direct inversion iterative subspace (DIIS) algorithm. The numerical tests suggest that CIAH is a stable, reliable and efficient algorithm for orbital optimization problem.
A hybrid optimization method for biplanar transverse gradient coil design
Qi Feng [Key Laboratory for Quantum Information and Measurements, Ministry of Education, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871 (China); Tang Xin [Beijing Key Laboratory of Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871 (China); Jin Zhe [Key Laboratory for Quantum Information and Measurements, Ministry of Education, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871 (China); Jiang Zhongde [Beijing Key Laboratory of Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871 (China); Shen Yifei [Key Laboratory for Quantum Information and Measurements, Ministry of Education, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871 (China); Meng Bin [Key Laboratory for Quantum Information and Measurements, Ministry of Education, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871 (China); Zu Donglin [Beijing Key Laboratory of Medical Physics and Engineering, Institute of Heavy Ion Physics, School of Physics, Peking University, Beijing 100871 (China); Wang Weimin [Key Laboratory for Quantum Information and Measurements, Ministry of Education, School of Electronics Engineering and Computer Science, Peking University, Beijing 100871 (China)
2007-05-07
The optimization of transverse gradient coils is one of the fundamental problems in designing magnetic resonance imaging gradient systems. A new approach is presented in this paper to optimize the transverse gradient coils' performance. First, in the traditional spherical harmonic target field method, high order coefficients, which are commonly ignored, are used in the first stage of the optimization process to give better homogeneity. Then, some cosine terms are introduced into the series expansion of stream function. These new terms provide simulated annealing optimization with new freedoms. Comparison between the traditional method and the optimized method shows that the inhomogeneity in the region of interest can be reduced from 5.03% to 1.39%, the coil efficiency increased from 3.83 to 6.31 mT m{sup -1} A{sup -1} and the minimum distance of these discrete coils raised from 1.54 to 3.16 mm.
Machine Self-Teaching Methods for Parameter Optimization.
1986-12-01
A199 285 MACHINE SELF- TEACHING METHODS FOR PARAMETER / OPTIMIZATION(U) NAVAL OCEAN SYSTEMS CENTER SAN DIEGO CA R A DILLARD DEC 86 NOSC/TR-1S39...Technical Document 1039 C) ,December 1986 Machine Self- Teaching Methods for Parameter Optimization Robin A. Dillard DTICS ELECTE MAY i01 STAra Approved...ELEMEWt NO PROECi’ No TASK NO ARC Locally FundedI I1 I TE (ewd* Seawmy Cft*Wi., Machine Self- Teaching Methods for Parameter Optimization it PERSONAL
Process control and optimization with simple interval calculation method
Pomerantsev, A.; Rodionova, O.; Høskuldsson, Agnar
2006-01-01
the series of expanding PLS/SIC models in order to support the on-line process improvements. This method helps to predict the effect of planned actions on the product quality and thus enables passive quality control. We have also considered an optimization approach that proposes the correcting actions......Methods of process control and optimization are presented and illustrated with a real world example. The optimization methods are based on the PLS block modeling as well as on the simple interval calculation methods of interval prediction and object status classification. It is proposed to employ...... for the quality improvement in the course of production. The latter is an active quality optimization, which takes into account the actual history of the process. The advocate approach is allied to the conventional method of multivariate statistical process control (MSPC) as it also employs the historical process...
Method of generating features optimal to a dataset and classifier
Bruillard, Paul J.; Gosink, Luke J.; Jarman, Kenneth D.
2016-10-18
A method of generating features optimal to a particular dataset and classifier is disclosed. A dataset of messages is inputted and a classifier is selected. An algebra of features is encoded. Computable features that are capable of describing the dataset from the algebra of features are selected. Irredundant features that are optimal for the classifier and the dataset are selected.
Optimization of Nanostructuring Burnishing Technological Parameters by Taguchi Method
Kuznetsov, V. P.; Dmitriev, A. I.; Anisimova, G. S.; Semenova, Yu V.
2016-04-01
On the basis of application of Taguchi optimization method, an approach for researching influence of nanostructuring burnishing technological parameters, considering the surface layer microhardness criterion, is developed. Optimal values of burnishing force, feed and number of tool passes for hardened steel AISI 420 hardening treatment are defined.
Maximum super angle optimization method for array antenna pattern synthesis
Wu, Ji; Roederer, A. G
1991-01-01
Different optimization criteria related to antenna pattern synthesis are discussed. Based on the maximum criteria and vector space representation, a simple and efficient optimization method is presented for array and array fed reflector power pattern synthesis. A sector pattern synthesized by a 20...
Exact and useful optimization methods for microeconomics
Balder, E.J.
2011-01-01
This paper points out that the treatment of utility maximization in current textbooks on microeconomic theory is deficient in at least three respects: breadth of coverage, completeness-cum-coherence of solution methods and mathematical correctness. Improvements are suggested in the form of a Kuhn-Tu
Cho, Tae Min; Lee, Byung Chai [Korea Advanced Institute of Science and Technology, Daejeon (Korea, Republic of)
2010-01-15
In this study, an effective method for reliability-based design optimization (RBDO) is proposed enhancing sequential optimization and reliability assessment (SORA) method by convex approximations. In SORA, reliability estimation and deterministic optimization are performed sequentially. The sensitivity and function value of probabilistic constraint at the most probable point (MPP) are obtained in the reliability analysis loop. In this study, the convex approximations for probabilistic constraint are constructed by utilizing the sensitivity and function value of the probabilistic constraint at the MPP. Hence, the proposed method requires much less function evaluations of probabilistic constraints in the deterministic optimization than the original SORA method. The efficiency and accuracy of the proposed method were verified through numerical examples
Lattice Boltzmann method for shape optimization of fluid distributor
Wang, Limin; Luo, Lingai
2013-01-01
This paper presents the shape optimization of a flat-type arborescent fluid distributor for the purpose of process intensification. A shape optimization algorithm based on the lattice Boltzmann method (LBM) is proposed with the objective of decreasing the flow resistance of such distributor at the constraint of constant fluid volume. Prototypes of the initial distributor as well as the optimized one are designed. Fluid distribution and hydraulic characteristics of these distributors are investigated numerically. Results show that the pressure drop of the optimized distributor is between 15.9% and 25.1% lower than that of the initial reference while keeping a uniform flow distribution, demonstrating the process intensification in fluid distributor, and suggesting the interests of the proposed optimization algorithm in engineering optimal design.
Optimization Methods for Supply Chain Activities
Balasescu S.
2014-12-01
Full Text Available This paper approach the theme of supply chain activities for medium and large companies which run many operations and need many facilities. The first goal is to analyse the influence of optimisation methods of supply chain activities on the success rate for a business. The second goal is to compare some logistic strategies applied by companies with the same profile to see which is the most effective. The final goal is to show which is the necessity of strategic optimum for a company and how can be achieved the considering the demand uncertainty.
OPTIMAL SIGNAL PROCESSING METHODS IN GPR
Saeid Karamzadeh
2014-01-01
Full Text Available In the past three decades, a lot of various applications of Ground Penetrating Radar (GPR took place in real life. There are important challenges of this radar in civil applications and also in military applications. In this paper, the fundamentals of GPR systems will be covered and three important signal processing methods (Wavelet Transform, Matched Filter and Hilbert Huang will be compared to each other in order to get most accurate information about objects which are in subsurface or behind the wall.
Optimization Design based on BP Neural Network and GA Method
Bing Wang
2013-12-01
Full Text Available This study puts forward one kind optimization controlling solution method on complicated system. At first modeling using neural network then adopt the real data to structure the neural network model of pertinence, make the parameter to seek to the neural network model excellently by mixing GA finally, thus got intelligence to the complicated system to optimize and control. The method can identify network configuration and network training methods. By adopting the number coding and effectively reducing the network size and the network convergence time, increase the network training speed. The study provides this and optimizes relevant MATLAB procedure which controls the method, so long as adjust a little to the concrete problem, can believe this procedure well the optimization of the complicated system controls the problem in the reality of solving.
Numerical methods for solving terminal optimal control problems
Gornov, A. Yu.; Tyatyushkin, A. I.; Finkelstein, E. A.
2016-02-01
Numerical methods for solving optimal control problems with equality constraints at the right end of the trajectory are discussed. Algorithms for optimal control search are proposed that are based on the multimethod technique for finding an approximate solution of prescribed accuracy that satisfies terminal conditions. High accuracy is achieved by applying a second-order method analogous to Newton's method or Bellman's quasilinearization method. In the solution of problems with direct control constraints, the variation of the control is computed using a finite-dimensional approximation of an auxiliary problem, which is solved by applying linear programming methods.
Research of the Optimization Methods for Mass Customization (MC)
无
2002-01-01
A group of graphical models and mathematical models a re used to describe the methods of mass customization (MC). The relationships am ong the models and methods are shown in Fig.1. Fig.1 Relationships among optimization methods for MCThe methods for MC relate to both of product and process, also customized quanti ty and deepness. The methods for MC are integrated by the work in the paper, whi ch can help to understand and use MC better. The optimization and standardization of product is the key for M...
Wang, Liang; Liu, Zunying; Zhao, Yuanhui; Dong, Shiyuan; Zeng, Mingyong; Yang, Huicheng
2015-08-01
Three freezing-point regulators (glycine, sodium chloride and D-sorbitol) were employed to optimize thermophysical properties of Pacific white shrimp (Litopenaeus vannamei) using response surface methodology (RSM). The independent variables were glycine content (0.250-1.250 %), sodium chloride content (0.500-2.500 %) and D-sorbitol content (0.125-0.625 %) and analysis of variance showed that the effects of glycine, sodium chloride and D-sorbitol on the thermophysical properties were statistically significant (P < 0.05). The coefficient of determination, R (2) values for initial freezing point (T i ), unfreezable water mass fraction (W u ), apparent specific heat (C app ) and Enthalpy (H) were 0.896 ~ 0.999. The combined effects of these independent variables on T i , W u , C app and H were investigated. The results indicated that T i , C app and H varied curvilinearly with increasing of glycine, sodium chloride and D-sorbitol content whereas W u increased nearly linearly. Based on response plots and desirability functions, the optimum combination of process variables for Pacific white shrimp previously treated with freezing-point regulators were 0.876 % for glycine content, 2.298 % for sodium chloride content and 0.589 % for D-sorbitol content, correspondently the optimized thermophysical properties were T i , - 5.086 °C; W u , 17.222 %; C app , 41.038 J/g °C and H, 155.942 J/g, respectively. Briefly, the application of freezing-point regulators depressed T i and obtained the optimum W u , C app and H, which would be obviously beneficial for the exploitation of various thermal processing and food storage.
Comparison of optimal design methods in inverse problems
Banks, H. T.; Holm, K.; Kappel, F.
2011-07-01
Typical optimal design methods for inverse or parameter estimation problems are designed to choose optimal sampling distributions through minimization of a specific cost function related to the resulting error in parameter estimates. It is hoped that the inverse problem will produce parameter estimates with increased accuracy using data collected according to the optimal sampling distribution. Here we formulate the classical optimal design problem in the context of general optimization problems over distributions of sampling times. We present a new Prohorov metric-based theoretical framework that permits one to treat succinctly and rigorously any optimal design criteria based on the Fisher information matrix. A fundamental approximation theory is also included in this framework. A new optimal design, SE-optimal design (standard error optimal design), is then introduced in the context of this framework. We compare this new design criterion with the more traditional D-optimal and E-optimal designs. The optimal sampling distributions from each design are used to compute and compare standard errors; the standard errors for parameters are computed using asymptotic theory or bootstrapping and the optimal mesh. We use three examples to illustrate ideas: the Verhulst-Pearl logistic population model (Banks H T and Tran H T 2009 Mathematical and Experimental Modeling of Physical and Biological Processes (Boca Raton, FL: Chapman and Hall/CRC)), the standard harmonic oscillator model (Banks H T and Tran H T 2009) and a popular glucose regulation model (Bergman R N, Ider Y Z, Bowden C R and Cobelli C 1979 Am. J. Physiol. 236 E667-77 De Gaetano A and Arino O 2000 J. Math. Biol. 40 136-68 Toffolo G, Bergman R N, Finegood D T, Bowden C R and Cobelli C 1980 Diabetes 29 979-90).
An improved optimal elemental method for updating finite element models
Duan Zhongdong(段忠东); Spencer B.F.; Yan Guirong(闫桂荣); Ou Jinping(欧进萍)
2004-01-01
The optimal matrix method and optimal elemental method used to update finite element models may not provide accurate results. This situation occurs when the test modal model is incomplete, as is often the case in practice. An improved optimal elemental method is presented that defines a new objective function, and as a byproduct, circumvents the need for mass normalized modal shapes, which are also not readily available in practice. To solve the group of nonlinear equations created by the improved optimal method, the Lagrange multiplier method and Matlab function fmincon are employed. To deal with actual complex structures,the float-encoding genetic algorithm (FGA) is introduced to enhance the capability of the improved method. Two examples, a 7-degree of freedom (DOF) mass-spring system and a 53-DOF planar frame, respectively, are updated using the improved method.Thc example results demonstrate the advantages of the improved method over existing optimal methods, and show that the genetic algorithm is an effective way to update the models used for actual complex structures.
Moreno, Santiago; González, Juan; Lekander, Ingrid; Martí, Belén; Oyagüez, Itziar; Sánchez-de la Rosa, Rainel; Casado, Miguel Angel
2010-12-01
The aim of this work was to evaluate the cost-effectiveness, from the perspective of the Spanish health care system, of optimized background therapy (OBT) plus maraviroc 300 mg BID versus OBT plus placebo in previously treated patients with R5 HIV-1 infection. A lifetime cohort model was developed, based on 24- and 48-week pooled results from the Maraviroc Versus Optimized Therapy in Viremic Antiretroviral Treatment-Experienced Patients (MOTIVATE) studies 1 and 2, to reflect the Spanish health care system's perspective. Treatment duration was based on clinical trial follow-up from MOTIVATE 1 and 2. Clinical data, cohort characteristics, success probability, CD4 increase rate, CD4 cell status link to disease states, and adverse-event probability were taken from the MOTIVATE trials and other published literature. Other input parameters were taken from published sources. Antiretroviral (ARV) costs were derived from local sources. Non-ARV drug costs were obtained from published literature and a cost database. All costs were calculated as year-2009 euros. The annual discount rate was set at 3.0%. The main outcomes were cost per life-year gained (LYG) and cost per quality-adjusted life-year (QALY) gained. Uncertainty was assessed with one-way and probabilistic sensitivity analyses. In the model analysis, adding maraviroc to OBT was associated with an increase of 0.952 LYG and 0.909 QALY. Total costs were €275,970 for maraviroc plus OBT and €254,655 for placebo plus OBT (difference: €21,315). The incremental cost per LYG was €22,398 and the incremental cost per QALY gained was €23,457. The model appeared to be robust for variations in key parameters. Results from the probabilistic sensitivity analyses indicated that the probability of the cost per QALY being below €30,000 was 99%. Despite the limitations of the model, our analysis suggested that OBT plus maraviroc 300 mg BID is a clinically valuable option, and cost-effective from the perspective of the
A Numerical Embedding Method for Solving the Nonlinear Optimization Problem
田保锋; 戴云仙; 孟泽红; 张建军
2003-01-01
A numerical embedding method was proposed for solving the nonlinear optimization problem. By using the nonsmooth theory, the existence and the continuation of the following path for the corresponding homotopy equations were proved. Therefore the basic theory for the algorithm of the numerical embedding method for solving the non-linear optimization problem was established. Based on the theoretical results, a numerical embedding algorithm was designed for solving the nonlinear optimization problem, and prove its convergence carefully. Numerical experiments show that the algorithm is effective.
Method and system for SCR optimization
Lefebvre, Wesley Curt; Kohn, Daniel W.
2009-03-10
Methods and systems are provided for controlling SCR performance in a boiler. The boiler includes one or more generally cross sectional areas. Each cross sectional area can be characterized by one or more profiles of one or more conditions affecting SCR performance and be associated with one or more adjustable desired profiles of the one or more conditions during the operation of the boiler. The performance of the boiler can be characterized by boiler performance parameters. A system in accordance with one or more embodiments of the invention can include a controller input for receiving a performance goal for the boiler corresponding to at least one of the boiler performance parameters and for receiving data values corresponding to boiler control variables and to the boiler performance parameters. The boiler control variables include one or more current profiles of the one or more conditions. The system also includes a system model that relates one or more profiles of the one or more conditions in the boiler to the boiler performance parameters. The system also includes an indirect controller that determines one or more desired profiles of the one or more conditions to satisfy the performance goal for the boiler. The indirect controller uses the system model, the received data values and the received performance goal to determine the one or more desired profiles of the one or more conditions. The system model also includes a controller output that outputs the one or more desired profiles of the one or more conditions.
Local Approximation and Hierarchical Methods for Stochastic Optimization
Cheng, Bolong
In this thesis, we present local and hierarchical approximation methods for two classes of stochastic optimization problems: optimal learning and Markov decision processes. For the optimal learning problem class, we introduce a locally linear model with radial basis function for estimating the posterior mean of the unknown objective function. The method uses a compact representation of the function which avoids storing the entire history, as is typically required by nonparametric methods. We derive a knowledge gradient policy with the locally parametric model, which maximizes the expected value of information. We show the policy is asymptotically optimal in theory, and experimental works suggests that the method can reliably find the optimal solution on a range of test functions. For the Markov decision processes problem class, we are motivated by an application where we want to co-optimize a battery for multiple revenue, in particular energy arbitrage and frequency regulation. The nature of this problem requires the battery to make charging and discharging decisions at different time scales while accounting for the stochastic information such as load demand, electricity prices, and regulation signals. Computing the exact optimal policy becomes intractable due to the large state space and the number of time steps. We propose two methods to circumvent the computation bottleneck. First, we propose a nested MDP model that structure the co-optimization problem into smaller sub-problems with reduced state space. This new model allows us to understand how the battery behaves down to the two-second dynamics (that of the frequency regulation market). Second, we introduce a low-rank value function approximation for backward dynamic programming. This new method only requires computing the exact value function for a small subset of the state space and approximate the entire value function via low-rank matrix completion. We test these methods on historical price data from the
Thickness optimization of laminated composites using the discrete material optimization method
Sørensen, Søren Nørgaard; Sørensen, Rene; Lund, Erik
2012-01-01
This work concerns a novel large scale multi-material topology optimization method for simultaneous determination of the optimum variable integer thickness and fiber orientation throughout laminated composites with fixed outer geometries while adhering to certain manufacturing constraints....... The conceptual combinatorial/integer problem is relaxed to a continuous problem and solved on basis of the so-called Discrete Material Optimization method, explicitly including the manufacturing constraints as linear constraints....
Optimized protein extraction methods for proteomic analysis of Rhizoctonia solani.
Lakshman, Dilip K; Natarajan, Savithiry S; Lakshman, Sukla; Garrett, Wesley M; Dhar, Arun K
2008-01-01
Rhizoctonia solani (Teleomorph: Thanatephorus cucumeris, T. praticola) is a basidiomycetous fungus and a major cause of root diseases of economically important plants. Various isolates of this fungus are also beneficially associated with orchids, may serve as biocontrol agents or remain as saprophytes with roles in decaying and recycling of soil organic matter. R. solani displays several hyphal anastomosis groups (AG) with distinct host and pathogenic specializations. Even though there are reports on the physiological and histological basis of Rhizoctonia-host interactions, very little is known about the molecular biology and control of gene expression early during infection by this pathogen. Proteamic technologies are powerful tools for examining alterations in protein profiles. To aid studies on its biology and host pathogen interactions, a two-dimensional (2-D) gel-based global proteomic study has been initiated. To develop an optimized protein extraction protocol for R. solani, we compared two previously reported protein extraction protocols for 2-D gel analysis of R. solani (AG-4) isolate Rs23. Both TCA-acetone precipitation and phosphate solubilization before TCA-acetone precipitation worked well for R. solani protein extraction, although selective enrichment of some proteins was noted with either method. About 450 spots could be detected with the densitiometric tracing of Coomassie blue-stained 2-D PAGE gels covering pH 4-7 and 6.5-205 kDa. Selected protein spots were subjected to mass spectrometric analysis with matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). Eleven protein spots were positively identified based on peptide mass fingerprinting match with fungal proteins in public databases with the Mascot search engine. These results testify to the suitability of the two optimized protein extraction protocols for 2-D proteomic studies of R. solani.
Method for Determining Optimal Residential Energy Efficiency Retrofit Packages
Polly, B.; Gestwick, M.; Bianchi, M.; Anderson, R.; Horowitz, S.; Christensen, C.; Judkoff, R.
2011-04-01
Businesses, government agencies, consumers, policy makers, and utilities currently have limited access to occupant-, building-, and location-specific recommendations for optimal energy retrofit packages, as defined by estimated costs and energy savings. This report describes an analysis method for determining optimal residential energy efficiency retrofit packages and, as an illustrative example, applies the analysis method to a 1960s-era home in eight U.S. cities covering a range of International Energy Conservation Code (IECC) climate regions. The method uses an optimization scheme that considers average energy use (determined from building energy simulations) and equivalent annual cost to recommend optimal retrofit packages specific to the building, occupants, and location. Energy savings and incremental costs are calculated relative to a minimum upgrade reference scenario, which accounts for efficiency upgrades that would occur in the absence of a retrofit because of equipment wear-out and replacement with current minimum standards.
On some other preferred method for optimizing the welded joint
Pejović Branko B.
2016-01-01
Full Text Available The paper shows an example of performed optimization of sizes in terms of welding costs in a characteristic loaded welded joint. Hence, in the first stage, the variables and constant parameters are defined, and mathematical shape of the optimization function is determined. The following stage of the procedure defines and places the most important constraint functions that limit the design of structures, that the technologist and the designer should take into account. Subsequently, a mathematical optimization model of the problem is derived, that is efficiently solved by a proposed method of geometric programming. Further, a mathematically based thorough optimization algorithm is developed of the proposed method, with a main set of equations defining the problem that are valid under certain conditions. Thus, the primary task of optimization is reduced to the dual task through a corresponding function, which is easier to solve than the primary task of the optimized objective function. The main reason for this is a derived set of linear equations. Apparently, a correlation is used between the optimal primary vector that minimizes the objective function and the dual vector that maximizes the dual function. The method is illustrated on a computational practical example with a different number of constraint functions. It is shown that for the case of a lower level of complexity, a solution is reached through an appropriate maximization of the dual function by mathematical analysis and differential calculus.
Review: Optimization methods for groundwater modeling and management
Yeh, William W.-G.
2015-09-01
Optimization methods have been used in groundwater modeling as well as for the planning and management of groundwater systems. This paper reviews and evaluates the various optimization methods that have been used for solving the inverse problem of parameter identification (estimation), experimental design, and groundwater planning and management. Various model selection criteria are discussed, as well as criteria used for model discrimination. The inverse problem of parameter identification concerns the optimal determination of model parameters using water-level observations. In general, the optimal experimental design seeks to find sampling strategies for the purpose of estimating the unknown model parameters. A typical objective of optimal conjunctive-use planning of surface water and groundwater is to minimize the operational costs of meeting water demand. The optimization methods include mathematical programming techniques such as linear programming, quadratic programming, dynamic programming, stochastic programming, nonlinear programming, and the global search algorithms such as genetic algorithms, simulated annealing, and tabu search. Emphasis is placed on groundwater flow problems as opposed to contaminant transport problems. A typical two-dimensional groundwater flow problem is used to explain the basic formulations and algorithms that have been used to solve the formulated optimization problems.
Cai, Lanlan; Li, Peng; Luo, Qi; Zhai, Pengcheng; Zhang, Qingjie
2017-03-01
As no single thermoelectric material has presented a high figure-of-merit (ZT) over a very wide temperature range, segmented thermoelectric generators (STEGs), where the p- and n-legs are formed of different thermoelectric material segments joined in series, have been developed to improve the performance of thermoelectric generators. A crucial but difficult problem in a STEG design is to determine the optimal values of the geometrical parameters, like the relative lengths of each segment and the cross-sectional area ratio of the n- and p-legs. Herein, a multi-parameter and nonlinear optimization method, based on the Improved Powell Algorithm in conjunction with the discrete numerical model, was implemented to solve the STEG's geometrical optimization problem. The multi-parameter optimal results were validated by comparison with the optimal outcomes obtained from the single-parameter optimization method. Finally, the effect of the hot- and cold-junction temperatures on the geometry optimization was investigated. Results show that the optimal geometry parameters for maximizing the specific output power of a STEG are different from those for maximizing the conversion efficiency. Data also suggest that the optimal geometry parameters and the interfacial temperatures of the adjacent segments optimized for maximum specific output power or conversion efficiency vary with changing hot- and cold-junction temperatures. Through the geometry optimization, the CoSb3/Bi2Te3-based STEG can obtain a maximum specific output power up to 1725.3 W/kg and a maximum efficiency of 13.4% when operating at a hot-junction temperature of 823 K and a cold-junction temperature of 298 K.
Ruisheng Sun
2016-01-01
Full Text Available This paper presents a new parametric optimization approach based on a modified particle swarm optimization (PSO to design a class of impulsive-correction projectiles with discrete, flexible-time interval, and finite-energy control. In terms of optimal control theory, the task is described as the formulation of minimum working number of impulses and minimum control error, which involves reference model linearization, boundary conditions, and discontinuous objective function. These result in difficulties in finding the global optimum solution by directly utilizing any other optimization approaches, for example, Hp-adaptive pseudospectral method. Consequently, PSO mechanism is employed for optimal setting of impulsive control by considering the time intervals between two neighboring lateral impulses as design variables, which makes the briefness of the optimization process. A modification on basic PSO algorithm is developed to improve the convergence speed of this optimization through linearly decreasing the inertial weight. In addition, a suboptimal control and guidance law based on PSO technique are put forward for the real-time consideration of the online design in practice. Finally, a simulation case coupled with a nonlinear flight dynamic model is applied to validate the modified PSO control algorithm. The results of comparative study illustrate that the proposed optimal control algorithm has a good performance in obtaining the optimal control efficiently and accurately and provides a reference approach to handling such impulsive-correction problem.
Evtushenko Yury
2016-01-01
Full Text Available Paper deals with the non-uniform covering method that is aimed at deterministic global optimization. This method finds a feasible solution to the optimization problem numerically and proves that the obtained solution differs from the optimal by no more than a given accuracy. Numerical proof consists of constructing a set of covering sets - the coverage. The number of elements in the coverage can be very large and even exceed the total amount of available computer resources. Basic method of coverage construction is the comparison of upper and lower bounds on the value of the objective function. In this work we propose to use necessary optimality conditions of first and second order for reducing the search for boxconstrained problems. We provide the algorithm description and prove its correctness. The efficiency of the proposed approach is studied on test problems.
A weak Hamiltonian finite element method for optimal control problems
Hodges, Dewey H.; Bless, Robert R.
1990-01-01
A temporal finite element method based on a mixed form of the Hamiltonian weak principle is developed for dynamics and optimal control problems. The mixed form of Hamilton's weak principle contains both displacements and momenta as primary variables that are expanded in terms of nodal values and simple polynomial shape functions. Unlike other forms of Hamilton's principle, however, time derivatives of the momenta and displacements do not appear therein; instead, only the virtual momenta and virtual displacements are differentiated with respect to time. Based on the duality that is observed to exist between the mixed form of Hamilton's weak principle and variational principles governing classical optimal control problems, a temporal finite element formulation of the latter can be developed in a rather straightforward manner. Several well-known problems in dynamics and optimal control are illustrated. The example dynamics problem involves a time-marching problem. As optimal control examples, elementary trajectory optimization problems are treated.
Weak Hamiltonian finite element method for optimal control problems
Hodges, Dewey H.; Bless, Robert R.
1991-01-01
A temporal finite element method based on a mixed form of the Hamiltonian weak principle is developed for dynamics and optimal control problems. The mixed form of Hamilton's weak principle contains both displacements and momenta as primary variables that are expanded in terms of nodal values and simple polynomial shape functions. Unlike other forms of Hamilton's principle, however, time derivatives of the momenta and displacements do not appear therein; instead, only the virtual momenta and virtual displacements are differentiated with respect to time. Based on the duality that is observed to exist between the mixed form of Hamilton's weak principle and variational principles governing classical optimal control problems, a temporal finite element formulation of the latter can be developed in a rather straightforward manner. Several well-known problems in dynamics and optimal control are illustrated. The example dynamics problem involves a time-marching problem. As optimal control examples, elementary trajectory optimization problems are treated.
Efficient Hybrid Optimal Design Method for Power Electronics Converters
AUTHOR|(SzGeCERN)697719; Aguglia, Davide; Viarouge, Philippe; Cros, Jérôme
2015-01-01
This paper presents a novel design methodology for dimensioning optimal power-electronic converters, which is able to achieve the precision of numerical simulation-based optimization procedures, however minimizing the overall computation time. The approach is based on the utilization of analytical and frequency-domain design models for a numerical optimization process, a validation with numerical simulations of the intermediate optimal solutions, and the correction of the analytical design models precision from the numerical simulation results. This method allows using the numerical simulation in an efficient way, where typically less than ten correction iterations are required. In order to demonstrate the performances of the proposed methodology, the calculation of the control parameters for an H-bridge DC-DC converter and the optimal dimensioning of a damped output filter for a buck converter using the proposed approach is presented.
Robust Collaborative Optimization Method Based on Dual-response Surface
WANG Wei; FAN Wenhui; CHANG Tianqing; YUAN Yuming
2009-01-01
A novel method for robust collaborative design of complex products based on dual-response surface (DRS-RCO) is proposed to solve multidisciplinary design optimization (MDO) problems under uncertainty. Collaborative optimization (CO) which decomposes the whole system into a double-level nonlinear optimization problem is widely Accepted as an efficient method to solve MDO problems. In order to improve the quality of complex product in design process, robust collaborative optimization (RCO) is developed to solve those problems under uncertain conditions. RCO does opfmiTation on the linear sum of mean and standard deviation of objective function and gets an optimal solution with high robustnmess. Response surfaces method is an important way to do approximation in robust design. DRS-RCO is an improved RCO method in which dual-response surface replaces system uncertainty analysis module of CO. The dual-response surface is the approximate model of mean and standard deviation of objective function respectively. In DRS-RCO, All the information of subsystems is included in dual-response surfaces. As an additional item, the standard deviation of objective function is added to the subsystem optimization. This item guarantee both the mean and standard deviation of this subsystem is reaching the minima at the same time. Finally, a test problem with two coupled subsystems is conducted to verify the feasibility and effectiveness of DRS-RCO.
RELATIVE CAMERA POSE ESTIMATION METHOD USING OPTIMIZATION ON THE MANIFOLD
C. Cheng
2017-05-01
Full Text Available To solve the problem of relative camera pose estimation, a method using optimization with respect to the manifold is proposed. Firstly from maximum-a-posteriori (MAP model to nonlinear least squares (NLS model, the general state estimation model using optimization is derived. Then the camera pose estimation model is applied to the general state estimation model, while the parameterization of rigid body transformation is represented by Lie group/algebra. The jacobian of point-pose model with respect to Lie group/algebra is derived in detail and thus the optimization model of rigid body transformation is established. Experimental results show that compared with the original algorithms, the approaches with optimization can obtain higher accuracy both in rotation and translation, while avoiding the singularity of Euler angle parameterization of rotation. Thus the proposed method can estimate relative camera pose with high accuracy and robustness.
Development of optimization method for plate heat exchanger with undulation
Dvořák Václav
2016-01-01
Full Text Available The article deals with optimization of undulated heat transfer surface of plate heat exchanger. The goal of optimization is not only to increase effectiveness of heat transfer but also to reduce the pressure drop. A combined pattern of undulation which combines herringbone pattern and wavy pattern was optimized and best values of four parameters were found; angle of herringbone pattern, number, phase and amplitude of longitudinal waves of wavy pattern. The optimization procedure looked for maximum of objective function which was a linear combination of effectiveness and pressure drop. We used simple Monte Carlo method and the optimum was searched for four values of reference pressure drop. Four different optimization were run and we investigated the effect of various definition of objective function and parameters of undulation. It was found that during optimization of combined pattern, the herringbone pattern is more favoured than wavy pattern. It is caused by the fact that herringbone pattern was described by the only one free parameter, which was the angle of undulation, and therefore it is more likely to be found by a stochastic method. This assumption was confirmed when simple wavy pattern was optimized and higher values of objective function and effectiveness were found.
A class of trust-region methods for parallel optimization
P. D. Hough; J. C. Meza
1999-03-01
The authors present a new class of optimization methods that incorporates a Parallel Direct Search (PDS) method within a trust-region Newton framework. This approach combines the inherent parallelism of PDS with the rapid and robust convergence properties of Newton methods. Numerical tests have yielded favorable results for both standard test problems and engineering applications. In addition, the new method appears to be more robust in the presence of noisy functions that are inherent in many engineering simulations.
System Design Support by Optimization Method Using Stochastic Process
Yoshida, Hiroaki; Yamaguchi, Katsuhito; Ishikawa, Yoshio
We proposed the new optimization method based on stochastic process. The characteristics of this method are to obtain the approximate solution of the optimum solution as an expected value. In numerical calculation, a kind of Monte Carlo method is used to obtain the solution because of stochastic process. Then, it can obtain the probability distribution of the design variable because it is generated in the probability that design variables were in proportion to the evaluation function value. This probability distribution shows the influence of design variables on the evaluation function value. This probability distribution is the information which is very useful for the system design. In this paper, it is shown the proposed method is useful for not only the optimization but also the system design. The flight trajectory optimization problem for the hang-glider is shown as an example of the numerical calculation.
Optimal Combination of Aircraft Maintenance Tasks by a Novel Simplex Optimization Method
Huaiyuan Li
2015-01-01
Full Text Available Combining maintenance tasks into work packages is not only necessary for arranging maintenance activities, but also critical for the reduction of maintenance cost. In order to optimize the combination of maintenance tasks by fuzzy C-means clustering algorithm, an improved fuzzy C-means clustering model is introduced in this paper. In order to reduce the dimension, variables representing clustering centers are eliminated in the improved cluster model. So the improved clustering model can be directly solved by the optimization method. To optimize the clustering model, a novel nonlinear simplex optimization method is also proposed in this paper. The novel method searches along all rays emitting from the center to each vertex, and those search directions are rightly n+1 positive basis. The algorithm has both theoretical convergence and good experimental effect. Taking the optimal combination of some maintenance tasks of a certain aircraft as an instance, the novel simplex optimization method and the clustering model both exhibit excellent performance.
Optimal Route Selection Method Based on Vague Sets
GUO Rui; DU Li min; WANG Chun
2015-01-01
Optimal route selection is an important function of vehicle trac flow guidance system. Its core is to determine the index weight for measuring the route merits and to determine the evaluation method for selecting route. In this paper, subjective weighting method which relies on driver preference is used to determine the weight and the paper proposes the multi-criteria weighted decision method based on vague sets for selecting the optimal route. Examples show that, the usage of vague sets to describe route index value can provide more decision-making information for route selection.
Deterministic operations research models and methods in linear optimization
Rader, David J
2013-01-01
Uniquely blends mathematical theory and algorithm design for understanding and modeling real-world problems Optimization modeling and algorithms are key components to problem-solving across various fields of research, from operations research and mathematics to computer science and engineering. Addressing the importance of the algorithm design process. Deterministic Operations Research focuses on the design of solution methods for both continuous and discrete linear optimization problems. The result is a clear-cut resource for understanding three cornerstones of deterministic operations resear
Adaptive Mixed Finite Element Methods for Parabolic Optimal Control Problems
Zuliang Lu
2011-01-01
We will investigate the adaptive mixed finite element methods for parabolic optimal control problems. The state and the costate are approximated by the lowest-order Raviart-Thomas mixed finite element spaces, and the control is approximated by piecewise constant elements. We derive a posteriori error estimates of the mixed finite element solutions for optimal control problems. Such a posteriori error estimates can be used to construct more efficient and reliable adaptive mixed finite element ...
Mehdi Neshat
2015-11-01
Full Text Available In this article, the objective was to present effective and optimal strategies aimed at improving the Swallow Swarm Optimization (SSO method. The SSO is one of the best optimization methods based on swarm intelligence which is inspired by the intelligent behaviors of swallows. It has been able to offer a relatively strong method for solving optimization problems. However, despite its many advantages, the SSO suffers from two shortcomings. Firstly, particles movement speed is not controlled satisfactorily during the search due to the lack of an inertia weight. Secondly, the variables of the acceleration coefficient are not able to strike a balance between the local and the global searches because they are not sufficiently flexible in complex environments. Therefore, the SSO algorithm does not provide adequate results when it searches in functions such as the Step or Quadric function. Hence, the fuzzy adaptive Swallow Swarm Optimization (FASSO method was introduced to deal with these problems. Meanwhile, results enjoy high accuracy which are obtained by using an adaptive inertia weight and through combining two fuzzy logic systems to accurately calculate the acceleration coefficients. High speed of convergence, avoidance from falling into local extremum, and high level of error tolerance are the advantages of proposed method. The FASSO was compared with eleven of the best PSO methods and SSO in 18 benchmark functions. Finally, significant results were obtained.
Cost optimal river dike design using probabilistic methods
Bischiniotis, K.; Kanning, W.; Jonkman, S.N.
2014-01-01
This research focuses on the optimization of river dikes using probabilistic methods. Its aim is to develop a generic method that automatically estimates the failure probabilities of many river dike cross-sections and gives the one with the least cost, taking into account the boundary conditions and
Multiplier methods for optimization problems with Lipschitzian derivatives
Izmailov, A. F.; Kurennoy, A. S.
2012-12-01
Optimization problems for which the objective function and the constraints have locally Lipschitzian derivatives but are not assumed to be twice differentiable are examined. For such problems, analyses of the local convergence and the convergence rate of the multiplier (or the augmented Lagrangian) method and the linearly constraint Lagrangian method are given.
The piecewise constant method in gait design through optimization
Yizhen Wei
2014-01-01
The objective of this paper is to introduce the piecewise constant method in gait design of a planar, under actuated, five-link biped robot model and to discuss the advantages and disadvantages. The piecewise constant method transforms the dynamic optimal control problem into a static problem.
An intermediate targets method for time parallelization in optimal control
Maday, Yvon; Riahi, Kamel
2011-01-01
In this paper, we present a method that enables to solve in parallel the Euler-Lagrange system associated with the optimal control of a parabolic equation. Our approach is based on an iterative update of a sequence of intermediate targets and gives rise independent sub-problems that can be solved in parallel. Numerical experiments show the efficiency of our method.
Cost optimal river dike design using probabilistic methods
Bischiniotis, K.; Kanning, W.; Jonkman, S.N.
2014-01-01
This research focuses on the optimization of river dikes using probabilistic methods. Its aim is to develop a generic method that automatically estimates the failure probabilities of many river dike cross-sections and gives the one with the least cost, taking into account the boundary conditions and
CONIC TRUST REGION METHOD FOR LINEARLY CONSTRAINED OPTIMIZATION
Wen-yu Sun; Jin-yun Yuan; Ya-xiang Yuan
2003-01-01
In this paper we present a trust region method of conic model for linearly constrainedoptimization problems. We discuss trust region approaches with conic model subproblems.Some equivalent variation properties and optimality conditions are given. A trust regionalgorithm based on conic model is constructed. Global convergence of the method isestablished.
A Combined Method in Parameters Optimization of Hydrocyclone
Jing-an Feng
2016-01-01
Full Text Available To achieve efficient separation of calcium hydroxide and impurities in carbide slag by using hydrocyclone, the physical granularity property of carbide slag, hydrocyclone operation parameters for slurry concentration, and the slurry velocity inlet are designed to be optimized. The optimization methods are combined with the Design of Experiment (DOE method and the Computational Fluid Dynamics (CFD method. Based on Design Expert software, the central composite design (CCD with three factors and five levels amounting to five groups of 20 test responses was constructed, and the experiments were performed by numerical simulation software FLUENT. Through the analysis of variance deduced from numerical simulation experiment results, the regression equations of pressure drop, overflow concentration, purity, and separation efficiencies of two solid phases were, respectively, obtained. The influences of factors were analyzed by the responses, respectively. Finally, optimized results were obtained by the multiobjective optimization method through the Design Expert software. Based on the optimized conditions, the validation test by numerical simulation and separation experiment were separately proceeded. The results proved that the combined method could be efficiently used in studying the hydrocyclone and it has a good performance in application engineering.
Optimization of digital breast tomosynthesis using the Taguchi method
Lee, Taewon; Min, Jonghwan; Cho, Seungryong [KAIST, Daejeon (Korea, Republic of). Dept. of Nuclear and Quantum Engineering
2011-07-01
Digital breast tomosynthesis (DBT) has been demonstrated to be a promising technique in early breast cancer detection. The DBT performance is generally affected by many factors including scanning parameters such as limited angle and limited dose value, and reconstruction method. Many investigators have studied the effects of those factors on image quality of DBT, and optimized the factors accordingly. The suggested scanning parameters, however, vary widely among the investigators. Optimization in DBT can be challenging partly due to the large number of parameters that are involved in the optimization, and also due to diverse imaging tasks under consideration. In this work, we propose an optimization method for DBT based on the Taguchi design-of-experiment method. It should be noted that we are not searching for a universal, optimum DBT technique, which we believe is very difficult if not impossible, but instead we would like to demonstrate that the Taguchi method provides an efficient and systematic way of optimizing many parameters for a given DBT system and a given imaging task. As a preliminary, we conducted a numerical simulation study, and showed that the Taguchi method effectively selected the (near-) optimum parameters for a mass detection task. (orig.)
A solution quality assessment method for swarm intelligence optimization algorithms.
Zhang, Zhaojun; Wang, Gai-Ge; Zou, Kuansheng; Zhang, Jianhua
2014-01-01
Nowadays, swarm intelligence optimization has become an important optimization tool and wildly used in many fields of application. In contrast to many successful applications, the theoretical foundation is rather weak. Therefore, there are still many problems to be solved. One problem is how to quantify the performance of algorithm in finite time, that is, how to evaluate the solution quality got by algorithm for practical problems. It greatly limits the application in practical problems. A solution quality assessment method for intelligent optimization is proposed in this paper. It is an experimental analysis method based on the analysis of search space and characteristic of algorithm itself. Instead of "value performance," the "ordinal performance" is used as evaluation criteria in this method. The feasible solutions were clustered according to distance to divide solution samples into several parts. Then, solution space and "good enough" set can be decomposed based on the clustering results. Last, using relative knowledge of statistics, the evaluation result can be got. To validate the proposed method, some intelligent algorithms such as ant colony optimization (ACO), particle swarm optimization (PSO), and artificial fish swarm algorithm (AFS) were taken to solve traveling salesman problem. Computational results indicate the feasibility of proposed method.
Timofeev, M E; Shapoval'iants, S G; Fedorov, E D; Polushkin, V G
2014-01-01
The article presents the use of laparoscopic interventions in 38 patients with Acute Adhesive Small Bowel Obstruction (AASBO) in patients without previous history of abdominal surgery. Clinical, radiological and ultrasound patterns of disease are analyzed. The use of laparoscopy has proved itself the most effective and relatively safe diagnostic procedure. In 14 (36.8%) patients convertion to laparotomy was made due to contraindications for laparoscopy. In 24 (63.2%) patients laparosopic adhesyolisis was performed and AASBO subsequently treated with complications rate of 4.2%.
Methods of optimal control choice of non-Keplerian orbits
Starinova, Olga
2017-01-01
The developed method of iterative optimization of interplanetary missions with the low thrust, using sequence of movement specified mathematical models and design shape spacecraft, is realized. The new articular analytical decision describing planar movement of spacecraft with solar electric propulsion is described, allowing to construct initial approach in the iterative scheme of optimization. Developed a method of modelling and optimization of interplanetary missions ballistic schemes, based on a combination of Pontryagin's maximum principle formalism conditions of transversality and methods of the mathematical programming, allowing to consider restrictions characteristic for concrete interplanetary missions. Recommendations for choice design-ballistic parameters of the interplanetary missions spacecraft, received taking into account features nuclear and solar power plant for missions on delivery of a payload to orbits of Mars, expeditions the Earth-Mars-Earth are received.
Optimization method for electron beam melting and refining of metals
Donchev, Veliko; Vutova, Katia
2014-03-01
Pure metals and special alloys obtained by electron beam melting and refining (EBMR) in vacuum, using electron beams as a heating source, have a lot of applications in nuclear and airspace industries, electronics, medicine, etc. An analytical optimization problem for the EBMR process based on mathematical heat model is proposed. The used criterion is integral functional minimization of a partial derivative of the temperature in the metal sample. The investigated technological parameters are the electron beam power, beam radius, the metal casting velocity, etc. The optimization problem is discretized using a non-stationary heat model and corresponding adapted Pismen-Rekford numerical scheme, developed by us and multidimensional trapezional rule. Thus a discrete optimization problem is built where the criterion is a function of technological process parameters. The discrete optimization problem is heuristically solved by cluster optimization method. Corresponding software for the optimization task is developed. The proposed optimization scheme can be applied for quality improvement of the pure metals (Ta, Ti, Cu, etc.) produced by the modern and ecological-friendly EBMR process.
Optimal image-fusion method based on nonsubsampled contourlet transform
Dou, Jianfang; Li, Jianxun
2012-10-01
The optimization of image fusion is researched. Based on the properties of nonsubsampled contourlet transform (NSCT), shift invariance, multiscale and multidirectional expansion, the fusion parameters of the multiscale decompostion scheme is optimized. In order to meet the requirement of feedback optimization, a new image fusion quality metric of image quality index normalized edge association (IQI-NEA) is built. A polynomial model is adopted to establish the relationship between the IQI_NEA metric and several decomposition levels. The optimal fusion includes four steps. First, the source images are decomposed in NSCT domain for several given levels. Second, principal component analysis is adopted to fuse the low frequency coefficients and the maximum fusion rule is utilized to fuse the high frequency coefficients to obtain the fused coefficients and the fused result is reconstructed from the obtained fused coefficients. Third, calculate the fusion quality metric IQI_NEA for the source images and fused images. Finally, the optimal fused image and optimal level are obtained through extremum properties of polynomials function. The visual and statistical results show that the proposed method has optimized the fusion performance compared to the existing fusion schemes, in terms of the visual effects and quantitative fusion evaluation indexes.
Compensating Interpolation Distortion by Using New Optimized Modular Method
Tofighi, Mohammad; Marvasti, Farokh
2012-01-01
A modular method was suggested before to recover a band limited signal from the sample and hold and linearly interpolated (or, in general, an nth-order-hold) version of the regular samples. In this paper a novel approach for compensating the distortion of any interpolation based on modular method has been proposed. In this method the performance of the modular method is optimized by adding only some simply calculated coefficients. This approach causes drastic improvement in terms of signal-to-noise ratios with fewer modules compared to the classical modular method. Simulation results clearly confirm the improvement of the proposed method and also its superior robustness against additive noise.
Optimization methods and silicon solar cell numerical models
Girardini, K.; Jacobsen, S. E.
1986-01-01
An optimization algorithm for use with numerical silicon solar cell models was developed. By coupling an optimization algorithm with a solar cell model, it is possible to simultaneously vary design variables such as impurity concentrations, front junction depth, back junction depth, and cell thickness to maximize the predicted cell efficiency. An optimization algorithm was developed and interfaced with the Solar Cell Analysis Program in 1 Dimension (SCAP1D). SCAP1D uses finite difference methods to solve the differential equations which, along with several relations from the physics of semiconductors, describe mathematically the performance of a solar cell. A major obstacle is that the numerical methods used in SCAP1D require a significant amount of computer time, and during an optimization the model is called iteratively until the design variables converge to the values associated with the maximum efficiency. This problem was alleviated by designing an optimization code specifically for use with numerically intensive simulations, to reduce the number of times the efficiency has to be calculated to achieve convergence to the optimal solution.
QUADRO: A SUPERVISED DIMENSION REDUCTION METHOD VIA RAYLEIGH QUOTIENT OPTIMIZATION.
Fan, Jianqing; Ke, Zheng Tracy; Liu, Han; Xia, Lucy
We propose a novel Rayleigh quotient based sparse quadratic dimension reduction method-named QUADRO (Quadratic Dimension Reduction via Rayleigh Optimization)-for analyzing high-dimensional data. Unlike in the linear setting where Rayleigh quotient optimization coincides with classification, these two problems are very different under nonlinear settings. In this paper, we clarify this difference and show that Rayleigh quotient optimization may be of independent scientific interests. One major challenge of Rayleigh quotient optimization is that the variance of quadratic statistics involves all fourth cross-moments of predictors, which are infeasible to compute for high-dimensional applications and may accumulate too many stochastic errors. This issue is resolved by considering a family of elliptical models. Moreover, for heavy-tail distributions, robust estimates of mean vectors and covariance matrices are employed to guarantee uniform convergence in estimating non-polynomially many parameters, even though only the fourth moments are assumed. Methodologically, QUADRO is based on elliptical models which allow us to formulate the Rayleigh quotient maximization as a convex optimization problem. Computationally, we propose an efficient linearized augmented Lagrangian method to solve the constrained optimization problem. Theoretically, we provide explicit rates of convergence in terms of Rayleigh quotient under both Gaussian and general elliptical models. Thorough numerical results on both synthetic and real datasets are also provided to back up our theoretical results.
Towards Robust Designs Via Multiple-Objective Optimization Methods
Man Mohan, Rai
2006-01-01
evolutionary method (DE) is first used to solve a relatively difficult problem in extended surface heat transfer wherein optimal fin geometries are obtained for different safe operating base temperatures. The objective of maximizing the safe operating base temperature range is in direct conflict with the objective of maximizing fin heat transfer. This problem is a good example of achieving robustness in the context of changing operating conditions. The evolutionary method is then used to design a turbine airfoil; the two objectives being reduced sensitivity of the pressure distribution to small changes in the airfoil shape and the maximization of the trailing edge wedge angle with the consequent increase in airfoil thickness and strength. This is a relevant example of achieving robustness to manufacturing tolerances and wear and tear in the presence of other objectives.
Parareal in time intermediate targets methods for optimal control problem
Maday, Yvon; Salomon, Julien
2012-01-01
In this paper, we present a method that enables solving in parallel the Euler-Lagrange system associated with the optimal control of a parabolic equation. Our approach is based on an iterative update of a sequence of intermediate targets that gives rise to independent sub-problems that can be solved in parallel. This method can be coupled with the parareal in time algorithm. Numerical experiments show the efficiency of our method.
An Optimization Method for Simulator Using Probability Statistic Model
无
2006-01-01
An optimization method was presented to be easily applied in retargetable simulator. The substance of this method is to reduce the redundant information of operation code which is caused by the variety of execution frequencies of instructions. By recoding the operation code in the loading part of simulator, times of bit comparison in identification of an instruction will get reduced. Thus the performance of the simulator will be improved. The theoretical analysis and experimental results both prove the validity of this method.
METHOD OF CALCULATING THE OPTIMAL HEAT EMISSION GEOTHERMAL WELLS
A. I. Akaev
2015-01-01
Full Text Available This paper presents a simplified method of calculating the optimal regimes of the fountain and the pumping exploitation of geothermal wells, reducing scaling and corrosion during operation. Comparative characteristics to quantify the heat of formation for these methods of operation under the same pressure at the wellhead. The problem is solved graphic-analytical method based on a balance of pressure in the well with the heat pump.
Inversion method based on stochastic optimization for particle sizing.
Sánchez-Escobar, Juan Jaime; Barbosa-Santillán, Liliana Ibeth; Vargas-Ubera, Javier; Aguilar-Valdés, Félix
2016-08-01
A stochastic inverse method is presented based on a hybrid evolutionary optimization algorithm (HEOA) to retrieve a monomodal particle-size distribution (PSD) from the angular distribution of scattered light. By solving an optimization problem, the HEOA (with the Fraunhofer approximation) retrieves the PSD from an intensity pattern generated by Mie theory. The analyzed light-scattering pattern can be attributed to unimodal normal, gamma, or lognormal distribution of spherical particles covering the interval of modal size parameters 46≤α≤150. The HEOA ensures convergence to the near-optimal solution during the optimization of a real-valued objective function by combining the advantages of a multimember evolution strategy and locally weighted linear regression. The numerical results show that our HEOA can be satisfactorily applied to solve the inverse light-scattering problem.
Exergetic optimization of turbofan engine with genetic algorithm method
Turan, Onder [Anadolu University, School of Civil Aviation (Turkey)], e-mail: onderturan@anadolu.edu.tr
2011-07-01
With the growth of passenger numbers, emissions from the aeronautics sector are increasing and the industry is now working on improving engine efficiency to reduce fuel consumption. The aim of this study is to present the use of genetic algorithms, an optimization method based on biological principles, to optimize the exergetic performance of turbofan engines. The optimization was carried out using exergy efficiency, overall efficiency and specific thrust of the engine as evaluation criteria and playing on pressure and bypass ratio, turbine inlet temperature and flight altitude. Results showed exergy efficiency can be maximized with higher altitudes, fan pressure ratio and turbine inlet temperature; the turbine inlet temperature is the most important parameter for increased exergy efficiency. This study demonstrated that genetic algorithms are effective in optimizing complex systems in a short time.
Optimal mesh hierarchies in Multilevel Monte Carlo methods
Von Schwerin, Erik
2016-01-08
I will discuss how to choose optimal mesh hierarchies in Multilevel Monte Carlo (MLMC) simulations when computing the expected value of a quantity of interest depending on the solution of, for example, an Ito stochastic differential equation or a partial differential equation with stochastic data. I will consider numerical schemes based on uniform discretization methods with general approximation orders and computational costs. I will compare optimized geometric and non-geometric hierarchies and discuss how enforcing some domain constraints on parameters of MLMC hierarchies affects the optimality of these hierarchies. I will also discuss the optimal tolerance splitting between the bias and the statistical error contributions and its asymptotic behavior. This talk presents joint work with N.Collier, A.-L.Haji-Ali, F. Nobile, and R. Tempone.
Investigation of Optimal Integrated Circuit Raster Image Vectorization Method
Leonas Jasevičius
2011-03-01
Full Text Available Visual analysis of integrated circuit layer requires raster image vectorization stage to extract layer topology data to CAD tools. In this paper vectorization problems of raster IC layer images are presented. Various line extraction from raster images algorithms and their properties are discussed. Optimal raster image vectorization method was developed which allows utilization of common vectorization algorithms to achieve the best possible extracted vector data match with perfect manual vectorization results. To develop the optimal method, vectorized data quality dependence on initial raster image skeleton filter selection was assessed.Article in Lithuanian
Optimizing the atmospheric sampling sites using fuzzy mathematic methods
无
2003-01-01
A new approach applying fuzzy mathematic theorems, including the Primary Matrix Element Theorem and the Fisher ClassificationMethod, was established to solve the optimization problem of atmospheric environmental sampling sites. According to its basis, an applicationin the optimization of sampling sites in the atmospheric environmental monitoring was discussed. The method was proven to be suitable andeffective. The results were admitted and applied by the Environmental Protection Bureau (EPB) of many cities of China. A set of computersoftware of this approach was also completely compiled and used.
Unconstrained steepest descent method for multicriteria optimization on Riemmanian manifolds
Bento, G C; Oliveira, P R
2010-01-01
In this paper we present a steepest descent method with Armijo's rule for multicriteria optimization in the Riemannian context. The well definedness of the sequence generated by the method is guaranteed. Under mild assumptions on the multicriteria function, we prove that each accumulation point (if they exist) satisfies first-order necessary conditions for Pareto optimality. Moreover, assuming quasi-convexity of the multicriteria function and non-negative curvature of the Riemannian manifold, we prove full convergence of the sequence to a Pareto critical.
Sørensen, Søren Nørgaard; Lund, Erik
2012-01-01
This work concerns a novel large-scale multi-material topology optimization method for simultaneous determination of the optimum variable integer thickness and fiber orientation throughout laminate structures with fixed outer geometries while adhering to certain manufacturing constraints. The con......This work concerns a novel large-scale multi-material topology optimization method for simultaneous determination of the optimum variable integer thickness and fiber orientation throughout laminate structures with fixed outer geometries while adhering to certain manufacturing constraints....... The conceptual combinatorial/integer problem is relaxed to a continuous problem and solved on basis of the so-called Discrete Material Optimization method, explicitly including the manufacturing constraints as linear constraints....
Shape optimization for viscous flows by reduced basis methods and free-form deformation
Manzoni, Andrea; Quarteroni, Alfio; Rozza, Gianluigi
2011-01-01
In this paper we further develop an approach previously introduced in [Lassila and Rozza, C.M.A.M.E 2010] for shape optimization that combines a suitable low-dimensional parametrization of the geometry (yielding a geometrical reduction) with reduced basis methods (yielding a reduction of computational complexity). More precisely, free-form deformation techniques are considered for the geometry description and its parametrization, while reduced basis methods are used upon a finite element dis...
Methods of centers and methods of feasible directions for the solution of optimal control problems.
Polak, E.; Mukai, H.; Pironneau, O.
1971-01-01
Demonstration of the applicability of methods of centers and of methods of feasible directions to optimal control problems. Presented experimental results show that extensions of Frank-Wolfe (1956), Zoutendijk (1960), and Pironneau-Polak (1971) algorithms for nonlinear programming problems can be quite efficient in solving optimal control problems.
Nozzle Mounting Method Optimization Based on Robot Kinematic Analysis
Chen, Chaoyue; Liao, Hanlin; Montavon, Ghislain; Deng, Sihao
2016-08-01
Nowadays, the application of industrial robots in thermal spray is gaining more and more importance. A desired coating quality depends on factors such as a balanced robot performance, a uniform scanning trajectory and stable parameters (e.g. nozzle speed, scanning step, spray angle, standoff distance). These factors also affect the mass and heat transfer as well as the coating formation. Thus, the kinematic optimization of all these aspects plays a key role in order to obtain an optimal coating quality. In this study, the robot performance was optimized from the aspect of nozzle mounting on the robot. An optimized nozzle mounting for a type F4 nozzle was designed, based on the conventional mounting method from the point of view of robot kinematics validated on a virtual robot. Robot kinematic parameters were obtained from the simulation by offline programming software and analyzed by statistical methods. The energy consumptions of different nozzle mounting methods were also compared. The results showed that it was possible to reasonably assign the amount of robot motion to each axis during the process, so achieving a constant nozzle speed. Thus, it is possible optimize robot performance and to economize robot energy.
Optimization of Moving Bed Biofilm ReactorUsing Taguchi Method
R Nabizadeh Nodehi
2009-07-01
Full Text Available "n "nBackgrounds and Objectives: in recent years, mobile bed biological reactors have been used progressively for municipal and industrial wastewaters treatment. Dissented experiment is a trial that significant changes will accrue for influent variables in the process, and generally used for identification of the effective factors and optimization of the process. The scope of this study was determination of the optimized conditions for the MBBR process by using of Taguchi method. "nMaterials and Methods: Reactor start up was done by using of the recycled activated sludge from Ahwaz wastewater treatment plant. After that and passing the acclimation period, with hydraulic residence time equal to 9 hours matched for 1000, 2000 and 3000 mg/l based on COD respectively, for optimization determination of the acclimated microbial growth, the variables change (pH, nitrogen source, chemical oxygen demand and salinity were determined in 9 steps, and all of the results were analyzed by Qualitek -4 (w32b."nResults:In this study, organic load removal based on COD was 97% and best optimized condition for MBBR were (inf. COD=1000 mg/l, pH= 8, salinity = 5% and the Nitrogen source= NH4CL"nConclusion: Based on our finding, we may conclude that Taguchi method is on of the appropriate procedure in determination the optimized condition for increasing removal efficiency of MBBR.
Reliability-based design optimization using a moment method and a kriging metamodel
Ju, Byeong Hyeon; Chai Lee, Byung
2008-05-01
Reliability-based design optimization (RBDO) has been used for optimizing engineering systems with uncertainties in design variables and system parameters. RBDO involves reliability analysis, which requires a large amount of computational effort, so it is important to select an efficient method for reliability analysis. Of the many methods for reliability analysis, a moment method, which is called the fourth moment method, is known to be less expensive for moderate size problems and requires neither iteration nor the computation of derivatives. Despite these advantages, previous research on RBDO has been mainly based on the first-order reliability method and relatively little attention has been paid to moment-based RBDO. This article considers difficulties in implementing the moment method into RBDO; they are solved using a kriging metamodel with an active constraint strategy. Three numerical examples are tested and the results show that the proposed method is efficient and accurate.
A method for optimizing the performance of buildings
Pedersen, Frank
2007-01-01
This thesis describes a method for optimizing the performance of buildings. Design decisions made in early stages of the building design process have a significant impact on the performance of buildings, for instance, the performance with respect to the energy consumption, economical aspects......, and the indoor environment. The method is intended for supporting design decisions for buildings, by combining methods for calculating the performance of buildings with numerical optimization methods. The method is able to find optimum values of decision variables representing different features of the building......, such as its shape, the amount and type of windows used, and the amount of insulation used in the building envelope. The parties who influence design decisions for buildings, such as building owners, building users, architects, consulting engineers, contractors, etc., often have different and to some extent...
How to optimize the drop plate method for enumerating bacteria.
Herigstad, B; Hamilton, M; Heersink, J
2001-03-01
The drop plate (DP) method can be used to determine the number of viable suspended bacteria in a known beaker volume. The drop plate method has some advantages over the spread plate (SP) method. Less time and effort are required to dispense the drops onto an agar plate than to spread an equivalent total sample volume into the agar. By distributing the sample in drops, colony counting can be done faster and perhaps more accurately. Even though it has been present in the laboratory for many years, the drop plate method has not been standardized. Some technicians use 10-fold dilutions, others use twofold. Some technicians plate a total volume of 0.1 ml, others plate 0.2 ml. The optimal combination of such factors would be useful to know when performing the drop plate method. This investigation was conducted to determine (i) the standard deviation of the bacterial density estimate, (ii) the cost of performing the drop plate procedure, (iii) the optimal drop plate design, and (iv) the advantages of the drop plate method in comparison to the standard spread plate method. The optimal design is the combination of factor settings that achieves the smallest standard deviation for a fixed cost. Computer simulation techniques and regression analysis were used to express the standard deviation as a function of the beaker volume, dilution factor, and volume plated. The standard deviation expression is also applicable to the spread plate method.
Some optimal iterative methods and their with memory variants
F. Soleymani
2013-07-01
Full Text Available Based on the fourth-order method of Liu et al. [10], eighth-order three-step iterative methods without memory, which are totally free from derivative calculation and reach the optimal efficiency index are presented. The extension of one of the methods for multiple zeros without the knowledge of multiplicity is presented. Further accelerations will be provided through the concept of with memory iteration methods. Moreover, it is shown by way of illustration that the novel methods are useful on a series of relevant numerical problems when high precision computing is required.
Nguyen, Nhan T.; Hornby, Gregory; Ishihara, Abe
2013-01-01
This paper describes two methods of trajectory optimization to obtain an optimal trajectory of minimum-fuel- to-climb for an aircraft. The first method is based on the adjoint method, and the second method is based on a direct trajectory optimization method using a Chebyshev polynomial approximation and cubic spine approximation. The approximate optimal trajectory will be compared with the adjoint-based optimal trajectory which is considered as the true optimal solution of the trajectory optimization problem. The adjoint-based optimization problem leads to a singular optimal control solution which results in a bang-singular-bang optimal control.
Optimization of MIMO Systems Capacity Using Large Random Matrix Methods
Philippe Loubaton
2012-11-01
Full Text Available This paper provides a comprehensive introduction of large random matrix methods for input covariance matrix optimization of mutual information of MIMO systems. It is first recalled informally how large system approximations of mutual information can be derived. Then, the optimization of the approximations is discussed, and important methodological points that are not necessarily covered by the existing literature are addressed, including the strict concavity of the approximation, the structure of the argument of its maximum, the accuracy of the large system approach with regard to the number of antennas, or the justification of iterative water-filling optimization algorithms. While the existing papers have developed methods adapted to a specific model, this contribution tries to provide a unified view of the large system approximation approach.
Control and Optimization Methods for Electric Smart Grids
Ilić, Marija
2012-01-01
Control and Optimization Methods for Electric Smart Grids brings together leading experts in power, control and communication systems,and consolidates some of the most promising recent research in smart grid modeling,control and optimization in hopes of laying the foundation for future advances in this critical field of study. The contents comprise eighteen essays addressing wide varieties of control-theoretic problems for tomorrow’s power grid. Topics covered include: Control architectures for power system networks with large-scale penetration of renewable energy and plug-in vehicles Optimal demand response New modeling methods for electricity markets Control strategies for data centers Cyber-security Wide-area monitoring and control using synchronized phasor measurements. The authors present theoretical results supported by illustrative examples and practical case studies, making the material comprehensible to a wide audience. The results reflect the exponential transformation that today’s grid is going...
Optimal Joint Multiple Resource Allocation Method for Cloud Computing Environments
Kuribayashi, Shin-ichi
2011-01-01
Cloud computing is a model for enabling convenient, on-demand network access to a shared pool of configurable computing resources. To provide cloud computing services economically, it is important to optimize resource allocation under the assumption that the required resource can be taken from a shared resource pool. In addition, to be able to provide processing ability and storage capacity, it is necessary to allocate bandwidth to access them at the same time. This paper proposes an optimal resource allocation method for cloud computing environments. First, this paper develops a resource allocation model of cloud computing environments, assuming both processing ability and bandwidth are allocated simultaneously to each service request and rented out on an hourly basis. The allocated resources are dedicated to each service request. Next, this paper proposes an optimal joint multiple resource allocation method, based on the above resource allocation model. It is demonstrated by simulation evaluation that the p...
Reliability-Based Shape Optimization using Stochastic Finite Element Methods
Enevoldsen, Ib; Sørensen, John Dalsgaard; Sigurdsson, G.
1991-01-01
Application of first-order reliability methods FORM (see Madsen, Krenk & Lind [8)) in structural design problems has attracted growing interest in recent years, see e.g. Frangopol [4), Murotsu, Kishi, Okada, Yonezawa & Taguchi [9) and Sørensen [14). In probabilistically based optimal design...
Practical inventory routing: A problem definition and an optimization method
Geiger, Martin Josef
2011-01-01
The global objective of this work is to provide practical optimization methods to companies involved in inventory routing problems, taking into account this new type of data. Also, companies are sometimes not able to deal with changing plans every period and would like to adopt regular structures for serving customers.
Optimal Variational Asymptotic Method for Nonlinear Fractional Partial Differential Equations.
Baranwal, Vipul K; Pandey, Ram K; Singh, Om P
2014-01-01
We propose optimal variational asymptotic method to solve time fractional nonlinear partial differential equations. In the proposed method, an arbitrary number of auxiliary parameters γ 0, γ 1, γ 2,… and auxiliary functions H 0(x), H 1(x), H 2(x),… are introduced in the correction functional of the standard variational iteration method. The optimal values of these parameters are obtained by minimizing the square residual error. To test the method, we apply it to solve two important classes of nonlinear partial differential equations: (1) the fractional advection-diffusion equation with nonlinear source term and (2) the fractional Swift-Hohenberg equation. Only few iterations are required to achieve fairly accurate solutions of both the first and second problems.
An Optimal Calibration Method for a MEMS Inertial Measurement Unit
Bin Fang
2014-02-01
Full Text Available An optimal calibration method for a micro-electro-mechanical inertial measurement unit (MIMU is presented in this paper. The accuracy of the MIMU is highly dependent on calibration to remove the deterministic errors of systematic errors, which also contain random errors. The overlapping Allan variance is applied to characterize the types of random error terms in the measurements. The calibration model includes package misalignment error, sensor-to-sensor misalignment error and bias, and a scale factor is built. The new concept of a calibration method, which includes a calibration scheme and a calibration algorithm, is proposed. The calibration scheme is designed by D-optimal and the calibration algorithm is deduced by a Kalman filter. In addition, the thermal calibration is investigated, as the bias and scale factor varied with temperature. The simulations and real tests verify the effectiveness of the proposed calibration method and show that it is better than the traditional method.
A method for optimizing the performance of buildings
Pedersen, Frank
2006-07-01
This thesis describes a method for optimizing the performance of buildings. Design decisions made in early stages of the building design process have a significant impact on the performance of buildings, for instance, the performance with respect to the energy consumption, economical aspects, and the indoor environment. The method is intended for supporting design decisions for buildings, by combining methods for calculating the performance of buildings with numerical optimization methods. The method is able to find optimum values of decision variables representing different features of the building, such as its shape, the amount and type of windows used, and the amount of insulation used in the building envelope. The parties who influence design decisions for buildings, such as building owners, building users, architects, consulting engineers, contractors, etc., often have different and to some extent conflicting requirements to buildings. For instance, the building owner may be more concerned about the cost of constructing the building, rather than the quality of the indoor climate, which is more likely to be a concern of the building user. In order to support the different types of requirements made by decision-makers for buildings, an optimization problem is formulated, intended for representing a wide range of design decision problems for buildings. The problem formulation involves so-called performance measures, which can be calculated with simulation software for buildings. For instance, the annual amount of energy required by the building, the cost of constructing the building, and the annual number of hours where overheating occurs, can be used as performance measures. The optimization problem enables the decision-makers to specify many different requirements to the decision variables, as well as to the performance of the building. Performance measures can for instance be required to assume their minimum or maximum value, they can be subjected to upper or
Pump-and-treat optimization using analytic element method flow models
Matott, L. Shawn; Rabideau, Alan J.; Craig, James R.
2006-05-01
Plume containment using pump-and-treat (PAT) technology continues to be a popular remediation technique for sites with extensive groundwater contamination. As such, optimization of PAT systems, where cost is minimized subject to various remediation constraints, is the focus of an important and growing body of research. While previous pump-and-treat optimization (PATO) studies have used discretized (finite element or finite difference) flow models, the present study examines the use of analytic element method (AEM) flow models. In a series of numerical experiments, two PATO problems adapted from the literature are optimized using a multi-algorithmic optimization software package coupled with an AEM flow model. The experiments apply several different optimization algorithms and explore the use of various pump-and-treat cost and constraint formulations. The results demonstrate that AEM models can be used to optimize the number, locations and pumping rates of wells in a pump-and-treat containment system. Furthermore, the results illustrate that a total outflux constraint placed along the plume boundary can be used to enforce plume containment. Such constraints are shown to be efficient and reliable alternatives to conventional particle tracking and gradient control techniques. Finally, the particle swarm optimization (PSO) technique is identified as an effective algorithm for solving pump-and-treat optimization problems. A parallel version of the PSO algorithm is shown to have linear speedup, suggesting that the algorithm is suitable for application to problems that are computationally demanding and involve large numbers of wells.
NOVEL METHOD SOLVING NUMERICAL INSTABILITIES IN TOPOLOGY OPTIMIZATION
无
2002-01-01
Numerical instabilities are often encountered in FE solution of continuum topology optimization. The essence of the numerical instabilities is given from the inverse partial differential equation (PDE) point of view. On the basis of the strict mathematical theory, a novel method, named as window filter and multi-grid method, which solves the numerical instabilities, is proposed. Convergent analyses and a numerical example are presented.
METHODS FOR DETERMINATION AND OPTIMIZATION OF LOGISTICS COSTS
Mihaela STET
2016-01-01
The paper is dealing with the problems of logistics costs, highlighting some methods for estimation and determination of specific costs for different transport modes in freight distribution. There are highlighted, besides costs of transports, the other costs in supply chain, as well as costing methods used in logistics activities. In this context, there are also revealed some optimization means of transport costs in logistics chain.
Apparatus and methods for manipulation and optimization of biological systems
Ho, Chih-Ming (Inventor); Wong, Pak Kin (Inventor); Sun, Ren (Inventor); Yu, Fuqu (Inventor)
2012-01-01
The invention provides systems and methods for manipulating, e.g., optimizing and controlling, biological systems, e.g., for eliciting a more desired biological response of biological sample, such as a tissue, organ, and/or a cell. In one aspect, systems and methods of the invention operate by efficiently searching through a large parametric space of stimuli and system parameters to manipulate, control, and optimize the response of biological samples sustained in the system, e.g., a bioreactor. In alternative aspects, systems include a device for sustaining cells or tissue samples, one or more actuators for stimulating the samples via biochemical, electromagnetic, thermal, mechanical, and/or optical stimulation, one or more sensors for measuring a biological response signal of the samples resulting from the stimulation of the sample. In one aspect, the systems and methods of the invention use at least one optimization algorithm to modify the actuator's control inputs for stimulation, responsive to the sensor's output of response signals. The compositions and methods of the invention can be used, e.g., to for systems optimization of any biological manufacturing or experimental system, e.g., bioreactors for proteins, e.g., therapeutic proteins, polypeptides or peptides for vaccines, and the like, small molecules (e.g., antibiotics), polysaccharides, lipids, and the like. Another use of the apparatus and methods includes combination drug therapy, e.g. optimal drug cocktail, directed cell proliferations and differentiations, e.g. in tissue engineering, e.g. neural progenitor cells differentiation, and discovery of key parameters in complex biological systems.
Gao, Hao
2016-04-01
For the treatment planning during intensity modulated radiation therapy (IMRT) or volumetric modulated arc therapy (VMAT), beam fluence maps can be first optimized via fluence map optimization (FMO) under the given dose prescriptions and constraints to conformally deliver the radiation dose to the targets while sparing the organs-at-risk, and then segmented into deliverable MLC apertures via leaf or arc sequencing algorithms. This work is to develop an efficient algorithm for FMO based on alternating direction method of multipliers (ADMM). Here we consider FMO with the least-square cost function and non-negative fluence constraints, and its solution algorithm is based on ADMM, which is efficient and simple-to-implement. In addition, an empirical method for optimizing the ADMM parameter is developed to improve the robustness of the ADMM algorithm. The ADMM based FMO solver was benchmarked with the quadratic programming method based on the interior-point (IP) method using the CORT dataset. The comparison results suggested the ADMM solver had a similar plan quality with slightly smaller total objective function value than IP. A simple-to-implement ADMM based FMO solver with empirical parameter optimization is proposed for IMRT or VMAT.
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.
Chebyshev-Legendre method for discretizing optimal control problems
ZHANG Wen; MA He-ping
2009-01-01
In this paper, a numerical method for solving the optimal control (OC) problems is presented. The method is enlightened by the Chebyshev-Legendre (CL) method for solving the partial differential equations (PDEs). The Legen-dre expansions are used to approximate both the control and the state functions. The constraints are discretized over the Chebyshev-Gauss-Lobatto (CGL) collocation points. A Legendre technique is used to approximate the integral involved in the performance index. The OC problem is changed into an equivalent nonlinear programming problem which is directly solved. The fast Legendre transform is employed to reduce the computation time. Several further illustrative examples demonstrate the efficiency of the proposed method.
An ant colony optimization method for generalized TSP problem
Jinhui Yang; Xiaohu Shi; Maurizio Marchese; Yanchun Liang
2008-01-01
Focused on a variation of the euclidean traveling salesman problem (TSP), namely, the generalized traveling salesman problem (GTSP), this paper extends the ant colony optimization method from TSP to this field. By considering the group influence, an improved method is further improved. To avoid locking into local minima, a mutation process and a local searching technique are also introduced into this method. Numerical results show that the proposed method can deal with the GTSP problems fairly well, and the developed mutation process and local search technique are effective.
Optimization method for quantitative calculation of clay minerals in soil
Libo Hao; Qiaoqiao Wei; Yuyan Zhao; Zilong Lu; Xinyun Zhao
2015-04-01
Determination of types and amounts for clay minerals in soil are important in environmental, agricultural, and geological investigations. Many reliable methods have been established to identify clay mineral types. However, no reliable method for quantitative analysis of clay minerals has been established so far. In this study, an attempt was made to propose an optimization method for the quantitative determination of clay minerals in soil based on bulk chemical composition data. The fundamental principles and processes of the calculation are elucidated. Some samples were used for reliability verification of the method and the results prove the simplicity and efficacy of the approach.
Mathematical foundation of the optimization-based fluid animation method
Erleben, Kenny; Misztal, Marek Krzysztof; Bærentzen, Jakob Andreas
2011-01-01
We present the mathematical foundation of a fluid animation method for unstructured meshes. Key contributions not previously treated are the extension to include diffusion forces and higher order terms of non-linear force approximations. In our discretization we apply a fractional step method to ...
Method of Fire Image Identification Based on Optimization Theory
无
2002-01-01
In view of some distinctive characteristics of the early-stage flame image, a corresponding method of characteristic extraction is presented. Also introduced is the application of the improved BP algorithm based on the optimization theory to identifying fire image characteristics. First the optimization of BP neural network adopting Levenberg-Marquardt algorithm with the property of quadratic convergence is discussed, and then a new system of fire image identification is devised. Plenty of experiments and field tests have proved that this system can detect the early-stage fire flame quickly and reliably.
A New Method for Optimal Harmonic Meter Placement
A. Ketabi
2008-01-01
Full Text Available In this research, a new method based on Singular Value Decomposition (SVD is proposed to solve the problem of optimal placement of meters for static estimation of harmonic sources in a power system. Also, the Binary Genetic Algorithm (BGA is used to solve the problem of optimization. IEEE 14-bus test system is provided to validate the measurement placement algorithm. It has been observed that the quality of estimation is improved and the Number of Observable Variable (NOV is increased. Moreover, the BGA-based meter placement strategy yields the same solution as obtained from the complete enumeration technique but in shorter time.
Chen, Peiyuan; Siano, Pierluigi; Chen, Zhe;
2010-01-01
limit requirements. The method combines the Genetic Algorithm (GA), gradient-based constrained nonlinear optimization algorithm and sequential Monte Carlo simulation (MCS). The GA searches for the optimal locations and capacities of WTs. The gradient-based optimization finds the optimal power factor...
Chen, Peiyuan; Siano, Pierluigi; Chen, Zhe
2010-01-01
limit requirements. The method combines the Genetic Algorithm (GA), gradient-based constrained nonlinear optimization algorithm and sequential Monte Carlo simulation (MCS). The GA searches for the optimal locations and capacities of WTs. The gradient-based optimization finds the optimal power factor...
Autonomous guided vehicles methods and models for optimal path planning
Fazlollahtabar, Hamed
2015-01-01
This book provides readers with extensive information on path planning optimization for both single and multiple Autonomous Guided Vehicles (AGVs), and discusses practical issues involved in advanced industrial applications of AGVs. After discussing previously published research in the field and highlighting the current gaps, it introduces new models developed by the authors with the goal of reducing costs and increasing productivity and effectiveness in the manufacturing industry. The new models address the increasing complexity of manufacturing networks, due for example to the adoption of flexible manufacturing systems that involve automated material handling systems, robots, numerically controlled machine tools, and automated inspection stations, while also considering the uncertainty and stochastic nature of automated equipment such as AGVs. The book discusses and provides solutions to important issues concerning the use of AGVs in the manufacturing industry, including material flow optimization with A...
The construction of optimal stated choice experiments theory and methods
Street, Deborah J
2007-01-01
The most comprehensive and applied discussion of stated choice experiment constructions available The Construction of Optimal Stated Choice Experiments provides an accessible introduction to the construction methods needed to create the best possible designs for use in modeling decision-making. Many aspects of the design of a generic stated choice experiment are independent of its area of application, and until now there has been no single book describing these constructions. This book begins with a brief description of the various areas where stated choice experiments are applicable, including marketing and health economics, transportation, environmental resource economics, and public welfare analysis. The authors focus on recent research results on the construction of optimal and near-optimal choice experiments and conclude with guidelines and insight on how to properly implement these results. Features of the book include: Construction of generic stated choice experiments for the estimation of main effects...
Immune clonal selection optimization method with combining mutation strategies
无
2007-01-01
In artificial immune optimization algorithm, the mutation of immune cells has been considered as the key operator that determines the algorithm performance. Traditional immune optimization algorithms have used a single mutation operator, typically a Gaussian. Using a variety of mutation operators that can be combined during evolution to generate different probability density function could hold the potential for producing better solutions with less computational effort. In view of this, a linear combination mutation operator of Gaussian and Cauchy mutation is presented in this paper, and a novel clonal selection optimization method based on clonal selection principle is proposed also. The simulation results show the combining mutation strategy can obtain the same performance as the best of pure strategies or even better in some cases.
A Survey of Methods for Gas-Lift Optimization
Kashif Rashid
2012-01-01
Full Text Available This paper presents a survey of methods and techniques developed for the solution of the continuous gas-lift optimization problem over the last two decades. These range from isolated single-well analysis all the way to real-time multivariate optimization schemes encompassing all wells in a field. While some methods are clearly limited due to their neglect of treating the effects of inter-dependent wells with common flow lines, other methods are limited due to the efficacy and quality of the solution obtained when dealing with large-scale networks comprising hundreds of difficult to produce wells. The aim of this paper is to provide an insight into the approaches developed and to highlight the challenges that remain.
Application of Taguchi method in optimization of cervical ring cage.
Yang, Kai; Teo, Ee-Chon; Fuss, Franz Konstantin
2007-01-01
The Taguchi method is a statistical approach to overcome the limitation of the factorial and fractional factorial experiments by simplifying and standardizing the fractional factorial design. The objective of the current study is to illustrate the procedures and strengths of the Taguchi method in biomechanical analysis by using a case study of a cervical ring cage optimization. A three-dimensional finite element (FE) model of C(5)-C(6) with a generic cervical ring cage inserted was modelled. Taguchi method was applied in the optimization of the cervical ring cage in material property and dimensions for producing the lowest stress on the endplate to reduce the risk of cage subsidence, as in the following steps: (1) establishment of objective function; (2) determination of controllable factors and their levels; (3) identification of uncontrollable factors and test conditions; (4) design of Taguchi crossed array layout; (5) execution of experiments according to trial conditions; (6) analysis of results; (7) determination of optimal run; (8) confirmation of optimum run. The results showed that a cage with larger width, depth and wall thickness can produce the lower von Mises stress under various conditions. The contribution of implant materials is found trivial. The current case study illustrates that the strengths of the Taguchi method lie in (1) consistency in experimental design and analysis; (2) reduction of time and cost of experiments; (3) robustness of performance with removing the noise factors. The Taguchi method will have a great potential application in biomechanical field when factors of the issues are at discrete level.
Novel Method for Optimal Synthesis of 5G Millimeter Wave Linear Antenna Array
Zarko Rosic
2017-01-01
Full Text Available We will propose a useful method for 5G mm wave antenna array synthesis, based on Genetic Algorithm for the synthesis of linear array with nonuniform interelement spacing. Our design method was used to obtain the optimal position of the elements in order to get the minimum side lobe level and nulls in desired directions. The simulation results verify that proposed method outperforms the previously published methods in terms of suppression side lobe level while maintaining nulls in specified directions. The flexibility of proposed algorithm shows good potential for the antenna array synthesis.
A method for optimizing the performance of buildings
Pedersen, Frank
2006-07-01
This thesis describes a method for optimizing the performance of buildings. Design decisions made in early stages of the building design process have a significant impact on the performance of buildings, for instance, the performance with respect to the energy consumption, economical aspects, and the indoor environment. The method is intended for supporting design decisions for buildings, by combining methods for calculating the performance of buildings with numerical optimization methods. The method is able to find optimum values of decision variables representing different features of the building, such as its shape, the amount and type of windows used, and the amount of insulation used in the building envelope. The parties who influence design decisions for buildings, such as building owners, building users, architects, consulting engineers, contractors, etc., often have different and to some extent conflicting requirements to buildings. For instance, the building owner may be more concerned about the cost of constructing the building, rather than the quality of the indoor climate, which is more likely to be a concern of the building user. In order to support the different types of requirements made by decision-makers for buildings, an optimization problem is formulated, intended for representing a wide range of design decision problems for buildings. The problem formulation involves so-called performance measures, which can be calculated with simulation software for buildings. For instance, the annual amount of energy required by the building, the cost of constructing the building, and the annual number of hours where overheating occurs, can be used as performance measures. The optimization problem enables the decision-makers to specify many different requirements to the decision variables, as well as to the performance of the building. Performance measures can for instance be required to assume their minimum or maximum value, they can be subjected to upper or
Direct trajectory optimization based on a mapped Chebyshev pseudospectral method
Guo Xiao; Zhu Ming
2013-01-01
In view of generating optimal trajectories of Bolza problems,standard Chebyshev pseudospectral (PS) method makes the points' accumulation near the extremities and rarefaction of nodes close to the center of interval,which causes an ill-condition of differentiation matrix and an oscillation of the optimal solution.For improvement upon the difficulties,a mapped Chebyshev pseudospectral method is proposed.A conformal map is applied to Chebyshev points to move the points closer to equidistant nodes.Condition number and spectral radius of differentiation matrices from both methods are presented to show the improvement.Furthermore,the modification keeps the Chebyshev pseudospectral method's advantage,the spectral convergence rate.Based on three numerical examples,a comparison of the execution time,convergence and accuracy is presented among the standard Chebyshev pseudospectral method,other collocation methods and the proposed one.In one example,the error of results from mapped Chebyshev pseudospectral method is reduced to 5％ of that from standard Chebyshev pseudospectral method.
Kumar, Ajeet
2009-01-01
We introduce a new and efficient numerical method for multicriterion optimal control and single criterion optimal control under integral constraints. The approach is based on extending the state space to include information on a "budget" remaining to satisfy each constraint; the augmented Hamilton-Jacobi-Bellman PDE is then solved numerically. The efficiency of our approach hinges on the causality in that PDE, i.e., the monotonicity of characteristic curves in one of the newly added dimensions. A semi-Lagrangian "marching" method is used to approximate the discontinuous viscosity solution efficiently. We compare this to a recently introduced "weighted sum" based algorithm for the same problem. We illustrate our method using examples from flight path planning and robotic navigation in the presence of friendly and adversarial observers.
First-principle optimal local pseudopotentials construction via optimized effective potential method
Mi, Wenhui; Zhang, Shoutao; Wang, Yanchao; Ma, Yanming; Miao, Maosheng
2016-04-01
The local pseudopotential (LPP) is an important component of orbital-free density functional theory, a promising large-scale simulation method that can maintain information on a material's electron state. The LPP is usually extracted from solid-state density functional theory calculations, thereby it is difficult to assess its transferability to cases involving very different chemical environments. Here, we reveal a fundamental relation between the first-principles norm-conserving pseudopotential (NCPP) and the LPP. On the basis of this relationship, we demonstrate that the LPP can be constructed optimally from the NCPP for a large number of elements using the optimized effective potential method. Specially, our method provides a unified scheme for constructing and assessing the LPP within the framework of first-principles pseudopotentials. Our practice reveals that the existence of a valid LPP with high transferability may strongly depend on the element.
2008-01-01
In this paper, to overcome the drawbacks of POT which minimize the reminder, not only the optimal functional with minimal residual is put forward, but also the method of global optimization is used. Under the condition of large reminder, the optimal bases can also be obtained through the method of POTres without the approaching of initial conditions. In addition, compared with local optimization, the advantage of global optimization is remarkable. On the one hand, we can expect that the dynamical system based on the global optimal bases will include more information than the ones based on the local optimal bases, since the global optimal bases are much more precise than the local optimal bases. On the other hand, from the point of view of error, the global optimal bases are independent of the choice of the object functional and the initial bases.
A method for unified optimization of systems and controllers
Abildgaard, Ole
1990-01-01
A unified method for solving control system optimization problems is suggested. All system matrices are allowed to be functions of the design variables. The method makes use of an implementation of a sequential quadratic programming algorithm (NLPQL) for solution of general constrained nonlinear...... programming problems. It is shown how to compute the gradients of the objective function and the constraint functions imposing eigenvalue constraints. In an example it is demonstrated how the method can solve a high-dimensional problem, where the initial condition covariance assumption is used to ensure...
Optimal sensor placement using FRFs-based clustering method
Li, Shiqi; Zhang, Heng; Liu, Shiping; Zhang, Zhe
2016-12-01
The purpose of this work is to develop an optimal sensor placement method by selecting the most relevant degrees of freedom as actual measure position. Based on observation matrix of a structure's frequency response, two optimal criteria are used to avoid the information redundancy of the candidate degrees of freedom. By using principal component analysis, the frequency response matrix can be decomposed into principal directions and their corresponding singular. A relatively small number of principal directions will maintain a system's dominant response information. According to the dynamic similarity of each degree of freedom, the k-means clustering algorithm is designed to classify the degrees of freedom, and effective independence method deletes the sensors which are redundant of each cluster. Finally, two numerical examples and a modal test are included to demonstrate the efficient of the derived method. It is shown that the proposed method provides a way to extract sub-optimal sets and the selected sensors are well distributed on the whole structure.
A new method for nonlinear optimization - experimental results
Loskovska, S.; Percinkova, B.
1994-12-31
In this paper an application of a new method for nonlinear optimization problems suggested and presented by B. Percinkova is performed. The method is originally developed and applicated on nonlinear systems. Basis of the method is following: A system of n-nonlinear equations gives as F{sub i}(x{sub 1}, x{sub 2}, x{sub 3}, ..., x{sub n}) = 0; 1 = 1, 2, ..., n and solution domain x{sub pi} {<=} x{sub i} {<=} x{sub ki} i = 1, 2, ..., n is modified by introducing a new variable z. The new system is given by: F{sub i}(x{sub 1}, x{sub 2}, x{sub 3}, ..., x{sub n}) = z; i = 1, 2, ..., n. The system defines a curve in (n + 1) dimensional space. System`s point X = (x{sub i}, x{sub 2}, x{sub 3}, ..., x{sub n}, z) that, the solution of the system is obtained using an interative procedure moving along the curve until the point with z = 0 is reached. In order to applicate method on optimization problems, a basic optimization model given with (min, max)F{sub i}(x{sub 1}, x{sub 2}, x{sub 3}, ..., x{sub n}) with the following optimization space: F{sub i}(x{sub 1}, x{sub 2}, x{sub 3}, ..., x{sub n}) ({<=}{>=})0 : i = 1, 2, ..., n is transformed into a system equivalent to system (2) by (dF/dx{sub i}) = z; i - 1, 2, ..., n. The main purpose of this work is to make relevant evaluation of the method by standard test problems.
Design of large Francis turbine using optimal methods
Flores, E.; Bornard, L.; Tomas, L.; Liu, J.; Couston, M.
2012-11-01
Among a high number of Francis turbine references all over the world, covering the whole market range of heads, Alstom has especially been involved in the development and equipment of the largest power plants in the world : Three Gorges (China -32×767 MW - 61 to 113 m), Itaipu (Brazil- 20x750 MW - 98.7m to 127m) and Xiangjiaba (China - 8x812 MW - 82.5m to 113.6m - in erection). Many new projects are under study to equip new power plants with Francis turbines in order to answer an increasing demand of renewable energy. In this context, Alstom Hydro is carrying out many developments to answer those needs, especially for jumbo units such the planned 1GW type units in China. The turbine design for such units requires specific care by using the state of the art in computation methods and the latest technologies in model testing as well as the maximum feedback from operation of Jumbo plants already in operation. We present in this paper how a large Francis turbine can be designed using specific design methods, including the global and local optimization methods. The design of the spiral case, the tandem cascade profiles, the runner and the draft tube are designed with optimization loops involving a blade design tool, an automatic meshing software and a Navier-Stokes solver, piloted by a genetic algorithm. These automated optimization methods, presented in different papers over the last decade, are nowadays widely used, thanks to the growing computation capacity of the HPC clusters: the intensive use of such optimization methods at the turbine design stage allows to reach very high level of performances, while the hydraulic flow characteristics are carefully studied over the whole water passage to avoid any unexpected hydraulic phenomena.
METAHEURISTIC OPTIMIZATION METHODS FOR PARAMETERS ESTIMATION OF DYNAMIC SYSTEMS
V. Panteleev Andrei
2017-01-01
Full Text Available The article considers the usage of metaheuristic methods of constrained global optimization: “Big Bang - Big Crunch”, “Fireworks Algorithm”, “Grenade Explosion Method” in parameters of dynamic systems estimation, described with algebraic-differential equations. Parameters estimation is based upon the observation results from mathematical model behavior. Their values are derived after criterion minimization, which describes the total squared error of state vector coordinates from the deduced ones with precise values observation at different periods of time. Paral- lelepiped type restriction is imposed on the parameters values. Used for solving problems, metaheuristic methods of constrained global extremum don’t guarantee the result, but allow to get a solution of a rather good quality in accepta- ble amount of time. The algorithm of using metaheuristic methods is given. Alongside with the obvious methods for solving algebraic-differential equation systems, it is convenient to use implicit methods for solving ordinary differen- tial equation systems. Two ways of solving the problem of parameters evaluation are given, those parameters differ in their mathematical model. In the first example, a linear mathematical model describes the chemical action parameters change, and in the second one, a nonlinear mathematical model describes predator-prey dynamics, which characterize the changes in both kinds’ population. For each of the observed examples there are calculation results from all the three methods of optimization, there are also some recommendations for how to choose methods parameters. The obtained numerical results have demonstrated the efficiency of the proposed approach. The deduced parameters ap- proximate points slightly differ from the best known solutions, which were deduced differently. To refine the results one should apply hybrid schemes that combine classical methods of optimization of zero, first and second orders and
Selection of an optimal treatment method for acute periodontitis disease.
Aliev, Rafik A; Aliyev, B F; Gardashova, Latafat A; Huseynov, Oleg H
2012-04-01
The present paper is devoted to selection of an optimal treatment method for acute periodontitis by using fuzzy Choquet integral-based approach. We consider application of different treatment methods depending on development stages and symptoms of the disease. The effectiveness of application of different treatment methods in each stage of the disease is linguistically evaluated by a dentist. The stages of the disease are also linguistically described by a dentist. Dentist's linguistic evaluations are represented by fuzzy sets. The total effectiveness of the each considered treatment method is calculated by using fuzzy Choquet integral with fuzzy number-valued integrand and fuzzy number-valued fuzzy measure. The most effective treatment method is determined by using fuzzy ranking method.
Optimal Rapid Restart of Heuristic Methods of NP Hard Problems
侯越先; 王芳
2004-01-01
Many heuristic search methods exhibit a remarkable variability in the time required to solve some particular problem instances. Their cost distributions are often heavy-tailed. It has been demonstrated that, in most cases, rapid restart (RR) method can prominently suppress the heavy-tailed nature of the instances and improve computation efficiency. However, it is usually time-consuming to check whether an algorithm on a specific instance is heavy-tailed or not. Moreover, if the heavy-tailed distribution is confirmed and the RR method is relevant, an optimal RR threshold should be chosen to facilitate the RR mechanism. In this paper, an approximate approach is proposed to quickly check whether an algorithm on a specific instance is heavy-tailed or not.The method is realized by means of calculating the maximal Lyapunov exponent of its generic running trace.Then a statistical formula to estimate the optimal RR threshold is educed. The method is based on common nonparametric estimation, e. g. , Kernel estimation. Two heuristic methods are selected to verify our method. The experimental results are consistent with the theoretical consideration perfectly.
A Localization Method for Multistatic SAR Based on Convex Optimization.
Xuqi Zhong
Full Text Available In traditional localization methods for Synthetic Aperture Radar (SAR, the bistatic range sum (BRS estimation and Doppler centroid estimation (DCE are needed for the calculation of target localization. However, the DCE error greatly influences the localization accuracy. In this paper, a localization method for multistatic SAR based on convex optimization without DCE is investigated and the influence of BRS estimation error on localization accuracy is analysed. Firstly, by using the information of each transmitter and receiver (T/R pair and the target in SAR image, the model functions of T/R pairs are constructed. Each model function's maximum is on the circumference of the ellipse which is the iso-range for its model function's T/R pair. Secondly, the target function whose maximum is located at the position of the target is obtained by adding all model functions. Thirdly, the target function is optimized based on gradient descent method to obtain the position of the target. During the iteration process, principal component analysis is implemented to guarantee the accuracy of the method and improve the computational efficiency. The proposed method only utilizes BRSs of a target in several focused images from multistatic SAR. Therefore, compared with traditional localization methods for SAR, the proposed method greatly improves the localization accuracy. The effectivity of the localization approach is validated by simulation experiment.
A Localization Method for Multistatic SAR Based on Convex Optimization.
Zhong, Xuqi; Wu, Junjie; Yang, Jianyu; Sun, Zhichao; Huang, Yuling; Li, Zhongyu
2015-01-01
In traditional localization methods for Synthetic Aperture Radar (SAR), the bistatic range sum (BRS) estimation and Doppler centroid estimation (DCE) are needed for the calculation of target localization. However, the DCE error greatly influences the localization accuracy. In this paper, a localization method for multistatic SAR based on convex optimization without DCE is investigated and the influence of BRS estimation error on localization accuracy is analysed. Firstly, by using the information of each transmitter and receiver (T/R) pair and the target in SAR image, the model functions of T/R pairs are constructed. Each model function's maximum is on the circumference of the ellipse which is the iso-range for its model function's T/R pair. Secondly, the target function whose maximum is located at the position of the target is obtained by adding all model functions. Thirdly, the target function is optimized based on gradient descent method to obtain the position of the target. During the iteration process, principal component analysis is implemented to guarantee the accuracy of the method and improve the computational efficiency. The proposed method only utilizes BRSs of a target in several focused images from multistatic SAR. Therefore, compared with traditional localization methods for SAR, the proposed method greatly improves the localization accuracy. The effectivity of the localization approach is validated by simulation experiment.
Geophysical Inversion With Multi-Objective Global Optimization Methods
Lelièvre, Peter; Bijani, Rodrigo; Farquharson, Colin
2016-04-01
We are investigating the use of Pareto multi-objective global optimization (PMOGO) methods to solve numerically complicated geophysical inverse problems. PMOGO methods can be applied to highly nonlinear inverse problems, to those where derivatives are discontinuous or simply not obtainable, and to those were multiple minima exist in the problem space. PMOGO methods generate a suite of solutions that minimize multiple objectives (e.g. data misfits and regularization terms) in a Pareto-optimal sense. This allows a more complete assessment of the possibilities and provides opportunities to calculate statistics regarding the likelihood of particular model features. We are applying PMOGO methods to four classes of inverse problems. The first are discrete-body problems where the inversion determines values of several parameters that define the location, orientation, size and physical properties of an anomalous body represented by a simple shape, for example a sphere, ellipsoid, cylinder or cuboid. A PMOGO approach can determine not only the optimal shape parameters for the anomalous body but also the optimal shape itself. Furthermore, when one expects several anomalous bodies in the subsurface, a PMOGO inversion approach can determine an optimal number of parameterized bodies. The second class of inverse problems are standard mesh-based problems where the physical property values in each cell are treated as continuous variables. The third class of problems are lithological inversions, which are also mesh-based but cells can only take discrete physical property values corresponding to known or assumed rock units. In the fourth class, surface geometry inversions, we consider a fundamentally different type of problem in which a model comprises wireframe surfaces representing contacts between rock units. The physical properties of each rock unit remain fixed while the inversion controls the position of the contact surfaces via control nodes. Surface geometry inversion can be
Metastable legged locomotion: methods to quantify and optimize reliability
Saglam, Cenk O.; Byl, Katie
2015-05-01
Measuring the stability of highly-dynamic bipedal locomotion is a challenging but essential task for more capable human-like walking. By discretizing the walking dynamics, we treat the system as a Markov chain, which lends itself to an easy quantification of failure rates by the expected number of steps before falling. This meaningful and intuitive metric is then used for optimizing and benchmarking given controllers. While this method is applicable to any controller scheme, we illustrate the results with two case demonstrations. One scheme is the now-familiar hybrid zero dynamics approach and the other is a method using piece-wise reference trajectories with a sliding mode control. We optimize low-level controllers, to minimize failure rates for any one gait, and we adopt a hierarchical control structure to switch among low-level gaits, providing even more dramatic improvements on the system performance.
Time-dependent optimal heater control using finite difference method
Li, Zhen Zhe; Heo, Kwang Su; Choi, Jun Hoo; Seol, Seoung Yun [Chonnam National Univ., Gwangju (Korea, Republic of)
2008-07-01
Thermoforming is one of the most versatile and economical process to produce polymer products. The drawback of thermoforming is difficult to control thickness of final products. Temperature distribution affects the thickness distribution of final products, but temperature difference between surface and center of sheet is difficult to decrease because of low thermal conductivity of ABS material. In order to decrease temperature difference between surface and center, heating profile must be expressed as exponential function form. In this study, Finite Difference Method was used to find out the coefficients of optimal heating profiles. Through investigation, the optimal results using Finite Difference Method show that temperature difference between surface and center of sheet can be remarkably minimized with satisfying temperature of forming window.
Optimal and adaptive methods of processing hydroacoustic signals (review)
Malyshkin, G. S.; Sidel'nikov, G. B.
2014-09-01
Different methods of optimal and adaptive processing of hydroacoustic signals for multipath propagation and scattering are considered. Advantages and drawbacks of the classical adaptive (Capon, MUSIC, and Johnson) algorithms and "fast" projection algorithms are analyzed for the case of multipath propagation and scattering of strong signals. The classical optimal approaches to detecting multipath signals are presented. A mechanism of controlled normalization of strong signals is proposed to automatically detect weak signals. The results of simulating the operation of different detection algorithms for a linear equidistant array under multipath propagation and scattering are presented. An automatic detector is analyzed, which is based on classical or fast projection algorithms, which estimates the background proceeding from median filtering or the method of bilateral spatial contrast.
Novel electromagnetism-like mechanism method for multiobjective optimization problems
Lixia Han,Shujuan Jiang,; Shaojiang Lan
2015-01-01
As a new-style stochastic algorithm, the electro-magnetism-like mechanism (EM) method gains more and more attention from many researchers in recent years. A novel model based on EM (NMEM) for multiobjective optimization problems is proposed, which regards the charge of al particles as the con-straints in the current population and the measure of the uniformity of non-dominated solutions as the objective function. The charge of the particle is evaluated based on the dominated concept, and its magnitude determines the direction of a force between two particles. Numerical studies are carried out on six complex test functions and the experimental results demonstrate that the pro-posed NMEM algorithm is a very robust method for solving the multiobjective optimization problems.
Circulating fluidized bed coal-saving optimization control method
Jiang, Tengfei; Li, Dewei; Xi, Yugeng; Zhou, Wu [Shanghai Jiao Tong Univ., Shanghai (China). Dept. of Automation; Ministry of Education, Shanghai (China). Key Lab. of System Control and Information Processing; Yin, Debin [Shanghai Xinhua Control Technology (Group) Co., Ltd., Shanghai (China)
2013-07-01
The circulating fluidized bed boiler is widely used in thermal power plants. With the proposal of energy-saving emission reduction, how to reduce coal consumption while ensure the output steam quality at the same time has become an important topic. This paper combines the technology of RTO (real-time optimization) and zone control in DMC (dynamic matrix control) to achieve this goal. The proposed method adds the coal consumption into the objective function of DMC controller and the operation point of the boiler is permitted to change within a zone which can be set according to the actual requirements of the circulating fluidized bed boiler. The zone control in DMC provides the freedom to reduce the coal consumption and achieves the economic optimal target. Compared to the simple use of constrained DMC control, the proposed method is verified to be remarkable coal-saving by the case study of a 150 t/h boiler of a power plant in Sichuan.
Optimized Adaptor Polymerase Chain Reaction Method for Efficient Genomic Walking
Peng XU; Rui-Ying HU; Xiao-Yan DING
2006-01-01
Genomic walking is one of the most useful approaches in genome-related research. Three kinds of PCR-based methods are available for this purpose. However, none of them has been generally applied because they are either insensitive or inefficient. Here we present an efficient PCR protocol, an optimized adaptor PCR method for genomic walking. Using a combination of a touchdown PCR program and a special adaptor, the optimized adaptor PCR protocol achieves high sensitivity with low background noise. By applying this protocol, the insertion sites of a gene trap mouse line and two gene promoters from the incompletely sequenced Xenopus laevis genome were successfully identified with high efficiency. The general application of this protocol in genomic walking was promising.
Carver, Charles S.; Scheier, Michael F.; Segerstrom, Suzanne C.
2010-01-01
Optimism is an individual difference variable that reflects the extent to which people hold generalized favorable expectancies for their future. Higher levels of optimism have been related prospectively to better subjective well-being in times of adversity or difficulty (i.e., controlling for previous well-being). Consistent with such findings, optimism has been linked to higher levels of engagement coping and lower levels of avoidance, or disengagement, coping. There is evidence that optimism is associated with taking proactive steps to protect one's health, whereas pessimism is associated with health-damaging behaviors. Consistent with such findings, optimism is also related to indicators of better physical health. The energetic, task-focused approach that optimists take to goals also relates to benefits in the socioeconomic world. Some evidence suggests that optimism relates to more persistence in educational efforts and to higher later income. Optimists also appear to fare better than pessimists in relationships. Although there are instances in which optimism fails to convey an advantage, and instances in which it may convey a disadvantage, those instances are relatively rare. In sum, the behavioral patterns of optimists appear to provide models of living for others to learn from. PMID:20170998
A Novel Global Path Planning Method for Mobile Robots Based on Teaching-Learning-Based Optimization
Zongsheng Wu
2016-07-01
Full Text Available The Teaching-Learning-Based Optimization (TLBO algorithm has been proposed in recent years. It is a new swarm intelligence optimization algorithm simulating the teaching-learning phenomenon of a classroom. In this paper, a novel global path planning method for mobile robots is presented, which is based on an improved TLBO algorithm called Nonlinear Inertia Weighted Teaching-Learning-Based Optimization (NIWTLBO algorithm in our previous work. Firstly, the NIWTLBO algorithm is introduced. Then, a new map model of the path between start-point and goal-point is built by coordinate system transformation. Lastly, utilizing the NIWTLBO algorithm, the objective function of the path is optimized; thus, a global optimal path is obtained. The simulation experiment results show that the proposed method has a faster convergence rate and higher accuracy in searching for the path than the basic TLBO and some other algorithms as well, and it can effectively solve the optimization problem for mobile robot global path planning.
Experimental methods for the analysis of optimization algorithms
Bartz-Beielstein, Thomas; Paquete, Luis; Preuss, Mike
2010-01-01
In operations research and computer science it is common practice to evaluate the performance of optimization algorithms on the basis of computational results, and the experimental approach should follow accepted principles that guarantee the reliability and reproducibility of results. However, computational experiments differ from those in other sciences, and the last decade has seen considerable methodological research devoted to understanding the particular features of such experiments and assessing the related statistical methods. This book consists of methodological contributions on diffe
Method for optimization of statically determined articulated structures
Parras Galán, L.; Montes Tubío, M.; García Guzmán, A.; Entrenas Angulo, J.A.; de Dios Palomares, R.
1985-01-01
An algorithm of mathematical programmation for the optimum disign of iso-static articulate structures has been developed. The characteristics of the proposed algorithm are: a) It not requires that the objective function or the restriction equations be derivable. b) They use unidimensional methods of unidimensional optimization to pass over from a given solution to a better one. c) To introduce contingent procedures to obtain Information which leads to a better solution. Those characteristics ...
Gradient Gene Algorithm: a Fast Optimization Method to MST Problem
无
2001-01-01
The extension of Minimum Spanning Tree(MST) problem is an NP hardproblem which does not exit a polynomial time algorithm. In this paper, a fast optimizat ion method on MST problem--the Gradient Gene Algorithm is introduced. Compar ed with other evolutionary algorithms on MST problem, it is more advanced: firstly, very simple and easy to realize; then, efficient and accurate; finally general on other combination optimization problems.
An Optimized Method for Terrain Reconstruction Based on Descent Images
Xu Xinchao
2016-02-01
Full Text Available An optimization method is proposed to perform high-accuracy terrain reconstruction of the landing area of Chang’e III. First, feature matching is conducted using geometric model constraints. Then, the initial terrain is obtained and the initial normal vector of each point is solved on the basis of the initial terrain. By changing the vector around the initial normal vector in small steps a set of new vectors is obtained. By combining these vectors with the direction of light and camera, the functions are set up on the basis of a surface reflection model. Then, a series of gray values is derived by solving the equations. The new optimized vector is recorded when the obtained gray value is closest to the corresponding pixel. Finally, the optimized terrain is obtained after iteration of the vector field. Experiments were conducted using the laboratory images and descent images of Chang’e III. The results showed that the performance of the proposed method was better than that of the classical feature matching method. It can provide a reference for terrain reconstruction of the landing area in subsequent moon exploration missions.
Split Bregman's optimization method for image construction in compressive sensing
Skinner, D.; Foo, S.; Meyer-Bäse, A.
2014-05-01
The theory of compressive sampling (CS) was reintroduced by Candes, Romberg and Tao, and D. Donoho in 2006. Using a priori knowledge that a signal is sparse, it has been mathematically proven that CS can defY Nyquist sampling theorem. Theoretically, reconstruction of a CS image relies on the minimization and optimization techniques to solve this complex almost NP-complete problem. There are many paths to consider when compressing and reconstructing an image but these methods have remained untested and unclear on natural images, such as underwater sonar images. The goal of this research is to perfectly reconstruct the original sonar image from a sparse signal while maintaining pertinent information, such as mine-like object, in Side-scan sonar (SSS) images. Goldstein and Osher have shown how to use an iterative method to reconstruct the original image through a method called Split Bregman's iteration. This method "decouples" the energies using portions of the energy from both the !1 and !2 norm. Once the energies are split, Bregman iteration is used to solve the unconstrained optimization problem by recursively solving the problems simultaneously. The faster these two steps or energies can be solved then the faster the overall method becomes. While the majority of CS research is still focused on the medical field, this paper will demonstrate the effectiveness of the Split Bregman's methods on sonar images.
A conjugate gradient method with descent direction for unconstrained optimization
Yuan, Gonglin; Lu, Xiwen; Wei, Zengxin
2009-11-01
A modified conjugate gradient method is presented for solving unconstrained optimization problems, which possesses the following properties: (i) The sufficient descent property is satisfied without any line search; (ii) The search direction will be in a trust region automatically; (iii) The Zoutendijk condition holds for the Wolfe-Powell line search technique; (iv) This method inherits an important property of the well-known Polak-Ribière-Polyak (PRP) method: the tendency to turn towards the steepest descent direction if a small step is generated away from the solution, preventing a sequence of tiny steps from happening. The global convergence and the linearly convergent rate of the given method are established. Numerical results show that this method is interesting.
New design method for valves internals, to optimize process
Jorge, Leonardo [PDVSA (Venezuela)
2011-07-01
In the heavy oil industry, various methods can be used to reduce the viscosity of oil, one of them being the injection of diluent. This method is commonly used in the Orinoco oil belt but it requires good control of the volume of diluent injected as well as the gas flow to optimize production; thus flow control valves need to be accurate. A new valve with a new method was designed with the characteristic of being very reliable and was then bench tested and compared with the other commercially available valves. Results showed better repeatability, accuracy and reliability with lower maintenance for the new method. The use of this valve provides significant savings while distributing the exact amount of fluids; up to date a less than 2% failure rate has been recorded in the field. The new method developed demonstrated impressive performance and PDVSA has decided to use it in mass.
Optimization design of thumbspica splint using finite element method.
Huang, Tz-How; Feng, Chi-Kung; Gung, Yih-Wen; Tsai, Mei-Wun; Chen, Chen-Sheng; Liu, Chien-Lin
2006-12-01
De Quervain's tenosynovitis is often observed on repetitive flexion of the thumb. In the clinical setting, the conservative treatment is usually an applied thumbspica splint to immobilize the thumb. However, the traditional thumbspica splint is bulky and heavy. Thus, this study used the finite element (FE) method to remove redundant material in order to reduce the splint's weight and increase ventilation. An FE model of a thumbspica splint was constructed using ANSYS9.0 software. A maximum lateral thumb pinch force of 98 N was used as the input loading condition for the FE model. This study implemented topology optimization and design optimization to seek the optimal thickness and shape of the splint. This new design was manufactured and compared with the traditional thumbspica splint. Ten thumbspica splints were tested in a materials testing system, and statistically analyzed using an independent t test. The optimal thickness of the thumbspica splint was 3.2 mm. The new design is not significantly different from the traditional splint in the immobilization effect. However, the volume of this new design has been reduced by about 35%. This study produced a new thumbspica splint shape with less volume, but had a similar immobilization effect compared to the traditional shape. In a clinical setting, this result can be used by the occupational therapist as a reference for manufacturing lighter thumbspica splints for patients with de Quervain's tenosynovitis.
Robust Optimal Adaptive Control Method with Large Adaptive Gain
Nguyen, Nhan T.
2009-01-01
In the presence of large uncertainties, a control system needs to be able to adapt rapidly to regain performance. Fast adaptation is referred to the implementation of adaptive control with a large adaptive gain to reduce the tracking error rapidly. However, a large adaptive gain can lead to high-frequency oscillations which can adversely affect robustness of an adaptive control law. A new adaptive control modification is presented that can achieve robust adaptation with a large adaptive gain without incurring high-frequency oscillations as with the standard model-reference adaptive control. The modification is based on the minimization of the Y2 norm of the tracking error, which is formulated as an optimal control problem. The optimality condition is used to derive the modification using the gradient method. The optimal control modification results in a stable adaptation and allows a large adaptive gain to be used for better tracking while providing sufficient stability robustness. Simulations were conducted for a damaged generic transport aircraft with both standard adaptive control and the adaptive optimal control modification technique. The results demonstrate the effectiveness of the proposed modification in tracking a reference model while maintaining a sufficient time delay margin.
Continuous Control Artificial Potential Function Methods and Optimal Control
2014-03-27
Method, namely r̈VDSVAPF = −K̇SKR∇φ−KSK̇R∇φ−KSKRH(φ)ṙ −KD (KSKR∇φ+ ṙ) . The above dynamics are very nonlinear due to the trigonometric functions (inside...constraints (on KS and θ) and the deletion of trigonometric functions . The suspected reasons for the larger computa- tional expense are twofold. First, this...Continuous Control Artificial Potential Function Methods and Optimal Control THESIS R. Andrew Fields, Civ, USAF AFIT-ENY-14-M-20 DEPARTMENT OF THE
Optimization in engineering sciences approximate and metaheuristic methods
Stefanoiu, Dan; Popescu, Dumitru; Filip, Florin Gheorghe; El Kamel, Abdelkader
2014-01-01
The purpose of this book is to present the main metaheuristics and approximate and stochastic methods for optimization of complex systems in Engineering Sciences. It has been written within the framework of the European Union project ERRIC (Empowering Romanian Research on Intelligent Information Technologies), which is funded by the EU's FP7 Research Potential program and has been developed in co-operation between French and Romanian teaching researchers. Through the principles of various proposed algorithms (with additional references) this book allows the reader to explore various methods o
The Value of Methodical Management: Optimizing Science Results
Saby, Linnea
2016-01-01
As science progresses, making new discoveries in radio astronomy becomes increasingly complex. Instrumentation must be incredibly fine-tuned and well-understood, scientists must consider the skills and schedules of large research teams, and inter-organizational projects sometimes require coordination between observatories around the globe. Structured and methodical management allows scientists to work more effectively in this environment and leads to optimal science output. This report outlines the principles of methodical project management in general, and describes how those principles are applied at the National Radio Astronomy Observatory (NRAO) in Charlottesville, Virginia.
Optimal turning method of composting regarding hygienic safety
Masafumi Tateda; Le Duc Trung; Michihiko Ike; Masanori Fujita
2005-01-01
The new turning method was proposed and verified its effectiveness to pathogens by laboratory scale experiments. Considering the results obtained from the previous studies, it could be said that turning of a composting pile was essential in terms of hygienic aspects but the number of turning should be minimized. Effectiveness of inactivation was estimated for each composting run. From this estimation,turning by layers, which is different from conventional turning that mixes compost pile entirely, was proposed and investigated its performance by experiments. Composting operations with static pile method, complete mix(conventional) turning method, and proposed turning( layer turning) method were done and their effectiveness on inactivation of indicator microorganism was evaluated and compared.As results, the conventional turning method was not a proper method in terms of pathogen inactivation, whereas, the proposed turning method showed an excellent performance and should be employed in a composting operation.
Wang, Liang; Liu, Zunying; Zhao, Yuanhui; Dong, Shiyuan; Zeng, Mingyong; Yang, HuiCheng
2014-01-01
Three freezing-point regulators (glycine, sodium chloride and D-sorbitol) were employed to optimize thermophysical properties of Pacific white shrimp (Litopenaeus vannamei) using response surface methodology (RSM). The independent variables were glycine content (0.250–1.250 %), sodium chloride content (0.500–2.500 %) and D-sorbitol content (0.125–0.625 %) and analysis of variance showed that the effects of glycine, sodium chloride and D-sorbitol on the thermophysical properties were statistic...
Design optimization methods for genomic DNA tiling arrays.
Bertone, Paul; Trifonov, Valery; Rozowsky, Joel S; Schubert, Falk; Emanuelsson, Olof; Karro, John; Kao, Ming-Yang; Snyder, Michael; Gerstein, Mark
2006-02-01
A recent development in microarray research entails the unbiased coverage, or tiling, of genomic DNA for the large-scale identification of transcribed sequences and regulatory elements. A central issue in designing tiling arrays is that of arriving at a single-copy tile path, as significant sequence cross-hybridization can result from the presence of non-unique probes on the array. Due to the fragmentation of genomic DNA caused by the widespread distribution of repetitive elements, the problem of obtaining adequate sequence coverage increases with the sizes of subsequence tiles that are to be included in the design. This becomes increasingly problematic when considering complex eukaryotic genomes that contain many thousands of interspersed repeats. The general problem of sequence tiling can be framed as finding an optimal partitioning of non-repetitive subsequences over a prescribed range of tile sizes, on a DNA sequence comprising repetitive and non-repetitive regions. Exact solutions to the tiling problem become computationally infeasible when applied to large genomes, but successive optimizations are developed that allow their practical implementation. These include an efficient method for determining the degree of similarity of many oligonucleotide sequences over large genomes, and two algorithms for finding an optimal tile path composed of longer sequence tiles. The first algorithm, a dynamic programming approach, finds an optimal tiling in linear time and space; the second applies a heuristic search to reduce the space complexity to a constant requirement. A Web resource has also been developed, accessible at http://tiling.gersteinlab.org, to generate optimal tile paths from user-provided DNA sequences.
Optimized optical clearing method for imaging central nervous system
Yu, Tingting; Qi, Yisong; Gong, Hui; Luo, Qingming; Zhu, Dan
2015-03-01
The development of various optical clearing methods provides a great potential for imaging entire central nervous system by combining with multiple-labelling and microscopic imaging techniques. These methods had made certain clearing contributions with respective weaknesses, including tissue deformation, fluorescence quenching, execution complexity and antibody penetration limitation that makes immunostaining of tissue blocks difficult. The passive clarity technique (PACT) bypasses those problems and clears the samples with simple implementation, excellent transparency with fine fluorescence retention, but the passive tissue clearing method needs too long time. In this study, we not only accelerate the clearing speed of brain blocks but also preserve GFP fluorescence well by screening an optimal clearing temperature. The selection of proper temperature will make PACT more applicable, which evidently broaden the application range of this method.
Optimization and modification of the method for detection of rhamnolipids
Takeshi Tabuchi
2015-10-01
Full Text Available Use of biosurfactants in bioremediation, facilitates and accelerates microbial degradation of hydrocarbons. CTAB/MB agar method created by Siegmund & Wagner for screening of rhamnolipids (RL producing strains, has been widely used but has not improved significantly for more than 20 years. To optimize the technique as a quantitative method, CTAB/MB agar plates were made and different variables were tested, like incubation time, cooling, CTAB concentration, methylene blue presence, wells diameter and inocula volume. Furthermore, a new method for RL detection within halos was developed: precipitation of RL with HCl, allows the formation a new halos pattern, easier to observe and to measure. This research reaffirm that this method is not totally suitable for a fine quantitative analysis, because of the difficulty to accurately correlate RL concentration and the area of the halos. RL diffusion does not seem to have a simple behavior and there are a lot of factors that affect RL migration rate.
An Optimized Constraint Decomposition Method in Concurrent Engineering
无
2003-01-01
In order to facilitate solution, a complex problem is normally decomposed into many small sub-problems during product development process. Teams are formed to resolve each sub-problem. The original problem is resolved from solutions of sub-problems. Ideally, sub-problems are not only mutually independent but also inherent parameters of original problem. Solution of original problem can be directly derived from the collection of solutions from simplified sub-problems. In practice, the degree of interdependency is indeed reduced, sub-problems are neither totally independent nor all inherent parameters of original problem. This paper discusses team coordination under this condition and design solution from each team, which not only satisfies total requirements but also is an optimal one. The suggested optimized constraint decomposition method will insure workable Pareto solution.
Stochastic Recursive Algorithms for Optimization Simultaneous Perturbation Methods
Bhatnagar, S; Prashanth, L A
2013-01-01
Stochastic Recursive Algorithms for Optimization presents algorithms for constrained and unconstrained optimization and for reinforcement learning. Efficient perturbation approaches form a thread unifying all the algorithms considered. Simultaneous perturbation stochastic approximation and smooth fractional estimators for gradient- and Hessian-based methods are presented. These algorithms: • are easily implemented; • do not require an explicit system model; and • work with real or simulated data. Chapters on their application in service systems, vehicular traffic control and communications networks illustrate this point. The book is self-contained with necessary mathematical results placed in an appendix. The text provides easy-to-use, off-the-shelf algorithms that are given detailed mathematical treatment so the material presented will be of significant interest to practitioners, academic researchers and graduate students alike. The breadth of applications makes the book appropriate for reader from sim...
Active control of transient rotordynamic vibration by optimal control methods
Palazzolo, A. B.; Lin, R. R.; Alexander, R. M.; Kascak, A. F.
1988-01-01
Although considerable effort has been put into the study of steady state vibration control, there are few methods applicable to transient vibration control of rotorbearing systems. In this paper optimal control theory has been adopted to minimize rotor vibration due to sudden imbalance, e.g., blade loss. The system gain matrix is obtained by choosing the weighting matrices and solving the Riccati equation. Control forces are applied to the system via a feedback loop. A seven mass rotor system is simulated for illustration. A relationship between the number of sensors and the number of modes used in the optimal control model is investigated. Comparisons of responses are made for various configurations of modes, sensors, and actuators. Furthermore, spillover effect is examined by comparing results from collocated and noncollocated sensor configurations. Results show that shaft vibration is significantly attenuated in the closed loop system.
Suleimanov, Yury V
2015-01-01
We present a simple protocol which allows fully automated discovery of elementary chemical reaction steps using in cooperation single- and double-ended transition-state optimization algorithms - the freezing string and Berny optimization methods, respectively. To demonstrate the utility of the proposed approach, the reactivity of several systems of combustion and atmospheric chemistry importance is investigated. The proposed algorithm allowed us to detect without any human intervention not only "known" reaction pathways, manually detected in the previous studies, but also new, previously "unknown", reaction pathways which involve significant atom rearrangements. We believe that applying such a systematic approach to elementary reaction path finding will greatly accelerate the possibility of discovery of new chemistry and will lead to more accurate computer simulations of various chemical processes.
Suleimanov, Yury V; Green, William H
2015-09-08
We present a simple protocol which allows fully automated discovery of elementary chemical reaction steps using in cooperation double- and single-ended transition-state optimization algorithms--the freezing string and Berny optimization methods, respectively. To demonstrate the utility of the proposed approach, the reactivity of several single-molecule systems of combustion and atmospheric chemistry importance is investigated. The proposed algorithm allowed us to detect without any human intervention not only "known" reaction pathways, manually detected in the previous studies, but also new, previously "unknown", reaction pathways which involve significant atom rearrangements. We believe that applying such a systematic approach to elementary reaction path finding will greatly accelerate the discovery of new chemistry and will lead to more accurate computer simulations of various chemical processes.
Quasi-Newton Method for Optimal Blank Allowance Balancing
CHEN Manyi
2006-01-01
A balancing technique for casting or forging parts to be machined is presented in this paper. It allows an optimal part setup to make sure that no shortage of material (undercut) will occur during machining. Particularly in the heavy part industry, where the resulting casting size and shape may deviate from expectations, the balancing process discovers whether or not the design model is totally enclosed in the actual part to be machined. The alignment is an iterative process involving nonlinear constrained optimization, which forces data points to lie outside the nominal model under a specific order of priority. Newton methods for non-linear numerical minimization are rarely applied to this problem because of the high cost of computing. In this paper, Newton methods are applied to the balancing of blank part. The aforesaid algorithm is demonstrated in term of a marine propeller blade, and result shows that The Newton methods are more efficient and accurate than those implemented in past research and have distinct advantages compared to the registration methods widely used today.
Optimal Recognition Method of Human Activities Using Artificial Neural Networks
Oniga Stefan
2015-12-01
Full Text Available The aim of this research is an exhaustive analysis of the various factors that may influence the recognition rate of the human activity using wearable sensors data. We made a total of 1674 simulations on a publically released human activity database by a group of researcher from the University of California at Berkeley. In a previous research, we analyzed the influence of the number of sensors and their placement. In the present research we have examined the influence of the number of sensor nodes, the type of sensor node, preprocessing algorithms, type of classifier and its parameters. The final purpose is to find the optimal setup for best recognition rates with lowest hardware and software costs.
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 a...
Advanced Topology Optimization Methods for Conceptual Architectural Design
Aage, Niels; Amir, Oded; Clausen, Anders
2015-01-01
in topological optimization: Interactive control and continuous visualization; embedding flexible voids within the design space; consideration of distinct tension / compression properties; and optimization of dual material systems. In extension, optimization procedures for skeletal structures such as trusses...
Optimizing ETL by a Two-level Data Staging Method
Iftikhar, Nadeem
2016-01-01
In data warehousing, the data from source systems are populated into a central data warehouse (DW) through extraction, transformation and loading (ETL). The standard ETL approach usually uses sequential jobs to process the data with dependencies, such as dimension and fact data. It is a non......-/late-arriving data, and fast-/slowly-changing data. The introduced additional staging area decouples loading process from data extraction and transformation, which improves ETL flexibility and minimizes intervention to the data warehouse. This paper evaluates the proposed method empirically, which shows......-trivial task to process the so-called early-/late-arriving data, which arrive out of order. This paper proposes a two-level data staging area method to optimize ETL. The proposed method is an all-in-one solution that supports processing different types of data from operational systems, including early...
Structured light system calibration method with optimal fringe angle.
Li, Beiwen; Zhang, Song
2014-11-20
For structured light system calibration, one popular approach is to treat the projector as an inverse camera. This is usually performed by projecting horizontal and vertical sequences of patterns to establish one-to-one mapping between camera points and projector points. However, for a well-designed system, either horizontal or vertical fringe images are not sensitive to depth variation and thus yield inaccurate mapping. As a result, the calibration accuracy is jeopardized if a conventional calibration method is used. To address this limitation, this paper proposes a novel calibration method based on optimal fringe angle determination. Experiments demonstrate that our calibration approach can increase the measurement accuracy up to 38% compared to the conventional calibration method with a calibration volume of 300(H) mm×250(W) mm×500(D) mm.
Methods to optimize myxobacterial fermentations using off-gas analysis
Hüttel Stephan
2012-05-01
Full Text Available Abstract Background The influence of carbon dioxide and oxygen on microbial secondary metabolite producers and the maintenance of these two parameters at optimal levels have been studied extensively. Nevertheless, most studies have focussed on their influence on specific product formation and condition optimization of established processes. Considerably less attention has been paid to the influence of reduced or elevated carbon dioxide and oxygen levels on the overall metabolite profiles of the investigated organisms. The synergistic action of both gases has garnered even less attention. Results We show that the composition of the gas phase is highly important for the production of different metabolites and present a simple approach that enables the maintenance of defined concentrations of both O2 and CO2 during bioprocesses over broad concentration ranges with a minimal instrumental setup by using endogenously produced CO2. The metabolite profiles of a myxobacterium belonging to the genus Chondromyces grown under various concentrations of CO2 and O2 showed considerable differences. Production of two unknown, highly cytotoxic compounds and one antimicrobial substance was found to increase depending on the gas composition. In addition, the observation of CO2 and O2 in the exhaust gas allowed optimization and control of production processes. Conclusions Myxobacteria are becoming increasingly important due to their potential for bioactive secondary metabolite production. Our studies show that the influence of different gas partial pressures should not be underestimated during screening processes for novel compounds and that our described method provides a simple tool to investigate this question.
Performance enhancement of a pump impeller using optimal design method
Jeon, Seok-Yun; Kim, Chul-Kyu; Lee, Sang-Moon; Yoon, Joon-Yong; Jang, Choon-Man
2017-04-01
This paper presents the performance evaluation of a regenerative pump to increase its efficiency using optimal design method. Two design parameters which define the shape of the pump impeller, are introduced and analyzed. Pump performance is evaluated by numerical simulation and design of experiments(DOE). To analyze three-dimensional flow field in the pump, general analysis code, CFX, is used in the present work. Shear stress turbulence model is employed to estimate the eddy viscosity. Experimental apparatus with an open-loop facility is set up for measuring the pump performance. Pump performance, efficiency and pressure, obtained from numerical simulation are validated by comparison with the results of experiments. Throughout the shape optimization of the pump impeller at the operating flow condition, the pump efficiency is successfully increased by 3 percent compared to the reference pump. It is noted that the pressure increase of the optimum pump is mainly caused by higher momentum force generated inside blade passage due to the optimal blade shape. Comparisons of pump internal flow on the reference and optimum pump are also investigated and discussed in detail.
Multiobjective Optimization Methods for Congestion Management in Deregulated Power Systems
K. Vijayakumar
2012-01-01
Full Text Available Congestion management is one of the important functions performed by system operator in deregulated electricity market to ensure secure operation of transmission system. This paper proposes two effective methods for transmission congestion alleviation in deregulated power system. Congestion or overload in transmission networks is alleviated by rescheduling of generators and/or load shedding. The two objectives conflicting in nature (1 transmission line over load and (2 congestion cost are optimized in this paper. The multiobjective fuzzy evolutionary programming (FEP and nondominated sorting genetic algorithm II methods are used to solve this problem. FEP uses the combined advantages of fuzzy and evolutionary programming (EP techniques and gives better unique solution satisfying both objectives, whereas nondominated sorting genetic algorithm (NSGA II gives a set of Pareto-optimal solutions. The methods propose an efficient and reliable algorithm for line overload alleviation due to critical line outages in a deregulated power markets. The quality and usefulness of the algorithm is tested on IEEE 30 bus system.
AN ADAPTIVE TRUST REGION METHOD FOR EQUALITY CONSTRAINED OPTIMIZATION
ZHANG Juliang; ZHANG Xiangsun; ZHUO Xinjian
2003-01-01
In this paper, a trust region method for equality constrained optimization based on nondifferentiable exact penalty is proposed. In this algorithm, the trail step is characterized by computation of its normal component being separated from computation of its tangential component, i.e., only the tangential component of the trail step is constrained by trust radius while the normal component and trail step itself have no constraints. The other main characteristic of the algorithm is the decision of trust region radius. Here, the decision of trust region radius uses the information of the gradient of objective function and reduced Hessian. However, Maratos effect will occur when we use the nondifferentiable exact penalty function as the merit function. In order to obtain the superlinear convergence of the algorithm, we use the twice order correction technique. Because of the speciality of the adaptive trust region method, we use twice order correction when p = 0 (the definition is as in Section 2) and this is different from the traditional trust region methods for equality constrained optimization. So the computation of the algorithm in this paper is reduced. What is more, we can prove that the algorithm is globally and superlinearly convergent.
Optimization of Statistical Methods Impact on Quantitative Proteomics Data.
Pursiheimo, Anna; Vehmas, Anni P; Afzal, Saira; Suomi, Tomi; Chand, Thaman; Strauss, Leena; Poutanen, Matti; Rokka, Anne; Corthals, Garry L; Elo, Laura L
2015-10-02
As tools for quantitative label-free mass spectrometry (MS) rapidly develop, a consensus about the best practices is not apparent. In the work described here we compared popular statistical methods for detecting differential protein expression from quantitative MS data using both controlled experiments with known quantitative differences for specific proteins used as standards as well as "real" experiments where differences in protein abundance are not known a priori. Our results suggest that data-driven reproducibility-optimization can consistently produce reliable differential expression rankings for label-free proteome tools and are straightforward in their application.
Experimental Methods for the Analysis of Optimization Algorithms
of solution quality, runtime and other measures; and the third part collects advanced methods from experimental design for configuring and tuning algorithms on a specific class of instances with the goal of using the least amount of experimentation. The contributor list includes leading scientists...... in algorithm design, statistical design, optimization and heuristics, and most chapters provide theoretical background and are enriched with case studies. This book is written for researchers and practitioners in operations research and computer science who wish to improve the experimental assessment...
A ROBUST SQP METHOD FOR OPTIMIZATION WITH INEQUALITY CONSTRAINTS
Juliang Zhang; Xiangsun Zhang
2003-01-01
A new algorithm for inequality constrained optimization is presented, which solves a linear programming subproblem and a quadratic subproblem at each iteration. The algorithm can circumvent the difficulties associated with the possible inconsistency of QP subproblem of the original SQP method. Moreover, the algorithm can converge to a point which satisfies a certain first-order necessary condition even if the original problem is itself infeasible. Under certain condition, some global convergence results are proved and local superlinear convergence results are also obtained. Preliminary numerical results are reported.
Adaptive nonmonotone line search method for unconstrained optimization
Qunyan ZHOU; Wenyu SUN
2008-01-01
In this paper, an adaptive nonmonotone line search method for unconstrained minimization problems is proposed. At every iteration, the new algorithm selects only one of the two directions: a Newton-type direc-tion and a negative curvature direction, to perform the line search. The nonmonotone technique is included in the backtracking line search when the Newton-type direction is the search direction. Furthermore, if the negative curvature direction is the search direction, we increase the steplength un-der certain conditions. The global convergence to a stationary point with second-order optimality conditions is established. Some numerical results which show the efficiency of the new algorithm are reported.
An Optimal Method for Developing Global Supply Chain Management System
Hao-Chun Lu
2013-01-01
Full Text Available Owing to the transparency in supply chains, enhancing competitiveness of industries becomes a vital factor. Therefore, many developing countries look for a possible method to save costs. In this point of view, this study deals with the complicated liberalization policies in the global supply chain management system and proposes a mathematical model via the flow-control constraints, which are utilized to cope with the bonded warehouses for obtaining maximal profits. Numerical experiments illustrate that the proposed model can be effectively solved to obtain the optimal profits in the global supply chain environment.
Comparison of Optimal Design Methods in Inverse Problems
2011-05-11
corresponding FIM can be estimated by F̂ (τ) = F̂ (τ, θ̂OLS) = (Σ̂ N (θ̂OLS)) −1. (13) The asymptotic standard errors are given by SEk (θ0) = √ (ΣN0 )kk, k...1, . . . , p. (14) These standard errors are estimated in practice (when θ0 and σ0 are not known) by SEk (θ̂OLS) = √ (Σ̂N (θ̂OLS))kk, k = 1... SEk (θ̂boot) = √ Cov(θ̂boot)kk. We will compare the optimal design methods using the standard errors resulting from the op- timal time points each
Optimal Control for Bufferbloat Queue Management Using Indirect Method with Parametric Optimization
Amr Radwan
2016-01-01
Full Text Available Because memory buffers become larger and cheaper, they have been put into network devices to reduce the number of loss packets and improve network performance. However, the consequences of large buffers are long queues at network bottlenecks and throughput saturation, which has been recently noticed in research community as bufferbloat phenomenon. To address such issues, in this article, we design a forward-backward optimal control queue algorithm based on an indirect approach with parametric optimization. The cost function which we want to minimize represents a trade-off between queue length and packet loss rate performance. Through the integration of an indirect approach with parametric optimization, our proposal has advantages of scalability and accuracy compared to direct approaches, while still maintaining good throughput and shorter queue length than several existing queue management algorithms. All numerical analysis, simulation in ns-2, and experiment results are provided to solidify the efficiency of our proposal. In detailed comparisons to other conventional algorithms, the proposed procedure can run much faster than direct collocation methods while maintaining a desired short queue (≈40 packets in simulation and 80 (ms in experiment test.
Yin, Xin'an; Yang, Zhifeng; Liu, Cailing; Zhao, Yanwei
2015-09-01
In this research, a new method is developed to determine the optimal contract load for a hydropower reservoir, which is achieved by incorporating environmental flows into the determination process to increase hydropower revenues, while mitigating the negative impacts of hydropower generation on riverine ecosystems. In this method, the degree of natural flow regime alteration is adopted as a constraint of hydropower generation to protect riverine ecosystems, and the maximization of mean annual revenue is set as the optimization objective. The contract load in each month and the associated reservoir operating parameters were simultaneously optimized by a genetic algorithm. The proposed method was applied to China's Wangkuai Reservoir to test its effectiveness. The new method offers two advantages over traditional studies. First, it takes into account both the economic benefits and the ecological needs of riverine systems, rather than only the economic benefits, as in previous methods. Second, although many measures have been established to mitigate the negative ecological impacts of hydropower generation, few have been applied to the hydropower planning stage. Thus, since the contract load is an important planning parameter for hydropower generation, influencing both economic benefits and riverine ecosystem protection, this new method could provide guidelines for the establishment of river protection measures at the hydropower planning stage.
Joint Geophysical Inversion With Multi-Objective Global Optimization Methods
Lelievre, P. G.; Bijani, R.; Farquharson, C. G.
2015-12-01
Pareto multi-objective global optimization (PMOGO) methods generate a suite of solutions that minimize multiple objectives (e.g. data misfits and regularization terms) in a Pareto-optimal sense. Providing a suite of models, as opposed to a single model that minimizes a weighted sum of objectives, allows a more complete assessment of the possibilities and avoids the often difficult choice of how to weight each objective. We are applying PMOGO methods to three classes of inverse problems. The first class are standard mesh-based problems where the physical property values in each cell are treated as continuous variables. The second class of problems are also mesh-based but cells can only take discrete physical property values corresponding to known or assumed rock units. In the third class we consider a fundamentally different type of inversion in which a model comprises wireframe surfaces representing contacts between rock units; the physical properties of each rock unit remain fixed while the inversion controls the position of the contact surfaces via control nodes. This third class of problem is essentially a geometry inversion, which can be used to recover the unknown geometry of a target body or to investigate the viability of a proposed Earth model. Joint inversion is greatly simplified for the latter two problem classes because no additional mathematical coupling measure is required in the objective function. PMOGO methods can solve numerically complicated problems that could not be solved with standard descent-based local minimization methods. This includes the latter two classes of problems mentioned above. There are significant increases in the computational requirements when PMOGO methods are used but these can be ameliorated using parallelization and problem dimension reduction strategies.
Practical optimization of Steiner trees via the cavity method
Braunstein, Alfredo; Muntoni, Anna
2016-07-01
The optimization version of the cavity method for single instances, called Max-Sum, has been applied in the past to the minimum Steiner tree problem on graphs and variants. Max-Sum has been shown experimentally to give asymptotically optimal results on certain types of weighted random graphs, and to give good solutions in short computation times for some types of real networks. However, the hypotheses behind the formulation and the cavity method itself limit substantially the class of instances on which the approach gives good results (or even converges). Moreover, in the standard model formulation, the diameter of the tree solution is limited by a predefined bound, that affects both computation time and convergence properties. In this work we describe two main enhancements to the Max-Sum equations to be able to cope with optimization of real-world instances. First, we develop an alternative ‘flat’ model formulation that allows the relevant configuration space to be reduced substantially, making the approach feasible on instances with large solution diameter, in particular when the number of terminal nodes is small. Second, we propose an integration between Max-Sum and three greedy heuristics. This integration allows Max-Sum to be transformed into a highly competitive self-contained algorithm, in which a feasible solution is given at each step of the iterative procedure. Part of this development participated in the 2014 DIMACS Challenge on Steiner problems, and we report the results here. The performance on the challenge of the proposed approach was highly satisfactory: it maintained a small gap to the best bound in most cases, and obtained the best results on several instances in two different categories. We also present several improvements with respect to the version of the algorithm that participated in the competition, including new best solutions for some of the instances of the challenge.
Optimal experimental design with the sigma point method.
Schenkendorf, R; Kremling, A; Mangold, M
2009-01-01
Using mathematical models for a quantitative description of dynamical systems requires the identification of uncertain parameters by minimising the difference between simulation and measurement. Owing to the measurement noise also, the estimated parameters possess an uncertainty expressed by their variances. To obtain highly predictive models, very precise parameters are needed. The optimal experimental design (OED) as a numerical optimisation method is used to reduce the parameter uncertainty by minimising the parameter variances iteratively. A frequently applied method to define a cost function for OED is based on the inverse of the Fisher information matrix. The application of this traditional method has at least two shortcomings for models that are nonlinear in their parameters: (i) it gives only a lower bound of the parameter variances and (ii) the bias of the estimator is neglected. Here, the authors show that by applying the sigma point (SP) method a better approximation of characteristic values of the parameter statistics can be obtained, which has a direct benefit on OED. An additional advantage of the SP method is that it can also be used to investigate the influence of the parameter uncertainties on the simulation results. The SP method is demonstrated for the example of a widely used biological model.
Using tailored methodical approaches to achieve optimal science outcomes
Wingate, Lory M.
2016-08-01
The science community is actively engaged in research, development, and construction of instrumentation projects that they anticipate will lead to new science discoveries. There appears to be very strong link between the quality of the activities used to complete these projects, and having a fully functioning science instrument that will facilitate these investigations.[2] The combination of using internationally recognized standards within the disciplines of project management (PM) and systems engineering (SE) has been demonstrated to lead to achievement of positive net effects and optimal project outcomes. Conversely, unstructured, poorly managed projects will lead to unpredictable, suboptimal project outcomes ultimately affecting the quality of the science that can be done with the new instruments. The proposed application of these two specific methodical approaches, implemented as a tailorable suite of processes, are presented in this paper. Project management (PM) is accepted worldwide as an effective methodology used to control project cost, schedule, and scope. Systems engineering (SE) is an accepted method that is used to ensure that the outcomes of a project match the intent of the stakeholders, or if they diverge, that the changes are understood, captured, and controlled. An appropriate application, or tailoring, of these disciplines can be the foundation upon which success in projects that support science can be optimized.
Lopes, Ana C.; Fernandes, Maria G.A.; Ribeiro, J. E.; Fonseca, E.M.M.
2016-01-01
Dental implant is used to replace the natural dental root. The process to fix the dental implant in the maxillary bone needs a previous drilling operation. This machining operation involves the increasing of temperature in the drilled region which can reach values higher than 47°C and for this temperature is possible to occur the osseous necrosis [I]. The main goal of this work is to implement an optimization method to define the optimal drilling parameters that cou...
Construction of optimal supersaturated designs by the packing method
FANG; Kaitai; GE; Gennian; LIU; Minqian
2004-01-01
A supersaturated design is essentially a factorial design with the equal occurrence of levels property and no fully aliased factors in which the number of main efits potential in factor screening experiments. A packing design is an important object in combinatorial design theory. In this paper, a strong link between the two apparently unrelated kinds of designs is shown. Several criteria for comparing supersaturated designs are proposed, their properties and connections with other existing criteria are discussed.A combinatorial approach, called the packing method, for constructing optimal supersaturated designs is presented, and properties of the resulting designs are also investigated.Comparisons between the new designs and other existing designs are given, which show that our construction method and the newly constructed designs have good properties.
PARAMETER OPTIMIZATION OF CARBIDIC AUSTEMPERED DUCTILE IRON USING TAGUCHI METHOD
P.DHANAPAL
2010-08-01
Full Text Available Carbidic austempered ductile iron [CADI] is the family of ductile iron containing wear resistance alloy carbides in the ausferrite matrix. This CADI is manufactured by selecting proper material composition through the melting route.In an effort to obtain the optimal production parameters, Taguchi method is applied. To analyse the effect of production parameters on the machanical properties, signal-to-noise (S/N ratio is calculated based on the design ofexperiments and the linear graph. The analysis of varience is calculated to find the amount of contribution of factors on individual mechanical properties and its significancy. The analytical results of taguchi method are compared with the experimental values, and it shows both are identical.
Methods for Optimizing CRISPR-Cas9 Genome Editing Specificity.
Tycko, Josh; Myer, Vic E; Hsu, Patrick D
2016-08-04
Advances in the development of delivery, repair, and specificity strategies for the CRISPR-Cas9 genome engineering toolbox are helping researchers understand gene function with unprecedented precision and sensitivity. CRISPR-Cas9 also holds enormous therapeutic potential for the treatment of genetic disorders by directly correcting disease-causing mutations. Although the Cas9 protein has been shown to bind and cleave DNA at off-target sites, the field of Cas9 specificity is rapidly progressing, with marked improvements in guide RNA selection, protein and guide engineering, novel enzymes, and off-target detection methods. We review important challenges and breakthroughs in the field as a comprehensive practical guide to interested users of genome editing technologies, highlighting key tools and strategies for optimizing specificity. The genome editing community should now strive to standardize such methods for measuring and reporting off-target activity, while keeping in mind that the goal for specificity should be continued improvement and vigilance.
Comparison of operation optimization methods in energy system modelling
Ommen, Torben Schmidt; Markussen, Wiebke Brix; Elmegaard, Brian
2013-01-01
, possibilities for decoupling production constraints may be valuable. Introduction of heat pumps in the district heating network may pose this ability. In order to evaluate if the introduction of heat pumps is economically viable, we develop calculation methods for the operation patterns of each of the used...... energy technologies. In the paper, three frequently used operation optimization methods are examined with respect to their impact on operation management of the combined technologies. One of the investigated approaches utilises linear programming for optimisation, one uses linear programming with binary...... operation constraints, while the third approach uses nonlinear programming. In the present case the non-linearity occurs in the boiler efficiency of power plants and the cv-value of an extraction plant. The linear programming model is used as a benchmark, as this type is frequently used, and has the lowest...
Optimal PMU placement using topology transformation method in power systems.
Rahman, Nadia H A; Zobaa, Ahmed F
2016-09-01
Optimal phasor measurement units (PMUs) placement involves the process of minimizing the number of PMUs needed while ensuring the entire power system completely observable. A power system is identified observable when the voltages of all buses in the power system are known. This paper proposes selection rules for topology transformation method that involves a merging process of zero-injection bus with one of its neighbors. The result from the merging process is influenced by the selection of bus selected to merge with the zero-injection bus. The proposed method will determine the best candidate bus to merge with zero-injection bus according to the three rules created in order to determine the minimum number of PMUs required for full observability of the power system. In addition, this paper also considered the case of power flow measurements. The problem is formulated as integer linear programming (ILP). The simulation for the proposed method is tested by using MATLAB for different IEEE bus systems. The explanation of the proposed method is demonstrated by using IEEE 14-bus system. The results obtained in this paper proved the effectiveness of the proposed method since the number of PMUs obtained is comparable with other available techniques.
HongZhong Tang
2016-01-01
Full Text Available Optimizing the mutual coherence of a learned dictionary plays an important role in sparse representation and compressed sensing. In this paper, a efficient framework is developed to learn an incoherent dictionary for sparse representation. In particular, the coherence of a previous dictionary (or Gram matrix is reduced sequentially by finding a new dictionary (or Gram matrix, which is closest to the reference unit norm tight frame of the previous dictionary (or Gram matrix. The optimization problem can be solved by restricting the tightness and coherence alternately at each iteration of the algorithm. The significant and different aspect of our proposed framework is that the learned dictionary can approximate an equiangular tight frame. Furthermore, manifold optimization is used to avoid the degeneracy of sparse representation while only reducing the coherence of the learned dictionary. This can be performed after the dictionary update process rather than during the dictionary update process. Experiments on synthetic and real audio data show that our proposed methods give notable improvements in lower coherence, have faster running times, and are extremely robust compared to several existing methods.
Shobeiri, Vahid
2016-03-01
In this article, the bi-directional evolutionary structural optimization (BESO) method based on the element-free Galerkin (EFG) method is presented for topology optimization of continuum structures. The mathematical formulation of the topology optimization is developed considering the nodal strain energy as the design variable and the minimization of compliance as the objective function. The EFG method is used to derive the shape functions using the moving least squares approximation. The essential boundary conditions are enforced by the method of Lagrange multipliers. Several topology optimization problems are presented to show the effectiveness of the proposed method. Many issues related to topology optimization of continuum structures, such as chequerboard patterns and mesh dependency, are studied in the examples.
Optimal grid-based methods for thin film micromagnetics simulations
Muratov, C. B.; Osipov, V. V.
2006-08-01
Thin film micromagnetics are a broad class of materials with many technological applications, primarily in magnetic memory. The dynamics of the magnetization distribution in these materials is traditionally modeled by the Landau-Lifshitz-Gilbert (LLG) equation. Numerical simulations of the LLG equation are complicated by the need to compute the stray field due to the inhomogeneities in the magnetization which presents the chief bottleneck for the simulation speed. Here, we introduce a new method for computing the stray field in a sample for a reduced model of ultra-thin film micromagnetics. The method uses a recently proposed idea of optimal finite difference grids for approximating Neumann-to-Dirichlet maps and has an advantage of being able to use non-uniform discretization in the film plane, as well as an efficient way of dealing with the boundary conditions at infinity for the stray field. We present several examples of the method's implementation and give a detailed comparison of its performance for studying domain wall structures compared to the conventional FFT-based methods.
Development of an optimal velocity selection method with velocity obstacle
Kim, Min Geuk; Oh, Jun Ho [KAIST, Daejeon (Korea, Republic of)
2015-08-15
The Velocity obstacle (VO) method is one of the most well-known methods for local path planning, allowing consideration of dynamic obstacles and unexpected obstacles. Typical VO methods separate a velocity map into a collision area and a collision-free area. A robot can avoid collisions by selecting its velocity from within the collision-free area. However, if there are numerous obstacles near a robot, the robot will have very few velocity candidates. In this paper, a method for choosing optimal velocity components using the concept of pass-time and vertical clearance is proposed for the efficient movement of a robot. The pass-time is the time required for a robot to pass by an obstacle. By generating a latticized available velocity map for a robot, each velocity component can be evaluated using a cost function that considers the pass-time and other aspects. From the output of the cost function, even a velocity component that will cause a collision in the future can be chosen as a final velocity if the pass-time is sufficiently long enough.
Optimization Method for Girder of Wind Turbine Blade
Yuqiao Zheng
2014-01-01
Full Text Available This paper presents a recently developed numerical multidisciplinary optimization method for design of wind turbine blade. The objective was the highest possible blade weight under specified atmospheric conditions, determined by the design giving girder layer and location parameter. Wind turbine blade on box-section beams girder is calculated by ply thickness, main girder and trailing edge. In this study, a realistic 30 m blade from a 1.2 MW wind turbine model of blade girder parameters is established. The optimization evolves a structure which transforms along the length of the blade, changing from a design with spar caps at the maximum thickness and a trailing edge mass to a design with spar caps toward the tip. In addition, the cross-section structural properties and the modal characteristics of a 62 m rotor blade were predicted by the developed beam finite element. In summary, these findings indicate that the conventional structural layout of a wind turbine blade is suboptimal under the static load conditions, suggesting an opportunity to reduce blade weight and cost.
Process parameter optimization for fly ash brick by Taguchi method
Prabir Kumar Chaulia; Reeta Das [Central Mechanical Engineering Research Institute, Durgapur (India)
2008-04-15
This paper presents the results of an experimental investigation carried out to optimize the mix proportions of the fly ash brick by Taguchi method of parameter design. The experiments have been designed using an L9 orthogonal array with four factors and three levels each. Small quantity of cement has been mixed as binding materials. Both cement and the fly ash used are indicated as binding material and water binder ratio has been considered as one of the control factors. So the effects of water/binder ratio, fly ash, coarse sand, and stone dust on the performance characteristic are analyzed using signal-to-noise ratios and mean response data. According to the results, water/binder ratio and stone dust play the significant role on the compressive strength of the brick. Furthermore, the estimated optimum values of the process parameters are corresponding to water/binder ratio of 0.4, fly ash of 39%, coarse sand of 24% and stone dust of 30%. The mean value of optimal strength is predicted as 166.22 kg.cm{sup -2} with a tolerance of 10.97 kg.cm{sup -2}. The confirmatory experimental result obtained for the optimum conditions is 160.17 kg.cm{sup -2}. 13 refs., 3 figs., 7 tabs.
A hybrid method for optimization of the adaptive Goldstein filter
Jiang, Mi; Ding, Xiaoli; Tian, Xin; Malhotra, Rakesh; Kong, Weixue
2014-12-01
The Goldstein filter is a well-known filter for interferometric filtering in the frequency domain. The main parameter of this filter, alpha, is set as a power of the filtering function. Depending on it, considered areas are strongly or weakly filtered. Several variants have been developed to adaptively determine alpha using different indicators such as the coherence, and phase standard deviation. The common objective of these methods is to prevent areas with low noise from being over filtered while simultaneously allowing stronger filtering over areas with high noise. However, the estimators of these indicators are biased in the real world and the optimal model to accurately determine the functional relationship between the indicators and alpha is also not clear. As a result, the filter always under- or over-filters and is rarely correct. The study presented in this paper aims to achieve accurate alpha estimation by correcting the biased estimator using homogeneous pixel selection and bootstrapping algorithms, and by developing an optimal nonlinear model to determine alpha. In addition, an iteration is also merged into the filtering procedure to suppress the high noise over incoherent areas. The experimental results from synthetic and real data show that the new filter works well under a variety of conditions and offers better and more reliable performance when compared to existing approaches.
A new fuzzy optimal data replication method for data grid
Zeinab Ghilavizadeh
2013-03-01
Full Text Available These days, There are several applications where we face with large data set and it has become an important part of common resources in different scientific areas. In fact, there are many applications where there are literally huge amount of information handled either in terabyte or in petabyte. Many scientists apply huge amount of data distributed geographically around the world through advanced computing systems. The huge volume data and calculations have created new problems in accessing, processing and distribution of data. The challenges of data management infrastructure have become very difficult under a large amount of data, different geographical spaces, and complicated involved calculations. Data Grid is a remedy to all mentioned problems. In this paper, a new method of dynamic optimal data replication in data grid is introduced where it reduces the total job execution time and increases the locality in accessibilities by detecting and impacting the factors influencing the data replication. Proposed method is composed of two main phases. During the first phase is the phase of file application and replication operation. In this phase, we evaluate three factors influencing the data replication and determine whether the requested file can be replicated or it can be used from distance. In the second phase or the replacement phase, the proposed method investigates whether there is enough space in the destination to store the requested file or not. In this phase, the proposed method also chooses a replica with the lowest value for deletion by considering three replica factors to increase the performance of system. The results of simulation also indicate the improved performance of our proposed method compared with other replication methods represented in the simulator Optorsim.
A Glowworm Optimization Method for the Design of Web Services
Koffka Khan
2012-09-01
Full Text Available A method for adaptive usability evaluation of B2C eCommerce web services is proposed. For measuring eCommerce usability a checklist integrating eCommerce quality and usability is developed. By a Glowworm swarm optimization (GSO neural networks-based model the usability dimensions and their checklist items are adaptively selected. A case study for usability evaluation of an eCommerce anthurium retail website is carried out. The experimental results show that GSO with neural networks supports the allocation of usability problems and the defining of relevant improvement measures. The main advantage of the approach is the adaptive selection of most significant checklist dimensions and items and thus significant reduction of the time for usability evaluation and design.
Hybrid Neural Network and Support Vector Machine Method for Optimization
Rai, Man Mohan (Inventor)
2007-01-01
System and method for optimization of a design associated with a response function, using a hybrid neural net and support vector machine (NN/SVM) analysis to minimize or maximize an objective function, optionally subject to one or more constraints. As a first example, the NN/SVM analysis is applied iteratively to design of an aerodynamic component, such as an airfoil shape, where the objective function measures deviation from a target pressure distribution on the perimeter of the aerodynamic component. As a second example, the NN/SVM analysis is applied to data classification of a sequence of data points in a multidimensional space. The NN/SVM analysis is also applied to data regression.
Gradient type optimization methods for electronic structure calculations
Zhang, Xin; Wen, Zaiwen; Zhou, Aihui
2013-01-01
The density functional theory (DFT) in electronic structure calculations can be formulated as either a nonlinear eigenvalue or direct minimization problem. The most widely used approach for solving the former is the so-called self-consistent field (SCF) iteration. A common observation is that the convergence of SCF is not clear theoretically while approaches with convergence guarantee for solving the latter are often not competitive to SCF numerically. In this paper, we study gradient type methods for solving the direct minimization problem by constructing new iterations along the gradient on the Stiefel manifold. Global convergence (i.e., convergence to a stationary point from any initial solution) as well as local convergence rate follows from the standard theory for optimization on manifold directly. A major computational advantage is that the computation of linear eigenvalue problems is no longer needed. The main costs of our approaches arise from the assembling of the total energy functional and its grad...
Shape optimized headers and methods of manufacture thereof
Perrin, Ian James
2013-11-05
Disclosed herein is a shape optimized header comprising a shell that is operative for collecting a fluid; wherein an internal diameter and/or a wall thickness of the shell vary with a change in pressure and/or a change in a fluid flow rate in the shell; and tubes; wherein the tubes are in communication with the shell and are operative to transfer fluid into the shell. Disclosed herein is a method comprising fixedly attaching tubes to a shell; wherein the shell is operative for collecting a fluid; wherein an internal diameter and/or a wall thickness of the shell vary with a change in pressure and/or a change in a fluid flow rate in the shell; and wherein the tubes are in communication with the shell and are operative to transfer fluid into the shell.
Convex functions and optimization methods on Riemannian manifolds
Udrişte, Constantin
1994-01-01
This unique monograph discusses the interaction between Riemannian geometry, convex programming, numerical analysis, dynamical systems and mathematical modelling. The book is the first account of the development of this subject as it emerged at the beginning of the 'seventies. A unified theory of convexity of functions, dynamical systems and optimization methods on Riemannian manifolds is also presented. Topics covered include geodesics and completeness of Riemannian manifolds, variations of the p-energy of a curve and Jacobi fields, convex programs on Riemannian manifolds, geometrical constructions of convex functions, flows and energies, applications of convexity, descent algorithms on Riemannian manifolds, TC and TP programs for calculations and plots, all allowing the user to explore and experiment interactively with real life problems in the language of Riemannian geometry. An appendix is devoted to convexity and completeness in Finsler manifolds. For students and researchers in such diverse fields as pu...
[Optimization for determination methods of alpha-, beta- carotene in vegetables].
Cao, Mengjin; Zhang, Xuesong; Wang, Xiaojiig; Shen, Xiang; Wang, Zhu
2016-05-01
To optimize the determination method of carotene in order to separate the alpha and beta carotene. The homogenized samples were extracted with ethanol before saponification, and then extracted with petroleum ether followed by wash, dry and concentration. At last, the separation was performed on the high performance liquid chromatographic with YMC Carotenoid C30 column (3 microm 4.6 mm x 150 mm). The mobile phase was consisted of A (methanol: acetonitrile: water = 73.5: 24.5:2) and B (methyl tert-butyl ether), with a linear gradient elution. The detection wavelength was 450 nm. The C30-HPLC column was used throughout the experiment exhibiting a significant benefit for the separation of carotienes and isomers. Multifactor analysis of variance showed that heating by the anhydrous ethyl alcohol before the saponification was more suitable for extracting the carotenoids from vegetables. Under the optimal condition, the linear range of alpha-carotene was 0.495-9.90 microg/mL, and all-trans-beta-carotene was 0.552-10.4 microg/mL, with the linear correlation coefficient were above 0. 999. The relative standard deviation for the alpha and beta carotene were less than 10% (n = 6). The average spike recoveries for alpha and beta carotene in vegetables were 81.6%-93.3%. The detection and quantification limits for alpha and beta carotene were 0.5 microg/100 g and 1.5 microg/100 g, respectively, when taking 5g samples. The method is accurate, reliable, and fit for analyzing alpha-carotene and beta-carotene in fresh vegetables.
An automatic and effective parameter optimization method for model tuning
T. Zhang
2015-05-01
Full Text Available Physical parameterizations in General Circulation Models (GCMs, having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determines parameter sensitivity and the other chooses the optimum initial value of sensitive parameters, are introduced before the downhill simplex method to reduce the computational cost and improve the tuning performance. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9%. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameters tuning during the model development stage.
Optimizing restriction fragment fingerprinting methods for ordering large genomic libraries.
Branscomb, E; Slezak, T; Pae, R; Galas, D; Carrano, A V; Waterman, M
1990-10-01
We present a statistical analysis of the problem of ordering large genomic cloned libraries through overlap detection based on restriction fingerprinting. Such ordering projects involve a large investment of effort involving many repetitious experiments. Our primary purpose here is to provide methods of maximizing the efficiency of such efforts. To this end, we adopt a statistical approach that uses the likelihood ratio as a statistic to detect overlap. The main advantages of this approach are that (1) it allows the relatively straightforward incorporation of the observed statistical properties of the data; (2) it permits the efficiency of a particular experimental method for detecting overlap to be quantitatively defined so that alternative experimental designs may be compared and optimized; and (3) it yields a direct estimate of the probability that any two library members overlap. This estimate is a critical tool for the accurate, automatic assembly of overlapping sets of fragments into islands called "contigs." These contigs must subsequently be connected by other methods to provide an ordered set of overlapping fragments covering the entire genome.
Convex Optimization methods for computing the Lyapunov Exponent of matrices
Protasov, Vladimir Yu
2012-01-01
We introduce a new approach to evaluate the largest Lyapunov exponent of a family of nonnegative matrices. The method is based on using special positive homogeneous functionals on $R^{d}_+,$ which gives iterative lower and upper bounds for the Lyapunov exponent. They improve previously known bounds and converge to the real value. The rate of convergence is estimated and the efficiency of the algorithm is demonstrated on several problems from applications (in functional analysis, combinatorics, and lan- guage theory) and on numerical examples with randomly generated matrices. The method computes the Lyapunov exponent with a prescribed accuracy in relatively high dimensions (up to 60). We generalize this approach to all matrices, not necessar- ily nonnegative, derive a new universal upper bound for the Lyapunov exponent, and show that such a lower bound, in general, does not exist.
Pan Jun; Fan Xiumin; Ma Dengzhe; Jin Ye
2003-01-01
Virtual product development (VPD) is essentially based on simulation. Due to computational inefficiency, traditional engineering simulation software and optimization methods are inadequate to analyze optimization problems in VPD. Optimization method based on simulation metamodel for virtual product development is proposed to satisfy the needs of complex optimal designs driven by VPD. This method extends the current design of experiments (DOE) by various metamodeling technologies. Simulation metamodels are built to approximate detailed simulation codes, so as to provide link between optimization and simulation, or serve as a bridge for simulation software integration among different domains. An example of optimal design for composite material structure is used to demonstrate the newly introduced method.
Zhang De-Sheng; Chen Jian; Shi Wei-Dong; Shi Lei; Geng Lin-Lin
2016-01-01
Both efficiency and cavitation performance of the hydrofoil are the key technologies to design the tidal current turbine. In this paper, the hydrofoil efficiency and lift coefficient were improved based on particle swarm optimization method and XFoil codes. The cavitation performance of the optimized hydrofoil was also discussed by the computational fluid dynamic. Numerical results show the efficiency of the optimized hydrofoil was improved 11% ranging from...
Alenius, Malin; Hammarlund-Udenaes, Margareta; Honoré, Per Gustaf Hartvig
2009-01-01
, cross-sectional study was performed using patient interviews and information from patient files. The new classification method CANSEPT, which combines the Camberwell Assessment of Need rating scale, the Udvalg for Kliniske Undersøgelser side effect rating scale (SE), and the patient's previous treatment...... history (PT), was used to group the patients according to treatment response. CANSEPT was evaluated by comparison of expected and observed results. RESULTS: In the patient population (n = 123), the patients in functional remission, as defined by CANSEPT, had higher quality of life, fewer hospitalizations...
Classification and uptake of reservoir operation optimization methods
Dobson, Barnaby; Pianosi, Francesca; Wagener, Thorsten
2016-04-01
Reservoir operation optimization algorithms aim to improve the quality of reservoir release and transfer decisions. They achieve this by creating and optimizing the reservoir operating policy; a function that returns decisions based on the current system state. A range of mathematical optimization algorithms and techniques has been applied to the reservoir operation problem of policy optimization. In this work, we propose a classification of reservoir optimization approaches by focusing on the formulation of the water management problem rather than the optimization algorithm type. We believe that decision makers and operators will find it easier to navigate a classification system based on the problem characteristics, something they can clearly define, rather than the optimization algorithm. Part of this study includes an investigation regarding the extent of algorithm uptake and the possible reasons that limit real world application.
Jui-Chang Lin
2015-01-01
Full Text Available The three-dimensional tube (or pipe is manufactured by CNC tube bending machine. The key techniques are determined by tube diameter, wall thickness, material, and bending radius. The obtained technique through experience and the trial and error method is unreliable. Finite element method (FEM simulation for the tube bending process before production can avoid wasting manpower and raw materials. The computer-aided engineering (CAE software ABAQUS 6.12 is applied to simulate bending characteristics and to explore the maximum stress and strain conditions. The Taguchi method is used to find the optimal parameters of bending. The confirmation experiment is performed according to optimal parameters. Results indicate that the strain error between CAE simulation and bending experiments is within 6.39%.
DONG Zhao-yang; SUN Shu-dong
2006-01-01
The three levels optimizing strategy is put forward for the networked manufacturing resources optimizing configuration, namely, the optimizing of a logical manufacturing process,the optimizing of simulation-based integration of process planning and scheduling, and the optimizing of networked production scheduling. Then, the web services-based architecture of networked manufacturing resources optimizing configuration is brought forward. Finally, the key algorithm of the networked manufacturing resources optimizing configuration is discussed, namely, the two phases manufacturing partners selection method, which including the group technology-based manufacturing resources pre-configuration and the genetic algorithm-based executable manufacturing process optimizing.
Pipeline heating method based on optimal control and state estimation
Vianna, F.L.V. [Dept. of Subsea Technology. Petrobras Research and Development Center - CENPES, Rio de Janeiro, RJ (Brazil)], e-mail: fvianna@petrobras.com.br; Orlande, H.R.B. [Dept. of Mechanical Engineering. POLI/COPPE, Federal University of Rio de Janeiro - UFRJ, Rio de Janeiro, RJ (Brazil)], e-mail: helcio@mecanica.ufrj.br; Dulikravich, G.S. [Dept. of Mechanical and Materials Engineering. Florida International University - FIU, Miami, FL (United States)], e-mail: dulikrav@fiu.edu
2010-07-01
In production of oil and gas wells in deep waters the flowing of hydrocarbon through pipeline is a challenging problem. This environment presents high hydrostatic pressures and low sea bed temperatures, which can favor the formation of solid deposits that in critical operating conditions, as unplanned shutdown conditions, may result in a pipeline blockage and consequently incur in large financial losses. There are different methods to protect the system, but nowadays thermal insulation and chemical injection are the standard solutions normally used. An alternative method of flow assurance is to heat the pipeline. This concept, which is known as active heating system, aims at heating the produced fluid temperature above a safe reference level in order to avoid the formation of solid deposits. The objective of this paper is to introduce a Bayesian statistical approach for the state estimation problem, in which the state variables are considered as the transient temperatures within a pipeline cross-section, and to use the optimal control theory as a design tool for a typical heating system during a simulated shutdown condition. An application example is presented to illustrate how Bayesian filters can be used to reconstruct the temperature field from temperature measurements supposedly available on the external surface of the pipeline. The temperatures predicted with the Bayesian filter are then utilized in a control approach for a heating system used to maintain the temperature within the pipeline above the critical temperature of formation of solid deposits. The physical problem consists of a pipeline cross section represented by a circular domain with four points over the pipe wall representing heating cables. The fluid is considered stagnant, homogeneous, isotropic and with constant thermo-physical properties. The mathematical formulation governing the direct problem was solved with the finite volume method and for the solution of the state estimation problem
Proposed optimal LSP selection method in MPLS networks
Kuribayashi, Shin-ichi
2012-01-01
Multi-Protocol Label Switching (MPLS) had been deployed by many data networking service providers, including the next-generation mobile backhaul networks, because of its undeniable potential in terms of virtual private network (VPN) management, traffic engineering, etc. In MPLS networks, IP packets are transmitted along a Label Switched Path (LSP) established between edge nodes. To improve the efficiency of resource use in MPLS networks, it is essential to utilize the LSPs efficiently. This paper proposes a method of selecting the optimal LSP pair from among multiple LSP pairs which are established between the same pair of edge nodes, on the assumption that both the upward and downward LSPs are established as a pair (both-way operation). It is supposed that both upward and downward bandwidths are allocated simultaneously in the selected LSP pair for each service request. It is demonstrated by simulation evaluations that the proposal method could reduce the total amount of the bandwidth required by up to 15% c...
Research on multi-fidelity aerodynamic optimization methods
Huang Likeng; Gao Zhenghong; Zhang Dehu
2013-01-01
Constructing high approximation accuracy surrogate model with lower computational cost has great engineering significance.In this paper,using co-Kriging method,an efficient multifidelity surrogate model is constructed based on two independent high and low fidelity samples.Co-Kriging method can use a greater quantity of low-fidelity information to enhance the accuracy of a surrogate of the high-fidelity model by modeling the correlation between high and low fidelity model,thus computational cost of building surrogate model can be greatly reduced.A wing-body problem is taken as an example to compare characteristics of co-Kriging multi-fidelity (CKMF)model with traditional Kriging based multi-fidelity (KMF) model.A sampling convergence of the CKMF model and the KMF model is conducted,and an appropriate sampling design is selected through the sampling convergence analysis.The results indicate that CKMF model has higher approximation accuracy with the same high-fidelity samples,and converges at less high-fidelity samples.A wing-body drag reduction optimization design using genetic algorithm is implemented.Satisfying design results are obtained,which validate the feasibility of CKMF model in engineering design.
Simple optimization method for partitioning purification of hydrogen networks
W.M. Shehata
2015-03-01
Full Text Available The Egyptian petroleum fuel market is increasing rapidly nowadays. These fuels must be in the standard specifications of the Egyptian General Petroleum Corporation (EGPC, which required lower sulfur gasoline and diesel fuels. So the fuels must be deep hydrotreated which resulted in increasing hydrogen (H2 consumption for deeper hydrotreating. Along with increased H2 consumption for deeper hydrotreating, additional H2 is needed for processing heavier and higher sulfur crude slates especially in hydrocracking process, in addition to hydrotreating unit, isomerization units and lubricant plants. Purification technology is used to increase the amount of recycled hydrogen. If the amount of recycled hydrogen is increased, the amount of hydrogen that is sent to the furnaces with the off gas will decrease. In this work, El Halwagi et al. (2003 and El Halwagi (2012 optimization methods which are used for recycle/reuse integration systems have been extended to be used in the partitioning purification of hydrogen networks to minimize the hydrogen consumption and the hydrogen discharge. An actual case study and two case studies from the literature are solved to illustrate the proposed method.
Martian Radiative Transfer Modeling Using the Optimal Spectral Sampling Method
Eluszkiewicz, J.; Cady-Pereira, K.; Uymin, G.; Moncet, J.-L.
2005-01-01
The large volume of existing and planned infrared observations of Mars have prompted the development of a new martian radiative transfer model that could be used in the retrievals of atmospheric and surface properties. The model is based on the Optimal Spectral Sampling (OSS) method [1]. The method is a fast and accurate monochromatic technique applicable to a wide range of remote sensing platforms (from microwave to UV) and was originally developed for the real-time processing of infrared and microwave data acquired by instruments aboard the satellites forming part of the next-generation global weather satellite system NPOESS (National Polarorbiting Operational Satellite System) [2]. As part of our on-going research related to the radiative properties of the martian polar caps, we have begun the development of a martian OSS model with the goal of using it to perform self-consistent atmospheric corrections necessary to retrieve caps emissivity from the Thermal Emission Spectrometer (TES) spectra. While the caps will provide the initial focus area for applying the new model, it is hoped that the model will be of interest to the wider Mars remote sensing community.
Zhengnan Li; Tao Yang; Zhiwei Feng
2016-01-01
To solve the multiobjective optimization problem on hypersonic glider vehicle trajectory design subjected to complex constraints, this paper proposes a multiobjective trajectory optimization method that combines the boundary intersection method and pseudospectral method. The multiobjective trajectory optimization problem (MTOP) is established based on the analysis of the feature of hypersonic glider vehicle trajectory. The MTOP is translated into a set of general optimization subproblems by u...
Optimal Control with Time Delays via the Penalty Method
Mohammed Benharrat
2014-01-01
Full Text Available We prove necessary optimality conditions of Euler-Lagrange type for a problem of the calculus of variations with time delays, where the delay in the unknown function is different from the delay in its derivative. Then, a more general optimal control problem with time delays is considered. Main result gives a convergence theorem, allowing us to obtain a solution to the delayed optimal control problem by considering a sequence of delayed problems of the calculus of variations.
Methods and tools for analysis and optimization of power plants
Assadi, Mohsen
2000-09-01
The most noticeable advantage of the introduction of the computer-aided tools in the field of power generation, has been the ability to study the plant's performance prior to the construction phase. The results of these studies have made it possible to change and adjust the plant layout to match the pre-defined requirements. Further development of computers in recent years has opened up for implementation of new features in the existing tools and also for the development of new tools for specific applications, like thermodynamic and economic optimization, prediction of the remaining component life time, and fault diagnostics, resulting in improvement of the plant's performance, availability and reliability. The most common tools for pre-design studies are heat and mass balance programs. Further thermodynamic and economic optimization of plant layouts, generated by the heat and mass balance programs, can be accomplished by using pinch programs, exergy analysis and thermoeconomics. Surveillance and fault diagnostics of existing systems can be performed by using tools like condition monitoring systems and artificial neural networks. The increased number of tools and their various construction and application areas make the choice of the most adequate tool for a certain application difficult. In this thesis the development of different categories of tools and techniques, and their application area are reviewed and presented. Case studies on both existing and theoretical power plant layouts have been performed using different commercially available tools to illuminate their advantages and shortcomings. The development of power plant technology and the requirements for new tools and measurement systems have been briefly reviewed. This thesis contains also programming techniques and calculation methods concerning part-load calculations using local linearization, which has been implemented in an inhouse heat and mass balance program developed by the author
A New Method for Horizontal Axis Wind Turbine (HAWT Blade Optimization
Mohammadreza Mohammadi
2016-02-01
Full Text Available Iran has a great potential for wind energy. This paper introduces optimization of 7 wind turbine blades for small and medium scales in a determined wind condition of Zabol site, Iran, where the average wind speed is considered 7 m /s. Considered wind turbines are 3 bladed and radius of 7 case study turbine blades are 4.5 m, 6.5 m, 8 m, 9 m, 10 m, 15.5 m and 20 m. As the first step, an initial design is performed using one airfoil (NACA 63-215 across the blade. In the next step, every blade is divided into three sections, while the 20 % of first part of the blade is considered as root, the 5% of last the part is considered as tip and the rest of the blade as mid part. Providing necessary input data, suitable airfoils for wind turbines including 43 airfoils are extracted and their experimental data are entered in optimization process. Three variables in this optimization problem would be airfoil type, attack angle and chord, where the objective function is maximum output torque. A MATLAB code was written for design and optimization of the blade, which was validated with a previous experimental work. In addition, a comparison was made to show the effect of optimization with two variables (airfoil type and attack angle versus optimization with three variables (airfoil type, attack angle and chord on output torque increase. Results of this research shows a dramatic increase in comparison to initial designed blade with one airfoil where two variable optimization causes 7.7% to 22.27 % enhancement and three variable optimization causes 17.91% up to 24.48% rise in output torque .Article History: Received Oct 15, 2015; Received in revised form January 2, 2016; Accepted January 14, 2016; Available online How to Cite This Article: Mohammadi, M., Mohammadi, A. and Farahat, S. (2016 A New Method for Horizontal Axis Wind Turbine (HAWT Blade Optimization. Int. Journal of Renewable Energy Development, 5(1,1-8. http://dx.doi.org/10.14710/ijred.5.1.1-8
Kazemi, Amir S; Kawka, Karina; Latulippe, David R
2016-10-01
There is considerable interest in developing microscale (i.e., high-throughput) methods that enable multiple filtration experiments to be run in parallel with smaller sample amounts and thus reduce the overall required time and associated cost to run the filtration tests. Previous studies to date have focused on simply evaluating the filtration capacity, not the separation performance. In this work, the stirred-well filtration (SWF) method was used in combination with design-of-experiment (DOE) methods to optimize the separation performance for three binary mixtures of bio-molecules: protein-protein, protein-polysaccharide, and protein-DNA. Using the parallel based format of the SWF method, eight constant-flux ultrafiltration experiments were conducted at once to study the effects of stirring conditions, permeate flux, and/or solution conditions (pH, ionic strength). Four separate filtration tests were conducted for each combination of process variables; in total, over 100 separate tests were conducted. The sieving coefficient and selectivity results are presented to match the DOE design format and enable a greater understanding of the effects of the different process variables that were studied. The method described herein can be used to rapidly determine the optimal combination of process factors that give the best separation performance for a range of membrane-based separations applications and thus obviate the need to run a large number of traditional lab-scale tests. Biotechnol. Bioeng. 2016;113: 2131-2139. © 2016 Wiley Periodicals, Inc.
Brewick, Patrick T.; Smyth, Andrew W.
2016-12-01
The authors have previously shown that many traditional approaches to operational modal analysis (OMA) struggle to properly identify the modal damping ratios for bridges under traffic loading due to the interference caused by the driving frequencies of the traffic loads. This paper presents a novel methodology for modal parameter estimation in OMA that overcomes the problems presented by driving frequencies and significantly improves the damping estimates. This methodology is based on finding the power spectral density (PSD) of a given modal coordinate, and then dividing the modal PSD into separate regions, left- and right-side spectra. The modal coordinates were found using a blind source separation (BSS) algorithm and a curve-fitting technique was developed that uses optimization to find the modal parameters that best fit each side spectra of the PSD. Specifically, a pattern-search optimization method was combined with a clustering analysis algorithm and together they were employed in a series of stages in order to improve the estimates of the modal damping ratios. This method was used to estimate the damping ratios from a simulated bridge model subjected to moving traffic loads. The results of this method were compared to other established OMA methods, such as Frequency Domain Decomposition (FDD) and BSS methods, and they were found to be more accurate and more reliable, even for modes that had their PSDs distorted or altered by driving frequencies.
Optimized Audio Classification and Segmentation Algorithm by Using Ensemble Methods
Saadia Zahid
2015-01-01
Full Text Available Audio segmentation is a basis for multimedia content analysis which is the most important and widely used application nowadays. An optimized audio classification and segmentation algorithm is presented in this paper that segments a superimposed audio stream on the basis of its content into four main audio types: pure-speech, music, environment sound, and silence. An algorithm is proposed that preserves important audio content and reduces the misclassification rate without using large amount of training data, which handles noise and is suitable for use for real-time applications. Noise in an audio stream is segmented out as environment sound. A hybrid classification approach is used, bagged support vector machines (SVMs with artificial neural networks (ANNs. Audio stream is classified, firstly, into speech and nonspeech segment by using bagged support vector machines; nonspeech segment is further classified into music and environment sound by using artificial neural networks and lastly, speech segment is classified into silence and pure-speech segments on the basis of rule-based classifier. Minimum data is used for training classifier; ensemble methods are used for minimizing misclassification rate and approximately 98% accurate segments are obtained. A fast and efficient algorithm is designed that can be used with real-time multimedia applications.
Activity optimization method in SPECT: A comparison with ROC analysis
D(I)AZ Marlén Pérez; RIZO Oscar Díaz; D(I)AZ Adlin López; APARICIO Eric Estévez; D(I)AZ Reinaldo Roque
2006-01-01
A discriminant method for optimizing activity in nuclear medicine studies is validated by comparison with ROC performed with a cardiac phantom. Three different lesions (L1, L2 and L3) were placed in the myocardium-wall by pairs for each SPECT. Three activities (84, 37 or 18.5 MBq) of 99mTc were used as background. Linear discriminant analysis was used to select the parameters that characterize image quality among the measured variables in the images [(Background-to-Lesion (B/Li) and Signal-to-Noise (Si/N) ratio s)]. Two clusters with different image quality (P=0.021) were obtained. The ratios B/L1, B/L2 and B/L3are the parameters used to construct the function with 100% of cases correctly classified into the clusters. The value of 37 MBq was the lowest tested activity for which good results for the B/Li ratios were obtained. The result coincides with the applied ROC-analysis (r=0.89).
Influence of Pareto optimality on the maximum entropy methods
Peddavarapu, Sreehari; Sunil, Gujjalapudi Venkata Sai; Raghuraman, S.
2017-07-01
Galerkin meshfree schemes are emerging as a viable substitute to finite element method to solve partial differential equations for the large deformations as well as crack propagation problems. However, the introduction of Shanon-Jayne's entropy principle in to the scattered data approximation has deviated from the trend of defining the approximation functions, resulting in maximum entropy approximants. Further in addition to this, an objective functional which controls the degree of locality resulted in Local maximum entropy approximants. These are based on information-theoretical Pareto optimality between entropy and degree of locality that are defining the basis functions to the scattered nodes. The degree of locality in turn relies on the choice of locality parameter and prior (weight) function. The proper choices of both plays vital role in attain the desired accuracy. Present work is focused on the choice of locality parameter which defines the degree of locality and priors: Gaussian, Cubic spline and quartic spline functions on the behavior of local maximum entropy approximants.
Multiobjective Optimization Method Based on Adaptive Parameter Harmony Search Algorithm
P. Sabarinath
2015-01-01
Full Text Available The present trend in industries is to improve the techniques currently used in design and manufacture of products in order to meet the challenges of the competitive market. The crucial task nowadays is to find the optimal design and machining parameters so as to minimize the production costs. Design optimization involves more numbers of design variables with multiple and conflicting objectives, subjected to complex nonlinear constraints. The complexity of optimal design of machine elements creates the requirement for increasingly effective algorithms. Solving a nonlinear multiobjective optimization problem requires significant computing effort. From the literature it is evident that metaheuristic algorithms are performing better in dealing with multiobjective optimization. In this paper, we extend the recently developed parameter adaptive harmony search algorithm to solve multiobjective design optimization problems using the weighted sum approach. To determine the best weightage set for this analysis, a performance index based on least average error is used to determine the index of each weightage set. The proposed approach is applied to solve a biobjective design optimization of disc brake problem and a newly formulated biobjective design optimization of helical spring problem. The results reveal that the proposed approach is performing better than other algorithms.
Control Methods Utilizing Energy Optimizing Schemes in Refrigeration Systems
Larsen, L.S; Thybo, C.; Stoustrup, Jakob;
2003-01-01
The potential energy savings in refrigeration systems using energy optimal control has been proved to be substantial. This however requires an intelligent control that drives the refrigeration systems towards the energy optimal state. This paper proposes an approach for a control, which drives...
石连栓; 孙焕纯; 冯恩民
2001-01-01
A method for topological optimization of structures with discrete variables subjected to dynamic stress and displacement constraints is presented. By using the quasistatic method, the structure optimization problem under dynamic stress and displacement constraints is converted into one subjected to static stress and displacement constraints. The comprehensive algorithm for topological optimization of structures with discrete variables is used to find the optimum solution.
Culture medium optimization for pigment production with RSM method
无
2005-01-01
Response surface methodology was used to optimize a medium for a red-pigmented marine bacterium S-9801 strain (Flavobacterium sp.). In the first optimization step the influence of yeast extract, peptone, glucose and sodium chloride on pigment production was evaluated using a fractional factorial design. Pigment production was positively influenced by glucose and sodium chloride while other components had no significant effect. In the second step the path of steepest ascent was used to approach the optimal region of the medium composition. In the third step the optimal concentration of glucose and sodium chloride was determined by a central composite design and response analysis. The optimized medium allowed pigment production (A 535～650) to be increased from 0.137 to0.559, being 320% higher than the original medium.
Laser: a Tool for Optimization and Enhancement of Analytical Methods
Preisler, Jan [Iowa State Univ., Ames, IA (United States)
1997-01-01
In this work, we use lasers to enhance possibilities of laser desorption methods and to optimize coating procedure for capillary electrophoresis (CE). We use several different instrumental arrangements to characterize matrix-assisted laser desorption (MALD) at atmospheric pressure and in vacuum. In imaging mode, 488-nm argon-ion laser beam is deflected by two acousto-optic deflectors to scan plumes desorbed at atmospheric pressure via absorption. All absorbing species, including neutral molecules, are monitored. Interesting features, e.g. differences between the initial plume and subsequent plumes desorbed from the same spot, or the formation of two plumes from one laser shot are observed. Total plume absorbance can be correlated with the acoustic signal generated by the desorption event. A model equation for the plume velocity as a function of time is proposed. Alternatively, the use of a static laser beam for observation enables reliable determination of plume velocities even when they are very high. Static scattering detection reveals negative influence of particle spallation on MS signal. Ion formation during MALD was monitored using 193-nm light to photodissociate a portion of insulin ion plume. These results define the optimal conditions for desorbing analytes from matrices, as opposed to achieving a compromise between efficient desorption and efficient ionization as is practiced in mass spectrometry. In CE experiment, we examined changes in a poly(ethylene oxide) (PEO) coating by continuously monitoring the electroosmotic flow (EOF) in a fused-silica capillary during electrophoresis. An imaging CCD camera was used to follow the motion of a fluorescent neutral marker zone along the length of the capillary excited by 488-nm Ar-ion laser. The PEO coating was shown to reduce the velocity of EOF by more than an order of magnitude compared to a bare capillary at pH 7.0. The coating protocol was important, especially at an intermediate pH of 7.7. The increase of p
Laser: a Tool for Optimization and Enhancement of Analytical Methods
Preisler, Jan
1997-01-01
In this work, we use lasers to enhance possibilities of laser desorption methods and to optimize coating procedure for capillary electrophoresis (CE). We use several different instrumental arrangements to characterize matrix-assisted laser desorption (MALD) at atmospheric pressure and in vacuum. In imaging mode, 488-nm argon-ion laser beam is deflected by two acousto-optic deflectors to scan plumes desorbed at atmospheric pressure via absorption. All absorbing species, including neutral molecules, are monitored. Interesting features, e.g. differences between the initial plume and subsequent plumes desorbed from the same spot, or the formation of two plumes from one laser shot are observed. Total plume absorbance can be correlated with the acoustic signal generated by the desorption event. A model equation for the plume velocity as a function of time is proposed. Alternatively, the use of a static laser beam for observation enables reliable determination of plume velocities even when they are very high. Static scattering detection reveals negative influence of particle spallation on MS signal. Ion formation during MALD was monitored using 193-nm light to photodissociate a portion of insulin ion plume. These results define the optimal conditions for desorbing analytes from matrices, as opposed to achieving a compromise between efficient desorption and efficient ionization as is practiced in mass spectrometry. In CE experiment, we examined changes in a poly(ethylene oxide) (PEO) coating by continuously monitoring the electroosmotic flow (EOF) in a fused-silica capillary during electrophoresis. An imaging CCD camera was used to follow the motion of a fluorescent neutral marker zone along the length of the capillary excited by 488-nm Ar-ion laser. The PEO coating was shown to reduce the velocity of EOF by more than an order of magnitude compared to a bare capillary at pH 7.0. The coating protocol was important, especially at an intermediate pH of 7.7. The increase of p
Models and Methods for Structural Topology Optimization with Discrete Design Variables
Stolpe, Mathias
Structural topology optimization is a multi-disciplinary research field covering optimal design of load carrying mechanical structures such as bridges, airplanes, wind turbines, cars, etc. Topology optimization is a collection of theory, mathematical models, and numerical methods and is often used...... such as bridges, airplanes, wind turbines, cars, etc. Topology optimization is a collection of theory, mathematical models, and numerical methods and is often used in the conceptual design phase to find innovative designs. The strength of topology optimization is the capability of determining both the optimal...... methods. The methods are often based on the concept of divide-and-conquer. Despite the proposed theoretical and numerical advances, this thesis clearly indicates that solving large-scale structural topology optimization problems with discrete design variables to proven global optimality is currently...
Analyses of Methods and Algorithms for Modelling and Optimization of Biotechnological Processes
Stoyan Stoyanov
2009-08-01
Full Text Available A review of the problems in modeling, optimization and control of biotechnological processes and systems is given in this paper. An analysis of existing and some new practical optimization methods for searching global optimum based on various advanced strategies - heuristic, stochastic, genetic and combined are presented in the paper. Methods based on the sensitivity theory, stochastic and mix strategies for optimization with partial knowledge about kinetic, technical and economic parameters in optimization problems are discussed. Several approaches for the multi-criteria optimization tasks are analyzed. The problems concerning optimal controls of biotechnological systems are also discussed.
Zhengnan Li
2016-01-01
Full Text Available To solve the multiobjective optimization problem on hypersonic glider vehicle trajectory design subjected to complex constraints, this paper proposes a multiobjective trajectory optimization method that combines the boundary intersection method and pseudospectral method. The multiobjective trajectory optimization problem (MTOP is established based on the analysis of the feature of hypersonic glider vehicle trajectory. The MTOP is translated into a set of general optimization subproblems by using the boundary intersection method and pseudospectral method. The subproblems are solved by nonlinear programming algorithm. In this method, the solution that has been solved is employed as the initial guess for the next subproblem so that the time consumption of the entire multiobjective trajectory optimization problem shortens. The maximal range and minimal peak heat problem is solved by the proposed method. The numerical results demonstrate that the proposed method can obtain the Pareto front of the optimal trajectory, which can provide the reference for the trajectory design of hypersonic glider vehicle.
Experimental Optimization Methods for Multi-Element Airfoils
Landman, Drew; Britcher, Colin P.
1996-01-01
A modern three element airfoil model with a remotely activated flap was used to investigate optimum flap testing position using an automated optimization algorithm in wind tunnel tests. Detailed results for lift coefficient versus flap vertical and horizontal position are presented for two angles of attack: 8 and 14 degrees. An on-line first order optimizer is demonstrated which automatically seeks the optimum lift as a function of flap position. Future work with off-line optimization techniques is introduced and aerodynamic hysteresis effects due to flap movement with flow on are discussed.
Genetic-evolution-based optimization methods for engineering design
Rao, S. S.; Pan, T. S.; Dhingra, A. K.; Venkayya, V. B.; Kumar, V.
1990-01-01
This paper presents the applicability of a biological model, based on genetic evolution, for engineering design optimization. Algorithms embodying the ideas of reproduction, crossover, and mutation are developed and applied to solve different types of structural optimization problems. Both continuous and discrete variable optimization problems are solved. A two-bay truss for maximum fundamental frequency is considered to demonstrate the continuous variable case. The selection of locations of actuators in an actively controlled structure, for minimum energy dissipation, is considered to illustrate the discrete variable case.
Genetic-evolution-based optimization methods for engineering design
Rao, S. S.; Pan, T. S.; Dhingra, A. K.; Venkayya, V. B.; Kumar, V.
1990-01-01
This paper presents the applicability of a biological model, based on genetic evolution, for engineering design optimization. Algorithms embodying the ideas of reproduction, crossover, and mutation are developed and applied to solve different types of structural optimization problems. Both continuous and discrete variable optimization problems are solved. A two-bay truss for maximum fundamental frequency is considered to demonstrate the continuous variable case. The selection of locations of actuators in an actively controlled structure, for minimum energy dissipation, is considered to illustrate the discrete variable case.
Oil Reservoir Production Optimization using Single Shooting and ESDIRK Methods
Capolei, Andrea; Völcker, Carsten; Frydendall, Jan;
2012-01-01
Conventional recovery techniques enable recovery of 10-50% of the oil in an oil field. Advances in smart well technology and enhanced oil recovery techniques enable significant larger recovery. To realize this potential, feedback model-based optimal control technologies are needed to manipulate...... the injections and oil production such that flow is uniform in a given geological structure. Even in the case of conventional water flooding, feedback based optimal control technologies may enable higher oil recovery than with conventional operational strategies. The optimal control problems that must be solved...... for sensitivity computation. We demonstrate the procedure on a water ooding example with conventional injectors and producers....
Optimizing Fungal DNA Extraction Methods from Aerosol Filters
Jimenez, G.; Mescioglu, E.; Paytan, A.
2016-12-01
Fungi and fungal spores can be picked up from terrestrial ecosystems, transported long distances, and deposited into marine ecosystems. It is important to study dust-borne fungal communities, because they can stay viable and effect the ambient microbial populations, which are key players in biogeochemical cycles. One of the challenges of studying dust-borne fungal populations is that aerosol samples contain low biomass, making extracting good quality DNA very difficult. The aim of this project was to increase DNA yield by optimizing DNA extraction methods. We tested aerosol samples collected from Haifa, Israel (polycarbonate filter), Monterey Bay, CA (quartz filter) and Bermuda (quartz filter). Using the Qiagen DNeasy Plant Kit, we tested the effect of altering bead beating times and incubation times, adding three freeze and thaw steps, initially washing the filters with buffers for various lengths of time before using the kit, and adding a step with 30 minutes of sonication in 65C water. Adding three freeze/thaw steps, adding a sonication step, washing with a phosphate buffered saline overnight, and increasing incubation time to two hours, in that order, resulted in the highest increase in DNA for samples from Israel (polycarbonate). DNA yield of samples from Monterey (quart filter) increased about 5 times when washing with buffers overnight (phosphate buffered saline and potassium phophate buffer), adding a sonication step, and adding three freeze and thaw steps. Samples collected in Bermuda (quartz filter) had the highest increase in DNA yield from increasing incubation to 2 hours, increasing bead beating time to 6 minutes, and washing with buffers overnight (phosphate buffered saline and potassium phophate buffer). Our results show that DNA yield can be increased by altering various steps of the Qiagen DNeasy Plant Kit protocol, but different types of filters collected at different sites respond differently to alterations. These results can be used as
Otomori, Masaki; Yamada, Takayuki; Izui, Kazuhiro;
2012-01-01
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......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...
Iron Pole Shape Optimization of IPM Motors Using an Integrated Method
JABBARI, A.
2010-02-01
Full Text Available An iron pole shape optimization method to reduce cogging torque in Interior Permanent Magnet (IPM motors is developed by using the reduced basis technique coupled by finite element and design of experiments methods. Objective function is defined as the minimum cogging torque. The experimental design of Taguchi method is used to build the approximation model and to perform optimization. This method is demonstrated on the rotor pole shape optimization of a 4-poles/24-slots IPM motor.
Using the Taguchi method for rapid quantitative PCR optimization with SYBR Green I.
Thanakiatkrai, Phuvadol; Welch, Lindsey
2012-01-01
Here, we applied the Taguchi method, an engineering optimization process, to successfully determine the optimal conditions for three SYBR Green I-based quantitative PCR assays. This method balanced the effects of all factors and their associated levels by using an orthogonal array rather than a factorial array. Instead of running 27 experiments with the conventional factorial method, the Taguchi method achieved the same optimal conditions using only nine experiments, saving valuable resources.
A new method of determining the optimal embedding dimension based on nonlinear prediction
Meng Qing-Fang; Peng Yu-Hua; Xue Pei-Jun
2007-01-01
A new method is proposed to determine the optimal embedding dimension from a scalar time series in this paper. This method determines the optimal embedding dimension by optimizing the nonlinear autoregressive prediction model parameterized by the embedding dimension and the nonlinear degree. Simulation results show the effectiveness of this method. And this method is applicable to a short time series, stable to noise, computationally efficient, and without any purposely introduced parameters.
Zhang, Songchuan; Xia, Youshen
2016-12-28
Much research has been devoted to complex-variable optimization problems due to their engineering applications. However, the complex-valued optimization method for solving complex-variable optimization problems is still an active research area. This paper proposes two efficient complex-valued optimization methods for solving constrained nonlinear optimization problems of real functions in complex variables, respectively. One solves the complex-valued nonlinear programming problem with linear equality constraints. Another solves the complex-valued nonlinear programming problem with both linear equality constraints and an ℓ₁-norm constraint. Theoretically, we prove the global convergence of the proposed two complex-valued optimization algorithms under mild conditions. The proposed two algorithms can solve the complex-valued optimization problem completely in the complex domain and significantly extend existing complex-valued optimization algorithms. Numerical results further show that the proposed two algorithms have a faster speed than several conventional real-valued optimization algorithms.
Optimal design of structures for earthquake loads by a hybrid RBF-BPSO method
Eysa Salajegheh; Saeed Gholizadeh; Mohsen Khatibina
2008-01-01
The optimal seismic design of structures requires that time history analyses (THA) be carried out repeatedly. This makes the optimal design process inefficient, in particular, if an evolutionary algorithm is used. To reduce the overall time required for structural optimization, two artificial intelligence strategies are employed. In the first strategy, radial basis function (RBF) neural networks are used to predict the time history responses of structures in the optimization flow. In the second strategy, a binary particle swarm optimization (BPSO) is used to find the optimum design. Combining the RBF and BPSO, a hybrid RBF-BPSO optimization method is proposed in this paper, which achieves fast optimization with high computational performance. Two examples are presented and compared to determine the optimal weight of structures under earthquake loadings using both exact and approximate analyses. The numerical results demonstrate the computational advantages and effectiveness of the proposed hybrid RBF-BPSO optimization method for the seismic design of structures.
Structural Topology Optimization : Basic Theory, Methods and Applications
2013-01-01
The thesis is written as an introduction to topology optimization, aiming to help knowledge development in design optimization techniques, as well as aiding the adaptation of a sustainable culture with direct application to similar products like the two test cases supplied by the EC SuPLight project. These components are; a Door Connection Joint for a business jet and a Front Lower Control Arm from a McPherson suspension. The thesis has no intention of covering all aspects concerning topology...
Gradient-based stochastic optimization methods in Bayesian experimental design
2012-01-01
Optimal experimental design (OED) seeks experiments expected to yield the most useful data for some purpose. In practical circumstances where experiments are time-consuming or resource-intensive, OED can yield enormous savings. We pursue OED for nonlinear systems from a Bayesian perspective, with the goal of choosing experiments that are optimal for parameter inference. Our objective in this context is the expected information gain in model parameters, which in general can only be estimated u...
Immune clonal selection optimization method with combining mutation strategies
无
2007-01-01
In artificial immune optimization algorithm, the mutation of immune cells has been considered as the key operator that determines the algorithm performance. Traditional immune optimization algorithms have used a single mutation operator, typically a Gaussian. Using a variety of mutation operators that can be combined during evolution to generate different probability density function could hold the potential for producing better solutions with less computational effort. In view of this, a linear combination...
A new method to optimize natural convection heat sinks
Lampio, K.; Karvinen, R.
2017-08-01
The performance of a heat sink cooled by natural convection is strongly affected by its geometry, because buoyancy creates flow. Our model utilizes analytical results of forced flow and convection, and only conduction in a solid, i.e., the base plate and fins, is solved numerically. Sufficient accuracy for calculating maximum temperatures in practical applications is proved by comparing the results of our model with some simple analytical and computational fluid dynamics (CFD) solutions. An essential advantage of our model is that it cuts down on calculation CPU time by many orders of magnitude compared with CFD. The shorter calculation time makes our model well suited for multi-objective optimization, which is the best choice for improving heat sink geometry, because many geometrical parameters with opposite effects influence the thermal behavior. In multi-objective optimization, optimal locations of components and optimal dimensions of the fin array can be found by simultaneously minimizing the heat sink maximum temperature, size, and mass. This paper presents the principles of the particle swarm optimization (PSO) algorithm and applies it as a basis for optimizing existing heat sinks.
Topology optimization based on spline-based meshfree method using topological derivatives
Hur, Junyoung; Youn, Sung-Kie [KAIST, Daejeon (Korea, Republic of); Kang, Pilseong [Korea Research Institute of Standards and Science, Daejeon (Korea, Republic of)
2017-05-15
Spline-based meshfree method (SBMFM) is originated from the Isogeometric analysis (IGA) which integrates design and analysis through Non-uniform rational B-spline (NURBS) basis functions. SBMFM utilizes trimming technique of CAD system by representing the domain using NURBS curves. In this work, an explicit boundary topology optimization using SBMFM is presented with an effective boundary update scheme. There have been similar works in this subject. However unlike the previous works where semi-analytic method for calculating design sensitivities is employed, the design update is done by using topological derivatives. In this research, the topological derivative is used to derive the sensitivity of boundary curves and for the creation of new holes. Based on the values of topological derivatives, the shape of boundary curves is updated. Also, the topological change is achieved by insertion and removal of the inner holes. The presented approach is validated through several compliance minimization problems.
Arakawa, Masahiro; Fuyuki, Masahiko; Inoue, Ichiro
Aiming at the elimination of tardy jobs in a job shop production schedule, an optimization-oriented simulation-based scheduling (OSBS) method incorporating capacity adjustment function is proposed. In order to determine the pertinent additional capacities and to control job allocations simultaneously the proposed method incorporates the parameter-space search improvement (PSSI) method into the scheduling procedure. In previous papers, we have introduced four parameters; two of them are used to control the upper limit to the additional capacity and the balance of the capacity distribution among machines, while the others are used to control the job allocation procedure. We found that a ‘direct' optimization procedure which uses the enumeration method produces a best solution with practical significance, but it takes too much computation time for practical use. In this paper, we propose a new method which adopts a pattern search method in the schedule generation procedure to obtain an approximate optimal solution. It is found that the computation time becomes short enough for a practical use. Moreover, the extension of the parameter domain yields an approximate optimal solution which is better than the best solution obtained by the ‘direct' optimization.
Pan Pan
2012-11-01
Full Text Available This paper presents an optimization method for the structural design of horizontal-axis wind turbine (HAWT blades based on the particle swarm optimization algorithm (PSO combined with the finite element method (FEM. The main goal is to create an optimization tool and to demonstrate the potential improvements that could be brought to the structural design of HAWT blades. A multi-criteria constrained optimization design model pursued with respect to minimum mass of the blade is developed. The number and the location of layers in the spar cap and the positions of the shear webs are employed as the design variables, while the strain limit, blade/tower clearance limit and vibration limit are taken into account as the constraint conditions. The optimization of the design of a commercial 1.5 MW HAWT blade is carried out by combining the above method and design model under ultimate (extreme flap-wise load conditions. The optimization results are described and compared with the original design. It shows that the method used in this study is efficient and produces improved designs.
Development of Combinatorial Methods for Alloy Design and Optimization
Pharr, George M.; George, Easo P.; Santella, Michael L
2005-07-01
The primary goal of this research was to develop a comprehensive methodology for designing and optimizing metallic alloys by combinatorial principles. Because conventional techniques for alloy preparation are unavoidably restrictive in the range of alloy composition that can be examined, combinatorial methods promise to significantly reduce the time, energy, and expense needed for alloy design. Combinatorial methods can be developed not only to optimize existing alloys, but to explore and develop new ones as well. The scientific approach involved fabricating an alloy specimen with a continuous distribution of binary and ternary alloy compositions across its surface--an ''alloy library''--and then using spatially resolved probing techniques to characterize its structure, composition, and relevant properties. The three specific objectives of the project were: (1) to devise means by which simple test specimens with a library of alloy compositions spanning the range interest can be produced; (2) to assess how well the properties of the combinatorial specimen reproduce those of the conventionally processed alloys; and (3) to devise screening tools which can be used to rapidly assess the important properties of the alloys. As proof of principle, the methodology was applied to the Fe-Ni-Cr ternary alloy system that constitutes many commercially important materials such as stainless steels and the H-series and C-series heat and corrosion resistant casting alloys. Three different techniques were developed for making alloy libraries: (1) vapor deposition of discrete thin films on an appropriate substrate and then alloying them together by solid-state diffusion; (2) co-deposition of the alloying elements from three separate magnetron sputtering sources onto an inert substrate; and (3) localized melting of thin films with a focused electron-beam welding system. Each of the techniques was found to have its own advantages and disadvantages. A new and very
Comfort filters in a total energy demand optimization method for the passive design of a building
2015-01-01
The effective design of sustainable buildings results from an accurate optimization process of all the interrelated variables. The authors developed a replicable methodology for the optimization of the building envelope design. Following a previous work, where in the pre-processing and the optimization phases the minimization of the total energy demand is performed by coupling TRNSYS® with GenOpt®, this paper is focused on the post-processing phase of the methodology, in which the results are...
A. P. Karpenko
2014-01-01
Full Text Available We consider a class of stochastic search algorithms of global optimization which in various publications are called behavioural, intellectual, metaheuristic, inspired by the nature, swarm, multi-agent, population, etc. We use the last term.Experience in using the population algorithms to solve challenges of global optimization shows that application of one such algorithm may not always effective. Therefore now great attention is paid to hybridization of population algorithms of global optimization. Hybrid algorithms unite various algorithms or identical algorithms, but with various values of free parameters. Thus efficiency of one algorithm can compensate weakness of another.The purposes of the work are development of hybrid algorithm of global optimization based on known algorithms of harmony search (HS and swarm of particles (PSO, software implementation of algorithm, study of its efficiency using a number of known benchmark problems, and a problem of dimensional optimization of truss structure.We set a problem of global optimization, consider basic algorithms of HS and PSO, give a flow chart of the offered hybrid algorithm called PSO HS , present results of computing experiments with developed algorithm and software, formulate main results of work and prospects of its development.
Application of multi-stage Monte Carlo method for solving machining optimization problems
Miloš Madić
2014-08-01
Full Text Available Enhancing the overall machining performance implies optimization of machining processes, i.e. determination of optimal machining parameters combination. Optimization of machining processes is an active field of research where different optimization methods are being used to determine an optimal combination of different machining parameters. In this paper, multi-stage Monte Carlo (MC method was employed to determine optimal combinations of machining parameters for six machining processes, i.e. drilling, turning, turn-milling, abrasive waterjet machining, electrochemical discharge machining and electrochemical micromachining. Optimization solutions obtained by using multi-stage MC method were compared with the optimization solutions of past researchers obtained by using meta-heuristic optimization methods, e.g. genetic algorithm, simulated annealing algorithm, artificial bee colony algorithm and teaching learning based optimization algorithm. The obtained results prove the applicability and suitability of the multi-stage MC method for solving machining optimization problems with up to four independent variables. Specific features, merits and drawbacks of the MC method were also discussed.
张春宜; 宋鲁凯; 费成巍; 郝广平; 刘令君
2016-01-01
To improve the computational efficiency of the reliability-based design optimization (RBDO) of flexible mechanism, particle swarm optimization-advanced extremum response surface method (PSO-AERSM) was proposed by integrating particle swarm optimization (PSO) algorithm and advanced extremum response surface method (AERSM). Firstly, the AERSM was developed and its mathematical model was established based on artificial neural network, and the PSO algorithm was investigated. And then the RBDO model of flexible mechanism was presented based on AERSM and PSO. Finally, regarding cross-sectional area as design variable, the reliability optimization of flexible mechanism was implemented subject to reliability degree and uncertainties based on the proposed approach. The optimization results show that the cross-section sizes obviously reduce by 22.96 mm2 while keeping reliability degree. Through the comparison of methods, it is demonstrated that the AERSM holds high computational efficiency while keeping computational precision for the RBDO of flexible mechanism, and PSO algorithm minimizes the response of the objective function. The efforts of this work provide a useful sight for the reliability optimization of flexible mechanism, and enrich and develop the reliability theory as well.
PCNN document segmentation method based on bacterial foraging optimization algorithm
Liao, Yanping; Zhang, Peng; Guo, Qiang; Wan, Jian
2014-04-01
Pulse Coupled Neural Network(PCNN) is widely used in the field of image processing, but it is a difficult task to define the relative parameters properly in the research of the applications of PCNN. So far the determination of parameters of its model needs a lot of experiments. To deal with the above problem, a document segmentation based on the improved PCNN is proposed. It uses the maximum entropy function as the fitness function of bacterial foraging optimization algorithm, adopts bacterial foraging optimization algorithm to search the optimal parameters, and eliminates the trouble of manually set the experiment parameters. Experimental results show that the proposed algorithm can effectively complete document segmentation. And result of the segmentation is better than the contrast algorithms.
Intelligent and nature inspired optimization methods in medicine
Marinakis, Yannis; Marinaki, Magdalene; Dounias, Georgios;
2009-01-01
, decrease noise and improve speed by the elimination of irrelevant or redundant features. The present paper deals with the optimization of nearest neighbour classifiers via intelligent and nature inspired algorithms for a very significant medical problem, the Pap smear cell classification problem......The classification problem consists of using some known objects, usually described by a large vector of features, to induce a model that classifies others into known classes. Feature selection is widely used as the first stage of the classification task to reduce the dimension of the problem....... The algorithms used include tabu search, genetic algorithms, particle swarm optimization and ant colony optimization. The proposed complete algorithmic scheme is tested on two sets of data. The first consists of 917 images of Pap smear cells and the second set consists of 500 images, classified carefully...
A hybrid nonlinear programming method for design optimization
Rajan, S. D.
1986-01-01
Solutions to engineering design problems formulated as nonlinear programming (NLP) problems usually require the use of more than one optimization technique. Moreover, the interaction between the user (analysis/synthesis) program and the NLP system can lead to interface, scaling, or convergence problems. An NLP solution system is presented that seeks to solve these problems by providing a programming system to ease the user-system interface. A simple set of rules is used to select an optimization technique or to switch from one technique to another in an attempt to detect, diagnose, and solve some potential problems. Numerical examples involving finite element based optimal design of space trusses and rotor bearing systems are used to illustrate the applicability of the proposed methodology.
Optimization of Classical Hydraulic Engine Mounts Based on RMS Method
J. Christopherson
2005-01-01
Full Text Available Based on RMS averaging of the frequency response functions of the absolute acceleration and relative displacement transmissibility, optimal parameters describing the hydraulic engine mount are determined to explain the internal mount geometry. More specifically, it is shown that a line of minima exists to define a relationship between the absolute acceleration and relative displacement transmissibility of a sprung mass using a hydraulic mount as a means of suspension. This line of minima is used to determine several optimal systems developed on the basis of different clearance requirements, hence different relative displacement requirements, and compare them by means of their respective acceleration and displacement transmissibility functions. In addition, the transient response of the mount to a step input is also investigated to show the effects of the optimization upon the time domain response of the hydraulic mount.
Optimal reliability design method for remote solar systems
Suwapaet, Nuchida
A unique optimal reliability design algorithm is developed for remote communication systems. The algorithm deals with either minimizing an unavailability of the system within a fixed cost or minimizing the cost of the system with an unavailability constraint. The unavailability of the system is a function of three possible failure occurrences: individual component breakdown, solar energy deficiency (loss of load probability), and satellite/radio transmission loss. The three mathematical models of component failure, solar power failure, transmission failure are combined and formulated as a nonlinear programming optimization problem with binary decision variables, such as number and type (or size) of photovoltaic modules, batteries, radios, antennas, and controllers. Three possible failures are identified and integrated in computer algorithm to generate the parameters for the optimization algorithm. The optimization algorithm is implemented with a branch-and-bound technique solution in MS Excel Solver. The algorithm is applied to a case study design for an actual system that will be set up in remote mountainous areas of Peru. The automated algorithm is verified with independent calculations. The optimal results from minimizing the unavailability of the system with the cost constraint case and minimizing the total cost of the system with the unavailability constraint case are consistent with each other. The tradeoff feature in the algorithm allows designers to observe results of 'what-if' scenarios of relaxing constraint bounds, thus obtaining the most benefit from the optimization process. An example of this approach applied to an existing communication system in the Andes shows dramatic improvement in reliability for little increase in cost. The algorithm is a real design tool, unlike other existing simulation design tools. The algorithm should be useful for other stochastic systems where component reliability, random supply and demand, and communication are
A VIRTUAL MESH METHOD FOR THE OPTIMIZED DESIGN OF COMPLEX STRUCTURES
DuTaisheng; HllangRongjie; SongTianxia; ChenChuanyao
2003-01-01
Concepts for a virtual 3D space and a hyper-sphere are proposed and the formulae for determining the computable nodes of the mesh are derived. Then a new optimization design method ('Virtual Mesh Method' or V.M.M) is developed. Three examples are given, showing that the method proposed is especially suitable for the optimized design of complex structures, and that the global approximate optimal solution can be searched with remarkably reduced computational work.
A numerical method for solving optimal control problems using state parametrization
Mehne, H.; Borzabadi, A.
2006-06-01
A numerical method for solving a special class of optimal control problems is given. The solution is based on state parametrization as a polynomial with unknown coefficients. This converts the problem to a non-linear optimization problem. To facilitate the computation of optimal coefficients, an improved iterative method is suggested. Convergence of this iterative method and its implementation for numerical examples are also given.
Qin Ni; Ch. Zillober; K. Schittkowski
2005-01-01
In this paper, we describe a method to solve large-scale structural optimization problems by sequential convex programming (SCP). A predictor-corrector interior point method is applied to solve the strictly convex subproblems. The SCP algorithm and the topology optimization approach are introduced. Especially, different strategies to solve certain linear systems of equations are analyzed. Numerical results are presented to show the efficiency of the proposed method for solving topology optimization problems and to compare different variants.
Topology Optimal Design of Material Microstructures Using Strain Energy-based Method
Zhang Weihong; Wang Fengwen; Dai Gaoming; Sun Shiping
2007-01-01
Sensitivity analysis and topology optimization of microstructures using strain energy-based method is presented. Compared with homogenization method, the strain energy-based method has advantages of higher computing efficiency and simplified programming.Both the dual convex programming method and perimeter constraint scheme are used to optimize the 2D and 3D microstructures. Numerical results indicate that the strain energy-based method has the same effectiveness as that of homogenization method for orthotropic materials.
ChenChangya; PanJin; WangDeyu
2005-01-01
With the development of satellite structure technology, more and more design parameters will affect its structural performance. It is desirable to obtain an optimal structure design with a minimum weight, including optimal configuration and sizes. The present paper aims to describe an optimization analysis for a satellite structure, including topology optimization and size optimization. Based on the homogenization method, the topology optimization is carried out for the main supporting frame of service module under given constraints and load conditions, and then the sensitivity analysis is made of 15 structural size parameters of the whole satellite and the optimal sizes are obtained. The numerical result shows that the present optimization design method is very effective.
Models and Methods for Structural Topology Optimization with Discrete Design Variables
Stolpe, Mathias
Structural topology optimization is a multi-disciplinary research field covering optimal design of load carrying mechanical structures such as bridges, airplanes, wind turbines, cars, etc. Topology optimization is a collection of theory, mathematical models, and numerical methods and is often used...... or stresses, or fundamental frequencies. The design variables are either continuous or discrete and model dimensions, thicknesses, densities, or material properties. Structural topology optimization is a multi-disciplinary research field covering optimal design of load carrying mechanical structures...... in the conceptual design phase to find innovative designs. The strength of topology optimization is the capability of determining both the optimal shape and the topology of the structure. In some cases also the optimal material properties can be determined. Optimal structural design problems are modeled...
Taguchi Method Applied in Optimization of Shipley 5740 Positive Resist Deposition
Hui, Allan; Wiberg, Dean V.; Blosiu, Julian
1997-01-01
Taguchi Methods of Robust Design Presents a way to optimize output process performance through organized experiments, by using orthogonal arrays for the evaluation of the process controlleable parameters.
Optimal traffic light control method for a single intersection based on hybrid systems
赵晓华; 陈阳舟; 崔平远
2003-01-01
A single intersection of two phases is selected as a model to put forward a new optimal time-planning scheme for traffic light based on the model of hybrid automata for single intersection. A method of optimization is proposed for hybrid systems, and the average queue length over all queues is used as an objective function to find an optimal switching scheme for traffic light. It is illustrated that traffic light control for single intersection is a typical hybrid system, and the optimal planning-time scheme can be obtained using the optimal hybrid systems control based on the two stages method.
Overview: Applications of numerical optimization methods to helicopter design problems
Miura, H.
1984-01-01
There are a number of helicopter design problems that are well suited to applications of numerical design optimization techniques. Adequate implementation of this technology will provide high pay-offs. There are a number of numerical optimization programs available, and there are many excellent response/performance analysis programs developed or being developed. But integration of these programs in a form that is usable in the design phase should be recognized as important. It is also necessary to attract the attention of engineers engaged in the development of analysis capabilities and to make them aware that analysis capabilities are much more powerful if integrated into design oriented codes. Frequently, the shortcoming of analysis capabilities are revealed by coupling them with an optimization code. Most of the published work has addressed problems in preliminary system design, rotor system/blade design or airframe design. Very few published results were found in acoustics, aerodynamics and control system design. Currently major efforts are focused on vibration reduction, and aerodynamics/acoustics applications appear to be growing fast. The development of a computer program system to integrate the multiple disciplines required in helicopter design with numerical optimization technique is needed. Activities in Britain, Germany and Poland are identified, but no published results from France, Italy, the USSR or Japan were found.
A monotonic method for solving nonlinear optimal control problems
Salomon, Julien
2009-01-01
Initially introduced in the framework of quantum control, the so-called monotonic algorithms have shown excellent numerical results when dealing with various bilinear optimal control problems. This paper aims at presenting a unified formulation of such procedures and the intrinsic assumptions they require. In this framework, we prove the feasibility of the general algorithm. Finally, we explain how these assumptions can be relaxed.
A topology optimization method for design of negative permeability metamaterials
Diaz, A. R.; Sigmund, Ole
2010-01-01
A methodology based on topology optimization for the design of metamaterials with negative permeability is presented. The formulation is based on the design of a thin layer of copper printed on a dielectric, rectangular plate of fixed dimensions. An effective media theory is used to estimate...... are presented for illustration. New metamaterial concepts are uncovered, beyond the classical split-ring inspired layouts....
A method for optimizing the location of wind farms
McWilliam, M.K.; Kooten, van G.C.; Crawford, C.
2012-01-01
The optimal location and configuration of wind farms in a large region is important information for policy makers, electricity system planners and wind farm developers. The model developed in this paper uses wind resource data, population data and transmission line locations to find the configuratio
Research on inverse methods and optimization in Italy
Larocca, Francesco
1991-01-01
The research activities in Italy on inverse design and optimization are reviewed. The review is focused on aerodynamic aspects in turbomachinery and wing section design. Inverse design of blade rows and ducts of turbomachinery in subsonic and transonic regime are illustrated by the Politecnico di Torino and turbomachinery industry (FIAT AVIO).
Resampling: an optimization method for inverse planning in robotic radiosurgery.
Schweikard, Achim; Schlaefer, Alexander; Adler, John R
2006-11-01
By design, the range of beam directions in conventional radiosurgery are constrained to an isocentric array. However, the recent introduction of robotic radiosurgery dramatically increases the flexibility of targeting, and as a consequence, beams need be neither coplanar nor isocentric. Such a nonisocentric design permits a large number of distinct beam directions to be used in one single treatment. These major technical differences provide an opportunity to improve upon the well-established principles for treatment planning used with GammaKnife or LINAC radiosurgery. With this objective in mind, our group has developed over the past decade an inverse planning tool for robotic radiosurgery. This system first computes a set of beam directions, and then during an optimization step, weights each individual beam. Optimization begins with a feasibility query, the answer to which is derived through linear programming. This approach offers the advantage of completeness and avoids local optima. Final beam selection is based on heuristics. In this report we present and evaluate a new strategy for utilizing the advantages of linear programming to improve beam selection. Starting from an initial solution, a heuristically determined set of beams is added to the optimization problem, while beams with zero weight are removed. This process is repeated to sample a set of beams much larger compared with typical optimization. Experimental results indicate that the planning approach efficiently finds acceptable plans and that resampling can further improve its efficiency.
Hydrodynamic Optimization Method and Design Code for Stall-Regulated Hydrokinetic Turbine Rotors
Sale, D.; Jonkman, J.; Musial, W.
2009-08-01
This report describes the adaptation of a wind turbine performance code for use in the development of a general use design code and optimization method for stall-regulated horizontal-axis hydrokinetic turbine rotors. This rotor optimization code couples a modern genetic algorithm and blade-element momentum performance code in a user-friendly graphical user interface (GUI) that allows for rapid and intuitive design of optimal stall-regulated rotors. This optimization method calculates the optimal chord, twist, and hydrofoil distributions which maximize the hydrodynamic efficiency and ensure that the rotor produces an ideal power curve and avoids cavitation. Optimizing a rotor for maximum efficiency does not necessarily create a turbine with the lowest cost of energy, but maximizing the efficiency is an excellent criterion to use as a first pass in the design process. To test the capabilities of this optimization method, two conceptual rotors were designed which successfully met the design objectives.
Fuel-optimal low-thrust formation reconfiguration via Radau pseudospectral method
Li, Jing
2016-07-01
This paper investigates fuel-optimal low-thrust formation reconfiguration near circular orbit. Based on the Clohessy-Wiltshire equations, first-order necessary optimality conditions are derived from the Pontryagin's maximum principle. The fuel-optimal impulsive solution is utilized to divide the low-thrust trajectory into thrust and coast arcs. By introducing the switching times as optimization variables, the fuel-optimal low-thrust formation reconfiguration is posed as a nonlinear programming problem (NLP) via direct transcription using multiple-phase Radau pseudospectral method (RPM), which is then solved by a sparse nonlinear optimization software SNOPT. To facilitate optimality verification and, if necessary, further refinement of the optimized solution of the NLP, formulas for mass costate estimation and initial costates scaling are presented. Numerical examples are given to show the application of the proposed optimization method. To fix the problem, generic fuel-optimal low-thrust formation reconfiguration can be simplified as reconfiguration without any initial and terminal coast arcs, whose optimal solutions can be efficiently obtained from the multiple-phase RPM at the cost of a slight fuel increment. Finally, influence of the specific impulse and maximum thrust magnitude on the fuel-optimal low-thrust formation reconfiguration is analyzed. Numerical results shown the links and differences between the fuel-optimal impulsive and low-thrust solutions.
Graph-Structured Multi-task Regression and an Efficient Optimization Method for General Fused Lasso
Chen, Xi; Lin, Qihang; Carbonell, Jaime G; Xing, Eric P
2010-01-01
We consider the problem of learning a structured multi-task regression, where the output consists of multiple responses that are related by a graph and the correlated response variables are dependent on the common inputs in a sparse but synergistic manner. Previous methods such as l1/l2-regularized multi-task regression assume that all of the output variables are equally related to the inputs, although in many real-world problems, outputs are related in a complex manner. In this paper, we propose graph-guided fused lasso (GFlasso) for structured multi-task regression that exploits the graph structure over the output variables. We introduce a novel penalty function based on fusion penalty to encourage highly correlated outputs to share a common set of relevant inputs. In addition, we propose a simple yet efficient proximal-gradient method for optimizing GFlasso that can also be applied to any optimization problems with a convex smooth loss and the general class of fusion penalty defined on arbitrary graph stru...
Bernadette T Gillick
2014-09-01
Full Text Available Background- Transcranial direct current stimulation (tDCS has been investigated mainly in adults and doses may not be appropriate in pediatric applications. In perinatal stroke where potential applications are promising, rational adaptation of dosage for children remains under investigation.Objective - Construct child-specific tDCS dosing parameters through case study within a perinatal stroke tDCS safety and feasibility trial. Methods- 10-year-old subject with a diagnosis of presumed perinatal ischemic stroke and hemiparesis was identified. T1 MRI scans used to derive computerized model for current flow and electrode positions. Workflow using modeling results and consideration of dosage in previous clinical trials was incorporated. Prior Ad hoc adult montages versus de novo optimized montages provided distinct risk benefit analysis. Approximating adult dose required consideration of changes in both peak brain current flow and distribution which further tradeoff between maximizing efficacy and adding safety factors. Electrode size, position, current intensity, compliance voltage, and duration were controlled independently in this process.Results- Brain electric fields modeled and compared to values previously predicted models. Approximating conservative brain current flow patterns and intensities used in previous adult trials for comparable indications, the optimal current intensity established was 0.7 mA for 10 minutes with a tDCS C3/C4 montage. Specifically 0.7 mA produced comparable peak brain current intensity of an average adult receiving 1.0 mA. Electrode size of 5x7 cm2 with 1.0 mA and low-voltage tDCS was employed to maximize tolerability. Safety and feasibility confirmed with subject tolerating the session well and no serious adverse events.Conclusion- Rational approaches to dose customization, with steps informed by computational modeling, may improve guidance for pediatric stroke tDCS trials.
Zhang De-Sheng
2016-01-01
Full Text Available Both efficiency and cavitation performance of the hydrofoil are the key technologies to design the tidal current turbine. In this paper, the hydrofoil efficiency and lift coefficient were improved based on particle swarm optimization method and XFoil codes. The cavitation performance of the optimized hydrofoil was also discussed by the computational fluid dynamic. Numerical results show the efficiency of the optimized hydrofoil was improved 11% ranging from the attack angle of 0-7° compared to the original NACA63-818 hydrofoil. The minimum pressure on leading edge of the optimized hydrofoil dropped above 15% at the high attack angle conditions of 10°, 15°, and 20°, respectively, which is benefit for the hydrofoil to avoiding the cavitation.
Scalable Optimization Methods for Distribution Networks with High PV Integration
Guggilam, Swaroop S.; Dall' Anese, Emiliano; Chen, Yu Christine; Dhople, Sairaj V.; Giannakis, Georgios B.
2016-07-01
This paper proposes a suite of algorithms to determine the active- and reactive-power setpoints for photovoltaic (PV) inverters in distribution networks. The objective is to optimize the operation of the distribution feeder according to a variety of performance objectives and ensure voltage regulation. In general, these algorithms take a form of the widely studied ac optimal power flow (OPF) problem. For the envisioned application domain, nonlinear power-flow constraints render pertinent OPF problems nonconvex and computationally intensive for large systems. To address these concerns, we formulate a quadratic constrained quadratic program (QCQP) by leveraging a linear approximation of the algebraic power-flow equations. Furthermore, simplification from QCQP to a linearly constrained quadratic program is provided under certain conditions. The merits of the proposed approach are demonstrated with simulation results that utilize realistic PV-generation and load-profile data for illustrative distribution-system test feeders.
Optimized Audio Classification and Segmentation Algorithm by Using Ensemble Methods
Saadia Zahid; Fawad Hussain; Muhammad Rashid; Muhammad Haroon Yousaf; Hafiz Adnan Habib
2015-01-01
Audio segmentation is a basis for multimedia content analysis which is the most important and widely used application nowadays. An optimized audio classification and segmentation algorithm is presented in this paper that segments a superimposed audio stream on the basis of its content into four main audio types: pure-speech, music, environment sound, and silence. An algorithm is proposed that preserves important audio content and reduces the misclassification rate without using large amount o...
Methods for reliability based design optimization of structural components
Dersjö, Tomas
2012-01-01
Cost and quality are key properties of a product, possibly even the two most important. Onedefinition of quality is fitness for purpose. Load-bearing products, i.e. structural components,loose their fitness for purpose if they fail. Thus, the ability to withstand failure is a fundamentalmeasure of quality for structural components. Reliability based design optimization(RBDO) is an approach for development of structural components which aims to minimizethe cost while constraining the probabili...
Multigrid Methods in Network Optimization: Overview and Appraisal
1994-03-01
matrix and c is a given n-vector (a row vector). (NOTE: There are other versions of the lemma; this is taken from Bazaraa , Jarvis and Sherali, 1990). We...of A. C. KARUSH-KUHN-TUCKER (KKT) OPTIMALUTY CONDITIONS We take this version of the KKT ciptimality conditions from Bazaraa , Jarvis and Sherali, 1990...0, or 1. This fact allows for integer optimal solutions when both supplies and demands are integer values ( Bazaraa et al..., 1990) B. TRANSPORTATION
P.K. Bharti
2010-09-01
Full Text Available Injection molding has been a challenging process for many manufacturers and researchers to produce products meeting requirements at the lowest cost. Faced with global competition in injection molding industry, using the trialand- error approach to determine the process parameters for injection molding is no longer good enough. Factors that affect the quality of a molded part can be classified into four categories: part design, mold design, machineperformance and processing conditions. The part and mold design are assumed as established and fixed. During production, quality characteristics may deviate due to drifting or shifting of processing conditions caused by machine wear, environmental change or operator fatigue. Determining optimal process parameter settings critically influences productivity, quality, and cost of production in the plastic injection molding (PIM industry. Previously, production engineers used either trial-and-error method or Taguchi’s parameter design method to determine optimal process parameter settings for PIM. However, these methods are unsuitable in present PIM because of the increasing complexity of product design and the requirement of multi-response quality characteristics. This article aims to review the recent research in designing and determining process parameters of injection molding. A number of research works based on various approaches have been performed in the domain of the parameter setting for injection molding. These approaches, including mathematical models, Taguchi method, Artificial Neural Networks (ANN,Fuzzy logic, Case Based Reasoning (CBR, Genetic Algorithms (GA, Finite Element Method(FEM,Non Linear Modeling, Response Surface Methodology, Linear Regression Analysis ,Grey Rational Analysis and Principle Component Analysis (PCA are described in this article. The strength and theweakness of individual approaches are discussed. It is then followed by conclusions and discussions of the potential
Application of Artificial Intelligence Methods of Tool Path Optimization in CNC Machines: A Review
Khashayar Danesh Narooei
2014-08-01
Full Text Available Today, in most of metal machining process, Computer Numerical Control (CNC machine tools have been very popular due to their efficiencies and repeatability to achieve high accuracy positioning. One of the factors that govern the productivity is the tool path travel during cutting a work piece. It has been proved that determination of optimal cutting parameters can enhance the machining results to reach high efficiency and minimum the machining cost. In various publication and articles, scientist and researchers adapted several Artificial Intelligence (AI methods or hybrid method for tool path optimization such as Genetic Algorithms (GA, Artificial Neural Network (ANN, Artificial Immune Systems (AIS, Ant Colony Optimization (ACO and Particle Swarm Optimization (PSO. This study presents a review of researches in tool path optimization with different types of AI methods that show the capability of using different types of optimization methods in CNC machining process.
NOVEL METHOD OF REALIZING THE OPTIMAL TRANSMISSION OF THE CRANK-AND-ROCKER MECHANISM DESIGN
无
2003-01-01
A novel method of realizing the optimal transmission of the crank-and-rocker mechanism is presented. The optimal combination design is made by finding the related optimal transmission parameters. The diagram of the optimal transmission is drawn. In the diagram, the relation among minimum transmission angle, the coefficient of travel speed variation, the oscillating angle of the rocker and the length of the bars is shown, concisely, conveniently and directly. The method possesses the main characteristic. That it is to achieve the optimal transmission parameters under the transmission angle by directly choosing in the diagram, according to the given requirements. The characteristics of the mechanical transmission can be improved to gain the optimal transmission effect by the method. Especially, the method is simple and convenient in practical use.
Blade pitch optimization methods for vertical-axis wind turbines
Kozak, Peter
Vertical-axis wind turbines (VAWTs) offer an inherently simpler design than horizontal-axis machines, while their lower blade speed mitigates safety and noise concerns, potentially allowing for installation closer to populated and ecologically sensitive areas. While VAWTs do offer significant operational advantages, development has been hampered by the difficulty of modeling the aerodynamics involved, further complicated by their rotating geometry. This thesis presents results from a simulation of a baseline VAWT computed using Star-CCM+, a commercial finite-volume (FVM) code. VAWT aerodynamics are shown to be dominated at low tip-speed ratios by dynamic stall phenomena and at high tip-speed ratios by wake-blade interactions. Several optimization techniques have been developed for the adjustment of blade pitch based on finite-volume simulations and streamtube models. The effectiveness of the optimization procedure is evaluated and the basic architecture for a feedback control system is proposed. Implementation of variable blade pitch is shown to increase a baseline turbine's power output between 40%-100%, depending on the optimization technique, improving the turbine's competitiveness when compared with a commercially-available horizontal-axis turbine.
An Efficient Optimization Method for Solving Unsupervised Data Classification Problems
Parvaneh Shabanzadeh
2015-01-01
Full Text Available Unsupervised data classification (or clustering analysis is one of the most useful tools and a descriptive task in data mining that seeks to classify homogeneous groups of objects based on similarity and is used in many medical disciplines and various applications. In general, there is no single algorithm that is suitable for all types of data, conditions, and applications. Each algorithm has its own advantages, limitations, and deficiencies. Hence, research for novel and effective approaches for unsupervised data classification is still active. In this paper a heuristic algorithm, Biogeography-Based Optimization (BBO algorithm, was adapted for data clustering problems by modifying the main operators of BBO algorithm, which is inspired from the natural biogeography distribution of different species. Similar to other population-based algorithms, BBO algorithm starts with an initial population of candidate solutions to an optimization problem and an objective function that is calculated for them. To evaluate the performance of the proposed algorithm assessment was carried on six medical and real life datasets and was compared with eight well known and recent unsupervised data classification algorithms. Numerical results demonstrate that the proposed evolutionary optimization algorithm is efficient for unsupervised data classification.
An Optimal Power Flow (OPF) Method with Improved Power System Stability
Su, Chi; Chen, Zhe
2010-01-01
This paper proposes an optimal power flow (OPF) method taking into account small signal stability as additional constraints. Particle swarm optimization (PSO) algorithm is adopted to realize the OPF process. The method is programmed in MATLAB and implemented to a nine-bus test power system which...... has large-scale wind power integration. The results show the ability of the proposed method to find optimal (or near-optimal) operating points in different cases. Based on these results, the analysis of the impacts of wind power integration on the system small signal stability has been conducted....
A Survey of Stochastic Simulation and Optimization Methods in Signal Processing
Pereyra, Marcelo; Schniter, Philip; Chouzenoux, Emilie; Pesquet, Jean-Christophe; Tourneret, Jean-Yves; Hero, Alfred O.; McLaughlin, Steve
2016-03-01
Modern signal processing (SP) methods rely very heavily on probability and statistics to solve challenging SP problems. SP methods are now expected to deal with ever more complex models, requiring ever more sophisticated computational inference techniques. This has driven the development of statistical SP methods based on stochastic simulation and optimization. Stochastic simulation and optimization algorithms are computationally intensive tools for performing statistical inference in models that are analytically intractable and beyond the scope of deterministic inference methods. They have been recently successfully applied to many difficult problems involving complex statistical models and sophisticated (often Bayesian) statistical inference techniques. This survey paper offers an introduction to stochastic simulation and optimization methods in signal and image processing. The paper addresses a variety of high-dimensional Markov chain Monte Carlo (MCMC) methods as well as deterministic surrogate methods, such as variational Bayes, the Bethe approach, belief and expectation propagation and approximate message passing algorithms. It also discusses a range of optimization methods that have been adopted to solve stochastic problems, as well as stochastic methods for deterministic optimization. Subsequently, areas of overlap between simulation and optimization, in particular optimization-within-MCMC and MCMC-driven optimization are discussed.
A Global Optimal Coherence Method for Multi-baseline InSAR Elevation Inversion
HUA Fenfen
2015-11-01
Full Text Available A global optimal coherence method for elevation inversion from multi-baseline polarimetric InSAR data is proposed. The multi-baseline polarimetric InSAR data used in experiments were obtained by Chinese X-SAR system and Germany's E-SAR system. Through combining several full polarimetric InSAR images, the proposed method constructs the multi-baseline polarimetric InSAR coherency matrix, and solves the optimal interferograms under global optimal coherence criterion. The optimal interferograms generated by global optimal coherence method were used to calculate the elevation of target with multi-baseline InSAR elevation inversion method. The proposed method reduces the influence of different scattering centers effectively using multi-baseline InSAR, which improves the accuracy and reliability of the interferometric phase and eventually improves the accuracy of DEM. The results verify the validity of the proposed method.
Gekeler, Simon
2016-01-01
The book provides suggestions on how to start using bionic optimization methods, including pseudo-code examples of each of the important approaches and outlines of how to improve them. The most efficient methods for accelerating the studies are discussed. These include the selection of size and generations of a study’s parameters, modification of these driving parameters, switching to gradient methods when approaching local maxima, and the use of parallel working hardware. Bionic Optimization means finding the best solution to a problem using methods found in nature. As Evolutionary Strategies and Particle Swarm Optimization seem to be the most important methods for structural optimization, we primarily focus on them. Other methods such as neural nets or ant colonies are more suited to control or process studies, so their basic ideas are outlined in order to motivate readers to start using them. A set of sample applications shows how Bionic Optimization works in practice. From academic studies on simple fra...
Second decision-making method of the fuzzy optimal model and its application
刘金禄; 陈守煜
2004-01-01
In the process of concept design offshore platforms, it is necessary to select the best from feasible alternatives through comparison and filtering. Fuzzy optimal selection in multiple stages is an useful method in engineering decisionmaking, but in some cases, it is difficult to make a distinction between the first alternative and the second. Therefore it is necessary to improve the fuzzy optimal selection method with multiple stages. In this paper, using fuzzy cross iteration optimal modeling, the weight sensitivity analysis is made firstly. Then a second decision-making method to solve fuzzy optimal problems is proposed. Its basic principle is the inverse reasoning in mathematics. This method expands the applied range of fuzzy optimal selection. Finally this method is used in the concept design of offshore platform. A case study shows that this method is scientific, reasonable and easy to use in practice.
The use of least squares methods in functional optimization of energy use prediction models
Bourisli, Raed I.; Al-Shammeri, Basma S.; AlAnzi, Adnan A.
2012-06-01
The least squares method (LSM) is used to optimize the coefficients of a closed-form correlation that predicts the annual energy use of buildings based on key envelope design and thermal parameters. Specifically, annual energy use is related to a number parameters like the overall heat transfer coefficients of the wall, roof and glazing, glazing percentage, and building surface area. The building used as a case study is a previously energy-audited mosque in a suburb of Kuwait City, Kuwait. Energy audit results are used to fine-tune the base case mosque model in the VisualDOE{trade mark, serif} software. Subsequently, 1625 different cases of mosques with varying parameters were developed and simulated in order to provide the training data sets for the LSM optimizer. Coefficients of the proposed correlation are then optimized using multivariate least squares analysis. The objective is to minimize the difference between the correlation-predicted results and the VisualDOE-simulation results. It was found that the resulting correlation is able to come up with coefficients for the proposed correlation that reduce the difference between the simulated and predicted results to about 0.81%. In terms of the effects of the various parameters, the newly-defined weighted surface area parameter was found to have the greatest effect on the normalized annual energy use. Insulating the roofs and walls also had a major effect on the building energy use. The proposed correlation and methodology can be used during preliminary design stages to inexpensively assess the impacts of various design variables on the expected energy use. On the other hand, the method can also be used by municipality officials and planners as a tool for recommending energy conservation measures and fine-tuning energy codes.
Peng, Haijun; Wang, Xinwei; Zhang, Sheng; Chen, Biaosong
2017-07-01
Nonlinear state-delayed optimal control problems have complex nonlinear characters. To solve this complex nonlinear problem, an iterative symplectic pseudospectral method based on quasilinearization techniques, the dual variational principle and pseudospectral methods is proposed in this paper. First, the proposed method transforms the original nonlinear optimal control problem into a series of linear quadratic optimal control problems. Then, a symplectic pseudospectral method is developed to solve these converted linear quadratic state-delayed optimal control problems. Coefficient matrices in the proposed method are sparse and symmetric since the dual variational principle is used, which makes the proposed method highly efficient. Converged numerical solutions with high precision can be obtained after a few iterations due to the benefit of the local pseudospectral method and quasilinearization techniques. In the numerical simulations, other numerical methods were used for comparisons. The numerical simulation results show that the proposed method is highly accurate, efficient and robust.
A Monte Carlo Method for Multi-Objective Correlated Geometric Optimization
2014-05-01
performs a Monte Carlo optimization to provide geospatial intelligence on entity placement using OpenCL framework. The solutions for optimal...Geometric optimization,Monte Carlo method, parallel computing, OpenCL 22 Song J. Park 410-278-5444Unclassified Unclassified Unclassified UU ii...given threat and target positions, and • AMonte Carlo method development in the OpenCL programming model for vendor-agnostic architecture support and
DMTO – a method for Discrete Material and Thickness Optimization of laminated composite structures
Sørensen, Søren Nørgaard; Sørensen, Rene; Lund, Erik
2014-01-01
are optimized simultaneously through interpolation functions with penalization. Numerical results for several parameterizations of a finite element model of a generic main spar from a wind turbine blade are presented. The different parameterizations represent different levels of complexity with respect......This paper presents a gradient based topology optimization method for Discrete Material and Thickness Optimization of laminated composite structures, labelled the DMTOmethod. The capabilities of the proposed method are demonstrated on mass minimization, subject to constraints on the structural...
无
2009-01-01
The problem of real-time trajectory optimization for small solid launch vehicle of operational responsive space (ORS) was studied by using pseudospectral method. According to the characteristic of the trajectory design, the dynamics model was set up in the inertia right-angled reference frame, and the equation and parameter at the orbit injection point were simplified and converted. The infinite dimension dynamic optimal control problem was converted to a finite dimension static state optimization problem and the algorithm reduced the complexity so as to become a general algorithm in trajectories optimization. With the trajectories optimization of a three-stage solid vehicle with a liquor upper stage as example, the model of the trajectory optimization was set up and simulations were carried out. The results demonstrated the advantage and validity of the pseudospectral method. The rejection time of fairing was also analyzed by the simulation results, and the optimal flight procedure and trajectory were obtained.
Development Optimization and Uncertainty Analysis Methods for Oil and Gas Reservoirs
Ettehadtavakkol, Amin, E-mail: amin.ettehadtavakkol@ttu.edu [Texas Tech University (United States); Jablonowski, Christopher [Shell Exploration and Production Company (United States); Lake, Larry [University of Texas at Austin (United States)
2017-04-15
Uncertainty complicates the development optimization of oil and gas exploration and production projects, but methods have been devised to analyze uncertainty and its impact on optimal decision-making. This paper compares two methods for development optimization and uncertainty analysis: Monte Carlo (MC) simulation and stochastic programming. Two example problems for a gas field development and an oilfield development are solved and discussed to elaborate the advantages and disadvantages of each method. Development optimization involves decisions regarding the configuration of initial capital investment and subsequent operational decisions. Uncertainty analysis involves the quantification of the impact of uncertain parameters on the optimum design concept. The gas field development problem is designed to highlight the differences in the implementation of the two methods and to show that both methods yield the exact same optimum design. The results show that both MC optimization and stochastic programming provide unique benefits, and that the choice of method depends on the goal of the analysis. While the MC method generates more useful information, along with the optimum design configuration, the stochastic programming method is more computationally efficient in determining the optimal solution. Reservoirs comprise multiple compartments and layers with multiphase flow of oil, water, and gas. We present a workflow for development optimization under uncertainty for these reservoirs, and solve an example on the design optimization of a multicompartment, multilayer oilfield development.
Pingen, Georg; Evgrafov, Anton; Maute, Kurt
2009-01-01
We present an adjoint parameter sensitivity analysis formulation and solution strategy for the lattice Boltzmann method (LBM). The focus is on design optimization applications, in particular topology optimization. The lattice Boltzmann method is briefly described with an in-depth discussion...
A Method for Surveying Control Network Optimization Based on Reliability Properties
ZHANG Zhenglu; DENG Yong; LUO Changlin
2007-01-01
Surveying control network optimization design is related to standards,such as precision,reliability,sensitivity and the cost,and these standards are related closely to each other.A new method for surveying control network simulation optimization design is proposed.This method is based on the inner reliability index of the observation values.
A primal-dual interior point method for large-scale free material optimization
Weldeyesus, Alemseged Gebrehiwot; Stolpe, Mathias
2015-01-01
optimization problem is a nonlinear semidefinite program with many small matrix inequalities for which a special-purpose optimization method should be developed. The objective of this article is to propose an efficient primal-dual interior point method for FMO that can robustly and accurately solve large...
Damage approach: A new method for topology optimization with local stress constraints
Verbart, Alexander; Langelaar, Matthijs; van Keulen, Fred
2016-01-01
In this paper, we propose a new method for topology optimization with local stress constraints. In this method, material in which a stress constraint is violated is considered as damaged. Since damaged material will contribute less to the overall performance of the structure, the optimizer will p...
Comparison of Direct Multiobjective Optimization Methods for the Design of Electric Vehicles
2006-01-01
International audience; "System design oriented methodologies" are discussed in this paper through the comparison of multiobjective optimization methods applied to heterogeneous devices in electrical engineering. Avoiding criteria function derivatives, direct optimization algorithms are used. In particular, deterministic geometric methods such as the Hooke & Jeeves heuristic approach are compared with stochastic evolutionary algorithms (Pareto genetic algorithms). Different issues relative to...
An unconstrained optimization method using nonmonotone second order Goldstein's line search
Wen-yu; SUN; Qun-yan; ZHOU
2007-01-01
In this paper, an unconstrained optimization method using the nonmonotone second order Goldstein's line search is proposed. By using the negative curvature information from the Hessian,the sequence generated is shown to converge to a stationary point with the second order optimality conditions. Numerical tests on a set of standard test problems confirm the efficiency of our new method.
Yamaleev, N. K.; Diskin, B.; Nielsen, E. J.
2009-01-01
.We study local-in-time adjoint-based methods for minimization of ow matching functionals subject to the 2-D unsteady compressible Euler equations. The key idea of the local-in-time method is to construct a very accurate approximation of the global-in-time adjoint equations and the corresponding sensitivity derivative by using only local information available on each time subinterval. In contrast to conventional time-dependent adjoint-based optimization methods which require backward-in-time integration of the adjoint equations over the entire time interval, the local-in-time method solves local adjoint equations sequentially over each time subinterval. Since each subinterval contains relatively few time steps, the storage cost of the local-in-time method is much lower than that of the global adjoint formulation, thus making the time-dependent optimization feasible for practical applications. The paper presents a detailed comparison of the local- and global-in-time adjoint-based methods for minimization of a tracking functional governed by the Euler equations describing the ow around a circular bump. Our numerical results show that the local-in-time method converges to the same optimal solution obtained with the global counterpart, while drastically reducing the memory cost as compared to the global-in-time adjoint formulation.
Computational Methods for Identification, Optimization and Control of PDE Systems
2010-04-30
for an air-breathing hypersonic vehicle in the presence of unmodeled dynamics, AIAA-2007-6527, August. 2007. 6. Lizette Zietsman, A Numerical Study of...1, 182–196. 17. E.M. Cliff, R.J. Kraus, J.C. Luby and C.A. Woolsey, Optimal Control of an Un- dersea Glider in a Symmetric Pull-Up, International...Cao, E.M. CLiff, N. Hovakimyan, A. Kurdila, and K. Wise, Design of an L1 Adaptive Controller for a Hypersonic Vehicle with Flexible Body Dy- namics
Trip optimization system and method for a train
Kumar, Ajith Kuttannair; Shaffer, Glenn Robert; Houpt, Paul Kenneth; Movsichoff, Bernardo Adrian; Chan, David So Keung
2017-08-15
A system for operating a train having one or more locomotive consists with each locomotive consist comprising one or more locomotives, the system including a locator element to determine a location of the train, a track characterization element to provide information about a track, a sensor for measuring an operating condition of the locomotive consist, a processor operable to receive information from the locator element, the track characterizing element, and the sensor, and an algorithm embodied within the processor having access to the information to create a trip plan that optimizes performance of the locomotive consist in accordance with one or more operational criteria for the train.
Yoshida, Hiroaki; Yamaguchi, Katsuhito; Ishikawa, Yoshio
The conventional optimization methods were based on a deterministic approach, since their purpose is to find out an exact solution. However, these methods have initial condition dependence and risk of falling into local solution. In this paper, we propose a new optimization method based on a concept of path integral method used in quantum mechanics. The method obtains a solutions as an expected value (stochastic average) using a stochastic process. The advantages of this method are not to be affected by initial conditions and not to need techniques based on experiences. We applied the new optimization method to a design of the hang glider. In this problem, not only the hang glider design but also its flight trajectory were optimized. The numerical calculation results showed that the method has a sufficient performance.
ZHANG Juliang; ZHANG Xiangsun
2001-01-01
In this paper, we use the smoothing penalty function proposed in [1] as the merit function of SQP method for nonlinear optimization with inequality constraints. The global convergence of the method is obtained.
Optimal regime of jet fuel water impregnation by ultrasonic dispersion method
В. Г. Бережний
2000-09-01
Full Text Available Analyzed is the efficiency of existing method and devices of jet fuel water impregnation, their advantages and disadvantages. Proposed is a principal scheme of installation and optimal regime of jet fuel water impregnation by ultrasonic dispersion method
A Pareto-optimal refinement method for protein design scaffolds.
Nivón, Lucas Gregorio; Moretti, Rocco; Baker, David
2013-01-01
Computational design of protein function involves a search for amino acids with the lowest energy subject to a set of constraints specifying function. In many cases a set of natural protein backbone structures, or "scaffolds", are searched to find regions where functional sites (an enzyme active site, ligand binding pocket, protein-protein interaction region, etc.) can be placed, and the identities of the surrounding amino acids are optimized to satisfy functional constraints. Input native protein structures almost invariably have regions that score very poorly with the design force field, and any design based on these unmodified structures may result in mutations away from the native sequence solely as a result of the energetic strain. Because the input structure is already a stable protein, it is desirable to keep the total number of mutations to a minimum and to avoid mutations resulting from poorly-scoring input structures. Here we describe a protocol using cycles of minimization with combined backbone/sidechain restraints that is Pareto-optimal with respect to RMSD to the native structure and energetic strain reduction. The protocol should be broadly useful in the preparation of scaffold libraries for functional site design.
Detecting anthropogenic climate change with an optimal fingerprint method
Hegerl, G.C. [Max-Planck-Institut fuer Meteorologie, Hamburg (Germany); Storch, H. von [Max-Planck-Institut fuer Meteorologie, Hamburg (Germany); Hasselmann, K. [Max-Planck-Institut fuer Meteorologie, Hamburg (Germany); Santer, B.D. [Lawrence Livermore National Lab., CA (United States). Program for Climate Model Diagnosis and Intercomparison; Cubasch, U. [Deutsches Klimarechenzentrum (DKRZ), Hamburg (Germany); Jones, P.D. [East Anglia Univ., Norwich (United Kingdom). Climatic Research Unit
1994-09-01
We propose a general fingerprint strategy to detect anthropogenic climate change and present application to near surface temperature trends. An expected time-space-variable pattern of anthropogenic climate change (the `signal`) is identified through application of an appropriate optimally matched space-time filter (the `fingerprint`) to the observations. The signal and the fingerprint are represented in a space with sufficient observed and simulated data. The signal pattern is derived from a model-generated prediction of anthropogenic climate change. Application of the fingerprint filter to the data yields a scalar detection variable. The statistically optimal fingerprint is obtained by weighting the model-predicted pattern towards low-noise directions. A combination of model output and observations is used to estimate the noise characteristics of the detection variable, arising from the natural variability of climate in the absence of external forcing. We test then the null hypothesis that the observed climate change is part of natural climate variability. We conclude that a statistically significant externally induced warming has been observed, with the caveat of a possibly inadequate estimate of the internal climate variability. In order to attribute this warming uniquely to anthropogenic greenhouse gas forcing, more information on the climate`s response to other forcing mechanisms (e.g. changes in solar radiation, volcanic or anthropogenic aerosols) and their interaction is needed. (orig./KW)
Chaotic Charged System Search with a Feasible-Based Method for Constraint Optimization Problems
B. Nouhi
2013-01-01
Full Text Available Recently developed chaotic charged system search was combined to feasible-based method to solve constraint engineering optimization problems. Using chaotic maps into the CSS increases the global search mobility for a better global optimization. In the present method, an improved feasible-based method is utilized to handle the constraints. Some constraint design examples are tested using the new chaotic-based methods, and the results are compared to recognize the most efficient and powerful algorithm.
Discovery and Optimization of Low-Storage Runge-Kutta Methods
2015-06-01
more stage evaluation than RK4, it takes fewer operations to attain the same error level. This savings is one reason why some low storage methods are an...for automatically generating Runge-Kutta trees, order conditions, and truncation error coefficients,” ACM Transac- tions on Mathematical Software...methods. We then focus on optimizing the truncation error coefficients for LSRK to discover new methods. Reusing the tools from the optimization method, we
Spectral fitting of NMR spectra using an alternating optimization method with a priori knowledge.
Bi, Z; Bruner, A P; Li, J; Scott, K N; Liu, Z S; Stopka, C B; Kim, H W; Wilson, D C
1999-09-01
As alternatives to the fast Fourier transform, advanced parametric methods based on the damped sinusoidal data model have been devised to better quantify the nuclear magnetic resonance (NMR) spectroscopy time-domain data. Previously, linear prediction (LP) fitting methods using Householder triangularization and singular value decomposition (SVD) techniques have been applied to the NMR spectroscopy data analysis. In this paper, we propose an alternating optimization method to quantify the time-domain NMR spectroscopy data. The proposed algorithm uses the a priori knowledge of the possible frequency intervals of the damped sinusoids to obtain more accurate parameter estimates when the NMR spectroscopy data are obtained under low signal-to-noise ratio conditions and the peaks are close together. None of the LP and SVD type of methods can use such approximate a priori knowledge. We have shown with measured NMR spectroscopy data that the proposed algorithm can be used to obtain accurate parameter estimates of frequencies, amplitudes, and damping ratios of the damped sinusoids and therefore the ultimate fit of the spectrum by using the a priori knowledge about the possible frequency intervals of the damped sinusoids. Copyright 1999 Academic Press.
Ducru, Pablo; Josey, Colin; Dibert, Karia; Sobes, Vladimir; Forget, Benoit; Smith, Kord
2017-04-01
This article establishes a new family of methods to perform temperature interpolation of nuclear interactions cross sections, reaction rates, or cross sections times the energy. One of these quantities at temperature T is approximated as a linear combination of quantities at reference temperatures (Tj). The problem is formalized in a cross section independent fashion by considering the kernels of the different operators that convert cross section related quantities from a temperature T0 to a higher temperature T - namely the Doppler broadening operation. Doppler broadening interpolation of nuclear cross sections is thus here performed by reconstructing the kernel of the operation at a given temperature T by means of linear combination of kernels at reference temperatures (Tj). The choice of the L2 metric yields optimal linear interpolation coefficients in the form of the solutions of a linear algebraic system inversion. The optimization of the choice of reference temperatures (Tj) is then undertaken so as to best reconstruct, in the L∞ sense, the kernels over a given temperature range [Tmin ,Tmax ]. The performance of these kernel reconstruction methods is then assessed in light of previous temperature interpolation methods by testing them upon isotope 238U. Temperature-optimized free Doppler kernel reconstruction significantly outperforms all previous interpolation-based methods, achieving 0.1% relative error on temperature interpolation of 238U total cross section over the temperature range [ 300 K , 3000 K ] with only 9 reference temperatures.
Hou, Gene J.-W.; Gumbert, Clyde R.; Newman, Perry A.
2004-01-01
A major step in a most probable point (MPP)-based method for reliability analysis is to determine the MPP. This is usually accomplished by using an optimization search algorithm. The optimal solutions associated with the MPP provide measurements related to safety probability. This study focuses on two commonly used approximate probability integration methods; i.e., the Reliability Index Approach (RIA) and the Performance Measurement Approach (PMA). Their reliability sensitivity equations are first derived in this paper, based on the derivatives of their respective optimal solutions. Examples are then provided to demonstrate the use of these derivatives for better reliability analysis and Reliability-Based Design Optimization (RBDO).
A Novel Method for Assessing and Optimizing Software Project Process Based Risk Control
无
2006-01-01
A new approach for assessing and optimizing software project process based on software risk control pre-sented, which evaluates and optimizes software project process from the view of controlling the software project risks. A model for optimizing software risk control is given, a discrete optimization algorithm based on dynamic programming is proposed and an example of using above method to solve a problem is also included in this paper. By improving the old passive post-project control into an active effective pre-action, this new method can greatly promote the possibility of success of software projects.
Design Tool Using a New Optimization Method Based on a Stochastic Process
Yoshida, Hiroaki; Yamaguchi, Katsuhito; Ishikawa, Yoshio
Conventional optimization methods are based on a deterministic approach since their purpose is to find out an exact solution. However, such methods have initial condition dependence and the risk of falling into local solution. In this paper, we propose a new optimization method based on the concept of path integrals used in quantum mechanics. The method obtains a solution as an expected value (stochastic average) using a stochastic process. The advantages of this method are that it is not affected by initial conditions and does not require techniques based on experiences. We applied the new optimization method to a hang glider design. In this problem, both the hang glider design and its flight trajectory were optimized. The numerical calculation results prove that performance of the method is sufficient for practical use.
Optimization of a Shell and Tube Condenser using Numerical Method
Pradeep Wagh
2015-07-01
Full Text Available The purpose of this study was to investigate the effect of installation of the tube external surfaces, their parameter and variable in a shell-and-tube condenser. Variation of heat transfer coefficient with each variable of shell and tube condenser was measured each test. The optimization tube outside diameter size was analyzed and use extended surface area attached tube with tube material and tube layout and arrangement (Number of tube a triangular or hexagonal arrangement on shell-and tube condenser. The computer programming was used to get faster output in less time. Results suggest that mean heat transfer coefficient in variable condition were mainly at velocity is fixed. And also average additional surfaces and tube layout and the arrangement comparison with the quantity of the heat transfer.
Multidimensional CAE methods for car body structure optimization
Men Yongxin; Ma Fangwu; Zhu Zhenying; Zhang Jiyou; Yuan Liantai
2012-01-01
The computer aided engineering ( CAE ) analysis technique has become one of the most important means in modern automobile development due to its remarkably computing capability. With the development of CAE bussiness software and intensive research on automotive development process, the CAE technique, which was only used as an aux- iliary validation tool in the later product development in the past, is gradually used in each stage of product development now. Furthermore, accurate and fast analysis data can be supplied effectively. Especially in current CAE application, based on some optimization technologies such as sensitivity, feature and topology, the parametric design gradually ena- bles a CAE engineer to be free from the condition which a convention analysis usually lags behind a design, and makes it be parallel to or even anterior to the design. Under the circumstance, the CAE resources can be maximized in each project development department, thereby promoting the project progress and quality, and creating a new philosophy of "analysis-driven design".
Control and optimization system and method for chemical looping processes
Lou, Xinsheng; Joshi, Abhinaya; Lei, Hao
2015-02-17
A control system for optimizing a chemical loop system includes one or more sensors for measuring one or more parameters in a chemical loop. The sensors are disposed on or in a conduit positioned in the chemical loop. The sensors generate one or more data signals representative of an amount of solids in the conduit. The control system includes a data acquisition system in communication with the sensors and a controller in communication with the data acquisition system. The data acquisition system receives the data signals and the controller generates the control signals. The controller is in communication with one or more valves positioned in the chemical loop. The valves are configured to regulate a flow of the solids through the chemical loop.
CONVEXIFICATION AND CONCAVIFICATION METHODS FOR SOME GLOBAL OPTIMIZATION PROBLEMS
WU Zhiyou; ZHANG Liansheng; BAI Fusheng; YANG Xinmin
2004-01-01
In this paper, firstly, we propose several convexification and concavification transformations to convert a strictly monotone function into a convex or concave function,then we propose several convexification and concavification transformations to convert a non-convex and non-concave objective function into a convex or concave function in the programming problems with convex or concave constraint functions, and propose several convexification and concavification transformations to convert a non-monotone objective function into a convex or concave function in some programming problems with strictly monotone constraint functions. Finally, we prove that the original programming problem can be converted into an equivalent concave minimization problem, or reverse convex programming problem or canonical D.C. Programming problem. Then the global optimal solution of the original problem can be obtained by solving the converted concave minimization problem, or reverse convex programming problem or canonical D.C. Programming problem using the existing algorithms about them.
Szczepanik, M.; Poteralski, A.
2016-11-01
The paper is devoted to an application of the evolutionary methods and the finite element method to the optimization of shell structures. Optimization of thickness of a car wheel (shell) by minimization of stress functional is considered. A car wheel geometry is built from three surfaces of revolution: the central surface with the holes destined for the fastening bolts, the surface of the ring of the wheel and the surface connecting the two mentioned earlier. The last one is subjected to the optimization process. The structures are discretized by triangular finite elements and subjected to the volume constraints. Using proposed method, material properties or thickness of finite elements are changing evolutionally and some of them are eliminated. As a result the optimal shape, topology and material or thickness of the structures are obtained. The numerical examples demonstrate that the method based on evolutionary computation is an effective technique for solving computer aided optimal design.
A Novel Parametric Modeling Method and Optimal Design for Savonius Wind Turbines
Baoshou Zhang
2017-03-01
Full Text Available Under the inspiration of polar coordinates, a novel parametric modeling and optimization method for Savonius wind turbines was proposed to obtain the highest power output, in which a quadratic polynomial curve was bent to describe a blade. Only two design parameters are needed for the shape-complicated blade. Therefore, this novel method reduces sampling scale. A series of transient simulations was run to get the optimal performance coefficient (power coefficient C p for different modified turbines based on computational fluid dynamics (CFD method. Then, a global response surface model and a more precise local response surface model were created according to Kriging Method. These models defined the relationship between optimization objective Cp and design parameters. Particle swarm optimization (PSO algorithm was applied to find the optimal design based on these response surface models. Finally, the optimal Savonius blade shaped like a “hook” was obtained. Cm (torque coefficient, Cp and flow structure were compared for the optimal design and the classical design. The results demonstrate that the optimal Savonius turbine has excellent comprehensive performance. The power coefficient Cp is significantly increased from 0.247 to 0.262 (6% higher. The weight of the optimal blade is reduced by 17.9%.
Six-DOF trajectory optimization for reusable launch vehicles via Gauss pseudospectral method
Zhen Wang; Zhong Wu
2016-01-01
To be close to the practical flight process and in-crease the precision of optimal trajectory, a six-degree-of- freedom (6-DOF) trajectory is optimized for the reusable launch vehicle (RLV) using the Gauss pseudospectral method (GPM). Different from the traditional trajectory optimization problem which generaly considers the RLV as a point mass, the coupling between translational dynamics and rotational dynamics is taken into account. An optimization problem is formulated to minimize a performance index subject to 6-DOF equations of motion, including translational and rotational dynamics. A two-step optimal strategy is then introduced to reduce the large calculations caused by multiple variables and convergence confinement in 6-DOF trajectory optimization. The simulation results demonstrate that the 6-DOF trajectory optimal strategy for RLV is feasible.
An adaptive image denoising method based on local parameters optimization
Hari Om; Mantosh Biswas
2014-08-01
In image denoising algorithms, the noise is handled by either modifying term-by-term, i.e., individual pixels or block-by-block, i.e., group of pixels, using suitable shrinkage factor and threshold function. The shrinkage factor is generally a function of threshold and some other characteristics of the neighbouring pixels of the pixel to be thresholded (denoised). The threshold is determined in terms of the noise variance present in the image and its size. The VisuShrink, SureShrink, and NeighShrink methods are important denoising methods that provide good results. The first two, i.e., VisuShrink and SureShrink methods follow term-by-term approach, i.e., modify the individual pixel and the third one, i.e., NeighShrink and its variants: ModiNeighShrink, IIDMWD, and IAWDMBMC, follow block-by-block approach, i.e., modify the pixels in groups, in order to remove the noise. The VisuShrink, SureShrink, and NeighShrink methods however do not give very good visual quality because they remove too many coefficients due to their high threshold values. In this paper, we propose an image denoising method that uses the local parameters of the neighbouring coefficients of the pixel to be denoised in the noisy image. In our method, we propose two new shrinkage factors and the threshold at each decomposition level, which lead to better visual quality. We also establish the relationship between both the shrinkage factors. We compare the performance of our method with that of the VisuShrink and NeighShrink including various variants. Simulation results show that our proposed method has high peak signal-to-noise ratio and good visual quality of the image as compared to the traditional methods:Weiner filter, VisuShrink, SureShrink, NeighBlock, NeighShrink, ModiNeighShrink, LAWML, IIDMWT, and IAWDMBNC methods.
Optimization Method for Indoor Thermal Comfort Based on Interactive Numerical Calculation
无
2008-01-01
In order to implement the optimal design of the indoor thermal comfort based on the numerical modeling method, the numerical calculation platform is combined seamlessly with the data-processing platform, and an interactive numerical calculation platform which includes the functions of numerical simulation and optimization is established. The artificial neural network (ANN) and the greedy strategy are introduced into the hill-climbing pattern heuristic search process, and the optimizing search direction can be predicted by using small samples; when searching along the direction using the greedy strategy, the optimal values can be quickly approached. Therefore, excessive external calling of the numerical modeling process can be avoided,and the optimization time is decreased obviously. The experimental results indicate that the satisfied output parameters of air conditioning can be quickly given out based on the interactive numerical calculation platform and the improved search method, and the optimization for indoor thermal comfort can be completed.
A New Optimization Method for Centrifugal Compressors Based on 1D Calculations and Analyses
Pei-Yuan Li
2015-05-01
Full Text Available This paper presents an optimization design method for centrifugal compressors based on one-dimensional calculations and analyses. It consists of two parts: (1 centrifugal compressor geometry optimization based on one-dimensional calculations and (2 matching optimization of the vaned diffuser with an impeller based on the required throat area. A low pressure stage centrifugal compressor in a MW level gas turbine is optimized by this method. One-dimensional calculation results show that D3/D2 is too large in the original design, resulting in the low efficiency of the entire stage. Based on the one-dimensional optimization results, the geometry of the diffuser has been redesigned. The outlet diameter of the vaneless diffuser has been reduced, and the original single stage diffuser has been replaced by a tandem vaned diffuser. After optimization, the entire stage pressure ratio is increased by approximately 4%, and the efficiency is increased by approximately 2%.
A level set method for reliability-based topology optimization of compliant mechanisms
2008-01-01
Based on the level set model and the reliability theory, a numerical approach of reliability-based topology optimization for compliant mechanisms with multiple inputs and outputs is presented. A multi-objective topology optimal model of compliant mechanisms considering uncertainties of the loads, material properties, and member geometries is developed. The reliability analysis and topology optimization are integrated in the optimal iterative process. The reliabilities of the compliant mechanisms are evaluated by using the first order reliability method. Meanwhile, the problem of structural topology optimization is solved by the level set method which is flexible in handling complex topological changes and concise in describing the boundary shape of the mechanism. Numerical examples show the importance of considering the stochastic nature of the compliant mechanisms in the topology optimization process.
Natural frequency optimization of structures using a soft-kill BESO method
Huang, Xiaodong; Xie, Yi Min
2010-06-01
Frequency optimization is of great importance in the design of machines and structures subjected to dynamic loading. When the natural frequencies of considered structures are maximized using the Solid Isotropic Material with Penalization (SIMP) model, artificial localized modes may occur in areas where elements are assigned with low density values. In this paper, a modified SIMP model is developed to effectively avoid the artificial modes. Based on this model, a new bi-directional evolutionary structural optimization (BESO) method combined with rigorous optimality criteria is developed for topology frequency optimization problems. Numerical results show that the proposed BESO method is efficient and convergent and solid-void or bi-material optimal solutions can be achieved for a variety of frequency optimization problems of continuum structures.
Topology optimization of bounded acoustic problems using the hybrid finite element-wave based method
Goo, Seongyeol; Wang, Semyung; Kook, Junghwan
2017-01-01
This paper presents an alternative topology optimization method for bounded acoustic problems that uses the hybrid finite element-wave based method (FE-WBM). The conventional method for the topology optimization of bounded acoustic problems is based on the finite element method (FEM), which...... is limited to low frequency applications due to considerable computational efforts. To this end, we propose a gradient-based topology optimization method that uses the hybrid FE-WBM whereby the entire domain of a problem is partitioned into design and non-design domains. In this respect, the FEM is used...... as a design domain of topology optimization, and the WBM is used as a non-design domain to increase computational efficiency. The adjoint variable method based on the hybrid FE-WBM is also proposed as a means of computing design sensitivities. Numerical examples are presented to demonstrate the effectiveness...
Optimization of Screening Method for Feed Nutrition and Processing Conditions
YangYun－qing; WangYi－tong; 等
1999-01-01
On the basis of regressional relationship between nutritional indexes and technological conditions,when confronted with feed quality that contains multiple nutritional indexes,a comprehensive equation of evaluating feed quality was developed by using fuzzy mathematical method at first in this study,and then the optimum technological conditions were established by introduction of statistical frequency analysis method pursuant to the comprehensive evaluating equation mentioned above compared with conventional multi-aim nonlinear programming,this method can lead to easy solutions,and also can make adjustment of the importance of nutritional indexes to feed quality according to practical considerations.An example was given of soybean extruding to show how the method worked.
Optimization of Screening Method for Feed Nutrition and Processing Conditions
无
1999-01-01
On the basis of regressional relationship between nutritional indexes and technological conditions, when confronted with feed quality that contains multiple nutritional indexes ,a comprehensive equation of evaluating feed quality was developed by using fuzzy mathematical method at first in this study,and then the optimum technological conditions were established by introduction of statistical frequency analysis method pursuant to the comprehensive evaluating equation mentioned above compared with conventional multi-aim nonlinear programming,this method can lead to easy solutions ,and also can make adjustment of the importance of nu- tritional indexes to feed quality according to practical considerations. An example was given of soybean ex- truding to show how the method worked.
USING OF THE COVER AMOUNTS METHOD FOR OPTIMIZATION OF INCOME
A. V. Volkov
2007-01-01
Full Text Available The method of cover amounts (marginal income gives possibility to determine profitableness of each kind of the production and their real contribution into the result of work of enterprise.
Pseudospectral Collocation Methods for the Direct Transcription of Optimal Control Problems
2003-04-01
solving optimal control problems for trajectory optimization, spacecraft attitude control, jet thruster control, missile guidance and many other... optimal control problems using a pseudospectral direct transcription method. These problems are stated here so that they may be referred to elsewhere...e.g., [7]. 2.3 Prototypical Examples Throughout this thesis two example problems are used to demonstrate various prop- erties associated with solving
Karimi, N
2011-01-01
In the present paper, an exact analytic solution for the optimal unambiguous state discrimination (OPUSD) problem involving an arbitrary number of pure linearly independent quantum states with real and complex inner product is presented. Using semidefinite programming and Karush-Kuhn-Tucker convex optimization method, we derive an analytical formula which shows the relation between optimal solution of unambiguous state discrimination problem and an arbitrary number of pure linearly independent quantum states.
Utopian preference mapping and the utopian preference method for group multiobjective optimization
HU Yuda; HONG Zhenjie; ZHOU Xuanwei
2003-01-01
The individual utopian preference and the group utopian preference on a set of alternatives, and the concept of the utopian preference mapping from the individual utopian preferences, to the group utopian preference, based on the utopian points of the corresponding multiobjective optimization models proposed by decision makers are introduced. Through studying the various fundamental properties of the utopian preference mapping, a method for solving group multiobjective optimization problems with multiple multiobjective optimization models is constructed.
Iván Machín
2015-03-01
Full Text Available This paper presents a set of procedures based on mathematical optimization methods to establish optimal active sulphide phases with higher HDS activity. This paper proposes a list of active phases as a guide for orienting the experimental work in the search of new catalysts that permit optimize the HDS process. Studies in this paper establish Co-S, Cr-S, Nb-S and Ni-S systems have the greatest potential to improve HDS activity.
Fernández, J.
2001-12-01
Full Text Available The maintenance of genetic diversity is, from a genetic point of view, a key objective of conservation programmes. The selection of individuals contributing offspring and the decision of the mating scheme are the steps on which managers can control genetic diversity, specially on ‘ex situ’ programmes. Previous studies have shown that the optimal management strategy is to look for the parents’ contributions that yield minimum group coancestry (overall probability of identity by descent in the population and, then, to arrange mating couples following minimum pairwise coancestry. However, physiological constraints make it necessary to account for mating restrictions when deciding the contributions and, therefore, these should be implemented in a single step along with the mating plan. In the present paper, a single-step method is proposed to optimise the management of a conservation programme when restrictions on the mating scheme exist. The performance of the method is tested by computer simulation. The strategy turns out to be as efficient as the two-step method, regarding both the genetic diversity preserved and the fitness of the population.
Applying the Taguchi method to river water pollution remediation strategy optimization.
Yang, Tsung-Ming; Hsu, Nien-Sheng; Chiu, Chih-Chiang; Wang, Hsin-Ju
2014-04-15
Optimization methods usually obtain the travel direction of the solution by substituting the solutions into the objective function. However, if the solution space is too large, this search method may be time consuming. In order to address this problem, this study incorporated the Taguchi method into the solution space search process of the optimization method, and used the characteristics of the Taguchi method to sequence the effects of the variation of decision variables on the system. Based on the level of effect, this study determined the impact factor of decision variables and the optimal solution for the model. The integration of the Taguchi method and the solution optimization method successfully obtained the optimal solution of the optimization problem, while significantly reducing the solution computing time and enhancing the river water quality. The results suggested that the basin with the greatest water quality improvement effectiveness is the Dahan River. Under the optimal strategy of this study, the severe pollution length was reduced from 18 km to 5 km.
Junction termination extension (JTE) with variation lateral doping (VLD) optimization method
Chernyavskiy, Evgeny
2016-01-01
A simple and effective method for the junction termination design was suggested. Optimization method uses lateral charge function F(x) which depends from two arguments and can be changed in wide range of shapes. To demonstrate method effectiveness, design and optimization example for the HV diode (1800 V) edge termination was shown. Achieved breakdown voltage is 93% of the corresponding 1D structure breakdown voltage.
Epsilon-net method for optimizations over separable states
Shi, Yaoyun
2011-01-01
We give algorithms for the optimization problem: $\\max_\\rho \\ip{Q}{\\rho}$, where $Q$ is a Hermitian matrix, and the variable $\\rho$ is a bipartite {\\em separable} quantum state. This problem lies at the heart of several problems in quantum computation and information, such as the complexity of QMA(2). While the problem is NP-hard, our algorithms are better than brute force for several instances of interest. In particular, they give PSPACE upper bounds on promise problems admitting a QMA(2) protocol in which the verifier performs only logarithmic number of elementary gate on both proofs, as well as the promise problem of deciding if a bipartite local Hamiltonian has large or small ground energy. For $Q\\ge0$, our algorithm runs in time exponential in $\\|Q\\|_F$. While the existence of such an algorithm was first proved recently by Brand{\\~a}o, Christandl and Yard [{\\em Proceedings of the 43rd annual ACM Symposium on Theory of Computation}, 343--352, 2011], our algorithm is conceptually simpler.
XFEL diffraction: developing processing methods to optimize data quality
Sauter, Nicholas K., E-mail: nksauter@lbl.gov [Lawrence Berkeley National Laboratory, Berkeley, CA 94720 (United States)
2015-01-29
Bragg spots recorded from a still crystal necessarily give partial measurements of the structure factor intensity. Correction to the full-spot equivalent, relying on both a physical model for crystal disorder and postrefinement of the crystal orientation, improves the electron density map in serial crystallography. Serial crystallography, using either femtosecond X-ray pulses from free-electron laser sources or short synchrotron-radiation exposures, has the potential to reveal metalloprotein structural details while minimizing damage processes. However, deriving a self-consistent set of Bragg intensities from numerous still-crystal exposures remains a difficult problem, with optimal protocols likely to be quite different from those well established for rotation photography. Here several data processing issues unique to serial crystallography are examined. It is found that the limiting resolution differs for each shot, an effect that is likely to be due to both the sample heterogeneity and pulse-to-pulse variation in experimental conditions. Shots with lower resolution limits produce lower-quality models for predicting Bragg spot positions during the integration step. Also, still shots by their nature record only partial measurements of the Bragg intensity. An approximate model that corrects to the full-spot equivalent (with the simplifying assumption that the X-rays are monochromatic) brings the distribution of intensities closer to that expected from an ideal crystal, and improves the sharpness of anomalous difference Fourier peaks indicating metal positions.
Decomposition method of complex optimization model based on global sensitivity analysis
Qiu, Qingying; Li, Bing; Feng, Peien; Gao, Yu
2014-07-01
The current research of the decomposition methods of complex optimization model is mostly based on the principle of disciplines, problems or components. However, numerous coupling variables will appear among the sub-models decomposed, thereby make the efficiency of decomposed optimization low and the effect poor. Though some collaborative optimization methods are proposed to process the coupling variables, there lacks the original strategy planning to reduce the coupling degree among the decomposed sub-models when we start decomposing a complex optimization model. Therefore, this paper proposes a decomposition method based on the global sensitivity information. In this method, the complex optimization model is decomposed based on the principle of minimizing the sensitivity sum between the design functions and design variables among different sub-models. The design functions and design variables, which are sensitive to each other, will be assigned to the same sub-models as much as possible to reduce the impacts to other sub-models caused by the changing of coupling variables in one sub-model. Two different collaborative optimization models of a gear reducer are built up separately in the multidisciplinary design optimization software iSIGHT, the optimized results turned out that the decomposition method proposed in this paper has less analysis times and increases the computational efficiency by 29.6%. This new decomposition method is also successfully applied in the complex optimization problem of hydraulic excavator working devices, which shows the proposed research can reduce the mutual coupling degree between sub-models. This research proposes a decomposition method based on the global sensitivity information, which makes the linkages least among sub-models after decomposition, and provides reference for decomposing complex optimization models and has practical engineering significance.
Design and Optimization Method of a Two-Disk Rotor System
Huang, Jingjing; Zheng, Longxi; Mei, Qing
2016-04-01
An integrated analytical method based on multidisciplinary optimization software Isight and general finite element software ANSYS was proposed in this paper. Firstly, a two-disk rotor system was established and the mode, humorous response and transient response at acceleration condition were analyzed with ANSYS. The dynamic characteristics of the two-disk rotor system were achieved. On this basis, the two-disk rotor model was integrated to the multidisciplinary design optimization software Isight. According to the design of experiment (DOE) and the dynamic characteristics, the optimization variables, optimization objectives and constraints were confirmed. After that, the multi-objective design optimization of the transient process was carried out with three different global optimization algorithms including Evolutionary Optimization Algorithm, Multi-Island Genetic Algorithm and Pointer Automatic Optimizer. The optimum position of the two-disk rotor system was obtained at the specified constraints. Meanwhile, the accuracy and calculation numbers of different optimization algorithms were compared. The optimization results indicated that the rotor vibration reached the minimum value and the design efficiency and quality were improved by the multidisciplinary design optimization in the case of meeting the design requirements, which provided the reference to improve the design efficiency and reliability of the aero-engine rotor.
LI Qiang; WU Jianxin; SUN Yan
2009-01-01
Dynamic optimization of electromechanical coupling system is a significant engineering problem in the field of mechatronics. The performance improvement of electromechanical equipment depends on the system design parameters. Aiming at the spindle unit of refitted machine tool for solid rocket, the vibration acceleration of tool is taken as objective function, and the electromechanical system design parameters are appointed as design variables. Dynamic optimization model is set up by adopting Lagrange-Maxwell equations, Park transform and electromechanical system energy equations. In the procedure of seeking high efficient optimization method, exponential function is adopted to be the weight function of particle swarm optimization algorithm. Exponential inertia weight particle swarm algorithm(EPSA), is formed and applied to solve the dynamic optimization problem of electromechanical system. The probability density function of EPSA is presented and used to perform convergence analysis. After calculation, the optimized design parameters of the spindle unit are obtained in limited time period. The vibration acceleration of the tool has been decreased greatly by the optimized design parameters. The research job in the paper reveals that the problem of dynamic optimization of electromechanical system can be solved by the method of combining system dynamic analysis with reformed swarm particle optimization. Such kind of method can be applied in the design of robots, NC machine, and other electromechanical equipments.
Optimal method for exoplanet detection by angular differential imaging.
Mugnier, Laurent M; Cornia, Alberto; Sauvage, Jean-François; Rousset, Gérard; Fusco, Thierry; Védrenne, Nicolas
2009-06-01
We propose a novel method for the efficient direct detection of exoplanets from the ground using angular differential imaging. The method combines images appropriately, then uses the combined images jointly in a maximum-likelihood framework to estimate the position and intensity of potential planets orbiting the observed star. It takes into account the mixture of photon and detector noises and a positivity constraint on the planet's intensity. A reasonable detection criterion is also proposed based on the computation of the noise propagation from the images to the estimated intensity of the potential planet. The implementation of this method is tested on simulated data that take into account static aberrations before and after the coronagraph, residual turbulence after adaptive optics correction, and noise.
Global Synthesis Method for the Optimization of Multifeed EBG Antennas
Julien Drouet
2008-01-01
Full Text Available This paper presents a novel technique for synthesizing a given radiation pattern from an EBG antenna with an array feed. The method determines the optimum sets of input waves and input impedances for the feed ports in order to perform simultaneously the radiation pattern and the impedance matching of all the radiating probes that form the array feed. The method is validated through a numerical design of an EBG antenna excited with four patch antennas. The structure is designed to radiate with a single lobe scanned at =30∘ in the E-plane. The interactions between each patch inside the EBG resonator are characterized with the CST MWS software. The optimum weights and the input impedances which simultaneously perform the objective radiation and the matching of all feeding ports are calculated by the developed global synthesis method. The feed network is designed with the Agilent ADS software in order to perform the specified weights and the impedances matching.
A SQP optimization method for shimming a permanent MRI magnet
Zhe Jin; Xing Tang; Bin Meng; Donglin Zu; Weimin Wang
2009-01-01
Based on the sequential quadratic programming (SQP) method, a new approach is presented in this paper to gain a uniform magnetic field for a permanent MRI magnet with biplanar poles. First, the adopted shimming piece is modeled as a magnetic dipole moment to calculate its effect on the background field over the imaging region of interest. Then, the SQP method is utilized to determine the ideal solution for the shimming equation. Finally, the ideal solution is discrete, and the quantization error control technique is used for special cases. This new method helps to reduce the inhomogeneity from 1234.5 ppm to 21.4 ppm over a 36 cm diameter spherical volume (DSV), within hours in practical shimming work.
Experimental optimization of a landmine detection facility using PGNAA method
Hashem MIRI-HAKIMABAD; Hamed PANJEH; Alireza VEJDANI-NOGHREIYAN
2008-01-01
The optimum moderator geometry increases the performance of prompt gamma neutron activation analysis (PGNAA) method considerably. In this work an 241Am-Be source was used in the moderator geometry for detecting buried landmines by PGNAA method. Experiments were done to find the best moderator geometry for the moderated 241 Am-Be source, by replacing the mine with a neutron detector and counting the thermal neutron flux. The flux of thermal neutrons at the place of mine was used as a determining factor to introduce the best moderator geometry.
Application of nonlinear optimization method to sensitivity analysis of numerical model
XU Hui; MU Mu; LUO Dehai
2004-01-01
A nonlinear optimization method is applied to sensitivity analysis of a numerical model. Theoretical analysis and numerical experiments indicate that this method can give not only a quantitative assessment whether the numerical model is able to simulate the observations or not, but also the initial field that yields the optimal simulation. In particular, when the simulation results are apparently satisfactory, and sometimes both model error and initial error are considerably large, the nonlinear optimization method, under some conditions, can identify the error that plays a dominant role.
Ji-ting Qu
2013-01-01
Full Text Available A new optimal method is presented by combining the weight coefficient with the theory of force analogy method. Firstly, a new mathematical model of location index is proposed, which deals with the determination of a reasonable number of dampers according to values of the location index. Secondly, the optimal locations of dampers are given. It can be specific from stories to spans. Numerical examples are illustrated to verify the effectiveness and feasibility of the proposed mathematical model and optimal method. At last, several significant conclusions are given based on numerical results.
Yoon, Gil Ho; Park, Y.K.; Kim, Y.Y.
2007-01-01
A new topology optimization scheme, called the element stacking method, is developed to better handle design optimization involving material-dependent boundary conditions and selection of elements of different types. If these problems are solved by existing standard approaches, complicated finite...... element models or topology optimization reformulation may be necessary. The key idea of the proposed method is to stack multiple elements on the same discretization pixel and select a single or no element. In this method, stacked elements on the same pixel have the same coordinates but may have...
A Swarm Optimization Based Method for Urban Growth Modelling
Sassan Mohammady
2014-10-01
Full Text Available Land use activity is a major issue and challenge for town and country planners. Urban planners must be able to allocate urban land area to different applications with a special focus on the role and function of the city, its economy, and the ability to simulate the effect of user interaction with each other. Continuing migration of rural population to cities and population increases has caused many problems of today's cities including the expansion of urban areas, lack of infrastructure and urban services as well as environmental pollution. Local governments that implement urban growth boundaries need to estimate the amount of urban land required in the future given anticipated growth of housing, business, recreation and other urban activities. Urban growth is a complex process that encounters a number of sophisticated parameters that interact to produce the urban growth pattern. Urban growth modelling aims to understand the dynamic processes. Therefore, interpretability of models is becoming increasingly important. Different approaches have been applied in spatial modelling. In this study, Particle Swarm Optimization (PSO has been used for modelling of urban growth in Qazvin city area (Iran during 2005 to 2011. Landsat imageries, taken in 2005 and 2011 have been used in the study. Main parameters in this study are distance to residential area, distance to industrial area, slope, accessibility, land price and number of urban cell in a 3*3 neighbourhood. Figure of Merit and Kappa statistics have been used for estimating accuracy of the proposed model. DOI: http://dx.doi.org/10.5755/j01.erem.69.3.6653
An Adaptive Finite Element Method Based on Optimal Error Estimates for Linear Elliptic Problems
汤雁
2004-01-01
The subject of the work is to propose a series of papers about adaptive finite element methods based on optimal error control estimate. This paper is the third part in a series of papers on adaptive finite element methods based on optimal error estimates for linear elliptic problems on the concave corner domains. In the preceding two papers (part 1:Adaptive finite element method based on optimal error estimate for linear elliptic problems on concave corner domain; part 2:Adaptive finite element method based on optimal error estimate for linear elliptic problems on nonconvex polygonal domains), we presented adaptive finite element methods based on the energy norm and the maximum norm. In this paper, an important result is presented and analyzed. The algorithm for error control in the energy norm and maximum norm in part 1 and part 2 in this series of papers is based on this result.
A new three-dimensional topology optimization method based on moving morphable components (MMCs)
Zhang, Weisheng; Li, Dong; Yuan, Jie; Song, Junfu; Guo, Xu
2017-04-01
In the present paper, a new method for solving three-dimensional topology optimization problem is proposed. This method is constructed under the so-called moving morphable components based solution framework. The novel aspect of the proposed method is that a set of structural components is introduced to describe the topology of a three-dimensional structure and the optimal structural topology is found by optimizing the layout of the components explicitly. The standard finite element method with ersatz material is adopted for structural response analysis and the shape sensitivity analysis only need to be carried out along the structural boundary. Compared to the existing methods, the description of structural topology is totally independent of the finite element/finite difference resolution in the proposed solution framework and therefore the number of design variables can be reduced substantially. Some widely investigated benchmark examples, in the three-dimensional topology optimization designs, are presented to demonstrate the effectiveness of the proposed approach.
A novel optimized LCL-filter designing method for grid connected converter
Guohong, Zeng; Rasmussen, Tonny Wederberg; Teodorescu, Remus
2010-01-01
capacity of all filter components, with clear physical meaning of minimum cost and volume, a set of optimal values of attenuation ratio and inductancesplit- ratio is obtained for deciding all LCL-filter parameters. With this method, filter overall capacity can be minimized while the grid limit of switching......This paper presents a new LCL-filters optimized designing method for grid connected voltage source converter. This method is based on the analysis of converter output voltage components and inherent relations among LCL-filter parameters. By introducing an optimizing index of equivalent total...... frequency distortion is fulfilled. Compared to the existing methods, the proposed method contains only four steps without try-and-error process, so it is efficient and easy to implement. Simulation results of a 50kVA grid-connected inverter with two sets of LCL-filter parameters under different optimizing...
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.
A generic method to optimize instructions for the control of evacuations
Huibregtse, O.L.; Hoogendoorn, S.P.; Pel, A.J.; Bliemer, M.C.J.
2010-01-01
A method is described to develop a set of optimal instructions to evacuate by car the population of a region threatened by a hazard. By giving these instructions to the evacuees, traffic conditions and therefore the evacuation efficiency can be optimized. The instructions, containing a departure
Topology optimization of unsteady flow problems using the lattice Boltzmann method
Nørgaard, Sebastian Arlund; Sigmund, Ole; Lazarov, Boyan Stefanov
2016-01-01
This article demonstrates and discusses topology optimization for unsteady incompressible fluid flows. The fluid flows are simulated using the lattice Boltzmann method, and a partial bounceback model is implemented to model the transition between fluid and solid phases in the optimization problems...
Topology optimization of heat conduction problems using the finite volume method
Gersborg-Hansen, Allan; Bendsøe, Martin P.; Sigmund, Ole
2006-01-01
This note addresses the use of the finite volume method (FVM) for topology optimization of a heat conduction problem. Issues pertaining to the proper choice of cost functions, sensitivity analysis and example test problems are used to illustrate the effect of applying the FVM as an analysis tool...... checkerboards do not form during the topology optimization process....
Liu, Chengxi; Qin, Nan; Bak, Claus Leth;
2015-01-01
This paper proposes a hybrid optimization method to optimally control the voltage and reactive power with minimum power loss in transmission grid. This approach is used for the Danish automatic voltage control (AVC) system which is typically a non-linear non-convex problem mixed with both continu...
Van Dijk, N.P.
2012-01-01
This thesis aims at understanding and improving topology optimization techniques focusing on density-based level-set methods and geometrical nonlinearities. Central in this work are the numerical modeling of the mechanical response of a design and the consistency of the optimization process itself.
Zhang Xishun
2013-03-01
Full Text Available Aiming at the drawbacks of the optimization and design methods and the practical production goal of least energy consumption, a new theory is raised that the gas of the layer released energy in the lifting process including two parts: dissolved-gas expansion energy and free-gas expansion energy. The motor’s input power of rod pumping system is divided into hydraulic horse power, gas expansion power, surface mechanical loss power, subsurface loss power. Using the theory of energy-conservation, the simulation model of free-gas expansion power has been established, the simulating models of the motor’s input power which are based on the energy method have been improved and the simulation precision of system efficiency has been enhanced. The entire optimization design models have been set up in which the single-well output is taken as the optimum design variable, the planed production of all oil wells in an overall oilfield as the restraint condition and the least input power of the overall oilfield as the object. Synthesizing the optimization design results of the single well and the entire oilfield, the optimal output and the optimal swabbing parameters of all wells can be got. The actual optimizing examples show that the total power consumption designed by the entire optimization method is less 12.95% than that by the single optimization method.
A Primal-Dual Augmented Lagrangian Method for Optimal Control of ...
A Primal-Dual Augmented Lagrangian Method for Optimal Control of ... Log in or Register to get access to full text downloads. ... In this paper we are concerned with time-varying optimal control problems whose cost is quadratic and whose ...
A new method for ship inner shell optimization based on parametric technique
Yu Yan-Yun
2015-01-01
Full Text Available A new method for ship Inner Shell optimization, which is called Parametric Inner Shell Optimization Method (PISOM, is presented in this paper in order to improve both hull performance and design efficiency of transport ship. The foundation of PISOM is the parametric Inner Shell Plate (ISP model, which is a fully-associative model driven by dimensions. A method to create parametric ISP model is proposed, including geometric primitives, geometric constraints, geometric constraint solving etc. The standard optimization procedure of ship ISP optimization based on parametric ISP model is put forward, and an efficient optimization approach for typical transport ship is developed based on this procedure. This approach takes the section area of ISP and the other dominant parameters as variables, while all the design requirements such as propeller immersion, fore bottom wave slap, bridge visibility, longitudinal strength etc, are made constraints. The optimization objective is maximum volume of cargo oil tanker/cargo hold, and the genetic algorithm is used to solve this optimization model. This method is applied to the optimization of a product oil tanker and a bulk carrier, and it is proved to be effective, highly efficient, and engineering practical.
Advanced Control Methods for Optimization of Arc Welding
Thomsen, J. S.
Gas Metal Arc Welding (GMAW) is a proces used for joining pieces of metal. Probably, the GMAW process is the most successful and widely used welding method in the industry today. A key issue in welding is the quality of the welds produced. The quality of a weld is influenced by several factors...
An Optimized Culture Method of Rat Dorsal Root Ganglion Neurons
LIUYin; CHENJing-Hong; GONGZe-Hui
2004-01-01
AIM: To establish a primary culture technique of acutely isolated dorsal root ganglion (DRG) neurons, and provide a simple & useful in vitro model for study of analgesia. Methods: Acutely isolated dorsal root ganglion (DRG) neurons were planted and cultured; the configuration and growth characters of DRG neurons were observed through inverted microscope.
Optimization of statistical methods impact on quantitative proteomics data
Pursiheimo, A.; Vehmas, A.P.; Afzal, S.; Suomi, T.; Chand, T.; Strauss, L.; Poutanen, M.; Rokka, A.; Corthals, G.L.; Elo, L.L.
2015-01-01
As tools for quantitative label-free mass spectrometry (MS) rapidly develop, a consensus about the best practices is not apparent. In the work described here we compared popular statistical methods for detecting differential protein expression from quantitative MS data using both controlled
An automatic and effective parameter optimization method for model tuning
T. Zhang
2015-11-01
simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9 %. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameter tuning during the model development stage.