Global smoothing and continuation for large-scale molecular optimization
More, J.J.; Wu, Zhijun
1995-10-01
We discuss the formulation of optimization problems that arise in the study of distance geometry, ionic systems, and molecular clusters. We show that continuation techniques based on global smoothing are applicable to these molecular optimization problems, and we outline the issues that must be resolved in the solution of large-scale molecular optimization problems.
Handling inequality constraints in continuous nonlinear global optimization
Wang, Tao; Wah, B.W.
1996-12-31
In this paper, we present a new method to handle inequality constraints and apply it in NOVEL (Nonlinear Optimization via External Lead), a system we have developed for solving constrained continuous nonlinear optimization problems. In general, in applying Lagrange-multiplier methods to solve these problems, inequality constraints are first converted into equivalent equality constraints. One such conversion method adds a slack variable to each inequality constraint in order to convert it into an equality constraint. The disadvantage of this conversion is that when the search is inside a feasible region, some satisfied constraints may still pose a non-zero weight in the Lagrangian function, leading to possible oscillations and divergence when a local optimum lies on the boundary of a feasible region. We propose a new conversion method called the MaxQ method such that all satisfied constraints in a feasible region always carry zero weight in the Lagrange function; hence, minimizing the Lagrange function in a feasible region always leads to local minima of the objective function. We demonstrate that oscillations do not happen in our method. We also propose methods to speed up convergence when a local optimum lies on the boundary of a feasible region. Finally, we show improved experimental results in applying our proposed method in NOVEL on some existing benchmark problems and compare them to those obtained by applying the method based on slack variables.
NASA Astrophysics Data System (ADS)
Mikhalev, A. S.; Rouban, A. I.
2016-04-01
The algorithms of global non-differentiable minimization of functions on set of the mixed variables: continuous and discrete with unordered specific possible values are constructed. The method of optimization is based on selective averaging of required variables, on adaptive reorganization of the sizes of admissible domain of trial movements and on use of relative values for minimised functions. Existence of discrete variables leads to solution of a sequence of global minimization problems of the functions in space of only continuous variables at the presence: 1) of their inequality restrictions for each problem; 2) of the general inequality restrictions for all problems (i.e. at the absence of dependence of functions fore inequality restrictions from discrete variables). In the first case, presence of discrete variables with unordered non-numeric possible values leads to solution of sequence of problems of global minimization of multiextreme functions on set only of continuous variables at the presence of their inequality restrictions. As a result, among the received optimum solutions the best is selected. In the second variant all minimized functions is convoluted in each sampling point in one multiextreme function and this function is minimised on continuous variables.
NASA Astrophysics Data System (ADS)
Tsoulos, Ioannis G.; Lagaris, Isaac E.
2006-01-01
A new stochastic method for locating the global minimum of a multidimensional function inside a rectangular hyperbox is presented. A sampling technique is employed that makes use of the procedure known as grammatical evolution. The method can be considered as a "genetic" modification of the Controlled Random Search procedure due to Price. The user may code the objective function either in C++ or in Fortran 77. We offer a comparison of the new method with others of similar structure, by presenting results of computational experiments on a set of test functions. Program summaryTitle of program: GenPrice Catalogue identifier:ADWP Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADWP Program available from: CPC Program Library, Queen's University of Belfast, N. Ireland Computer for which the program is designed and others on which it has been tested: the tool is designed to be portable in all systems running the GNU C++ compiler Installation: University of Ioannina, Greece Programming language used: GNU-C++, GNU-C, GNU Fortran-77 Memory required to execute with typical data: 200 KB No. of bits in a word: 32 No. of processors used: 1 Has the code been vectorized or parallelized?: no No. of lines in distributed program, including test data, etc.:13 135 No. of bytes in distributed program, including test data, etc.: 78 512 Distribution format: tar. gz Nature of physical problem: A multitude of problems in science and engineering are often reduced to minimizing a function of many variables. There are instances that a local optimum does not correspond to the desired physical solution and hence the search for a better solution is required. Local optimization techniques are frequently trapped in local minima. Global optimization is hence the appropriate tool. For example, solving a nonlinear system of equations via optimization, employing a "least squares" type of objective, one may encounter many local minima that do not correspond to solutions, i.e. minima with values
Towards continuous global measurements and optimal emission estimates of NF3
NASA Astrophysics Data System (ADS)
Arnold, T.; Muhle, J.; Salameh, P.; Harth, C.; Ivy, D. J.; Weiss, R. F.
2011-12-01
We present an analytical method for the continuous in situ measurement of nitrogen trifluoride (NF3) - an anthropogenic gas with a global warming potential of ~16800 over a 100 year time horizon. NF3 is not included in national reporting emissions inventories under the United Nations Framework Convention on Climate Change (UNFCCC). However, it is a rapidly emerging greenhouse gas due to emission from a growing number of manufacturing facilities with increasing output and modern end-use applications, namely in microcircuit etching, and in production of flat panel displays and thin-film photovoltaic cells. Despite success in measuring the most volatile long lived halogenated species such as CF4, the Medusa preconcentration GC/MS system of Miller et al. (2008) is unable to detect NF3 under remote operation. Using altered techniques of gas separation and chromatography after initial preconcentration, we are now able to make continuous atmospheric measurements of NF3 with average precisions < 1.5% (1 s.d.) for modern background air samples. Most notably, the suite of gases previously measured by Medusa (the significant halogenated species listed under both the Montreal and Kyoto Protocols), can also be quantified from the same sample. Our technique was used to extend the most recent atmospheric measurements into 2011 and complete the background Southern Hemispheric trend over the past three decades using samples from the Cape Grim Air Archive. Using these latest results and those from Weiss et al. (2008) we present optimised annual emission estimates using a 2D atmospheric transport model (AGAGE 12-box model) and an inverse method (Rigby et al., 2011). We calculate emissions during 2010 of 7.6 +/- 1.3 kt (equivalent to 13 million metric tons of CO2), which is estimated to be around 6% of the total NF3 produced. Emission factors are shown to have reduced over the last decade; however, rising production and end-use have caused the average global atmospheric concentration
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.
Dengue: a continuing global threat
Guzman, Maria G.; Halstead, Scott B.; Artsob, Harvey; Buchy, Philippe; Farrar, Jeremy; Gubler, Duane J.; Hunsperger, Elizabeth; Kroeger, Axel; Margolis, Harold S.; Martínez, Eric; Nathan, Michael B.; Pelegrino, Jose Luis; Simmons, Cameron; Yoksan, Sutee; Peeling, Rosanna W.
2014-01-01
Dengue fever and dengue haemorrhagic fever are important arthropod-borne viral diseases. Each year, there are ~50 million dengue infections and ~500,000 individuals are hospitalized with dengue haemorrhagic fever, mainly in Southeast Asia, the Pacific and the Americas. Illness is produced by any of the four dengue virus serotypes. A global strategy aimed at increasing the capacity for surveillance and outbreak response, changing behaviours and reducing the disease burden using integrated vector management in conjunction with early and accurate diagnosis has been advocated. Antiviral drugs and vaccines that are currently under development could also make an important contribution to dengue control in the future. PMID:21079655
Dengue: a continuing global threat.
Guzman, Maria G; Halstead, Scott B; Artsob, Harvey; Buchy, Philippe; Farrar, Jeremy; Gubler, Duane J; Hunsperger, Elizabeth; Kroeger, Axel; Margolis, Harold S; Martínez, Eric; Nathan, Michael B; Pelegrino, Jose Luis; Simmons, Cameron; Yoksan, Sutee; Peeling, Rosanna W
2010-12-01
Dengue fever and dengue haemorrhagic fever are important arthropod-borne viral diseases. Each year, there are ∼50 million dengue infections and ∼500,000 individuals are hospitalized with dengue haemorrhagic fever, mainly in Southeast Asia, the Pacific and the Americas. Illness is produced by any of the four dengue virus serotypes. A global strategy aimed at increasing the capacity for surveillance and outbreak response, changing behaviours and reducing the disease burden using integrated vector management in conjunction with early and accurate diagnosis has been advocated. Antiviral drugs and vaccines that are currently under development could also make an important contribution to dengue control in the future.
Enhancing Polyhedral Relaxations for Global Optimization
ERIC Educational Resources Information Center
Bao, Xiaowei
2009-01-01
During the last decade, global optimization has attracted a lot of attention due to the increased practical need for obtaining global solutions and the success in solving many global optimization problems that were previously considered intractable. In general, the central question of global optimization is to find an optimal solution to a given…
Intervals in evolutionary algorithms for global optimization
Patil, R.B.
1995-05-01
Optimization is of central concern to a number of disciplines. Interval Arithmetic methods for global optimization provide us with (guaranteed) verified results. These methods are mainly restricted to the classes of objective functions that are twice differentiable and use a simple strategy of eliminating a splitting larger regions of search space in the global optimization process. An efficient approach that combines the efficient strategy from Interval Global Optimization Methods and robustness of the Evolutionary Algorithms is proposed. In the proposed approach, search begins with randomly created interval vectors with interval widths equal to the whole domain. Before the beginning of the evolutionary process, fitness of these interval parameter vectors is defined by evaluating the objective function at the center of the initial interval vectors. In the subsequent evolutionary process the local optimization process returns an estimate of the bounds of the objective function over the interval vectors. Though these bounds may not be correct at the beginning due to large interval widths and complicated function properties, the process of reducing interval widths over time and a selection approach similar to simulated annealing helps in estimating reasonably correct bounds as the population evolves. The interval parameter vectors at these estimated bounds (local optima) are then subjected to crossover and mutation operators. This evolutionary process continues for predetermined number of generations in the search of the global optimum.
Building a global business continuity programme.
Lazcano, Michael
2014-01-01
Business continuity programmes provide an important function within organisations, especially when aligned with and supportive of the organisation's goals, objectives and organisational culture. Continuity programmes for large, complex international organisations, unlike those for compact national companies, are more difficult to design, build, implement and maintain. Programmes for international organisations require attention to structural design, support across organisational leadership and hierarchy, seamless integration with the organisation's culture, measured success and demonstrated value. This paper details practical, but sometimes overlooked considerations for building successful global business continuity programmes.
Building a global business continuity programme.
Lazcano, Michael
2014-01-01
Business continuity programmes provide an important function within organisations, especially when aligned with and supportive of the organisation's goals, objectives and organisational culture. Continuity programmes for large, complex international organisations, unlike those for compact national companies, are more difficult to design, build, implement and maintain. Programmes for international organisations require attention to structural design, support across organisational leadership and hierarchy, seamless integration with the organisation's culture, measured success and demonstrated value. This paper details practical, but sometimes overlooked considerations for building successful global business continuity programmes. PMID:24854730
Optimization of Secondary Concentrators with the Continuous Information Entropy Strategy
NASA Astrophysics Data System (ADS)
Schmidt, Tobias Christian; Ries, Harald
2010-10-01
In this contribution, a method for global optimization of noisy functions, the Continuous Information Entropy Strategy (CIES), is explained and its applicability for the optimization of solar concentrators is shown. The CIES is efficient because all decisions made during optimizations are based on criteria that are derived from the concept of information entropy. Two secondary concentrators have been optimized with the CIES. The optimized secondary concentrators convert circular light distributions of round focal spots to square light distributions to match with the shape of square PV cells. The secondary concentrators are highly efficient and have geometrical concentration ratios of 2.25 and 8 respectively. Part of this material has been published in: T. C. Schmidt, "Information Entropy-Based Decision Making in Optimization", Ph.D. Thesis, Philipps University Marburg, 2010.
Multiplier-continuation algorthms for constrained optimization
NASA Technical Reports Server (NTRS)
Lundberg, Bruce N.; Poore, Aubrey B.; Bing, Yang
1989-01-01
Several path following algorithms based on the combination of three smooth penalty functions, the quadratic penalty for equality constraints and the quadratic loss and log barrier for inequality constraints, their modern counterparts, augmented Lagrangian or multiplier methods, sequential quadratic programming, and predictor-corrector continuation are described. In the first phase of this methodology, one minimizes the unconstrained or linearly constrained penalty function or augmented Lagrangian. A homotopy path generated from the functions is then followed to optimality using efficient predictor-corrector continuation methods. The continuation steps are asymptotic to those taken by sequential quadratic programming which can be used in the final steps. Numerical test results show the method to be efficient, robust, and a competitive alternative to sequential quadratic programming.
A novel metaheuristic for continuous optimization problems: Virus optimization algorithm
NASA Astrophysics Data System (ADS)
Liang, Yun-Chia; Rodolfo Cuevas Juarez, Josue
2016-01-01
A novel metaheuristic for continuous optimization problems, named the virus optimization algorithm (VOA), is introduced and investigated. VOA is an iteratively population-based method that imitates the behaviour of viruses attacking a living cell. The number of viruses grows at each replication and is controlled by an immune system (a so-called 'antivirus') to prevent the explosive growth of the virus population. The viruses are divided into two classes (strong and common) to balance the exploitation and exploration effects. The performance of the VOA is validated through a set of eight benchmark functions, which are also subject to rotation and shifting effects to test its robustness. Extensive comparisons were conducted with over 40 well-known metaheuristic algorithms and their variations, such as artificial bee colony, artificial immune system, differential evolution, evolutionary programming, evolutionary strategy, genetic algorithm, harmony search, invasive weed optimization, memetic algorithm, particle swarm optimization and simulated annealing. The results showed that the VOA is a viable solution for continuous optimization.
Global optimality of extremals: An example
NASA Technical Reports Server (NTRS)
Kreindler, E.; Newman, F.
1980-01-01
The question of the existence and location of Darboux points is crucial for minimally sufficient conditions for global optimality and for computation of optimal trajectories. A numerical investigation is presented of the Darboux points and their relationship with conjugate points for a problem of minimum fuel, constant velocity, and horizontal aircraft turns to capture a line. This simple second order optimal control problem shows that ignoring the possible existence of Darboux points may play havoc with the computation of optimal trajectories.
Global optimization of digital circuits
NASA Astrophysics Data System (ADS)
Flandera, Richard
1991-12-01
This thesis was divided into two tasks. The first task involved developing a parser which could translate a behavioral specification in Very High-Speed Integrated Circuits (VHSIC) Hardware Description Language (VHDL) into the format used by an existing digital circuit optimization tool, Boolean Reasoning In Scheme (BORIS). Since this tool is written in Scheme, a dialect of Lisp, the parser was also written in Scheme. The parser was implemented is Artez's modification of Earley's Algorithm. Additionally, a VHDL tokenizer was implemented in Scheme and a portion of the VHDL grammar was converted into the format which the parser uses. The second task was the incorporation of intermediate functions into BORIS. The existing BORIS contains a recursive optimization system that optimizes digital circuits by using circuit outputs as inputs into other circuits. Intermediate functions provide a greater selection of functions to be used as circuits inputs. Using both intermediate functions and output functions, the costs of the circuits in the test set were reduced by 43 percent. This is a 10 percent reduction when compared to the existing recursive optimization system. Incorporating intermediate functions into BORIS required the development of an intermediate-function generator and a set of control methods to keep the computation time from increasing exponentially.
Optimal directed searches for continuous gravitational waves
NASA Astrophysics Data System (ADS)
Ming, Jing; Krishnan, Badri; Papa, Maria Alessandra; Aulbert, Carsten; Fehrmann, Henning
2016-03-01
Wide parameter space searches for long-lived continuous gravitational wave signals are computationally limited. It is therefore critically important that the available computational resources are used rationally. In this paper we consider directed searches, i.e., targets for which the sky position is known accurately but the frequency and spin-down parameters are completely unknown. Given a list of such potential astrophysical targets, we therefore need to prioritize. On which target(s) should we spend scarce computing resources? What parameter space region in frequency and spin-down should we search through? Finally, what is the optimal search setup that we should use? In this paper we present a general framework that allows us to solve all three of these problems. This framework is based on maximizing the probability of making a detection subject to a constraint on the maximum available computational cost. We illustrate the method for a simplified problem.
Optimal signal processing for continuous qubit readout
NASA Astrophysics Data System (ADS)
Ng, Shilin; Tsang, Mankei
2014-08-01
The measurement of a quantum two-level system, or a qubit in modern terminology, often involves an electromagnetic field that interacts with the qubit, before the field is measured continuously and the qubit state is inferred from the noisy field measurement. During the measurement, the qubit may undergo spontaneous transitions, further obscuring the initial qubit state from the observer. Taking advantage of some well-known techniques in stochastic detection theory, here we propose a signal processing protocol that can infer the initial qubit state optimally from the measurement in the presence of noise and qubit dynamics. Assuming continuous quantum-nondemolition measurements with Gaussian or Poissonian noise and a classical Markov model for the qubit, we derive analytic solutions to the protocol in some special cases of interest using Itō calculus. Our method is applicable to multihypothesis testing for robust qubit readout and relevant to experiments on qubits in superconducting microwave circuits, trapped ions, nitrogen-vacancy centers in diamond, semiconductor quantum dots, or phosphorus donors in silicon.
Enlightening Globalization: An Opportunity for Continuing Education
ERIC Educational Resources Information Center
Reimers, Fernando
2009-01-01
Globalization presents a new social context for educational institutions from elementary schools to universities. In response to this new context, schools and universities are slowly changing their ways. These changes range from altering the curriculum so that students understand the process of globalization itself, or developing competencies…
FPSO global strength and hull optimization
NASA Astrophysics Data System (ADS)
Ma, Junyuan; Xiao, Jianhua; Ma, Rui; Cao, Kai
2014-03-01
Global strength is a significant item for floating production storage and offloading (FPSO) design, and steel weight plays an important role in the building costs of FPSO. It is the main task to consider and combine these two aspects by optimizing hull dimensions. There are many optional methods for the global strength analysis. A common method is to use the ABS FPSO Eagle software to analyze the global strength including the rule check and direct strength analysis. And the same method can be adopted for the FPSO hull optimization by changing the depth. After calculation and optimization, the results are compared and analyzed. The results can be used as a reference for the future design or quotation purpose.
Globally optimal impulsive transfers via Green's theorem
NASA Astrophysics Data System (ADS)
Hazelrigg, G. A., Jr.
1984-08-01
For certain classes of trajectories the cost function (characteristic velocity) can be written as a 'quasilinear' function of the change in state. In the case presented, impulsive transfers between coplanar, coaxial orbits with transfer time and angle unrestricted, Green's theorem can be used to determine the optimal transfer between given terminal states. This is done in a manner which places no restrictions on the number of impulses used and leads to globally optimal results. These results are used to show that the Hohmann transfer and the biparabolic transfer provide global minima in their respective regions. The regions in which monoelliptic and biparabolic trajectories are globally optimal are also defined for elliptic terminal states. The results are applicable to the case in which restrictions are placed on the radius of closest approach or greatest recession from the center of the force field.
Global Design Optimization for Fluid Machinery Applications
NASA Technical Reports Server (NTRS)
Shyy, Wei; Papila, Nilay; Tucker, Kevin; Vaidyanathan, Raj; Griffin, Lisa
2000-01-01
Recent experiences in utilizing the global optimization methodology, based on polynomial and neural network techniques for fluid machinery design are summarized. Global optimization methods can utilize the information collected from various sources and by different tools. These methods offer multi-criterion optimization, handle the existence of multiple design points and trade-offs via insight into the entire design space can easily perform tasks in parallel, and are often effective in filtering the noise intrinsic to numerical and experimental data. Another advantage is that these methods do not need to calculate the sensitivity of each design variable locally. However, a successful application of the global optimization method needs to address issues related to data requirements with an increase in the number of design variables and methods for predicting the model performance. Examples of applications selected from rocket propulsion components including a supersonic turbine and an injector element and a turbulent flow diffuser are used to illustrate the usefulness of the global optimization method.
Global search acceleration in the nested optimization scheme
NASA Astrophysics Data System (ADS)
Grishagin, Vladimir A.; Israfilov, Ruslan A.
2016-06-01
Multidimensional unconstrained global optimization problem with objective function under Lipschitz condition is considered. For solving this problem the dimensionality reduction approach on the base of the nested optimization scheme is used. This scheme reduces initial multidimensional problem to a family of one-dimensional subproblems being Lipschitzian as well and thus allows applying univariate methods for the execution of multidimensional optimization. For two well-known one-dimensional methods of Lipschitz optimization the modifications providing the acceleration of the search process in the situation when the objective function is continuously differentiable in a vicinity of the global minimum are considered and compared. Results of computational experiments on conventional test class of multiextremal functions confirm efficiency of the modified methods.
Electronic neural networks for global optimization
NASA Technical Reports Server (NTRS)
Thakoor, A. P.; Moopenn, A. W.; Eberhardt, S.
1990-01-01
An electronic neural network with feedback architecture, implemented in analog custom VLSI is described. Its application to problems of global optimization for dynamic assignment is discussed. The convergence properties of the neural network hardware are compared with computer simulation results. The neural network's ability to provide optimal or near optimal solutions within only a few neuron time constants, a speed enhancement of several orders of magnitude over conventional search methods, is demonstrated. The effect of noise on the circuit dynamics and the convergence behavior of the neural network hardware is also examined.
Global Optimality of the Successive Maxbet Algorithm.
ERIC Educational Resources Information Center
Hanafi, Mohamed; ten Berge, Jos M. F.
2003-01-01
It is known that the Maxbet algorithm, which is an alternative to the method of generalized canonical correlation analysis and Procrustes analysis, may converge to local maxima. Discusses an eigenvalue criterion that is sufficient, but not necessary, for global optimality of the successive Maxbet algorithm. (SLD)
Local and Global Comparison of Continuous Functions
Edelsbrunner, H; Harer, J; Natarajan, V; Pascucci, V
2004-12-16
We introduce local and global comparison measures for a collection of k {<=} d real-valued smooth functions on a common d-dimensional Riemannian manifold. For k = d = 2 we relate the measures to the set of critical points of one function restricted to the level sets of the other. The definition of the measures extends to piecewise linear functions for which they are easy to compute. The computation of the measures forms the centerpiece of a software tool which we use to study scientific datasets.
A Novel Particle Swarm Optimization Algorithm for Global Optimization
Wang, Chun-Feng; Liu, Kui
2016-01-01
Particle Swarm Optimization (PSO) is a recently developed optimization method, which has attracted interest of researchers in various areas due to its simplicity and effectiveness, and many variants have been proposed. In this paper, a novel Particle Swarm Optimization algorithm is presented, in which the information of the best neighbor of each particle and the best particle of the entire population in the current iteration is considered. Meanwhile, to avoid premature, an abandoned mechanism is used. Furthermore, for improving the global convergence speed of our algorithm, a chaotic search is adopted in the best solution of the current iteration. To verify the performance of our algorithm, standard test functions have been employed. The experimental results show that the algorithm is much more robust and efficient than some existing Particle Swarm Optimization algorithms. PMID:26955387
Global optimization of bilinear engineering design models
Grossmann, I.; Quesada, I.
1994-12-31
Recently Quesada and Grossmann have proposed a global optimization algorithm for solving NLP problems involving linear fractional and bilinear terms. This model has been motivated by a number of applications in process design. The proposed method relies on the derivation of a convex NLP underestimator problem that is used within a spatial branch and bound search. This paper explores the use of alternative bounding approximations for constructing the underestimator problem. These are applied in the global optimization of problems arising in different engineering areas and for which different relaxations are proposed depending on the mathematical structure of the models. These relaxations include linear and nonlinear underestimator problems. Reformulations that generate additional estimator functions are also employed. Examples from process design, structural design, portfolio investment and layout design are presented.
Global optimization algorithm for heat exchanger networks
Quesada, I.; Grossmann, I.E. )
1993-03-01
This paper deals with the global optimization of heat exchanger networks with fixed topology. It is shown that if linear area cost functions are assumed, as well as arithmetic mean driving force temperature differences in networks with isothermal mixing, the corresponding nonlinear programming (NLP) optimization problem involves linear constraints and a sum of linear fractional functions in the objective which are nonconvex. A rigorous algorithm is proposed that is based on a convex NLP underestimator that involves linear and nonlinear estimators for fractional and bilinear terms which provide a tight lower bound to the global optimum. This NLP problem is used within a spatial branch and bound method for which branching rules are given. Basic properties of the proposed method are presented, and its application is illustrated with several example problems. The results show that the proposed method only requires few nodes in the branch and bound search.
Solving global optimization problems on GPU cluster
NASA Astrophysics Data System (ADS)
Barkalov, Konstantin; Gergel, Victor; Lebedev, Ilya
2016-06-01
The paper contains the results of investigation of a parallel global optimization algorithm combined with a dimension reduction scheme. This allows solving multidimensional problems by means of reducing to data-independent subproblems with smaller dimension solved in parallel. The new element implemented in the research consists in using several graphic accelerators at different computing nodes. The paper also includes results of solving problems of well-known multiextremal test class GKLS on Lobachevsky supercomputer using tens of thousands of GPU cores.
On optimal nonlinear estimation. I - Continuous observation.
NASA Technical Reports Server (NTRS)
Lo, J. T.
1973-01-01
A generalization of Bucy's (1965) representation theorem is obtained under very weak hypotheses. The generalized theorem is shown to play the same role in the case of general optimal estimation for an arbitrary random process as does the Bucy theorem in the case of optimal filtering for a diffusion process. At least for the models considered, the possibility is pointed out to reduce all sequential estimation problems to the problem of filtering. Hence, filtering theory is seen to represent the core of estimation theory, and is believed to define the direction in which future research should be focused.
Globally Optimal Segmentation of Permanent-Magnet Systems
NASA Astrophysics Data System (ADS)
Insinga, A. R.; Bjørk, R.; Smith, A.; Bahl, C. R. H.
2016-06-01
Permanent-magnet systems are widely used for generation of magnetic fields with specific properties. The reciprocity theorem, an energy-equivalence principle in magnetostatics, can be employed to calculate the optimal remanent flux density of the permanent-magnet system, given any objective functional that is linear in the magnetic field. This approach, however, yields a continuously varying remanent flux density, while in practical applications, magnetic assemblies are realized by combining uniformly magnetized segments. The problem of determining the optimal shape of each of these segments remains unsolved. We show that the problem of optimal segmentation of a two-dimensional permanent-magnet assembly with respect to a linear objective functional can be reduced to the problem of piecewise linear approximation of a plane curve by perimeter maximization. Once the problem has been cast into this form, the globally optimal solution can be easily computed employing dynamic programming.
Global Grazing Systems: Their Continuing Importance in Meeting Global Demand
NASA Astrophysics Data System (ADS)
Davis, K. F.; D'Odorico, P.
2014-12-01
Animal production exerts significant demand on land, water and food resources and is an extensive means by which humans modify natural systems. Demand for animal source foods has more than tripled over the past 50 years due to population growth and dietary change. To meet this demand, livestock intensification (e.g. concentrated animal feeding operations) has increased and with it the water, nitrogen and carbon footprints of animal production. However, grass-fed systems continue to contribute significantly to overall animal production. To date, little is known about the contributions of grass- and grain-fed systems to animal calorie production, how this has changed through time and to what extent these two systems are sensitive to climate. Using a calorie-based approach we hypothesize that grain-fed systems are increasing in importance (with serious implications for water and nutrient demand) and that rangeland productivity is correlated with rainfall. Our findings show that grass-fed systems made up the majority of animal calorie production since 1960 years but that the relative contribution of grain-fed system has increased (from 27% to 49%). This rapid transition towards grain-fed animal production is largely a result of changing diets demand, as we found the growth of grass-fed production only kept pace with population growth. On a regional scale, we find that Asia has been the major contributor to the increase in grass-fed animal calorie production and that Africa has undergone the most drastic transition from grass-fed to grain-fed dependence. Finally, as expected we see a positive relationship between rangeland productivity and precipitation and a shift from dairy- to meat-dominated production going from drier to wetter climates. This study represents a new means of analyzing the food security of animal products and an important step in understanding the historic trends of animal production, their relation to climate, their prospects for the future and their
On Global Optimal Sailplane Flight Strategy
NASA Technical Reports Server (NTRS)
Sander, G. J.; Litt, F. X.
1979-01-01
The derivation and interpretation of the necessary conditions that a sailplane cross-country flight has to satisfy to achieve the maximum global flight speed is considered. Simple rules are obtained for two specific meteorological models. The first one uses concentrated lifts of various strengths and unequal distance. The second one takes into account finite, nonuniform space amplitudes for the lifts and allows, therefore, for dolphin style flight. In both models, altitude constraints consisting of upper and lower limits are shown to be essential to model realistic problems. Numerical examples illustrate the difference with existing techniques based on local optimality conditions.
Wu, Zong-Sheng; Fu, Wei-Ping; Xue, Ru
2015-01-01
Teaching-learning-based optimization (TLBO) algorithm is proposed in recent years that simulates the teaching-learning phenomenon of a classroom to effectively solve global optimization of multidimensional, linear, and nonlinear problems over continuous spaces. In this paper, an improved teaching-learning-based optimization algorithm is presented, which is called nonlinear inertia weighted teaching-learning-based optimization (NIWTLBO) algorithm. This algorithm introduces a nonlinear inertia weighted factor into the basic TLBO to control the memory rate of learners and uses a dynamic inertia weighted factor to replace the original random number in teacher phase and learner phase. The proposed algorithm is tested on a number of benchmark functions, and its performance comparisons are provided against the basic TLBO and some other well-known optimization algorithms. The experiment results show that the proposed algorithm has a faster convergence rate and better performance than the basic TLBO and some other algorithms as well.
Wu, Zong-Sheng; Fu, Wei-Ping; Xue, Ru
2015-01-01
Teaching-learning-based optimization (TLBO) algorithm is proposed in recent years that simulates the teaching-learning phenomenon of a classroom to effectively solve global optimization of multidimensional, linear, and nonlinear problems over continuous spaces. In this paper, an improved teaching-learning-based optimization algorithm is presented, which is called nonlinear inertia weighted teaching-learning-based optimization (NIWTLBO) algorithm. This algorithm introduces a nonlinear inertia weighted factor into the basic TLBO to control the memory rate of learners and uses a dynamic inertia weighted factor to replace the original random number in teacher phase and learner phase. The proposed algorithm is tested on a number of benchmark functions, and its performance comparisons are provided against the basic TLBO and some other well-known optimization algorithms. The experiment results show that the proposed algorithm has a faster convergence rate and better performance than the basic TLBO and some other algorithms as well. PMID:26421005
NASA Astrophysics Data System (ADS)
Gao, Wei
2016-05-01
The objective function of displacement back analysis for rock parameters in underground engineering is a very complicated nonlinear multiple hump function. The global optimization method can solve this problem very well. However, many numerical simulations must be performed during the optimization process, which is very time consuming. Therefore, it is important to improve the computational efficiency of optimization back analysis. To improve optimization back analysis, a new global optimization, immunized continuous ant colony optimization, is proposed. This is an improved continuous ant colony optimization using the basic principles of an artificial immune system and evolutionary algorithm. Based on this new global optimization, a new displacement optimization back analysis for rock parameters is proposed. The computational performance of the new back analysis is verified through a numerical example and a real engineering example. The results show that this new method can be used to obtain suitable parameters of rock mass with higher accuracy and less effort than previous methods. Moreover, the new back analysis is very robust.
Global Design Optimization for Aerodynamics and Rocket Propulsion Components
NASA Technical Reports Server (NTRS)
Shyy, Wei; Papila, Nilay; Vaidyanathan, Rajkumar; Tucker, Kevin; Turner, James E. (Technical Monitor)
2000-01-01
Modern computational and experimental tools for aerodynamics and propulsion applications have matured to a stage where they can provide substantial insight into engineering processes involving fluid flows, and can be fruitfully utilized to help improve the design of practical devices. In particular, rapid and continuous development in aerospace engineering demands that new design concepts be regularly proposed to meet goals for increased performance, robustness and safety while concurrently decreasing cost. To date, the majority of the effort in design optimization of fluid dynamics has relied on gradient-based search algorithms. Global optimization methods can utilize the information collected from various sources and by different tools. These methods offer multi-criterion optimization, handle the existence of multiple design points and trade-offs via insight into the entire design space, can easily perform tasks in parallel, and are often effective in filtering the noise intrinsic to numerical and experimental data. However, a successful application of the global optimization method needs to address issues related to data requirements with an increase in the number of design variables, and methods for predicting the model performance. In this article, we review recent progress made in establishing suitable global optimization techniques employing neural network and polynomial-based response surface methodologies. Issues addressed include techniques for construction of the response surface, design of experiment techniques for supplying information in an economical manner, optimization procedures and multi-level techniques, and assessment of relative performance between polynomials and neural networks. Examples drawn from wing aerodynamics, turbulent diffuser flows, gas-gas injectors, and supersonic turbines are employed to help demonstrate the issues involved in an engineering design context. Both the usefulness of the existing knowledge to aid current design
Continuously Optimized Reliable Energy (CORE) Microgrid: Models & Tools (Fact Sheet)
Not Available
2013-07-01
This brochure describes Continuously Optimized Reliable Energy (CORE), a trademarked process NREL employs to produce conceptual microgrid designs. This systems-based process enables designs to be optimized for economic value, energy surety, and sustainability. Capabilities NREL offers in support of microgrid design are explained.
LDRD Final Report: Global Optimization for Engineering Science Problems
HART,WILLIAM E.
1999-12-01
For a wide variety of scientific and engineering problems the desired solution corresponds to an optimal set of objective function parameters, where the objective function measures a solution's quality. The main goal of the LDRD ''Global Optimization for Engineering Science Problems'' was the development of new robust and efficient optimization algorithms that can be used to find globally optimal solutions to complex optimization problems. This SAND report summarizes the technical accomplishments of this LDRD, discusses lessons learned and describes open research issues.
STP: A Stochastic Tunneling Algorithm for Global Optimization
Oblow, E.M.
1999-05-20
A stochastic approach to solving continuous function global optimization problems is presented. It builds on the tunneling approach to deterministic optimization presented by Barhen et al, by combining a series of local descents with stochastic searches. The method uses a rejection-based stochastic procedure to locate new local minima descent regions and a fixed Lipschitz-like constant to reject unpromising regions in the search space, thereby increasing the efficiency of the tunneling process. The algorithm is easily implemented in low-dimensional problems and scales easily to large problems. It is less effective without further heuristics in these latter cases, however. Several improvements to the basic algorithm which make use of approximate estimates of the algorithms parameters for implementation in high-dimensional problems are also discussed. Benchmark results are presented, which show that the algorithm is competitive with the best previously reported global optimization techniques. A successful application of the approach to a large-scale seismology problem of substantial computational complexity using a low-dimensional approximation scheme is also reported.
A quantitative method for optimized placement of continuous air monitors.
Whicker, Jeffrey J; Rodgers, John C; Moxley, John S
2003-11-01
Alarming continuous air monitors (CAMs) are a critical component for worker protection in facilities that handle large amounts of hazardous materials. In nuclear facilities, continuous air monitors alarm when levels of airborne radioactive materials exceed alarm thresholds, thus prompting workers to exit the room to reduce inhalation exposures. To maintain a high level of worker protection, continuous air monitors are required to detect radioactive aerosol clouds quickly and with good sensitivity. This requires that there are sufficient numbers of continuous air monitors in a room and that they are well positioned. Yet there are no published methodologies to quantitatively determine the optimal number and placement of continuous air monitors in a room. The goal of this study was to develop and test an approach to quantitatively determine optimal number and placement of continuous air monitors in a room. The method we have developed uses tracer aerosol releases (to simulate accidental releases) and the measurement of the temporal and spatial aspects of the dispersion of the tracer aerosol through the room. The aerosol dispersion data is then analyzed to optimize continuous air monitor utilization based on simulated worker exposure. This method was tested in a room within a Department of Energy operated plutonium facility at the Savannah River Site in South Carolina, U.S. Results from this study show that the value of quantitative airflow and aerosol dispersion studies is significant and that worker protection can be significantly improved while balancing the costs associated with CAM programs.
SamACO: variable sampling ant colony optimization algorithm for continuous optimization.
Hu, Xiao-Min; Zhang, Jun; Chung, Henry Shu-Hung; Li, Yun; Liu, Ou
2010-12-01
An ant colony optimization (ACO) algorithm offers algorithmic techniques for optimization by simulating the foraging behavior of a group of ants to perform incremental solution constructions and to realize a pheromone laying-and-following mechanism. Although ACO is first designed for solving discrete (combinatorial) optimization problems, the ACO procedure is also applicable to continuous optimization. This paper presents a new way of extending ACO to solving continuous optimization problems by focusing on continuous variable sampling as a key to transforming ACO from discrete optimization to continuous optimization. The proposed SamACO algorithm consists of three major steps, i.e., the generation of candidate variable values for selection, the ants' solution construction, and the pheromone update process. The distinct characteristics of SamACO are the cooperation of a novel sampling method for discretizing the continuous search space and an efficient incremental solution construction method based on the sampled values. The performance of SamACO is tested using continuous numerical functions with unimodal and multimodal features. Compared with some state-of-the-art algorithms, including traditional ant-based algorithms and representative computational intelligence algorithms for continuous optimization, the performance of SamACO is seen competitive and promising.
An approximation based global optimization strategy for structural synthesis
NASA Technical Reports Server (NTRS)
Sepulveda, A. E.; Schmit, L. A.
1991-01-01
A global optimization strategy for structural synthesis based on approximation concepts is presented. The methodology involves the solution of a sequence of highly accurate approximate problems using a global optimization algorithm. The global optimization algorithm implemented consists of a branch and bound strategy based on the interval evaluation of the objective function and constraint functions, combined with a local feasible directions algorithm. The approximate design optimization problems are constructed using first order approximations of selected intermediate response quantities in terms of intermediate design variables. Some numerical results for example problems are presented to illustrate the efficacy of the design procedure setforth.
Optimality of Gaussian attacks in continuous-variable quantum cryptography.
Navascués, Miguel; Grosshans, Frédéric; Acín, Antonio
2006-11-10
We analyze the asymptotic security of the family of Gaussian modulated quantum key distribution protocols for continuous-variables systems. We prove that the Gaussian unitary attack is optimal for all the considered bounds on the key rate when the first and second momenta of the canonical variables involved are known by the honest parties.
A global optimization algorithm for protein surface alignment
2010-01-01
Background A relevant problem in drug design is the comparison and recognition of protein binding sites. Binding sites recognition is generally based on geometry often combined with physico-chemical properties of the site since the conformation, size and chemical composition of the protein surface are all relevant for the interaction with a specific ligand. Several matching strategies have been designed for the recognition of protein-ligand binding sites and of protein-protein interfaces but the problem cannot be considered solved. Results In this paper we propose a new method for local structural alignment of protein surfaces based on continuous global optimization techniques. Given the three-dimensional structures of two proteins, the method finds the isometric transformation (rotation plus translation) that best superimposes active regions of two structures. We draw our inspiration from the well-known Iterative Closest Point (ICP) method for three-dimensional (3D) shapes registration. Our main contribution is in the adoption of a controlled random search as a more efficient global optimization approach along with a new dissimilarity measure. The reported computational experience and comparison show viability of the proposed approach. Conclusions Our method performs well to detect similarity in binding sites when this in fact exists. In the future we plan to do a more comprehensive evaluation of the method by considering large datasets of non-redundant proteins and applying a clustering technique to the results of all comparisons to classify binding sites. PMID:20920230
Parameter-space correlations of the optimal statistic for continuous gravitational-wave detection
Pletsch, Holger J.
2008-11-15
The phase parameters of matched-filtering searches for continuous gravitational-wave signals are sky position, frequency, and frequency time-derivatives. The space of these parameters features strong global correlations in the optimal detection statistic. For observation times smaller than 1 yr, the orbital motion of the Earth leads to a family of global-correlation equations which describes the 'global maximum structure' of the detection statistic. The solution to each of these equations is a different hypersurface in parameter space. The expected detection statistic is maximal at the intersection of these hypersurfaces. The global maximum structure of the detection statistic from stationary instrumental-noise artifacts is also described by the global-correlation equations. This permits the construction of a veto method which excludes false candidate events.
Aerodynamic design optimization by using a continuous adjoint method
NASA Astrophysics Data System (ADS)
Luo, JiaQi; Xiong, JunTao; Liu, Feng
2014-07-01
This paper presents the fundamentals of a continuous adjoint method and the applications of this method to the aerodynamic design optimization of both external and internal flows. General formulation of the continuous adjoint equations and the corresponding boundary conditions are derived. With the adjoint method, the complete gradient information needed in the design optimization can be obtained by solving the governing flow equations and the corresponding adjoint equations only once for each cost function, regardless of the number of design parameters. An inverse design of airfoil is firstly performed to study the accuracy of the adjoint gradient and the effectiveness of the adjoint method as an inverse design method. Then the method is used to perform a series of single and multiple point design optimization problems involving the drag reduction of airfoil, wing, and wing-body configuration, and the aerodynamic performance improvement of turbine and compressor blade rows. The results demonstrate that the continuous adjoint method can efficiently and significantly improve the aerodynamic performance of the design in a shape optimization problem.
GMG: A Guaranteed, Efficient Global Optimization Algorithm for Remote Sensing.
D'Helon, CD
2004-08-18
The monocular passive ranging (MPR) problem in remote sensing consists of identifying the precise range of an airborne target (missile, plane, etc.) from its observed radiance. This inverse problem may be set as a global optimization problem (GOP) whereby the difference between the observed and model predicted radiances is minimized over the possible ranges and atmospheric conditions. Using additional information about the error function between the predicted and observed radiances of the target, we developed GMG, a new algorithm to find the Global Minimum with a Guarantee. The new algorithm transforms the original continuous GOP into a discrete search problem, thereby guaranteeing to find the position of the global minimum in a reasonably short time. The algorithm is first applied to the golf course problem, which serves as a litmus test for its performance in the presence of both complete and degraded additional information. GMG is further assessed on a set of standard benchmark functions and then applied to various realizations of the MPR problem.
GMG - A guaranteed global optimization algorithm: Application to remote sensing
D'Helon, Cassius; Protopopescu, Vladimir A; Wells, Jack C; Barhen, Jacob
2007-01-01
We investigate the role of additional information in reducing the computational complexity of the global optimization problem (GOP). Following this approach, we develop GMG -- an algorithm to find the Global Minimum with a Guarantee. The new algorithm breaks up an originally continuous GOP into a discrete (grid) search problem followed by a descent problem. The discrete search identifies the basin of attraction of the global minimum after which the actual location of the minimizer is found upon applying a descent algorithm. The algorithm is first applied to the golf course problem, which serves as a litmus test for its performance in the presence of both complete and degraded additional information. GMG is further assessed on a set of standard benchmark functions. We then illustrate the performance of the the validated algorithm on a simple realization of the monocular passive ranging (MPR) problem in remote sensing, which consists of identifying the range of an airborne target (missile, plane, etc.) from its observed radiance. This inverse problem is set as a GOP whereby the difference between the observed and model predicted radiances is minimized over the possible ranges and atmospheric conditions. We solve the GOP using GMG and report on the performance of the algorithm.
WFH: closing the global gap--achieving optimal care.
Skinner, Mark W
2012-07-01
For 50 years, the World Federation of Hemophilia (WFH) has been working globally to close the gap in care and to achieve Treatment for All patients, men and women, with haemophilia and other inherited bleeding disorders, regardless of where they might live. The WFH estimates that more than one in 1000 men and women has a bleeding disorder equating to 6,900,000 worldwide. To close the gap in care between developed and developing nations a continued focus on the successful strategies deployed heretofore will be required. However, in response to the rapid advances in treatment and emerging therapeutic advances on the horizon it will also require fresh approaches and renewed strategic thinking. It is difficult to predict what each therapeutic advance on the horizon will mean for the future, but there is no doubt that we are in a golden age of research and development, which has the prospect of revolutionizing treatment once again. An improved understanding of "optimal" treatment is fundamental to the continued evolution of global care. The challenges of answering government and payer demands for evidence-based medicine, and cost justification for the introduction and enhancement of treatment, are ever-present and growing. To sustain and improve care it is critical to build the body of outcome data for individual patients, within haemophilia treatment centers (HTCs), nationally, regionally and globally. Emerging therapeutic advances (longer half-life therapies and gene transfer) should not be justified or brought to market based only on the notion that they will be economically more affordable, although that may be the case, but rather more importantly that they will be therapeutically more advantageous. Improvements in treatment adherence, reductions in bleeding frequency (including microhemorrhages), better management of trough levels, and improved health outcomes (including quality of life) should be the foremost considerations. As part of a new WFH strategic plan
WFH: closing the global gap--achieving optimal care.
Skinner, Mark W
2012-07-01
For 50 years, the World Federation of Hemophilia (WFH) has been working globally to close the gap in care and to achieve Treatment for All patients, men and women, with haemophilia and other inherited bleeding disorders, regardless of where they might live. The WFH estimates that more than one in 1000 men and women has a bleeding disorder equating to 6,900,000 worldwide. To close the gap in care between developed and developing nations a continued focus on the successful strategies deployed heretofore will be required. However, in response to the rapid advances in treatment and emerging therapeutic advances on the horizon it will also require fresh approaches and renewed strategic thinking. It is difficult to predict what each therapeutic advance on the horizon will mean for the future, but there is no doubt that we are in a golden age of research and development, which has the prospect of revolutionizing treatment once again. An improved understanding of "optimal" treatment is fundamental to the continued evolution of global care. The challenges of answering government and payer demands for evidence-based medicine, and cost justification for the introduction and enhancement of treatment, are ever-present and growing. To sustain and improve care it is critical to build the body of outcome data for individual patients, within haemophilia treatment centers (HTCs), nationally, regionally and globally. Emerging therapeutic advances (longer half-life therapies and gene transfer) should not be justified or brought to market based only on the notion that they will be economically more affordable, although that may be the case, but rather more importantly that they will be therapeutically more advantageous. Improvements in treatment adherence, reductions in bleeding frequency (including microhemorrhages), better management of trough levels, and improved health outcomes (including quality of life) should be the foremost considerations. As part of a new WFH strategic plan
Strategies for Global Optimization of Temporal Preferences
NASA Technical Reports Server (NTRS)
Morris, Paul; Morris, Robert; Khatib, Lina; Ramakrishnan, Sailesh
2004-01-01
A temporal reasoning problem can often be naturally characterized as a collection of constraints with associated local preferences for times that make up the admissible values for those constraints. Globally preferred solutions to such problems emerge as a result of well-defined operations that compose and order temporal assignments. The overall objective of this work is a characterization of different notions of global preference, and to identify tractable sub-classes of temporal reasoning problems incorporating these notions. This paper extends previous results by refining the class of useful notions of global temporal preference that are associated with problems that admit of tractable solution techniques. This paper also answers the hitherto open question of whether problems that seek solutions that are globally preferred from a Utilitarian criterion for global preference can be found tractably.
GenMin: An enhanced genetic algorithm for global optimization
NASA Astrophysics Data System (ADS)
Tsoulos, Ioannis G.; Lagaris, I. E.
2008-06-01
A new method that employs grammatical evolution and a stopping rule for finding the global minimum of a continuous multidimensional, multimodal function is considered. The genetic algorithm used is a hybrid genetic algorithm in conjunction with a local search procedure. We list results from numerical experiments with a series of test functions and we compare with other established global optimization methods. The accompanying software accepts objective functions coded either in Fortran 77 or in C++. Program summaryProgram title: GenMin Catalogue identifier: AEAR_v1_0 Program summary URL:http://cpc.cs.qub.ac.uk/summaries/AEAR_v1_0.html Program obtainable from: CPC Program Library, Queen's University, Belfast, N. Ireland Licensing provisions: Standard CPC licence, http://cpc.cs.qub.ac.uk/licence/licence.html No. of lines in distributed program, including test data, etc.: 35 810 No. of bytes in distributed program, including test data, etc.: 436 613 Distribution format: tar.gz Programming language: GNU-C++, GNU-C, GNU Fortran 77 Computer: The tool is designed to be portable in all systems running the GNU C++ compiler Operating system: The tool is designed to be portable in all systems running the GNU C++ compiler RAM: 200 KB Word size: 32 bits Classification: 4.9 Nature of problem: A multitude of problems in science and engineering are often reduced to minimizing a function of many variables. There are instances that a local optimum does not correspond to the desired physical solution and hence the search for a better solution is required. Local optimization techniques are frequently trapped in local minima. Global optimization is hence the appropriate tool. For example, solving a nonlinear system of equations via optimization, employing a least squares type of objective, one may encounter many local minima that do not correspond to solutions (i.e. they are far from zero). Solution method: Grammatical evolution and a stopping rule. Running time: Depending on the
Applications of parallel global optimization to mechanics problems
NASA Astrophysics Data System (ADS)
Schutte, Jaco Francois
Global optimization of complex engineering problems, with a high number of variables and local minima, requires sophisticated algorithms with global search capabilities and high computational efficiency. With the growing availability of parallel processing, it makes sense to address these requirements by increasing the parallelism in optimization strategies. This study proposes three methods of concurrent processing. The first method entails exploiting the structure of population-based global algorithms such as the stochastic Particle Swarm Optimization (PSO) algorithm and the Genetic Algorithm (GA). As a demonstration of how such an algorithm may be adapted for concurrent processing we modify and apply the PSO to several mechanical optimization problems on a parallel processing machine. Desirable PSO algorithm features such as insensitivity to design variable scaling and modest sensitivity to algorithm parameters are demonstrated. A second approach to parallelism and improving algorithm efficiency is by utilizing multiple optimizations. With this method a budget of fitness evaluations is distributed among several independent sub-optimizations in place of a single extended optimization. Under certain conditions this strategy obtains a higher combined probability of converging to the global optimum than a single optimization which utilizes the full budget of fitness evaluations. The third and final method of parallelism addressed in this study is the use of quasiseparable decomposition, which is applied to decompose loosely coupled problems. This yields several sub-problems of lesser dimensionality which may be concurrently optimized with reduced effort.
Hybrid and adaptive meta-model-based global optimization
NASA Astrophysics Data System (ADS)
Gu, J.; Li, G. Y.; Dong, Z.
2012-01-01
As an efficient and robust technique for global optimization, meta-model-based search methods have been increasingly used in solving complex and computation intensive design optimization problems. In this work, a hybrid and adaptive meta-model-based global optimization method that can automatically select appropriate meta-modelling techniques during the search process to improve search efficiency is introduced. The search initially applies three representative meta-models concurrently. Progress towards a better performing model is then introduced by selecting sample data points adaptively according to the calculated values of the three meta-models to improve modelling accuracy and search efficiency. To demonstrate the superior performance of the new algorithm over existing search methods, the new method is tested using various benchmark global optimization problems and applied to a real industrial design optimization example involving vehicle crash simulation. The method is particularly suitable for design problems involving computation intensive, black-box analyses and simulations.
An adaptive ant colony system algorithm for continuous-space optimization problems.
Li, Yan-jun; Wu, Tie-jun
2003-01-01
Ant colony algorithms comprise a novel category of evolutionary computation methods for optimization problems, especially for sequencing-type combinatorial optimization problems. An adaptive ant colony algorithm is proposed in this paper to tackle continuous-space optimization problems, using a new objective-function-based heuristic pheromone assignment approach for pheromone update to filtrate solution candidates. Global optimal solutions can be reached more rapidly by self-adjusting the path searching behaviors of the ants according to objective values. The performance of the proposed algorithm is compared with a basic ant colony algorithm and a Square Quadratic Programming approach in solving two benchmark problems with multiple extremes. The results indicated that the efficiency and reliability of the proposed algorithm were greatly improved. PMID:12656341
Interior search algorithm (ISA): a novel approach for global optimization.
Gandomi, Amir H
2014-07-01
This paper presents the interior search algorithm (ISA) as a novel method for solving optimization tasks. The proposed ISA is inspired by interior design and decoration. The algorithm is different from other metaheuristic algorithms and provides new insight for global optimization. The proposed method is verified using some benchmark mathematical and engineering problems commonly used in the area of optimization. ISA results are further compared with well-known optimization algorithms. The results show that the ISA is efficiently capable of solving optimization problems. The proposed algorithm can outperform the other well-known algorithms. Further, the proposed algorithm is very simple and it only has one parameter to tune.
Optimal packing characteristics of rolled, continuous stationary-phase columns.
Li, Chenghong; Ladisch, Christine M; Yang, Yiqi; Hendrickson, Richard; Keim, Craig; Mosier, Nathan; Ladisch, Michael R
2002-01-01
Rolled, continuous stationary phases were constructed by tightly rolling and inserting a whole textile fabric into a chromatography column. This work reports the column performance, in terms of plate height, void fraction, and resolution, of 10 cellulose-based fabrics. The relation between fabric structural properties of yarn diameter, fabric count, fabric compressibility, and column performance are quantitated. General requirements, including reproducibility of packing, for choosing fabrics to make a good SEC column are identified. This research showed that the packed columns have an optimal mass of fabric that minimizes plate height and maximizes resolution, in a manner that is consistent with chromatography theory. Mass of material packed is then an important column parameter to consider when optimizing columns for the rapid desalting of proteins. Proteins were completely separated from salt and glucose in less than 8 min at a pressure drop less than 500 psi on the rolled, continuous stationary-phase columns. These results, together with stability and reproducibility, suggest potential industrial applications for cellulose-based rolled, continuous stationary-phase columns where speed is a key parameter in the production process. PMID:11934301
Optimal scheduling of multiple sensors in continuous time.
Wu, Xiang; Zhang, Kanjian; Sun, Changyin
2014-05-01
This paper considers an optimal sensor scheduling problem in continuous time. In order to make the model more close to the practical problems, suppose that the following conditions are satisfied: only one sensor may be active at any one time; an admissible sensor schedule is a piecewise constant function with a finite number of switches; and each sensor either doesn't operate or operates for a minimum non-negligible amount of time. However, the switching times are unknown, and the feasible region isn't connected. Thus, it's difficult to solve the problem by conventional optimization techniques. To overcome this difficulty, by combining a binary relaxation, a time-scaling transformation and an exact penalty function, an algorithm is developed for solving this problem. Numerical results show that the algorithm is effective.
Optimal continuous variable quantum teleportation protocol for realistic settings
NASA Astrophysics Data System (ADS)
Luiz, F. S.; Rigolin, Gustavo
2015-03-01
We show the optimal setup that allows Alice to teleport coherent states | α > to Bob giving the greatest fidelity (efficiency) when one takes into account two realistic assumptions. The first one is the fact that in any actual implementation of the continuous variable teleportation protocol (CVTP) Alice and Bob necessarily share non-maximally entangled states (two-mode finitely squeezed states). The second one assumes that Alice's pool of possible coherent states to be teleported to Bob does not cover the whole complex plane (| α | < ∞). The optimal strategy is achieved by tuning three parameters in the original CVTP, namely, Alice's beam splitter transmittance and Bob's displacements in position and momentum implemented on the teleported state. These slight changes in the protocol are currently easy to be implemented and, as we show, give considerable gain in performance for a variety of possible pool of input states with Alice.
Nonlinear Global Optimization Using Curdling Algorithm
1996-03-01
An algorithm for performing curdling optimization which is a derivative-free, grid-refinement approach to nonlinear optimization was developed and implemented in software. This approach overcomes a number of deficiencies in existing approaches. Most notably, it finds extremal regions rather than only single external extremal points. The program is interactive and collects information on control parameters and constraints using menus. For up to four dimensions, function convergence is displayed graphically. Because the algorithm does not compute derivatives,more » gradients or vectors, it is numerically stable. It can find all the roots of a polynomial in one pass. It is an inherently parallel algorithm. Constraints are handled as being initially fuzzy, but become tighter with each iteration.« less
Neural network training with global optimization techniques.
Yamazaki, Akio; Ludermir, Teresa B
2003-04-01
This paper presents an approach of using Simulated Annealing and Tabu Search for the simultaneous optimization of neural network architectures and weights. The problem considered is the odor recognition in an artificial nose. Both methods have produced networks with high classification performance and low complexity. Generalization has been improved by using the backpropagation algorithm for fine tuning. The combination of simple and traditional search methods has shown to be very suitable for generating compact and efficient networks.
Global nonlinear optimization of spacecraft protective structures design
NASA Technical Reports Server (NTRS)
Mog, R. A.; Lovett, J. N., Jr.; Avans, S. L.
1990-01-01
The global optimization of protective structural designs for spacecraft subject to hypervelocity meteoroid and space debris impacts is presented. This nonlinear problem is first formulated for weight minimization of the space station core module configuration using the Nysmith impact predictor. Next, the equivalence and uniqueness of local and global optima is shown using properties of convexity. This analysis results in a new feasibility condition for this problem. The solution existence is then shown, followed by a comparison of optimization techniques. Finally, a sensitivity analysis is presented to determine the effects of variations in the systemic parameters on optimal design. The results show that global optimization of this problem is unique and may be achieved by a number of methods, provided the feasibility condition is satisfied. Furthermore, module structural design thicknesses and weight increase with increasing projectile velocity and diameter and decrease with increasing separation between bumper and wall for the Nysmith predictor.
More on conditions of local and global minima coincidence in discrete optimization problems
Lebedeva, T.T.; Sergienko, I.V.; Soltan, V.P.
1994-05-01
In some areas of discrete optimization, it is necessary to isolate classes of problems whose target functions do not have local or strictly local minima that differ from the global minima. Examples include optimizations on discrete metric spaces and graphs, lattices and partially ordered sets, and linear combinatorial problems. A unified schema that to a certain extent generalizes the convexity models on which the above-cited works are based has been presented in articles. This article is a continuation of that research.
Dispositional optimism and terminal decline in global quality of life.
Zaslavsky, Oleg; Palgi, Yuval; Rillamas-Sun, Eileen; LaCroix, Andrea Z; Schnall, Eliezer; Woods, Nancy F; Cochrane, Barbara B; Garcia, Lorena; Hingle, Melanie; Post, Stephen; Seguin, Rebecca; Tindle, Hilary; Shrira, Amit
2015-06-01
We examined whether dispositional optimism relates to change in global quality of life (QOL) as a function of either chronological age or years to impending death. We used a sample of 2,096 deceased postmenopausal women from the Women's Health Initiative clinical trials who were enrolled in the 2005-2010 Extension Study and for whom at least 1 global QOL and optimism measure were analyzed. Growth curve models were examined. Competing models were contrasted using model fit criteria. On average, levels of global QOL decreased with both higher age and closer proximity to death (e.g., M(score) = 7.7 eight years prior to death vs. M(score) = 6.1 one year prior to death). A decline in global QOL was better modeled as a function of distance to death (DtD) than as a function of chronological age (Bayesian information criterion [BIC](DtD) = 22,964.8 vs. BIC(age) = 23,322.6). Optimism was a significant correlate of both linear (estimate(DtD) = -0.01, SE(DtD) = 0.005; ρ = 0.004) and quadratic (estimate(DtD) = -0.006, SE(DtD) = 0.002; ρ = 0.004) terminal decline in global QOL so that death-related decline in global QOL was steeper among those with a high level of optimism than those with a low level of optimism. We found that dispositional optimism helps to maintain positive psychological perspective in the face of age-related decline. Optimists maintain higher QOL compared with pessimists when death-related trajectories were considered; however, the gap between those with high optimism and those with low optimism progressively attenuated with closer proximity to death, to the point that is became nonsignificant at the time of death.
Geophysical Inversion With Multi-Objective Global Optimization Methods
NASA Astrophysics Data System (ADS)
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
Humans Can Continuously Optimize Energetic Cost during Walking.
Selinger, Jessica C; O'Connor, Shawn M; Wong, Jeremy D; Donelan, J Maxwell
2015-09-21
People prefer to move in ways that minimize their energetic cost. For example, people tend to walk at a speed that minimizes energy use per unit distance and, for that speed, they select a step frequency that makes walking less costly. Although aspects of this preference appear to be established over both evolutionary and developmental timescales, it remains unclear whether people can also optimize energetic cost in real time. Here we show that during walking, people readily adapt established motor programs to minimize energy use. To accomplish this, we used robotic exoskeletons to shift people's energetically optimal step frequency to frequencies higher and lower than normally preferred. In response, we found that subjects adapted their step frequency to converge on the new energetic optima within minutes and in response to relatively small savings in cost (<5%). When transiently perturbed from their new optimal gait, subjects relied on an updated prediction to rapidly re-converge within seconds. Our collective findings indicate that energetic cost is not just an outcome of movement, but also plays a central role in continuously shaping it.
Optimizing human activity patterns using global sensitivity analysis
Hickmann, Kyle S.; Mniszewski, Susan M.; Del Valle, Sara Y.; Hyman, James M.
2014-01-01
Implementing realistic activity patterns for a population is crucial for modeling, for example, disease spread, supply and demand, and disaster response. Using the dynamic activity simulation engine, DASim, we generate schedules for a population that capture regular (e.g., working, eating, and sleeping) and irregular activities (e.g., shopping or going to the doctor). We use the sample entropy (SampEn) statistic to quantify a schedule’s regularity for a population. We show how to tune an activity’s regularity by adjusting SampEn, thereby making it possible to realistically design activities when creating a schedule. The tuning process sets up a computationally intractable high-dimensional optimization problem. To reduce the computational demand, we use Bayesian Gaussian process regression to compute global sensitivity indices and identify the parameters that have the greatest effect on the variance of SampEn. We use the harmony search (HS) global optimization algorithm to locate global optima. Our results show that HS combined with global sensitivity analysis can efficiently tune the SampEn statistic with few search iterations. We demonstrate how global sensitivity analysis can guide statistical emulation and global optimization algorithms to efficiently tune activities and generate realistic activity patterns. Though our tuning methods are applied to dynamic activity schedule generation, they are general and represent a significant step in the direction of automated tuning and optimization of high-dimensional computer simulations. PMID:25580080
Optimizing human activity patterns using global sensitivity analysis
Fairchild, Geoffrey; Hickmann, Kyle S.; Mniszewski, Susan M.; Del Valle, Sara Y.; Hyman, James M.
2013-12-10
Implementing realistic activity patterns for a population is crucial for modeling, for example, disease spread, supply and demand, and disaster response. Using the dynamic activity simulation engine, DASim, we generate schedules for a population that capture regular (e.g., working, eating, and sleeping) and irregular activities (e.g., shopping or going to the doctor). We use the sample entropy (SampEn) statistic to quantify a schedule’s regularity for a population. We show how to tune an activity’s regularity by adjusting SampEn, thereby making it possible to realistically design activities when creating a schedule. The tuning process sets up a computationally intractable high-dimensional optimization problem. To reduce the computational demand, we use Bayesian Gaussian process regression to compute global sensitivity indices and identify the parameters that have the greatest effect on the variance of SampEn. Here we use the harmony search (HS) global optimization algorithm to locate global optima. Our results show that HS combined with global sensitivity analysis can efficiently tune the SampEn statistic with few search iterations. We demonstrate how global sensitivity analysis can guide statistical emulation and global optimization algorithms to efficiently tune activities and generate realistic activity patterns. Finally, though our tuning methods are applied to dynamic activity schedule generation, they are general and represent a significant step in the direction of automated tuning and optimization of high-dimensional computer simulations.
Nallasivam, Ulaganathan; Shah, Vishesh H.; Shenvi, Anirudh A.; Huff, Joshua; Tawarmalani, Mohit; Agrawal, Rakesh
2016-02-10
We present a general Global Minimization Algorithm (GMA) to identify basic or thermally coupled distillation configurations that require the least vapor duty under minimum reflux conditions for separating any ideal or near-ideal multicomponent mixture into a desired number of product streams. In this algorithm, global optimality is guaranteed by modeling the system using Underwood equations and reformulating the resulting constraints to bilinear inequalities. The speed of convergence to the globally optimal solution is increased by using appropriate feasibility and optimality based variable-range reduction techniques and by developing valid inequalities. As a result, the GMA can be coupled with already developedmore » techniques that enumerate basic and thermally coupled distillation configurations, to provide for the first time, a global optimization based rank-list of distillation configurations.« less
Communication: Optimal parameters for basin-hopping global optimization based on Tsallis statistics
NASA Astrophysics Data System (ADS)
Shang, C.; Wales, D. J.
2014-08-01
A fundamental problem associated with global optimization is the large free energy barrier for the corresponding solid-solid phase transitions for systems with multi-funnel energy landscapes. To address this issue we consider the Tsallis weight instead of the Boltzmann weight to define the acceptance ratio for basin-hopping global optimization. Benchmarks for atomic clusters show that using the optimal Tsallis weight can improve the efficiency by roughly a factor of two. We present a theory that connects the optimal parameters for the Tsallis weighting, and demonstrate that the predictions are verified for each of the test cases.
Communication: Optimal parameters for basin-hopping global optimization based on Tsallis statistics
Shang, C. Wales, D. J.
2014-08-21
A fundamental problem associated with global optimization is the large free energy barrier for the corresponding solid-solid phase transitions for systems with multi-funnel energy landscapes. To address this issue we consider the Tsallis weight instead of the Boltzmann weight to define the acceptance ratio for basin-hopping global optimization. Benchmarks for atomic clusters show that using the optimal Tsallis weight can improve the efficiency by roughly a factor of two. We present a theory that connects the optimal parameters for the Tsallis weighting, and demonstrate that the predictions are verified for each of the test cases.
Similarity-based global optimization of buildings in urban scene
NASA Astrophysics Data System (ADS)
Zhu, Quansheng; Zhang, Jing; Jiang, Wanshou
2013-10-01
In this paper, an approach for the similarity-based global optimization of buildings in urban scene is presented. In the past, most researches concentrated on single building reconstruction, making it difficult to reconstruct reliable models from noisy or incomplete point clouds. To obtain a better result, a new trend is to utilize the similarity among the buildings. Therefore, a new similarity detection and global optimization strategy is adopted to modify local-fitting geometric errors. Firstly, the hierarchical structure that consists of geometric, topological and semantic features is constructed to represent complex roof models. Secondly, similar roof models can be detected by combining primitive structure and connection similarities. At last, the global optimization strategy is applied to preserve the consistency and precision of similar roof structures. Moreover, non-local consolidation is adapted to detect small roof parts. The experiments reveal that the proposed method can obtain convincing roof models and promote the reconstruction quality of 3D buildings in urban scene.
A deterministic global approach for mixed-discrete structural optimization
NASA Astrophysics Data System (ADS)
Lin, Ming-Hua; Tsai, Jung-Fa
2014-07-01
This study proposes a novel approach for finding the exact global optimum of a mixed-discrete structural optimization problem. Although many approaches have been developed to solve the mixed-discrete structural optimization problem, they cannot guarantee finding a global solution or they adopt too many extra binary variables and constraints in reformulating the problem. The proposed deterministic method uses convexification strategies and linearization techniques to convert a structural optimization problem into a convex mixed-integer nonlinear programming problem solvable to obtain a global optimum. To enhance the computational efficiency in treating complicated problems, the range reduction technique is also applied to tighten variable bounds. Several numerical experiments drawn from practical structural design problems are presented to demonstrate the effectiveness of the proposed method.
Joint Geophysical Inversion With Multi-Objective Global Optimization Methods
NASA Astrophysics Data System (ADS)
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.
Optimizing human activity patterns using global sensitivity analysis
Fairchild, Geoffrey; Hickmann, Kyle S.; Mniszewski, Susan M.; Del Valle, Sara Y.; Hyman, James M.
2013-12-10
Implementing realistic activity patterns for a population is crucial for modeling, for example, disease spread, supply and demand, and disaster response. Using the dynamic activity simulation engine, DASim, we generate schedules for a population that capture regular (e.g., working, eating, and sleeping) and irregular activities (e.g., shopping or going to the doctor). We use the sample entropy (SampEn) statistic to quantify a schedule’s regularity for a population. We show how to tune an activity’s regularity by adjusting SampEn, thereby making it possible to realistically design activities when creating a schedule. The tuning process sets up a computationally intractable high-dimensional optimizationmore » problem. To reduce the computational demand, we use Bayesian Gaussian process regression to compute global sensitivity indices and identify the parameters that have the greatest effect on the variance of SampEn. Here we use the harmony search (HS) global optimization algorithm to locate global optima. Our results show that HS combined with global sensitivity analysis can efficiently tune the SampEn statistic with few search iterations. We demonstrate how global sensitivity analysis can guide statistical emulation and global optimization algorithms to efficiently tune activities and generate realistic activity patterns. Finally, though our tuning methods are applied to dynamic activity schedule generation, they are general and represent a significant step in the direction of automated tuning and optimization of high-dimensional computer simulations.« less
Optimization of cortical hierarchies with continuous scales and ranges.
Reid, Andrew T; Krumnack, Antje; Wanke, Egon; Kötter, Rolf
2009-08-15
Although information flow in the neocortex has an apparent hierarchical organization, there is much ambiguity with respect to the definition of such a hierarchy, particularly in higher cortical regions. This ambiguity has been addressed by utilizing observable anatomical criteria, based upon tract tracing experiments, to constrain the definition of hierarchy [Felleman D.J. and van Essen D.C., 1991. Distributed hierarchical processing in the primate. Cereb. Cortex. 1(1), 1-47.]. There are, however, a high number of equally optimal hierarchies that fit these constraints [Hilgetag C.C., O'Neill M.A., Young M.P., 1996. Indeterminate organization of the visual system. Science. 271(5250), 776-777.]. Here, we propose a refined constraint set for optimization which utilizes continuous, rather than discrete, hierarchical levels, and permits a range of acceptable values rather than attempting to fit fixed hierarchical distances. Using linear programming to obtain hierarchies across a number of range sizes, we find a clear hierarchical pattern for both the original and refined versions of the Felleman and Van Essen [Felleman D.J. and van Essen D.C., 1991. Distributed hierarchical processing in the primate. Cereb. Cortex. 1(1), 1-47.] visual network. We also obtain an optimal hierarchy from a refined set of anatomical criteria which allows for the direct specification of hierarchical distance from the laminar distribution of labelled cells (Barone P., Batardiere A., Knoblauch K., Kennedy H., 2000. Laminar distribution of neurons in extrastriate areas projecting to visual areas V1 and V4 correlates with the hierarchical rank and indicates the operation of a distance rule. J. Neurosci. 20(9), 3263-3281.), and discuss the limitations and further possible refinements of such an approach.
Dutta, Amit K; Tan, Jasmine; Napadensky, Boris; Zydney, Andrew L; Shinkazh, Oleg
2016-03-01
Recent studies have demonstrated that continuous countercurrent tangential chromatography (CCTC) can effectively purify monoclonal antibodies from clarified cell culture fluid. CCTC has the potential to overcome many of the limitations of conventional packed bed protein A chromatography. This paper explores the optimization of CCTC in terms of product yield, impurity removal, overall productivity, and buffer usage. Modeling was based on data from bench-scale process development and CCTC experiments for protein A capture of two clarified Chinese Hamster Ovary cell culture feedstocks containing monoclonal antibodies provided by industrial partners. The impact of resin binding capacity and kinetics, as well as staging strategy and buffer recycling, was assessed. It was found that optimal staging in the binding step provides better yield and increases overall system productivity by 8-16%. Utilization of higher number of stages in the wash and elution steps can lead to significant decreases in buffer usage (∼40% reduction) as well as increased removal of impurities (∼2 log greater removal). Further reductions in buffer usage can be obtained by recycling of buffer in the wash and regeneration steps (∼35%). Preliminary results with smaller particle size resins show that the productivity of the CCTC system can be increased by 2.5-fold up to 190 g of mAb/L of resin/hr due to the reduction in mass transfer limitations in the binding step. These results provide a solid framework for designing and optimizing CCTC technology for capture applications. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:430-439, 2016. PMID:26914276
Orbit design and optimization based on global telecommunication performance metrics
NASA Technical Reports Server (NTRS)
Lee, Seungwon; Lee, Charles H.; Kerridge, Stuart; Cheung, Kar-Ming; Edwards, Charles D.
2006-01-01
The orbit selection of telecommunications orbiters is one of the critical design processes and should be guided by global telecom performance metrics and mission-specific constraints. In order to aid the orbit selection, we have coupled the Telecom Orbit Analysis and Simulation Tool (TOAST) with genetic optimization algorithms. As a demonstration, we have applied the developed tool to select an optimal orbit for general Mars telecommunications orbiters with the constraint of being a frozen orbit. While a typical optimization goal is to minimize tele-communications down time, several relevant performance metrics are examined: 1) area-weighted average gap time, 2) global maximum of local maximum gap time, 3) global maximum of local minimum gap time. Optimal solutions are found with each of the metrics. Common and different features among the optimal solutions as well as the advantage and disadvantage of each metric are presented. The optimal solutions are compared with several candidate orbits that were considered during the development of Mars Telecommunications Orbiter.
NASA Technical Reports Server (NTRS)
Washburn, R. B.; Mehra, R. K.; Sajan, S.
1979-01-01
The singular perturbation theory (SPT) approximation of optimal feedback control laws is presented and methods for on-line application of these approximations are discussed. It is demonstrated that SPT control laws break down when the current state is near the terminal target state. The use of continuation methods to improve the accuracy of the SPT approximation and to obtain global solutions of two-point boundary value problems is also discussed. As an illustration, consideration is given to the minimum-time control of a supersonic aircraft for a three-dimensional intercept problem.
Application of clustering global optimization to thin film design problems.
Lemarchand, Fabien
2014-03-10
Refinement techniques usually calculate an optimized local solution, which is strongly dependent on the initial formula used for the thin film design. In the present study, a clustering global optimization method is used which can iteratively change this initial formula, thereby progressing further than in the case of local optimization techniques. A wide panel of local solutions is found using this procedure, resulting in a large range of optical thicknesses. The efficiency of this technique is illustrated by two thin film design problems, in particular an infrared antireflection coating, and a solar-selective absorber coating. PMID:24663856
Application of clustering global optimization to thin film design problems.
Lemarchand, Fabien
2014-03-10
Refinement techniques usually calculate an optimized local solution, which is strongly dependent on the initial formula used for the thin film design. In the present study, a clustering global optimization method is used which can iteratively change this initial formula, thereby progressing further than in the case of local optimization techniques. A wide panel of local solutions is found using this procedure, resulting in a large range of optical thicknesses. The efficiency of this technique is illustrated by two thin film design problems, in particular an infrared antireflection coating, and a solar-selective absorber coating.
A global optimization paradigm based on change of measures
Sarkar, Saikat; Roy, Debasish; Vasu, Ram Mohan
2015-01-01
A global optimization framework, COMBEO (Change Of Measure Based Evolutionary Optimization), is proposed. An important aspect in the development is a set of derivative-free additive directional terms, obtainable through a change of measures en route to the imposition of any stipulated conditions aimed at driving the realized design variables (particles) to the global optimum. The generalized setting offered by the new approach also enables several basic ideas, used with other global search methods such as the particle swarm or the differential evolution, to be rationally incorporated in the proposed set-up via a change of measures. The global search may be further aided by imparting to the directional update terms additional layers of random perturbations such as ‘scrambling’ and ‘selection’. Depending on the precise choice of the optimality conditions and the extent of random perturbation, the search can be readily rendered either greedy or more exploratory. As numerically demonstrated, the new proposal appears to provide for a more rational, more accurate and, in some cases, a faster alternative to many available evolutionary optimization schemes. PMID:26587268
Global Optimal Trajectory in Chaos and NP-Hardness
NASA Astrophysics Data System (ADS)
Latorre, Vittorio; Gao, David Yang
This paper presents an unconventional theory and method for solving general nonlinear dynamical systems. Instead of the direct iterative methods, the discretized nonlinear system is first formulated as a global optimization problem via the least squares method. A newly developed canonical duality theory shows that this nonconvex minimization problem can be solved deterministically in polynomial time if a global optimality condition is satisfied. The so-called pseudo-chaos produced by linear iterative methods are mainly due to the intrinsic numerical error accumulations. Otherwise, the global optimization problem could be NP-hard and the nonlinear system can be really chaotic. A conjecture is proposed, which reveals the connection between chaos in nonlinear dynamics and NP-hardness in computer science. The methodology and the conjecture are verified by applications to the well-known logistic equation, a forced memristive circuit and the Lorenz system. Computational results show that the canonical duality theory can be used to identify chaotic systems and to obtain realistic global optimal solutions in nonlinear dynamical systems. The method and results presented in this paper should bring some new insights into nonlinear dynamical systems and NP-hardness in computational complexity theory.
Simple proof of the global optimality of the Hohmann transfer
NASA Technical Reports Server (NTRS)
Prussing, John E.
1992-01-01
The case of two-impulse transfer between coplanar circular orbits is considered. The global optimality of the Hohmann transfer among the class of two-impulse transfers is proved via ordinary calculus by using the familiar orbital elements, eccentricity e and parameter (semilatus rectum) p. It is noted that this proof is simpler than existing proofs in the literature.
Simple proof of the global optimality of the Hohmann transfer
NASA Astrophysics Data System (ADS)
Prussing, John E.
1992-08-01
The case of two-impulse transfer between coplanar circular orbits is considered. The global optimality of the Hohmann transfer among the class of two-impulse transfers is proved via ordinary calculus by using the familiar orbital elements, eccentricity e and parameter (semilatus rectum) p. It is noted that this proof is simpler than existing proofs in the literature.
Obstetricians’ Opinions of the Optimal Caesarean Rate: A Global Survey
Cavallaro, Francesca L.; Cresswell, Jenny A.; Ronsmans, Carine
2016-01-01
Background The debate surrounding the optimal caesarean rate has been ongoing for several decades, with the WHO recommending an “acceptable” rate of 5–15% since 1997, despite a weak evidence base. Global expert opinion from obstetric care providers on the optimal caesarean rate has not been documented. The objective of this study was to examine providers’ opinions of the optimal caesarean rate worldwide, among all deliveries and within specific sub-groups of deliveries. Methods A global online survey of medical doctors who had performed at least one caesarean in the last five years was conducted between August 2013 and January 2014. Respondents were asked to report their opinion of the optimal caesarean rate—defined as the caesarean rate that would minimise poor maternal and perinatal outcomes—at the population level and within specific sub-groups of deliveries (including women with demographic and clinical risk factors for caesareans). Median reported optimal rates and corresponding inter-quartile ranges (IQRs) were calculated for the sample, and stratified according to national caesarean rate, institutional caesarean rate, facility level, and respondent characteristics. Results Responses were collected from 1,057 medical doctors from 96 countries. The median reported optimal caesarean rate was 20% (IQR: 15–30%) for all deliveries. Providers in private for-profit facilities and in facilities with high institutional rates reported optimal rates of 30% or above, while those in Europe, in public facilities and in facilities with low institutional rates reported rates of 15% or less. Reported optimal rates were lowest among low-risk deliveries and highest for Absolute Maternal Indications (AMIs), with wide IQRs observed for most categories other than AMIs. Conclusions Three-quarters of respondents reported an optimal caesarean rate above the WHO 15% upper threshold. There was substantial variation in responses, highlighting a lack of consensus around
Landsat Data Continuity Mission (LDCM) - Optimizing X-Band Usage
NASA Technical Reports Server (NTRS)
Garon, H. M.; Gal-Edd, J. S.; Dearth, K. W.; Sank, V. I.
2010-01-01
The NASA version of the low-density parity check (LDPC) 7/8-rate code, shortened to the dimensions of (8160, 7136), has been implemented as the forward error correction (FEC) schema for the Landsat Data Continuity Mission (LDCM). This is the first flight application of this code. In order to place a 440 Msps link within the 375 MHz wide X band we found it necessary to heavily bandpass filter the satellite transmitter output . Despite the significant amplitude and phase distortions that accompanied the spectral truncation, the mission required BER is maintained at < 10(exp -12) with less than 2 dB of implementation loss. We utilized a band-pass filter designed ostensibly to replicate the link distortions to demonstrate link design viability. The same filter was then used to optimize the adaptive equalizer in the receiver employed at the terminus of the downlink. The excellent results we obtained could be directly attributed to the implementation of the LDPC code and the amplitude and phase compensation provided in the receiver. Similar results were obtained with receivers from several vendors.
Automated parameterization of intermolecular pair potentials using global optimization techniques
NASA Astrophysics Data System (ADS)
Krämer, Andreas; Hülsmann, Marco; Köddermann, Thorsten; Reith, Dirk
2014-12-01
In this work, different global optimization techniques are assessed for the automated development of molecular force fields, as used in molecular dynamics and Monte Carlo simulations. The quest of finding suitable force field parameters is treated as a mathematical minimization problem. Intricate problem characteristics such as extremely costly and even abortive simulations, noisy simulation results, and especially multiple local minima naturally lead to the use of sophisticated global optimization algorithms. Five diverse algorithms (pure random search, recursive random search, CMA-ES, differential evolution, and taboo search) are compared to our own tailor-made solution named CoSMoS. CoSMoS is an automated workflow. It models the parameters' influence on the simulation observables to detect a globally optimal set of parameters. It is shown how and why this approach is superior to other algorithms. Applied to suitable test functions and simulations for phosgene, CoSMoS effectively reduces the number of required simulations and real time for the optimization task.
Local and global strategies for optimal selective mass scaling
NASA Astrophysics Data System (ADS)
Tkachuk, Anton; Bischoff, Manfred
2014-06-01
The problem of optimal selective mass scaling for linearized elasto-dynamics is discussed. Optimal selective mass scaling should provide solutions for dynamical problems that are close to the ones obtained with a lumped mass matrix, but at much smaller computational costs. It should be equally applicable to all structurally relevant load cases. The three main optimality criteria, namely eigenmode preservation, small number of non-zero entries and good conditioning of the mass matrix are explicitly formulated in the article. An example of optimal mass scaling which relies on redistribution of mass on a global system level is constructed. Alternative local mass scaling strategies are proposed and compared with existing methods using one modal and two transient numerical examples.
Global optimization for multisensor fusion in seismic imaging
Barhen, J.; Protopopescu, V.; Reister, D.
1997-06-01
The accurate imaging of subsurface structures requires the fusion of data collected from large arrays of seismic sensors. The fusion process is formulated as an optimization problem and yields an extremely complex energy surface. Due to the very large number of local minima to be explored and escaped from, the seismic imaging problem has typically been tackled with stochastic optimization methods based on Monte Carlo techniques. Unfortunately, these algorithms are very cumbersome and computationally intensive. Here, the authors present TRUST--a novel deterministic algorithm for global optimization that they apply to seismic imaging. The excellent results demonstrate that TRUST may provide the necessary breakthrough to address major scientific and technological challenges in fields as diverse as seismic modeling, process optimization, and protein engineering.
A global optimization approach to multi-polarity sentiment analysis.
Li, Xinmiao; Li, Jing; Wu, Yukeng
2015-01-01
Following the rapid development of social media, sentiment analysis has become an important social media mining technique. The performance of automatic sentiment analysis primarily depends on feature selection and sentiment classification. While information gain (IG) and support vector machines (SVM) are two important techniques, few studies have optimized both approaches in sentiment analysis. The effectiveness of applying a global optimization approach to sentiment analysis remains unclear. We propose a global optimization-based sentiment analysis (PSOGO-Senti) approach to improve sentiment analysis with IG for feature selection and SVM as the learning engine. The PSOGO-Senti approach utilizes a particle swarm optimization algorithm to obtain a global optimal combination of feature dimensions and parameters in the SVM. We evaluate the PSOGO-Senti model on two datasets from different fields. The experimental results showed that the PSOGO-Senti model can improve binary and multi-polarity Chinese sentiment analysis. We compared the optimal feature subset selected by PSOGO-Senti with the features in the sentiment dictionary. The results of this comparison indicated that PSOGO-Senti can effectively remove redundant and noisy features and can select a domain-specific feature subset with a higher-explanatory power for a particular sentiment analysis task. The experimental results showed that the PSOGO-Senti approach is effective and robust for sentiment analysis tasks in different domains. By comparing the improvements of two-polarity, three-polarity and five-polarity sentiment analysis results, we found that the five-polarity sentiment analysis delivered the largest improvement. The improvement of the two-polarity sentiment analysis was the smallest. We conclude that the PSOGO-Senti achieves higher improvement for a more complicated sentiment analysis task. We also compared the results of PSOGO-Senti with those of the genetic algorithm (GA) and grid search method. From
p-MEMPSODE: Parallel and irregular memetic global optimization
NASA Astrophysics Data System (ADS)
Voglis, C.; Hadjidoukas, P. E.; Parsopoulos, K. E.; Papageorgiou, D. G.; Lagaris, I. E.; Vrahatis, M. N.
2015-12-01
A parallel memetic global optimization algorithm suitable for shared memory multicore systems is proposed and analyzed. The considered algorithm combines two well-known and widely used population-based stochastic algorithms, namely Particle Swarm Optimization and Differential Evolution, with two efficient and parallelizable local search procedures. The sequential version of the algorithm was first introduced as MEMPSODE (MEMetic Particle Swarm Optimization and Differential Evolution) and published in the CPC program library. We exploit the inherent and highly irregular parallelism of the memetic global optimization algorithm by means of a dynamic and multilevel approach based on the OpenMP tasking model. In our case, tasks correspond to local optimization procedures or simple function evaluations. Parallelization occurs at each iteration step of the memetic algorithm without affecting its searching efficiency. The proposed implementation, for the same random seed, reaches the same solution irrespectively of being executed sequentially or in parallel. Extensive experimental evaluation has been performed in order to illustrate the speedup achieved on a shared-memory multicore server.
A deterministic global optimization using smooth diagonal auxiliary functions
NASA Astrophysics Data System (ADS)
Sergeyev, Yaroslav D.; Kvasov, Dmitri E.
2015-04-01
In many practical decision-making problems it happens that functions involved in optimization process are black-box with unknown analytical representations and hard to evaluate. In this paper, a global optimization problem is considered where both the goal function f (x) and its gradient f‧ (x) are black-box functions. It is supposed that f‧ (x) satisfies the Lipschitz condition over the search hyperinterval with an unknown Lipschitz constant K. A new deterministic 'Divide-the-Best' algorithm based on efficient diagonal partitions and smooth auxiliary functions is proposed in its basic version, its convergence conditions are studied and numerical experiments executed on eight hundred test functions are presented.
Comments upon the usage of derivatives in Lipschitz global optimization
NASA Astrophysics Data System (ADS)
Sergeyev, Yaroslav D.; Kvasov, Dmitri E.; Mukhametzhanov, Marat S.
2016-06-01
An optimization problem is considered where the objective function f (x) is black-box and multiextremal and the information about its gradient ∇ f (x) is available during the search. It is supposed that ∇ f (x) satisfies the Lipschitz condition over the admissible hyperinterval with an unknown Lipschitz constant K. Some numerical Lipschitz global optimization methods based on geometric ideas with the usage of different estimates of the Lipschitz constant K are presented. Results of their systematic experimental investigation are reported and commented on.
Global Optimization Methods for Gravitational Lens Systems with Regularized Sources
NASA Astrophysics Data System (ADS)
Rogers, Adam; Fiege, Jason D.
2012-11-01
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 by applying our code to a subset of the lens systems included in the SLACS survey.
Global optimization using the y-ybar diagram
NASA Astrophysics Data System (ADS)
Brown, Daniel M.
1991-12-01
Software is under development at Teledyne Brown Engineering to represent a lens configuration as a y-ybar or Delano diagram. The program determines third-order Seidel and chromatic aberrations for each configuration. It performs a global search through all valid permutations of configuration space and determines, to within a step increment of the space, the configuration with smallest third-order aberrations. The program was developed to generate first-order optical layouts which promised to reach global minima during subsequent conventional optimization. Other operations allowed by the program are: add or delete surfaces, couple surfaces (for Mangin mirrors), shift the stop position, and display first-order properties and the optical layout (surface radii and thicknesses) for subsequent entry into a conventional lens-design program with automatic optimization. Algorithms for performing some of the key functions, not covered by previous authors, are discussed in this paper.
Multi-fidelity global design optimization including parallelization potential
NASA Astrophysics Data System (ADS)
Cox, Steven Edward
The DIRECT global optimization algorithm is a relatively new space partitioning algorithm designed to determine the globally optimal design within a designated design space. This dissertation examines the applicability of the DIRECT algorithm to two classes of design problems: unimodal functions where small amplitude, high frequency fluctuations in the objective function make optimization difficult; and multimodal functions where multiple local optima are formed by the underlying physics of the problem (as opposed to minor fluctuations in the analysis code). DIRECT is compared with two other multistart local optimization techniques on two polynomial test problems and one engineering conceptual design problem. Three modifications to the DIRECT algorithm are proposed to increase the effectiveness of the algorithm. The DIRECT-BP algorithm is presented which alters the way DIRECT searches the neighborhood of the current best point as optimization progresses. The algorithm reprioritizes which points to analyze at each iteration. This is to encourage analysis of points that surround the best point but that are farther away than the points selected by the DIRECT algorithm. This increases the robustness of the DIRECT search and provides more information on the characteristics of the neighborhood of the point selected as the global optimum. A multifidelity version of the DIRECT algorithm is proposed to reduce the cost of optimization using DIRECT. By augmenting expensive high-fidelity analysis with cheap low-fidelity analysis, the optimization can be performed with fewer high-fidelity analyses. Two correction schemes are examined using high- and low-fidelity results at one point to correct the low-fidelity result at a nearby point. This corrected value is then used in place of a high-fidelity analysis by the DIRECT algorithm. In this way the number of high-fidelity analyses required is reduced and the optimization became less expensive. Finally the DIRECT algorithm is
Asynchronous global optimization techniques for medium and large inversion problems
Pereyra, V.; Koshy, M.; Meza, J.C.
1995-04-01
We discuss global optimization procedures adequate for seismic inversion problems. We explain how to save function evaluations (which may involve large scale ray tracing or other expensive operations) by creating a data base of information on what parts of parameter space have already been inspected. It is also shown how a correct parallel implementation using PVM speeds up the process almost linearly with respect to the number of processors, provided that the function evaluations are expensive enough to offset the communication overhead.
Continued global warming after CO2 emissions stoppage
NASA Astrophysics Data System (ADS)
Froelicher, T. L.; Winton, M.; Sarmiento, J. L.
2014-12-01
Recent studies have suggested that global mean surface temperature would remain approximately constant on multi-century timescales after CO2 emissions are stopped. These studies suggest that the cooling effect of reduction in radiative forcing due to the decrease in atmospheric CO2 is roughly balanced by the warming effect of reduction in ocean heat uptake. Here we use Earth system model simulations of such a stoppage to demonstrate that in some models, surface temperature may actually increase on multi-century timescales after an initial century-long decrease. For example, global mean surface temperature may increase by 0.6°C after carbon emissions are stopped at 2°C above preindustrial. Surprisingly, the temperature increase occurs in spite of a decline in radiative forcing that exceeds the decline in ocean heat uptake—a circumstance that would otherwise be expected to lead to a decline in global temperature. The reason is that the warming effect of decreasing ocean heat uptake together with feedback effects arising in response to the geographic structure of ocean heat uptake overcompensates the cooling effect of decreasing atmospheric CO2 on multi-century timescales in these models. We show that ocean heat uptake, which occurs preferentially at subpolar latitudes, has a larger temperature impact per watt per square meter than the CO2 radiative forcing. In other words, the cooling effect of a high-latitude heat sink is larger than that of an equivalent tropical heat sink. The implications of our results for estimates of allowable carbon emissions required to remain below a specific global warming target will be discussed.
Continued global warming after CO2 emissions stoppage
NASA Astrophysics Data System (ADS)
Frölicher, Thomas; Winton, Michael; Sarmiento, Jorge
2014-05-01
Recent studies have suggested that global mean surface temperature would remain approximately constant on multi-century timescales after CO2 emissions are stopped. Here we use Earth system model simulations of such a stoppage to demonstrate that in some models, surface temperature may actually increase on multi-century timescales after an initial century-long decrease. For example, global mean surface temperature may increase by 0.6°C after a carbon emissions stoppage at 2-degree. This increase occurs in spite of a decline in radiative forcing that exceeds the decline in ocean heat uptake—a circumstance that would otherwise be expected to lead to a decline in global temperature. The reason is that the warming effect of decreasing ocean heat uptake together with feedback effects arising in response to the geographic structure of ocean heat uptake overcompensates the cooling effect of decreasing atmospheric CO2 on multi-century timescales. Our study also reveals that equilibrium climate sensitivity estimates based on a widely used method of regressing the Earth's energy imbalance against surface temperature change are biased. Uncertainty in the magnitude of the feedback effects associated with the magnitude and geographic distribution of ocean heat uptake therefore contributes substantially to the uncertainty in allowable carbon emissions for a given multi-century warming target.
Multidisciplinary optimization of controlled space structures with global sensitivity equations
NASA Technical Reports Server (NTRS)
Padula, Sharon L.; James, Benjamin B.; Graves, Philip C.; Woodard, Stanley E.
1991-01-01
A new method for the preliminary design of controlled space structures is presented. The method coordinates standard finite element structural analysis, multivariable controls, and nonlinear programming codes and allows simultaneous optimization of the structures and control systems of a spacecraft. Global sensitivity equations are a key feature of this method. The preliminary design of a generic geostationary platform is used to demonstrate the multidisciplinary optimization method. Fifteen design variables are used to optimize truss member sizes and feedback gain values. The goal is to reduce the total mass of the structure and the vibration control system while satisfying constraints on vibration decay rate. Incorporating the nonnegligible mass of actuators causes an essential coupling between structural design variables and control design variables. The solution of the demonstration problem is an important step toward a comprehensive preliminary design capability for structures and control systems. Use of global sensitivity equations helps solve optimization problems that have a large number of design variables and a high degree of coupling between disciplines.
Methods for accurate homology modeling by global optimization.
Joo, Keehyoung; Lee, Jinwoo; Lee, Jooyoung
2012-01-01
High accuracy protein modeling from its sequence information is an important step toward revealing the sequence-structure-function relationship of proteins and nowadays it becomes increasingly more useful for practical purposes such as in drug discovery and in protein design. We have developed a protocol for protein structure prediction that can generate highly accurate protein models in terms of backbone structure, side-chain orientation, hydrogen bonding, and binding sites of ligands. To obtain accurate protein models, we have combined a powerful global optimization method with traditional homology modeling procedures such as multiple sequence alignment, chain building, and side-chain remodeling. We have built a series of specific score functions for these steps, and optimized them by utilizing conformational space annealing, which is one of the most successful combinatorial optimization algorithms currently available.
Continued global warming after CO2 emissions stoppage
NASA Astrophysics Data System (ADS)
Frölicher, Thomas Lukas; Winton, Michael; Sarmiento, Jorge Louis
2014-01-01
Recent studies have suggested that global mean surface temperature would remain approximately constant on multi-century timescales after CO2 emissions are stopped. Here we use Earth system model simulations of such a stoppage to demonstrate that in some models, surface temperature may actually increase on multi-century timescales after an initial century-long decrease. This occurs in spite of a decline in radiative forcing that exceeds the decline in ocean heat uptake--a circumstance that would otherwise be expected to lead to a decline in global temperature. The reason is that the warming effect of decreasing ocean heat uptake together with feedback effects arising in response to the geographic structure of ocean heat uptake overcompensates the cooling effect of decreasing atmospheric CO2 on multi-century timescales. Our study also reveals that equilibrium climate sensitivity estimates based on a widely used method of regressing the Earth's energy imbalance against surface temperature change are biased. Uncertainty in the magnitude of the feedback effects associated with the magnitude and geographic distribution of ocean heat uptake therefore contributes substantially to the uncertainty in allowable carbon emissions for a given multi-century warming target.
Avoiding spurious submovement decompositions : a globally optimal algorithm.
Rohrer, Brandon Robinson; Hogan, Neville
2003-07-01
Evidence for the existence of discrete submovements underlying continuous human movement has motivated many attempts to extract them. Although they produce visually convincing results, all of the methodologies that have been employed are prone to produce spurious decompositions. Examples of potential failures are given. A branch-and-bound algorithm for submovement extraction, capable of global nonlinear minimization (and hence capable of avoiding spurious decompositions), is developed and demonstrated.
Global structual optimizations of surface systems with a genetic algorithm
Chuang, Feng-Chuan
2005-01-01
Global structural optimizations with a genetic algorithm were performed for atomic cluster and surface systems including aluminum atomic clusters, Si magic clusters on the Si(111) 7 x 7 surface, silicon high-index surfaces, and Ag-induced Si(111) reconstructions. First, the global structural optimizations of neutral aluminum clusters Al_{n} algorithm in combination with tight-binding and first-principles calculations were performed to study the structures of magic clusters on the Si(111) 7 x 7 surface. Extensive calculations show that the magic cluster observed in scanning tunneling microscopy (STM) experiments consist of eight Si atoms. Simulated STM images of the Si magic cluster exhibit a ring-like feature similar to STM experiments. Third, a genetic algorithm coupled with a highly optimized empirical potential were used to determine the lowest energy structure of high-index semiconductor surfaces. The lowest energy structures of Si(105) and Si(114) were determined successfully. The results of Si(105) and Si(114) are reported within the framework of highly optimized empirical potential and first-principles calculations. Finally, a genetic algorithm coupled with Si and Ag tight-binding potentials were used to search for Ag-induced Si(111) reconstructions at various Ag and Si coverages. The optimized structural models of √3 x √3, 3 x 1, and 5 x 2 phases were reported using first-principles calculations. A novel model is found to have lower surface energy than the proposed double-honeycomb chained (DHC) model both for Au/Si(111) 5 x 2 and Ag/Si(111) 5 x 2 systems.
A Novel Hybrid Firefly Algorithm for Global Optimization
Zhang, Lina; Liu, Liqiang; Yang, Xin-She; Dai, Yuntao
2016-01-01
Global optimization is challenging to solve due to its nonlinearity and multimodality. Traditional algorithms such as the gradient-based methods often struggle to deal with such problems and one of the current trends is to use metaheuristic algorithms. In this paper, a novel hybrid population-based global optimization algorithm, called hybrid firefly algorithm (HFA), is proposed by combining the advantages of both the firefly algorithm (FA) and differential evolution (DE). FA and DE are executed in parallel to promote information sharing among the population and thus enhance searching efficiency. In order to evaluate the performance and efficiency of the proposed algorithm, a diverse set of selected benchmark functions are employed and these functions fall into two groups: unimodal and multimodal. The experimental results show better performance of the proposed algorithm compared to the original version of the firefly algorithm (FA), differential evolution (DE) and particle swarm optimization (PSO) in the sense of avoiding local minima and increasing the convergence rate. PMID:27685869
A Global Optimization Approach to Multi-Polarity Sentiment Analysis
Li, Xinmiao; Li, Jing; Wu, Yukeng
2015-01-01
Following the rapid development of social media, sentiment analysis has become an important social media mining technique. The performance of automatic sentiment analysis primarily depends on feature selection and sentiment classification. While information gain (IG) and support vector machines (SVM) are two important techniques, few studies have optimized both approaches in sentiment analysis. The effectiveness of applying a global optimization approach to sentiment analysis remains unclear. We propose a global optimization-based sentiment analysis (PSOGO-Senti) approach to improve sentiment analysis with IG for feature selection and SVM as the learning engine. The PSOGO-Senti approach utilizes a particle swarm optimization algorithm to obtain a global optimal combination of feature dimensions and parameters in the SVM. We evaluate the PSOGO-Senti model on two datasets from different fields. The experimental results showed that the PSOGO-Senti model can improve binary and multi-polarity Chinese sentiment analysis. We compared the optimal feature subset selected by PSOGO-Senti with the features in the sentiment dictionary. The results of this comparison indicated that PSOGO-Senti can effectively remove redundant and noisy features and can select a domain-specific feature subset with a higher-explanatory power for a particular sentiment analysis task. The experimental results showed that the PSOGO-Senti approach is effective and robust for sentiment analysis tasks in different domains. By comparing the improvements of two-polarity, three-polarity and five-polarity sentiment analysis results, we found that the five-polarity sentiment analysis delivered the largest improvement. The improvement of the two-polarity sentiment analysis was the smallest. We conclude that the PSOGO-Senti achieves higher improvement for a more complicated sentiment analysis task. We also compared the results of PSOGO-Senti with those of the genetic algorithm (GA) and grid search method. From
NASA Astrophysics Data System (ADS)
Paasche, H.; Tronicke, J.
2012-04-01
In many near surface geophysical applications multiple tomographic data sets are routinely acquired to explore subsurface structures and parameters. Linking the model generation process of multi-method geophysical data sets can significantly reduce ambiguities in geophysical data analysis and model interpretation. Most geophysical inversion approaches rely on local search optimization methods used to find an optimal model in the vicinity of a user-given starting model. The final solution may critically depend on the initial model. Alternatively, global optimization (GO) methods have been used to invert geophysical data. They explore the solution space in more detail and determine the optimal model independently from the starting model. Additionally, they can be used to find sets of optimal models allowing a further analysis of model parameter uncertainties. Here we employ particle swarm optimization (PSO) to realize the global optimization of tomographic data. PSO is an emergent methods based on swarm intelligence characterized by fast and robust convergence towards optimal solutions. The fundamental principle of PSO is inspired by nature, since the algorithm mimics the behavior of a flock of birds searching food in a search space. In PSO, a number of particles cruise a multi-dimensional solution space striving to find optimal model solutions explaining the acquired data. The particles communicate their positions and success and direct their movement according to the position of the currently most successful particle of the swarm. The success of a particle, i.e. the quality of the currently found model by a particle, must be uniquely quantifiable to identify the swarm leader. When jointly inverting disparate data sets, the optimization solution has to satisfy multiple optimization objectives, at least one for each data set. Unique determination of the most successful particle currently leading the swarm is not possible. Instead, only statements about the Pareto
hp-Pseudospectral method for solving continuous-time nonlinear optimal control problems
NASA Astrophysics Data System (ADS)
Darby, Christopher L.
2011-12-01
In this dissertation, a direct hp-pseudospectral method for approximating the solution to nonlinear optimal control problems is proposed. The hp-pseudospectral method utilizes a variable number of approximating intervals and variable-degree polynomial approximations of the state within each interval. Using the hp-discretization, the continuous-time optimal control problem is transcribed to a finite-dimensional nonlinear programming problem (NLP). The differential-algebraic constraints of the optimal control problem are enforced at a finite set of collocation points, where the collocation points are either the Legendre-Gauss or Legendre-Gauss-Radau quadrature points. These sets of points are chosen because they correspond to high-accuracy Gaussian quadrature rules for approximating the integral of a function. Moreover, Runge phenomenon for high-degree Lagrange polynomial approximations to the state is avoided by using these points. The key features of the hp-method include computational sparsity associated with low-order polynomial approximations and rapid convergence rates associated with higher-degree polynomials approximations. Consequently, the hp-method is both highly accurate and computationally efficient. Two hp-adaptive algorithms are developed that demonstrate the utility of the hp-approach. The algorithms are shown to accurately approximate the solution to general continuous-time optimal control problems in a computationally efficient manner without a priori knowledge of the solution structure. The hp-algorithms are compared empirically against local (h) and global (p) collocation methods over a wide range of problems and are found to be more efficient and more accurate. The hp-pseudospectral approach developed in this research not only provides a high-accuracy approximation to the state and control of an optimal control problem, but also provides high-accuracy approximations to the costate of the optimal control problem. The costate is approximated by
PROSPECT: A Computer System for Globally-Optimal Threading
Xu, D.; Xu, Y.
1999-08-06
This paper presents a new computer system, PROSPECT, for protein threading. PROSPECT employs an energy function that consists of three additive terms: (1) a singleton fitness term, (2) a distance-dependent pairwise-interaction preference term, and (3) alignment gap penalty; and currently uses FSSP as its threading template database. PROSPECT uses a divide-and-conquer algorithm to find an alignment between a query protein sequence and a protein fold template, which is guaranteed to be globally optimal for its energy function. The threading algorithm presented here significantly improves the computational efficiency of our previously-published algorithm, which makes PROSPECT a practical tool even for large protein threading problems. Mathematically, PROSPECT finds a globally-optimal threading between a query sequence of n residues and a fold template of m residues and M core secondary structures in O(nm + MnN{sup 1.5C{minus}1}) time and O(nm + nN{sup C{minus}1}) space, where C, the topological complexity of the template fold as we term, is a value which characterizes the overall structure of the considered pairwise interactions in the fold; and N represents the maximum number of possible alignments between an individual core of the fold and the query sequence when its neighboring cores are already aligned. PROSPECT allows a user to incorporate known biological constraints about the query sequence during the threading process. For given constraints, the system finds a globally-optimal threading which satisfies the constraints. Currently PROSPECT can deal with constraints which reflect geometrical relationships among residues of disulfide bonds, active sites, or determined by the NOE constraints of (low-resolution) NMR spectral data.
Practical strategy for global optimization of zoom lenses
NASA Astrophysics Data System (ADS)
Kuper, Thomas G.; Harris, Thomas I.
1998-09-01
The effectiveness of global optimizers for non-zoomed lenses has been steadily improving, but until recently their application to zoom lens design has been less successful. Although some methods have been able to make minor improvements to initial design forms, the algorithms have not consistently discovered new solutions with different group power distributions in a single run. In many cases, the difficulty appears related to how effective focal length (EFL) is controlled across zoom positions. Improvements made to the Global SynthesisTM (GS) algorithm in Code VTM, together with a revised strategy for controlling the EFL via weighted constraints, have significantly improved the ability of GS to discover distinct zoom lens solutions, including those with different group powers. We offer a plausible explanation for the success of these changes, and we discuss an example zoom lens design problem based on a 2-group, 7-element patent design.
An Adaptive Unified Differential Evolution Algorithm for Global Optimization
Qiang, Ji; Mitchell, Chad
2014-11-03
In this paper, we propose a new adaptive unified differential evolution algorithm for single-objective global optimization. Instead of the multiple mutation strate- gies proposed in conventional differential evolution algorithms, this algorithm employs a single equation unifying multiple strategies into one expression. It has the virtue of mathematical simplicity and also provides users the flexibility for broader exploration of the space of mutation operators. By making all control parameters in the proposed algorithm self-adaptively evolve during the process of optimization, it frees the application users from the burden of choosing appro- priate control parameters and also improves the performance of the algorithm. In numerical tests using thirteen basic unimodal and multimodal functions, the proposed adaptive unified algorithm shows promising performance in compari- son to several conventional differential evolution algorithms.
Design and global optimization of high-efficiency thermophotovoltaic systems.
Bermel, Peter; Ghebrebrhan, Michael; Chan, Walker; Yeng, Yi Xiang; Araghchini, Mohammad; Hamam, Rafif; Marton, Christopher H; Jensen, Klavs F; Soljačić, Marin; Joannopoulos, John D; Johnson, Steven G; Celanovic, Ivan
2010-09-13
Despite their great promise, small experimental thermophotovoltaic (TPV) systems at 1000 K generally exhibit extremely low power conversion efficiencies (approximately 1%), due to heat losses such as thermal emission of undesirable mid-wavelength infrared radiation. Photonic crystals (PhC) have the potential to strongly suppress such losses. However, PhC-based designs present a set of non-convex optimization problems requiring efficient objective function evaluation and global optimization algorithms. Both are applied to two example systems: improved micro-TPV generators and solar thermal TPV systems. Micro-TPV reactors experience up to a 27-fold increase in their efficiency and power output; solar thermal TPV systems see an even greater 45-fold increase in their efficiency (exceeding the Shockley-Quiesser limit for a single-junction photovoltaic cell).
Global distribution and continuing spread of Aedes albopictus.
Knudsen, A B
1995-12-01
Aedes albopictus ranks second only to Ae. aegypti in importance to man as a vector of dengue and dengue haemorrhagic fever (DHF) which viruses place at risk a potential population of 2 billion people living in tropical and sub-tropical regions. Due to its predilection for breeding in a plethora of habitat within urban and suburban environs as well as peri-rural areas it is spreading rapidly where suitable breeding is available. It exhibits strain differences ranging from the cold-hardy to tropic loving, yet despite limited flight range, it has spread beyond the Orient to China, the Pacific, the Indian Ocean islands, the Americas, parts of continental Africa and into southern Europe. This has been done principally by means of transport of eggs in used tyres via rapid air and sea transport. Egg positive used tyres, when shipped, and later rehydrated by rainfall, produce adult mosquitoes within a few days rapidly infesting new areas. Although dengue and other vector-borne arboviral diseases have not been in Europe in epidemic form for many decades, travelers do not infrequently return from dengue endemic areas with dengue and other similar infections. Aedes albopictus is a potential vector of a number of arboviruses and can transmit them in a vertical or transvenereal manner in nature, thereby providing a means for their maintenance and transmission. Where Ae. albopictus newly occurs, the affected populace immediately are aware of a new daytime, nuisance biting mosquito and complaints addressed to local mosquito control authorities increase significantly. The biological characteristics of the mosquito make its spread within Europe highly probable. The paper offers several avenues to be pursued to reduce the global spread of Ae. albopictus, when examined within the context of Europe and the wider world community.
A Unified Differential Evolution Algorithm for Global Optimization
Qiang, Ji; Mitchell, Chad
2014-06-24
Abstract?In this paper, we propose a new unified differential evolution (uDE) algorithm for single objective global optimization. Instead of selecting among multiple mutation strategies as in the conventional differential evolution algorithm, this algorithm employs a single equation as the mutation strategy. It has the virtue of mathematical simplicity and also provides users the flexbility for broader exploration of different mutation strategies. Numerical tests using twelve basic unimodal and multimodal functions show promising performance of the proposed algorithm in comparison to convential differential evolution algorithms.
A self-learning particle swarm optimizer for global optimization problems.
Li, Changhe; Yang, Shengxiang; Nguyen, Trung Thanh
2012-06-01
Particle swarm optimization (PSO) has been shown as an effective tool for solving global optimization problems. So far, most PSO algorithms use a single learning pattern for all particles, which means that all particles in a swarm use the same strategy. This monotonic learning pattern may cause the lack of intelligence for a particular particle, which makes it unable to deal with different complex situations. This paper presents a novel algorithm, called self-learning particle swarm optimizer (SLPSO), for global optimization problems. In SLPSO, each particle has a set of four strategies to cope with different situations in the search space. The cooperation of the four strategies is implemented by an adaptive learning framework at the individual level, which can enable a particle to choose the optimal strategy according to its own local fitness landscape. The experimental study on a set of 45 test functions and two real-world problems show that SLPSO has a superior performance in comparison with several other peer algorithms.
Multivariable optimization of a multiproduct continuous chemicals facility.
Subawalla, Hoshang; Zehnder, Anthony J; Turcotte, Michael; Bonin, Millard J
2004-01-01
Multivariable optimization (MVO) is a powerful nonlinear steady-state flowsheet simulation technique used widely in the chemical process industry to optimize plant performance by increasing plant capacity and/or reducing energy usage. The user supplies the objective function(s), constraints, and variable limits based on operating heuristics, prior experience, and observed process behavior. In this paper we describe how we used MVO in conjunction with improved automation and statistical process monitoring to increase capacity and reduce energy consumption. This project was conducted over a two-year period in a methylamines facility that produces three products: monomethylamine (MMA), dimethylamine (DMA), and trimethylamine (TMA). It led to a 14% improvement in MMA capacity and a 7% improvement in TMA capacity. Energy consumption per pound of main product decreased by approximately 20% for the six-month period (April-September, 2000) when compared to a three-year average (1997-2000). It was accompanied by a 60% improvement in process capability and 31% improvement in product quality. We attribute this improvement to several factors: smoother transition between optimum set points determined by the process optimizer, improved disturbance rejection due to controller installation and tuning, and reduced operator intervention because of controlled operation at or near the optimum set points.
Optimal landing position in reading isolated words and continuous text.
Vitu, F; O'Regan, J K; Mittau, M
1990-06-01
During isolated-word reading, within-word eye-movement tactics (i.e., whether the eye makes one or more fixations on the word) depend strongly on the eye's first fixation position in the word; there exists an optimal landing position where the probability of having to refixate the word is much smaller than when the eye first fixates other parts of the word. The present experiment was designed to test whether the optimal landing position effect still exists during text reading, and to compare the nature and strength of the effect with the effect found for isolated words. The results confirmed the existence of an optimal landing position in both reading conditions, but the effect for words in texts was weaker than it was for isolated words, probably because of the presence of factors such as reading rhythm and linguistic context. However, the effect still existed in text reading; within-word tactics during text reading are dependent on the eye's initial landing position in words. Moreover, individual fixation durations were dependent on within-word tactics. Thus, the initial landing position in words must be taken into account if one wishes to understand eye-movement behavior during text reading. Further results concerned the effects of word length and word frequency in both reading conditions.
New Algorithms for Global Optimization and Reaction Path Determination.
Weber, D; Bellinger, D; Engels, B
2016-01-01
We present new schemes to improve the convergence of an important global optimization problem and to determine reaction pathways (RPs) between identified minima. Those methods have been implemented into the CAST program (Conformational Analysis and Search Tool). The first part of this chapter shows how to improve convergence of the Monte Carlo with minimization (MCM, also known as Basin Hopping) method when applied to optimize water clusters or aqueous solvation shells using a simple model. Since the random movement on the potential energy surface (PES) is an integral part of MCM, we propose to employ a hydrogen bonding-based algorithm for its improvement. We show comparisons of the results obtained for random dihedral and for the proposed random, rigid-body water molecule movement, giving evidence that a specific adaption of the distortion process greatly improves the convergence of the method. The second part is about the determination of RPs in clusters between conformational arrangements and for reactions. Besides standard approaches like the nudged elastic band method, we want to focus on a new algorithm developed especially for global reaction path search called Pathopt. We started with argon clusters, a typical benchmark system, which possess a flat PES, then stepwise increase the magnitude and directionality of interactions. Therefore, we calculated pathways for a water cluster and characterize them by frequency calculations. Within our calculations, we were able to show that beneath local pathways also additional pathways can be found which possess additional features. PMID:27497166
Optimizing a global alignment of protein interaction networks
Chindelevitch, Leonid; Ma, Cheng-Yu; Liao, Chung-Shou; Berger, Bonnie
2013-01-01
Motivation: The global alignment of protein interaction networks is a widely studied problem. It is an important first step in understanding the relationship between the proteins in different species and identifying functional orthologs. Furthermore, it can provide useful insights into the species’ evolution. Results: We propose a novel algorithm, PISwap, for optimizing global pairwise alignments of protein interaction networks, based on a local optimization heuristic that has previously demonstrated its effectiveness for a variety of other intractable problems. PISwap can begin with different types of network alignment approaches and then iteratively adjust the initial alignments by incorporating network topology information, trading it off for sequence information. In practice, our algorithm efficiently refines other well-studied alignment techniques with almost no additional time cost. We also show the robustness of the algorithm to noise in protein interaction data. In addition, the flexible nature of this algorithm makes it suitable for different applications of network alignment. This algorithm can yield interesting insights into the evolutionary dynamics of related species. Availability: Our software is freely available for non-commercial purposes from our Web site, http://piswap.csail.mit.edu/. Contact: bab@csail.mit.edu or csliao@ie.nthu.edu.tw Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24048352
Parallel global optimization with the particle swarm algorithm.
Schutte, J F; Reinbolt, J A; Fregly, B J; Haftka, R T; George, A D
2004-12-01
Present day engineering optimization problems often impose large computational demands, resulting in long solution times even on a modern high-end processor. To obtain enhanced computational throughput and global search capability, we detail the coarse-grained parallelization of an increasingly popular global search method, the particle swarm optimization (PSO) algorithm. Parallel PSO performance was evaluated using two categories of optimization problems possessing multiple local minima-large-scale analytical test problems with computationally cheap function evaluations and medium-scale biomechanical system identification problems with computationally expensive function evaluations. For load-balanced analytical test problems formulated using 128 design variables, speedup was close to ideal and parallel efficiency above 95% for up to 32 nodes on a Beowulf cluster. In contrast, for load-imbalanced biomechanical system identification problems with 12 design variables, speedup plateaued and parallel efficiency decreased almost linearly with increasing number of nodes. The primary factor affecting parallel performance was the synchronization requirement of the parallel algorithm, which dictated that each iteration must wait for completion of the slowest fitness evaluation. When the analytical problems were solved using a fixed number of swarm iterations, a single population of 128 particles produced a better convergence rate than did multiple independent runs performed using sub-populations (8 runs with 16 particles, 4 runs with 32 particles, or 2 runs with 64 particles). These results suggest that (1) parallel PSO exhibits excellent parallel performance under load-balanced conditions, (2) an asynchronous implementation would be valuable for real-life problems subject to load imbalance, and (3) larger population sizes should be considered when multiple processors are available.
Parallel global optimization with the particle swarm algorithm
Schutte, J. F.; Reinbolt, J. A.; Fregly, B. J.; Haftka, R. T.; George, A. D.
2007-01-01
SUMMARY Present day engineering optimization problems often impose large computational demands, resulting in long solution times even on a modern high-end processor. To obtain enhanced computational throughput and global search capability, we detail the coarse-grained parallelization of an increasingly popular global search method, the particle swarm optimization (PSO) algorithm. Parallel PSO performance was evaluated using two categories of optimization problems possessing multiple local minima—large-scale analytical test problems with computationally cheap function evaluations and medium-scale biomechanical system identification problems with computationally expensive function evaluations. For load-balanced analytical test problems formulated using 128 design variables, speedup was close to ideal and parallel efficiency above 95% for up to 32 nodes on a Beowulf cluster. In contrast, for load-imbalanced biomechanical system identification problems with 12 design variables, speedup plateaued and parallel efficiency decreased almost linearly with increasing number of nodes. The primary factor affecting parallel performance was the synchronization requirement of the parallel algorithm, which dictated that each iteration must wait for completion of the slowest fitness evaluation. When the analytical problems were solved using a fixed number of swarm iterations, a single population of 128 particles produced a better convergence rate than did multiple independent runs performed using sub-populations (8 runs with 16 particles, 4 runs with 32 particles, or 2 runs with 64 particles). These results suggest that (1) parallel PSO exhibits excellent parallel performance under load-balanced conditions, (2) an asynchronous implementation would be valuable for real-life problems subject to load imbalance, and (3) larger population sizes should be considered when multiple processors are available. PMID:17891226
Global Optimization and Broadband Analysis Software for Interstellar Chemistry (GOBASIC)
NASA Astrophysics Data System (ADS)
Rad, Mary L.; Zou, Luyao; Sanders, James L.; Widicus Weaver, Susanna L.
2016-01-01
Context. Broadband receivers that operate at millimeter and submillimeter frequencies necessitate the development of new tools for spectral analysis and interpretation. Simultaneous, global, multimolecule, multicomponent analysis is necessary to accurately determine the physical and chemical conditions from line-rich spectra that arise from sources like hot cores. Aims: We aim to provide a robust and efficient automated analysis program to meet the challenges presented with the large spectral datasets produced by radio telescopes. Methods: We have written a program in the MATLAB numerical computing environment for simultaneous global analysis of broadband line surveys. The Global Optimization and Broadband Analysis Software for Interstellar Chemistry (GOBASIC) program uses the simplifying assumption of local thermodynamic equilibrium (LTE) for spectral analysis to determine molecular column density, temperature, and velocity information. Results: GOBASIC achieves simultaneous, multimolecule, multicomponent fitting for broadband spectra. The number of components that can be analyzed at once is only limited by the available computational resources. Analysis of subsequent sets of molecules or components is performed iteratively while taking the previous fits into account. All features of a given molecule across the entire window are fitted at once, which is preferable to the rotation diagram approach because global analysis is less sensitive to blended features and noise features in the spectra. In addition, the fitting method used in GOBASIC is insensitive to the initial conditions chosen, the fitting is automated, and fitting can be performed in a parallel computing environment. These features make GOBASIC a valuable improvement over previously available LTE analysis methods. A copy of the sofware is available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (ftp://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/585/A23
Optimized continuous pharmaceutical manufacturing via model-predictive control.
Rehrl, Jakob; Kruisz, Julia; Sacher, Stephan; Khinast, Johannes; Horn, Martin
2016-08-20
This paper demonstrates the application of model-predictive control to a feeding blending unit used in continuous pharmaceutical manufacturing. The goal of this contribution is, on the one hand, to highlight the advantages of the proposed concept compared to conventional PI-controllers, and, on the other hand, to present a step-by-step guide for controller synthesis. The derivation of the required mathematical plant model is given in detail and all the steps required to develop a model-predictive controller are shown. Compared to conventional concepts, the proposed approach allows to conveniently consider constraints (e.g. mass hold-up in the blender) and offers a straightforward, easy to tune controller setup. The concept is implemented in a simulation environment. In order to realize it on a real system, additional aspects (e.g., state estimation, measurement equipment) will have to be investigated. PMID:27317987
Optimized continuous pharmaceutical manufacturing via model-predictive control.
Rehrl, Jakob; Kruisz, Julia; Sacher, Stephan; Khinast, Johannes; Horn, Martin
2016-08-20
This paper demonstrates the application of model-predictive control to a feeding blending unit used in continuous pharmaceutical manufacturing. The goal of this contribution is, on the one hand, to highlight the advantages of the proposed concept compared to conventional PI-controllers, and, on the other hand, to present a step-by-step guide for controller synthesis. The derivation of the required mathematical plant model is given in detail and all the steps required to develop a model-predictive controller are shown. Compared to conventional concepts, the proposed approach allows to conveniently consider constraints (e.g. mass hold-up in the blender) and offers a straightforward, easy to tune controller setup. The concept is implemented in a simulation environment. In order to realize it on a real system, additional aspects (e.g., state estimation, measurement equipment) will have to be investigated.
Gradient gravitational search: An efficient metaheuristic algorithm for global optimization.
Dash, Tirtharaj; Sahu, Prabhat K
2015-05-30
The adaptation of novel techniques developed in the field of computational chemistry to solve the concerned problems for large and flexible molecules is taking the center stage with regard to efficient algorithm, computational cost and accuracy. In this article, the gradient-based gravitational search (GGS) algorithm, using analytical gradients for a fast minimization to the next local minimum has been reported. Its efficiency as metaheuristic approach has also been compared with Gradient Tabu Search and others like: Gravitational Search, Cuckoo Search, and Back Tracking Search algorithms for global optimization. Moreover, the GGS approach has also been applied to computational chemistry problems for finding the minimal value potential energy of two-dimensional and three-dimensional off-lattice protein models. The simulation results reveal the relative stability and physical accuracy of protein models with efficient computational cost. PMID:25779670
Quantum-inspired immune clonal algorithm for global optimization.
Jiao, Licheng; Li, Yangyang; Gong, Maoguo; Zhang, Xiangrong
2008-10-01
Based on the concepts and principles of quantum computing, a novel immune clonal algorithm, called a quantum-inspired immune clonal algorithm (QICA), is proposed to deal with the problem of global optimization. In QICA, the antibody is proliferated and divided into a set of subpopulation groups. The antibodies in a subpopulation group are represented by multistate gene quantum bits. In the antibody's updating, the general quantum rotation gate strategy and the dynamic adjusting angle mechanism are applied to accelerate convergence. The quantum not gate is used to realize quantum mutation to avoid premature convergences. The proposed quantum recombination realizes the information communication between subpopulation groups to improve the search efficiency. Theoretical analysis proves that QICA converges to the global optimum. In the first part of the experiments, 10 unconstrained and 13 constrained benchmark functions are used to test the performance of QICA. The results show that QICA performs much better than the other improved genetic algorithms in terms of the quality of solution and computational cost. In the second part of the experiments, QICA is applied to a practical problem (i.e., multiuser detection in direct-sequence code-division multiple-access systems) with a satisfying result.
ABCluster: the artificial bee colony algorithm for cluster global optimization.
Zhang, Jun; Dolg, Michael
2015-10-01
Global optimization of cluster geometries is of fundamental importance in chemistry and an interesting problem in applied mathematics. In this work, we introduce a relatively new swarm intelligence algorithm, i.e. the artificial bee colony (ABC) algorithm proposed in 2005, to this field. It is inspired by the foraging behavior of a bee colony, and only three parameters are needed to control it. We applied it to several potential functions of quite different nature, i.e., the Coulomb-Born-Mayer, Lennard-Jones, Morse, Z and Gupta potentials. The benchmarks reveal that for long-ranged potentials the ABC algorithm is very efficient in locating the global minimum, while for short-ranged ones it is sometimes trapped into a local minimum funnel on a potential energy surface of large clusters. We have released an efficient, user-friendly, and free program "ABCluster" to realize the ABC algorithm. It is a black-box program for non-experts as well as experts and might become a useful tool for chemists to study clusters. PMID:26327507
Optimizing global liver function in radiation therapy treatment planning
NASA Astrophysics Data System (ADS)
Wu, Victor W.; Epelman, Marina A.; Wang, Hesheng; Romeijn, H. Edwin; Feng, Mary; Cao, Yue; Ten Haken, Randall K.; Matuszak, Martha M.
2016-09-01
Liver stereotactic body radiation therapy (SBRT) patients differ in both pre-treatment liver function (e.g. due to degree of cirrhosis and/or prior treatment) and radiosensitivity, leading to high variability in potential liver toxicity with similar doses. This work investigates three treatment planning optimization models that minimize risk of toxicity: two consider both voxel-based pre-treatment liver function and local-function-based radiosensitivity with dose; one considers only dose. Each model optimizes different objective functions (varying in complexity of capturing the influence of dose on liver function) subject to the same dose constraints and are tested on 2D synthesized and 3D clinical cases. The normal-liver-based objective functions are the linearized equivalent uniform dose (\\ell \\text{EUD} ) (conventional ‘\\ell \\text{EUD} model’), the so-called perfusion-weighted \\ell \\text{EUD} (\\text{fEUD} ) (proposed ‘fEUD model’), and post-treatment global liver function (GLF) (proposed ‘GLF model’), predicted by a new liver-perfusion-based dose-response model. The resulting \\ell \\text{EUD} , fEUD, and GLF plans delivering the same target \\ell \\text{EUD} are compared with respect to their post-treatment function and various dose-based metrics. Voxel-based portal venous liver perfusion, used as a measure of local function, is computed using DCE-MRI. In cases used in our experiments, the GLF plan preserves up to 4.6 % ≤ft(7.5 % \\right) more liver function than the fEUD (\\ell \\text{EUD} ) plan does in 2D cases, and up to 4.5 % ≤ft(5.6 % \\right) in 3D cases. The GLF and fEUD plans worsen in \\ell \\text{EUD} of functional liver on average by 1.0 Gy and 0.5 Gy in 2D and 3D cases, respectively. Liver perfusion information can be used during treatment planning to minimize the risk of toxicity by improving expected GLF; the degree of benefit varies with perfusion pattern. Although fEUD model optimization is computationally inexpensive and
Arasomwan, Martins Akugbe; Adewumi, Aderemi Oluyinka
2013-01-01
Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the algorithm approaches its terminal point. Researchers have tried to address this shortcoming by modifying LDIW-PSO or proposing new PSO variants. Some of these variants have been claimed to outperform LDIW-PSO. The major goal of this paper is to experimentally establish the fact that LDIW-PSO is very much efficient if its parameters are properly set. First, an experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems. Second, using the experimentally obtained values, five well-known benchmark optimization problems were used to show the outstanding performance of LDIW-PSO over some of its competitors which have in the past claimed superiority over it. Two other recent PSO variants with different inertia weight strategies were also compared with LDIW-PSO with the latter outperforming both in the simulation experiments conducted. PMID:24324383
The L_infinity constrained global optimal histogram equalization technique for real time imaging
NASA Astrophysics Data System (ADS)
Ren, Qiongwei; Niu, Yi; Liu, Lin; Jiao, Yang; Shi, Guangming
2015-08-01
Although the current imaging sensors can achieve 12 or higher precision, the current display devices and the commonly used digital image formats are still only 8 bits. This mismatch causes significant waste of the sensor precision and loss of information when storing and displaying the images. For better usage of the precision-budget, tone mapping operators have to be used to map the high-precision data into low-precision digital images adaptively. In this paper, the classic histogram equalization tone mapping operator is reexamined in the sense of optimization. We point out that the traditional histogram equalization technique and its variants are fundamentally improper by suffering from local optimum problems. To overcome this drawback, we remodel the histogram equalization tone mapping task based on graphic theory which achieves the global optimal solutions. Another advantage of the graphic-based modeling is that the tone-continuity is also modeled as a vital constraint in our approach which suppress the annoying boundary artifacts of the traditional approaches. In addition, we propose a novel dynamic programming technique to solve the histogram equalization problem in real time. Experimental results shows that the proposed tone-preserved global optimal histogram equalization technique outperforms the traditional approaches by exhibiting more subtle details in the foreground while preserving the smoothness of the background.
Equivalence between entanglement and the optimal fidelity of continuous variable teleportation.
Adesso, Gerardo; Illuminati, Fabrizio
2005-10-01
We devise the optimal form of Gaussian resource states enabling continuous-variable teleportation with maximal fidelity. We show that a nonclassical optimal fidelity of N-user teleportation networks is necessary and sufficient for N-party entangled Gaussian resources, yielding an estimator of multipartite entanglement. The entanglement of teleportation is equivalent to the entanglement of formation in a two-user protocol, and to the localizable entanglement in a multiuser one. Finally, we show that the continuous-variable tangle, quantifying entanglement sharing in three-mode Gaussian states, is defined operationally in terms of the optimal fidelity of a tripartite teleportation network.
Equivalence between entanglement and the optimal fidelity of continuous variable teleportation.
Adesso, Gerardo; Illuminati, Fabrizio
2005-10-01
We devise the optimal form of Gaussian resource states enabling continuous-variable teleportation with maximal fidelity. We show that a nonclassical optimal fidelity of N-user teleportation networks is necessary and sufficient for N-party entangled Gaussian resources, yielding an estimator of multipartite entanglement. The entanglement of teleportation is equivalent to the entanglement of formation in a two-user protocol, and to the localizable entanglement in a multiuser one. Finally, we show that the continuous-variable tangle, quantifying entanglement sharing in three-mode Gaussian states, is defined operationally in terms of the optimal fidelity of a tripartite teleportation network. PMID:16241708
Structure of CIMS in large-scale continuous manufacturing industry and its optimization strategy
NASA Astrophysics Data System (ADS)
Yao, Jianchu; Wang, Gaofeng; Wang, Boxing; Zhou, Ji; Yu, Jun
1995-08-01
This paper focuses on the large scale petroleum refinery manufacturing industry and has analyzed the characteristics and functional requirements of CIMS in continuous process industries. Then it compares the continuous manufacturing industry with the discrete manufacturing industry on CIMS conceptual model, and presents the functional model frame and key technologies of CIPS. The paper also proposes the optimization model and solution strategy for the CIMS in continuous industry.
Optimal control of nonlinear continuous-time systems in strict-feedback form.
Zargarzadeh, Hassan; Dierks, Travis; Jagannathan, Sarangapani
2015-10-01
This paper proposes a novel optimal tracking control scheme for nonlinear continuous-time systems in strict-feedback form with uncertain dynamics. The optimal tracking problem is transformed into an equivalent optimal regulation problem through a feedforward adaptive control input that is generated by modifying the standard backstepping technique. Subsequently, a neural network-based optimal control scheme is introduced to estimate the cost, or value function, over an infinite horizon for the resulting nonlinear continuous-time systems in affine form when the internal dynamics are unknown. The estimated cost function is then used to obtain the optimal feedback control input; therefore, the overall optimal control input for the nonlinear continuous-time system in strict-feedback form includes the feedforward plus the optimal feedback terms. It is shown that the estimated cost function minimizes the Hamilton-Jacobi-Bellman estimation error in a forward-in-time manner without using any value or policy iterations. Finally, optimal output feedback control is introduced through the design of a suitable observer. Lyapunov theory is utilized to show the overall stability of the proposed schemes without requiring an initial admissible controller. Simulation examples are provided to validate the theoretical results. PMID:26111400
Liu, Liqiang; Dai, Yuntao; Gao, Jinyu
2014-01-01
Ant colony optimization algorithm for continuous domains is a major research direction for ant colony optimization algorithm. In this paper, we propose a distribution model of ant colony foraging, through analysis of the relationship between the position distribution and food source in the process of ant colony foraging. We design a continuous domain optimization algorithm based on the model and give the form of solution for the algorithm, the distribution model of pheromone, the update rules of ant colony position, and the processing method of constraint condition. Algorithm performance against a set of test trials was unconstrained optimization test functions and a set of optimization test functions, and test results of other algorithms are compared and analyzed to verify the correctness and effectiveness of the proposed algorithm. PMID:24955402
Liu, Liqiang; Dai, Yuntao
2014-01-01
Ant colony optimization algorithm for continuous domains is a major research direction for ant colony optimization algorithm. In this paper, we propose a distribution model of ant colony foraging, through analysis of the relationship between the position distribution and food source in the process of ant colony foraging. We design a continuous domain optimization algorithm based on the model and give the form of solution for the algorithm, the distribution model of pheromone, the update rules of ant colony position, and the processing method of constraint condition. Algorithm performance against a set of test trials was unconstrained optimization test functions and a set of optimization test functions, and test results of other algorithms are compared and analyzed to verify the correctness and effectiveness of the proposed algorithm. PMID:24955402
NASA Astrophysics Data System (ADS)
Petukhov, V. G.
2008-06-01
The problem of optimization of interplanetary trajectories is considered for spacecraft with a small-thrust ideally regulated engine. When the maximum principle is used, determination of the optimal trajectory is reduced to solution of a two-point boundary value problem for a system of ordinary differential equations. In order to solve this boundary value problem, the method of continuation in parameter is used, and with the help of it the formal reduction of the boundary value problem to a Cauchy problem is performed. Different variants of the continuation method are considered, including the method of continuation in the gravitational parameter which allows one to find extreme trajectories with a preset angular distance. The issues of numerical realization of the continuation method are discussed, and numerical examples of its use for solving the problems of optimization of interplanetary trajectories are presented.
Continuation of the NVAP Global Water Vapor Data Sets for Pathfinder Science Analysis
NASA Technical Reports Server (NTRS)
VonderHaar, Thomas H.; Engelen, Richard J.; Forsythe, John M.; Randel, David L.; Ruston, Benjamin C.; Woo, Shannon; Dodge, James (Technical Monitor)
2001-01-01
This annual report covers August 2000 - August 2001 under NASA contract NASW-0032, entitled "Continuation of the NVAP (NASA's Water Vapor Project) Global Water Vapor Data Sets for Pathfinder Science Analysis". NASA has created a list of Earth Science Research Questions which are outlined by Asrar, et al. Particularly relevant to NVAP are the following questions: (a) How are global precipitation, evaporation, and the cycling of water changing? (b) What trends in atmospheric constituents and solar radiation are driving global climate? (c) How well can long-term climatic trends be assessed or predicted? Water vapor is a key greenhouse gas, and an understanding of its behavior is essential in global climate studies. Therefore, NVAP plays a key role in addressing the above climate questions by creating a long-term global water vapor dataset and by updating the dataset with recent advances in satellite instrumentation. The NVAP dataset produced from 1988-1998 has found wide use in the scientific community. Studies of interannual variability are particularly important. A recent paper by Simpson, et al. that examined the NVAP dataset in detail has shown that its relative accuracy is sufficient for the variability studies that contribute toward meeting NASA's goals. In the past year, we have made steady progress towards continuing production of this high-quality dataset as well as performing our own investigations of the data. This report summarizes the past year's work on production of the NVAP dataset and presents results of analyses we have performed in the past year.
Zou, Feng; Chen, Debao; Wang, Jiangtao
2016-01-01
An improved teaching-learning-based optimization with combining of the social character of PSO (TLBO-PSO), which is considering the teacher's behavior influence on the students and the mean grade of the class, is proposed in the paper to find the global solutions of function optimization problems. In this method, the teacher phase of TLBO is modified; the new position of the individual is determined by the old position, the mean position, and the best position of current generation. The method overcomes disadvantage that the evolution of the original TLBO might stop when the mean position of students equals the position of the teacher. To decrease the computation cost of the algorithm, the process of removing the duplicate individual in original TLBO is not adopted in the improved algorithm. Moreover, the probability of local convergence of the improved method is decreased by the mutation operator. The effectiveness of the proposed method is tested on some benchmark functions, and the results are competitive with respect to some other methods. PMID:27057157
Zou, Feng; Chen, Debao; Wang, Jiangtao
2016-01-01
An improved teaching-learning-based optimization with combining of the social character of PSO (TLBO-PSO), which is considering the teacher's behavior influence on the students and the mean grade of the class, is proposed in the paper to find the global solutions of function optimization problems. In this method, the teacher phase of TLBO is modified; the new position of the individual is determined by the old position, the mean position, and the best position of current generation. The method overcomes disadvantage that the evolution of the original TLBO might stop when the mean position of students equals the position of the teacher. To decrease the computation cost of the algorithm, the process of removing the duplicate individual in original TLBO is not adopted in the improved algorithm. Moreover, the probability of local convergence of the improved method is decreased by the mutation operator. The effectiveness of the proposed method is tested on some benchmark functions, and the results are competitive with respect to some other methods. PMID:27057157
Direct approach for bioprocess optimization in a continuous flat-bed photobioreactor system.
Kwon, Jong-Hee; Rögner, Matthias; Rexroth, Sascha
2012-11-30
Application of photosynthetic micro-organisms, such as cyanobacteria and green algae, for the carbon neutral energy production raises the need for cost-efficient photobiological processes. Optimization of these processes requires permanent control of many independent and mutably dependent parameters, for which a continuous cultivation approach has significant advantages. As central factors like the cell density can be kept constant by turbidostatic control, light intensity and iron content with its strong impact on productivity can be optimized. Both are key parameters due to their strong dependence on photosynthetic activity. Here we introduce an engineered low-cost 5 L flat-plate photobioreactor in combination with a simple and efficient optimization procedure for continuous photo-cultivation of microalgae. Based on direct determination of the growth rate at constant cell densities and the continuous measurement of O₂ evolution, stress conditions and their effect on the photosynthetic productivity can be directly observed. PMID:22789478
Global optimization of fuel consumption in rendezvous scenarios by the method of interval analysis
NASA Astrophysics Data System (ADS)
Ma, Hongliang; Xu, Shijie
2015-03-01
To reduce the optimal but large Δv of the fixed-short-time two impulse Lambert rendezvous between two spacecrafts along two coplanar circular orbits, the three-impulse Lambert rendezvous optimized via the optimization algorithm-interval analysis (IA) is proposed in this paper. The purpose of optimization is to minimize the velocity increment of the fixed-short-time three-impulse Lambert rendezvous. The optimization algorithm IA is given for solving the rendezvous optimization problem with multiple uncertain variables, and strong nonlinearity and nonconvexity. Numerical examples of the time-open, coplanar-circular-orbit, multiple-revolution Lambert rendezvous with a parking time optimized via the optimization algorithm IA are firstly undertaken to validate the feasibility of the optimization algorithm IA by comparing the optimization results with those of the globally optimal Hohmann transfer. The results indicate that the globally optimal parameters of the time-open coplanar-circular-orbit multiple-revolution Lambert rendezvous can be obtained by the optimization algorithm IA, and the initial separation angle of two spacecrafts with different orbit radius can be adjusted to obtain the globally optimal and small Δv by distributing an optimal parking time. After that, for the fixed-short-time two-impulse Lambert rendezvous problem without sufficient time to adjust the separation angle by distributing a parking time like the open-time Lambert rendezvous problem, three-impulse Lambert rendezvous involving multiple optimization variables is given and the variables are optimized by the optimization algorithm IA to obtain an optimal and small Δv. Numerical simulation indicates that the optimal and small Δv of the fixed short time, three-impulse Lambert rendezvous can be obtained using the optimization algorithm IA.
Fournier, René; Mohareb, Amir
2016-01-14
We devised a global optimization (GO) strategy for optimizing molecular properties with respect to both geometry and chemical composition. A relative index of thermodynamic stability (RITS) is introduced to allow meaningful energy comparisons between different chemical species. We use the RITS by itself, or in combination with another calculated property, to create an objective function F to be minimized. Including the RITS in the definition of F ensures that the solutions have some degree of thermodynamic stability. We illustrate how the GO strategy works with three test applications, with F calculated in the framework of Kohn-Sham Density Functional Theory (KS-DFT) with the Perdew-Burke-Ernzerhof exchange-correlation. First, we searched the composition and configuration space of CmHnNpOq (m = 0-4, n = 0-10, p = 0-2, q = 0-2, and 2 ≤ m + n + p + q ≤ 12) for stable molecules. The GO discovered familiar molecules like N2, CO2, acetic acid, acetonitrile, ethane, and many others, after a small number (5000) of KS-DFT energy evaluations. Second, we carried out a GO of the geometry of CumSnn (+) (m = 1, 2 and n = 9-12). A single GO run produced the same low-energy structures found in an earlier study where each CumSnn (+) species had been optimized separately. Finally, we searched bimetallic clusters AmBn (3 ≤ m + n ≤ 6, A,B= Li, Na, Al, Cu, Ag, In, Sn, Pb) for species and configurations having a low RITS and large highest occupied Molecular Orbital (MO) to lowest unoccupied MO energy gap (Eg). We found seven bimetallic clusters with Eg > 1.5 eV. PMID:26772561
NASA Astrophysics Data System (ADS)
Fournier, René; Mohareb, Amir
2016-01-01
We devised a global optimization (GO) strategy for optimizing molecular properties with respect to both geometry and chemical composition. A relative index of thermodynamic stability (RITS) is introduced to allow meaningful energy comparisons between different chemical species. We use the RITS by itself, or in combination with another calculated property, to create an objective function F to be minimized. Including the RITS in the definition of F ensures that the solutions have some degree of thermodynamic stability. We illustrate how the GO strategy works with three test applications, with F calculated in the framework of Kohn-Sham Density Functional Theory (KS-DFT) with the Perdew-Burke-Ernzerhof exchange-correlation. First, we searched the composition and configuration space of CmHnNpOq (m = 0-4, n = 0-10, p = 0-2, q = 0-2, and 2 ≤ m + n + p + q ≤ 12) for stable molecules. The GO discovered familiar molecules like N2, CO2, acetic acid, acetonitrile, ethane, and many others, after a small number (5000) of KS-DFT energy evaluations. Second, we carried out a GO of the geometry of Cu m Snn + (m = 1, 2 and n = 9-12). A single GO run produced the same low-energy structures found in an earlier study where each Cu m S nn + species had been optimized separately. Finally, we searched bimetallic clusters AmBn (3 ≤ m + n ≤ 6, A,B= Li, Na, Al, Cu, Ag, In, Sn, Pb) for species and configurations having a low RITS and large highest occupied Molecular Orbital (MO) to lowest unoccupied MO energy gap (Eg). We found seven bimetallic clusters with Eg > 1.5 eV.
Summers, R. J.; Boudreaux, D. P.; Srinivasan, V. R.
1979-01-01
Steady-state continuous culture was used to optimize lean chemically defined media for a Cellulomonas sp. and Bacillus cereus strain T. Both organisms were extremely sensitive to variations in trace-metal concentrations. However, medium optimization by this technique proved rapid, and multifactor screening was easily conducted by using a minimum of instrumentation. The optimized media supported critical dilution rates of 0.571 and 0.467 h−1 for Cellulomonas and Bacillus, respectively. These values approximated maximum growth rate values observed in batch culture. PMID:16345417
Continuous Firefly Algorithm for Optimal Tuning of Pid Controller in Avr System
NASA Astrophysics Data System (ADS)
Bendjeghaba, Omar
2014-01-01
This paper presents a tuning approach based on Continuous firefly algorithm (CFA) to obtain the proportional-integral- derivative (PID) controller parameters in Automatic Voltage Regulator system (AVR). In the tuning processes the CFA is iterated to reach the optimal or the near optimal of PID controller parameters when the main goal is to improve the AVR step response characteristics. Conducted simulations show the effectiveness and the efficiency of the proposed approach. Furthermore the proposed approach can improve the dynamic of the AVR system. Compared with particle swarm optimization (PSO), the new CFA tuning method has better control system performance in terms of time domain specifications and set-point tracking.
CFD optimization of continuous stirred-tank (CSTR) reactor for biohydrogen production.
Ding, Jie; Wang, Xu; Zhou, Xue-Fei; Ren, Nan-Qi; Guo, Wan-Qian
2010-09-01
There has been little work on the optimal configuration of biohydrogen production reactors. This paper describes three-dimensional computational fluid dynamics (CFD) simulations of gas-liquid flow in a laboratory-scale continuous stirred-tank reactor used for biohydrogen production. To evaluate the role of hydrodynamics in reactor design and optimize the reactor configuration, an optimized impeller design has been constructed and validated with CFD simulations of the normal and optimized impeller over a range of speeds and the numerical results were also validated by examination of residence time distribution. By integrating the CFD simulation with an ethanol-type fermentation process experiment, it was shown that impellers with different type and speed generated different flow patterns, and hence offered different efficiencies for biohydrogen production. The hydrodynamic behavior of the optimized impeller at speeds between 50 and 70 rev/min is most suited for economical biohydrogen production.
Global Optimization of Low-Thrust Interplanetary Trajectories Subject to Operational Constraints
NASA Technical Reports Server (NTRS)
Englander, Jacob A.; Vavrina, Matthew A.; Hinckley, David
2016-01-01
Low-thrust interplanetary space missions are highly complex and there can be many locally optimal solutions. While several techniques exist to search for globally optimal solutions to low-thrust trajectory design problems, they are typically limited to unconstrained trajectories. The operational design community in turn has largely avoided using such techniques and has primarily focused on accurate constrained local optimization combined with grid searches and intuitive design processes at the expense of efficient exploration of the global design space. This work is an attempt to bridge the gap between the global optimization and operational design communities by presenting a mathematical framework for global optimization of low-thrust trajectories subject to complex constraints including the targeting of planetary landing sites, a solar range constraint to simplify the thermal design of the spacecraft, and a real-world multi-thruster electric propulsion system that must switch thrusters on and off as available power changes over the course of a mission.
Nonlinear Global Optimization Using Curdling Algorithm in Mathematica Environmet
Craig Loehle, Ph. D.
1997-08-05
An algorithm for performing optimization which is a derivative-free, grid-refinement approach to nonlinear optimization was developed and implemented in software as OPTIMIZE. This approach overcomes a number of deficiencies in existing approaches. Most notably, it finds extremal regions rather than only single extremal points. the program is interactive and collects information on control parameters and constraints using menus. For up to two (and potentially three) dimensions, function convergence is displayed graphically. Because the algorithm does not compute derivatives, gradients, or vectors, it is numerically stable. It can find all the roots of a polynomial in one pass. It is an inherently parallel algorithm. OPTIMIZE-M is a modification of OPTIMIZE designed for use within the Mathematica environment created by Wolfram Research.
Nonlinear Global Optimization Using Curdling Algorithm in Mathematica Environmet
1997-08-05
An algorithm for performing optimization which is a derivative-free, grid-refinement approach to nonlinear optimization was developed and implemented in software as OPTIMIZE. This approach overcomes a number of deficiencies in existing approaches. Most notably, it finds extremal regions rather than only single extremal points. the program is interactive and collects information on control parameters and constraints using menus. For up to two (and potentially three) dimensions, function convergence is displayed graphically. Because the algorithm doesmore » not compute derivatives, gradients, or vectors, it is numerically stable. It can find all the roots of a polynomial in one pass. It is an inherently parallel algorithm. OPTIMIZE-M is a modification of OPTIMIZE designed for use within the Mathematica environment created by Wolfram Research.« less
Albano Farias, L.; Stephany, J.
2010-12-15
We analyze the statistics of observables in continuous-variable (CV) quantum teleportation in the formalism of the characteristic function. We derive expressions for average values of output-state observables, in particular, cumulants which are additive in terms of the input state and the resource of teleportation. Working with a general class of teleportation resources, the squeezed-bell-like states, which may be optimized in a free parameter for better teleportation performance, we discuss the relation between resources optimal for fidelity and those optimal for different observable averages. We obtain the values of the free parameter of the squeezed-bell-like states which optimize the central momenta and cumulants up to fourth order. For the cumulants the distortion between in and out states due to teleportation depends only on the resource. We obtain optimal parameters {Delta}{sub (2)}{sup opt} and {Delta}{sub (4)}{sup opt} for the second- and fourth-order cumulants, which do not depend on the squeezing of the resource. The second-order central momenta, which are equal to the second-order cumulants, and the photon number average are also optimized by the resource with {Delta}{sub (2)}{sup opt}. We show that the optimal fidelity resource, which has been found previously to depend on the characteristics of input, approaches for high squeezing to the resource that optimizes the second-order momenta. A similar behavior is obtained for the resource that optimizes the photon statistics, which is treated here using the sum of the squared differences in photon probabilities of input versus output states as the distortion measure. This is interpreted naturally to mean that the distortions associated with second-order momenta dominate the behavior of the output state for large squeezing of the resource. Optimal fidelity resources and optimal photon statistics resources are compared, and it is shown that for mixtures of Fock states both resources are equivalent.
Development of a new adaptive ordinal approach to continuous-variable probabilistic optimization.
Romero, Vicente JosÔe; Chen, Chun-Hung (George Mason University, Fairfax, VA)
2006-11-01
A very general and robust approach to solving continuous-variable optimization problems involving uncertainty in the objective function is through the use of ordinal optimization. At each step in the optimization problem, improvement is based only on a relative ranking of the uncertainty effects on local design alternatives, rather than on precise quantification of the effects. One simply asks ''Is that alternative better or worse than this one?'' -not ''HOW MUCH better or worse is that alternative to this one?'' The answer to the latter question requires precise characterization of the uncertainty--with the corresponding sampling/integration expense for precise resolution. However, in this report we demonstrate correct decision-making in a continuous-variable probabilistic optimization problem despite extreme vagueness in the statistical characterization of the design options. We present a new adaptive ordinal method for probabilistic optimization in which the trade-off between computational expense and vagueness in the uncertainty characterization can be conveniently managed in various phases of the optimization problem to make cost-effective stepping decisions in the design space. Spatial correlation of uncertainty in the continuous-variable design space is exploited to dramatically increase method efficiency. Under many circumstances the method appears to have favorable robustness and cost-scaling properties relative to other probabilistic optimization methods, and uniquely has mechanisms for quantifying and controlling error likelihood in design-space stepping decisions. The method is asymptotically convergent to the true probabilistic optimum, so could be useful as a reference standard against which the efficiency and robustness of other methods can be compared--analogous to the role that Monte Carlo simulation plays in uncertainty propagation.
Optimal Detection of Global Warming using Temperature Profiles
NASA Technical Reports Server (NTRS)
Leroy, Stephen S.
1997-01-01
Optimal fingerprinting is applied to estimate the amount of time it would take to detect warming by increased concentrations of carbon dioxide in monthly averages of temperature profiles over the Indian Ocean.
NASA Astrophysics Data System (ADS)
Afshar, M. H.; Rohani, M.
2012-01-01
In this article, cellular automata based hybrid methods are proposed for the optimal design of sewer networks and their performance is compared with some of the common heuristic search methods. The problem of optimal design of sewer networks is first decomposed into two sub-optimization problems which are solved iteratively in a two stage manner. In the first stage, the pipe diameters of the network are assumed fixed and the nodal cover depths of the network are determined by solving a nonlinear sub-optimization problem. A cellular automata (CA) method is used for the solution of the optimization problem with the network nodes considered as the cells and their cover depths as the cell states. In the second stage, the nodal cover depths calculated from the first stage are fixed and the pipe diameters are calculated by solving a second nonlinear sub-optimization problem. Once again a CA method is used to solve the optimization problem of the second stage with the pipes considered as the CA cells and their corresponding diameters as the cell states. Two different updating rules are derived and used for the CA of the second stage depending on the treatment of the pipe diameters. In the continuous approach, the pipe diameters are considered as continuous variables and the corresponding updating rule is derived mathematically from the original objective function of the problem. In the discrete approach, however, an adhoc updating rule is derived and used taking into account the discrete nature of the pipe diameters. The proposed methods are used to optimally solve two sewer network problems and the results are presented and compared with those obtained by other methods. The results show that the proposed CA based hybrid methods are more efficient and effective than the most powerful search methods considered in this work.
Efficient Parallel Global Optimization for High Resolution Hydrologic and Climate Impact Models
NASA Astrophysics Data System (ADS)
Shoemaker, C. A.; Mueller, J.; Pang, M.
2013-12-01
High Resolution hydrologic models are typically computationally expensive, requiring many minutes or perhaps hours for one simulation. Optimization can be used with these models for parameter estimation or for analyzing management alternatives. However Optimization of these computationally expensive simulations requires algorithms that can obtain accurate answers with relatively few simulations to avoid infeasibly long computation times. We have developed a number of efficient parallel algorithms and software codes for optimization of expensive problems with multiple local minimum. This is open source software we are distributing. It runs in Matlab and Python, and has been run on Yellowstone supercomputer. The talk will quickly discuss the characteristics of the problem (e.g. the presence of integer as well as continuous variables, the number of dimensions, the availability of parallel/grid computing, the number of simulations that can be allowed to find a solution, etc. ) that determine which algorithms are most appropriate for each type of problem. A major application of this optimization software is for parameter estimation for nonlinear hydrologic models, including contaminant transport in subsurface (e.g. for groundwater remediation or multi-phase flow for carbon sequestration), nutrient transport in watersheds, and climate models. We will present results for carbon sequestration plume monitoring (multi-phase, multi-constiuent), for groundwater remediation, and for the CLM climate model. The carbon sequestration example is based on the Frio CO2 field site and the groundwater example is for a 50,000 acre remediation site (with model requiring about 1 hour per simulation). Parallel speed-ups are excellent in most cases, and our serial and parallel algorithms tend to outperform alternative methods on complex computationally expensive simulations that have multiple global minima.
NASA Astrophysics Data System (ADS)
Libraro, Paola
The general electric propulsion orbit-raising maneuver of a spacecraft must contend with four main limiting factors: the longer time of flight, multiple eclipses prohibiting continuous thrusting, long exposure to radiation from the Van Allen belt and high power requirement of the electric engines. In order to optimize a low-thrust transfer with respect to these challenges, the choice of coordinates and corresponding equations of motion used to describe the kinematical and dynamical behavior of the satellite is of critical importance. This choice can potentially affect the numerical optimization process as well as limit the set of mission scenarios that can be investigated. To increase the ability to determine the feasible set of mission scenarios able to address the challenges of an all-electric orbit-raising, a set of equations free of any singularities is required to consider a completely arbitrary injection orbit. For this purpose a new quaternion-based formulation of a spacecraft translational dynamics that is globally nonsingular has been developed. The minimum-time low-thrust problem has been solved using the new set of equations of motion inside a direct optimization scheme in order to investigate optimal low-thrust trajectories over the full range of injection orbit inclinations between 0 and 90 degrees with particular focus on high-inclinations. The numerical results consider a specific mission scenario in order to analyze three key aspects of the problem: the effect of the initial guess on the shape and duration of the transfer, the effect of Earth oblateness on transfer time and the role played by, radiation damage and power degradation in all-electric minimum-time transfers. Finally trade-offs between mass and cost savings are introduced through a test case.
Vrabie, Draguna; Lewis, Frank
2009-04-01
In this paper we present in a continuous-time framework an online approach to direct adaptive optimal control with infinite horizon cost for nonlinear systems. The algorithm converges online to the optimal control solution without knowledge of the internal system dynamics. Closed-loop dynamic stability is guaranteed throughout. The algorithm is based on a reinforcement learning scheme, namely Policy Iterations, and makes use of neural networks, in an Actor/Critic structure, to parametrically represent the control policy and the performance of the control system. The two neural networks are trained to express the optimal controller and optimal cost function which describes the infinite horizon control performance. Convergence of the algorithm is proven under the realistic assumption that the two neural networks do not provide perfect representations for the nonlinear control and cost functions. The result is a hybrid control structure which involves a continuous-time controller and a supervisory adaptation structure which operates based on data sampled from the plant and from the continuous-time performance dynamics. Such control structure is unlike any standard form of controllers previously seen in the literature. Simulation results, obtained considering two second-order nonlinear systems, are provided.
Vrabie, Draguna; Lewis, Frank
2009-04-01
In this paper we present in a continuous-time framework an online approach to direct adaptive optimal control with infinite horizon cost for nonlinear systems. The algorithm converges online to the optimal control solution without knowledge of the internal system dynamics. Closed-loop dynamic stability is guaranteed throughout. The algorithm is based on a reinforcement learning scheme, namely Policy Iterations, and makes use of neural networks, in an Actor/Critic structure, to parametrically represent the control policy and the performance of the control system. The two neural networks are trained to express the optimal controller and optimal cost function which describes the infinite horizon control performance. Convergence of the algorithm is proven under the realistic assumption that the two neural networks do not provide perfect representations for the nonlinear control and cost functions. The result is a hybrid control structure which involves a continuous-time controller and a supervisory adaptation structure which operates based on data sampled from the plant and from the continuous-time performance dynamics. Such control structure is unlike any standard form of controllers previously seen in the literature. Simulation results, obtained considering two second-order nonlinear systems, are provided. PMID:19362449
Optimization of continuous one-dimensional structures under steady harmonic excitation
NASA Technical Reports Server (NTRS)
Johnson, E. H.; Segenreich, S. A.; Ashley, H.; Rizzi, P.
1976-01-01
The paper examines some questions relating to the optimal design of continuous one-dimensional structures driven by harmonically oscillating loads. Optimal-control methods are applied to a cantilever bar driven sinusoidally by an axial force at its tip to illustrate the minimum-weight design of one-dimensional structures under dynamic excitation. Realistic constraints are imposed during the optimizations, including a maximum allowable stress amplitude at any point along the bar and a minimum cross-sectional area. It is shown that in the absence of damping, the design space may contain many disjoint feasible regions, and multiple optima can exist. Detailed solutions are obtained for continuous bars with an excitation frequency less than, and then greater than, the fundamental free-vibration frequency. It is found that above a certain excitation frequency, two or more arcs with different constraints characterize the optimal designs. It is concluded that when more than two different constrained arcs characterize the optimal solution, the continuum approach may be impractical, and finite-element approximations may offer the only alternative.
NASA Astrophysics Data System (ADS)
Younis, Adel; Dong, Zuomin
2012-07-01
Surrogate-based modeling is an effective search method for global design optimization over well-defined areas using complex and computationally intensive analysis and simulation tools. However, indentifying the appreciate surrogate models and their suitable areas remains a challenge that requires extensive human intervention. In this work, a new global optimization algorithm, namely Mixed Surrogate and Space Elimination (MSSE) method, is introduced. Representative surrogate models, including Quadratic Response Surface, Radial Basis function, and Kriging, are mixed with different weight ratios to form an adaptive metamodel with best tested performance. The approach divides the field of interest into several unimodal regions; identifies and ranks the regions that likely contain the global minimum; fits the weighted surrogate models over each promising region using additional design experiment data points from Latin Hypercube Designs and adjusts the weights according to the performance of each model; identifies its minimum and removes the processed region; and moves to the next most promising region until all regions are processed and the global optimum is identified. The proposed algorithm was tested using several benchmark problems for global optimization and compared with several widely used space exploration global optimization algorithms, showing reduced computation efforts, robust performance and comparable search accuracy, making the proposed method an excellent tool for computationally intensive global design optimization problems.
Sartelli, Massimo; Weber, Dieter G; Ruppé, Etienne; Bassetti, Matteo; Wright, Brian J; Ansaloni, Luca; Catena, Fausto; Coccolini, Federico; Abu-Zidan, Fikri M; Coimbra, Raul; Moore, Ernest E; Moore, Frederick A; Maier, Ronald V; De Waele, Jan J; Kirkpatrick, Andrew W; Griffiths, Ewen A; Eckmann, Christian; Brink, Adrian J; Mazuski, John E; May, Addison K; Sawyer, Rob G; Mertz, Dominik; Montravers, Philippe; Kumar, Anand; Roberts, Jason A; Vincent, Jean-Louis; Watkins, Richard R; Lowman, Warren; Spellberg, Brad; Abbott, Iain J; Adesunkanmi, Abdulrashid Kayode; Al-Dahir, Sara; Al-Hasan, Majdi N; Agresta, Ferdinando; Althani, Asma A; Ansari, Shamshul; Ansumana, Rashid; Augustin, Goran; Bala, Miklosh; Balogh, Zsolt J; Baraket, Oussama; Bhangu, Aneel; Beltrán, Marcelo A; Bernhard, Michael; Biffl, Walter L; Boermeester, Marja A; Brecher, Stephen M; Cherry-Bukowiec, Jill R; Buyne, Otmar R; Cainzos, Miguel A; Cairns, Kelly A; Camacho-Ortiz, Adrian; Chandy, Sujith J; Che Jusoh, Asri; Chichom-Mefire, Alain; Colijn, Caroline; Corcione, Francesco; Cui, Yunfeng; Curcio, Daniel; Delibegovic, Samir; Demetrashvili, Zaza; De Simone, Belinda; Dhingra, Sameer; Diaz, José J; Di Carlo, Isidoro; Dillip, Angel; Di Saverio, Salomone; Doyle, Michael P; Dorj, Gereltuya; Dogjani, Agron; Dupont, Hervé; Eachempati, Soumitra R; Enani, Mushira Abdulaziz; Egiev, Valery N; Elmangory, Mutasim M; Ferrada, Paula; Fitchett, Joseph R; Fraga, Gustavo P; Guessennd, Nathalie; Giamarellou, Helen; Ghnnam, Wagih; Gkiokas, George; Goldberg, Staphanie R; Gomes, Carlos Augusto; Gomi, Harumi; Guzmán-Blanco, Manuel; Haque, Mainul; Hansen, Sonja; Hecker, Andreas; Heizmann, Wolfgang R; Herzog, Torsten; Hodonou, Adrien Montcho; Hong, Suk-Kyung; Kafka-Ritsch, Reinhold; Kaplan, Lewis J; Kapoor, Garima; Karamarkovic, Aleksandar; Kees, Martin G; Kenig, Jakub; Kiguba, Ronald; Kim, Peter K; Kluger, Yoram; Khokha, Vladimir; Koike, Kaoru; Kok, Kenneth Y Y; Kong, Victory; Knox, Matthew C; Inaba, Kenji; Isik, Arda; Iskandar, Katia; Ivatury, Rao R; Labbate, Maurizio; Labricciosa, Francesco M; Laterre, Pierre-François; Latifi, Rifat; Lee, Jae Gil; Lee, Young Ran; Leone, Marc; Leppaniemi, Ari; Li, Yousheng; Liang, Stephen Y; Loho, Tonny; Maegele, Marc; Malama, Sydney; Marei, Hany E; Martin-Loeches, Ignacio; Marwah, Sanjay; Massele, Amos; McFarlane, Michael; Melo, Renato Bessa; Negoi, Ionut; Nicolau, David P; Nord, Carl Erik; Ofori-Asenso, Richard; Omari, AbdelKarim H; Ordonez, Carlos A; Ouadii, Mouaqit; Pereira Júnior, Gerson Alves; Piazza, Diego; Pupelis, Guntars; Rawson, Timothy Miles; Rems, Miran; Rizoli, Sandro; Rocha, Claudio; Sakakhushev, Boris; Sanchez-Garcia, Miguel; Sato, Norio; Segovia Lohse, Helmut A; Sganga, Gabriele; Siribumrungwong, Boonying; Shelat, Vishal G; Soreide, Kjetil; Soto, Rodolfo; Talving, Peep; Tilsed, Jonathan V; Timsit, Jean-Francois; Trueba, Gabriel; Trung, Ngo Tat; Ulrych, Jan; van Goor, Harry; Vereczkei, Andras; Vohra, Ravinder S; Wani, Imtiaz; Uhl, Waldemar; Xiao, Yonghong; Yuan, Kuo-Ching; Zachariah, Sanoop K; Zahar, Jean-Ralph; Zakrison, Tanya L; Corcione, Antonio; Melotti, Rita M; Viscoli, Claudio; Viale, Perluigi
2016-01-01
Intra-abdominal infections (IAI) are an important cause of morbidity and are frequently associated with poor prognosis, particularly in high-risk patients. The cornerstones in the management of complicated IAIs are timely effective source control with appropriate antimicrobial therapy. Empiric antimicrobial therapy is important in the management of intra-abdominal infections and must be broad enough to cover all likely organisms because inappropriate initial antimicrobial therapy is associated with poor patient outcomes and the development of bacterial resistance. The overuse of antimicrobials is widely accepted as a major driver of some emerging infections (such as C. difficile), the selection of resistant pathogens in individual patients, and for the continued development of antimicrobial resistance globally. The growing emergence of multi-drug resistant organisms and the limited development of new agents available to counteract them have caused an impending crisis with alarming implications, especially with regards to Gram-negative bacteria. An international task force from 79 different countries has joined this project by sharing a document on the rational use of antimicrobials for patients with IAIs. The project has been termed AGORA (Antimicrobials: A Global Alliance for Optimizing their Rational Use in Intra-Abdominal Infections). The authors hope that AGORA, involving many of the world's leading experts, can actively raise awareness in health workers and can improve prescribing behavior in treating IAIs. PMID:27429642
Sartelli, Massimo; Weber, Dieter G; Ruppé, Etienne; Bassetti, Matteo; Wright, Brian J; Ansaloni, Luca; Catena, Fausto; Coccolini, Federico; Abu-Zidan, Fikri M; Coimbra, Raul; Moore, Ernest E; Moore, Frederick A; Maier, Ronald V; De Waele, Jan J; Kirkpatrick, Andrew W; Griffiths, Ewen A; Eckmann, Christian; Brink, Adrian J; Mazuski, John E; May, Addison K; Sawyer, Rob G; Mertz, Dominik; Montravers, Philippe; Kumar, Anand; Roberts, Jason A; Vincent, Jean-Louis; Watkins, Richard R; Lowman, Warren; Spellberg, Brad; Abbott, Iain J; Adesunkanmi, Abdulrashid Kayode; Al-Dahir, Sara; Al-Hasan, Majdi N; Agresta, Ferdinando; Althani, Asma A; Ansari, Shamshul; Ansumana, Rashid; Augustin, Goran; Bala, Miklosh; Balogh, Zsolt J; Baraket, Oussama; Bhangu, Aneel; Beltrán, Marcelo A; Bernhard, Michael; Biffl, Walter L; Boermeester, Marja A; Brecher, Stephen M; Cherry-Bukowiec, Jill R; Buyne, Otmar R; Cainzos, Miguel A; Cairns, Kelly A; Camacho-Ortiz, Adrian; Chandy, Sujith J; Che Jusoh, Asri; Chichom-Mefire, Alain; Colijn, Caroline; Corcione, Francesco; Cui, Yunfeng; Curcio, Daniel; Delibegovic, Samir; Demetrashvili, Zaza; De Simone, Belinda; Dhingra, Sameer; Diaz, José J; Di Carlo, Isidoro; Dillip, Angel; Di Saverio, Salomone; Doyle, Michael P; Dorj, Gereltuya; Dogjani, Agron; Dupont, Hervé; Eachempati, Soumitra R; Enani, Mushira Abdulaziz; Egiev, Valery N; Elmangory, Mutasim M; Ferrada, Paula; Fitchett, Joseph R; Fraga, Gustavo P; Guessennd, Nathalie; Giamarellou, Helen; Ghnnam, Wagih; Gkiokas, George; Goldberg, Staphanie R; Gomes, Carlos Augusto; Gomi, Harumi; Guzmán-Blanco, Manuel; Haque, Mainul; Hansen, Sonja; Hecker, Andreas; Heizmann, Wolfgang R; Herzog, Torsten; Hodonou, Adrien Montcho; Hong, Suk-Kyung; Kafka-Ritsch, Reinhold; Kaplan, Lewis J; Kapoor, Garima; Karamarkovic, Aleksandar; Kees, Martin G; Kenig, Jakub; Kiguba, Ronald; Kim, Peter K; Kluger, Yoram; Khokha, Vladimir; Koike, Kaoru; Kok, Kenneth Y Y; Kong, Victory; Knox, Matthew C; Inaba, Kenji; Isik, Arda; Iskandar, Katia; Ivatury, Rao R; Labbate, Maurizio; Labricciosa, Francesco M; Laterre, Pierre-François; Latifi, Rifat; Lee, Jae Gil; Lee, Young Ran; Leone, Marc; Leppaniemi, Ari; Li, Yousheng; Liang, Stephen Y; Loho, Tonny; Maegele, Marc; Malama, Sydney; Marei, Hany E; Martin-Loeches, Ignacio; Marwah, Sanjay; Massele, Amos; McFarlane, Michael; Melo, Renato Bessa; Negoi, Ionut; Nicolau, David P; Nord, Carl Erik; Ofori-Asenso, Richard; Omari, AbdelKarim H; Ordonez, Carlos A; Ouadii, Mouaqit; Pereira Júnior, Gerson Alves; Piazza, Diego; Pupelis, Guntars; Rawson, Timothy Miles; Rems, Miran; Rizoli, Sandro; Rocha, Claudio; Sakakhushev, Boris; Sanchez-Garcia, Miguel; Sato, Norio; Segovia Lohse, Helmut A; Sganga, Gabriele; Siribumrungwong, Boonying; Shelat, Vishal G; Soreide, Kjetil; Soto, Rodolfo; Talving, Peep; Tilsed, Jonathan V; Timsit, Jean-Francois; Trueba, Gabriel; Trung, Ngo Tat; Ulrych, Jan; van Goor, Harry; Vereczkei, Andras; Vohra, Ravinder S; Wani, Imtiaz; Uhl, Waldemar; Xiao, Yonghong; Yuan, Kuo-Ching; Zachariah, Sanoop K; Zahar, Jean-Ralph; Zakrison, Tanya L; Corcione, Antonio; Melotti, Rita M; Viscoli, Claudio; Viale, Perluigi
2016-01-01
Intra-abdominal infections (IAI) are an important cause of morbidity and are frequently associated with poor prognosis, particularly in high-risk patients. The cornerstones in the management of complicated IAIs are timely effective source control with appropriate antimicrobial therapy. Empiric antimicrobial therapy is important in the management of intra-abdominal infections and must be broad enough to cover all likely organisms because inappropriate initial antimicrobial therapy is associated with poor patient outcomes and the development of bacterial resistance. The overuse of antimicrobials is widely accepted as a major driver of some emerging infections (such as C. difficile), the selection of resistant pathogens in individual patients, and for the continued development of antimicrobial resistance globally. The growing emergence of multi-drug resistant organisms and the limited development of new agents available to counteract them have caused an impending crisis with alarming implications, especially with regards to Gram-negative bacteria. An international task force from 79 different countries has joined this project by sharing a document on the rational use of antimicrobials for patients with IAIs. The project has been termed AGORA (Antimicrobials: A Global Alliance for Optimizing their Rational Use in Intra-Abdominal Infections). The authors hope that AGORA, involving many of the world's leading experts, can actively raise awareness in health workers and can improve prescribing behavior in treating IAIs.
Local-global analysis of crack growth in continuously reinfoced ceramic matrix composites
NASA Technical Reports Server (NTRS)
Ballarini, Roberto; Ahmed, Shamim
1989-01-01
This paper describes the development of a mathematical model for predicting the strength and micromechanical failure characteristics of continuously reinforced ceramic matrix composites. The local-global analysis models the vicinity of a propagating crack tip as a local heterogeneous region (LHR) consisting of spring-like representation of the matrix, fibers and interfaces. Parametric studies are conducted to investigate the effects of LHR size, component properties, and interface conditions on the strength and sequence of the failure processes in the unidirectional composite system.
Global prediction of continuous hydrocarbon accumulations in self-sourced reservoirs
Eoff, Jennifer D.
2012-01-01
This report was first presented as an abstract in poster format at the American Association of Petroleum Geologists (AAPG) 2012 Annual Convention and Exhibition, April 22-25, Long Beach, Calif., as Search and Discovery Article no. 90142. Shale resource plays occur in predictable tectonic settings within similar orders of magnitude of eustatic events. A conceptual model for predicting the presence of resource-quality shales is essential for evaluating components of continuous petroleum systems. Basin geometry often distinguishes self-sourced resource plays from conventional plays. Intracratonic or intrashelf foreland basins at active margins are the predominant depositional settings among those explored for the development of self-sourced continuous accumulations, whereas source rocks associated with conventional accumulations typically were deposited in rifted passive margin settings (or other cratonic environments). Generally, the former are associated with the assembly of supercontinents, and the latter often resulted during or subsequent to the breakup of landmasses. Spreading rates, climate, and eustasy are influenced by these global tectonic events, such that deposition of self-sourced reservoirs occurred during periods characterized by rapid plate reconfiguration, predominantly greenhouse climate conditions, and in areas adjacent to extensive carbonate sedimentation. Combined tectonic histories, eustatic curves, and paleogeographic reconstructions may be useful in global predictions of organic-rich shale accumulations suitable for continuous resource development. Accumulation of marine organic material is attributed to upwellings that enhance productivity and oxygen-minimum bottom waters that prevent destruction of organic matter. The accumulation of potential self-sourced resources can be attributed to slow sedimentation rates in rapidly subsiding (incipient, flexural) foreland basins, while flooding of adjacent carbonate platforms and other cratonic highs
NASA Technical Reports Server (NTRS)
Tralli, David M.
1991-01-01
Temporal power spectral density models of noise in continuous crustal deformation measurements obtained with the Global Positioning System (GPS) and high-quality strainmeters are compared. The crossover frequency at which GPS measurement noise is less than that of strainmeters is determined. Assuming GPS precision of 0.1 to 1 cm in horizontal components for baselines up to 100 km in length, local deformation monitoring with GPS may be preferable to strainmeters for observations of short-term deformation in under 6 months of continuous (at least daily) measurements. Short-period tropospheric path delays and multipath effects, which may obscure GPS-determined strain signals in local network measurements, also are discussed.
NASA Astrophysics Data System (ADS)
Yang, Xiong; Liu, Derong; Wang, Ding
2014-03-01
In this paper, an adaptive reinforcement learning-based solution is developed for the infinite-horizon optimal control problem of constrained-input continuous-time nonlinear systems in the presence of nonlinearities with unknown structures. Two different types of neural networks (NNs) are employed to approximate the Hamilton-Jacobi-Bellman equation. That is, an recurrent NN is constructed to identify the unknown dynamical system, and two feedforward NNs are used as the actor and the critic to approximate the optimal control and the optimal cost, respectively. Based on this framework, the action NN and the critic NN are tuned simultaneously, without the requirement for the knowledge of system drift dynamics. Moreover, by using Lyapunov's direct method, the weights of the action NN and the critic NN are guaranteed to be uniformly ultimately bounded, while keeping the closed-loop system stable. To demonstrate the effectiveness of the present approach, simulation results are illustrated.
NASA Astrophysics Data System (ADS)
Hu, Li-Yun; Liao, Zeyang; Ma, Shengli; Zubairy, M. Suhail
2016-03-01
We introduce three tunable parameters to optimize the fidelity of quantum teleportation with continuous variables in a nonideal scheme. By using the characteristic-function formalism, we present the condition that the teleportation fidelity is independent of the amplitude of input coherent states for any entangled resource. Then we investigate the effects of tunable parameters on the fidelity with or without the presence of the environment and imperfect measurements by analytically deriving the expression of fidelity for three different input coherent-state distributions. It is shown that, for the linear distribution, the optimization with three tunable parameters is the best one with respect to single- and two-parameter optimization. Our results reveal the usefulness of tunable parameters for improving the fidelity of teleportation and the ability against decoherence.
Global stability and optimal control of an SIRS epidemic model on heterogeneous networks
NASA Astrophysics Data System (ADS)
Chen, Lijuan; Sun, Jitao
2014-09-01
In this paper, we consider an SIRS epidemic model with vaccination on heterogeneous networks. By constructing suitable Lyapunov functions, global stability of the disease-free equilibrium and the endemic equilibrium of the model is investigated. Also we firstly study an optimally controlled SIRS epidemic model on complex networks. We show that an optimal control exists for the control problem. Finally some examples are presented to show the global stability and the efficiency of this optimal control. These results can help in adopting pragmatic treatment upon diseases in structured populations.
Global optimization for future gravitational wave detector sites
NASA Astrophysics Data System (ADS)
Hu, Yi-Ming; Raffai, Péter; Gondán, László; Heng, Ik Siong; Kelecsényi, Nándor; Hendry, Martin; Márka, Zsuzsa; Márka, Szabolcs
2015-05-01
We consider the optimal site selection of future generations of gravitational wave (GW) detectors. Previously, Raffai et al optimized a two-detector network with a combined figure of merit (FoM). This optimization was extended to networks with more than two detectors in a limited way by first fixing the parameters of all other component detectors. In this work we now present a more general optimization that allows the locations of all detectors to be simultaneously chosen. We follow the definition of Raffai et al on the metric that defines the suitability of a certain detector network. Given the locations of the component detectors in the network, we compute a measure of the network's ability to distinguish the polarization, constrain the sky localization and reconstruct the parameters of a GW source. We further define the ‘flexibility index’ for a possible site location, by counting the number of multi-detector networks with a sufficiently high FoM that include that site location. We confirm the conclusion of Raffai et al, that in terms of the flexibility index as defined in this work, Australia hosts the best candidate site to build a future generation GW detector. This conclusion is valid for either a three-detector network or a five-detector network. For a three-detector network, site locations in Northern Europe display a comparable flexibility index to sites in Australia. However, for a five-detector network, Australia is found to be a clearly better candidate than any other location.
An evolutionary algorithm for global optimization based on self-organizing maps
NASA Astrophysics Data System (ADS)
Barmada, Sami; Raugi, Marco; Tucci, Mauro
2016-10-01
In this article, a new population-based algorithm for real-parameter global optimization is presented, which is denoted as self-organizing centroids optimization (SOC-opt). The proposed method uses a stochastic approach which is based on the sequential learning paradigm for self-organizing maps (SOMs). A modified version of the SOM is proposed where each cell contains an individual, which performs a search for a locally optimal solution and it is affected by the search for a global optimum. The movement of the individuals in the search space is based on a discrete-time dynamic filter, and various choices of this filter are possible to obtain different dynamics of the centroids. In this way, a general framework is defined where well-known algorithms represent a particular case. The proposed algorithm is validated through a set of problems, which include non-separable problems, and compared with state-of-the-art algorithms for global optimization.
Wagner, Caroline S.; Park, Han Woo; Leydesdorff, Loet
2015-01-01
Global collaboration continues to grow as a share of all scientific cooperation, measured as coauthorships of peer-reviewed, published papers. The percent of all scientific papers that are internationally coauthored has more than doubled in 20 years, and they account for all the growth in output among the scientifically advanced countries. Emerging countries, particularly China, have increased their participation in global science, in part by doubling their spending on R&D; they are increasingly likely to appear as partners on internationally coauthored scientific papers. Given the growth of connections at the international level, it is helpful to examine the phenomenon as a communications network and to consider the network as a new organization on the world stage that adds to and complements national systems. When examined as interconnections across the globe over two decades, a global network has grown denser but not more clustered, meaning there are many more connections but they are not grouping into exclusive ‘cliques’. This suggests that power relationships are not reproducing those of the political system. The network has features an open system, attracting productive scientists to participate in international projects. National governments could gain efficiencies and influence by developing policies and strategies designed to maximize network benefits—a model different from those designed for national systems. PMID:26196296
A Lyapunov-Based Extension to Particle Swarm Dynamics for Continuous Function Optimization
Bhattacharya, Sayantani; Konar, Amit; Das, Swagatam; Han, Sang Yong
2009-01-01
The paper proposes three alternative extensions to the classical global-best particle swarm optimization dynamics, and compares their relative performance with the standard particle swarm algorithm. The first extension, which readily follows from the well-known Lyapunov's stability theorem, provides a mathematical basis of the particle dynamics with a guaranteed convergence at an optimum. The inclusion of local and global attractors to this dynamics leads to faster convergence speed and better accuracy than the classical one. The second extension augments the velocity adaptation equation by a negative randomly weighted positional term of individual particle, while the third extension considers the negative positional term in place of the inertial term. Computer simulations further reveal that the last two extensions outperform both the classical and the first extension in terms of convergence speed and accuracy. PMID:22303158
NASA Astrophysics Data System (ADS)
Petronevich, V. V.
2016-10-01
The paper observes the issues related to the increase of efficiency and information content of experimental research in transonic wind tunnels (WT). In particular, questions of optimizing the WT Data Acquisition and Control Systems (DACS) to provide the continuous mode test method are discussed. The problem of Mach number (M number) stabilization in the test section of the large transonic compressor-type wind tunnels at subsonic flow conditions with continuous change of the aircraft model angle of attack is observed on the example of T-128 wind tunnel. To minimize the signals distortion in T-128 DACS measurement channels the optimal MGCplus filter settings of the data acquisition system used in T-128 wind tunnel to measure loads were experimentally determined. As a result of the tests performed a good agreement of the results of balance measurements for pitch/pause and continuous test modes was obtained. Carrying out balance tests for pitch/pause and continuous test methods was provided by the regular data acquisition and control system of T-128 wind tunnel with unified software package POTOK. The architecture and functional abilities of POTOK software package are observed.
Fast globally optimal segmentation of cells in fluorescence microscopy images.
Bergeest, Jan-Philip; Rohr, Karl
2011-01-01
Accurate and efficient segmentation of cells in fluorescence microscopy images is of central importance for the quantification of protein expression in high-throughput screening applications. We propose a new approach for segmenting cell nuclei which is based on active contours and convex energy functionals. Compared to previous work, our approach determines the global solution. Thus, the approach does not suffer from local minima and the segmentation result does not depend on the initialization. We also suggest a numeric approach for efficiently computing the solution. The performance of our approach has been evaluated using fluorescence microscopy images of different cell types. We have also performed a quantitative comparison with previous segmentation approaches.
Global Optimization, Local Adaptation, and the Role of Growth in Distribution Networks
NASA Astrophysics Data System (ADS)
Ronellenfitsch, Henrik; Katifori, Eleni
2016-09-01
Highly optimized complex transport networks serve crucial functions in many man-made and natural systems such as power grids and plant or animal vasculature. Often, the relevant optimization functional is nonconvex and characterized by many local extrema. In general, finding the global, or nearly global optimum is difficult. In biological systems, it is believed that such an optimal state is slowly achieved through natural selection. However, general coarse grained models for flow networks with local positive feedback rules for the vessel conductivity typically get trapped in low efficiency, local minima. In this work we show how the growth of the underlying tissue, coupled to the dynamical equations for network development, can drive the system to a dramatically improved optimal state. This general model provides a surprisingly simple explanation for the appearance of highly optimized transport networks in biology such as leaf and animal vasculature.
NASA Astrophysics Data System (ADS)
Vaziri Yazdi Pin, Mohammad
Electric power distribution systems are the last high voltage link in the chain of production, transport, and delivery of the electric energy, the fundamental goals of which are to supply the users' demand safely, reliably, and economically. The number circuit miles traversed by distribution feeders in the form of visible overhead or imbedded underground lines, far exceed those of all other bulk transport circuitry in the transmission system. Development and expansion of the distribution systems, similar to other systems, is directly proportional to the growth in demand and requires careful planning. While growth of electric demand has recently slowed through efforts in the area of energy management, the need for a continued expansion seems inevitable for the near future. Distribution system and expansions are also independent of current issues facing both the suppliers and the consumers of electrical energy. For example, deregulation, as an attempt to promote competition by giving more choices to the consumers, while it will impact the suppliers' planning strategies, it cannot limit the demand growth or the system expansion in the global sense. Curiously, despite presence of technological advancements and a 40-year history of contributions in the area, many of the major utilities still relay on experience and resort to rudimentary techniques when planning expansions. A comprehensive literature review of the contributions and careful analyses of the proposed algorithms for distribution expansion, confirmed that the problem is a complex, multistage and multiobjective problem for which a practical solution remains to be developed. In this research, based on the 15-year experience of a utility engineer, the practical expansion problem has been clearly defined and the existing deficiencies in the previous work identified and analyzed. The expansion problem has been formulated as a multistage planning problem in line with a natural course of development and industry
OPTIMIZE-M. Nonlinear Global Optimization Using Curdling Algorithm in Mathematica Environmet
Loehle, C.
1997-07-01
An algorithm for performing optimization which is a derivative-free, grid-refinement approach to nonlinear optimization was developed and implemented in software as OPTIMIZE. This approach overcomes a number of deficiencies in existing approaches. Most notably, it finds extremal regions rather than only single extremal points. the program is interactive and collects information on control parameters and constraints using menus. For up to two (and potentially three) dimensions, function convergence is displayed graphically. Because the algorithm does not compute derivatives, gradients, or vectors, it is numerically stable. It can find all the roots of a polynomial in one pass. It is an inherently parallel algorithm. OPTIMIZE-M is a modification of OPTIMIZE designed for use within the Mathematica environment created by Wolfram Research.
A Global Optimization Methodology for Rocket Propulsion Applications
NASA Technical Reports Server (NTRS)
2001-01-01
While the response surface method is an effective method in engineering optimization, its accuracy is often affected by the use of limited amount of data points for model construction. In this chapter, the issues related to the accuracy of the RS approximations and possible ways of improving the RS model using appropriate treatments, including the iteratively re-weighted least square (IRLS) technique and the radial-basis neural networks, are investigated. A main interest is to identify ways to offer added capabilities for the RS method to be able to at least selectively improve the accuracy in regions of importance. An example is to target the high efficiency region of a fluid machinery design space so that the predictive power of the RS can be maximized when it matters most. Analytical models based on polynomials, with controlled level of noise, are used to assess the performance of these techniques.
Metamodel-based global optimization using fuzzy clustering for design space reduction
NASA Astrophysics Data System (ADS)
Li, Yulin; Liu, Li; Long, Teng; Dong, Weili
2013-09-01
High fidelity analysis are utilized in modern engineering design optimization problems which involve expensive black-box models. For computation-intensive engineering design problems, efficient global optimization methods must be developed to relieve the computational burden. A new metamodel-based global optimization method using fuzzy clustering for design space reduction (MGO-FCR) is presented. The uniformly distributed initial sample points are generated by Latin hypercube design to construct the radial basis function metamodel, whose accuracy is improved with increasing number of sample points gradually. Fuzzy c-mean method and Gath-Geva clustering method are applied to divide the design space into several small interesting cluster spaces for low and high dimensional problems respectively. Modeling efficiency and accuracy are directly related to the design space, so unconcerned spaces are eliminated by the proposed reduction principle and two pseudo reduction algorithms. The reduction principle is developed to determine whether the current design space should be reduced and which space is eliminated. The first pseudo reduction algorithm improves the speed of clustering, while the second pseudo reduction algorithm ensures the design space to be reduced. Through several numerical benchmark functions, comparative studies with adaptive response surface method, approximated unimodal region elimination method and mode-pursuing sampling are carried out. The optimization results reveal that this method captures the real global optimum for all the numerical benchmark functions. And the number of function evaluations show that the efficiency of this method is favorable especially for high dimensional problems. Based on this global design optimization method, a design optimization of a lifting surface in high speed flow is carried out and this method saves about 10 h compared with genetic algorithms. This method possesses favorable performance on efficiency, robustness
NASA Technical Reports Server (NTRS)
Childs, A. G.
1971-01-01
A discrete steepest ascent method which allows controls which are not piecewise constant (for example, it allows all continuous piecewise linear controls) was derived for the solution of optimal programming problems. This method is based on the continuous steepest ascent method of Bryson and Denham and new concepts introduced by Kelley and Denham in their development of compatible adjoints for taking into account the effects of numerical integration. The method is a generalization of the algorithm suggested by Canon, Cullum, and Polak with the details of the gradient computation given. The discrete method was compared with the continuous method for an aerodynamics problem for which an analytic solution is given by Pontryagin's maximum principle, and numerical results are presented. The discrete method converges more rapidly than the continuous method at first, but then for some undetermined reason, loses its exponential convergence rate. A comparsion was also made for the algorithm of Canon, Cullum, and Polak using piecewise constant controls. This algorithm is very competitive with the continuous algorithm.
A policy iteration approach to online optimal control of continuous-time constrained-input systems.
Modares, Hamidreza; Naghibi Sistani, Mohammad-Bagher; Lewis, Frank L
2013-09-01
This paper is an effort towards developing an online learning algorithm to find the optimal control solution for continuous-time (CT) systems subject to input constraints. The proposed method is based on the policy iteration (PI) technique which has recently evolved as a major technique for solving optimal control problems. Although a number of online PI algorithms have been developed for CT systems, none of them take into account the input constraints caused by actuator saturation. In practice, however, ignoring these constraints leads to performance degradation or even system instability. In this paper, to deal with the input constraints, a suitable nonquadratic functional is employed to encode the constraints into the optimization formulation. Then, the proposed PI algorithm is implemented on an actor-critic structure to solve the Hamilton-Jacobi-Bellman (HJB) equation associated with this nonquadratic cost functional in an online fashion. That is, two coupled neural network (NN) approximators, namely an actor and a critic are tuned online and simultaneously for approximating the associated HJB solution and computing the optimal control policy. The critic is used to evaluate the cost associated with the current policy, while the actor is used to find an improved policy based on information provided by the critic. Convergence to a close approximation of the HJB solution as well as stability of the proposed feedback control law are shown. Simulation results of the proposed method on a nonlinear CT system illustrate the effectiveness of the proposed approach. PMID:23706414
NASA Astrophysics Data System (ADS)
Lv, Yongfeng; Na, Jing; Yang, Qinmin; Wu, Xing; Guo, Yu
2016-01-01
An online adaptive optimal control is proposed for continuous-time nonlinear systems with completely unknown dynamics, which is achieved by developing a novel identifier-critic-based approximate dynamic programming algorithm with a dual neural network (NN) approximation structure. First, an adaptive NN identifier is designed to obviate the requirement of complete knowledge of system dynamics, and a critic NN is employed to approximate the optimal value function. Then, the optimal control law is computed based on the information from the identifier NN and the critic NN, so that the actor NN is not needed. In particular, a novel adaptive law design method with the parameter estimation error is proposed to online update the weights of both identifier NN and critic NN simultaneously, which converge to small neighbourhoods around their ideal values. The closed-loop system stability and the convergence to small vicinity around the optimal solution are all proved by means of the Lyapunov theory. The proposed adaptation algorithm is also improved to achieve finite-time convergence of the NN weights. Finally, simulation results are provided to exemplify the efficacy of the proposed methods.
Hurtado, F J; Kaiser, A S; Zamora, B
2015-03-15
Continuous stirred tank reactors (CSTR) are widely used in wastewater treatment plants to reduce the organic matter and microorganism present in sludge by anaerobic digestion. The present study carries out a numerical analysis of the fluid dynamic behaviour of a CSTR in order to optimize the process energetically. The characterization of the sludge flow inside the digester tank, the residence time distribution and the active volume of the reactor under different criteria are determined. The effects of design and power of the mixing system on the active volume of the CSTR are analyzed. The numerical model is solved under non-steady conditions by examining the evolution of the flow during the stop and restart of the mixing system. An intermittent regime of the mixing system, which kept the active volume between 94% and 99%, is achieved. The results obtained can lead to the eventual energy optimization of the mixing system of the CSTR. PMID:25635665
Ren, N Q; Chua, H; Chan, S Y; Tsang, Y F; Wang, Y J; Sin, N
2007-07-01
In this study, the optimal fermentation type and the operating conditions of anaerobic process in continuous-flow acidogenic reactors was investigated for the maximization of bio-hydrogen production using mixed cultures. Butyric acid type fermentation occurred at pH>6, propionic acid type fermentation occurred at pH about 5.5 with E(h) (redox potential) >-278mV, and ethanol-type fermentation occurred at pH<4.5. The representative strains of these fermentations were Clostridium sp., Propionibacterium sp. and Bacteriodes sp., respectively. Ethanol fermentation was optimal type by comparing the operating stabilities and hydrogen production capacities between the fermentation types, which remained stable when the organic loading rate (OLR) reached the highest OLR at 86.1kgCOD/m(3)d. The maximum hydrogen production reached up to 14.99L/d.
Liu, Haorui; Yi, Fengyan; Yang, Heli
2016-01-01
The shuffled frog leaping algorithm (SFLA) easily falls into local optimum when it solves multioptimum function optimization problem, which impacts the accuracy and convergence speed. Therefore this paper presents grouped SFLA for solving continuous optimization problems combined with the excellent characteristics of cloud model transformation between qualitative and quantitative research. The algorithm divides the definition domain into several groups and gives each group a set of frogs. Frogs of each region search in their memeplex, and in the search process the algorithm uses the "elite strategy" to update the location information of existing elite frogs through cloud model algorithm. This method narrows the searching space and it can effectively improve the situation of a local optimum; thus convergence speed and accuracy can be significantly improved. The results of computer simulation confirm this conclusion.
Confidence intervals for the symmetry point: an optimal cutpoint in continuous diagnostic tests.
López-Ratón, Mónica; Cadarso-Suárez, Carmen; Molanes-López, Elisa M; Letón, Emilio
2016-01-01
Continuous diagnostic tests are often used for discriminating between healthy and diseased populations. For this reason, it is useful to select an appropriate discrimination threshold. There are several optimality criteria: the North-West corner, the Youden index, the concordance probability and the symmetry point, among others. In this paper, we focus on the symmetry point that maximizes simultaneously the two types of correct classifications. We construct confidence intervals for this optimal cutpoint and its associated specificity and sensitivity indexes using two approaches: one based on the generalized pivotal quantity and the other on empirical likelihood. We perform a simulation study to check the practical behaviour of both methods and illustrate their use by means of three real biomedical datasets on melanoma, prostate cancer and coronary artery disease. PMID:26756550
Hurtado, F J; Kaiser, A S; Zamora, B
2015-03-15
Continuous stirred tank reactors (CSTR) are widely used in wastewater treatment plants to reduce the organic matter and microorganism present in sludge by anaerobic digestion. The present study carries out a numerical analysis of the fluid dynamic behaviour of a CSTR in order to optimize the process energetically. The characterization of the sludge flow inside the digester tank, the residence time distribution and the active volume of the reactor under different criteria are determined. The effects of design and power of the mixing system on the active volume of the CSTR are analyzed. The numerical model is solved under non-steady conditions by examining the evolution of the flow during the stop and restart of the mixing system. An intermittent regime of the mixing system, which kept the active volume between 94% and 99%, is achieved. The results obtained can lead to the eventual energy optimization of the mixing system of the CSTR.
Liu, Haorui; Yi, Fengyan; Yang, Heli
2016-01-01
The shuffled frog leaping algorithm (SFLA) easily falls into local optimum when it solves multioptimum function optimization problem, which impacts the accuracy and convergence speed. Therefore this paper presents grouped SFLA for solving continuous optimization problems combined with the excellent characteristics of cloud model transformation between qualitative and quantitative research. The algorithm divides the definition domain into several groups and gives each group a set of frogs. Frogs of each region search in their memeplex, and in the search process the algorithm uses the “elite strategy” to update the location information of existing elite frogs through cloud model algorithm. This method narrows the searching space and it can effectively improve the situation of a local optimum; thus convergence speed and accuracy can be significantly improved. The results of computer simulation confirm this conclusion. PMID:26819584
A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems.
Cao, Leilei; Xu, Lihong; Goodman, Erik D
2016-01-01
A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared. PMID:27293421
A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems
Cao, Leilei; Xu, Lihong; Goodman, Erik D.
2016-01-01
A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared. PMID:27293421
A Guiding Evolutionary Algorithm with Greedy Strategy for Global Optimization Problems.
Cao, Leilei; Xu, Lihong; Goodman, Erik D
2016-01-01
A Guiding Evolutionary Algorithm (GEA) with greedy strategy for global optimization problems is proposed. Inspired by Particle Swarm Optimization, the Genetic Algorithm, and the Bat Algorithm, the GEA was designed to retain some advantages of each method while avoiding some disadvantages. In contrast to the usual Genetic Algorithm, each individual in GEA is crossed with the current global best one instead of a randomly selected individual. The current best individual served as a guide to attract offspring to its region of genotype space. Mutation was added to offspring according to a dynamic mutation probability. To increase the capability of exploitation, a local search mechanism was applied to new individuals according to a dynamic probability of local search. Experimental results show that GEA outperformed the other three typical global optimization algorithms with which it was compared.
Glocker, Ben; Paragios, Nikos; Komodakis, Nikos; Tziritas, Georgios; Navab, Nassir
2007-01-01
In this paper we propose a novel non-rigid volume registration based on discrete labeling and linear programming. The proposed framework reformulates registration as a minimal path extraction in a weighted graph. The space of solutions is represented using a set of a labels which are assigned to predefined displacements. The graph topology corresponds to a superimposed regular grid onto the volume. Links between neighborhood control points introduce smoothness, while links between the graph nodes and the labels (end-nodes) measure the cost induced to the objective function through the selection of a particular deformation for a given control point once projected to the entire volume domain, Higher order polynomials are used to express the volume deformation from the ones of the control points. Efficient linear programming that can guarantee the optimal solution up to (a user-defined) bound is considered to recover the optimal registration parameters. Therefore, the method is gradient free, can encode various similarity metrics (simple changes on the graph construction), can guarantee a globally sub-optimal solution and is computational tractable. Experimental validation using simulated data with known deformation, as well as manually segmented data demonstrate the extreme potentials of our approach. PMID:17633717
NASA Astrophysics Data System (ADS)
Ulybyshev, S. Yu.
2016-07-01
We present a method for designing nonuniform satellite systems for continuous global coverage using a combination of equatorial and near-polar satellite segments in circular orbits. Equations are derived to determine the basic design parameters of the satellite system itself and the conditions of its closure at the joint of near-polar and equatorial segments. We analyze specific features of near-polar and equatorial satellite systems and their advantages and disadvantages compared with existing classes of near-polar phased and kinematically correct satellite systems. We estimate the minimum required number of spacecrafts in satellite systems for a given fold of coverage and present calculated dependences for classes of near-polar phased and equatorial satellite systems with different types of closure. For the class of kinematically correct satellite systems, we analyze the characteristics of systems with a minimum spacecraft flight height and reveal that the number of satellites in the orbital plane depends on the flight height for different folds of coverage. We bring examples of the best near-polar equatorial satellite systems of global coverage for different folds and a class of satellite systems with a fixed number of spacecrafts and orbital planes in them.
NASA Astrophysics Data System (ADS)
Mastellone, Daniela; Fedi, Maurizio; Ialongo, Simone; Paoletti, Valeria
2014-12-01
Many methods have been used to upward continue potential field data. Most techniques employ the Fast Fourier transform, which is an accurate, quick way to compute level-to-level upward continuation or spatially varying scale filters for level-to-draped surfaces. We here propose a new continuation approach based on the minimum-length solution of the inverse potential field problem, which we call Volume Continuation (VOCO). For real data the VOCO is obtained as the regularized solution to the Tikhonov problem. We tested our method on several synthetic examples involving all types of upward continuation and downward continuation (level-to-level, level-to-draped, draped-to-level, draped-to-draped). We also employed the technique to upward continue to a constant height (2500 m a.s.l.), the high-resolution draped aeromagnetic data of the Ischia Island in Southern Italy. We found that, on the average, they are consistent with the aeromagnetic regional data measured at the same altitude. The main feature of our method is that it does not only provide continued data over a specified surface, but it yields a volume of upward continuation. For example, the continued data refers to a volume and thus, any surface may be easily picked up within the volume to get upward continuation to different surfaces. This approach, based on inversion of the measured data, tends to be especially advantageous over the classical techniques when dealing with draped-to-level upward continuation. It is also useful to obtain a more stable downward continuation and to continue noisy data. The inversion procedure involved in the method implies moderate computational costs, which are well compensated by getting a 3D set of upward continued data to achieve high quality results.
Method for using global optimization to the estimation of surface-consistent residual statics
Reister, David B.; Barhen, Jacob; Oblow, Edward M.
2001-01-01
An efficient method for generating residual statics corrections to compensate for surface-consistent static time shifts in stacked seismic traces. The method includes a step of framing the residual static corrections as a global optimization problem in a parameter space. The method also includes decoupling the global optimization problem involving all seismic traces into several one-dimensional problems. The method further utilizes a Stochastic Pijavskij Tunneling search to eliminate regions in the parameter space where a global minimum is unlikely to exist so that the global minimum may be quickly discovered. The method finds the residual statics corrections by maximizing the total stack power. The stack power is a measure of seismic energy transferred from energy sources to receivers.
An intelligent factory-wide optimal operation system for continuous production process
NASA Astrophysics Data System (ADS)
Ding, Jinliang; Chai, Tianyou; Wang, Hongfeng; Wang, Junwei; Zheng, Xiuping
2016-03-01
In this study, a novel intelligent factory-wide operation system for a continuous production process is designed to optimise the entire production process, which consists of multiple units; furthermore, this system is developed using process operational data to avoid the complexity of mathematical modelling of the continuous production process. The data-driven approach aims to specify the structure of the optimal operation system; in particular, the operational data of the process are used to formulate each part of the system. In this context, the domain knowledge of process engineers is utilised, and a closed-loop dynamic optimisation strategy, which combines feedback, performance prediction, feed-forward, and dynamic tuning schemes into a framework, is employed. The effectiveness of the proposed system has been verified using industrial experimental results.
GOSIM: A multi-scale iterative multiple-point statistics algorithm with global optimization
NASA Astrophysics Data System (ADS)
Yang, Liang; Hou, Weisheng; Cui, Chanjie; Cui, Jie
2016-04-01
Most current multiple-point statistics (MPS) algorithms are based on a sequential simulation procedure, during which grid values are updated according to the local data events. Because the realization is updated only once during the sequential process, errors that occur while updating data events cannot be corrected. Error accumulation during simulations decreases the realization quality. Aimed at improving simulation quality, this study presents an MPS algorithm based on global optimization, called GOSIM. An objective function is defined for representing the dissimilarity between a realization and the TI in GOSIM, which is minimized by a multi-scale EM-like iterative method that contains an E-step and M-step in each iteration. The E-step searches for TI patterns that are most similar to the realization and match the conditioning data. A modified PatchMatch algorithm is used to accelerate the search process in E-step. M-step updates the realization based on the most similar patterns found in E-step and matches the global statistics of TI. During categorical data simulation, k-means clustering is used for transforming the obtained continuous realization into a categorical realization. The qualitative and quantitative comparison results of GOSIM, MS-CCSIM and SNESIM suggest that GOSIM has a better pattern reproduction ability for both unconditional and conditional simulations. A sensitivity analysis illustrates that pattern size significantly impacts the time costs and simulation quality. In conditional simulations, the weights of conditioning data should be as small as possible to maintain a good simulation quality. The study shows that big iteration numbers at coarser scales increase simulation quality and small iteration numbers at finer scales significantly save simulation time.
Optimization of global model composed of radial basis functions using the term-ranking approach
Cai, Peng; Tao, Chao Liu, Xiao-Jun
2014-03-15
A term-ranking method is put forward to optimize the global model composed of radial basis functions to improve the predictability of the model. The effectiveness of the proposed method is examined by numerical simulation and experimental data. Numerical simulations indicate that this method can significantly lengthen the prediction time and decrease the Bayesian information criterion of the model. The application to real voice signal shows that the optimized global model can capture more predictable component in chaos-like voice data and simultaneously reduce the predictable component (periodic pitch) in the residual signal.
Inamdar, Shrirang Appasaheb; Surwase, Shripad Nagnath; Jadhav, Shekhar Bhagwan; Bapat, Vishwas Anant; Jadhav, Jyoti Prafull
2013-01-01
L-DOPA (3,4-dihydroxyphenyl-L-alanine), a modified amino acid, is an expansively used drug for the Parkinson's disease treatment. In the present study, optimization of nutritional parameters influencing L-DOPA production was attempted using the response surface methodology (RSM) from Mucuna monosperma callus. Optimization of the four factors was carried out using the Box-Behnken design. The optimized levels of factors predicted by the model include tyrosine 0.894 g l(-1), pH 4.99, ascorbic acid 31.62 mg l(-1)and copper sulphate 23.92 mg l(-1), which resulted in highest L-DOPA yield of 0.309 g l(-1). The optimization of medium using RSM resulted in a 3.45-fold increase in the yield of L-DOPA. The ANOVA analysis showed a significant R (2) value (0.9912), model F-value (112.465) and probability (0.0001), with insignificant lack of fit. Optimized medium was used in the laboratory scale column reactor for continuous production of L-DOPA. Uninterrupted flow column exhibited maximum L-DOPA production rate of 200 mg L(-1) h(-1) which is one of the highest values ever reported using plant as a biotransformation source. L-DOPA production was confirmed by HPTLC and HPLC analysis. This study demonstrates the synthesis of L- DOPA using Mucuna monosperma callus using a laboratory scale column reactor. PMID:25674405
An adaptive metamodel-based global optimization algorithm for black-box type problems
NASA Astrophysics Data System (ADS)
Jie, Haoxiang; Wu, Yizhong; Ding, Jianwan
2015-11-01
In this article, an adaptive metamodel-based global optimization (AMGO) algorithm is presented to solve unconstrained black-box problems. In the AMGO algorithm, a type of hybrid model composed of kriging and augmented radial basis function (RBF) is used as the surrogate model. The weight factors of hybrid model are adaptively selected in the optimization process. To balance the local and global search, a sub-optimization problem is constructed during each iteration to determine the new iterative points. As numerical experiments, six standard two-dimensional test functions are selected to show the distributions of iterative points. The AMGO algorithm is also tested on seven well-known benchmark optimization problems and contrasted with three representative metamodel-based optimization methods: efficient global optimization (EGO), GutmannRBF and hybrid and adaptive metamodel (HAM). The test results demonstrate the efficiency and robustness of the proposed method. The AMGO algorithm is finally applied to the structural design of the import and export chamber of a cycloid gear pump, achieving satisfactory results.
NASA Astrophysics Data System (ADS)
Raimunda da Silva, E.; Camacho, R. G. R.; Filho, N. M.
2010-08-01
It presents a methodology for global optimization with constraints of expensive functions using response surfaces models for aerodynamic cascade representing the turbomachine axial with profiles of family NACA65. For the calculation of flow, is used Fluent CFD software, which is on a local and global variations in the flow field. It has been verified that small geometric on the stagger angle, format airfoil and the spacing between the blades, can lead to changes in the efficiency of the blade. Accordingly, we intend to integrate the solution flow through CFD optimization programs based on the construction of metamodels, aiming to obtain considerable gains in computational time. Integration with the optimization programs is necessary to build "script" command to automatically generate the mesh, where the design variables that define the geometry of the blade cascade as stagger angle, pitch to chord and the camber be modified among pre-established limits based on optimization algorithms, in order to achieve an objective function pre-defined, how to obtain the maximum ratio of Cl/ Cd (lift/drag). This methodology for global optimization based on the construction of metamodels together with the random search algorithm controlled (CRSA) is based on iterative construction of response surfaces with radial basis functions (multiquadric) and the application of heuristic criteria to update the database during the optimization process. Cyclical patterns of search are iteratively used to determine the candidate points to be included in the database.
SfM with MRFs: discrete-continuous optimization for large-scale structure from motion.
Crandall, David J; Owens, Andrew; Snavely, Noah; Huttenlocher, Daniel P
2013-12-01
Recent work in structure from motion (SfM) has built 3D models from large collections of images downloaded from the Internet. Many approaches to this problem use incremental algorithms that solve progressively larger bundle adjustment problems. These incremental techniques scale poorly as the image collection grows, and can suffer from drift or local minima. We present an alternative framework for SfM based on finding a coarse initial solution using hybrid discrete-continuous optimization and then improving that solution using bundle adjustment. The initial optimization step uses a discrete Markov random field (MRF) formulation, coupled with a continuous Levenberg-Marquardt refinement. The formulation naturally incorporates various sources of information about both the cameras and points, including noisy geotags and vanishing point (VP) estimates. We test our method on several large-scale photo collections, including one with measured camera positions, and show that it produces models that are similar to or better than those produced by incremental bundle adjustment, but more robustly and in a fraction of the time. PMID:24136425
SfM with MRFs: Discrete-Continuous Optimization for Large-Scale Structure from Motion.
Crandall, David J; Owens, Andrew; Snavely, Noah; Huttenlocher, Daniel P
2012-10-01
Recent work in structure from motion (SfM) has built 3D models from large collections of images downloaded from the Internet. Many approaches to this problem use incremental algorithms that solve progressively larger bundle adjustment problems. These incremental techniques scale poorly as the image collection grows, and can suffer from drift or local minima. We present an alternative framework for SfM based on finding a coarse initial solution using hybrid discrete-continuous optimization, and then improving that solution using bundle adjustment. The initial optimization step uses a discrete Markov random field (MRF) formulation, coupled with a continuous Levenberg-Marquardt refinement. The formulation naturally incorporates various sources of information about both the cameras and points, including noisy geotags and vanishing point estimates. We test our method on several large-scale photo collections, including one with measured camera positions, and show that it produces models that are similar to or better than those produced by incremental bundle adjustment, but more robustly and in a fraction of the time. PMID:23045369
Anastassiadis, Savas; Aivasidis, Alexander; Wandrey, Christian; Rehm, Hans-Jürgen
2005-08-20
This study was focused on the optimization of a new fermentation process for continuous gluconic acid production by the isolated yeast-like strain Aureobasidium pullulans DSM 7085 (isolate 70). Operational fermentation parameters were optimized in chemostat cultures, using a defined glucose medium. Different optima were found for growth and gluconic acid production for each set of operation parameters. Highest productivity was recorded at pH values between 6.5 and 7.0 and temperatures between 29 and 31 degrees C. A gluconic acid concentration higher than 230 g/L was continuously produced at residence times of 12 h. A steady state extracellular gluconic acid concentration of 234 g/L was measured at pH 6.5. 122% air saturation yielded the highest volumetric productivity and product concentration. The biomass-specific productivity increased steadily upon raising air saturation. An intracellular gluconic acid concentration of about 159 g/L (0.83 mol) was determined at 31 degrees C. This is to be compared with an extracellular concentration of 223 g/L (1.16 mol), which indicates the possible existence of an active transport system for gluconic acid secretion, or the presence of extracellular glucose oxidizing enzymes. The new process provides significant advantages over the traditional discontinuous fungi operations. The process control becomes easier, thus offering stable product quality and quantity.
Optimization Strategies for Single-Stage, Multi-Stage and Continuous ADRs
NASA Technical Reports Server (NTRS)
Shirron, Peter J.
2014-01-01
Adiabatic Demagnetization Refrigerators (ADR) have many advantages that are prompting a resurgence in their use in spaceflight and laboratory applications. They are solid-state coolers capable of very high efficiency and very wide operating range. However, their low energy storage density translates to larger mass for a given cooling capacity than is possible with other refrigeration techniques. The interplay between refrigerant mass and other parameters such as magnetic field and heat transfer points in multi-stage ADRs gives rise to a wide parameter space for optimization. This paper first presents optimization strategies for single ADR stages, focusing primarily on obtaining the largest cooling capacity per stage mass, then discusses the optimization of multi-stage and continuous ADRs in the context of the coordinated heat transfer that must occur between stages. The goal for the latter is usually to obtain the largest cooling power per mass or volume, but there can also be many secondary objectives, such as limiting instantaneous heat rejection rates and producing intermediate temperatures for cooling of other instrument components.
NASA Astrophysics Data System (ADS)
Paul, Bryan
Waveform design that allows for a wide variety of frequency-modulation (FM) has proven benefits. However, dictionary based optimization is limited and gradient search methods are often intractable. A new method is proposed using differential evolution to design waveforms with instantaneous frequencies (IFs) with cubic FM functions whose coefficients are constrained to the surface of the three dimensional unit sphere. Cubic IF functions subsume well-known IF functions such as linear, quadratic monomial, and cubic monomial IF functions. In addition, all nonlinear IF functions sufficiently approximated by a third order Taylor series over the unit time sequence can be represented in this space. Analog methods for generating polynomial IF waveforms are well established allowing for practical implementation in real world systems. By sufficiently constraining the search space to these waveforms of interest, alternative optimization methods such as differential evolution can be used to optimize tracking performance in a variety of radar environments. While simplified tracking models and finite waveform dictionaries have information theoretic results, continuous waveform design in high SNR, narrowband, cluttered environments is explored.
NASA Astrophysics Data System (ADS)
Li, Yuan; Gosálvez, Miguel A.; Pal, Prem; Sato, Kazuo; Xing, Yan
2015-05-01
We combine the particle swarm optimization (PSO) method and the continuous cellular automaton (CCA) in order to simulate deep reactive ion etching (DRIE), also known as the Bosch process. By considering a generic growth/etch process, the proposed PSO-CCA method provides a general, integrated procedure to optimize the parameter values of any given theoretical model conceived to describe the corresponding experiments, which are simulated by the CCA method. To stress the flexibility of the PSO-CCA method, two different theoretical models of the DRIE process are used, namely, the ballistic transport and reaction (BTR) model, and the reactant concentration (RC) model. DRIE experiments are designed and conducted to compare the simulation results with the experiments on different machines and process conditions. Previously reported experimental data are also considered to further test the flexibility of the proposed method. The agreement between the simulations and experiments strongly indicates that the PSO-CCA method can be used to adjust the theoretical parameters by using a limited amount of experimental data. The proposed method has the potential to be applied on the modeling and optimization of other growth/etch processes.
NASA Astrophysics Data System (ADS)
Jacobson, Gloria; Rella, Chris; Farinas, Alejandro
2014-05-01
Technological advancement of instrumentation in atmospheric and other geoscience disciplines over the past decade has lead to a shift from discrete sample analysis to continuous, in-situ monitoring. Standard error analysis used for discrete measurements is not sufficient to assess and compare the error contribution of noise and drift from continuous-measurement instruments, and a different statistical analysis approach should be applied. The Allan standard deviation analysis technique developed for atomic clock stability assessment by David W. Allan [1] can be effectively and gainfully applied to continuous measurement instruments. As an example, P. Werle et al has applied these techniques to look at signal averaging for atmospheric monitoring by Tunable Diode-Laser Absorption Spectroscopy (TDLAS) [2]. This presentation will build on, and translate prior foundational publications to provide contextual definitions and guidelines for the practical application of this analysis technique to continuous scientific measurements. The specific example of a Picarro G2401 Cavity Ringdown Spectroscopy (CRDS) analyzer used for continuous, atmospheric monitoring of CO2, CH4 and CO will be used to define the basics features the Allan deviation, assess factors affecting the analysis, and explore the time-series to Allan deviation plot translation for different types of instrument noise (white noise, linear drift, and interpolated data). In addition, the useful application of using an Allan deviation to optimize and predict the performance of different calibration schemes will be presented. Even though this presentation will use the specific example of the Picarro G2401 CRDS Analyzer for atmospheric monitoring, the objective is to present the information such that it can be successfully applied to other instrument sets and disciplines. [1] D.W. Allan, "Statistics of Atomic Frequency Standards," Proc, IEEE, vol. 54, pp 221-230, Feb 1966 [2] P. Werle, R. Miicke, F. Slemr, "The Limits
Autonomous Modelling of X-ray Spectra Using Robust Global Optimization Methods
NASA Astrophysics Data System (ADS)
Rogers, Adam; Safi-Harb, Samar; Fiege, Jason
2015-08-01
The standard approach to model fitting in X-ray astronomy is by means of local optimization methods. However, these local optimizers suffer from a number of problems, such as a tendency for the fit parameters to become trapped in local minima, and can require an involved process of detailed user intervention to guide them through the optimization process. In this work we introduce a general GUI-driven global optimization method for fitting models to X-ray data, written in MATLAB, which searches for optimal models with minimal user interaction. We directly interface with the commonly used XSPEC libraries to access the full complement of pre-existing spectral models that describe a wide range of physics appropriate for modelling astrophysical sources, including supernova remnants and compact objects. Our algorithm is powered by the Ferret genetic algorithm and Locust particle swarm optimizer from the Qubist Global Optimization Toolbox, which are robust at finding families of solutions and identifying degeneracies. This technique will be particularly instrumental for multi-parameter models and high-fidelity data. In this presentation, we provide details of the code and use our techniques to analyze X-ray data obtained from a variety of astrophysical sources.
Climate, Agriculture, Energy and the Optimal Allocation of Global Land Use
NASA Astrophysics Data System (ADS)
Steinbuks, J.; Hertel, T. W.
2011-12-01
The allocation of the world's land resources over the course of the next century has become a pressing research question. Continuing population increases, improving, land-intensive diets amongst the poorest populations in the world, increasing production of biofuels and rapid urbanization in developing countries are all competing for land even as the world looks to land resources to supply more environmental services. The latter include biodiversity and natural lands, as well as forests and grasslands devoted to carbon sequestration. And all of this is taking place in the context of faster than expected climate change which is altering the biophysical environment for land-related activities. The goal of the paper is to determine the optimal profile for global land use in the context of growing commercial demands for food and forest products, increasing non-market demands for ecosystem services, and more stringent GHG mitigation targets. We then seek to assess how the uncertainty associated with the underlying biophysical and economic processes influences this optimal profile of land use, in light of potential irreversibility in these decisions. We develop a dynamic long-run, forward-looking partial equilibrium framework in which the societal objective function being maximized places value on food production, liquid fuels (including biofuels), timber production, forest carbon and biodiversity. Given the importance of land-based emissions to any GHG mitigation strategy, as well as the potential impacts of climate change itself on the productivity of land in agriculture, forestry and ecosystem services, we aim to identify the optimal allocation of the world's land resources, over the course of the next century, in the face of alternative GHG constraints. The forestry sector is characterized by multiple forest vintages which add considerable computational complexity in the context of this dynamic analysis. In order to solve this model efficiently, we have employed the
Zhong, Shangping; Chen, Tianshun; He, Fengying; Niu, Yuzhen
2014-09-01
For a practical pattern classification task solved by kernel methods, the computing time is mainly spent on kernel learning (or training). However, the current kernel learning approaches are based on local optimization techniques, and hard to have good time performances, especially for large datasets. Thus the existing algorithms cannot be easily extended to large-scale tasks. In this paper, we present a fast Gaussian kernel learning method by solving a specially structured global optimization (SSGO) problem. We optimize the Gaussian kernel function by using the formulated kernel target alignment criterion, which is a difference of increasing (d.i.) functions. Through using a power-transformation based convexification method, the objective criterion can be represented as a difference of convex (d.c.) functions with a fixed power-transformation parameter. And the objective programming problem can then be converted to a SSGO problem: globally minimizing a concave function over a convex set. The SSGO problem is classical and has good solvability. Thus, to find the global optimal solution efficiently, we can adopt the improved Hoffman's outer approximation method, which need not repeat the searching procedure with different starting points to locate the best local minimum. Also, the proposed method can be proven to converge to the global solution for any classification task. We evaluate the proposed method on twenty benchmark datasets, and compare it with four other Gaussian kernel learning methods. Experimental results show that the proposed method stably achieves both good time-efficiency performance and good classification performance. PMID:24929345
Zhong, Shangping; Chen, Tianshun; He, Fengying; Niu, Yuzhen
2014-09-01
For a practical pattern classification task solved by kernel methods, the computing time is mainly spent on kernel learning (or training). However, the current kernel learning approaches are based on local optimization techniques, and hard to have good time performances, especially for large datasets. Thus the existing algorithms cannot be easily extended to large-scale tasks. In this paper, we present a fast Gaussian kernel learning method by solving a specially structured global optimization (SSGO) problem. We optimize the Gaussian kernel function by using the formulated kernel target alignment criterion, which is a difference of increasing (d.i.) functions. Through using a power-transformation based convexification method, the objective criterion can be represented as a difference of convex (d.c.) functions with a fixed power-transformation parameter. And the objective programming problem can then be converted to a SSGO problem: globally minimizing a concave function over a convex set. The SSGO problem is classical and has good solvability. Thus, to find the global optimal solution efficiently, we can adopt the improved Hoffman's outer approximation method, which need not repeat the searching procedure with different starting points to locate the best local minimum. Also, the proposed method can be proven to converge to the global solution for any classification task. We evaluate the proposed method on twenty benchmark datasets, and compare it with four other Gaussian kernel learning methods. Experimental results show that the proposed method stably achieves both good time-efficiency performance and good classification performance.
NASA Astrophysics Data System (ADS)
Shabbir, Faisal; Omenzetter, Piotr
2014-04-01
Much effort is devoted nowadays to derive accurate finite element (FE) models to be used for structural health monitoring, damage detection and assessment. However, formation of a FE model representative of the original structure is a difficult task. Model updating is a branch of optimization which calibrates the FE model by comparing the modal properties of the actual structure with these of the FE predictions. As the number of experimental measurements is usually much smaller than the number of uncertain parameters, and, consequently, not all uncertain parameters are selected for model updating, different local minima may exist in the solution space. Experimental noise further exacerbates the problem. The attainment of a global solution in a multi-dimensional search space is a challenging problem. Global optimization algorithms (GOAs) have received interest in the previous decade to solve this problem, but no GOA can ensure the detection of the global minimum either. To counter this problem, a combination of GOA with sequential niche technique (SNT) has been proposed in this research which systematically searches the whole solution space. A dynamically tested full scale pedestrian bridge is taken as a case study. Two different GOAs, namely particle swarm optimization (PSO) and genetic algorithm (GA), are investigated in combination with SNT. The results of these GOA are compared in terms of their efficiency in detecting global minima. The systematic search enables to find different solutions in the search space, thus increasing the confidence of finding the global minimum.
Optimal Design of Grid-Stiffened Composite Panels Using Global and Local Buckling Analysis
Ambur, D.R.; Jaunky, N.; Knight, N.F. Jr.
1996-04-01
A design strategy for optimal design of composite grid-stiffened panels subjected to global and local buckling constraints is developed using a discrete optimizer. An improved smeared stiffener theory is used for the global buckling analysis. Local buckling of skin segments is assessed using a Rayleigh-Ritz method that accounts for material anisotropy and transverse shear flexibility. The local buckling of stiffener segments is also assessed. Design variables are the axial and transverse stiffener spacing, stiffener height and thickness, skin laminate, and stiffening configuration. The design optimization process is adapted to identify the lightest-weight stiffening configuration and pattern for grid stiffened composite panels given the overall panel dimensions, design in-plane loads, material properties, and boundary conditions of the grid-stiffened panel.
Optimal Design of Grid-Stiffened Composite Panels Using Global and Local Buckling Analysis
NASA Technical Reports Server (NTRS)
Ambur, Damodar R.; Jaunky, Navin; Knight, Norman F., Jr.
1996-01-01
A design strategy for optimal design of composite grid-stiffened panels subjected to global and local buckling constraints is developed using a discrete optimizer. An improved smeared stiffener theory is used for the global buckling analysis. Local buckling of skin segments is assessed using a Rayleigh-Ritz method that accounts for material anisotropy and transverse shear flexibility. The local buckling of stiffener segments is also assessed. Design variables are the axial and transverse stiffener spacing, stiffener height and thickness, skin laminate, and stiffening configuration. The design optimization process is adapted to identify the lightest-weight stiffening configuration and pattern for grid stiffened composite panels given the overall panel dimensions, design in-plane loads, material properties, and boundary conditions of the grid-stiffened panel.
Namiki, Ryo; Koashi, Masato; Imoto, Nobuyuki
2006-03-15
We investigate the security of continuous-variable quantum key distribution using coherent states and reverse reconciliation against Gaussian individual attacks based on an optimal Gaussian 1{yields}2 cloning machine. We provide an implementation of the optimal Gaussian individual attack. We also find a Bell-measurement attack which works without delayed choice of measurements and has better performance than the cloning attack.
A Bell-Curved Based Algorithm for Mixed Continuous and Discrete Structural Optimization
NASA Technical Reports Server (NTRS)
Kincaid, Rex K.; Weber, Michael; Sobieszczanski-Sobieski, Jaroslaw
2001-01-01
An evolutionary based strategy utilizing two normal distributions to generate children is developed to solve mixed integer nonlinear programming problems. This Bell-Curve Based (BCB) evolutionary algorithm is similar in spirit to (mu + mu) evolutionary strategies and evolutionary programs but with fewer parameters to adjust and no mechanism for self adaptation. First, a new version of BCB to solve purely discrete optimization problems is described and its performance tested against a tabu search code for an actuator placement problem. Next, the performance of a combined version of discrete and continuous BCB is tested on 2-dimensional shape problems and on a minimum weight hub design problem. In the latter case the discrete portion is the choice of the underlying beam shape (I, triangular, circular, rectangular, or U).
Vercruysse, J; Peeters, E; Fonteyne, M; Cappuyns, P; Delaet, U; Van Assche, I; De Beer, T; Remon, J P; Vervaet, C
2015-01-01
Since small scale is key for successful introduction of continuous techniques in the pharmaceutical industry to allow its use during formulation development and process optimization, it is essential to determine whether the product quality is similar when small quantities of materials are processed compared to the continuous processing of larger quantities. Therefore, the aim of this study was to investigate whether material processed in a single cell of the six-segmented fluid bed dryer of the ConsiGma™-25 system (a continuous twin screw granulation and drying system introduced by GEA Pharma Systems, Collette™, Wommelgem, Belgium) is predictive of granule and tablet quality during full-scale manufacturing when all drying cells are filled. Furthermore, the performance of the ConsiGma™-1 system (a mobile laboratory unit) was evaluated and compared to the ConsiGma™-25 system. A premix of two active ingredients, powdered cellulose, maize starch, pregelatinized starch and sodium starch glycolate was granulated with distilled water. After drying and milling (1000 μm, 800 rpm), granules were blended with magnesium stearate and compressed using a Modul™ P tablet press (tablet weight: 430 mg, main compression force: 12 kN). Single cell experiments using the ConsiGma™-25 system and ConsiGma™-1 system were performed in triplicate. Additionally, a 1h continuous run using the ConsiGma™-25 system was executed. Process outcomes (torque, barrel wall temperature, product temperature during drying) and granule (residual moisture content, particle size distribution, bulk and tapped density, hausner ratio, friability) as well as tablet (hardness, friability, disintegration time and dissolution) quality attributes were evaluated. By performing a 1h continuous run, it was detected that a stabilization period was needed for torque and barrel wall temperature due to initial layering of the screws and the screw chamber walls with material. Consequently, slightly deviating
Vercruysse, J; Peeters, E; Fonteyne, M; Cappuyns, P; Delaet, U; Van Assche, I; De Beer, T; Remon, J P; Vervaet, C
2015-01-01
Since small scale is key for successful introduction of continuous techniques in the pharmaceutical industry to allow its use during formulation development and process optimization, it is essential to determine whether the product quality is similar when small quantities of materials are processed compared to the continuous processing of larger quantities. Therefore, the aim of this study was to investigate whether material processed in a single cell of the six-segmented fluid bed dryer of the ConsiGma™-25 system (a continuous twin screw granulation and drying system introduced by GEA Pharma Systems, Collette™, Wommelgem, Belgium) is predictive of granule and tablet quality during full-scale manufacturing when all drying cells are filled. Furthermore, the performance of the ConsiGma™-1 system (a mobile laboratory unit) was evaluated and compared to the ConsiGma™-25 system. A premix of two active ingredients, powdered cellulose, maize starch, pregelatinized starch and sodium starch glycolate was granulated with distilled water. After drying and milling (1000 μm, 800 rpm), granules were blended with magnesium stearate and compressed using a Modul™ P tablet press (tablet weight: 430 mg, main compression force: 12 kN). Single cell experiments using the ConsiGma™-25 system and ConsiGma™-1 system were performed in triplicate. Additionally, a 1h continuous run using the ConsiGma™-25 system was executed. Process outcomes (torque, barrel wall temperature, product temperature during drying) and granule (residual moisture content, particle size distribution, bulk and tapped density, hausner ratio, friability) as well as tablet (hardness, friability, disintegration time and dissolution) quality attributes were evaluated. By performing a 1h continuous run, it was detected that a stabilization period was needed for torque and barrel wall temperature due to initial layering of the screws and the screw chamber walls with material. Consequently, slightly deviating
Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization.
He, Xiangzhu; Huang, Jida; Rao, Yunqing; Gao, Liang
2016-01-01
Recently, teaching-learning-based optimization (TLBO), as one of the emerging nature-inspired heuristic algorithms, has attracted increasing attention. In order to enhance its convergence rate and prevent it from getting stuck in local optima, a novel metaheuristic has been developed in this paper, where particular characteristics of the chaos mechanism and Lévy flight are introduced to the basic framework of TLBO. The new algorithm is tested on several large-scale nonlinear benchmark functions with different characteristics and compared with other methods. Experimental results show that the proposed algorithm outperforms other algorithms and achieves a satisfactory improvement over TLBO. PMID:26941785
Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization.
He, Xiangzhu; Huang, Jida; Rao, Yunqing; Gao, Liang
2016-01-01
Recently, teaching-learning-based optimization (TLBO), as one of the emerging nature-inspired heuristic algorithms, has attracted increasing attention. In order to enhance its convergence rate and prevent it from getting stuck in local optima, a novel metaheuristic has been developed in this paper, where particular characteristics of the chaos mechanism and Lévy flight are introduced to the basic framework of TLBO. The new algorithm is tested on several large-scale nonlinear benchmark functions with different characteristics and compared with other methods. Experimental results show that the proposed algorithm outperforms other algorithms and achieves a satisfactory improvement over TLBO.
Chaotic Teaching-Learning-Based Optimization with Lévy Flight for Global Numerical Optimization
He, Xiangzhu; Huang, Jida; Rao, Yunqing; Gao, Liang
2016-01-01
Recently, teaching-learning-based optimization (TLBO), as one of the emerging nature-inspired heuristic algorithms, has attracted increasing attention. In order to enhance its convergence rate and prevent it from getting stuck in local optima, a novel metaheuristic has been developed in this paper, where particular characteristics of the chaos mechanism and Lévy flight are introduced to the basic framework of TLBO. The new algorithm is tested on several large-scale nonlinear benchmark functions with different characteristics and compared with other methods. Experimental results show that the proposed algorithm outperforms other algorithms and achieves a satisfactory improvement over TLBO. PMID:26941785
On the choice of the optimal periodic operation for a continuous fermentation process.
D'Avino, G; Crescitelli, S; Maffettone, P L; Grosso, M
2010-01-01
In this contribution we investigate the impact of the forcing waveform on the productivity of a continuous bioreactor governed by an unstructured, nonlinear kinetic model. The (periodic) forcing is applied on the substrate concentration in the feed. To this end, some alternative waveforms commonly encountered in practice are evaluated and their performance is compared. An analytical/numerical approach is used. The preliminary analytical step is based on the π-criterion that gives useful information for small amplitudes. The extension to larger amplitudes, when significant improvements are expected, is then performed through a continuation-optimization procedure. It is found that the choice of the specific waveform has an impact on the performance of the process and there is no unique best forcing for any process condition, but its choice depends on the operating parameters and the forcing amplitude and frequency values. Further, the influence of the waveform functions on the wash-out conditions are extensively examined. The analysis shows that all the waveforms examined in this work may lead to significant enlargement of the nontrivial regime with respect to a steady state operation. In particular, square-wave forcing leads in practice to the extinction of the wash-out conditions for any feed substrate concentration and for a well defined choice of the forcing parameters.
A comparative study of expected improvement-assisted global optimization with different surrogates
NASA Astrophysics Data System (ADS)
Wang, Hu; Ye, Fan; Li, Enying; Li, Guangyao
2016-08-01
Efficient global optimization (EGO) uses the surrogate uncertainty estimator called expected improvement (EI) to guide the selection of the next sampling candidates. Theoretically, any modelling methods can be integrated with the EI criterion. To improve the convergence ratio, a multi-surrogate efficient global optimization (MSEGO) was suggested. In practice, the EI-based optimization methods with different surrogates show widely divergent characteristics. Therefore, it is important to choose the most suitable algorithm for a certain problem. For this purpose, four single-surrogate efficient global optimizations (SSEGOs) and an MSEGO involving four surrogates are investigated. According to numerical tests, both the SSEGOs and the MSEGO are feasible for weak nonlinear problems. However, they are not robust for strong nonlinear problems, especially for multimodal and high-dimensional problems. Moreover, to investigate the feasibility of EGO in practice, a material identification benchmark is designed to demonstrate the performance of EGO methods. According to the tests in this study, the kriging EGO is generally the most robust method.
Global optimization for optical thin-film design using Latin Squares
NASA Astrophysics Data System (ADS)
Li, Dong-guang; Watson, Anthony
1997-10-01
There are many advanced local and global optimization techniques, such as Gradient, Simplex, Flip-flop, Needle, Genetic and Simulated Annealing, which have been successfully applied to optical thin-film design. However, all these optimization techniques either require a selection of a reasonable starting design, which is a big obstacle to an inexperienced designer, or they have some kind of inbuilt random feature, which may give rise to different answers each time. To find the true global optimized solution for a thin film design problem, we need to solve an array of interlinked multi-dimensional simultaneous equations. Until recently, for more than just a few layers, this has been a very difficult task, requiring the use of a supercomputer and highly skilled programming. By using orthogonal Latin Square theory and an experimental design methodology in a search space reduction process, a Windows based program has been written that can operate on even a 20 MHz 386 computer. It can find the global optimum design for up to 23 layers using as many dispersive and lossy materials as one wishes, within a period of hours. Additionally this methodology (called DGL-Optimization) allows the use of multiple target spectra with such as both s & p polarization, for reflection and transmission simultaneously.
NASA Astrophysics Data System (ADS)
Wang, Xuewu; Shi, Yingpan; Ding, Dongyan; Gu, Xingsheng
2016-02-01
Spot-welding robots have a wide range of applications in manufacturing industries. There are usually many weld joints in a welding task, and a reasonable welding path to traverse these weld joints has a significant impact on welding efficiency. Traditional manual path planning techniques can handle a few weld joints effectively, but when the number of weld joints is large, it is difficult to obtain the optimal path. The traditional manual path planning method is also time consuming and inefficient, and cannot guarantee optimality. Double global optimum genetic algorithm-particle swarm optimization (GA-PSO) based on the GA and PSO algorithms is proposed to solve the welding robot path planning problem, where the shortest collision-free paths are used as the criteria to optimize the welding path. Besides algorithm effectiveness analysis and verification, the simulation results indicate that the algorithm has strong searching ability and practicality, and is suitable for welding robot path planning.
Global Optimization of Interplanetary Trajectories in the Presence of Realistic Mission Contraints
NASA Technical Reports Server (NTRS)
Hinckley, David, Jr.; Englander, Jacob; Hitt, Darren
2015-01-01
Interplanetary missions are often subject to difficult constraints, like solar phase angle upon arrival at the destination, velocity at arrival, and altitudes for flybys. Preliminary design of such missions is often conducted by solving the unconstrained problem and then filtering away solutions which do not naturally satisfy the constraints. However this can bias the search into non-advantageous regions of the solution space, so it can be better to conduct preliminary design with the full set of constraints imposed. In this work two stochastic global search methods are developed which are well suited to the constrained global interplanetary trajectory optimization problem.
NASA Astrophysics Data System (ADS)
Kanazaki, Masahiro; Matsuno, Takashi; Maeda, Kengo; Kawazoe, Hiromitsu
2015-09-01
A kriging-based genetic algorithm called efficient global optimization (EGO) was employed to optimize the parameters for the operating conditions of plasma actuators. The aerodynamic performance was evaluated by wind tunnel testing to overcome the disadvantages of time-consuming numerical simulations. The proposed system was used on two design problems to design the power supply for a plasma actuator. The first case was the drag minimization problem around a semicircular cylinder. In this case, the inhibitory effect of flow separation was also observed. The second case was the lift maximization problem around a circular cylinder. This case was similar to the aerofoil design, because the circular cylinder has potential to work as an aerofoil owing to the control of the flow circulation by the plasma actuators with four design parameters. In this case, applicability to the multi-variant design problem was also investigated. Based on these results, optimum designs and global design information were obtained while drastically reducing the number of experiments required compared to a full factorial experiment.
The fully actuated traffic control problem solved by global optimization and complementarity
NASA Astrophysics Data System (ADS)
Ribeiro, Isabel M.; de Lurdes de Oliveira Simões, Maria
2016-02-01
Global optimization and complementarity are used to determine the signal timing for fully actuated traffic control, regarding effective green and red times on each cycle. The average values of these parameters can be used to estimate the control delay of vehicles. In this article, a two-phase queuing system for a signalized intersection is outlined, based on the principle of minimization of the total waiting time for the vehicles. The underlying model results in a linear program with linear complementarity constraints, solved by a sequential complementarity algorithm. Departure rates of vehicles during green and yellow periods were treated as deterministic, while arrival rates of vehicles were assumed to follow a Poisson distribution. Several traffic scenarios were created and solved. The numerical results reveal that it is possible to use global optimization and complementarity over a reasonable number of cycles and determine with efficiency effective green and red times for a signalized intersection.
Guo, Y C; Wang, H; Wu, H P; Zhang, M Q
2015-12-21
Aimed to address the defects of the large mean square error (MSE), and the slow convergence speed in equalizing the multi-modulus signals of the constant modulus algorithm (CMA), a multi-modulus algorithm (MMA) based on global artificial fish swarm (GAFS) intelligent optimization of DNA encoding sequences (GAFS-DNA-MMA) was proposed. To improve the convergence rate and reduce the MSE, this proposed algorithm adopted an encoding method based on DNA nucleotide chains to provide a possible solution to the problem. Furthermore, the GAFS algorithm, with its fast convergence and global search ability, was used to find the best sequence. The real and imaginary parts of the initial optimal weight vector of MMA were obtained through DNA coding of the best sequence. The simulation results show that the proposed algorithm has a faster convergence speed and smaller MSE in comparison with the CMA, the MMA, and the AFS-DNA-MMA.
Algorithms for optimized maximum entropy and diagnostic tools for analytic continuation.
Bergeron, Dominic; Tremblay, A-M S
2016-08-01
Analytic continuation of numerical data obtained in imaginary time or frequency has become an essential part of many branches of quantum computational physics. It is, however, an ill-conditioned procedure and thus a hard numerical problem. The maximum-entropy approach, based on Bayesian inference, is the most widely used method to tackle that problem. Although the approach is well established and among the most reliable and efficient ones, useful developments of the method and of its implementation are still possible. In addition, while a few free software implementations are available, a well-documented, optimized, general purpose, and user-friendly software dedicated to that specific task is still lacking. Here we analyze all aspects of the implementation that are critical for accuracy and speed and present a highly optimized approach to maximum entropy. Original algorithmic and conceptual contributions include (1) numerical approximations that yield a computational complexity that is almost independent of temperature and spectrum shape (including sharp Drude peaks in broad background, for example) while ensuring quantitative accuracy of the result whenever precision of the data is sufficient, (2) a robust method of choosing the entropy weight α that follows from a simple consistency condition of the approach and the observation that information- and noise-fitting regimes can be identified clearly from the behavior of χ^{2} with respect to α, and (3) several diagnostics to assess the reliability of the result. Benchmarks with test spectral functions of different complexity and an example with an actual physical simulation are presented. Our implementation, which covers most typical cases for fermions, bosons, and response functions, is available as an open source, user-friendly software.
Algorithms for optimized maximum entropy and diagnostic tools for analytic continuation.
Bergeron, Dominic; Tremblay, A-M S
2016-08-01
Analytic continuation of numerical data obtained in imaginary time or frequency has become an essential part of many branches of quantum computational physics. It is, however, an ill-conditioned procedure and thus a hard numerical problem. The maximum-entropy approach, based on Bayesian inference, is the most widely used method to tackle that problem. Although the approach is well established and among the most reliable and efficient ones, useful developments of the method and of its implementation are still possible. In addition, while a few free software implementations are available, a well-documented, optimized, general purpose, and user-friendly software dedicated to that specific task is still lacking. Here we analyze all aspects of the implementation that are critical for accuracy and speed and present a highly optimized approach to maximum entropy. Original algorithmic and conceptual contributions include (1) numerical approximations that yield a computational complexity that is almost independent of temperature and spectrum shape (including sharp Drude peaks in broad background, for example) while ensuring quantitative accuracy of the result whenever precision of the data is sufficient, (2) a robust method of choosing the entropy weight α that follows from a simple consistency condition of the approach and the observation that information- and noise-fitting regimes can be identified clearly from the behavior of χ^{2} with respect to α, and (3) several diagnostics to assess the reliability of the result. Benchmarks with test spectral functions of different complexity and an example with an actual physical simulation are presented. Our implementation, which covers most typical cases for fermions, bosons, and response functions, is available as an open source, user-friendly software. PMID:27627408
Algorithms for optimized maximum entropy and diagnostic tools for analytic continuation
NASA Astrophysics Data System (ADS)
Bergeron, Dominic; Tremblay, A.-M. S.
2016-08-01
Analytic continuation of numerical data obtained in imaginary time or frequency has become an essential part of many branches of quantum computational physics. It is, however, an ill-conditioned procedure and thus a hard numerical problem. The maximum-entropy approach, based on Bayesian inference, is the most widely used method to tackle that problem. Although the approach is well established and among the most reliable and efficient ones, useful developments of the method and of its implementation are still possible. In addition, while a few free software implementations are available, a well-documented, optimized, general purpose, and user-friendly software dedicated to that specific task is still lacking. Here we analyze all aspects of the implementation that are critical for accuracy and speed and present a highly optimized approach to maximum entropy. Original algorithmic and conceptual contributions include (1) numerical approximations that yield a computational complexity that is almost independent of temperature and spectrum shape (including sharp Drude peaks in broad background, for example) while ensuring quantitative accuracy of the result whenever precision of the data is sufficient, (2) a robust method of choosing the entropy weight α that follows from a simple consistency condition of the approach and the observation that information- and noise-fitting regimes can be identified clearly from the behavior of χ2 with respect to α , and (3) several diagnostics to assess the reliability of the result. Benchmarks with test spectral functions of different complexity and an example with an actual physical simulation are presented. Our implementation, which covers most typical cases for fermions, bosons, and response functions, is available as an open source, user-friendly software.
NASA Astrophysics Data System (ADS)
de Pascale, P.; Vasile, M.; Casotto, S.
The design of interplanetary trajectories requires the solution of an optimization problem, which has been traditionally solved by resorting to various local optimization techniques. All such approaches, apart from the specific method employed (direct or indirect), require an initial guess, which deeply influences the convergence to the optimal solution. The recent developments in low-thrust propulsion have widened the perspectives of exploration of the Solar System, while they have at the same time increased the difficulty related to the trajectory design process. Continuous thrust transfers, typically characterized by multiple spiraling arcs, have a broad number of design parameters and thanks to the flexibility offered by such engines, they typically turn out to be characterized by a multi-modal domain, with a consequent larger number of optimal solutions. Thus the definition of the first guesses is even more challenging, particularly for a broad search over the design parameters, and it requires an extensive investigation of the domain in order to locate the largest number of optimal candidate solutions and possibly the global optimal one. In this paper a tool for the preliminary definition of interplanetary transfers with coast-thrust arcs and multiple swing-bys is presented. Such goal is achieved combining a novel methodology for the description of low-thrust arcs, with a global optimization algorithm based on a hybridization of an evolutionary step and a deterministic step. Low thrust arcs are described in a 3D model in order to account the beneficial effects of low-thrust propulsion for a change of inclination, resorting to a new methodology based on an inverse method. The two-point boundary values problem (TPBVP) associated with a thrust arc is solved by imposing a proper parameterized evolution of the orbital parameters, by which, the acceleration required to follow the given trajectory with respect to the constraints set is obtained simply through
Ringed Seal Search for Global Optimization via a Sensitive Search Model.
Saadi, Younes; Yanto, Iwan Tri Riyadi; Herawan, Tutut; Balakrishnan, Vimala; Chiroma, Haruna; Risnumawan, Anhar
2016-01-01
The efficiency of a metaheuristic algorithm for global optimization is based on its ability to search and find the global optimum. However, a good search often requires to be balanced between exploration and exploitation of the search space. In this paper, a new metaheuristic algorithm called Ringed Seal Search (RSS) is introduced. It is inspired by the natural behavior of the seal pup. This algorithm mimics the seal pup movement behavior and its ability to search and choose the best lair to escape predators. The scenario starts once the seal mother gives birth to a new pup in a birthing lair that is constructed for this purpose. The seal pup strategy consists of searching and selecting the best lair by performing a random walk to find a new lair. Affected by the sensitive nature of seals against external noise emitted by predators, the random walk of the seal pup takes two different search states, normal state and urgent state. In the normal state, the pup performs an intensive search between closely adjacent lairs; this movement is modeled via a Brownian walk. In an urgent state, the pup leaves the proximity area and performs an extensive search to find a new lair from sparse targets; this movement is modeled via a Levy walk. The switch between these two states is realized by the random noise emitted by predators. The algorithm keeps switching between normal and urgent states until the global optimum is reached. Tests and validations were performed using fifteen benchmark test functions to compare the performance of RSS with other baseline algorithms. The results show that RSS is more efficient than Genetic Algorithm, Particles Swarm Optimization and Cuckoo Search in terms of convergence rate to the global optimum. The RSS shows an improvement in terms of balance between exploration (extensive) and exploitation (intensive) of the search space. The RSS can efficiently mimic seal pups behavior to find best lair and provide a new algorithm to be used in global
Ringed Seal Search for Global Optimization via a Sensitive Search Model
Saadi, Younes; Yanto, Iwan Tri Riyadi; Herawan, Tutut; Balakrishnan, Vimala; Chiroma, Haruna; Risnumawan, Anhar
2016-01-01
The efficiency of a metaheuristic algorithm for global optimization is based on its ability to search and find the global optimum. However, a good search often requires to be balanced between exploration and exploitation of the search space. In this paper, a new metaheuristic algorithm called Ringed Seal Search (RSS) is introduced. It is inspired by the natural behavior of the seal pup. This algorithm mimics the seal pup movement behavior and its ability to search and choose the best lair to escape predators. The scenario starts once the seal mother gives birth to a new pup in a birthing lair that is constructed for this purpose. The seal pup strategy consists of searching and selecting the best lair by performing a random walk to find a new lair. Affected by the sensitive nature of seals against external noise emitted by predators, the random walk of the seal pup takes two different search states, normal state and urgent state. In the normal state, the pup performs an intensive search between closely adjacent lairs; this movement is modeled via a Brownian walk. In an urgent state, the pup leaves the proximity area and performs an extensive search to find a new lair from sparse targets; this movement is modeled via a Levy walk. The switch between these two states is realized by the random noise emitted by predators. The algorithm keeps switching between normal and urgent states until the global optimum is reached. Tests and validations were performed using fifteen benchmark test functions to compare the performance of RSS with other baseline algorithms. The results show that RSS is more efficient than Genetic Algorithm, Particles Swarm Optimization and Cuckoo Search in terms of convergence rate to the global optimum. The RSS shows an improvement in terms of balance between exploration (extensive) and exploitation (intensive) of the search space. The RSS can efficiently mimic seal pups behavior to find best lair and provide a new algorithm to be used in global
Ringed Seal Search for Global Optimization via a Sensitive Search Model.
Saadi, Younes; Yanto, Iwan Tri Riyadi; Herawan, Tutut; Balakrishnan, Vimala; Chiroma, Haruna; Risnumawan, Anhar
2016-01-01
The efficiency of a metaheuristic algorithm for global optimization is based on its ability to search and find the global optimum. However, a good search often requires to be balanced between exploration and exploitation of the search space. In this paper, a new metaheuristic algorithm called Ringed Seal Search (RSS) is introduced. It is inspired by the natural behavior of the seal pup. This algorithm mimics the seal pup movement behavior and its ability to search and choose the best lair to escape predators. The scenario starts once the seal mother gives birth to a new pup in a birthing lair that is constructed for this purpose. The seal pup strategy consists of searching and selecting the best lair by performing a random walk to find a new lair. Affected by the sensitive nature of seals against external noise emitted by predators, the random walk of the seal pup takes two different search states, normal state and urgent state. In the normal state, the pup performs an intensive search between closely adjacent lairs; this movement is modeled via a Brownian walk. In an urgent state, the pup leaves the proximity area and performs an extensive search to find a new lair from sparse targets; this movement is modeled via a Levy walk. The switch between these two states is realized by the random noise emitted by predators. The algorithm keeps switching between normal and urgent states until the global optimum is reached. Tests and validations were performed using fifteen benchmark test functions to compare the performance of RSS with other baseline algorithms. The results show that RSS is more efficient than Genetic Algorithm, Particles Swarm Optimization and Cuckoo Search in terms of convergence rate to the global optimum. The RSS shows an improvement in terms of balance between exploration (extensive) and exploitation (intensive) of the search space. The RSS can efficiently mimic seal pups behavior to find best lair and provide a new algorithm to be used in global
Economic optimization of a global strategy to address the pandemic threat.
Pike, Jamison; Bogich, Tiffany; Elwood, Sarah; Finnoff, David C; Daszak, Peter
2014-12-30
Emerging pandemics threaten global health and economies and are increasing in frequency. Globally coordinated strategies to combat pandemics, similar to current strategies that address climate change, are largely adaptive, in that they attempt to reduce the impact of a pathogen after it has emerged. However, like climate change, mitigation strategies have been developed that include programs to reduce the underlying drivers of pandemics, particularly animal-to-human disease transmission. Here, we use real options economic modeling of current globally coordinated adaptation strategies for pandemic prevention. We show that they would be optimally implemented within 27 y to reduce the annual rise of emerging infectious disease events by 50% at an estimated one-time cost of approximately $343.7 billion. We then analyze World Bank data on multilateral "One Health" pandemic mitigation programs. We find that, because most pandemics have animal origins, mitigation is a more cost-effective policy than business-as-usual adaptation programs, saving between $344.0.7 billion and $360.3 billion over the next 100 y if implemented today. We conclude that globally coordinated pandemic prevention policies need to be enacted urgently to be optimally effective and that strategies to mitigate pandemics by reducing the impact of their underlying drivers are likely to be more effective than business as usual. PMID:25512538
Economic optimization of a global strategy to address the pandemic threat.
Pike, Jamison; Bogich, Tiffany; Elwood, Sarah; Finnoff, David C; Daszak, Peter
2014-12-30
Emerging pandemics threaten global health and economies and are increasing in frequency. Globally coordinated strategies to combat pandemics, similar to current strategies that address climate change, are largely adaptive, in that they attempt to reduce the impact of a pathogen after it has emerged. However, like climate change, mitigation strategies have been developed that include programs to reduce the underlying drivers of pandemics, particularly animal-to-human disease transmission. Here, we use real options economic modeling of current globally coordinated adaptation strategies for pandemic prevention. We show that they would be optimally implemented within 27 y to reduce the annual rise of emerging infectious disease events by 50% at an estimated one-time cost of approximately $343.7 billion. We then analyze World Bank data on multilateral "One Health" pandemic mitigation programs. We find that, because most pandemics have animal origins, mitigation is a more cost-effective policy than business-as-usual adaptation programs, saving between $344.0.7 billion and $360.3 billion over the next 100 y if implemented today. We conclude that globally coordinated pandemic prevention policies need to be enacted urgently to be optimally effective and that strategies to mitigate pandemics by reducing the impact of their underlying drivers are likely to be more effective than business as usual.
Perera, Marlon; Lawrentschuk, Nathan; Romanic, Diana; Papa, Nathan; Bolton, Damien
2015-01-01
Background Journal clubs are an essential tool in promoting clinical evidence-based medical education to all medical and allied health professionals. Twitter represents a public, microblogging forum that can facilitate traditional journal club requirements, while also reaching a global audience, and participation for discussion with study authors and colleagues. Objective The aim of the current study was to evaluate the current state of social media–facilitated journal clubs, specifically Twitter, as an example of continuing professional development. Methods A systematic review of literature databases (Medline, Embase, CINAHL, Web of Science, ERIC via ProQuest) was performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A systematic search of Twitter, the followers of identified journal clubs, and Symplur was also performed. Demographic and monthly tweet data were extracted from Twitter and Symplur. All manuscripts related to Twitter-based journal clubs were included. Statistical analyses were performed in MS Excel and STATA. Results From a total of 469 citations, 11 manuscripts were included and referred to five Twitter-based journal clubs (#ALiEMJC, #BlueJC, #ebnjc, #urojc, #meded). A Twitter-based journal club search yielded 34 potential hashtags/accounts, of which 24 were included in the final analysis. The median duration of activity was 11.75 (interquartile range [IQR] 19.9, SD 10.9) months, with 7 now inactive. The median number of followers and participants was 374 (IQR 574) and 157 (IQR 272), respectively. An overall increasing establishment of active Twitter-based journal clubs was observed, resulting in an exponential increase in total cumulative tweets (R 2=.98), and tweets per month (R 2=.72). Cumulative tweets for specific journal clubs increased linearly, with @ADC_JC, @EBNursingBMJ, @igsjc, @iurojc, and @NephJC, and showing greatest rate of change, as well as total impressions per month since
SU-E-J-130: Automating Liver Segmentation Via Combined Global and Local Optimization
Li, Dengwang; Wang, Jie; Kapp, Daniel S.; Xing, Lei
2015-06-15
Purpose: The aim of this work is to develop a robust algorithm for accurate segmentation of liver with special attention paid to the problems with fuzzy edges and tumor. Methods: 200 CT images were collected from radiotherapy treatment planning system. 150 datasets are selected as the panel data for shape dictionary and parameters estimation. The remaining 50 datasets were used as test images. In our study liver segmentation was formulated as optimization process of implicit function. The liver region was optimized via local and global optimization during iterations. Our method consists five steps: 1)The livers from the panel data were segmented manually by physicians, and then We estimated the parameters of GMM (Gaussian mixture model) and MRF (Markov random field). Shape dictionary was built by utilizing the 3D liver shapes. 2)The outlines of chest and abdomen were located according to rib structure in the input images, and the liver region was initialized based on GMM. 3)The liver shape for each 2D slice was adjusted using MRF within the neighborhood of liver edge for local optimization. 4)The 3D liver shape was corrected by employing SSR (sparse shape representation) based on liver shape dictionary for global optimization. Furthermore, H-PSO(Hybrid Particle Swarm Optimization) was employed to solve the SSR equation. 5)The corrected 3D liver was divided into 2D slices as input data of the third step. The iteration was repeated within the local optimization and global optimization until it satisfied the suspension conditions (maximum iterations and changing rate). Results: The experiments indicated that our method performed well even for the CT images with fuzzy edge and tumors. Comparing with physician delineated results, the segmentation accuracy with the 50 test datasets (VOE, volume overlap percentage) was on average 91%–95%. Conclusion: The proposed automatic segmentation method provides a sensible technique for segmentation of CT images. This work is
NASA Astrophysics Data System (ADS)
Göktürkler, G.; Balkaya, Ç.
2012-10-01
Three naturally inspired meta-heuristic algorithms—the genetic algorithm (GA), simulated annealing (SA) and particle swarm optimization (PSO)—were used to invert some of the self-potential (SP) anomalies originated by some polarized bodies with simple geometries. Both synthetic and field data sets were considered. The tests with the synthetic data comprised of the solutions with both noise-free and noisy data; in the tests with the field data some SP anomalies observed over a copper belt (India), graphite deposits (Germany) and metallic sulfide (Turkey) were inverted. The model parameters included the electric dipole moment, polarization angle, depth, shape factor and origin of the anomaly. The estimated parameters were compared with those from previous studies using various optimization algorithms, mainly least-squares approaches, on the same data sets. During the test studies the solutions by GA, PSO and SA were characterized as being consistent with each other; a good starting model was not a requirement to reach the global minimum. It can be concluded that the global optimization algorithms considered in this study were able to yield compatible solutions with those from widely used local optimization algorithms.
NASA Technical Reports Server (NTRS)
Jaunky, N.; Ambur, D. R.; Knight, N. F., Jr.
1998-01-01
A design strategy for optimal design of composite grid-stiffened cylinders subjected to global and local buckling constraints and strength constraints was developed using a discrete optimizer based on a genetic algorithm. An improved smeared stiffener theory was used for the global analysis. Local buckling of skin segments were assessed using a Rayleigh-Ritz method that accounts for material anisotropy. The local buckling of stiffener segments were also assessed. Constraints on the axial membrane strain in the skin and stiffener segments were imposed to include strength criteria in the grid-stiffened cylinder design. Design variables used in this study were the axial and transverse stiffener spacings, stiffener height and thickness, skin laminate stacking sequence and stiffening configuration, where stiffening configuration is a design variable that indicates the combination of axial, transverse and diagonal stiffener in the grid-stiffened cylinder. The design optimization process was adapted to identify the best suited stiffening configurations and stiffener spacings for grid-stiffened composite cylinder with the length and radius of the cylinder, the design in-plane loads and material properties as inputs. The effect of having axial membrane strain constraints in the skin and stiffener segments in the optimization process is also studied for selected stiffening configurations.
Jerish Joyner, J; Yadav, B K
2015-12-01
Black gram kernels with three initial moisture contents (10, 14 & 18 % w.b.) were steam treated in a continuous steaming unit at three inlet steam pressures (2, 3 & 4 kg/cm(2)) for three grain residence times (2, 4 & 6 min) in order to determine best treatment condition for maximizing the dhal yield while limiting the colour change in acceptable range. The dhal yield, dehulling loss and the colour difference (Delta E*) of the dehulled dhal were found to vary respectively, from 56.4 to 78.8 %, 30.8 to 8.6 % and 2.1 to 9.5 with increased severity of treatment. Optimization was done in order to obtain higher dhal yield while limiting the colour difference (Delta E*) within acceptable range i.e. 2.0 to 3.5 using response surface methodology. The best condition was obtained with the samples having 13.1 % initial moisture treated with 4 kg/cm(2) for about 6 min to achieve a dhal yield of 71.2 % and dehulling loss of 15.5 %. PMID:26604354
Process design and optimization of novel wheat-based continuous bioethanol production system.
Arifeen, Najmul; Wang, Ruohang; Kookos, Ioannis K; Webb, Colin; Koutinas, Apostolis A
2007-01-01
A novel design of a wheat-based biorefinery for bioethanol production, including wheat milling, gluten extraction as byproduct, fungal submerged fermentation for enzyme production, starch hydrolysis, fungal biomass autolysis for nutrient regeneration, yeast fermentation with recycling integrated with a pervaporation membrane for ethanol concentration, and fuel-grade ethanol purification by pressure swing distillation (PSD), was optimized in continuous mode using the equation-based software General Algebraic Modelling System (GAMS). The novel wheat biorefining strategy could result in a production cost within the range of dollars 0.96-0.50 gal(-1) ethanol (dollars 0.25-0.13 L(-1) ethanol) when the production capacity of the plant is within the range of 10-33.5 million gal y(-1) (37.85-126.8 million L y(-1)). The production of value-added byproducts (e.g., bran-rich pearlings, gluten, pure yeast cells) was identified as a crucial factor for improving the economics of fuel ethanol production from wheat. Integration of yeast fermentation with pervaporation membrane could result in the concentration of ethanol in the fermentation outlet stream (up to 40 mol %). The application of a PSD system that consisted of a low-pressure and a high-pressure column and employing heat integration between the high- and low-pressure columns resulted in reduced operating cost (up to 44%) for fuel-grade ethanol production.
Shah, Siddharth; Sharma, S K; Singh, Parminder; Muruganathan, A; Das, Ashok Kumar
2014-07-01
The prevalence of diabetes continues to increase despite advances in detection and therapy. Majority of the patients fail to achieve desired glycaemic targets even with maximal tolerated doses of oral anti-hyperglycaemic drugs, necessitating insulin therapy. Although, much attention has been given to early insulin initiation, yet substantial proportion of patients do not achieve glycaemic targets as they fail to initiate or intensify insulin therapy at the appropriate time. The choice of an insulin regimen and timely initiation and intensification of insulin therapy are key factors in achieving optimal glycaemic control. This current consensus guideline developed by a panel of experts aims to provide specific recommendations based on existing guidelines and published data on initiation and intensification of insulin therapy in management of type 2 diabetes mellitus (T2DM) using basal, premixed and basal-bolus insulin regimens in Indian clinical practice. The panel recognized the need to upgrade the existing guidelines for management of T2DM and endorsed recommendations that are in line with Indian insulin guidelines.
Jerish Joyner, J; Yadav, B K
2015-12-01
Black gram kernels with three initial moisture contents (10, 14 & 18 % w.b.) were steam treated in a continuous steaming unit at three inlet steam pressures (2, 3 & 4 kg/cm(2)) for three grain residence times (2, 4 & 6 min) in order to determine best treatment condition for maximizing the dhal yield while limiting the colour change in acceptable range. The dhal yield, dehulling loss and the colour difference (Delta E*) of the dehulled dhal were found to vary respectively, from 56.4 to 78.8 %, 30.8 to 8.6 % and 2.1 to 9.5 with increased severity of treatment. Optimization was done in order to obtain higher dhal yield while limiting the colour difference (Delta E*) within acceptable range i.e. 2.0 to 3.5 using response surface methodology. The best condition was obtained with the samples having 13.1 % initial moisture treated with 4 kg/cm(2) for about 6 min to achieve a dhal yield of 71.2 % and dehulling loss of 15.5 %.
NASA Technical Reports Server (NTRS)
Malone, Brett; Mason, W. H.
1992-01-01
An extension of our parametric multidisciplinary optimization method to include design results connecting multiple objective functions is presented. New insight into the effect of the figure of merit (objective function) on aircraft configuration size and shape is demonstrated using this technique. An aircraft concept, subject to performance and aerodynamic constraints, is optimized using the global sensitivity equation method for a wide range of objective functions. These figures of merit are described parametrically such that a series of multiobjective optimal solutions can be obtained. Computational speed is facilitated by use of algebraic representations of the system technologies. Using this method, the evolution of an optimum design from one objective function to another is demonstrated. Specifically, combinations of minimum takeoff gross weight, fuel weight, and maximum cruise performance and productivity parameters are used as objective functions.
2014-01-01
Background Optimization is the key to solving many problems in computational biology. Global optimization methods, which provide a robust methodology, and metaheuristics in particular have proven to be the most efficient methods for many applications. Despite their utility, there is a limited availability of metaheuristic tools. Results We present MEIGO, an R and Matlab optimization toolbox (also available in Python via a wrapper of the R version), that implements metaheuristics capable of solving diverse problems arising in systems biology and bioinformatics. The toolbox includes the enhanced scatter search method (eSS) for continuous nonlinear programming (cNLP) and mixed-integer programming (MINLP) problems, and variable neighborhood search (VNS) for Integer Programming (IP) problems. Additionally, the R version includes BayesFit for parameter estimation by Bayesian inference. The eSS and VNS methods can be run on a single-thread or in parallel using a cooperative strategy. The code is supplied under GPLv3 and is available at http://www.iim.csic.es/~gingproc/meigo.html. Documentation and examples are included. The R package has been submitted to BioConductor. We evaluate MEIGO against optimization benchmarks, and illustrate its applicability to a series of case studies in bioinformatics and systems biology where it outperforms other state-of-the-art methods. Conclusions MEIGO provides a free, open-source platform for optimization that can be applied to multiple domains of systems biology and bioinformatics. It includes efficient state of the art metaheuristics, and its open and modular structure allows the addition of further methods. PMID:24885957
Peña, J M; Lozano, J A; Larrañaga, P
2005-01-01
Many optimization problems are what can be called globally multimodal, i.e., they present several global optima. Unfortunately, this is a major source of difficulties for most estimation of distribution algorithms, making their effectiveness and efficiency degrade, due to genetic drift. With the aim of overcoming these drawbacks for discrete globally multimodal problem optimization, this paper introduces and evaluates a new estimation of distribution algorithm based on unsupervised learning of Bayesian networks. We report the satisfactory results of our experiments with symmetrical binary optimization problems.
Song, Qiankun; Yan, Huan; Zhao, Zhenjiang; Liu, Yurong
2016-09-01
This paper investigates the stability problem for a class of impulsive complex-valued neural networks with both asynchronous time-varying and continuously distributed delays. By employing the idea of vector Lyapunov function, M-matrix theory and inequality technique, several sufficient conditions are obtained to ensure the global exponential stability of equilibrium point. When the impulsive effects are not considered, several sufficient conditions are also given to guarantee the existence, uniqueness and global exponential stability of equilibrium point. Two examples are given to illustrate the effectiveness and lower level of conservatism of the proposed criteria in comparison with some existing results.
Optimal Multi-scale Demand-side Management for Continuous Power-Intensive Processes
NASA Astrophysics Data System (ADS)
Mitra, Sumit
With the advent of deregulation in electricity markets and an increasing share of intermittent power generation sources, the profitability of industrial consumers that operate power-intensive processes has become directly linked to the variability in energy prices. Thus, for industrial consumers that are able to adjust to the fluctuations, time-sensitive electricity prices (as part of so-called Demand-Side Management (DSM) in the smart grid) offer potential economical incentives. In this thesis, we introduce optimization models and decomposition strategies for the multi-scale Demand-Side Management of continuous power-intensive processes. On an operational level, we derive a mode formulation for scheduling under time-sensitive electricity prices. The formulation is applied to air separation plants and cement plants to minimize the operating cost. We also describe how a mode formulation can be used for industrial combined heat and power plants that are co-located at integrated chemical sites to increase operating profit by adjusting their steam and electricity production according to their inherent flexibility. Furthermore, a robust optimization formulation is developed to address the uncertainty in electricity prices by accounting for correlations and multiple ranges in the realization of the random variables. On a strategic level, we introduce a multi-scale model that provides an understanding of the value of flexibility of the current plant configuration and the value of additional flexibility in terms of retrofits for Demand-Side Management under product demand uncertainty. The integration of multiple time scales leads to large-scale two-stage stochastic programming problems, for which we need to apply decomposition strategies in order to obtain a good solution within a reasonable amount of time. Hence, we describe two decomposition schemes that can be applied to solve two-stage stochastic programming problems: First, a hybrid bi-level decomposition scheme with
Tsai, Ko-Fan; Chu, Shu-Chun
2016-09-19
The one-time ray-tracing optimization method is a fast way to design LED illumination systems [Opt. Express22, 5357 (2014)10.1364/OE.22.005357]. The method optimizes the performance of LED illumination systems by modifying the LEDs' luminous intensity distribution curve (LIDC) with a freeform lens, instead of modifying the illumination system structure. In finding the LEDs' LIDC for optimizing the illumination system's performance, the LEDs' LIDC found by means of a general gradient descent method can be trapped in a local solution. This study develops a matrix operation method to directly find the global solution of the LEDs' LIDC for the optimization of the illumination system's performance for any initial design of an illumination system structure. As compared with the gradient descent method, using the proposed characteristic matrix operation method to find the best LEDs' LIDC reduces the cost in time by several orders of magnitude. The proposed characteristic matrix operation method ensures that the one-time ray-tracing optimization method is an efficient and reliable method for designing LED illumination systems. PMID:27661876
Tsai, Ko-Fan; Chu, Shu-Chun
2016-09-19
The one-time ray-tracing optimization method is a fast way to design LED illumination systems [Opt. Express22, 5357 (2014)10.1364/OE.22.005357]. The method optimizes the performance of LED illumination systems by modifying the LEDs' luminous intensity distribution curve (LIDC) with a freeform lens, instead of modifying the illumination system structure. In finding the LEDs' LIDC for optimizing the illumination system's performance, the LEDs' LIDC found by means of a general gradient descent method can be trapped in a local solution. This study develops a matrix operation method to directly find the global solution of the LEDs' LIDC for the optimization of the illumination system's performance for any initial design of an illumination system structure. As compared with the gradient descent method, using the proposed characteristic matrix operation method to find the best LEDs' LIDC reduces the cost in time by several orders of magnitude. The proposed characteristic matrix operation method ensures that the one-time ray-tracing optimization method is an efficient and reliable method for designing LED illumination systems.
ERIC Educational Resources Information Center
National Univ. Continuing Education Association, Washington, DC.
This fourth report in the National University Continuing Education Association (NUCEA) Challenges for Continuing Higher Education Leadership series draws attention to the wider and more complex set of issues surrounding the "internationalization" of U.S. society and its educational componets. The report consists of papers prsented at a…
Global-Local Analysis and Optimization of a Composite Civil Tilt-Rotor Wing
NASA Technical Reports Server (NTRS)
Rais-Rohani, Masound
1999-01-01
This report gives highlights of an investigation on the design and optimization of a thin composite wing box structure for a civil tilt-rotor aircraft. Two different concepts are considered for the cantilever wing: (a) a thin monolithic skin design, and (b) a thick sandwich skin design. Each concept is examined with three different skin ply patterns based on various combinations of 0, +/-45, and 90 degree plies. The global-local technique is used in the analysis and optimization of the six design models. The global analysis is based on a finite element model of the wing-pylon configuration while the local analysis uses a uniformly supported plate representing a wing panel. Design allowables include those on vibration frequencies, panel buckling, and material strength. The design optimization problem is formulated as one of minimizing the structural weight subject to strength, stiffness, and d,vnamic constraints. Six different loading conditions based on three different flight modes are considered in the design optimization. The results of this investigation reveal that of all the loading conditions the one corresponding to the rolling pull-out in the airplane mode is the most stringent. Also the frequency constraints are found to drive the skin thickness limits, rendering the buckling constraints inactive. The optimum skin ply pattern for the monolithic skin concept is found to be (((0/+/-45/90/(0/90)(sub 2))(sub s))(sub s), while for the sandwich skin concept the optimal ply pattern is found to be ((0/+/-45/90)(sub 2s))(sub s).
Optimizing rice yields while minimizing yield-scaled global warming potential.
Pittelkow, Cameron M; Adviento-Borbe, Maria A; van Kessel, Chris; Hill, James E; Linquist, Bruce A
2014-05-01
To meet growing global food demand with limited land and reduced environmental impact, agricultural greenhouse gas (GHG) emissions are increasingly evaluated with respect to crop productivity, i.e., on a yield-scaled as opposed to area basis. Here, we compiled available field data on CH4 and N2 O emissions from rice production systems to test the hypothesis that in response to fertilizer nitrogen (N) addition, yield-scaled global warming potential (GWP) will be minimized at N rates that maximize yields. Within each study, yield N surplus was calculated to estimate deficit or excess N application rates with respect to the optimal N rate (defined as the N rate at which maximum yield was achieved). Relationships between yield N surplus and GHG emissions were assessed using linear and nonlinear mixed-effects models. Results indicate that yields increased in response to increasing N surplus when moving from deficit to optimal N rates. At N rates contributing to a yield N surplus, N2 O and yield-scaled N2 O emissions increased exponentially. In contrast, CH4 emissions were not impacted by N inputs. Accordingly, yield-scaled CH4 emissions decreased with N addition. Overall, yield-scaled GWP was minimized at optimal N rates, decreasing by 21% compared to treatments without N addition. These results are unique compared to aerobic cropping systems in which N2 O emissions are the primary contributor to GWP, meaning yield-scaled GWP may not necessarily decrease for aerobic crops when yields are optimized by N fertilizer addition. Balancing gains in agricultural productivity with climate change concerns, this work supports the concept that high rice yields can be achieved with minimal yield-scaled GWP through optimal N application rates. Moreover, additional improvements in N use efficiency may further reduce yield-scaled GWP, thereby strengthening the economic and environmental sustainability of rice systems. PMID:24115565
Optimizing rice yields while minimizing yield-scaled global warming potential.
Pittelkow, Cameron M; Adviento-Borbe, Maria A; van Kessel, Chris; Hill, James E; Linquist, Bruce A
2014-05-01
To meet growing global food demand with limited land and reduced environmental impact, agricultural greenhouse gas (GHG) emissions are increasingly evaluated with respect to crop productivity, i.e., on a yield-scaled as opposed to area basis. Here, we compiled available field data on CH4 and N2 O emissions from rice production systems to test the hypothesis that in response to fertilizer nitrogen (N) addition, yield-scaled global warming potential (GWP) will be minimized at N rates that maximize yields. Within each study, yield N surplus was calculated to estimate deficit or excess N application rates with respect to the optimal N rate (defined as the N rate at which maximum yield was achieved). Relationships between yield N surplus and GHG emissions were assessed using linear and nonlinear mixed-effects models. Results indicate that yields increased in response to increasing N surplus when moving from deficit to optimal N rates. At N rates contributing to a yield N surplus, N2 O and yield-scaled N2 O emissions increased exponentially. In contrast, CH4 emissions were not impacted by N inputs. Accordingly, yield-scaled CH4 emissions decreased with N addition. Overall, yield-scaled GWP was minimized at optimal N rates, decreasing by 21% compared to treatments without N addition. These results are unique compared to aerobic cropping systems in which N2 O emissions are the primary contributor to GWP, meaning yield-scaled GWP may not necessarily decrease for aerobic crops when yields are optimized by N fertilizer addition. Balancing gains in agricultural productivity with climate change concerns, this work supports the concept that high rice yields can be achieved with minimal yield-scaled GWP through optimal N application rates. Moreover, additional improvements in N use efficiency may further reduce yield-scaled GWP, thereby strengthening the economic and environmental sustainability of rice systems.
Cappione, Amedeo; Mabuchi, Masaharu; Briggs, David; Nadler, Timothy
2015-04-01
Protein immuno-detection encompasses a broad range of analytical methodologies, including western blotting, flow cytometry, and microscope-based applications. These assays which detect, quantify, and/or localize expression for one or more proteins in complex biological samples, are reliant upon fluorescent or enzyme-tagged target-specific antibodies. While small molecule labeling kits are available with a range of detection moieties, the workflow is hampered by a requirement for multiple dialysis-based buffer exchange steps that are both time-consuming and subject to sample loss. In a previous study, we briefly described an alternative method for small-scale protein labeling with small molecule dyes whereby all phases of the conjugation workflow could be performed in a single centrifugal diafiltration device. Here, we expand on this foundational work addressing functionality of the device at each step in the workflow (sample cleanup, labeling, unbound dye removal, and buffer exchange/concentration) and the implications for optimizing labeling efficiency. When compared to other common buffer exchange methodologies, centrifugal diafiltration offered superior performance as measured by four key parameters (process time, desalting capacity, protein recovery, retain functional integrity). Originally designed for resin-based affinity purification, the device also provides a platform for up-front antibody purification or albumin carrier removal. Most significantly, by exploiting the rapid kinetics of NHS-based labeling reactions, the process of continuous diafiltration minimizes reaction time and long exposure to excess dye, guaranteeing maximal target labeling while limiting the risks associated with over-labeling. Overall, the device offers a simplified workflow with reduced processing time and hands-on requirements, without sacrificing labeling efficiency, final yield, or conjugate performance.
Research on global path planning based on ant colony optimization for AUV
NASA Astrophysics Data System (ADS)
Wang, Hong-Jian; Xiong, Wei
2009-03-01
Path planning is an important issue for autonomous underwater vehicles (AUVs) traversing an unknown environment such as a sea floor, a jungle, or the outer celestial planets. For this paper, global path planning using large-scale chart data was studied, and the principles of ant colony optimization (ACO) were applied. This paper introduced the idea of a visibility graph based on the grid workspace model. It also brought a series of pheromone updating rules for the ACO planning algorithm. The operational steps of the ACO algorithm are proposed as a model for a global path planning method for AUV. To mimic the process of smoothing a planned path, a cutting operator and an insertion-point operator were designed. Simulation results demonstrated that the ACO algorithm is suitable for global path planning. The system has many advantages, including that the operating path of the AUV can be quickly optimized, and it is shorter, safer, and smoother. The prototype system successfully demonstrated the feasibility of the concept, proving it can be applied to surveys of unstructured unmanned environments.
Schafroth, Heather D; Floudas, Christodoulos A
2004-02-15
Development of a computational prediction method based on molecular modeling, global optimization, and implicit solvation has produced accurate structure and relative binding affinity predictions for peptide amino acids binding to five pockets of the MHC molecule HLA-DRB1*0101. Because peptide binding to MHC molecules is essential to many immune responses, development of such a method for understanding and predicting the forces that drive binding is crucial for pharmaceutical design and disease treatment. Underlying the development of this prediction method are two hypotheses. The first is that pockets formed by the peptide binding groove of MHC molecules are independent, separating the prediction of peptide amino acids that bind within individual pockets from those that bind between pockets. The second hypothesis is that the native state of a system composed of an amino acid bound to a protein pocket corresponds to the system's lowest free energy. The prediction method developed from these hypotheses uses atomistic-level modeling, deterministic global optimization, and three methods of implicit solvation: solvent-accessible area, solvent-accessible volume, and Poisson-Boltzmann electrostatics. The method predicts relative binding affinities of peptide amino acids for pockets of HLA-DRB1*0101 by determining computationally an amino acid's global minimum energy conformation. Prediction results from the method are in agreement with X-ray crystallography data and experimental binding assays.
CH4 parameter estimation in CLM4.5bgc using surrogate global optimization
NASA Astrophysics Data System (ADS)
Müller, J.; Paudel, R.; Shoemaker, C. A.; Woodbury, J.; Wang, Y.; Mahowald, N.
2015-10-01
Over the anthropocene methane has increased dramatically. Wetlands are one of the major sources of methane to the atmosphere, but the role of changes in wetland emissions is not well understood. The Community Land Model (CLM) of the Community Earth System Models contains a module to estimate methane emissions from natural wetlands and rice paddies. Our comparison of CH4 emission observations at 16 sites around the planet reveals, however, that there are large discrepancies between the CLM predictions and the observations. The goal of our study is to adjust the model parameters in order to minimize the root mean squared error (RMSE) between model predictions and observations. These parameters have been selected based on a sensitivity analysis. Because of the cost associated with running the CLM simulation (15 to 30 min on the Yellowstone Supercomputing Facility), only relatively few simulations can be allowed in order to find a near-optimal solution within an acceptable time. Our results indicate that the parameter estimation problem has multiple local minima. Hence, we use a computationally efficient global optimization algorithm that uses a radial basis function (RBF) surrogate model to approximate the objective function. We use the information from the RBF to select parameter values that are most promising with respect to improving the objective function value. We show with pseudo data that our optimization algorithm is able to make excellent progress with respect to decreasing the RMSE. Using the true CH4 emission observations for optimizing the parameters, we are able to significantly reduce the overall RMSE between observations and model predictions by about 50 %. The methane emission predictions of the CLM using the optimized parameters agree better with the observed methane emission data in northern and tropical latitudes. With the optimized parameters, the methane emission predictions are higher in northern latitudes than when the default parameters are
A Globally Optimal Particle Tracking Technique for Stereo Imaging Velocimetry Experiments
NASA Technical Reports Server (NTRS)
McDowell, Mark
2008-01-01
An important phase of any Stereo Imaging Velocimetry experiment is particle tracking. Particle tracking seeks to identify and characterize the motion of individual particles entrained in a fluid or air experiment. We analyze a cylindrical chamber filled with water and seeded with density-matched particles. In every four-frame sequence, we identify a particle track by assigning a unique track label for each camera image. The conventional approach to particle tracking is to use an exhaustive tree-search method utilizing greedy algorithms to reduce search times. However, these types of algorithms are not optimal due to a cascade effect of incorrect decisions upon adjacent tracks. We examine the use of a guided evolutionary neural net with simulated annealing to arrive at a globally optimal assignment of tracks. The net is guided both by the minimization of the search space through the use of prior limiting assumptions about valid tracks and by a strategy which seeks to avoid high-energy intermediate states which can trap the net in a local minimum. A stochastic search algorithm is used in place of back-propagation of error to further reduce the chance of being trapped in an energy well. Global optimization is achieved by minimizing an objective function, which includes both track smoothness and particle-image utilization parameters. In this paper we describe our model and present our experimental results. We compare our results with a nonoptimizing, predictive tracker and obtain an average increase in valid track yield of 27 percent
Liang, Faming; Cheng, Yichen; Lin, Guang
2014-06-13
Simulated annealing has been widely used in the solution of optimization problems. As known by many researchers, the global optima cannot be guaranteed to be located by simulated annealing unless a logarithmic cooling schedule is used. However, the logarithmic cooling schedule is so slow that no one can afford to have such a long CPU time. This paper proposes a new stochastic optimization algorithm, the so-called simulated stochastic approximation annealing algorithm, which is a combination of simulated annealing and the stochastic approximation Monte Carlo algorithm. Under the framework of stochastic approximation Markov chain Monte Carlo, it is shown that the new algorithm can work with a cooling schedule in which the temperature can decrease much faster than in the logarithmic cooling schedule, e.g., a square-root cooling schedule, while guaranteeing the global optima to be reached when the temperature tends to zero. The new algorithm has been tested on a few benchmark optimization problems, including feed-forward neural network training and protein-folding. The numerical results indicate that the new algorithm can significantly outperform simulated annealing and other competitors.
Comparison of global optimization approaches for robust calibration of hydrologic model parameters
NASA Astrophysics Data System (ADS)
Jung, I. W.
2015-12-01
Robustness of the calibrated parameters of hydrologic models is necessary to provide a reliable prediction of future performance of watershed behavior under varying climate conditions. This study investigated calibration performances according to the length of calibration period, objective functions, hydrologic model structures and optimization methods. To do this, the combination of three global optimization methods (i.e. SCE-UA, Micro-GA, and DREAM) and four hydrologic models (i.e. SAC-SMA, GR4J, HBV, and PRMS) was tested with different calibration periods and objective functions. Our results showed that three global optimization methods provided close calibration performances under different calibration periods, objective functions, and hydrologic models. However, using the agreement of index, normalized root mean square error, Nash-Sutcliffe efficiency as the objective function showed better performance than using correlation coefficient and percent bias. Calibration performances according to different calibration periods from one year to seven years were hard to generalize because four hydrologic models have different levels of complexity and different years have different information content of hydrological observation. Acknowledgements This research was supported by a grant (14AWMP-B082564-01) from Advanced Water Management Research Program funded by Ministry of Land, Infrastructure and Transport of Korean government.
Energy landscape paving with local search for global optimization of the BLN off-lattice model
NASA Astrophysics Data System (ADS)
Liu, Jingfa; Huang, Weibo; Liu, Wenjie; Song, Beibei; Sun, Yuanyuan; Chen, Mao
2014-02-01
The optimization problem for finding the global minimum energy structure is one of the main problems of protein structure prediction and is known to be an NP-hard problem in computational molecular biology. The low-energy conformational search problem in the hydrophobic-hydrophilic-neutral (BLN) off-lattice model is studied. We convert the problem into an unconstrained optimization problem by introducing the penalty function. By putting forward a new updating mechanism of the histogram function in the energy landscape paving (ELP) method and incorporating heuristic conformation update strategies into the ELP method, we obtain an improved ELP (IELP) method. Subsequently, by combining the IELP method with the local search (LS) based on the gradient descent method, we propose a hybrid algorithm, denoted by IELP-LS, for the conformational search of the off-lattice BLN model. Simulation results indicate that IELP-LS can find lower-energy states than other methods in the literature, showing that the proposed method is an effective tool for global optimization in the BLN off-lattice protein model.
2012-01-01
Background In workforces that are traditionally mobile and have long lead times for new supply, such as health, effective global indicators of tertiary education are increasingly essential. Difficulties with transportability of qualifications and cross-accreditation are now recognised as key barriers to meeting the rapidly shifting international demands for health care providers. The plethora of mixed education and service arrangements poses challenges for employers and regulators, let alone patients; in determining equivalence of training and competency between individuals, institutions and geographical locations. Discussion This paper outlines the shortfall of the current indicators in assisting the process of global certification and competency recognition in the health care workforce. Using Organisation for Economic Cooperation and Development (OECD) data we highlight how International standardisation in the tertiary education sector is problematic for the global health workforce. Through a series of case studies, we then describe a model which enables institutions to compare themselves internally and with others internationally using bespoke or prioritised parameters rather than standards. Summary The mobility of the global health workforce means that transportability of qualifications is an increasing area of concern. Valid qualifications based on workplace learning and assessment requires at least some variables to be benchmarked in order to judge performance. PMID:22776517
Response of snow-dependent hydrologic extremes to continued global warming
Diffenbaugh, Noah S.; Scherer, Martin; Ashfaq, Moetasim
2013-01-01
Snow accumulation is critical for water availability in the northern hemisphere 1,2, raising concern that global warming could have important impacts on natural and human systems in snow-dependent regions 1,3. Although regional hydrologic changes have been observed (e.g., 1,3–5), the time of emergence of extreme changes in snow accumulation and melt remains a key unknown for assessing climate change impacts 3,6,7. We find that the CMIP5 global climate model ensemble exhibits an imminent shift towards low snow years in the northern hemisphere, with areas of western North America, northeastern Europe, and the Greater Himalaya showing the strongest emergence during the near-term decades and at 2°C global warming. The occurrence of extremely low snow years becomes widespread by the late-21st century, as do the occurrence of extremely high early-season snowmelt and runoff (implying increasing flood risk), and extremely low late-season snowmelt and runoff (implying increasing water stress). Our results suggest that many snow-dependent regions of the northern hemisphere are likely to experience increasing stress from low snow years within the next three decades, and from extreme changes in snow-dominated water resources if global warming exceeds 2°C above the pre-industrial baseline. PMID:24015153
NASA Technical Reports Server (NTRS)
Sabaka, T. J.; Rowlands, D. D.; Luthcke, S. B.; Boy, J.-P.
2010-01-01
We describe Earth's mass flux from April 2003 through November 2008 by deriving a time series of mas cons on a global 2deg x 2deg equal-area grid at 10 day intervals. We estimate the mass flux directly from K band range rate (KBRR) data provided by the Gravity Recovery and Climate Experiment (GRACE) mission. Using regularized least squares, we take into account the underlying process dynamics through continuous space and time-correlated constraints. In addition, we place the mascon approach in the context of other filtering techniques, showing its equivalence to anisotropic, nonsymmetric filtering, least squares collocation, and Kalman smoothing. We produce mascon time series from KBRR data that have and have not been corrected (forward modeled) for hydrological processes and fmd that the former produce superior results in oceanic areas by minimizing signal leakage from strong sources on land. By exploiting the structure of the spatiotemporal constraints, we are able to use a much more efficient (in storage and computation) inversion algorithm based upon the conjugate gradient method. This allows us to apply continuous rather than piecewise continuous time-correlated constraints, which we show via global maps and comparisons with ocean-bottom pressure gauges, to produce time series with reduced random variance and full systematic signal. Finally, we present a preferred global model, a hybrid whose oceanic portions are derived using forward modeling of hydrology but whose land portions are not, and thus represent a pure GRACE-derived signal.
Gutowski, William J.; Prusa, Joseph M.; Smolarkiewicz, Piotr K.
2012-05-08
This project had goals of advancing the performance capabilities of the numerical general circulation model EULAG and using it to produce a fully operational atmospheric global climate model (AGCM) that can employ either static or dynamic grid stretching for targeted phenomena. The resulting AGCM combined EULAG's advanced dynamics core with the "physics" of the NCAR Community Atmospheric Model (CAM). Effort discussed below shows how we improved model performance and tested both EULAG and the coupled CAM-EULAG in several ways to demonstrate the grid stretching and ability to simulate very well a wide range of scales, that is, multi-scale capability. We leveraged our effort through interaction with an international EULAG community that has collectively developed new features and applications of EULAG, which we exploited for our own work summarized here. Overall, the work contributed to over 40 peer-reviewed publications and over 70 conference/workshop/seminar presentations, many of them invited. 3a. EULAG Advances EULAG is a non-hydrostatic, parallel computational model for all-scale geophysical flows. EULAG's name derives from its two computational options: EULerian (flux form) or semi-LAGrangian (advective form). The model combines nonoscillatory forward-in-time (NFT) numerical algorithms with a robust elliptic Krylov solver. A signature feature of EULAG is that it is formulated in generalized time-dependent curvilinear coordinates. In particular, this enables grid adaptivity. In total, these features give EULAG novel advantages over many existing dynamical cores. For EULAG itself, numerical advances included refining boundary conditions and filters for optimizing model performance in polar regions. We also added flexibility to the model's underlying formulation, allowing it to work with the pseudo-compressible equation set of Durran in addition to EULAG's standard anelastic formulation. Work in collaboration with others also extended the demonstrated range of
Global Optimization of N-Maneuver, High-Thrust Trajectories Using Direct Multiple Shooting
NASA Technical Reports Server (NTRS)
Vavrina, Matthew A.; Englander, Jacob A.; Ellison, Donald H.
2016-01-01
The performance of impulsive, gravity-assist trajectories often improves with the inclusion of one or more maneuvers between flybys. However, grid-based scans over the entire design space can become computationally intractable for even one deep-space maneuver, and few global search routines are capable of an arbitrary number of maneuvers. To address this difficulty a trajectory transcription allowing for any number of maneuvers is developed within a multi-objective, global optimization framework for constrained, multiple gravity-assist trajectories. The formulation exploits a robust shooting scheme and analytic derivatives for computational efficiency. The approach is applied to several complex, interplanetary problems, achieving notable performance without a user-supplied initial guess.
Global Optimization of N-Maneuver, High-Thrust Trajectories Using Direct Multiple Shooting
NASA Technical Reports Server (NTRS)
Vavrina, Matthew A.; Englander, Jacob A.; Ellison, Donald H.
2015-01-01
The performance of impulsive, gravity-assist trajectories often improves with the inclusion of one or more maneuvers between flybys. However, grid-based scans over the entire design space can become computationally intractable for even one deep-space maneuver, and few global search routines are capable of an arbitrary number of maneuvers. To address this difficulty a trajectory transcription allow-ing for any number of maneuvers is developed within a multi-objective, global optimization framework for constrained, multiple gravity-assist trajectories. The formulation exploits a robust shooting scheme and analytic derivatives for com-putational efficiency. The approach is applied to several complex, interplanetary problems, achieving notable performance without a user-supplied initial guess.
Lithological and Surface Geometry Joint Inversions Using Multi-Objective Global Optimization Methods
NASA Astrophysics Data System (ADS)
Lelièvre, Peter; Bijani, Rodrigo; Farquharson, Colin
2016-04-01
surfaces are set to a priori values. The inversion is tasked with calculating the geometry of the contact surfaces instead of some piecewise distribution of properties in a mesh. Again, no coupling measure is required and joint inversion is simplified. Both of these inverse problems involve high nonlinearity and discontinuous or non-obtainable derivatives. They can also involve the existence of multiple minima. Hence, one can not apply the standard descent-based local minimization methods used to solve typical minimum-structure inversions. Instead, we are applying Pareto multi-objective global optimization (PMOGO) methods, which 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. While there are definite advantages to PMOGO joint inversion approaches, the methods come with significantly increased computational requirements. We are researching various strategies to ameliorate these computational issues including parallelization and problem dimension reduction.
Lee, JongHyup; Pak, Dohyun
2016-01-01
For practical deployment of wireless sensor networks (WSN), WSNs construct clusters, where a sensor node communicates with other nodes in its cluster, and a cluster head support connectivity between the sensor nodes and a sink node. In hybrid WSNs, cluster heads have cellular network interfaces for global connectivity. However, when WSNs are active and the load of cellular networks is high, the optimal assignment of cluster heads to base stations becomes critical. Therefore, in this paper, we propose a game theoretic model to find the optimal assignment of base stations for hybrid WSNs. Since the communication and energy cost is different according to cellular systems, we devise two game models for TDMA/FDMA and CDMA systems employing power prices to adapt to the varying efficiency of recent wireless technologies. The proposed model is defined on the assumptions of the ideal sensing field, but our evaluation shows that the proposed model is more adaptive and energy efficient than local selections. PMID:27589743
Lee, JongHyup; Pak, Dohyun
2016-01-01
For practical deployment of wireless sensor networks (WSN), WSNs construct clusters, where a sensor node communicates with other nodes in its cluster, and a cluster head support connectivity between the sensor nodes and a sink node. In hybrid WSNs, cluster heads have cellular network interfaces for global connectivity. However, when WSNs are active and the load of cellular networks is high, the optimal assignment of cluster heads to base stations becomes critical. Therefore, in this paper, we propose a game theoretic model to find the optimal assignment of base stations for hybrid WSNs. Since the communication and energy cost is different according to cellular systems, we devise two game models for TDMA/FDMA and CDMA systems employing power prices to adapt to the varying efficiency of recent wireless technologies. The proposed model is defined on the assumptions of the ideal sensing field, but our evaluation shows that the proposed model is more adaptive and energy efficient than local selections. PMID:27589743
NASA Astrophysics Data System (ADS)
Do, Khac Duc
2015-03-01
This paper presents a design of optimal controllers with respect to a meaningful cost function to force an underactuated omni-directional intelligent navigator (ODIN) under unknown constant environmental loads to track a reference trajectory in two-dimensional space. Motivated by the vehicle's steering practice, the yaw angle regarded as a virtual control plus the surge thrust force are used to force the position of the vehicle to globally track its reference trajectory. The control design is based on several recent results developed for inverse optimal control and stability analysis of nonlinear systems, a new design of bounded disturbance observers, and backstepping and Lyapunov's direct methods. Both state- and output-feedback control designs are addressed. Simulations are included to illustrate the effectiveness of the proposed results.
Lee, JongHyup; Pak, Dohyun
2016-08-29
For practical deployment of wireless sensor networks (WSN), WSNs construct clusters, where a sensor node communicates with other nodes in its cluster, and a cluster head support connectivity between the sensor nodes and a sink node. In hybrid WSNs, cluster heads have cellular network interfaces for global connectivity. However, when WSNs are active and the load of cellular networks is high, the optimal assignment of cluster heads to base stations becomes critical. Therefore, in this paper, we propose a game theoretic model to find the optimal assignment of base stations for hybrid WSNs. Since the communication and energy cost is different according to cellular systems, we devise two game models for TDMA/FDMA and CDMA systems employing power prices to adapt to the varying efficiency of recent wireless technologies. The proposed model is defined on the assumptions of the ideal sensing field, but our evaluation shows that the proposed model is more adaptive and energy efficient than local selections.
Using support vector machine and dynamic parameter encoding to enhance global optimization
NASA Astrophysics Data System (ADS)
Zheng, Z.; Chen, X.; Liu, C.; Huang, K.
2016-05-01
This study presents an approach which combines support vector machine (SVM) and dynamic parameter encoding (DPE) to enhance the run-time performance of global optimization with time-consuming fitness function evaluations. SVMs are used as surrogate models to partly substitute for fitness evaluations. To reduce the computation time and guarantee correct convergence, this work proposes a novel strategy to adaptively adjust the number of fitness evaluations needed according to the approximate error of the surrogate model. Meanwhile, DPE is employed to compress the solution space, so that it not only accelerates the convergence but also decreases the approximate error. Numerical results of optimizing a few benchmark functions and an antenna in a practical application are presented, which verify the feasibility, efficiency and robustness of the proposed approach.
A genetic algorithm for first principles global structure optimization of supported nano structures
Vilhelmsen, Lasse B.; Hammer, Bjørk
2014-07-28
We present a newly developed publicly available genetic algorithm (GA) for global structure optimisation within atomic scale modeling. The GA is focused on optimizations using first principles calculations, but it works equally well with empirical potentials. The implementation is described and benchmarked through a detailed statistical analysis employing averages across many independent runs of the GA. This analysis focuses on the practical use of GA’s with a description of optimal parameters to use. New results for the adsorption of M{sub 8} clusters (M = Ru, Rh, Pd, Ag, Pt, Au) on the stoichiometric rutile TiO{sub 2}(110) surface are presented showing the power of automated structure prediction and highlighting the diversity of metal cluster geometries at the atomic scale.
Electronic neural network for solving traveling salesman and similar global optimization problems
NASA Technical Reports Server (NTRS)
Thakoor, Anilkumar P. (Inventor); Moopenn, Alexander W. (Inventor); Duong, Tuan A. (Inventor); Eberhardt, Silvio P. (Inventor)
1993-01-01
This invention is a novel high-speed neural network based processor for solving the 'traveling salesman' and other global optimization problems. It comprises a novel hybrid architecture employing a binary synaptic array whose embodiment incorporates the fixed rules of the problem, such as the number of cities to be visited. The array is prompted by analog voltages representing variables such as distances. The processor incorporates two interconnected feedback networks, each of which solves part of the problem independently and simultaneously, yet which exchange information dynamically.
Model-data fusion across ecosystems: from multi-site optimizations to global simulations
NASA Astrophysics Data System (ADS)
Kuppel, S.; Peylin, P.; Maignan, F.; Chevallier, F.; Kiely, G.; Montagnani, L.; Cescatti, A.
2014-05-01
This study uses a variational data assimilation framework to simultaneously constrain a global ecosystem model with eddy covariance measurements of daily net carbon (NEE) and latent heat (LE) fluxes from a large number of sites grouped in seven plant functional types (PFTs). It is an attempt to bridge the gap between the numerous site-specific parameter optimization works found in the literature and the generic parameterization used by most land surface models within each PFT. The present multi-site approach allows deriving PFT-generic sets of optimized parameters enhancing the agreement between measured and simulated fluxes at most of the sites considered, with performances often comparable to those of the corresponding site-specific optimizations. Besides reducing the PFT-averaged model-data root-mean-square difference (RMSD) and the associated daily output uncertainty, the optimization improves the simulated CO2 balance at tropical and temperate forests sites. The major site-level NEE adjustments at the seasonal scale are: reduced amplitude in C3 grasslands and boreal forests, increased seasonality in temperate evergreen forests, and better model-data phasing in temperate deciduous broadleaf forests. Conversely, the poorer performances in tropical evergreen broadleaf forests points to deficiencies regarding the modeling of phenology and soil water stress for this PFT. An evaluation with data-oriented estimates of photosynthesis (GPP) and ecosystem respiration (Reco) rates indicates distinctively improved simulations of both gross fluxes. The multi-site parameter sets are then tested against CO2 concentrations measured at 53 locations around the globe, showing significant adjustments of the modeled seasonality of atmospheric CO2 concentration, whose relevance seems PFT-dependent, along with an improved interannual variability. Lastly, a global scale evaluation with remote sensing NDVI measurements indicates an improvement of the simulated seasonal variations of
Model-data fusion across ecosystems: from multisite optimizations to global simulations
NASA Astrophysics Data System (ADS)
Kuppel, S.; Peylin, P.; Maignan, F.; Chevallier, F.; Kiely, G.; Montagnani, L.; Cescatti, A.
2014-11-01
This study uses a variational data assimilation framework to simultaneously constrain a global ecosystem model with eddy covariance measurements of daily net ecosystem exchange (NEE) and latent heat (LE) fluxes from a large number of sites grouped in seven plant functional types (PFTs). It is an attempt to bridge the gap between the numerous site-specific parameter optimization works found in the literature and the generic parameterization used by most land surface models within each PFT. The present multisite approach allows deriving PFT-generic sets of optimized parameters enhancing the agreement between measured and simulated fluxes at most of the sites considered, with performances often comparable to those of the corresponding site-specific optimizations. Besides reducing the PFT-averaged model-data root-mean-square difference (RMSD) and the associated daily output uncertainty, the optimization improves the simulated CO2 balance at tropical and temperate forests sites. The major site-level NEE adjustments at the seasonal scale are reduced amplitude in C3 grasslands and boreal forests, increased seasonality in temperate evergreen forests, and better model-data phasing in temperate deciduous broadleaf forests. Conversely, the poorer performances in tropical evergreen broadleaf forests points to deficiencies regarding the modelling of phenology and soil water stress for this PFT. An evaluation with data-oriented estimates of photosynthesis (GPP - gross primary productivity) and ecosystem respiration (Reco) rates indicates distinctively improved simulations of both gross fluxes. The multisite parameter sets are then tested against CO2 concentrations measured at 53 locations around the globe, showing significant adjustments of the modelled seasonality of atmospheric CO2 concentration, whose relevance seems PFT-dependent, along with an improved interannual variability. Lastly, a global-scale evaluation with remote sensing NDVI (normalized difference vegetation index
Liu, Derong; Wang, Ding; Li, Hongliang
2014-02-01
In this paper, using a neural-network-based online learning optimal control approach, a novel decentralized control strategy is developed to stabilize a class of continuous-time nonlinear interconnected large-scale systems. First, optimal controllers of the isolated subsystems are designed with cost functions reflecting the bounds of interconnections. Then, it is proven that the decentralized control strategy of the overall system can be established by adding appropriate feedback gains to the optimal control policies of the isolated subsystems. Next, an online policy iteration algorithm is presented to solve the Hamilton-Jacobi-Bellman equations related to the optimal control problem. Through constructing a set of critic neural networks, the cost functions can be obtained approximately, followed by the control policies. Furthermore, the dynamics of the estimation errors of the critic networks are verified to be uniformly and ultimately bounded. Finally, a simulation example is provided to illustrate the effectiveness of the present decentralized control scheme. PMID:24807039
Response of snow-dependent hydrologic extremes to continued global warming
Diffenbaugh, Noah; Scherer, Martin; Ashfaq, Moetasim
2012-01-01
Snow accumulation is critical for water availability in the Northern Hemisphere1,2, raising concern that global warming could have important impacts on natural and human systems in snow-dependent regions1,3. Although regional hydrologic changes have been observed (for example, refs 1,3 5), the time of emergence of extreme changes in snow accumulation and melt remains a key unknown for assessing climate- change impacts3,6,7. We find that the CMIP5 global climate model ensemble exhibits an imminent shift towards low snow years in the Northern Hemisphere, with areas of western North America, northeastern Europe and the Greater Himalaya showing the strongest emergence during the near- termdecadesandat2 Cglobalwarming.Theoccurrenceof extremely low snow years becomes widespread by the late twenty-first century, as do the occurrences of extremely high early-season snowmelt and runoff (implying increasing flood risk), and extremely low late-season snowmelt and runoff (implying increasing water stress). Our results suggest that many snow-dependent regions of the Northern Hemisphere are likely to experience increasing stress from low snow years within the next three decades, and from extreme changes in snow-dominated water resources if global warming exceeds 2 C above the pre-industrial baseline.
NASA Astrophysics Data System (ADS)
Shaltev, M.
2016-02-01
The search for continuous gravitational waves in a wide parameter space at a fixed computing cost is most efficiently done with semicoherent methods, e.g., StackSlide, due to the prohibitive computing cost of the fully coherent search strategies. Prix and Shaltev [Phys. Rev. D 85, 084010 (2012)] have developed a semianalytic method for finding optimal StackSlide parameters at a fixed computing cost under ideal data conditions, i.e., gapless data and a constant noise floor. In this work, we consider more realistic conditions by allowing for gaps in the data and changes in the noise level. We show how the sensitivity optimization can be decoupled from the data selection problem. To find optimal semicoherent search parameters, we apply a numerical optimization using as an example the semicoherent StackSlide search. We also describe three different data selection algorithms. Thus, the outcome of the numerical optimization consists of the optimal search parameters and the selected data set. We first test the numerical optimization procedure under ideal conditions and show that we can reproduce the results of the analytical method. Then we gradually relax the conditions on the data and find that a compact data selection algorithm yields higher sensitivity compared to a greedy data selection procedure.
The Global Challenge in Basic Education: Why Continued Investment in Basic Education Is Important
ERIC Educational Resources Information Center
Mertaugh, Michael T.; Jimenez, Emmanuel Y.; Patrinos, Harry A.
2009-01-01
This paper documents the importance of continued investment in basic education and argues that investments need to be carefully targeted to address the constraints that limit the coverage and quality of education if they are to provide expected benefits. Part I begins with a discussion of the returns to investment in education. Part II then…
ERIC Educational Resources Information Center
Ekanem, Ekpenyong E.; Ekpiken, William E.
2013-01-01
Continuous assessment is an important management tool for transforming university education. Although this policy employed measurable criteria to retain students' interest and objectivity, most academic staff of Nigerian universities lack basic knowledge and skills in test construction and interpretation and are thus, ineffective in continuous…
Global design optimization for an axial-flow tandem pump based on surrogate method
NASA Astrophysics Data System (ADS)
Li, D. H.; Zhao, Y.; Y Wang, G.
2013-12-01
Tandem pump, compared with multistage pump, goes without guide vanes between impellers. Better cavitation performance and significant reduction of the axial geometry scale is important for high-speed propulsion. This study presents a global design optimization method based on surrogated method for an axial-flow tandem pump to enhance trade-off performances: energy and cavitation performances. At the same time, interactions between impellers and impacts on the performances are analyzed. Fixed angle of blades in impellers and phase angle are performed as design variables. Efficiency and minimum average pressure coefficient (MAPC) on axial sectional surface in front impeller are the objective function, which can represent energy and cavitation performances well. Different surrogate models are constructed, and Global Sensitivity Analysis and Pareto Front method are used. The results show that, 1) Influence from phase angle on performances can be neglected compared with other two design variables, 2) Impact ratio of fixed angle of blades in two impellers on efficiency are the same as their designed loading distributions, which is 4:6, 3) The optimization results can enhance the trade-off performances well: efficiency is improved by 0.6%, and the MAPC is improved by 4.5%.
An optimal adaptive design to address local regulations in global clinical trials.
Luo, Xiaolong; Shih, Weichung Joe; Ouyang, S Peter; Delap, Robert J
2010-01-01
After multi-regional clinical trials (MRCTs) have demonstrated overall significant effects, evaluation for a region-specific effect is often important. Recent guidance from regulatory authorities regarding evaluation for possible country-specific effects has led to research on statistical designs that incorporate such evaluations in MRCTs. These statistical designs are intended to use the MRCTs to address the requirements for global registration of a medicinal product. Adding a regional requirement could change the probability for declaring positive effect for the region when there is indeed no treatment difference as well as when there is in fact a true difference within the region. In this paper, we first quantify those probability structures based on the guidance issued by the Ministry of Health, Labour and Welfare (MHLW) of Japan. An adaptive design is proposed to consider those probabilities and to optimize the efficiency for regional objectives. This two-stage approach incorporates comprehensive global objectives into an integrated study design and may mitigate the need for a separate local bridging study. A procedure is used to optimize region-specific enrollment based on an objective function. The overall sample size requirement is assessed. We will use simulation analyses to illustrate the performance of the proposed study design. PMID:20872620
Hierarchical Grid-based Multi-People Tracking-by-Detection With Global Optimization.
Chen, Lili; Wang, Wei; Panin, Giorgio; Knoll, Alois
2015-11-01
We present a hierarchical grid-based, globally optimal tracking-by-detection approach to track an unknown number of targets in complex and dense scenarios, particularly addressing the challenges of complex interaction and mutual occlusion. Frame-by-frame detection is performed by hierarchical likelihood grids, matching shape templates through a fast oriented distance transform. To allow recovery from misdetections, common heuristics such as nonmaxima suppression within observations is eschewed. Within a discretized state-space, the data association problem is formulated as a grid-based network flow model, resulting in a convex problem casted into an integer linear programming form, giving a global optimal solution. In addition, we show how a behavior cue (body orientation) can be integrated into our association affinity model, providing valuable hints for resolving ambiguities between crossing trajectories. Unlike traditional motion-based approaches, we estimate body orientation by a hybrid methodology, which combines the merits of motion-based and 3D appearance-based orientation estimation, thus being capable of dealing also with still-standing or slowly moving targets. The performance of our method is demonstrated through experiments on a large variety of benchmark video sequences, including both indoor and outdoor scenarios.
NASA Technical Reports Server (NTRS)
Kurits, Inna; Lewis, M. J.; Hamner, M. P.; Norris, Joseph D.
2007-01-01
Heat transfer rates are an extremely important consideration in the design of hypersonic vehicles such as atmospheric reentry vehicles. This paper describes the development of a data reduction methodology to evaluate global heat transfer rates using surface temperature-time histories measured with the temperature sensitive paint (TSP) system at AEDC Hypervelocity Wind Tunnel 9. As a part of this development effort, a scale model of the NASA Crew Exploration Vehicle (CEV) was painted with TSP and multiple sequences of high resolution images were acquired during a five run test program. Heat transfer calculation from TSP data in Tunnel 9 is challenging due to relatively long run times, high Reynolds number environment and the desire to utilize typical stainless steel wind tunnel models used for force and moment testing. An approach to reduce TSP data into convective heat flux was developed, taking into consideration the conditions listed above. Surface temperatures from high quality quantitative global temperature maps acquired with the TSP system were then used as an input into the algorithm. Preliminary comparison of the heat flux calculated using the TSP surface temperature data with the value calculated using the standard thermocouple data is reported.
Ong, M L; Ng, E Y K
2005-12-01
In the lower brain, body temperature is continually being regulated almost flawlessly despite huge fluctuations in ambient and physiological conditions that constantly threaten the well-being of the body. The underlying control problem defining thermal homeostasis is one of great enormity: Many systems and sub-systems are involved in temperature regulation and physiological processes are intrinsically complex and intertwined. Thus the defining control system has to take into account the complications of nonlinearities, system uncertainties, delayed feedback loops as well as internal and external disturbances. In this paper, we propose a self-tuning adaptive thermal controller based upon Hebbian feedback covariance learning where the system is to be regulated continually to best suit its environment. This hypothesis is supported in part by postulations of the presence of adaptive optimization behavior in biological systems of certain organisms which face limited resources vital for survival. We demonstrate the use of Hebbian feedback covariance learning as a possible self-adaptive controller in body temperature regulation. The model postulates an important role of Hebbian covariance adaptation as a means of reinforcement learning in the thermal controller. The passive system is based on a simplified 2-node core and shell representation of the body, where global responses are captured. Model predictions are consistent with observed thermoregulatory responses to conditions of exercise and rest, and heat and cold stress. An important implication of the model is that optimal physiological behaviors arising from self-tuning adaptive regulation in the thermal controller may be responsible for the departure from homeostasis in abnormal states, e.g., fever. This was previously unexplained using the conventional "set-point" control theory.
Export dynamics as an optimal growth problem in the network of global economy
Caraglio, Michele; Baldovin, Fulvio; Stella, Attilio L.
2016-01-01
We analyze export data aggregated at world global level of 219 classes of products over a period of 39 years. Our main goal is to set up a dynamical model to identify and quantify plausible mechanisms by which the evolutions of the various exports affect each other. This is pursued through a stochastic differential description, partly inspired by approaches used in population dynamics or directed polymers in random media. We outline a complex network of transfer rates which describes how resources are shifted between different product classes, and determines how casual favorable conditions for one export can spread to the other ones. A calibration procedure allows to fit four free model-parameters such that the dynamical evolution becomes consistent with the average growth, the fluctuations, and the ranking of the export values observed in real data. Growth crucially depends on the balance between maintaining and shifting resources to different exports, like in an explore-exploit problem. Remarkably, the calibrated parameters warrant a close-to-maximum growth rate under the transient conditions realized in the period covered by data, implying an optimal self organization of the global export. According to the model, major structural changes in the global economy take tens of years. PMID:27530505
Export dynamics as an optimal growth problem in the network of global economy.
Caraglio, Michele; Baldovin, Fulvio; Stella, Attilio L
2016-01-01
We analyze export data aggregated at world global level of 219 classes of products over a period of 39 years. Our main goal is to set up a dynamical model to identify and quantify plausible mechanisms by which the evolutions of the various exports affect each other. This is pursued through a stochastic differential description, partly inspired by approaches used in population dynamics or directed polymers in random media. We outline a complex network of transfer rates which describes how resources are shifted between different product classes, and determines how casual favorable conditions for one export can spread to the other ones. A calibration procedure allows to fit four free model-parameters such that the dynamical evolution becomes consistent with the average growth, the fluctuations, and the ranking of the export values observed in real data. Growth crucially depends on the balance between maintaining and shifting resources to different exports, like in an explore-exploit problem. Remarkably, the calibrated parameters warrant a close-to-maximum growth rate under the transient conditions realized in the period covered by data, implying an optimal self organization of the global export. According to the model, major structural changes in the global economy take tens of years.
Export dynamics as an optimal growth problem in the network of global economy.
Caraglio, Michele; Baldovin, Fulvio; Stella, Attilio L
2016-01-01
We analyze export data aggregated at world global level of 219 classes of products over a period of 39 years. Our main goal is to set up a dynamical model to identify and quantify plausible mechanisms by which the evolutions of the various exports affect each other. This is pursued through a stochastic differential description, partly inspired by approaches used in population dynamics or directed polymers in random media. We outline a complex network of transfer rates which describes how resources are shifted between different product classes, and determines how casual favorable conditions for one export can spread to the other ones. A calibration procedure allows to fit four free model-parameters such that the dynamical evolution becomes consistent with the average growth, the fluctuations, and the ranking of the export values observed in real data. Growth crucially depends on the balance between maintaining and shifting resources to different exports, like in an explore-exploit problem. Remarkably, the calibrated parameters warrant a close-to-maximum growth rate under the transient conditions realized in the period covered by data, implying an optimal self organization of the global export. According to the model, major structural changes in the global economy take tens of years. PMID:27530505
Export dynamics as an optimal growth problem in the network of global economy
NASA Astrophysics Data System (ADS)
Caraglio, Michele; Baldovin, Fulvio; Stella, Attilio L.
2016-08-01
We analyze export data aggregated at world global level of 219 classes of products over a period of 39 years. Our main goal is to set up a dynamical model to identify and quantify plausible mechanisms by which the evolutions of the various exports affect each other. This is pursued through a stochastic differential description, partly inspired by approaches used in population dynamics or directed polymers in random media. We outline a complex network of transfer rates which describes how resources are shifted between different product classes, and determines how casual favorable conditions for one export can spread to the other ones. A calibration procedure allows to fit four free model-parameters such that the dynamical evolution becomes consistent with the average growth, the fluctuations, and the ranking of the export values observed in real data. Growth crucially depends on the balance between maintaining and shifting resources to different exports, like in an explore-exploit problem. Remarkably, the calibrated parameters warrant a close-to-maximum growth rate under the transient conditions realized in the period covered by data, implying an optimal self organization of the global export. According to the model, major structural changes in the global economy take tens of years.
Song, Zhaoliang; Parr, Jeffrey F.; Guo, Fengshan
2013-01-01
The occlusion of carbon (C) by phytoliths, the recalcitrant silicified structures deposited within plant tissues, is an important persistent C sink mechanism for croplands and other grass-dominated ecosystems. By constructing a silica content-phytolith content transfer function and calculating the magnitude of phytolith C sink in global croplands with relevant crop production data, this study investigated the present and potential of phytolith C sinks in global croplands and its contribution to the cropland C balance to understand the cropland C cycle and enhance long-term C sequestration in croplands. Our results indicate that the phytolith sink annually sequesters 26.35±10.22 Tg of carbon dioxide (CO2) and may contribute 40±18% of the global net cropland soil C sink for 1961–2100. Rice (25%), wheat (19%) and maize (23%) are the dominant contributing crop species to this phytolith C sink. Continentally, the main contributors are Asia (49%), North America (17%) and Europe (16%). The sink has tripled since 1961, mainly due to fertilizer application and irrigation. Cropland phytolith C sinks may be further enhanced by adopting cropland management practices such as optimization of cropping system and fertilization. PMID:24066067
NASA Astrophysics Data System (ADS)
Hew, Y. M.; Linscott, I.; Close, S.
2015-12-01
Meteoroids and orbital debris, collectively referred to as hypervelocity impactors, travel between 7 and 72 km/s in free space. Upon their impact onto the spacecraft, the energy conversion from kinetic to ionization/vaporization occurs within a very brief timescale and results in a small and dense expanding plasma with a very strong optical flash. The radio frequency (RF) emission produced by this plasma can potentially lead to electrical anomalies within the spacecraft. In addition, space weather, such as solar activity and background plasma, can establish spacecraft conditions which can exaggerate the damages done by these impacts. During the impact, a very strong impact flash will be generated. Through the studying of this emission spectrum of the impact, we hope to study the impact generated gas cloud/plasma properties. The impact flash emitted from a ground-based hypervelocity impact test is long expected by many scientists to contain the characteristics of the impact generated plasma, such as plasma temperature and density. This paper presents a method for the time-resolved plasma temperature estimation using three-color visible band photometry data with a global pattern search optimization method. The equilibrium temperature of the plasma can be estimated using an optical model which accounts for both the line emission and continuum emission from the plasma. Using a global pattern search based optimizer, the model can isolate the contribution of the continuum emission versus the line emission from the plasma. The plasma temperature can thus be estimated. Prior to the optimization step, a Gaussian process is also applied to extract the optical emission signal out of the noisy background. The resultant temperature and line-to-continuum emission weighting factor are consistent with the spectrum of the impactor material and current literature.
NASA Astrophysics Data System (ADS)
Huang, Zhipeng; Gao, Lihong; Wang, Yangwei; Wang, Fuchi
2016-06-01
The Johnson-Cook (J-C) constitutive model is widely used in the finite element simulation, as this model shows the relationship between stress and strain in a simple way. In this paper, a cluster global optimization algorithm is proposed to determine the J-C constitutive model parameters of materials. A set of assumed parameters is used for the accuracy verification of the procedure. The parameters of two materials (401 steel and 823 steel) are determined. Results show that the procedure is reliable and effective. The relative error between the optimized and assumed parameters is no more than 4.02%, and the relative error between the optimized and assumed stress is 0.2% × 10-5. The J-C constitutive parameters can be determined more precisely and quickly than the traditional manual procedure. Furthermore, all the parameters can be simultaneously determined using several curves under different experimental conditions. A strategy is also proposed to accurately determine the constitutive parameters.
Stellar structure modeling using a parallel genetic algorithm for objective global optimization
NASA Astrophysics Data System (ADS)
Metcalfe, Travis S.; Charbonneau, Paul
2003-02-01
Genetic algorithms are a class of heuristic search techniques that apply basic evolutionary operators in a computational setting. We have designed a fully parallel and distributed hardware/software implementation of the generalized optimization subroutine PIKAIA, which utilizes a genetic algorithm to provide an objective determination of the globally optimal parameters for a given model against an observational data set. We have used this modeling tool in the context of white dwarf asteroseismology, i.e., the art and science of extracting physical and structural information about these stars from observations of their oscillation frequencies. The efficient, parallel exploration of parameter-space made possible by genetic-algorithm-based numerical optimization led us to a number of interesting physical results: (1) resolution of a hitherto puzzling discrepancy between stellar evolution models and prior asteroseismic inferences of the surface helium layer mass for a DBV white dwarf; (2) precise determination of the central oxygen mass fraction in a white dwarf star; and (3) a preliminary estimate of the astrophysically important but experimentally uncertain rate for the 12C(α,γ)16O nuclear reaction. These successes suggest that a broad class of computationally intensive modeling applications could also benefit from this approach.
NASA Astrophysics Data System (ADS)
Huang, Zhipeng; Gao, Lihong; Wang, Yangwei; Wang, Fuchi
2016-09-01
The Johnson-Cook (J-C) constitutive model is widely used in the finite element simulation, as this model shows the relationship between stress and strain in a simple way. In this paper, a cluster global optimization algorithm is proposed to determine the J-C constitutive model parameters of materials. A set of assumed parameters is used for the accuracy verification of the procedure. The parameters of two materials (401 steel and 823 steel) are determined. Results show that the procedure is reliable and effective. The relative error between the optimized and assumed parameters is no more than 4.02%, and the relative error between the optimized and assumed stress is 0.2% × 10-5. The J-C constitutive parameters can be determined more precisely and quickly than the traditional manual procedure. Furthermore, all the parameters can be simultaneously determined using several curves under different experimental conditions. A strategy is also proposed to accurately determine the constitutive parameters.
Searchlight Correlation Detectors: Optimal Seismic Monitoring Using Regional and Global Networks
NASA Astrophysics Data System (ADS)
Gibbons, Steven J.; Kværna, Tormod; Näsholm, Sven Peter
2015-04-01
The sensitivity of correlation detectors increases greatly when the outputs from multiple seismic traces are considered. For single-array monitoring, a zero-offset stack of individual correlation traces will provide significant noise suppression and enhanced sensitivity for a source region surrounding the hypocenter of the master event. The extent of this region is limited only by the decrease in waveform similarity with increasing hypocenter separation. When a regional or global network of arrays and/or 3-component stations is employed, the zero-offset approach is only optimal when the master and detected events are co-located exactly. In many monitoring situations, including nuclear test sites and geothermal fields, events may be separated by up to many hundreds of meters while still retaining sufficient waveform similarity for correlation detection on single channels. However, the traveltime differences resulting from the hypocenter separation may result in significant beam loss on the zero-offset stack and a deployment of many beams for different hypothetical source locations in geographical space is required. The beam deployment necessary for optimal performance of the correlation detectors is determined by an empirical network response function which is most easily evaluated using the auto-correlation functions of the waveform templates from the master event. The correlation detector beam deployments for providing optimal network sensitivity for the North Korea nuclear test site are demonstrated for both regional and teleseismic monitoring configurations.
Selection of Thermal Worst-Case Orbits via Modified Efficient Global Optimization
NASA Technical Reports Server (NTRS)
Moeller, Timothy M.; Wilhite, Alan W.; Liles, Kaitlin A.
2014-01-01
Efficient Global Optimization (EGO) was used to select orbits with worst-case hot and cold thermal environments for the Stratospheric Aerosol and Gas Experiment (SAGE) III. The SAGE III system thermal model changed substantially since the previous selection of worst-case orbits (which did not use the EGO method), so the selections were revised to ensure the worst cases are being captured. The EGO method consists of first conducting an initial set of parametric runs, generated with a space-filling Design of Experiments (DoE) method, then fitting a surrogate model to the data and searching for points of maximum Expected Improvement (EI) to conduct additional runs. The general EGO method was modified by using a multi-start optimizer to identify multiple new test points at each iteration. This modification facilitates parallel computing and decreases the burden of user interaction when the optimizer code is not integrated with the model. Thermal worst-case orbits for SAGE III were successfully identified and shown by direct comparison to be more severe than those identified in the previous selection. The EGO method is a useful tool for this application and can result in computational savings if the initial Design of Experiments (DoE) is selected appropriately.
Comparison of batch and continuous multi-column protein A capture processes by optimal design.
Baur, Daniel; Angarita, Monica; Müller-Späth, Thomas; Steinebach, Fabian; Morbidelli, Massimo
2016-07-01
Multi-column capture processes show several advantages compared to batch capture. It is however not evident how many columns one should use exactly. To investigate this issue, twin-column CaptureSMB, 3- and 4-column periodic counter-current chromatography (PCC) and single column batch capture are numerically optimized and compared in terms of process performance for capturing a monoclonal antibody using protein A chromatography. Optimization is carried out with respect to productivity and capacity utilization (amount of product loaded per cycle compared to the maximum amount possible), while keeping yield and purity constant. For a wide range of process parameters, all three multi-column processes show similar maximum capacity utilization and performed significantly better than batch. When maximizing productivity, the CaptureSMB process shows optimal performance, except at high feed titers, where batch chromatography can reach higher productivity values than the multi-column processes due to the complete decoupling of the loading and elution steps, albeit at a large cost in terms of capacity utilization. In terms of trade-off, i.e. how much the capacity utilization decreases with increasing productivity, CaptureSMB is optimal for low and high feed titers, whereas the 3-column process is optimal in an intermediate region. Using these findings, the most suitable process can be chosen for different production scenarios.
Comparison of batch and continuous multi-column protein A capture processes by optimal design.
Baur, Daniel; Angarita, Monica; Müller-Späth, Thomas; Steinebach, Fabian; Morbidelli, Massimo
2016-07-01
Multi-column capture processes show several advantages compared to batch capture. It is however not evident how many columns one should use exactly. To investigate this issue, twin-column CaptureSMB, 3- and 4-column periodic counter-current chromatography (PCC) and single column batch capture are numerically optimized and compared in terms of process performance for capturing a monoclonal antibody using protein A chromatography. Optimization is carried out with respect to productivity and capacity utilization (amount of product loaded per cycle compared to the maximum amount possible), while keeping yield and purity constant. For a wide range of process parameters, all three multi-column processes show similar maximum capacity utilization and performed significantly better than batch. When maximizing productivity, the CaptureSMB process shows optimal performance, except at high feed titers, where batch chromatography can reach higher productivity values than the multi-column processes due to the complete decoupling of the loading and elution steps, albeit at a large cost in terms of capacity utilization. In terms of trade-off, i.e. how much the capacity utilization decreases with increasing productivity, CaptureSMB is optimal for low and high feed titers, whereas the 3-column process is optimal in an intermediate region. Using these findings, the most suitable process can be chosen for different production scenarios. PMID:26992151
NASA Astrophysics Data System (ADS)
Walsh, Jonathan A.; Romano, Paul K.; Forget, Benoit; Smith, Kord S.
2015-11-01
In this work we propose, implement, and test various optimizations of the typical energy grid-cross section pair lookup algorithm in Monte Carlo particle transport codes. The key feature common to all of the optimizations is a reduction in the length of the vector of energies that must be searched when locating the index of a particle's current energy. Other factors held constant, a reduction in energy vector length yields a reduction in CPU time. The computational methods we present here are physics-informed. That is, they are designed to utilize the physical information embedded in a simulation in order to reduce the length of the vector to be searched. More specifically, the optimizations take advantage of information about scattering kinematics, neutron cross section structure and data representation, and also the expected characteristics of a system's spatial flux distribution and energy spectrum. The methods that we present are implemented in the OpenMC Monte Carlo neutron transport code as part of this work. The gains in computational efficiency, as measured by overall code speedup, associated with each of the optimizations are demonstrated in both serial and multithreaded simulations of realistic systems. Depending on the system, simulation parameters, and optimization method employed, overall code speedup factors of 1.2-1.5, relative to the typical single-nuclide binary search algorithm, are routinely observed.
Prusa, Joseph
2012-05-08
This project had goals of advancing the performance capabilities of the numerical general circulation model EULAG and using it to produce a fully operational atmospheric global climate model (AGCM) that can employ either static or dynamic grid stretching for targeted phenomena. The resulting AGCM combined EULAG's advanced dynamics core with the physics of the NCAR Community Atmospheric Model (CAM). Effort discussed below shows how we improved model performance and tested both EULAG and the coupled CAM-EULAG in several ways to demonstrate the grid stretching and ability to simulate very well a wide range of scales, that is, multi-scale capability. We leveraged our effort through interaction with an international EULAG community that has collectively developed new features and applications of EULAG, which we exploited for our own work summarized here. Overall, the work contributed to over 40 peer- reviewed publications and over 70 conference/workshop/seminar presentations, many of them invited.
NASA Astrophysics Data System (ADS)
Hafner, K.; Davis, J. P.; Wilson, D.; Woodward, R.
2015-12-01
The Global Seismographic Network (GSN) is a 151 station, globally distributed permanent network of state-of-the-art seismological and geophysical sensors that is a result of an ongoing successful partnership between IRIS, the USGS, the University of California at San Diego, NSF and numerous host institutions worldwide. In recent years, the GSN has standardized their dataloggers to the Quanterra Q330HR data acquisition system at all but three stations. Current equipment modernization efforts are focused on the development of a new very broadband borehole sensor to replace failing KS-54000 instruments and replacing the aging Streckeisen STS-1 surface instruments at many GSN stations. Aging of GSN equipment and discoveries of quality problems with GSN data (e.g., the long period response of the STS-1 sensors) have resulted in the GSN placing major emphasis on quantifying, validating and maintaining data quality. This has resulted in the implementation of MUSTANG and DQA systems for analyzing GSN data quality and enables both network operators and data end users to quickly characterize the performance of stations and networks. We will present summary data quality metrics for the GSN as obtained via these quality assurance tools. Data from the GSN are used not only for research, but on a daily basis are part of the operational missions of the USGS NEIC, NOAA tsunami warning centers, the Comprehensive Nuclear-Test-Ban-Treaty Organization as well as other organizations. The primary challenges for the GSN include maintaining these operational capabilities while simultaneously developing and replacing the primary borehole sensors, replacing as needed the primary vault sensors, maintaining high quality data and repairing station infrastructure, all during a period of very tight federal budgets. We will provide an overview of the operational status of the GSN, with a particular emphasis on the status of the primary borehole and vault sensors.
Multiple actor-critic structures for continuous-time optimal control using input-output data.
Song, Ruizhuo; Lewis, Frank; Wei, Qinglai; Zhang, Hua-Guang; Jiang, Zhong-Ping; Levine, Dan
2015-04-01
In industrial process control, there may be multiple performance objectives, depending on salient features of the input-output data. Aiming at this situation, this paper proposes multiple actor-critic structures to obtain the optimal control via input-output data for unknown nonlinear systems. The shunting inhibitory artificial neural network (SIANN) is used to classify the input-output data into one of several categories. Different performance measure functions may be defined for disparate categories. The approximate dynamic programming algorithm, which contains model module, critic network, and action network, is used to establish the optimal control in each category. A recurrent neural network (RNN) model is used to reconstruct the unknown system dynamics using input-output data. NNs are used to approximate the critic and action networks, respectively. It is proven that the model error and the closed unknown system are uniformly ultimately bounded. Simulation results demonstrate the performance of the proposed optimal control scheme for the unknown nonlinear system.
NASA Astrophysics Data System (ADS)
Kavvadias, I. S.; Papoutsis-Kiachagias, E. M.; Dimitrakopoulos, G.; Giannakoglou, K. C.
2015-11-01
In this article, the gradient of aerodynamic objective functions with respect to design variables, in problems governed by the incompressible Navier-Stokes equations coupled with the k-ω SST turbulence model, is computed using the continuous adjoint method, for the first time. Shape optimization problems for minimizing drag, in external aerodynamics (flows around isolated airfoils), or viscous losses in internal aerodynamics (duct flows) are considered. Sensitivity derivatives computed with the proposed adjoint method are compared to those computed with finite differences or a continuous adjoint variant based on the frequently used assumption of frozen turbulence; the latter proves the need for differentiating the turbulence model. Geometries produced by optimization runs performed with sensitivities computed by the proposed method and the 'frozen turbulence' assumption are also compared to quantify the gain from formulating and solving the adjoint to the turbulence model equations.
Piro, M. H. A.; Simunovic, S.
2016-03-17
Several global optimization methods are reviewed that attempt to ensure that the integral Gibbs energy of a closed isothermal isobaric system is a global minimum to satisfy the necessary and sufficient conditions for thermodynamic equilibrium. In particular, the integral Gibbs energy function of a multicomponent system containing non-ideal phases may be highly non-linear and non-convex, which makes finding a global minimum a challenge. Consequently, a poor numerical approach may lead one to the false belief of equilibrium. Furthermore, confirming that one reaches a global minimum and that this is achieved with satisfactory computational performance becomes increasingly more challenging in systemsmore » containing many chemical elements and a correspondingly large number of species and phases. Several numerical methods that have been used for this specific purpose are reviewed with a benchmark study of three of the more promising methods using five case studies of varying complexity. A modification of the conventional Branch and Bound method is presented that is well suited to a wide array of thermodynamic applications, including complex phases with many constituents and sublattices, and ionic phases that must adhere to charge neutrality constraints. Also, a novel method is presented that efficiently solves the system of linear equations that exploits the unique structure of the Hessian matrix, which reduces the calculation from a O(N3) operation to a O(N) operation. As a result, this combined approach demonstrates efficiency, reliability and capabilities that are favorable for integration of thermodynamic computations into multi-physics codes with inherent performance considerations.« less
How best to optimize a global process-based carbon land surface model ?
NASA Astrophysics Data System (ADS)
Peylin, Philippe; Bacour, Cedric; MacBean, Natasha; Leonard, Sebastien; Maignan, Fabienne; Thum, Tea; Chevallier, Frederic; Ciais, Philippe; Cadule, Patricia; Santaren, Diego
2014-05-01
Global process-based land surface models are used to predict the response of the Earth's ecosystems to environmental changes. However, the estimated water and carbon fluxes remain subject to large uncertainties, partly because of unknown or poorly calibrated parameters. Assimilation of in situ data, remote sensing products, and/or atmospheric trace gas concentrations, into these models is a promising approach to optimize key parameters, providing that all major processes are well represented. So far, most of the studies have focused on using one single data stream, either remotely sensed estimates of the vegetation activity (fAPAR or NDVI) to constrain the modeled plant phenology, in situ measurements of net CO2 and latent heat fluxes (NEE, LE at FluxNet sites) or atmospheric CO2 concentrations (through the use of a transport model) to provide constraint on the net carbon fluxes at hourly to inter-annual time-scales. However, the combination of these data streams is expected to provide a much larger constraint on ecosystem carbon, water and energy dynamics. At LSCE we have constructed a global Carbon Cycle Multi-Data Assimilation System (CCDAS) to assimilate i) MODIS-NDVI observations at around 15 points for each plant functional type (PFT) in the model, ii) in situ NEE and LE fluxes at around 70 FluxNet sites and iii) atmospheric CO2 measurements at more than 80 sites. We used different methods of data assimilation (including a 4D-Var approach), depending on the number and type of data streams that are considered in order to optimize the main parameters of the global vegetation model ORCHIDEE (around 15 parameters per PFT). Using such a CCDAS, we investigated several methodological to scientific questions: How does a variational scheme perform compared to a "Monte Carlo" approach (the genetic algorithm) to minimize an objective function (using FluxNet data)? What is the additional information brought by the measurements of above ground biomass data on the top of
Aerodynamic Design Optimization on Unstructured Grids with a Continuous Adjoint Formulation
NASA Technical Reports Server (NTRS)
Anderson, W. Kyle; Venkatakrishnan, V.
1997-01-01
A continuous adjoint approach for obtaining sensitivity derivatives on unstructured grids is developed and analyzed. The derivation of the costate equations is presented, and a second-order accurate discretization method is described. The relationship between the continuous formulation and a discrete formulation is explored for inviscid, as well as for viscous flow. Several limitations in a strict adherence to the continuous approach are uncovered, and an approach that circumvents these difficulties is presented. The issue of grid sensitivities, which do not arise naturally in the continuous formulation, is investigated and is observed to be of importance when dealing with geometric singularities. A method is described for modifying inviscid and viscous meshes during the design cycle to accommodate changes in the surface shape. The accuracy of the sensitivity derivatives is established by comparing with finite-difference gradients and several design examples are presented.
Pantong, Natee; Su, Jianzhong; Shan, Hua; Klibanov, Michael V; Liu, Hanli
2009-03-01
A new numerical imaging algorithm is presented for reconstruction of optical absorption coefficients from near-infrared light data with a continuous-wave source. As a continuation of our earlier efforts in developing a series of methods called "globally convergent reconstruction methods" [J. Opt. Soc. Am. A23, 2388 (2006)], this numerical algorithm solves the inverse problem through solution of a boundary-value problem for a Volterra-type integral partial differential equation. We deal here with the particular issues in solving the inverse problems in an arbitrary convex shape domain. It is demonstrated in numerical studies that this reconstruction technique is highly efficient and stable with respect to the complex distribution of actual unknown absorption coefficients. The method is particularly useful for reconstruction from a large data set obtained from a tissue or organ of particular shape, such as the prostate. Numerical reconstructions of a simulated prostate-shaped phantom with three different settings of absorption-inclusions are presented.
Dill, K.A.; Phillips, A.T.; Rosen, J.B.
1997-12-01
Proteins require specific three-dimensional conformations to function properly. These {open_quotes}native{close_quotes} conformations result primarily from intramolecular interactions between the atoms in the macromolecule, and also intermolecular interactions between the macromolecule and the surrounding solvent. Although the folding process can be quite complex, the instructions guiding this process are specified by the one-dimensional primary sequence of the protein or nucleic acid: external factors, such as helper (chaperone) proteins, present at the time of folding have no effect on the final state of the protein. Many denatured proteins spontaneously refold into functional conformations once denaturing conditions are removed. Indeed, the existence of a unique native conformation, in which residues distant in sequence but close in proximity exhibit a densely packed hydrophobic core, suggests that this three-dimensional structure is largely encoded within the sequential arrangement of these specific amino acids. In any case, the native structure is often the conformation at the global minimum energy. In addition to the unique native (minimum energy) structure, other less stable structures exist as well, each with a corresponding potential energy. These structures, in conjunction with the native structure, make up an energy landscape that can be used to characterize various aspects of the protein structure. 22 refs., 10 figs., 2 tabs.
Spotlight on Global Malnutrition: A Continuing Challenge in the 21st Century.
Steiber, Alison; Hegazi, Refaat; Herrera, Marianella; Zamor, Marie Landy; Chimanya, Kudakwashe; Pekcan, Ayla Gülden; Redondo-Samin, Divina Cristy D; Correia, Maria Isabel T D; Ojwang, Alice A
2015-08-01
Malnutrition as undernutrition, overnutrition, or an imbalance of specific nutrients, can be found in all countries and in both community and hospital settings around the world. The prevalence of malnutrition is unacceptably high in all settings and affects children, adolescents, pregnant women, and sick and older adults. Malnutrition has multiple underlying issues (food insecurity, chronic and acute illnesses, sanitation and safety, and aging in the community), which need to be addressed. At the same time, direct nutrition interventions (food supplements and micronutrient supplementation) help support immediate resolution of malnutrition. Awareness of malnutrition issues in the community and in clinical setting must be stimulated in order to provide better care. Different countries have implemented a wide range of interventions to prevent and treat malnutrition. These include nutrition education, engagement of the community, resolution of sanitation problems affecting food and water, routine screening and assessment and diagnosis of malnutrition (when feasible), and food supplements and micronutrients. Such programs are achieving improved outcomes; however, further engagement and training is needed for more community and clinical health workers. Many countries lack qualified nutrition and dietetics practitioners or have low dietitian-to-patient ratios with suboptimal salaries. Thus, an increase in number of and empowerment of nutrition and dietetics practitioners is desperately needed to help prevent and treat malnutrition globally.
NASA Astrophysics Data System (ADS)
Schlutz, Juergen; Hufenbach, Bernhard; Laurini, Kathy; Spiero, Francois
2016-07-01
Future space exploration goals call for sending humans and robots beyond low Earth orbit and establishing sustained access to destinations such as the Moon, asteroids and Mars. Space agencies participating in the International Space Exploration Coordination Group (ISECG) are discussing an international approach for achieving these goals, documented in ISECG's Global Exploration Roadmap (GER). The GER reference scenario reflects a step-wise evolution of critical capabilities from ISS to missions in the lunar vicinity in preparation for the journey of humans to Mars. As ISECG agencies advance their individual planning, they also advance the mission themes and reference architecture of the GER to consolidate common goals, near-term mission scenarios and initial opportunities for collaboration. In this context, particular focus has been given to the Better understanding and further refinement of cislunar infrastructure and potential lunar transportation architecture Interaction with international science communities to identify and articulate the scientific opportunities of the near-term exploration mission themes Coordination and consolidation of interest in lunar polar volatiles prospecting and potential for in-situ resource utilisation Identification and articulation of the benefits from exploration and the technology transfer activities The paper discusses the ongoing roadmapping activity of the ISECG agencies. It provides an insight into the status of the above activities and an outlook towards the evolution of the GER that is currently foreseen in the 2017 timeframe.
NASA Astrophysics Data System (ADS)
Debiane, L.; Ivorra, B.; Mohammadi, B.; Nicoud, F.; Poinsot, T.; Ern, A.; Pitsch, H.
2006-02-01
Controlling flame shapes and emissions is a major objective for all combustion engineers. Considering the complexity of reacting flows, novel optimization methods are required: this paper explores the application of control theory for partial differential equations to combustion. Both flame temperature and pollutant levels are optimized in a laminar Bunsen burner computed with complex chemistry using a recursive semi-deterministic global optimization algorithm. In order to keep the computational time low, the optimization procedure is coupled with mesh adaptation and incomplete gradient techniques.
Optimizing Orbit-Instrument Configuration for Global Precipitation Mission (GPM) Satellite Fleet
NASA Technical Reports Server (NTRS)
Smith, Eric A.; Adams, James; Baptista, Pedro; Haddad, Ziad; Iguchi, Toshio; Im, Eastwood; Kummerow, Christian; Einaudi, Franco (Technical Monitor)
2001-01-01
Following the scientific success of the Tropical Rainfall Measuring Mission (TRMM) spearheaded by a group of NASA and NASDA scientists, their external scientific collaborators, and additional investigators within the European Union's TRMM Research Program (EUROTRMM), there has been substantial progress towards the development of a new internationally organized, global scale, and satellite-based precipitation measuring mission. The highlights of this newly developing mission are a greatly expanded scope of measuring capability and a more diversified set of science objectives. The mission is called the Global Precipitation Mission (GPM). Notionally, GPM will be a constellation-type mission involving a fleet of nine satellites. In this fleet, one member is referred to as the "core" spacecraft flown in an approximately 70 degree inclined non-sun-synchronous orbit, somewhat similar to TRMM in that it carries both a multi-channel polarized passive microwave radiometer (PMW) and a radar system, but in this case it will be a dual frequency Ku-Ka band radar system enabling explicit measurements of microphysical DSD properties. The remainder of fleet members are eight orbit-synchronized, sun-synchronous "constellation" spacecraft each carrying some type of multi-channel PMW radiometer, enabling no worse than 3-hour diurnal sampling over the entire globe. In this configuration the "core" spacecraft serves as a high quality reference platform for training and calibrating the PMW rain retrieval algorithms used with the "constellation" radiometers. Within NASA, GPM has advanced to the pre-formulation phase which has enabled the initiation of a set of science and technology studies which will help lead to the final mission design some time in the 2003 period. This presentation first provides an overview of the notional GPM program and mission design, including its organizational and programmatic concepts, scientific agenda, expected instrument package, and basic flight
Automated reconstruction of dendritic and axonal trees by global optimization with geometric priors.
Türetken, Engin; González, Germán; Blum, Christian; Fua, Pascal
2011-09-01
We present a novel probabilistic approach to fully automated delineation of tree structures in noisy 2D images and 3D image stacks. Unlike earlier methods that rely mostly on local evidence, ours builds a set of candidate trees over many different subsets of points likely to belong to the optimal tree and then chooses the best one according to a global objective function that combines image evidence with geometric priors. Since the best tree does not necessarily span all the points, the algorithm is able to eliminate false detections while retaining the correct tree topology. Manually annotated brightfield micrographs, retinal scans and the DIADEM challenge datasets are used to evaluate the performance of our method. We used the DIADEM metric to quantitatively evaluate the topological accuracy of the reconstructions and showed that the use of the geometric regularization yields a substantial improvement. PMID:21573886
NASA Technical Reports Server (NTRS)
Crassidis, John L.; Lightsey, E. Glenn; Markley, F. Landis
1998-01-01
In this paper, a new and efficient algorithm is developed for attitude determination from Global Positioning System signals. The new algorithm is derived from a generalized nonlinear predictive filter for nonlinear systems. This uses a one time-step ahead approach to propagate a simple kinematics model for attitude determination. The advantages of the new algorithm over previously developed methods include: it provides optimal attitudes even for coplanar baseline configurations; it guarantees convergence even for poor initial conditions; it is a non-iterative algorithm; and it is computationally efficient. These advantages clearly make the new algorithm well suited to on-board applications. The performance of the new algorithm is tested on a dynamic hardware simulator. Results indicate that the new algorithm accurately estimates the attitude of a moving vehicle, and provides robust attitude estimates even when other methods, such as a linearized least-squares approach, fail due to poor initial starting conditions.
Globally optimal rotation alignment of spherical surfaces with associated scalar values
NASA Astrophysics Data System (ADS)
Pan, Rongjiang; Skala, Vaclav; Müller, Rolf
2013-09-01
We propose a new approach to global optimization algorithm based on controlled random search techniques for rotational alignment of spherical surfaces with associated scalar values. To reduce the distortion in correspondence and increase efficiency, the spherical surface is first re-sampled using a geodesic sphere. The rotation in space is represented using the modified Rodrigues parameters. Correspondence between two spherical surfaces is implemented in the parametric domain. We applied the methods to the alignment of beam patterns computed from the outer ear shapes of bats. The proposed method is compared with other approaches such as principal component analysis (PCA), exhaustive search in the discrete space of rotations defined by Euler angles and direct search using uniform samples over the special orthogonal group of rotations in 3D space. Experimental results demonstrate that the rotation alignment obtained using the proposed algorithm has a high degree of precision and gives the best results among the four approaches. [Figure not available: see fulltext.
NASA Astrophysics Data System (ADS)
Mitroi, V.; de Coninck, A.; Vinçon-Leite, B.; Deroubaix, J.-F.
2014-09-01
The (re)construction of the ecological continuity is stated as one of the main objectives of the European Water Framework Directive for watershed management in Europe. Analysing the social, political, technical and scientific processes characterising the implementation of different projects of ecological continuity in two adjacent peri-urban territories in Ile-de-France, we observed science-driven approaches disregarding the social contexts. We show that, in urbanized areas, ecological continuity requires not only important technical and ecological expertise, but also social and political participation to the definition of a common vision and action plan. Being a challenge for both, technical water management institutions and "classical" ecological policies, we propose some social science contributions to deal with ecological unpredictability and reconsider stakeholder resistance to this kind of project.
Liu, Derong; Wang, Ding; Wang, Fei-Yue; Li, Hongliang; Yang, Xiong
2014-12-01
In this paper, the infinite horizon optimal robust guaranteed cost control of continuous-time uncertain nonlinear systems is investigated using neural-network-based online solution of Hamilton-Jacobi-Bellman (HJB) equation. By establishing an appropriate bounded function and defining a modified cost function, the optimal robust guaranteed cost control problem is transformed into an optimal control problem. It can be observed that the optimal cost function of the nominal system is nothing but the optimal guaranteed cost of the original uncertain system. A critic neural network is constructed to facilitate the solution of the modified HJB equation corresponding to the nominal system. More importantly, an additional stabilizing term is introduced for helping to verify the stability, which reinforces the updating process of the weight vector and reduces the requirement of an initial stabilizing control. The uniform ultimate boundedness of the closed-loop system is analyzed by using the Lyapunov approach as well. Two simulation examples are provided to verify the effectiveness of the present control approach. PMID:25415951
Pozo, Carlos; Guillén-Gosálbez, Gonzalo; Sorribas, Albert; Jiménez, Laureano
2012-01-01
Optimization models in metabolic engineering and systems biology focus typically on optimizing a unique criterion, usually the synthesis rate of a metabolite of interest or the rate of growth. Connectivity and non-linear regulatory effects, however, make it necessary to consider multiple objectives in order to identify useful strategies that balance out different metabolic issues. This is a fundamental aspect, as optimization of maximum yield in a given condition may involve unrealistic values in other key processes. Due to the difficulties associated with detailed non-linear models, analysis using stoichiometric descriptions and linear optimization methods have become rather popular in systems biology. However, despite being useful, these approaches fail in capturing the intrinsic nonlinear nature of the underlying metabolic systems and the regulatory signals involved. Targeting more complex biological systems requires the application of global optimization methods to non-linear representations. In this work we address the multi-objective global optimization of metabolic networks that are described by a special class of models based on the power-law formalism: the generalized mass action (GMA) representation. Our goal is to develop global optimization methods capable of efficiently dealing with several biological criteria simultaneously. In order to overcome the numerical difficulties of dealing with multiple criteria in the optimization, we propose a heuristic approach based on the epsilon constraint method that reduces the computational burden of generating a set of Pareto optimal alternatives, each achieving a unique combination of objectives values. To facilitate the post-optimal analysis of these solutions and narrow down their number prior to being tested in the laboratory, we explore the use of Pareto filters that identify the preferred subset of enzymatic profiles. We demonstrate the usefulness of our approach by means of a case study that optimizes the
Bell-Curve Genetic Algorithm for Mixed Continuous and Discrete Optimization Problems
NASA Technical Reports Server (NTRS)
Kincaid, Rex K.; Griffith, Michelle; Sykes, Ruth; Sobieszczanski-Sobieski, Jaroslaw
2002-01-01
In this manuscript we have examined an extension of BCB that encompasses a mix of continuous and quasi-discrete, as well as truly-discrete applications. FVe began by testing two refinements to the discrete version of BCB. The testing of midpoint versus fitness (Tables 1 and 2) proved inconclusive. The testing of discrete normal tails versus standard mutation showed was conclusive and demonstrated that the discrete normal tails are better. Next, we implemented these refinements in a combined continuous and discrete BCB and compared the performance of two discrete distance on the hub problem. Here we found when "order does matter" it pays to take it into account.
NASA Astrophysics Data System (ADS)
Mulder, W. A.; Shamasundar, R.
2016-07-01
We consider isotropic elastic wave propagation with continuous mass-lumped finite elements on tetrahedra with explicit time stepping. These elements require higher-order polynomials in their interior to preserve accuracy after mass lumping and are only known up to degree 3. Global assembly of the symmetric stiffness matrix is a natural approach but requires large memory. Local assembly on the fly, in the form of matrix-vector products per element at each time step, has a much smaller memory footprint. With dedicated expressions for local assembly, our code ran about 1.3 times faster for degree 2 and 1.9 times for degree 3 on a simple homogeneous test problem, using 24 cores. This is similar to the acoustic case. For a more realistic problem, the gain in efficiency was a factor 2.5 for degree 2 and 3 for degree 3. For the lowest degree, the linear element, the expressions for both the global and local assembly can be further simplified. In that case, global assembly is more efficient than local assembly. Among the three degrees, the element of degree 3 is the most efficient in terms of accuracy at a given cost.
NASA Astrophysics Data System (ADS)
Mulder, W. A.; Shamasundar, R.
2016-10-01
We consider isotropic elastic wave propagation with continuous mass-lumped finite elements on tetrahedra with explicit time stepping. These elements require higher-order polynomials in their interior to preserve accuracy after mass lumping and are only known up to degree 3. Global assembly of the symmetric stiffness matrix is a natural approach but requires large memory. Local assembly on the fly, in the form of matrix-vector products per element at each time step, has a much smaller memory footprint. With dedicated expressions for local assembly, our code ran about 1.3 times faster for degree 2 and 1.9 times for degree 3 on a simple homogeneous test problem, using 24 cores. This is similar to the acoustic case. For a more realistic problem, the gain in efficiency was a factor 2.5 for degree 2 and 3 for degree 3. For the lowest degree, the linear element, the expressions for both the global and local assembly can be further simplified. In that case, global assembly is more efficient than local assembly. Among the three degrees, the element of degree 3 is the most efficient in terms of accuracy at a given cost.
Multiple actor-critic structures for continuous-time optimal control using input-output data.
Song, Ruizhuo; Lewis, Frank; Wei, Qinglai; Zhang, Hua-Guang; Jiang, Zhong-Ping; Levine, Dan
2015-04-01
In industrial process control, there may be multiple performance objectives, depending on salient features of the input-output data. Aiming at this situation, this paper proposes multiple actor-critic structures to obtain the optimal control via input-output data for unknown nonlinear systems. The shunting inhibitory artificial neural network (SIANN) is used to classify the input-output data into one of several categories. Different performance measure functions may be defined for disparate categories. The approximate dynamic programming algorithm, which contains model module, critic network, and action network, is used to establish the optimal control in each category. A recurrent neural network (RNN) model is used to reconstruct the unknown system dynamics using input-output data. NNs are used to approximate the critic and action networks, respectively. It is proven that the model error and the closed unknown system are uniformly ultimately bounded. Simulation results demonstrate the performance of the proposed optimal control scheme for the unknown nonlinear system. PMID:25730830
Optimizing global CO concentrations and emissions based on DART/CAM-CHEM
NASA Astrophysics Data System (ADS)
Gaubert, B.; Arellano, A. F.; Barre, J.; Worden, H. M.; Emmons, L. K.; Wiedinmyer, C.; Anderson, J. L.; Deeter, M. N.; Mizzi, A. P.; Edwards, D. P.
2014-12-01
Atmospheric Carbon Monoxide (CO) is an important trace gas in tropospheric chemistry through its impact on the oxidizing capacity of the troposphere, as precursor of ozone, and as a good tracer of combustion from both anthropogenic sources and wildfires. We will investigate the potential of the assimilation of TERRA/MOPITT observations to constrain the regional to global CO budget using DART (Data assimilation Research Testbed) together with the global Community Atmospheric Model (CAM-Chem). DART/CAM-Chem is based on an ensemble adjustment Kalman filter (EAKF) framework which facilitates statistical estimation of error correlations between chemical states (CO and related species) and parameters (including sources) in the model using the ensemble statistics derived from dynamical and chemical perturbations in the model. Here, we estimate CO emissions within DART/CAM-Chem using a state augmentation approach where CO emissions are added to the CO state vector being analyzed. We compare these optimized emissions to estimates derived from a traditional Bayesian synthesis inversion using the CO analyses (assimilated CO states) as observational constraints. The spatio-temporal distribution of CO and other chemical species will be compared to profile measurements from aircraft and other satellite instruments (e.g., INTEX-B, ARCTAS).
Improving global CD uniformity by optimizing post-exposure bake and develop sequences
NASA Astrophysics Data System (ADS)
Osborne, Stephen P.; Mueller, Mark; Lem, Homer; Reyland, David; Baik, KiHo
2003-12-01
Improvements in the final uniformity of masks can be shrouded by error contributions from many sources. The final Global CD Uniformity (GCDU) of a mask is degraded by individual contributions of the writing tool, the Post Applied Bake (PAB), the Post Exposure Bake (PEB), the Develop sequence and the Etch step. Final global uniformity will improve by isolating and minimizing the variability of the PEB and Develop. We achieved this de-coupling of the PEB and Develop process from the whole process stream by using "dark loss" which is the loss of unexposed resist during the develop process. We confirmed a correspondence between Angstroms of dark loss and nanometer sized deviations in the chrome CD. A plate with a distinctive dark loss pattern was related to a nearly identical pattern in the chrome CD. This pattern was verified to have originated during the PEB process and displayed a [Δ(Final CD)/Δ(Dark Loss)] ratio of 6 for TOK REAP200 resist. Previous papers have reported a sensitive linkage between Angstroms of dark loss and nanometers in the final uniformity of the written plate. These initial studies reported using this method to improve the PAB of resists for greater uniformity of sensitivity and contrast. Similarly, this paper demonstrates an outstanding optimization of PEB and Develop processes.
Corzo, Gerald; Solomatine, Dimitri
2007-05-01
Natural phenomena are multistationary and are composed of a number of interacting processes, so one single model handling all processes often suffers from inaccuracies. A solution is to partition data in relation to such processes using the available domain knowledge or expert judgment, to train separate models for each of the processes, and to merge them in a modular model (committee). In this paper a problem of water flow forecast in watershed hydrology is considered where the flow process can be presented as consisting of two subprocesses -- base flow and excess flow, so that these two processes can be separated. Several approaches to data separation techniques are studied. Two case studies with different forecast horizons are considered. Parameters of the algorithms responsible for data partitioning are optimized using genetic algorithms and global pattern search. It was found that modularization of ANN models using domain knowledge makes models more accurate, if compared with a global model trained on the whole data set, especially when forecast horizon (and hence the complexity of the modelled processes) is increased.
Local search for optimal global map generation using mid-decadal landsat images
Khatib, L.; Gasch, J.; Morris, R.; Covington, S.
2007-01-01
NASA and the US Geological Survey (USGS) are seeking to generate a map of the entire globe using Landsat 5 Thematic Mapper (TM) and Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor data from the "mid-decadal" period of 2004 through 2006. The global map is comprised of thousands of scene locations and, for each location, tens of different images of varying quality to chose from. Furthermore, it is desirable for images of adjacent scenes be close together in time of acquisition, to avoid obvious discontinuities due to seasonal changes. These characteristics make it desirable to formulate an automated solution to the problem of generating the complete map. This paper formulates a Global Map Generator problem as a Constraint Optimization Problem (GMG-COP) and describes an approach to solving it using local search. Preliminary results of running the algorithm on image data sets are summarized. The results suggest a significant improvement in map quality using constraint-based solutions. Copyright ?? 2007, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
Nacelle Chine Installation Based on Wind-Tunnel Test Using Efficient Global Optimization
NASA Astrophysics Data System (ADS)
Kanazaki, Masahiro; Yokokawa, Yuzuru; Murayama, Mitsuhiro; Ito, Takeshi; Jeong, Shinkyu; Yamamoto, Kazuomi
Design exploration of a nacelle chine installation was carried out. The nacelle chine improves stall performance when deploying multi-element high-lift devices. This study proposes an efficient design process using a Kriging surrogate model to determine the nacelle chine installation point in wind-tunnel tests. The design exploration was conducted in a wind-tunnel using the JAXA high-lift aircraft model at the JAXA Large-scale Low-speed Wind Tunnel. The objective was to maximize the maximum lift. The chine installation points were designed on the engine nacelle in the axial and chord-wise direction, while the geometry of the chine was fixed. In the design process, efficient global optimization (EGO) which includes Kriging model and genetic algorithm (GA) was employed. This method makes it possible both to improve the accuracy of the response surface and to explore the global optimum efficiently. Detailed observations of flowfields using the Particle Image Velocimetry method confirmed the chine effect and design results.
ERIC Educational Resources Information Center
Foley, Greg
2011-01-01
Continuous feed and bleed ultrafiltration, modeled with the gel polarization model for the limiting flux, is shown to provide a rich source of non-linear algebraic equations that can be readily solved using numerical and graphical techniques familiar to undergraduate students. We present a variety of numerical problems in the design, analysis, and…
NASA Technical Reports Server (NTRS)
Shepperd, Stanley W.
1988-01-01
A family of functions involving integrals of universal functions is introduced. These functions have some interesting mathematical properties including the fact that they may be expressed as Gaussian continued fractions. A unique method of performing the integration is demonstrated which indicates why these functions may be important in the variation of Kepler's equation.
How can we Optimize Global Satellite Observations of Glacier Velocity and Elevation Changes?
NASA Astrophysics Data System (ADS)
Willis, M. J.; Pritchard, M. E.; Zheng, W.
2015-12-01
We have started a global compilation of glacier surface elevation change rates measured by altimeters and differencing of Digital Elevation Models and glacier velocities measured by Synthetic Aperture Radar (SAR) and optical feature tracking as well as from Interferometric SAR (InSAR). Our goal is to compile statistics on recent ice flow velocities and surface elevation change rates near the fronts of all available glaciers using literature and our own data sets of the Russian Arctic, Patagonia, Alaska, Greenland and Antarctica, the Himalayas, and other locations. We quantify the percentage of the glaciers on the planet that can be regarded as fast flowing glaciers, with surface velocities of more than 50 meters per year, while also recording glaciers that have elevation change rates of more than 2 meters per year. We examine whether glaciers have significant interannual variations in velocities, or have accelerated or stagnated where time series of ice motions are available. We use glacier boundaries and identifiers from the Randolph Glacier Inventory. Our survey highlights glaciers that are likely to react quickly to changes in their mass accumulation rates. The study also identifies geographical areas where our knowledge of glacier dynamics remains poor. Our survey helps guide how frequently observations must be made in order to provide quality satellite-derived velocity and ice elevation observations at a variety of glacier thermal regimes, speeds and widths. Our objectives are to determine to what extent the joint NASA and Indian Space Research Organization Synthetic Aperture Radar mission (NISAR) will be able to provide global precision coverage of ice speed changes and to determine how to optimize observations from the global constellation of satellite missions to record important changes to glacier elevations and velocities worldwide.
Optimal estimation for global ground-level fine particulate matter concentrations
NASA Astrophysics Data System (ADS)
Donkelaar, Aaron; Martin, Randall V.; Spurr, Robert J. D.; Drury, Easan; Remer, Lorraine A.; Levy, Robert C.; Wang, Jun
2013-06-01
We develop an optimal estimation (OE) algorithm based on top-of-atmosphere reflectances observed by the MODIS satellite instrument to retrieve near-surface fine particulate matter (PM2.5). The GEOS-Chem chemical transport model is used to provide prior information for the Aerosol Optical Depth (AOD) retrieval and to relate total column AOD to PM2.5. We adjust the shape of the GEOS-Chem relative vertical extinction profiles by comparison with lidar retrievals from the CALIOP satellite instrument. Surface reflectance relationships used in the OE algorithm are indexed by land type. Error quantities needed for this OE algorithm are inferred by comparison with AOD observations taken by a worldwide network of sun photometers (AERONET) and extended globally based upon aerosol speciation and cross correlation for simulated values, and upon land type for observational values. Significant agreement in PM2.5 is found over North America for 2005 (slope = 0.89; r = 0.82; 1-σ error = 1 µg/m3 + 27%), with improved coverage and correlation relative to previous work for the same region and time period, although certain subregions, such as the San Joaquin Valley of California are better represented by previous estimates. Independently derived error estimates of the OE PM2.5 values at in situ locations over North America (of ±(2.5 µg/m3 + 31%) and Europe of ±(3.5 µg/m3 + 30%) are corroborated by comparison with in situ observations, although globally (error estimates of ±(3.0 µg/m3 + 35%), may be underestimated. Global population-weighted PM2.5 at 50% relative humidity is estimated as 27.8 µg/m3 at 0.1° × 0.1° resolution.
A hybrid cuckoo search algorithm with Nelder Mead method for solving global optimization problems.
Ali, Ahmed F; Tawhid, Mohamed A
2016-01-01
Cuckoo search algorithm is a promising metaheuristic population based method. It has been applied to solve many real life problems. In this paper, we propose a new cuckoo search algorithm by combining the cuckoo search algorithm with the Nelder-Mead method in order to solve the integer and minimax optimization problems. We call the proposed algorithm by hybrid cuckoo search and Nelder-Mead method (HCSNM). HCSNM starts the search by applying the standard cuckoo search for number of iterations then the best obtained solution is passing to the Nelder-Mead algorithm as an intensification process in order to accelerate the search and overcome the slow convergence of the standard cuckoo search algorithm. The proposed algorithm is balancing between the global exploration of the Cuckoo search algorithm and the deep exploitation of the Nelder-Mead method. We test HCSNM algorithm on seven integer programming problems and ten minimax problems and compare against eight algorithms for solving integer programming problems and seven algorithms for solving minimax problems. The experiments results show the efficiency of the proposed algorithm and its ability to solve integer and minimax optimization problems in reasonable time. PMID:27217988
A hybrid cuckoo search algorithm with Nelder Mead method for solving global optimization problems.
Ali, Ahmed F; Tawhid, Mohamed A
2016-01-01
Cuckoo search algorithm is a promising metaheuristic population based method. It has been applied to solve many real life problems. In this paper, we propose a new cuckoo search algorithm by combining the cuckoo search algorithm with the Nelder-Mead method in order to solve the integer and minimax optimization problems. We call the proposed algorithm by hybrid cuckoo search and Nelder-Mead method (HCSNM). HCSNM starts the search by applying the standard cuckoo search for number of iterations then the best obtained solution is passing to the Nelder-Mead algorithm as an intensification process in order to accelerate the search and overcome the slow convergence of the standard cuckoo search algorithm. The proposed algorithm is balancing between the global exploration of the Cuckoo search algorithm and the deep exploitation of the Nelder-Mead method. We test HCSNM algorithm on seven integer programming problems and ten minimax problems and compare against eight algorithms for solving integer programming problems and seven algorithms for solving minimax problems. The experiments results show the efficiency of the proposed algorithm and its ability to solve integer and minimax optimization problems in reasonable time.
Need for coordinated programs to improve global health by optimizing salt and iodine intake.
Campbell, Norm R C; Dary, Omar; Cappuccio, Francesco P; Neufeld, Lynnette M; Harding, Kim B; Zimmermann, Michael B
2012-10-01
High dietary salt is a major cause of increased blood pressure, the leading risk for death worldwide. The World Health Organization (WHO) has recommended that salt intake be less than 5 g/day, a goal that only a small proportion of people achieve. Iodine deficiency can cause cognitive and motor impairment and, if severe, hypothyroidism with serious mental and growth retardation. More than 2 billion people worldwide are at risk of iodine deficiency. Preventing iodine deficiency by using salt fortified with iodine is a major global public health success. Programs to reduce dietary salt are technically compatible with programs to prevent iodine deficiency through salt fortification. However, for populations to fully benefit from optimum intake of salt and iodine, the programs must be integrated. This review summarizes the scientific basis for salt reduction and iodine fortification programs, the compatibility of the programs, and the steps that need to be taken by the WHO, national governments, and nongovernmental organizations to ensure that populations fully benefit from optimal intake of salt and iodine. Specifically, expert groups must be convened to help countries implement integrated programs and context-specific case studies of successfully integrated programs; lessons learned need to be compiled and disseminated. Integrated surveillance programs will be more efficient and will enhance current efforts to optimize intake of iodine and salt. For populations to fully benefit, governments need to place a high priority on integrating these two important public health programs. PMID:23299289
NASA Astrophysics Data System (ADS)
Arteaga, Lionel; Pahlow, Markus; Oschlies, Andreas
2014-07-01
The widely used concept of constant "Redfield" phytoplankton stoichiometry is often applied for estimating which nutrient limits phytoplankton growth in the surface ocean. Culture experiments, in contrast, show strong relations between growth conditions and cellular stoichiometry with often substantial deviations from Redfield stoichiometry. Here we investigate to what extent both views agree by analyzing remote sensing and in situ data with an optimality-based model of nondiazotrophic phytoplankton growth in order to infer seasonally varying patterns of colimitation by light, nitrogen (N), and phosphorus (P) in the global ocean. Our combined model-data analysis suggests strong N and N-P colimitation in the tropical ocean, seasonal light, and N-P colimitation in the Northern Hemisphere, and strong light limitation only during winter in the Southern Ocean. The eastern equatorial Pacific appears as the only ocean area that is essentially not limited by N, P, or light. Even though our optimality-based approach specifically accounts for flexible stoichiometry, inferred patterns of N and P limitation are to some extent consistent with those obtained from an analysis of surface inorganic nutrients with respect to the Redfield N:P ratio. Iron is not part of our analysis, implying that we cannot accurately predict N cell quotas in high-nutrient, low-chlorophyll regions. Elsewhere, we do not expect a major effect of iron on the relative distribution of N, P, and light colimitation areas. The relative importance of N, P, and light in limiting phytoplankton growth diagnosed here by combining observations and an optimal growth model provides a useful constraint for models used to predict future marine biological production under changing environmental conditions. 2014. American Geophysical Union. All Rights Reserved.
Recursive Ant Colony Global Optimization: a new technique for the inversion of geophysical data
NASA Astrophysics Data System (ADS)
Gupta, D. K.; Gupta, J. P.; Arora, Y.; Singh, U. K.
2011-12-01
We present a new method called Recursive Ant Colony Global Optimization (RACO) technique, a modified form of general ACO, which can be used to find the best solutions to inversion problems in geophysics. RACO simulates the social behaviour of ants to find the best path between the nest and the food source. A new term depth has been introduced, which controls the extent of recursion. A selective number of cities get qualified for the successive depth. The results of one depth are used to construct the models for the next depth and the range of values for each of the parameters is reduced without any change to the number of models. The three additional steps performed after each depth, are the pheromone tracking, pheromone updating and city selection. One of the advantages of RACO over ACO is that if a problem has multiple solutions, then pheromone accumulation will take place at more than one city thereby leading to formation of multiple nested ACO loops within the ACO loop of the previous depth. Also, while the convergence of ACO is almost linear, RACO shows exponential convergence and hence is faster than the ACO. RACO proves better over some other global optimization techniques, as it does not require any initial values to be assigned to the parameters function. The method has been tested on some mathematical functions, synthetic self-potential (SP) and synthetic gravity data. The obtained results reveal the efficiency and practicability of the method. The method is found to be efficient enough to solve the problems of SP and gravity anomalies due to a horizontal cylinder, a sphere, an inclined sheet and multiple idealized bodies buried inside the earth. These anomalies with and without noise were inverted using the RACO algorithm. The obtained results were compared with those obtained from the conventional methods and it was found that RACO results are more accurate. Finally this optimization technique was applied to real field data collected over the Surda
Uauy, Ricardo; Corvalan, Camila; Dangour, Alan D
2009-02-01
Optimal health and well-being are now considered the true measures of human development. Integrated strategies for infant, child and adult nutrition are required that take a life-course perspective to achieve life-long health. The major nutrition challenges faced today include: (a) addressing the pending burden of undernutrition (low birth weight, severe wasting, stunting and Zn, retinol, Fe, iodine and folic acid deficits) affecting those individuals living in conditions of poverty and deprivation; (b) preventing nutrition-related chronic diseases (obesity, diabetes, CVD, some forms of cancer and osteoporosis) that, except in sub-Saharan Africa, are the main causes of death and disability globally. This challenge requires a life-course perspective as effective prevention starts before conception and continues at each stage of life. While death is unavoidable, premature death and disability can be postponed by providing the right amount and quality of food and by maintaining an active life; (c) delaying or avoiding, via appropriate nutrition and physical activity interventions, the functional declines associated with advancing age. To help tackle these challenges, it is proposed that the term 'malnutrition in all its forms', which encompasses the full spectrum of nutritional disorders, should be used to engender a broader understanding of global nutrition problems. This term may prove particularly helpful when interacting with policy makers and the public. Finally, a greater effort by the UN agencies and private and public development partners is called for to strengthen local, regional and international capacity to support the much needed change in policy and programme activities focusing on all forms of malnutrition with a unified agenda.
Uauy, Ricardo; Corvalan, Camila; Dangour, Alan D
2009-02-01
Optimal health and well-being are now considered the true measures of human development. Integrated strategies for infant, child and adult nutrition are required that take a life-course perspective to achieve life-long health. The major nutrition challenges faced today include: (a) addressing the pending burden of undernutrition (low birth weight, severe wasting, stunting and Zn, retinol, Fe, iodine and folic acid deficits) affecting those individuals living in conditions of poverty and deprivation; (b) preventing nutrition-related chronic diseases (obesity, diabetes, CVD, some forms of cancer and osteoporosis) that, except in sub-Saharan Africa, are the main causes of death and disability globally. This challenge requires a life-course perspective as effective prevention starts before conception and continues at each stage of life. While death is unavoidable, premature death and disability can be postponed by providing the right amount and quality of food and by maintaining an active life; (c) delaying or avoiding, via appropriate nutrition and physical activity interventions, the functional declines associated with advancing age. To help tackle these challenges, it is proposed that the term 'malnutrition in all its forms', which encompasses the full spectrum of nutritional disorders, should be used to engender a broader understanding of global nutrition problems. This term may prove particularly helpful when interacting with policy makers and the public. Finally, a greater effort by the UN agencies and private and public development partners is called for to strengthen local, regional and international capacity to support the much needed change in policy and programme activities focusing on all forms of malnutrition with a unified agenda. PMID:19012808
Optimal radiation field in one-dimensional continuous flow heterogeneous photocatalytic reactors
NASA Astrophysics Data System (ADS)
Davydov, L.; Tsekov, R.; Smirniotis, P. G.
2001-08-01
A general kinetic model of oxidation in photocatalytic reactors has been used to describe the balances of active species and reactants. Approximate analytical solutions for two realistic cases have been developed for this model. Two particular cases (high conversion and low conversion of the reactant) were considered. It was found that both cases adequately represent the original non-linear system of equations in their respective ranges. The approximate analytical solutions for both cases were used to express the reactor output as a function of the axial distribution of radiation inside the vessel. As a result, an optimum radiation profile resulting in maximal output was found using optimal control methods. The latter involved forming the performance index and solving Euler-Lagrange equation. These profiles represent monotonically decreasing curves with higher intensity at the beginning of the reactor. The degree of enhancement by using the optimal radiation strategy was expressed as a ratio of the relative output concentration in the reactor to that in a uniformly irradiated photoreactor. For the case of high conversion this ratio monotonically decreased with the increase of the process parameters (such as light intensity and space time), while for the case of high conversion it passed through a minimum. An exhaustive parametric study was performed on the approximate analytical solution. The most meaningful parameters under identical irradiation conditions have been isolated, which significantly affect the reactor performance. These are: the ratio of radical generation to electron hole recombination rates, the ratio of radical recombination to surface reaction rates, and the surface reaction rate constant. The latter increases the importance of the non-linear terms in the equation, thus allowing for more significant optimization. On the contrary, relatively slow reaction is almost unaffected by the radiation profile in the reactor.
Closing the loop from continuous M-health monitoring to fuzzy logic-based optimized recommendations.
Benharref, Abdelghani; Serhani, Mohamed Adel; Nujum, Al Ramzana
2014-01-01
Continuous sensing of health metrics might generate a massive amount of data. Generating clinically validated recommendations, out of these data, to patients under monitoring is of prime importance to protect them from risk of falling into severe health degradation. Physicians also can be supported with automated recommendations that gain from historical data and increasing learning cycles. In this paper, we propose a Fuzzy Expert System that relies on data collected from continuous monitoring. The monitoring scheme implements preprocessing of data for better data analytics. However, data analytics implements the loopback feature in order to constantly improve fuzzy rules, knowledge base, and generated recommendations. Both techniques reduced data quantity, improved data quality and proposed recommendations. We evaluate our solution through a series of experiments and the results we have obtained proved that our fuzzy expert system combined with the intelligent monitoring and analytic techniques provide a high accuracy of collected data and valid advices.
Shoukri, Mohamed M; Elkum, Nasser; Walter, Stephen D
2006-01-01
Background In this paper we propose the use of the within-subject coefficient of variation as an index of a measurement's reliability. For continuous variables and based on its maximum likelihood estimation we derive a variance-stabilizing transformation and discuss confidence interval construction within the framework of a one-way random effects model. We investigate sample size requirements for the within-subject coefficient of variation for continuous and binary variables. Methods We investigate the validity of the approximate normal confidence interval by Monte Carlo simulations. In designing a reliability study, a crucial issue is the balance between the number of subjects to be recruited and the number of repeated measurements per subject. We discuss efficiency of estimation and cost considerations for the optimal allocation of the sample resources. The approach is illustrated by an example on Magnetic Resonance Imaging (MRI). We also discuss the issue of sample size estimation for dichotomous responses with two examples. Results For the continuous variable we found that the variance stabilizing transformation improves the asymptotic coverage probabilities on the within-subject coefficient of variation for the continuous variable. The maximum like estimation and sample size estimation based on pre-specified width of confidence interval are novel contribution to the literature for the binary variable. Conclusion Using the sample size formulas, we hope to help clinical epidemiologists and practicing statisticians to efficiently design reliability studies using the within-subject coefficient of variation, whether the variable of interest is continuous or binary. PMID:16686943
Smolensky, Paul; Goldrick, Matthew; Mathis, Donald
2014-08-01
Mental representations have continuous as well as discrete, combinatorial properties. For example, while predominantly discrete, phonological representations also vary continuously; this is reflected by gradient effects in instrumental studies of speech production. Can an integrated theoretical framework address both aspects of structure? The framework we introduce here, Gradient Symbol Processing, characterizes the emergence of grammatical macrostructure from the Parallel Distributed Processing microstructure (McClelland, Rumelhart, & The PDP Research Group, 1986) of language processing. The mental representations that emerge, Distributed Symbol Systems, have both combinatorial and gradient structure. They are processed through Subsymbolic Optimization-Quantization, in which an optimization process favoring representations that satisfy well-formedness constraints operates in parallel with a distributed quantization process favoring discrete symbolic structures. We apply a particular instantiation of this framework, λ-Diffusion Theory, to phonological production. Simulations of the resulting model suggest that Gradient Symbol Processing offers a way to unify accounts of grammatical competence with both discrete and continuous patterns in language performance.
Using R for Global Optimization of a Fully-distributed Hydrologic Model at Continental Scale
NASA Astrophysics Data System (ADS)
Zambrano-Bigiarini, M.; Zajac, Z.; Salamon, P.
2013-12-01
Nowadays hydrologic model simulations are widely used to better understand hydrologic processes and to predict extreme events such as floods and droughts. In particular, the spatially distributed LISFLOOD model is currently used for flood forecasting at Pan-European scale, within the European Flood Awareness System (EFAS). Several model parameters can not be directly measured, and they need to be estimated through calibration, in order to constrain simulated discharges to their observed counterparts. In this work we describe how the free software 'R' has been used as a single environment to pre-process hydro-meteorological data, to carry out global optimization, and to post-process calibration results in Europe. Historical daily discharge records were pre-processed for 4062 stream gauges, with different amount and distribution of data in each one of them. The hydroTSM, raster and sp R packages were used to select ca. 700 stations with an adequate spatio-temporal coverage. Selected stations span a wide range of hydro-climatic characteristics, from arid and ET-dominated watersheds in the Iberian Peninsula to snow-dominated watersheds in Scandinavia. Nine parameters were selected to be calibrated based on previous expert knowledge. Customized R scripts were used to extract observed time series for each catchment and to prepare the input files required to fully set up the calibration thereof. The hydroPSO package was then used to carry out a single-objective global optimization on each selected catchment, by using the Standard Particle Swarm 2011 (SPSO-2011) algorithm. Among the many goodness-of-fit measures available in the hydroGOF package, the Nash-Sutcliffe efficiency was used to drive the optimization. User-defined functions were developed for reading model outputs and passing them to the calibration engine. The long computational time required to finish the calibration at continental scale was partially alleviated by using 4 multi-core machines (with both GNU
Inamdar, Shrirang; Joshi, Swati; Bapat, Vishwas; Jadhav, Jyoti
2014-01-20
Melanins are predominantly indolic polymers which are having extensive applications in cosmetics, agriculture and medicine. In the present study, optimization of nutritional parameters influencing melanin production by Mucuna monosperma callus cultures was attempted using the response surface methodology (RSM). Standardization of four factors was carried out using the Box-Behnken design. The optimized levels of factors predicted by the model include tyrosine 0.978gL(-1), pH 5.85, SDS 34.55mgL(-1)and copper sulphate 21.14mgL(-1) tyrosine, which resulted in highest melanin yield of 0.887gL(-1). The optimization of medium using RSM resulted in a 3.06-fold increase in the yield of melanin. The ANOVA analysis showed a significant R(2)-value (0.9995), model F-value (1917.72) and probability (0.0001), with insignificant lack of fit. Optimized medium was used in the laboratory scale column reactor for the continuous production of melanin. Uninterrupted flow column exhibited maximum melanin production rate of 250mgL(-1)h(-1) which is the highest value ever reported using plant as a biotransformation source. Melanin production was confirmed by spectrophotometric and chemical analysis. Thus, this study demonstrates the production of melanin by M. monosperma callus, using a laboratory scale column reactor.
Inamdar, Shrirang; Joshi, Swati; Bapat, Vishwas; Jadhav, Jyoti
2014-01-20
Melanins are predominantly indolic polymers which are having extensive applications in cosmetics, agriculture and medicine. In the present study, optimization of nutritional parameters influencing melanin production by Mucuna monosperma callus cultures was attempted using the response surface methodology (RSM). Standardization of four factors was carried out using the Box-Behnken design. The optimized levels of factors predicted by the model include tyrosine 0.978gL(-1), pH 5.85, SDS 34.55mgL(-1)and copper sulphate 21.14mgL(-1) tyrosine, which resulted in highest melanin yield of 0.887gL(-1). The optimization of medium using RSM resulted in a 3.06-fold increase in the yield of melanin. The ANOVA analysis showed a significant R(2)-value (0.9995), model F-value (1917.72) and probability (0.0001), with insignificant lack of fit. Optimized medium was used in the laboratory scale column reactor for the continuous production of melanin. Uninterrupted flow column exhibited maximum melanin production rate of 250mgL(-1)h(-1) which is the highest value ever reported using plant as a biotransformation source. Melanin production was confirmed by spectrophotometric and chemical analysis. Thus, this study demonstrates the production of melanin by M. monosperma callus, using a laboratory scale column reactor. PMID:24291190
Xu, Dong; Zhang, Yang
2012-01-01
Ab initio protein folding is one of the major unsolved problems in computational biology due to the difficulties in force field design and conformational search. We developed a novel program, QUARK, for template-free protein structure prediction. Query sequences are first broken into fragments of 1–20 residues where multiple fragment structures are retrieved at each position from unrelated experimental structures. Full-length structure models are then assembled from fragments using replica-exchange Monte Carlo simulations, which are guided by a composite knowledge-based force field. A number of novel energy terms and Monte Carlo movements are introduced and the particular contributions to enhancing the efficiency of both force field and search engine are analyzed in detail. QUARK prediction procedure is depicted and tested on the structure modeling of 145 non-homologous proteins. Although no global templates are used and all fragments from experimental structures with template modeling score (TM-score) >0.5 are excluded, QUARK can successfully construct 3D models of correct folds in 1/3 cases of short proteins up to 100 residues. In the ninth community-wide Critical Assessment of protein Structure Prediction (CASP9) experiment, QUARK server outperformed the second and third best servers by 18% and 47% based on the cumulative Z-score of global distance test-total (GDT-TS) scores in the free modeling (FM) category. Although ab initio protein folding remains a significant challenge, these data demonstrate new progress towards the solution of the most important problem in the field. PMID:22411565
NASA Astrophysics Data System (ADS)
Portnoy, David; Feuerbach, Robert; Heimberg, Jennifer
2011-10-01
Today there is a tremendous amount of interest in systems that can detect radiological or nuclear threats. Many of these systems operate in extremely high throughput situations where delays caused by false alarms can have a significant negative impact. Thus, calculating the tradeoff between detection rates and false alarm rates is critical for their successful operation. Receiver operating characteristic (ROC) curves have long been used to depict this tradeoff. The methodology was first developed in the field of signal detection. In recent years it has been used increasingly in machine learning and data mining applications. It follows that this methodology could be applied to radiological/nuclear threat detection systems. However many of these systems do not fit into the classic principles of statistical detection theory because they tend to lack tractable likelihood functions and have many parameters, which, in general, do not have a one-to-one correspondence with the detection classes. This work proposes a strategy to overcome these problems by empirically finding parameter values that maximize the probability of detection for a selected number of probabilities of false alarm. To find these parameter values a statistical global optimization technique that seeks to estimate portions of a ROC curve is proposed. The optimization combines elements of simulated annealing with elements of genetic algorithms. Genetic algorithms were chosen because they can reduce the risk of getting stuck in local minima. However classic genetic algorithms operate on arrays of Booleans values or bit strings, so simulated annealing is employed to perform mutation in the genetic algorithm. The presented initial results were generated using an isotope identification algorithm developed at Johns Hopkins University Applied Physics Laboratory. The algorithm has 12 parameters: 4 real-valued and 8 Boolean. A simulated dataset was used for the optimization study; the "threat" set of spectra
OPTIMAL STRATEGIES FOR CONTINUOUS GRAVITATIONAL WAVE DETECTION IN PULSAR TIMING ARRAYS
Ellis, J. A.; Siemens, X.; Creighton, J. D. E.
2012-09-10
Supermassive black hole binaries (SMBHBs) are expected to emit a continuous gravitational wave signal in the pulsar timing array (PTA) frequency band (10{sup -9} to 10{sup -7} Hz). The development of data analysis techniques aimed at efficient detection and characterization of these signals is critical to the gravitational wave detection effort. In this paper, we leverage methods developed for LIGO continuous wave gravitational searches and explore the use of the F-statistic for such searches in pulsar timing data. Babak and Sesana have used this approach in the context of PTAs to show that one can resolve multiple SMBHB sources in the sky. Our work improves on several aspects of prior continuous wave search methods developed for PTA data analysis. The algorithm is implemented fully in the time domain, which naturally deals with the irregular sampling typical of PTA data and avoids spectral leakage problems associated with frequency domain methods. We take into account the fitting of the timing model and have generalized our approach to deal with both correlated and uncorrelated colored noise sources. We also develop an incoherent detection statistic that maximizes over all pulsar-dependent contributions to the likelihood. To test the effectiveness and sensitivity of our detection statistics, we perform a number of Monte Carlo simulations. We produce sensitivity curves for PTAs of various configurations and outline an implementation of a fully functional data analysis pipeline. Finally, we present a derivation of the likelihood maximized over the gravitational wave phases at the pulsar locations, which results in a vast reduction of the search parameter space.
Chapman, Julia L; Serinel, Yasmina; Marshall, Nathaniel S; Grunstein, Ronald R
2016-09-01
Excessive daytime sleepiness (EDS) is common in obstructive sleep apnea (OSA), but it is also common in the general population. When sleepiness remains after continuous positive airway pressure (CPAP) treatment of OSA, comorbid conditions or permanent brain injury before CPAP therapy may be the cause of the residual sleepiness. There is currently no broad approach to treating residual EDS in patients with OSA. Individual assessment must be made of comorbid conditions and medications, and of lifestyle factors that may be contributing to the sleepiness. Modafinil and armodafinil are the only pharmacologic agents indicated for residual sleepiness in these patients. PMID:27542881
Use of continuous optimization methods to find carbon links in 2D INADEQUATE spectra
NASA Astrophysics Data System (ADS)
Anand, Christopher Kumar; Bain, Alex D.; Watson, Sean C.
2011-05-01
The 2-D INADEQUATE experiment is a useful experiment for determining carbon structures of organic molecules, which is known for having low signal-to-noise ratios. A non-linear optimization method for solving low-signal spectra resulting from this experiment is introduced to compensate. The method relies on the peak locations defined by the INADEQUATE experiment to create boxes around these areas and measure the signal in each. By measuring pairs of these boxes and applying penalty functions that represent a priori information, we are able to quickly and reliably solve spectra with an acquisition time approximately a quarter of that required by traditional methods. Examples are shown using the spectrum of sucrose.
Li, Xingyuan; He, Zhili; Zhou, Jizhong
2005-10-30
The oligonucleotide specificity for microarray hybridizationcan be predicted by its sequence identity to non-targets, continuousstretch to non-targets, and/or binding free energy to non-targets. Mostcurrently available programs only use one or two of these criteria, whichmay choose 'false' specific oligonucleotides or miss 'true' optimalprobes in a considerable proportion. We have developed a software tool,called CommOligo using new algorithms and all three criteria forselection of optimal oligonucleotide probes. A series of filters,including sequence identity, free energy, continuous stretch, GC content,self-annealing, distance to the 3'-untranslated region (3'-UTR) andmelting temperature (Tm), are used to check each possibleoligonucleotide. A sequence identity is calculated based on gapped globalalignments. A traversal algorithm is used to generate alignments for freeenergy calculation. The optimal Tm interval is determined based on probecandidates that have passed all other filters. Final probes are pickedusing a combination of user-configurable piece-wise linear functions andan iterative process. The thresholds for identity, stretch and freeenergy filters are automatically determined from experimental data by anaccessory software tool, CommOligo_PE (CommOligo Parameter Estimator).The program was used to design probes for both whole-genome and highlyhomologous sequence data. CommOligo and CommOligo_PE are freely availableto academic users upon request.
NASA Astrophysics Data System (ADS)
Scirè Mammano, Giovanni; Dragoni, Eugenio
2015-04-01
A relatively unexplored but extremely attractive field for the application of the shape memory technology is the area of rotary actuators, especially for generating continuous rotations. This paper deals with a novel design of a rotary motor based on SMA wires and overrunning clutches which features high output torque and boundless angular stroke in a compact package. The concept uses a long SMA wire wound round a low-friction cylindrical drum upon which the wire can contract and extend with minimum effort and limited space demand. Fitted to the output shaft by means of an overrunning clutch the output shaft rotates unidirectionally despite the sequence of contractions-elongation cycles of the wire. Following a design procedure developed in a former paper, a six-stage miniature prototype is built and tested showing excellent performance in terms of torque, speed and power density. Characteristic performances of the motor are as follows: size envelope = 48×22×30 mm3; maximum torque = 20 Nmm; specific torque = 6.31×10-4 Nmm/mm3; rotation per module = 15 deg; continuous speed (unloaded) = 4 rpm.
NASA Astrophysics Data System (ADS)
Zhang, X.; Cai, X.; Zhu, T.
2013-12-01
Biofuels is booming in recent years due to its potential contributions to energy sustainability, environmental improvement and economic opportunities. Production of biofuels not only competes for land and water with food production, but also directly pushes up food prices when crops such as maize and sugarcane are used as biofuels feedstock. Meanwhile, international trade of agricultural commodities exports and imports water and land resources in a virtual form among different regions, balances overall water and land demands and resource endowment, and provides a promising solution to the increasingly severe food-energy competition. This study investigates how to optimize water and land resources uses for overall welfare at global scale in the framework of 'virtual resources'. In contrast to partial equilibrium models that usually simulate trades year-by-year, this optimization model explores the ideal world where malnourishment is minimized with optimal resources uses and trade flows. Comparing the optimal production and trade patterns with historical data can provide meaningful implications regarding how to utilize water and land resources more efficiently and how the trade flows would be changed for overall welfare at global scale. Valuable insights are obtained in terms of the interactions among food, water and bioenergy systems. A global hydro-economic optimization model is developed, integrating agricultural production, market demands (food, feed, fuel and other), and resource and environmental constraints. Preliminary results show that with the 'free market' mechanism and land as well as water resources use optimization, the malnourished population can be reduced by as much as 65%, compared to the 2000 historical value. Expected results include: 1) optimal trade paths to achieve global malnourishment minimization, 2) how water and land resources constrain local supply, 3) how policy affects the trade pattern as well as resource uses. Furthermore, impacts of
Donner, René; Menze, Bjoern H; Bischof, Horst; Langs, Georg
2013-12-01
The accurate localization of anatomical landmarks is a challenging task, often solved by domain specific approaches. We propose a method for the automatic localization of landmarks in complex, repetitive anatomical structures. The key idea is to combine three steps: (1) a classifier for pre-filtering anatomical landmark positions that (2) are refined through a Hough regression model, together with (3) a parts-based model of the global landmark topology to select the final landmark positions. During training landmarks are annotated in a set of example volumes. A classifier learns local landmark appearance, and Hough regressors are trained to aggregate neighborhood information to a precise landmark coordinate position. A non-parametric geometric model encodes the spatial relationships between the landmarks and derives a topology which connects mutually predictive landmarks. During the global search we classify all voxels in the query volume, and perform regression-based agglomeration of landmark probabilities to highly accurate and specific candidate points at potential landmark locations. We encode the candidates' weights together with the conformity of the connecting edges to the learnt geometric model in a Markov Random Field (MRF). By solving the corresponding discrete optimization problem, the most probable location for each model landmark is found in the query volume. We show that this approach is able to consistently localize the model landmarks despite the complex and repetitive character of the anatomical structures on three challenging data sets (hand radiographs, hand CTs, and whole body CTs), with a median localization error of 0.80 mm, 1.19 mm and 2.71 mm, respectively. PMID:23664450
Waldman, Yedael Y; Tuller, Tamir; Shlomi, Tomer; Sharan, Roded; Ruppin, Eytan
2010-05-01
Various studies in unicellular and multicellular organisms have shown that codon bias plays a significant role in translation efficiency (TE) by co-adaptation to the tRNA pool. Yet, in humans and other mammals the role of codon bias is still an open question, with contradictory results from different studies. Here we address this question, performing a large-scale tissue-specific analysis of TE in humans, using the tRNA Adaptation Index (tAI) as a direct measure for TE. We find tAI to significantly correlate with expression levels both in tissue-specific and in global expression measures, testifying to the TE of human tissues. Interestingly, we find significantly higher correlations in adult tissues as opposed to fetal tissues, suggesting that the tRNA pool is more adjusted to the adult period. Optimization based analysis suggests that the tRNA pool-codon bias co-adaptation is globally (and not tissue-specific) driven. Additionally, we find that tAI correlates with several measures related to the protein functionally importance, including gene essentiality. Using inferred tissue-specific tRNA pools lead to similar results and shows that tissue-specific genes are more adapted to their tRNA pool than other genes and that related sets of functional gene groups are translated efficiently in each tissue. Similar results are obtained for other mammals. Taken together, these results demonstrate the role of codon bias in TE in humans, and pave the way for future studies of tissue-specific TE in multicellular organisms. PMID:20097653
Donner, René; Menze, Bjoern H.; Bischof, Horst; Langs, Georg
2013-01-01
The accurate localization of anatomical landmarks is a challenging task, often solved by domain specific approaches. We propose a method for the automatic localization of landmarks in complex, repetitive anatomical structures. The key idea is to combine three steps: (1) a classifier for pre-filtering anatomical landmark positions that (2) are refined through a Hough regression model, together with (3) a parts-based model of the global landmark topology to select the final landmark positions. During training landmarks are annotated in a set of example volumes. A classifier learns local landmark appearance, and Hough regressors are trained to aggregate neighborhood information to a precise landmark coordinate position. A non-parametric geometric model encodes the spatial relationships between the landmarks and derives a topology which connects mutually predictive landmarks. During the global search we classify all voxels in the query volume, and perform regression-based agglomeration of landmark probabilities to highly accurate and specific candidate points at potential landmark locations. We encode the candidates’ weights together with the conformity of the connecting edges to the learnt geometric model in a Markov Random Field (MRF). By solving the corresponding discrete optimization problem, the most probable location for each model landmark is found in the query volume. We show that this approach is able to consistently localize the model landmarks despite the complex and repetitive character of the anatomical structures on three challenging data sets (hand radiographs, hand CTs, and whole body CTs), with a median localization error of 0.80 mm, 1.19 mm and 2.71 mm, respectively. PMID:23664450
Optimization of the continuous biosorption of copper with sugar-beet pectin gels.
Mata, Y N; Blázquez, M L; Ballester, A; González, F; Muñoz, J A
2009-04-01
Sugar-beet pectin xerogels obtained from residues of the sugar industry are an adequate material for metal recovery from effluents in continuous systems. The xerogels were used as a biosorbent for copper removal in a fixed-bed column. The performance of the system was evaluated in different experimental conditions: flow rate, bed height, inlet metal concentration and feeding system (drop and reverse). The effect on the biosorption parameters (saturation time, amount of adsorbed and treated metal, column performance and metal uptake) and the shape of the breakthrough curves was determined. The saturation time increased with increasing bed height but decreased with increasing feed flow rate and inlet metal concentration. Preferential flow channels greatly influenced the metal uptake and column performance. Copper was completely desorbed with 0.1M HNO(3). Additionally, the column data fitted both the linear and nonlinear expressions of the Thomas model.
Covariance and crossover matrix guided differential evolution for global numerical optimization.
Li, YongLi; Feng, JinFu; Hu, JunHua
2016-01-01
Differential evolution (DE) is an efficient and robust evolutionary algorithm and has wide application in various science and engineering fields. DE is sensitive to the selection of mutation and crossover strategies and their associated control parameters. However, the structure and implementation of DEs are becoming more complex because of the diverse mutation and crossover strategies that use distinct parameter settings during the different stages of the evolution. A novel strategy is used in this study to improve the crossover and mutation operations. The crossover matrix, instead of a crossover operator and its control parameter CR, is proposed to implement the function of the crossover operation. Meanwhile, Gaussian distribution centers the best individuals found in each generation based on the proposed covariance matrix, which is generated between the best individual and several better individuals. Improved mutation operator based on the crossover matrix is randomly selected to generate the trial population. This operator is used to generate high-quality solutions to improve the capability of exploitation and enhance the preference of exploration. In addition, the memory population is randomly chosen from previous generation and used to control the search direction in the novel mutation strategy. Accordingly, the diversity of the population is improved. Thus, CCDE, which is a novel efficient and simple DE variant, is presented in this paper. CCDE has been tested on 30 benchmarks and 5 real-world optimization problems from the IEEE Congress on Evolutionary Computation (CEC) 2014 and CEC 2011, respectively. Experimental and statistical results demonstrate the effectiveness of CCDE for global numerical and engineering optimization. CCDE can solve the test benchmark functions and engineering problems more successfully than the other DE variants and algorithms from CEC 2014.
Template based protein structure modeling by global optimization in CASP11.
Joo, Keehyoung; Joung, InSuk; Lee, Sun Young; Kim, Jong Yun; Cheng, Qianyi; Manavalan, Balachandran; Joung, Jong Young; Heo, Seungryong; Lee, Juyong; Nam, Mikyung; Lee, In-Ho; Lee, Sung Jong; Lee, Jooyoung
2016-09-01
For the template-based modeling (TBM) of CASP11 targets, we have developed three new protein modeling protocols (nns for server prediction and LEE and LEER for human prediction) by improving upon our previous CASP protocols (CASP7 through CASP10). We applied the powerful global optimization method of conformational space annealing to three stages of optimization, including multiple sequence-structure alignment, three-dimensional (3D) chain building, and side-chain remodeling. For more successful fold recognition, a new alignment method called CRFalign was developed. It can incorporate sensitive positional and environmental dependence in alignment scores as well as strong nonlinear correlations among various features. Modifications and adjustments were made to the form of the energy function and weight parameters pertaining to the chain building procedure. For the side-chain remodeling step, residue-type dependence was introduced to the cutoff value that determines the entry of a rotamer to the side-chain modeling library. The improved performance of the nns server method is attributed to successful fold recognition achieved by combining several methods including CRFalign and to the current modeling formulation that can incorporate native-like structural aspects present in multiple templates. The LEE protocol is identical to the nns one except that CASP11-released server models are used as templates. The success of LEE in utilizing CASP11 server models indicates that proper template screening and template clustering assisted by appropriate cluster ranking promises a new direction to enhance protein 3D modeling. Proteins 2016; 84(Suppl 1):221-232. © 2015 Wiley Periodicals, Inc.
Covariance and crossover matrix guided differential evolution for global numerical optimization.
Li, YongLi; Feng, JinFu; Hu, JunHua
2016-01-01
Differential evolution (DE) is an efficient and robust evolutionary algorithm and has wide application in various science and engineering fields. DE is sensitive to the selection of mutation and crossover strategies and their associated control parameters. However, the structure and implementation of DEs are becoming more complex because of the diverse mutation and crossover strategies that use distinct parameter settings during the different stages of the evolution. A novel strategy is used in this study to improve the crossover and mutation operations. The crossover matrix, instead of a crossover operator and its control parameter CR, is proposed to implement the function of the crossover operation. Meanwhile, Gaussian distribution centers the best individuals found in each generation based on the proposed covariance matrix, which is generated between the best individual and several better individuals. Improved mutation operator based on the crossover matrix is randomly selected to generate the trial population. This operator is used to generate high-quality solutions to improve the capability of exploitation and enhance the preference of exploration. In addition, the memory population is randomly chosen from previous generation and used to control the search direction in the novel mutation strategy. Accordingly, the diversity of the population is improved. Thus, CCDE, which is a novel efficient and simple DE variant, is presented in this paper. CCDE has been tested on 30 benchmarks and 5 real-world optimization problems from the IEEE Congress on Evolutionary Computation (CEC) 2014 and CEC 2011, respectively. Experimental and statistical results demonstrate the effectiveness of CCDE for global numerical and engineering optimization. CCDE can solve the test benchmark functions and engineering problems more successfully than the other DE variants and algorithms from CEC 2014. PMID:27512635
Efficiency of Pareto joint inversion of 2D geophysical data using global optimization methods
NASA Astrophysics Data System (ADS)
Miernik, Katarzyna; Bogacz, Adrian; Kozubal, Adam; Danek, Tomasz; Wojdyła, Marek
2016-04-01
Pareto joint inversion of two or more sets of data is a promising new tool of modern geophysical exploration. In the first stage of our investigation we created software enabling execution of forward solvers of two geophysical methods (2D magnetotelluric and gravity) as well as inversion with possibility of constraining solution with seismic data. In the algorithm solving MT forward solver Helmholtz's equations, finite element method and Dirichlet's boundary conditions were applied. Gravity forward solver was based on Talwani's algorithm. To limit dimensionality of solution space we decided to describe model as sets of polygons, using Sharp Boundary Interface (SBI) approach. The main inversion engine was created using Particle Swarm Optimization (PSO) algorithm adapted to handle two or more target functions and to prevent acceptance of solutions which are non - realistic or incompatible with Pareto scheme. Each inversion run generates single Pareto solution, which can be added to Pareto Front. The PSO inversion engine was parallelized using OpenMP standard, what enabled execution code for practically unlimited amount of threads at once. Thereby computing time of inversion process was significantly decreased. Furthermore, computing efficiency increases with number of PSO iterations. In this contribution we analyze the efficiency of created software solution taking under consideration details of chosen global optimization engine used as a main joint minimization engine. Additionally we study the scale of possible decrease of computational time caused by different methods of parallelization applied for both forward solvers and inversion algorithm. All tests were done for 2D magnetotelluric and gravity data based on real geological media. Obtained results show that even for relatively simple mid end computational infrastructure proposed solution of inversion problem can be applied in practice and used for real life problems of geophysical inversion and interpretation.
NASA Astrophysics Data System (ADS)
Wei, Qing-Lai; Song, Rui-Zhuo; Sun, Qiu-Ye; Xiao, Wen-Dong
2015-09-01
This paper estimates an off-policy integral reinforcement learning (IRL) algorithm to obtain the optimal tracking control of unknown chaotic systems. Off-policy IRL can learn the solution of the HJB equation from the system data generated by an arbitrary control. Moreover, off-policy IRL can be regarded as a direct learning method, which avoids the identification of system dynamics. In this paper, the performance index function is first given based on the system tracking error and control error. For solving the Hamilton-Jacobi-Bellman (HJB) equation, an off-policy IRL algorithm is proposed. It is proven that the iterative control makes the tracking error system asymptotically stable, and the iterative performance index function is convergent. Simulation study demonstrates the effectiveness of the developed tracking control method. Project supported by the National Natural Science Foundation of China (Grant Nos. 61304079 and 61374105), the Beijing Natural Science Foundation, China (Grant Nos. 4132078 and 4143065), the China Postdoctoral Science Foundation (Grant No. 2013M530527), the Fundamental Research Funds for the Central Universities, China (Grant No. FRF-TP-14-119A2), and the Open Research Project from State Key Laboratory of Management and Control for Complex Systems, China (Grant No. 20150104).
Optimization of the signal processing in frequency modulated continuous wave laser ranging system
NASA Astrophysics Data System (ADS)
Meng, Xiangsong; Zhang, Fumin; Qu, Xinghua
2015-02-01
Based on a dual interferometry frequency modulated wave laser (FMCW) laser ranging system, three steps to optimize the signal processing is proposed in this paper. The first step is signal re-sampling, by which the sampling signal is turned to be equal optical frequency intervals. The second step is splicing the re-sampled signal, by which can break though the tuning range of the laser source limitation. The last step is the all-phase pretreatment of the signal, its means that the all-phase Fast Fourier Transformation (apFFT) is used to handle the re-sampled signal, which could reduce the phase error of the signal. The experiments shows that the noise effect due to the tuning nonlinearity of laser can be reduced by re-sampling the signal, 50μm range resolution can be easily obtained by this method, the apFFT is more reliable and effective than FFT in the processing to reduce the phase error and improve the speed of operation.
Optimization of a continuous hybrid impeller mixer via computational fluid dynamics.
Othman, N; Kamarudin, S K; Takriff, M S; Rosli, M I; Engku Chik, E M F; Meor Adnan, M A K
2014-01-01
This paper presents the preliminary steps required for conducting experiments to obtain the optimal operating conditions of a hybrid impeller mixer and to determine the residence time distribution (RTD) using computational fluid dynamics (CFD). In this paper, impeller speed and clearance parameters are examined. The hybrid impeller mixer consists of a single Rushton turbine mounted above a single pitched blade turbine (PBT). Four impeller speeds, 50, 100, 150, and 200 rpm, and four impeller clearances, 25, 50, 75, and 100 mm, were the operation variables used in this study. CFD was utilized to initially screen the parameter ranges to reduce the number of actual experiments needed. Afterward, the residence time distribution (RTD) was determined using the respective parameters. Finally, the Fluent-predicted RTD and the experimentally measured RTD were compared. The CFD investigations revealed that an impeller speed of 50 rpm and an impeller clearance of 25 mm were not viable for experimental investigations and were thus eliminated from further analyses. The determination of RTD using a k-ε turbulence model was performed using CFD techniques. The multiple reference frame (MRF) was implemented and a steady state was initially achieved followed by a transient condition for RTD determination. PMID:25170524
Denault, J.; Guillemenet, J.
1996-12-31
The objective of this work was to optimize the processing conditions of polypropylene/carbon, PP/C, and polypropylene/glass, PP/G, composites. Investigation of the effects of molding parameters such as molding temperature and residence time and cooling rate on the tensile performance of PP/C and PP/G was undertaken. It is well known that the mechanical performance of composite based on thermoplastic matrix such as polypropylene is closely related to crystalline morphology which is dependent on the thermal history. Since the compression molding process involves kinetic behavior of systems undergoing phase transformations under non-isothermal conditions, the crystallization behavior of PP matrix in the presence of carbon and glass fibers was investigated under non-isothermal conditions. The effects of processing temperature, residence time and cooling rate on the crystallization temperature, degree of crystallinity, crystallization rate and kinetics of crystallization were analyzed. The tensile behavior of the {+-}45{degrees} laminate of PP/C and PP/G and their interfacial properties were evaluated as a function of molding parameters. The variation in the tensile strength of the {+-}45{degrees} laminates as a function of molding temperature was found to show three distinct regions: the tensile strength first increases with molding temperature, attains a plateau region, and finally decreases at high molding temperature. DSC analysis done in order to simulate phase transformation under non-isothermal conditions also revealed similar behavior suggesting a close relationship between mechanical performance and matrix properties.
Shan, Hai; Yasuda, Toshiyuki; Ohkura, Kazuhiro
2015-06-01
The artificial bee colony (ABC) algorithm is one of popular swarm intelligence algorithms that inspired by the foraging behavior of honeybee colonies. To improve the convergence ability, search speed of finding the best solution and control the balance between exploration and exploitation using this approach, we propose a self adaptive hybrid enhanced ABC algorithm in this paper. To evaluate the performance of standard ABC, best-so-far ABC (BsfABC), incremental ABC (IABC), and the proposed ABC algorithms, we implemented numerical optimization problems based on the IEEE Congress on Evolutionary Computation (CEC) 2014 test suite. Our experimental results show the comparative performance of standard ABC, BsfABC, IABC, and the proposed ABC algorithms. According to the results, we conclude that the proposed ABC algorithm is competitive to those state-of-the-art modified ABC algorithms such as BsfABC and IABC algorithms based on the benchmark problems defined by CEC 2014 test suite with dimension sizes of 10, 30, and 50, respectively.
Shan, Hai; Yasuda, Toshiyuki; Ohkura, Kazuhiro
2015-06-01
The artificial bee colony (ABC) algorithm is one of popular swarm intelligence algorithms that inspired by the foraging behavior of honeybee colonies. To improve the convergence ability, search speed of finding the best solution and control the balance between exploration and exploitation using this approach, we propose a self adaptive hybrid enhanced ABC algorithm in this paper. To evaluate the performance of standard ABC, best-so-far ABC (BsfABC), incremental ABC (IABC), and the proposed ABC algorithms, we implemented numerical optimization problems based on the IEEE Congress on Evolutionary Computation (CEC) 2014 test suite. Our experimental results show the comparative performance of standard ABC, BsfABC, IABC, and the proposed ABC algorithms. According to the results, we conclude that the proposed ABC algorithm is competitive to those state-of-the-art modified ABC algorithms such as BsfABC and IABC algorithms based on the benchmark problems defined by CEC 2014 test suite with dimension sizes of 10, 30, and 50, respectively. PMID:25982071
Optimization of a Continuous Hybrid Impeller Mixer via Computational Fluid Dynamics
Othman, N.; Kamarudin, S. K.; Takriff, M. S.; Rosli, M. I.; Engku Chik, E. M. F.; Meor Adnan, M. A. K.
2014-01-01
This paper presents the preliminary steps required for conducting experiments to obtain the optimal operating conditions of a hybrid impeller mixer and to determine the residence time distribution (RTD) using computational fluid dynamics (CFD). In this paper, impeller speed and clearance parameters are examined. The hybrid impeller mixer consists of a single Rushton turbine mounted above a single pitched blade turbine (PBT). Four impeller speeds, 50, 100, 150, and 200 rpm, and four impeller clearances, 25, 50, 75, and 100 mm, were the operation variables used in this study. CFD was utilized to initially screen the parameter ranges to reduce the number of actual experiments needed. Afterward, the residence time distribution (RTD) was determined using the respective parameters. Finally, the Fluent-predicted RTD and the experimentally measured RTD were compared. The CFD investigations revealed that an impeller speed of 50 rpm and an impeller clearance of 25 mm were not viable for experimental investigations and were thus eliminated from further analyses. The determination of RTD using a k-ε turbulence model was performed using CFD techniques. The multiple reference frame (MRF) was implemented and a steady state was initially achieved followed by a transient condition for RTD determination. PMID:25170524
NASA Astrophysics Data System (ADS)
Lera, Daniela; Sergeyev, Yaroslav D.
2015-06-01
In this paper, the global optimization problem miny∈S F (y) with S being a hyperinterval in RN and F (y) satisfying the Lipschitz condition with an unknown Lipschitz constant is considered. It is supposed that the function F (y) can be multiextremal, non-differentiable, and given as a 'black-box'. To attack the problem, a new global optimization algorithm based on the following two ideas is proposed and studied both theoretically and numerically. First, the new algorithm uses numerical approximations to space-filling curves to reduce the original Lipschitz multi-dimensional problem to a univariate one satisfying the Hölder condition. Second, the algorithm at each iteration applies a new geometric technique working with a number of possible Hölder constants chosen from a set of values varying from zero to infinity showing so that ideas introduced in a popular DIRECT method can be used in the Hölder global optimization. Convergence conditions of the resulting deterministic global optimization method are established. Numerical experiments carried out on several hundreds of test functions show quite a promising performance of the new algorithm in comparison with its direct competitors.
Optimal estimation of regional N2O emissions using a three-dimensional global model
NASA Astrophysics Data System (ADS)
Huang, J.; Golombek, A.; Prinn, R.
2004-12-01
In this study, we use the MATCH (Model of Atmospheric Transport and Chemistry) model and Kalman filtering techniques to optimally estimate N2O emissions from seven source regions around the globe. The MATCH model was used with NCEP assimilated winds at T62 resolution (192 longitude by 94 latitude surface grid, and 28 vertical levels) from July 1st 1996 to December 31st 2000. The average concentrations of N2O in the lowest four layers of the model were then compared with the monthly mean observations from six national/global networks (AGAGE, CMDL (HATS), CMDL (CCGG), CSIRO, CSIR and NIES), at 48 surface sites. A 12-month-running-mean smoother was applied to both the model results and the observations, due to the fact that the model was not able to reproduce the very small observed seasonal variations. The Kalman filter was then used to solve for the time-averaged regional emissions of N2O for January 1st 1997 to June 30th 2000. The inversions assume that the model stratospheric destruction rates, which lead to a global N2O lifetime of 130 years, are correct. It also assumes normalized emission spatial distributions from each region based on previous studies. We conclude that the global N2O emission flux is about 16.2 TgN/yr, with {34.9±1.7%} from South America and Africa, {34.6±1.5%} from South Asia, {13.9±1.5%} from China/Japan/South East Asia, {8.0±1.9%} from all oceans, {6.4±1.1%} from North America and North and West Asia, {2.6±0.4%} from Europe, and {0.9±0.7%} from New Zealand and Australia. The errors here include the measurement standard deviation, calibration differences among the six groups, grid volume/measurement site mis-match errors estimated from the model, and a procedure to account approximately for the modeling errors.
Jenny, Richard M; Jasper, Micah N; Simmons, Otto D; Shatalov, Max; Ducoste, Joel J
2015-10-15
Alternative disinfection sources such as ultraviolet light (UV) are being pursued to inactivate pathogenic microorganisms such as Cryptosporidium and Giardia, while simultaneously reducing the risk of exposure to carcinogenic disinfection by-products (DBPs) in drinking water. UV-LEDs offer a UV disinfecting source that do not contain mercury, have the potential for long lifetimes, are robust, and have a high degree of design flexibility. However, the increased flexibility in design options will add a substantial level of complexity when developing a UV-LED reactor, particularly with regards to reactor shape, size, spatial orientation of light, and germicidal emission wavelength. Anticipating that LEDs are the future of UV disinfection, new methods are needed for designing such reactors. In this research study, the evaluation of a new design paradigm using a point-of-use UV-LED disinfection reactor has been performed. ModeFrontier, a numerical optimization platform, was coupled with COMSOL Multi-physics, a computational fluid dynamics (CFD) software package, to generate an optimized UV-LED continuous flow reactor. Three optimality conditions were considered: 1) single objective analysis minimizing input supply power while achieving at least (2.0) log10 inactivation of Escherichia coli ATCC 11229; and 2) two multi-objective analyses (one of which maximized the log10 inactivation of E. coli ATCC 11229 and minimized the supply power). All tests were completed at a flow rate of 109 mL/min and 92% UVT (measured at 254 nm). The numerical solution for the first objective was validated experimentally using biodosimetry. The optimal design predictions displayed good agreement with the experimental data and contained several non-intuitive features, particularly with the UV-LED spatial arrangement, where the lights were unevenly populated throughout the reactor. The optimal designs may not have been developed from experienced designers due to the increased degrees of
Jenny, Richard M; Jasper, Micah N; Simmons, Otto D; Shatalov, Max; Ducoste, Joel J
2015-10-15
Alternative disinfection sources such as ultraviolet light (UV) are being pursued to inactivate pathogenic microorganisms such as Cryptosporidium and Giardia, while simultaneously reducing the risk of exposure to carcinogenic disinfection by-products (DBPs) in drinking water. UV-LEDs offer a UV disinfecting source that do not contain mercury, have the potential for long lifetimes, are robust, and have a high degree of design flexibility. However, the increased flexibility in design options will add a substantial level of complexity when developing a UV-LED reactor, particularly with regards to reactor shape, size, spatial orientation of light, and germicidal emission wavelength. Anticipating that LEDs are the future of UV disinfection, new methods are needed for designing such reactors. In this research study, the evaluation of a new design paradigm using a point-of-use UV-LED disinfection reactor has been performed. ModeFrontier, a numerical optimization platform, was coupled with COMSOL Multi-physics, a computational fluid dynamics (CFD) software package, to generate an optimized UV-LED continuous flow reactor. Three optimality conditions were considered: 1) single objective analysis minimizing input supply power while achieving at least (2.0) log10 inactivation of Escherichia coli ATCC 11229; and 2) two multi-objective analyses (one of which maximized the log10 inactivation of E. coli ATCC 11229 and minimized the supply power). All tests were completed at a flow rate of 109 mL/min and 92% UVT (measured at 254 nm). The numerical solution for the first objective was validated experimentally using biodosimetry. The optimal design predictions displayed good agreement with the experimental data and contained several non-intuitive features, particularly with the UV-LED spatial arrangement, where the lights were unevenly populated throughout the reactor. The optimal designs may not have been developed from experienced designers due to the increased degrees of
Optimal and continuous anaemia control in a cohort of dialysis patients in Switzerland
Mathieu, Claudine M; Teta, Daniel; Lötscher, Nathalie; Golshayan, Dela; Gabutti, Luca; Kiss, Denes; Martin, Pierre-Yves; Burnier, Michel
2008-01-01
Background Guidelines for the management of anaemia in patients with chronic kidney disease (CKD) recommend a minimal haemoglobin (Hb) target of 11 g/dL. Recent surveys indicate that this requirement is not met in many patients in Europe. In most studies, Hb is only assessed over a short-term period. The aim of this study was to examine the control of anaemia over a continuous long-term period in Switzerland. Methods A prospective multi-centre observational study was conducted in dialysed patients treated with recombinant human epoetin (EPO) beta, over a one-year follow-up period, with monthly assessments of anaemia parameters. Results Three hundred and fifty patients from 27 centres, representing 14% of the dialysis population in Switzerland, were included. Mean Hb was 11.9 ± 1.0 g/dL, and remained stable over time. Eighty-five % of the patients achieved mean Hb ≥ 11 g/dL. Mean EPO dose was 155 ± 118 IU/kg/week, being delivered mostly by subcutaneous route (64–71%). Mean serum ferritin and transferrin saturation were 435 ± 253 μg/L and 30 ± 11%, respectively. At month 12, adequate iron stores were found in 72.5% of patients, whereas absolute and functional iron deficiencies were observed in only 5.1% and 17.8%, respectively. Multivariate analysis showed that diabetes unexpectedly influenced Hb towards higher levels (12.1 ± 0.9 g/dL; p = 0.02). One year survival was significantly higher in patients with Hb ≥ 11 g/dL than in those with Hb <11 g/dL (19.7% vs 7.3%, p = 0.006). Conclusion In comparison to European studies of reference, this survey shows a remarkable and continuous control of anaemia in Swiss dialysis centres. These results were reached through moderately high EPO doses, mostly given subcutaneously, and careful iron therapy management. PMID:19077225
NASA Technical Reports Server (NTRS)
Dunn, D.; Lusignan, B.
1972-01-01
A set of analytical capabilities that are needed to assess the role satellite communications technology will play in public and other services was developed. It is user oriented in that it starts from descriptions of user demand and develops the ability to estimate the cost of satisfying that demand with the lowest cost communications system. To ensure that the analysis could cope with the complexities of the real users, two services were chosen as examples, continuing professional education and medical services. Telecommunications costs are effected greatly by demographic factors, involving distribution of users in urban areas and distances between towns in rural regions. For this reason the analytical tools were exercised on sample locations. San Jose, California and Denver, Colorado were used to represent an urban area and the Rocky Mountain states were used to represent a rural region. In assessing the range of satellite system costs, two example coverage areas were considered, one appropriate to cover the contiguous forty-eight states, a second appropriate to cover about one-third that area.
Segmentation of bone structures in 3D CT images based on continuous max-flow optimization
NASA Astrophysics Data System (ADS)
Pérez-Carrasco, J. A.; Acha-Piñero, B.; Serrano, C.
2015-03-01
In this paper an algorithm to carry out the automatic segmentation of bone structures in 3D CT images has been implemented. Automatic segmentation of bone structures is of special interest for radiologists and surgeons to analyze bone diseases or to plan some surgical interventions. This task is very complicated as bones usually present intensities overlapping with those of surrounding tissues. This overlapping is mainly due to the composition of bones and to the presence of some diseases such as Osteoarthritis, Osteoporosis, etc. Moreover, segmentation of bone structures is a very time-consuming task due to the 3D essence of the bones. Usually, this segmentation is implemented manually or with algorithms using simple techniques such as thresholding and thus providing bad results. In this paper gray information and 3D statistical information have been combined to be used as input to a continuous max-flow algorithm. Twenty CT images have been tested and different coefficients have been computed to assess the performance of our implementation. Dice and Sensitivity values above 0.91 and 0.97 respectively were obtained. A comparison with Level Sets and thresholding techniques has been carried out and our results outperformed them in terms of accuracy.
Lihoreau, Mathieu; Ings, Thomas C; Chittka, Lars; Reynolds, Andy M
2016-01-01
Simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a search space. It is frequently implemented in computers working on complex optimization problems but until now has not been directly observed in nature as a searching strategy adopted by foraging animals. We analysed high-speed video recordings of the three-dimensional searching flights of bumblebees (Bombus terrestris) made in the presence of large or small artificial flowers within a 0.5 m(3) enclosed arena. Analyses of the three-dimensional flight patterns in both conditions reveal signatures of simulated annealing searches. After leaving a flower, bees tend to scan back-and forth past that flower before making prospecting flights (loops), whose length increases over time. The search pattern becomes gradually more expansive and culminates when another rewarding flower is found. Bees then scan back and forth in the vicinity of the newly discovered flower and the process repeats. This looping search pattern, in which flight step lengths are typically power-law distributed, provides a relatively simple yet highly efficient strategy for pollinators such as bees to find best quality resources in complex environments made of multiple ephemeral feeding sites with nutritionally variable rewards.
Lihoreau, Mathieu; Ings, Thomas C; Chittka, Lars; Reynolds, Andy M
2016-01-01
Simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a search space. It is frequently implemented in computers working on complex optimization problems but until now has not been directly observed in nature as a searching strategy adopted by foraging animals. We analysed high-speed video recordings of the three-dimensional searching flights of bumblebees (Bombus terrestris) made in the presence of large or small artificial flowers within a 0.5 m(3) enclosed arena. Analyses of the three-dimensional flight patterns in both conditions reveal signatures of simulated annealing searches. After leaving a flower, bees tend to scan back-and forth past that flower before making prospecting flights (loops), whose length increases over time. The search pattern becomes gradually more expansive and culminates when another rewarding flower is found. Bees then scan back and forth in the vicinity of the newly discovered flower and the process repeats. This looping search pattern, in which flight step lengths are typically power-law distributed, provides a relatively simple yet highly efficient strategy for pollinators such as bees to find best quality resources in complex environments made of multiple ephemeral feeding sites with nutritionally variable rewards. PMID:27459948
NASA Astrophysics Data System (ADS)
Lihoreau, Mathieu; Ings, Thomas C.; Chittka, Lars; Reynolds, Andy M.
2016-07-01
Simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a search space. It is frequently implemented in computers working on complex optimization problems but until now has not been directly observed in nature as a searching strategy adopted by foraging animals. We analysed high-speed video recordings of the three-dimensional searching flights of bumblebees (Bombus terrestris) made in the presence of large or small artificial flowers within a 0.5 m3 enclosed arena. Analyses of the three-dimensional flight patterns in both conditions reveal signatures of simulated annealing searches. After leaving a flower, bees tend to scan back-and forth past that flower before making prospecting flights (loops), whose length increases over time. The search pattern becomes gradually more expansive and culminates when another rewarding flower is found. Bees then scan back and forth in the vicinity of the newly discovered flower and the process repeats. This looping search pattern, in which flight step lengths are typically power-law distributed, provides a relatively simple yet highly efficient strategy for pollinators such as bees to find best quality resources in complex environments made of multiple ephemeral feeding sites with nutritionally variable rewards.
NASA Astrophysics Data System (ADS)
Yang, Jian; Cong, Weijian; Chen, Yang; Fan, Jingfan; Liu, Yue; Wang, Yongtian
2014-02-01
The clinical value of the 3D reconstruction of a coronary artery is important for the diagnosis and intervention of cardiovascular diseases. This work proposes a method based on a deformable model for reconstructing coronary arteries from two monoplane angiographic images acquired from different angles. First, an external force back-projective composition model is developed to determine the external force, for which the force distributions in different views are back-projected to the 3D space and composited in the same coordinate system based on the perspective projection principle of x-ray imaging. The elasticity and bending forces are composited as an internal force to maintain the smoothness of the deformable curve. Second, the deformable curve evolves rapidly toward the true vascular centerlines in 3D space and angiographic images under the combination of internal and external forces. Third, densely matched correspondence among vessel centerlines is constructed using a curve alignment method. The bundle adjustment method is then utilized for the global optimization of the projection parameters and the 3D structures. The proposed method is validated on phantom data and routine angiographic images with consideration for space and re-projection image errors. Experimental results demonstrate the effectiveness and robustness of the proposed method for the reconstruction of coronary arteries from two monoplane angiographic images. The proposed method can achieve a mean space error of 0.564 mm and a mean re-projection error of 0.349 mm.
Lihoreau, Mathieu; Ings, Thomas C.; Chittka, Lars; Reynolds, Andy M.
2016-01-01
Simulated annealing is a powerful stochastic search algorithm for locating a global maximum that is hidden among many poorer local maxima in a search space. It is frequently implemented in computers working on complex optimization problems but until now has not been directly observed in nature as a searching strategy adopted by foraging animals. We analysed high-speed video recordings of the three-dimensional searching flights of bumblebees (Bombus terrestris) made in the presence of large or small artificial flowers within a 0.5 m3 enclosed arena. Analyses of the three-dimensional flight patterns in both conditions reveal signatures of simulated annealing searches. After leaving a flower, bees tend to scan back-and forth past that flower before making prospecting flights (loops), whose length increases over time. The search pattern becomes gradually more expansive and culminates when another rewarding flower is found. Bees then scan back and forth in the vicinity of the newly discovered flower and the process repeats. This looping search pattern, in which flight step lengths are typically power-law distributed, provides a relatively simple yet highly efficient strategy for pollinators such as bees to find best quality resources in complex environments made of multiple ephemeral feeding sites with nutritionally variable rewards. PMID:27459948
Duprey, Sonia; Cheze, Laurence; Dumas, Raphaël
2010-10-19
In order to obtain the lower limb kinematics from skin-based markers, the soft tissue artefact (STA) has to be compensated. Global optimization (GO) methods rely on a predefined kinematic model and attempt to limit STA by minimizing the differences between model predicted and skin-based marker positions. Thus, the reliability of GO methods depends directly on the chosen model, whose influence is not well known yet. This study develops a GO method that allows to easily implement different sets of joint constraints in order to assess their influence on the lower limb kinematics during gait. The segment definition was based on generalized coordinates giving only linear or quadratic joint constraints. Seven sets of joint constraints were assessed, corresponding to different kinematic models at the ankle, knee and hip: SSS, USS, PSS, SHS, SPS, UHS and PPS (where S, U and H stand for spherical, universal and hinge joints and P for parallel mechanism). GO was applied to gait data from five healthy males. Results showed that the lower limb kinematics, except hip kinematics, knee and ankle flexion-extension, significantly depend on the chosen ankle and knee constraints. The knee parallel mechanism generated some typical knee rotation patterns previously observed in lower limb kinematic studies. Furthermore, only the parallel mechanisms produced joint displacements. Thus, GO using parallel mechanism seems promising. It also offers some perspectives of subject-specific joint constraints.
Dutta, Samrat; Patchaikani, Prem Kumar; Behera, Laxmidhar
2016-07-01
This paper presents a single-network adaptive critic-based controller for continuous-time systems with unknown dynamics in a policy iteration (PI) framework. It is assumed that the unknown dynamics can be estimated using the Takagi-Sugeno-Kang fuzzy model with arbitrary precision. The successful implementation of a PI scheme depends on the effective learning of critic network parameters. Network parameters must stabilize the system in each iteration in addition to approximating the critic and the cost. It is found that the critic updates according to the Hamilton-Jacobi-Bellman formulation sometimes lead to the instability of the closed-loop systems. In the proposed work, a novel critic network parameter update scheme is adopted, which not only approximates the critic at current iteration but also provides feasible solutions that keep the policy stable in the next step of training by combining a Lyapunov-based linear matrix inequalities approach with PI. The critic modeling technique presented here is the first of its kind to address this issue. Though multiple literature exists discussing the convergence of PI, however, to the best of our knowledge, there exists no literature, which focuses on the effect of critic network parameters on the convergence. Computational complexity in the proposed algorithm is reduced to the order of (Fz)(n-1) , where n is the fuzzy state dimensionality and Fz is the number of fuzzy zones in the states space. A genetic algorithm toolbox of MATLAB is used for searching stable parameters while minimizing the training error. The proposed algorithm also provides a way to solve for the initial stable control policy in the PI scheme. The algorithm is validated through real-time experiment on a commercial robotic manipulator. Results show that the algorithm successfully finds stable critic network parameters in real time for a highly nonlinear system. PMID:26259150
Guo, Fumin; Yuan, Jing; Rajchl, Martin; Svenningsen, Sarah; Capaldi, Dante P I; Sheikh, Khadija; Fenster, Aaron; Parraga, Grace
2015-07-01
Pulmonary imaging using hyperpolarized (3)He/(129)Xe gas is emerging as a new way to understand the regional nature of pulmonary ventilation abnormalities in obstructive lung diseases. However, the quantitative information derived is completely dependent on robust methods to segment both functional and structural/anatomical data. Here, we propose an approach to jointly segment the lung cavity from (1)H and (3)He pulmonary magnetic resonance images (MRI) by constraining the spatial consistency of the two segmentation regions, which simultaneously employs the image features from both modalities. We formulated the proposed co-segmentation problem as a coupled continuous min-cut model and showed that this combinatorial optimization problem can be solved globally and exactly by means of convex relaxation. In particular, we introduced a dual coupled continuous max-flow model to study the convex relaxed coupled continuous min-cut model under a primal and dual perspective. This gave rise to an efficient duality-based convex optimization algorithm. We implemented the proposed algorithm in parallel using general-purpose programming on graphics processing unit (GPGPU), which substantially increased its computational efficiency. Our experiments explored a clinical dataset of 25 subjects with chronic obstructive pulmonary disease (COPD) across a wide range of disease severity. The results showed that the proposed co-segmentation approach yielded superior performance compared to single-channel image segmentation in terms of precision, accuracy and robustness.
A GPS-Based Pitot-Static Calibration Method Using Global Output-Error Optimization
NASA Technical Reports Server (NTRS)
Foster, John V.; Cunningham, Kevin
2010-01-01
Pressure-based airspeed and altitude measurements for aircraft typically require calibration of the installed system to account for pressure sensing errors such as those due to local flow field effects. In some cases, calibration is used to meet requirements such as those specified in Federal Aviation Regulation Part 25. Several methods are used for in-flight pitot-static calibration including tower fly-by, pacer aircraft, and trailing cone methods. In the 1990 s, the introduction of satellite-based positioning systems to the civilian market enabled new inflight calibration methods based on accurate ground speed measurements provided by Global Positioning Systems (GPS). Use of GPS for airspeed calibration has many advantages such as accuracy, ease of portability (e.g. hand-held) and the flexibility of operating in airspace without the limitations of test range boundaries or ground telemetry support. The current research was motivated by the need for a rapid and statistically accurate method for in-flight calibration of pitot-static systems for remotely piloted, dynamically-scaled research aircraft. Current calibration methods were deemed not practical for this application because of confined test range size and limited flight time available for each sortie. A method was developed that uses high data rate measurements of static and total pressure, and GPSbased ground speed measurements to compute the pressure errors over a range of airspeed. The novel application of this approach is the use of system identification methods that rapidly compute optimal pressure error models with defined confidence intervals in nearreal time. This method has been demonstrated in flight tests and has shown 2- bounds of approximately 0.2 kts with an order of magnitude reduction in test time over other methods. As part of this experiment, a unique database of wind measurements was acquired concurrently with the flight experiments, for the purpose of experimental validation of the
Eckermann, Simon; Willan, Andrew R
2013-05-01
Risk sharing arrangements relate to adjusting payments for new health technologies given evidence of their performance over time. Such arrangements rely on prospective information regarding the incremental net benefit of the new technology, and its use in practice. However, once the new technology has been adopted in a particular jurisdiction, randomized clinical trials within that jurisdiction are likely to be infeasible and unethical in the cases where they would be most helpful, i.e. with current evidence of positive while uncertain incremental health and net monetary benefit. Informed patients in these cases would likely be reluctant to participate in a trial, preferring instead to receive the new technology with certainty. Consequently, informing risk sharing arrangements within a jurisdiction is problematic given the infeasibility of collecting prospective trial data. To overcome such problems, we demonstrate that global trials facilitate trialling post adoption, leading to more complete and robust risk sharing arrangements that mitigate the impact of costs of reversal on expected value of information in jurisdictions who adopt while a global trial is undertaken. More generally, optimally designed global trials offer distinct advantages over locally optimal solutions for decision makers and manufacturers alike: avoiding opportunity costs of delay in jurisdictions that adopt; overcoming barriers to evidence collection; and improving levels of expected implementation. Further, the greater strength and translatability of evidence across jurisdictions inherent in optimal global trial design reduces barriers to translation across jurisdictions characteristic of local trials. Consequently, efficiently designed global trials better align the interests of decision makers and manufacturers, increasing the feasibility of risk sharing and the expected strength of evidence over local trials, up until the point that current evidence is globally sufficient.
Eckermann, Simon; Willan, Andrew R
2013-05-01
Risk sharing arrangements relate to adjusting payments for new health technologies given evidence of their performance over time. Such arrangements rely on prospective information regarding the incremental net benefit of the new technology, and its use in practice. However, once the new technology has been adopted in a particular jurisdiction, randomized clinical trials within that jurisdiction are likely to be infeasible and unethical in the cases where they would be most helpful, i.e. with current evidence of positive while uncertain incremental health and net monetary benefit. Informed patients in these cases would likely be reluctant to participate in a trial, preferring instead to receive the new technology with certainty. Consequently, informing risk sharing arrangements within a jurisdiction is problematic given the infeasibility of collecting prospective trial data. To overcome such problems, we demonstrate that global trials facilitate trialling post adoption, leading to more complete and robust risk sharing arrangements that mitigate the impact of costs of reversal on expected value of information in jurisdictions who adopt while a global trial is undertaken. More generally, optimally designed global trials offer distinct advantages over locally optimal solutions for decision makers and manufacturers alike: avoiding opportunity costs of delay in jurisdictions that adopt; overcoming barriers to evidence collection; and improving levels of expected implementation. Further, the greater strength and translatability of evidence across jurisdictions inherent in optimal global trial design reduces barriers to translation across jurisdictions characteristic of local trials. Consequently, efficiently designed global trials better align the interests of decision makers and manufacturers, increasing the feasibility of risk sharing and the expected strength of evidence over local trials, up until the point that current evidence is globally sufficient. PMID:23529209
Developments of global greenhouse gas retrieval algorithm based on Optimal Estimation Method
NASA Astrophysics Data System (ADS)
Kim, W. V.; Kim, J.; Lee, H.; Jung, Y.; Boesch, H.
2013-12-01
After the industrial revolution, atmospheric carbon dioxide concentration increased drastically over the last 250 years. It is still increasing and over than 400ppm of carbon dioxide was measured at Mauna Loa observatory for the first time which value was considered as important milestone. Therefore, understanding the source, emission, transport and sink of global carbon dioxide is unprecedentedly important. Currently, Total Carbon Column Observing Network (TCCON) is operated to observe CO2 concentration by ground base instruments. However, the number of site is very few and concentrated to Europe and North America. Remote sensing of CO2 could supplement those limitations. Greenhouse Gases Observing SATellite (GOSAT) which was launched 2009 is measuring column density of CO2 and other satellites are planned to launch in a few years. GOSAT provide valuable measurement data but its low spatial resolution and poor success rate of retrieval due to aerosol and cloud, forced the results to cover less than half of the whole globe. To improve data availability, accurate aerosol information is necessary, especially for East Asia region where the aerosol concentration is higher than other region. For the first step, we are developing CO2 retrieval algorithm based on optimal estimation method with VLIDORT the vector discrete ordinate radiative transfer model. Proto type algorithm, developed from various combinations of state vectors to find best combination of state vectors, shows appropriate result and good agreement with TCCON measurements. To reduce calculation cost low-stream interpolation is applied for model simulation and the simulation time is drastically reduced. For the further study, GOSAT CO2 retrieval algorithm will be combined with accurate GOSAT-CAI aerosol retrieval algorithm to obtain more accurate result especially for East Asia.
Wu, Xiao; Lin, Hongjian; Zhu, Jun
2013-05-01
This study investigated and optimized the operational conditions for continuous hydrogen production from sugar beet molasses, co-fermented with liquid swine manure in an anaerobic sequencing batch reactor. Results indicated that pH, HRT and total solids content in the swine manure (TS) had significant impact on all the responses such as biogas production rate (BPR), hydrogen content (HC), hydrogen production rate (HPR), and hydrogen yield (HY), although the highest level of each response was achieved at different combination of the three variables. The maximum BPR, HC, HPR and HY of 32.21 L/d, 30.51%, 2.23 L/d/L and 1.57 mol-H2/mol-sugar were estimated at the optimal pH, HRT, and TS of 5.55, 15.78 h, and 0.71% for BPR; 5.22, 12.04, and 0.69 for HC; 5.32, 15.62, and 0.78% for HPR; and 5.36, 17.56, and 0.74% for HY, respectively. Good linear relationships of the predicted and tested results for all the parameters were observed.
NASA Astrophysics Data System (ADS)
Lera, Daniela; Sergeyev, Yaroslav D.
2016-06-01
In this paper the global optimization problem where the objective function is multiextremal and satisfying the Lipschitz condition over a hyperinterval is considered. An algorithm that uses Peano-type space-filling curves to reduce the original Lipschitz multi-dimensional problem to a univariate one satisfying the Hölder condition is proposed. The algorithm at each iteration applies a new geometric technique working with a number of possible Hölder constants chosen from a set of values varying from zero to infinity showing so that ideas introduced in a popular DIRECT method can be used in the Hölder global optimization, as well. Convergence condition are given. Numerical experiments show quite a promising performance of the new technique.
Korenromp, Eline L.; Glaziou, Philippe; Fitzpatrick, Christopher; Floyd, Katherine; Hosseini, Mehran; Raviglione, Mario; Atun, Rifat; Williams, Brian
2012-01-01
Background The Global Plan to Stop TB estimates funding required in low- and middle-income countries to achieve TB control targets set by the Stop TB Partnership within the context of the Millennium Development Goals. We estimate the contribution and impact of Global Fund investments under various scenarios of allocations across interventions and regions. Methodology/Principal Findings Using Global Plan assumptions on expected cases and mortality, we estimate treatment costs and mortality impact for diagnosis and treatment for drug-sensitive and multidrug-resistant TB (MDR-TB), including antiretroviral treatment (ART) during DOTS for HIV-co-infected patients, for four country groups, overall and for the Global Fund investments. In 2015, China and India account for 24% of funding need, Eastern Europe and Central Asia (EECA) for 33%, sub-Saharan Africa (SSA) for 20%, and other low- and middle-income countries for 24%. Scale-up of MDR-TB treatment, especially in EECA, drives an increasing global TB funding need – an essential investment to contain the mortality burden associated with MDR-TB and future disease costs. Funding needs rise fastest in SSA, reflecting increasing coverage need of improved TB/HIV management, which saves most lives per dollar spent in the short term. The Global Fund is expected to finance 8–12% of Global Plan implementation costs annually. Lives saved through Global Fund TB support within the available funding envelope could increase 37% if allocations shifted from current regional demand patterns to a prioritized scale-up of improved TB/HIV treatment and secondly DOTS, both mainly in Africa − with EECA region, which has disproportionately high per-patient costs, funded from alternative resources. Conclusions/Significance These findings, alongside country funding gaps, domestic funding and implementation capacity and equity considerations, should inform strategies and policies for international donors, national governments and disease
Near-surface ocean velocity from infrared images: Global Optimal Solution to an inverse model
NASA Astrophysics Data System (ADS)
Chen, Wei; Mied, Richard P.; Shen, Colin Y.
2008-10-01
We address the problem of obtaining ocean surface velocities from sequences of thermal (AVHRR) space-borne images by inverting the heat conservation equation (including sources of surface heat fluxes and vertical entrainment). We demonstrate the utility of the technique by deriving surface velocities from (1) The motion of a synthetic surface tracer in a numerical model and (2) a sequence of five actual AVHRR images from 1 day. Typical formulations of this tracer inversion problem yield too few equations at each pixel, which is often remedied by imposing additional constraints (e.g., horizontal divergence, vorticity, and energy). In contrast, we propose an alternate strategy to convert the underdetermined equation set to an overdetermined one. We divide the image scene into many subarrays and define velocities and sources within each subarray using bilinear expressions in terms of the corner points (called knots). In turn, all velocities and sources on the knots can be determined by seeking an optimum solution to these linear equations over the large scale, which we call the Global Optimal Solution (GOS). We test the accuracy of the GOS by contaminating the model output with up to 10% white noise but find that filtering the data with a Gaussian convolution filter yields velocities nearly indistinguishable from those without the added noise. We compare the GOS velocity fields with those from the numerical model and from the Maximum Cross Correlation (MCC) technique. A histogram of the difference between GOS and numerical model velocities is narrower and more peaked than the similar comparison with MCC, irrespective of the time interval (Δt = 2 or 4 h) between images. The calculation of the root mean square error difference between the GOS (and MCC) results and the model velocities indicates that the GOS/model error is only half that of the MCC/model error irrespective of the time interval (Δt = 2 or 4 h) between images. Finally, the application of the technique to
ERIC Educational Resources Information Center
Boyd, Donna J., Ed.
These proceedings record the addresses, concurrent sessions, and business meetings of the annual meeting of the Association for Continuing Higher Education (ACHE). Part 1 consists of three addresses: "World Collaboration for a Global Perspective" (Beverly Cassara); "When Chaos Is the Solution: A Paradigm for 21st Century Mandates and Strategies"…
Order-Constrained Solutions in K-Means Clustering: Even Better than Being Globally Optimal
ERIC Educational Resources Information Center
Steinley, Douglas; Hubert, Lawrence
2008-01-01
This paper proposes an order-constrained K-means cluster analysis strategy, and implements that strategy through an auxiliary quadratic assignment optimization heuristic that identifies an initial object order. A subsequent dynamic programming recursion is applied to optimally subdivide the object set subject to the order constraint. We show that…
NASA Astrophysics Data System (ADS)
Fan, Shu-Kai S.; Chang, Ju-Ming
2010-05-01
This article presents a novel parallel multi-swarm optimization (PMSO) algorithm with the aim of enhancing the search ability of standard single-swarm PSOs for global optimization of very large-scale multimodal functions. Different from the existing multi-swarm structures, the multiple swarms work in parallel, and the search space is partitioned evenly and dynamically assigned in a weighted manner via the roulette wheel selection (RWS) mechanism. This parallel, distributed framework of the PMSO algorithm is developed based on a master-slave paradigm, which is implemented on a cluster of PCs using message passing interface (MPI) for information interchange among swarms. The PMSO algorithm handles multiple swarms simultaneously and each swarm performs PSO operations of its own independently. In particular, one swarm is designated for global search and the others are for local search. The first part of the experimental comparison is made among the PMSO, standard PSO, and two state-of-the-art algorithms (CTSS and CLPSO) in terms of various un-rotated and rotated benchmark functions taken from the literature. In the second part, the proposed multi-swarm algorithm is tested on large-scale multimodal benchmark functions up to 300 dimensions. The results of the PMSO algorithm show great promise in solving high-dimensional problems.
NASA Astrophysics Data System (ADS)
Theos, F. V.; Lagaris, I. E.; Papageorgiou, D. G.
2004-05-01
We present two sequential and one parallel global optimization codes, that belong to the stochastic class, and an interface routine that enables the use of the Merlin/MCL environment as a non-interactive local optimizer. This interface proved extremely important, since it provides flexibility, effectiveness and robustness to the local search task that is in turn employed by the global procedures. We demonstrate the use of the parallel code to a molecular conformation problem. Program summaryTitle of program: PANMIN Catalogue identifier: ADSU Program summary URL:http://cpc.cs.qub.ac.uk/summaries/ADSU Program obtainable from: CPC Program Library, Queen's University of Belfast, N. Ireland Computer for which the program is designed and others on which it has been tested: PANMIN is designed for UNIX machines. The parallel code runs on either shared memory architectures or on a distributed system. The code has been tested on a SUN Microsystems ENTERPRISE 450 with four CPUs, and on a 48-node cluster under Linux, with both the GNU g77 and the Portland group compilers. The parallel implementation is based on MPI and has been tested with LAM MPI and MPICH Installation: University of Ioannina, Greece Programming language used: Fortran-77 Memory required to execute with typical data: Approximately O( n2) words, where n is the number of variables No. of bits in a word: 64 No. of processors used: 1 or many Has the code been vectorised or parallelized?: Parallelized using MPI No. of bytes in distributed program, including test data, etc.: 147163 No. of lines in distributed program, including the test data, etc.: 14366 Distribution format: gzipped tar file Nature of physical problem: A multitude of problems in science and engineering are often reduced to minimizing a function of many variables. There are instances that a local optimum does not correspond to the desired physical solution and hence the search for a better solution is required. Local optimization techniques can be
NASA Astrophysics Data System (ADS)
Alberti, Luca; Oberto, Gabriele; Pianosi, Francesca; Castelletti, Andrea
2013-04-01
Infiltration galleries and scavenger wells are often constructed to prevent saltwater intrusion in coastal aquifers. The optimal design of these infrastructures can be framed as a multi-objective optimization problem balancing availability of fresh water supply and installation/operation costs. High fidelity simulation models of the flow and transport processes can be used to link design parameters (e.g. wells location, size and pumping rates) to objective functions. However, the incorporation of these simulation models within an optimization-based planning framework is not straightforward because of the computational requirements of the model itself and the computational limitations of the optimization algorithms. In this study we investigate the potential for the Global Interactive Response Surface (GIRS) methodology to overcome these technical limitations. The GIRS methodology is used to recursively build a non-dynamic emulator of the process-based simulation model that maps design options into objectives values and can be used in place of the original model to more quickly explore the design space. The approach is used to plan infrastructural interventions for controlling saltwater intrusion and ensuring sustainable groundwater supply for Nauru, a Pacific island republic in Micronesia. GIRS is used to emulate a SEAWAT density driven groundwater flow-and-transport simulation model. Results show the potential applicability of the proposed approach for optimal planning of coastal aquifers.
Jarrar, Mu’taman; Rahman, Hamzah Abdul; Don, Mohammad Sobri
2016-01-01
Background and Objective: Demand for health care service has significantly increased, while the quality of healthcare and patient safety has become national and international priorities. This paper aims to identify the gaps and the current initiatives for optimizing the quality of care and patient safety in Malaysia. Design: Review of the current literature. Highly cited articles were used as the basis to retrieve and review the current initiatives for optimizing the quality of care and patient safety. The country health plan of Ministry of Health (MOH) Malaysia and the MOH Malaysia Annual Reports were reviewed. Results: The MOH has set four strategies for optimizing quality and sustaining quality of life. The 10th Malaysia Health Plan promotes the theme “1 Care for 1 Malaysia” in order to sustain the quality of care. Despite of these efforts, the total number of complaints received by the medico-legal section of the MOH Malaysia is increasing. The current global initiatives indicted that quality performance generally belong to three main categories: patient; staffing; and working environment related factors. Conclusions: There is no single intervention for optimizing quality of care to maintain patient safety. Multidimensional efforts and interventions are recommended in order to optimize the quality of care and patient safety in Malaysia. PMID:26755459
NASA Astrophysics Data System (ADS)
Leaci, Paola; Prix, Reinhard
2015-05-01
We derive simple analytic expressions for the (coherent and semicoherent) phase metrics of continuous-wave sources in low-eccentricity binary systems for the two regimes of long and short segments compared to the orbital period. The resulting expressions correct and extend previous results found in the literature. We present results of extensive Monte Carlo studies comparing metric mismatch predictions against the measured loss of detection statistics for binary parameter offsets. The agreement is generally found to be within ˜10 %- 30 % . For an application of the metric template expressions, we estimate the optimal achievable sensitivity of an Einstein@Home directed search for Scorpius X-1, under the assumption of sufficiently small spin wandering. We find that such a search, using data from the upcoming advanced detectors, would be able to beat the torque-balance level [R. V. Wagoner, Astrophys. J. 278, 345 (1984); L. Bildsten, Astrophys. J. 501, L89 (1998).] up to a frequency of ˜500 - 600 Hz , if orbital eccentricity is well constrained, and up to a frequency of ˜160 - 200 Hz for more conservative assumptions about the uncertainty on orbital eccentricity.
Mutation-Based Artificial Fish Swarm Algorithm for Bound Constrained Global Optimization
NASA Astrophysics Data System (ADS)
Rocha, Ana Maria A. C.; Fernandes, Edite M. G. P.
2011-09-01
The herein presented mutation-based artificial fish swarm (AFS) algorithm includes mutation operators to prevent the algorithm to falling into local solutions, diversifying the search, and to accelerate convergence to the global optima. Three mutation strategies are introduced into the AFS algorithm to define the trial points that emerge from random, leaping and searching behaviors. Computational results show that the new algorithm outperforms other well-known global stochastic solution methods.
Gálvez, Akemi; Iglesias, Andrés; Cabellos, Luis
2014-01-01
The problem of data fitting is very important in many theoretical and applied fields. In this paper, we consider the problem of optimizing a weighted Bayesian energy functional for data fitting by using global-support approximating curves. By global-support curves we mean curves expressed as a linear combination of basis functions whose support is the whole domain of the problem, as opposed to other common approaches in CAD/CAM and computer graphics driven by piecewise functions (such as B-splines and NURBS) that provide local control of the shape of the curve. Our method applies a powerful nature-inspired metaheuristic algorithm called cuckoo search, introduced recently to solve optimization problems. A major advantage of this method is its simplicity: cuckoo search requires only two parameters, many fewer than other metaheuristic approaches, so the parameter tuning becomes a very simple task. The paper shows that this new approach can be successfully used to solve our optimization problem. To check the performance of our approach, it has been applied to five illustrative examples of different types, including open and closed 2D and 3D curves that exhibit challenging features, such as cusps and self-intersections. Our results show that the method performs pretty well, being able to solve our minimization problem in an astonishingly straightforward way. PMID:24977175
Gálvez, Akemi; Iglesias, Andrés; Cabellos, Luis
2014-01-01
The problem of data fitting is very important in many theoretical and applied fields. In this paper, we consider the problem of optimizing a weighted Bayesian energy functional for data fitting by using global-support approximating curves. By global-support curves we mean curves expressed as a linear combination of basis functions whose support is the whole domain of the problem, as opposed to other common approaches in CAD/CAM and computer graphics driven by piecewise functions (such as B-splines and NURBS) that provide local control of the shape of the curve. Our method applies a powerful nature-inspired metaheuristic algorithm called cuckoo search, introduced recently to solve optimization problems. A major advantage of this method is its simplicity: cuckoo search requires only two parameters, many fewer than other metaheuristic approaches, so the parameter tuning becomes a very simple task. The paper shows that this new approach can be successfully used to solve our optimization problem. To check the performance of our approach, it has been applied to five illustrative examples of different types, including open and closed 2D and 3D curves that exhibit challenging features, such as cusps and self-intersections. Our results show that the method performs pretty well, being able to solve our minimization problem in an astonishingly straightforward way.
Gálvez, Akemi; Iglesias, Andrés; Cabellos, Luis
2014-01-01
The problem of data fitting is very important in many theoretical and applied fields. In this paper, we consider the problem of optimizing a weighted Bayesian energy functional for data fitting by using global-support approximating curves. By global-support curves we mean curves expressed as a linear combination of basis functions whose support is the whole domain of the problem, as opposed to other common approaches in CAD/CAM and computer graphics driven by piecewise functions (such as B-splines and NURBS) that provide local control of the shape of the curve. Our method applies a powerful nature-inspired metaheuristic algorithm called cuckoo search, introduced recently to solve optimization problems. A major advantage of this method is its simplicity: cuckoo search requires only two parameters, many fewer than other metaheuristic approaches, so the parameter tuning becomes a very simple task. The paper shows that this new approach can be successfully used to solve our optimization problem. To check the performance of our approach, it has been applied to five illustrative examples of different types, including open and closed 2D and 3D curves that exhibit challenging features, such as cusps and self-intersections. Our results show that the method performs pretty well, being able to solve our minimization problem in an astonishingly straightforward way. PMID:24977175
NASA Astrophysics Data System (ADS)
Ladefoged, Claes N.; Benoit, Didier; Law, Ian; Holm, Søren; Kjær, Andreas; Højgaard, Liselotte; Hansen, Adam E.; Andersen, Flemming L.
2015-10-01
The reconstruction of PET brain data in a PET/MR hybrid scanner is challenging in the absence of transmission sources, where MR images are used for MR-based attenuation correction (MR-AC). The main challenge of MR-AC is to separate bone and air, as neither have a signal in traditional MR images, and to assign the correct linear attenuation coefficient to bone. The ultra-short echo time (UTE) MR sequence was proposed as a basis for MR-AC as this sequence shows a small signal in bone. The purpose of this study was to develop a new clinically feasible MR-AC method with patient specific continuous-valued linear attenuation coefficients in bone that provides accurate reconstructed PET image data. A total of 164 [18F]FDG PET/MR patients were included in this study, of which 10 were used for training. MR-AC was based on either standard CT (reference), UTE or our method (RESOLUTE). The reconstructed PET images were evaluated in the whole brain, as well as regionally in the brain using a ROI-based analysis. Our method segments air, brain, cerebral spinal fluid, and soft tissue voxels on the unprocessed UTE TE images, and uses a mapping of R2* values to CT Hounsfield Units (HU) to measure the density in bone voxels. The average error of our method in the brain was 0.1% and less than 1.2% in any region of the brain. On average 95% of the brain was within ±10% of PETCT, compared to 72% when using UTE. The proposed method is clinically feasible, reducing both the global and local errors on the reconstructed PET images, as well as limiting the number and extent of the outliers.
Ladefoged, Claes N; Benoit, Didier; Law, Ian; Holm, Søren; Kjær, Andreas; Højgaard, Liselotte; Hansen, Adam E; Andersen, Flemming L
2015-10-21
The reconstruction of PET brain data in a PET/MR hybrid scanner is challenging in the absence of transmission sources, where MR images are used for MR-based attenuation correction (MR-AC). The main challenge of MR-AC is to separate bone and air, as neither have a signal in traditional MR images, and to assign the correct linear attenuation coefficient to bone. The ultra-short echo time (UTE) MR sequence was proposed as a basis for MR-AC as this sequence shows a small signal in bone. The purpose of this study was to develop a new clinically feasible MR-AC method with patient specific continuous-valued linear attenuation coefficients in bone that provides accurate reconstructed PET image data. A total of 164 [(18)F]FDG PET/MR patients were included in this study, of which 10 were used for training. MR-AC was based on either standard CT (reference), UTE or our method (RESOLUTE). The reconstructed PET images were evaluated in the whole brain, as well as regionally in the brain using a ROI-based analysis. Our method segments air, brain, cerebral spinal fluid, and soft tissue voxels on the unprocessed UTE TE images, and uses a mapping of R(*)2 values to CT Hounsfield Units (HU) to measure the density in bone voxels. The average error of our method in the brain was 0.1% and less than 1.2% in any region of the brain. On average 95% of the brain was within ±10% of PETCT, compared to 72% when using UTE. The proposed method is clinically feasible, reducing both the global and local errors on the reconstructed PET images, as well as limiting the number and extent of the outliers.
NASA Astrophysics Data System (ADS)
Ait moussa, Abdellah; Jassemnejad, Bahaeddin
2014-05-01
Nanocomposites with high-aspect ratio fillers attract enormous attention because of the superior physical properties of the composite over the parent matrix. Nanocomposites with functionalized graphene as fillers did not produce the high thermal conductivity expected due to the high interfacial thermal resistance between the functional groups and graphene flakes. We report here a robust and efficient technique that identifies the configuration of the functionalities for improved thermal conductivity. The method combines linearization of the interatomic interactions, calculation, and optimization of the thermal conductivity using the globalized and bounded Nelder-Mead algorithm.
Multi-objective global optimization of a butterfly valve using genetic algorithms.
Corbera, Sergio; Olazagoitia, José Luis; Lozano, José Antonio
2016-07-01
A butterfly valve is a type of valve typically used for isolating or regulating flow where the closing mechanism takes the form of a disc. For a long time, the attention of many researchers has focused on carrying out structural (FEM) and computational fluid dynamics (CFD) analysis in order to increase the performance of this type of flow-control device. This paper proposes a novel multi-objective approach for the design optimization of a butterfly valve using advanced genetic algorithms based on Pareto dominance. Firstly, after defining the need for this study and analyzing previous papers on the subject, the initial butterfly valve is presented and the initial fluid and structural analysis are carried out. Secondly, the optimization problem is defined and the optimization strategy is presented. The design variables are identified and a parameterization model of the valve is made. Thirdly, initial design candidates are generated by DOE and design optimization using genetic algorithms is performed. In this part of the process structural and CFD analysis are calculated for each candidate simultaneously. The optimization process involves various types of software and Python scripts are needed for their interaction and the connection of all steps. Finally, a set of optimal solutions is obtained and the optimum design that provides a 65.4% stress reduction, a 5% mass reduction and a 11.3% flow increase is selected in accordance with manufacturer preferences. Validation of the results is provided by comparing experimental test results with the values obtained for the initial design. The results demonstrate the capability and potential of the proposed methodology.
Global Optimization of Interplanetary Trajectories in the Presence of Realistic Mission Constraints
NASA Technical Reports Server (NTRS)
Hinckley, David; Englander, Jacob; Hitt, Darren
2015-01-01
Single trial evaluations Trial creation by Phase-wise GA-style or DE-inspired recombination Bin repository structure requires an initialization period Non-exclusionary Kill Distance Population collapse mechanic Main loop Creation Probabilistic switch between GA and DE creation types Locally optimize Submit to repository Repeat.
NASA Astrophysics Data System (ADS)
Naser, Samer Fahim
The design of an extractant molecule for use in liquid-liquid extraction, traditionally a combinatorial optimization problem, has been solved using continuous optimization. UNIFAC, a thermodynamic group contribution method which allows the calculation of an activity coefficient of a component from its chemical structure, was used as the basis for all calculations. A computer system was developed which employs a three step procedure. First, the error in the liquid-liquid equilibrium relations resulting from the specification of a target separation criteria is minimized by continuously varying the functional groups in the design group pool. Second, the theoretical molecule obtained from the first step is used as a starting point to optimize up to seven separation criteria by variation of functional groups and mole fractions to obtain the optimum theoretical extractant molecule which satisfies the equilibrium relations. Third, the theoretical molecule is used to generate alternative extractant molecules which contain integer functional group values only. Numeric molecular structure constraints were developed which help maintain the feasibility of molecules in the first two steps, and allow the rejection of infeasible molecules in the third step. These constraints include limits on boiling point and molecular weight. The system developed was successfully tested on several separation problems and has suggested extractants as good or better than ones currently in use. This is the first reported use of continuous optimization in molecular design. For large design pools, this approach, as opposed to combinatorial optimization, is several orders of magnitude faster.
Daily Time Step Refinement of Optimized Flood Control Rule Curves for a Global Warming Scenario
NASA Astrophysics Data System (ADS)
Lee, S.; Fitzgerald, C.; Hamlet, A. F.; Burges, S. J.
2009-12-01
Pacific Northwest temperatures have warmed by 0.8 °C since 1920 and are predicted to further increase in the 21st century. Simulated streamflow timing shifts associated with climate change have been found in past research to degrade water resources system performance in the Columbia River Basin when using existing system operating policies. To adapt to these hydrologic changes, optimized flood control operating rule curves were developed in a previous study using a hybrid optimization-simulation approach which rebalanced flood control and reservoir refill at a monthly time step. For the climate change scenario, use of the optimized flood control curves restored reservoir refill capability without increasing flood risk. Here we extend the earlier studies using a detailed daily time step simulation model applied over a somewhat smaller portion of the domain (encompassing Libby, Duncan, and Corra Linn dams, and Kootenai Lake) to evaluate and refine the optimized flood control curves derived from monthly time step analysis. Moving from a monthly to daily analysis, we found that the timing of flood control evacuation needed adjustment to avoid unintended outcomes affecting Kootenai Lake. We refined the flood rule curves derived from monthly analysis by creating a more gradual evacuation schedule, but kept the timing and magnitude of maximum evacuation the same as in the monthly analysis. After these refinements, the performance at monthly time scales reported in our previous study proved robust at daily time scales. Due to a decrease in July storage deficits, additional benefits such as more revenue from hydropower generation and more July and August outflow for fish augmentation were observed when the optimized flood control curves were used for the climate change scenario.
Kamph, Jerome Henri; Robinson, Darren; Wetter, Michael
2009-09-01
There is an increasing interest in the use of computer algorithms to identify combinations of parameters which optimise the energy performance of buildings. For such problems, the objective function can be multi-modal and needs to be approximated numerically using building energy simulation programs. As these programs contain iterative solution algorithms, they introduce discontinuities in the numerical approximation to the objective function. Metaheuristics often work well for such problems, but their convergence to a global optimum cannot be established formally. Moreover, different algorithms tend to be suited to particular classes of optimization problems. To shed light on this issue we compared the performance of two metaheuristics, the hybrid CMA-ES/HDE and the hybrid PSO/HJ, in minimizing standard benchmark functions and real-world building energy optimization problems of varying complexity. From this we find that the CMA-ES/HDE performs well on more complex objective functions, but that the PSO/HJ more consistently identifies the global minimum for simpler objective functions. Both identified similar values in the objective functions arising from energy simulations, but with different combinations of model parameters. This may suggest that the objective function is multi-modal. The algorithms also correctly identified some non-intuitive parameter combinations that were caused by a simplified control sequence of the building energy system that does not represent actual practice, further reinforcing their utility.
A global earthquake discrimination scheme to optimize ground-motion prediction equation selection
Garcia, Daniel; Wald, David J.; Hearne, Michael
2012-01-01
We present a new automatic earthquake discrimination procedure to determine in near-real time the tectonic regime and seismotectonic domain of an earthquake, its most likely source type, and the corresponding ground-motion prediction equation (GMPE) class to be used in the U.S. Geological Survey (USGS) Global ShakeMap system. This method makes use of the Flinn–Engdahl regionalization scheme, seismotectonic information (plate boundaries, global geology, seismicity catalogs, and regional and local studies), and the source parameters available from the USGS National Earthquake Information Center in the minutes following an earthquake to give the best estimation of the setting and mechanism of the event. Depending on the tectonic setting, additional criteria based on hypocentral depth, style of faulting, and regional seismicity may be applied. For subduction zones, these criteria include the use of focal mechanism information and detailed interface models to discriminate among outer-rise, upper-plate, interface, and intraslab seismicity. The scheme is validated against a large database of recent historical earthquakes. Though developed to assess GMPE selection in Global ShakeMap operations, we anticipate a variety of uses for this strategy, from real-time processing systems to any analysis involving tectonic classification of sources from seismic catalogs.
NASA Astrophysics Data System (ADS)
Miyazaki, Takahiko; Akisawa, Atsushi; Kashiwagi, Takao; Akahira, Akira
The study used the particle swarm optimization to maximize the specific cooling capacity (SCC) of a single-stage adsorption chiller, as well as to maximize the coefficient of performance (COP) at part load conditions of the chiller. The cycle time, which consists of adsorption/desorption time and pre-heating/ pre-cooling time, was chosen as a design parameter. The simulation results of a mathematical model showed a good agreement with experimental results on SCC and COP. It was shown that the SCC could be improved by the optimum cycle time as much as by 30% compared with that by the fixed cycle time. It was also presented that the part load COP would be significantly increased by the cycle time optimization at part load conditions.
Use of a generalized fisher equation for global optimization in chemical kinetics.
Villaverde, Alejandro F; Ross, John; Morán, Federico; Balsa-Canto, Eva; Banga, Julio R
2011-08-01
A new approach for parameter estimation in chemical kinetics has been recently proposed (Ross et al. Proc. Natl. Acad. Sci. U.S.A. 2010, 107, 12777). It makes use of an optimization criterion based on a Generalized Fisher Equation (GFE). Its utility has been demonstrated with two reaction mechanisms, the chlorite-iodide and Oregonator, which are computationally stiff systems. In this Article, the performance of the GFE-based algorithm is compared to that obtained from minimization of the squared distances between the observed and predicted concentrations obtained by solving the corresponding initial value problem (we call this latter approach "traditional" for simplicity). Comparison of the proposed GFE-based optimization method with the "traditional" one has revealed their differences in performance. This difference can be seen as a trade-off between speed (which favors GFE) and accuracy (which favors the traditional method). The chlorite-iodide and Oregonator systems are again chosen as case studies. An identifiability analysis is performed for both of them, followed by an optimal experimental design based on the Fisher Information Matrix (FIM). This allows to identify and overcome most of the previously encountered identifiability issues, improving the estimation accuracy. With the new data, obtained from optimally designed experiments, it is now possible to estimate effectively more parameters than with the previous data. This result, which holds for both GFE-based and traditional methods, stresses the importance of an appropriate experimental design. Finally, a new hybrid method that combines advantages from the GFE and traditional approaches is presented.
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
Laguzet, Laetitia; Turinici, Gabriel
2015-05-01
This work focuses on optimal vaccination policies for an Susceptible-Infected-Recovered (SIR) model; the impact of the disease is minimized with respect to the vaccination strategy. The problem is formulated as an optimal control problem and we show that the value function is the unique viscosity solution of an Hamilton-Jacobi-Bellman (HJB) equation. This allows to find the best vaccination policy. At odds with existing literature, it is seen that the value function is not always smooth (sometimes only Lipschitz) and the optimal vaccination policies are not unique. Moreover we rigorously analyze the situation when vaccination can be modeled as instantaneous (with respect to the time evolution of the epidemic) and identify the global optimum solutions. Numerical applications illustrate the theoretical results. In addition the pertussis vaccination in adults is considered from two perspectives: first the maximization of DALY averted in presence of vaccine side-effects; then the impact of the herd immunity on the cost-effectiveness analysis is discussed on a concrete example. PMID:25771436
Loizzo, Joseph
2009-08-01
This overview surveys the new optimism about the aging mind/brain, focusing on the potential for self-regulation practices to advance research in stress-protection and optimal health. It reviews recent findings and offers a research framework. The review links the age-related biology of stress and regeneration to the variability of mind/brain function found under a range of conditions from trauma to enrichment. The framework maps this variation along a biphasic continuum from atrophic dysfunction to peak performance. It adopts the concept of allostatic load as a measure of the wear-and-tear caused by stress, and environmental enrichment as a measure of the use-dependent enhancement caused by positive reinforcement. It frames the dissociation, aversive affect and stereotyped reactions linked with stress as cognitive, affective and behavioral forms of allostatic drag; and the association, positive affect, and creative responses in enrichment as forms of allostatic lift. It views the human mind/brain as a heterarchy of higher intelligence systems that shift between a conservative, egocentric mode heightening self-preservation and memory and a generative, altruistic mode heightening self-correction and learning. Cultural practices like meditation and psychotherapy work by teaching the self-regulation of shifts from the conservative to the generative mode. This involves a systems shift from allostatic drag to allostatic lift, minimizing wear-and-tear and optimizing plasticity and learning. For cultural practices to speed research and application, a universal typology is needed. This framework includes a typology aligning current brain models of stress and learning with traditional Indo-Tibetan models of meditative stress-cessation and learning enrichment.
ERIC Educational Resources Information Center
Rafiq, Azhar; Merrell, Ronald C.
2005-01-01
Health care practices continue to evolve with technological advances integrating computer applications and patient information management into telemedicine systems. Telemedicine can be broadly defined as the use of information technology to provide patient care and share clinical information from one geographic location to another. Telemedicine…
Optimal integer resolution for attitude determination using global positioning system signals
NASA Technical Reports Server (NTRS)
Crassidis, John L.; Markley, F. Landis; Lightsey, E. Glenn
1998-01-01
In this paper, a new motion-based algorithm for GPS integer ambiguity resolution is derived. The first step of this algorithm converts the reference sightline vectors into body frame vectors. This is accomplished by an optimal vectorized transformation of the phase difference measurements. The result of this transformation leads to the conversion of the integer ambiguities to vectorized biases. This essentially converts the problem to the familiar magnetometer-bias determination problem, for which an optimal and efficient solution exists. Also, the formulation in this paper is re-derived to provide a sequential estimate, so that a suitable stopping condition can be found during the vehicle motion. The advantages of the new algorithm include: it does not require an a-priori estimate of the vehicle's attitude; it provides an inherent integrity check using a covariance-type expression; and it can sequentially estimate the ambiguities during the vehicle motion. The only disadvantage of the new algorithm is that it requires at least three non-coplanar baselines. The performance of the new algorithm is tested on a dynamic hardware simulator.
NASA Astrophysics Data System (ADS)
Ma, Hongliang; Xu, Shijie
2016-11-01
By defining two open-time impulse points, the optimization of a two-impulse, open-time terminal rendezvous and docking with target spacecraft on large-eccentricity elliptical orbit is proposed in this paper. The purpose of optimization is to minimize the velocity increment for a terminal elliptic-reference-orbit rendezvous and docking. Current methods for solving this type of optimization problem include for example genetic algorithms and gradient based optimization. Unlike these methods, interval methods can guarantee that the globally best solution is found for a given parameterization of the input. The non-linear Tschauner- Hempel(TH) equations of the state transitions for a terminal elliptic target orbit are transformed form time domain to target orbital true anomaly domain. Their homogenous solutions and approximate state transition matrix for the control with a short true anomaly interval can be used to avoid interval integration. The interval branch and bound optimization algorithm is introduced for solving the presented rendezvous and docking optimization problem and optimizing two open-time impulse points and thruster pulse amplitudes, which systematically eliminates parts of the control and open-time input spaces that do not satisfy the path and final time state constraints. Several numerical examples are undertaken to validate the interval optimization algorithm. The results indicate that the sufficiently narrow spaces containing the global optimization solution for the open-time two-impulse terminal rendezvous and docking with target spacecraft on large-eccentricity elliptical orbit can be obtained by the interval algorithm (IA). Combining the gradient-based method, the global optimization solution for the discontinuous nonconvex optimization problem in the specifically remained search space can be found. Interval analysis is shown to be a useful tool and preponderant in the discontinuous nonconvex optimization problem of the terminal rendezvous and
ERIC Educational Resources Information Center
Smolensky, Paul; Goldrick, Matthew; Mathis, Donald
2014-01-01
Mental representations have continuous as well as discrete, combinatorial properties. For example, while predominantly discrete, phonological representations also vary continuously; this is reflected by gradient effects in instrumental studies of speech production. Can an integrated theoretical framework address both aspects of structure? The…
Contact-assisted protein structure modeling by global optimization in CASP11.
Joo, Keehyoung; Joung, InSuk; Cheng, Qianyi; Lee, Sung Jong; Lee, Jooyoung
2016-09-01
We have applied the conformational space annealing method to the contact-assisted protein structure modeling in CASP11. For Tp targets, where predicted residue-residue contact information was provided, the contact energy term in the form of the Lorentzian function was implemented together with the physical energy terms used in our template-free modeling of proteins. Although we observed some structural improvement of Tp models over the models predicted without the Tp information, the improvement was not substantial on average. This is partly due to the inaccuracy of the provided contact information, where only about 18% of it was correct. For Ts targets, where the information of ambiguous NOE (Nuclear Overhauser Effect) restraints was provided, we formulated the modeling in terms of the two-tier optimization problem, which covers: (1) the assignment of NOE peaks and (2) the three-dimensional (3D) model generation based on the assigned NOEs. Although solving the problem in a direct manner appears to be intractable at first glance, we demonstrate through CASP11 that remarkably accurate protein 3D modeling is possible by brute force optimization of a relevant energy function. For 19 Ts targets of the average size of 224 residues, generated protein models were of about 3.6 Å Cα atom accuracy. Even greater structural improvement was observed when additional Tc contact information was provided. For 20 out of the total 24 Tc targets, we were able to generate protein structures which were better than the best model from the rest of the CASP11 groups in terms of GDT-TS. Proteins 2016; 84(Suppl 1):189-199. © 2015 Wiley Periodicals, Inc.
Islam, Sk Minhazul; Das, Swagatam; Ghosh, Saurav; Roy, Subhrajit; Suganthan, Ponnuthurai Nagaratnam
2012-04-01
Differential evolution (DE) is one of the most powerful stochastic real parameter optimizers of current interest. In this paper, we propose a new mutation strategy, a fitness-induced parent selection scheme for the binomial crossover of DE, and a simple but effective scheme of adapting two of its most important control parameters with an objective of achieving improved performance. The new mutation operator, which we call DE/current-to-gr_best/1, is a variant of the classical DE/current-to-best/1 scheme. It uses the best of a group (whose size is q% of the population size) of randomly selected solutions from current generation to perturb the parent (target) vector, unlike DE/current-to-best/1 that always picks the best vector of the entire population to perturb the target vector. In our modified framework of recombination, a biased parent selection scheme has been incorporated by letting each mutant undergo the usual binomial crossover with one of the p top-ranked individuals from the current population and not with the target vector with the same index as used in all variants of DE. A DE variant obtained by integrating the proposed mutation, crossover, and parameter adaptation strategies with the classical DE framework (developed in 1995) is compared with two classical and four state-of-the-art adaptive DE variants over 25 standard numerical benchmarks taken from the IEEE Congress on Evolutionary Computation 2005 competition and special session on real parameter optimization. Our comparative study indicates that the proposed schemes improve the performance of DE by a large magnitude such that it becomes capable of enjoying statistical superiority over the state-of-the-art DE variants for a wide variety of test problems. Finally, we experimentally demonstrate that, if one or more of our proposed strategies are integrated with existing powerful DE variants such as jDE and JADE, their performances can also be enhanced.
Rafiq, Azhar; Merrell, Ronald C
2005-01-01
Health care practices continue to evolve with technological advances integrating computer applications and patient information management into telemedicine systems. Telemedicine can be broadly defined as the use of information technology to provide patient care and share clinical information from one geographic location to another. Telemedicine can lower costs and increase access to health care, especially for those who live in remote or underserved areas. The mechanism of telemedicine raises some difficult legal and regulatory issues as well since technology provides remote diagnosis and treatment across state lines resulting in unclear definitions for liability coverage. Physician licensing becomes an issue because telemedicine facilitates consultations without respect to state or national borders. With the increased access to current information and resources, continuing medical education becomes more feasible with synchronous or asynchronous access to educational content. The challenge in implementation of these unique educational tools is the inclusion for standards of practice and appropriate regulatory mechanisms to cover the audiences.
Sabesan, Shivkumar; Chakravarthy, Niranjan; Tsakalis, Kostas; Pardalos, Panos; Iasemidis, Leon
2009-01-01
Epileptic seizures are manifestations of intermittent spatiotemporal transitions of the human brain from chaos to order. Measures of chaos, namely maximum Lyapunov exponents (STL(max)), from dynamical analysis of the electroencephalograms (EEGs) at critical sites of the epileptic brain, progressively converge (diverge) before (after) epileptic seizures, a phenomenon that has been called dynamical synchronization (desynchronization). This dynamical synchronization/desynchronization has already constituted the basis for the design and development of systems for long-term (tens of minutes), on-line, prospective prediction of epileptic seizures. Also, the criterion for the changes in the time constants of the observed synchronization/desynchronization at seizure points has been used to show resetting of the epileptic brain in patients with temporal lobe epilepsy (TLE), a phenomenon that implicates a possible homeostatic role for the seizures themselves to restore normal brain activity. In this paper, we introduce a new criterion to measure this resetting that utilizes changes in the level of observed synchronization/desynchronization. We compare this criterion's sensitivity of resetting with the old one based on the time constants of the observed synchronization/desynchronization. Next, we test the robustness of the resetting phenomena in terms of the utilized measures of EEG dynamics by a comparative study involving STL(max), a measure of phase (ϕ(max)) and a measure of energy (E) using both criteria (i.e. the level and time constants of the observed synchronization/desynchronization). The measures are estimated from intracranial electroencephalographic (iEEG) recordings with subdural and depth electrodes from two patients with focal temporal lobe epilepsy and a total of 43 seizures. Techniques from optimization theory, in particular quadratic bivalent programming, are applied to optimize the performance of the three measures in detecting preictal entrainment. It is
William J. Gutowski; Joseph M. Prusa, Piotr K. Smolarkiewicz
2012-04-09
This project had goals of advancing the performance capabilities of the numerical general circulation model EULAG and using it to produce a fully operational atmospheric global climate model (AGCM) that can employ either static or dynamic grid stretching for targeted phenomena. The resulting AGCM combined EULAG's advanced dynamics core with the 'physics' of the NCAR Community Atmospheric Model (CAM). Effort discussed below shows how we improved model performance and tested both EULAG and the coupled CAM-EULAG in several ways to demonstrate the grid stretching and ability to simulate very well a wide range of scales, that is, multi-scale capability. We leveraged our effort through interaction with an international EULAG community that has collectively developed new features and applications of EULAG, which we exploited for our own work summarized here. Overall, the work contributed to over 40 peer-reviewed publications and over 70 conference/workshop/seminar presentations, many of them invited.
Optimal ?-Control for the Global Cauchy Problem of The Relativistic Vlasov-Poisson System
NASA Astrophysics Data System (ADS)
Young, Brent
2011-12-01
Recently, M.K.-H. Kiessling and A.S. Tahvildar-Zadeh proved that a unique global classical solution to the relativistic Vlasov-Poisson system exists whenever the positive, integrable initial datum is spherically symmetric, compactly supported in momentum space, vanishes on characteristics with vanishing angular momentum, and for β⩾3/2 has ?-norm strictly below a positive, critical value ?. Everything else being equal, data leading to finite time blow-up can be found with ?-norm surpassing ? for any β>1, with ? if and only if β⩾3/2. In their paper, the critical value for β=3/2 is calculated explicitly while the value for all other β is merely characterized as the infimum of a functional over an appropriate function space. In this work, the existence of minimizers is established, and the exact expression of ? is calculated in terms of the famous Lane-Emden functions. Numerical computations of the ? are presented along with some elementary asymptotics near the critical exponent 3/2.
NASA Astrophysics Data System (ADS)
Selva, D.
2014-10-01
Requirements from the different disciplines of the Earth sciences on satellite missions have become considerably more stringent in the past decade, while budgets in space organizations have not increased to support the implementation of new systems meeting these requirements. At the same time, new technologies such as optical communications, electrical propulsion, nanosatellite technology, and new commercial agents and models such as hosted payloads are now available. The technical and programmatic environment is thus ideal to conduct architectural studies that look with renewed breadth and adequate depth to the myriad of new possible architectures for Earth Observing Systems. Such studies are challenging tasks, since they require formidable amounts of data and expert knowledge in order to be conducted. Indeed, trade-offs between hundreds or thousands of requirements from different disciplines need to be considered, and millions of combinations of instrument technologies and orbits are possible. This paper presents a framework and tool to support the exploration of such large architectural tradespaces. The framework can be seen as a model-based, executable science traceability matrix that can be used to compare the relative value of millions of different possible architectures. It is demonstrated with an operational climate-centric case study. Ultimately, this framework can be used to assess opportunities for international collaboration and look at architectures for a global Earth observing system, including space, air, and ground assets.
Guiding automated NMR structure determination using a global optimization metric, the NMR DP score.
Huang, Yuanpeng Janet; Mao, Binchen; Xu, Fei; Montelione, Gaetano T
2015-08-01
ASDP is an automated NMR NOE assignment program. It uses a distinct bottom-up topology-constrained network anchoring approach for NOE interpretation, with 2D, 3D and/or 4D NOESY peak lists and resonance assignments as input, and generates unambiguous NOE constraints for iterative structure calculations. ASDP is designed to function interactively with various structure determination programs that use distance restraints to generate molecular models. In the CASD-NMR project, ASDP was tested and further developed using blinded NMR data, including resonance assignments, either raw or manually-curated (refined) NOESY peak list data, and in some cases (15)N-(1)H residual dipolar coupling data. In these blinded tests, in which the reference structure was not available until after structures were generated, the fully-automated ASDP program performed very well on all targets using both the raw and refined NOESY peak list data. Improvements of ASDP relative to its predecessor program for automated NOESY peak assignments, AutoStructure, were driven by challenges provided by these CASD-NMR data. These algorithmic improvements include (1) using a global metric of structural accuracy, the discriminating power score, for guiding model selection during the iterative NOE interpretation process, and (2) identifying incorrect NOESY cross peak assignments caused by errors in the NMR resonance assignment list. These improvements provide a more robust automated NOESY analysis program, ASDP, with the unique capability of being utilized with alternative structure generation and refinement programs including CYANA, CNS, and/or Rosetta. PMID:26081575
Skilton, Ryan A; Parrott, Andrew J; George, Michael W; Poliakoff, Martyn; Bourne, Richard A
2013-10-01
The use of automated continuous flow reactors is described, with real-time online Fourier transform infrared spectroscopy (FT-IR) analysis to enable rapid optimization of reaction yield using a self-optimizing feedback algorithm. This technique has been applied to the solvent-free methylation of 1-pentanol with dimethyl carbonate using a γ-alumina catalyst. Calibration of the FT-IR signal was performed using gas chromatography to enable quantification of yield over a wide variety of flow rates and temperatures. The use of FT-IR as a real-time analytical technique resulted in an order of magnitude reduction in the time and materials required compared to previous studies. This permitted a wide exploration of the parameter space to provide process understanding and validation of the optimization algorithms. PMID:24067568
Ona, Ofelia; Facelli, Julio C.; Bazterra, Victor E.; Caputo, Maria C.; Ferraro, Marta B.
2005-11-15
The results of ab initio global optimizations of the structures of Si{sub n}H, n=4-10, atomic clusters using a parallel genetic algorithm are presented. Driving the global search with the parallel implementation of the genetic algorithm are presented and using the density functional theory as implemented in the Carr-Parinello molecular dynamics code to calculate atomic cluster energies and perform the local optimization of their structures, we have been able to demonstrate that it is possible to perform global optimizations of the structure of atomic clusters using ab initio methods. The results show that this approach is able to find many structures that were not previously reported in the literature. Moreover, in most cases the new structures have considerable lower energies than those previously known. The results clearly demonstrate that these calculations are now possible and in spite of their larger computational demands provide more reliable results.
Lin, Jingjing; Jing, Honglei
2016-01-01
Artificial immune system is one of the most recently introduced intelligence methods which was inspired by biological immune system. Most immune system inspired algorithms are based on the clonal selection principle, known as clonal selection algorithms (CSAs). When coping with complex optimization problems with the characteristics of multimodality, high dimension, rotation, and composition, the traditional CSAs often suffer from the premature convergence and unsatisfied accuracy. To address these concerning issues, a recombination operator inspired by the biological combinatorial recombination is proposed at first. The recombination operator could generate the promising candidate solution to enhance search ability of the CSA by fusing the information from random chosen parents. Furthermore, a modified hypermutation operator is introduced to construct more promising and efficient candidate solutions. A set of 16 common used benchmark functions are adopted to test the effectiveness and efficiency of the recombination and hypermutation operators. The comparisons with classic CSA, CSA with recombination operator (RCSA), and CSA with recombination and modified hypermutation operator (RHCSA) demonstrate that the proposed algorithm significantly improves the performance of classic CSA. Moreover, comparison with the state-of-the-art algorithms shows that the proposed algorithm is quite competitive. PMID:27698662
Lin, Jingjing; Jing, Honglei
2016-01-01
Artificial immune system is one of the most recently introduced intelligence methods which was inspired by biological immune system. Most immune system inspired algorithms are based on the clonal selection principle, known as clonal selection algorithms (CSAs). When coping with complex optimization problems with the characteristics of multimodality, high dimension, rotation, and composition, the traditional CSAs often suffer from the premature convergence and unsatisfied accuracy. To address these concerning issues, a recombination operator inspired by the biological combinatorial recombination is proposed at first. The recombination operator could generate the promising candidate solution to enhance search ability of the CSA by fusing the information from random chosen parents. Furthermore, a modified hypermutation operator is introduced to construct more promising and efficient candidate solutions. A set of 16 common used benchmark functions are adopted to test the effectiveness and efficiency of the recombination and hypermutation operators. The comparisons with classic CSA, CSA with recombination operator (RCSA), and CSA with recombination and modified hypermutation operator (RHCSA) demonstrate that the proposed algorithm significantly improves the performance of classic CSA. Moreover, comparison with the state-of-the-art algorithms shows that the proposed algorithm is quite competitive.
NASA Astrophysics Data System (ADS)
Cai, X.; Zhang, X.; Zhu, T.
2014-12-01
Global food security is constrained by local and regional land and water availability, as well as other agricultural input limitations and inappropriate national and global regulations. In a theoretical context, this study assumes that optimal water and land uses in local food production to maximize food security and social welfare at the global level can be driven by global trade. It follows the context of "virtual resources trade", i.e., utilizing international trade of agricultural commodities to reduce dependency on local resources, and achieves land and water savings in the world. An optimization model based on the partial equilibrium of agriculture is developed for the analysis, including local commodity production and land and water resources constraints, demand by country, and global food market. Through the model, the marginal values (MVs) of social welfare for water and land at the level of so-called food production units (i.e., sub-basins with similar agricultural production conditions) are derived and mapped in the world. In this personation, we will introduce the model structure, explain the meaning of MVs at the local level and their distribution around the world, and discuss the policy implications for global communities to enhance global food security. In particular, we will examine the economic values of water and land under different world targets of food security (e.g., number of malnourished population or children in a future year). In addition, we will also discuss the opportunities on data to improve such global modeling exercises.
Cooper, Joel A; Tichauer, Kenneth M; Boulton, Melfort; Elliott, Jonathan; Diop, Mamadou; Arango, Miguel; Lee, Ting-Yim; St Lawrence, Keith
2011-06-01
Treatment of intracranial aneurysms by surgical clipping carries a risk of intraoperative ischemia, caused mainly by prolonged temporary occlusion of cerebral arteries. The objective of this study was to develop a near-infrared spectroscopy (NIRS) technique for continuous monitoring of cerebral blood flow (CBF) during surgery. With this approach, cerebral hemodynamics prior to clipping are measured by a bolus-tracking method that uses indocyanine green as an intravascular contrast agent. The baseline hemodynamic measurements are then used to convert the continuous Hb difference (HbD) signal (HbD = oxyhemoglobin - deoxyhemoglobin) acquired during vessel occlusion to units of CBF. To validate the approach, HbD signal changes, along with the corresponding CBF changes, were measured in pigs following occlusion of the common carotid arteries or a middle cerebral artery. For both occlusion models, the predicted CBF change derived from the HbD signal strongly correlated with the measured change in CBF. Linear regression of the predicted and measured CBF changes resulted in a slope of 0.962 (R(2) = 0.909) following carotid occlusion and 0.939 (R(2) = 0.907) following middle cerebral artery occlusion. These results suggest that calibrating the HbD signal by baseline hemodynamic measurements provides a clinically feasible method of monitoring CBF changes during neurosurgery. PMID:21454747
Chao, Ming; Wei, Jie; Li, Tianfang; Yuan, Yading; Rosenzweig, Kenneth E; Lo, Yeh-Chi
2016-04-21
We present a study of extracting respiratory signals from cone beam computed tomography (CBCT) projections within the framework of the Amsterdam Shroud (AS) technique. Acquired prior to the radiotherapy treatment, CBCT projections were preprocessed for contrast enhancement by converting the original intensity images to attenuation images with which the AS image was created. An adaptive robust z-normalization filtering was applied to further augment the weak oscillating structures locally. From the enhanced AS image, the respiratory signal was extracted using a two-step optimization approach to effectively reveal the large-scale regularity of the breathing signals. CBCT projection images from five patients acquired with the Varian Onboard Imager on the Clinac iX System Linear Accelerator (Varian Medical Systems, Palo Alto, CA) were employed to assess the proposed technique. Stable breathing signals can be reliably extracted using the proposed algorithm. Reference waveforms obtained using an air bellows belt (Philips Medical Systems, Cleveland, OH) were exported and compared to those with the AS based signals. The average errors for the enrolled patients between the estimated breath per minute (bpm) and the reference waveform bpm can be as low as -0.07 with the standard deviation 1.58. The new algorithm outperformed the original AS technique for all patients by 8.5% to 30%. The impact of gantry rotation on the breathing signal was assessed with data acquired with a Quasar phantom (Modus Medical Devices Inc., London, Canada) and found to be minimal on the signal frequency. The new technique developed in this work will provide a practical solution to rendering markerless breathing signal using the CBCT projections for thoracic and abdominal patients.
NASA Astrophysics Data System (ADS)
Chao, Ming; Wei, Jie; Li, Tianfang; Yuan, Yading; Rosenzweig, Kenneth E.; Lo, Yeh-Chi
2016-04-01
We present a study of extracting respiratory signals from cone beam computed tomography (CBCT) projections within the framework of the Amsterdam Shroud (AS) technique. Acquired prior to the radiotherapy treatment, CBCT projections were preprocessed for contrast enhancement by converting the original intensity images to attenuation images with which the AS image was created. An adaptive robust z-normalization filtering was applied to further augment the weak oscillating structures locally. From the enhanced AS image, the respiratory signal was extracted using a two-step optimization approach to effectively reveal the large-scale regularity of the breathing signals. CBCT projection images from five patients acquired with the Varian Onboard Imager on the Clinac iX System Linear Accelerator (Varian Medical Systems, Palo Alto, CA) were employed to assess the proposed technique. Stable breathing signals can be reliably extracted using the proposed algorithm. Reference waveforms obtained using an air bellows belt (Philips Medical Systems, Cleveland, OH) were exported and compared to those with the AS based signals. The average errors for the enrolled patients between the estimated breath per minute (bpm) and the reference waveform bpm can be as low as -0.07 with the standard deviation 1.58. The new algorithm outperformed the original AS technique for all patients by 8.5% to 30%. The impact of gantry rotation on the breathing signal was assessed with data acquired with a Quasar phantom (Modus Medical Devices Inc., London, Canada) and found to be minimal on the signal frequency. The new technique developed in this work will provide a practical solution to rendering markerless breathing signal using the CBCT projections for thoracic and abdominal patients.
Chao, Ming; Wei, Jie; Li, Tianfang; Yuan, Yading; Rosenzweig, Kenneth E; Lo, Yeh-Chi
2016-04-21
We present a study of extracting respiratory signals from cone beam computed tomography (CBCT) projections within the framework of the Amsterdam Shroud (AS) technique. Acquired prior to the radiotherapy treatment, CBCT projections were preprocessed for contrast enhancement by converting the original intensity images to attenuation images with which the AS image was created. An adaptive robust z-normalization filtering was applied to further augment the weak oscillating structures locally. From the enhanced AS image, the respiratory signal was extracted using a two-step optimization approach to effectively reveal the large-scale regularity of the breathing signals. CBCT projection images from five patients acquired with the Varian Onboard Imager on the Clinac iX System Linear Accelerator (Varian Medical Systems, Palo Alto, CA) were employed to assess the proposed technique. Stable breathing signals can be reliably extracted using the proposed algorithm. Reference waveforms obtained using an air bellows belt (Philips Medical Systems, Cleveland, OH) were exported and compared to those with the AS based signals. The average errors for the enrolled patients between the estimated breath per minute (bpm) and the reference waveform bpm can be as low as -0.07 with the standard deviation 1.58. The new algorithm outperformed the original AS technique for all patients by 8.5% to 30%. The impact of gantry rotation on the breathing signal was assessed with data acquired with a Quasar phantom (Modus Medical Devices Inc., London, Canada) and found to be minimal on the signal frequency. The new technique developed in this work will provide a practical solution to rendering markerless breathing signal using the CBCT projections for thoracic and abdominal patients. PMID:27008349
Pleban, Dariusz
2014-01-01
This paper describes the results of a study aimed at developing a tool for optimizing the location of machinery and workstations. A global index of acoustic assessment of machines was developed for this purpose. This index and a genetic algorithm were used in a computer tool for predicting noise emission of machines as well as optimizing the location of machines and workstations in industrial rooms. The results of laboratory and simulation tests demonstrate that the developed global index and the genetic algorithm support measures aimed at noise reduction at workstations.
Wei, Qinglai; Song, Ruizhuo; Yan, Pengfei
2016-02-01
This paper is concerned with a new data-driven zero-sum neuro-optimal control problem for continuous-time unknown nonlinear systems with disturbance. According to the input-output data of the nonlinear system, an effective recurrent neural network is introduced to reconstruct the dynamics of the nonlinear system. Considering the system disturbance as a control input, a two-player zero-sum optimal control problem is established. Adaptive dynamic programming (ADP) is developed to obtain the optimal control under the worst case of the disturbance. Three single-layer neural networks, including one critic and two action networks, are employed to approximate the performance index function, the optimal control law, and the disturbance, respectively, for facilitating the implementation of the ADP method. Convergence properties of the ADP method are developed to show that the system state will converge to a finite neighborhood of the equilibrium. The weight matrices of the critic and the two action networks are also convergent to finite neighborhoods of their optimal ones. Finally, the simulation results will show the effectiveness of the developed data-driven ADP methods.
Caproni, A.; Toffoli, R. T.; Monteiro, H.; Abraham, Z.; Teixeira, D. M.
2011-07-20
We present a new technique for obtaining model fittings to very long baseline interferometric images of astrophysical jets. The method minimizes a performance function proportional to the sum of the squared difference between the model and observed images. The model image is constructed by summing N{sub s} elliptical Gaussian sources characterized by six parameters: two-dimensional peak position, peak intensity, eccentricity, amplitude, and orientation angle of the major axis. We present results for the fitting of two main benchmark jets: the first constructed from three individual Gaussian sources, the second formed by five Gaussian sources. Both jets were analyzed by our cross-entropy technique in finite and infinite signal-to-noise regimes, the background noise chosen to mimic that found in interferometric radio maps. Those images were constructed to simulate most of the conditions encountered in interferometric images of active galactic nuclei. We show that the cross-entropy technique is capable of recovering the parameters of the sources with a similar accuracy to that obtained from the very traditional Astronomical Image Processing System Package task IMFIT when the image is relatively simple (e.g., few components). For more complex interferometric maps, our method displays superior performance in recovering the parameters of the jet components. Our methodology is also able to show quantitatively the number of individual components present in an image. An additional application of the cross-entropy technique to a real image of a BL Lac object is shown and discussed. Our results indicate that our cross-entropy model-fitting technique must be used in situations involving the analysis of complex emission regions having more than three sources, even though it is substantially slower than current model-fitting tasks (at least 10,000 times slower for a single processor, depending on the number of sources to be optimized). As in the case of any model fitting
Kaltenbrunner, Oliver; Diaz, Luis; Hu, Xiaochun; Shearer, Michael
2016-07-01
Recently, continuous downstream processing has become a topic of discussion and analysis at conferences while no industrial applications of continuous downstream processing for biopharmaceutical manufacturing have been reported. There is significant potential to increase the productivity of a Protein A capture step by converting the operation to simulated moving bed (SMB) mode. In this mode, shorter columns are operated at higher process flow and corresponding short residence times. The ability to significantly shorten the product residence time during loading without appreciable capacity loss can dramatically increase productivity of the capture step and consequently reduce the amount of Protein A resin required in the process. Previous studies have not considered the physical limitations of how short columns can be packed and the flow rate limitations due to pressure drop of stacked columns. In this study, we are evaluating the process behavior of a continuous Protein A capture column cycling operation under the known pressure drop constraints of a compressible media. The results are compared to the same resin operated under traditional batch operating conditions. We analyze the optimum system design point for a range of feed concentrations, bed heights, and load residence times and determine achievable productivity for any feed concentration and any column bed height. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:938-948, 2016. PMID:27111828
NASA Astrophysics Data System (ADS)
Carroll, Rosemary W. H.; Pohll, Greg M.; Earman, Sam; Hershey, Ronald L.
2007-10-01
SummaryAs part of a larger study to estimate groundwater recharge volumes in the area of the eastern Nevada Test Site (NTS), [Campana, M.E., 1975. Finite-state models of transport phenomena in hydrologic systems, PhD Dissertation: University of Arizona, Tucson] Discrete-state compartment model (DSCM) was re-coded to simulate steady-state groundwater concentrations of a conservative tracer. It was then dynamically linked with the shuffled complex evolution (SCE) optimization algorithm [Duan, Q., Soroosh, S., Gupta, V., 1992. Effective and efficient global optimization for conceptual rainfall-runoff models. Water Resources Research 28(4), 1015-1031] in which both flow direction and magnitude were adjusted to minimize errors in predicted tracer concentrations. Code validation on a simple four-celled model showed the algorithm consistent in model predictions and capable of reproducing expected cell outflows with relatively little error. The DSCM-SCE code was then applied to a 15-basin (cell) eastern NTS model developed for the DSCM. Auto-calibration of the NTS model was run given two modeling scenarios, (a) assuming known groundwater flow directions and solving only for magnitudes and, (b) solving for groundwater flow directions and magnitudes. The SCE is a fairly robust algorithm, unlike simulated annealing or modified Gauss-Newton approaches. The DSCM-SCE improves upon its original counterpart by being more user-friendly and by auto-calibrating complex models in minutes to hours. While the DSCM-SCE can provide numerical support to a working hypothesis, it can not definitively define a flow system based solely on δD values given few hydrogeologic constraints on boundary conditions and cell-to-cell interactions.
NASA Astrophysics Data System (ADS)
Chen, Fang; Chang, Honglong; Yuan, Weizheng; Wilcock, Reuben; Kraft, Michael
2012-10-01
This paper describes a novel multiobjective parameter optimization method based on a genetic algorithm (GA) for the design of a sixth-order continuous-time, force feedback band-pass sigma-delta modulator (BP-ΣΔM) interface for the sense mode of a MEMS gyroscope. The design procedure starts by deriving a parameterized Simulink model of the BP-ΣΔM gyroscope interface. The system parameters are then optimized by the GA. Consequently, the optimized design is tested for robustness by a Monte Carlo analysis to find a solution that is both optimal and robust. System level simulations result in a signal-to-noise ratio (SNR) larger than 90 dB in a bandwidth of 64 Hz with a 200° s-1 angular rate input signal; the noise floor is about -100 dBV Hz-1/2. The simulations are compared to measured data from a hardware implementation. For zero input rotation with the gyroscope operating at atmospheric pressure, the spectrum of the output bitstream shows an obvious band-pass noise shaping and a deep notch at the gyroscope resonant frequency. The noise floor of measured power spectral density (PSD) of the output bitstream agrees well with simulation of the optimized system level model. The bias stability, rate sensitivity and nonlinearity of the gyroscope controlled by an optimized BP-ΣΔM closed-loop interface are 34.15° h-1, 22.3 mV °-1 s-1, 98 ppm, respectively. This compares to a simple open-loop interface for which the corresponding values are 89° h-1, 14.3 mV °-1 s-1, 7600 ppm, and a nonoptimized BP-ΣΔM closed-loop interface with corresponding values of 60° h-1, 17 mV °-1 s-1, 200 ppm.
Arai, Hidenao; Nishigaki, Koichi; Nemoto, Naoto; Suzuki, Miho; Husimi, Yuzuru
2014-01-01
The norovirus RNA replicase (NV3D(pol), 56 kDa, single chain monomeric protein) can amplify double-stranded (ds) RNA isothermally. It will play an alternative role in the in vitro evolution against traditional Qβ RNA replicase, which cannot amplify dsRNA and consists of four subunits, three of which are borrowed from host E.coli. In order to identify the optimal 3'-terminal sequence of the RNA template for NV3D(pol), an in vitro selection using the serial transfer was performed for a random library having the 3'-terminal sequence of ---UUUUUUNNNN-3'. The population landscape on the 4-dimensional sequence space of the 17(th) round of transfer gave a main peak around ---CAAC-3'. In the preceding studies on the batch amplification reaction starting from a single-stranded RNA, a template with 3'-terminal C-stretch was amplified effectively. It was confirmed that in the batch amplification the ---CCC-3' was much more effective than the ---CAAC-3', but in the serial transfer condition in which the ----CAAC-3' was sustained stably, the ---CCC-3' was washed out. Based on these results we proposed the existence of the "shuttle mode" replication of dsRNA. We also proposed the optimal terminal sequences of RNA for in vitro evolution with NV3D(pol). PMID:27493494
NASA Astrophysics Data System (ADS)
Zhen, Wu; Wanji, Chen
2010-04-01
A C0-type global-local higher order theory including interlaminar stress continuity is proposed for the cross-ply laminated composite and sandwich plates in this paper, which is able to a priori satisfy the continuity conditions of transverse shear stresses at interfaces. Moreover, total number of unknowns involved in the model is independent of number of layers. Compared to other higher-order theories satisfying the continuity conditions of transverse shear stresses at interfaces, merit of the proposed model is that the first derivatives of transverse displacement w have been taken out from the in-plane displacement fields, so that the C0 interpolation functions is only required during its finite element implementation. To verify the present model, a C0 three-node triangular element is used for bending analysis of laminated composite and sandwich plates. It ought to be shown that all variables involved in present model are discretized by only using linear interpolation functions within an element. Numerical results show that the C0 plate element based on the present theory may accurately calculate transverse shear stresses without any postprocessing, and the present results agree well with those obtained from the C1-type higher order theory. Compared with the C1 plate bending element, the present finite element is simple, convenient to use and accurate enough.
NASA Astrophysics Data System (ADS)
Bos, Brent J.; Howard, Joseph M.; Young, Philip J.; Gracey, Renee; Seals, Lenward T.; Ohl, Raymond G.
2012-09-01
During cryogenic vacuum testing of the James Webb Space Telescope (JWST) Integrated Science Instrument Module (ISIM), the global alignment of the ISIM with respect to the designed interface of the JWST optical telescope element (OTE) will be measured through a series of optical characterization tests. These tests will determine the locations and orientations of the JWST science instrument projected focal surfaces and entrance pupils with respect to their corresponding OTE optical interfaces. Thermal, finite element and optical modeling will then be used to predict the on-orbit optical performance of the observatory. If any optical performance non-compliances are identified, the ISIM will be adjusted to improve its performance. If this becomes necessary, ISIM has a variety of adjustments that can be made. The lengths of the six kinematic mount struts that attach the ISIM to the OTE can be modified and five science instrument focus positions and two pupil positions can be individually adjusted as well. In order to understand how to manipulate the ISIM’s degrees of freedom properly and to prepare for the ISIM flight model testing, we have completed a series of optical-mechanical analyses to develop and identify the best approaches for bringing a non-compliant ISIM Element back into compliance. During this work several unknown misalignment scenarios were produced and the simulated optical performance metrics were input into various mathematical modeling and optimization tools to determine how the ISIM degrees of freedom should be adjusted to provide the best overall optical performance.
Zhang, N.; Chen, F. Y.; Wu, X.Q.
2015-01-01
The structure of 38 atoms Ag-Cu cluster is studied by using a combination of a genetic algorithm global optimization technique and density functional theory (DFT) calculations. It is demonstrated that the truncated octahedral (TO) Ag32Cu6 core-shell cluster is less stable than the polyicosahedral (pIh) Ag32Cu6 core-shell cluster from the atomistic models and the DFT calculation shows an agreeable result, so the newfound pIh Ag32Cu6 core-shell cluster is further investigated for potential application for O2 dissociation in oxygen reduction reaction (ORR). The activation energy barrier for the O2 dissociation on pIh Ag32Cu6 core-shell cluster is 0.715 eV, where the d-band center is −3.395 eV and the density of states at the Fermi energy level is maximal for the favorable absorption site, indicating that the catalytic activity is attributed to a maximal charge transfer between an oxygen molecule and the pIh Ag32Cu6 core-shell cluster. This work revises the earlier idea that Ag32Cu6 core-shell nanoparticles are not suitable as ORR catalysts and confirms that Ag-Cu nanoalloy is a potential candidate to substitute noble Pt-based catalyst in alkaline fuel cells. PMID:26148904
Deshpande, Nandini; Hewston, Patricia; Yoshikawa, Mika
2015-04-01
The ability to safely perform cognitive-motor dual-tasks is critical for independence of older adults. We compared age-associated differences in global and segmental control during dual-task walking in sub-optimal sensory conditions. Thirteen young (YA) and 13 healthy older (OA) adults walked a straight pathway with cognitive dual-task of walking-while-talking (WT) or no-WT under four sensory conditions. On randomly selected trials, visual and vestibular inputs were manipulated using blurring goggles (BV) and Galvanic Vestibular Stimulation (GVS), respectively. Gait speed decreased more in YA than OA during WT. Gait speed increased with GVS with normal vision but not BV. Step length considerably decreased with WT. Trunk roll significantly decreased only in OA with GVS in WT. Head roll significantly decreased with GVS regardless of age. Results indicate GVS-induced adaptations were dependent on available visual information. YA reduced their gait speed more than OA to achieve a similar pace to safely perform WT. GVS resulted in both age-groups to reduce head movement. However, with the addition of WT during GVS, OA also stiffened their trunk. Therefore, with increased attentional demands healthy OA employed different compensatory strategies than YA to maintain postural control.
Nakajima, T Y; Nakajima, T; Nakajima, M; Fukushima, H; Kuji, M; Uchiyama, A; Kishino, M
1998-05-20
The channel specifications of the Global Imager onboard the Advanced Earth Observing Satellite II have been determined by extensive numerical experiments. The results show that there is an optimum feasible position for each ocean color channel. The bandwidth of the 0.763-microm channel should be less than 10 nm for good sensitivity to the cloud top height and geometric thickness of the cloud layer; a 40-nm bandwidth is suitable for the 1.38-microm channel to have the strongest contrast between cloudy and clear radiance with a sufficient radiant energy; and a 3.7-microm channel is better than a 3.95-microm channel for estimation of the sea surface temperature (SST) and determination of the cloud particle size when the bandwidth of the channel is 0.33 microm. A three-wavelength combination of 6.7, 7.3, and 7.5 microm is an optimized choice for water vapor profiling. The combination of 8.6, 10.8, and 12.0 microm is suitable for cloud microphysics and SST retrievals with the split-window technique.
Dong, Futao; Du, Linxiu; Liu, Xianghua; Xue, Fei
2013-10-15
The influence of Mn,S and B contents on microstructural characteristics, mechanical properties and hydrogen trapping ability of low-carbon Al-killed enamel steel was investigated. The materials were produced and processed in a laboratory and the ultra-fast continuous annealing processing was performed using a continuous annealing simulator. It was found that increasing Mn,S contents in steel can improve its hydrogen trapping ability which is attributed by refined ferrite grains, more dispersed cementite and added MnS inclusions. Nevertheless, it deteriorates mechanical properties of steel sheet. Addition of trace boron results in both good mechanical properties and significantly improved hydrogen trapping ability. The boron combined with nitrogen segregating at grain boundaries, cementite and MnS inclusions, provides higher amount of attractive hydrogen trapping sites and raises the activation energy for hydrogen desorption from them. - Highlights: • We study microstructures and properties in low-carbon Al-killed enamel steel. • Hydrogen diffusion coefficients are measured to reflect fish-scale resistance. • Manganese improves hydrogen trapping ability but decrease deep-drawing ability. • Boron improves both hydrogen trapping ability and deep-drawing ability. • Both excellent mechanical properties and fish-scale resistance can be matched.
Cao, Ping; Müller, Tobias K H; Ketterer, Benedikt; Ewert, Stephanie; Theodosiou, Eirini; Thomas, Owen R T; Franzreb, Matthias
2015-07-17
Continued advance of a new temperature-controlled chromatography system, comprising a column filled with thermoresponsive stationary phase and a travelling cooling zone reactor (TCZR), is described. Nine copolymer grafted thermoresponsive cation exchangers (thermoCEX) with different balances of thermoresponsive (N-isopropylacrylamide), hydrophobic (N-tert-butylacrylamide) and negatively charged (acrylic acid) units were fashioned from three cross-linked agarose media differing in particle size and pore dimensions. Marked differences in grafted copolymer composition on finished supports were sourced to base matrix hydrophobicity. In batch binding tests with lactoferrin, maximum binding capacity (qmax) increased strongly as a function of charge introduced, but became increasingly independent of temperature, as the ability of the tethered copolymer networks to switch between extended and collapsed states was lost. ThermoCEX formed from Sepharose CL-6B (A2), Superose 6 Prep Grade (B2) and Superose 12 Prep Grade (C1) under identical conditions displayed the best combination of thermoresponsiveness (qmax,50°C/qmax,10°C ratios of 3.3, 2.2 and 2.8 for supports 'A2', 'B2' and 'C1' respectively) and lactoferrin binding capacity (qmax,50°C∼56, 29 and 45mg/g for supports 'A2', 'B2' and 'C1' respectively), and were selected for TCZR chromatography. With the cooling zone in its parked position, thermoCEX filled columns were saturated with lactoferrin at a binding temperature of 35°C, washed with equilibration buffer, before initiating the first of 8 or 12 consecutive movements of the cooling zone along the column at 0.1mm/s. A reduction in particle diameter (A2→B2) enhanced lactoferrin desorption, while one in pore diameter (B2→C1) had the opposite effect. In subsequent TCZR experiments conducted with thermoCEX 'B2' columns continuously fed with lactoferrin or 'lactoferrin+bovine serum albumin' whilst simultaneously moving the cooling zone, lactoferrin was
Thompson, Kimberly M; Duintjer Tebbens, Radboud J
2016-07-01
Managing the dynamics of vaccine supply and demand represents a significant challenge with very high stakes. Insufficient vaccine supplies can necessitate rationing, lead to preventable adverse health outcomes, delay the achievements of elimination or eradication goals, and/or pose reputation risks for public health authorities and/or manufacturers. This article explores the dynamics of global vaccine supply and demand to consider the opportunities to develop and maintain optimal global vaccine stockpiles for universal vaccines, characterized by large global demand (for which we use measles vaccines as an example), and nonuniversal (including new and niche) vaccines (for which we use oral cholera vaccine as an example). We contrast our approach with other vaccine stockpile optimization frameworks previously developed for the United States pediatric vaccine stockpile to address disruptions in supply and global emergency response vaccine stockpiles to provide on-demand vaccines for use in outbreaks. For measles vaccine, we explore the complexity that arises due to different formulations and presentations of vaccines, consideration of rubella, and the context of regional elimination goals. We conclude that global health policy leaders and stakeholders should procure and maintain appropriate global vaccine rotating stocks for measles and rubella vaccine now to support current regional elimination goals, and should probably also do so for other vaccines to help prevent and control endemic or epidemic diseases. This work suggests the need to better model global vaccine supplies to improve efficiency in the vaccine supply chain, ensure adequate supplies to support elimination and eradication initiatives, and support progress toward the goals of the Global Vaccine Action Plan. PMID:25109229
Thompson, Kimberly M; Duintjer Tebbens, Radboud J
2016-07-01
Managing the dynamics of vaccine supply and demand represents a significant challenge with very high stakes. Insufficient vaccine supplies can necessitate rationing, lead to preventable adverse health outcomes, delay the achievements of elimination or eradication goals, and/or pose reputation risks for public health authorities and/or manufacturers. This article explores the dynamics of global vaccine supply and demand to consider the opportunities to develop and maintain optimal global vaccine stockpiles for universal vaccines, characterized by large global demand (for which we use measles vaccines as an example), and nonuniversal (including new and niche) vaccines (for which we use oral cholera vaccine as an example). We contrast our approach with other vaccine stockpile optimization frameworks previously developed for the United States pediatric vaccine stockpile to address disruptions in supply and global emergency response vaccine stockpiles to provide on-demand vaccines for use in outbreaks. For measles vaccine, we explore the complexity that arises due to different formulations and presentations of vaccines, consideration of rubella, and the context of regional elimination goals. We conclude that global health policy leaders and stakeholders should procure and maintain appropriate global vaccine rotating stocks for measles and rubella vaccine now to support current regional elimination goals, and should probably also do so for other vaccines to help prevent and control endemic or epidemic diseases. This work suggests the need to better model global vaccine supplies to improve efficiency in the vaccine supply chain, ensure adequate supplies to support elimination and eradication initiatives, and support progress toward the goals of the Global Vaccine Action Plan.
Chow, Yvonne; Tu, Wang Yung; Wang, David; Ng, Daphne H P; Lee, Yuan Kun
2015-10-01
The microalga Dunaliella tertiolecta synthesizes intracellular glycerol as an osmoticum to counteract external osmotic pressure in high saline environments. The species has recently been found to release and accumulate extracellular glycerol, making it a suitable candidate for sustainable industrial glycerol production if a sufficiently high product titre yield can be achieved. While macronutrients such as nitrogen and phosphorus are essential and well understood, this study seeks to understand the influence of the micronutrient profile on glycerol production. The effects of metallic elements calcium, magnesium, manganese, zinc, cobalt, copper, and iron, as well as boron, on glycerol production as well as cell growth were quantified. The relationship between cell density and glycerol productivity was also determined. Statistically, manganese recorded the highest improvement in glycerol production as well as cell growth. Further experiments showed that manganese availability was associated with higher superoxide dismutase formation, thus suggesting that glycerol production is negatively affected by oxidative stress and the manganese bound form of this enzyme is required in order to counteract reactive oxygen species in the cells. A minimum concentration of 8.25 × 10(-5) g L(-1) manganese was sufficient to overcome this problem and achieve 10 g L(-1) extracellular glycerol, compared to 4 g L(-1) without the addition of manganese. Unlike cell growth, extracellular glycerol production was found to be negatively affected by the amount of calcium present in the normal growth medium, most likely due to the lower cell permeability at high calcium concentrations. The inhibitory effects of iron also affected extracellular glycerol production more significantly than cell growth and several antagonistic interaction effects between various micronutrients were observed. This study indicates how the optimization of these small amounts of nutrients in a two
Godman, Brian; Malmström, Rickard E.; Diogene, Eduardo; Jayathissa, Sisira; McTaggart, Stuart; Cars, Thomas; Alvarez-Madrazo, Samantha; Baumgärtel, Christoph; Brzezinska, Anna; Bucsics, Anna; Campbell, Stephen; Eriksson, Irene; Finlayson, Alexander; Fürst, Jurij; Garuoliene, Kristina; Gutiérrez-Ibarluzea, Iñaki; Hviding, Krystyna; Herholz, Harald; Joppi, Roberta; Kalaba, Marija; Laius, Ott; Malinowska, Kamila; Pedersen, Hanne B.; Markovic-Pekovic, Vanda; Piessnegger, Jutta; Selke, Gisbert; Sermet, Catherine; Spillane, Susan; Tomek, Dominik; Vončina, Luka; Vlahović-Palčevski, Vera; Wale, Janet; Wladysiuk, Magdalena; van Woerkom, Menno; Zara, Corinne; Gustafsson, Lars L.
2014-01-01
Background: There are potential conflicts between authorities and companies to fund new premium priced drugs especially where there are effectiveness, safety and/or budget concerns. Dabigatran, a new oral anticoagulant for the prevention of stroke in patients with non-valvular atrial fibrillation (AF), exemplifies this issue. Whilst new effective treatments are needed, there are issues in the elderly with dabigatran due to variable drug concentrations, no known antidote and dependence on renal elimination. Published studies showed dabigatran to be cost-effective but there are budget concerns given the prevalence of AF. These concerns resulted in extensive activities pre- to post-launch to manage its introduction. Objective: To (i) review authority activities across countries, (ii) use the findings to develop new models to better manage the entry of new drugs, and (iii) review the implications based on post-launch activities. Methodology: (i) Descriptive review and appraisal of activities regarding dabigatran, (ii) development of guidance for key stakeholder groups through an iterative process, (iii) refining guidance following post launch studies. Results: Plethora of activities to manage dabigatran including extensive pre-launch activities, risk sharing arrangements, prescribing restrictions and monitoring of prescribing post launch. Reimbursement has been denied in some countries due to concerns with its budget impact and/or excessive bleeding. Development of a new model and future guidance is proposed to better manage the entry of new drugs, centering on three pillars of pre-, peri-, and post-launch activities. Post-launch activities include increasing use of patient registries to monitor the safety and effectiveness of new drugs in clinical practice. Conclusion: Models for introducing new drugs are essential to optimize their prescribing especially where concerns. Without such models, new drugs may be withdrawn prematurely and/or struggle for funding. PMID
Chow, Yvonne; Tu, Wang Yung; Wang, David; Ng, Daphne H P; Lee, Yuan Kun
2015-10-01
The microalga Dunaliella tertiolecta synthesizes intracellular glycerol as an osmoticum to counteract external osmotic pressure in high saline environments. The species has recently been found to release and accumulate extracellular glycerol, making it a suitable candidate for sustainable industrial glycerol production if a sufficiently high product titre yield can be achieved. While macronutrients such as nitrogen and phosphorus are essential and well understood, this study seeks to understand the influence of the micronutrient profile on glycerol production. The effects of metallic elements calcium, magnesium, manganese, zinc, cobalt, copper, and iron, as well as boron, on glycerol production as well as cell growth were quantified. The relationship between cell density and glycerol productivity was also determined. Statistically, manganese recorded the highest improvement in glycerol production as well as cell growth. Further experiments showed that manganese availability was associated with higher superoxide dismutase formation, thus suggesting that glycerol production is negatively affected by oxidative stress and the manganese bound form of this enzyme is required in order to counteract reactive oxygen species in the cells. A minimum concentration of 8.25 × 10(-5) g L(-1) manganese was sufficient to overcome this problem and achieve 10 g L(-1) extracellular glycerol, compared to 4 g L(-1) without the addition of manganese. Unlike cell growth, extracellular glycerol production was found to be negatively affected by the amount of calcium present in the normal growth medium, most likely due to the lower cell permeability at high calcium concentrations. The inhibitory effects of iron also affected extracellular glycerol production more significantly than cell growth and several antagonistic interaction effects between various micronutrients were observed. This study indicates how the optimization of these small amounts of nutrients in a two
Halim, Siti Fatimah Abdul; Kamaruddin, Azlina Harun; Fernando, W J N
2009-01-01
This study aimed to develop an optimal continuous procedure of lipase-catalyzes transesterification of waste cooking palm oil in a packed bed reactor to investigate the possibility of large scale production further. Response surface methodology (RSM) based on central composite rotatable design (CCRD) was used to optimize the two important reaction variables packed bed height (cm) and substrate flow rate(ml/min) for the transesterification of waste cooking palm oil in a continuous packed bed reactor. The optimum condition for the transesterification of waste cooking palm oil was as follows: 10.53 cm packed bed height and 0.57 ml/min substrate flow rate. The optimum predicted fatty acid methyl ester (FAME) yield was 80.3% and the actual value was 79%. The above results shows that the RSM study based on CCRD is adaptable for FAME yield studied for the current transesterification system. The effect of mass transfer in the packed bed reactor has also been studied. Models for FAME yield have been developed for cases of reaction control and mass transfer control. The results showed very good agreement compatibility between mass transfer model and the experimental results obtained from immobilized lipase packed bed reactor operation, showing that in this case the FAME yield was mass transfer controlled.
NASA Astrophysics Data System (ADS)
Stackhouse, P. W.; Mikovitz, J. C.; Cox, S. J.; Zhang, T.; Perez, R.; Schlemmer, J.; Sengupta, M.; Knapp, K. R.
2014-12-01
As renewable energy system become more prevalent, improved global long-term, up-to-date records are needed to better understand and quantify the solar resource and variability. Toward this end, a project involving NASA, DOE NREL, SUNY-Albany and the NOAA National Climatic Data Center (NCDC) was initiated to provide NREL with a solar resource mapping production system for improved depiction of global long-term solar resources that provides the capacity for continual updates. This new production system is made possible by the efforts of NOAA and NASA to completely reprocess the International Satellite Cloud Climatology Project (ISCCP) data set that provides satellite visible and infrared radiances together with retrieved cloud and surface properties on a 3-hourly basis beginning from July 1983 at an effective 10 km resolution. Thus, working with SUNY and NCDC, NASA will develop and test an improved production system that will yield an operational production system for NREL to continually update the Earth's solar resource. In this presentation, we provide a general overview of this project together with samples of the new solar irradiance mapped data products and comparisons to surface measurements at various locations across the world. Here, a three-year prototype of the anticipated ISCCP data set called GridSat is used to assess the algorithms and demonstrate the production system. GridSat maps together cross-calibrated visible and IR reflectances from all the world's geosynchronous satellites at 10 km and 3-hourly respectively. The results are shown and discussed in comparison to existing solar data products. Additionally, the solar irradiance values are compared to various Baseline Surface Radiation Network surface site measurements and other high quality surface measurements. The statistics of the agreement between the measurements and new satellite estimates are also reviewed. The team is now testing a beta release of the revised ISCCP data set through the NOAA
Zhang, Jilie; Zhang, Huaguang; Liu, Zhenwei; Wang, Yingchun
2015-07-01
In this paper, we consider the problem of developing a controller for continuous-time nonlinear systems where the equations governing the system are unknown. Using the measurements, two new online schemes are presented for synthesizing a controller without building or assuming a model for the system, by two new implementation schemes based on adaptive dynamic programming (ADP). To circumvent the requirement of the prior knowledge for systems, a precompensator is introduced to construct an augmented system. The corresponding Hamilton-Jacobi-Bellman (HJB) equation is solved by adaptive dynamic programming, which consists of the least-squared technique, neural network approximator and policy iteration (PI) algorithm. The main idea of our method is to sample the information of state, state derivative and input to update the weighs of neural network by least-squared technique. The update process is implemented in the framework of PI. In this paper, two new implementation schemes are presented. Finally, several examples are given to illustrate the effectiveness of our schemes.
Zhang, Jilie; Zhang, Huaguang; Liu, Zhenwei; Wang, Yingchun
2015-07-01
In this paper, we consider the problem of developing a controller for continuous-time nonlinear systems where the equations governing the system are unknown. Using the measurements, two new online schemes are presented for synthesizing a controller without building or assuming a model for the system, by two new implementation schemes based on adaptive dynamic programming (ADP). To circumvent the requirement of the prior knowledge for systems, a precompensator is introduced to construct an augmented system. The corresponding Hamilton-Jacobi-Bellman (HJB) equation is solved by adaptive dynamic programming, which consists of the least-squared technique, neural network approximator and policy iteration (PI) algorithm. The main idea of our method is to sample the information of state, state derivative and input to update the weighs of neural network by least-squared technique. The update process is implemented in the framework of PI. In this paper, two new implementation schemes are presented. Finally, several examples are given to illustrate the effectiveness of our schemes. PMID:25704057
Lu, Lei; Xin, Xin; Lu, Hang; Zhu, Liao-dong; Xie, Si-jian; Wu, Yong
2015-10-01
The mature aerobic granular sludge (AGS) was inoculated in a continuous-flow joint constructor reactor to treat low chemical oxygen demand/nitrogen (COD/N) ratio sewage. The effects of aeration intensity and hydraulic retention time (HRT) on the denitrification and phosphorus removal efficiencies and the granular sludge stabilization were investigated. When the aeration intensity was 300 mL x min(-1) (superficial air upflow velocity of 1.2 cm x s(-1)) and the HRT was 7.5 h, the average removal efficiencies of COD, TN and TP were 76.34%, 51.23% and 53.70%, respectively. The mixed liquor suspended solids (MLSS) was only about 2 000 mg x L(-1), the sludge volume index ( SVI) was below 50 mL x g(-1), and the AGS exhibited complete forms and good settling performances. Additionally, the low COD/N ratios sewage could promote the production of extracellular polymeric substances (EPS) of AGS, and the PN proteins in EPS played a pivotal role in the maintenance of AGS stabilization. PMID:26841612
NASA Astrophysics Data System (ADS)
Dusek, Jaromir; Dohnal, Michal; Snehota, Michal; Sobotkova, Martina; Ray, Chittaranjan; Vogel, Tomas
2015-04-01
The fate of pesticides in tropical soils is still not understood as well as it is for soils in temperate regions. In this study, water flow and transport of bromide tracer and five pesticides (atrazine, imazaquin, sulfometuron methyl, S-metolachlor, and imidacloprid) through an undisturbed soil column of tropical Oxisol were analyzed using a one-dimensional numerical model. The numerical model is based on Richards' equation for solving water flow, and the advection-dispersion equation for solving solute transport. Data from a laboratory column leaching experiment were used in the uncertainty analysis using a global optimization methodology to evaluate the model's sensitivity to transport parameters. All pesticides were found to be relatively mobile (sorption distribution coefficients lower than 2 cm3 g- 1). Experimental data indicated significant non-conservative behavior of bromide tracer. All pesticides, with the exception of imidacloprid, were found less persistent (degradation half-lives smaller than 45 days). Three of the five pesticides (atrazine, sulfometuron methyl, and S-metolachlor) were better described by the linear kinetic sorption model, while the breakthrough curves of imazaquin and imidacloprid were more appropriately approximated using nonlinear instantaneous sorption. Sensitivity analysis suggested that the model is most sensitive to sorption distribution coefficient. The prediction limits contained most of the measured points of the experimental breakthrough curves, indicating adequate model concept and model structure for the description of transport processes in the soil column under study. Uncertainty analysis using a physically-based Monte Carlo modeling of pesticide fate and transport provides useful information for the evaluation of chemical leaching in Hawaii soils.
Dusek, Jaromir; Dohnal, Michal; Snehota, Michal; Sobotkova, Martina; Ray, Chittaranjan; Vogel, Tomas
2015-01-01
The fate of pesticides in tropical soils is still not understood as well as it is for soils in temperate regions. In this study, water flow and transport of bromide tracer and five pesticides (atrazine, imazaquin, sulfometuron methyl, S-metolachlor, and imidacloprid) through an undisturbed soil column of tropical Oxisol were analyzed using a one-dimensional numerical model. The numerical model is based on Richards' equation for solving water flow, and the advection-dispersion equation for solving solute transport. Data from a laboratory column leaching experiment were used in the uncertainty analysis using a global optimization methodology to evaluate the model's sensitivity to transport parameters. All pesticides were found to be relatively mobile (sorption distribution coefficients lower than 2 cm(3) g(-1)). Experimental data indicated significant non-conservative behavior of bromide tracer. All pesticides, with the exception of imidacloprid, were found less persistent (degradation half-lives smaller than 45 days). Three of the five pesticides (atrazine, sulfometuron methyl, and S-metolachlor) were better described by the linear kinetic sorption model, while the breakthrough curves of imazaquin and imidacloprid were more appropriately approximated using nonlinear instantaneous sorption. Sensitivity analysis suggested that the model is most sensitive to sorption distribution coefficient. The prediction limits contained most of the measured points of the experimental breakthrough curves, indicating adequate model concept and model structure for the description of transport processes in the soil column under study. Uncertainty analysis using a physically-based Monte Carlo modeling of pesticide fate and transport provides useful information for the evaluation of chemical leaching in Hawaii soils. PMID:25703186
Rossi, Giulia; Ferrando, Riccardo; Rapallo, Arnaldo; Fortunelli, Alessandro; Curley, Benjamin C; Lloyd, Lesley D; Johnston, Roy L
2005-05-15
Genetic algorithm global optimization of Ag-Pd, Ag-Au, and Pd-Pt clusters is performed. The 34- and 38-atom clusters are optimized for all compositions. The atom-atom interactions are modeled by a semiempirical potential. All three systems are characterized by a small size mismatch and a weak tendency of the larger atoms to segregate at the surface of the smaller ones. As a result, the global minimum structures exhibit a larger mixing than in Ag-Cu and Ag-Ni clusters. Polyicosahedral structures present generally favorable energetic configurations, even though they are less favorable than in the case of the size-mismatched systems. A comparison between all the systems studied here and in the previous paper (on size-mismatched systems) is presented.
Aranow, Cynthia
2015-12-01
The Physician Global Assessment (PGA) is an important and useful outcome measurement of lupus disease activity, but consensus on whether the PGA should be performed prior to or after the receipt of laboratory values is lacking. The objective of this study was to collect preliminary data on the optimal time to perform a PGA. In this pilot study, a PGA was performed by a single clinician upon completion of an outpatient clinical encounter and again after receipt of pertinent laboratory values. Laboratory values obtained at each clinical visit included a CBC, comprehensive chemistries, C3, C4, anti-dsDNA antibody levels, urinalysis and, if pertinent, a spot urinary protein/creatinine ratio. Disease activity was additionally determined by the SELENA-SLEDAI. Fifty-four patients, 3 males and 51 females with an average SLE disease duration of 12.3 (SD 10.5) years contributed 74 assessments to this study. The average SELENA-SLEDAI was 2.2. The average pre-laboratory PGA was 0.46, and the average post-laboratory PGA was 0.55 (p < 0.02 paired Student's t test). Among the 48 encounters with active disease and a mean SELENA-SLEDAI of 3.37, concordance of the pre-laboratory and post-laboratory PGAs occurred in only third of the patient encounters. Both pre- and post-PGA correlated with the SELENA-SLEDAI. However, the correlation of the post-PGA with the SELENA-SLEDAI was significantly greater than the correlation of the pre-PGA and SELENA-SLEDAI [r = 0.69 vs 0.79, respectively (p < 0.0179)]. In some lupus patients, the PGA determined prior to receipt of laboratory values may be the same as the PGA determined after laboratory values are received. However, in these preliminary data, there was a significant difference between pre-laboratory and post-laboratory PGA with a significantly greater correlation of the post-laboratory PGA with the SELENA-SLEDAI. Further studies in a larger patient population with a greater range of disease activity are needed to confirm and extend these
Kameyama, Shumpei; Imaki, Masaharu; Hirano, Yoshihito; Ueno, Shinichi; Kawakami, Shuji; Sakaizawa, Daisuke; Kimura, Toshiyoshi; Nakajima, Masakatsu
2011-05-10
A feasibility study is carried out on a 1.6 μm continuous-wave modulation laser absorption spectrometer system for measurement of global CO(2)concentration from a satellite. The studies are performed for wavelength selection and both systematic and random error analyses. The systematic error in the differential absorption optical depth (DAOD) is mainly caused by the temperature estimation error, surface pressure estimation error, altitude estimation error, and ON wavelength instability. The systematic errors caused by unwanted backscattering from background aerosols and dust aerosols can be reduced to less than 0.26% by using a modulation frequency of around 200 kHz, when backscatter coefficients of these unwanted backscattering have a simple profile on altitude. The influence of backscattering from cirrus clouds is much larger than that of dust aerosols. The transmission power required to reduce the random error in the DAOD to 0.26% is determined by the signal-to-noise ratio and the carrier-to-noise ratio calculations. For a satellite altitude of 400 km and receiving aperture diameter of 1 m, the required transmission power is approximately 18 W and 70 W when albedo is 0.31 and 0.08, respectively; the total measurement time in this case is 4 s, which corresponds to a horizontal resolution of 28 km.
Arun, C; Sivashanmugam, P
2015-10-01
Reuse and management of organic solid waste, reduce the environmental impact on human health and increase the economic status by generating valuable products for current and novel applications. Garbage enzyme is one such product produced from fermentation of organic solid waste and it can be used as liquid fertilizer, antimicrobial agents, treatment of domestic wastewater, municipal and industrial sludge treatment, etc. The semi-continuous production of garbage enzyme in large quantity at minimal time period and at lesser cost is needed to cater for treatment of increasing quantities of industrial waste activated sludge. This necessitates a parameter for monitoring and control for the scaling up of current process on semi-continuous basis. In the present study a RP-HPLC (Reversed Phase-High Performance Liquid Chromatography) method is used for quantification of standard organic acid at optimized condition 30°C column oven temperature, pH 2.7, and 0.7 ml/min flow rate of the mobile phase (potassium dihydrogen phosphate in water) at 50mM concentration. The garbage enzyme solution collected in 15, 30, 45, 60, 75 and 90 days were used as sample to determine the concentration of organic acid. Among these, 90th day sample showed the maximum concentration of 78.14 g/l of acetic acid in garbage enzyme, whereas other organic acids concentration got decreased when compare to the 15th day sample. This result confirms that the matured garbage enzyme contains a higher concentration of acetic acid and thus it can be used as a monitoring parameter for semi-continuous production of garbage enzyme in large scale.
Arun, C; Sivashanmugam, P
2015-10-01
Reuse and management of organic solid waste, reduce the environmental impact on human health and increase the economic status by generating valuable products for current and novel applications. Garbage enzyme is one such product produced from fermentation of organic solid waste and it can be used as liquid fertilizer, antimicrobial agents, treatment of domestic wastewater, municipal and industrial sludge treatment, etc. The semi-continuous production of garbage enzyme in large quantity at minimal time period and at lesser cost is needed to cater for treatment of increasing quantities of industrial waste activated sludge. This necessitates a parameter for monitoring and control for the scaling up of current process on semi-continuous basis. In the present study a RP-HPLC (Reversed Phase-High Performance Liquid Chromatography) method is used for quantification of standard organic acid at optimized condition 30°C column oven temperature, pH 2.7, and 0.7 ml/min flow rate of the mobile phase (potassium dihydrogen phosphate in water) at 50mM concentration. The garbage enzyme solution collected in 15, 30, 45, 60, 75 and 90 days were used as sample to determine the concentration of organic acid. Among these, 90th day sample showed the maximum concentration of 78.14 g/l of acetic acid in garbage enzyme, whereas other organic acids concentration got decreased when compare to the 15th day sample. This result confirms that the matured garbage enzyme contains a higher concentration of acetic acid and thus it can be used as a monitoring parameter for semi-continuous production of garbage enzyme in large scale. PMID:26205805
Optimization of the GOSAT global observation from space with region-by-region target-mode operations
NASA Astrophysics Data System (ADS)
kuze, A.; Suto, H.; Shiomi, K.; Kawakami, S.; Nakajima, M.
2013-12-01
Since its launch in 2009, the Thermal And Near infrared Sensor for carbon Observation Fourier-Transform Spectrometer (TANSO-FTS) onboard the Greenhouse gases Observing SATellite (GOSAT) has continued its grid observation and acquired about 20,000 samples per day. Now that more than 10 programs are planned or proposed to monitor greenhouse gases from space. TANSO-FTS is the only instrument that uses a Fourier transfer spectrometer. It is not an imaging spectrometer but has a symmetrical instrument line shape function (ILSF) that can be expressed to high precision for all wavelengths with a simple analytical function and can reduce fitting errors for atmosphere remote sensing. Therefore, other future instruments can cross-calibrate their data with accurate and precise GOSAT spectra. Since August 2010, TANSO-FTS has selected 3-point cross-track scan mode, which has the current best pointing stability and observes a single point three times in 14 sec. Column-averaged dry air mole fractions of CO2 (XCO2) and CH4 (XCH4) have been well validated at the TCCON sites, where surface albedo is not high and aerosol optical thickness is small. Long term GOSAT data show seasonal and latitudinal variation and annual increase accurately and precisely. JAXA has been processing and providing all the Level 1B spectra data that were acquired on-orbit. Thus the distribution of the Level 1B is spatially equal. The Level 2 users are retrieving XCO2 and XCH4 from the Level 1 by filtering cloud contaminated, aerosol thick, and low signal-to-noise ratio scenes. As a result, the yield rate at cloudy area such as Amazon, south-east Asia, and Central America, low surface albedo area such as snow and ice, bay and channels is very low. Aerosol thick area such as Sahara also has larger errors. Now that GOSAT demonstrated accurate XCO2 and XCH4 remote sensing, demand for emission source measurements of mega cities, power plants, gas fields, and volcanos has increased. In addition to grid
NASA Astrophysics Data System (ADS)
Auluck, S. K. H.
2014-12-01
Dense plasma focus (DPF) is known to produce highly energetic ions, electrons and plasma environment which can be used for breeding short-lived isotopes, plasma nanotechnology and other material processing applications. Commercial utilization of DPF in such areas would need a design tool that can be deployed in an automatic search for the best possible device configuration for a given application. The recently revisited (Auluck 2013 Phys. Plasmas 20 112501) Gratton-Vargas (GV) two-dimensional analytical snowplow model of plasma focus provides a numerical formula for dynamic inductance of a Mather-type plasma focus fitted to thousands of automated computations, which enables the construction of such a design tool. This inductance formula is utilized in the present work to explore global optimization, based on first-principles optimality criteria, in a four-dimensional parameter-subspace of the zero-resistance GV model. The optimization process is shown to reproduce the empirically observed constancy of the drive parameter over eight decades in capacitor bank energy. The optimized geometry of plasma focus normalized to the anode radius is shown to be independent of voltage, while the optimized anode radius is shown to be related to capacitor bank inductance.
NASA Astrophysics Data System (ADS)
Biswas, A.; Sharma, S. P.
2012-12-01
Self-Potential anomaly is an important geophysical technique that measures the electrical potential due natural source of current in the Earth's subsurface. An inclined sheet type model is a very familiar structure associated with mineralization, fault plane, groundwater flow and many other geological features which exhibits self potential anomaly. A number of linearized and global inversion approaches have been developed for the interpretation of SP anomaly over different structures for various purposes. Mathematical expression to compute the forward response over a two-dimensional dipping sheet type structures can be described in three different ways using five variables in each case. Complexities in the inversion using three different forward approaches are different. Interpretation of self-potential anomaly using very fast simulated annealing global optimization has been developed in the present study which yielded a new insight about the uncertainty and equivalence in model parameters. Interpretation of the measured data yields the location of the causative body, depth to the top, extension, dip and quality of the causative body. In the present study, a comparative performance of three different forward approaches in the interpretation of self-potential anomaly is performed to assess the efficacy of the each approach in resolving the possible ambiguity. Even though each forward formulation yields the same forward response but optimization of different sets of variable using different forward problems poses different kinds of ambiguity in the interpretation. Performance of the three approaches in optimization has been compared and it is observed that out of three methods, one approach is best and suitable for this kind of study. Our VFSA approach has been tested on synthetic, noisy and field data for three different methods to show the efficacy and suitability of the best method. It is important to use the forward problem in the optimization that yields the
Continuous Maximal Flows and Wulff Shapes: Application to MRFs
Zach, Christopher; Niethammer, Marc; Frahm, Jan-Michael
2014-01-01
Convex and continuous energy formulations for low level vision problems enable efficient search procedures for the corresponding globally optimal solutions. In this work we extend the well-established continuous, isotropic capacity-based maximal flow framework to the anisotropic setting. By using powerful results from convex analysis, a very simple and efficient minimization procedure is derived. Further, we show that many important properties carry over to the new anisotropic framework, e.g. globally optimal binary results can be achieved simply by thresholding the continuous solution. In addition, we unify the anisotropic continuous maximal flow approach with a recently proposed convex and continuous formulation for Markov random fields, thereby allowing more general smoothness priors to be incorporated. Dense stereo results are included to illustrate the capabilities of the proposed approach. PMID:25729263
NASA Astrophysics Data System (ADS)
Fang, Xiaohua; Ma, Yingjuan; Brain, David; Dong, Yaxue; Lillis, Robert
2015-12-01
We present a time-dependent MHD study of the controlling effects of the Mars crustal field on atmospheric escape. We calculate globally integrated planetary ion loss rates under quiet solar conditions considering the continuous rotation of crustal anomalies with the planet. It is found that the rotating crustal field plays an important role in controlling atmospheric escape. Significant time variation of ˜20% and ˜50% is observed during the entire rotation period for O+ and for O2+ and CO2+, respectively. The control is exerted mainly through two processes. First, the crustal magnetic pressure over the subsolar regime controls solar wind penetration and mass loading and therefore the escaping planetary ion source. There is a strong negative correlation between the magnetic pressure and ion loss, with a time lag of <1 h for O+ and ˜2.5 h for O2+ and CO2+. Second, the crustal magnetic pressure near the terminator region controls the cross-section area between the induced magnetospheric boundary and 100 km altitude at the terminator. The change in day-night connection regulates the extent to which planetary ions created on the dayside can be ultimately carried away by the solar wind and escape Mars. There is a strong positive correlation between the cross-section area and ion loss, with no significant time lag. As the planet rotates, the dayside process and the terminator process work together to control the total amount of escaping planetary ions. However, their relative importance changes with the local time of the strong crustal field region.
NASA Astrophysics Data System (ADS)
Chandra, Rishabh
Partial differential equation-constrained combinatorial optimization (PDECCO) problems are a mixture of continuous and discrete optimization problems. PDECCO problems have discrete controls, but since the partial differential equations (PDE) are continuous, the optimization space is continuous as well. Such problems have several applications, such as gas/water network optimization, traffic optimization, micro-chip cooling optimization, etc. Currently, no efficient classical algorithm which guarantees a global minimum for PDECCO problems exists. A new mapping has been developed that transforms PDECCO problem, which only have linear PDEs as constraints, into quadratic unconstrained binary optimization (QUBO) problems that can be solved using an adiabatic quantum optimizer (AQO). The mapping is efficient, it scales polynomially with the size of the PDECCO problem, requires only one PDE solve to form the QUBO problem, and if the QUBO problem is solved correctly and efficiently on an AQO, guarantees a global optimal solution for the original PDECCO problem.
Reck, R.
1993-12-31
This paper will discuss possible United States policy responses to global warming. The components of a voluntary program for emissions control will be presented as well as regulatory options, including a carbon tax and tradeable permits. The advantages and disadvantages of both options will be discussed as well as the need for a consistent overall policy response to climate change.
Assured Optimism in a Scottish Girls' School: Habitus and the (Re)production of Global Privilege
ERIC Educational Resources Information Center
Forbes, Joan; Lingard, Bob
2015-01-01
This paper examines how high levels of social-cultural connectedness and academic excellence, inflected by gender and social class, constitute a particular school habitus of "assured optimism" at an elite Scottish girls' school. In Bourdieuian terms, Dalrymple is a "forcing ground" for the "intense cultivation"…
Castillo, Edward; Castillo, Richard; Fuentes, David; Guerrero, Thomas
2014-01-01
Purpose: Block matching is a well-known strategy for estimating corresponding voxel locations between a pair of images according to an image similarity metric. Though robust to issues such as image noise and large magnitude voxel displacements, the estimated point matches are not guaranteed to be spatially accurate. However, the underlying optimization problem solved by the block matching procedure is similar in structure to the class of optimization problem associated with B-spline based registration methods. By exploiting this relationship, the authors derive a numerical method for computing a global minimizer to a constrained B-spline registration problem that incorporates the robustness of block matching with the global smoothness properties inherent to B-spline parameterization. Methods: The method reformulates the traditional B-spline registration problem as a basis pursuit problem describing the minimal l1-perturbation to block match pairs required to produce a B-spline fitting error within a given tolerance. The sparsity pattern of the optimal perturbation then defines a voxel point cloud subset on which the B-spline fit is a global minimizer to a constrained variant of the B-spline registration problem. As opposed to traditional B-spline algorithms, the optimization step involving the actual image data is addressed by block matching. Results: The performance of the method is measured in terms of spatial accuracy using ten inhale/exhale thoracic CT image pairs (available for download at www.dir-lab.com) obtained from the COPDgene dataset and corresponding sets of expert-determined landmark point pairs. The results of the validation procedure demonstrate that the method can achieve a high spatial accuracy on a significantly complex image set. Conclusions: The proposed methodology is demonstrated to achieve a high spatial accuracy and is generalizable in that in can employ any displacement field parameterization described as a least squares fit to block match
Chen, Tinggui; Xiao, Renbin
2014-01-01
Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic algorithm (GA), artificial colony optimization (ACO), and particle swarm optimization (PSO). However, the ABC still has some limitations. For example, ABC can easily get trapped in the local optimum when handing in functions that have a narrow curving valley, a high eccentric ellipse, or complex multimodal functions. As a result, we proposed an enhanced ABC algorithm called EABC by introducing self-adaptive searching strategy and artificial immune network operators to improve the exploitation and exploration. The simulation results tested on a suite of unimodal or multimodal benchmark functions illustrate that the EABC algorithm outperforms ACO, PSO, and the basic ABC in most of the experiments. PMID:24772023
Chen, Tinggui; Xiao, Renbin
2014-01-01
Artificial bee colony (ABC) algorithm, inspired by the intelligent foraging behavior of honey bees, was proposed by Karaboga. It has been shown to be superior to some conventional intelligent algorithms such as genetic alg